Knowledge Sharing in Platform Ecosystems through Sponsored Online Communities: The Influence of User Roles and Media Richness

Platform ecosystems are characterized by knowledge boundaries that arise between the platform owner and third-party developers. Although major platform owners such as Microsoft and SAP nurture sponsored online communities to overcome knowledge boundaries in their ecosystem, the peculiarities of such communities are yet to be examined. Drawing upon the lead user and media richness theory, we investigate how different user roles and media types influence the value of a knowledge contribution in such communities. Analyzing one million answers from the SAP Community, we uncovered that both lead users and sponsor representatives are more likely to provide valuable knowledge contributions compared to normal users. Moreover, we show that attachments, code snippets, and links significantly enhance the value of a knowledge contribution. Surprisingly, we find a strong negative moderation effect of code snippets on the contributions of sponsor representatives, but a strong positive moderation effect on the contributions of lead users

Stray Off Topic to Stay On Topic: Preserving Interaction and Team Morale in a Highly Collaborative Course while at a Distance

The coronavirus disease of 2019 (COVID-19) pandemic has prompted schools and universities to shift their teaching to virtual classrooms from one day to the other. As a unique example, we had to virtualize the second half of a two-semester course on human-centered innovation, which heavily relies on direct interaction with and among students in small groups. In going virtual, we found adapting assignments to be only the tip of the iceberg. Despite being familiar with the students, we faced challenges in preserving high levels of creative interaction and in surveying team morale and status. Reflecting on our experiences, we detail solutions related to the lack of creative interaction by fostering off-topic chit-chat and surveying team morale by introducing more explicit communication and seeking team consent. To help teachers adapt to virtual teaching, we discuss how our mitigation approaches, which we developed in an extreme setting that required close, creative collaboration, may apply to virtual teaching in general

Detecting Feature Requests of Third-Party Developers through Machine Learning: A Case Study of the SAP Community

The elicitation of requirements is central for the development of successful software products. While traditional requirement elicitation techniques such as user interviews are highly labor-intensive, data-driven elicitation techniques promise enhanced scalability through the exploitation of new data sources like app store reviews or social media posts. For enterprise software vendors, requirements elicitation remains challenging because app store reviews are scarce and vendors have no direct access to users. Against this background, we investigate whether enterprise software vendors can elicit requirements from their sponsored developer communities through data-driven techniques. Following the design science methodology, we collected data from the SAP Community and developed a supervised machine learning classifier, which automatically detects feature requests of third-party developers. Based on a manually labeled data set of 1,500 questions, our classifier reached a high accuracy of 0.819. Our findings reveal that supervised machine learning models are an effective means for the identification of feature requests

Individual Enterprise Social Network Adoption: The Influence of Perceived Network Externalities and Perceived Social Capital Advantage

Empirical evidence indicates that Enterprise Social Networks facilitate intra-organizational knowledge sharing. While organizations continue to invest in Enterprise Social Networks, many implementation projects fail due to insuffi-cient user adoption. Against this background, this paper investigates factors that influence individuals’ adoption of Enterprise Social Networks. We thoroughly reviewed the existing literature and crafted a comprehensive adoption model. Be-sides commonly known adoption factors, we introduce perceived network exter-nalities and perceived social capital advantage to account for the specific context of Enterprise Social Networks. We tested our model using structural equation modeling and empirical survey data of 155 respondents. Our results show that perceived network externalities are by far the strongest predictor for enterprise social network adoption, followed by perceived enjoyment and perceived social capital advantage. In contrast to other studies, we find perceived usefulness and perceived ease of use to be insignificant