Unrevealing the Dynamic Impact of Extended Reality Adoption on Offline Channel Performance

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Fang, Jie; Xiong, Bingqing; Li, Wilson; Song, Xiaofei

Abstract: The substantial attention of extended reality (XR) technology poses challenges ... Expand

Abstract: The substantial attention of extended reality (XR) technology poses challenges for multichannel retailers. On the one hand, retailers' adoption of XR can enhance consumers' online shopping experience and stimulate their purchases. On the other hand, XR technology replace the necessity for multichannel retailers to operate physical stores, which reflects the need to optimize channel structures while adopting XR. To ascertain how the multichannel retailers can outperform under such circumstances, this study combines the data sources of online website information and offline transaction information for over 1,100 global multichannel retailers from 2018 to 2022, thus involves 13497 monthly-brand observations. By using the fixed-effects model, the current study found that XR adoption duration has a non-linear impact on offline store conversion rate, which can initially increase customers' purchase decision in offline stores, but then turns to result in the negative impacts. Moreover, there are nuanced effects for XR with and without the affordance of create-ability. Findings of the paper aims to provide managerial implications for multichannel retailers to optimize channel structure and adopt XR technology for creating positive cross-channel synergy. Collapse

Topics: user experience technology adoption website information system use database system

Methods: Mann Whitney U test nonparametric test instrumental variables estimation difference in differences statistical hypothesis test

Theories: theory of affordance

Barriers to Data Sharing among Private Sector Organizations

2023 | HICSS | Citations: 1

Authors: Fassnacht, Marcel; Benz, Carina; Heinz, Daniel; Leimstoll, Jannis; Satzger, Gerhard

Abstract: In today’s digital world, sharing data among private sector organizations to rea ... Expand

Abstract: In today’s digital world, sharing data among private sector organizations to realm mutual benefits, such as innovation and value co-creation, is considered a promising yet barely explored and realized approach. Although private sector organizations are pursuing data sharing, successful real-world examples are sparse due to a multitude of barriers. However, knowledge on barriers to data sharing among private sector organizations is scarcely existent in scientific literature. Therefore, we apply an exploratory research approach by triangulating insights from fourteen expert interviews and a systematic literature review to identify barriers which we group along five distinct perspectives. By exploring the multi-faceted barriers to data sharing among private sector organizations, our work contributes to a better understanding of data sharing in this field and lays the foundation for future studies. For practitioners, we identify key challenges to successful data sharing among private sector organizations and, hence call for additional endeavors in data sharing. Collapse

Topics: database system digital ecosystem

Methods: qualitative interview literature sample structured literature research literature study triangulation

Regulation of data-driven market power in the digital economy: Business value creation and competitive advantages from big data

2023 | Journal of Information Technology | Citations: 0

Authors: Fast, Victoria; Schnurr, Daniel; Wohlfarth, Michael

Abstract: Recent high-profile antitrust cases and policy proposals have put a spotlight on ... Expand

Abstract: Recent high-profile antitrust cases and policy proposals have put a spotlight on the relationship between firms’ access to big data and sustained competitive advantages in digital markets. In Europe, concerns about data-driven market power have led policymakers to propose far-reaching regulations of information technology (IT) in these markets. Despite the global policy relevance, the regulation of big data and its competitive effects has so far received little attention in information systems (IS) research. This article addresses this research gap by developing an overarching framework for future IS research on the role of IT for the regulation of data-driven market power. The proposed research framework builds upon a three-part analysis: First, we review the academic literature and show that there is extensive, although nuanced, empirical evidence for business value creation from big (user) data. Second, we draw on the resource-based view of the firm and recent policy reports to derive six facilitating factors that enable firms to establish market power based on sustained data-driven competitive advantages. Third, we characterize three regulatory approaches to govern market power and competition in data-driven digital markets: (i) empowering consumers, (ii) data openness, and (iii) limiting data scale. For each of these approaches, we highlight the key role of IT artifacts in mediating the effect of regulatory rules on actual practice. Collapse

Topics: electronic market competitive advantage organizational value value creation big data

Methods: design artifact machine learning literature study theory development archival research

Theories: transaction cost economics resource based view of the firm

Trending topics workshop

2023 | Information Systems Journal | Citations: 0

Authors: Eckhardt, Andreas

Abstract: ... Expand

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Introduction to Analytics and Decision Support for Green IS and Sustainability Applications

2023 | HICSS | Citations: 0

Authors: El-Gayar, O.; Leung, P.; Scharl, A.

Abstract: ... Expand

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Near-Infrared Spectroscopy for Bladder Monitoring: A Machine Learning Approach

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Fechner, Pascal; König, Fabian; Kratsch, Wolfgang; Lockl, Jannik; Röglinger, Maximilian

Abstract: Patients living with neurogenic bladder dysfunction can lose the sensation of th ... Expand

Abstract: Patients living with neurogenic bladder dysfunction can lose the sensation of their bladder filling. To avoid over-distension of the urinary bladder and prevent long-term damage to the urinary tract, the gold standard treatment is clean intermittent catheterization at predefined time intervals. However, the emptying schedule does not consider actual bladder volume, meaning that catheterization is performed more often than necessary, which can lead to complications such as urinary tract infections. Time-consuming catheterization also interferes with patients' daily routines and, in the case of an empty bladder, uses human and material resources unnecessarily. To enable individually tailored and volume-responsive bladder management, we design a model for the continuous monitoring of bladder volume. During our design science research process, we evaluate the model's applicability and usefulness through interviews with affected patients, prototyping, and application to a real-world in vivo dataset. The developed prototype predicts bladder volume based on relevant sensor data (i.e., near-infrared spectroscopy and acceleration) and the time elapsed since the previous micturition. Our comparison of several supervised state-of-the-art machine and deep learning models reveals that a long short-term memory network architecture achieves a mean absolute error of 116.7 ml that can improve bladder management for patients. Collapse

Topics: computer hardware open source mobile application user interface programming language

Methods: design artifact qualitative interview design process support vector machine computational algorithm

The Power of Trust: Designing Trustworthy Machine Learning Systems in Healthcare

2023 | International Conference on Information Systems | Citations: 0

Authors: Fecho, Mariska; Zöll, Anne

Abstract: Machine Learning (ML) systems have an enormous potential to improve medical car ... Expand

Abstract: Machine Learning (ML) systems have an enormous potential to improve medical care, but skepticism about their use persists. Their inscrutability is a major concern which can lead to negative attitudes reducing end users trust and resulting in rejection. Consequently, many ML systems in healthcare suffer from a lack of user-centricity. To overcome these challenges, we designed a user-centered, trustworthy ML system by applying design science research. The design includes meta-requirements and design principles instantiated by mockups. The design is grounded on our kernel theory, the Trustworthy Artificial Intelligence principles. In three design cycles, we refined the design through focus group discussions (N1=8), evaluation of existing applications, and an online survey (N2=40). Finally, an effectiveness test was conducted with end users (N3=80) to assess the perceived trustworthiness of our design. The results demonstrated that the end users did indeed perceive our design as more trustworthy. Collapse

Topics: health information system artificial intelligence information privacy concern privacy collaborative work

Methods: survey design science kernel theory experimental group design artifact

Theories: trust theory

SOLVING THE DATA-DRIVEN NEWSVENDOR WITH ATTENTION TO TIME

2023 | European Conference On Information Systems | Citations: 0

Authors: Feddersen, Leif; Cleophas, Catherine

Abstract: Inventory management systems support firms in planning for an uncertain future ... Expand

Abstract: Inventory management systems support firms in planning for an uncertain future by using demand forecasts and optimization models to make restocking decisions. Recent work on the "data-driven" newsvendor found that incorporating machine learning (ML) can improve the success of inventory management by accounting for demand-driving information. However, ML methods are infamously hard to interpret, which may hinder their acceptance. To ameliorate this, we show how to apply an interpretable attention-based architecture, the Temporal Fusion Transformer (TFT), to the data-driven newsvendor problem. Our approach replicates and extends the original TFT time series forecasting method to the inventory management domain. We evaluate our method on two real-world retail datasets, each covering 260 perishable food items, and provide domain-specific benchmarks. The computational study illustrates TFT's interpretable predictions and their comparatively high accuracy. Our work aims to lay the groundwork for further design science research on transparency in human-AI collaboration in this domain. Collapse

Topics: human AI collaboration human computer interaction Python decision making accounting

Methods: time series analysis longitudinal research artificial neural network feature engineering machine learning

Theories: learning theory

Distant and Local Knowledge: Investigating the Effect of Changing Interest in Knowledge Generation

2023 | International Conference on Information Systems | Citations: 0

Authors: Feiter, Tim; Resch, Christian; Kock, Alexander

Abstract: This study examines how changes between drawing inspiration from distant knowle ... Expand

Abstract: This study examines how changes between drawing inspiration from distant knowledge and focusing on local knowledge affect contributions in online communities. The research compares two theoretical frameworks for understanding knowledge generation: The tension-based view highlights the tensional perspective of initially engaging with distant knowledge before narrowing the focus to specific domains to foster creative behavior. Conversely, the foundational view posits that creative behavior requires local expertise before it is combined with insights from distant knowledge domains. We collected data from 15 Q&A forums hosted by Stack Exchange and used natural language processing to analyze users' contributions and changes in interest. Our findings suggest that both theories explain knowledge generation. Individuals need to engage with more distant knowledge over time but also streamline their interests between local and distant knowledge domains to generate more valuable and novel contributions. The study enriches understanding of knowledge generation in online communities and offers insights into how to support creative individuals. Collapse

Topics: online community social network open innovation innovation management database system

Methods: BERT natural language processing transformer model archival research linear regression analysis

Theories: creativity theory

Avoiding the Diffusion of Responsibility in Social Networking Groups: A Field Experiment on Responses to Online Help-Request Referrals

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Feng, Nan; Hu, Zhongtao; Wu, Yi; Choi, Ben

Abstract: Help-request referral (HRR) campaigns are being increasingly adopted by online s ... Expand

Abstract: Help-request referral (HRR) campaigns are being increasingly adopted by online service providers to attract online traffic and engage new users. In these campaigns, participants are required to collect a specific amount of help from their social network in exchange for attractive rewards. Participants often face challenges in obtaining positive feedback from their social networking groups due to the diffusion of responsibility. Based on the triangle model of responsibility, this study proposes and empirically tests a research model that examines two key determinants (i.e., request personalization and relational closeness) of recipients’ perceived responsibility that further shape their responses to HRR. Our field experiment empirically supports the research hypotheses. Overall, this study enriches the literature on online social referrals by focusing on HRR, where social referrals are broadcasted and senders reap referral benefits. The study also provides practical insights for HRR designers and social networking service providers. Collapse

Topics: personalization usability WeChat marketing management online word of mouth

Methods: survey experiment field experiment logistic regression analysis of covariance