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ItemSocial Collaboration Analytics for Enterprise Collaboration Systems: Providing Business Intelligence on Collaboration Activities( 2017-01-04)The success of public Social Media has led to the emergence of Enterprise Social Software (ESS), a new type of collaboration software for organizations that incorporates “social features”. Surveys show that many companies are trying to implement ESS but that adoption is slower than expected. We believe that in order to understand the issues with its implementation we need to first examine and understand the “social” interactions that are taking place in this new kind of collaboration software. We propose Social Collaboration Analytics (SCA), a specialized form of examination of log files and content data, to gain a better understanding of the actual usage of ESS. Our research was guided by the CRISP-DM approach. We first analyzed the data available in a leading ESS. Together with leading user companies of this ESS, we then developed a framework for Social Collaboration Analysis, which we present in this paper.
ItemCollaborative Software Performance Engineering for Enterprise Applications( 2017-01-04)In the domain of enterprise applications, organizations usually implement third-party standard software components in order to save costs. Hence, application performance monitoring activities constantly produce log entries that are comparable to a certain extent, holding the potential for valuable collaboration across organizational borders. Taking advantage of this fact, we propose a collaborative knowledge base, aimed to support decisions of performance engineering activities, carried out during early design phases of planned enterprise applications. To verify our assumption of cross-organizational comparability, machine learning algorithms were trained on monitoring logs of 18,927 standard application instances productively running at different organizations around the globe. Using random forests, we were able to predict the mean response time for selected standard business transactions with a mean relative error of 23.19 percent. Hence, the approach combines benefits of existing measurement-based and model-based performance prediction techniques, leading to competitive advantages, enabled by inter-organizational collaboration.
ItemCollaboration for Success in Crowdsourced Innovation Projects: Knowledge Creation, Team Diversity, and Tacit Coordination( 2017-01-04)When innovation projects are crowdsourced, individuals are allowed to form teams and collaborate to develop a successful solution. In this environment, teams will be competing with each other, as only the winning ones take the award home. Should a worker work alone, so that she or he does not need to share the award when she or he wins, or should she or he form a team for a better chance to win? In this paper, we studied the behaviors of workers in the context of crowdsourced innovation projects (CIPs). Building upon the theoretical framework of the organizational knowledge creation theory (OKCT), we linked team performance to team formation factors, including team diversity, team coordination, and task complexity. Our preliminary analysis showed that team coordination was an important factor for success. Team diversity in terms of connectivity was a positive factor towards better performance, whereas other factors were not significant. Our study indicates that workers in CIPs are likely to benefit from collaborations, connectivity diversity, and role diversity.