An investigation of the acquisition and sharing of tacit knowledge in software development teams

Knowledge in general, and tacit knowledge in particular, has been hailed as an important factor for successful performance in knowledge-worker teams. Despite claims of the importance of tacit knowledge, few researchers have studied the concept empirically, due in part to the confusion surrounding its conceptualisation. The present study examined the acquisition and sharing o f tacit knowledge and the consequent effect on team performance, through social interaction and the development of a transactive memory system (TMS). TMSs are important for the acquisition and sharing of tacit knowledge, since they enact ‘collective minds’ of teams, and are also a factor in successful team performance. In order to conduct this research, a team-level operational definition of tacit knowledge was forwarded and a direct measure of tacit knowledge for software development teams, called the Team Tacit Knowledge Measure (TTKM ) was developed and validated. To investigate the main premise of this research an empirical survey study was conducted which involved 48 software development teams (n = 181 individuals), from Ireland and the UK. Software developers were chosen as the example of knowledge-worker teams because they work with intangible cognitive processes. It was concluded that tacit knowledge was acquired and shared directly through good quality social interactions and through the development of a TMS. Quality of social interaction was found to be a more important route through which teams can learn and share tacit knowledge, than was transactive memory. However, transactive memory was not a mediator between social interaction and team tacit knowledge, indicating that both provided separate contributions. Team tacit knowledge was found to predict team performance above and beyond transactive memory, though both were significant. Based on these findings recommendations were made for the management of software development teams and for future research directions

Komunitas SLiMS Semarang sebagai ruang inovasi pustakawan

Senayan Library Management System (SLiMS) software formed by SLiMS developers in 2008. Members of the SLiMS Community are librarians who play an important role in making innovations from interactions in an exchange of knowledge and produce various works. This study aimed to identify the Semarang SLiMS Community as a space for librarians' innovation in developing their competence. The method used is qualitative with a case study approach. Collecting data techniques was carried out using semi-structured interviews with four informants. The collected data was then analyzed using thematic analysis. Analysis results showed that the SLiMS Community was a space for librarians to improve their competence, especially in the field of SLiMS development. The patterns identified included motivation, knowledge sharing, collaboration, and innovation. Motivational factors included the desire to share knowledge, increase competence, and raise professional degrees to job demands. The second stage is knowledge sharing, conducted using ‘Sinau Bareng’, ‘Library Clinic’, workshops, and others. The third stage of collaboration is the interaction of community members with other communities to produce innovations. The last stage is innovation, describing outputs or achievements such as products, ideas, and new activities. Innovation is the beginning of the formation of new knowledge. This study concludes that the Semarang SLiMS Community can generate knowledge cycles that lead to innovation

Towards a Reference Architecture with Modular Design for Large-scale Genotyping and Phenotyping Data Analysis: A Case Study with Image Data

With the rapid advancement of computing technologies, various scientific research communities have been extensively using cloud-based software tools or applications. Cloud-based applications allow users to access software applications from web browsers while relieving them from the installation of any software applications in their desktop environment. For example, Galaxy, GenAP, and iPlant Colaborative are popular cloud-based systems for scientific workflow analysis in the domain of plant Genotyping and Phenotyping. These systems are being used for conducting research, devising new techniques, and sharing the computer assisted analysis results among collaborators. Researchers need to integrate their new workflows/pipelines, tools or techniques with the base system over time. Moreover, large scale data need to be processed within the time-line for more effective analysis. Recently, Big Data technologies are emerging for facilitating large scale data processing with commodity hardware. Among the above-mentioned systems, GenAp is utilizing the Big Data technologies for specific cases only. The structure of such a cloud-based system is highly variable and complex in nature. Software architects and developers need to consider totally different properties and challenges during the development and maintenance phases compared to the traditional business/service oriented systems. Recent studies report that software engineers and data engineers confront challenges to develop analytic tools for supporting large scale and heterogeneous data analysis. Unfortunately, less focus has been given by the software researchers to devise a well-defined methodology and frameworks for flexible design of a cloud system for the Genotyping and Phenotyping domain. To that end, more effective design methodologies and frameworks are an urgent need for cloud based Genotyping and Phenotyping analysis system development that also supports large scale data processing. In our thesis, we conduct a few studies in order to devise a stable reference architecture and modularity model for the software developers and data engineers in the domain of Genotyping and Phenotyping. In the first study, we analyze the architectural changes of existing candidate systems to find out the stability issues. Then, we extract architectural patterns of the candidate systems and propose a conceptual reference architectural model. Finally, we present a case study on the modularity of computation-intensive tasks as an extension of the data-centric development. We show that the data-centric modularity model is at the core of the flexible development of a Genotyping and Phenotyping analysis system. Our proposed model and case study with thousands of images provide a useful knowledge-base for software researchers, developers, and data engineers for cloud based Genotyping and Phenotyping analysis system development

ELISA, a demonstrator environment for information systems architecture design

This paper describes an approach of reusability of software engineering technology in the area of ground space system design. System engineers have lots of needs similar to software developers: sharing of a common data base, capitalization of knowledge, definition of a common design process, communication between different technical domains. Moreover system designers need to simulate dynamically their system as early as possible. Software development environments, methods and tools now become operational and widely used. Their architecture is based on a unique object base, a set of common management services and they host a family of tools for each life cycle activity. In late '92, CNES decided to develop a demonstrative software environment supporting some system activities. The design of ground space data processing systems was chosen as the application domain. ELISA (Integrated Software Environment for Architectures Specification) was specified as a 'demonstrator', i.e. a sufficient basis for demonstrations, evaluation and future operational enhancements. A process with three phases was implemented: system requirements definition, design of system architectures models, and selection of physical architectures. Each phase is composed of several activities that can be performed in parallel, with the provision of Commercial Off the Shelves Tools. ELISA has been delivered to CNES in January 94, currently used for demonstrations and evaluations on real projects (e.g. SPOT4 Satellite Control Center). It is on the way of new evolutions

Knowledge sharing as spontaneous order : on the emergence of strong and weak ties

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Coopetition of software firms in Open source software ecosystems

Software firms participate in an ecosystem as a part of their innovation strategy to extend value creation beyond the firms boundary. Participation in an open and independent environment also implies the competition among firms with similar business models and targeted markets. Hence, firms need to consider potential opportunities and challenges upfront. This study explores how software firms interact with others in OSS ecosystems from a coopetition perspective. We performed a quantitative and qualitative analysis of three OSS projects. Finding shows that software firms emphasize the co-creation of common value and partly react to the potential competitiveness on OSS ecosystems. Six themes about coopetition were identified, including spanning gatekeepers, securing communication, open-core sourcing and filtering shared code. Our work contributes to software engineering research with a rich description of coopetition in OSS ecosystems. Moreover, we also come up with several implications for software firms in pursing a harmony participation in OSS ecosystems.Comment: This is the author's version of the work. Copyright owner's version can be accessed at https://link.springer.com/chapter/10.1007/978-3-319-69191-6_10, Coopetition of software firms in Open source software ecosystems, 8th ICSOB 2017, Essen, Germany (2017