Data Analytics, Strategic Leadership, and Value Creation
Permanent URI for this collection
Browse
Recent Submissions
Item From Human Cashiers to Machine: An Empirical Analysis of Self-Service Technologies in the Retail Stores(2025-01-07) Kim, Jeongha; Kwon, Hyeokkoo Eric; Lee, Dongwon; Lee, Hyunseok; Lee, KyuhanUsing detailed data from a leading retailer in Korea, where some stores adopted Self-Service Technologies (SSTs) in their check-out, this study examines the effect of SSTs on sales dynamics, labor productivity, and customer segmentation. Based on the difference-in-differences approach, we find that the SST adoption reduces the number of transactions at a traditional POS counter while increasing a basket value, indicating a trend towards fewer but larger transactions at a POS counter. Naturally, we find fewer cashiers at POS counters after the SST adoption, but their workload has also decreased, leading to lower labor productivity. From a customer segmentation standpoint, we find that the number of unregistered customers decreased after the SST adoption. Having a more identified customer base is essential to retailers as they can use more focused marketing strategies and optimize their operations. We further examine the heterogeneity effect of SST on different customer types and operational periods.Item Data Analytics based on MCDM Methods for Business Sustainability – What's Behind and What Lies Ahead?(2025-01-07) Jefmański, Bartłomiej; Kusterka-Jefmańska, Marta; Gross-Gołacka, Elwira; Błoński, KrzysztofData analytics plays a key role in promoting and implementing sustainable development in business. Thanks to advanced data analysis techniques, enterprises can make more informed decisions, optimize their operations and achieve sustainable development goals. Due to the complex nature of the issue of enterprise sustainability, a particularly useful class of data analysis methods are multi-criteria decision making methods (MDCM). In order to indicate the usefulness of this approach, a bibliometric analysis was carried out in the article in the period 2007-2024. Thematic maps were developed and analyzed, and the thematic evolution was analyzed. The results of the analysis indicated the AHP, ANP and TOPSIS methods as the leading MCDM methods in the implementation of sustainable business development. It also identified the increasing importance of fuzzy modifications of these methods proposed to account for uncertainty in business decision making.Item Performance after Rewards and Penalties: An Empirical Study of Gain-Loss Incentive Structure in an mHealth App(2025-01-07) Kang, Kyungpyo; Jin, Seungwook; Kang, Keumseok; Park, Jae HongThe rapid advancement of digital technology and growing interest in well-being have fueled the mobile health (mHealth) market. mHealth apps aim to support positive behavioral changes and maintain high user motivation through various health-related activities. This study examines the impact of a gain-loss incentive structure within mHealth apps, where users make a monetary deposit that could result in rewards, penalties, or neither, based on performance. Using data from a leading mHealth app platform in South Korea, we explore how gain and loss incentives affect users’ subsequent performance. Our findings show that gains enhance performance, while losses diminish it. Unlike previous studies, we reveal that users who experience losses exhibit risk-averse behavior by lowering their deposit amounts rather than improving performance. These insights improve our understanding of financial incentives in mHealth and highlight potential drawbacks, offering valuable guidance for mHealth app platforms implementing such systems.Item Creating Value from Data the Right Way - A Framework for Assessing Data Value Creation Use Cases(2025-01-07) Kakuschke, Nick; Legner, Christine; Jung, ReinhardThe opportunities for organizations to create value from data are steadily increasing. However, assessing data value to justify the required investments continues to pose challenges for many organizations. To provide a compelling rationale, data value creation use cases must be defined for the specific application context and evaluated in a comprehensive manner. Based on a comprehensive literature review, this study identifies and synthesizes relevant assessment criteria across the dimensions of desirability, feasibility, and viability. The proposed framework supports organizations in prioritizing data value creation use cases to optimize resource allocation and return on investment and provides a foundation for developing tailored decision-making tools. For researchers, this holistic overview of relevant assessment criteria from different literature streams and research areas fosters a more coherent understanding of data value creation in academia.Item Opportunities and Challenges for AI-supported Business Intelligence Systems – A Delphi Study(2025-01-07) Stahmann, Philip; Röver, Jannis; Ciftci, Seyyid; Rodda, Alena; Janiesch, ChristianIn a progressively data-driven environment, business intelligence has emerged as a crucial function fostering efficiency, competitiveness, and innovation. Thus, the growing volume of data necessitates more extensive analysis. In addition, ensuing data complexity requires increasing capacities of skilled workers. Using artificial intelligence appears promising to not only handle but utilize increasing data volume and variety. While artificial intelligence adoption offers numerous opportunities, it also presents challenges alike. The objective of our research is to identify and synthesize the opportunities and challenges associated with incorporating artificial intelligence into business intelligence systems. To this end, we conducted a structured literature review and subsequently evaluated our findings through a Delphi study with experts from practice. Our results contribute to both practice and academia regarding potentials for harnessing opportunities and mastering challenges towards the future of artificial intelligence in business intelligence systems.Item Introduction to the Minitrack on Data Analytics, Strategic Leadership, and Value Creation(2025-01-07) Xu, Tu; Lim, Jee-Hae