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Social interactions and relationships matter for the well-being of a software development organization. These efforts work as a vehicle to convey the diffusion of information during the software development process. Therefore, software development is considered to be a human endeavor (i.e., intellectually intensive team effort) (Fairley, 2009). Consequently, the human and social aspect of software engineering has turned into an important topic to investigate for both scholars and practitioners who strive to improve organizational efficiency. Today, it is commonly accepted as the productivity of a software development team not only depends on the degree of its members’ experience, skills, and competences but also how well its members socially interact. In fact, it is not surprising to observe that experiencing greater production success heavily relies on how the teams socially communicate, and utilize their interactions. These interactions however, should be governed and coordinated to achieve the desired productivity levels both for an individual and a team. By understanding software development as a social activity (Dittrich, Floyd, & Klischewski, 2002), we begin to investigate social capital as a network-based shared norm or a value that supports collective outcomes and to explore the related factors for software development. The goal is to measure the socioeconomic constraints for organizations by exploring several social and productivity factors that are highlighted by software development participants. A software development organization should enable their teams to use their social capital (for example while creating optimal team configurations) and use this value to understand and measure the needs to improve its economically effective levels.
The process of forming a conceptual definition of social capital and customizing a method for its measurement are both very challenging tasks especially in applied settings. Nevertheless, we claim that social capital should be considered as an adequate value to be identified, measured and used for productivity improvement in a software development environment. There are several context specific definitions of the term social capital emerged and used within the empirical research. Social capital should be a factor to improve the productivity of social structure. It should be defined as a network based (hidden) resource identified by the size and the value of an individual’s social connections. Therefore, understanding the notion of social capital has a potential of improving social aspects of software development.
Our research agenda has two important objectives: First determining factors that are affecting productivity and second, investigating a method or a model to determine a way to measure the correlations among these factors. In our preliminary study (Yilmaz & O’Connor, 2011), we have analyzed the relationship between the social factors potentially affecting productivity and productivity based upon software productivity literature refined by our focus group studies. Consequently, we designed a SEM model to investigate productivity factors and sought empirical support for our proposed approach. Based on the selected data collected from a preliminary study, in this work, first we plan to revise our previous models, and second we design a new SEM model to examine the relationship between social productivity and social capital.
The rest of this paper is organized as follows: In the next section, we introduce several definitions for the social and value dynamics of software development landscapes such as software artifacts, productivity, the value, social capital, social productivity. The following section describes some of the techniques that are used in social network analysis (SNA). The next section describes the analysis models and methods proposed and used in this research. It presents some of our findings that validates our proposed model, and verifies our empirical approach. Finally, the last section concludes the paper with a brief summary of contributions and some directions for future research.