A Behavioral Theory of the Reciprocal Relationship between

A BEHAVIORAL THEORY OF THE RECIPROCAL RELATIONSHIP BETWEEN

INNOVATIVE OUTPUT AND R&D ALLIANCES

BEVERLY B. TYLER*

North Carolina State University

Poole College of Management

Campus Box 7229

Raleigh, NC 27539-7229

Tel.: (919) 515-1652

Fax: (919) 515-6943

E-mail: beverly_tyler@ncsu.edu

TURANAY CANER*

North Carolina State University

Poole College of Management

Campus Box 7229

Raleigh, NC 27539-7229

Tel.: (919) 515-6946

Fax: (919) 515-6943

E-mail: turanay_caner@ncsu.edu

*Both Authors’ contributed equally to this manuscript.

Submitted to Strategic Management

April 19, 2012

We would like to acknowledge helpful feedback from Ted Baker, Janet Bercovitz, Nandini

Lahiri, Joseph Mahoney, research seminar participants at North Carolina State University and the

University of Illinois at Urbana Champaign, as well as several biopharmaceutical industry experts, on earlier versions of this manuscript.

Innovative Output and R&D Alliances

A BEHAVIORAL THEORY OF THE RECIPROCAL RELATIONSHIP BETWEEN

INNOVATIVE OUTPUT AND R&D ALLIANCES

ABSTRACT

The behavioral theory of the firm maintains that when firms perform below their aspiration, they will engage in problemistic search and invest in novel solutions to improve subsequent performance. Using a pooled time series analysis of 667 observations for 131 biopharmaceutical firms operating in the U.S. from 1990 to 2007, we find support for behavioral theory expectations. Furthermore, our data suggest that the relationship between past below aspiration innovative output and subsequent innovative output, defined as new product introductions, is mediated by the number of R&D alliances established, while the relationship between past above aspiration performance and innovative output is not. We contribute to the behavioral theory and alliance literature, illustrate how firms’ innovative output influences R&D alliance formation, and suggest directions for future research.

Keywords : behavioral theory, innovative output, R&D alliances,

1


INTRODUCTION

Empirical tests of the tenets of the behavioral theory of the firm proposed by Cyert and

March (1963) have found general support for the theory (Gavetti, et al., 2012). There has been wide support for the proposed decrease in risk taking when performance is above aspiration, but the effect of below aspiration performance on risk-taking and problemistic search processes remains the subject of active debate

1

(Audia & Greve, 2006; Chen & Miller, 2007; Greve, 2011).

Aspiration, defined as “the smallest outcome that would be deemed satisfactory by the decision maker” (Schneider, 1992: 1053), is argued to determine the boundary between what decision makers consider success and failure in continuous measures of performance when assessing the effect of past performance on problemistic search , defined as “search that is stimulated by a problem…and is directed toward finding a solution to that problem” (Cyert & March, 1963:

121). According to behavioral theory as search becomes focused on a problem, adaptations in search routines and changes in search mechanisms, improve the chances of locating alternative solutions to the problem (Mahoney, 2005). Thus, research testing the propositions of behavioral theory consider how firms’ performance below and above aspiration affect problemistic search processes and the adaptation of search routines used to locate solutions to problems.

We see two gaps in the behavioral theory literature that can inform the debate on the effects of below aspiration performance effects on problemistic search and adaptation. First,

Cyert and March (1963) argued that subunit (marketing, purchasing, R&D) performance relative to goal aspirations should affect the allocation of resources, consistent with the behavioral theory premises. While studies testing behavioral theory tenets have considered firms’ financial performance when they calculate below or above aspiration performance (e.g., Miller &

1 For theoretical clarity, in this paper we limit our discussion to the logic provided by the behavioral theory of the firm (Cyert &

March, 1963) and do not explain why performance below aspiration level might lead to risk aversion (Audia & Greve, 2006).

2


Leiblein, 1996; Singh, 1986; Wiseman & Bromiley, 1996), research that evaluates subunit and departmental performance relative to aspirational goals is rare. Secondly, Cyert add March

(1963) said that studies of how R&D resource allocations were made “should be especially useful in providing clues to the ways in which organizations avoid uncertainty and learn in ambiguous situations” (274). These arguments have lead to research evaluating the effect of financial performance below and above aspirations on R&D intensity and innovative output

(e.g., Audia & Greve, 2006; Chen & Miller, 2007; Greve, 2003). A next step would be to test the proposed relationship between innovative performance

2

relative to aspirations and investments in R&D alliances, a common resource investment made by firms to access solutions to product development problems more distant from the firms’ established knowledge search routines (Gavetti, et al., 2012; Powell, Koput, & Smith-Doerr, 1996; Teece, 1989).

Since alliances are a common source of distant and novel knowledge required for new product development and have been shown to have a positive effect on innovative output

(Danzon, Nicholson, & Pereira, 2005; Shan, Walker, & Kogut, 1994; Teece, 1992), we propose that R&D alliances will be influenced by past innovative output and will influence future innovative output. More specifically, based on behavioral theory we submit that the greater the distance innovative output is below aspirations the greater the likelihood firms will adapt their search processes to locate novel solutions to product development problems and negotiate an increased number of R&D alliances. We also propose that this increase in R&D alliances mediates the relationship between past below aspiration innovative output and future innovative output, as the increased number of R&D alliances provide diverse and novel knowledge required for future successful new product introductions. On the other hand, we predict that greater innovative output above the aspiration level will not trigger problemistic search or an increase in

2 Based on Cyert and March (1963), we assume that aggregate innovative output reflects firms’ R&D subunits performance.

3

Innovative Output and R&D Alliances the number of R&D alliances subsequently formed, and, thus, R&D alliances do not mediate the relationship between past above aspiration innovative output and future innovative output. Thus, in an effort to inform the behavioral theory debate on when below aspiration performance result in risky organizational investments, we conducted a study focused on two research questions.

First, does the distance of innovative output below aspiration levels, defined by prior performance, increase firms’ subsequent R&D alliance formation? Second, does subsequent

R&D alliance formation mediate the relationship between innovative output below and above aspiration and firms’ future innovative output?

Our study contributes to the literature in three primary ways. First, we consider the effects of innovative output below aspirations, a subunit goal rather than financial performance, to test behavioral theory in the context of new product introductions in the biopharmaceutical industry. We find support for the fundamental premises of the theory regarding the effects of subunit performance below goal aspirations on firms’ problemistic search and adaptation of search routines. Past empirical publications have primarily been limited to the consideration of the effect of below and above aspiration financial performance (Chen, 2008; Chen & Miller,

2007; Hu, Blettner, & Bettis, 2011; Greve, 2003). Second, we evaluate the reciprocal relationship between innovative output and R&D alliances. The alliance literature has long predicted a strong positive relationship between the formation of alliances and subsequent performance (Shan, et al., 1994; Teece, 1992), but some have proposed that it is the positive performance that attracts alliance partners in the first place (Chan, et al., 1997; Lee, 2010). The current study finds that increases in R&D alliance formation in the biopharmaceutical industry are not just the result of positive past innovative output. Rather firms that have below aspiration innovative output are more likely to establish a relatively greater number of R&D alliances than

4

Innovative Output and R&D Alliances firms that have above average aspiration innovative output. Finally, we recognize explicitly the mating theory 3 of search and its implications for behavioral theory (Cyert & March, 1963). We argue that not only are firms with below aspiration innovative output searching for technical partners, but potential partners are simultaneously looking to leverage their technical resources, create real options for the future, and learn how to cope with uncertainty through R&D alliances

(Beckman & Haunschild, 2002; Beckman, et al., 2004; Kogut, 1991).

We begin theory development by reviewing the basic tenets of behavioral theory and the literature that considers the effects of below and above aspiration performance on R&D investments. Next, we discuss the alliance literature which has proposed a strong positive link between R&D alliance formation and innovative output. Then we develop our hypotheses, and describe our empirical context, analyses, and results. We conclude with a discussion of our findings, the study’s limitations, and implications for future research and practice.

THEORY DEVELOPMENT

Cyert and March (1963) proposed a behavioral theory of the firm that presented logic for how goals and objectives are formed, knowledge search strategies evolve, and decisions are reached in organizations consisting of bounded rational individuals and groups with conflicting goals. An important component of behavioral theory is its prediction of when particular units in the organization will attend to particular goals and the direction of their search, given that all of the organizations objectives cannot receive focused attention at the same time. Building on prior work (March & Simon, 1958; Simon, 1947), they proposed that decision makers’ awareness of below goal performance relative to aspirations triggers actions which focus organizations’ attention on tasks important to goal performance improvement. This attention focus directs

3

Cyert and March (1963: 80) differentiated between mating theory and prospect theory which assumes that the organization is searching for alternative that are passive elements distributed in some fashion in the environment. A mating theory on the other hand asserts that not only is the organization searching for alternatives but alternatives are also looking for organizations.

5

Innovative Output and R&D Alliances problemistic search processes and adaptations of search routines used to find solutions to problems (Mahoney, 2005; Simon, 1947). They posited that problemistic search proceeds on the basis of a simple model of causality unless driven to a more complex one (Baum & Dahlin,

2007). Cyert and March (1963) maintained that initially problemistic search will focus on the immediate neighborhood of the problem and solution alternatives that are already known, reflecting the notion that the cause of the problem will be near its negative effect. When the search for the cause of the problem is not successful, they assumed that organizations will make adjustments and use increasingly complex (distant) search mechanisms to determine the cause of the problem and potential solutions. On the other hand, they proposed that firms without recognized problems will continue their “regular, planned search [which] is relatively unimportant in inducing changes in existing solutions that are viewed as adequate” (page 121).

They said that when an organization discovers a solution to a problem by searching in a particular way, it will be more likely to search in that same way to address future problems of the same type using routine behavior providing organizational regularity (Argote & Greve, 2007).

Cyert and March (1963) also commented specifically on the application of behavioral theory propositions to the allocation of resources to research and development and innovation.

In discussing subunit resource allocations, they stated that decisions on allocations to regular subunits would be quite sensitive to past experience, the experience of similar subunits, and the relevance of the subunit to other parts of the organization, and that expenditures for research and development would be based on simple rules that change slowly on the basis of experience but that would be voided in the short-run by the pressure of failure (Cyert & March, 1963: 274).

Furthermore, they predicted that firms that have been relatively unsuccessful are more likely to innovate than those that have been relatively successful, if everything else is equal.

6


In support of these contentions, research has shown that thresholds of performance aspirations differ systematically across firms, and that top managers influence the firms’ willingness to accept lower levels of performance and information search practices (Cooper,

Folta, & Woo, 1995; Gimeno, et al., 1997). Defining attention as noticing, encoding, interpreting, and focusing of time and effort by organizational decision-makers on issues and answers (Ocasio, 1997), research has found that managerial attention patterns related to noticing and constructing meaning have been found to influence firms’ strategic choices (Cho &

Hambrick, 2006; Levy, 2005), which in turn determine what capabilities are developed and what investments are made (Gavetti, 2005). Research and development expenditures have been of particular interest to researchers involved in this stream of research, because innovative outcomes are highly consequential and thought to be dependent of top manager sponsorship, although investments are typically done in subunits (Gavetti, et al., 2012). Furthermore, this research posits that top managers seeking to improve organizational performance are likely to search for ways to improve product development and increase R&D investments in an effort to increase innovation launches (e.g., Yadav, Prabhu, & Chandy, 2007).

Researchers have also considered how below aspiration financial performance influences

R&D search intensity and innovative output. For example, Greve (2003) predicted the intensity of R&D efforts, based on financial performance below aspiration, and the result of these investments on innovation launches in the Japanese shipbuilding industry. He posited that low financial performance relative to aspiration increases managerial search for solutions and tolerance for risk related to increased R&D and new ways of doing R&D, when decision makers believe that upgrading their technology or product portfolio can solve the performance problem

(Bolton, 1993; Hundley, Jacobson & Park, 1996) He suggested that increased R&D

7

Innovative Output and R&D Alliances expenditures would be channeled to both new R&D projects and increased support of existing projects, especially those that are near completion. Greve found support for his hypothesis regarding R&D expenditures when he estimated the coefficient with only the below performance variable in the model, but he found a positive but insignificant effect for below aspiration level on innovation launches. His findings caused him to call for future research that examines the behavioral pattern of launching innovations in response to low performance.

Chen and Miller (2007) and Chen (2008) extended Greve’s (2003) discussion of institutionalized search as represented in ongoing R&D expenditures to include conformity to industry trends in R&D expenditures, by examining R&D investments across a wide range of manufacturing industries. Chen and Miller (2007) found that the further past performance falls below aspirations the higher the firm’s spending on R&D; however, they did not find a significant relationship between the distance above firm aspirations and R&D search intensity.

They maintained that institutional determinants of search within the tradition of Cyert and

March’s (1963) behavioral theory of the firm have received little empirical research attention, and argued for the value of research which considers how the focus of managerial attention can lead to variations in R&D intensity. Consistent with behavioral theory, Chen (2008) found the predicted negative relationship between the distance below firm aspiration performance and

R&D intensity, and a positive relationship between the distance above firm aspiration performance and R&D intensity, when controlling for the positive effects of slack.

Given that R&D intensity is affected by financial performance below and above aspirations, a next step would be a consideration of how innovative output below aspirations affect investments in R&D alliances, and how R&D alliances mediate the relationship between past below and above innovative output and future innovative output.

8


R&D Alliances and Innovative Output

R&D alliances, defined as contractual agreements among two or more organizations which include research and/or development activities (Hagedoorn, 1993), are considered important sources of new knowledge necessary to innovative output (Powell, et al., 1996; Teece,

1989). In fact a great deal of alliance research has been conducted to substantiate the proposed positive impact of R&D alliance intensity on innovative output. For, example, research has shown that innovative output of startup biotechnology firms is enhanced by both the number of alliances they had at founding and the number of their current cooperative relationships (Baum,

Calabrese, & Silverman, 2000; Shan, et al., 1994). Baum and his colleagues (2000) argued that biotechnology startups configure their alliances to provide access to diverse information and capabilities with minimum costs of redundancy, conflict, and complexity and judiciously ally with potential rivals that present opportunities for learning and less risk for intra-alliance rivalry and found that these performance effects were most clearly and strongly reflected in patenting and R&D spending. Furthermore, pharmaceutical-biotechnology R&D products developed in an alliance have been found to have a higher probability of success, at least in the more complex phase 2 and phase 3 trials (Danzon, et al., 2005).

More generally, alliance research has shown that a firm’s portfolio of R&D alliances can have a profound positive influence on its innovative and overall performance (Rosenkopf &

Nerkar, 2001; Sampson, 2005), the alliance capabilities it develops (Kale, Dyer & Singh, 2002), and the knowledge it is able to access (Cohen & Levinthal, 1990; Grant & Baden-Fuller, 2004;

Powell, et al., 1996). Research has shown that because the knowledge base of a high technology industries are often complex and expanding and the sources are widely disbursed, firms may need to form R&D alliances in order to access the knowledge needed to develop new innovations

9


(Powell et al., 1996). Alliances provide firms with the opportunity to both generate new knowledge through interaction with external partners and apply knowledge accessed from partners efficiently in the production of complex goods (Grant & Baden-Fuller, 2004; March,

1991). Collaborative relationships appear to help businesses develop routines needed to commercialize complex goods, suggesting that firms develop managerial skills and organizational routines to both work closely with current partners and at the same time identify possible new partners (Mitchell, & Singh, 1996; Teece, 1992). As firms form more alliances they develop routines that allow them to identify, access, and assimilate new knowledge from external partners and often form a dedicated alliance function to assist them in managing their portfolio of alliances (Kale, et al., 2002; Lewin, Massini, & Peeters, 2011).

Hypothesis Development

Applying behavioral theory as briefly reviewed, we maintain that when the firm’s top managers become aware of the firms’ below aspiration innovative output they are motivated to focus the firms’ attention on locating solutions to improve performance through two primary mechanisms: problemistic search and adaptation of established search routines (Cyert & March,

1963; Lant, Milliken, & Batra, 1992; Ocasio, 1997). Given that below aspiration financial performance has been found to result in increased R&D intensity (Chen & Miller, 2007), we suggest that below aspiration innovative output is even more likely to focus firms’ attention on problemistic search, a search for problems constraining innovation and sources of solutions, because of the clear link between innovation related problems and innovative output. Although

R&D alliances are not necessarily established by top managers, we suggest that adaptations to routine search behavior will result from top managements’ endorsement of subunit actions and monetary allocations, creating opportunities for R&D managers to seek to solve product

10

Innovative Output and R&D Alliances development problems with more distant novel solutions by forming R&D alliances (Gavetti, et al., 2012). Furthermore, we posit that the firm-specific and market-specific uncertainties revealed during problemistic search will predict the adaptations firms’ make to their established search routines associated with R&D alliance formation (Beckman et al., 2004). We propose that firms with below aspiration innovative output will search for and be successful negotiating agreements for R&D alliances that each party considers beneficial, consistent with the mating theory of search proposed by Cyert and March (1963: 80).

More specifically, we propose that established search routines will be adapted to include a greater tolerance for risk, increased R&D investments, and new ways of doing R&D (e.g.

Bolton, 1993; Chen & Miller, 2007; Hundley, et al., 1996), and that these adaptations will result in the formation of a greater number of R&D alliances as firms increase their external search for riskier and more unique R&D opportunities with external partners (Baum, et al., 2005). We posit that firms manage the uncertainties they face by modifying their R&D alliance portfolio, resulting in more new relationships with partners in an effort to broaden the scope and diversity of unique information when they perceive firm-specific uncertainty, and additional relationships with existing partners to establish stronger ties when they perceive market-specific uncertainty

(Beckman et al., 2004). Technical uncertainty, uncertainty about the probability of technical success and the related costs (McGrath, 1997), is arguably firm specific to the extent that other firms have different probabilities of success because they have different capabilities. This is consistent with Teece (1989) and Powell et al. (1996) who argue that firms uncertain of how to develop new knowledge and innovate are motivated to establish new relationships. While firmspecific uncertainty is largely internal and unique, market-specific uncertainty is external and shared across a set of firms. Market-specific uncertainty, uncertainty related to competitive

11

Innovative Output and R&D Alliances dynamics, consumer demand, industry level technology trajectories, and input costs, makes partner quality hard to assess (Burgers et al., 1993; McGrath, 1997). Research has shown that under conditions of high market-specific uncertainty establishing additional relationships with familiar partners is the best strategy (Podolny, 1994). Thus, any increase in decision makers perceptions of firm-specific or market-specific uncertainty as the result of problemistic search, will result in adaptations to routine search behavior, increasing the number of R&D alliances as firms seek partners, either new or old, to help them address these primary sources of uncertainty.

While some have proposed that prior good performance is in fact the probable cause of alliance formation (Chan, et al., 1997; Lee, 2010), consistent with a mating theory of search

Beckman et al. (2004) proposed four reasons why new and past partners might choose to establish new R&D alliances with a firm that has in the past year introduced fewer new products than they had historically. First, firms can benefit from partnering with those that have different experiences, even negative experiences, as this diversity of firm-specific experience helps them make better choices (Beckman & Haunschild, 2002). Second, by allying with a firm experiencing firm-specific technological uncertainty a partner is likely to establish an option for future value while obtaining terms that are favorable in the current environment (Kogut, 1991).

Third, firms in the same industry are facing the same market uncertainty and have similar motivations to reinforce their existing relationship by establishing new R&D alliances, increasing partner reliability and trust. Finally, partners outside the focal firm’s industry can learn from the firm’s industry experience managing market uncertainty, as learning what not to do or how firms in other industries address weaknesses may save the partner time and money.

Thus, we propose that as innovative output relative to firms’ aspiration levels decrease, the number of R&D alliances subsequently established will increase. We posit that they will

12

Innovative Output and R&D Alliances have the opportunity to establish a greater number of R&D alliances than they had previously, because of the potential for partners to learn from their experiences.

Hypothesis 1: There is a negative relationship between past innovative output below aspiration levels and the number of R&D alliances subsequently established.

When firms have above aspiration innovative output, there is no reason why top managers’ would be motivated to redirect the firms’ attention toward problemistic search or adapt the firms’ regular, established search routines related to new product development, so firms are unlikely to make changes in existing routines that are viewed as adequate (Cyert &

March 1963; March & Simon, 1958; Simon, 1947). Given their success in discovering solutions to innovation related problems by searching in particular ways, they are likely to search in those same ways to address future problems of the same type using routine behavior providing organizational regularity (Argote & Greve, 2007). Furthermore, success may lead to risk-aversion as decision makers choose low-risk organizational changes or no changes at all.

These arguments are consistent with research that has found a declining rate for R&D intensity and innovation launches as financial performance increases above aspirations in the Japanese shipbuilding industry (Greve, 2003). However, two studies of US manufacturing industries found somewhat different results. Chen and Miller (2007) found that R&D search decreases as financial performance rises above the industry average, but did not find a consistent significant relationship between distance above firm aspirations based on firm performance and R&D intensity. Chen (2008), on the other hand, found evidence to suggest that firms whose financial performance rises above firm aspiration increase search and firms whose performance exceeds industry aspiration decrease search. Still, these studies lend support to our contention that firms that have above aspiration innovative output are unlikely to invest in a larger number of R&D alliances than they have historically.

13


Some alliance researchers have posited a reciprocal relationship between the number of cooperative relationships established and performance, but research has not substantiated a clear link between past innovative output and the number of R&D alliances firms subsequently form.

There is evidence that operating performance affects subsequent partnering (Chan, et al. 1997) and that inventors with superior track records are more apt to form collaborations (Lee, 2010).

However, while Shan et al. (1994) found a positive relationship between the number of cooperative relationships biotechnology startups had and the number patents issued they did not find support for the alternative explanation that established firms form relationships with startups that have demonstrated their innovative capabilities. While startups who have received interorganizational endorsements in the form of venture capital have been found to have more cooperative partners and subsequently better patenting performance (Stuart, Hoang, & Hybels,

1999), little evidence has been forthcoming which shows a clear link between firms innovative output and subsequent alliance formation in ongoing enterprises.

We propose the reason previous research has been inconclusive is that the link between past innovative output and subsequent R&D alliance formation is driven by whether the output is below or above aspirations. If it is below aspirations, the process described above leads the firm to establish more R&D alliances in an effort to access the prerequisite knowledge needed to address uncertainty and be more successful in producing innovative outputs. Thus, we would expect that R&D alliances established because of poor innovative output will mediate the relationship between past below aspiration innovate output and subsequent innovative output.

However, if innovative output is above aspirations, decision makers are unlikely to activate problemistic search or adapt the firms’ established search routines. Under this condition we expect the firms’ managers to continue to invest in established search routines to access the

14

Innovative Output and R&D Alliances technical knowledge required to produce innovations in subsequent periods, if slack is limited.

These arguments suggest the following hypothesized relationships.

Hypothesis 2a: The number of R&D alliances subsequently established mediates the relationship between firms’ past innovative output below their aspiration level and their future innovative output.

Hypothesis 2b: The number of R&D alliances subsequently established does not mediate the relationship between firms’ past innovative output above their aspiration level and their future innovative output.

METHODS

Research Setting, Sample, and Data Sources

The context of our study is the U.S. biopharmaceutical industry which includes firms with SICs 2833 through 2836. We selected this context to test our hypotheses developed based on the behavioral theory of the firm for four main reasons. First, the biopharmaceutical industry is considered a high discretion industry (Finkelstein & Hambrick, 1996), a factor that affects both managerial attention patterns (Abrahamson & Hambrick, 1997) and the relationship between attention and strategic choice (Finkelstein & Hambrick, 1990). Second, in response to high cost and uncertainty in the outcome of research and development in biopharmaceutical product development, biopharmaceutical firms have formed extensive R&D alliances (Lane &

Probert, 2007; Roijakkers & Hagedoorn, 2006). Third, the biopharmaceutical industry has been highly regulated since the 1930s and new products must be approved by the U.S. Food and Drug

Administration (FDA) to be marketed and sold. This allowed us to obtain reliable new drug approval data over a long period of time in order to construct variables with long lag times, required to test our theoretical predictions. Finally, the biopharmaceutical firms patent extensively to protect novel ideas and products at various stages of R&D enabling us to construct

15

Innovative Output and R&D Alliances several variables to control for the effects of firms’ knowledge diversity and knowledge search capabilities on innovative output.

We tested hypotheses in this study with panel data of 667 firm year observations for 131 biopharmaceutical firms. While we designed the study as pooled time series analysis spanning the years from 1997 to 2007, we collected data for variables from 1990 to 2007 to create time lags and windows required to calculate measures for our variables. We began with a list of 485 active public biopharmaceutical firms as well as their consolidated financial data obtained from the Compustat database as of 2007. Due to missing data and use of extensive lags to construct variables we were left with 131 firms for which we had 667 firm year observations. The data used for all the variables in this study were aggregated at the corporate level, i.e., all the subsidiary data are combined. This was done through a process of subsidiary identification using the corporate affiliations database by the LexisNexis Business Data Group. Subsidiary information was also validated from company websites.

Data were compiled from multiple sources. First, in assembling the data on number of new drug approvals (NDAs), we followed an approach used by Cardinal (2001) and Yeoh and

Roth (1999). We use the U.S. FDA classification of a biopharmaceutical product as being a new drug approval if it constitutes a novel chemical composition.

We obtained the number of NDAs in 8 chemical categories

4

identified by the FDA. The U.S. FDA approved 1,411 new drugs between 1990 and 2007, of these approvals 1,185 (84 %) belong to our sample firms

( http://www.fda.gov

). Second, we collected annual R&D alliance data for firms and noted the number of R&D alliances in a given year, 0 when appropriate. We collected data on R&D alliances for our focal firms from the Bioscan alliance database, which has a qualitative section

4 Chemical type categories: 1) New Molecular Entity (NME) 2) New derivative 3) New formulation 4) New combination 5)

Marketed drug product but a new manufacturer 6) Marketed drug product, but a new use 7) Drug already legally marketed without an approved NDA 8) OTC switch. We do not distinguish newness according to these categories in NDA counts.

16

Innovative Output and R&D Alliances describing alliances in detail. We obtained all the alliances involving activities related to research, discovery, and development of biopharmaceutical products. In order to have the greatest coverage of new R&D alliance activity for the firm/years in our sample, we also complemented and validated our coverage of R&D alliance data with the Recap alliance database. In total, for our sample firms, Bioscan and Recap had 2,082 R&D alliances for the years 1990 –2007. Third, patent data came from the U.S. Patent and Trademark Office (USPTO) database. According to the concordance between the U.S. Patent Classification (USPC) System and the Standard Industrial Code (SIC) System

5

, 64 three digit patent classes correspond most closely to biopharmaceutical inventions. We gathered all patents granted within these classes (a total of 54,652) to each of the sample firms. Finally, data to control for firm specific factors such as R&D intensity, number of employees, assets, and liabilities are obtained from Compustat.

Variables

Number of R&D alliances.

We calculated the number of active R&D alliances in year t by summing the number of R&D alliances that a firm formed over five years ( t-4 to t ). Unless alliance termination dates were available, we kept all the alliances active for 5 years beginning from their announcement date for the data collection period (1990-2007). We used a five year window to calculate R&D alliances, because it takes several years for new capabilities to be accessed from R&D alliances and incorporated into new products. Furthermore, in our dataset the average life of alliances on which we had termination dates was 4.2 years. Similarly, Zollo,

Reuer, and Singh (2002) found that the average life of biotech alliances were 54 months or 4.5 years. This 5 year window is a standard assumption in alliance research (e.g., Lavie, 2007).

Innovative output. We measured a firm’s innovative output as the average number of new drug approvals (NDAs) it received over a two year period. The formula is:

5 The concordance link is available on the website: http://www.uspto.gov/web/offices/ac/ido/oeip/taf/brochure.htm#Patent_Data.

17


. We used a two year window to obtain an average innovative output measure, because firms may not have a new drug approved every year due to long and uncertain drug development process in biopharmaceutical industry. Taking an annual count might have resulted in erroneously overstating firms as not innovative if they did not have an NDA approval for a single year. We also used 3 and 5 year windows and our results were similar, however, we decided to use the 2 year window because it allowed us to use the maximum number of data points. The innovative NDA counts exclude generic drug approvals, as they are not considered novel and do not proceed through the same development stages. The use of NDAs as firms’ innovative output measure is consistent with prior studies that examined innovation outcomes (Cardinal, 2001; Yeoh & Roth, 1999). Our innovative output measure, however, differs from other studies that have studied innovation but used patent (citation) data to measure innovations (e.g. Shan, et al., 1994). In this study we focus on innovative output, hence, we distinguish between invention and innovation because they entail different activities. Invention is the creation of new ideas while innovation refers to the outcome of invention in the form of commercializable products (Ahuja and Lampert, 2001). The conversion process from invention to innovation requires an extensive pool of technical knowledge in order to move from idea creation to development, design, production and sales

(Artz, Norman, Hatfield, and Cardinal, 2010). NDAs must be ready for the market to be approved by the FDA (i.e., manufacturing, packaging, etc.), thus, NDAs are a better proxy than patents to measure innovative output, or commercializable products, (Trajtenberg, 1990).

Innovative output below (above) aspiration level.

We calculated these variables in two steps. First, we created a variable called aspiration level for innovative output . Consistent with prior research which has suggested that firms adjust their aspiration level based on their

18

Innovative Output and R&D Alliances performance in previous years (Chen, 2008; Chen and Miller, 2007; Greve 2003, Levinthal and

March 1981), we used the past new drug approval data as the indicator of past innovative output to calculate a firm’s aspiration level. More specifically, for each firm year we calculated an aspiration level innovative output value using the actual innovative output values as calculated in the dependent variable section above. The formula is as follows: where where A is the aspiration level innovative output, P is the actual innovative output, is different weights assigned to actual innovative output in past years, i is firm and t is the time period. We elaborate on the time lags used in the lag structure of variables in the section below. Since there is not enough knowledge about how firms weight their past innovative performance when determining their innovative output aspiration levels, in the above formula we chose to weight performance at the earlier year by 0.4 and at the later year by 0.6. Thus, similar to Chen (2008) the most recent innovative output data used to calculate aspiration is weighted higher than the innovative output in the earliest year. We checked the robustness of our weighting by using other weights in our calculations (0.3-0.7 and 0.2-0.8).

Second, we computed distance or the difference between actual innovative output and the aspiration level innovative output for each firm year. The formula is:

. Our hypotheses suggest that firms R&D alliances and current innovative output are influenced by whether firms perform below or above their innovative output aspiration level. Thus, following Chen (2008), we created an indicator variable to determine whether a firm performed below or above its aspired innovative output level, I

1

, which equals to 1 if firm i

’s innovative output is below aspirations. Thus,

( indicates firms whose innovative output is below aspiration level and

19


( indicates firms whose innovative output meets or exceeds their aspiration level for innovative output.

Mediator and control variables . The number of R&D alliances is a mediator variable in hypotheses 2a and 2b. Since this variable is the same variable that was used as a dependent variable in hypotheses 1 and described above, we do not repeat its measurement here. In addition to the independent and mediator variables, several additional controls were included in the analysis. First, knowledge depth and scope were included to control for firms’ efforts to reuse existing knowledge and to explore new knowledge to improve innovative performance

(Katila & Ahuja, 2002). We adopt the depth and scope measures developed by Katila and Ahuja

(2002) using patent citation data. We allowed a five year window to calculate knowledge measures because prior research has indicated that knowledge loses value in approximately five years (Argote, 1999). Thus, knowledge search capability variables calculated at time (t) indicate the firm’s knowledge search from year t-1 to t-5, inclusive. The knowledge depth variable illustrates accumulation of previously used knowledge items by the firm and is measured as the average number of times a firm repeatedly used the patent citations on patents granted in the focal year, during the prior five years. The formula is:

where the count of repeated citations is equal to the sum of annual citations that were repeatedly cited by firm i in the previous 5 years.

Knowledge scope was measured as the ratio of previously unused (new) patent citations, in a firm’s annual list of citations. We identified new citations as those citations that were in the current year but were not found in the past five years of the citation list.

, where total new citations it

= count of new cited patents by

20

Innovative Output and R&D Alliances firm i in year t, which were not cited in the past five years (t-1 to t-5); total all citations it

= count of all cited patents by firm i in year t.

Second, we account for firm differences that may influence R&D alliances and innovative output by including controls for: R&D intensity , measured as the ratio of R&D expenditures to total assets; firm size , measured as log of number of employees; return on equity

(ROE) measured as equity divided by revenue; and firm type , dummy variable, to indicate whether a firm in our sample is a pharmaceutical (=1) or a biotechnology company (=0). Third, we control for firms’ slack resources because prior research found that firms’ slack resources influences their R&D search intensity (Chen & Miller, 2007) which may also influence their

R&D alliance and innovative activities. Consistent with prior studies we employ three measures of slack (e.g. Chen, 2008; Greve, 2003). Available slack is measured as the ratio of current assets to current liabilities and captures the liquid resources that are uncommitted to liabilities.

R ecoverable slack represents absorption of slack related to capitalization and is measured by the working capital to sales ratio. Potential slack is measured by debt to equity ratio to proxy for firms’ ability to further borrowing. Finally, to control for the effects of time , we create dummy variables for years that constitute our analysis period (1997–2007), year 2007 is the omitted year.

Lag Structure of Variables and Empirical approach

We lagged the independent variables in each equation discussed in the section below, to better establish the hypothesized causal relationships in the biopharmaceutical industry. The innovative output below (above) aspiration level is lagged by 8 years with respect to the current innovative output because new drug development timelines average 6 to 10 years from discovery to launch for pharmaceuticals (Chandy et al., 2006). Thus, we calculate aspiration levels for innovative output for the year t-8 to allow the necessary time lags to observe the influence of

21

Innovative Output and R&D Alliances innovative output below (above) aspiration in t-8 on the current innovative output in time t . The number of R&D alliances is lagged by 4 years with respect to the current innovative output; this

4 year lag is reasonable because it allows us to account for the effect of alliances that are formed during the time period between ( t-8 ) and ( t ) when past innovative output and current innovative outputs are measured, respectively. Consistent with past innovative output below (above) aspiration lag, all other control and instrumental variables are lagged by 8 years with respect to the dependent variable, current innovative output. Our results were also robust to a 7 year alternative lag structure, discussed in the robustness checks section below.

In our theoretical model we hypothesize that: i) firms’ innovative output below aspiration innovative output influences the number of their subsequent R&D alliances (hypothesis 1) and ii)

R&D alliances mediate the relationship between innovative output below (above) aspiration and current innovative output (hypotheses 2a and 2b). We specify the following two equations to test the above predictions.

(Equation 1)

In equations 1 and 2,

(Equation 2) refers to innovative output below innovative aspiration level and β

2

( refers to innovative output above aspiration level. A standard way to proceed with hypothesis testing would be to estimate these equations independently. However, the estimation of these equations independently is based on the strict

22

Innovative Output and R&D Alliances assumption that the error terms in Equations (1) and (2) are uncorrelated. Nonetheless, unobserved omitted variables and measurement error (both of which are likely to exist given the nature of data used in the management research) can violate this assumption and lead to correlation of error terms (endogeneity) across Equations (1) and (2), resulting in biased and inconsistent parameter estimates due to endogeneity (Shaver, 2005). Shaver (2005) suggests that the problem of endogeneity can be overcome through the use of instrumental variable estimation.

Consistent with Shaver’s (2005) suggestion we account for endogeneity in our models by employing instrumental variable regression and estimated equations (1) and (2) simultaneously by using three staged least squares regression (3SLS).

Identifying (instrumental) variables

In conducting our hypotheses tests by estimating equations (1) and (2) simultaneously, we had to find instrumental variables that would enable identification of the two equations.

Identification is required to obtain unbiased parameter estimates of equations estimated simultaneously. To ensure identification, it is necessary but not sufficient that in a particular equation the number of instrumental variables excluded is greater than or equal to the number of endogenous variables. In our equations we have two endogenous variables (number of R&D alliances and innovative output). Thus, it is necessary that equation (1) has at least one instrumental variable not appearing in equation (2) and vice versa. To address the identification issue, we used partner experience and partner patent stock to identify equation (1) and firm knowledge diversity and backward citations to identify equation (2). The strength of the instrumental variables was empirically confirmed based on their significance estimating the endogenous variables. The effects of both partner experience and partner patent stock on the number of R&D alliances are positive and significant (Table 2; Model1:1, panel A). Regarding

23

Innovative Output and R&D Alliances equation (2) we found that firm knowledge diversity and backward patent citations significantly influences innovative output (Table 2, Model 2:1 panel B).

The inclusion of the first set of instrumental variables in equation (1) is justified based on theoretical grounds. Firms that have (partner) experience in managing partnerships are more likely to form R&D alliances (Powell et al, 1996). Prior partner experience may also increase the number of R&D alliances because familiarity enhances collaboration (Reuer, Zollo, and Singh,

2002). We define partner experience , similar to Reuer, et al., (2002), as having alliances with the same partner and calculate as: Partner experience = number of current partners with partnerships in last 5 years / total number of partners in current year. Regarding the second instrumental variable, partner patent stock

, number of focal firm partners’ patents, literature suggests that firms that have high levels of patenting are considered to be on technological frontier and also indicate the innovative quality of firms (Arora & Gambardella, 1990). Thus, above average patenting increases the likelihood of focal firms being selected as a R&D alliance partners, thereby increasing the number of R&D alliances. To measure partner patent stock we count the number of patents that are granted (in 64 patent classes) to each partner of a focal firm within the past five years and then sum these to assign to the focal firm.

The second set of instrumental variables, used to identify equation (2), is also theoretically justified . Firm knowledge diversity is positively associated with innovative output

(Sampson, 2007). We measure firm knowledge diversity as the distribution of patents across all

64 biopharmaceutical classes, using the inverse of the nonbiased Herfindahl Index (HHI) proposed by Hall (2002).

6

The computational formula is:

, where and i= focal firm;

6

We adopt Equation 6 on page 3 of paper: A Note on the Bias in Herfindahl-type Measures Based on Count Data by Hall (2002), available at: http://elsa.berkeley.edu/~bhhall/papers/BHH05_hhibias.pdf

24

Innovative Output and R&D Alliances k=patent classes, N ik

= number of patents in class k by the focal firm i; and N i

= total number of patents in all classes by the focal firm i. This estimator adjusts for bias caused by the size of firms’ patent portfolios, specifically by increasing the value of the Herfindahl index for firms with fewer patents (Hall, 2002). The index value can range between 0 and 1, a smaller value indicating that a firm has lower levels of firm knowledge diversity. The second instrumental variable in equation 2, backward patent citations , is associated with innovative output (Chandy et al, 2006). Backward patent citations include the citations that the focal firms’ patents make to previous patents. We include the number of citations made (backward citations) by the focal firm as an instrumental variable in equation 2 because these reflect localness of search which influences innovative output by increasing the likelihood of making incremental improvements in existing new products (Fleming & Sorenson, 2001). We measure this variable by summing the number of citations made by each patent of a focal firm until the observation year.

RESULTS

Tables 1a and 1b presents the descriptive statistics for the dependent, independent, and instrumental variables in equations (1) and (2). Although correlations are within the acceptable range, we also conducted a Variance Inflation Factor (VIF) analysis to see whether multicollinearity is a concern in our models and we find that it is not, as the maximum VIF across our models is 1.59 (Table2), well below the threshold of 10 (Kennedy, 1992).

[Place Table 1 and 2 about here]

Table 2 shows estimates of our R&D alliance and innovative output equations in Panel A and Panel B, respectively. In the R&D alliance equation (Panel A) Model 1:1 represents the base model which includes the effect of control and instrumental variables on the number of R&D alliances. Among control variables we find that firm R&D intensity and size positively and

25

Innovative Output and R&D Alliances significantly influences the number of R&D alliances they subsequently form. The instrumental variables, partner experience and partner patent stock are also significant in the control model.

Models 1:2 and 1:3 include the influence of innovative output below aspiration and above aspiration. In the innovative output equation (Panel B) Model 2:1 represents the base model with the effect of control and instrumental variables on innovative output. The results of Model 2:1 shows that R&D intensity and firm size is positively and significantly associated with firms’ innovative output. Pharmaceutical firms have higher innovative output than biotechnology firms, indicated by the positive and significant sign of firm type variable. The results of slack variables show that firms’ available slack has a negative but potential slack has a positive effect on innovative output. As for instrumental variables, we find that firms’ knowledge diversity and the number of backward citations have a positive and significant effect on innovative output.

We rely on models 1:3, 2:2, and 2:3 to test our hypotheses. In hypothesis 1 we propose that the number of R&D alliances that firms form increases as their innovative output relative to aspiration decreases. Model 1:3 shows that the coefficient estimate of innovative output below aspiration is negative and significant (β= -0.942, p<0.05), supporting hypothesis 1. Hypothesis

2a predicts that firms’ R&D alliances mediate the relationship between innovative output below aspiration and current innovative output. We adopt Baron and Kenny’s (1986) widely used method to test for the mediation effects. We supplement this analysis with Sobel’s (1982) test to establish the nature and significance of the mediation effect (MacKinnon et al., 2002). In order for a mediating effect to be present four criteria must be present in the regression analysis (Baron

& Kenny, 1986; MacKinnon, 2008). First, the independent variable must influence the outcome variable. Second, independent variable must influence the mediator. Third, the mediator variable must influence the outcome variable. Fourth, to establish mediation, the effect of

26

Innovative Output and R&D Alliances independent variable on the outcome variable must change when controlling for the mediating variable (the effects of third and fourth steps are estimated in the same equation). In a simulation study MacKinnon et al. (2002) found that the most important criteria are the second and third criteria and maintained that mediation is established if the second and third criteria are met. All four of the proposed criteria are satisfied in support of hypothesis 2a. First, in model 2:2 (Panel

B) we find that there is a negative and significant relationship between innovative output below aspiration and current innovative output (β= -0.096, p<0.05). Second, as also presented above in testing hypothesis 1 (Model, 1:3, Panel A), innovative output below aspiration is negatively associated with the number of R&D alliances (β= -0.942, p<0.001). Third, we find that the number of R&D alliances and current innovative output are positively associated in Model 2:3

(β= 0.028, p<0.05). Fourth, the coefficient indicating the effect of innovative output below aspiration on current innovative output changes when we control for the mediating effect of

R&D alliances in Model 2:3. The coefficient of innovative output below aspiration is no longer significant and there is a %17 change in the coefficient value when we include the mediating effect of the number of R&D alliances in the model (the coefficients of innovative output below aspiration are -0.096 and -0.080, in models 2:2 and 2:3, respectively in Panel B). These results indicate that the number of R&D alliances mediates the relationship between innovative output below aspiration and current innovative output.

To further investigate the nature of this mediation we utilized the Sobel test to examine the type of mediation (i.e. full or partial). According to the Sobel test, there is no significant indirect effect if the Sobel test z-value is not significant; the mediation relationship is partial if the Sobel test z-value is significant and the effect ratio is lower than 0.8; and the mediation relationship is full if the Sobel test z-value is significant and the effect ratio is over 0.8 (Jose,

27


2008). We find that the mediation effect is significant as indicated by the Sobel test (p<0.10), but the effect size is below 0.8 (Table 3). Thus, we find support for our hypothesis 2a and the nature of mediation is partial.

7

This finding is interesting for two reasons. First, it supports prior literature that the number of R&D alliances positively affects innovative output controlling for endogeneity of the number of R&D alliances. Second, if firms’ innovative output is below their aspiration then they take actions in terms of increasing their R&D alliances which enhances the influence of innovative output below aspiration on subsequent innovative output.

In hypothesis 2b we predict that firms’ R&D alliances do not mediate the relationship between innovative output above aspiration and current innovative output. According to the criteria to test for a mediation effect, described above in testing for hypothesis 2a, we find that there is no significant relationship between innovative output above aspiration level and R&D alliances, i.e. step 2 of the mediation analysis was not confirmed. Thus we find that there is no mediation between innovative output above aspiration and current innovative output by firms’

R&D alliances. This finding suggests that if firms are performing at or above their aspirations they expect to do well in the future and they do not change their routines or engage in new strategic actions such as forming more R&D alliances.

[Place Table 3 about here]

Robustness checks

Apart from the results presented in table 2 we conducted several additional analyses to check the robustness of our results. First, before estimating our equations by 3SLS we undertook

Durbin-Wu-Hausman (DWH) test (Davidson and MacKinnon, 1993) for endogeneity in our

7

One may be concerned that the mediation model we have is not a conventional mediation model because the direct effect and indirect effects are of different signs. We have a negative relationship between the independent and dependent variable and a positive relationship between the mediator and the dependent variable. We e-mailed Prof. MacKinnon and Prof. Kenny (experts in the mediation analysis) with this concern. Both stated that we have a mediation model in such instances and that this type of mediation analysis is referred to as an ‘inconsistent mediation’ analysis. Further information can be found at website: http://davidakenny.net/cm/mediate.htm#IM

28

Innovative Output and R&D Alliances equations, which compares the parameters estimated by OLS with the parameters estimated by simultaneous equations model, using instrumental variables. If there is no significant difference in the parameter estimates then the null hypothesis that the regressors are exogenous is accepted and OLS will provide unbiased parameter estimates. In our case the null hypothesis of exogeneity was rejected indicating endogeneity in our equations (p<0.01). Second, while the strength of instruments was confirmed based on their significance in estimating the endogenous variables (all instrumental variables were significant in Models 1:1 and 2:1, Table 2) we conducted additional instrument validity tests. In particular, we conducted the Sargan-Hansen test, a test of overidentifying restrictions. The joint null hypothesis is that the instruments are valid instruments, i.e., uncorrelated with the error term, and that the excluded instruments are correctly excluded from the estimated equation. A rejection casts doubt on the validity of the instruments. The result of Sargan/Hansen test showed that our instruments are valid as we do not reject the null hypothesis (p<0.44).

Third, we considered alternative estimation techniques. We estimated equations (1) and

(2) using two stage least squares (2SLS) and instrumental variable regression with Generalized

Method of Moments (GMM) estimation. Our results remain consistent with the reported results when we use 2SLS and GMM estimators in our regression analysis. Fourth, we analyzed models with a 7 year lag specification for innovative output below (above) aspiration, a 4 year lag for

R&D alliances, and 7 year lag for control and instrumental variables, with respect to the current innovative output; our results remained similar. Finally, we estimated our models with an innovative performance variable calculated in a different way. More specifically, we calculated the average innovative performance of firms based on a five year moving window. Then we included 10 year lag of innovative performance, 5 year lag of R&D alliances, and 10 year lag of

29

Innovative Output and R&D Alliances the control and instrumental variables with respect to the current innovative performance in both our equations (1) and (2). As a result of analysis of this specification we found that there is a negative relationship between past performance and the number of R&D alliances and there is a positive relationship between R&D alliances and current innovative performance. This finding corroborates our findings reported here and suggests that the weaker (stronger) the past innovative performance the higher (lower) the number of R&D alliances and that R&D alliances positively influences current innovative performance after accounting for its endogeneity.

DISCUSSION AND CONCLUSION

This study makes three contributes to the debate on when performance relative to aspirations shifts firms’ attention to problemistic search and adaptations of established search routines. First, we investigate the effect of a subunit goal, innovative output, on problemistic search and adaptations of firms search routines as reflected in the number of R&D alliances subsequently formed rather than considering the effects of financial performance to test the basic propositions of behavioral theory of the firm. We believe an investigation of the effects of innovative output below levels is important because innovative output: 1) is an important source of competitive advantage for firms, 2) is a subunit goal that is necessary to the success of other parts of the organization, and 3) is based on search routines that change slowly but are voided by short-term pressures of failure (Cyert & March, 1963). Second, we evaluate the reciprocal relationship between innovative output and R&D alliances. While the alliance literature has predicted a strong positive relationship between alliance formation and subsequent performance

(e.g., Shan, et al., 1994; Teece, 1992), some have argued that it is the positive performance that attracts alliance partners in the first place (e.g., Chan, et al., 1997; Lee, 2010). Although we recognize that firms must have resources to attract resources (Eisenhardt & Schoonhoven, 1996;

Danneels, 2010), we posit that high technology industries are often complex and expanding and

30

Innovative Output and R&D Alliances the sources of knowledge are widely disbursed. Because of this dispersion firms may need to form R&D alliances in order to access the knowledge needed to develop new innovations

(Powell et al., 1996). Our data suggests that increases in R&D alliances in the biopharmaceutical industry are not just the result of positive past innovative output. Rather firms that have below aspiration innovative output are more likely to establish a relatively greater number of R&D alliances than firms that have above average aspiration innovative output. Finally, we recognize explicitly the mating theory of search (Cyert & March, 1963). We argue that not only are firms with below aspiration innovative output searching for technical partners, but potential partners are simultaneously looking to leverage their technical resources, creating real options for the future, and learning how to cope with uncertainty through R&D alliances (Beckman &

Haunschild, 2002; Beckman, Haunschild, & Phillips, 2004; Kogut, 1991).

We also complement prior research that has looked at the effects of below and above aspiration financial performance on R&D investment decisions. The results of our study are consistent with studies of the shipbuilding industry and a broad spectrum of manufacturing industries that have found a link between the distance below aspiration performance and increases in R&D intensity and innovation launches (Chen, 2008; Chen & Miller, 2007: Greve,

2003). However, our review of this stream of research suggests that the effect of firm performance above aspiration on R&D investment is less conclusive than the broader literature would suggest. Greve (2003) found a decrease in the rate of launching innovations when firm performance increases above the aspiration level, Chen and Miller (2007) reported no significant relationship, and Chen (2008) found that R&D intensity increased for firms whose performance is above firm aspirations. Our results are most consistent with Chen and Miller (2007), but

31

Innovative Output and R&D Alliances future research should seek to clarify why the effects of above aspiration firm performance on

R&D investments to date have been inconsistent.

The study also has some limitations that provide direction for future research. First, this is a single industry study, which could limit the generalizability of our results. We acknowledge that the biopharmaceutical industry is unique in some respects, such as the constraints and clear oversight of the FDA on new product approvals. However, this fact allowed us to access data on innovations ready for the market rather than inventions that are not yet commercialized.

Furthermore, we believe that the knowledge base in other high technology industries is also often complex and expanding, and the sources are widely disbursed leading firms to form R&D alliances in order to access the knowledge needed to develop new innovations (Mitchell &

Singh, 1996; Powell et al., 1996). We encourage future research to replicate and extend our study of the effect of below and above aspiration subunit goal performance on R&D investments, particularly R&D alliance formation. Second, we examine firms innovative output with respect to their prior performance and do not consider their performance with respect to the industry performance or other kinds of social comparisons. We encourage future research to consider the differential effects of firms past performance and industry performance relative to aspirations, given that firms may have differing reference points and their strategic behavior may change depending on firms’ performance position in relation to these reference points (Audia & Greve,

2006; Chen, 2008; Hu, Blettner, & Bettis, 2011). Third, we limited our discussion of adaptations of search routines as a result of problemistic search initiated by below aspiration innovative output to increases in R&D alliance formation. R&D investments to access external knowledge can include hiring technical personnel, encouraging employees to access knowledge through professional associations and personal networks, or through acquisitions. In this regard, future

32

Innovative Output and R&D Alliances research can examine the extent to which different kinds of investments can be substituted when making investment decisions to address below aspiration innovative outcomes. Finally, we posit that decision makers become aware of firms’ below aspiration innovative output and then redirect firm attention to problemistic search and adaptations to search routines, but we do not measure this cognitive process. Behavioral strategy links cognitive and social psychology with firm behavior (Powell, Lovallo, & Fox, 2011), but like many strategy studies we infer cognitive awareness from firm behavior. Future research should seek to understand managerial cognition and the link between factors that trigger cognitive awareness and how firms’ mange resources such as R&D investments to create value (Danneels, 2010; Sirmon, Hitt, & Ireland, 2007).

In their review of behavioral theory over the past forty years, Argote & Greve (2007) said the weakest part of the empirical work in A Behavioral Theory of the Firm is the quantitative testing of propositions. They suggested future research use event history or panel-data analysis to study causes and effects of organizational change in organizational processes. It is our hope that the theoretical logic we developed to apply behavioral theory tenets to a study of the effects of subunit goal performance relative to aspiration levels on R&D alliance formation, and our panel-data analysis of new product approvals in the biopharmaceutical industry, will encourage future research that extends our understanding of the cognitive and social psychological mechanisms that explain which paths firms do and do not take.

33


REFERENCES

Abrahamson E, Hambrick DC. 1997. Attentional homogeneity in industries: the effect of discretion . Journal of Organizational Behavior 18 : 513-532.

Ahuja G, Lampert C M. 2001. Entrepreneurship in the large corporation: A longitudinal study of how established firms create breakthrough inventions. Strategic Management Journal 22

(6-7): 521–543.

Argote L. 1999. Organizational learning: Creating, retaining, and transferring knowledge.

Kluwer Academic: Boston, MA.

Argote L, Greve, HR. 2007. A behavioral theory of the firm – 40 years and counting: introduction and impact. Organization Science 18 (3): 337-349.

Arora A, Gambardella A, 1990. Complementarity and External Linkages: The Strategies of the

Large Firms in Biotechnology”,

Journal of Industrial Economics 38 (4):361-379.

Artz KW, Norman PM, Hatfield DE, Cardinal LB. 2010. Longitudinal Study of the Impact of

R&D, Patents, and Product Innovation on Firm Performance .

Journal of Product

Innovation Management 27 (5):725–740.

Audia PG, Greve HR. 2006. Less likely to fail: low performance, firm size, and factory expansion in the shipbuilding industry. Management Science 52 (1): 83-94.

Baron RM, Kenny DA. 1986. The Moderator-Mediator Variable Distinction in Social

Psychological Research: Conceptual, Strategic, and Statistical Considerations. Journal of

Personality and Social Psychology 51 (6): 1173-1182

Baum JAC, Calabrese T, Silverman BS. 2000. Don’t go it alone: alliance network composition and startups’ performance in Canadian biotechnology.

Strategic Management Journal

21 (3): 267-294.

Baum JAC, Dahlin KB. 2007. Aspiration performance and railroads’ patterns of learning from train wrecks and crashes. Organization Science 18 (3): 368-385.

Baum JAC, Rowley TJ, Shipilov AV, Chuang YT. 2005. Dancing with strangers: aspiration performance and the search for underwriting syndicate partners. Administrative Science

Quarterly 50 (4): 536-575.

Beckman CM, Haunschild PR. 2002. Network learning: the effects of partners’ heterogeneity of experience on corporate acquisitions. Administrative Science Quarterly 47 (1): 92-124.

Beckman CM, Haunschild PR, Phillips DJ. 2004. Friends or strangers? Firm-specific uncertainty, market-specific uncertainty, and network partner selection. Organization

Science 15 (3): 259-275.

Bolton MK. 1993. Organizational innovation and substandard performance: when is necessity the mother of invention? Organization Science 4 (1): 57-75.

Burgers WP, Hill CWL, Kim WC. 1993. The theory of global strategic alliances: the case of the global auto industry. Strategic Management Journal 14 (6): 419-432.

Cardinal LB. 2001. Technological Innovation in the pharmaceutical industry: The use of organizational control in managing research and development. Organization Science

12 (1):19-36.

34


Chan SH, Kensinger JW, Keown AJ, Martin JD. 1997. Do alliances create value? Journal of

Financial Economics 46 (2): 199-221.

Chandy R, Hopstaken B, Narasimhan O, Prabhu J. 2006. From invention to innovation:

Conversion ability in product development. Journal of Marketing Research 43 (3): 494-

508.

Chen WR. 2008. Determinants of firms’ backward- and forward-looking R&D search behavior.

Organization Science 19 (4): 609-622.

Chen WR, Miller KD. 2007. Situational and institutional determinants of firms’ R&D intensity.

Strategic Management Journal 28 (4): 369-381.

Cho TS, Hambrick DC. 2006. Attention as the mediator between top management team characteristics and strategic change: the case of airline deregulation. Organization

Science 17 (4): 453-469.

Cohen, W., Levinthal, D. 1990. Absorptive Capacity: A new perspective on learning and innovation. Administrative Science Quarterly 35 : 128-152.

Cooper AC, Folta TB, Woo C. 1995. Entrepreneurial information search. Journal of Business

Venturing 10 : 107-120.

Cyert RM, March JG. 1963. A Behavioral Theory of the Firm . Prentice-Hall: Englewood Cliffs,

NJ.

Danneels E. 2010. Trying to become a different type of company: dynamic capability at Smith

Corona. Strategic Management Journal 32 (1): 1-31.

Danzon PM, Nicholson S, Pereira NS. 2005. Productivity in pharmaceutical-biotechnology

R&D: the role of experience and alliances. Journal of Health Economics 24 (2): 317-339.

Davidson, R., MacKinnon, JG.

1993 . Estimation and Inference in Econometrics , Oxford

University Press: New York, NY.

Dougherty D, Hardy C. 1996. Sustained product innovation in large, mature organizations: overcoming innovation-to-organization problems. Academy of Management Journal

39 (5): 1120-1153.

Eisenhardt KM, Schoonhoven CB. 1996. Resource-based view of strategic alliance formation: strategic and social effects in entrepreneurial firms. Organization Science 7 (2): 136-150.

Finkelstein S, Hambrick DC. 1990. Top management team tenure and organizational outcomes: the moderating role of managerial discretion. Administrative Science Quarterly 35 (3):

484-503.

Finkelstein S, Hambrick DC. 1996. Strategic leadership: top executives and their effects on organizations . West Publishing: St. Paul, MN.

Fleming L, Sorenson O. 2001. Technology as a complex adaptive system: Evidence from patent data. Research Policy 30 (7) 1019-1039.

Gavetti, G. 2005. Cognition and hierarchy: Rethinking the foundations of capabilities’ development. Organization Science . 16 (6): 599–617.

Gavetti G, Greve HR, Levinthal DA, Ocasio W. 2012. The behavioral theory of the firm: assessment and prospects. The Academy of Management Annals 6 (1): 1-40.

35


Gimeno J, Folta TB, Cooper AC, Woo CY. 1997. Survival of the fittest? Entrepreneurial human capital and the persistence of underperforming firms. Administrative Science Quarterly

42 : 750-783.

Grant RM, Baden-Fuller C. 2004. A knowledge assessing theory of strategic alliances. Journal of Management Studies 41 (1): 61-84.

Greve HR. 2003a. A behavioral theory of R&D expenditures and innovations: evidence from shipbuilding. Academy of Management Journal 46 (6): 685-702.

Greve HR. 2011. Positional rigidity: low performance and resource acquisition in large and small firms. Strategic Management Journal 32 (1): 103-114.

Hagedoorn J. 1993. Understanding the rationale for strategic technology partnering: interorganizational models of co-operation and sectoral differences. Strategic

Management Journal 14 (5): 371-386.

Hall B. 2002. A Note on the bias in Herfindahl-type measures based on count data. Available at: http://elsa.berkeley.edu/~bhhall/papers/BHH05_hhibias.pdf. Last accessed on April 25,

2010.

Hu S, Blettner D, Bettis RA. 2011. Adaptive aspirations: performance consequences of risk preferences at extremes and alternative reference groups. Strategic Management Journal

32 (13): 1426-1436.

Hundley G, Jacobson CK, Parks SH. 1996. Effects of profitability and liquidity on R&D intensity: Japanese and U.S. companies compared. Academy of Management Journal

39 (6): 1659-1674.

Jose P. 2008. Workshop on statistical mediation and moderation: statistical mediation. SASP

Conference , Victoria University of Wellington, New Zealand, 27 March.

Kale, P., Dyer, J., & Singh, H. 2002. Alliance capability, stock market response and long-term alliance success: The role of the alliance function. Strategic Management Journal, 23 (8):

747–767.

Kale P, Singh H. 2007. Building firm capabilities through learning the role of the alliance learning process in alliance capability and firm-level alliance success. Strategic


Katila R, Ahuja G. 2002. Something old, something new: A longitudinal study of search behavior and new product introduction. Academy of Management Journal 45 (6): 1183-

1194.

Kogut B. 1991. Joint ventures and the option to expand and acquire. Management Science 37 (1):

19-33.

Lane C, Probert J. 2007. The external sourcing of technological knowledge by US pharmaceutical companies: Strategic goals and inter-organizational relationships.

Industry and Innovation 14 (1) 5-25.

Lavie D. 2007. Alliance portfolios and firm performance: A study of value creation and appropriation in the US software industry. Strategic Management Journal 28 (12): 1187-

1212.

36


Lant TK, Milliken FJ, Batra B. 1992. The role of managerial learning and interpretation in strategic persistence and reorientation: an empirical exploration. Strategic Management

Journal 13 (8): 585-608.

Lee J. 2010. Hetergeneity, brokerage, and innovative performance: endogenous formation of collaborative inventor networks. Organization Science 21 (4): 804-822.

Levy O. 2005. The influence of top management team attention patterns on global strategic posture of firms. Journal of Organization Behavior 26 : 797-819.

Levinthal, D., J. G. March. 1981. A model of adaptive organizational search. Journal of

Economic Behavior and Organization 2 307–333.

Lewin AY, Massini S, Peeters C. 2011. Microfoundations of internal and external absorptive capacity routines. Organization Science 22 (1): 81-98.

Leonard-Barton D. 1995. Wellsprings of Knowledge: Building and Sustaining the Sources of

Innovation . Harvard Business School Press: Boston, MA.

MacKinnon DP, Lockwood CM, Hoffmann WH, West SG, Sheets V. 2002. A comparison of methods to test the significance of mediated effect. Psychological Methods 7 : 83-104.

MacKinnon, DP. 2008. Introduction to Statistical mediation Analysis.

Taylor and Francis Group:

New York.

Mahoney JT. 2005. Economic Foundations of Strategy . Sage Publications: New York, NY.

March JG. 1991. Exporation and exploitation in organizational learning. Organization Science

2 (1): 71-87.

March JG, Simon HA. 1958 . Organizations . John Wiley & Sons: New York, NY.

McGrath RG. 1997. A real options logic for initiating technology positioning investments.

Academy of Management Review 22 (4): 974-996.

Miller KD, Leiblein MJ. 1996. Corporate risk-return relations: returns variability versus downside risk. Academy of Management Journal 39 (1): 91-122.

Mitchell W, Singh K. 1996. Survival of businesses using collaborative relationships to commercialize complex goods. Strategic Management Journal 17 (3): 169-195.

Ocasio W. 1997. Towards an attention-based view of the firm. Strategic Management Journal

18 (summer special issue): 187-206.

Powell WW, Koput KW, Smith-Doerr L. 1996. Interorganizational collaboration and the locus of innovation: networks of learning in biotechnology. Administrative Science Quarterly

41 (1): 116-145.

Powell TC, Lovallo D, Fox CR. 2011. Behavioral strategy. Strategic Management Journal

32 (13): 1369-1386.

Podolny J. 1994. Market uncertainty and the social character of economic exchange.

Administrative Science Quarterly 39 (3): 458-483.

Reuer, J, Zollo M, Singh H. 2002. Post-formation dynamics in strategic alliances . Strategic

Management Journal 23 (2) 135-151

Roijakkers N, Hagedoorn J. 2006. Inter-firm R&D partnering in pharmaceutical biotechnology since 1975: trends, patterns, and networks. Research Policy 35 (3) 431-446.

37


Rosenkopf L, Nerkar A. 2001. Beyond local search: boundary-spanning exploration, and impact in the optical disk industry. Strategic Management Journal 22 (4): 287-306.

Sampson RC. 2005. Experience effects and collaborative returns to R&D alliances. Strategic


Sampson R. 2007. R&D alliances and firm performance: The impact of technological diversity and alliance organization on innovation. Academy of Management J ournal 50 (2): 364-

386

Schneider SL. 1992. Framing and conflict: aspiration level contingency, the starts quo, and current theories of risky choice. Journal of Experimental Psychology: Learning, Memory, and Cognition 18 (5): 1040-1057.

Shan W, Walker G, Kogut B. 1994. Interfirm cooperation and startup innovation in the biotechnology industry. Strategic Management Journal 15 (5): 387-394.

Shaver JM. 2005.

Testing for mediating variables in management research: Concerns, implications, and alternative strategies.

Journal of Management 31(3): 330-353.

Simon HA. 1947. Administrative Behavior.

Free Press: New York, NY.

Singh JV. 1986. Performance, slack, and risk taking in organizational decision making. Academy of Management Journal 29 (3): 562-585.

Sirmon DG, Hitt MA, Ireland RD. 2007. Managing firm resources in dynamic environments to create value: looking inside the black box. Academy of Management Review 32 (1): 273-

292.

Sobel, M. E. 1982. Asymptotic intervals for indirect effects in structural equations models. In S.

Leinhart (Ed.), Sociological methodology 1982 (pp.290-312). San Francisco: Jossey-

Bass.

Stuart TE, Hoang H, Hybels RC. 1999. Interorganizational endorsements and the performance of entrepreneurial ventures. Administrative Science Quarterly 44 (2): 315-349.

Teece DJ. 1989. Inter-organizational requirements of the innovation process. Managerial

Decision Economics (spring special issue):35-42.

Teece DJ. 1992. Competition, cooperation, and innovation: organizational arrangements for regimes of rapid technological progress. Journal of Economic Behavior and Organization

18 (1): 1-25.

Trajtenberg M. 1990. A Penny for Your Quotes: Patent Citations and the Value of Innovations.

RAND Journal of Economics 21 (1) 172-187.

Wiseman RM Bromiley P. 1996. Toward a model of risk in declining organizations: an empirical examination of risk, performance and decline. Organization Science 7 (5): 524-543.

Yadav MS, Prabhu JC, Chandy RK. 2007. Managing the future: CEO attention and innovative outcomes. Journal of Marketing 71 (October): 84-101.

Yeoh PL, Roth K. 1999. An empirical analysis of sustained advantage in the U.S. pharmaceutical industry: Impact of firm. Strategic Management Journal 20 (7): 637-653.

Zollo M, Reuer JJ, Singh H. 2002. Interorganizational routines and performance in strategic alliances. Organization Science 13 (6): 701-713.

38

Table 1a. Summary statistics and correlations: Variables in the equation estimating the number of R&D alliances

1 R&D alliances

2 Knowledge depth

3 Knowledge scope

4 R&D intensity

5 Firm size

6 ROE

7 Firm type

8 Available slack

9 Recoverable slack

10 Potential slack

11 Partner experience

12 Partner patent stock

13 Innovative output below aspiration

14 Innovative output above aspiration

Mean

Std. Dev.

Min

Max

Correlations >0.06 are significant at 5%

1

1

0.169

-0.044

-0.005

-0.145

0.350

0.466

-0.269

0.257

2

0.050

0.017

-0.013

-0.097

0.211

0.260

-0.069

0.031

3

0.192 1

0.289 -0.001 1

-0.111 0.057 -0.038

0.096

0.006

-0.031

-0.073

0.179

0.120

-0.137

0.104

4

1

0.649 0.128 0.336 -0.260

-0.188

-0.102

-0.030

-0.102

-0.054

-0.054

0.105

-0.099

5

1

0.026 0.004 0.047 -0.009 0.047

0.287

-0.198

-0.029

-0.194

0.363

0.348

-0.438

0.343

6

-0.004 -0.054 0.070

0.023 -0.089 0.518

0.025

0.011

-0.024

0.020

7

1

0.014 1

0.032 -0.160

0.044

0.141

-0.115

0.161

8

1

0.044

-0.005

0.085

-0.087

9 10 11

1

0.098 1

-0.028 -0.069 1

-0.013 -0.066 0.325

0.012 0.087 -0.233

-0.009 -0.087 0.123

12

1

-0.170

0.174

13

1

0.060

14

6.082 1.346 0.247 0.280 0.745

11.064 3.501 0.329 0.383 1.238

-0.541

8.393

0.573

0.495

4.106

4.226

46.340

930.495

1.696

2.257

0.231

0.318

11.856

44.419

-0.144

0.589

0.105

0.432

0.000 0.000 0.000 0.004 0.001 -157.671 0.000 0.007 -2131.500 -0.706 0.000

91.000 36.387 1.000 5.481 4.814 33.236 1.000 50.627 28109.430 23.788 0.947

0.000

451.000

-10.700

0.000

0.000

4.900

1


Table 1b. Summary statistics and correlations: Variables in the equation estimating current innovative performance

1 Current innovative output

2 Knowledge depth

3 Knowledge scope

4 R&D intensity

5 Firm size

6 ROE

7 Firm type

8 Available slack

9 Recoverable slack

10 Potential slack

11 Firm knowledge diversity

12 Backward citations (log)

13 Innovative output below aspiration

14 Innovative output above aspiration

15 R&D alliances

Mean

Std. Dev.

Min

Max

Correlations >0.06 are significant at 5%

1

0.597

0.527

2

0.031

0.192

3

0.104

0.289

4

-0.099

-0.111

5

1.000

0.079 1.000

0.248 -0.001 1.000

-0.173 0.057 -0.038 1.000

0.731 0.128 0.336 -0.260 1.000

0.033 0.004 0.047 -0.009 0.047

0.293 0.050 0.096 -0.188 0.287

-0.167 0.017 0.006 -0.102 -0.198

-0.017 -0.013 -0.031 -0.030 -0.029

-0.147 -0.097 -0.073 -0.102 -0.194

-0.080 -0.142 -0.350 -0.031 -0.086

0.426 0.439 0.484 -0.085 0.621

-0.238 -0.069 -0.137 0.105 -0.438

0.343

0.649

6

0.020

0.026

7 8

1.000

0.014 1.000

0.032 -0.160 1.000

-0.004 -0.054 0.070

0.023 -0.089 0.518

-0.036 -0.097 -0.015

0.038 0.151 -0.044

-0.024 -0.115 0.085

0.161

0.169

-0.087

-0.044

9 10 11 12 13 14 15

1.000

0.098 1.000

0.041 0.098 1.000

-0.047 -0.177 -0.478 1.000

0.012 0.087 0.022 -0.276

-0.009 -0.087 -0.025 0.194

1.000

0.060 1.000

-0.005 -0.145 -0.107 0.510 -0.269 0.257 1.000

0.395 1.346 0.247 0.280 0.745 -0.541 0.573 4.106 46.340 1.696 0.613 2.755 -0.144 0.105 6.082

1.018 3.501 0.329 0.383 1.238 8.393 0.495 4.226 930.495 2.257 0.332 2.405 0.589 0.432 11.064

0.000 0.000 0.000 0.004 0.001 -157.671 0.000 0.007 -2131.500 -0.706 0.000 0.000 -10.700 0.000 0.000

7.500 36.387 1.000 5.481 4.814 33.236 1.000 50.627 28109.430 23.788 1.000 7.955 0.000 4.900 91.000

40


Table 2. Regression results

Knowledge depth

Knowledge scope

R&D intensity

(t-8)

(t-8)

(t-8)

R&D alliance

(t-4)

equation: Panel A Current innovative perf.

(t)

equation: Panel B

Model 1:1 Model 1:2 Model 1:3 Model 2:1 Model 2:2 Model 2:3

0.201

(0.165)

0.187

(0.164)

0.180

(0.164)

-0.033

(0.019)

-0.027

(0.018)

-0.030

(0.019)

1.673

(1.021)

1.667*

(0.838)

1.698

(1.025)

1.665*

(0.835)

1.610

(0.815)

1.634*

(0.835)

0.222

(0.132)

0.203†

(0.128)

0.188

(0.121)

0.179

(0.125)

0.157

(0.121)

0.099

(0.129)

Firm size

ROE

(t-8)

Firm type

(t-8)

(t-8)

Available slack

(t-8)

Recoverable slack

(t-8)

0.775*** 0.758*** 0.717*** 0.108***

(0.082) (0.088) (0.088) (0.009)

0.006

(0.038)

1.028

0.007

(0.037)

1.049

0.006

(0.037)

1.054

0.000

(0.004)

0.370***

(0.688)

0.183

(0.107)

0.003

(0.005)

(0.691)

0.171

(0.108)

(0.003)

(0.005)

(0.691)

0.158

(0.109)

(0.003)

(0.005)

(0.076)

-0.038***

(0.012)

0.000

(0.000)

Potential slack

(t-8)

Partner experience

(t-8)

Partner patent stock

(t-8)

Firm knowledge diversity

(t-8)

0.143

(0.137)

0.135

(0.137)

0.127

(0.137)

7.662*** 7.594*** 7.693***

(1.125) (1.126) (1.125)

0.032*** 0.032*** 0.033***

0.026†

(0.015)

(0.005) (0.005) (0.005)

0.323*

(0.137)

0.085**

(0.024)

Backward citations

(t-8)

Innovative output below aspiration

Innovative output above aspiration

(t-8)

(t-8)

-0.956*

(0.485)

0.413

(0.605)

-0.942*

(0.470)

0.533

(0.604)

R&D alliances

(t-4)

Year effects

Average VIF

Included

1.280

Included

1.300

Included

1.300

R-square 0.460 0.461 0.462

Significance levels: † p<0.1, * p<0.05, ** p<0.01, *** p<0.001; Year dummies included in each model

Included

1.580

0.458

0.097***

(0.008)

0.000

(0.003)

0.359***

(0.071)

-0.033***

(0.011)

0.000

(0.000)

0.022†

(0.013)

0.199*

(0.119)

0.060*

(0.022)

-0.096*

(0.048)

0.118*

(0.057)

Included

1.550

0.469

0.071***

(0.013)

0.000

(0.003)

0.350***

(0.071)

-0.029***

(0.011)

0.000

(0.000)

0.017

(0.014)

0.188*

(0.140)

0.045

(0.034)

-0.080

(0.050)

0.104*

(0.050)

0.028*

(0.010)

Included

1.590

0.474

41


Table 3. Results of Sobel test and effect ratio

Mediator: Number of R&D alliances c a sa b sb Sobel test statistic

-1.63

†

Effect ratio

0.27 Mediating effect of # of R&D alliances for the innovative output below aspiration and current innovative output relationship

-0.096 -0.942 0.470 0.028 0.010

Mediating effect of # of R&D alliances for the innovative output above aspiration and current innovative output relationship

† p<0.1

0.118 0.533 0.604 0.028 0.010 0.84 0.13 c: coefficient of innovative output below (above) aspiration (total effect, without the mediator’s effect in the model, of innovative performance below (above) aspiration on current innovative output; model 2:2, panel B of Table2) a: coefficient of innovative output below (above) aspiration (effect of innovative output below (above) aspiration on the mediator, Model 1:3, panel A of Table2) sa: standard error of a (Model 1:3, panel A of Table2) b: coefficient of the mediator (# of R&D alliances) (Model3, panel B of Table2) sb: standard error of b (Model3, panel B of Table2)

Sobel test statistic: a*b/SQRT (b 2 *sa 2 + a 2 *sb 2 )

Effect ratio: a × b/c (indirect effect/total effect)

42

A Behavioral Theory of the Reciprocal Relationship between

Related documents

Products

Support

A Behavioral Theory of the Reciprocal Relationship between

Related documents

Add this document to collection(s)

Add this document to saved

Suggest us how to improve StudyLib