Absorptive Capacity in R&D Project Teams: A ... - IEEE Xplore

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, VOL. 57, NO. 4, NOVEMBER 2010

Absorptive Capacity in R&D Project Teams: A Conceptualization and Empirical Test Louise A. Nemanich, Robert T. Keller, Dusya Vera, and Wynne W. Chin

Abstract—The purpose of this study is to answer a call for the rejuvenation of the absorptive capacity (ACAP) construct by offering a novel conceptualization and empirical test of a multidimensional model of R&D project team ACAP that portrays it as a capability distinct from prior knowledge, specifies each dimension’s level of analysis, distinguishes between individual and collective assimilation, and considers the moderating effects of team structure. Using a dataset from survey and archival sources on 100 innovations by R&D project teams, we find that the capability of R&D team members to evaluate external knowledge is related to their ability to assimilate it and that both individual assimilation capabilities and collective assimilation capabilities, in the form of ability to reach a shared understanding, are important to the team’s ability to apply external knowledge. We also find that prior knowledge negatively moderates the relationship between individual assimilation and application ability and that team autonomy positively moderates this relationship. By clarifying levels of analysis and encompassing multiple dimensions of ACAP, this work leads to a more fine-grained understanding of the complex nature of ACAP. Implications of these findings for future research and R&D team management are presented. Index Terms—Absorptive capacity, autonomy, computer industry, external knowledge, prior knowledge, R&D teams, shared mental models.

I. INTRODUCTION OHEN and Levinthal [1], [2] were the first to describe absorptive capacity (ACAP) as the “ability of the firm to recognize the value of new external information, assimilate it, and apply it to commercial ends.” The ability to absorb knowledge from sources external to the firm is critical to the successful management of technology and innovation because it triggers divergent thinking through new knowledge resources transferred from external sources [1], [3]–[6]. In a critical review of the ACAP construct, Lane et al. [7] conclude that the construct has become reified; i.e., become a taken-for-granted, generalpurpose concept. They identify fundamental weaknesses in our understanding of ACAP and call for a “rejuvenation of the construct.” Specifically, they ask researchers to engage in empirical analysis and integrative theoretical work that characterizes the construct as a capability rather than a knowledge asset, focuses at the microlevel to broaden understanding of the role of team cognition, emphasizes the multiple dimensions of the concept,

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Manuscript received January 8, 2009; revised June 5, 2009 and September 18, 2009; accepted November 12, 2009. Date of publication May 10, 2010; date of current version October 20, 2010. Review of this manuscript was arranged by Department Editor P. E. Bierly. L. A. Nemanich is with the WP Carey School of Business, Arizona State University, Phoenix, AZ 85069-7100 USA (e-mail: [email protected]). R. T. Keller, D. Vera, and W. W. Chin are with the C.T. Bauer College of Business, University of Houston, Houston, TX 77204-6021 USA (e-mail: [email protected]; [email protected]; [email protected]). Digital Object Identifier 10.1109/TEM.2009.2037736

and considers the impact of team structural factors on the efficiency of applying assimilated knowledge. Such deepening of our basic understanding of the ACAP construct would provide an essential foundation to improve the reliability and validity of future research and valuable insight into the effective management of R&D teams. The purpose of this study is to answer this call for rejuvenation by offering a novel conceptualization and empirical test of a multidimensional model of ACAP in R&D teams that portrays it as a capability distinct from prior knowledge, specifies each dimension’s level of analysis, distinguishes between individual and collective assimilation, and considers the moderating effect of project team structure. Our research extends prior theoretical work [7]–[9] by clarifying the appropriate level of analysis for each dimension of ACAP and the relationships among them. We augment the very scarce empirical data on ACAP dimensionality by testing our conceptual model against field data because prior research has identified that the dimensions have different antecedents [10] and outcomes [11], suggesting that full understanding of all dimensions is essential to avoid the bias problems of underspecified models. Zahra and George [9] theorized that unspecified social integration mechanisms moderate the relationship between abilities to assimilate knowledge and to apply it by altering the efficiency with which a firm uses assimilated knowledge. We extend this work by drawing on both group dynamics and learning research to propose two contingency factors in this relationship, team autonomy and team members’ prior knowledge, reflecting the influence of R&D team structure on the team’s ability to collectively exploit the assimilated knowledge of members. This research makes four distinctive contributions to the literature. First, we develop an ACAP model at the R&D projectteam level by incorporating the three traditional dimensions of firm ACAP (evaluate, assimilate, and apply), plus a new dimension, shared cognition. Second, we develop new theory clarifying the level of analysis appropriate to each ACAP dimension and offer empirical evidence of relationships among dimensions. Third, we evaluate team autonomy and prior knowledge as team structural characteristics that moderate the relationship between assimilation capability and the ability to apply external knowledge for commercial ends. Lastly, we validate new measures for ACAP dimensions, emphasizing their nature as capabilities. II. THEORY A. Theoretical Background Cohen and Levinthal [1] defined firm ACAP, highlighted its critical functionality in R&D, identified its recursive

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NEMANICH et al.: ABSORPTIVE CAPACITY IN R&D PROJECT TEAMS: A CONCEPTUALIZATION AND EMPIRICAL TEST

relationship with knowledge accumulation, and proposed a role in predicting future technological advances. Their definition of firm ACAP has gained widespread use. Empirical research generally has followed Cohen and Levinthal [1] in focusing on its importance to firm R&D and in using a unidimensional proxy (R&D expense) for the ACAP construct. Unidimensional treatment of a multifaceted construct (evaluate, assimilate, and apply) assumes a reflective higher order construct (i.e., the dimensions are highly correlated, interchangeable, and share common antecedents and outcomes) [12], [13]. Recent theoretical research questions this assumption for ACAP and is supported by empirical data showing different antecedents and outcomes among the dimensions [7], [10]. This focus on R&D expense as a convenient proxy for ACAP has left the role of individual and social cognition in the ACAP of R&D teams much less well understood [7]. A body of theoretical work has begun to develop that is broadly consistent in its descriptions of ACAP’s dimensions, but is still somewhat inconsistent in the number of and relationships among dimensions. Zahra and George [9] propose a sequential relationship between potential ACAP (acquisition [evaluation] and assimilate dimensions) and realized ACAP (transformation and exploitation [application] dimensions), on the basis that a firm cannot apply knowledge that it has not previously assimilated. They point out that firms may have a strong capability to understand complex problems, but are weaker skilled at translating that knowledge into action [9]. Lane et al. [7] later argued for a return to Cohen and Levinthal’s [1] three dimensions (recognize [evaluate], assimilate, and apply) as sequential processes comprising ACAP. Todorova and Durisin [8] propose five dimensions: evaluate, acquire, assimilate, transform, and exploit (apply). They suggest that transform and assimilate perform the same functions and are differentiated only by how easily the new external knowledge fits with existing knowledge frameworks. They argue that realized ACAP and the apply dimension are equivalent and that all other dimensions are part of potential ACAP. B. Conceptual Model Our model of team ACAP extends previous work by adding team shared cognition to the three traditional dimensions of ACAP—evaluate, assimilate, and apply capabilities—and by recognizing that these capabilities manifest themselves in different levels of analysis, individual or team. In doing this, we build on the 4I framework of organizational learning [15], which proposes that learning takes place at the individual, group, and organizational levels, and that four subprocesses link the three levels, involving both behavioral and cognitive changes. Mintzberg et al. [14] summarize the four subprocesses embedded in the 4I framework: Intuiting is a subconscious process that occurs at the level of the individual. It is the start of learning and must happen in a single mind. Interpreting then picks up on the conscious elements of this individual learning and shares it at the group level. Integrating follows to change collective understanding at the group level and bridges to the level of the whole organization. Finally, institutionalizing incor-

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porates that learning across the organization by embedding it in its systems, structures, routines, and practices. (p. 212)

One difference between the 4I framework and our model of ACAP is that the former describes learning as mainly originated from individual insight, while the later focuses on the type of learning originated by valuable external information initially evaluated by an individual mind. Next, we build on the microprocesses of learning to create a foundation of a multilevel theory of ACAP. We define the capability to evaluate knowledge as the ability of individual team members to accurately assess the most valuable external knowledge to target for assimilation. The assimilate capability is the ability of individual team members to learn and develop a deep understanding of valuable external knowledge. While recognizing the fine distinctions between the processes of acquiring knowledge and assimilating/transforming it, we assert that assimilation of external knowledge implies that the knowledge has been acquired and that an ability to acquire knowledge, divorced from the ability to understand it, has little meaning. Thus, as in Cohen and Levinthal [1] our definition of the assimilate capability encompasses the functions implied in Todorova and Durisin’s [8] acquire, assimilate, and transform constructs. Collective assimilation is represented by team shared cognition capability, or the ability of the team to reach a mutual understanding of external knowledge acquired by individual team members. The apply dimension is defined as the team’s capability to exploit external knowledge commercially in an innovation [1]. The capabilities to evaluate and assimilate external knowledge are highly cognitive in nature, and thus, depend upon the abilities of individual team members [15]. Both capabilities are based upon expert intuition, including skill at pattern recognition and associative learning ability (ability to establish linkages between new information and preexisting concepts), and upon the ability to interpret the meaning of that knowledge to oneself [1], [16]. Intuition involves preconscious pattern recognition and the ability to make novel connections to discern new relationships [16] that are the critical processes for external knowledge evaluation. Such expert intuition is a learning process that is unique to individuals, not teams or organizations, suggesting the importance of integrating an understanding of microlevel learning processes into ACAP theory [16]. Interpretation at the individual level is the process of making explicit knowledge connections using cognitive maps [16]. ACAP in R&D teams does not depend upon the evaluation and assimilation capabilities of all members equally [1]. These abilities are critical for boundary spanning individuals who interface directly with external sources of knowledge; other team members only need sufficient commonality of language and technical competence to understand communications from the boundary spanner [1], [17]. Knowledge arising from intuition and self-interpretation is transmitted to the team by the source individual through a social interpretation process [16]. Interpretation is also associated with what Nonaka [18] calls an externalization process of knowledge creation, where individuals are able to translate their intuitive and tacit understandings into

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explicit knowledge that they can communicate to their team. Rogers [19] describes this relationship as the two-step flow model in which the boundary spanner’s process for absorbing external knowledge relies on cognitive abilities and diffusion of that information to others is more a function of influence skills. Cohen and Levinthal [1] emphasize that ACAP relies upon both boundary spanning individuals’ assimilation abilities for external knowledge and the capability to share this knowledge internally for collective assimilation. Collective assimilation capability relies upon a somewhat different skill set than individual assimilation capability, and requires that the unique external knowledge gathered externally by an individual be shared among others through social interpretation processes for integration at the team level [20]. We adopt a concept often used in the team learning literature for collective assimilation, shared cognition, defined as “an organized understanding of relevant knowledge that is shared by team members” [21, p. 89]. The 4I framework of organizational learning [16] describes the learning process that is central to shared cognition capability as interpretation. Interpretation is the process of achieving a shared understanding of knowledge by reducing equivocality among cognitive maps held by team members [16]. Shared cognition is also related to Nonaka’s [18] socialization and combination processes of the knowledge spiral, where socialization describes the implicit sharing of tacit knowledge, often even without the use of language (for example, through shared experience), and combination passes codified knowledge among people. Collective assimilation has rarely been explored in the ACAP literature. The few ACAP empirical studies that have incorporated a concept related to collective assimilation have operationalized it as either access to general knowledge or transactive memory systems (TMS). Including collective assimilation as an explicit dimension of ACAP highlights the role of collective sensemaking as a social process that shapes interpretations, interpreting, and conduct [22]. As part of the collective assimilation dimension of ACAP, collective sensemaking is about placing items into frameworks, comprehending, constructing meaning, interacting in pursuit of mutual understanding, and patterning [22]. Because teams are defined in terms of their task interdependence [23], team knowledge application is a collective capability. In contrast to cognitive capabilities, knowledge application requires both cognitive skills and the collective behavioral skills that are essential to team task effectiveness, such as consensus decision-making and team problem solving [24]. The 4I framework of organizational learning describes the process whereby teams make mutual adjustments and take coherent collective action as integration [16], which is a learning process of teams rather than individuals. Integration is closely associated with the notion of internalization proposed by Nonaka [18], where team members learn by doing and new collective knowledge is embodied by team members and becomes part of their repertoire of action. Finally, while the scope of our paper excludes ACAP at the organizational level, we capture in our model some aspects of the process of institutionalization, included in the 4I framework, in the concept of prior knowledge. Institutionalization refers to the process of capturing team level learning into nonhuman reposi-

tories of organizational knowledge such as systems, procedures, products, and strategies [15]. In our model, prior knowledge represents institutionalization of knowledge at the team level, in particular, knowledge of prior innovations in the R&D team. C. Hypotheses The ability to evaluate external knowledge enhances the ability to assimilate that knowledge [8] by directing attention and by providing the individual with cognitive maps for understanding [25]. Cognitive maps direct search processes in new areas [26] that allow information to be categorized by its value, new knowledge to be processed, and thus, assimilated more easily. People adept at evaluating the usefulness of knowledge for innovation will be more efficient at assimilating external knowledge [27] because their attention is quickly focused on the most valuable knowledge. Hence, we propose the following hypothesis. Hypothesis 1: The capability of individual R&D project team members to evaluate external knowledge will be positively related to their ability to assimilate external knowledge.

The relationship between individual assimilation capability and project team application capability is complex in that it involves a shift from the individual to the team and from cognition to behavior. Organizational learning researchers suggest an interrelationship between cognition (assimilation) and behavior (application), stating that one cannot be divorced from the other [16], [28], [29]. Individuals with higher abilities to assimilate external information bring greater knowledge breadth to the team, offering a richer source for exploitation [7]. Individuals with strong assimilation skills will be more effective at intuition and the initiation of team interpretation. The ability of individual team members to assimilate external knowledge contributes to the team’s ability to apply knowledge because knowledge application requires teams to retrieve and exploit knowledge that their members already possess [9]. The creativity literature, in fact, argues that individuals with an ability to accumulate a larger and more diverse set of knowledge will be able to come up with more numerous applications of ideas [30]. Factual knowledge about a new technology and evaluative knowledge of its costs, benefits, and risks held by group members are positively related to the extent to which that technology is applied by the team [31]. Thus, we have the following hypothesis. Hypothesis 2: The capability of individual R&D project team members to assimilate external knowledge will be positively related to the team’s ability to apply external knowledge for commercial ends.

Many researchers have emphasized the importance of prior experience to ACAP development. Prior knowledge has been identified as a proxy for overall ACAP [32]–[34], as a proxy for only one ACAP dimension (the ability to evaluate external knowledge) [35], or as an antecedent to ACAP [1], [9]. Prior knowledge has been shown to moderate the performance benefits of external knowledge to venture capital firms, such that external knowledge was less important to firms with high stocks of relevant prior knowledge [36]. On the other hand, Lane and Lubatkin [11] find that prior knowledge (R&D spending)


is not significantly correlated with learning from an external organization. Given these inconsistencies, we turn to group dynamics and the dynamic capabilities perspective to consider how prior knowledge might influence relationships among ACAP dimensions. We challenge conventional thinking about the value of prior knowledge by arguing for a substitution effect between prior knowledge as a static team resource and individual assimilation capability as a dynamic process influencing the team’ ability to apply knowledge. Two different views about the value of prior knowledge versus the value of the ability to create new knowledge can be found in the literature. On one hand, group dynamics researchers have identified the aggregated expertise of R&D project team members as crucial to group effectiveness especially for performing the intellectual tasks central to R&D work, such as problem solving and creativity [37], [38]. Prior knowledge held in memory includes both declarative (facts and events) and procedural memory (procedures and routines) [39]. In this perspective, prior knowledge provides the potential for both recombining prior knowledge in new ways and replicating actions [40]. On the other hand, organizational learning has been proposed as the ultimate high-order capability that influences dynamic and operational capabilities, and static resources, including knowledge [2], [41]. In this sense, we make a distinction between prior knowledge that has been institutionalized at the team level as a static asset of the team, and individual team members’ assimilation capability as a dynamic process that renews the team’s pool of knowledge. Knowledge stocks may become obsolete but the ability to assimilate knowledge helps to renew knowledge. We argued in Hypothesis 2 that the more ability team members have to assimilate new knowledge the more effective they would be at applying new knowledge and creating an innovation. By adding the team’s prior knowledge as a moderating condition, we argue that more experienced teams have a large base of existing knowledge and more practice in creating innovations and that these knowledge resources and operational routines can to a certain extent substitute for the dynamic capability of assimilating more new knowledge resources and learning new capabilities. Thus, the experienced team’s rich storehouse of prior knowledge can reduce both the need for and the incentive to assimilate new knowledge. This interpretation is consistent with Crossan et al.’s description in the 4I framework of the ability of knowledge that is institutionalized to block the ability of individuals to generate new insights and new knowledge [15]. Hence, we have the following hypothesis. Hypothesis 3: The higher the level of prior knowledge held by individual members of an R&D project team, the weaker the positive relationship between the capability of individual team members to assimilate external knowledge and the team’s ability to apply external knowledge for commercial ends.

Among teams of R&D professionals and other knowledge workers, research shows that team autonomy is a social integration mechanism. Decisions by firms can affect the degree of team autonomy, and autonomy has substantial effects on team processes and outcomes [42]. These integrative characteristics

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bring a team closer together and improve its ability to solve problems and share expertise [9], [43], [44]. Cohesiveness has been found to be an important predictor of R&D project team performance, and team autonomy can allow for the intensive interactions and activities needed to form these interpersonal bonds [3]. These interpersonal bonds can then facilitate the transformation of assimilated knowledge into applications by team members who have complementary skills for application and are motivated to work together via cohesiveness. Teams with strong assimilation capability are likely to be more efficient in applying their assimilated knowledge when they have greater autonomy, will be able to access the knowledge assimilation capability of team members more accurately, and use it to greater advantage. The knowledge gathered through assimilation capability improves a team’s ability to exploit that knowledge when the team is structured to be autonomous [45]. Furthermore, teams that assimilate new cognitive learning, but are thwarted from acting upon that knowledge due to lack of autonomy of action, may experience “blocked learning” [46]. Blocked learning frustrates team members and reduces their incentive to continue to try to build their capability to apply external knowledge. On the other hand, teams with low assimilation capability may benefit from supervisory control. Through the process of gaining supervisory approval for design changes, teams with lower assimilation capability may be able to benefit from the assimilation capability or knowledge of the supervisor. For teams with low assimilation capability, supervisory control may act to augment the team’s ability to exploit the full potential of its assimilation capability. Therefore, we have the following hypothesis. Hypothesis 4: The higher the level of R&D project team’s autonomy, the stronger the positive relationship between the capability of individual team members to assimilate external knowledge and the team’s ability to apply that knowledge for commercial ends.

In addition to the assimilation capability of individual team members, the team’s shared cognition capability also contributes to its ability to apply external knowledge. Walsh and Ungson [47] are mindful of the interconnection between cognitive and social levels when they describe organizations as networks of inter-subjectively shared meanings that are sustained through the development and use of a common language and everyday social interaction. Repeated social interactions increase the breadth and depth of information exchange and provide incentive for the dynamic process of investing in knowledgesharing activities [48]. To this, Weick [22] adds in his theory of sensemaking that conduct is contingent on the conduct of others, whether those others are imagined or physically present. Thus, in the context of ACAP, individual and collective cognition are both critical for the actual application of external knowledge. Weick and Roberts [49] conceptualize shared cognition as a pattern of heedful interrelations of actions in a social system. Shared cognition is a positive influence on both the cognitive and behavioral aspects of external knowledge application because it includes both common assessment of stimuli (cognition) and common understanding of what to do about it (action) [50].

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Shared cognition leads to improved task processes, such as more efficient communication, more accurate expectations, greater consensus, improved coordination [51], and reduced “glitches” in applied activities of product development teams when shared knowledge is high [52]. Shared cognition facilitates the team consensus building and joint decision-making necessary for the interdependent tasks in knowledge application. Open communication among team members, generally a prerequisite for developing common cognitive maps, is positively related to team problem solving and teamwork success [53], [54]. When shared cognition is available to facilitate communication, interactions among team members with complementary knowledge facilitate the making of novel linkages critical to successfully applying knowledge in R&D team innovations [1]. Thus, we have the following hypothesis. Hypothesis 5: An R&D project team’s shared cognition capability will be positively related to the team’s ability to apply external knowledge for commercial ends.

III. METHOD A. Sample and Procedure U.S.-based research teams receiving patents in the prior year in the most common computer technology patent classes (345, 365, 707, and 709) comprised our sampling frame. Recent innovations were selected to enable respondents to accurately remember their development. The computer field was selected because its pace of change creates a high value for innovation. Indeed, average R&D spending is 18% of sales in the software industry and 7% of sales in the hardware industry, compared with 2% in consumer products and energy [55]. A research project team was defined as the group of people who worked together in creating a patented innovation. We used the principal investigator listed in the patent as the key informant. Although team research that collects data on member perceptions of team climate often employs multiple respondents per team, we were collecting data on team abilities. Key informants are appropriate in this context because they are preferred over multiple informants when the selected person is uniquely qualified to respond to the issues under investigation [56], [57]. Input from less informed team members, such as those not dedicated full-time to the project or not interacting with all team members, could bias the results if it were based on incomplete information. We randomly chose patents from the selected classes that met the following three criteria: a minimum of two inventors composing the team, an identifiable mailing address, and a unique lead inventor. We received 151 responses, but missing data reduced the sample to 100 teams with complete responses. Due to high levels of mobility in this industry and difficulties in connecting patent inventors to specific mail codes within some labs, we had a large number of returned and undeliverable requests. Based on an analysis of the returned mail, we estimate the response rate to be 16%. We compared the respondent sample to the full sampling frame on the basis of team size, invention scope (number of technological subclasses into which the patent was classified), and invention size (number of unique claims for

the innovation approved by the patent examiner), and found no significant differences (p = 0.75, 0.70, and 0.97, respectively). We also did an analysis of variance (ANOVA) analysis of all variables comparing early respondents to late respondents and found no significant differences. Together, these analyses suggest that the respondent sample was representative. B. Measures Prior research has developed some measurement scales for ACAP [10], [44], [58]. None of these scales met our needs, either because they do not capture the capability character of the construct, do not capture separate dimensions (two key shortfalls of prior ACAP research identified by Lane et al. [7]), do not focus on the team level, or are not specifically focused on external knowledge. Thus, survey measures were newly developed for this study. A seven-point Likert scale from strongly disagree (1) to strongly agree (7) was used. All survey items and questions are shown in Table I. 1) Team Absorptive Capacity: The dependent variable, the apply dimension of ACAP, was measured with a four-item scale structured to capture the full construct definition, including the ability to exploit knowledge and the ability to apply knowledge for commercial ends. Whereas much of the team learning and innovation literature characterizes any knowledge from outside the team as external, team ACAP research is more sharply focused on knowledge external to the firm (as in [59]). Knowledge external to the firm is more difficult to access, interpret, and richly understand [60], and represents a source of more diverse ideas than knowledge that is internal to the firm and external only to the focal team. A sample item was “With respect to using new technical knowledge from outside the company while designing this innovation, our team was very competent at exploiting it.” Chronbach’s alpha was 0.91. The evaluate and assimilate dimensions were measured with three-item scales. In information processing theory every team member need not be adept at evaluating and assimilating external knowledge; it is only necessary that at least one person, the gatekeeper, be able to do so [1]. Items for these dimensions began as “With respect to new technological developments originating outside your company, at least some individual team members were . . .” An evaluate sample item continued “very capable at accurately evaluating their worth.” An assimilate sample item continued “very adept at absorbing knowledge about them.” Cronbach’s alpha was 0.89 for evaluate and 0.93 for assimilate. Team shared cognition was measured with a threeitem scale designed to assess the team’s abilities to share external knowledge and reach a common understanding of it. A sample item was “With respect to communicating about valuable new technical knowledge, our team was very skillful at combining it to create a shared understanding.” Chronbach’s alpha was 0.89. 2) Moderators: In the ACAP literature, prior knowledge has been determined as the aggregation of individual prior knowledge, e.g., aggregated numbers of patents, publications or number of technical employees [15], [61]. Following this practice, we measured the prior knowledge of the team members as the aggregated number of patents previously granted to them, as in


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TABLE I FACTOR ANALYSIS

Penner-Hahn and Shaver [34]. This measure was particularly applicable for this study because it measured a team’s prior knowledge of tasks closely related to the one for which we are measuring their ability to apply knowledge. Typically, for count data this variable was highly skewed, so we utilized the natural logarithm of the patent count [62, p. 232]. Team autonomy was measured with a three-item scale designed to assess the team’s ability to take action without seeking management approval. Given the emphasis on innovativeness for R&D teams, we focused specifically on team autonomy with respect to product design. A sample item was “Without seeking management

approval, our team could make our own decisions about the innovation design.” Chronbach’s alpha was 0.89. 3) Control Variables: We rigorously controlled for characteristics of the innovation, the knowledge environment, and the team. We controlled for TMS, which is a collective memory system for group knowledge that develops over time as group members work together and come to rely upon each other’s complementary areas of expertise [63]. TMS was measured as the number of patents on which team members had previously worked together. TMS was chosen as a control because it overlaps to some extent with one of our independent variables, shared

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cognition ability [64]. There is some ambiguity in the literature regarding the need to control for type of innovation. Some studies group patent classes by broad categories within a single industry [e.g., 65], while others do not control for innovation type at all [e.g., 66]. We controlled for the technology categories of hardware and software using a dummy variable. We also controlled for innovation size, using the number of claims made in the patent [67], and for innovation scope, using the number of patent subclasses [68]. Knowledge spillover varies by geographic location due to the intensity of R&D investment in some locations [69]; therefore, we controlled for knowledge environment with a dummy variable set to 1 if the principle investigator was based in Silicon Valley and 0 otherwise. From the literature on team learning, we controlled for inter-team differences with two variables: team size and whether the team was collocated or virtual. The number of inventors listed on the patent represented team size. We used a dummy variable for collocation: it was set equal to 1 if all of the team members’ hometowns (listed in the patent) were in the same metropolitan area and 0 otherwise. TMS develops over time through shared experience working together on similar problems [63]; hence, we used as a proxy the number of patents that team members had previously worked on jointly to control for TMS. 4) Other Variables: We also measured several additional variables to assess construct validity of new measurement scales. A two-item scale was developed to measure the extent to which the team was able to gather external knowledge through an externally connected boundary spanner. A sample item was “At least some individual team members were very capable at gathering news about new technological developments outside your company.” An item from Sivadas and Dwyer’s [70] scale was used to measure innovation radicalness, “This invention was pioneering (first of its kind).” A two-item scale was used to measure team cohesiveness. A sample item was “While working on this innovation our team was cohesive.” A three-item scale was used to measure goal clarity. A sample item was “From the beginning of this invention project, our team was very clear about its objectives.” We used one item to measure time commitment, “The members of this team committed all of their time solely to this project while developing this innovation.” The ability to apply external knowledge was also measured using archival data from patent applications. Similar to previous researchers who have used patent citation analysis to determine the percentage of the precursor knowledge for the invention that was sourced from outside the firm [e.g., [71], we calculated the percentage of the cited references that were from sources outside the firm. Some variables were empirically constructed for the structural equation model. To control for common method variance, using the technique proposed by Podsakoff et al. [72], we set up a latent variable, Method, with all items measured using survey data shown as manifest variables. Two Interaction terms were created. The interaction of autonomy and assimilate is composed of nine cross-product terms (three items × three items). The interaction of prior knowledge and assimilate is composed of three cross-product terms (one item × three items).

C. Validation of Constructs An initial set of items was generated for each of the dependent and independent variables. This list of items went through several iterations to verify face validity, adherence to the construct definition, and full capture of the construct. First, the items were refined separately by three authors and differences resolved. Then, they were reviewed by a fourth author with expertise in scale development and differences of interpretation resolved. Reliability for each measure exceeded 0.89. Descriptive statistics and correlations are shown in Table II. An exploratory factor analysis of the independent variables (see Table I) generated a clean solution with each item loading above 0.70 on the expected construct and below 0.40 on the other constructs [73]. No items were dropped. In a further test, average variance extracted (AVE), the variance shared between a construct and its indicators, was computed for each variable (shown on the diagonal in Table II). Discriminant validity is supported when the square root of AVE for a variable exceeds the correlations between that variable and other variables [74]. This test was met for all variables. The measurement model (see Table III) developed using AMOS 7.0 also tests discriminant validity among the four multimeasure constructs. A close fitting model will have factor loadings greater than 0.70 and fit statistics of: χ2 /df < 2.0, comparative fit index (CFI) > 0.90, high parsimonious CFI (PCFI), and root-mean-square error of approximation (RMSEA) < 0.10 [75], [76]. All factor loadings for the measurement model exceeded 0.82 (p < 0.001), and fit statistics were: χ2 /df = 1.9, CFI = 0.96, PCFI = 0.70, and RMSEA = 0.09, indicating a good fit and high-quality scales. In addition to exceeding typical standards for close fit, a good model should also have better fit statistics than alternative models (not shown in tables). Fit statistics for our theoretical model were significantly better than those for the standard null (independence) model (χ2 /df = 14.9, CFI = 0, and RMSEA = 0.37). Our theoretical model also had a better model fit than a model combining the two most highly correlated variables, shared cognition and assimilate (χ2 /df = 4.2, CFI = 0.82, and RMSEA = 0.18), suggesting that discrimination between those variables improves the model. Per Churchill [77], we also verified construct validity for the perceptual scales by testing whether the variables behaved as they should within a nomological net. We compared each variable to a construct that it should be theoretically related to and tested for a significant positive correlation. The ability to evaluate external knowledge is linked to seeking external knowledge from research communities [78]. Therefore, evaluate was compared to external connectedness and found to be positively correlated (r = 0.66, p < 0.001). Assimilation of knowledge from diverse sources is related to the development of pioneering inventions [79]. Therefore, we compared the assimilate scale with innovation radicalness and found a positive correlation (r = 0.31, p < 0.01). Cohesion contributes to the formation of team mental models [50] and has been found in other research to be strongly related (r = 0.69) to a similar construct, free flow of information among team members [54]. Therefore, we


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TABLE II MEANS, STANDARD DEVIATIONS, AND CORRELATIONS

compared shared cognition with cohesion and found a positive correlation (r = 0.51, p < 0.001). Autonomy has been found to contribute to goal clarity in other research (r = 0.31) [80]. We compared team autonomy with goal clarity and found a positive correlation (r = 0.24, p < 0.05). Since the apply dimension is the dependent variable in this study, we used separate source data for validation. The team leader’s assessment of the team’s ability to apply external knowledge was positively correlated with the percentage of external knowledge actually applied in the invention per the patent application (r = 0.34, p < 0.001), suggesting good construct validity. In addition to these individual tests for relatedness to similar constructs, we also compared each scale to the measure of team commitment for which there was no theoretical reason to expect a relationship. Correlations between this item and each of the perceptual scales were not significantly different from zero. Overall, all tests provided evidence for the construct validity of each ACAP dimension scale and discriminant validity among the scales. IV. RESULTS We use structural equation modeling (AMOS 7.0) to test the hypotheses. The results are shown in Fig. 1. We follow the recommendation of Podsakoff et al. [72] by including a latent factor in the main effects structural model (see Table III)

reflected by all of the manifest variables that were measured using a common method (the survey). This technique removes the effect of common method variance from the structural path coefficient estimates, although it is conservative in that it may also partial out some of the substantive relationships among the constructs [72]. Three correlations between exogenous variables and one correlated pair of error terms from the measurement model (see Table I) are included in the main effects structural model. Heywood cases were resolved by fixing two variances to match the measurement model estimates [81]. Three control variables are significantly related to the dependent variable: TMS, invention type, and collocation. The results support all of the hypothesized relationships among the dimensions of ACAP. Evaluate is positively related to assimilate (r = 0.69, p < 0.001), supporting Hypothesis 1. As predicted in Hypothesis 2, assimilate is positively related to apply; moreover, it fully mediates the relationship between evaluate and apply. Shared cognition is positively related to apply, supporting Hypothesis 5. The main effects model explains 35% of the variance in the apply dimension. Our results also indicated a strong, unhypothesized correlation (.59, p < 0.001) between the ability of some individuals in a team to evaluate and understand new information and the team’s ability to share that knowledge. We ran two post hoc tests to further explore these relationships. In one test, individual assimilation lead to team shared cognition; in the second test, the

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TABLE III STRUCTURAL EQUATION MODEL RESULTS


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TABLE III CONTINUE.

Fig. 1. Model of project-team level absorptive capacity. Notes: Model fit statistics for prior knowledge interaction model: χ 2 /df = 1.15, CFI = 0.97, PCFI = 0.83, RMSEA = 0.04. Model fit statistics for autonomy interaction model: χ 2 /df = 1.17, CFI = 0.98, PCFI = 0.85, RMSEA = 0.04.

opposite relationship was modeled. The model with individual assimilation leading to team shared cognition yields the same conclusions regarding our hypotheses, but has significantly better model fit. Following the latest recommended procedure to model interactions among latent variables, we orthogonalized the crossproduct indicators of the interaction term [82]. Separate models were used to test each interaction to conserve statistical power. The prior knowledge interaction model results support Hypothesis 3; the interaction of assimilate and prior knowledge is negatively related to apply and explains an additional 2% of the variance. The autonomy interaction model (see Table III) provides evidence in support of Hypothesis 4; the interaction between assimilate and autonomy is significant and explains an additional 4% of the variance. This method for estimating interactions between two latent variables requires that the cross-product error terms be correlated. Therefore, the autonomy interaction model includes 14 more error-term correlations than the main effects model (10 are significant). Heywood cases in this model were resolved by fixing two additional variances to match the main effects model estimates.

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pability are more effective in knowledge application than teams designed with greater controls. Recent research on the publishing activities of teams of pharmaceutical scientists [84] suggests that while star scientists produce a large percentage of the industry’s innovative output, these scientists may have a greater effect by enhancing the innovative output of the nonstar scientists. We conducted a post hoc analysis of our data to determine whether our computer industry teams that combined star scientists and nonstar scientists had different levels of team ability to achieve shared understanding of new external knowledge, ability to apply that knowledge, or team autonomy than teams with only star scientists or only nonstar scientists. We found no significant differences in these variables by team composition. V. DISCUSSION

Fig. 2. Moderating effects of prior knowledge and autonomy on relationship between assimilate and apply dimensions. Notes: The plots were constructed using high and low levels of the moderators set at ±1 standard deviation from their means. All other variables were held constant at their means.

Using MacCallum et al.’s procedure [83], statistical power of the main effects, prior knowledge interaction, and autonomy interaction models are 0.92, 0.97, and 0.99, respectively. All models were highly parsimonious and have good fit statistics. Confidence intervals on the RMSEA estimate can be used to test two hypotheses of poor and close approximate fits. The LO 90 number is used to test close approximate fit and the HI 90 number is used to test poor approximate fit. The LO and HI ranges suggest that the hypotheses of close approximate fit cannot be rejected, but that the hypotheses of poor approximate fit can be rejected [76]. To facilitate interpretation of the interaction effects, the simple slopes for each interaction are illustrated in Fig. 2. As recommended by Cohen et al. [62], high and low levels of the moderators are set at ±1 standard deviation from the mean. The negative interaction effect of prior knowledge results in a relationship in which knowledge substitutes for assimilation capability. Less knowledgeable teams rely to a greater extent on their assimilation capability in order to be able to apply external knowledge. On the other hand, assimilation capability is less important to teams that have more extensive experience in knowledge application. In the case of team autonomy, the positive interaction effect of autonomy and assimilation suggests that teams with high assimilation capabilities are better able to exploit their external knowledge when operating with a high degree of autonomy. In contrast, teams with low assimilation ca-

The ability to exploit external knowledge is a critical capability for innovation, yet research on ACAP at the fundamental level for technological innovation, R&D project teams, has been scant. We answer Lane et al.’s [7] call for a rejuvenation of the ACAP construct with research that recognizes the capability nature of ACAP, builds construct understanding upward from the individual level to the team level, takes a multidimensional approach, and considers team structural factors affecting the team’s capability to apply assimilated knowledge. Our model represents unique theoretical contributions in that we specifically address the level of analysis appropriate to each dimension of ACAP in the context of innovation by R&D project teams, question previous assumptions about the relationship between team prior experience and ACAP, and offer specific thinking about structural moderators between the dimensions. It is important to note the empirical rigor of this study due to the use of seven control variables, plus a latent factor for common method variance, and validation of the dependent variable with an external source of objective data. Our findings support theory proposing that ACAP is a multidimensional, multilevel construct, and that these dimensions build upon each other [7], [9]. First, we develop and find strong empirical support for a 4-D model of team ACAP. We find that the capability of team members to evaluate external knowledge is related to their ability to assimilate it, and that both individual assimilation capabilities and collective assimilation capabilities, in the form of ability to reach a shared understanding of knowledge, are important to the team’s ability to apply knowledge. Second, our findings build upon ideas included in Cohen and Levinthal’s original work and work by Matusik and Heely suggesting that ACAP is a multilevel capability in which individuals play an important role [1], [15]. Third, we find that individual assimilation capability fully mediates the relationship between the ability to evaluate knowledge and the ability to apply it, supporting the “pipeline” structure for ACAP first proposed by Zahra and George [9]. Our work also explores Zahra and George’s [9] notion that there are “efficiency factors” that moderate the relationship between the ability to assimilate external knowledge and the ability to exploit it. We find that the relationship between individual


assimilation and team application abilities is complex, i.e., we find that a team of members with high prior knowledge weakens the relationship, while a team with high autonomy strengthens the relationship. Consistent with prior research [3], high team autonomy can help to develop and strengthen the interpersonal bonds needed for team members to work together to transform assimilated knowledge into applications. Hence, team autonomy should be included as an important variable in R&D project team processes and performance. Future multilevel research on ACAP should look not only at team autonomy, but explore how organizational variables, such as culture, norms, and structure, affect team autonomy and team and organizational ACAP. We offer new thinking about the role of prior knowledge in ACAP that diverges from the traditional view [9] that prior knowledge enhances the ability to evaluate and assimilate external knowledge. Similar to Lane and Lubatkin’s nonsignificant finding for the relationship between R&D spending (prior knowledge) and variance measures of ACAP, we found no significant correlation between prior knowledge and any dimension of ACAP. Our results support a hypothesis based on the organizational learning literature that prior knowledge negatively moderates the relationship between the ability to assimilate knowledge and the ability to exploit it. Low levels of prior knowledge increase both the need for and the benefits from the dynamic capability to assimilate new knowledge. We find that each of the dimensions and moderator relationships provides an incremental explanation of variance in the ability to apply knowledge. While we sought to capture the complex relationships among ACAP dimensions, we also had to make a tradeoff between parsimony and descriptive power. We hypothesized a relationship between the ability of individual team members to evaluate and assimilate new knowledge (essentially the role of boundary spanning) with the team’s ability to apply that knowledge. We also hypothesized a relationship between the team’s cognitive capability to reach a shared understanding of new knowledge and the team’s ability to apply that knowledge. We now believe that the unhypothesized relationship between an individual’s ability to assimilate knowledge and the team’s ability to share that knowledge is consistent with the 4I framework of organizational learning, which suggests a recursive relationship between individual intuition and group interpretation and integration. Our finding in support of a formative model for team ACAP in which dimensions build upon each other suggests that future operationalizations of collective ACAP should incorporate all four dimensions separately since the omission of one or more dimensions may lead to misspecification bias [12]. Research into whether internal learning is a precursor for external learning is also warranted [1], [85]. In addition, our work at the team level has implications for the processes and performance of R&D teams by highlighting the influence of social and individual cognition on ACAP at different levels. Questions that might be considered are the effects on the ACAP of a firm’s R&D function of the average ACAP of all R&D teams, the variance of the ACAP of all teams, the capability of the team with the highest ACAP, or the ACAP of boundary spanning teams.

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Our results extend Crossan et al.’s [16] view of learning. In its original proposal, the 4I framework mentions that external stimuli trigger individual intuition and insight, but does not address the particular phenomenon of ACAP as a capability to evaluate, assimilate, and apply external knowledge [17]. We extend the boundaries of the 4I framework to show how the microprocesses of learning apply not only to the creation of internal knowledge but also to the assimilation and application of external knowledge. Our model positions ACAP as a dynamic capability; a perspective that was not present in Crossan et al.’s [16] framework. Since 1999, the literature on dynamic capabilities has developed rapidly, and processes, in particular learning processes, are now understood as the cornerstone of dynamic capabilities and the ultimate high-order capability. We describe ACAP as a learning capability composed of processes at different levels, connecting in this way classical learning theory with new insights from dynamic capabilities. We developed perceptual measures to tap each dimension of a team’s ACAP for use in future research. In contrast to the measures that operationalize the 4I framework, our measures seek to capture dynamic capability processes rather than the knowledge stocks and flows. They offer a combined set of advantages not present together in earlier ACAP scales [10], [44], [58]. They include separate scales for each dimension, focus specifically on external knowledge, emphasize ACAP as a capability, and are usable across a range of samples. The scales show good convergent and discriminant validities and have strong factor loadings and good internal reliability. A study limitation is that to control for differences in technology our sample was limited to computer technology innovations. In other respects, the respondent group was quite broad and included teams from 53 firms in industry, academia, and government. While the majority of teams worked in the computer industry, there also were teams from the telecom, medical equipment, and aerospace industries, providing some confidence for inference to other high technology arenas. Our findings are relevant for practicing R&D managers because they offer insights into how ACAP can be built within R&D project teams. R&D project team leaders should understand that each dimension of team ACAP is based on a capability that is important to the whole, but relies on a different set of processes. For example, the team shared cognition capability relies on internal communication and development of a shared language, while the application ability requires effective experimentation skills. Also, while the capabilities to evaluate and assimilate external knowledge rely heavily on individual intuition and cognitive abilities, R&D project leaders need to maintain a balance between expecting these abilities to be present in all team members versus making sure that boundary spanners have these recognition and interpretation abilities and the rest of the team can effectively communicate with them. As a result of the complex relationships between ACAP processes, team leaders should develop the capabilities of both the individuals on the team and the socially constructed capabilities of the team as a group because both influence the team’s capability to exploit external knowledge. These findings suggest the importance of considering not only individual cognitive skills when selecting

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members to a R&D project team, but also the ability of the team members to reach a shared interpretation of knowledge. Project leaders should encourage cohesiveness in the team that can enhance knowledge sharing and diffusion. Our work also contributes to R&D practice by identifying the importance of structuring R&D project teams in an optimum manner to build the team’s ACAP capability. For example, our work shows that high levels of task autonomy are more appropriate for teams with strong capability to assimilate external knowledge. Research efforts to further understand the microprocesses of ACAP will be important for practicing R&D managers leading technology innovation. Future research in large R&D organizations could explore the differential effects of new knowledge external to the R&D team but inside the firm compared to new knowledge external to the firm.

REFERENCES [1] W. M. Cohen and D. A. Levinthal, “Absorptive capacity: A new perspective on learning and innovation,” Admin. Sci. Q., vol. 35, pp. 128–153, 1990. [2] S. G. Winter, “Understanding dynamic capabilities,” Strateg. Manag. J., vol. 24, pp. 911–995, 2003. [3] M. D. Santoro and P. Bierly, “Facilitators of knowledge transfer in university-industry collaborations: A knowledge-based perspective,” IEEE Trans. Eng. Manage., vol. 53, no. 4, pp. 495–507, Nov. 2006. [4] R. T. Keller, “Predictors of the performance of project groups in R&D organizations,” Acad. Manage. J., vol. 29, pp. 715–726, 1986. [5] R. V. Wijk, J. J. P. Jansen, and M. A. Lyles, “Inter- and intra-organizational knowledge transfer: A meta-analytic review and assessment of its antecedents and consequences,” J. Manage. Stud., vol. 45, pp. 830–853, 2008. [6] U. Lichtenthaler, “Absorptive capacity, environmental turbulence, and the complementarity of organizational learning processes,” Acad. Manage. J., vol. 52, pp. 822–846, 2009. [7] P. J. Lane, B. Koka, and S. Pathak, “The reification of absorptive capacity: A critical review and rejuvenation of the construct,” Acad. Manage. Rev., vol. 31, 2006. [8] G. Todorova and B. Durisin, “Absorptive capacity: Valuing a reconceptualization,” Acad. Manage. Rev., vol. 32, pp. 774–786, 2007. [9] S. A. Zahra and G. George, “Absorptive capacity: A review, reconceptualization, and extension,” Acad. Manage. Rev., vol. 27, pp. 185–203, 2002. [10] J. J. P. Jansen, F. A. J. Van Den Bosch, and H. W. Volberda, “Managing potential and realized absorptive capacity: How do organizational antecedents matter?,” Acad. Manage. J., vol. 48, pp. 999–1015, 2005. [11] P. J. Lane and M. H. Lubatkin, “Relative absorptive capacity and interorganizational learning,” Strateg. Manage. J., vol. 19, pp. 461–477, 1998. [12] S. B. MacKensie, P. M. Podsakoff, and C. B. Jarvis, “The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions,” J. Appl. Psychol., vol. 90, pp. 710–730, 2005. [13] W. W. Chin, “Issues and opinion on structural equation modeling,” MIS Q., vol. 22, pp. vii–xvi, 1998. [14] H. Mintzberg, B. Ahlstrand, and J. Lampel, Strategy Safari: A Guided Tour Through the Wilds of Strategic Management. New York: Free Press, 1998. [15] S. F. Matusik and M. B. Heeley, “Absorptive capacity in the software industry: Identifying dimensions that affect knowledge and knowledge creation activities,” J. Manage., vol. 31, pp. 549–572, Aug. 2005. [16] M. Crossan, H. W. Lane, and R. White, “An organizational learning framework: From intuition to institution,” Acad. Manage. J., vol. 24, pp. 522– 537, 1999. [17] T. J. Allen, Managing the Flow of Technology. Cambridge, MA: MIT Press, 1977. [18] I. Nonaka, “A dynamic theory of organizational knowledge creation,” Organ. Sci., vol. 5, pp. 14–37, 1994. [19] E. M. Rogers, “New product adoption and diffusion,” J. Consum. Res., vol. 2, pp. 290–301, 1976.

[20] L. Argote, P. Ingram, J. M. Levine, and R. L. Moreland, “Knowledge transfer in organizations: Learning from the experience of others,” Organ. Behav. Hum. Decis. Process., vol. 82, pp. 1–8, 2000. [21] S. Mohammed and B. C. Dumville, “Team mental models in a team knowledge framework: Expanding theory and measurement across disciplinary boundaries,” J. Organ. Behav., vol. 22, pp. 89–106, 2001. [22] K. E. Weick, Sensemaking in Organizations. Thousand Oaks, CA: Sage, 1995. [23] L. Argote, Organizational Learning: Creating, Retaining, and Transferring Knowledge. Norwell, MA: Kluwer, 1999. [24] D. Knight, C. L. Pearce, K. G. Smith, J. D. Olian, H. P. Sims, K. A. Smith, and P. C. Flood, “Top management team diversity, group process, and strategic consensus,” Strateg. Manage. J., vol. 20, pp. 445–465, 1999. [25] G. P. Huber, “Organizational learning: The contributing processes and the literature,” Organ. Sci., vol. 2, pp. 88–115, 1991. [26] M. Tripsas and G. Gavetti, “Capabilities, cognition, and inertia: Evidence from digital imaging,” Strateg. Manage. J., vol. 21, pp. 1147–1161, 2000. [27] C. Letti, C. Hienerth, and H. G. Gemuenden, “Exploring how lead users develop radical innovations; Opportunity recognition and exploitation in the field of medical equipment technology,” IEEE Trans. Eng. Manage., vol. 55, pp. 219–233, May 2008. [28] K. E. Weick, The Social Psychology of Organizing. Reading, MA: Addison-Wesley, 1979. [29] U. Neisser, Cognition and Reality. San Francisco, CA: Freeman, 1976. [30] T. M. Amabile, R. Conti, H. Coon, J. Lazenby, and M. Heron, “Assessing the work environment for creativity,” Acad. Manag. J., vol. 39, pp. 1154– 1184, 1996. [31] T. Ravichandran, “Organizational assimilation of complex technologies: An empirical study of component-based software development,” IEEE Trans. Eng. Manage., vol. 52, no. 2, pp. 249–268, May 2005. [32] S. F. Matusik, “An empirical investigation of firm public and private knowledge,” Strateg. Manage. J., vol. 23, pp. 457–467, May 2002. [33] B. L. Simonin, “Ambiguity and the process of knowledge transfer in strategic alliances,” Strateg. Manage. J., vol. 20, pp. 595–623, 1999. [34] J. Penner-Hahn and J. M. Shaver, “Does international research and development increase patent output? An analysis of Japanese pharmaceutical firms,” Strateg. Manage. J., vol. 26, pp. 121–140, Feb. 2005. [35] P. J. Lane, J. E. Salk, and M. Lyles, “Absorptive capacity, learning, and performance in international joint ventures,” Strateg. Manage. J., vol. 22, pp. 1139–1161, 2001. [36] D. D. Clercq and D. Dimov, “Internal knowledge development and external knowledge access in venture capital investment performance,” J. Manage. Stud., vol. 45, pp. 585–612, 2008. [37] P. E. Laughlin, “Ability and group problem solving,” J. Res. Dev. Educ., vol. 12, pp. 114–120, 1978. [38] J. E. McGrath and D. A. Kravitz, “Group research,” Annu. Rev. Psychol., vol. 33, pp. 195–230, 1982. [39] C. Moorman and A. S. Miner, “Organizational improvisation and organizational memory,” Acad. Manage. Rev., vol. 23, pp. 696–723, 1998. [40] A. Hargadon and A. Fanelli, “Action and possibility: Reconciling dual perspectives of knowledge in organizations,” Organ. Sci., vol. 13, pp. 290– 302, May/Jun. 2002. [41] M. Zollo and S. G. Winter, “Deliberate learning and the evolution of dynamic capabilities,” Organ. Sci., vol. 13, pp. 339–351, 2002. [42] S. G. Cohen and D. E. Bailey, “What makes teams work: Group effectiveness research from the shop floor to the executive suite,” J. Manage., vol. 23, pp. 239–290, 1997. [43] W. A. Sheremata, “Centrifugal and centripetal forces in radical new product development under time pressure,” Acad. Manage. Rev., vol. 25, pp. 389–408, 2000. [44] A. Tiwana and E. R. McLean, “Expertise integration and creativity in information systems development,” J. Manage. Inf. Syst., vol. 22, pp. 13– 43, Sum. 2005. [45] M. R. Haas, “Knowledge gathering, team capabilities, and project performance in challenging work environments,” Manage. Sci., vol. 52, pp. 1170–1184, Aug. 2006. [46] M. M. Crossan, H. W. Lane, R. White, and L. Djurfeldt, “Organizational learning: Dimensions for a theory,” Int. J. Organ. Anal., vol. 3, pp. 337– 360, 1995. [47] J. P. Walsh and G. R. Ungson, “Organizational memory,” Acad. Manage. Rev., vol. 16, pp. 57–91, 1991. [48] H. Yli-renko, E. Autio, and H. J. Sapienza, “Social capital, knowledge acquisition, and knowledge exploitation in young technology-based firms,” Strateg. Manage. J., vol. 22, pp. 587–613, 2001. [49] K. E. Weick and K. H. Roberts, “Collective mind in organizations: Heedful inter-relating on flight decks,” Admin. Sci. Q., vol. 38, pp. 357–381, 1993.


[50] R. J. Klimoski and S. Mohammed, “Team mental model: Construct or metaphor?,” J. Manage., vol. 20, pp. 403–437, 1994. [51] J. A. Cannon-Bowers and E. Salas, “Reflections on shared cognition,” J. Organ. Behav., vol. 22, pp. 195–202, Mar. 2001. [52] D. G. Hoopes and S. Postrel, “Shared knowledge, “glitches”, and product development performance,” Strateg. Manage. J., vol. 20, pp. 837–865, 1999. [53] K. Atuahene-Gima, “The effects of centrifugal and centripetal forces on product development speed and quality: How does problem solving matter,” Acad. Manage. J., vol. 46, pp. 359–373, 2003. [54] M. Hoegl and H. G. Gemuenden, “Teamwork quality and the success of innovative projects: A theoretical concept and empirical evidence,” Organ. Sci., vol. 12, pp. 435–449, 2001. [55] M. Hoegl and H. G. Gemuenden, “R&D 2004,” Technol. Rev., vol. 107, pp. 61–71, 2004. [56] J. H. Dyer and N. W. Hatch, “Relation-specific capabilities and barriers to knowledge transfers: Creating advantage through network relationships,” Strateg. Manage. J., vol. 27, pp. 701–719, 2006. [57] N. Kumar, L. W. Stern, and J. C. Anderson, “Conducting interorganizational research using key informants,” Acad. Manage. J., vol. 36, pp. 1633–1651, 1993. [58] G. Szulanski, “Exploring internal stickiness: Impediments to the transfer of best practice within the firm,” Strateg. Manage. J., vol. 17, pp. 27–43, 1996. [59] M. T. Hansen, M. L. Mors, and B. Lovas, “Knowledge sharing in organizations: Multiple networks, multiple phases,” Acad. Manage. J., vol. 48, pp. 776–793, Oct. 2005. [60] P. Bierly and A. Chakrabarti, “Generic knowledge strategies in the U.S. pharmaceutical industry,” Strateg. Manage. J., vol. 17, pp. 123–135, 1996. [61] K. G. Smith, C. Collins, and K. Clark, “Existing knowledge, knowledge creation capability, and the rate of new product introduction in high technology firms,” Acad. Manage. J., vol. 48, pp. 346–357, 2005. [62] J. Cohen, P. Cohen, S. G. West, and L. S. Aiken, Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Mahwah, NJ: Lawrence Erlbaum Associates, 2003. [63] K. Lewis, D. Lange, and L. Gillis, “Transactive memory systems, learning, and learning transfer,” Organ. Sci., vol. 16, pp. 581–598, 2005. [64] A. E. Akgun, J. C. Byrne, H. Keskin, and G. S. Lynn, “Transactive memory system in new product development teams,” IEEE Trans. Eng. Manag., vol. 53, pp. 95–111, Feb. 2006. [65] M. Gittelman and B. Kogut, “Does good science lead to valuable knowledge? Biotechnology firms and the evolutionary logic of citation patterns,” Manag. Sci., vol. 49, pp. 366–382, 2003. [66] L. Fleming and O. Sorenson, “Science as a map in technological search,” Strateg. Manag. J., vol. 25, pp. 909–928, 2004. [67] X. Tong and J. D. Frame, “Measuring national technological performance with patent claims data,” Res. Policy, vol. 23, pp. 133–141, 1994. [68] J. Lerner, “The importance of patent scope: An empirical analysis,” RAND J. Econ., vol. 25, pp. 319–333, 1994. [69] A. B. Jaffe, M. Trajtenberg, and R. M. Henderson, “Geographic localization of knowledge spillovers as evidenced by patent citations,” in Patents, Citations, & Innovations; A Window on the Knowledge Economy, A. Jaffe and M. Trajtenberg, Eds. Cambridge, MA: MIT Press, 2002, pp. 155– 178. [70] E. Sivadas and F. R. Dwyer, “An examination of organizational factors influencing new product success in internal and alliance-based processes,” J. Mark., vol. 64, pp. 31–49, 2000. [71] F. T. Rothaermel and M. Thursby, “University-incubator firm knowledge flows: Assessing their impact on incubator firm performance,” Res. Policy, vol. 34, pp. 305–320, Apr. 2005. [72] P. M. Podsakoff, S. B. MacKensie, J.-Y. Lee, and N. P. Podsakoff, “Common method biases in behavioral research: A critical review of the literature and recommended remedies,” J. Appl. Psychol., vol. 88, pp. 879–903, 2003. [73] J. F. Hair, Jr., W. C. Black, B. J. Babin, R. E. Anderson, and R. L. Tatham, Multivariate Data Analysis, 6th ed. Upper Saddle River, NJ: Pearson Prentice-Hall, 2006. [74] C. Fornell and D. F. Larker, “Evaluating structural equation models with unobservable variables and measurement error,” J. Mark. Res., vol. 18, pp. 39–50, 1981. [75] T. Goles and W. W. Chin, “Information systems outsourcing relationship factors: Detailed conceptualization and initial evidence,” Data Base Adv. Inf. Syst., vol. 36, pp. 47–67, 2005. [76] R. B. Klein, Principles and Practice of Structural Equation Modeling. New York: The Guildford Press, 2005.

687

[77] G. A. Churchill, Jr., “A paradigm for developing better measures of marketing constructs,” J. Mark. Res., vol. 16, pp. 64–73, 1979. [78] D. L. Deeds, “The role of R&D intensity, technical development, and absorptive capacity in creating entrepreneurial wealth in high technology start-ups,” J. Eng. Technol. Manag., vol. 18, pp. 29–47, 2001. [79] N. Amara and R. Landry, “Sources of information as determinants of novelty of innovation in manufacturing firms: Evidence from the 1999 statistics Canada innovation survey,” Technovation, vol. 25, pp. 245–259, 2005. [80] J. E. Sawyer, “Goal and process clarity: Specification of multiple constructs and role ambiguity and a structural equation model of their antecedents and consequences,” J. Appl. Psychol., vol. 77, pp. 130–142, 1992. [81] B. Byrne, Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. Mahway, NJ: Lawrence Erlbaum Associates, 2001. [82] T. D. Little, J. A. Bovaird, and K. F. Widaman, “On the merits of orthogonalizing powered and product terms: Implictations for modeling interactions among latent variables,” Struct. Equation Model., vol. 13, pp. 497–519, 2006. [83] R. C. MacCallum, M. W. Browne, and H. M. Sugawara, “Power analysis and determination of sample size for covariance structure modeling,” Psychol. Methods, vol. 1, pp. 130–149, 1996. [84] F. Rothaermel and A. Hess, “Building dynamic capabilities: Innovation driven by individual-, firm-, and network-level effects,” Organ. Sci., vol. 18, pp. 898–921, 2007. [85] E. H. Kessler, P. Bierly, and S. Gopalakrishnan, “Internal vs. external learning in new product development: Effects on speed, costs, and competitive advantage,” R&D Manag., vol. 30, pp. 213–223, 2000.

Louise A. Nemanich received the B.S. degree in chemical engineering from West Virginia University, Morgantown, WV, and the MBA and Ph.D. degrees in strategic management from the C.T. Bauer College of Business, University of Houston, Houston, TX. She is currently a Clinical Associate Professor of strategic management with the W. P. Carey School of Business, Arizona State University, Phoenix, AZ. She was an executive leader at major multinational firms in the energy industry. Her current research interests include strategic issues in the management of innovation, including absorptive capacity, the tension between exploration and exploitation, improvisation, knowledge transfer, and the role of senior leadership in organizational learning. She has authored or coauthored papers, which are published in The Leadership Quarterly, Decision Sciences Journal of Innovative Education, and the International Journal of Innovation and Technology Management.

Robert T. Keller received the Ph.D. degree in management from The Pennsylvania State University, University Park, PA. He is currently the Baker Hughes Professor of Business Administration with the University of Houston, Houston, TX. He has authored or coauthored more than 100 journal articles and professional papers. His research on technological innovation in R&D organizations has been supported by the National Science Foundation, the Center for Innovation Management Studies at Lehigh University, Shell Oil Foundation, and the German Marshall Fund. He is currently a member of the Editorial Board of the Journal of High Technology Management Research, and the Leadership Quarterly, and has been a member of the Editorial Review Board of the Academy of Management Journal two times. He is the Past Chair of the Technology and Innovation Management Division of the Academy of Management, a member of the Academy Council, and a Charter Member of the Academy of Management Journal’s Hall of Fame. Professor Keller has conducted research and lectured on the management of technology in the U.S., Germany, U.K., Hong Kong, and Mexico. His current research interests include cross-national factors and technological innovation, R&D project teams for innovation and speed-to-market, and the performance of scientists and engineers. Prof. Keller is currently a member of the Editorial Board of the IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT (past Department Editor).

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Dusya Vera received the Ph.D. degree in strategic management from the University of Western Ontario, London, ON, Canada. She is currently an Associate Professor of strategic management with the C.T. Bauer College of Business, University of Houston, Houston, TX. Her research interests include improvisation, organizational learning, and strategic leadership. She has authored or coauthored papers, which are published in journals such as the Academy of Management Review, Organization Science, the Leadership Quarterly, Organization Studies, and Organizational Dynamics.

Wynne W. Chin received the A.B. degree in biophysics from University of California, Berkeley, the MS degree in biomedical/chemical engineering from the Northwestern University, Evanston, IL, and the MBA and Ph.D. degrees in computers and information systems from the University of Michigan, Ann Arbor. He is currently the C.T. Bauer Professor of MIS with the Department of Decision and Information Sciences, C.T. Bauer College of Business, University of Houston, Houston, TX. He was engaged in teaching with the University of Calgary, Wayne State University, and the University of Michigan and has been a Visiting Fellow with the University of Canterbury, Queens University, City University of Hong Kong, and the University of New South Wales. His current research interests include sales force automation, IT adoption, outsourcing, acceptance, satisfaction, group cohesion and negotiation, and psychometric modeling issues. He is a member of the Editorial Board of the Structural Equation Modeling Journal, Journal of Information Technology, and previously a Co-editor of Data Base and a member of the boards of Journal of AIS, Information Systems Research, and MIS Quarterly. He is also the developer of PLS-Graph, the first graphical based software dating back to 1990 to perform partial least squares analysis. Dr. Chin is a member of the Editorial Board of the IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT.