Information Systems Research

Journal Information
ISSN / EISSN: 10477047 / 15265536
Total articles ≅ 1,480

Latest articles in this journal

Tongxin Zhou, Yingfei Wang, ,
Information Systems Research; https://doi.org/10.1287/isre.2022.1191

Abstract:
Choice overload is a common problem in many online settings, including healthcare. Online healthcare platforms tend to provide a large variety of behavior intervention information or programs to help individuals modify their lifestyles to improve wellness. However, having too many options can significantly increase searching cost, prevent users from discovering the truly relevant interventions, and harm users’ long-term healthcare decision-making efficiency. This motivates us to propose a personalized healthcare recommendation system to provide tailored support for individuals’ intervention participation. The proposed framework, a deep-learning and diversity-enhanced multiarmed bandit (DLDE-MAB), integrates several predictive and prescriptive analytics components to combat the unique challenges presented in the healthcare recommendation setting. It leverages online machine learning to provide adaptive and real-time support, a theory-guided diversity promotion scheme to cover multiple healthcare needs, and deep learning to further enhance dynamic context representation. Through extensive experiments, we show that the proposed framework outperforms various competing models in terms of its adaptivity to data dynamics, diversity, and uncertainty. The proposed model and evaluation results provide important implications for business intelligence and personalized, contextualized, and agile healthcare decision making.
Nan Zhang, Heng Xu
Information Systems Research; https://doi.org/10.1287/isre.2022.1195

Abstract:
A hallmark of information technology use in disaster management is the wide adoption of complex information systems for risk assessment, portfolio management, and ratemaking in catastrophe insurance. Whereas the importance of catastrophe insurance to disaster preparedness is beyond dispute, catastrophe insurers are increasingly reckoning with the potential impact of inequality in insurance practices. Historically, the presence of inequalities in insurance, from redlining to pricing disparity, has had a devastating impact on minority communities. Even recently, people living in predominantly African American communities can still be charged more for the same insurance coverage than people living in other communities. Whereas the fairness of insurance ratemaking is studied in general, we identify a unique challenge for catastrophe insurance that sets it apart from other lines of insurance. Drawing upon the recent advances in machine learning for fair data valuation, we reveal striking connections between the two seemingly unrelated problems and lean on insights from machine learning to study the fairness of ratemaking methods for catastrophe insurance. Our results indicate the potential existence of disparate impact against minorities across existing methods, pointing to a unique solution that can satisfy a few commonly assumed properties of fair ratemaking for catastrophe insurance.
Nasim Mousavi, Panagiotis Adamopoulos,
Information Systems Research; https://doi.org/10.1287/isre.2022.1197

Abstract:
Recommendation systems and the decoy effect are two popular marketing techniques that have been used for facilitating decision making. Practitioners often use decoys to help drive demand for specific items, and prior research has shown the decoy effect to be robust in traditional choice settings, with consistent reporting of an overall positive impact. Recommendation systems are also increasingly being used to present item choice sets to customers and users, assisting users in their decision-making process. However, previous work has not examined the decoy effect in the context of recommendations. The decoy effect may facilitate consumer decision making and positively impact user behavior when used with recommendation systems. However, in the recommendation context, customers often have different expectations for the reliability and quality of the presented information. Hence, a decoy as a recommendation could signal issues in system reliability, resulting in a negative effect. Our study demonstrates that depending on the recommendation context, the decoy effect can be beneficial or counterproductive. Specifically, we find in the personalized context, including a decoy minimizes the demand for the target option and pushes consumers to opt out of purchase, which deviates from the traditional decoy effect. However, a decoy increases the target item’s demand in the nonpersonalized context, following the conventional decoy effect.
Gang Chen, Shuaiyong Xiao, Chenghong Zhang, Huimin Zhao
Information Systems Research; https://doi.org/10.1287/isre.2022.1196

Abstract:
A Theory-Driven Deep Learning Method for Voice Chat–Based Customer Response Prediction In this study, we target the task of voice chat–based customer response prediction in an emerging online interaction–based commercial mode, the invite-online-and-experience-in-store mode. Prior research shows that satisfaction, which can be revealed by the discrepancy between prior expectation and actual experience, is a key factor to disentangle customers’ purchase intention, whereas black box deep learning methods empirically promise us with advantageous capabilities in dealing with complex voice data, for example, text and audio information incorporated in voice chat. To this end, we propose a theory-driven deep learning method that enables us to (1) learn customers’ personalized product preferences and dynamic satisfaction in the absence of their profile information, (2) model customers’ actual experiences based on multiview voice chat information in an interlaced way, and (3) enhance the customer response prediction performance of a black box deep learning model with theory-driven dynamic satisfaction. Empirical evaluation results demonstrate the advantageous prediction performance of our proposed method over state-of-the-art deep learning alternatives. Investigation of cumulative satisfaction reveals the collaborative predictive roles of theory-driven dynamic satisfaction and deep representation features for customer response prediction. Explanatory analysis further renders insights into customers’ personalized preferences and dynamic satisfaction for key product attributes.
Tawfiq Alashoor, Mark Keil, H. Jeff Smith, Allen R. McConnell
Information Systems Research; https://doi.org/10.1287/isre.2022.1182

Abstract:
Policy-oriented Abstract Data privacy is one of the most pressing issues today. The world is thirsty for novel, effective, and efficient policies to strike an appropriate balance between protecting individuals’ privacy and creating economic value from their personal information. Whereas governmental efforts, such as the enaction of General Data Protection Regulation, California Consumer Privacy Act, and other privacy regulations, have been pushing boundaries to strike this balance, the effects of these types of initiatives on individuals’ privacy awareness and behavior are uncertain, likely to be nuanced, and will take time to sort out. In this paper, we explain the privacy paradox, a phenomenon with important implications that apply to policymakers, industry professionals, and individuals. The privacy paradox refers to a mismatch between individuals’ stated privacy concerns and their actual disclosure behaviors. In three behavioral experiments, we show how the paradox is revealed when individuals are cognitively tired especially when they are in a good mood. These findings do not indicate that individuals do not care about privacy because they do when they are not cognitively tired especially when they are in a bad mood. By explaining the privacy paradox, we inform existing and future privacy policies to strike that balance we all strive for.
, Vandith Pamuru, Yaroslav Rosokha
Information Systems Research; https://doi.org/10.1287/isre.2022.1187

Abstract:
Generalized second-price auction is the preferred mechanism for sponsored-search advertisements. In this paper, we take a twofold approach using Q-learning-based simulations and human-subject experiments to show that the low-value advertisers (who do not win the auction) exhibit highly exploratory behavior and impact the allocative efficiency of the mechanism. Moreover, we find the presence of bid-adjustment frictions (e.g., bid fee) moderates this phenomenon and results in higher allocative efficiency of the auction. Our focus on the bid-adjustment costs is motivated by the fact that both the sponsored-search platforms and policymakers can best observe and influence these types of frictions as compared with frictions that are difficult to observe (e.g., resources spent on the analysis of the market and sophistication of algorithms by the advertisers).
Yun Young Hur, Fujie Jin, Xitong Li, Yuan Cheng,
Information Systems Research; https://doi.org/10.1287/isre.2022.1189

Abstract:
We examine how social influence interacts with other information sources to affect user behaviors in the context of medical crowdfunding. We conduct a large-scale randomized field experiment on a leading medical crowdfunding platform, showing friends’ donation information to donors in the treatment group and not showing such information in the control group, and examine how the likelihood to donate differs. In addition, we conduct a survey on Amazon Mechanical Turk to evaluate the informational value of different case attributes in conveying the patients’ need for help to donors. We find that for cases containing attributes with high informational value (e.g., minor patient, severe conditions), social influence is insignificant. In contrast, for cases lacking attributes with high informational value, social influence significantly increases donors’ likelihood to donate. Overall, our results show that the impact of social influence depends on the informational value of other information sources, suggesting that the social influence in our context is primarily informational. Our findings indicate that rather than generating an entrenchment effect, where cases with attributes of high informational value attract disproportionate benefits, social influence can increase donation likelihood to cases that lack such attributes, promoting more equal access to resources overall. History: Olivia Liu Sheng, Senior Editor; Yuliang (Oliver) Yao, Associate Editor. Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2022.1189 .
Kexin Yin, , Bintong Chen, Olivia R. Liu Sheng
Information Systems Research; https://doi.org/10.1287/isre.2022.1174

Abstract:
Link recommendation, such as “People You May Know” on LinkedIn, recommends links to connect unlinked online social network users. Existing link recommendation methods tend to recommend similar friends to a user but overlook the fact that different users have different diversity preferences when making friends in a social network. That is, some users prefer to connect with friends of similar profiles while some others prefer to befriend those of different profiles. For example, Jane prefers to connect with those primarily majoring in mathematics, whereas Jack prefers to befriend those in many different majors. To address this research gap, we define and operationalize the concept of diversity preference and propose a new link recommendation problem: the diversity preference-aware link recommendation problem. We then develop a novel link recommendation method that recommends friends to cater each user’s diversity preference. Our study informs researchers and practitioners about a new perspective on link recommendation – diversity preference-aware link recommendation. Our study also suggests that recommender systems need to be designed to meet each user’s diversity preference rather than indiscriminately increase the diversity of recommended items for every user.
Elizabeth Han, , Han Zhang
Information Systems Research; https://doi.org/10.1287/isre.2022.1179

Abstract:
The rise of emotional intelligence technology and the recent debate about the possibility of a “sentient” artificial intelligence (AI) urge the need to study the role of emotion during people’s interactions with AIs. In customer service, human employees are increasingly replaced by AI agents, such as chatbots, and often these AI agents are equipped with emotion-expressing capabilities to replicate the positive impact of human-expressed positive emotion. But is it indeed beneficial? This research explores how, when, and why an AI agent’s expression of positive emotion affects customers’ service evaluations. Through controlled experiments in which the subjects interacted with a service agent (AI or human) to resolve a hypothetical service issue, we provide answers to these questions. We show that AI-expressed positive emotion can influence customers affectively (by evoking customers’ positive emotions) and cognitively (by violating customers’ expectations) in opposite directions. Thus, positive emotion expressed by an AI agent (versus a human employee) is less effective in facilitating service evaluations. We further underscore that, depending on customers’ expectations toward their relationship with a service agent, AI-expressed positive emotion may enhance or hurt service evaluations. Overall, our work provides useful guidance on how and when companies can best deploy emotion-expressing AI agents.
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