Social Information Systems
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Item Automatically Quantifying Customer Need Tweets: Towards a Supervised Machine Learning Approach(2018-01-03) Kühl, Niklas; Mühlthaler, Marius; Goutier, MarcThe elicitation of customer needs is an important task for businesses in order to design customer-centric products and services. While there are different approaches available, most lack automation, scalability and monitoring capabilities. In this work, we demonstrate the feasibility to automatically identify and quantify customer needs by training and evaluating on previously-labeled Twitter data. To achieve that, we utilize a supervised machine learning approach. Our results show that the classification performances are statistically superior-”but can be further improved in the future.Item Archetypes of Enterprise Social Network Users(2018-01-03) Oettl, Christian; Berger, Thomas; Böhm, Markus; Wiesche, Manuel; Krcmar, HelmutInvestments in enterprise social networks (ESNs) have increased rapidly in recent years. However, an ESN utilization intensity develops slowly, and there are a few well-grounded approaches to understand ESN usage. To elaborate on different archetypes of ESN users, we conducted a case study that comprised 28 interviews with a large IT services company. We present a model to characterize ESN users and classify them as archetypes based on the following two dimensions: individual openness to ESNs and perceived task-fit. We determine six archetypes of ESN users, namely, power users, limited users, reluctant users, repudiators, hidden champions, and question marks. From a theoretical viewpoint, this study contributes to the discussion around user typology of ESN users and the utilization intensity, acceptance, and value contribution of ESNs. In practice, results provide an orientation to organizations that intend to address both ESN users and the organization to increase the utilization intensity of ESNs.Item Social Environment of Virtual Collaboration Using Mobile Social Media(2018-01-03) Yang, Zhaojun; Sun, Jun; Zhang, Yali; Wang, YingMobile social media such as WhatsApp and WeChat greatly facilitate virtual collaboration within and across organizations. Based on the theory of self-interest and collective action, this study investigates how social environment influences user behavior. Corresponding to social capital, weak ties, and adoption thresholds, extrinsic motivation, communication climate and top management support are identified respectively as the main factors of member environment, group environment and organization environment that impact virtual collaboration. The research model hypothesizes that these social-level variables interact with the psychological processes related to technology use at the individual level. The survey results from virtual teams provide supporting evidence to most hypothesized relationships. The findings yield some interesting theoretical and practical implications for the collaborative use of social information systems.Item Exploring Affordances of Slack Integrations and Their Actualization Within Enterprises - Towards an Understanding of How Chatbots Create Value(2018-01-03) Stoeckli, Emanuel; Uebernickel, Falk; Brenner, WalterThe rise of chatbots poses new possibilities to link social interactions within instant messengers with third-party systems and business processes. While many companies use chatbots within the enterprise in the form of Slack apps and integrations, little is known about their affordances. Grounded in a qualitative research endeavour, we conducted 12 explorative interviews in 8 organizational settings to inductively gain rich contextual insights. Our results reveal 14 functional affordances in 4 categories, elucidating how their actualization leads to the perception of higher level affordances and constraints. First, we discuss how chatbots augment social information systems with affordances of traditional enterprise systems, and therefore, enable bottom-up automation. Second, we elaborate on how the actualization of an affordance by one user may facilitate its perception by other users. Thus, we contribute towards a better understanding of how chatbots create value.Item Introduction to the Minitrack on Social Information Systems(2018-01-03) Schmidt, Rainer; Alt, Rainer; Nurcan, Selmin