How AI-Based Systems Can Induce Reflections: The Case of AI-Augmented Diagnostic Work

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Abdel-Karim, Benjamin; Pfeuffer, Nicolas; Carl, K. Valerie; Hinz, Oliver

Abstract: This paper addresses a thus-far neglected dimension in human-artificial intelli ... Expand

Abstract: This paper addresses a thus-far neglected dimension in human-artificial intelligence (AI) augmentation: machine-induced reflections. By establishing a grounded theoretical-informed model of machine-induced reflection, we contribute to the ongoing discussion in information systems (IS) regarding AI and research on reflection theories. In our multistage study, physicians used a machine learning-based (ML) clinical decision support system (CDSS) to see if and how this interaction can stimulate reflective practice in the context of an X-ray diagnosis task. By analyzing verbal protocols, performance metrics, and survey data, we developed an integrative theoretical foundation to explain how ML-based systems can help stimulate reflective practice. Individuals engage in more critical or shallower modes depending on whether they perceive a conflict or agreement with these CDSS systems, which in turn leads to different levels of reflection depth. By uncovering the process of machine-induced reflections, we offer IS research a different perspective on how such AI-based systems can help individuals become more reflective, and consequently more effective, professionals. This perspective stands in stark contrast to the traditional, efficiency-focused view of MLbased decision support systems and also enriches theories on human-AI augmentation. Collapse

Topics: artificial intelligence decision making decision support system algorithmic transparency recommender system

Methods: cognitive walkthrough survey theory development qualitative interview Student's t-test

Theories: learning theory information systems theory theory of abduction

Human vs. Automated Sales Agents: How and Why Customer Responses Shift Across Sales Stages

2023 | Information Systems Research | Citations: 0

Authors: Adam, Martin; Roethke, Konstantin; Benlian, Alexander

Abstract: Customers in sales processes increasingly encounter automated sales agents (ASAs ... Expand

Abstract: Customers in sales processes increasingly encounter automated sales agents (ASAs) that complement or replace human sales agents (HSAs). Yet, little is known about whether, how, and why customers respond to ASAs in contrast to HSAs across successive decision stages of the same sales process. Even less is known about customer responses to HSA-ASA combinations, where both agents assume distinct roles and focus on complementary tasks that are traditionally performed by only one single sales agent. Against this backdrop, this paper explores the influence of increasingly common sales representative (rep) types (i.e., ASA, HSA, and HSA-ASA) on customer decisions across sales stages. Drawing on information processing theory and the literature on hedonic-utilitarian decision making, we investigate customer responses to text-based ASAs from vendor companies in two common early stages of email sales processes (i.e., sales initiation stages) when customers successively decide whether to indicate their initial interest in an offer and then, whether to provide their contact information. Specifically, we conducted two complementary multi-decision experiments, namely (1) a randomized field experiment in a high-stakes sales initiation setting (n = 325) and (2) a subsequent randomized online experiment to complement the real-world insights (n = 408). Our core findings reveal reversing effect patterns of sales rep types across stages: although customers are more likely to indicate their initial interest to HSAs (versus ASAs) because of HSAs’ higher levels of social presence, they are less likely to provide contact information to HSAs because of HSAs’ lower levels of performance expectancy and effort expectancy. We also show that HSA-ASA combinations can be reasonable options for single ASAs, yet contextual features of the sales setting may affect differential customer responses to HSA-ASA combinations (versus ASAs) in each sales stage. Taken together, we uncover shifting effect patterns in customer responses to sales rep types across successive sales stages and shed light on the consecutive underlying mechanisms that explain these shifts. These findings have significant implications for vendor companies seeking to allocate HSAs and/or ASAs effectively across various decision stages in sales processes and beyond. History: Wonseok Oh, Senior Editor and Khim Yong Goh; Associate Editor. Funding: This work was supported by the Center for Responsible Digitality (ZEVEDI) and the German Research Foundation (DFG) [Grant 471168026]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1171. Collapse

Topics: electronic mail social presence decision making recommender system information system use

Methods: experiment randomized field experiment regression analysis method experimental task field experiment

Theories: information processing theory

Privacy Regulation and Its Unintended Consequence on Consumption Behaviors: Evidence From CCPA

2023 | International Conference on Information Systems | Citations: 0

Authors: Bae, Donghwa; Mayya, Raveesh; Nian, Tingting

Abstract: This study explores the unintended consequences of data protection regulations ... Expand

Abstract: This study explores the unintended consequences of data protection regulations on consumer behavior. Specifically, we examine the California Consumer Privacy Act, which restricts companies from collecting Californians' personal information. Privacy regulations may limit firms' ability to suggest products aligned with consumers' taste predicted from data, potentially influencing subsequent consumer behavior. These regulations especially pose a challenge to digital platforms, as their success hinges on seamless data flow between firms and consumers. Using a difference-in-differences approach with transaction data, we find that post-CCPA, Californians reduce purchase by 4.1%, increase returns by 2.9%. Moreover, we find that Californians spend more time online and visit more webpages, potentially indicating increased search efforts. Mechanism analysis suggests that firms under CCPA proactively alter their data collection strategy to reduce the liability. These results reveal the complex interplay between privacy regulation and consumer behavior, highlighting the need for a nuanced understanding of the trade-offs between privacy protection and economic outcomes. Collapse

Topics: information privacy law legal context consumer behavior advertising management website

Methods: experimental group longitudinal research natural experiment archival research difference in differences

Influencer is the New Recommender: insights for Theorising Social Recommender Systems

2023 | Information Systems Frontiers | Citations: 0

Authors: Bawack, Ransome Epie; Bonhoure, Emilie

Abstract: Designing theory-driven social recommender systems (SRSs) has been a significant ... Expand

Abstract: Designing theory-driven social recommender systems (SRSs) has been a significant research challenge for over a decade. This study aims to identify behavioural factors that could improve the persuasiveness and quality of recommendations made by SRSs. Given both research streams’ striking similarity, it uses the recent yet rich research on social media influencers (SMI) to inform SRS research. Drawing on 72 publications, we classified 52 independent variables into 12 categories regrouped into three broad categories that characterise the relationships between the consumer and the (i) recommender system, (ii) product or brand, and (iii) and advert. The metanalysis results determined the relative importance of each category in predicting purchase intentions, placing recommender credibility and attitude towards the recommended product or brand at the top of the charts. Our findings are expected to facilitate more refined theory-building efforts and theory-driven designs in SRS research and practice. Collapse

Topics: advertising management database system Twitter TikTok IS discipline

Methods: literature study theory development computational algorithm descriptive statistic sensitivity analysis

Theories: social learning theory behavioral theory theory of planned behavior

Household Digital Twin for Disaster Response

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Bonke, Chaitali; Sun, Jun; Owusu, Gabriel; Wang, Yin

Abstract: Using simulation of digital twins can help save lives and reduce the impact of ... Expand

Abstract: Using simulation of digital twins can help save lives and reduce the impact of disasters on households. By accessing a virtual process and making recommendations to families before a disaster hits, the risk of harm can be minimized. Developing a household digital twin for disaster response can lay the foundation for effective household disaster management. This study proposes a design of household digital twin for disaster response and demonstrates how the platform can be used to simulate hazardous processes before they occur for damage control. Collapse

Topics: production management digital twin hurricane recommender system emergency management

Methods: design artifact simulation

Product Recommendation and Consumer Search

2023 | Journal of Management Information Systems | Citations: 0

Authors: Choudhary, Vidyanand; Zhang, Zhe

Abstract: We study an online environment where a firm provides strategic product recommend ... Expand

Abstract: We study an online environment where a firm provides strategic product recommendations to consumers. We develop an analytical framework to integrate recommendations into the consumer search process. The firm sells two imperfectly substitutable products with different profit margins and makes a personalized product recommendation to each consumer based on its uncertainty (lack of knowledge) of his preferences. We define recommendation bias as the firm's deliberate decision to recommend a product to a consumer that does not minimize expected misfit cost of the consumer. Consumers can accept the product recommendation, search for the nonrecommended product, or leave the website. We identify five consumer segments based on consumers' responses to the firm's recommendations. We show that the recommendation bias, profit, and consumer surplus depend on the interaction between the firm's uncertainty regarding consumer preferences and consumer search costs. A reduction in its uncertainty about consumers leads to a corresponding increase in the firm's profit but does not necessarily result in a reduction in consumer surplus. An increase in search costs can lead to nonmonotonic changes in the firm's recommendation strategy, causing an increase or decrease in recommendation bias when the firm's uncertainty about consumers is low. Furthermore, the firm's profit can behave non-monotonically with respect to search costs: the firm benefits from an increase in search costs when these costs are small and uncertainty about consumers is low, but it can be adversely affected when search costs are moderate. Interestingly, consumer surplus may increase when search costs increase. Collapse

Topics: consumer surplus IS economics website recommender system digital service

Methods: mathematical model

Theories: economic theory

A temporal graph framework for intelligence extraction in social media networks

2023 | Information & Management | Citations: 0

Authors: Chung, Wingyan; Lai, Vincent S.

Abstract: Social media networks (SMNs) are increasingly used in professional management of ... Expand

Abstract: Social media networks (SMNs) are increasingly used in professional management of knowledge workers and related assets. However, the factors affecting behavioral trends and activity levels in these networks are not well understood. Although social and cognitive theories can help to explain human behavior in traditional social networks, their application to SMNs has not been validated. Traditional social network modeling techniques may not accurately predict real-world SMN activities. This research developed a temporal graph framework for in­ telligence extraction in SMNs. Theory-based, data-driven models (Conformity Model (COM), Recency-Primacy Model (REM), Trend Interaction Model (TIM), Periodic Interaction Model (PIM)) were developed based on the framework to capture various aspects of user behavior: conformity effect, recency, primacy, periodicity, and dynamic trend. The models capture the activity history and dynamically combine pricing information to enhance predictive accuracy. Using data of 83,536 GitHub software repositories on cryptocurrency, this article reports the results of experiments that compare the models’ performance in predicting SMN activities over time. Experi­ mental results show that the model (REM) that captures recency/primacy effects of human cognitive processing outperformed other models in 9 (out of 18) measures pertaining to engagement, contribution, influence, and popularity. Primacy plays a dominant role in predicting engagement, contribution, and popularity, whereas recency plays a key role in predicting influence. Short-term trend (modeled with TIM) was found to yield significantly better performance on predicting user contribution. The models also outperformed an integrated machine learning (IML) model by most measures. Overall, the effects modeled by REM and TIM were found to be more significant than the effects modeled by COM, PIM, and IML. The research contributes to enhancing un­ derstanding of SMN behavior, developing new models to simulate and predict SMN activities, and designing new artifacts for information systems practitioners to manage knowledge assets and to extract SMN intelligence. Collapse

Topics: cryptocurrency social network systems development situation awareness user behavior

Methods: experiment machine learning longitudinal research computational algorithm time series analysis

Theories: psychological theory social contagion theory social interactionist theory social impact theory social sciences theory

Controversies' Roots in Digital Business Models—The Case of Academic Publishing

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Engert, Simon

Abstract: Digital business models (DBMs) are built on digital technologies with complexit ... Expand

Abstract: Digital business models (DBMs) are built on digital technologies with complexity-inducing characteristics. Thus, they form ever-more complex (eco)systems as they connect heterogeneous actors for value cocreation and blur the boundaries of organizations, markets, and industries. The co-existence of actors' multiple logics in pluralistic settings has been associated with tensions and controversies. However, information systems (IS) research has ignored controversies' role in DBMs in settings of logic multiplicity. Drawing on the 'orders of worth' framework, we follow the controversies over DBMs in the academic publishing ecosystem. Building on our in-depth case study data, we develop a model highlighting three roots of controversies in sociotechnical DBMs. We add to the literature by furthering our understanding of controversies' role in DBMs and enriching the community's methodological toolbox in applying a theory from French pragmatic sociology to study pluralistic contexts. Collapse

Topics: participatory design open access innovation management recommender system digital business model

Methods: qualitative interview design methodology case study personal interview company material

Theories: sociological theory

Formation and Action of a Learning Community with Collaborative Learning Software

2023 | Journal of Management Information Systems | Citations: 0

Authors: Eryilmaz, Evren; Thoms, Brian; Ahmed, Zafor; Lee, Howard

Abstract: This paper explores the formation of a learning community facilitated by custom ... Expand

Abstract: This paper explores the formation of a learning community facilitated by custom collaborative learning software. Drawing on research in group cognition, knowledge building discourse, and learning analytics, we conducted a mixed-methods field study involving an asynchronous online discussion consisting of 259 messages posted by 50 participants. The cluster analysis results provide evidence that the recommender system within the software can support the formation of a learning community with a small peripheral cluster. Regarding knowledge building discourse, we identified the distinct roles of central, intermediate (i.e., middle of three clusters), and peripheral clusters within a learning community. Furthermore, we found that message lexical complexity does not correlate to the stages of knowledge building. Overall, this study contributes to the group cognition theory to deepen our understanding about collaboration to construct new knowledge in online discussions. Moreover, we add a much-needed text mining perspective to the qualitative interaction analysis model. Collapse

Topics: recommender system collaborative filtering pandemic online learning system open source

Methods: qualitative content analysis descriptive statistic sociometry synthesis mixed method

Theories: cognitive theory

Live streaming recommendations based on dynamic representation learning

2023 | Decision Support Systems | Citations: 0

Authors: Gao, Ge; Liu, Hongyan; Zhao, Kang

Abstract: As an emerging form of social media, live streaming services (e.g., Twitch and C ... Expand

Abstract: As an emerging form of social media, live streaming services (e.g., Twitch and Clubhouse) allow users to interact with hosts and peers in real time while enjoying shows or participating in discussions. These platforms are also dynamic, with shows or discussions changing quickly inside a room and users frequently switching between rooms. To improve user engagement and experience on such platforms, we design a new recommendation model named Dynamic Representations for Live Streaming Rooms (DRIVER) to provide room recommendations. Guided by the Integrated Framework for Consumer Path Modeling and the social affordance theory, DRIVER infers dynamic representations of live streaming rooms by leveraging users’ behavior paths in entering, staying in, and leaving rooms. One contribution of our model is a new and efficient dynamic learning framework to model instantaneous and ever-changing inter-room relationships by considering individual users’ behavior paths after leaving a room. Also supported by social affordance theory, another methodological novelty of our model is to capture dynamic characteristics of a room by incorporating features of the current audience inside the room. Experiments on real-world datasets from two different types of live streaming platforms demonstrate that DRIVER outperforms state-of-the-art representation learning methods and sequential recommender systems. The proposed method also has implications for recommender system design in other contexts, in which items are characterized by users’ dynamic behavior paths and ongoing social interactions. Collapse

Topics: live streaming platform recommender system user experience knowledge sharing systems design

Methods: experiment sensitivity analysis ablation analysis descriptive statistic deep learning

Theories: theory of affordance