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Conceptualizing the Impact of Social Capital on Knowledge Creation

Conceptualizing the Impact of Social Capital on Knowledge Creation: Mediating Roles of Socialization and Externalization in a Multi-Stakeholder Agricultural Innovation Platform Research-in-Progress Getahun Semeon IT Doctoral Program, Addis Ababa University [email protected] Million Meshesha Addis Ababa University [email protected]

Monica J. Garfield Bentley University [email protected] Dessa David Morgan State University [email protected]

ABSTRACT

In a collaborative knowledge creation environment knowledge is socially constructed and the nature of knowledge being shared, integrated and converted is dominantly tacit. Achieving the required level of exchange, proliferation and extraction of tacit knowledge in a social network context is intimately tied to social capital and the socialization and externalization modes of knowledge conversion. Empirical support is scarce on how social capital dynamically influences socialization and externalization and the knowledge creation outcomes. Through an in-depth field study to be conducted in two multistakeholder innovation platforms in Ethiopia, this research-in-progress will examine how social capital impact the knowledge creation outcomes with mediating roles of socialization and externalization. Therefore, this study addresses the following two research questions: How do multiple stakeholders interact and create knowledge in a collaborative agricultural innovation platform? How do factors of social capital impact socialization and externalization processes and then the knowledge creation outcome? Keywords

Multi-stakeholder, innovation platform, collaborative knowledge creation, SECI, socialization, externalization, social capital, structural capital, relational capital, cognitive capital INTRODUCTION

In a collaborative knowledge creation environment knowledge is socially constructed in interactions among people (Jakubik, 2008) and it is created, shared, amplified, enlarged and justified through social and collaborative processes (Jun and Weiguo, 2008; Chou and Chang, 2008; Bouwen and Taillieu, 2004; Nonaka, 1994;). Through collaboration different mental models, experiences, practices and expertise are shared and integrated; creativity is enhanced; and a rapid mechanism of feedback is provided (Javadi and Gebauer, 2009; Jun and Weiguo, 2008; Swan, et al., 2000). Because of its potential benefits, collaborative knowledge creation continued to be an area of research focus (Mitchell and Nicholas, 2006). Nonaka’s (1994) SECI (Socialization, Externalization, Combination, Internalization) model is the most widely adopted model (Wipawayangkool, 2011). The model is considered relevant to the study since the focus is on collaborative knowledge creation where the nature of knowledge being shared, integrated and converted is more of tacit (know-how, skill, experience, expertise) and dispersed across multiple stakeholders. In this environment more focus is given to tacit knowledge since it is a basis for generation of new knowledge and creative problem solving and serves as a critical vehicle to successful transfer of best practices within communities (Wang, 2012; Robert, et al., 2008; Greenman, 2006). Achieving the required level of exchange, proliferation and extraction of tacit knowledge in a social network context is intimately tied to social capital and the socialization and externalization modes of knowledge conversion (Wipawayangkool 2011; Costa et al., 2008; June and Weiguo, 2008; Natalya, 2010; Anderson and Mohan, 2011). Thus, in addition to SECI model social capital theory is also proposed as a theoretical framework to understand the process of collaborative knowledge creation (Nahapiet & Ghoshal, 1998) in a multi-stakeholder innovation environment.

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Regardless of the growing number of research in social capital and collaborative knowledge creation little attention has been paid towards synthesizing the way in which different components of social capital dynamically influence the knowledge creation process in the social network context (Jakubik, 2011; Janhonena and Johansonb, 2011; Zheng, 2010; Nonaka and von Krogh, 2009). Previous studies are conducted by taking organization as a unit of analysis and gave more emphasis to knowledge transfer and acquisition between and within organization (McFadyen and Cannella, 2004). The mechanism of knowledge creation through integration of tacit knowledge in the context of complex relationships involving multiple and diverse stakeholders is not well addressed. Such environment is characterized by stakeholders who are loosely coupled and coordinated in a more voluntary basis where leadership which is one of the enabling factors of knowledge creation (Nonaka, 2000) is not perfectly working. In addition, little is known about how social capital impact socialization and externalization modes of knowledge creation in the context of an innovation platform formed by multiple stakeholders. In this research, we address these limitations by studying how social capital impact the knowledge creation outcomes in the context of loosely structured multi-stakeholder agricultural innovation platform with particular reference to the mediating roles of socialization and externalization. Therefore, this study addresses the following two research questions: • •

How do multiple stakeholders interact and create knowledge in a collaborative agricultural innovation platform? How do factors of social capital impact socialization and externalization processes and then the knowledge creation outcome?

The study will be conducted by taking multi-stakeholder agricultural innovation platforms named Agricultural Development Partners Linkage Advisory Councils (ADPLACs) in Ethiopia as a case. The platform have been established since 2008 at Federal, Regional, Zonal, District/Wereda and Kebele levels to solve extensive problems of the agricultural sector. The rest of this paper is structured as follows: the next section will provide the theoretical framework with possible propositions and the conceptual framework for the study. The next section will present the methodology that will be used in the study. The final section constitutes the concluding remark and major contributions of the study. THEORETICAL FRAMEWORK Definition of Knowledge

Knowledge is defined as a mix of framed experience, important values, contextual information, and expert insight (Davenport and Prusak, 1998) which exists at individual, group and organizational levels (Hahn and Subramani, 2000). This study follows the commodity view which considers knowledge as an object that can be found outside individual and captured, stored, manipulated and transferred (Nonaka and Takeuchi 1994, 1995; Davenport and Prusak 1998; Brown and Duguid, 2000). Knowledge is divided into tacit and explicit. Tacit knowledge is an unarticulated mental model, rooted in senses, values, emotion, intuition, skills, commitment, action, routines, procedures and implicit rules of thumb which are shaped by the individual’s past experience (Nonaka and von Krogh, 2009; Hargadon & Fanelli, 2002; Nonaka et al. 1996, 2000). Tacit knowledge involves technical dimensions (know-how, crafts, skills that apply to specific contexts) and cognitive dimensions (mental models – schemata, paradigms, beliefs and viewpoints that provide perspectives that help individuals to perceive and define their world) (Nonaka, 1994). Although tacit knowledge is hard to formalize and communicate (Nonaka, 1994) it can be accessible through consciousness if it leans towards the explicit side of the continuum (Nonaka and von Krogh, 2009). According to the commodity view, tacit knowledge develops through the process of conversion between tacit and explicit knowledge. Studies confirm that tacit knowledge can be converted to explicit knowledge by “reflection in action” (Schön, 1983) by the use of metaphor and analogy (Nonaka, 1994) or by using mentoring and storytelling. The technical dimension of tacit knowledge, i.e., know-how and skill can still be transferred in a more implicit way like apprenticing, observation and mentoring, imitative application, participating in routines, and personal experience (Leonard and Sensiper, 1998; Nonaka, 1994; Spender, 1996; McIver, et al., 2012) while the cognitive form of tacit knowledge can be articulated through metaphor and analogy (Nonaka, 1994). Explicit knowledge is defined as formal and systematic knowledge that can be easily articulated in the form of text, diagrams, and product specifications and presented in the form of reports, documents, and manuals (Sunassee and Sewrya, 2002; Chou and Chang, 2008; Smuts, et al., 2009). In knowledge application and creation more emphasis is given to the tacit form of knowledge (Wang, 2012) because it enables dynamic responses to context-specific problems (Vat, 2004); it is a critical vehicle to successfully transfer best practices within communities (Greenman, 2006); it is the basis for creative problem solving and innovation (Robert, et al.,

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2008; Wang, 2012); and it ensures core competitiveness (Zhu, et al., 2007). On the other hand, explicit knowledge has limited value in guiding social practice since it fails to represent the practical circumstances surrounding individual tacit knowledge acquisition (Nonaka and von Krogh, 2009). The application of explicit knowledge rather depends on tacit knowledge which is embedded in the social practice in the form of hidden “rules” of performance, procedures, problem understanding, problem solutions, and tasks that have the potential to be articulated (Nonaka and von Krogh, 2009). Knowledge Creation

According to the knowledge-based theory knowledge creation occurs through the alteration between tacit and explicit knowledge, one enhancing the other through the processes of socialization, externalization, combination, and internalization (SECI) (Nonaka and Takeuchi, 1995). Tacit and explicit knowledge dynamically interact with each other in creative activities by individuals and groups (Janhonena and Johanson, 2011). This study builds on the basic assumption that new knowledge is created in a social context based collaborative behaviour and collaborative group environment that facilitates group creativity. Collective Knowledge Creation is a process in which groups of individuals like communities of practice (CoPs) learn and develop knowledge together through social interactions (Jakubik, 2008). Knowledge creation is a social process where individuals are involved in tacit knowledge sharing and concept creation (Korgh, 1998). In such collaborative environment knowledge emerges through discussions and active dialogues among members of the group aiming at achieving a shared understanding (Jakubic, 2008). Socialization

The direct exchange of tacit knowledge through socialization is critical for knowledge creation and the process of innovation (Greiner, et al., 2007). Socialization facilitates the exchange of tacit knowledge via joint activities including sharing experiences, brainstorming, dialoguing, commenting, discussing and conceptualizing (Chen, 2008; Jakubik, 2008) with the final outcome of creating tacit knowledge such as shared mental models and technical skills (Chen, 2008). Specific meanings and understandings are extensively negotiated through socialization (Nahapiet and Ghoshal, 1998). In the socialization process, personal subjective knowledge are socially justified and brought together with other’s knowledge so that the knowledge keeps expanding (Janhonena and Johanson, 2011). Socialization requires ‘ba’ (Nonaka and Konno, 1998) which could take the form of physical space (meeting room), virtual space (e-mail or a virtual community) or mental space (shared ideas and mental models). Socialization ‘ba’ creates a trustful atmosphere among actors, increases social cohesion and it is extremely important in a heterogeneous multi-actor innovation network where the background of the networked actors is also very different (Wu, et al. 2010). Socialization through dialoguing requires language, communication skills, listening skills, argumentation, questioning, trust, empathy, willingness to help, and positive feelings (Jakubik, 2008; Korgh, 1998) Externalization

Externalization is the articulation of tacit knowledge into comprehensive forms of explicit knowledge through the process of concept creation triggered by dialogue, metaphor, hypotheses or collective reflection (Chen, 2008; Senaratne and Sxton, 2008). Individuals justify their beliefs through observation of objects, events, and relationships and make their knowledge increasingly explicit along the continuum (Nonaka et al. 1996). According to Jakubik (2008), creation of new knowledge may require innovative vocabulary that can correctly represent the new knowledge and easily understood by the community. Storytelling, such as on the failed attempts to the implementation of a technology (Korgh, 1998) may have significant role in making personal knowledge explicit. However, some part of the tacit knowledge becomes a basis for explicit knowledge (Nonaka and von Krogh, 2009). Knowledge loses some of its “tacitness” through the process of externalization and become more explicit (Nonaka and von Krogh, 2009). In the externalization ‘ba’ actors are engaged in development of further innovative product, service or process ideas (Harmaakorpi & Melkas, 2005). In the example of a software engineer named Tanaka (Nonaka and Takeuchi, 1995), after the group members discussed and experimented together at the socialization stage with the clear understanding of the baking skills, the next step for the group was externalizing their tacit knowledge in the form of creating a prototype which was delivered to the business unit as explicit knowledge carrier (Wu, et al., 2010). Therefore, as we have discussed above, the two critical and relevant processes of collaborative knowledge creation are socialization and externalization. The two social constructs can be further analyzed and understood by social capital (Nahapiet & Ghoshal, 1998). Next, we discuss social capital and its dimensions and how it contributes to knowledge creation.

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Social Capital

Social capital is defined as the interpersonal relationships of a person, as well as the resources embedded in those relationships (Nahapiet & Ghoshal, 1998). It is a collective resource emerging from and embedded inside a social network, as a result of the interconnected relationship of its members (Nov, et al., 2012). The three dimensions of social capital include structural, cognitive and relational capital. Structural capital mainly refers to the degree of closure or interconnectedness among members of the network (Nahapiet & Ghoshal, 1998) and represents configurations and patterns of connections between people (Zheng, 2010). It constitutes properties including network density, connectivity and hierarchy and represented by network size, structural holes, tie strength and centrality (Zheng, 2010). Tie strength is strength of relationship among member of the social network. Tie strength can be network closure (Colman, 1990) where social capital is created by a network of strongly interconnected elements or structural hole (Burt, 2001) where social capital is created by a network in which people can broker connections between otherwise disconnected segments. From the generation of new knowledge perspective, structural hole or sparse network allows the group to have access to new ideas from people with diverse perspectives, different outlooks, varying interests, and diverse approaches to problems (Rost, 2011). Relational capital refers to personal relationships individuals have developed with each other through a history of interaction such as trust, obligations and expectation, and identification (Nahapiet and Ghoshal, 1998). Trust is defined as assuming the best when interpreting another’s motives and actions (Zheng, 2010). High trust increases the willingness of people to engage in social exchange, cooperative interaction and innovation (Farshchi and Brown, 2011). Norms are more related to organizational culture in the form of implicit rules that regulate members’ behaviour by demonstrating what are appropriate or inappropriate attitudes and behaviors (Zheng, 2010). Risk taking cultures, rewards for change and openness and team work are some of the dimension of norms identified by different authors for promoting knowledge creation (Zheng, 2010). Relational capital captures the quality of the relationships that increases the chance of innovation-inducing interactions (Zheng, 2010). Cognitive capital is embodied in attributes like shared code or paradigm that facilitates a common understanding of collective goals and proper ways of acting in a social system (Tsai and Ghoshal, 1998). It is embodied in attributes such as shared representations, behavioral and linguistic codes, systems of meaning, paradigms, understandings and interpretations, as well as shared vision and/or a set of common values that facilitate communication in a group and collective knowledge creation (Tsai and Ghoshal 1998,; Nahapiet & Ghoshal, 1998). It refers to a mutually held cognitive frame of reference and common knowledge between the interaction partners. Shared vision is a construct deemed significant in innovation studies (Zheng, 2010) and it refers to a common mental model of a future state by a group or members of an organization (Zheng, 2010). Cognitive dimension of common set of goals and values as well as shared languages, codes and practices facilitate the process of social interaction. Base on the above discussion on socialization and externalization processes of knowledge creation and the structural, relational and cognitive dimensions of social capital, the next section will discuss how dimensions of social capital impact socialization and externalization constructs and then the knowledge creation outcome. Construct Relationship, Propositions and Conceptual Model

Judging from existing findings, network size (greater social contacts) has a generally positive influence on generation of new knowledge through creating access to a large volume and diversity of information and resources (Zheng, 2010). Tie strength also enables members of the network to develop shared understandings, habits, trust, and a base of language and experience that facilitate smooth interaction (McFadyen and Cannella, 2004). The impact of structural holes and network closure can be divided in to two socialization stages: problem perception and idea formation stage and innovation development and implementation stage (Zheng, 2010). Structural holes are more important at the stage of identifying different bottlenecks of agricultural sector and identifying innovative ideas through brainstorming sessions among wider and diverse stakeholders in the sector. Network closure/tie strength are more relevant at the time of developing and implementing problem solving solutions (Zheng, 2010). We use the structural hole instead of network size since more network size means more contacts with more disconnections and less density (Zheng, 2010). Proposition 1: Structural capital has positive impact on socialization through creating smooth interaction and access to a large volume and diversity of information and resources Through relational capital stakeholders are able to socialize as well as access and leverage knowledge resources embedded in relationships (Nahapiet & Ghoshal, 1998). Trust and norms are considered as key dimensions of relational capital impacting

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the collective knowledge creation. Trust makes individuals to decide on contributing their knowledge freely in the course of socialization with the intention of enhancing the success of the collaborative effort. In the same manner, norms contribute positively to the socialization process by providing structure for the team discussion (Deeter-Schemlz and Ramsey 2003), by promoting a cooperative motivational environment through encouraging risk taking and tolerance of mistakes (Caldwell and O’Reilly 2003). Proposition 2: Relational capital positively impact on socialization through developing trust and trustworthiness in relationships and providing structure and cooperative motivational environment for interaction. Cognitive capital facilitates exchange and integration of tacit knowledge through socialization process by providing shared understanding (Nahapiet and Ghoshal 1998) and building shared cognitive ground (Nonaka & Takeuchi, 1995). Shared experience increases joint cognitive ground because building on overlapping experience helps the listener to sense intuitively what the speaker is trying to express (Nonaka & Takeuchi, 1995). In the process of building joint cognitive ground, individual actors reflects on their own experiences, uses language to remind themselves of what they already know, thematize certain circumstances, and discuss them with others (Tsoukas, 2003). Cognitive capital also impact the externalization process by providing shared representations or mental models, linguistic codes or innovative vocabulary, shared collective narratives in the form of stories, metaphors, systems of meaning, paradigms, understandings and interpretations (Jakubik, 2008; Nahapiet & Ghoshal, 1998). Shared mental model which is a socially constructed symbolic representation of reality serves as a bonding mechanism that facilitates the integration of knowledge and encoding of tacit knowledge (Zheng, 2010; Inkpen and Tsang, 2005). Proposition 3: Cognitive capital positively impact socialization through building shared cognitive ground Proposition 4: Cognitive capital positively impact externalization by providing shared mental models, language and codes and collective narratives Regarding the impact of socialization on externalization, the interaction among actors exposes them to each other’s tacit understandings, codes, and language systems. The social interaction is a means to articulate and amplify the tacit knowledge possessed by individuals and group (Nonaka, 1994; Nonaka et al., 2000). The outcome of socialization is creating tacit knowledge such as shared mental models and technical skills (Chen, 2008) which are prerequisites for externalization. Proposition 5: Socialization positively impact externalization through building shared understanding of tacit knowledge, language and codes The knowledge creation outcome include agricultural technology, product and process innovations; new agricultural techniques and practices, policies, strategies, enhanced capacity to act that enable improved or new definition of problems and solution, more effective performance based on the newly acquired knowledge and effective group decision making (Nonaka and von Krogh, 2009). These outcomes are results of socialization and externalization processes through shared insights, language, mental models, knowledge about expertise, problem-solving capabilities, etc. Socialization impact the collective knowledge creation outcome through the creation of group tacit knowledge (Nonaka and von Krogh, 2009). Externalization impact knowledge creation outcome through the articulation of such socially constructed tacit knowledge into explicit forms of knowledge through concept creation (Wu, et al., 2010). Proposition 6: Socialization has positive impact on collective knowledge creation outcome through creation of group tacit knowledge Proposition 7: Externalization has positive impact on collective knowledge creation outcome through concept creation Based on the above propositions the following conceptual framework has been proposed to guide our research. Structural Capital

Socialization Knowledge Creation Outcome

Relational Capital Externalization Cognitive Capital

Fig. 1: Conceptual Model of Collective Knowledge Creation

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METHODOLOGY

This case study research applies a mixed method approach by combining both qualitative and quantitative methods. The quantitative aspect uses social network analysis (SNA). Based on the recommendation of experts from Ministry of Agriculture, two most successful Zonal ADPLACs (West Arsi Zone ADPLAC & East Shewa Zone ADPLAC) are selected as research site from Oromia Regional State. The population of the study constitutes all key stakeholders involved in the two platforms. 100 respondents represented from each category of stakeholders will be involved in the quantitative social network analysis study since it requires larger number of respondents. For the qualitative study, sampling of the interviewees will be based on the results of the social network analysis which identifies individuals at central network position and reach in information. About 30 respondents will be involved in the qualitative study. The quantitative data will be collected using standard questionnaire developed for studying social network which is going to be customized for our particular context. Semi-structured interviews will be conducted with key stakeholders in the two innovation platforms. The theoretical frameworks used for the study (Social Capital Theory and SECI model) will inform the interview protocol. Pertinent documents will be analyzed in order to understand the research context and key processes involved in collective knowledge creation. The data collected will be analyzed using both quantitative and qualitative analytical techniques by using UCINET software for the SNA and NVivo 10 for qualitative data analysis. CONCLUSION

The study investigates how social capital impacts the knowledge creation outcomes in the context of loosely structured multistakeholder agricultural innovation platform with particular reference to the mediating roles of socialization and externalization. Based on Nonaka’s (1994) SECI model and social capital theory a conceptual model of collaborative knowledge creation have been proposed. The study will have both theoretical and practical contribution. In the case theoretical contribution, the concept of linking social capital with socialization & externalization to explain the creation of knowledge through integration of tacit knowledge of diverse and loosely coupled stakeholders is the major contribution to the literature related to collaborative knowledge creation. Testing and validation of the conceptual model proposed could also contribute to the development of collaborative knowledge creation theory. The study will contribute for better understanding of the emerging community view of knowledge and social processes of knowledge creation. It also leads to the oprationalization of the western concept of social capital, socialization and externalization in the context of a different culture and social practices of third world country like Ethiopia. From the practical contribution perspective, the identification of patterns of interaction among stakeholders, the processes of exchange and integration of their tacit knowledge and identification of different factors impacting the process may contribute to the development of a more effective strategy of enhancing social capital that could promote collaborative knowledge creation. REFERENCES

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