Linking social capital to knowledge productivity

In an environment where knowledge is the main organizational driver, the ability to learn fast, adapt regularly to new challenges and acquire technical and interactive capabilities to continuously improve and innovate is crucial (Harrison & Kessels, 2004). This ability is referred to as knowledge productivity (Kessels, 1995, 2001b). Knowledge productivity is the process of identifying, gathering and interpreting relevant information, using this information to develop new capabilities and applying these capabilities to improve and radically innovate work processes, products and services (Kessels, 1995, 2001b). Learning with the intention of innovating requires that relevant parties cooperate. Cooperation is in its nature a fundamentally social activity. In the field of Human Resource Development (HRD) there is a growing interest in studying relations instead of purely individuals (Sanders, 2005). Simply, because when people are at work, connections with others compose the fabric of their daily activities (Dutton & Heaphy, 2003). Insight into how to facilitate and support this social dimension to enable knowledge productivity is an important future challenge in the field of learning and development (Harrison & Kessels, 2004). The relevance of learning in today’s organizational setting is rarely under debate. Despite this, the innovation debate is still strongly biased towards technical innovation (Volberda,Van Den Bosch & Jansen, 2006), thereby neglecting the various workplaces in organizations where innovation can take place (Verdonschot, 2009). Taking into account that planned organizational innovation often does not have the desired effect (Chesbourgh, 2006), academics are increasingly beginning to look at social capital and network theory to explain innovation processes in the day-to-day workplace (Burt, 2005; Obstfeld, 2005; Tsai & Ghoshal, 1998). This study aims to develop a theoretical framework that provides insight into how characteristics of social capital impact knowledge productivity within networks.

This chapter explores relevant literature describing the interaction between knowledge productivity, learning and social capital theory. Given today’s division of labor and the accompanying fragmentation and specialization, knowledge productivity is a fundamental means to achieving collective outcomes that maintain competitive advantage. Knowledge productivity is based on powerful learning processes. There is increasing evidence that learning is inherently a social and situated process that is strongly impacted by characteristics of social capital (Field, 2008; Van Der Sluis & De Jong, 2009). Social capital makes any cooperative group into more than a collection of individuals that only focus on achieving their own private purposes. Social capital connects the dots between people as it aims to understand productive relations. The main proposition of this chapter is that in a knowledge society, the competitive advantage of organizations depends on their ability to adapt to a changing environment through the continuous generation and application of new knowledge (Harrison & Kessels, 2004). Knowledge productivity focuses on these innovation processes. Knowledge productivity is the process of identifying, gathering and interpreting relevant information, using this information to develop new abilities, and applying these abilities to improve and radically innovate work processes, products and services (Kessels, 1995, 2001b). Knowledge productivity as a research concept brings together notions of innovation and learning (Verdonschot, 2009). For this reason, it is a helpful concept as it focuses on the process of learning that is strongly related to specific improvements and innovations of work processes, products and services.

Chapter 2 explores relevant theory on social capital, social networks, learning and knowledge productivity and results in a first framework of their possible interaction. The goal of this chapter is explore in practice how characteristics of social capital relate to knowledge-productive learning processes in networks; and to subsequently determine in what way social relations support to these learning processes. This serves as input for the conceptual framework of this study in Chapter 4. Chapter 2 describes three theoretical perspectives that support this notion:

The five case studies presented in Chapter 3 serve as input to explore the relationship between learning processes in networks and to determine how specific characteristics of social capital support knowledge productivity. The conclusions of the cross-case analysis and the reflection on the selected method are useful input for the design of the conceptual framework in this chapter. The following sections present the findings that serve as input for the design of the conceptual framework. This is done by reflecting on the main research variables of this study: social learning processes, knowledge productivity, social capital and network as unit of analysis. The chapter concludes with the refined research objectives, a revised conceptual framework and a set of research questions.

This chapter presents the research design of the study. The main objective of this study is to design a suitable method to understand how characteristics of social capital within networks impact learning processes that enable knowledge productivity. The main research questions are:

This chapter describes the findings of the second series of case studies. The aim of this study is to provide insight into what characteristics of social capital influence social learning processes that lead to knowledge productivity. The second series of case study consists of a case study research of 17 networks. The study of these networks serves as an input to validate and possibly extend the conceptual framework presented in Chapter 4. Chapter 7 presents a cross-case analysis of the case studies. The research activities focus on 17 networks across 15 organizations geographically dispersed in the Netherlands. The research activities took place between December 2007 and May of 2009. The 17 networks are studied over a longer period in time in order to include the development of social capital within the networks and to be able to study knowledge productive results that often take time to be realized. The research activities in each network took between six to twelve months. During this timeframe a group of co-researchers observed network meetings, frequently interviewed participants and organized reflection meetings. The aim of the reflection meeting is to present preliminary findings of the case study to the members of the network in order to validate the findings. This also leads to the identification of stimulating and inhibiting factors in the network that are presented after each case description. Each network is studied by a minimum of two co-researchers. The validated findings serve as input to organize a discussion meeting within the organization to check if the activities of the network resulted in knowledge productivity within the relevant organization.

Chapter 7 compares the 17 case studies in a cross-site analysis. The within-site analyses of Chapter 6 provide material for comparison of cross-site analyses as it enables to compare the main research variables of this study. The last cross-case analysis of this chapter presents the cases ranked according to their effect variable: knowledge productivity. The effect variable consists of two components: the results of the improvements, innovations and secondly the increased sustainable capability to innovate. In the process of drawing conclusions the qualitative findings have been transformed to Likert scales so that additional statistical computation can be performed. The findings in the cross-case display are rated on a five-point scale (1 = absent; 5 very strongly represented). Based on the numeric data a correlation matrix is constructed, that offers additional insight to what extent the different variables of the study relate to each other. The findings are presented in this chapter. Table 7.3 is used to discuss the patterns from the cross-case analysis. The results of a consultation session with representatives of the case studies are included in this chapter in order to better understand the meaning of the various findings and to share interpretations of the participants that took part in the research.

This chapter presents the conclusions of this study, its possible limitations and exploration of new directions for further research. The first section of this chapter recapitulates the objective of this study and the research questions. The next section presents the main conclusions by elaborating on the main research variables, their constituting elements and relationships. These insights and the main conclusions serve as a starting point to explore two conceptual frameworks. The first framework elaborates on the relation between social learning processes and the cognitive and relational dimension of social capital. The second framework focuses on specific phases in the development of knowledge productive networks. Henceforth, the scientific, practical and societal relevance of this study is reflected upon. In addition, the research design and its limitations are discussed. Observations are made on the internal validity, external validity and reliability of the case study research. The last paragraph explores possible directions for further research and study.