The effect of governance on global software development: An empirical research in transactive memory systems
Introduction
Over the years globalization of software development activities turned into a common practice. Factors such as the coordination and synchronization of the activities across locations and different time zones, the communication and the knowledge management between distributed teams became familiar among scholars and practitioners. And while research in global software development (GSD) evolves and new practices emerge [1], Herbsleb [2] notes that “there is little reason to expect that these factors will diminish”.
The purpose of this paper is not to try to diminish those factors influencing GSD collaboration, but rather identify them and use them as a tool for a “cause and effect” analysis. Particularly, we are interested in investigating how different decisions that companies take on how to govern their GSD activities, affect knowledge management processes, and more specifically the development of transactive memory systems (TMSs). In the following paragraphs, we elaborate on that purpose.
With the continuous and evolving strategies in global software development, there is a turn of interest towards the challenges and the key issues of managing GSD activities [3], [4], [5]. As a result, global software development governance turned into an emerging field of research, as a subfield of information technology (IT) governance. The purpose of GSD governance is to identify those aspects that are necessary for an effective coordination and collaboration among distributed teams. As Bannerman suggests [6], “governance is the infrastructure needed to ensure the satisfaction of direct and indirect stakeholders”. For instance, when engaged in global activities one can decide to create a captive center in a remote location, or to make a “client–supplier” contract with an external partner. Managers also need to decide on how to structure their teams, and how to allocate tasks among geographically dispersed members. What parts of the projects will be outsourced to the remote partners, and which parts will remain in site? What kind of responsibilities will be delegated to the offshore partners and how much to delegate? These are all questions that frame the governance structure that a company builds for its global activities.
Furthermore, different working practices, geographic proximity and/or legal barriers between remote offices, influence the development of transactive memory systems (TMSs) [7]. Transactive memory is the kind of memory that the team members develop and which helps them identify “who knows what” within the team [8]. In order for the team members to develop such a memory, they have to engage into various transactions through which expertise knowledge is created, shared and stored. As a result, a cognitive system (transactive memory system) is created, where members are aware of each others expertise domain and they are able to access it, update it, share it and facilitate its storage.
Coming back to the main purpose of this study, we pursue the following research question: How do GSD governance decisions affect transactive memory systems? We examine four case studies, in two multi-national companies, and we identify their governance structure. Based on the different governance decisions that each case study employs, we report on the differences in the development of transactive memory systems. We present those differences as a “cause and effect” analysis, in order to explain how different GSD governance modes (a set of governance decisions) affect the collaboration and communication of the distributed teams.
Section snippets
Global software development governance
Research on software development governance is rather recent, and as Dubinsky et al. note [9] it is also the result of an increased focus on the human aspects of software development, such as team work and social collaboration. When the software development activities are globally distributed, the need for a clearly defined governance model increases [10]. For instance, the authors in [11] highlight two “moments” of governance in outsourcing relationships; the moment of the promissory contract
Transactive memory systems
A transactive memory system is a shared cognitive system for encoding, storing, and retrieving knowledge between members of a group. Within TMS, the group members have a collective awareness of each others’ specialized knowledge domain which helps them identify and locate where expertise resides within the group [27]. Another characterization of a TMS is as a shared understanding of “who knows what” within the group.
Recent studies in global software development turned their attention to the
Project overview
The research was conducted in two multi-national companies. The first company, which we call Eco, is developing printing systems on a global scale. Eco is headquartered in the Netherlands, with offices in more than 100 countries and over 20,000 employees. Research and development departments work on hardware as well as software innovations, and they are distributed across nine different countries.
The second company, which we call Ricon, is active in the telecommunications field in more than 180
Data collection and analysis
To analyze the projects and answer our main research question, we use both quantitative and qualitative data. Surveys are the most common method for collecting social network data [42], [43]. Additionally, the purpose of our study is to collect data regarding transactive memory processes, which refer to the cognitive memory of individuals. Therefore, we choose to collect social network data using an online survey. We distributed the survey to all project members of Eco and Ricon. The
Clustering
We begin the analysis with plotting the sociographs of all four cases, from both Eco and Ricon (Fig. 4, Fig. 5). We also applied a color-based graphical convention to help with the interpretation of the sociographs; every color refers to a location, with orange denoting people from site NL, blue is site A, black is site B, yellow is site C and finally green refers to site D. For the structural representation and analysis, we used UCINET [45] a software package for the analysis of social network
Centrality and boundary spanners
The second step in our analysis is to look at the centrality measures of the networks. The centrality measures attempt to describe the position of the actors in the network and identify the most prominent, the most central ones. Here we discuss three of the most important centrality measures: degree centrality, betweenness centrality and closeness centrality.
Degree centrality is the number of direct connections that an actor has. The idea behind degree centrality measure is that an actor who
Network core
The last step is to look at the core–periphery structures of the networks. The core of the network tends to have a more central position, which indicates that the actors of the core are not only very well connected with each other but they are also well connected with the rest of the actors in the periphery. Core–periphery structure in Open Source Software projects (OSS) is sometimes referred to as the onion structure [55]. The core developers are at the center of the onion, they contribute
Lessons learned
There are no “one size fits all” solutions in global software development. Every company is different and every person is different. With this in mind, we proposed a way to identify and analyze the different governance decisions of GSD companies, and observe how such decisions affect transactive memory systems. Our findings are summarized in Fig. 8. We present four different governance modes, based on decisions in business strategy, team structure and composition, and task allocation.
Limitations
The credibility of a study refers to the degree of confidence we have on its conclusions [59]. Although we have addressed the validity of our study from the beginning of this research, it is important to point out several aspects that might be a threat. We do so for two reasons; first because we want to establish the integrity and credibility of our observations and results, and second for suggesting future improvements.
To tackle internal validity in our study, we used data triangulation. We
Conclusion
In this paper, we examined how different governance decisions influence transactive memory systems. We analyzed governance decisions according to business strategy, task allocation and, team structure and composition. Additionally, we used a twofold approach in analysing transactive memory systems, based on the structure as well as the processes involved in such systems. We conclude with the presentation of a “cause and effect” method that we can use to examine how different governance
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