In search for unobservables

COPENHAGENBUSINESSSCHOOL
June 25th, 2012 Insearchforunobservables
What drives success of the acquiring firm in online business acquisitions?
Andrea Wiholm – Cand.Merc.FSM Bettina Nyquist – Cand.Merc.AEF Supervisor: Jens Gammelgaard Department of International Economics and Management Pages 116 Words 39.845 Characters 256.952 Pharagraphs
699 Lines 3.230 Executive Summary Through the lens of the Resource‐Based View and its branch theories – Dynamic Capabilities, Knowledge‐based View and Resource‐Based Model, this paper places focus on firm resources as well as the combination of these and how they affect acquirer’s abnormal stock return. By testing a carefully selected number of factors, previously confirmed to have an impact on acquirer’s abnormal stock return, the paper seeks to increase consensus in the currently scattered literature on the subject. Analysing these factors in a new context – the online business – aims to shed light over a surprisingly underrepresented research context, and to test the theory’s applicability to more dynamic contexts. From each branch the researchers have derived one key ‘aspect’, argued to drive performance in acquisitions. These are ‘adaptive capability’ Dynamic Capabilities, ‘alignment of strategic emphases’ from Resource‐Based Model and ‘absorptive capacity’ from the Knowledge‐based View. While the three branches empirically have been examined isolated, this paper proposes a consolidated model to predict performance drivers in acquisitions. An event study is used, based on the assumption that the stock market has all publicly available information, and that the markets can rationally assess the three factors given the information available to them. All three aspects have been proven on a significant level to have a positive impact on performance, though not all measures tested for have contributed to these findings. Following three out of seven hypotheses are confirmed: –
In the online business, complementing alignment between acquirer and target’s strategic emphases has a positive impact on acquirer’s performance –
Absolute size of acquirer’s knowledge base has a positive impact on acquirer’s performance –
In the online industry, a broad market focus of acquirer has a positive impact on acquirer’s performance In addition the following hypothesis has been rejected at a significant level: –
Prior acquisition experience of the acquirer has a positive impact on acquirer’s performance 1 Tableofcontent
1. PART I ‐ Introduction and research design ................................................................................................ 5 1.1. Field of Research ............................................................................................................................... 6 1.2. Delimitations ......................................................................................................................................... 7 1.3. Disposition .............................................................................................................................................. 8 1.4. Discussion and definition of key concepts ............................................................................................ 9 1.4.1. Resources, knowledge and capabilities ................................................................................... 10 1.4.2. Competitive advantage ................................................................................................................ 12 1.4.3. Mergers and Acquisitions ............................................................................................................ 13 1.4.4. The online business ..................................................................................................................... 14 2. PART II ‐ Theoretical and contextual discussion ...................................................................................... 15 2.1. Initial theory review ......................................................................................................................... 15 2.2. A short note on epistemology ......................................................................................................... 15 2.3. The Resource‐Based View ............................................................................................................... 16 2.3.1. The evolution and key characteristics of the Resource‐Based View ....................................... 17 2.3.2 Critique of the Resource‐Based View ...................................................................................... 23 2.4. Identification of research gap .......................................................................................................... 25 2.5. Contribution to academia ................................................................................................................ 28 2.6. Contextual analysis ‐ The online business ....................................................................................... 29 2.6.1. Rational for choosing the online business as connect for analysis ......................................... 29 2.6.2. Industry development ............................................................................................................. 30 2.6.3. M&A activities in the online business ..................................................................................... 31 2.6.4. Academic work on the industry ............................................................................................... 32 2.7. In‐depth theoretical review and discussion .................................................................................... 34 2.7.1. The three branches of the Resource‐Based View ....................................................................... 35 2.7.2. The Resource‐Based Model ......................................................................................................... 35 2.7.2.1. Key concept ‐ Alignment of strategic emphases .................................................................. 36 2.7.2.2. Theoretical discussion and hypothesis generation .............................................................. 38 2.7.3. The Knowledge‐Based View ........................................................................................................ 40 2.7.3.1. Key concept ‐ Absorptive capacity ....................................................................................... 41 2 2.7.3.2. 2.7.4. Key concept ‐ Adaptive capability ........................................................................................ 50 2.7.4.2. Theoretical discussion and hypothesis generation ............................................................. 51 Theoretical conclusion ..................................................................................................................... 55 PART III – Method and empirical analysis ............................................................................................... 58 3.1. 4. Dynamic Capabilities ................................................................................................................... 48 2.7.4.1. 2.8. 3. Theoretical discussion and hypothesis generation .............................................................. 43 Empirical research method.............................................................................................................. 58 3.1.1. Choice of research method ..................................................................................................... 58 3.1.2. Sample and data ...................................................................................................................... 61 3.1.3. Choice of measurements ......................................................................................................... 64 3.1.4. Specifying the statistic model .................................................................................................. 78 3.1.5. Estimation of the econometric model and its parameters ..................................................... 78 3.1.6. Hypothesis testing about the individual regression coefficients ............................................ 79 3.2. Conclusion of methodical approach ................................................................................................ 80 3.3. Empirical Analysis ............................................................................................................................ 81 3.3.1. Introduction ............................................................................................................................. 81 3.3.2. Adaptive capability .................................................................................................................. 85 3.3.3. Alignment of strategic emphasis ............................................................................................. 87 3.3.4. Absorptive capacity ................................................................................................................. 90 3.3.5. Conclusion empirical analysis .................................................................................................. 93 PART IV – Discussion ................................................................................................................................ 96 4.1. Adaptive capability ............................................................................................................................. 96 4.1.1. Hypotheses 1a ‐ Formal organizational structure ....................................................................... 96 4.1.2. Hypothesis 1b ‐ Market focus ...................................................................................................... 97 4.1.3. Concluding on hypothesis one ..................................................................................................... 98 4.1.4. A few perspectives on ADCAP ..................................................................................................... 98 4.2. Alignment of strategic emphases ..................................................................................................... 100 4.2.1. Hypothesis 2a ‐ Complementarity versus supplementary resource alignment ........................ 100 4.2.2. Hypothesis 2b ‐ Specific alignment of strategic emphases ....................................................... 100 4.2.3. Concluding on hypothesis two .................................................................................................. 102 4.2.4. A note on the failed attempt to apply a motivation dimension to RBM ................................... 102 4.3. Absorptive capacity .......................................................................................................................... 103 4.3.1. Hypothesis 3a ‐ Prior acquisition experience ............................................................................ 104 3 4.3.2. Hypothesis 3b and 3c ‐ Absolute and relative size of knowledge base ..................................... 108 4.3.4. Concluding on hypothesis three ................................................................................................ 109 4.4. Final discussion ................................................................................................................................. 111 4.4.1. A model to help explain acquirer’s performance ...................................................................... 111 4.4.2. Theoretical implications ............................................................................................................ 112 4.4.3. Practical implications ................................................................................................................. 114 4.5. Conclusion ......................................................................................................................................... 115 List of literature ............................................................................................................................................. 118 4 1.
PARTI‐Introductionandresearchdesign
Existing literature does not seem to have consistently identified what drives acquisition performance, yet firms continue to acquire without an established framework to guide the acquisition process (Hitt et al., 1998; Hoskisson et al., 1994; King et al., 2008; King et al., 2004). The M&A literature is filled with various theoretical attempts to explain value creation, and researchers have battled to agree on whether firms should be valued for the resources they possess, or by the outputs they produce. While there are many contradicting theories, the resource based view has been acknowledged to hold explanations of strong accountability (King et al., 2004; Newbert, 2007). From its earliest mentioning in 1959, by Penrose, a long line of research has followed, aiming to discover new ground‐breaking explanations as to why some firms experience more success than others, all other things equal. As a result of these empirical efforts can be discovered a vivid debate of whether the unobservable can be measured at all (Armstrong, 2007). Pursuing the goal of contributing to existing theory in detecting value drivers in acquisitions, the authors find the online industry an intriguing context to examine. The online business has faced a rapidly changing environment the last 15 years, from large first‐
movers dominating the field, to a constantly shifting market where late‐movers seem to hold the competitive advantage (Eisenmann, 2006), as recently seen with successful players such as Amazon and Groupon. Since the internet emerged, acquiring online competencies has been a quick solution as to enter the ever increasing field of online business. As in other industries, acquiring competencies that is not currently at hand is a much debated subject. Do acquisitions create value or are they rather destroying? With the online market continuously ‘morphing’ into new forms and an increasing number of players entering the field, M&A activities have become an attractive solution in the attempt to achieve superior performance. With a desire to find drivers of success in the online battle field, the resource based lens appears to be highly relevant. The online industry is particularly characterized by the resources that can be found. After the dotcom‐bubble burst, acquisitions in the online industry were no longer solely reserved entire firms, but could also be broken down to acquiring specific employees with high level of knowledge. With this in mind, it follows to examine the acquisition field in the years 5 following, to see if the acquiring firm still could gain from acquiring a firm with desired online competencies. With offset in this paradigm of the resource based view, this paper explores drivers of acquisition performance in the online business. Through exploring an extensive body of resource based literature, three branches of the RBV theory have arisen as highly intriguing; Dynamic Capabilities, Knowledge‐based view and the Resource‐Based model. Within each of these areas can be found a key factor in which the authors believe the key driver(s) can be found; adaptive capability, alignment of strategic emphasis and absorptive capacity. Through focusing on online acquisitions made in last seven years, the paper aims to contribute to existing literature, and detect key drivers of success within online acquisitions. 1.1. FieldofResearch
Intrigued by academia’s inability to provide comprehensive insights of performance drivers in acquisitions, this research paper aims to investigate the observed heterogeneity of acquirers’ abnormal returns in the online business. Taking point of departure in the academic consensus that few acquisitions result in value creation, the research question driving the forthcoming paper is: What drives success of the acquiring firm in online business acquisitions? By applying the lens of the Resource‐Based View and its branch theories – Dynamic Capabilities, Knowledge‐based View and Resource‐Based Model, focus is placed on firm resources as well as the combination of these and how they affect acquirer’s abnormal stock return. By testing a carefully selected number of factors, previously confirmed to have an impact on acquirer’s abnormal stock return, the paper seeks to increase consensus in the currently scattered literature on the subject. Analysing these factors in a new context – the online business – aims to shed light over a surprisingly underrepresented research context, and to challenge the common notion that RBV is lacks the dynamic aspect to provide valuable insights in such turbulent environments. Given that the M&A‐field of research has been a fast growing focus area within the RBV literature, future research could benefit from further insights into the black box of acquisition performance. 6 1.2. Delimitations In order to be able to say anything with certainty, a research paper must have a clear focus, which also results in well‐defined delimitations. In the search to explain what factors drive acquirer’s acquisition performance in the online business, this research paper has several demarcations the reader should be aware of. First of all, the aim of generating generalizable insights has led to the choice of a quantitative research method, where objective proxies have been used in an event study. By this choice, the paper delimits itself from multiple relevant aspects that cannot be adequately measured quantitatively (Locket and Thomson, 2009). Given the complex nature of RBV, in‐depth case studies have the ability to better explain the true un‐observable drivers of firms’ sustained competitive advantage (Ghauri and Grønhaug, 2005; Yin, 1994; Zue and Ghauri, 2008). As RBV is a reaction to other theories’ simplifying shortcomings (that firms are homogenous), the optimal research method would be to treat all firms as unique, the focus should than be to identify the positive effect of certain resources and capabilities on certain key characteristics of firms. However, as a case study does not offer generalizability beyond the specific context of research, this was not found suitable. Instead, Case studies have been used both in the theory discussion as well as in discussion of findings, to highlight aspects where quantitative research studies fall short of explanation1. The in‐between option; surveys, was disregarded as it runs a high risk of personal biases: people tend overemphasize importance of recent events, managers tend to be overconfident and exaggerate, people tend to choose the middle answer when they are unsure our uninterested etc. (Armstrong and Shimizu, 2007; Hayward and Hambrick, 1997; Rouse and Daellenbach, 1999). Secondly, the aim to investigate acquisitions using a RBV lens demarcates this research paper from applying other theories that also applies to this field of study. However, given the chosen focus of research, the authors acknowledge that there is a plethora of other aspects that could have 1
Itisimportanttonotethatlargesamplesalsodoexistinthequalitativecasestudymethod,thoughtheyare
ratherrare(ArmstrongandShimizu,2007).However,thisapproachisarguedtobebettersuitedforaPh.D.or
anotherresearchprojectwithaconsiderablylargertimeframethanforthepresentstudyduetotheextensive
timeitwouldtaketoidentifyandconductinterviewswithalargenumberofrelevantacquisitionsparticipants
frommultiplecountriesandindustries.
7 helped to increase the explanatory power of our analysis. The literature on M&A is vast and some purely financial aspects have managed to provide valuable insight on value creation, such as type of payment method, ownership and financial structure, etc. Still, as the general consensus of traditional M&A academia is that acquisitions on average are value neutral, or even slightly value destroying, the need for a new theoretical lens has been articulated (King et al., 2004). Instead, some of these measures have been used as control variable. A third delimitation important to stipulate is that it is not the aim of this paper to explain ‘sustained competitive advantage’ (SCA), rather the positive abnormal return of acquirer’s stock performance will be used as an indication of competitive advantage. The last important delimitation of this paper, which is the choice to look at the stock market’s immediate reaction, rather than to use lagged performance‐indicators to assess post‐acquisition performance. This choice is further discussed in section 3.1.2. but some of its resulting demarcations are worth mentioning here. As post‐acquisition performance is not investigated, nothing can be concluded on the sustainability of the firms’ performance. In general, when empirically investigating SCA, there is a need for more longitudinal studies (Armstrong and Shimizu, 2011). Furthermore, though a large part of the theoretical reasoning backing up the hypotheses is based on the concept of ‘resource redeployment’ between acquirer and target, nothing can be concluded on the actual redeployment of resources. 1.3.Disposition
In order for the reader to gain an overview of the research paper at hand a research disposition is provided, both graphically (see figure 1) as well as articulated. 8 Figure 1 – Research disposition 1.4.Discussionanddefinitionofkeyconcepts
In the below section, the key concepts used throughout this research paper will be presented and discussed, with the aim to generate clear and relevant definitions. For each concept, point of departure has been taken in the academic literature to identify current definitions, or lack of such. The concepts have then been discussed out from a more practical point of view and concrete 9 definitions provided. The key concepts are: 1.) Resources, knowledge and capabilities, 2.) Mergers and Acquisitions, 3.) Competitive advantage and 4.) The online business. 1.4.1. Resources,knowledgeandcapabilities
Resource is the key concept in RBV as well as the RBM. In initial works broad definitions are applied, including all resources and capabilities tied to the firm (Penrose, 1959; Rumelt, 1984; Wenerfelt, 1984). Dierickx and Cool (1989) use the term strategic assets stocks, which are defined as non‐tradable, non‐imitable and non‐substitutable assets, however, assets are not defined. In a similar manner Barney (1986) fails to properly define the term resource in his concept ‘strategic resource’. In RBM, Barney (1991) is the first to properly define the meaning of the term resources: “Firm resources include all assets, capabilities, organizational processes, firm attributes, information, knowledge etc. controlled by a firm that enable the firm to conceive of and implement strategies that improve its efficiency and effectiveness” (Barney, 1991: 101) This definition has been criticized for being too broad (Priem and Butler, 2001a; Denrell et al., 2003). However, it fits the purpose of this paper. First it includes both knowledge and capabilities, which are the two main concepts used in KBV and DC. KBV’s key term knowledge forms part of Barney’s (1991) ‘human capital resources’, and since Grant (1996b) focuses on characteristics rather than a definition of the concept (accept for “that which is known”2 (Grant, 1996; 110) knowledge is viewed in this paper in the same manner as Barney (1991). In terms of capabilities, the phrasing has been more properly defined by Teece et al.: “The term 'capabilities' emphasizes the key role of strategic management in appropriately adapting, integrating, and reconfiguring internal and external organizational skills, resources, and functional competences to match the requirements of a changing environment” (1997: 515) 2
In defining knowledge, Grant puts forth that he does not wish to compete in grounding consensus in answering the much debated question, “What is knowledge?”, and merely states that there are many sorts of knowledge relevant to the firm 10 Here, resource forms part of the definition, indicating that they can be seen as inputs to capabilities, in a similar manner as raw materials are put into a machine (Kraaijenbrink et al., 2010). Summing up the literature’s definitions of the three terms it can be concluded that knowledge is seen as a resource that in turn is seen as an input to capabilities. However, capabilities are seen as a resource, which creates a tautological reasoning leading to confusion. In addition to reviewing the literature’s definition of the key terms, it is also important to look at the terms relevance in the context of the study. In the online business, knowledge (in its human capital definition) is argued to be of key importance (Amit and Zott, 2001; Uhlenbruck et al., 2006), and seen as input into technology and marketing capabilities, which in turn are argued to be the main drivers of performance (Park et al., 2004). This is not to say that other types of resources (brand equity, physical computers, organizational processes etc.) are not relevant; however, in the absence of technology and marketing, a firm cannot compete. It is necessary is to separate resources used as inputs for capabilities, from the broad concept of resources, where capabilities are included. In solution to this the term assets will be used to denote resources that are used as inputs to capabilities, while the term resource is kept in Barney’s broad definition. As such, this paper focuses on capabilities, as defined by Teece et al. (1997), to represent the dynamic nature of the online business. Knowledge, in all its forms3, is seen as the key asset used in order to develop the necessary capabilities. Assets can further incorporate other relevant inputs to capabilities, such as powerful computers, a large customer base and strategic partnerships. Finally, capabilities and knowledge are both seen as resources. Once this clarification has been made it is easier to follow the terminology used by the different theories. As such, in RBM, ‘resources’ will be used which includes both knowledge and capabilities; in KBV, ‘knowledge’ will be used, and in DC, ‘capabilities’ will be used were assets and thus knowledge are seen as in input. 3
TheauthorsfollowGrant(1996)inneglectingtodefinesomethingasabstractastheconceptof‘knowledge’.
Thus, “In all its forms” means all the types of knowledge defined in the literature, such as tacit and explicit
knowledge(NonakaandTakeuchi,1995),individualandorganizationalknowledge(Grant1996b)etc.
11 1.4.2.Competitiveadvantage
The RBV tries to explain how firms can create and sustain competitive advantage in market equilibrium by acquiring, developing, combining and deploying a set of resources and capabilities that makes it more efficient than its competitors (Dierickx and Cool, 1989; Nelson and Winter, 1982; Penrose, 1959; Wernerfelt, 1984). An important aspect of competitive advantage is whether or not it is sustainable. In RBV, sustainability is an equilibrium definition that prevails after duplication efforts by competitors have ceased (Lippman and Rumelt, 1982; Rumelt, 1984; Barney, 1991). Penrose (1959) debates the sustainability of the competitive advantage, especially of incumbent firms, highlighting the possible erosion of any competitive advantage in the face of a ‘Shumpeterian shock’4. Similar critique has been raised by D’Aveni (1994), Eisenhardt and Martin (2000) and Fiol (2001), all arguing that a more dynamic view is needed to explain SCA in turbulent industries that evolves fast without necessary experiencing disruptive shocks. As a response to the increasingly dynamic nature of markets multiple scholars have, based on theorization and empirical work, proposed the concept of multiple ‘temporary competitive advantages’ (TCA), rather than SCA (Brown and Eisenhardt, 1998; D’Aveni, 1994; Fiol, 2001; Wiggins and Ruefli, 2005). Most M&A literature, on the other hand, has not been concerned with SCA5. Instead focus is on CA, which is measured by different types of performance indicators. The most common ways to measure CA in the M&A literature is by using accounting profit, innovation output or positive abnormal stock performance (Armstrong and Shimizu, 2007; Newbert, 2007). Of interest is that while SCA often is discussed in the theoretical part, little or nothing is often said about why it is not tested for in the analysis. The logical reason is that SCA, given its non‐calendar definition, is very hard to assess, even using longitudinal studies (Armstrong and Shimizu, 2007). This paper acknowledges that given the event study methodology applied, nothing can be determined about the acquirer’s ability to attain SCA or even if it will achieve TCA. Rather any observation of positive abnormal stock returns made will be assumed to be an indicator of competitive advantage, for which the following definition has been generated for this paper: 4
A Shumpetarian shock is when a new (technological) innovation disrupt old technology and render existing product less competitive 5
A good example is It is Wiggins and Ruefli (2005) that uses a calendar time of 20 years as a measurement in their analysis of SCA and TCA 12 Competitive advantage is superior performance of firms compared to competitors created by the combination and deployment of a set of resources, in its broad definition explained above, that makes it more efficient than its competitors. 1.4.3. Mergers and Acquisitions The aim of this paper is to explain acquirer’s performance in acquisitions. In both academia and business, ‘acquisitions’ (when one company acquires a majority stake in another) and ‘merger of equals’, or simply ‘merger’ (the combination of two firms of about the similar size to form a single company) are used interchangeably or combined in the term M&A. This practice can be questioned, as an acquisition in fact is seen as highly different to a merger in multiple ways. In principle, while reason for an acquisition can be categorized rather easily (resources, market power, cost synergies, market access etc.) the reason behind a merger is often less clear. Multiple mergers are done for the wrong reasons; they do not require cash, it is a cheaper way for a private company to get listed (‘reverse merger’), tax benefits etc. (Mastracchio et al., 2002). Secondly, in an acquisition the roles of the target and acquirer are clear, while in mergers they often are more ambiguous, resulting in value destroying power battles in top management: “Although a merger involves a combination of two or more entities, they are rarely equal participants.” (Mastracchio et al., 2002: 40). Finally, mergers are typically more expensive than acquisitions, often due to legal costs. Connected to this is that value creation of merger is often worse than for acquisition, and they experience an even higher failure rate (when the parties “divorce” into two entities again). Given the reasons stated above, this paper has chosen to exclude all mergers from the analysis (data sample). As such the term ‘acquisition’ is used whenever referring to the sample, hypothesis or analysis. However, given the fact that most literature have focused on both mergers and acquisitions, solely using the term acquisition in relation to literature review and theoretical discussion would be misleading. As such, the term M&A will be used to denote research done by prior scholars. 13 1.4.4. The online business The online business is rather different from other, more traditional industries. Rather than being defined by a specific type of service or product it is as much a technology, as a service or distribution channel. In order to find a proper definition for the online business a good point of departure was deemed to be the Internet, as this is the back‐bone of the online universe. The Internet, however, is not an industry but a general purpose technology affecting many aspects of a firm’s business system (Amit and Zott, 2001). Instead, the literature has focused more on either electronic or virtual markets which are facilitated by the Internet (Uhlenbruck et al., 2006). This has been termed e‐business, or online business, using the terms internet firm and online firm interchangeably. However, there is a lack of clear definitions to these concepts. Some scholars have used revenue to define e‐business, applying a minimum percentage, (e.g. 50 or 90 per cent) of revenue must come from online activities in order for the firm to be classified as an e‐business (Amit and Zott. 2001; Eisenmann, 2006; Park et al., 2004). Others choose to adhered to the newer versions of industry codes (SEC, NAIC 2007 etc.), which have specific online related business categories (Eisenmann, 2006). However, in order to apply them, a clear definition must first be crafted. After reading industry reports as well as academic and more practical articles on the subject, the following definition has been constructed: The online business comprises firms who offer products, services and/or other enablers for all Internet related activities The fact that some revenue is generated by traditional, offline business, is not an excluding factor, which other scholars have argued (Park et al., 2004). In connection to this definition the following key words have been identified as relevant when conducting our initial sample search: Internet, Online, Web, E‐commerce, Digital, Software, Wi‐Fi, and Virtual. Any relevant concept must also have clear delimitations (Bryman and Bell, 2007). In accordance with multiple other scholars, this paper excludes companies that purely produce hardware required for the Internet, e.g. computers, cables etc. (Amit and Zott, 2001; Uhlenbruck et al., 2006). Telecoms are further excluded, even though they provide connectivity infrastructure for the online business. This is because telecoms are regarded as communication, rather than online 14 (Eisenmann, 2006). However, today the boundaries between the traditional industries connected to online business have begun to erode, making it harder, but also more important to be clear on delimitations (Hagel et al., 2008). 2.
PARTII‐Theoreticalandcontextualdiscussion
Part I comprises the theoretic section, which encompasses an initial literature review as well as a contextual analysis of the online business. Subsequently, it contains theoretical discussions of the chosen theories aiming to generate empirically testable hypotheses for what factors are believed to drive success of the acquiring firm in online business acquisitions. The intention is to create a strong theoretical foundation for the empirical analysis put forward in Part II. 2.1.
Initialtheoryreview
In principal, the literature review aims to expand the cognitive boundaries of the authors and guide focus of the study. Through the use of reference analysis, the structure and trends of the applied theories are laid out, which provides a valuable road map for the researchers (Acedo et al., 2006). For the reader, the essence of the conducted literature review is to become familiarized with the RBV theories and their focus on the M&A research field. As such the aim is to prove a fundamental understanding of theoretical foundation and obtain a comprehensive overview of theory development and empirical focus of former works. The literature review further intends to identify gaps in the literature and stipulate how the study wishes to contribute to the RBV academia. However, before the initial review is conducted, a brief discussion about the paper’s view of knowledge and theory application will be put forward. 2.2. Ashortnoteonepistemology
This paper has no intention of providing an in‐depth discussion on research philosophy. In order to make a valid contribution or even discussion on this subject one has to have considerably more 15 knowledge of the subject than the researchers at hand possess. However, it has been deemed relevant for the reader to know that the authors adhere to the ‘realist’ approach to epistemology6. As such, it is assumed that a well‐developed theory and research design can make it possible for researchers to draw conclusions about the unobservable – by studying observable entities and their reactions to different stimuli. This means that a carefully constructed analysis, founded on solid theoretical reasoning and backed up by empirical evidence can provide insight about things that is not directly observable for the researcher. (Godfrey, and Hill, 1995) This philosophical assumption is argued to be of high relevance when dealing with the RBV, mostly due to the theory’s fundamental assumption of causal ambiguity and imitability and their role in creating superior performance for companies: “The power of the (RBV) theory to explain performance persistence over time is based upon the assumption that certain resources are by their nature unobservable, and hence give rise to high barriers to imitation… In short, if there are no unobservable resources, the RBV loses much of its explanatory power.” (Godfrey, and Hill, 1995: 523) Without a realist approach to research in the field of RBV, scholars could never be sure of the existence of many un‐observables, such as tacit knowledge, organizational routines and bundles of capabilities, which are believed to create and sustained competitive performance (Godfrey, and Hill, 1995). Even though the choice of philosophic reasoning has been stated, the limitation on the research strategy implied by the key concepts of RBV will still be discussed as they appear in the remaining part of the method section. 2.3. TheResource‐BasedView
The Resource‐Based View (RBV) (Wernerfelt, 1984) aims to explain the firm’s competitive position by taking an internal focus on its bundle of resources, capabilities and relationships (Barney 1986, 1991; Nelson and Winter 1982; Rumlet, 1984; Teece et al., 1997). Further it looks at how the firm deploys these in order to gain a competitive advantage over other firms by being more efficient and effective in its use of these resources (Dierickx and Cool, 1989; Kor and Mahoney 2004; Lippman and Rumelt 1982; Penrose 1959). Superior efficiency is often expressed as rents, which in 6Epistemologyisthestudyofknowledge
16 turn is defined as the excess value created by a resource, compared to the resource owner’s opportunity costs of capital (Tollison, 1982). As such, RBV deals with Ricardian rents that are created due to resource scarcity relative to demand as well as ‘Pareto rents’7, which is the difference in efficiency (profit) between the first and second best usage of a resource (Mahoney and Pandian, 1992). RBV and its three branches ‐ RBM (Barney 1991), KBV (Grant 1996b) and the DC (Teece et al., 1997), have during the past twenty years played a major role in the research arena of management science (Acedo et al., 2006; Kraaijenbrink et al., 2010). In addition, it has been actively applied to the M&A research area, trying to explain the large and growing M&A activity in a superior manner than other economic theories (King et al., 2004; Newbert, 2007) 2.3.1.
TheevolutionandkeycharacteristicsoftheResource‐BasedView
Aspects of the RBV can be found in seminal works of the economic literature such as Coase’s (1937) work on transaction cost economics, Chandler’s (1962) contribution about scale and scope and Rumelt’s (1974) paper on diversification. However, the evolution of RBV itself traces back to eight influential, and to some extent consecutive, works written between 1959 and 1989. These are: Penrose (1959), Demsetz (1972), Nelson and Winter (1982), Lippman and Rumelt (1982), Rumelt (1984), Wernerfelt (1984), Barney (1986a) and Dierickx and Cool (1989) (Acedo et al., 2006; Rugman and Verbeke 2002; Pitelis, 2004). These epic works each draw upon earlier authors, add new key aspects and provide inspiration for the subsequent scholar(s). It is important to note that others also have contributed to the emergence of RBV (Burgeois and Eisenhardt 1988; Conner, 1991; Mintzberg and McHugh, 1985; Mahoney and Pandian, 1992; Teece, 1982; Teece et al., 1990) though to a lesser extent than the ones mentioned above. Already in its early years, the RBV was discussed, theorized and applied by multiple scholars with different focus (Acedo et al., 2006; Rugman and Verbeke, 2002). However, some common traits emerged rather quickly, which can be summarized as followed: 1.) The firm is a bundle of resources and some create more value for the firm than others 2.) Resource heterogeneity and immobility result in superior performance by some firms 3.) Growth, and its constraints, comes from simultaneously exploiting existing resources and exploring new ones, and 4.) There is a path dependency connected to a firm’s resources. (Acedo et al., 2006; Lockett and Thomson, 2009; 7
Also called ‘quasi rents’ (Klein et al., 1978) 17 Rugman and Verbeke, 2002) These five characteristics, (mostly) formed by the founding papers mentioned above, can be identified in the initial work and further development of all the three branches discussed below; however, different characteristics have been emphasized in each of them. Below, each of the main characteristics of RBV will be discussed. 2.3.1.1. Thefirmisabundleofresourcesandsomecreatemorevalueforthefirmthanothers
This notion is articulated by Penrose (1959) and emphasized by Wernerfelt in his famous quote: “For the firm, resources and products are two sides of the same coin” (1984:171). In short it means that a firm should be seen as a combination of all its resources, knowledge and capabilities that it possesses at a specific point in time. In the same way, all products and services that are produced by the firm can be traced back to its resources and their deployment. The term bundle is interesting as it hints towards the possibility of multiple combinations, i.e. the firm has many possibilities to deploy what it has (Penrose, 1959). As such, resources provide the link between the firm’s services, productive opportunities and profitable growth (Penrose 1959). The next step in the RBV logic is that not all resources are equally valuable; rather, there are a few key resources that the firm use to compete and the quality of these resources determines whether the firms is able to obtain and sustain its competitive advantage. This is the key statement in Wernerfelt (1984), who identifies ‘rareness’ and ‘non‐substitutability’ as two main attributes of superior resources. Rareness of a resource, though first properly defined by Barney (1991) is a way to ensure that superior performance cannot be copied. While it is acknowledged that rareness per se, does not make a resource valuable, valuable resources that are not rare have not been given much focus in the RBV literature8. In a similar manner the resource needs to be non‐substitutable, because otherwise its rareness is irrelevant (Wernerfelt, 1984). RBV acknowledges that the value of a resource can be different to different firms (Barney, 1986; Penrose, 1959; Wernerfelt, 1984). This implies that if one firm values a resource higher than another, it can acquire it, either on the open market or by acquire the whole firm that owns the valuable resource (Wernerfelt, 1984). However, in order for acquisitions to create value, the target must indeed be more valuable to the acquirer than on its own: 8AnotableexceptionisBarney1989,whicharguesthatavaluableresourcecan,ifnotrare,keepthefirmata
competitiveparity,i.e.makingitsurviveeventhoughitisnotperformingsuperiortoitscompetitors
18 “Since acquisition is a purchase in which the price must be agreeable to the seller as well as to the buyer, the acquired firm must be more valuable to the acquiring firm (or combined with it) than it is to itself (or by itself); for only under these conditions will the one be willing to sell and the other be willing to buy at the same price.” (Penrose: 1959, p. 129) This quote lies at the heart of the RBV’s M&A literature, which tries to explain the large and growing volume of acquisitions and mergers made each year. One way for the acquirer to ensure value creation is if it is more efficient in deploying the target’s resources (Penrose, 1959). Still, it is generally considered of key importance to know the true value of the target. Firms with superior knowledge about the target’s true value will outperform peers in acquisition activities in two ways. On the one hand they will avoid the ‘winners curse’ (Bazerman and Samueteon, 1983) of paying a premium for the target, and on the other they will be able to attain undervalued targets due to incorrect valuations by the market. In order to gain superior knowledge, firms should focus on analysing internal resources and capabilities, such as specific production techniques and unique managerial experience, and how these can match with potential new resources (Barney, 1986a). Though luck also plays a part in picking the right targets, the more accurate the acquirer’s expectations are the less importance this exogenous factor plays (Barney, 1986a). 2.3.1.2. Resourceheterogeneityandimmobilityresultinsuperiorperformancebysomefirms
The term heterogeneity, and its endogenous nature in the RBV, is unique compared to many other economic theories that try to explain firms’ existence and prosperity9 (Conner 1991; Mahoney and Pandian, 1992). RBV focus both on the emergence of heterogeneity (Barney, 1986; Dierickx and Cool, 1989; Penrose, 1959; Wernerfelt, 1984) and its persistence (Demsetz, 1973, Lippman and Rumelt, 1982, Rumelt, 1984). Heterogeneity among firms emerges due to the above explained differences in firms’ resources as well as their deployment. A simple explanation to this initial 9
Other economic theories view the firm either as a way to overcome market failures, e.g. transaction cost economics by Coase, (1937), or ignore it completely like Porter’s five forces (1980). Sometheoriesacknowledgesfirmsizeand
productivityasdistinctivefactorsamongfirms,butmoregranularexplanationsarenotgiven(Conner1991)
19 heterogeneity is the unique historical context and related learning process attached to each firm. However, most scholars within the RBV have tried to explain the emergence of heterogeneity in more sophisticated ways. The ambiguous aspect of the future value of assets makes it hard for firms to determine which resources to focus on to ensure future performance. This creates ex ante limits to competition (Barney, 1986a; Peteraf, 1993; Rumelt, 1984) and as a consequence, firms with superior knowledge of the true value of their and others’ resources can outperform their peers (Barney, 1986a). In addition to having different resources and capabilities, firms also differ in the way they deploy these. De facto, firms with identical resources might still produce different services due to idiosyncratic deployments, leading to dissimilar productive opportunities and consequently different financial performance: “The service that resources will yield depends on the capacity of the men using them, but the development of the men is partly shaped by the resources men deal with. The two together create the special productive opportunity of a particular firm”. (Penrose, 1959: 78‐79) This quote highlights the nature and impact of resource heterogeneity among firms. The individual firm’s value creation depends on a complex interaction between prior and current resources, and the current ability of the firm to deploy them. As such, ex ante limits to competition and idiosyncratic deployments ensures the emergence of heterogeneity. ‘Isolating mechanisms’, then, ensure ex post limitations to competition, as they prevent imitation of resource allocation (Lippman and Rumelt, 1982; Rumelt 1984). This can be due to multiple reasons. First of all, most resources are tied to firms at least ‘semi‐permanently’, meaning they are sticky and thus hard to acquire, develop or divest in the short‐term (Wernerfelt, 1984). Furthermore not all valuable resources are tradable (Dierickx and Cool, 1989). As a consequence, the losers must either substitute or imitate the valuable resource(s) of the winners (Dierickx and Cool, 1989). Imitability is thus a central concept to the RBV, which means that competitors are not able to copy successful performance. This can be due to property rights, cost of learning and development, or ‘casual ambiguity’, which means that the relationship between performance and firm resources is unclear. The term ‘uncertain imitability’, coined by Lippman and Rumelt (1982), which is a product of causal ambiguity (Demsetz, 1973) indicates competitors’ 20 inability to copy successful strategies due to their failure to understand what drives performance. This ambiguity is seen as a key reason for both for the emergence and perseverance of heterogeneity among firms, making it a key isolating mechanism: “Thus, the ambiguity that generates the initial heterogeneity will also act to block its homogenization through imitability” (Rumelt, 1984: 562). Firms’ M&A activities are thought to be a way to circumvent the problem posed by isolating mechanisms, as it allows firms to obtain otherwise non‐marketable bundles of resources (Wernerfelt, 1984) and in that way ensure superior performance (Demsetz, 1973). Another reason for imitability is the process connected to ‘resource accumulation’ (Dierickx and Cool, 1989, Nelson and Winter, 1982). Factors such as ‘time compression diseconomies’ (it is hard to generate the same value in a shorter period of time), ‘asset erosion’ (resources need to be maintained in order not to lose value), ‘asset mass efficiency’ (incremental resource investments are more valuable when existing asset stock is large) and routines (knowledge of how to search is based on past search experience) will affect the imitability of strategic resources, assets and capabilities. (Dierickx and Cool, 1989; Nelson and Winter, 1982) Again, acquiring a company can be a solution to many problems connected to resource accumulation. A good example is when companies deploy an M&A strategy as a complementary or substitute strategy for in‐house R&D (Capron, 1999; Cefis, 2009; DeMan and Duysters, 2005). In highly dynamic industries where in‐
house research often is an ineffective way of keeping up with market development, platform building M&A strategies are a normal phenomenon (see section 2.6. for further discussion) (Viguerie et al., 2008). Thus, the two first key attributes of RBV summarises that firms are made up of heterogeneous resources, and that these and their deployment will determine the firm’s competitive position relative to its peers. Acquisition is seen as a way to obtain resources that the firm cannot develop effectively in‐house, and which cannot be bought in isolation. Thus the key to value creating acquisition is that the acquirer can create more value by using the target’s resources than the target could have done by itself. The last two of the RBV’s key characteristics are today more connected to the branch‐theories of KBV (Grant, 1996a, b) and DC (Teece et al., 1997); however, dynamic aspects of RBV can indeed be traced back to RBV’s cradle. 21 2.3.1.3. Growth,anditsconstraints,comesfromsimultaneouslyexploitingexistingresources
andexploringnewones
In order to grow, the firm must strike a balance between exploiting current internal resources and exploring new ones, either internally or externally (Penrose, 1959, Rugman and Verbeke 2002). This balance between ‘routines’ (gaining experience in performing certain tasks) and ‘search’ (looking for alternative ways of resources and their deployment), is regarded as an optimal evolution for the firm (Nelson and Winter, 1982). As a firm gets more efficient in deploying its resources, slack (un‐used resources) is created. By exploiting this slack, the firm is able to grow by market penetration or related diversification (Penrose, 1959). The notion is also supported by Dierickx and Cool (1989) in their discussion about growing and maintaining strategic assets stocks and their importance for the development of the firm. However, in order to ensure competitiveness, the firm must also explore new resources (Penrose, 1959), which can be done using ‘stepping stone’ resources that bridge current and desired resources (Wernerfelt, 1984). Exploration of new resources is also of key importance for firms wishing to venture into unrelated markets (Rumelt, 1974). Here, managers are thought to play a central role as they are responsible for identifying, exploiting and developing profitable opportunities (Castanias and Helfat, 1991; Penrose, 1959). In a similar way that resources enable growth, they also restrain it. In order to grow, the firm needs resources. This internal re‐deployment of resources and increased organizational burden can distort operational effectiveness and thus lead to decreased performance or stagnation in the subsequent period, an often observed phenomenon in fast‐growing firms (Penrose, 1959). Acquisitions have been acknowledged as a short‐term solution to this so‐called ‘Penrose effect’ (Hay and Morris, 1991) as they allow the firm an injection of resources, which can result in sustained profitable growth over multiple periods. However, it still poses problems of managerial time constraints due to the necessary focus on integration (Penrose, 1959). 2.3.1.4. Thereisapathdependencyconnectedtoafirm’sresources
Path dependency is articulated and discussed in depth by Teece et al. (1997) (see section 2.7.4.), however, its implication on firm growth was already recognized by the RBV’s founding scholars. Paths play a central role in a firm’s expansion strategies (Wernerfelt, 1984) as firms can be 22 expected to behave in the future according to the routines they have employed in the past, a concept termed ‘dominant logic’ (Prahalad and Bettis, 1986). In short this means that history matters (Nelson and Winter, 1982). This is due to the cognitive and motivational biases of managers and employees were sunk cost often plays an important role. Managerial participation in the firm’s acquisition and development of resources is a key explanation of path dependency, helping to explain firm heterogeneity (Penrose, 1959). As managers, rather than being as value‐
maximizing robots, are unique in terms of competencies and biases, they heavily influence the faith of the firm. A further implication of paths is the cumulative aspect of technology (R&D) for the development of firms (Nelson and Winter, 1982). Using the metaphor of a bath tub, Dierickx and Cool (1989) emphasise path dependency by stating that while flows of the asset stock through in/divestments can be adjusted instantaneously (in by the tap and out through the tub drain), stocks (the actual water in the tub) cannot be adjusted as fast. As such, past choices of firms will affect future range of possible choices and subsequently performance (Dierickx and Cool, 1989). Even though M&A activities can be argued to overcome path dependency (Eisenmann 2006; Noe and Parker, 2005) they are also highly affected by it. One implication is that an initial strategic motivation for an acquisition (for example growth), tends to transform into a so called dominant logic for future acquisitions, even though the strategic focus of the firm might have changed (Côté et al., 1999). Furthermore, due to the path dependency connected to both firms and resources, it might not always be possible to successfully absorb and exploit even tradable resources (Nelson and Winter, 1982). Above, a first review of the RBV, its four cornerstones and implication for firms’ acquisition activities have been outlined and discussed. These attributes can be identified in all of the three branches discussed in below sections – RBM, KBV and DC, though different characteristics have been emphasized in each of them. 2.3.2 CritiqueoftheResource‐BasedView
When reviewing RBV and its three branch theories, of notice is that much of the critique is reoccurring. A simple explanation is that all theories are built on the same basic assumptions and have similar key concepts, though they have chosen to focus on different aspect of the RBV’s 23 concept of resources. As such, all critique relevant for this paper has been summarized below, rather than being repeated in connection to each branch theory. The key criticism to the RBV is that it is tautological, reducing its empirical testability (Kraaijenbrink et al., 2010; Lockett and Thomson, 2009; Priem and Butler, 2001a, b). Priem and Butler (2001a) conclude that RBV is built upon analytic statements, as it defines competitive advantage in similar terms as ‘resources’. This is a valid critique and applying it to the study at hand it could imply that superior acquisition performance of some acquirers actually was due to the high performance of the targets they had acquired. Most key concepts connected to the RBV and its branch theories have further been accused for being inadequately defined: ‘Resources’ are thought too broad (Kraaijenbrink, 2010); ‘knowledge’ is too abstract and simplified (Felin and Foss, 2005; Håkanson, 2010) and ‘capabilities’ have too many definitions (Winter, 2003; Danneels, 2008). Secondly, a lot of scholars have raised the concern that empirical studies within RBV do not really measure the true source of superior performance (Priem and Butler, 2001a, Lockett and Thomson, 2009, Kraaijenbrink, 2010). This is connected to the problem of ‘causal ambiguity’, which results in ‘uncertain imitability’10. Related to this is an observation made by Barney (1988) himself, which has some interesting implications for the field of M&A study. Building on the lack of coherent empirical evidence that M&A creates value (Lubatkin, 1987; Singh and Montgomery, 1987), Barney argues that: “Firms cannot expect to obtain above‐normal returns from acquiring targets when several other bidding firms all value these targets in the same way” (1988: 78). Thus, the only way for M&A to generated abnormal returns is when the true value of the target to the acquirer (the ‘synergetic cash flow’) is un‐known to both target and acquirer at the time of acquisition, or if the resource making the synergetic cash flow is imitable11 (Barney, 1988). This has even more implications for the concept of ‘knowledge’ in acquisitions. If the acquirer plans to incorporate the target in order to absorb its knowledge, it will benefit from codified and explicit knowledge (Kogut and Zander, 1992). However, if the knowledge is of an explicit nature, the risk of competitors copying it increases and the acquisition will thus become less valuable (Winter, 1987). 10Theseconceptsmeanthatcompetitorsareunabletocopysuperiorperformanceduetotheuncertaintyofhow
itiscreated,i.e.whichcombinationofresourcesthatareinplay.Seesection2.7.2.forfurtherdiscussion
11Imitablemeansthatitcannotbecopied.Seesection2.7.2.forfurtherdiscussion
24 The above discussed could be a valid explanation for why the literature indeed has struggled to establish proof that M&A is value creating (King et al., 2004)12. The final criticism of the RBV relevant to this study is the fact that RBV has been argued by many to be a highly static approach to competitive advantage and SCA (Lockett and Thomson, 2009; Priem and Butler, 2001a; Barney 2002). The general notion is that the RBV produces a snap‐shot like view of a firm’s current resources. As a result, it has problems explaining superior performance in dynamic industries with high technological and market change (Barney, 2002; Kraaijenbrink et al., 2010), such as the online business. These areas of critique have serious implication for the paper at hand. The risk of tautological elements in the findings and conclusions is present, both due to the concepts and measurements used, as well as the fact that the analysis is an event rather than longitudinal study, which creates a more static view, increasing risk of tautological reasoning. To mitigate this risk, all key concepts have been thoroughly defined (see section 1.3.). In a similar manner great emphasis has been put into the measures to ensure that they measure what is relevant rather than what is feasible (see section 3.1.3.). In addition, by using abnormal stock returns to measure performance, the risk of tautological reasoning should be mitigated. Given the assumption that the stock market has access to all relevant information, prior performance per se should not result in abnormal returns. Finally, the problem of RBV being static is acknowledged by authors. As the online business constantly evolves, a dynamic theory is necessary to create valuable insights. However, it is argued that the RBV indeed has dynamic elements and the paper at hand has focused on these when building the model to empirically investigate the online business. 2.4. Identificationofresearchgap
Reviewing the literature sheds light over many valuable insights that RBV has generated in terms of firms’ motive(s) for engaging in M&A activities; as a way to acquire resources otherwise unobtainable, to complement or supplement existing resources, or to take over resources that currently are deployed inefficiently. However, even though current academia is impressive, 12 A solution to this paradox is that the target’s knowledge indeed is tacit, and that the acquirer, rather than
incorporatingitwillmakeitintoacentreofexcellence.Thoughinteresting,centresofexcellenceisregardedout
ofscopeastheyarenotrelatedtotheacquirersAC.
25 especially considering its rather young age, there are still several gaps that need to be filled by future research. Three central aspects have been identified and add up to what is defined as the research gap, in which this paper aims to make a contribution to the existing literature: 1.) Lack of attention to the direct link between firm’s resources and acquisition performance 2.) Disperse theory application leading to lack of academic consensus, and 3.) Underrepresentation of studies focusing on the online business and related industries. Even though multiple scholars have investigated M&A through the lenses of RBV, there is still a need to extend the understanding of resources’ impact in M&A performance (King et al., 2008). It is notable that the majority of RBV researchers focus on more indirect links between resources and SCA. Scholars have investigated resources’ impact on firms’: innovation output (Ahuja and Katila, 2001; Cloodt et al., 2006; Cohen and Levinthal 1989, 1990), knowledge transfer (Castro and Neira, 2005; Haspeslagh and Jemison, 1991; Ranft, 1997; Ranft and Lord, 2000), strategic flexibility (Henderson and Cockburn., 1994) and absorptive capacity (Puranam et al. 2009; Vermulen and Barkema, 2001; Very and Shweiger, 2001). Even though these factors in general are believed to lead to increased performance, focusing on the direct link between a proposed factor and M&A performance would positively contribute to the literature (Barreto et al., 2010). Secondly, in pace with the growing body of RBV literature on the subject of M&A, diffusion of focus, concepts and measurements is increasingly creating confusion in the field: “For many basic questions in the (RBV) literature, the reader will find contrary findings, different measurements, unclear international implications and the use of a range of partly overlapping constructs” (Cloodt et al., 2006: 643) This problem is believed to have two roots, a self‐centred approach to theory development and lack of consensus how to apply the RBV theory empirically. Already in 1999, Larsson and Finkelstein pointed out that the M&A researchers only are vaguely informed about each other’s work. As opposed to applied science, social researchers seldom replicate each other’s studies (Hubbard and Vetter, 1992, 1997; Fuess, 1996; Cloodt et al., 2006), which can be seen as an 26 explanation to the above quote13. In addition, while RBV argue that unique and valuable bundles of resources, rather than single ones, are the true source of SCA, empirical scholars have mostly focus on one or a few factors which they argue drives M&A performance. These isolated resources, knowledge stocks or capabilities, have further been tested on widely different data samples using different measures and dependent variables. Recent years’ increase in literature reviews can be seen as an indication of this confusion as younger scholars try to structure the ever increasing body of research, create sense of it and raise awareness of the diffusion problem (Acedo et al., 2006; Armstrong and Shimizu, 2007; Barreto, 2010; Kraaijenbrink et al., 2010; Locket and Thomson, 2009; Newbert, 2007; Wang and Ahmed, 2007). In addition to the noted self‐centrism, the flexibility of RBV has been misused resulting in lack of consensus of how to apply the theory empirically. Some scholars stringently adhere to one of the branch theories (King et al., 2008; Matusik and Heeley, 2005; Zahra and George, 2002) and argue that all relevant concepts from the RBV belong to their chosen branch. Others apply a pluck‐and‐
play attitude, using whatever fit their purpose without acknowledging that many of the key concepts steam from different branch theories to the RBV (Ahuja and Katila, 2001; Uhlenbruck et al., 2006). It can be argued that a core strength of RBV is its flexibility which allows scholars to mould it to different situations. Still, this paper argues that the given field of literature would benefit from having more specified frameworks that were flexible enough to be applied to different empirical settings. Finally, prior RBV research have focused either on overarching samples including firms from multiple industries (Swaminathan et al., 2008), or traditional industries where the pharmaceutical and biotech industry have been the most popular (Arora and Gambardella, 1990; Kirchhoff and Schiereck, 2001; Sorescu et al., 2003). However, surprisingly few scholars have focused on the online business and its related industries and segments, especially after 200114. If looking at other field of academia, a much larger interest is noted, with specific theories being developed as well as 13
A few notable distinctions exist where influential papers have focused on re‐examining past findings such as Cloodt et al. (2006) and King et al. (2008). 14 Indeed, finding recent academic papers focusing on the online business has been very hard. There are off
courseafewnotableexceptionssuchasUhlenbrucketal.(2006)andEisenmann,2007.Pleaseseesection2.6.4.
forfurtherelaborationonacademicworkfocusedontheonlinebusiness
27 traditional ones extended to fit the new context of the online universe. One plausible explanation for this lack of focus is that the online business is a constantly evolving phenomenon, making it a moving target few scholars chose to invest effort in as the time span for research relevance gets shorter. Another reason, identified above, is that the RBV is not deemed fit for such dynamic environments as the online business. Either way, this absence is not sustainable for a theory wishing to lead the field of strategic management as the online business already is on the top of all managers’ agenda. Concluding on the above discussion this paper argues that there is a prominent gap in the RBV literature’s ability to explain value creation in acquisitions. This gap relates to the theory’s inability to leverage insights from its broad conceptual spectra, to create a structured and holistic model that can investigate the direct link between specific bundles of resources and financial performance. This model should furthermore be flexible and dynamic enough to allow for application to a multitude of industries, even the more turbulent ones such as the online business. 2.5.
Contribution to academia To state that this research paper provides an undisputed solution to the above identified challenges of RBV would be to fall into the self‐centred trap argued for above. Instead, the aim of the researchers has been to identify, based on a thorough literature review, an operational framework that uses prominent concepts within the RBV to explain the key drivers of performance in acquisitions. The framework is flexible enough to be applied to different acquisition contexts, reducing the need for a constant re‐invention of the wheel. Operationalizing this framework using the complex online business as empirical field of study creates further value as previously confirmed drivers of performance are re‐tested in a new context. This helps to bring consensus about what factors should be investigated when trying to explain acquirer’s performance in acquisitions. This contribution has been asked for by multiple scholars arguing that in order to contribute to further development of the RBV, time and effort must be put into creating a comprehensive overview and increased consensus in the current research pool (Armstrong and Shimizu, 2007; Kraaijenbrink et al., 2010; Locket and Thomson, 2009). A solid analysis of the online 28 business has been conducted, which contributes to understanding of key drivers and challenges, as well as how acquisitions have been used as a response to these. Above, an initial literature review of the RBV has been outlaid in order to provide a general theoretical introduction as well as a mapping of early influential academia. In addition to providing a basic understanding of RBV’s cornerstones and key concepts, the review has provided insight into some of the existing gaps where this paper hopes to contribute to the literature. Before continuing with the more in‐depth review and theoretical discussion of RBV’s three branch theories, it is deemed necessary to get a thorough understanding of the research context – the online business. As mentioned, this is a surprisingly under‐studied research setting, further increasing the relevance of the forthcoming section. 2.6.
Contextualanalysis‐Theonlinebusiness
In a similar manner as the literature review helps to establish frames for the theoretical discussion and hypothesis generation, it is important to understand the context of the research as this too has implications for further theoretical discussion. The aim of the following section is three folded: To put forward a brief introduction to the development of the online business and its M&A activity; to identify important performance drivers of the industry; and to review academic work on the subject in order to identify any important research gap this paper helps to fill. By doing this the paper at hand hopes to create a contextual lens that can be used in the second part of the literature review and theoretical discussion. This lens will help to identify key concepts to use and influence how to build the hypotheses. First, however, a brief rational for why the online business has been chosen as research context will be put forward. 2.6.1. Rationalforchoosingtheonlinebusinessasconnectforanalysis
There are two main reasons for why the online business is regarded as an interesting and relevant context for applying the RBV to identify performance drivers behind acquisitions. In principle the characteristics of the online business fits with the need to extend the RBV’s contextual reach into more turbulent industries. The online business is characterized by so‐called ‘hyper‐competition’: 29 “An environment characterized by intense and rapid competitive moves, in which competitors must move quickly to build advantage and erode the advantage of their rivals” (D’Aveni, 1994:217–218) Due to the highly dynamic nature of the online business, companies must be able to quickly absorb information and knowledge externally, in order to constantly keep up with technological development and changing customer demand (Ahuja and Katila, 2001). In addition, organizational agility is highly important due to the constant development and restructuring of the industries connected to the online business. Secondly, marketing and technology capabilities have both been identified as the two key capabilities in the online business (Park et al., 2004). This is partly due to the fact that other traditional activities, such as production, are less common for online companies. However, it is also because technology is the backbone and key driver of the industry, and the ability to attract and retain customers is a key driver of competitive advantage in markets characterized by low entry barriers and high and constantly changing customer demands (Noe and Parker, 2005). As such, the common critique that measures of marketing and technology fail to provide insight of the true source of competitive advantage is less relevant in the online business context. 2.6.2. Industrydevelopment
An in‐depth historical overview of the development of the online business is neither deemed possible nor necessary for the scope of this research paper. Instead, some key developments affecting the industry will be presented. In the late 1990s, the disruptive innovation of the Internet led to an IPO and M&A boom. Small entrepreneurs tried their wings on the public market and in 2000, Internet related acquisitions accounted for over 20 per cent of global M&A activities, measured in both number of deals and value (Marock et al., 2000). A lot changed with the bursting of the Dotcom bubble in March 2000, and the subsequent bear market. As multiple online companies went bankrupted skilled labour was freed up. This meant that companies could now hire employees to provide the capabilities they earlier had to acquire a whole company to get, resulting in a fall of online related acquisitions. The IPO activity also decreased, due to the lack of liquidity and investors’ risk adversity towards online firms. (Uhlenbruck et al., 2006) This, however, did not mean that the 30 Internet revolution had stopped. A whole new business sector aroused with the creation of e‐
commerce (Park et al., 2004), and already in 2002 it was estimated that over 90 per cent of all U.S firms had some part of their trade conducted over the Internet (Amit and Zott, 2001). In 2008, approximately one billion people were estimated to have access to the internet, a number which is expected to increase rapidly with the growing middle class in emerging countries. In addition to pure expansion, the online universe has also developed incredibly in terms of technical applications. The Web 2.0 is a good example. This concept is used to describe the plethora of technologies and services used by companies to improve communication with customers and end users. These technologies allow for customer feedback, interaction and co‐
creation of new products and services (Keen, 2008). It has resulted in greater ability to share ideas; improved access to knowledge experts; reduced costs of communications and operations, and decreased the time‐to‐market for products (Rehm et al., 2012). Another recent development is that digital giants (Google, Facebook, Apple etc.) have started to cross into each other’s industries, eroding traditional boarders of the related online segments (Hagel et al., 2008). Apple, who earlier was mainly a hardware producer, has recently started to provide both online enabling software (iCloud) and content (iTunes). Google, who started up as an online platform provider, now has entered into bordering segments such as online content (YouTube and music streaming) and also mobile platforms (Android). 2.6.3. M&A activities in the online business Even though the M&A activity within the online business slowed down after the Dotcom bubble (relative to pace of online acquisitions prior to the market correction), online companies are still acquired in a speedy manner, and an acquisition strategy is still the fastest way to implement new technologies and other strategic initiatives within this segment. The rapidly shifting environment exhibited in the online business makes acquisitions a good way to reduce bounded rationality and time compression diseconomies (Nelson and Winter, 1982; Noe and Parker, 2005). Like in most other industries, M&A activities in the online industry have been one way for companies to mitigate late mover threats (Eisenmann, 2006). In fact, multiple firms that helped to form the Internet have later had troubles keeping up and online giants have used acquisitions to stay in the 31 game (Rindova and Kotha, 2001). In addition it has been argued that constant change is a way to keep competition behind, since many online businesses otherwise are subject to imitation due to their relatively transparent business models (Amit and Zott, 2001; Porter, 2001; Rindova and Kotha, 2001). Acquirers of online firms are typically large, listed companies, both off‐ and online. Targets, on the other hand, are characterized by smaller size and a more entrepreneurial nature, where firms often are backed by venture funds or other private investors (Eisenmann, 2006; McKinsey Quarterly, 2012; Uhlenbruck et al., 2006). The rationale behind online acquisitions was in the early 2000s more competence seeking, either as firms acquiring online firms to gain access to completely new technologies, or online firms acquiring other online firms in order to extend their core business or product line (Uhlenbruck et al., 2006; McKinsey Quarterly 2001). The online industry is also characterized by a rather high level of positive feedback15¸ resulting in winner‐takes all scenarios such as Facebook, EBay and Google (McIntyre and Subramaniam, 2009). While some scholars argue this to be exogenous factors affecting online related industries (Noe and Parker, 2005) other empirical studies show that firm strategy is highly important (Shilling, 2002). Acquisitions can play a central role in creating this positive feedback, e.g. through access to increased customer base (Evans and Wurster, 1999) and acquiring complementary technologies and products that enhance customer value (McIntyre and Subramaniam, 2009; Schilling, 2002). 2.6.4. Academic work on the industry Academic work relating to the Internet and online industry must be regarded sparse considering its economic impact on the world during the last 20 years (Park et al., 2002), and even more so after the Dotcom bubble busted in March 2000 (Uhlenbruck et al., 2006). Initial theoretical research discussed whether the online industry needed a new set of theoretical lenses than traditional industries (Amit and Zott, 2001) or if the Internet rather should be seen as: 15
Positivefeedbackisatendencyforleadingfirmstofurtherreinforcetheirlead,whereasthosefallingbehindacceleratetheirdecline
(Arthur,1996;McIntyreandSubramaniam,2009).
32 “…a powerful set of tools that can be used, wisely or unwisely, in almost any industry and as part of almost any strategy.” (Porter, 2001:64) Viewing the internet simply as a set of tools, would open for the possibility of detecting an unidentified subgroup with positive abnormal returns in the sample, through classifying the targets into industry categories. This said, the online business has been labelled an industry for the research purpose, consistent with Uhlenbruck (2006). In terms of more concrete theory generation fit for the unique circumstances of the online universe, Amit and Zott’s work (2001) is the only academic paper found which has been cited by multiple relevant sources. Arguing that none of the traditional economic theories can fully explain the value drivers of e‐business, they identified the four key levers to be: transaction efficiency, vertical and horizontal product and service complementary, lock‐in (switching costs) and novelty in product and service offering. The value creation of the Internet has been one key focus, where marketing and customer relationships (Eisenmann, 2006), as well as cost savings resulting from increased operational efficiencies (Litan and Rivlin, 2001) have been identified. The value to the end‐consumer has also been investigated and confirmed, e.g. how the Internet lowers prices through increased transparency and lower search costs (Litan and Rivlin, 2001). A second focus area of research has been on the value drivers of the online business. Here, economies of scale have been deemed important (McKinsey Quarterly, 2001; Park et al., 2004), taking form of dominant technological standards (Hill, 1997) and/or network effects (McIntyre and Subramaniam, 2009). In connection to value drivers the focus can be found on growth strategies of Internet firms. Internationalization of online firms has mainly been driven by brand reputation as well as home page clicks (Kotha et al., 2001). Of interest is that empirical evidence show that late movers in the Internet industry after 2001 seem to experience an advantage compared to first‐movers. This is opposite to many other industries and to what might have been expected. (Eisenmann, 2006) In terms of M&A research in the online business, major works have tried to prove that Internet acquisition per se creates value (Uhlenbruck et al., 2006) and that online M&A activities by key players affect competitors’ stock prices negatively (Akhigbe and Martin, 2002). Finally, Uhlenbruck et al. (2006) have also investigated whether the combination of offline and online, or online and 33 online acquisitions create more value, though without any clear results. Due to the fact that the majority of prior researchers’ observations were made before the stock market correction of 2000, there is a clear need for a new sample after the dot.com bubble. While both theoretical and empirical studies have been conducted on Internet and online related samples, the authors have failed to find any study trying to identify key drivers of M&A performance within the online business. The hypothesis generated in this study have been tested in more traditional industries such as biotech (Kirchhoff and Schiereck, 2011), chemicals (Ahuja and Katila, 2001), foods (Swaminathan et al., 2008) and “high tech” (Cloodt et al., 2006). However, no similar study has been conducted in an online relevant context. It is highly important to test whether these variables thought to affect acquisition performance in traditional industries also have predictive ability in the online business. There are several factors that distinguish e‐business from traditional bricks‐and‐mortar sectors, such as the role of information (Porter, 2001), Transaction costs and customer reach (Amit and Zott, 2001) and network effects (McIntyre and Subramaniam, 2009; Shilling, 2002). As such this paper tries to fill the gap identified by using RBV to build an empirically testable model that can investigate performance drivers in online business acquisitions. After conducting a contextual analysis on the online business focus will no return to the RBV, where a more in‐depth analysis of its three branch theories –RBM, KBV and DC, will be made. The insight generated above will help guide the review literature and theoretical discussion, as well as the hypotheses generation of which factors are thought to drive acquirer’s performance in the online industry. 2.7.
In‐depththeoreticalreviewanddiscussion
As the study at hand draws upon three theories within the RBV – RMB, KBV and DC, the subsequent theoretical section is rather extensive. Below, the review and discussion have been structured into silos, one for each theoretical branch, in order to create a better overview for the reader and then provide more in‐depth analyses of the central concepts chosen in each theory. It 34 should however be noted that the theories are highly inter‐related, and as such a certain level of repetition has been hard to avoid. 2.7.1. The three branches of the Resource‐Based View In the beginning of the 1990s, the RBV gets branched off into three distinct, yet connected theories (Acedo et al., 2006; Locket and Thomson, 2009) ‐ the Resource‐Based Model (RBM) (Barney 1991), the Knowledge‐Based View (KBV) (Grant 1996a, b) and Dynamic Capabilities (DC) (Teece et al., 1997). The RBM focuses on the characteristics of resources that can lead to (sustained) competitive advantage, introducing the VRIN criteria where a resource must be ‘valuable’, ‘rare’, ‘imitable’ and ‘non‐substitutable’ (Barney 1991). The KBV focuses on knowledge as the most important resource for firms to create and SCA (Grant, 1996a, b). Finally, DC builds on a more dynamic view of resources (Dierickx and Cool, 1989; Nelson and Winter, 1982; Penrose, 1959), stating that is the ability to constantly identify, obtain, develop and divest strategic resources that is the true source of competitive advantage (Teece et al., 1997; Eisenhardt and Martin, 2000). Below, each of the three branches will be introduced and discussed with a focus on the key concepts chosen for further analysis, their relevance for the M&A literature up to date. Each theory sections end with a main and two to three sub hypotheses being generated that are believed to help explain superior acquisition performers in the online business. 2.7.2. The Resource‐Based Model The Resource‐Based Model (RBM) tries to concretize the RBV by testing whether or not specific ‘resources’ hold the potential of competitive advantage and SCA (Barney, 1991; Wernerfelt, 1984) 16
. The model used is called VRIN and it determines whether a resource is heterogeneous and immobile. VRIN evaluates the resource on four parameters: ‘Value’, the extent to which it helps the firm to take advantage of opportunities and reduce threats (Porter, 1980, 1985); ‘Rareness’, meaning that fewer firms control it than is needed for perfect competition conditions to apply (Barney, 1991, Hirshleifer, 1980); ‘Imitability’, the failure of other companies to copy it, which can be due to its unique historical conditions (Arthur et al., 1987), causal ambiguity (Demsetz, 1973; Lippman and Rumelt, 1982; Rumelt, 1984) and/or social complexness (Barney, 1986b; Dierickx and 16
In a similar, though less concrete manner, Prahalas and Hamel (1990) try to connect a firm’s ‘core competences’, with ‘core‐ ’ and ‘end products’ which determines the firm’s competitiveness. Even though Prahalas and Hamel’s paper came first, Barney’s (1991) is still seen as the founding paper of RBM. 35 Cool, 1989); and finally ‘Non‐substitutability’ by other resources that are not rare and/or imitable themselves (Wernerfelt, 1984). Of interest is that Barney (1991), in contradiction to Barney (1986) concludes that resources that are VRIN, and thus holds SCA potential, cannot be acquired through M&A activities: “Rather, such advantages must be found in the rare, imperfectly imitable, and non‐substitutable resources already controlled by the firm.” (Barney, 1991: 117) By this statement, Barney (1991) adheres to Dierickx and Cool (1989) theoretical proposition that only resources developed within the firm can lead to superior performance. This indicates that the RBM should be less applicable for analysing firms that wish to obtain resources through acquisitions rather than from internal development. However, judging from the large pool of theoretical and empirical work made on M&A using VRIN assumptions, subsequent researchers have challenged Barney’s (1991) conclusion. In fact, the RBM has been applied on a plethora of empirical studies trying to explain the nature of M&A and determine when and how M&A activities create value for the firms involved and their shareholders. In line with Uhlenbruck et al. (2006), this paper’s objective is to detect a possible subgroup in a rapidly shifting environment, which may or may not experience abnormal returns. In dynamic environments, organizations are often limited as to what extent and how fast they can develop new resources (Dierickx and Cool, 1989). This bottle neck can be solved by acquisitions, facilitating the opportunity to transfer resources, capabilities and key employees between organizations (Ahuja and Katila, 2001; Karim and Mitchell, 2000). Even though the “make versus buy” discussion is highly interesting and relevant for the online business, it has been deemed outside the scope of this paper. Rather, the remaining part of the section will review the part of RBM that has tried to explain M&A performance by examining the strategic resource fit of the target and acquirer. 2.7.2.1.
Key concept ‐ Alignment of strategic emphases In a descriptive analysis Capron et al. (1998) investigate the nature of resource re‐deployment between acquirer and target. They find that acquirers look for two types of targets, firms with strong resources they can use, or resources gaps they can fill. Some researchers have focused on specific resources that ensure value creation in M&A (James, 2002; Uhlenbruck et al., 2006) 36 though few have been confirmed17. Focus on the combination of resources has been more insightful, even though consensus has not yet been reached (Datta et al., 1992; Shelton, 1988; Hitt et al., 1998; Swaminathan et al., 2008). The paper at hand has termed this focus ‘alignment of strategic emphases’ (ASE), where strategic emphasis indicates which resources the firm uses to compete. Multiple scholars have investigated the interconnectedness between acquirer and target firm resources (King et al., 2004), whether supplementary (Datta et al., 1992; Shelton, 1988; Singh and Montgommery, 1987) or complementary resources (Harrison et al., 1991, 2001; Hess and Rothaermel, 2011; Hitt et al., 1998; Uhlenbruck et al., 2006) is key to value creation in M&A. King et al. proposes that both substitution and complementing can be value‐adding in acquisitions: “Identifying a role for both resource substitution and resource complements in acquisitions also offers new insights into target firm selection, the source of value from firm resources, and acquirer performance.” (2008: 335). This belief, that context matters is further investigated by Swaminathan et al. (2008). Introducing acquisition motive as context specific angle, they conclude that when the motive of the M&A is diversification, complementary resources creates greater value, while supplementary resources result in higher value when firms acquire to consolidate18. This paper adheres to the theoretical belief that the fit between acquirer’s and target’s strategic emphases (complementing versus supplementing resources and capabilities) will have greater influence on acquirer’s performance in acquisitions, than any specific resource on its own. 17
ItwasaninitialinterestoftheresearchertoidentifyVRINconnectedmeasures.Newbert(2007)findsstrong
empirical support for empirical studies, analysing the different elements of VRIN. However, when taking a
second look at the studies, few are applicable for the chosen research method. The only VRIN measure
investigated in quantitative studies is ‘imitability’, which have only been measured using patents, which is
deemedanun‐optimalmeasurefortheonlineindustry.
18
Thisground‐breakinglensofcontextwasinitiallychosenforfurtherinvestigation,asitwasbelievedtohold
potentialofaddingconsiderablevaluetotheliterature.Howeverduetooverlapping,tautologicaldefinitionsand
highpresenceofsubjectivityinmeasurements,thecontextspecificanglewasnotincludedinthefinalmodel.It
willinsteadbecommentedoninsection4.2.4.
37 2.7.2.2.
Theoretical discussion and hypothesis generation As mentioned above it is believed that a key driver of acquirer’s acquisition performance is how the resources currently held fit with those added by the target, i.e. the alignment of strategic emphases (ASE). The subject adds to existing research by using RBM to explore heterogeneity in firm performance through an examination of target and acquiring firm resource interactions (King et al., 2008). The rationale is as much founded in theory and empirics as in logic thinking. The combined value of entity A and B should be highly dependent on how the resources of these two entities interact, whether they enhance or diminish each other’s existing effectiveness and efficiency. However, the logic for which type of fit creates the highest value is not as straight forward. Earlier studies offer a highly contradicting discussion on whether resource supplementary (Shelton, 1988; Singh and Montgommery, 1987) or complementary (Harrison et al., 1991, 2001; Hitt et al., 1998; Larsson and Finkelstein, 1999) resources generates greater post M&A performance. Supplementary resources are by definition equal or highly similar to existing ones. Similar knowledge is easier for the acquiring firm to absorb and assimilate; often making the integration process often runs smoother with less disruption of existing value adding activities for both firms (Helfat and Lieberman, 2002). In addition, supplementing resources increases ability to leverage economies of scale, resulting in cost savings and increased market power (Shepherd, 1979). Even though this notion often is related to more traditional, manufacturing industries, it has also shown to have impact on newer, more dynamic industries such as the online business19. However, scholars have also identified negative effects of supplementary resources. If new resources are too similar to existing ones, the opportunity for learning is reduced (Hitt et al., 1996; Ahuja and Lampert, 2001), and can thus affect the innovation output of the acquirer negatively (Cassiman et al., 2005). This can be explained by increased path dependency (Makri et al., 2010; Ahuja and Lampert, 2001). The ‘familiarity’ ‐, and ‘propinquity traps articulated by Ahuja and Lampert (2001) state that firms that constantly seek supplementing resources and technologies, tend to limit their novel inventions and instead produce familiar solutions close to existing ones. Hess and Rothaermel (2011) argue that resources that are too similar can create a substitutive relationship, 19
Foradiscussionofeconomiesofscaleintheonlinebusinesspleaseseesection2.6. 38 where the presence of one resource decreases the marginal return another, and destroying value in the organization. This hypothesis is further empirically confirmed to hamper the firm’s innovative performance. Complimentary, on the other hand, increases the scope of the acquirers existing resources. As such, value is created either by extending existing resources, or providing new ones. A good example of the former would be Google acquiring YouTube in order to extend its capabilities in online content offering (video streaming). Google’s acquisition of Motorola, on the other hand, is an example of the company acquiring additional capabilities. In both examples, the acquisition of complementary resources increases Google’s ability to create value for its customers through product innovation and development. Taking a RBM perspective, multiple scholars hypothesize and empirically confirm that acquisition performance will be higher when acquiring and target firm resources complement one another (Capron and Pistre 2002; Hitt et al. 1998, 2001; Hess and Rothaermel, 2011; King et al. 2003; Puranam et al. 2006). However, resources and capabilities much different from existing ones are harder to integrate and require more resources (Ahuja and Katila, 2001; Grant, 1996). As a result of this, performance of the acquirer can actually decrease post M&A performance (Hitt et al., 1996). Recently, attention has been paid to the importance of specific resource fits (King et al., 2008; Christman 2000; Tanriverdi and Venkatraman 2005), with positive consensus of the combination of target’s technological resources with acquirer’s marketing resources (Datta et al., 1999; King et al., 2008; Moorman and Slotegraaf, 1999; Song et al., 2005). Taking point of departure in King et al. (2008) this paper investigates the relevance of ASE in the online industry. More specifically it is the aim is to confirm whether complementary alignment between acquirer’s and target’s strategic emphases increases acquirer’s acquisition performance. In addition, it will further be investigated whether the specific alignment between acquirer’s strategic marketing emphasis and target’s strategic technology emphasis outperforms other combinations of strategic emphases alignment20. In section 2.6., it is argued that marketing and technology capabilities both have been identified as the two key capabilities in the online business (Park et al., 2004), and that 20
Other possible alignments would be acquirer’s technology with target’s marketing emphasis, or complementary alignments in terms of marketing‐marketing or technology‐technology. 39 important acquisition motives comprise of the desire to attain technological capabilities and know‐how not possible to develop in‐house (Bower, 2001). Empirically, targets are often acquired for their technological resources, while acquirers are appraised for having strong marketing skills (King et al., 2008). However, given the key drivers of the online industry, it cannot be rejected that online targets can be sought for their marketing as well as. Based on the above discussion, this paper argues that the RBM literature can serve as a platform to forecast the best interconnection between target and acquirer resources as the ideal alignment between acquirer’s and targets strategic emphases. More specifically this paper stipulates the following hypotheses: Hypothesis 1 – Alignment of acquirer and target’s strategic emphases has a positive impact on acquirer’s acquisition performance In the online context this main hypothesis is believed to be supported by two sub hypotheses, namely: Hypothesis 1a – In the online business, complementing alignment between acquirer and target’s strategic emphases has a positive impact on acquirer’s performance Hypothesis 1b – In the online business, the alignment of acquirer’s strategic marketing emphasis and target’s strategic technology emphasis has a positive impact on acquirer’s performance. 2.7.3. The Knowledge‐Based View The second branch of RBV is KBV, which focuses on knowledge and how it can be used by the firm to reach competitive advantage. According to the KBV, knowledge is the most important resource, especially in dynamic markets (Quinn 1992) as unique sets of knowledge and their integration in the firm generate the capabilities that can lead to competitive advantage (and SCA) (Grant, 1996; Leonard‐Barton, 1995). It is primarily the ‘imitability’ (see section 2.7.2. for explanation) that makes knowledge so important (Grant, 1996b). Knowledge embedded in individuals, often referred to as ‘tacit knowledge’, can easily disappear if the employee quits. As such, the firm is viewed as an institution for integrating knowledge where its primary role is applying knowledge, 40 rather than creating it (Grant, 1996b). In a similar way as RBM, KBV also places a large focus on the two former key characteristics of RBV ‐ firms are made up by resources that are heterogeneous, where knowledge is seen as the key resource that distinguishes winners from the losers. However, it also directs attention to the latter two of the key characteristics – the balance of exploiting existing and building new knowledge stocks, and the aspect of path dependency. KBV strongly acknowledge the importance of developing new knowledge, which is believed to happen through the integration of external and internal knowledge (Grant 1996a, b; Kogut and Zander, 1992) In addition, the importance of path dependency is highlighted by most KBV scholars as how it affects the firm’s ability to identify and integrate new knowledge (Grant 1996a, b; Kogut and Zander, 1992). Given its nature, KBV is highly applicable on acquisitions, as they result in integration of internal and external knowledge and can be a way to overcome path dependency (Noe and Parker, 2005). Furthermore, as is discussed in section 2.6.3., this is even more relevant in the online industry where acquisitions often are made in order to stay in the game, or get back on track after a wrong turn due to path dependency. 2.7.3.1.
Key concept ‐ Absorptive capacity A key concept within the KBV is ‘absorptive capacity’ (AC), indicating the extent to which a firm can absorb external knowledge and add it to existing. More specifically, AC is defined as: “The ability of a firm to recognize the value of new, external information, assimilate it, and apply it to commercial ends.” (Cohen and Levintal, 1990:128) AC forms a large part of the RBV academia, both within KBV and DC. However, given its definition, the authors argue that it is more relevant for the KBV21. This definition indicates a strong focus on how knowledge is handled by the company, especially how it is taken from outside the firm and integrated with the aim to enhance existing knowledge. Even though AC often is referred to as something belonging to a company, it involves individuals, groups, and organizational levels. However, the focus of AC in this study is on corporate level, which includes “unobservables” such 21
Assuch,DCliteraturewillbeappliedsparselyandonlywhenconceptisdeemedhighlyrelevantforthe
analysisandnotconflictingwiththeKBV 41 as documentation procedures, routines, heuristics, and know‐how that create a common understanding of knowledge (Foss et al., 2010; Grant, 1996b; Matusik and Heeley, 2005). Though highly celebrated as a truly ground breaking theory (Foss et al., 2010; Van den Bosch et al.; 2003), AC’s complex nature makes it a challenging concept to apply in empirical studies. However, this complexity has also spurred a large interest in the subject. As a result, a plethora of empirical studies have focused on the key elements of AC, and how the firm can enhance it. A key element of AC is knowledge base (Ahuja and Katila, 2001; Cloodt et al., 2006; Cohen and Levinthal 1989, 1990), where both absolute and relative size affects chances of successful M&A. Prior experience is also believed to have a positive effect on AC, though empirical evidence are somewhat mixed (Vermulen and Barkema, 2001; Very and Shweiger, 2001; Inkpen et al., 2000). Finally, knowledge relatedness has been stipulated to have a positive effect on AC, as multiple scholars have shown that learning is facilitated by a similar knowledge base (Cohen and Levinthal, 1990; Lane and Lubatkin, 1998; Rosenkopf and Almeida, 2003). An interesting aspect of AC is the notion of ‘potential’ versus ‘realized’ AC (Zahra and George, 2002). Potential AC concerns the ‘acquisition’ (the ability to identify and acquire valuable external knowledge) and ‘assimilation’ (the ability to analyse and understand the information obtained) of knowledge, while realized AC involves ‘transformation’ (the ability to combine new knowledge with existing) and ‘exploitation’ (the ability to leverage new knowledge to extend existing, or create new capabilities) of knowledge (Zahra and George, 2002) (see figure 2). The acquisition of other companies plays an important part of the potential AC. This framework does however indicate that acquisitions per se cannot assure that the potential AC is actually realized. Realized AC is argued and empirically confirmed to have a stronger correlation with performance than potential AC (Lichtenthaler, 2009; Zahra and George, 2002) However, without potential AC, the firm will not be able to renew itself, making performance unsustainable (Zahra and George, 2002). 42 Figure 2 – Zahra and George’s (2002) framework for potential and realized absorptive capacity AC is argued by the authors to play an important role in the success, or failure, of acquisitions as it increases chances that the acquirer can identify the right knowledge (the right target) and properly integrate and leverage it to commercial ends. As such the following section is dedicated to provide a theoretical argumentation for the hypothesis that absorptive capacity of the acquirer increases acquisition performance. Furthermore, a discussion of the chosen way to measure AC will be put forward, where acquirer’s prior acquisition experience, absolute size of acquirer’s knowledge base and relative size of target’s knowledge base are believed to be three key factors affecting acquirer’s AC and thus its acquisition performance. 2.7.3.2.
Theoretical discussion and hypothesis generation The importance of AC in dynamic markets (Woiceshyn and Daellanbach, 2005) as well as in acquisitions (Auja and Katila, 2001) has both been theoretically and empirically proven (Foss et al., 2010). Acquisitions are often used by firms that wish to acquire knowledge they currently lack. A higher level of AC will not only increase the chances that the acquirer recognizes the “right” targets (that have the relevant knowledge), but also that it is able to integrate and apply this knowledge in a way to increase value. In terms of explaining AC, the literature provides a wide range of factors. Most of these are related to the nature of target’s knowledge and its relativeness to existing knowledge, the knowledge base of both acquirer and target and prior experience of acquirer (Foss et al., 2010). Though acknowledged as an important factor affecting AC, the nature and relatedness of knowledge has been excluded from the discussion. The reason is that it is deemed to be too overlapping with the 43 prior factor – ASE, to be valuable to the analysis. Instead, focus has been placed on the remaining factors, acquirer’s prior acquisition experience and acquirer and target’s knowledge bases, which will be elaborated on below. Prior acquisition experience is believed to strengthen the acquirer’s AC22. One of the many challenges that a firm has to face when dealing with acquisitions, is fostering learning from the acquisition process; both acquisition specific as well as general learning (Haspelagh and Jemison, 1991b). Acquiring firms strengthen their knowledge structures and capabilities related to the acquisition process by being exposed to a multitude of events in connection to the process (Levinthal and March, 1993; Levitt and March, 1988; March, 1991). This should result in an increased capacity to identify and leverage knowledge in future acquisitions. Knowledge flows between two different entities, such as acquirer and target, has been identified as a key influencer of AC (Foss, 2006). The success of this flow is further affected by prior experience (Foss et al., 2010). Furthermore, as the acquirer also obtains the target’s external knowledge domain, i.e. its AC, (Ahuja and Katila, 2001) each acquisition can be seen as an injection of additional AC to the acquirer’s existing. If adhering to Zahra and George’s (2002) framework of AC, prior acquisition experience is highly correlated with the first element, ‘acquiring’ knowledge. By looking at a company’s acquisition history, it is possible to determine if it is good at identifying knowledge that it deems valuable to its existing operations (see figure 3). In general, there is a lack of empirical consensus on the impact that prior experience has on M&A performance (Karim and Mitchell, 2000). Fuller et al. (2002), confirm a positive relationship between experience and stock market reaction, while Higgins and Rodriguez (2006) find evidence for a negative correlation. In their study of US acquisitions, Hitt et al. (1998) found that acquirer and target experiences a successful integration if acquirer had considerable M&A experience, and that synergies between acquirer and targets assets are more efficiently realized. Capron et al. (1998, 1999, 2002) concludes that a firm’s potential adaption and value creation increases by the resource integration that follows an acquisition, supported by Bruton et al. (2004) who found that 22TherearescholarswhoarguethatM&Aexperienceinfactshouldberegardedasadynamiccapabilitybyitself
(EisenhardtandMartin,2000).However,asarguedabove,AChasbeendeemedmorerelevantfortheKBVand
as such it is seen as an important element for firms to create value from new external value, rather than as a
specificcapability
44 previous M&A history leads to success in acquiring distressed firms. Meanwhile, Kirchoff and Schierek (2011) propose that the effect of prior acquisition history is not immediate, suggesting a quarantine following the merger, to allow the firm to reconfigure. Secondly, absolute size of the acquirer’s existing knowledge base is believed to affect AC positively in multiple ways. First of all, knowledge stocks and flows are argued to be related to the recognition, assimilation, and utilization of new knowledge (Foss, 2006): “The ability to evaluate and utilize outside knowledge is largely a function of the level of prior knowledge” (Cohen and Levinthal, 1990:128). This means that the more you know, the easier you have to identify and learn new knowledge. In their seminal work on AC, Cohen and Levinthal (1989, 1990) argue and empirically prove that by expanding internal knowledge base, firms also improves their ability to identify valuable external knowledge and further absorb and exploit it internally. This implies that absolute knowledge base is connected to both the ‘assimilation’ and ‘transformation’ elements of Zahra and George’s (2002) AC framework as it will increase the chances that the firm will understand the new knowledge acquired and find a way to combine it with existing resources. In addition, incremental value adding is believed to be higher when knowledge base is large (Dierickx and Cool, 1989). This argument can be illustrated by a simple example where company A and B both have the opportunity to invest one million USD in development of a new technology. Company A, has a large knowledge base, and thus have a lot of knowledge to leverage, both in terms of generally applicable knowledge and knowledge that might be related to the new technology. Company B, on the other hand, does not have a knowledge base. All other things equal, company A is more likely to gain a higher return on the investment than company B. The impact of absolute knowledge base of acquirer is empirically proven to have a positive effect on acquisition performance (Ahuja and Katila, 2001); however, the effect has been accused of being rather short term, especially in highly turbulent industries where knowledge depreciates fast (Cloodt et al., 2006). Finally, it has been found that relative size of target’s knowledge base also affect the acquirer’s absorptive capacity during the specific deal, though this relationship is negatively correlated. By simple logic it can be understood that it could be hard for an acquirer to absorb something that is 45 larger than itself, as this requires a lot of resources and creates the question of who is absorbing who. As such, relative size of target’s knowledge base can have an adverse effect on the ‘assimilation’ element of Zahra and George’s (2002) AC framework (see figure 3). A key driver of assimilation is comprehension. The share size of a large knowledge base can create a needle in a haystack problem for an acquirer if its own knowledge base not is large enough to comprehend all information. A number of empirical studies conclude that relative size of target’s knowledge base has a negative impact on M&A performance (Ahuja and Katila, 2001; Cloodt et al., 2006; Ranft and Lord, 2000; Nahavandi and Malekzadeh, 1988). In fact, solely acquiring small targets has been deemed as a key reason for acquirers’ success in multiple case studies (Drexhage, 1998; Chatman et al., 2005). The reason for this observation is believed to be that complexity, cost and time to identify, integrate and exploit target’s knowledge base increases with its relative size compared to the acquirer’s (Ahuja and Katila, 2001). Complex integration distorts resources, such as management and key employee time, and thus disturbs existing innovation, (Ahuja and Katila, 2001; Hitt et al., 1996) R&D activities (Gerpott, 1995) and other value creating activities for both target and acquirer (Chakrabarti, et al., 1994; Haspeslagh and Jemison, 1991). As the market knows that integration is a vital part of post‐acquisition performance (Haspeslagh, and Jemison, 1991) relative size of knowledge base will have an reversed affect acquirer’s performance (Ahuja and Katila, 2001; Cloodt et al., 2006; Ranft and Lord, 2000).23 Managers, on the other hand, do not seem aware of the fact that buying targets with too large knowledge bases can be value destroying. There might be several reasons for such a gap between theory and practice. First of all, managers might not know the actual size of the knowledge base they acquire (Granstrand and Sjolander, 1990). However, then it is not plausible that the stock market would be able to know it either. Rather, managers might be over‐confident in what they believe their firm can manage to absorb, i.e. they misjudge their own knowledge base (Ahuja and Katila, 2001). Due to the multitude of managerial biases reported in the literature this reason is argued more plausible24. 23AlltheabovescholarshavefocusedonM&Awherethegoalistointegratethetarget.However,ifthetargetis
kept as a separate unit, then relative size of target’s knowledge base should not affect the M&A performance
negatively. Multiple studies confirm that autonomy (Castro and Neira, 2005; Krug and Nigh, 2001; Ranft and
Lord,2000),centreofexcellence(HolmandPedersen,2000)andtotalseparation(Kaleetal.,2009)allaregood
strategiestoimprovevaluecreationofpostM&Aperformance.However,inorderfortheacquirertoabsorband
exploitthetarget’sknowledgeandcapabilities,acertainlevelofintegrationisneeded(Makrietal.,2010).
24TheAgencytheoryprovidesagoodoverviewofmanagerialbiasesthatmightleadtovaluedestructioninM&A
(seeforfurtherdiscussionofthesubject) 46 Figure 3 – Operationalization of Zahra and George (2002) model of AC To sum of the above discussion, this paper argues that AC contributes to acquirer’s performance in acquisition as this will help the firm to better leverage the new knowledge from the target. Looking at figure 3, it can be seen that these three indicators cover three out of the four elements stipulated by Zahra and George (2002) in their framework of AC25. This increases the credibility of the analysis in two ways. Firstly, multiple measures connected to different aspects of AC increases the chances that any insights indeed manages to conclude something about a firm’s AC, which is rather unobservable in itself. Secondly, it will create an opportunity to a more fine grained analysis of which aspects, except exploitation, that contribute to acquirer’s acquisition performance. Finally, by trying to operationalize an acknowledged theoretical framework this paper provides an example of how to add value to the literature while building on existing work, which hopefully can inspire others. As such, the following hypotheses are argued: Hypothesis 2: There is a positive relationship between acquirer’s absorptive capacity and its acquisition performance 25Thefinalelement–exploitation,wouldhavebeeninterestingtomeasure.However,givenitscharacteristicsit
canonlybemeasuredbyoutputrelatedmeasureswhichcouldnotbeidentifiedfortheonlinebusiness
47 Absorptive capacity is believed to be positively affected by prior acquisition activity of the acquirer, both due to prior injections of AC as well as the positive effect that prior knowledge flows. Absolute knowledge base is also important as prior knowledge is seen as a prerequisite to recognize and learn and leverage new. The relative size of the target’s knowledge base, on the other hand, has a negative effect on acquirer’s performance as it requires larger integration focus, which in turn distorts resources from value creating activities. Hypothesis 2a: Acquirer’s prior acquisition experience has a positive impact on acquirer’s performance Hypothesis 2b: Absolute size of acquirer’s knowledge base has a positive impact on acquirer’s performance Hypothesis 2c: The relative size of target’s knowledge base has a negative impact on acquirer’s performance 2.7.4. Dynamic Capabilities DC addresses a source of competitive advantage for firms that need to reconfigure in rapidly shifting environments (Amit and Schoemaker, 1993; Lee et al., 2002; Teece et al., 1997)26. Teece et al. (1997) were the first to define ‘dynamic capabilities’ as they are known today27: “We define dynamic capabilities as the firm's ability to integrate, build, and reconfigure intimal and external competences to address rapidly changing environments” (Teece et al., 1997: 516) Related to the first cornerstone of RBV28, DC can be classified into varying levels and hierarchies. In a more sophisticated model, firm ‘assets29’ and ‘capabilities’ can be segmented into a hierarchical order, ranked by their ability to drive competitive advantage. The zero‐order is assets that can achieve competitive advantage potential when they are VRIN. Following, capabilities can be found, 26ItisimportanttonotethatthispaperdiffersbetweenthetheoryofDynamicCapabilities,whichistermedDC,
andcapabilitiesthatarereferredtoasdynamic–‘dynamiccapabilities’
27 In the following decade, more than 1.500 articles cited Teece et al. (1997) when defining, applying and
developingthetheoryofDCinmultipledirections(Barreto,2010).
28Thefirmisabundleofresourcesandsomecreatemorevalueforthefirmthanothers
29Notethat‘assets’isusedtoindicate“inputs”.Seesection1.2.3.forfurtherdiscussion
48 leveraging the competitive advantage when they deploy the VRIN assets. The next order in the hierarchy is the bundle of assets and capabilities that strategically aim to secure the firm’s competitive advantage. Lastly, the highest level can be reached when assets and dynamic capabilities shift in pace with the environment in order to create SCA. (Zahra et al., 2006) If the firm, on the other hand, fails to adapt to its environment, the dynamic capabilities endure the risk of becoming ‘core rigidities’ (Leonard‐Barton, 1992). Core rigidities are irrelevant or even harming capabilities, which at the same time are a source, and a product of path dependency, paralyzing the firm (Leonard‐Barton, 1992). Recent arguments in DC suggests that firms both need to develop skills through internal development and external sourcing to be able to renew their capabilities and thrive over time (Helfat et al., 2006). Some of the key assets and capabilities constituting dynamic capabilities cannot be bought due to imperfect factor markets and non‐
tradability. Examples of these are intangible assets like values and culture as well as learning capabilities (Teece et al., 1997). External sourcing of new capabilities through acquisitions can help the firm to overcome this problem and develop dynamic capabilities that both guard against obsolescence and resolve organizational inertia (Karim and Mitchell, 2000; Rosenkorpf and Nektar, 2001; Vermeulen and Barkema, 2001). In addition acquisitions may trigger corporate restructuring, which is another type of renewal also an important capability in dynamic environments (Lei and Hitt, 1995). The DC has a strong focus on path‐dependency, though it is viewed rather differently by contributing scholars. Teece et al. (1997) argue that a particular path leads to a distinct capability and characterize dynamic capabilities as idiosyncratic processes that emerge from path‐dependent histories of individual firms. This is because endowments are sticky in the short run, meaning that firms are stuck with what they have, and have to live with what they lack (Teece et al., 1997). This stickiness can be found partially due to complexity in developing new capabilities in pace with the shifting environment (Dierickx and Cool, 1989), and partially to non‐tradability for tacit know‐how (Teece, 1980) and reputation (Dierickx and Cool, 1989). On the other hand, several paths can lead to the very similar dynamic capabilities, even though they are idiosyncratic in detail and thus never can be identical across firms (Eisenhardt and Martin, 2000). Either way, path dependency formulates that history matters, and that previous investments and repertoire of routines are of 49 importance. As discussed earlier, acquisitions are believed to both solve and enhance the problem of path dependency (Eisenmann, 2006; Noe and Parker, 2005; Côté et al., 1999) Some scholars link DC to rapidly changing environments (Burgelman, 1994; Lee et al., 2002). In high‐velocity markets, also called hyper‐competitive markets (D’aveni, 1994), changes are often unpredictable and these markets tend to be more ambiguous and shifting. Dynamic capabilities become less dependent on existing capabilities and more on rapidly creating new, situation‐
specific ones (Argote, 1999). Rindova and Kotha argue that “the focus of firms competing in such (hyper competitive) environments should be on renewing rather than protecting their sources of competitive advantage.” (2001: 1276). As such, dynamic capabilities in high‐velocity markets are most effective as simple and flexible routines that quickly can be applied to a new context, rather than complex procedures that slow down the flexibility of the firm’s learning (Burgelman, 1994; Eisenhardt and Martin, 2001; Eisenhardt and Sull, 2000). As discussed above, the online business is regarded as a high‐velocity market experiencing hyper competition. Looking at large players in this industry (Google, Cisco, IBM etc.) most seem to cope with the rapidly changing environment by executing an acquisition strategy buying multiple smaller targets with specific capabilities. Their success could imply that this might be the most efficient way to adapt to the key characteristics of fast technological development and changing customer demands. 2.7.4.1.
Key concept ‐ Adaptive capability Among the multitude of dynamic capabilities hypothesized, tested and discussed, this paper has recognized the ‘adaptive capabilities’ (ADCAP) as most relevant for the study of acquisition performance30. ADCAP can be defined as the ability to identify and capitalize on emerging market opportunities (Chakravarthy, 1992; Hooley et al., 1992), indicating the firm’s external focus and ability to take action and manage change (Thomas, 1996). These types of dynamic capabilities are reflected through the strategic flexibility of assets and capabilities, and the firm’s ability to align these with environmental changes (Rindova and Kotha, 2001). In order for a firm to generate ADCAP it must have organizational slack (unused resources), processes that ensures continuous 30Dynamiccapabilitiescanbebrokendowntothreecategories;adaptivecapabilities,absorptivecapabilitiesand
innovativecapabilities(WangandAhmed,2007).Absorptivecapabilityissynonymtoabsorptivecapacity,which
hasalreadybeendiscussedandappliedinKBV.Innovativecapabilities,thoughacknowledgedasimportantfor
firmstogenerateCAandSCA,arebelievedtohavelessimpactontheM&Aperformanceperse.
50 gathering of market information as well as organizational capacity that can leverage slack resources in order to respond to market information (Chakravarthy,1992). Given the characteristics of ADCAP, it is believed to play an important role in firms’ ability to secure performance in acquisitions. The rationale is that firms with a high degree of ADCAP will be able to identify threats and opportunities is their environment and analyse what assets and capabilities they need in order to react in a value maximizing manner. Finally, when acquisition is deemed the best alternative, ADCAP increases the ability to identify targets that 1) possess such assets and capabilities, and 2) matches the acquirer is other ways needed for an acquisition to create value (culture, valuation, organizational structure etc.). In a similar way, these firms will also know when acquisition is not the most value maximizing option, even though potential targets exist (Rindova and Kotha, 2001). As such, this paper argues that there is a positive relationship between the acquirer’s adaptive capability and its acquisition performance. This statement will be further elaborated below, as well as the factors considered by the authors to be indicators of ADCAP in the online business – a decentralized organizational structure and a broad market focus. 2.7.4.2.
Theoretical discussion and hypothesis generation In environments characterized by complexity and turbulence, ADCAP has been identified as a prerequisite for superior business survival, product development and SCA (Oktemgil and Greenley, 1997; Hurley and Hult, 1998; Vorhies et al., 1999). A reason for superior performance is that firms with higher ADCAP have the ability to anticipate change ahead of time and have an organization that is agile enough to quickly adapt to new challenges (Chakravarthy, 1992; Lant et al., 1992; Walker and Rukert, 1987). Rhindova and Kotha (2001) identify how online giants constantly “morph” in both form and function in order to stay competitive: “First, they (Yahoo and Exite) morphed from search engines (that is, providing navigational tools) into destination sites (providing content). Second, they morphed into Web portals (providing broad‐based Interactive services). Each transformation entailed (1) a strategic thrust to generate or regenerate competitive advantage and (2) evolution of the organization to respond to shifting competitive conditions.” (Rindova and Kotha, 2001: 1268) 51 The quote highlights how the two companies through their ability to identify and capitalize on emerging market opportunities managed to ensure competitive advantage. The case studies further highlight the strong relationship between ADCAP, successful acquisition strategies and executions, and the firms’ ability to survive and create competitive advantage.31 Success was reached by choosing when to focus on in‐house and when to acquire, who to acquire, and how to properly integrate the targets (Rindova and Kotha, 2001). Except for the integration, all the mentioned aspects argued to be highly correlated to ADCAP. On the other hand, some scholars argue and empirically confirm that there are no differences in performance in relation to level of adaptability (Miles and Snow, 1978; Slater and Narver, 1993). Hrebiniak (1980) concludes that market adaptability is positively associated with performance up to a point, but that at higher levels there is a negative association. The neutral or even negative relationship between ADCAP and performance is explained by inefficiencies. In order to have sustainable ADCAP, the firm need to keep a certain level of slack resources, which could be used to increase operational effectiveness (McKee et al., 1989; Zammuto, 1982). Slack is emphasized as being a pre‐requisite as well as indicator of ADCAP. Of notice is that none of these studies have been applied in a context of acquisitions, which seems like a rather unexplored field in the ADCAP literature. Rindova and Kotha (2001), even though not a specific focus on acquisitions, have a high context specific relevance to the paper at hand and have thus been used as one of few guiding papers. As such it is not only argued that ADCAP indeed is an important factor affecting acquirer’s performance in acquisitions, but further that an analysis of how it affect acquirer’s performance in acquisition greatly will contribute to the literature. ADCAP can be de‐coded into three different focus areas for the firm: technological, market and organizational. The technology aspect concerns the firm’s technological learning process and its agility in switching between different technologies (Tuominen et al., 2004). Here, so‐called technologies of scope (technologies with multiple applications) are the central theme and as a 31
Yahoo successfully used multiple acquisitions to ensure continuous “morphing” and turned out as the
dominantplayerinthewebnavigationsegment,whileExite’sacquisitionstrategyfailedresultinginalossofits
position.
52 result R&D is a key focus. As R&D forms the backbone of AC in this research paper, the technological focus will not be further treated in this section. Instead, the remaining part examines on the other focus areas, firms’ market and organizational focus. Organizational focus is affected by both structural aspects, such as report and control systems, and less hierarchical aspects of organizational processes, for example cross‐functional integration (Achrol, 1991; Tuominen et al., 2004). In order to maximize value, firms need to balance the structural aspect so that decision rights lie in the hands of those with superior knowledge regarding the matter (Jensen and Meckling, 1992). Centralized decision‐making often results in cost of transferring knowledge, while decentralized decision‐making increases control costs. While knowledge costs are hard to quantify they are seen as both direct costs related to transferring knowledge to the decision maker, and indirect cost associated with delays and errors due to the decision maker’s inferior understanding of the matter. In general, cost of knowledge transfer increases with the level of specialization. Control cost are connected to all processes put in place to ensure that managers act in accordance with owners’ interest (i.e. that they are pursuing value maximizing activities). Examples of control cost are reporting systems, incentive programs, internal auditors etc. (Christie et al., 2003). Which structural system that creates most value for the firm is highly context specific, as it depends on how the firm can ensure that the right decisions are made at the lowest cost possible. In industries characterized by fast technological development, specialized knowledge is highly important, increasing costs connected to knowledge transfer. Furthermore, when competition is fierce, the importance of speed in decision‐making is enhanced (Christie et al., 2003) As such, de‐centralization is regarded to be the most effective formal structure, as it increases the firms’ ability to make fast and accurate decisions to successfully adapt to its environment (Christie et al., 2003). This is further the organizational structure argued to be the most efficient for continuous “morphing” in the Internet industry (Rindova and Kotha, 2001), and is therefore also argued to be relevant for the online business analysed in this paper. A further sign of this is that M&A budgets have been a growing phenomenon for business units, becoming part of their growth strategy (Inkpen, 2000). Looking at the example with Google’s 74 acquisitions during the past seven years it is easy to understand that if all these had been the responsibility of the CEO (in a centralized company), little time would have been left for other strategic decisions. In addition, the cost of transferring all relevant 53 knowledge connected to each acquisition to top management would result in unsustainable costs of knowledge transfer. Furthermore, by giving business unit managers the responsibility to find their own targets, motivation is likely to increase, leading to increased employee commitment, more solid research and other similar factors believed to affect performance positively. Finally, a decentralized structure can be seen as an efficient way to handle slack resources, earlier argued to be a prerequisite for sustained ADCAP. The external focused is related to the firm’s ability to adapt its product and service offerings to shifting customer demand32. Firms can either have a broad or narrow market focus. Firms characterized by the former are ’market sensing’, meaning that they fast can identify changing market trends. The latter are ‘customer linking’ firms, which are more focused on serving the specific demand of a few customers, often resulting in close partnerships (Day, 1994; Heide and John, 1992). Again, context plays and important role in determining which of these focuses are connected to superior performance. This paper argues that a broad market focus is more likely to lead to competitive advantage in the online business. The combination of globalization and fast technological development means that the firm must have multiple antennas to keep up with embryotic technology innovations and indications of changes in customer preferences. As such, a market sensing strategy should equip the firm better than having to rely on the information provided by a limited group of customers. Furthermore it has been proven that customers’ demand often is based on their current situation, not potential future needs due to changing environments. As such, trusting customers to provide insight for future market trends can be dangerous due to their myopic biases (Christie et al., 2003). Concluding on the above discussion this paper argues that ADCAP is an important factor in determining the acquirer’s acquisition performance as it will enhance the firm’s ability to identify environmental changes and decide when and how to best capitalize on them through the use of acquisitions. The overall hypothesis is thus argued as following: 32Inadditiontocustomerfocusexternalfocuscanalsobemeasuredbycompetitorfocus.However,inorderto
graspafirm’sfocusoncompetitoraqualitativeresearchmethodisnecessary,ascompaniesseldomrevealpublic
dataontheirmonitoringofcompetitors
54 Hypothesis 3: There is a positive relationship between acquirer’s adaptive capability and its acquisition performance Seeing as ADCAP concerns the firm’s ability to scan the market and react fast and accurate to threats and opportunities identified. As such, a decentralized organisation with broad market focus is believed to generate ADCAP in the online business. Subsequently, the factors indicating presence of ADCAP are hypothesized as: Hypothesis 3a: In the online business, a decentralized structure in acquirer firm has a positive impact on acquirer’s performance Hypothesis 3b: In the online industry, a broad market focus of acquirer has a positive impact on acquirer’s performance 2.8.
Theoretical conclusion In the above section, a profound theoretical review and discussion has been applied to each of the three branch theories of the RBV with the aim to identify areas of the theories that can help clarify superior acquisition performance in the online business. In short, RBM, KBV and DC all try to explain firm’s competitive advantage by taking an internal look at resources, knowledge and capabilities and how the firm identifies, absorb and acquire, and reconfigure and exploit these. Though empirically debated, M&A is argued to be a viable alternative to attain resources; however, in order for the acquirer to ensure performance, multiple aspects must be taken into consideration. From RBM, a focus on the combination of resources is applied by looking at the alignment of acquirer and target’s strategic emphases. A complementary alignment is believed to be connected to positive abnormal return, specifically when the acquirer has a strategic marketing emphasis and the target a strategic technology emphasis. From KBV, the importance of the acquirer’s ability to identify new information externally, integrate, and leverage it to commercial ends is acknowledged through the concept of AC. AC contributes to acquirer’s performance in acquisition as this will help the firm to better leverage the new resources from the target in the online business. Both acquirer’s past experience in acquisitions and absolute knowledge base are argued 55 to increase its AC as existing knowledge enhances the ability recognize, learn and leverage new knowledge. The relative size of the target’s knowledge base, on the other hand, has a negative effect on acquirer’s performance as it is argued to require larger focus on integration, which in turn distorts resources from other, value creating activities. Finally, from DC, the importance of the firm’s ability to identify and adapt to external changes is recognized through the concept of ADCAP. ADCAP is an important factor in determining the acquirer’s acquisition performance as it will enhance the firm’s ability to decide when and how to best capitalize on identified threats and opportunities through the use of acquisitions. In order to ensure ADCAP in the online business, firms should have a decentralized organisation with broad market focus, in order to scan the market and react fast and accurate to environmental changes identified. After summarizing each of the key elements it is time to tie them together, into a theoretical model which can be used to empirically investigating acquisitions. Based on the profound theoretical analysis, this paper proposes a model to apply the three branch theories to RBV, in order to explain acquirer’s acquisition performance (see figure 4). Of notice to the reader is that the three elements have been switch around in the model so that they now occur in the order: ADCAP, ASE and AC. The reasoning behind this switch is that this order is thought to better represent the sequential process of an acquisition: Recognizing the need to change with the environment and identifying the target which possesses the relevant resource (ADCAP); control that target resources are aligned to acquirer’s (ASE); ensure that the strategically relevant resources of the target are recognized, assimilated and transformed properly (AC). It is here important to note that all elements indeed play a role during the whole acquisition process, e.g. AC is also important to identify the right target, and ASE will have an impact on the assimilation process. However, this has been deemed the most logical for the reader to follow and the remaining paper will thus present them in the stipulated order. As can be seen in figure 4, each element is built up by two or three operationalized factors, which are the so called observable indicators of the unobservable elements proposed. Finally, it should be noted that the hypotheses generated from the framework are specifically tailored to the online business, ensuring relevance for the analysis. 56 Figure 4 – Proposed theoretical model for analysing acquirer’s abnormal stock return This concludes the first part of the research paper, where the aim has been to create a theoretical foundation for how to empirically test the research question: What drives success of the acquiring firm in online business acquisitions? Through the initial literature review guiding academia was found, and a current void in the research literature identified. Next a thorough analysis of the research context – the online business, was conducted to better understand the drivers of acquisition in this industry. Returning to the theory an in‐depth analysis was put forward; with the aim to identify and evaluate the most relevant concepts prior scholars have found to influence acquisition performance. Here the findings from the online analysis where applied as a contextual lens to generate context specific hypotheses. The result can be viewed in figure 4, where the three key elements thought to drive acquirers’ abnormal return are operationalized by specific, observable factors, which further have been tailored to the online industry. 57 In the second part of this research paper, the hypotheses generated will be tested on a sample of acquisitions related to the online business. The section starts with an account for the chosen method of analysis, specification of measures and the sampling process, as well as the statistical model chosen for the regression analysis. 3.
PARTIII–Methodandempiricalanalysis
3.1.
Empiricalresearchmethod
The main ambition of this research paper has been to provide the RBV literature with generalizable insights about performance drivers for companies buying online businesses. This aim has driven choice of research method, methodology and measures, as well as sample. In the following section, all key decisions related to method, and their implication for the paper, will be accounted for. Furthermore the main issues and challenges encountered during the process will be discussed, as well as how they were solved. 3.1.1. Choiceofresearchmethod
Choosing which research method to apply is a key consideration for any empirical analysis, and arguably even more so when trying to explain what drives performance in acquisitions. Most empirical studies agree that M&A per se does not create value, but that it appears to exist subgroups that experience significant positive returns from their M&A activity (King et al., 2004; Uhlenbruck et al., 2006). However, academia has failed to identify variables that consistently can predict M&A performance (King et al., 2004). This failure is believed to be due to flaws in both existing M&A theory and research method (Armstrong and Shimizu, 2007; King et al., 2004). After a thorough literature review and theoretical discussion, the relevance of the RBV and its branch theories in explaining acquisition performance has been confirmed. In terms of research method, a quantitative research method using objective proxies in an event study has been chosen. The reasoning for this choice will be discussed below. As already stated, generalizability has been a key motivation for this research paper. By using objective proxies of a large sample of M&A deals, the findings will be easier to generalize and can 58 thus provide insights for both academia and management. Rather than understanding the performance drivers of a single or a few specific acquisition case studies this analysis will provide a more overall understanding of how the chosen factors affect acquisition performance. In new fields of research, where the need for theorization is important, the case study approach is deemed highly valuable (Amit and Zott, 2001). However, since performance drivers in acquisition is a rather well‐studied area it is seen as more important to validate hypotheses and measures on a large sample, rather than to add further in‐depth knowledge about a single or few companies. In addition, quantitative studies form the basis for the pivotal meta‐analyses provided on regular basis in the literature. These analyses bring insight to the research field about past and current practice, common challenges as well as recommendations for future practices (Acedo et al., 2006; King et al., 2004; Newbert, 2007). When choosing a quantitative research method it is important to be aware of the limitations connected to it, of which multiple are applicable to the paper at hand. Firstly, given the complex nature of many of the key concepts in RBV, several scholars have raised concern that the objective proxies used in most quantitative studies cannot measure the true source of superior performance and thus fail to provide insight. An often articulated issue is that what is truly relevant is not measurable, and what is measurable is generally not relevant (Armstrong and Shimizu, 2007; Hayek, 1989; Lockett and Thomson, 2009). This fundamental paradox is connected to two of the cornerstones of the RBV, namely rareness and imitability (Levitas and Chi, 2002; Lockett and Thomson, 2009). It is unlikely that something that is rare can be found in most companies’ publicly available data. Furthermore, in order to fulfil the imitability criteria, causal ambiguity is often a central theme (Barney, 1992; Rumelt, 1984) implying that neither managers, nor researchers know which resources lead to superior performance. Secondly, the basic notion of generalizing insights based on a large data sample is contradicting to another cornerstone of RBV – heterogeneity (Lockett and Thomson, 2009; Rouse and Deallenbach, 1999). By pooling a large group of firms and extracting key numbers and ratios, researchers implicitly assume that all firms are homogenous, which is basically what RBV argues to be the key issue with other economic theories (Barney, 1991; Wernerfelt, 1984). Finally, Lockett and Thomson (2009) argue that quantitative studies of RBV often suffer from high multicollinearity of independent variables. 59 In terms of the measurability paradox, the authors of this paper adhere to Godfrey and Hill’s argument that the job of an RBV researcher is to “theoretically identify what the observable consequence of unobservable resources are likely to be” (1995: 530). In the analysis of the online business context, clear indications have been made that the resources tested for indeed play and important role in competitive advantage. In addition, extensive discussion of the chosen measures will be conducted below in section 3.1.3. However, it is here important to note that observable measures, that are believed to indicate the unobservable resource, are used to measure performance. ADCAP cannot be measured, but the theoretical link between using formal organizational structure and market focus as measurable indicators and ADCAP has been theoretically argued. AC has the same problem, where the components absolute and relative knowledge base and prior acquisition experience serve as indicators, and is based on the same logic. As such, if the hypotheses connected to these three factors are confirmed, it can be argued that AC indeed has a positive effect on acquirer’s performance. The limitation of this rationale is that since ADCAP and AC cannot be directly measured, it might be the case that even though all hypotheses are confirmed, the reason for the factors effect on performance might be another than due to the existence of ADCAP and AC. The second issue, that quantitative sample generalization contradicts the basic assumption of heterogeneity, is indeed a valid and serious limitation. Looking at the chosen focus of targets with resources and capabilities related to online businesses it would be a clear mistake to assume homogeneity, as they differ widely in size, value proposition, nationality and place in the online value chain. Acquirers are an even more disperse group as few criteria concerning business activity have been applied (see section 3.1.2.). It could indeed be argued that the whole notion of generalizability based on analyses using homogeneity as an assumption is a research method not suitable for RBV empirical field. This issue has been widely discussed by multiple scholars (Armstrong and Shimizu, 2007; Godfrey, and Hill, 1995; Lockett and Thomson, 2009) and is something that could have great implication for future RBV researchers. As such it will be further discussed in connection with the results found in this study. However, even though the authors acknowledge this serious limitation, the quantitative research method is still chosen, given its better fit with the research ambition and general acceptance in the RBV literature. In addition, even though homogeneity cannot, and should not, be assumed, there are still some similar 60 characteristics of conducting business related to online. High level of technology related resources are of key importance in order to keep up with the constant evolving online universe (Noe and Parker, 2005). As is the ability to market products to new and existing customers in highly competitive markets characterized by low entry barriers and constantly changing customer demands (Park et al., 2004). Regarding the problems with multicollinearity, it will be further discussed in section 3.1.4. 3.1.2. Sampleanddata
The initial data sample was found using Zephyre data base, a global M&A database with comprehensive company information. Zephyre was chosen as it allows for highly detailed search criteria to be applied. As stated earlier, the focus of the study is companies that acquire online businesses. Finding the right sample within the online business segment was a rather challenging task as rather than being a pre‐defined industry, the online business is a growing segment within multiple industries. In contrast to most empirical studies, the acquirer’s industry could not be used as initial screening criteria. Instead, a two‐step research strategy was conducted to find the relevant deals, in a similar manner as Uhlenbruck et al. (2006). First, using the definition and key words found during the initial research of the online environment (see section 2.6.), the following NAICs (2007) codes were identified as relevant (see appendix 3 for more detailed specification): -
425110: Business to business electronic markets -
454111: Business to consumer retail sales internet sites -
454112: Auctions, internet retail -
511210: Applications software, computer, packaged -
518210: Application hosting -
519130: Advertising periodical publishers, exclusively on internet Furthermore, the key words (Internet, Online, Web, E‐commerce, Digital, Software, Wi‐Fi, and Virtual) were used to search for additional deals. Three addition criteria were applied. Firstly, deal type had to be specified as acquisition, where the acquirer could have maximum 49 per cent ownership before the acquisition and minimum 51 per cent after the deal (Kirchhoff and Schiereck, 2011; Swaminathan et al., 2008). 61 Secondly, both acquirer and target had to be listed in order to secure that the data for the chosen measures could be found. This is a clear limitation of the sample since a review of the online business (see section 2.6.) indicates that a majority of potential targets are small, unlisted companies, many backed by venture funds or other private investors. A good example of this was found during the screening for prior M&A experience. For Google, 75 online related deals were found, however, only one of them had a listed target. It was considered to include all deals and then exclude all the ones where information could not be found. However, after taking a test of 20 companies it was deemed to be a too time‐consuming task in terms of the output it created (complete data could not be found on any of the 20 companies). It is however acknowledged that the sample is not perfectly representative for the online business and might suffer from biases such as ‘large company’ bias, which can skew the sample negatively in terms of performance (Amit and Zott, 2001). Finally, the deal had to be completed between 31/12/2003 and 31/12/2012. The reason for choosing to start the time period in ultimo 2003 is due to indication that the stock crash connected to the Dotcom bubble ended (“hit rock bottom”) October 2002 (Los Angeles times, 2006). This resulted in an initial data sample of 228 transactions. Subsequently all the deals were screened in order to assure relevance. First of all, there were some 22 deals that had slipped through the search criteria having the wrong initial or final stake, or had not been completed. Secondly, all private equity funds, M&A vehicles or other investment companies (8 in total) were taken out, since their business model is considerably different. Subsequently, 19 deals where either target or acquirer was not public before the acquisition were taken out; of these 8 were so‐
called ‘reversed mergers’ where a private company acquires a public in order to bypass an IPO. Finally, two deals were found twice in the sample. As a result of the first screening 177 deals were left in the sample. In the second screening phase, each deal was cross‐checked against two additional M&A data bases (SDC Mergers and Acquisitions and Merger markets) as well as Factiva press archive. Through the two M&A databases, it was controlled that the deals had been completed, and simultaneously the announcement dates were verified. In case of conflicting announcement dates, the date given in Factiva’s press releases were used (Swaminathan et al., 2008). Factiva was 62 further consulted to ensure that no rumours about the acquisition had been published, or other major event affecting the firm had occurred at the same date, e.g. new CEO or release of annual or quarterly report (see section below for further discussion) (McWilliams and Siegel, 1997). Next, all targets were evaluated in Bloomberg business week to indeed be online businesses, using the definition earlier developed by the researchers33. Here 56 targets were found not relevant. All of these had the key word ‘software’, however when reviewing the companies nothing about online was mentioned. Rather, in these cases software was connected to things such as distribution, manufacturing and other clearly non‐online related activities. On the other hand, if the word software was connected to other key words such as web portal, home page, digital platform etc. it was deemed relevant. This led to the conclusion that software on its own is not sufficient for a company to be classified as having online business. Finally, the companies were screened for having the relevant data for all the measures (see below for further specification). In 53 of the deals data could not be found, either on the acquirer or the target. The eliminated companies were a mix of Asian businesses (where data in general is harder to obtain) and small companies that had not provided data on R&D and/or Selling and Marketing expenses. Furthermore, on Capital IQ it was not possible to get data earlier than 2002, which excluded the two companies from 2004 (due to the need to get cumulative R&D expenses). As such, the adjusted time period for the sample ended up being from the 31/12‐2004 to 31/12‐
2011. Finally, the stock data was gathered for the remaining companies, resulting in elimination of two companies due to incomplete estimation periods. After the secondary screening, 64 deals were left (n=64), representing 28 per cent of the initial sample. The authors acknowledge that this is a rather small sample size for a quantitative analysis which can be a result of multiple factors. In principle, as already stated there are relatively few online businesses that are public companies (compared to total size), and even fewer that are target potential. Secondly, due to the relative young age of the online business world, it is the 33
The online business comprises firms who offer products, services and/or other enablers for all Internet
relatedactivities 63 belief of the authors that many companies that have moved from being pure offline players to having a large part of their business online, still are not classified as such in the NAICs codes. 3.1.3. Choiceofmeasurements
In conducting a quantitative event study, three types of measures must be specified – the dependent variable, the independent variable(s) and a number of control variables. In this section, each chosen variable will be presented and discussed, with a clear emphasis on the independent variables. 3.1.3.1.
Dependentvariable
The dependent variable in this event study is the cumulated abnormal return of the acquirers’ stock price in the intervals (‐1, +1), (‐5, +5) and (‐20, +20). This is the most common measure used as dependant variable of performance in quantitative studies (Bild, 1998; King et al., 2004), and has further been applied in studies focused on the online segment (Lee, 2001; Park et al., 2004; Uhlenbruck et al., 2006). Before discussing the use of stock measures for assessing performance, it is important to briefly discuss the choice of performance as a relevant focus in itself. As empirical evidence of acquirers’ financial performance of M&A activities have been hard to establish, part of the literature has started questioning whether financial performance really is the right rational for acquiring another company. Instead, alternative rationales such as management of environmental and technological shocks and growth in scale and scope to reduce vulnerability have been suggested (King et al., 2004). However, given the fact that the chosen companies are listed and their obligation towards shareholders is to maximize shareholder value, financial performance is argued to be a relevant measure for the study at hand. This paper focuses on determining the drivers of acquisition performance, using solely acquirers’ performance as benchmark. The reason for this choice is that most empirical studies on M&A activities, as well as the meta analyses conducted in the field, agree that targets’ generally benefit from being acquired, showing positive abnormal performance (Datta et al., 1992; King et al., 2004). Similarly, most studies conclude that acquirers, on the other hand, on average show zero or slightly negative abnormal return in most event windows used. As such, the positive return from the target tends to net out the negative return of the acquirer. This is, however, not in the interest 64 of the researchers as the focus is not to determine whether acquisitions are value creating or value destroying. Instead, the goal is to separate the ”winners” from the ”losers” among firms that chose to engage in acquisition activities and as such acquirers’ abnormal return is deemed to be the sole relevant focus. When using stock measures, three basic assumptions must be articulated to the reader. Firstly, the stock market is assumed to be semi‐efficient. Hence, it knows all relevant, publicly available information about all companies in the market and that this information is represented in the share price at any given time. Secondly, the acquisition must not be expected by the market and finally, no other event must occur on the announcement date that could create noise in share prices. (McWilliams and Siegel, 1997) This implies that shareholders will be able to determine whether an acquisition will generate more value, either through new technologies, products or other types of innovation or through cost synergies, than the two entities would do by themselves (Brealey and Meyers, 1991). As in the section above, this assumption clashes with the notion of causal ambiguity and multiple scholars argue that only when the true value of the target to the acquirer is hidden or unknown, will the acquisition be able to gain profits, otherwise the additional value will be diminished by increasing price of the target (Barney, 1989; Lockett and Thompson, 2009). Still, this paper argues that it is the best possible measure for isolating the effects of the acquisition. Using accounting measures would require a significant time lag in order to assure that the effects of the acquisition would be measurable (Ahuja and Katila, 2001; Eisenmann, 2006). However, it is very hard, if not impossible, to isolate the effect of the acquisition on accounting performance one or more years after the event. Multiple other factors could have affected the firm’s performance, such as economic climate, competitor action, technological development, new regulation etc. In addition, accounting measures are further criticized for not providing objective measures of firm performance (McWilliams and Siegel, 1997). As this paper’s aim is to determine performance drivers in acquisitions, the stock performance is deemed a more relevant and accurate measure of abnormal performance, using the three assumptions stipulated by McWilliams and Siegel (1997). 3.1.3.1.1.
Calculatingabnormalreturns
The abnormal returns have been calculated using the market model (MacKinlay, 1997), which computes the returns as the difference between the realized return of acquirer and the expected 65 return, using a market index to benchmark the expected return. While the actual returns were gathered through extracting stock data from Compustat, the expected return has been computed from the MSCI world index. This global index has been selected to ensure a consistent benchmark for each security (Stulz, 1995). Together, these interrelated markets form one capital market for the purpose of estimating the risk of assets. The MSCI world index, measures the price performance of markets with the income from dividend payments, and is correlated with all the stocks in the sample (Stulz, 1995). This index was deemed the most relevant for the study, even though the sample contains deals from different countries. As investors today have seamless information of, and access to most of the world’s stock exchanges, the old praxis of using local indexes for each country represented in the sample (17) came forward as unrealistic (Stulz, 1995). In order to capture the true announcement effects circumventing the announcement date, different event windows need to be tested. Prior to the event window, a non‐overlapping estimation window of 250 trading days has been used, in order to ensure that no early rumours have interfered with the stock price (Eisenmann, 2006; Stulz, 1995). It is important to note that the stock data has not been corrected for confounding events in the estimation period. It is though assumed that all securities in the sample have had quarterly announcements in the period comprising 250 trading days. In the process of analysing, the following event windows were tested (0), (‐1,+1), (‐5,+5) and (‐20, +20) (Swaminathan et al., 2008; Stulz, 1995). In order to find the expected return of each security, the realized return needs to be regressed on the realized returns of the market index: = realized return of security j at time t = Realized returns of the market at time t Regressing the two variables on each other yields the intercept with the Y‐axis ( ) and the slope of the regression line needed to calculate the expected returns given in the market model: = expected return on security j at time t 66 = realized returns of the market index Subsequently, the abnormal returns (AR) can now be calculated for each security: = the realized return of stock j, on day t = the recently calculated expected return of stock j, on day t Finally, the cumulated abnormal returns (CAR) have been computed across the securities, for each day in the event window, as well as the CAR for each security within each event window: ∑
= Abnormal returns of stock j at t time N= number of days in the event window The stock returns of acquirer are not controlled for confounding effects i.e. quarterly announcements, which could affect the sample. As the lack of control of such is consistent throughout the sample, the authors have concluded this to be a minor risk as all the firms are assumed to have had announcements in the estimation window. 3.1.3.2.
Independentvariables
The choice of independent variables has been guided by two ambitions – to ensure that they represent strong indications of underlying resources and capabilities relevant for the hypotheses and that they to some extent already are anchored in the literature. A strong critique against the RBV’s empirical literature is the lack of replication studies (Cloodt et al., 2006; King et al., 2004; Lockett and Thomson, 2009). Rather than re‐test similar variables and/or samples (a practice highly common in applied science), researchers within the RBV field prefer to seek new resources, capabilities or other firm attributes that can explain difference in performance (Cloodt et al., 2006; Lockett and Thomson, 2009). The result has been a rather 67 confusing body of literature where the reader can find multiple answers for even the simplest question and almost everything confirmed by one or more studies has been disconfirmed by equally many papers. This disperse research has also resulted in a slower development of the RBV than would be expected given the high activity of research on the subject (Armstrong and Shimizu, 2007; Newbert, 2007). As such, this paper aims to use independent variables that have been tested and confirmed to have an impact on performance by earlier researcher in hope to provide insight of their consistent ability of explaining performance, or lack of such. However, it has still been deemed necessary to evaluate the relevance of the measures, given the sample and context of the paper at hand, rather than to simply copying the measures. Each time a measure has been modified, the rationale behind the modification will be put forward. Taking point of departure in the work of acknowledges scholars is also a first step in order to secure the main ambition ‐ to ensure that the chosen measures provide insight about underlying resources, which are unobservable to external (and sometimes also internal) actors. The chosen measures have been thoroughly discussed by more than one scholar and have furthermore shown signs of relevance in prior empirical work. However, academic acceptance per se is not sufficient to ensure relevance for the current study, as such; each measure will be further discussed below. Finally, in accordance with recommendations from the literature, multiple measures have been applied to both ADCAP and AC (Armstrong and Shimizu, 2007; King et al., 2004). The rationale is that by applying multiple measurements thought to be connected to a certain unobservable element, the possibility to estimate its effect on the dependant variable increases, in a triangulation type of manner (Godfrey and Hill, 1995; King et al., 2004). It is important to note that multiple measurements only increase the likelihood of valuable insight if all measurements used by themselves are deemed relevant for estimating underlying resources and capabilities. For the final factor tested, only one measure of ASE was found relevant. The other way of measuring strategic focus in the literature is through the use of industry codes (Swaminathan et al., 2008), which was not regarded as relevant by the authors due to the fact that online businesses can be applied to most industries. 68 3.1.3.2.1.
Measurementofadaptivecapability
As discussed in section 2.7.3.1., ADCAP is believed by many scholars to have a positive impact on acquisition performance. Below, the measurements for the two indicators, formal organizational structure and market focus, of ADCAP selected for the regression analysis will be operationalized. 3.1.3.2.1.1.
Decentralizationofdecisionrights
In an attempt to unveil a firm’s degree of decentralization, the organizational structure is put forth. An indicator of this can be the degree of decentralization of decision rights, i.e. the level of decentralization from CEO to next level of management. The level of decentralization can be determined by measuring firms as profit or cost centres (Christie et al., 2003). Profit centres are defined as companies where the second level of management is organized in independent revenue‐generating business units, such as different products or markets. In contrary, cost centres are organized into global units, such as HR, R&D, Marketing and Business Development. Functions such as Finance and Treasury are excluded from the screening process, as these particular units will be present in close to all firms regardless of level of decentralization. Profit centred companies are argued to show higher degree of decentralization; due to the fact that managers make decisions about both revenues and costs, and thus have more decision power to adapt the unit’s strategic decisions when the environment changes. In cost centred firms, managers control costs, and do not have the freedom to navigate the firm’s external focus (Christie et al., 2003) The firm’s formal organizational structure is thus determined through categorizing each firm into profit centred, cost centred or a mix of both (Christie et al., 2003). Through reviewing the firm’s annual report, web site and management profiles, the organizational structure was easily determined for majority of the firms. Some of the firms were a bit more complicated to detect, as they typically had listed only CEO, CFO and Board of Directors on their web page, and no additional information on organizational structure. Through examining annual reports two years prior to the deal, exporting management overviews from CAPITALIQ and searching the web, it succeeded to detect part of the remaining firms’ organizational structure, while the remaining unsolved were eliminated from the sample (these formed part of the 56 where data was missing). This categorization resulted in 28 per cent profit centred, 49 per cent cost centred; and 18 per cent representing a ‘mix’. The latter comprised companies that clearly had divided their second level management into revenue generating business unit managers (i.e. China or healthcare) as 69 well as global cost division managers (i.e. R&D, HR or Marketing) on the same level in an organizational diagram. 3.1.3.2.1.2.
Market focus Secondly, ADCAP can be measured through the extent of market focus, which is the way a company ensures flow of info. A broad market focus indicates the ability to identify needs in a changing market, while a narrow market focus indicates niche capabilities, addressing customers within a specific segment (Tuominen et al., 2004). While Tuominen measures market focus as the absolute number of customers of each firm, this paper defines a broad market focus to be represented by a high number of offices spread around the world (i.e. a form of globalization measure). Reversely, a narrow market focus is measured by a low level of international presence. The reason for this choice is two‐folded. First, the share number of customers are not believed to necessary indicate a broad focus in the term defined by this paper and relevant for the online business. A good example could be a large local niche player. While it has a lot of customers in its own region, it is unaware about what happens outside its own market. As such, this firm will be unaware of evolving global market trends that can affect the firm. Instead, by being present in multiple countries the firm is believed to have a broader customer base, which enhances its market sensing abilities. Secondly, number of customers is often highly confidential information, making it hard to obtain given the research method applied. The degree of market focus is measured in absolute number of countries in which the firm has physical offices (po.box offices not included). On a side note, other measures could have been selected to show the firms market focus. Empirically, measures such as foreign sales, ratio of foreign employees to total or foreign assets to total assets, are used. This would however show to what extent the firm is internationalized; not how broad or narrow its market focus is. The authors do not consider this measure accurate enough, and have therefore chosen Christie’s definition of market focus instead. 3.1.3.2.2.
Measuresofalignmentofstrategicemphases
3.1.3.2.2.1.
Alignment of strategic emphasis ASE was determined by the difference in strategic emphasis of the acquirer and target (Swaminathan et al., 2008). Strategic emphasis is related to the focus on building up resources and 70 capabilities within a certain area of the business (Mizik and Jacobson, 2003). Taking point of departure in Swaminathan et al. (2008), strategic emphasis is defined as the resource base where the firm has its strength (i.e. main focus). This is done through comparing size of marketing base to technological base, seen as these resource bases have been the ones in focus. By determining which resource is the base of the firm, relative to total sales, a numerical ratio (positive indicating marketing base, and negative indicating technological base) was denoted each firm. Computing this into one formula yields: /
= Selling and Marketing expenses, LY &
= R&D expenses, LY = Total sales, LY This measure of strategic emphasis (SE) is used as the authors agree with prior researchers that relative investment is a good proxy for where a company places its focus, and is also there it probably has most resources. The only measurement different from Swaminathan et al. (2008) in this equation is the Selling and Marketing expenses (used instead of advertisement expenditures). The argument for this is that the advertisement expense is believed by the authors to be a too narrow measure of marketing capabilities. Marketing capabilities are operationalized by the ability of the company to manage customer needs, relationships and processes, communication and distribution, and competitor information (Wang et al., 2004). Advertisement is an investment in pure brand awareness and interest (Keller, 2003), and this solely covers part of the total marketing, which makes it an incomplete measure. Selling and Marketing expenses encompasses a more complete focus on the whole go‐to‐market strategy and is therefore a more comprehensive indicator of the firms marketing resources. The R&D expense, on the other hand, is generally accepted to indicate the underlying technical capabilities of the company and is found to be a good indicator of the company’s focus on technical development (Ahuja and Katila, 2001; Ahuja and Lampert, 2001; Dutta et al., 2005). In addition, both R&D and Selling and Marketing expenditures are argued to be connected to 71 capabilities, rather than pure assets, which have empirically shown to be a better predictor of corporate performance (Newbert, 2007). In some industries, a large share of R&D expenses goes to obtaining and protecting patents (an asset). This is not the case in the online industry, where fast technological development makes patents less important and most of the R&D investments are put directly into technology and product innovation (King et al., 2008)34. Both R&D and Selling and Marketing expenditures were found in Capital IQ’s COMPUSTAT database. After computing the strategic emphasis for each company, the alignment between the acquirer’s and target’s strategic emphasis was determined through computed the difference in absolute values: High strategic emphasis alignment, when acquirer and target both are focused on either marketing or technology, is seen when the absolute value is close to zero. This indicates that a company with core capabilities in marketing is buying up another marketing focused company, either seeking to expand its core competencies, or to create cost synergies. When the value is large, either positive or negative, there is a low extent of strategic emphasis alignment, which means that the acquirer has bought a company with complimentary focus than itself. (Swaminathan et al., 2008) One important limitation of using expenses as measurement is that it contradicts the RBV’s assumption about heterogeneity. This is because expenses only measure what is put into “the black box”, neither what happens in it, nor what comes out (Anand and Khanna, 2000; Mowery et al., 1996). The authors fully acknowledge that there exists differences in efficiency and learning capabilities across companies, and that a higher monetary amount does not necessary mean superior capabilities. However, when return on investment is high, due to high learning and efficiency, investments also tend to increase; i.e. companies, like people, tend to focus their effort where they are good (Brealey and Meyers, 1991). It would be of substantial value to also test for an output measure, such as patents. However, as earlier mentioned, patents are not believed to 34Onlytwoacquirersfromthesamplewereindustrieswherepatentsaredeemedimportant.
72 be as important in the online business and given the difference of business activities in the sample, no other coherent output measure was found suitable. 3.1.3.2.2.2.
Specific resource complementarity The specific resource complementarity was determined by respectively examining the relative size of acquirer’s marketing base to target’s technological base, and the relative size of acquirer’s technological base to target's marketing base. Following King et al. (2008), combining the acquirer and target’s specific resource complementarity, will indicate which combination yields the highest (positive) impact on abnormal returns. Contrary to King et al. (2008) and Heeley et al. (2006), this study does not measure the cumulated stock of each resource, but rather emphasizes the importance of recent investments, to provide a true snapshot of state of resources at the time of acquisition, consistent with Dierickx and Cool (1989) implying that recent investments are more valuable. Following, the specific resource complementarity is computed through the firms’ relative size of resource base &
= Relative size of resource base (marketing) = Relative size of resource base (technology) = Selling and Marketing expenses, LY &
= R&D expenses, LY = Total sales, LY As oppose to the components of ASE, the specific resource complementarity measures relative size of the firms resource base, being either marketing or technology. Through connecting each of 73 the two resource combinations, the regression model is enabled to predict which of the interconnections that yields the highest influence on performance. 3.1.3.2.3.
Measuresconnectedtoabsorptivecapacity
As discussed in section 2.7.3.1., AC is believed by many scholars to have a positive impact on M&A performance. AC is a relevant example of an unobservable, discussed in section 2.2. This means that it is not possible to find one measure that with certainty can provide insight of the firm’s AC (Foss et al., 2010). Instead, AC is believed to be a result of multiple things connected to the company’s stock and flow of knowledge (Cohen and Levinthal, 1990). In the theoretical section, arguments for using the three indicators – absolute size of acquirer’s knowledge base, relative size of target’s knowledge base and acquirer’s prior acquisition experience, have been put forward. These will be operationalized below. 3.1.3.2.3.1.
Prioracquisitionexperienceoftheacquirer
Prior M&A experience of the acquirer is tested for by counting the number of acquisitions made by the acquirer during the seven years leading up to the acquisition. This measure has been used by multiple scholars, but with different time horizons (Bruton et al., 1994; Hitt et al., 1993, 1998, 2001). Bruton et al. (1994) used a four year period in their study but this was viewed as a too short time period. Experience in acquisitions is argued by the researchers to be a combination of organizational processes developed, e.g. specific screening procedures and advisory relations, and the personal experience of the people involved in the acquisition. While (almost) all employees’ play a part in an acquisition, top management is the one with the most responsibility. Average top management has since the beginning of 2000 had a tenure of approximately five to seven years (Practical accountant, 2001; Forbes, 2007; Booz & Co., 2010), and as such the higher limit has been used as time frame for measuring experience. It was further discussed whether to depreciate experience in a similar manner as R&D expense. However, no such measures could be found in the literature. Furthermore it was deemed too complicated to determine the optimal time lag for knowledge generation of an acquisition, especially given the assumption of heterogeneity. Data about acquirers’ prior M&A activities was acquired in Zephyre, using the following screening: 74 -
Only acquisitions were deemed relevant, were the acquirer could have max 49 per cent before the deal and min 51 per cent after -
An acquirers prior deal had to be completed prior to the announcement date of sample deals It should be noted that it was not deemed relevant whether the targets were public or not. The initial screening resulted in 1637 acquisitions. All deals were furthered screened for: -
Wrong acquirer were excluded (similar names had been included by Zephyre) -
Reverse acquisitions were excluded -
All deals of the sample were excluded -
All deals that had occurred after the deal, or more than 7 years before, were excluded Rather than using the absolute numbers of acquisitions, a threshold of 3 acquisitions has been used. The rationale for using three is that below that, the acquirer is still deemed inexperienced with low impact on AC and thus performance (Kirchhoff and Schiereck, 2011) 3.1.3.2.3.2.
Absolutesizeofknowledgebase
Absolute size of knowledge base is generally agreed among scholars to be the backbone of AC (Ahuja and Katila, 2001; Cohen and Levinthal, 1990; Henderson and Cockburn, 1996). Taken point of departure in Ahuja and Katila (2001), the connection between absolute size of acquirer’s knowledge base and acquisition performance will be tested for. However, their sample is taken from the chemical industry and as such they argue for the validity of using patent as an output measurement for absolute size of knowledge base. Since patents are not deemed relevant for the chosen sample of the study, other academic papers were screened to find a measurement believed to be more appropriate for the analysis at hand. The chosen measure for absolute size of knowledge base is the cumulative R&D expense for the past three years, with a depreciation of 15 per cent per year, divided by total sales (King et al., 2008; Kirchhoff and Schiereck, 2011). &
&
∗ 0,85
&
∗ 0,85^2 /TS 75 There are several possible issues related to this measure. First of all, R&D expense cannot be assumed to be the only measure of a company’s absolute knowledge base, even less so in the past three years (Lane and Lubatkin, 1998; Lichtenthaler, 2009). A lot of knowledge is embedded in the employees (tacit knowledge), which will not be captured by the R&D expenditure measurement (with the exception of employee training) (Hatch and Dyer, 2004). Furthermore, the company often has certain organizational routines that also contribute to the knowledge base, without the need for further expenditures (Becker, 2004). Secondly, a company’s knowledge base is something that has been accumulated since its birth, not only in the three years prior to the acquisition. However, knowledge in the online business is believed to get obsolete faster than in other industries, making newer knowledge much more valuable than old. In fact, it could be questioned whether 15 per cent depreciation is a too low number, favouring companies that made large investments that today are worth less than small continuous investments (Cloodt et al., 2006; Glazer and Weiss, 1993). Even though the researchers recognize that R&D expense does not capture the whole notion of knowledge base, it has already been argued to be a good proxy for technical capabilities and knowledge and the cumulative effect of this expense can be seen as the stock of knowledge the company currently has (Cohen and Levinthal, 1989; Dierickx and Cool 1989)35. While a longer accumulation period probably would provide a more accurate picture, three years is believed to be sufficient to account for any extraordinary years of high or low spending (King et al., 2008)36. Depreciation is further added to account for the belief that newer knowledge often is more valuable than old (Dierickx and Cool 1989; King et al., 2008). Finally, the measure is divided by total sales in order to make comparison between large and small companies possible (King et al., 2008). Multiple other measures such as total assets or total operating expenses could have been used to determine relative size. However as revenue is a good estimation of firm size, it was rendered to be the most relevant measure to apply. 35 A key argument why R&D is not a sufficient measure is that it fails to estimate other important types of
knowledge like market knowledge (Lichtenthaler, 2009; Teece, 2007; van den Bosch,). Here the market focus
usedinADCAPishighlycomplementary(seesection3.1.1.).
36 Dummy variables for year of acquisition could have helped to detect any biases of economic climate,
technologicalbreakthroughorotherexternalfactorsaffectingcompanies’propensitytoinvestinR&D.However,
duetothesmallsamplesizethiswasnotpossible(seesection3.1.1.),whichclearlycreatesimplicationforthe
generalizabilityoftheresults.
76 3.1.3.2.3.3.
Relativesizeofknowledgebase
The relative size is computed by dividing absolute size of target with absolute size of acquirer in accordance with Ahuja and Katila (2001). /
) The impact of target’s relative size of knowledge on AC already has been accounted for in section 2.7.3.1. No issues were encountered with the measurement other than the ones discussed in the above section. 3.1.3.3.
Controlvariables
To ensure that the estimates are not falsely given significance due to omitting important variables, central control variables are included. Relative size of target to acquirer is included as empirical evidence shows that it affects M&A performance (Seth, 1990; Singh and Montgomery 1987). If the gap between the two firms is sufficiently large, the integration process will suffer and lead to unrealized synergies (Seth, 1990; Swaminathan et al., 2008). The relative size is calculated as the ratio of target sales to acquirer sales for the year preceding the acquisition. Secondly, debt ratio of the acquiring firm was included as a control variable, measured as the ratio of liabilities to assets. When testing a sample across industries, it is advised to control for industry effects, as the resource must be seen in light of its specific context of operation (Barney, 2001). In a ‘hybrid industry’ such as online, it is uncertain whether this is the central context, as most firms have another industry classification. While the aim was to control for such through industry dummies, it turned out some industries comprised few firms, while other industries had a large majority share of firms. This led to ‘biased’ or ‘zeroed’ industry variables, due to uneven distribution, and industry variables are there for not included in the final model(s). As in the case of the industry variables, testing across a period of seven years emphasizes the possibility that isolated years can be in a market set back or the opposite. In worst case this could result in heteroscedasticity. To assure no presence of such, the data set is controlled for heteroscedasticity through White’s test. The classification of the firms showed the same pattern as with industry, with only two deals in 2011 and further. As the expected return is calculated on the global actual returns in the given year, it is though assumed that recessions and other states of the market are included in the computed 77 returns. Time is therefore not controlled for, in the final model(s). Finally, to rule out the possibility that the results can be influenced by the size of the firms’ resource bases, all of such have been included as control variables, namely target and acquirer’s marketing and technology bases. 3.1.4. Specifyingthestatisticmodel
In the process of specifying the model, it has never been the aim to explain all aspects of the acquirer’s abnormal stock performance, nor has it been the driving force to achieve the highest possible R‐squared value37. In contrary, the aim has been to test if the chosen ‘resources’ and their combination have, as stipulated in this paper, a positive influence on the stock market’s reaction to online acquisitions. The classic linear OLS regression model has been designated for the analysis, meaning it is linear in its parameters, not necessarily in its variables (Canavos and Miller, 1999). For further elaboration of the underlying assumptions of the model, see appendix 1. ∗
∗
∗
∗
∗
∗
∗
∗
∗
In order to conduct the regression analysis, the model needs to be transformed to an econometric model, see the following section. 3.1.5. Estimationoftheeconometricmodelanditsparameters
The statistical technique of regression analysis is the central tool to obtain the numerical estimates of the parameters in the main model. In order to be sure that the model will be able to predict reliable and logic results, there are some important corrections that need to be applied to the model. To correct the model as it is in the section above for kurtosis, some variables have been standardized in order to lower the fatness of the tails (Gujarati, 2009). The CAR was standardized through taking the square root of the variable, while the explanatory variables have been 37
An event study in its traditional aspect should aim to achieve the highest explanatory value as possible (Gujarati, 2009). However this paper does not focus on playing the “maximizing R‐squared game”. 78 corrected with logarithms. However the estimation process returned a negative R‐square with the squared CAR, and the normal CAR was therefore used instead. Through the process of estimating the parameters, the following variables were standardized: absolute knowledge base of acquirer, relative knowledge base of target to acquirer and relative size. Following the standardization of the parameters, the econometric model comprises: ∗
∗
∗
∗
∗
∗
∗
∗
∗
3.1.6. Hypothesistestingabouttheindividualregressioncoefficients
:
0 :
0 The null hypothesis states that, with all other parameters held constant, X has no influence on Y (abnormal performance). To test the null hypothesis, we use the t test to see if the computed t‐
value exceeds the critical t‐value at the chosen level of significance. If so, the hypothesis can be rejected; otherwise the hypothesis cannot be rejected. For the hypothesis testing, a significance level of one and five per cent has been applied. The degrees of freedom are determined by the sample size38 deducted number of parameters, subsequently 51‐15=36. The critical t‐value at the five per cent level, can be gathered a 30 or 40 df level, correspondingly 1.697 and 1.684. To provide the most accurate critical t‐value, the average of the two t‐values is computed, resulting in a critical t‐value of 1.690. 38
Recall that the sample size in the output is the sample size less the deals that could not be standardized, resulting in 51 deals 79 3.2. Conclusionofmethodicalapproach
The preceding chapter has guided the reader through the method that lies beneath the analysis to follow. For the paper at hand, a quantitative research method has been chosen, despite empirical issues with ‘measuring the unobservable’. A two‐step research strategy has been conducted to find the relevant deals. First, using the definition and key words found during the initial research of the online environment, a set of NAICs codes were identified as relevant. The key words (Internet, Online, Web, E‐commerce, Digital, Software, Wi‐Fi, and Virtual) were used to search for additional deals. Secondly, deal type had to be specified as acquisition, where the acquirer could have maximum 49 per cent ownership before the acquisition and minimum 51 per cent after the deal. Further, both acquirer and target had to be listed in order to secure that the data for the chosen measures could be found. Finally, the deal had to be completed between 31/12/2003 and 31/12/2012. After correcting for actual deal completion and relevant business models, the sample was run through the next screening phase, where beach deal was cross‐checked against two additional M&A data bases (SDC Mergers and Acquisitions and Merger markets) as well as Factiva press archive. Next, all targets were evaluated in Bloomberg business week to indeed be online businesses. After the final screening, 64 deals were left (n=64), representing 28 per cent of the initial sample. The dependent variable in this event study is the cumulated abnormal return of the acquirers’ stock price in the intervals (‐1, +1), (‐5, +5) and (‐20, +20). The abnormal returns have been calculated using the market model with the MSCI world index as benchmark. The independent variables cover multiple measures comprising ADCAP, AC and ASE. For ADCAP, two different measures were tested; decentralization and market focus. The degree of decentralization of decision rights has been seen as the level of decentralization from CEO to next level of management. Decentralization was determined by measuring firms as profit or cost centres. The degree of market focus is measured in absolute number of countries in which the firm has physical offices. ASE was measured through alignment of strategic emphasis, and specific resource complementarity. Two steps were conducted to determine the alignment; firstly comparing size of marketing base to technological base, then by determining which resource is the base of the firm, relative to total sales, a numerical ratio (positive indicating marketing base, and negative indicating technological base) was denoted each firm. After computing the strategic 80 emphasis for each company, the alignment between the acquirer’s and target’s strategic emphasis was determined through computed the difference in absolute values. The specific resource complementarity was determined by respectively examining the relative size of acquirer’s marketing base to target’s technological base, and the relative size of acquirer’s technological base to target's marketing base. Following, the specific resource complementarity was computed through the firms’ relative size of resource base. Next, AC has been tested through three different measures; Prior M&A experience, absolute knowledge base and relative knowledge base. Prior M&A experience of the acquirer is tested for by counting the number of acquisitions made by the acquirer during the seven years leading up to the acquisition. Absolute size of knowledge base is measured through the cumulative R&D expense for the past three years, with a depreciation of 15 per cent per year, divided by total sales. Relative size is computed by dividing absolute size of target with absolute size of acquirer. Lastly, debt and relative size of target to acquirer are included as control variables. In addition to controlling for variables that potentially could affect the sample, a number of statistical tests have been conducted to fill all econometric requirements. As a result of the testing and validating, an econometric model has been formed, and the regression results will be reported in the following. 3.3.
EmpiricalAnalysis
3.3.1. Introduction
The following section will guide the reader through a detailed body of the multiple‐regression analysis. First the overall model results will be presented and subsequently each factor will be looked at in isolation. All results will be presented as well as their implications for the connected hypothesis. One of the most common traits of studies in the management literature is the finding of non‐significant findings, which either can be explained by problems in the methodology, or due to in‐existence of impact on the dependent variable (Armstrong and Shimizu, 2007). While the conclusion of a non‐significant resource can be a highly significant contribution to the RBV literature, there is no question in doubt that there are certain issues with the methodology that also can have provoked the presence of such, most notably, the limited sample size. 81 Figure 6 – summary of fit Rsquare Rsquare Adjusted Root Mean Sq error Mean of response Observations 0.536 0.337 0.029 (0.006) 51 Overall the model has high explanatory power, with an adjusted R‐square value of 0.30939. The mean average return for the three day event window, is ‐0.00572. As can be seen from the summary, 13 of the deals were not regressed in the final output due to lack of numerical values as a result from the standardization process40, yielding 51 observations rather than the initial 64. This has downsized the sample even further, increasing the risk of detecting non‐significant findings. Figure 7 – Analysis of variance Source Model Error C. Total DF Squares Mean Square F‐Ratio 15 0.033 0.002 2.695 35 0.029 0.000 0.008* 50 0.062 As a whole, the model explains 33.4 per cent of the samples abnormal performance, with a likelihood of 99.3 per cent at a five per cent level of significance. As discussed in section 3.1.1. some relevant control variables were excluded due to the low sample size, which created biases. Had these control variables been included, a higher R‐square value would have been achieved. However, as earlier stated it has not been the goal to maximize the models explanatory power. The explanatory variables are tested for autocorrelation (see figure 8). The highest degree of autocorrelation (0.61) exists between absolute and relative size of knowledge base, this is somewhat expected due to target size of knowledge base being the only differentiator. To ensure that there is no presence of autocorrelation, the Durbin Watson test is been computed. 39 For a more trustworthy picture of the model’s explanatory power, adjusted Rsquare is used rather than
Rsquare
40Withoutstandardizationofthevariables,Rsquareadjustedreportsanegativevalue
82 Figure 8 – Durbin Watson Test DW 1.579 Number of Obs. AutoCorrelation Prob<DW
51 0.192 0.028* As can be seen, the DW statistic of 1.579 is significantly larger than the adjusted R‐squared (0.34), it can be reliably concluded that the results have not been inferred with autocorrelation (Gujarati, 2009). Due to the cross‐sectional regression, the White test for heteroscedacity is computed. Thus, the White test confirms that there is no presence of heteroscedacity in the sample. After performing the required tests, standardizing the independent variables and correcting for first presence of autocorrelation, the model is concluded to be significant and reliable to the extent which it is meant. The results of parameters are in the following reported 83 Figure 9 – Reported results of Model 1 84 From figure 9 the estimators of the explanatory variables can be found. In the following section, the results of the regression will be reported, and each sub‐hypothesis will be thoroughly walked through on the basis of the numerical estimates. To supplement the proposed model, each of the three components, ADCAP, ASE and AC have been isolated and tested for individually, resulting in three additional models (2‐4). These will be presented under each proceeding section. It should though be noted that the main model is the core referral, and all estimates reported will be pinpointed from Model 1 and not the individual testing. 3.3.2. Adaptivecapability
As presented in section 3.1.3., the acquirer’s adaptive capability is measured through two independent indicators; 1) the formal organizational structure and 2) the type of market focus. Hypothesis 1a: In the online business, a decentralized structure in acquirer firm has a positive impact on acquirer’s performance The first measure of ADCAP is devoted to the level of decentralization within the acquiring firm. Due to the presence of three categories; profit, cost and mixed, the two former have been included in the model as dummy variables using the ‘mix’ as benchmark. As mix is not deemed relevant for the reader, only profit and cost are presented in the following section. Computing the t‐test for profit concludes that the corresponding null hypothesis cannot be rejected: as the absolute t ratio of 0.70<1.690. The reported coefficient is +0.008, with a p‐value of 0.492. The p‐value confirms the t‐test, and the hypothesis of decentralization cannot be accepted. This noted, the hypothesis confirms the right proposed vector, and is in line with theoretical reasoning even though it is not validated. For cost centred firms, the null hypothesis cannot be rejected either with an absolute t ratio of 0.55<1.667. The reported coefficient is ‐0.008, with a p‐value of 0.586. Hypothesis 1a is rejected at the designated level of significance. 85 Hypothesis 1b: In the online industry, a broad market focus of acquirer has a positive impact on acquirer’s performance Computing the t‐test on hypothesis 1b concludes that the corresponding null hypothesis can be rejected: as the t‐ratio of 3.46>1.690. The reported coefficient is +0.001, with a p‐value of 0.001. This means that for each additional country the firm has an office in, it positively affects the abnormal performance of the acquirer with 0.045) per cent (ceteris paribus). Figure 10 – Leverage plot of AC As seen in the leverage plot, there are several ‘outliers’ that could have an impact on the results; as they are located quite far from the mean value, however the stipulated lines are evenly distant from the mean line, suggesting low influence on the regression coefficients. The reported results are concluded to be reliable. Hypothesis 1b is therefore accepted at the designated level of significance. When diving into the isolated regression of ADCAP on abnormal performance, the results have experienced slight reductions in significance, yet none of the levels of significance have been altered. Figure 11 – ‘Model 2’: The isolated effect of ADCAP on performance Term Market focus Cost centers Profit centers Estimate 0.001 (0.007) 0.004 Std error
0.000 0.013 0.011 t Ratio 1.80 (0.52) 0.39 Prob>t 0.077 0.608 0.702 86 To conclude on the section covering ADCAP, both measures tested indicate that the theoretical reasoning holds, though only hypothesis 1b holds at an adequate significance level. 3.3.3. Alignmentofstrategicemphasis
As reasoned in section 3.1.3., the ASE comprises two measures. Thus, the larger value of strategic emphasis, the lower alignment, hence the more complementary are the resources. Secondly, ASE is tested for which specific resource combination yields the best outcome. Hypothesis 2a – In the online business, complementing alignment between acquirer and target’s strategic emphases has a positive impact on acquirer’s performance In principle, the optimal resource combination (complementary vs. supplementary) is tested through the gap in ASE. As ASE is calculated in absolute numbers, the positive estimate indicates that with low ASE, i.e. complementary resources, the specific resource combination has a positive influence on acquirer’s abnormal returns. Computing the t‐test concludes that the corresponding null hypothesis can be rejected: as the t ratio of 1.93>1.667. The reported coefficient is +0.072, with a p‐value of 0.061. The hypothesis is accepted at a ten per cent significance level, and the results confirm that complementary resources is the optimal strategic fit, as a high level of emphasis alignment will have an opposite (negative effect) on CAR. Figure 12 – Leverage plot of ASE 87 The leverage plot shows a clustering of the residuals, yet the outliers are fairly wide spread. The stipulated lines do however seem to be equally distant from the mean on both sides, indicating that the outliers are not influencing the reported results. Hypothesis 2a is therefore accepted at the designated level of significance. Hypothesis 2b – In the online business, the alignment of acquirer’s strategic marketing emphasis and target’s strategic technology emphasis has a positive impact on acquirer’s performance. Both complementary combinations have been included in model 1, to determine which specific complementary combination of resources will benefit the acquirer most. As can be seen in the results, the proposed combination of acquirer’s technological resources and target’s marketing resources yields an estimate of 0.747 with a p‐value of 0.125. The t‐test verifies that the null hypothesis cannot be rejected, as the t‐value of 1.57 is slightly less than 1.690. Although not significant at a ten per cent level, the p‐value is close to being significant, and can be included as a strong indicator of the proposed hypothesis. Subsequently, when the strategic emphasis of acquirer is marketing and the strategic emphasis of target is technology the reported p‐value is quite the non‐significant at 0.43. Figure 13 – Leverage plot of both complementary resource combinations Taking a closer look at each of the two complementary resource combinations, slightly higher presence of outliers is seen in the first combination. Outliers should not necessarily be seen as a 88 bias for the sample, quite contrary they provide a representative view of the sample as contradicting, and emphasizes the difficulty of concluding in one direction. That said, the hypothesized combination displays an evident positive impact on performance, in comparison to the opposite combination which neither has neither high impact nor significance. Hypothesis 2b cannot be confirmed at the designated level of significance. Yet, it is seen as a strong indicator of a valid hypothesis. All though the estimators are not significant, they indicate that the combination of acquirer marketing and target technology delivers substantially higher returns. In model 3, the isolated effects of ASE on performance can be seen. While the above mentioned parameters overall experience loss in significance when isolated, the combination of resources seem to be affected in a peculiar manner. Figure 14 – ‘Model 3’: The isolated effect of ASE on performance Term ASE Comb 1: Tech (A) & Marketing (T) Comb 2: Marketing (A) & Tech (T) Estimate
0.050 0.354 0.561 Std Error
0.029 0.264 0.560 t Ratio 1.69 1.34 1.00 Prob>t 0.096 0.185 0.321 Under the isolated model ASE is still significant at the designated level of ten per cent. The proposed combination however, has shifted in ranking, yielding the opposite combination being the one with most significance. With this in mind, it can be concluded that other factors need to be present in order to detect the most optimal resource combination as put forward. This is most likely to be the control variables included in Model 1, but the other independent variables could also have influence. In conclusion of the above section, hypothesis 2a comprising ASE is confirmed at a standard level of significance, while hypothesis 2b cannot blindly be confirmed. With the reported results in model 1, the specific resource combination does however serve as an indicator of the theoretical discussion which it is based upon, though at a slightly higher level of significance. 89 3.3.4. Absorptivecapacity
For the final hypothesis, there are three measures indicating presence of absorptive capacity, namely prior acquisition experience of the acquiring firm, absolute knowledge base of acquirer and lastly the relative ratio of targets knowledge base to acquirer. Hypothesis 3a: Acquirer’s prior acquisition experience has a positive impact on acquirer’s performance The first hypothesis connected to AC is acquirer’s prior acquisition experience. As explained in section 3.1.3., the acquisition experience is quantified in regards to more than three completed deals as the benchmark. Computing the t‐test concludes that the corresponding null hypothesis can be rejected: as the t ratio (in absolute terms) of 4.82>1.690. The reported coefficient is negative at ‐0.062, with a p‐value of <0.0001. As the measure only classifies firms with three or more acquisitions within the last seven years (prior to the deal) as experienced, firms with two or less are not included. Figure 15 – Leverage plot of prior acquisition experience Examining the leverage plot, shows that the residuals are scattered in an highly observable pattern, a clear downward sloping line can be seen, confirmed by two straight stipulated lines in the same direction. Outliers are therefore considered to have low leverage and nearly no impact at all on the estimate. Hypothesis 3a is therefore rejected at the designated level of significance. In addition to rejecting the proposed hypothesis, the results have yielded significant results in the opposite direction, 90 meaning that prior acquisition experience can be concluded to have negative impact on performance. Hypothesis 3b: Absolute size of acquirer’s knowledge base has a positive impact on acquirer’s performance Computing the t‐test for hypothesis 3b concludes that the corresponding null hypothesis can be rejected, as the t ratio of 3.26>1.667. The reported coefficient is +0.037, with a p‐value of 0.003. As the parameter is regressed in its logarithmic value, the coefficient states that for one percentage increase in the logged value, the abnormal return will increase 0.032 per cent. Figure 16 – Leverage plot of absolute knowledge base The leverage plot for absolute knowledge base shows a high presence of outliers scattered across the plot of residuals. Even though there appears to be an even presence of negative as well as positive leverage points, both stipulated leverage lines are pointed in the positive direction. The steep curve can be seen as a visualization of the high impact that AKB is proven to have on performance. Hypothesis 3b is accepted at the designated significance level of ten per cent. Hypothesis 3c: The relative size of target’s knowledge base has a negative impact on acquirer’s performance 91 Computing the t‐test for hypothesis 3c concludes that the corresponding null hypothesis cannot be rejected: as the t ratio of 1.16<1.667. The reported coefficient is ‐0.010, with a p‐value of 0.25. This means that the hypothesis cannot be confirmed, as there is not sufficient significance present. Figure 17 – Leverage plot of absolute knowledge base The leverage plot shows outliers in both directions, with high leverage as well as substantial influence on the regression coefficients. Seemingly, the stipulated lines are exact the same distance from the mean value, and as they are opposite directions do therefore not have an impact on the residuals. Hypothesis 3c is rejected at a significance level of one per cent. In model 4, the isolated effects of AC on performance can be seen. The first hypothesis reports slightly less negative impact, though at the same level of significance. Hypothesis 3b and 3c however have altered in the isolated model, where absolute knowledge base has lost its significance and relative knowledge base is 90 per cent uncertain. Figure 18 – ‘Model 4’: The isolated effect of AC on performance Term Prior acquisiton experience Absolute knowledge base Relative knowledge base Estimate
(0.029) 0.005 0.001 Std Error
0.011 0.004 0.005 t Ratio (2.77) 1.19 0.12 Prob>t 0.008* 0.238 0.904 92 To sum up the hypotheses on AC, the regression shows some intriguing results. In principle, prior acquisition experience of acquirer is concluded, with significance of one per cent, to have a negative impact with acquirer’s performance. The acquirer’s absolute knowledge base is confirmed to be positively correlated with acquirer’s acquisition performance, at the designated level of significance. Finally relative size of knowledgebase indicates a negative impact to acquirer’s performance, though this cannot be concluded due to lack of significance. 3.3.5. Conclusionempiricalanalysis
Through the extensive use of multivariate regression, the three RBV components ADCAP (1), SE (2) and AC (3) have been tested through their sub hypothesizes, and will be reiterated in the following section. An overview over the findings can be seen in figure 19: 93 Figure 19: Hypothesis table 94 In principle, hypothesis 1 was generated through sub‐hypothesis 1a concerning the formal organizational structure of the acquiring firm and 1b covering the acquirer’s degree of market focus. Only the latter was confirmed at the designated level of significance, reporting a positive influence on performance. Secondly, hypothesis 2 was evaluated through two hypotheses; 2a, alignment of strategic emphasis and 2b, the specific resource combination. Here both were confirmed, though only at the designated level of significance. However, 2b clearly implied the proposed direction with a significance level only slightly above ten per cent. Lastly, hypothesis 3 was added up by 3a prior acquisition experience, 3b absolute knowledgebase and 3c, relative size of knowledgebase (target to acquirer). This section provided somewhat contradicting results; 3a and 3b were both significant, but while 3b confirmed the hypothesis with a fairly large contribution, 3a resulted in a negative impact of 6.2 per cent. Hypothesis 3c on the other hand could not be confirmed due to the low level of significance. While each of the three overall hypotheses (1, 2 and 3) were weighted equally at the models origin, it is interesting to reflect upon which of the components in fact have the largest impact on performance. In the model, the components are treated as ‘independent parameters’, meaning that they should not be related in terms of measures nor in the sense of being reliant on one of the other in order to maximise its value. That said technology serves as the interconnecting glue that acts as a core aspect in all three components. While AC and ASE both have had differing measures of the firms’ technological resources to indicate their presence, ADCAP has been ‘robbed’ for its technological focus41. Furthermore it could be argued that that two of the components in fact can be contradicting in a peculiar manner. ASE concludes that complementary resource bases, specifically marketing (acquirer) and technology (target) yield the best outcome. AC, on the other hand, concludes that acquirer’s knowledge base must be larger than target’s, in order to harvest positive abnormal returns. It could be argued that a target with technology emphasis often would have a larger knowledge base than its acquirer, if the latter’s strategic emphasis was marketing, given the fact that knowledge base is measured through R&D. However, acquirers are often considerably larger than targets in terms of absolute size42. As such, the 41
42
See section 2.7.4. elaboration of the elimination of ‘technological focus’ In the sample of analysis the target was on average one third of acquirer (0.31) in terms of market capitalization. 95 acquirer, even though it has a relative focus on marketing, still has a larger absolute size of knowledge base than the target. The research findings prove elements of each of the three overall hypotheses to be confirmed, though not every proposed hypothesis. From ADCAP, market focus is confirmed to have a positive impact on success. The measure of decentralization does however not prove to be a valid indication of adaptive capability, in this very context. ASE provides the study with the key finding that complementary resources have a stronger positive impact on success than supplementary resources are likely to have. Further the analysis has yielded an indication of the specific resource combination proposed to be attractive. Lastly, AC has returned interesting results, where prior acquisition experience is proven to have negative influence on performance. Absolute knowledge base is with certainty one of the key drivers behind success in online acquisitions, while the relative size does not exert confirming results. 4.
PARTIV–Discussion
In the following section the empirical findings will be discussed in detail. First each hypothesis connected to the three elements – ADCAP, ASE and AC will be analysed in isolation. Next, their consolidated implication for the unobservable elements’ (ADCAP, ASE and AC) ability to explain acquisition performance in the online business will be concluded. Finally, the implication of the theoretical model will be discussed, focusing on theoretical and practical implications. 4.1.Adaptivecapability
In section 3.3.2. it was shown that both hypotheses connected to ADCAP, and its positive relationship with acquirer’s acquisition performance, were confirmed, though only market focus at an acceptable significance level. 4.1.1.Hypotheses1a‐Formalorganizationalstructure
The first sub‐hypothesis to ADCAP, formal organizational structure, could not be confirmed at a standard level of significance. This creates a need to re‐visit the theoretical and methodological arguments in order to see where the line of reasoning might be wrong. That decentralization 96 indeed is the right formal organizational structure for the online business is thought a valid argument, given its rigid theoretical backing (see section 2.7.4.2.). However, given the fact that most multinational enterprises today must be strike the right balance between global integration and local responsiveness (Bartlett and Ghoshal, 1989, 1992; Sohn and Paik, 1995), it might be too narrow minded to state that decentralization always is the better option. There are, however, other reasons why the observed results deviate from the hypothesized. First of all, it can be a measurement error. Simply looking at the second level of management might not be sufficient in order to with certainty determine whether the firm is cost‐ or profit centred. A survey could for example more easily provide accurate information on this matter. It could also be that the profit/cost centre measurement not is the right way to determine decentralization. Another possible measure would be to look at the business units (BU) budgets, compared to the head quarters’. This would indicate how much decision power each BU have concerning for example marketing or R&D spending. This measure would be a continuous variable, rather than a dummy, which would increase the sophistication of the regression analysis. This measure again requires more in‐depth research and would most likely not be available for all public companies; rather it would be suitable for a qualitative research paper. Finally, even though profit centred acquirers indeed are more decentralized and thus better equipped to identify and integrate the right targets, the match between acquirer and target formal organizational structure might play an important role. It could be argued that integration would be easier if both companies had the same formal organizational structure, regardless if this was centralized or decentralized. However, since the formal organizational structure not has been gathered for the targets, this will be left to other researchers to determine. 4.1.2.Hypothesis1b‐Marketfocus
In terms of the acquirer’s market focus, it was confirmed that a broad, ‘market sensing’ focus increases acquirer’s acquisition performance. In a similar manner as formal organizational structure, this is believed to be context specific to industries experiencing high turbulence in technology, competitive situation and customer demand. This finding is in line with prior research, though the nuanced discussion requires some commenting. A close relationship with customers has been seen as pivotal in turbulent environment, which has questioned the sustainability of a 97 broad focus as: “the broad market focus limits the extent to which a company can customize its offerings.” (Tuominen et al., 2004:497). While acknowledging the proposed reasoning, this paper adheres to Christie et al. (2003) and argues that in turbulent environments with high rate of disruptive innovation, relying too much on customers’ perceived needs can be dangerous. There are multiple examples where products and services not asked for have turned out to change the whole structure of markets (i.e. Apples Ipad). Similarly, needs can be met in different ways, and disruptive innovations often render products that are focused on serving a specific need, uncompetitive (good examples are the WAP technology and regular mobile phones after the smartphones were introduced). Other scholars have used similar line of reasoning, warning for customers’ myopic behaviour (Zhou and Li, 2010) and their inability to foresee future demand in rapidly shifting environments (Hamel and Prahalad, 1994). This is especially applicable to the online business, where technological development, rather than customer needs, drive product and market development. In addition, the online business is highly globalized, which increases the need to have multiple antennas around the world to keep up with development and trends. It does not matter that a West European company has two large organizations in the USA and Latin America, if the newest technological innovations keep popping up around Asia and Eastern Europe. 4.1.3.Concludingonhypothesisone
Only the sub‐hypotheses concerning market focus has been confirmed at the designated level of significance. While the combination of decentralization and ‘market sensing’ was believed to enhance each other, this cannot be confirmed. As such, there is an indication that ADCAP is a performance driver in the online business, though nothing can be concluded with certainty. Still the operationalization of ADCAP is highly relevant for the literature, as it provide a concrete yet flexible framework as well as measurement to test similar hypotheses, though the decentralization measure could be modified as discussed above. 4.1.4.AfewperspectivesonADCAP
Of notice is that rather few empirical studies investigate the correlation between ADCAP and performance, and none was found focusing on the context of acquisitions. Scholars have been 98 busy formulating different frameworks, with no or little empirical backing (Wang and Ahmed, 2007), resulting in an articulated need for more specific measure of ADCAP (Zhou and Li, 2010)43. ADCAP is connected to the concept of ‘strategic flexibility’, comprising the flexibility of a firm’s assets and its ability to deploy them in a flexible manner (Carud and Kotha, 1994; Rindova and Kotha, 2001; Sanchez, 1995). Of interest is that in their in‐depth analysis of Internet giants, Rindova and Kotha (2001) argue that the concept of an “evolving organization” is believed to be something generated in top management, rather in the technological capabilities of the organization: “Thus, whereas Sanchez (1995) emphasized that strategic flexibility depends on flexible technologies, such as flexible manufacturing systems and point‐of‐purchase information systems, our data suggest that human and organizational assets are central in developing strategic flexibility. Our view echoes the idea that technology can be helpful but not sufficient for achieving the flexibility needed to respond to rapidly changing environments.” (Rindova and Kotha, 2001: 1275) Human aspects of ADCAP discussed by Rindova and Kotha (2001), such as key employees and leadership competencies, have not been measured in the study at hand. They are, however, believed to be of key importance to the online business, given the high level of tacit knowledge. Due to the technological fast development, it is not always cost efficient to code knowledge, which makes it more explicit and thus easier to copy. Instead, leading companies focus on constantly attracting the most up‐to‐date talents and keep educating them in the newest technologies. This is a great opportunity for future researchers to contribute. 43
While innovation (Tuominen et al., 2004) and investment opportunities (Christie et al., 2003) can
bearguedtohaveanimpactonperformancenodirectlinkagehasbeenempiricallyproven.
99 4.2.Alignmentofstrategicemphases
The two hypotheses connected to alignment of strategic emphasis are both confirmed, though the specific ASE of acquirer‐ marketing and target – cost only can be seen as an indication. As such, the findings are deemed interesting as they indicate similar results as prior studies. 4.2.1.Hypothesis2a‐Complementarityversussupplementaryresourcealignment
Hypothesis 2a confirms that acquirers should seek complementary alignment of strategic emphasis in order to ensure positive abnormal stock performance when acquiring online businesses. Even though the literature on one side of table is debating whether resources are best interconnected through supplementary resources (Datta et al., 1992; Shelton, 1988; Singh and Montgommery, 1987) this paper argues and empirically shows that complementary resources are found to be the best combination. This is in line with multiple prior works (Harrison et al., 1991, 2001; Hess and Rothaermel, 2011; Hitt et al., 1998; Uhlenbruck et al., 2006). Ahuja and Lampert (2001) state that when firms not seek complementary technologies, they tend to be constrained in their innovation output and are liable to produce solutions that are related to their existing ones. While Ahuja and Lampert (2001) and Makri et al. (2010) reason their findings with impact on innovation output, it yields an attention‐grabbing hypothesis to be tested on abnormal returns, in knowledge intensive industries such as the online business. Capron et al. (1998) argue that when one business is stronger on a relevant resource dimension, a greater redeployment of particular resources can be expected. Applying this line of reasoning to the above analysis of specific resource alignment it could be argued that a higher redeployment of marketing or technology resources is to be expected from the acquirer to the target, and vice versa, depending on the combination of complementary alignment. Further interpretation could even hypothesize, that the reason why complementary alignment of strategic emphases between acquirer and target technological resources result in a better performance, is that the market anticipates a better redeployment of these resources. Due to scope of the research paper, it has not been deemed relevant to examine the post‐acquisition effect of resource redeployment; however this very subject is proposed as a future research topic to be addressed. 4.2.2. Hypothesis 2b ‐ Specific alignment of strategic emphases In terms of hypothesis A2, the analysis indicates that the combination of acquirer’s marketing and target’s technology emphasis maximizes the abnormal return for the acquirer. In spite of the 100 stipulated importance of understanding value creating resource alignment (Tanriverdi and Venkatraman, 2005), there is an evident research gap in examining target and acquiring firm resource interactions on firm performance (Song et al., 2005). As such, this finding ad consensus to the few prior studies on the subject, and contributes with further indication of generalizability for online related acquisitions. While Capron et al. (1998) investigates the nature of resource re‐
deployment between acquirer and target, through a five part typology comprising R&D, manufacturing, marketing, managerial and financial resources, this study has consciously focused on the two resources that are said to be key drivers of online business. Confirming the empirical study of King et al. (2008), the specific complementary relationship of acquirer’s marketing resources and target’s technological resources can be seen as an indication of higher returns of the acquirer, even though the reported results only can be used as an indication. Remarkably enough, the opposite combination turned out positive as well, though to a much lesser extent than the former and not at a significant level. One rational behind the outcome could be that marketing and technological resources are both acknowledged as the key drivers of online businesses44. However, even though marketing capabilities indeed are important, the sole most important resource in the studied industry is technology. Furthermore the scope of technology in the online business makes it almost naïve to talk about technology as one type of resource. In this rapidly shifting high technological business, it can be stated with certainty that not all technological capabilities are the same. In retro perspective, classifying technological resources in a more granular manner would have been more suitable for the online industry. A good example of such an approach is done by Makri et al. (2010), who conclude that complementary scientific and technological resources generates the highest post M&A performance45. In the online industry it would have been interesting to look at the alignment between basic and specialized knowledge, as both are seen as key growth drivers (Amit and Zott, 2001; Uhlenbruck et al., 2006). Another area of interest for future researcher could be to re‐test Hess and Rothaermel (2011) study on the online industry, looking at strategic alliances and high‐performing employees as either 44Onareferringnotice,complementaritywasdeemedthebestoutcomebythefirsttest;andperhapstherefore
nosurprisethattheybothturnoutpositive.
45 Scientific knowledge is defined as the core design concepts and how they are implemented in a specific
component, while technological knowledge concerns the linkage of components to create a specific output
(Makrietal.,2010)
101 complementary or supplementary resources, depending on where in the value chain the firm choose to invest in them. 4.2.3. Concluding on hypothesis two With one confirmed and one indicating sub‐hypothesis there is support of the theoretical arguments backing up the overall hypothesis – alignment between acquirer’s and target’s strategic emphasis is positively related to acquirer’s performance. This is believed by the authors to be one of the key drivers that can be used to explain acquisition performance out from an RBV perspective. As the firm’s resources often are the key reason for acquisitions it should seem natural to investigate target and acquiring firms’ resources at a more comprehensive level before any conclusion of combined value can be drawn. The above stated findings contribute to existing literature by empirically testing acknowledged resource alignments, first confirming the superior performance of complementary ASE, and further indicate the specific alignment of acquirer’s strategic marketing emphasis, with target’s strategic technology emphasis as the best combination of resources. Through operationalization of unobservable resources, consensus is created as well as a new context studied ‐ the online business. The latter further ads value by confirming generalizability of the importance of ASE in driving acquirers’ performance. 4.2.4. A note on the failed attempt to apply a motivation dimension to RBM As mentioned, it has been an ambition to create a context for the ASE, which can be applied to shifting environments. As mentioned in section 2.7.2., it was therefore the initial aim to test resource alignment with a dynamic lens, namely examining the hypothesis that alignment between acquirer’s strategic focus (diversification versus consolidation) and alignment between acquirer and target’s strategic emphases (complementing versus supplementing resources and capabilities) will lead to superior performance in acquisitions. The idea was initially proposed by Swaminathan et al. (2008) and thus their methodology was used as guidance, though modifications were made in order to better fit the context of online business. However, when trying to replicate the method, severe tautological issues were encountered concerning the definitions of the motives ‐ diversification and consolidation, and the resource definitions ‐
102 complementary and supplementary46. In addition, the method of determining acquisition motive was too subjective, leading to the abandonment of the hypothesis. A thorough description of the applied method can be found in appendix 2. The authors recognize this failed attempt to contribute to the literature. Empirically, the case of both diversification and consolidation focused M&A activities have been argued and empirically confirmed (Anand and Singh, 1997; Capron et al., 1995; Rumelt, 1974; Varadarajan and Ramanujam, 1987). However, in order to create the dynamic angle, this motive should be crossed with resource alignment. Other definitions could evidently have been taken use of. Capron et al. (1998) suggests alternative acquisition motives; economies of scale, industry, overcapacity, financial diversification and target turnaround. The research focus could indeed have been altered to include a broader range of motives, a revised categorization of the deals, and a more comprehensive scope of concept. However, the risk of falling into the tautology trap became too big, leading to the choice to abandon the hypothesis. Still, the importance of adding a deal specific context factor should not be neglected. Even though this paper has failed to provide an operational way to measure acquisition motive, the motive as such is deemed relevant for the development of the RBM and RBV literature is still highly emphasized. 4.3. Absorptive capacity The analysis of AC yields some interesting, and unexpected result that affects the overall hypothesis (3): Absorptive capacity increases acquirer’s performance in online business acquisitions. The sub‐hypothesis concerning acquisition experience (3a) is rejected at the stipulated significance level, while the remaining sub‐hypotheses (3b) and (3c), covering absolute and relative size of knowledge base, are confirmed, though only the former at a significant level. In the following section all sub‐hypotheses will be discussed, using both a theoretical and contextual 46Theconceptofconsolidationisdefinedusingtheword‘similarproducts’whilediversificationisdefinedusing
theword‘relatedtechnologiesandproducts’.Thispaperarguesthatitcreatesatautologicalstatementassimilar
products, are synonymous to supplementing products, which is what the other independent variable is. In a
similarmannerrelatedproductsarearguedtobesynonymoustocomplementaryproducts.Seeappendix2for
moredetails.
103 lens. However, as seen in figure 4, AC is the element which not has an online specific focus47, which will result in relatively less contextual perspectives. 4.3.1. Hypothesis 3a ‐ Prior acquisition experience The rejection of this vividly debated factor is highly intriguing, as it leads to the question of where the theoretical reasoning is wrong48. Three potential scenarios are identified by the researchers. The first is that prior acquisition experience indeed increases AC, but AC does not always increase the acquirer’s performance. The second is that AC indeed increases acquirer’s acquisition performance, but that the performance is neutral or negatively affected by acquisition experience. The third plausible reason is that AC indeed increases acquisition performance and that acquisition experience can have a positive effect on AC, but that this effect not always is realized. The first reason is not deemed plausible by the researchers. In their theoretical model of AC, acquisitions are argued to be a key lever for the first element of potential AC (see figure 2). Even though few AC scholars have solely focused on AC in an M&A context, acquisition performance is often mentioned as a result of high AC (Ahuja and Katila, 2001; Cloodt et al., 2006; Cohen and Levinthal, 1990; King et al., 2008). The more a firm is able to “recognize the value of new, external information, assimilate it, and apply it to commercial ends” (Cohen and Levinthal, 1990: 128) the better it should perform in identifying the right targets, integrating this knowledge in the organization and exploiting it to increase value. The next reason is more plausible. As mentioned in section 2.7.3.3. there are scholars who have confirmed a neutral or negative relationship between acquisition experience and performance, especially when acquisitions are used instead of R&D (Higgins and Rodriguez, 2006; Kirchhoff and Schiereck, 2011). There are also aspects such as path dependency and dominant logic that can be enhanced by acquisitions (Côté, et al., 1999) as firms become used to doing things in a certain way and look for a specific type of targets. This could potentially have implications for the firm’s ability to recognize new, valuable knowledge (Ahuja and Lampert, 2001). This would be highly relevant in the turbulent online business, where 47ThismeansthatthehypothesesgeneratedunderACarebelievedtobeequallyrelevantforotherindustries,
whereastheothertwofactors(ADCAPandASE)bothhavegottenhypothesestailoredtotheonlineindustry.
48 In addition to these theoretical arguments there areother plausible reasonsthat could explain the negative
impactofprioracquisitionexperienceonperformance,suchasabadhistoryofpoorlyexecutedacquisitionsor
themarket’sworriesthatthecombinedorganizationrisksbecomingin‐efficientduetoexcessresources.Though
plausible,theyprovidelittleinsighttothediscussionathandandwillthusnotbefurtherelaborated.
104 firms trying to do things as they were done a few years back risk to get punished by the market. However, it does not seem plausible that all acquisitions fall into these categories. Rather, the final reason is believed to be the most probable, that acquisitions per se cannot be argued to increase AC and thus performance. This will be further discussed below. 4.3.1.1. When prior experience does not lead to increased AC The argued reason why prior acquisition experience does not lead to increased performance is that these experiences in fact have not managed to increase the firm’s AC. In order for acquisition to improve acquirer’s AC it must involve additional flow and/or stock of knowledge (Cohen and Levinthal, 1990; Foss 2006). There are multiple reasons for why an acquisition would not do either. First of all, if the acquirer’s prior acquisitions have not involved any knowledge transfer from the target, acquirer’s AC will not increase. An example of this would be if the acquirer’s motive of the acquisition was to leverage its superior resources in less effective competitors (Capron et al., 1998)49. Secondly, if prior targets have not been integrated but rather kept as separated firms (or centres of excellence) substantially less effect on the acquirer’s AC can be expected. Thirdly, even though the acquirer’s prior acquisitions indeed were aimed to increase its AC, ta reason for failure could be the acquirer’s inability to ‘assimilate’ and ‘transform’ the external knowledge acquired. This difference between transferring of knowledge and learning is highly relevant in the KBV literature, as while the former is more feasible, the latter is the element necessary to ensure competitive advantage. As discussed in section 2.7.3.1., potential AC can be seen as an enabler for performance, but not a driver; hence all successful deals producing positive abnormal returns have potential AC, but not all firms with potential AC experience superior performance. If the firm does not have AC from the start, it will not be able to integrate, assimilate and exploit the knowledge acquired. As such, a certain level of in‐house R&D is necessary for acquisitions to make sense (Higgins and Rodriguez, 2006; Rindova and Kotha, 2001). Empirically it has been proven that firms that use acquisitions as a substitute for in‐house R&D are punished by the stock market (Heeley et al., 2006; Higgins and Rodriguez, 2006). Too much focus on acquiring resources in early stages of technology development was also found to impact Excite negatively in the online navigation industry: 49Thisisarathercommonobservationinacquisitionswithconsolidationmotives
105 “Excite frequently acquired companies and hired and fired personnel in an effort to reconfigure its resources. Such frequency may impair a firm's ability to transform experience into meaningful learning…and to align newly acquired resources with the coordination mechanisms needed to use them effectively.” (Rindova and Kotha, 2001:1274) The insight of Excites reconfiguration strategy indicates the importance of having a base of knowledge that can capitalize on the external information gained through acquisitions and facilitate learning. Yahoo (which was concluded more successful than Excite) primarily focused on in‐house R&D in the beginning, to later start acquiring smaller firms to add specific knowledge to its existing portfolio (Rindova and Kotha, 2001). This type of M&A strategy is called ‘tactical’ and has shown to be one of the most value creating strategies for high‐tech companies (Rehm et al., 2012)50 Looking at the online business, this is also a common strategy, applied by Google, Apple and other successful online giants. In order to check if the sample used in the analysis shows a tendency for acquirers to substitute R&D with acquisitions, a simple regression was performed where number of prior deals where regressed on absolute knowledge base (see figure 19). The negative slope demonstrates that firms with a higher number of past acquisitions have smaller absolute knowledge base. This is indeed an indication that that the substitution problem is present in the sample. A more thorough analysis is needed in order to conclude that online firms are bought up as a R&D replacement strategy. However, the regression shows an important explanation to why prior acquisition experience seems to affect performance negatively. 50Atacticdealstrategyincorporatesdoingmultipleofsmalldealsthatarestrategicallyimportant(thoughnotin
terms of size), enabling the acquirer to attain specific resources needed to strengthen value proposition and
furthergrowth(Rehmetal.,2012).
106 Figure 20 – Regression of acquirer’s prior acquisition experience (A experience) and absolute size of knowledge base (AKB A) Finally, even if the motive of prior acquisition was to acquire knowledge from target and the acquirer was well equipped to assimilate and transform this knowledge, this might still have failed due to a plethora of other reasons connected to the post‐acquisition phase. See an extensive body of literature for examples (Buckley and Carter, 1999; Weber and Shenkar, 1996); Haspeslagh and Jemison, 1998; Marks and Mirvis, 1998; Castro and Neira, 2005; Inkpen et al., 2000; Ranftm 1997; Risberg, 2001). Given the above discussion it is possible to conclude that prior acquisitions per se will not be able to ensure increased AC; rather it must be acquisitions that have resulted in increased flows and/or increased stock of knowledge. This is connected to the argument that acquisitions only can provide ‘potential’ AC (Lichtenthaler, 2009; Zahra and George, 2002). The fact that a firm has a history with multiple acquisitions tells the researcher (and the market) nothing more than that this firm is good at identifying valuable knowledge externally51. This does not imply whether the knowledge has been successfully assimilated nor transformed nor exploited. Potential AC can be seen as an enabler for performance, but not a driver. In order to ensure that prior acquisitions 51Multipleotherreasonsforacquisitionsaretreatedbytheoriessuchasagencytheory;however,thesearede‐
limitedfromthepaper.
107 have led to assimilation or even transformation of the knowledge, the number of prior deals must be viewed in light of acquirer’s absolute knowledge base at the time of acquisitions, as well as the relative knowledge base of each target. 4.3.2. Hypothesis 3b and 3c ‐ Absolute and relative size of knowledge base After a thorough discussion of reasons and implications of the rejection of hypothesis 3a, hypotheses 3b, absolute size of acquirer’s knowledge base affects performance positively, and 3c, target’s relative size has a negative effect on acquirer’s performance, will now be evaluated. Both, can be empirically confirmed, though only the former at an acceptable level of significance. The findings for hypothesis 3b confirms prior empirical works on the subject (Ahuja and Katila, 2001; Cloodt et al., 2006; King et al., 2008). Of interest is that absolute knowledge base was also confirmed by Ahuja and Katila (2001), even though this study uses input rather than output measures. While the use of output in absolute knowledge base empowers Ahuja and Katila (2001) to predict the effectiveness of redeployment of resources, the shift from output to input disables this research paper to comment on any redeployment of resources. However, the results do opt for the reflection of whether there might be a connection between input and output, contrary to what other scholars have warned (Anand and Khanna, 2000; Mowery et al., 1996). Evidently, a study using both input as well as output indicators of R&D would facilitate researchers to better understand the firm’s efficiency in turning input into competitive advantage, decreasing the “black box” issue. However in the case where relevant output measures cannot be found, the findings of this paper indicates that input measures can tell part of the story. It is important to point out that this is not an indication that the heterogeneity assumption can be violated. Rather it can be seen as a sign that companies are capable of placing their focus (and investments) where they can create value. The nature of the online business makes it extremely important for smaller players to become specialists rather than generalists. The general strategy is to invest heavily in a specific technology to become a front‐runner, attracting the larger incumbents which are prepared to pay large sum to acquire knowledge they are too slow to develop in‐house. In addition to confirming prior researchers’ hypotheses concerning absolute knowledge, this paper has further contributed by placing absolute knowledge base as a key operational measure of Zahra 108 and George’s (2002) theoretical model of AC (see figure 2). Further research should, however, be put into evaluating if absolute knowledge base plays the role stipulated by this paper, in primarily enhancing assimilation and transformation of external knowledge. In extension of absolute knowledge base is the way knowledge is stored and retrieved. While argued to be highly important for the firm’s AC (Foss et al., 2010), it cannot be estimated from input measures. Effective knowledge management systems, as well as well‐developed informal networks are believed to explain a large part of heterogeneity among firms with similar knowledge bases (Van den Bosch et al. 1999). This is further argued to be particularly important in the online industry, due to the size and characteristic of the knowledge needed to be accumulated, as well as the importance of accumulation speed. Here, competitive advantage can steam from a well‐organized system to store knowledge in a way that facilitates retrieval by a new person when needed, as well as a culture that enhances sharing of tacit knowledge. However, as discussed above, the fast development of the industry often renders knowledge obsolete quickly, decreasing the value of spending money on coding and storing it. The hypothesis that relative size of knowledge base affects acquirer’s performance negatively cannot be confirmed at an adequate significance level. The theoretical reasoning, that it is hard for the acquirer to absorb and assimilate a knowledge base if it is similar or larger in size than the existing knowledge base, is acknowledged by multiple scholars (Ahuja and Katila, 2001; Cloodt et al., 2006). As such, it is plausible to believe that the failure to confirm this hypothesis lies either in the measurement, or in the fact that the online context differs from earlier tested industries. The measurement applied in this study has, as commented on, not been used by other scholars. However, since it is only a relative measurement based on the absolute size of knowledge base, this is not believed to be a root cause to the rejected hypothesis. Rather, it could be argued that relative size of knowledge base is of less importance in the online business, primarily because most targets are considerably smaller than their acquirers. 4.3.4. Concluding on hypothesis three After going through the three hypotheses connected to AC, some intriguing insights have been generated. Through rejection of hypothesis (3a), that acquisition experience increases acquirer’s performance, a rigid discussion of using acquisitions as objective proxies was put forward. Here it 109 was concluded that prior acquisitions need to be analysed in relationship to acquirer’s knowledge base at the time of these acquisitions, as well as prior targets’ knowledge bases. This will help to evaluate whether a prior acquisition is likely to have affected AC positively, which is the argued reason why acquisition experience will increase acquirer’s performance in future acquisitions. Absolute size of knowledge base was confirmed to be an important factor influencing acquirer’s performance and the theoretical reasoning that it is strongly connected to AC is deemed solid. The contribution made by this paper is confirming the hypothesis in a new context – the online business, and indicating that input measure of R&D show similar results as output measured used by prior researchers. This allows for improved possibilities for future research to use multiple measures to explain AC and helps to further the acknowledgement of using objective proxies in order to measure unobservable. The hypothesis that target’s relative size has a negative impact on acquirer’s acquisition performance was not confirmed. The two factors measured have further added value to the theoretical model of AC proposed by Zahra and George (2002). As such, the paper has contributed to increasing consensus in the literature, using acknowledged models when possible rather than constantly re‐inventing the wheel (Wang and Ahmed, 2007). While a vast number of scholars have developed the different components and tested the effect they have on AC, the literature has been slow to consolidate these findings and operationalize them. This paper takes a first step in doing so, by proposing two measures connected to the three elements of Zahra and George’s (2002) AC model –acquisition, assimilation and transformation (see figure 2). Prior acquisition experience can measure the first element of the AC model – acquisition of external knowledge. However, data needs to be gathered more thoroughly than in the present analysis in order to ensure that only experience that has increased acquirer’s AC is included. Absolute knowledge base positively influences both assimilation and transformation of external knowledge. Relative knowledge base of target, on the other hand, needs to be further investigated to see if it indeed is relevant for the online industry. The final measurement – exploitation, needs a more output focused measure which was not deemed plausible to find in the context of the online business. For other industries a measure such as patents will serve well. 110 4.4.Finaldiscussion
In the following section the consolidation of the three elements thought to drive acquirer’s performance in the online business will be put into perspective as well as the implication of creating a model for analysing acquirers’ performance through a RBV lens. Furthermore this chapter will reflect on implications of the findings provided in this research paper, both for the RBV literature and for practitioners. Figure 21 ‐ Proposed theoretical model for analysing acquirer’s abnormal stock return 4.4.1.Amodeltohelpexplainacquirer’sperformance
The model created in this paper states that ADCAP + ASE + AC = Increased probability of positive abnormal stock returns of acquirer. While acknowledging that these three elements are not the sole drivers of positive abnormal performance, they are believed to create a rather holistic picture of how the resources of acquirer and target can be expected to create value. This model has further been tested in the context of the online business, where it proved to have a fairly high explanatory power of variance in acquirers’ performance (R‐square adjusted of 0.34). The three 111 components ‐ ADCAP, ASE and AC, are equally weighted with regards to research priority and the analysis. Their individual impact however, is found to be more complex as was concluded in the previous section. The order in which they have been analysed, navigates the reader through the order in which they should be operationalized. As discussed above, it is argued that the firm needs ADCAP in order to identify the right targets, while the ASE ensures the match between target and acquirer. Finally, AC is essential for the firm to recognize, assimilate and transform the knowledge of the target. As such, the components should be seen as building blocks rather than in an ascending order of importance or impact. However, it is important to point out that all three components are believed to influence the whole process. AC also plays a part in finding the right target, while ADCAP also has positive effects on assimilation of the external knowledge. However, the proposed sequence indicates where the three influencing factors are thought to play the most central role. 4.4.2.Theoreticalimplications
This research paper sheds new light on the empirical use of RBV to predict acquisition performance, through interconnecting the dimensions of ADCAP, ASE and AC and in a holistic and dynamic model (Wang and Ahmed, 2007). Looking back at the identified research gap it can indeed be argued that this model adds value to existing RBV research on acquisitions in multiple ways. In principle, the model is flexible to fit different contexts, making it applicable to other industries. This flexibility stems from the model’s sub‐components which can be tailored to the specific characteristics of the market investigated. While the context of the online business led to the specific hypotheses generated for the online industry, other industries are thought to yield different combinations (see figure 20 for an illustrative example). Given this flexibility it would be highly interesting to test the model in other contexts, both to investigate how the hypotheses would differ, as well as their empirical support. That said, this paper argues that another key contribution has been to choose a highly dynamic environment, such as the online business, especially considering the low focused given to it in the existing academic literature. The paper at hand has disproved many of the critical claims that RBV cannot be applied to dynamic industries. By paying attention to more resent contributions, a dynamic model to empirically investigate acquisition performance can indeed be created using the RBV. Hopefully, the insights generated 112 can be used as foundation for further research in the online business area, as the RBV indeed needs to step up on this subject in order to remain a relevant management theory. Figure 22 – Illustration of how the proposed model could be applied in another empirical analysis Secondly, all measures used in the model have been either replicates or highly influenced by acknowledged empirical work. Even though one single research paper cannot overcome the void of self‐centrism discussed in section 2.3.2., it hopefully provides an example of how replication can be done still contributing new insights to the literature. A good example of this is that the analysis at hand implies that input‐measures predict similar results to those of output measures. Though this is merely an inference, it holds the possibility to further the research on how to tackle the “black box” issue52. Even though this paper has tested for stock market performance, other research could use innovation as the performance measure, given the large interest in this output measure. 52Asmentionedbefore,itisimportantnottomistakethisobservationasproofofhomogeneityacrossfirms.
113 Finally, while the models applicability is restricted to the acquisition context, the methodology behind it could be applied to other areas of the RBV literature. Prior scholars have either focused solely on one isolated branch, or applied a pluck‐and‐play attitude to the theory, using various aspects from different branches ‘claiming’ its relatedness (Locket and Thomson, 2009). In this paper, the authors have strived to underline the components respective roots, while consolidating them into a holistic model which can be tested empirically. If more scholars adhered to a similar approach to science it would surely help to improved consensus of the interconnection of RBV’s key concepts. Increasing both feasibility and incentives for scholars to replicate each other’s work would in turn further RBV’s development in a more coherent way than currently observed (Armstrong and Shimizu, 2007). 4.4.3.Practicalimplications
The model created in this paper does not only have implications for future research in RBV, but also for managers executing an acquisition strategy. Growth through acquisitions is becoming increasingly important for the firm. In fact, M&A was found to account for on average 35 per cent of a firms’ growth, considerably larger than growth through market share gains (Viguerie et al., 2008)53. M&A is further seen to play an even more important role in larger corporations’ growth strategies (Rehm et al., 2012). However, this observed growth in M&A does not seem to be correlated with an observed increase in value creation. Using the event study method, most scholars still confirm a value neutral or slightly negative effect from M&A (Kelly, 2009; King et al., 2008; Kirchhoff and Schiereck, 2011). Thus, it can be concluded that helping managers’ to understand how they can find the best match amongst the plethora of targets in the market. The proposed model’s illustrates in a neat manner which aspects managers of the acquiring firm need to contemplate in the phase of identifying its target. The authors believe that the model can be a valuable addition to the more financial models often applied in pre‐phase commercial due diligences. Given the fact that the majority of acquisitions motives concern access to a specific resource, it is about time that managers start investigating more wisely how the characteristics of these resources will affect their firm. By having a tool to guide pre‐phase research of attractive targets, managers are provided with more sober insights, rather than simple and rather naïve dream scenarios solely based on what the target can offer. There are, however, important factors 53Inastudywasconductedon100ofthelargestUScorporationsin17differentsectors.
114 related to acquisitions where this model cannot offer any insights, such as the many cultural aspects affecting post‐integration process (Buckley and Carter, 1999; Castro and Neira, 2005; Weber and Shenkar, 1996). In addition, the model is not believed to be applicable to specific condition such as private equity firms or other acquisition vehicles. 4.5.Conclusion
This research paper set out to answer the question: What drives success of the acquiring firm in online business acquisitions? The RBV was chosen as theoretical lens due to its internal focus on firms’ resources and their deployment. The theory’s flexibility and richness in insight makes it a valuable tool when investigating something as complex as the absorption of one firm by another. RBV has an impressive portfolio of both theoretical and empirical work shedding light over the M&A practice. Yet, reviewing the literature some serious gaps were identified, further intriguing the authors. As such, the aim of the paper at hand expanded to not only answer the initial research question. But in doing so the authors further aspire to contribute to the literature void identified in the RBV field by: 1.) Helping provide insight on the unsolved challenge for the academic literature to explain the observed heterogeneity in acquirers’ performance 2.) Improving consensus in the RBV literature by re‐testing factors argued to influence acquisition performance and consolidating the elements found most relevant into a structured yet dynamic model 3.) Providing insight into the drivers of performance in the online business and thus proving RBV to be dynamic enough to handle high turbulence environment. Studying the online business, which comprises firms who offer products, services and/or other enablers for all Internet related activities generated several valuable insights. The online business is characterized as hypercompetitive with large turbulence in technology development, competitive landscape and customer demand. Acquisitions play a fundamental role in firm growth. Successful companies have applied a tactical M&A strategy, simultaneously focusing on in‐house R&D while acquiring multiple small firms with strategically important resources. The resources that have been key in driving performance has been determined to be technology and marketing, though technology is the sole most important resource to survive and gain a competitive advantage in the online business. While academia was highly active in the years leading up to the 115 Dot.com bubble, surprisingly little has been written about the online business in recent years and even less so in the field of M&A. The same cannot be said about the more practically oriented literature, where online related studies have become the norm. Still, the analysis generated some valuable insights that later were applied to the theoretical review, discussion and hypothesis generation. In the theoretical analysis, the three main branches of RBV – RBM, KBV and DC were carefully studied with the aim to identify key elements that could provide insights of performance drivers in acquisitions. Though the empirical literature is highly dispersed, there is a general agreement in all theories that acquisitions indeed can create value. However, performance is dependent on a highly complex interaction of both acquirer and target resources. Three key elements were identified as key drivers of acquirers’ performance. ADCAP was found to be a possible factor of importance in determining the acquirer’s performance and is argued to enhance the ability to decide when and how to best capitalize on identified threats and opportunities through the use of acquisitions. ADCAP was believed to be affected by formal organizational structure and market focus. Specific for the online business, a decentralized organisation with broad market focus was hypothesized to create the most value, as this would allow the acquirer to scan the market and react fast and accurate to environmental changes identified. Alignment of strategic emphases (ASE) focuses on the combination of resources, where a complementary resource alignment was argued to create most value. More specifically it was further hypothesized that value would be greater when the acquirer has a strategic marketing emphasis and the target a strategic technology emphasis. Finally, the acquirer’s absorptive capacity (AC) was identified as a key element in explaining acquirer performance as it indicates the acquirer’s ability to identify new information externally, integrate, and leverage it to commercial ends. Both acquirer’s absolute knowledge base and past experience in acquisitions were believe to affect acquirer’s performance positively as existing knowledge enhances the ability recognize, learn and leverage new knowledge. The relative size of the target’s knowledge base, on the other hand, was argued to have a negative effect on acquirer’s performance due to the belief that integration of a large knowledge pool distorts resources from other, value creating activities. 116 An empirical analysis was conducted using an event study, where each hypothesis was operationalized using objective proxies. Here, effort was put into screening the literature for previously applied and confirmed measures. However, rather than simply copying the measures, each was evaluated to confirm its relevance to the online context. In cases where modifications were deemed value adding, these were made. The sample was generated using carefully selected screening criteria, in order to ensure that only deals were targets were defined as online businesses were included. Putting the result of the regression analysis into perspective, some rather intriguing insights was generated. While all three elements (ADCAP, ASE and AC) were confirmed to indeed have a positive impact on acquirer’s performance in the online business, the level of impact differed. ADCAP was only partly confirmed to have an impact, as the analysis could not find a relationship between a decentralized formal structure and performance. ASE showed to be a strong driving factor, though the proposed specific ASE of acquirer’s marketing and target’s technology emphases only showed an indication of influencing performance. Finally, AC produced mixed results. While s prior acquisition experience affected performance negatively instead of positively, absolute size of knowledge base was confirmed to drive acquirer’s performance. The final sub‐
hypotheses, relative size of knowledge base, were not found significant as its relevance for the online industry was later questioned by the authors. Finally, the model proposed for analysing acquirer’s performance in the online business was evaluated, and its implication for academia and practitioners evaluated. Though all hypotheses could not be confirmed, the model is still argued to be valid for analysing a dynamic environment such as the online business, given its dynamic and flexible characteristics . -
117 Listofliterature
-
Acedo, F. J., Barroso, C., & Galan, J. L. (2006). The resource‐based theory: Dissemination and main trends. Strategic Management Journal, 27, pp. 621‐636. Achrol, R. (1991). Evolution of the marketing organization: new forms for turbulent environments. J Mark, 55, pp.77–93 Ahuja G, Lampert CM. (2001). Entrepreneurship in the large corporation: a longitudinal study of how established firms create breakthrough inventions. Strategic Management Journal, June–July Special Issue 22, pp. 521–543. Ahuja, G. Katila. (2001) Technological Acquisitions and the Innovation Performance of Acquiring Firms: A Longitudinal Study. Strategic Management Journal, 22, pp. 197–220 Akhigbe, A. Martin, A. (2002). Do Microsoft Acquisitions Benefit the Computer Industry? Financial Analysts Journal; Vol. 58 Issue 4, pp. 19‐28 Ambrosini, V. and Bowman, C. (2001). Tacit knowledge: Some suggestions for operationalization. Journal of Management Studies 38, pp. 811‐829. Amit R, Zott C. (2001). Value creation in e‐business. Strategic Management Journal, Special Issue 22(6–7), pp. 493–520. Anand, B. N., & Khanna, T. (2000). Do firms learn to create value? The case of alliances. Strategic Management Journal, 21, pp. 295‐315. Anand, J and Singh, H. (1997). Asset redeployment, acquisition and corporate strategy in declining industries. Strategic management Journal, Vol. 18, pp. 99‐118 Andrade G, Mitchell M, Stafford E. (2001). New evidence and perspectives on mergers. Journal of Economic Perspectives 15(2): 103–120. Armstrong, C. E., Shimizu, K. (2007). A review of approaches to empirical research on the resource‐based view of the firm. Journal of Management, 33, pp. 959‐986. Arora, A., Gambardella, A. (1990) Complementary and external linkages: The strategies of the large firms in biotechnology. Journal of Industrial Economics, Jun90, Vol. 38 Issue 4, p361‐379, 19p, 3 Charts Arthur,W. B., Ermolieve, U., Kaniovsky,Y. (1987). Path dependent processes and the emergence of macro structure. European Journal of Operations Research 30:294–303. Barney, J. (1986). Organizational Culture: Can It Be a Source of Sustained Competitive Advantage? Academy o/Monogemen/Review. Vol. 11. No. 3. 656‐665. Barney, J. (1988). Returns to Bidding Firms in Mergers and Acquisitions: Reconsidering the Relatedness Hypothesis. Strategic Management Journal, Vol. 9, Special Issue: Strategy Content Research Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management; Mar91, Vol. 17:1, pp. 99‐121 Barney, J. B. (2001). Is the resource‐based “view” a useful perspective for strategic management research? Yes. Academy of Management Review, 26, pp. 41‐56. Barney, J. B. (2002). Gaining and sustaining competitive advantage. Upper Saddle River, NJ: Prentice Hall. Barney, J.B. & Hansen, M.H. (1994). Trustworthiness as a source of competitive advantage, Strategic Management Journal, Vol. 15, 175‐190. Bartlett, C. K., & Ghoshal, S. (1988). Organizing for worldwide effectiveness: The transnational solution. California Management Review, 31(1), pp. 54‐74 Bartlett, C. K., & Ghoshal, S. (1992). Matrix management: Not a structure, a frame of mind. Harvard Business Review, 68(4), pp. 138‐145. Bettis, R.A. (1981). Performance difference in related and unrelated diversification firms. Strategic Management Journal, 2, pp. 379‐394 Bild, M. (1998). Valuation of Takeovers. Economic Research Institute at the Stockholm School of Economics: Stockholm. Blonigen, B., C. Taylor. (2000). R&D intensity and acquisitions in high‐technology industries: Evidence from the US electronic and electrical equipment industries. J. Indust. Econom. 48, pp. 47–70. Booz&Co. (2010). Fewer CEOs Forced from Office in 2009, Reports Booz & Company in 10th Annual Global CEO Succession Study. White paper Bourgeois, L. J., & Eisenhardt, K. M. (1988). Strategic decision processes in high velocity environments: Four cases in the microcomputer industry. Management Science, 34, pp. 816‐835. Bower, J. (2001). Not All M&As Are Alike – And That Matters. Harvard Business Review, March, pp. 93‐101. 118 -
-
Brealey, A., Meyers, S. (1991). Principles of corporate finance. McGraw Hill, 4th International Edition Bromiley, P., & James‐Wade, S. (2003). Putting rational blinders behind us: Behavioural understandings of finance and strategic management. Long Range Planning, 36: 37‐48. Brown SL, Eisenhardt KM. (1998). Competing on the Edge: Strategy as Structured Chaos. Harvard Business School Press: Boston, MA. Bruton, G., Oviatt, B., White, M. (1994). Performance of Acquisitions of Distressed Firms. The Academy of Management Journal, 37:4, pp. 972‐989 Bryman, A., Bell, E. (2007). Business Research Method. Oxford University Press, 2nd edition Buckley, P. J., & Carter, M. J. (1999) Managing cross‐border complementary knowledge – conceptual developments in the business process approach to knowledge management in multinational firms. International Studies of Management & Organization, 29:1, pp. 80‐104. Canavos, G., Miller, D. (1999). An introduction to modern business statistics. 2nd edition, Duxbury Press Cannella, A., Hambrick, D. (1993). Effect of executive departures on the performance of acquired firms. Strategic Management Journal, 14, pp. 137‐152. Capron, L. (1999). The long term performance of horizontal acquisitions. Strategic Management Journal 20, pp. 987–1018. Capron, L., & Mitchell, W. (1998). Bilateral resource redeployment and capabilities improvement following horizontal acquisitions. Industrial and Corporate Change, 7 (3), pp. 453‐484. Capron, L., Dussauge, P., Mitchell, Wl. (1998). Resource redeployment following horizontal acquisitions in Europe and North America, 1988‐1992. Journal Strategic Management Journal, Vol. 19:7, pp. 631 Capron, L., Dussauge, P., Mitchell. D. (1995). Resource deployment following horizontal mergers and acquisitions in Europe and United States, 1988 – 1992. Working paper Capron, L., N. Pistre. 2002. When do acquirers earn abnormal returns? Strategic Management J. 23, pp. 781–794. Carud, R., & Kotha. S. (1994). Using the brain as a metaphor to model flexible production systems. Academy of Management Review, 19, pp. 671‐698. Cassiman B, Colombo M, Garrone P, Veugelers R. (2005). The impact of M&A on the R&D Process: an empirical analysis of the role of technological and market relatedness. Research Policy 34, pp. 195–220. Castanias, R.P. and Helfat, C.E. (2001). The managerial rents model: theory and empirical analysis. Journal of Management, Vol. 27, pp. 661‐78. Cefis, E. (2009). The impact of M&A on technology sourcing strategies. Economics of Innovation and New Technology, 19:1, pp. 27‐51 Chakrabarti, A., Hauschildt, J., Sueverkruep, C., (1994). Does it pay to acquire technological firms? R&D Management 24, pp. 47–56. Chakravarthy B. (1992). Adaptation: a promising metaphor for strategic management. Academic Management Review, 1, pp. 35– 44. Chandler, A. (1962). Strategy and Structure: Chapters in the History of the American Industrial Enterprise. Cambridge, MA: MIT Press Chatman, J., O'Reilly, C., Chang, V. (2005). Cisco Systems: Developing a human capital strategy Christie, A., Joye, M., Watts, R. (2003). Decentralization of the firm: theory and evidence. Journal of Corporate Finance, 9:1, pp. 3‐36 Cloodt, M., Hagedoorn, J., Kranenburg, H.V. (2006). Mergers and acquisitions: Their effect on the innovative performance of companies in high‐tech industries. Research Polic,y 35, 642–654 Coase, R. H. (1937). The Nature of the Firm. Economica, New Series, Vol. 4, No. 16, pp. 386‐405. Coff, R. (1997). Human assets and management dilemma: Coping with hazards on the road to resource‐based theory. Academy of Management Review, 22, 374‐402 Cohen, W., Levinthal, D. (1989). Innovation and learning: The two faces of R&D. Economic Journal, Vol, pp. 99 569–596. Cohen, W., Levinthal, D. (1990). Absorptive capacity: A new perspective on learning and innovation. Administration Science Quarterly, Vol 35, pp. 128–152. Conner, K. R. (1991). A historical comparison of resource‐based theory and five schools of thought within industrial organization economics: Do we have a new theory of the firm? Journal of Management, 17, pp. 121‐154. Côté, L., Langley, A., Pasquero, J. (1999). Acquisition Strategy and Dominant Logic in an Engineering Firm. Journal of Management Studies. Vol. 36:7, pp. 919‐952. Cronin MJ. (2000). Unchained Value: The New Logic of Digital Business. Harvard Business School Press: Boston, MA. D’Aveni, R. (1994). Hypercompetition: Managing the dynamics of strategic manoeuvring. New York: Free Press. 119 -
Danneels, E. (2008) "Organizational antecedents of second‐order competences," Strategic Management Journal, 29(5), 519‐543. Datta DK, Narayanan VK, Pinches GE. (1992). Factors influencing wealth creation from mergers and acquisitions: a meta‐
analysis. Strategic Management Journal 13(1): 67–84. Datta, D. (1991). Organizational Fit and Acquisition Performance: Effects of Post‐acquisition Integration, Strategic Management Journal, 12:4, pp. 281‐297. Datta, D.,Pinches, G. Narayanan, V. (1992), Factors Influencing Wealth Creation from Mergers and Acquisitions. Strategic Management Journal, 13 (January), pp. 67–84. Datta, Deepek K., George E. Pinches., V.K. Narayanan (1992). Factors Influencing Wealth Creation from Mergers and Acquisitions. Strategic Management Journal, 13 (January), 67–84. Day, G. (1994). The capabilities of market‐driven organizations. J Mark, 58, pp. 37– 52 De Carolis DM. (2003). Competencies and imitability in the pharmaceutical industry: an analysis of their relationship with firm performance. Journal of Management 29:1, pp. 27–50. DeMan, A.G., and G. Duysters. (2005). Collaboration and innovation: A review of the effects of mergers, acquisitions and alliances on innovation. Technovation 25:12, pp 1377–87. Demsetz, H. (1973). The Private Production of Public Goods: Reply. Journal of Law and Economics, University of Chicago Press, vol. 16(2), pp.413‐15 Denrell, J., C. Fang, and S. Winter (2003). The Economics of Strategic Opportunities. Strategic Management Journal, 24:10, pp. 977‐990 Dierickx, I., Cool, K. (1989). Asset stock accumulation and sustainability of competitive advantage. Management Science, 35, pp. 1504‐1511. Drexbage, G. (1998). How Cisco Bought its Way to the Top. Corporate Finance, 163, pp. 21‐25. Drori, I., Wrzesniewski, A., Ellis, S. (2011). Cultural clashes in a 'merger of equals': The case of high‐tech start‐ups. Human Resource Management, 50: 5, pp. 625‐649 Eisenhardt, K., Martin J. (2000). Dynamic capabilities: What are they? Strategic Management Journal. 21, pp. 1105–1121 Eisenmann, T. (2006) Internet Companies' Growth Strategies: Determinants of Investment Intensity and Long‐term Performance. Strategic Management Journal, 27, pp. 1183–1204 Felin, T. and N. J. Foss (2005), ‘Strategic organization: a field in search of micro‐foundations,’ Strategic Organization, 3, pp. 441–455. Felin, T. and W. S. Hesterley (2007). The knowledge‐based view, nested heterogeneity, and new value creation: philosophical considerations on the locus of knowledge. Academy of Management Review, 32, pp. 195–218. Fiol, C. M. (1991). Managing culture as a competitive resource: An identity‐based view of sustainable competitive advantage. Journal of Management, 17, pp. 191‐211. Fiol, C. M. (2001). Revisiting an identity‐based view of sustainable competitive advantage. Journal of Management, 27, pp. 691‐699. Foss, N,J., Volberda, H., Lyles, M. (2010). Absorbing the Concept of Absorptive Capacity: How to Realize Its Potential in the Organization Field. Organizational Science, 21:4, pp. 931–951 Foss, N. (2006). Knowledge and organization in the theory of the multinational enterprise. J. Management Governance, 10, pp. 3–20. Foss, N. J. (1996), ‘Knowledge‐based approaches to the theory of the firm: some critical comments,’ Organization Science, 7, pp. 470–476. Foss, N. J., Klein, P. G., Kor, Y. Y., & Mahoney, J. T. (2008). Entrepreneurship, subjectivism, and the resource‐based view: Toward a new synthesis. Strategic Entrepreneurship Journal, 2: 73‐94. Froelich, K.,McLagan II, J. (2008). Diversification Strategies in Electric Utilities: Who wins? Who loses? Academy of Strategic Management Journal, 7 Fuerst, W. (1995). Information Technology and Sustained Competitive Advantage: A Resource‐Based Analysis. MIS Quarterly/December Fuess Jr., S.M., (1996). On replication in business and economics research: the QJBE case. Quarterly Journal of Business and Economics 35, pp. 3–13. Fuller, K., Netter, J., Stegemoller, M. (2002). What do returns to acquiring firms tell us? Evidence from firms that make many acquisitions. Journal of Finance, 57, pp. 1763‐1793. Gammelgaard, G., Husted, K., Michailova, S. (2002). Knowledge Sharing Hostility in Acquisitions 120 -
Gerpott, T.J., (1995). Successful integration of R&D functions after acquisition: an exploratory empirical study. R&D Management 25, 161–178. Ghauri, P. N., Grønhaug, K. (2005). Research Methods in Business Studies. London: Prentice Hal Glazer, R.,Weiss, A., (1993). Marketing in turbulent environments: decision processes and the time‐sensitivity of information. Journal of Marketing Research, pp. 509–521. Godfrey, P. C., & Hill, C. W. L. (1995). The problem of unobservables in strategic management research. Strategic Management Journal, 16 pp. 519‐533. Granstrand O, Sjolander S. (1990). The acquisition of technology and small firms by large firms. Journal of Economic Behaviour & Organization, Vol 13,3, pp. 367–386. Grant, R. M. (1996a). Prospering in dynamically‐competitive environments: Organizational capability as knowledge integration. Organization Science 7(4) pp.375‐387. Grant, R. M. (1996b). Toward a knowledge‐based theory of the firm. Strategic Management Journal, 17: 109‐122. Gujarti, D., Porter, D. (2009) Basic Econometrics. McGraw Hill, Fifth International Edition Hagedoorn J, Duysters G. (2002). The effect of mergers and acquisitions on the technological performance of companies in a high‐tech environment. Technology Analysis & Strategic Management 14: 67–89. Hagel, J., J, Brown., L, Davison (2008). Shaping Strategy in a World of Constant Disruption, Harvard Business Review, October 2008, pp. 81‐89 Hall, B.H., (1990). The impact of corporate restructuring on industrial research and development. Brookings Papers on Economic Activity 3, 85–135. Hamel, G., & Prahalad, G. K. (1994). Competing for the future. Boston: Harvard Business School Press. Harrison, J. Hitt, M. Hoskisson, R. Ireland, R. (1991), Synergies and Post‐Acquisition Performance: Differences Versus Similarities in Resource Allocations, Journal of Management, 17 (March), 173–90. Haspeslagh, P., Jemison, D. (1991a). Managing Acquisitions: Creating Value Through Corporate Renewal. New York, NY: Free Press Haspeslagh, P.C., Jemison, D.B. (1991b). Managing Acquisitions: Creating Value Through Corporate Renewal. The Free Press, New York. Hatch NW, Dyer JH. (2004). Human capital and learning as a source of sustainable competitive advantage. Strategic Management Journal 25(12): 1155–1178. Hayek, F, A. (1989). The pretence of knowledge, American Economic Review, 79:6, pp. 3‐7. Heeley, M., D. King, J. Covin. (2006). R&D investment level and environment as predictors of firm acquisition. J. Management Stud. 43 1513–1536. Heide, J., John, G. (1992). Do norms matter in marketing relationships? J Mark, 56, pp. 32– 44. Helfat CE, Lieberman MB. (2002). The birth of capabilities: market entry and the importance of prehistory. Industrial and Corporate Change 11(4): 725–760. Helfat, C. E., & Peteraf, M. A. (2003). The dynamic resource‐based view: Capability lifecycles. Strategic Management Journal, 24: 997‐1010. Helfat, C. E., & Peteraf, M. A. (2003). The dynamic resource‐based view: Capability lifecycles. Strategic Management Journal, 24: 997‐1010. Henderson , R., Clark, K.. (1990). Architectural innovation: the reconfiguration of existing product technologies and the failure of established firms. Administrative Science Quarterly 35(1): 9–30. Henderson R, Cockburn I. (1996). Scale, scope, and spillovers: the determinants of research productivity in drug discovery. RAND Journal of Economics 27(1): 32–59. Hess, A. M., Rothaermel, F. T. (2011). When are assets complementary? Star scientists, strategic alliances and innovation in the pharmaceutical industry. Strategic Management Journal, 32 (8): 895‐909. Higgins, M., Rodriguez, D. (2006). The outsourcing of R&D through acquisitions in the pharmaceutical industry. Journal of Financial Economics, 80(2), pp. 351‐383 Hill, C. (1997). Establishing a standard: Competitive strategy and technological standards in winnertake‐all industries. Academy of Management Executive, 11, pp. 7‐18. Hirshliefer. J. (1980). Price theory and application (2nd addition). Englewood Cliffs, NJ: Practice‐Hall Hitt MA, Harrison JS, Ireland RD. (2001). Mergers & Acquisitions: A Guide to Creating Value for Stakeholders. Oxford University Press: New York. Hitt MA, Hoskisson RE, Johnson RA, Moesel, DD. (1996). The market for corporate control and firm innovation. Academy of Management Journal 39: 1084–1119. 121 -
Hitt, Hoskisson and Ireland, (1990) Hayward MLA, Hambrick DC. 1997. Explaining the premiums paid for large acquisitions: evidence of CEO hubris. Administrative Science Quarterly 42:103–127. Hitt, M., Harrison, Ireland, R., Best, a. (1998), Attributes of Successful and Unsuccessful Acquisitions of US Firms. British Journal of Management, 9 (June), 91–114. Hitt, M., J. Harrison, R. Ireland. (2001). Mergers and Acquisitions ‐ A Guide to Creating Value for Stakeholders. Oxford University Press, Oxford, UK. Hitt, M.A., Hoskisson, R.E., Johnson, R.A., Moesel, D.D., (1996). The market for corporate control and firm innovation. Academy of Management Journal 39, 1084–1119. Holm, U., Pedersen, T. (2000). The Emergence and Impact of MNC Centres of Excellence – A Subsidiary Perspective. Macmillan Press:houndsmill, Bas. Hooley, G., Lynch, J.,Jobber, D. (1992). Generic marketing strategies. International Journal of Research in Marketing, 9, pp. 75–89. Hoskisson, R., R. Johnson, D. Moesel. (1994). Corporate divestiture intensity of restructuring firms: Effects of governance, strategy, and performance. Acad. Management J. 37 1207–1251. Hurley, R., Hult, G.(1998) Innovation, market orientation, and organizational learning: an integration and empirical examination. J Mark, 62, pp. 42–54 Håkanson, L. (2010). The firm as an epistemic community: the knowledge‐based view revisited. Industrial and Corporate Change, Volume 19, Number 6, pp. 1801–1828 Inkpin, A., Sundaram, A. K., Rockwood, K. (2000). Cross‐border acquisitions of U.S. technology assets. California Management Review, 42:3, pp. 50‐71. Jacobson, R. (1988). The persistence of abnormal returns'. Strategic Management Journal, 9, pp. 415‐430 James, A. (2002 ).The Strategic Management of Mergers and Acquisitions in the Pharmaceutical Industry: Developing a Resource‐based Perspective. Technology Analysis & Strategic Management. Volume 14, Issue 3, Jansen, J., Van den Bosch, M., Volberda, H. (2005). Managing potential and realized absorptive capacity: How do organizational antecedents matter? Academic Management Journal, Vol 48, pp. 999–1015. Jensen MC, Ruback RS. (1983). The market for corporate control: the scientific evidence. Journal of Financial Economics 34, pp. 5–50. Jensen MC. (1988). Takeovers: their causes and consequences. Journal of Economic Perspectives 1: 21–48. Jensen, M.C., Meckling,W.H., (1992). Specific and general knowledge, and organizational structure. In: Werin, L., Wijkander, H. (Eds.), Main Currents in Contract Economics. Blackwell, ford, UK. Kale, P., Singh, H., Raman, A (2009). Don't Integrate Your Acquisitions, Partner with Them. Harvard Business Review, Dec2009, Vol. 87 Issue 12, p109‐115 Kanter RM. (2001). E‐Volve! Succeeding in the Digital Culture of Tomorrow. Harvard Business School Press: Boston, MA. Kaplan, S., A. Schenkel, G. von Krogh and C. Weber (2001), ‘Knowledge‐based theories of the firm in strategic management: a review and extension,’ MIT Sloan Working Paper #4216‐01, Karim and Mitchell. (2000). Path‐dependent and path‐breaking change: reconfiguring business resources following acquisitions in the U.S. medical sector, 1978–1995. Strategic Management Journal, 21: 10‐11, pp. 1061‐1081 Keen, A. (2008). The Cult of the Amateur. Doubleday Keller, K. (2003). Strategic Brand Management: Building, Measuring and Managing Brand Equity. Uppe rsaddler River, NJ: Prentice Hall Kelly, M. (2009). Wanted: Intelligent M&A. Ottawa Business Journal, Vol. 15:2, p7 King DR, Dalton DR, Daily CM, Covin J. (2004). Meta‐analyses of post‐acquisition performance: indicators of unidentified moderators. Strategic Management Journal 25(2): 187–200. King DR, Slotegraaf RJ, Kesner I. (2008). Performance implications of firm resource interactions in the acquisition of R&D intensive firms. Organization Science 19(2): 327–340. King, D., J. Driessnack. (2003). Investigating the integration of acquired firms in high‐technology industries: Implications for industrial policy. Acquisition Rev. Quart. 10(Summer) 260–283. Kirchoff, M., Schierek, D. (2011). Determinants of M&A Success in the Pharmaceutical and Biotechnological Industry, Journal of Business Strategy, 8:1, pp. 25‐50 Kirdahy, M. (2007). CEO Turnover Increased In 2007. Forbes.com Klein, B., R. G. Crawford and A. A. Alchian. (1978). Vertical integration, appropriable rents and the competitive contracting process. Journal of Law and Economics, 21, pp. 297‐326. 122 -
Kogut, B. and U. Zander (1992). Knowledge of the firm, combinative capabilities, and the replication of technology. Organization Science, 3, pp. 384–397. Kogut, B. and U. Zander (1993), ‘Knowledge of the firm and the evolutionary theory of the multinational corporation,’ Journal of International Business Studies, 24, 625–645. Kogut, B. and U. Zander (1996), ‘What firms do? Coordination, identity and learning,’ Organization Science, 7, 502–518. Kor, .Mahoney, (2004). Edith Penrose’s (1959) contributions to the resource‐based view of strategic management. Journal of Management Studies, 41, pp. 183–191. Kotha, S., Rindova, V., Rothaermel, F. (2001). Assets and Actions: Firm‐Specific Factors in the Internationalization of U.S. Internet Firms, Journal of International Business Studies, 32, 4, pp.769‐791 Krug, J.A., Nigh, D. (2001). Executive perceptions in foreign and domestic acquisitions: An analysis of foreign ownership and its effect on executive fate. Journal of World Business, 36:1, pp. 85‐105 Kraaijenbrink, J., Spender, J., Groen, A. (2010). The Resource‐Based View: A Review and Assessment of Its Critiques, Journal of Management, Vol. 36 No. 1, pp. 349‐372 Lane, P., Lubatkin, M. (1998). Relative absorptive capacity and inter‐organizational learning. Strategic Management Journal, 19, pp. 461–477. Larsson, Rikard; Finkelstein, Sydney. (1999). Integrating Strategic, Organizational, and Human Resource Perspectives on Mergers and Acquisitions: A Case Survey of Synergy Realization. Organization Science, Jan/Feb99, Vol. 10:1, pp. 1‐26 Lee J, Lee K & Rho S (2002). An evolutionary perspective on strategic group emergence: a genetic algorithm‐based model, Strategic Management Journal, 23:8, pp. 727–47. Lee PM. (2001). What’s in a name dot.com? The effects of ‘.com’ name changes on stock prices and trading activity. Strategic Management Journal 22(8): 793–804 Lemelin, A. (1982). Relatedness and patterns of industry diversification. A Review of Economics and Statistics. Vol. 64, No. 4 Leonard‐Barton, D. (1995) Wellsprings of Knowledge: Building and Sustaining the Sources of Innovation. Boston, MA: Harvard Business School Press Lepetit, L., Patry, S., Rous, P. (2004). Diversification versus specialization: an event study of M&As in the European banking industry. Applied Financial Economics, 14:9, pp. 663‐669 Levitas, E., & Chi, T. (2002). Rethinking Rouse and Daellenbach’s rethinking: Isolating vs. testing for sources of sustainable competitive advantage. Strategic Management Journal, 23: 957‐962. Lichtenthaler, U. (2009). Absorptive capacity, environmental turbulence, and the complementarity of organizational learning processes. Academic Management Journal, 52,4, pp. 822–846. Lippman, S. A.; Rumelt, R. P.(1982). Uncertain imitability: an analysis of inter‐firm differences in efficiency under competition. Bell Journal of Economics, Autumn82, Vol. 13 Issue 2, p418‐438 Litan, R.E. and Rivlin, A.M. (2001). Projecting the Economic Impact of the Internet. American Economic Review, Papers and Proceedings, 91:2, pp 313‐417. Lockett, A. (2005). Edith Penrose’s Legacy to the Resource‐Based View. Managerial and Decision Economics 26: pp. 83–98 Lockett, A., Thompson, S., & Morgenstern, U. (2009). The development of the resource‐based view of the firm: A critical appraisal. International Journal of Management Reviews, 11: pp. 9‐28. Gaither, C., Chmielewski, C. (2006). Fears of Dot‐Com Crash, Version 2.0. Los Angeles times, web edition Lubatkin, M. (1987). 'Merger strategies and stockholder value', Strategic Management Journal, 8, pp. 39‐53. MacKinlay, C. (1997). Event Studies in Economics and Finance. Journal of Economic Literature, 19, pp. 13‐39 Madhok, A. (1996), ‘The organization of economic activity: transaction costs, firm capabilities and the nature of governance,’ Organization Science, 7, 577–590. Mahoney, J.T. and Pandian, J.R. (1992). The resource‐based view within the conversation of strategic management. Strategic management Journal, 13, 363–380. Makri, M, MA Hitt and PJ Lane (2010). Complementary technologies, knowledge relatedness, and invention outcomes in high technology mergers and acquisitions. Strategic Management Journal, 31:6, pp. 602–628 Marock, D., Nas, J., Strickroth, H. (2000). Internet M&A Booms, McKinsey Quarterly Mata, F. J., Fuerst, W. L., & Barney, J. B. (1995). Information technology and sustained competitive advantage: A resource‐
based analysis. MIS Quarterly, 19:4, pp. 487. Matusik, S., Heeley, M. (2005). Absorptive capacity in the software industry: Identifying dimensions that affect knowledge and knowledge creation activities. Journal of Management 31, pp. 549–572. 123 -
McIntyre, D., Subramaniam, M. (2009). Strategy in network industries: A review and research agenda. Journal of Management, 35, pp.1494‐1517. McKee, D., Varadarajan, P.,Pride, W. (1989). Strategic adaptability and firm performance: a market‐contingent perspective. Journal of Marketing,53, pp. 21‐35. McWilliams A, Siegel D. (1997). Event studies in management research: theoretical and empirical issues. Academy of Management Journal 40, pp. 626–657 Meyer, C., Altenborg, E. (2007). The disintegrating effects of equality: A study of a failed international merger. British Journal of Management, 18, pp. 257–271. Miles, R., Snow, C. (1978). Organizational Strategy, Structure and Process. New York: McGraw‐Hill. Mintzberg, H. and McHugh, A. (1985). Strategy formation in an adhocracy. Administrative Science Quarterly, 30, pp. 160–
197. Mirowski, P. (1983). An Evolutionary Theory of Economic Change: A Review Article. Journal of Economic Issues, 3, pp. 757‐768 Mitchell, W., Capron, L. (2002). Managing Acquisitions to Change and Survive. European Business Forum, 9, Spring, pp. 51‐55. Mizik, Natalie and Robert Jacobson (2003), “Trading Off Between Value Creation and Value Appropriation: The Financial Implications of Shifts in Strategic Emphasis,” Journal of Marketing, 67 (January), pp. 63–76 Moorman, Christine and Rebecca J. Slotegraaf (1999). The Contingency Value of Complementary Capabilities in Product Development. Journal of Marketing Research, 36 (May), 239–57. Mowery, D., Oxley, J., Silverman, B. (1996). Strategic alliances and inter‐firm knowledge transfer. Strategic Management Journal, 17 (winter special issue): 77–91. Nahapiet, J. and S. Ghoshal (1998), ‘Social capital, intellectual capital and the organizational advantage,’ Academy of Management Review, 23, 242–266. Nahavandi, A., Malekzadeh, A.R. (1988). Acculturation in mergers and acquisitions. Academy of Management Review, 13, 79‐90 Nelson, R., Winter, S. (1982). An Evolutionary Theory of Economic Change. Harvard University Press. Newbert, S. L. (2007). Empirical research on the resource‐based view of the firm: An assessment and suggestions for future research. Strategic Management Journal, 28,pp. 121‐146. Nickerson, J. A. and T. R. Zenger (2004). A knowledge‐based theory of the firm – the problem‐solving perspective, Organization Science, 15, pp. 617–632. Noe, T., Parker, G. (2005). Winner Take All: Competition, Strategy, and the Structure of Returns in the Internet Economy. Journal of Economics & Management Strategy, Vol. 14 Issue 1, p141‐164 Nonaka, I., Takeuchi H. (1995). The knowledge‐creating company. NY: Oxford University Press Oktemgil M, Greenley G. (1997). Consequences of high and low adaptive capability in UK companies. Eur J Mark, 31(7) pp.445– 66. Ouinn, J. B. (1992). Intelligent Enterprise. New York: Free Press Park, N., Mezias, J.,Song, J. (2004). A Resource‐based View of Strategic Alliances and Firm Value in the Electronic Marketplace Journal of Management, Vol 30, pp. 7‐21 Penrose, E. T. (1959). The theory of the growth of the firm. New York: John Wiley. Pisano, G. (1990). The R&D boundaries of the firm: An empirical analysis. Admin. Sci. Quart. 35 153–176. Pitelis, C. (2004). Edith Penrose and the resource‐based view of (international) business strategy. International Business Review 13 (2004) 523–532 Porter, M.E. (1980) Competitive Strategy, Free Press, New York Porter, M.E. (1985) Competitive Advantage, Free Press, New York Porter, M.E. (2001). Strategy and the Internet, Harvard Business Review, March 2001, pp. 62–78. Powell, T., Dent‐Micallef, A. (1997) Information Technology as Competitive Advantage: The Role of Human, Business and Technology Resources, Strategic Management Journal. Prahalad. C. K. and R. A. Bettis (1986). The dominant logic: A new linkage between diversity and performance. Strategic Management Journa, 7:6. pp. 485‐501. Priem R., Butler J. (2001a) Tautology in the Resource‐Based View and the Implications of Externally Determined Resource Value: Further Comments, Academy of Management Review 26(1): pp. 57‐66. Priem R., Butler J. (2001b) Is the “Resource‐Based View"" a Useful Perspective for Strategic Management Research? Academy of Management Review 26(1): pp, 22‐41, 124 -
-
Puranam, P., H. Singh, M. Zollo. (2006). Organizing for innovation: Managing the coordination‐autonomy dilemma in technology acquisitions. Acad. Management J. 49, pp. 263–280 Puranam, P., H. Singh, S. Chaudhuri. (2009). Integrating acquired capabilities: When structural integration is (un)necessary. Organ. Sci. 20(2) 313–328. Ranft, A. (1997), Preserving and Transferring knowledge resources during post acquisition implementation. Doctoral Dissertation Ranft, A., Lord, M. (2000). Acquiring new knowledge: The role of retaining human capital in acquisition of high‐tech firms. The Journal of High Technology Management Research, Volume 11:2. PP. 295‐319 Ravenscraft, D., Scherer, F.M. (1987). Mergers, sell‐offs and economic efficiency. Washington D.C, the Brookings Institution Rayport, J. F. (1999). The truth about Internet business models. Strategy and Business, 16(3): 5–7. Reed, R., DeFillippi, R.J., (1990). Causal ambiguity, barriers to imitation, and sustainable competitive advantage. Academy of Management Review 15, 88–102. Rehm, W., Uhlaner, R., West, A. (2012). Taking a longer‐term look at M&A value creation. McKinsey Quarterly, January Ricardo, D. (1817) Principles of Political Economy and Taxation, J. Murray, London Rindova, V., Kotha, s. (2001). Continuous "Morphing": Competing through Dynamic Capabilities, Form and Function. Academy of Management fournal, Vol. 44. No, 6, pp. 1263‐1280. Risberg, A. (2001). Executives’ perceptions in foreign and domestic acquisitions: an analysis of foreign ownership and its effect on executive fate. Journal of World Business, 36 (1), 58‐84. Rondinelli, D. A./Black, S. S., (2000). Multinational Strategic Alliances and Acquisitions in Central and Eastern Europe: Partnerships in Privatization, Academy of Management Executive, 14, 4, pp. 85‐98. Rosenkopf, L., Almeida, P. (2003). Overcoming Local Search Through Alliances and Mobility. Management Science,49:6, pp. 751‐766 Rouse, M. J., & Daellenbach, U. S. (1999). Rethinking research methods for the resource‐based perspective: Isolation sources of sustainable competitive advantage. Strategic Management Journal, 20, pp. 487‐49 Rudner, R. S. (1966). Philosophy of social science. Englewood Cliffs, NJ: Prentice‐Hall. Rugman, A., Verbeke, A. (2002). Edith Penrose's contribution to the resource‐based view of strategic management. Strategic Management Journal, 23:8, pp. 769‐780 Rumelt, R,P. (1984). Toward a Strategic Theory of the Firm, in Competitive Strategic Management, R. Lamb (Ed.), Prentice Hall, Englewood Cliffs, pp. 556‐570 Rumelt, R. (1974) Strategy, Structure, and Economic Performance. Boston: Harvard University Press Rumelt, R. P. (1987). Theory, strategy, and entrepreneurship. In D. J. Teece (ed.). The Competitive Challenge, Ballinger Publishing, Cambridge, MA, 1987, pp. 137‐158. Samuelson, W. Bazerman, M. The Winner’s Curse in Bilateral Negotiations. (1985). Experimental Economics. Volume: 3, Pages: 105‐137 Schilling, M. (2002). Technology success and failure in winner‐take‐all markets: The impact of learning orientation, timing, and network externalities. Academy of Management Journal, 45: 387‐398. Seth, A. (1990). Value creation in acquisition: a re‐examination of performance issues. Strategic Management Journal, 11, pp. 99‐115 Shelton, L. (1988), Strategic Business Fits and Corporate Acquisition: Empirical Evidence. Strategic Management Journal, 9 (May–June), 279–87. Shepherd, W. G. 1979. The Economics of Industrial Organization. Englewood Cliffs, N.J.: Prentice‐Hall Singh, H. and C. Montgomery. (1987). Corporate acquisition strategies and economic performance, Strategic Management Journal, 8, pp. 377‐386. Singh, K., W. Mitchell. (2005). Growth dynamics: The bi‐directional relationship between inter‐firm collaboration and business sales in entrant and incumbent alliances. Strategic Management J. 26 497–522. Sirower ML. (1997). The Synergy Trap: How Companies Lose the Acquisition Game. Free Press: New York. Sohn, J. H., & Paik, Y. S. (1995). Social knowledge and the effectiveness of expatriate personnel in controlling overseas subsidiaries: A contingency approach. In D. Moore (Ed.), National Academy of Management 1994 Best Paper Proceedings (pp. 158‐162). Madison, WI: Omni Press. Song, M., Droge, C., Hanvanish, S., Calantone, R. (2005), Marketing and Technology Resources Complementarity: An Analysis of Their Interaction Effect in Two Environmental Contexts. Strategic Management Journal, 26, pp. 259–76. 125 -
Sorescu, A. B., Chandy, R. K., Prabhu, J. C. (2003), Sources and Financial Consequences of Radical Innovation: Insights from Pharmaceuticals, Journal of Marketing, 67, 82‐102. Stulz, R. M. (1995), The cost of capital in internationally integrated markets: The case of Nestlé. European Financial Management, 1, pp. 11–22. Swaminathan, V., Murshed, F., Hulland, J. (2008). Value Creation Following Merger and Acquisition Announcements: The Role of Strategic Emphasis Alignment. Journal of Marketing Research, 45:1, pp. 33‐47 Tanriverdi,H., Venkatraman, N. (2005). Knowledge relatedness and the performance of multibusiness firms. Strategic Managment journal, 26:2, pp. 97‐119 Teece et al. (1990) Firm Capabilities, Resources and the Concept of Strategy. Working Paper, EAP‐38. Teece, D. (1982), Towards an economic theory of the multiproduct firm, Journal of Economic Behavior and organization, 3, pp.39‐63. Teece, D. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28, pp. 1319–1350. Teece, D. J. (1986). Profiting from technological innovation: Implications for integration, collaboration, licensing, and public policy. Res. Policy 15 285–305. Teece, D. J., Pisano, G. P., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18: 509‐533. Thompson, S., Wright, M. (2005). Edith Penrose’s Contribution to Economics and Strategy: An Overview. Managerial and Decision Economics.26, pp. 57‐66 Tollison, R. D. (1982). Rent seeking: A survey, Kyklos, 35, 1982, pp. 575‐602. Tuominen, M., Rajala, A., Möller, K. (2004). How does adaptability drive firm innovativeness? Journal of Business Research, Vol. 57 Issue 5, pp. 495‐507 Uhlenbruck K, Hitt MA, Semadeni M. (2006). Market value effects of acquisitions involving Internet firms: a resource‐
based analysis. Strategic Management Journal 27(10): 899–913. Van den Bosch, F., Volberda, H., de Boer, M. (1999). Coevolution of firm absorptive capacity and knowledge environment: Organizational forms and combinative capabilities. Organization Science, 10, pp. 551–568. Vermeulen F, Barkema H. (2001). Learning through acquisitions. Academy of Management Journal 44, pp. 457–476. Very, P., Lubatkin, M., Calori, R., Veiga, J. (1997). Relative standing and the performance of recently acquired European firms. Strategic Management Journal, 18, 593‐614 Very, P., Shweiger, D. M. (2001). The acquisitions process as a learning process: evidence from a study of critical problems and solutions in domestic and cross‐boarder deals. Journal of World Business , 36 (1), 11‐31 Viguerie, P., Smit, s., Baghai, M. (2008). The Granularity of Growth. John Wiley & Sons Villalonga B, McGahan AM. (2005). The choice among acquisitions, alliances, and divestitures. Strategic Management Journal 26(13): 1183–1208.: Vorhies, D., Harker, M., Rao, C.(1993). The capabilities and performance advantages of market‐driven firms. Eur J Mark, 33, 11/12, pp. 1171– 202. Walker, O., Ruekert, W. (1987). Marketing’s role in the implementation of business strategies: a critical review and conceptual framework. J Mark, 51(3), pp. 15 – 33. Walsh, J.P. (1988). Top management turnover following mergers and acquisitions. Strategic Management journal, 9,173‐
183 Walsh, J.P. (1989). Doing a deal: Mergers and acquisitions negotiations and their impact upon target company top management turnover. Strategic Management Journal, 10, 307‐322 Wang Y, Lo H, Yang Y. (2004). The constituents of core competencies and firm performance: evidence from high‐
technology firms in china. Journal of Engineering and Technology Management 21(4): 249–280. Wang, C., Ahmed, P. (2007). Dynamic capabilities: A review and research agenda. International Journal of Management Reviews, 9:1, pp. 31‐51 Weber, Y., Shenkar, O. (1996). National and Corporate Cultural Fit in Mergers/Acquisitions: An Exploratory Study, Management Science, 42, 8, pp. 1215–1227. Wernerfelt, B. (1984), A Resource‐Based View of the Firm. Strategic Management Journal, 5:2, pp. 171‐180. Wernerfelt, B. and C. A. Montgomery. (1988). Tobin's q and the importance of focus in firm performance, American Economic Review, 78, pp. 246‐250. Wiggins, R., Ruefli, T. (2002). Sustained competitive advantage: Temporal dynamics and the incidence and persistence of superior economic performance. Organization Science, 13, pp. 82‐105. 126 -
Wiggins, R., Ruefli, T. (2005). Schumpeter’s ghost: Is hypercompetition making the best of time shorter? Strategic Management Journal, 26, pp. 887‐911. Wiklund, J. and Shepered, D. (2003). Knowledge‐based Resources, Entrepreneurial Orientation and the Performance of Small and Medium‐sized Businesses. Strategic Management Journal, 24, pp. 1307‐1314. Wiklund, J., & Shepherd, D. (2003). Aspiring for, and achieving growth: The moderating role of resources and opportunities. Journal of Management Studies, 40(8), 1919–1941. Winter, S. G. (2003). Understanding dynamic capabilities. Strategic Management Journal, 24(10): 991‐995. Winter, S., Szulanski, G. (2001). Replication as strategy. Organization Science, 12, 730–743. Woiceshyn, J. and Daellenbach, U. (2005). Integrative capability and technology adoption: evidence from oil firm. Industrial and Corporate Change, 14(2), pp. 307‐342. Yin, R. K. (1994). Case Study Research: Design and Methods, London: Sage Zaheer, S. Schomaker, M., Genc, M. (2003). Identity versus culture in merger of equals. European Management Journal, 21(2), 185–191. Zahra, S. A., G. George. (2002). Absorptive capacity: A review, reconceptualization, and extension. Academic Management Review, 27, pp. 185–203. Zahra, S. A., Sapienza, H. J. and Davidsson, P. (2006) ‘Entrepreneurship and Dynamic Capabilities: A Review, Model and Research Agenda’, Journal of Management Studies 43(4): 917–55. Zahra, S.A., Nielsen, A.P.(2002). Sources of capabilities, integration and technology commercialization. Strategic Management Journal, 23:5, pp. 377–398 Zammuto, R. (1982), Assessing Organizational Effectiveness, State University of New York Press, Albany, NY. Zander, U. and Kogut, B. (1995) Knowledge and the speed of the transfer and imitation of organizational capabilities: an empirical test, Organization Science 6(1): 76–92. Zhou, K., Li, C. (2010). How strategic orientations influence the building of dynamic capability in emerging economies. Journal of Business Research, Vol. 63, pp. 224–231 127 APPENDIX Table of Contents Appendix 1: Specification of model ............................................................................................................... 129 Model specification ................................................................................................................................... 129 Appendix 2: Abandoned hypothesis regarding acquisition motive and resource fit .................................... 131 Appendix 3 ‐ NAICs code descriptions ........................................................................................................... 135 Appendix 4 – Proof of word count ................................................................................................................ 136 128 Appendix 1: Specification of model Model specification In the process of specifying the model, it has never been the aim to explain all aspects of the acquirer’s abnormal stock performance, nor has it been the driving force to achieve the highest possible R‐squared value54. In contrary, the aim has been to test if the chosen ‘resources’ and their combination have, as stipulated in this paper, a positive influence on the stock market’s reaction to online acquisitions. As elaborated in section X, one in‐depth area has been examined within each of the theory branches, namely adaptive capabilities, strategic fit and absorptive capacity. The classic linear OLS regression model has been designated for the analysis, meaning it is linear in its parameters, not necessarily in its variables (Canavos & Miller, 1999). The following assumptions should be assumed made for the sample in order to operate with the linear model (Gujarati, 2009) -
The parameters are linear -
The x values are independent of the error terms (zero covariance between the dependant and the independent variables) -
Zero mean value of disturbance on Y -
Homoscedasticity or constant variance of Y -
No autocorrelation between the disturbances -
The number of n observations is greater than the number of parameters to be estimated -
There is variation in the values of the X variables -
No exact co‐linearity between the X variables (no exact linear relationship) -
There is no specification bias With the above assumptions fulfilled, the mathematical model can be specified as following ∗
∗
∗
In order to proceed with the model, it must to be broken down to the independent variables and needs further specification. 54
An event study in its traditional aspect should aim to achieve the highest explanatory value as possible (Gujarati, 2009). However this paper does not focus on playing the “maximizing R‐squared game”. 129 When specifying the econometric model there are additional criteria that the model should strive to fulfil (Gujarati, 2009). In principle the predictions must be admissible, meaning that the coefficients must be logical (i.e. a negative absolute knowledge base, which would mean that the more knowledge the firm has, the weaker performance would be expected). Secondly the model should be consistent with theory, and have weakly exogenous regressors. Further the model must exhibit parameter constancy, meaning the values of the variables must be somewhat stable in order to use the model in a predicting sense. The data must be coherent (which is tested through detection of heteroscedacity. Lastly, the model must be encompassing, the best possible fit. All of the above mentioned criteria have been taken into account when specifying the model, yet there are several specification errors that need to be accounted for. Some of the most critical errors when constructing a new model to address a specific research area (Gujarati, 2009): -
Omitting a relevant variable -
Including an irrelevant variable -
Measurement errors -
Wrong functional form of the model -
Interaction among the regressors In order to ensure not falling into any of the specification traps, a number of statistical tests have been conducted to ensure statistical validity, comprising Durbin‐Watson, White’s test of heteroscedacity, autocorrelation etc. Lastly a critical assumption is made in regards of the deals that were eliminated due to missing data, namely that the missing data is independent of the available observations, as oppose to a systematic relation to the available data. Taking the previously discussed measures into account, each of the three components (A, B and C) is tested for impact through multiple measures. Absorptive capacity is measured though prior M&A experience, absolute size of knowledge base and relative size of target knowledge base to acquirer. Decentralization and market focus comprise the measures for adaptive capability, while the strategic resource fit is measured through alignment of strategic emphasis and the specific resource interaction. These factors are the base of the econometric model specification: 130 (1.2) ∗
∗
∗
∗
∗
∗
∗
∗
∗
Appendix 2: Abandoned hypothesis regarding acquisition motive and resource fit In this appendix, a thorough description of the abandoned hypothesis that alignment between acquisition motive and acquirer’s and target’s strategic emphasis will be put forward in terms of theoretical reasoning and intended operationalization. This is done to highlight the high risk of falling into the ‘tautology trap’ connected to RBV, and which has resulted in an important learning for the researchers. Theoreticalargumentation
In a first moving article, Swaminathan et al. (2008) acknowledges the accuracy of both the supplementary and complementarity argument, proposing the idea that context plays a key role in determining value creation of M&A. More specifically Swaminathan et al. (2008) argue that managers must take the M&A motive into account when deciding whether to search for targets with supplementing or complimentary resources55. When diversification is the motive, targets with complementary resources should generate higher value56. The rationale is that if a company is looking to move into a new business area, the chances that it will need additional capabilities to what it already possesses seem likely. Either it can be in form of new technology or production capabilities, or maybe it needs to alter marketing practices, in order to attract and retain a new customer group. On the other hand, when consolidation is on the strategic agenda, targets should have supplementing resources and capabilities. A market leader in a declining industry would be 55
InthestudythisiscalledStrategicemphasis,anditisdefinedasthefirm’srelativefocusonR&Dormarketing
(MizikandJacobsen,2003;Swaminathanetal.,2008)
56
Itisimportanttonotethattheextensivediscussionofrelatedversusunrelateddiversification(Runelt,1974)
hasbeendeemedoutofscopeforthisresearchpaper.However,duetothegeneralnotionofthediversification
discount of un‐related diversification, these have been excluded from the sample. For further discussion see
sectionx. 131 more interesting in maximizing market share by increasing economies of scale. As such, acquiring competitors’ production facilities or work force would be strategically sound (Swaminathan et al., 2008). In their study, Swaminathan et al. (2008) conclude that when the merger motive is consolidation, supplementary resources and capabilities create superior M&A performance, while complementary resources are more valuable when the motive is diversification (Swaminathan et al., 2008). Based on both theoretical rationale and empirical evidence, adding acquisition motive as a deal specific context seemed seamed highly relevant for the online industry (Amit R, Zott C., 2001Rindova and Kotha, 2001; Uhlenbruck et al., 2006). As such, the following hypotheses were proposed: Hypothesis Xa – When the acquisition motive is consolidation, resource supplementary is positively related to acquirer’s performance in acquisitions. Hypothesis Xb ‐ When the acquisition motive is diversification, resource complementarity is positively related to acquirer’s performance in acquisitions. Method
The strategic focus of acquirer is a qualitative assessment of the rationale behind the acquisition, whether it was a consolidation or diversification. Taken point of departure in Swaminathan et al. (2008) the following definitions were given: Consolidation: An acquisition in which the primary objective is to lower cost and/or gain market power by combining two firms with similar products and serving similar markets. Diversification: An acquisition made in which firms gain access to related technology or related but clearly complementary products. It is important to note that the above definitions are not complete copies of the definitions provided in Swaminathan et al. (2008). In principle, the difference between related and 132 unrelated diversification has been excluded based on the hypothesis that most of the acquisition in the sample would be related diversification in terms of either products or technology. In addition, it is both theoretically and empirically proven that un‐related diversification leads to lower performance in M&A (Rumelt, 1974) and to avoid this bias any un‐
related diversification were agreed to be taken out of the sample. However, all diversification motives were deemed related. Secondly, focus has been placed less on difference in industry per se (different industry codes), and more on difference in technology and product line. Furthermore, it had to be clear that the products were truly complementing in order to be labelled diversification. The underlying data to assess acquisition motive was taken from press releases found in Factiva database and on the acquiring companies’ home pages. Prior scholars applying this measure have undergone rigorous testing to ensure reliability of their coding. Singh and Montgomery (1987), Larsson and Finkelstein (1999) and The following guidelines were Swaminathan et al. (2008) all used multiple independent judges to determine whether the motive could be classified as consolidation or diversification. This was not feasible for the paper at hand, so instead measurement were taken to ensure an unbiased codification process57. First three random samples of ten acquisitions were selected were both researcher determined the motive on their own. Following, a comparison of codification was done where any differences where discussed in order to determine key words, sentences and rationales that constituted either consolidation or diversification. In the principle thirty samples – initial agreement about the codes was achieved in 83.3 per cent (25/30), with 100 per cent agreement in the last test58 used for the different categories: Consolidation: -
Key words and sentences: “Cost savings”, “gaining market base/share”, “strengthening position in existing product market/industry”, “acquiring key competitor”, “economies of scale” “access to competitors customer base” 57
58
It should be clearly noted that the authors recognizes this as a serious limitation to the analysis First test agreement was 6 out of 10, second it was 9 out of 10 and in the last it was 10 out of 10. 133 -
New products but without emphasis on complementarity to existing products, e.g. “broadening of existing product portfolio” -
Pure geographical expansions Diversification: -
Key words and sentences: “new technology”, “ new product line”, access to new product market”, “combine two technologies”, “economies of scope” -
Complementary products that would “enhance value proposition”, “create an integrated user experience”, “allow access to new customers” -
Resource and capability related geographical market access The market seeking rational, which was found in six of the deals was a bit of an issue, since it does not perfectly fit with either of the two categories. It was decided that if the rational of pure geographical expansion was given, without the mentioning of any local knowledge or other type of resource or capability gain, it would be classified as a diversification (seen on the global market). However, if anything was mentioned about local know‐how, or market relevant resource or capability, diversification was chosen as merger motive. Subsequently, the rest of the sample was reviewed by both researchers and then compared. Out of the remaining 47 deals, initial agreement was found in 95.4 per cent (62/65), the final three were discussed and agreed. Reasoningforexcludinghypothesis
It was under the process of estimating the parameters of the model that it became obvious that clear overlaps, between the definition of diversification and complementary resources and the merger motives, existed. The strategic focus of acquirer was a qualitative assessment of the rationale behind the acquisition, whether it was a consolidation or diversification. However, during the these assessment it became clear how often consolidation motives mentioned the specific words of supplementing resources, while diversification motivated acquisitions in most cases used complementarity as a descriptive word. Revisiting the definitions of consolidation and 134 diversification it became clear that these indeed were tautological statements, rendering the measures useless for analysis. In addition to the tautological problem, though enhancing it, was the high reliability of subjective assessment concerning acquisition motives. Even though a standardized approach was set up by the researcher that seemed to ensure alignment of categorization (see above), the ability for others to replicate the study was questioned, further decreasing the perceived academic. Appendix3:NAICscodedescriptions
425110:Businesstobusinesselectronicmarkets
This industry comprises business‐to‐business electronic markets bringing together buyers and sellers of goods using the Internet or other electronic means and generally receiving a commission or fee for the service. Business‐to‐business electronic markets for durable and nondurable goods are included in this industry. 454111:Businesstoconsumerretailsalesinternetsites
This U.S. Industry comprises establishments engaged in retailing all types of merchandise using the Internet. 454112:Auctions,internetretail
This U.S. Industry comprises establishments engaged in providing sites for and facilitating consumer‐to‐consumer or business‐to‐consumer trade in new and used goods, on an auction basis, using the Internet. Establishments in this industry provide the electronic location for retail auctions, but do not take title to the goods being sold. 511210:Applicationssoftware,computer,packaged
This industry comprises establishments primarily engaged in computer software publishing or publishing and reproduction. Establishments in this industry carry out operations necessary for producing and distributing computer software, such as designing, providing documentation, assisting in installation, and providing support services to software purchasers. These establishments may design, develop, and publish, or publish only. 135 518210:Applicationhosting
This industry comprises establishments primarily engaged in providing infrastructure for hosting or data processing services. These establishments may provide specialized hosting activities, such as web hosting, streaming services or application hosting, provide application service provisioning, or may provide general time‐share mainframe facilities to clients. Data processing establishments provide complete processing and specialized reports from data supplied by clients or provide automated data processing and data entry services. 519130:Advertisingperiodicalpublishers,exclusivelyoninternet
This industry comprises establishments primarily engaged in 1) publishing and/or broadcasting content on the Internet exclusively or 2) operating Web sites that use a search engine to generate and maintain extensive databases of Internet addresses and content in an easily searchable format (and known as Web search portals). The publishing and broadcasting establishments in this industry do not provide traditional (non‐Internet) versions of the content that they publish or broadcast. They provide textual, audio, and/or video content of general or specific interest on the Internet exclusively. Establishments known as Web search portals often provide additional Internet services, such as e‐mail, connections to other web sites, auctions, news, and other limited content, and serve as a home base for Internet users. Appendix4:Proofofwordcount
136 137