Best Completed Research Paper
Bridging Fields of Practice: How Boundary Objects Enable Collaboration in Data Science Initiatives
N. Rahlmeier, K. Hopf
Data-intensive technologies draw high investments. Yet, data science projects are reported to suffer from poor collaboration, unrealistic expectations, and difficulties in realizing practical solutions between business and data science units. Moving beyond the currently prevalent approach to study data science practices, our study emphasizes the use of boundary objects between data science and collaborating fields. We interviewed collaborators from diverse fields in six organizational data science initiatives. Our inductive analysis of this rich data source uncovered six distinct mechanisms and six archetypes of boundary objects in data science projects. While archetypes that we label Alignment, Temporary, Collaboration, and Outcome are procedural and appear in selective stages of the data value creation process, the archetypes Infrastructure and Upskilling support projects along the value creation process. The archetypes and their mechanisms inform the management of data science initiatives, help to advance boundary object theory, and provide instruments to study data science initiatives.
Track: IT Strategy, Governance and Management
Best Completed Research Paper (Runner Up)
(When) Is a Summary Really Worth a Thousand Words? – How Textual and Visual Customer Review Summaries Affect Cognitive Load During Online Purchase Decisions
S. Erlebach
Online customer reviews are crucial to inform customers’ online purchase decisions. However, the complex nature and immense volume of customer reviews can easily overload customers’ cognitive resources, with significant negative consequences for customers and online shopping sites. In response, first online shopping sites provide summaries of textual review content (i.e., customer review summaries) alongside individual customer reviews. Building on cognitive load theory, this study examines how customer review summaries in textual and visual presentation formats affect customers’ cognitive load during online purchase decisions. With an incentive-compatible online experiment, we find that displaying customer review summaries reduces cognitive load. While textual and visual review summaries are equally effective, displaying both textual and visual review summaries reduces cognitive load the most. These findings contribute to literature on the design of customer review systems and provide relevant insights for practitioners on how to support customers’ online purchase decisions.
Track: Human Computer Interaction and Social Computing
Best Research-In-Progress Paper
Introducing Generative Feedback to Chatbots in Digital Higher Education
D. Benner
The rapid evolution of the educational landscape, accentuated by the rise of Generative Artificial Intelligence (GAI), calls for a re-evaluation of digital education methodologies to harness new technologies and meet the changing needs of learners. Focusing on the potential of GAI, this study introduces a novel approach called generative feedback and explores its impact on the motivation, learning experience and performance of students in higher education. Therefore, this study presents a prototype that implements a GAI-driven chat using OpenAI’s GPT-4 turbo model to provide feedback to students. Using an overlapping study design that combines a longitudinal field study and a two-stage online experiment, this study investigates how generative feedback affects learners. Initial findings suggest that GAI-driven chatbots can provide meaningful feedback to students and enhance their learning experience, setting the stage for further investigation towards a better theoretical understanding of the design and practical application of GAI-driven chatbot feedback.
Track: Digital Education & Learning
Best Student Paper
Hate Speech and Sentiment of YouTube Video Comments From Public and Private Sources Covering the Israel-Palestine Conflict
S. Hofmann, C. Sommermann, M. Kraus, P. Zschech, J. Rosenberger
This study explores the prevalence of hate speech (HS) and sentiment in YouTube video comments concerning the Israel-Palestine conflict by analyzing content from both public and private news sources. The research involved annotating 4983 comments for HS and sentiments (neutral, pro-Israel, and pro-Palestine). Subsequently, machine learning (ML) models were developed, demonstrating robust predictive capabilities with area under the receiver operating characteristic (AUROC) scores ranging from 0.83 to 0.90. These models were applied to the extracted comment sections of YouTube videos from public and private sources, uncovering a higher incidence of HS in public sources 40.4% compared to private sources 31.6%. Sentiment analysis revealed a predominantly neutral stance in both source types, with more pronounced sentiments towards Israel and Palestine observed in public sources. This investigation highlights the dynamic nature of online discourse surrounding the Israel-Palestine conflict and underscores the potential of moderating content in a politically charged environment.
Track: Student Track
