http://dl.gi.de/handle/20.500.12116/37059 2026-07-23T22:32:17Z 2026-07-23T22:32:17Z Köster, Antonia Matt, Christian Hess, Thomas http://dl.gi.de/handle/20.500.12116/37067 2021-08-26T19:34:59Z 2021-01-01T00:00:00Z

Do All Roads Lead to Rome? Exploring the Relationship Between Social Referrals, Referral Propensity and Stickiness to Video-on-Demand Websites Köster, Antonia; Matt, Christian; Hess, Thomas Content website providers have two main goals: They seek to attract consumers and to keep them on their websites as long as possible. To reach potential consumers, they can utilize several online channels, such as paid search results or advertisements on social media, all of which usually require a substantial marketing budget. However, with rising user numbers of online communication tools, website providers increasingly integrate social sharing buttons on their websites to encourage existing consumers to facilitate referrals to their social networks. While little is known about this social form of guiding consumers to a content website, the study proposes that the way in which consumers reach a website is related to their stickiness to the website and their propensity to refer content to others. By using a unique clickstream data set of a video-on-demand website, the study compares consumers referred by their social network to those consumers arriving at the website via organic search or social media advertisements in terms of stickiness to the website (e.g., visit length, number of page views, video starts) and referral likelihood. The results show that consumers referred through social referrals spend more time on the website, view more pages, and start more videos than consumers who respond to social media advertisements, but less than those coming through organic search. Concerning referral propensity, the results indicate that consumers attracted to a website through social referrals are more likely to refer content to others than those who came through organic search or social media advertisements. The study offers direct insights to managers and recommends an increase in their efforts to promote social referrals on their websites.

2021-01-01T00:00:00Z Sunyaev, Ali Kannengießer, Niclas Beck, Roman Treiblmaier, Horst Lacity, Mary Kranz, Johann Fridgen, Gilbert Spankowski, Ulli Luckow, André http://dl.gi.de/handle/20.500.12116/37065 2021-08-26T19:34:59Z 2021-01-01T00:00:00Z

Token Economy Sunyaev, Ali; Kannengießer, Niclas; Beck, Roman; Treiblmaier, Horst; Lacity, Mary; Kranz, Johann; Fridgen, Gilbert; Spankowski, Ulli; Luckow, André

2021-01-01T00:00:00Z Afflerbach, Patrick Dun, Christopher Gimpel, Henner Parak, Dominik Seyfried, Johannes http://dl.gi.de/handle/20.500.12116/37066 2021-08-26T19:34:59Z 2021-01-01T00:00:00Z

A Simulation-Based Approach to Understanding the Wisdom of Crowds Phenomenon in Aggregating Expert Judgment Afflerbach, Patrick; Dun, Christopher; Gimpel, Henner; Parak, Dominik; Seyfried, Johannes Research has shown that aggregation of independent expert judgments significantly improves the quality of forecasts as compared to individual expert forecasts. This “wisdom of crowds?? (WOC) has sparked substantial interest. However, previous studies on strengths and weaknesses of aggregation algorithms have been restricted by limited empirical data and analytical complexity. Based on a comprehensive analysis of existing knowledge on WOC and aggregation algorithms, this paper describes the design and implementation of a static stochastic simulation model to emulate WOC scenarios with a wide range of parameters. The model has been thoroughly evaluated: the assumptions are validated against propositions derived from literature, and the model has a computational representation. The applicability of the model is demonstrated by investigating aggregation algorithm behavior on a detailed level, by assessing aggregation algorithm performance, and by exploring previously undiscovered suppositions on WOC. The simulation model helps expand the understanding of WOC, where previous research was restricted. Additionally, it gives directions for developing aggregation algorithms and contributes to a general understanding of the WOC phenomenon.

2021-01-01T00:00:00Z Hacker, Janine Riemer, Kai http://dl.gi.de/handle/20.500.12116/37069 2021-08-26T19:34:59Z 2021-01-01T00:00:00Z

Identification of User Roles in Enterprise Social Networks: Method Development and Application Hacker, Janine; Riemer, Kai The importance of gaining insights into informal organizational structures for management purposes is acknowledged by both research and practice. However, “traditional?? approaches to analyzing informal organizational social networks involve significant manual effort and do not scale for larger datasets. Enterprise Social Networks (ESN) have emerged as important tools for informal employee interactions, such as for problem-solving and information sharing. While the analysis of ESN back end data might provide insights into the informal fabric of organizations, and in particular employees’ roles in such networks, there is a lack of systematic approaches for carrying out ESN analytics, such as for user role identification. Following a design science research process, a process-based method to identify user roles from ESN data was developed and evaluated. The method’s efficacy is demonstrated through an in-depth application in a case study of Australian professional services firm Deloitte. In doing so the paper shows how ESN data can be utilized to derive metrics that characterize participation behavior, message content, and structural network positions of ESN users.

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