- ZeitschriftenartikelDo All Roads Lead to Rome? Exploring the Relationship Between Social Referrals, Referral Propensity and Stickiness to Video-on-Demand Websites(Business & Information Systems Engineering: Vol. 63, No. 4, 2021) Köster, Antonia; Matt, Christian; Hess, ThomasContent 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.
- ZeitschriftenartikelToken Economy(Business & Information Systems Engineering: Vol. 63, No. 4, 2021) Sunyaev, Ali; Kannengießer, Niclas; Beck, Roman; Treiblmaier, Horst; Lacity, Mary; Kranz, Johann; Fridgen, Gilbert; Spankowski, Ulli; Luckow, André
- ZeitschriftenartikelA Simulation-Based Approach to Understanding the Wisdom of Crowds Phenomenon in Aggregating Expert Judgment(Business & Information Systems Engineering: Vol. 63, No. 4, 2021) Afflerbach, Patrick; Dun, Christopher; Gimpel, Henner; Parak, Dominik; Seyfried, JohannesResearch 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.
- ZeitschriftenartikelReviews Left and Right: The Link Between Reviewers’ Political Ideology and Online Review Language(Business & Information Systems Engineering: Vol. 63, No. 4, 2021) Graf-Vlachy, Lorenz; Goyal, Tarun; Ouardi, Yannick; König, AndreasOnline reviews, i.e., evaluations of products and services posted on websites, are ubiquitous. Prior research observed substantial variance in the language of such online reviews and linked it to downstream consequences like perceived helpfulness. However, the understanding of why the language of reviews varies is limited. This is problematic because it might have vital implications for the design of IT systems and user interactions. To improve the understanding of online review language, the paper proposes that consumers’ personality, as reflected in their political ideology, is a predictor of such online review language. Specifically, it is hypothesized that reviewers’ political ideology as measured by degree of conservatism on a liberal–conservative spectrum is negatively related to review depth (the number of words and the number of arguments in a review), cognitively complex language in reviews, diversity of arguments, and positive valence in language. Support for these hypotheses is obtained through the analysis of a unique dataset that links a sample of online reviews to reviewers’ political ideology as inferred from their online news consumption recorded in clickstream data.
- ZeitschriftenartikelDESERV IT: A Method for Devolving Service Tasks in IT Services(Business & Information Systems Engineering: Vol. 63, No. 4, 2021) Baer, Florian; Sandkuhl, Kurt; Leyer, Michael; Lantow, BirgerNowadays, IT operations devolve many tasks in IT services to internal customers (i.e., IT self-service). The rationale for this service task devolvement is often to reduce the IT personnel’s workload. However, prior research has shown that IT operations often fail to achieve this goal. Existing methods for modeling and analyzing services fall short of supporting service providers in identifying and specifying service tasks suitable to be devolved to (internal) customers. This paper presents, therefore, the first method for devolving service tasks in IT services (DESERV IT). DESERV IT is a compound of four method components encompassing a joint meta-model, a visual notation for modeling IT services, and procedural recommendations. The DESERV IT meta-model extends the meta-model of service blueprinting by means of concepts required to analyze service task devolvement. DESERV IT is evaluated in four evaluation episodes. The results of the evaluation episodes show that DESERV IT is perceived as effective, useful, complete, and generalizable by experts in the IT service management and enterprise architecture discipline. This paper contributes to enterprise modeling by demonstrating the feasibility of DESERV IT in an example case and describing DESERV IT’s evolution during the evaluation episodes. DESERV IT supports practitioners (e.g., request fulfillment managers) in modeling and analyzing IT services.
- ZeitschriftenartikelIdentification of User Roles in Enterprise Social Networks: Method Development and Application(Business & Information Systems Engineering: Vol. 63, No. 4, 2021) Hacker, Janine; Riemer, KaiThe 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.
- ZeitschriftenartikelCall for Papers, Issue 5/2023(Business & Information Systems Engineering: Vol. 63, No. 4, 2021) Spiekermann-Hoff, Sarah; Krasnova, Hanna; Hinz, Oliver
- ZeitschriftenartikelArtificial Intelligence as a Service(Business & Information Systems Engineering: Vol. 63, No. 4, 2021) Lins, Sebastian; Pandl, Konstantin D.; Teigeler, Heiner; Thiebes, Scott; Bayer, Calvin; Sunyaev, Ali
- ZeitschriftenartikelPersonality Profiles that Put Users at Risk of Perceiving Technostress(Business & Information Systems Engineering: Vol. 63, No. 4, 2021) Pflügner, Katharina; Maier, Christian; Mattke, Jens; Weitzel, TimSome information systems research has considered that individual personality traits influence whether users feel stressed by information and communication technologies. Personality research suggests, however, that personality traits do not act individually, but interact interdependently to constitute a personality profile that guides individual perceptions and behavior. The study relies on the differential exposure-reactivity model to investigate which personality profiles of the Big Five personality traits predispose users to perceive techno-stressors. Using a questionnaire, data was collected from 221 users working in different organizations. That data was analyzed using fuzzy set Qualitative Comparative Analysis. Based on the results, six different personality profiles that predispose to perceive high techno-stressors are identified. By investigating personality traits in terms of profiles, it is shown that a high and a low level of a personality trait can influence the perception of techno-stressors. The results will allow users and practitioners to identify individuals who are at risk of perceiving techno-stressors based on their personality profile. The post-survey analysis offers starting points for the prevention of perceived techno-stressors and the related negative consequences for specific personality profiles.
- ZeitschriftenartikelWelcome to Economies in IS!(Business & Information Systems Engineering: Vol. 63, No. 4, 2021) Weinhardt, Christof; Peukert, Christian; Hinz, Oliver; Aalst, Wil M. P.