Using external knowledge to improve organizational innovativeness: Understanding the knowledge leveraging process
By Xinchun Wang PhD Candidate Rawls College of Business Texas Tech University Area of Marketing Lubbock, Texas 79409
[email protected] Dennis B. Arnett John B. Malouf Professor of Marketing Rawls College of Business Texas Tech University Area of Marketing Lubbock, Texas 79409
[email protected] and Limin Hou Associate Professor of Marketing East China University of Science and Technology Shanghai, China
[email protected]
February 2015 Forthcoming in the Journal of Business & Industrial Marketing
Using external knowledge to improve organizational innovativeness: Understanding the knowledge leveraging process
Abstract Purpose – The purpose of this paper is to develop a better understanding of the process used by organizations to leverage external knowledge. A model of the knowledge leveraging process is developed, which hypothesizes (1) joint sensemaking is a key antecedent to both explicit and tacit knowledge exchange, (2) a dual role for explicit knowledge exchange (i.e., as an antecedent of both tacit knowledge exchange and absorptive capacity), and (3) absorptive capacity is a key mediator between knowledge exchange (both explicit and tacit) and organizational innovativeness. Design/methodology/approach – The hypothesized model is tested using survey data gathered from over 230 Chinese companies. The results from the analysis of the hypothesized model are compared to ones from a theory-based rival model. The analyses are performed using partial least squares (PLS) analysis. Findings – The results suggest key roles for both joint sensemaking and absorptive capacity in the knowledge exchange process. In addition, our findings provide evidence regarding the interplay between explicit and tacit knowledge exchange and their role in the knowledge leveraging process. Research limitations/implications – The cross-sectional nature of the study provides limited inferences regarding causality. In addition, organizational innovativeness is measured using selfreported, subjective assessments. However, the results provide valuable insights into the knowledge leveraging process. Practical implications – The study increases our understanding of how organizations leverage external knowledge to improve organizational innovativeness. In addition, it provides specific guidance for managers interested in leveraging external knowledge. Originality/value – Knowledge and knowledge management issues are receiving increased attention in the marketing literature. However, due to the complexity involved in transferring and using external knowledge, our understanding of the processes involved is limited. Our study provides some insights regarding how firms leverage external knowledge and, therefore should be of interest to both researchers and practitioners. Keywords joint sense-making; knowledge exchange, absorptive capacity; innovativeness; explicit knowledge; tacit knowledge; leveraging external knowledge Paper type Research paper
Introduction Organizations struggle constantly to develop competitive advantages over rivals (Hunt, 2000). Advantages are often short-lived. As a result, the marketplace is in a constant state of change. To be successful in this environment, managers must find ways to innovate – again and again (Hill et al., 2014). That is, they must continually introduce new processes, products, and/or ideas to the organization (Hult et al., 2004). Innovation is an agent of change for organizations, which often leads to sustainable competitive advantage. The question is, “How do organizations maintain their innovativeness?” Innovation requires companies to develop new knowledge and expertise. Yet, many organizations find it difficult to acquire the necessary capabilities on their own. Consequently, many of them turn to partners for assistance (Lambert and Enz, 2012; Tsang, 2002). For example, when Tesla and Toyota formed an alliance in 2010, they hoped that it would allow both companies to innovate. As Tesla CEO Elon Monk, emphasized “We look forward to learning and benefiting from Toyota's legendary engineering, manufacturing and production expertise” (Tam and Carlton, 2010). However, leveraging the knowledge of others is not easy. Currently, the Tesla-Toyota alliance seems to be unraveling and it is unclear whether the alliance has resulted in any meaningful innovations by either company (Trudell and Ohnsman, 2014). Both companies possess important knowledge and skills. Why are they unable to take advantage of each other’s knowhow? One explanation is that forming an alliance with a knowledgeable partner is not sufficient. Though interorganizational relationships can provide important foundations for organizational learning, the knowledge exchange process is complex (Arnett and Wittmann, 2014). First, when organizations attempt to share knowhow, some information (explicit knowledge) can be codified (e.g., routines and processes), while other information (tacit knowledge) is embedded in the mental models, beliefs, and perspectives of each organization and its employees (Nonaka, 2007). The
2 explicit knowledge can be stored and transferred using information technology systems (Speier and Venkatesh, 2002). In contrast, the tacit knowledge must be learned by doing, using give-and-take processes that allow people to understand the complexities of situations (Wu and Lin, 2013). As a result, organizations that wish to share expertise must use multiple methods to communicate the required knowledge, which adds to the complexity of the knowledge exchange process. Second, the knowledge exchange process can be hampered by other factors. For example, managers from different organizations often have divergent perspectives, which can prevent learning-related alliances from attaining their goals (Das and Kumar, 2010). Divergent perspectives result from managers using different mental models to impose order on the complex and uncertain environments in which they operate (Day and Nedungadi, 1994). Mental models specify what information is important and how it should be interpreted. Research suggests that associates can learn to understand each other’s viewpoints (Selnes and Sallis, 2003). Over time, they make “sense” of each other’s behaviors and communications (Weick et al., 2005). Joint sensemaking activities provide them with a common foundation, which promotes interactive learning (Thomas et al., 2001). Third, the sharing of knowledge is not sufficient to guarantee that organizations will benefit from it. As Dyer and Singh (1998) maintain, partners must generate routines that facilitate knowledge exchange and develop knowledge-absorbing capacities. That is, they must develop absorptive capacity (i.e., the ability to acquire, assimilate, and absorb external knowledge) (Li et al., 2010). Research suggests that absorptive capacity enables organizations to accumulate relevant knowledge bases and to leverage them for future endeavors (Lane and Lubatkin, 1998). Due to its complexity, our understanding of the knowledge leveraging process is limited. To extend knowledge in the area, we investigate the process by which external knowledge influences organizational innovativeness. Specifically, we address two questions. (1) What factors enhance an
3 organization’s ability to leverage information received from a partner to improve its innovativeness? (2) What are the roles of explicit and tacit knowledge exchange in the leveraging process? In order to answer these questions, we develop and test a model of the knowledge levering process (see Figure 1). The model proposes an important role for sensemaking (i.e., as an antecedent to both explicit and tacit knowledge exchange), a dual role for explicit knowledge exchange (i.e., as an antecedent to both tacit knowledge exchange and absorptive capacity), and that absorptive capacity is a key mediator between knowledge exchange (both explicit and tacit) and organizational innovativeness. The study is organized as follows. First, drawing on the organizational learning literature, a theory-based model is developed. Second, the model is tested using data gathered from over 230 Chinese companies. Third, the implications of the results are discussed. Finally, a number of suggestions are made for future research in this area.
A Model of the Knowledge Levering Process Joint sensemaking and knowledge exchange Over time, managers learn to understand the behaviors of the people around them. This process is referred to as sensemaking. It involves the ongoing retrospective development of plausible interpretations that rationalize what people are doing (Weick et al., 2005). It enables people to understand lived experiences through reflection (Woodside et al., 2005). Though research has mainly addressed sensemaking within organizations, sensemaking is especially important in interorganizational relationships (Medlin and Tönroos, 2014). For example, when employees from different organizations work together, they examine the work environment (e.g., tasks, activities, processes, and interactions). They seek and exchange information, ascribe meanings, interpret and explain situations, and determine any required actions (Cecez-Kecmanovic, 2004). These joint sensemaking activities result in a deep understanding of what has happened, what outcomes are
4 likely/unlikely to occur, and the reasoning (i.e., mental models) being used by others (Wilson and Woodside, 2001). These interactions enable organizations with different experiences and ways of operating to jointly develop common understandings (Medlin and Tönroos, 2014). Sensemaking has been linked to a number of factors that enhance organizational innovativeness, including coping with uncertainty (Hill and Levenhagen, 1995; Neill, McKee, and Rose, 2007), knowledge accumulation (“knowledge stock”) (Fang et al., 2011; Yang and Lai, 2012), creativity (Drazin, Glynn, and Kazanjian, 1999), identification of new patterns and trends (Neill, McKee, and Rose, 2007), unlearning (Sinkula, 2002), agenda construction (Möller, 2010), and the development of strategic resources (Wei and Wang, 2011). Joint sensemaking is important because it enables organizations to evaluate the importance of new, external knowledge and judge how it can be integrated into the organization (Day, 2002). Research suggests that sensemaking enhances organizational learning (Thomas et al., 2001). It provides a basis for collaboration by allowing people to find common ground from which they can develop shared meanings (Sinkula, 1994). It complements both knowledge building and decisionmaking processes (Choo, 1996). In general, sensemaking enables decision-makers to understand what factors are important and to “connect the dots” (i.e., to develop causal assertions). Johnson et al. (2004) find strong empirical evidence for a positive relationship between sensemaking and knowledge accumulation. They maintain that sensemaking allows organizations to come to terms with new knowledge and assimilate it into their knowledge stores, which consist of both explicit knowledge (e.g. descriptive information about current market conditions) and tacit knowledge (e.g. an ability to use marketplace knowledge to develop complex market offerings). Therefore, it is posited that: H1: Joint sensemaking is positively related to explicit knowledge exchange.
5 H2: Joint sensemaking is positively related to tacit knowledge exchange. Explicit and tacit knowledge exchange Knowledge is the lifeblood of organizations because it forms the raw materials from which innovations are developed (Filieri et al., 2014). Indeed, as Clausen (2014, p. 398) suggests, innovation can be thought of “as a search process in which firms learn to (re)combine new and old knowledge in new ways.” The process can be used to enhance a variety of organizational processes, including new product development (Li and Calantone, 1998; Pattinson and Woodside, 2008), relationship development (Cannon and Perreault, 1999; Johnson, Sohi, and Grewal, 2004; Selnes and Sallis, 2003), the development of competitive advantages (Ghingold and Johnson, 1998) and the implementation of the marketing concept (Tzokas and Saren, 2004). Given the value of knowledge in innovative organizations, it is not surprising that many companies are interested in exchanging knowhow with others. When sharing expertise, organizations exchange a combination of both explicit and tacit knowledge. Explicit knowledge manifests itself in a variety of ways, including sales data, organizational policies, structures, norms, rules, control mechanisms, patterns of activities, and types of actions (Cecez-Kecmanovic, 2004). It forms the building blocks that are used to learn more complex knowledge (Dhanaraj et al., 2004). For example, an organization can share its explicit knowledge concerning marketplace dynamics, such as changes related to consumer behavior, market structure, and technology. Then, it can provide training (tacit knowledge) to teach others how to use the knowledge to develop effective marketing strategies. Though explicit knowledge is an important component of an organization’s knowhow, it cannot sufficiently capture the more complex elements. For example, during World War I, the French military needed to increase the production of its 75mm field gun. It outsourced production to
6 the United States. Although detailed blueprints (explicit knowledge) for the weapon were sent to U.S. manufacturers, they were unable to successfully produce the guns because necessary skills (tacit knowledge) could not be expressed in the blueprints. A team of French technicians was dispatched to teach them the necessary tacit knowledge (Landes, 1998). In this situation, the tacit knowledge serves as the integrating mechanism for the explicit knowledge (Dhanaraj et al., 2004). The exchange of explicit knowledge can aid the exchange of tacit knowledge. First, when explicit knowledge forms the basis for the tacit knowledge, its exchange is required prior to any attempts to transfer the related tacit knowledge (Filieri et al., 2014). Second, since explicit knowledge can be exchanged easily (e.g., through electronic data interchange systems), it can be exchanged even in transactional relationships (i.e., arms-length transactions) and in the early stages of collaborative relationships. As a result, it may help establish effective and credible exchange relationships, which are required for the transfer of tacit knowledge (Li et al., 2010). Third, explicit knowledge exchange increases the amount of knowledge organizations have in common. Overlapping knowledge bases among organizations provide shared languages and meanings, which support more effective knowledge sharing in the future (Mowery et al., 1996). Therefore, it is posited that: H3: Explicit knowledge exchange is positively related to tacit knowledge exchange. Knowledge exchange, absorptive capacity, and organizational innovativeness Although researchers highlight the distinctions between explicit and tacit knowledge, they are linked in that tacit knowledge, which is constructed from personal experience, forms the basis for explicit knowledge (Polyani, 1958, 1966, 1969). As a result, both types of knowledge are critical for innovation (Brown and Duguid, 2002). For example, when a company has a specific competence, it creates associated explicit knowledge (e.g., procedures and training manuals), which is based on its
7 expertise. The explicit knowledge is imbued with the knowledge that resides in its knowhow and is essential in its efforts to socialize new employees and to educate partners. In addition, as Nonaka (1994) emphasizes, knowledge is enlarged and enriched by the interactions between explicit and tacit knowledge. Therefore, to more fully leverage knowledge, organizations should encourage the symbiotic use of both explicit and tacit knowledge (Jasimuddin et al., 2005). Although knowledge accumulation is important, to be effective, organizations must find ways to leverage their knowledge to improve marketplace performance. An organization’s ability to harness external knowledge to enhance its innovation processes is referred to as absorptive capacity (Cohen and Levinthal, 1990). It is considered an integral part of organizations’ learning systems (Kim, 1998). Specifically, it enables organizations to effectively use managers’ observations and experiences to understand, assimilate, and integrate new knowledge into their knowledge stores and recognize how new knowledge can be used to improve organizational efficiency and effectiveness (Johnson et al., 2004). An organization’s absorptive capacity is the result of a long-term process of investments and knowledge accumulation. The process can be broken down into four subprocesses: acquisition (i.e., the identification and attainment of external knowledge), assimilation (i.e., the analysis and interpretation of external knowledge), transformation (i.e., the combining of existing knowledge with newly assimilated knowledge), and exploitation (i.e., the application of knowledge) (Zahra and George, 2002). The exchange of knowledge with other organizations is crucial for the process. Without new sources of knowledge, organizations would be operating in a vacuum and would not derive any benefit from their absorptive capacity (Fosfuri and Tribó, 2008). Therefore, it is posited that: H4: Explicit knowledge exchange is positively related to absorptive capacity.
8 H5: Tacit knowledge exchange is positively related to absorptive capacity. As previously discussed, absorptive capacity refers not only to an organizations capacity to acquire knowledge but also to its ability to apply external knowledge commercially to achieve its objectives (Cohen and Levinthal, 1990). One factor that enhances organizations’ abilities to meet their objectives is innovativeness. Indeed, the incorporation of new processes, products, and ideas into an organization’s operations is considered one of the most important factors that influence business performance (Hult et al., 2004). Research suggests that absorptive capacity is an important determinant of organizational innovation (Chen et al., 2009; Nätti et al., 2014). Specifically, organizations characterized as having high levels of absorptive capacity develop routines and processes that facilitate the combining of exiting knowledge with newly acquired knowledge (Zahra and George, 2002). As Fosfuri and Tribó (2008, p. 174) emphasize, “Firms endowed with higher levels of absorptive capacity will be able to extract greater benefits from similar stocks of external knowledge, and therefore outperform rivals in their innovation activity.” Thus, it is posited that: H6: Absorptive capacity is positively related to organizational innovativeness.
A rival model Although absorptive capacity is often modeled as a key mediating construct between knowledge exchange and organizational innovativeness, other explanations suggest that knowledge exchange directly affects innovativeness (e.g., Noordhoff et al., 2011; Paswan and Wittmann, 2009). These views focus on the nature of the knowledge exchanged and assume that organizations can share “innovative knowledge,” which will enhance the introduction of new processes, products, and ideas (e.g., Noordhoff et al., 2011). For example, Cavusgil, Calantone, and Zhao (2003) emphasize that tacit knowledge, because it is harder for rivals to replicate, adds to a company’s innovation capability. In addition, absorptive capacity is also modeled as an antecedent to knowledge
9 accumulation (e.g., Lane et al., 2001). Therefore, following Bollen and Long (1992), we compare the hypothesized model with a theory-based rival model in which explicit and tacit knowledge exchange influence organizational innovativeness directly, as well as indirectly through absorptive capacity (see Figure 2).
Method Sample and data collection A sample frame of 300 Chinese companies was developed for the study by interviewing managers and executives, who were enrolled in a part-time executive MBA program in a Chinese university. Potential key respondents were interviewed to assess whether (1) their companies would be appropriate for the study and (2) they possessed the necessary experience and knowledge to answer questions regarding their organizations’ knowledge management practices. The final list of qualified respondents consisted of 300 middle- and high-level managers. Questionnaires were administered by email. Two-hundred forty-three (243) responses were received, which represents an 81% response rate. The median number of years respondents have been with their employers is 7. The majority of respondents (42.8%) work in companies with over 500 employees, 26.8% work at companies with 201 to 500 employees, 16.9% work at companies with 50 to 200 employees, and 13.5% work at companies with less than 50 employees. Over half (50.2%) of the companies represented are manufacturers, 22.2% are involved in high-tech, 16.1% are service organizations, and the remaining 11.5% are from other industries. Measures The study uses multi-item scales to measure four reflective constructs (joint sensemaking, explicit knowledge exchange, tacit knowledge exchange, and organizational innovativeness) and one index (absorptive capacity) (see Appendix). Each item is measured using a 7-point scale (1 = “strongly
10 disagree” to 7 = “strongly agree”). All scales have been used in prior research. Joint sensemaking, explicit knowledge exchange, and tacit knowledge exchange are measured using scales developed by Selnes and Sallis (2003). Innovativeness is measured using a scale developed by Hult et al. (2004). The measurement of absorptive capacity is measured using a scale developed by Jansen et al. (2002). The scales are used in the same context as in the original studies. Therefore, the items did not need to be adapted for the study. However, the items were translated into Mandarin Chinese. We used an iterative process in which four experts fluent in both Mandarin Chinese and English were given the translated scales. (1) The panel members compared the translated scales to the original ones. (2) Recommended changes were incorporated into the translated scales. (3) The scales were redistributed to the panel. This process continued until all members were satisfied that the scales were translated accurately.
Analysis and Results The data are analyzed using partial least squares (PLS) analysis (Wold, 1982). It is a nonparametric technique and, therefore, does not assume that the data are normally distributed. It allows researchers to work with more complex models than other causal modelling techniques. For example, it enables the modeling of both index-based scales and reflective scales (see Arnett et al., 2003). The objective of PLS analysis is the explanation of variance through an iterative ordinary least squares (OLS) procedure and, therefore, is predictive in a regression sense. First, the measurement properties of the constructs are examined. Specifically, the internal reliabilities, convergent validity, and discriminant validity are assessed. Second, the hypotheses represented by Figure 1 are tested. Measurement model In PLS analysis, the measurement model is tested within the imposed structure of the hypothesized model. The means, standard deviations, and intercorrelations of the constructs are shown in Table 1
11 and the measurement model results are provided in Table 2. All measures of internal consistency for the reflective scales are greater than or equal to 0.83, which is well above the 0.70 level recommended by Nunnally (1978) to indicate that a scale demonstrates internal reliability. The average variance extracted for each reflective construct is high (all values are ≥ 0.63). This high average variance extracted coupled with the strengths (𝑥̅ = 0.81) and low standard errors (𝑥̅ = 0.07) of the parameter estimates provide evidence of convergent validity (Wittmann et al., 2009). The Fornell and Larcker (1981) method is used to assess the discriminant validity of the constructs. The results show that the variance shared between each construct and its measures is higher than the variance shared between the construct and other constructs in the model, which provides evidence of discriminant validity. The measurement properties of the absorptive capacity index are assessed using guidelines suggested by Arnett et al. (2003). The results indicate that: (1) the indicator paths have low standard errors relative to their measurement path estimates, (2) the multicollinearity among the indicators is low (all tolerance statistics are below 0.50), and (3) the index demonstrates external validity (i.e., it relates, as theory suggests, to other constructs in the model). These results suggest that the index demonstrates acceptable measurement properties. To test for common method variance, the Harman one-factor test (Podsakoff and Organ, 1986; Scott and Bruce, 1994) was conducted. The test involves including all measurement items in a single principle components factor analysis. Then, the unrotated factor solution is examined. If a single factor emerges or if one general factor accounts for most of the covariance in the variables (i.e., both the independent and dependent variables), then evidence of common method variance exists. All 15 items were included in the factor analysis. The analysis produced five factors, with the
12 first factor explaining 32% of the variance. More importantly, no general factor was indicated by the unrotated factor solution. Therefore, it is appropriate to interpret the structural model. The hypothesized model In PLS analysis, the variance extracted is an important criterion for model assessment (Barclay, 1991). The results of the structural model are shown in Table 3. The hypothesized model accounts for a large percentage of variance in the endogenous constructs: 13% of the variance in explicit knowledge exchange, 37% of the variance in tacit knowledge exchange, 20% of the variance in absorptive capacity, and 27% of the variance in organization innovativeness. Therefore, there is evidence that the structural model is appropriate. The results reveal that 5 out of 6 (~83%) of the structural paths are supported (see Table 3). Joint sensemaking is positively related to both explicit knowledge exchange (γ = 0.37, SE = 0.11) and tacit knowledge exchange (γ = 0.47, SE = 0.09), which provides support for H1 and H2. Explicit knowledge exchange is positively related to tacit knowledge exchange ( = 0.40, SE = 0.11), which provides support for H3. Explicit knowledge exchange is not related to absorptive capacity. Therefore, H4 is not supported. Tacit knowledge exchange is positively related to absorptive capacity ( = 0.34, SE = 0.11), which provides support for H5. Absorptive capacity is positively related to organizational innovativeness ( = 0.53, SE = 0.08), which provides support for H6. The rival model Table 3 also shows the results for the rival model. The model accounts for 13% of the variance in explicit knowledge exchange, 37% of the variance in tacit knowledge exchange, 20% of the variance in absorptive capacity, and 29% of the variance in organization innovativeness. Five out of the eight (~63%) hypothesized paths are supported. However, and most importantly, neither of the two added structural paths is supported. That is, neither of the direct paths from explicit and tacit knowledge
13 exchange to organizational innovativeness is significant. Therefore, the hypothesized model performs better than the rival model.
Discussion What factors enhance an organization’s ability to leverage external information received from a partner to improve its innovativeness? What are the roles of explicit and tacit knowledge exchange in the knowledge leveraging process? To examine these questions, a model of the knowledge levering process is developed and tested (see Figure 1). The results suggest that joint sensemaking is an important antecedent to both explicit and tacit knowledge exchange. The positive relationship between it and tacit knowledge exchange supports research that suggests tacit knowledge exchange requires considerable social interaction among participants (e.g., Arnett and Wittmann, 2014). Since joint sensemaking is a give-and-take process, it allows participants to develop social connections, which foster tacit knowledge exchange (Medlin and Tönroos, 2014). The analysis reveals that joint sensemaking is also positively related to explicit knowledge exchange. Research on explicit knowledge emphasizes that its exchange does not require high levels of social interaction (e.g., Speier and Venkatesh, 2002). As a consequence, the effects of social interactions, such as sensemaking, are often not tested. However, our results provide evidence that social interaction improves the exchange of explicit knowledge. Further investigation of the results reveals that joint sensemaking influences tacit knowledge exchange more strongly than it influences explicit knowledge exchange (γ = 0.47 versus γ = 0.37.). These results are important for understanding the knowledge leveraging process. First, though joint sensemaking has a stronger influence on tacit knowledge exchange (as would be predicted), it also positively affects explicit knowledge exchange. Therefore, they provide evidence that joint sensemaking benefits knowledge exchange in general. Given that knowhow is composed of both explicit and tacit knowledge, these results are important.
14 They suggest that joint sensemaking is a mechanism that can be used to foster the exchange of expertise. Our results suggest that though both explicit knowledge and tacit knowledge exchange influence absorptive capacity, they do so differently. Tacit knowledge exchange directly influences absorptive capacity, which is consistent with research that suggests tacit knowledge is a crucial element of organizational learning. Specifically, it enables organizations to accumulate knowledge, which is a key element of absorptive capacity (Cohen and Levinthal, 1990). As knowledge stores grow, so do organizations’ abilities to evaluate and use outside knowledge. In contrast, explicit knowledge exchange does not directly influence absorptive capacity. Instead, it indirectly influences it through tacit knowledge exchange (indirect effects = 0.14). This provides empirical support for research that suggests explicit knowledge provides an important foundation for the transfer of tacit knowledge (Filieri et al., 2014) and for the interplay between explicit and tacit knowledge (Nonaka, 1994). The lack of relationship between explicit knowledge exchange and absorptive capacity may be an indication that explicit knowledge, in and of itself, may not provide the type of knowledge necessary to act as a knowledge base for absorptive capacity. One argument is that explicit knowledge provides “know-what” (i.e., factual knowledge) but not “know-how” (i.e., skills and expertise) (von Hippel, 1988). For example, a manual describing a manufacturing process contains explicit knowledge. However, the required “know-how” is only imperfectly represented in the description (see Kogut and Zander, 1992). As a result, multiple attempts may be required (often accompanied by instruction from experts) before the desired results are obtained. In contrast, unused “know-what” does not add to an organization’s experiences and expertise. Therefore, the sharing of explicit knowledge, in and of itself, may not add to an organization’s useable knowledge base.
15 However, the results do provide support for the theory that explicit knowledge forms the building blocks that are used to learn more complex knowledge (Dhanaraj et al., 2004). In general, these results provide a better understanding of the roles that explicit and tacit knowledge play in the knowledge leveraging process. The results provide evidence of the importance of absorptive capacity for organization that desire to become more innovative (i.e., the relationship between absorptive capacity and organizational innovation is significant and positive). This provides empirical support for research that suggests organizations endowed with higher levels of absorptive capacity extract greater benefits from external knowledge than those with lower levels, which enables them to be more innovative (Chen et al., 2009; Fosfuri and Tribó, 2008; Nätti et al., 2014; Zahra and George, 2002). To be successful, an organization must find ways to promote organizational innovativeness. We argue that organizational innovativeness results from at least three specific processes: joint sensemaking, explicit knowledge exchange, and tacit knowledge exchange. However, these factors do not promote organizational innovativeness directly. Rather, we hypothesize that these factors influence innovativeness through a key mediating construct—absorptive capacity. To provide a better test of this hypothesis, the hypothesized model is compared to a theory-based rival model, which allows both explicit and tacit knowledge exchange to directly influence organizational innovativeness (see Figure 2). If this model were to fit the data better than our hypothesized one, it would indicate that our model is not valid. The amount of variance explained in the endogenous constructs, by the two models, is almost identical, with the rival model explaining only slightly more variance in organizational innovativeness (R2 = 0.29) than does the hypothesized model (R2 = 0.27). More importantly, the analysis reveals that neither of the added pathways in the rival model is
16 significant. Therefore, there is strong evidence supporting our hypothesized model, which depicts absorptive capacity as a key mediator. Implications for managers The study provides insights for managers interested in leveraging partner knowledge to increase their innovativeness. First, organizations should encourage the use of joint sensemaking. It provides the context for the interpretation and application of external knowledge. Joint sensemaking enables managers from different organizations to reduce confusion by allowing them to ascribe common meanings and interpretations for events and situations (Cecez-Kecmanovic, 2004). Organizations can encourage joint sensemaking in a number of ways, including the promotion of a culture that emphasizes the receptivity of new ideas (Hatch and Schultz, 1997), increasing the number of joint activities with partners (e.g., joint development teams) (Selnes and Sallis, 2003), the development of appropriate governance mechanisms (Mohr and Sengupta, 2002), and supporting the development of social ties among one’s own employees and those of key partners (Ahmed and Rafiq, 2003). Second, given that both explicit and tacit knowledge are critical for organizational innovation, managers should promote organizational cultures that value both types of knowledge and encourage knowledge exchange (Brown and Duguid, 2002). Organizational culture can be influenced by a number of factors, including leadership practices (Schein, 2010), rewards/punishments (Kerr and Slocum, 2005), and socialization processes (e.g., recruitment and socialization processes) (Schein, 2010; Wilson, 2001). Therefore, managers should use these mechanisms to encourage employees to share explicit and tacit knowledge with each other and with key external partners. In addition, organizations should develop processes and procedures for sharing knowledge. Organizations can enhance explicit knowledge exchange by adopting information technology systems, such as electronic data interchange (EDI) systems (Speier and Venkatesh,
17 2002). To improve tacit knowledge exchange, organizations should increase the number of socialization opportunities with partners (e.g., cross-organizational teams) (Arnett and Wittmann, 2014). Third, organizations should develop their absorptive capacities. Research suggests that absorptive capacity plays a critical role in identifying, evaluating, and using external knowledge (Escribano et al., 2008). Organizations can develop their absorptive capacities in two ways. First, they can add to their knowledge stores by encouraging both explicit and tacit knowledge exchange. Second, they can engage in activities that enhance their ability to assimilate and use knowledge. For example, Jansen et al. (2005) find that increases in cross-functional interfaces and the use of job rotations positively influence an organization’s absorptive capacity. Future Research Though the results provide new insights into the external knowledge leveraging process, the crosssectional nature of the study provides limited inferences regarding causality. Therefore, additional testing of the theory is warranted. The knowledge leveraging process has the ability to affect a variety of business processes (e.g., new product development and the development of competitive advantage). Therefore, studies examining these areas are encouraged. Self-report measures are also used in the study. While this approach is common, it is not without concerns for same-source bias. Future research could benefit from using objective or multisource data. Though the respondents in this study were chosen because there was evidence that they possessed the necessary knowledge of their organizations’ knowledge management practices, there is always a concern with self-reported measures regarding the accuracy of people’s perceptions. Thus, a limitation of this study is that it does not use objective measures of organizational innovativeness. However, because the results do
18 suggest that both joint sensemaking and absorptive capacity influence, at a minimum, the perception of organizational innovativeness, further research is warranted.
19 Figure 1. A Model of the Knowledge Levering Process
Figure 2. A Rival Model
20
Table 1. Means, Standard Deviations, and Intercorrelations a,b Constructs
Mean
SD
1
1. Joint sensemaking
3.41
0.99
1.00
2. Explicit knowledge exchange
3.52
0.93
0.36
1.00
3. Tacit knowledge exchange
3.57
0.91
0.50
0.51
1.00
4. Absorptive capacity
3.63
0.78
0.30
0.35
0.41
1.00
5. Organizational innovativeness
3.68
0.85
0.23
0.22
0.33
0.52
a b
2
3
4
5
1.00
Means and standard deviations are based on the average of the indicators for each construct. All correlations are significant at the p < .05 level.
Table 2. Measurement Model Results. Estimates (SEa)
Constructs Joint sensemaking SM1
0.84 (0.05)
SM2
0.84 (0.06)
SM3
0.87 (0.03)
Explicit Knowledge Exchange EKE1
0.83 (0.05)
EKE2
0.86 (0.04)
EKE3
0.78 (0.08)
Tacit Knowledge Exchange TKE1
0.79 (0.06)
TKE2
0.84 (0.04)
TKE3 Absorptive capacity
b
Internal Consistency
0.72
0.89
0.68
0.86
0.63
0.83
̶
̶
0.67
0.86
0.75 (0.07) b
AC1
0.79 (0.06)
AC2
0.73 (0.13)
AC3
0.74 (0.15)
Organizational innovativeness
a
AVE
INN1
0.80 (0.06)
INN2
0.88 (0.03)
INN3
0.76 (0.08)
Path estimates are standardized. Standard errors are estimated using a jackknife procedure. Absorptive capacity is measured as an index and, therefore, measures of reliability and variance extracted are not appropriate.
21 Table 3. Structural Model Results.a
Explicit Knowledge Exchange (EKE) (H1) Joint sensemaking EKE Tacit Knowledge Exchange (TKE) (H2) Joint sensemaking TKE (H3) EKE TKE Absorptive Capacity (H4) EKE Absorptive Capacity (H5) TKE Absorptive Capacity
Hypothesized Model Path estimate (SE)a R2 0.13 0.37 (0.11)
Alternative Model Path estimate (SE) R2 0.13 0.37 (0.09)
0.37 0.47 (0.09) 0.40 (0.11)
0.37 0.50 (0.08) 0.39 (0.10)
0.20 not significant 0.34 (0.11)
0.20 not significant 0.34 (0.12)
0.27 Organization Innovativeness(ORGINN) (H6) Absorptive Capacity ORGINN 0.53 (0.08) 0.48 (0.12) EKE ORGINN ̶ not significant TKE ORGINN ̶ not significant a Path estimates are standardized. Standard errors are estimated using a jackknife procedure.
0.29
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Appendix – Measurement Scales Joint Sensemaking SM1 It is common to establish a joint team to solve operational problems in the relationship. SM2 It is common to establish a joint team to analyze and discuss strategy issues. SM3 The atmosphere in the relationship stimulates productive discussion encompassing a variety of opinions. Explicit Knowledge Exchange EKE1 Our companies exchange information related to changes in end-user needs, preferences and behaviors. EKE2 Our companies exchange information related to changes in market structure, such as mergers, acquisitions, or partnering. EKE3 Our companies exchange information related to changes in technology of the focal products. Tacit Knowledge Exchange TKE1 In the relationship, we frequently adjust our common understanding of enduser needs, preferences and behaviors. TKE2 In the relationship, we frequently adjust our common understanding of trends in technology related to our business. TKE3 In the relationship, we frequently evaluate and, if needed, adjust our routines in order-delivery process. Absorptive Capacity AC1 We quickly recognize the usefulness of new external knowledge to existing knowledge. AC2 We constantly consider how to better exploit knowledge. AC3 Employees have a common language regarding our products and services. Organization Innovativeness INN1 Our company tries out new ideas. INN2 Our company seeks out new ways to do things. INN3 Our company is creative in its methods of operation.
