Strategic Overlap in Alliance Partner Selection: Overlap in
Firm’s Strategic Orientations and Attention Given to
Resource Constraints as Predictors of Alliance Formation
Likelihood
Author: G. J. Veenstra – S1911880
First supervisor: P. M. M. de Faria
Second supervisor: F. Noseleit
University of Groningen
Faculty of Economics and Business
Master of Business Administration
Specialization: Strategic Innovation Management
June 23
th, 2014
Word count: 12.822
Strategic Overlap in Alliance Partner Selection: Overlap in
Firm’s Strategic Orientations and Attention Given to
Resource Constraints as Predictors of Alliance Formation
Likelihood
Veenstra, Geert Jan
Faculty of Economics and Business, University of Groningen, The Netherlands
Abstract
This study investigated the effects of strategic overlap in alliance partner selection, because prior research generally focused on resource complementarity in the search and selection process of alliance partners. By looking at the degrees of overlap in explorative orientation, exploitative orientation and attention given to resource constraints between firms, this paper studied the effects of similarities in strategies on the likelihood of alliance formation. The study was performed at the dyadic level with data on a sample of 90 pharmaceutical and biotechnology firms over the period of 2002-2012. In line with the expectations based on prior research, the findings show that similarity in explorative orientation and overlap in attention given to resource constraints of two firms positively influence the likelihood of these firms forming an alliance. The paper provides evidence that the partner selection process is not completely about resource complementarity, but also about the assessment of compatibility of strategic goals among the potential partners. The results of this paper help managers in their search and selection of potential partners by showing that strategic overlap plays a role in the selection of partners, and partners’ strategies should be compatible. Furthermore this paper helps managers to identify companies that are most likely to be willing to ally with them, and thereby can improve the efficiency of the search and selection process.
Keywords: Strategic alliances, partner selection, alliance formation, strategic orientation, resource
constraints, strategic overlap
1. INTRODUCTION
gained considerable attention is the partner selection process (e.g., Bierly & Gallagher, 2007; Cummings & Holmberg, 2012; Geringer, 1991; Hitt et al., 2000; Pansiri, 2005; Shah & Swaminathan, 2008). But most literature so far has studied the partner selection process from a resource complementarity perspective, and neglected the possible effects of overlap in strategies. By looking at the role of strategic overlap in alliance formation this paper will address this issue. One of the most important reasons for the difficulty of partner selection is that for knowledge to be complementary, a balance needs to be found between closeness and diversity of knowledge and resources (Ahuja & Katila, 2001; Harrison, Hitt, Hoskisson, & Ireland, 2001; Keil, Maula, Schildt, & Zahra, 2008). This process of choosing the right partner is also influenced by the differences in criteria firms use to select potential partners (Dacin, Hitt, & Levitas, 1997; Hitt et al., 2000). It is argued that based on the objectives and specific situation of the firm, partner selection criteria such as characteristics of the partner, degree of fit and complementarity of capabilities differ in content and importance (Wu, Shih, & Chan, 2009). In line with this, Cummings & Holmberg (2012) argue that environmental factors, corporate objectives and alliance objectives influence the importance of several critical success factors, arguing that a dynamic partner selection analysis is necessary for an appropriate analysis of fit. Understanding partners’ objectives and selection criteria prior to alliance formation can increase the likelihood of alliance success (Dacin et al., 1997).
each other as an alliance partner. Therefore this study proposes that the degree of overlap in explorative and exploitative orientation positively influences the likelihood of alliance formation.
Furthermore, this paper studies attention from a resource-based view perspective. This view stresses that value-creation by pooled resources is the rationale for alliances (Das & Teng, 2000; Tsang, 2000). Not all firms have equal resource availability and constraints (Pidduck, 2006), and alliances require some kind of similarity in the resources provided by both firms (Das & Teng, 2000). But since it is likely that a firm with a lot of interesting resources is favored over a partner with little to contribute, it is generally believed that firms will try to partner with a firm that has a lot of resources available. However, from a strategic point of view, the partner selection logic might be different. Companies with an abundance of resources, are unlikely to feel concerned with resource constraints and the decisions on how to apply and allocate these resources. A firm that is in another position, and does not posses all the resources it would have to be successful, might feel concerned with how to apply their resources. Also the willingness to share resources might be influenced by these concerns (Pidduck, 2006). If a company would select a partner that is less concerned with its resources, chances are that these firms do not have the same ideas and concerns about how to apply the contributed resources. Therefore, this means a risk of conflict. Because conflicts can negatively influence alliance operations and outcomes (Cummings & Holmberg, 2012), this paper suggests that firms that are concerned by their resource constraints might give more attention to this in their partner selection process, and are expected to look for companies with similar concerns and idea’s regarding their resources in order to avoid conflicts in a later stage.
This paper tests the proposed relations by comparing data on alliance formation with the strategic intentions of management as expressed in their yearly Letters to Shareholders because these Letters are an expression of management’s intentions (Short & Palmer, 2008). These Letters are analyzed using computer-aided content analysis, allowing for the testing of several variables. The study investigates alliance formation on the dyadic level, so alliances between two firms. This data is collected from a sample of 90 firms operating in the pharmaceutical and biotechnology industry, with a timeframe of 2002-2012. The results show support for two of the three hypotheses. Overlap in explorative orientation is found to positively influence the likelihood of firms forming an alliance, whereas overlap in exploitative orientation was not found to significantly influence alliance formation. Lastly, the proposed effect of overlap in the degree of attention given to resource constraints on the likelihood of alliance formation was proved.
for these companies. By doing so companies are better able to select the partners that have the highest probability of success, and also increase the efficiency of the partner search and selection process. This paper will continue with describing the theoretical context and the hypotheses that result from this. Next the data and methods are described, followed by the results of the analyses. In the discussion, the results will be explained and reflected to existing literature. The conclusion will finish with this paper’s contribution to literature, managerial implications, and finally the limitations and suggestions for further research are discussed.
2. LITERATURE REVIEW
Firms increasingly collaborate by forming strategic alliances to access needed capabilities, gain knowledge and seek competitive advantage (Cummings & Holmberg, 2012). Collaborations are essential for the competitive advantage of firms that cannot rely solely on internal resources to develop new products, processes and services (Bierly & Gallagher, 2007; Rosenkopf & Nerkar, 2001; Wu et al., 2009), especially in industries that experience increasing costs of developing new products and entering new markets (Hamel, Doz, & Prahalad, 1989). Strategic alliances are a much used formalized type of inter-organizational collaboration, and can be defined as voluntary arrangements between firms involving exchange, sharing, or co-development of products, technologies, or services. They can occur as a result of a wide range of motives and goals, take a variety of forms, and occur across vertical and horizontal boundaries (Gulati, 1998). By sharing knowledge or jointly creating knowledge alliances can facilitate innovation and economic growth. This capacity has been the primary reason for the large amount of recent research attention (Schilling, 2009).
such capabilities (Mowery, 1996). Tacit knowledge is both more difficult to transfer as more difficult to protect, and requires more learning (Douma, Bilderbeek, Idenburg, & Looise, 2000; Sampson, 2007). Another important distinction between types of alliances that needs to be considered is that of exploration and exploitation (Lavie & Rosenkopf, 2006). Based on definitions outside the alliance literature, Lavie & Rosenkopf relate exploration to the pursuit of new knowledge, including things captured in terms such as search, variation, risk taking, experimentation, play, flexibility, discovery and innovation. Exploitation involves the use and development of things already known and includes terms as refinement, choice, production, efficiency, selection, implementation and execution. They state that collaboration with partners facilitates learning by accessing new knowledge residing outside a firm’s boundaries and by collaboratively leveraging existing knowledge with partners. Knowledge generation points to alliances as vehicles of learning in which each member firm uses the alliance to transfer and absorb the partner’s knowledge base. Knowledge application points to a form of knowledge sharing in which each member firm accesses its partner’s stock of knowledge in order to exploit complementarities (Grant & Baden‐Fuller, 2004). A different way to distinguish between types of alliances is in the distance of the alliance from real market competition. Technological alliances can be classified as ‘pre-competitive’, when the technological effort associated is far away from the industrial or commercial phase, and ‘competitive’ when the companies involved in the alliance are competitors or the result of the alliance will provide two competing companies with a common component or product which will be integrated in their competing products or product lines (Nueno & Oosterveld, 1988). Furthermore partner types can be divided according to the different roles they can play in complementing a firm’s own resources and capabilities, distinguishing between vertical and horizontal inter-firm relations (Belderbos, Gilsing, & Lokshin, 2011). Vertical collaboration refers to collaboration between suppliers and customers, whereas horizontal refers to collaboration between competitors. The objectives and performance effects of vertical alliances have also been found to differ from those of horizontal alliances, with the latter frequently focusing on more radical innovations and the former on cost reduction or on reducing time to market (Belderbos et al., 2011).
success. Firms that have superior alliance organizational capabilities to manage alliances have greater success with their alliances than other firms do (Anand & Khanna, 2000). This means that experience with alliances is also relevant alliance performance.
However, most academics agree that fit between partners is the most crucial factor determining success, because the performance gap between expectation and reality is often related to misfit between partners (Bierly & Gallagher, 2007; Cummings & Holmberg, 2012; Douma et al., 2000). Brouthers, Brouthers, & Wilkinson (1995) identified the four Cs of strategic alliances. Many authors agree with them that alliances should be utilized when: complementary skills are offered by the partners; cooperative cultures exist between the firms; the firms have compatible goals; and commensurate levels of risk are involved. This means that prior to forming the alliance, firms need to know what complementarities they are looking for, how their culture can be characterized, what goals they have and what risks they are experiencing. It also means that firms need to be able to understand all this about the potential partner. All this information is necessary before an optimal decision can be made. Because firms need to evaluate the fit with potential partners prior to forming the alliance, the partner selection process is very important. This strategic alliance partner selection process has been cited as one of the most critical aspects that account for the successful implementation of strategic alliances (Cummings & Holmberg, 2012; Geringer, 1991; Hitt et al., 2000; Pansiri, 2005; Shah & Swaminathan, 2008). This importance is due to the fact that failure of many alliances can easily be traced to partner selection at the planning stage (Pansiri, 2005). The selection of partners cannot be reversed once the alliance is formed, and an inappropriate partner will most certainly lead to alliance failure. It is not easy to select the optimal partner however (Wu et al., 2009), since a lot of criteria need to be taken into account in order to be able to make the optimal decision. Therefore it is critical for prospective alliance partners to understand the process of partner selection and the variables that influence this process. Complementarity has repeatedly been argued to be a fundamental objective in partner selection, but it is hard to give insight into what this complementarity might entail (Geringer, 1991).
importance of success factors and selection criteria might differ, the importance of a dynamic partner selection process is stressed. In identifying the partner characteristics of trust, commitment, complementarity and financial pay-off Shah & Swaminathan (2008) also argue that partner characteristics can have differential effects on partner attractiveness and selection. Geringer (1991) also argued that the relative importance of partner selection criteria is determined by the strategic context. This links to the concept of strategic fit, one of the most common and rational explanations for the way in which the strategic and resource needs of alliance partners are met (Bierly & Gallagher, 2007).
The concept of strategic fit is also well studied. Strategic fit of the alliance means that the firms know each other’s real objectives in the alliance, and that these objectives can be accommodated in the alliance without harming the alliance or the partner firms (Das & Teng, 1999). Because in an alliance the partners remain more or less independent, it is crucial to balance the interests and backgrounds of the partners involved, so that a win–win situation is created. Within the context of alliances, fit is very much related to concepts such as complementary balance, mutual benefits, harmony and dependency (Douma et al., 2000). This stresses the importance that both partners have to be able to achieve equal benefits with the alliance, or at least benefits that correspond with the respective inputs. Das & Teng (2000) argue that in regard to this strategic fit, resource alignment among firms is critical. They state that the fit between one organization’s resource needs and another’s resource provision relative to an opportunity set, is essential for the utility of the contributed resources for achieving the goals of the alliance. These resources may take the form of capital, technology, capabilities or firm-specific assets, and are frequently key or critical success factors in an industry (Bierly & Gallagher, 2007). Goal congruence between the partners is an important factor in the selection of partners, but it is argued that in the case of technology related projects, firms should seek partners whose strategic goals converge, while their competitive goals diverge (Wu et al., 2009). This competitive divergence might be argued to depend on the goal of the alliance. But the recognition that the overall strategy of the firm and an alliance strategy are in close interplay is very important in this regard, since a lack of this insight is another important reason for alliance failure (Wu et al., 2009). Therefore it is important that firms not only have the same alliance objectives, but also have some degree of similarities in strategies.
communicate, share and combine knowledge effectively. This means that firms have to search for overlap, but somehow have to identify the optimal amount of overlap. Furthermore, Noseleit & de Faria (2013) argued that too different external knowledge acquisition also might have a negative effect on internal R&D efforts because the firm might not be able to fully understand and incorporate the knowledge. Therefore it is argued that a moderate distance of the knowledge base across partnering firms is best for optimal innovativeness generated with both internal and external efforts (Bos, Faems, & Leten, 2012; Noseleit & de Faria, 2013; Sampson, 2007). This finding also stresses the importance of looking for partners with moderate overlap in knowledge bases.
However, most of these authors have focused on the resource complementarity and technological fit between the partners. The discussion is about the complementarity and closeness of resources, and the optimal overlap to be able to get the best results from the alliance. But this is a too narrow perspective, since the aspect of strategic fit should also involve the actual strategies of the firms. Research already suggests that firm’s strategies should fit in order for an alliance to become successful (Brouthers et al., 1995). Since the overall strategy of the firm and an alliance strategy are in close interplay (Wu et al., 2009), there is reason to expect that firm’s strategies should ideally have some overlap. Furthermore, it is explained that alliances should be win-win; both partners need to be able to achieve the goals they have set for their company (Douma et al., 2000). Because firms seek partners that fit with what they try to do, having resources is not enough for a good fit. What is also important is what the firms want to do and how they think; their strategies. These kinds of things are expressed in management’s external communications such as Letters to Shareholders (Maula, Keil, & Zahra, 2013). There is reason to expect that firms seek partners that have overlap in their strategies. There is also logic involved in this reasoning because it can be expected that if there is less diversity in focus and goals, conflicts might be less likely to occur. The alliance formation literature so far did not focus on this overlap in strategies. Therefore this paper adds to the understanding of the alliance partner selection process by not focusing on resource, knowledge, capability or technological complementarity, but on the similarity in strategies firms follow to achieve their goals.
Hypotheses development
Sampson, 2007). This research generally agrees that complementarity is key for alliance success. But there is reason to expect that firms will look for similarity in terms of strategy when it comes to alliance partner selection. Since firm strategy and alliance strategy are in close interplay (Wu et al., 2009), firms are considered to search for companies that not only have complementary resources and capabilities, but also have a fit in terms of strategy. If potential partners have similarities in their strategies, so try to achieve comparable goals, firms are expected to have a better fit. This corresponds with the theory of Brouthers et al. (1995) that compatible goals are necessary if an alliance is formed and with the theory of Das & Teng (1999) that strategic fit means that firm's objectives can be accomodated in the alliance without harming the alliance or the partner firms. Therefore, this paper will look if similarity in strategies is a predictor of alliance formation.
Before the hypotheses can be formulated, some alliance strategy perspectives need to be distinguished. An important consideration for managers is to balance the two fundamentally different goal orientations and corresponding strategic foci, exploitation and exploration (March, 1991). Firm strategies and alliance objectives can have explorative or exploitative characteristics. Exploration includes things such as variation, experimentation, discovery and innovation, and mainly involves the pursuit of new knowledge, whereas exploitation includes things such as refinement, production, efficiency, selection, implementation and execution and mainly involves the exploitation of existing knowledge (Lavie & Rosenkopf, 2006). Generally, exploration is considered to span a longer time period whereas exploitation is considered to span a shorter time period, and exploration is characterized by more novelty and uncertainty. Because firms need partners that fit their strategies (Das & Teng, 1999), and firms with similar degrees of explorative orientation or exploitative orientation are likely to have similarity in strategies and goals, it would be likely that managers equally focusing on exploration or exploitation, are more likely to select each other as an alliance partner. This translates into the first two hypotheses:
H1: The degree of overlap in explorative orientation of two firms positively influences the likelihood of
those firms forming an alliance.
H2: The degree of overlap in exploitative orientation of two firms positively influences the likelihood of
those firms forming an alliance.
means that firms will always search for companies with a lot of useful resources, but each partner has to contribute more or less equally to the collaboration. However from a strategic viewpoint, something else might be important too; the resource constraints companies experience. A company can have resource constraints because it does not posses all the resources it could use in order to optimally compete and perform in the market. Such a situation can lead to concerns regarding this resource availability, and might also determine how firms apply their resources. Firms that are less concerned with their resource constraints can be less careful in applying and allocating these resources. This means that different degrees of concerns with resource constraints can result in varying resource application styles. When a firm that is in need of resources looks for a partner with an abundance of resources, this could mean that they have different degrees of concerns with their resources. The position of a firm regarding its resource availability and constraints might determine if they are willing to share their knowledge and skills with certain partners (Pidduck, 2006). Therefore a resource-concerned firm might be hesitant in selecting a less concerned firm, because of the fear that their contributed resources will be spilled or applied improperly and the conflicts that can arise from this. Because conflicts can negatively influence alliance operations and outcomes (Cummings & Holmberg, 2012), firms will do everything to reduce the risk of conflicts with potential partners. Therefore, firms that are concerned by their resource constraints might give more attention to this in their partner selection process, and are expected to look for companies with similar concerns and idea’s regarding their resources, because this will avoid the risk of conflicts. Furthermore, concerns about resource constraints might also be caused by the fact that firms with fewer knowledge are less able to assess the capabilities and reliability of a potential partner (Ahuja, Polidoro, & Mitchell, 2009). Therefore it might be argued that such firms are more concerned and careful in selecting partners that have more information than they do, because these asymmetries in available information could be used in their disadvantage. Because of these arguments, it is expected that firms that are more or less concerned with resource constraints are more likely to partner with a firm that is equally concerned. This leads to the third hypothesis:
H3: The overlap in the degree of attention given to resource constraints of two firms positively influences
the likelihood of those firms forming an alliance.
3. DATA AND METHODS
Shareholders will be used to measure and compare management’s strategic focus and attention. By doing so, this paper will measure the actual similarities or differences in strategic aspects as perceived by the management.
To be able to test the hypotheses, large-scale data collection and statistical data analysis is necessary (van Aken et al., 2012). For this study the empirical context is the pharmaceutical industry. This is a relevant industry as it has received considerable attention in the alliance literature because of its high level of inter-firm collaborations (Hagedoorn, 2002). A single industry is chosen to be better able to compare overlap of strategic focus and attention of firms that operate in the same industry. More specifically, the largest, R&D intensive pharmaceutical and biotechnology firms were selected. The reason for this is that this kind of firms are more likely to have larger variations in their strategic foci and goal orientation as compared to low-R&D performing firms, and therefore are more suitable for this research. Furthermore, data on large firms is more publicly available, resulting in fewer missing data and better reliability (Ahuja et al., 2009). A set of 90 firms was identified as the top R&D spending pharmaceuticals and biotech firms. All these firms are heavily engaged in the processes of research, development and marketing of drugs in a global context. The period under analysis is 2002-2012. This period was chosen, also because of data availability reasons, but also because alliances have become common and widely used in this period (Anand & Khanna, 2000).
Dependent variable
Alliance formation. – In this study the dependent variable is the formation of an alliance between
two organizations in a given year. This means that the unit of analysis is the dyad of two firms. This data, which was derived from the SDC database, resulted in a record of the number of alliances for each dyad for each year studied (2002-2012). This approach resulted in a set of dichotomous values for all possible dyad of firms in the sample for all years in the timeframe. This approach is essential in this type of analysis, to uncovering unbiased results (Ahuja et al., 2009; Gulati & Gargiulo, 1999). Gulati & Garguilo (1999) also used this approach in their research with dyads as the dependent variable, and tested with other risk sets. Including only dyads of which one firm entered at least one alliance before, or including only dyads of which both firms entered at least one alliance before, provided comparable results. Therefore this study also reports results based on the full risk set of all possible dyad of firms in the sample. Some alliances involved three partners. In line with Vasudeva & Anand (2011) each dyad was recorded separately, to account for all relationships that could possibly result in knowledge flows. Reverse-ordered pairs were excluded to avoid double counting. In total 135 unique dyads had entered one or more alliances within the period under analysis, containing in total 149 alliances. According to Vasudeva & Anand (2011) alliances typically last for more than one year, but termination dates are rarely reported. They argue that previous research has used alliance duration windows ranging from one to five years. Given this norm, they assumed a productive life span from a knowledge-building perspective of five years. This is in line with other alliance literature (e.g., Bennett, 1997; Cui, 2013) and therefore this life span will also be applied in this study. This means that if a dyad formed an alliance in a certain year, the subsequent 4 years will also have positive values for this dyad. Following this approach results in a validated measure for alliance formation on the dyadic level.
Independent variables
In this study the computer-assisted text analysis program LIWC (Linguistic Inquiry and Word Count) was used to perform the analyses (Tausczik & Pennebaker, 2009). LIWC has been used before multiple times in studies with comparable approaches (e.g., Maula et al., 2013; Moss et al., 2013; Zavyalova & Pfarrer, 2012). LIWC software provides output in the form of word counts per 100 words in each narrative (Moss et al., 2013). One of the important advantages of LIWC for this study is option to use both standard dictionaries already available in the program, but also user-defined dictionaries. The standard dictionaries are backed with evidence on the reliability and external validity (Tausczik & Pennebaker, 2009), whereas the user-defined dictionary allowed for the flexibility to analyze specific constructs.
Overlap in explorative orientation of two firms & Overlap in exploitative orientation of two firms. –
To be able to measure these two independent variables, a specific tool was necessary. In their research on strategic consistency of exploration and exploitation in family businesses, Moss et al. (2013) developed a very useful and detailed word list for measuring exploration and exploitation. Moss et al. (2013) ensured construct validity by using March's (1991) definitions for exploration and exploitation as a starting point. After this they included permutations of each word for each construct, to generate a comprehensive list of representative words. As a final and important step this deductive, theory-based approach was complemented by an inductive custom word list (Moss et al., 2013). The result is a very comprehensive word list for the exploration and exploitation measures. The use of both deductive and inductive word list is important for this study, because there is considerable variation in the length of the Letters to Shareholders. However the program accounts for this by calculating the values per 100 words, a larger word list per variable increases the chances of capturing all relevant terms, since it can be assumed that longer letters go more in depth and therefore could contain a bigger variety of relevant words. Table 1 presents the lists of words used to measure both variables. The use of this previously validated measure used in textual analysis of organizational exploration and exploitation contributes to the construct validity of this study.
Table 1. List of words used to measure exploration and exploitation (based on Moss et al., 2013)
Variable Words used in content analysis
Exploration (143 words)
Discover, Discoverability, Discoverable, Discoverably, Discovered, Discoverer, Discoverers, Discoveries, Discovering, Discoverist, Discoverists, Discoverment, Discoverments, Discovers, Discovery, Experiment, Experimental, Experimentalism, Experimentalist, Experimentalists,
Experimentalize, Experimentally, Experimentarian, Experimentarians, Experimentation,
Exploration (continued)
Riskiness, Risks, Risky, Search, Searchable, Searchableness, Searched, Searcher, Searchers, Searches, Searching, Searchingly, Variation, Variational, Variationally, Variations, Variative, Variatively, Adapt, Adapting, Adaptive, Adaptors, Create, Created, Creates, Creating, Creation, Creative, Creator, Develop, Developed, Developer, Developers, Developing, Development, Developmental, Develops, Inventions, Laboratories, Laboratory, Labs, Patent, Patented, Patents, Pioneer, Pioneered, Prospect, Prospecting, Prospective, Prospectively, Prospects, Research, Researcher, Researchers, Researching, Scientist, Scientists
Exploitation (186 words)
Choice, Choicer, Choices, Choicest, Efficience, Efficiencies, Efficiency, Efficient, Efficiently, Executable, Executant, Executant, Executants, Execute, Executed, Executer, Executers, Executes, Executing, Execution, Execution, Executional, Executioner, Executioners, Executions, Executions, Executively, Executiveness, Executor, Executorial, Executors, Executorship, Executory, Exploit, Exploitability, Exploitable, Exploitation, Exploitational, Exploitationally, Exploitations, Exploitative, Exploitatively, Exploitatory, Exploited, Exploiter, Exploiters, Exploiting, Exploitive, Exploitively, Exploits, Exploiture, Implement, Implementable, Implemental, Implementation, Implemented, Implementer, Implementers, Implementing, Implementor, Implementors, Implements, Production, Productional, Productions, Productivity, Refine, Refined, Refinedly, Refinedness, Refinement, Refiner, Refineries, Refiners, Refinery, Refines, Refining, Select, Selectability, Selectable, Selected, Selectedly, Selecting, Selection, Selectional, Selectionalism, Selectionist, Selectionists, Selections, Selective, Selectively, Selectiveness, Selectivities, Selectivity, Selectly, Selectness, Selector, Selector, Selectors, Selectors, Selects, Accountant, Accountants, Administering, Administration, Administrative, Advertise, Advertised, Advertisement, Advertisements, Advertiser, Advertisers, Advertising, Assemble, Assembled, Assembler, Assemblers, Assemblies, Assembly, Audited, Auditing, Auditors, Audits, Automate,
Automated, Automatic, Automatically, Automating, Automation, Commercialization,
Commercialize, Commercialized, Commercializing, Commercials, Commoditized, Commoditizing, Commodity, Conventional, Deploy, Deployable, Deployed, Deploying, Deployment, Deployments, Distributor, Distributors, Increment, Incremental, Incrementally, Increments, Launch, Launched, Launches, Maintain, Maintained, Maintaining, Maintains, Manufacture, Manufactured, Manufacturer, Manufacturers, Manufacturing, Marketed, Marketer, Marketers, Marketing, Optimization, Optimize, Optimizer, Optimizing, Optimum, Procured, Procurement, Promotion, Promotional, Promotions, Replicated, Replication, Replicators, Routine, Routinely, Salesforce, Salespeople, Salespersons, Standardized, Throughput
This study however, needs a measure for the overlap or similarity of the values of two firms in a dyad. This was done by first converting the output to values per firm per year, the same format as the dependent variable. Then this data was transformed to dyads of firms. This means that for the dyad of firm i and firm j the values for firm i and firm j were listed in separate columns. In a separate column, the value of firm j was extracted from the value of firm i. Because this results in both positive and negative values, whereas the objective is the relative difference, these values were transformed into absolute values. The lower the value, the more similar the firms in a dyad are regarding each construct. This calculation was done for both exploration and exploitation, resulting in two separate values for the two variables.
Overlap in attention given to resource constraints of two firms. – To measure this third
relevant words. Although this approach has not been used before for measuring the construct of concerns with resource constraints, based on the arguments taken from literature there is sufficient expectation that similarity in values of inhibition among firms in a dyad can be a predictor of alliance formation of a dyad. It is important to mention once again, that this variable does not relate so much to the by Das & Teng (2000) distinguished property-based, or financial resources, but mainly to the knowledge-based resources such as skills and knowledge. If management of a firm feels itself being constrained by these types of resources, it may describe this in their Letters to Shareholders using words such as blocked, constrained, restrained, inhibited etcetera. Therefore this measure of inhibition from the LIWC2007 dictionary is considered to be a valid construct for measuring resource constraints. Because this variable needs to be transformed to similarity of values per dyad, the same conversion approach as the former independent variables was used. The values were first extracted per firm per year, and then linked to all dyads the firms were engaged in. So again, for a dyad between firm i and firm j, the separate values were listed. After this, the value of firm j was extracted from the value of firm i, and finally converted to absolute values to dismiss the negative values. This resulted in a variable of similarity in resource constraints as experienced by the managements with values for each unique dyad of two firms.
Control variables
Firm size. – Firm size was included because numerous studies have found reasons to control for
the relationship between organizational size and alliance formation (e.g., Ahuja et al., 2009; Beckman, Haunschild, & Phillips, 2004; Gulati & Gargiulo, 1999). Furthermore there is evidence that firm size influences exploration and exploitation intensity (Lavie & Rosenkopf, 2006). Firm size was measured as the number of employees the firm had each year, since the number of employees is considered to be a good estimator of firm size (e.g., Li, Eden, Hitt, & Ireland, 2008; Moss et al., 2013; Yadav, Prabhu, & Chandy, 2006). Firm size data was collected from Compustat and Orbis, and completed with the firm’s annual reports. For each dyad, the difference in firm size of the two firms was taken because of the likelihood of smaller firm allying with larger firms or vice versa, also because of the above mentioned differences in tendency of larger firms focusing more on exploration and smaller firms more on exploitation (Beckman et al., 2004) and the propensity to be engaged in collaborations (Belderbos et al., 2011).
Alliance experience. – The study also controlled for alliance experience. Firms with frequent
factor when choosing alliance partners (Brouthers et al., 1995; Gulati, Nohria, & Zaheer, 2000). This means that if there is much difference in experience, the partners are expected to be less likely to collaborate, because of the risks of unequal benefits and the relative attractiveness of the potential partners. Alliance experience is calculated with the number of alliances a firm has been involved in over the last 5 years, because this is deemed to be a period in which an alliance can contribute to the firm’s level of experience (Duysters, Heimeriks, Lokshin, Meijer, & Sabidussi, 2012; Li et al., 2008). Following the approach of Duysters et al. (2012), the 5 year experience was considered low if it was below the average of the firms in the sample, and considered to be high if it was above the average of the firms in the sample. This resulted in a dummy variable with values for each firm for each year, if it was considered to have alliance experience or not. Again, like the independent variables this was then transferred to dyad level, indicating if 0, 1 or 2 firms in the dyad had alliance experience. As a robustness test, the absolute difference in the values of the sum of the experience over the last five years was also tested. This indicates the similarity in alliance experience of the firms in a dyad. Both measures will be included in the results.
Year and industry. – To account for potential difference in the several years under study, and
potential industry differences, year and industry were also controlled for. The sample contains firms from two industries; pharmaceutical (2834) and biotech (2836). Because firms in the same industry are considered to have less resource differences, firms with equal SIC codes are more likely to collaborate and therefore this needs to be controlled for (Cui, 2013). The SIC codes were derived from the SDC Platinum database.
Data analysis techniques
In line with the work of Ahuja et al. (2009) the hypotheses were tested using the probit regression model, which models alliance formation likelihood as the dependent variable, that is, πijt ,where πijt = Pr (alliance
formationijt = 1) and (1−πijt) = Pr (alliance formationijt = 0). In the probit model, Φ−1 (πijt) = Xijt−1β +εijt,
where Xijt−1 is a vector of lagged time-varying covariates, β is a vector of estimated coefficients, εijt is a
normally distributed error term, and Φ−1 is the inverse of the cumulative normal density function (Ahuja et
4. RESULTS
Descriptive statisticsTable 2 presents the descriptive statistics and correlation between the dependent variable alliance formation, the independent variables and the control variables. As can be seen in the number of observations for the dependent variable, the maximum number of dyads within the timeframe under study is 44055. This is based on 89 alliances for each of the 90 firms, for each of the 11 years under study, divided by two because reverse-ordered pairs were excluded. The independent variables have 18263 observations because these values are extracted from the 619 Letters to Shareholders. Control variable data were available for all firms and years of which data on the independent variables was present. Therefore, the analyses are based on 18263 unique dyads of two firms. The independent variables show no high correlations, but the control variables do show some higher correlations. Firm size seems to be related to both measures of alliance experience. This will be reflected upon in the next section. Both measures of alliance experience show correlations, but this is logical and no problem because the two measures will not be used in the same model. Table 2 also indicates the variance inflation factors (VIF). However the correlations show little reason to suspect multicollinearity, the VIF values ranging from 1.00 to 1.71 prove that multicollinearity is indeed not a problem (O’brien, 2007).
Results
by the fact that the effect of a change in one variable depends on the values of the other variables (Hoetker, 2007). Because this paper aims to investigate if relations between the dependent and independent variables exist, and the calculations of variable’s marginal effects is quite comprehensive and beyond the scope of this paper, this section will focus on the significance and the sign of the coefficients. This is in line with most prior research in strategic management using probit or logit models (Bowen & Wiersema, 2004).
Table 3 presents the results of the probit regressions. Model 1 includes the control variables. Firm size and both measures for alliance experience are significant. The sign is positive, meaning for firm size that the larger the difference in firm size of the two firms in the dyad, the more likely they are to form an alliance. This is in line with prior research (e.g., Beckman, Haunschild, & Phillips, 2004; Belderbos, Gilsing, & Lokshin, 2011). Alliance experience on the other hand, by also showing a positive sign, contradicts with the expectations. The assumption was that firms with more similar levels of alliance experience are more likely to collaborate, because this situation involves less risk of unequal benefits
from the alliance (Anand & Khanna, 2000; Gulati & Gargiulo, 1999). These results might be partially explained by the characteristics of firms in the pharmaceutical and biotech industry. Established and experienced pharmaceutical firms have the tendency to collaborate with new, small and less experienced firms, by for example forming alliances with new biotech firms, to discover and develop new drugs (Hill & Rothaermel, 2003). Besides this, to reap benefits from prior alliance experience, firms need to possess absorptive capacity, the potential to acquire, assimilate, transform and exploit new knowledge, something that might be more difficult for large firms than for small partners in this industry (Hoang & Rothaermel, 2005). The results for the control variables are consistent across all three models.
Table 3. Probit estimates of influence on likelihood of alliance formation
Predictor Model 1 Model 2 Model 3
Independent variables
Difference explorative orientation (H1) -0,12*a -0,16**
Difference exploitative orientation (H2) -0,10aa -0,09aa Difference resource constraints (H3) -0,19†a -0,21*a
Control variables
Difference firm size 0,00** 0,00** 0,00** Alliance experience (dummy) 0,31** 0,30**
In model 2 the independent variables difference in explorative orientation (H1), difference in exploitative orientation (H2) and difference in attention given to resource constraints (H3) were added to the analysis. The model shows support for the first hypothesis, that the degree of overlap in explorative orientation of two firms positively influences the likelihood of those firms forming an alliance, on a 95% confidence level. The relation is negative, meaning that the smaller the difference (so the more similarity), the more likely the firms in a dyad are to collaborate. The model shows no support for the second hypothesis, that the degree of overlap in exploitative orientation of two firms positively influences the likelihood of those firms forming an alliance. The model does show support for the third hypothesis, that the degree of overlap in attention given to resource constraints of two firms positively influences the likelihood of those firms forming an alliance, but only on a 90% confidence level. Because the dataset of 90 firms containing 149 alliances in total, is relatively small, it can be argued that significance at this confidence level provides enough reason to expect that relation is not totally random (Fisher, 1925). This relation is also negative, in line with the expectations. The smaller the difference in resource constraints, so the more similar, the more likely the two firms in a dyad are to collaborate.
Model 3 is identical to model 2, but includes the control variable with the absolute value of the difference in alliance experience, instead of the alliance experience dummy measure. This model shows support for the same hypotheses; H1 and H3 are supported but H2 is not. The confidence level at which these hypotheses are supported however, are different from the second model. The hypothesis that the degree of overlap in explorative orientation of two firms positively influences the likelihood of those firms forming an alliance is supported on a 99% confidence level, so more significant. The hypothesis that the degree of overlap in resource constraints of two firms positively influences the likelihood of those firms forming an alliance is also more significantly supported; the confidence level for H3 is 95%. The relations in the third model have the same sign as model 2; supporting the hypotheses that overlap in explorative orientation and overlap in resource constraints positively influences the likelihood of forming an alliance.
5. DISCUSSION
Overlap in explorative orientation
Prior literature showed that potential partners need to have some degree of overlap in their strategies and goals (Das & Teng, 2000; Wu et al., 2009). Furthermore, literature differentiates between two fundamentally different goal orientations and strategic foci, exploration and exploitation (March, 1991). Therefore this study hypothesized that the degree of overlap in explorative orientation of two firms positively influences the likelihood of those firms forming an alliance. The results provide evidence to support this hypothesis. This means that indeed overlap in explorative orientation by two firms increases the likelihood that these two firms will collaborate. Firms with overlap in strategic explorative orientation are likely to focus on similar goals, and therefore are more likely favor such partners. Similar strategies means that these firms are better able to align their goals in the alliance and optimally benefit from their joint efforts. This is in line with the relevant literature, however there is also research that suggests that a certain alliance can be explorative in nature for one partner, but simultaneously exploitative for the other partner (Lavie & Rosenkopf, 2006). In this kind of cases, the degree of overlap in explorative orientation cannot be expected to be a predictor of alliance formation likelihood. Literature argues that there is a risk of partners being too similar regarding resources and capabilities, providing little novelty (Sampson, 2007; Vassolo, Anand, & Folta, 2004). The results show that this doesn’t hold for the similarities in terms of strategy, where more similarity increases the probability of alliance formation. This is in line with the expectations based on Brouthers et al. (1995) that firms should have compatible goals. This finding is in practice helpful for managers that are in the challenging process of partner selection, because it points that focusing solely on resource complementarity might be risky. Since strategies and goals need to have some kind of overlap, managers focusing on explorative alliance activities should assess the fit of potential partner’s strategies with their own strategies. When managers know that some firms are more likely to ally with them than others, they can also use this information to focus attention to these firms and set favorable terms.
Overlap in exploitative orientation
be relevant. It could be the case that in regard to exploitative orientation, firms are more likely to find similarly exploitatively oriented firms, but do not select these firms as partners. When it comes to exploitative activities, firms might stress more importance to other aspects such as competition than the overlap in strategic orientation. Furthermore, in the pharmaceutical industry alliances are often used for explorative activities, and when it comes to exploitation, especially large firms choose for vertical integration to be able to develop and commercialize projects (Rothaermel & Deeds, 2004). This means that the nature of exploitative activities and the specific characteristics of the industry used in this study partly explain why no evidence was found to support this hypothesis.
Overlap in attention given to resource constraints
The third hypothesis relates to concerns with resource constraints. A firm’s concerns regarding resource availability were argued to determine a firm’s likelihood to ally with a potential partner, because this determines the constraints firms have regarding knowledge-based resources. The results show support for this expectation. This means that firms that experience similar degrees of concerns with resource constraints are more likely to collaborate with each other. However no specific literature regarding similarity of resource constraints existed yet, these results can be explained with the described literature. If firms have issues with their resources, they will be careful in sharing the resources they have (Pidduck, 2006). A firm with comparable issues is less likely to retain unequal benefits from the alliance, and therefore these firms are more likely to collaborate with each other. Managers that feel constrained in their resources might feel confident in a partner if this partner has similar issues, because the risks of conflicts are expected to be smaller. On the other hand, firms that have less concerns with resource constraints might value more resource concerned firms less attractive, because they could be to hesitant in sharing their assets, thereby inhibiting the outcomes of the alliance. This means that the results show that less constrained firms are more likely to select similar firms as partners. Managers that feel constrained by resources might thus feel more cautious about sharing their resources with less constrained firms. On the other hand are less constrained firms more hesitant in sharing their resources with more constrained firms because of the risk that these firms will contribute less to the collaboration. This knowledge can help managers with understanding which firms might be likely to collaborate, but also give insight into why sometimes firms are hesitant when a resource fit between the partners seems to exist. Since the risk of conflicts need to be minimized at all times, managers should have attention for the way potential partners see and apply there resources when selection partners.
6. CONCLUSIONS
orientation and concerns with resource constraints between firms, this paper studied the effects of similarities in attention on the likelihood of alliance formation. This study was performed with data on a sample of 90 pharmaceutical and biotechnology firms over the period of 2002-2012.
Existing literature studied the alliance formation process mainly from a resource complementarity perspective. But this is a too limited perspective, since partners should not only have a fit regarding resources, but also have a fit regarding strategic goals (Brouthers et al., 1995; Das & Teng, 2000; Wu et al., 2009). This study extends the knowledge on alliance formation by measuring and comparing Letters to Shareholders, because these are considered to reflect judgments about the importance of issues among senior management (Maula et al., 2013). Because these Letters are expressions of management’s intentions (Short & Palmer, 2008), this study was able to compare actual similarities or differences in strategic attention as perceived by management. The results appear to support the expectation that overlap in strategies of potential partners plays a role in the partner selection process. More specifically, overlap in explorative orientation of two firms increases the likelihood of alliance formation. Also, overlap in attention given to resource constraints of two firms increases the likelihood of alliance formation. These results cause that this paper not only enriches information on alliance formation, but also introduced a working method for comparing strategic orientations in relation to partner selection. Computer-assisted text analysis programs offer great opportunities to analyze and compare Letters to Shareholders on several other relevant aspects to get better insight in the partner selection process.
Managerial implications
The results of this study suggest that managers should be aware that potential partner’s strategies should be considered when selecting a partner. The partner selection process is not just a process of selecting the partner with the best resource complementarity; firms should have overlapping strategic goals in order for an alliance to become successful. Also their concerns regarding resource constraints should be evaluated in advance, because similar degrees of concerns can avoid the risk of conflicts. Furthermore, by showing which firms are most likely to be willing to collaborate, firms can anticipate on which particular firms are most likely to be interested in an alliance. This can improve the efficiency of the search and selection process, and can also help managers with setting favorable terms for these companies to stimulate alliance formation.
Limitations and future research
to other types of firms and other industries without further research. Also a replication with a bigger firm sample and a longer timespan could improve the external validity of the results, because the sample size of 90 firms, with in total 135 firm dyads is just a fraction of the organizations actively collaborating via alliances in this industry segment. But despite these remarks these results are helpful for the understanding of the role of strategic overlap and serve as a starting point for other research in this field. However the Letters to Shareholders are seen as good reflections of management’s intentions (Short & Palmer, 2008), they also have some disadvantages. First of all, the relative length of the Letters differs substantially among the different firms in the sample, and sometimes even between different years of a single firm. Although this study used a very extensive list of words to measure the constructs, and the software accounts for length by calculating values per 100 words, the effect of length cannot be excluded. Letters that have just one page or even shorter, are obviously very less detailed than Letters of, for example, ten pages. Therefore longer Letters are likely to score higher values than shorter ones. Future research might try to solve this issue, for example by selecting a larger sample size and excluding Letters below a certain length threshold. Another issue with these Letters is the writer. The study focused on the Letters to Shareholders written by the CEO, because this person is responsible for the day-to-day management of the firm. However, some Letters include Letters written by the CEO and the Chairman of the board, whereas others only have a Letter of the Chairman of the board and a CEO letter is missing. So while these other Letters are still useful, the expressions from the Letters might not be fully from the management. Despite these disadvantages, Letters to Shareholders are perceived to be the best estimator of management’s strategic intentions.
Another issue with the method was the inclusion of only alliance experience in general, and not specific experience of firms in a dyad. While Hoang & Rothaermel (2005) argued that general and partner-specific alliance experience should be viewed separately, the data in this study was too limited to provide a valid measure of partner-specific alliance experience. This problem could also be addressed in a follow-up study with a larger sample.
Acknowledgements
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