P2P lending and Natural Disasters: Is Altruistic Behavior conditional?

2023 | International Conference on Information Systems | Citations: 0

Authors: Agrawal, Lavlin; Sharman, Raj; Tiu, Christian; Joenväärä, Juha

Abstract: In the aftermath of natural disasters, borrowers seeking credit from traditiona ... Expand

Abstract: In the aftermath of natural disasters, borrowers seeking credit from traditional sources, such as banks, may encounter higher interest rates than in pre-disaster periods. We examine the lending behavior of peer-topeer (P2P) platforms in the wake of disasters. Since P2P platforms involve individual investors in making lending decisions, they accommodate both profit motives and empathetic responses. Empirical evidence from psychology suggests that empathy can lead to prosocial behavior. Consistent with this assertion, we find that loans affected by natural disasters have lower interest rates than comparable loans not issued in the wake of disasters. Our study finds that lenders are involved in a prosocial behavior and are ready to give away some part of their interest income to help the communities in need. Collapse

Topics: crowdsourcing natural hazard peer-to-peer lending peer-to-peer model LendingClub

Methods: instrumental variables estimation experimental group sensitivity analysis ordinary least square robustness check

Brand Crisis and Customer Relationship Management on Social Media: Evidence from a Natural Experiment from the Airline Industry

2023 | Information Systems Research | Citations: 0

Authors: Al Balawi, Ramah; Hu, Yuheng; Qiu, Liangfei

Abstract: In this study, we investigate the effect of a brand crisis on the customer relat ... Expand

Abstract: In this study, we investigate the effect of a brand crisis on the customer relationship management (CRM) efforts of brands on social media. Despite the opportunities social media offers to brands to connect and engage with customers, it is still unclear how a brand crisis can change a brand’s social CRM efforts. Social media platforms can amplify the negative consequences of a brand crisis. However, the unique and public nature of social media platforms can offer new means for brands to handle a brand crisis and publicly manage their customer relationships. Compared with traditional CRM, social media enables brands to communicate with their customers publicly, which can strengthen their relationships with customers and maintain higher levels of customer engagement. Leveraging a natural experiment setting, we investigate the impact of the United Airlines crisis on three dimensions of social CRM efforts: informativeness, timeliness, and attentiveness. Contrary to traditional CRM efforts and recommendations, we find that the brand crisis increases informativeness efforts but reduces timeliness and attentiveness efforts.History: Yong Tan, Senior Editor; Beibei Li, Associate Editor.Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1159. Collapse

Topics: social media Twitter customer service customer satisfaction customer relationship management

Methods: mediation analysis longitudinal research robustness check natural experiment experimental group

Does Analytics Help Resolve Equivocality in the Healthcare Context? Contrasting the Effects of Analyzability and Differentiation

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

Authors: Adjerid, Idris; Angst, Corey; Devaraj, Sarv; Berente, Nicholas

Abstract: Organizations are increasingly using data analytics to help make decisions and ... Expand

Abstract: Organizations are increasingly using data analytics to help make decisions and drive positive outcomes. But organizational scholarship has warned us that the sort of information processing associated with analytic capabilities, while effective for uncertainty reduction, may be less effective in equivocal contexts. Equivocality is evident when tasks are not easily analyzable (task analyzability) or when organizational departments are highly differentiated (differentiation). We hypothesize that analytics will be less effective in driving positive outcomes when equivocality is high because of low task analyzability. However, when an organization is more differentiated, resulting in high equivocality, we anticipate that analytics will be more effective in driving positive outcomes. To test this theory, we studied how clinical healthcare analytics influenced experiential quality (akin to patient satisfaction) in over 3,000 hospitals across nine years. Our results show that analytics capabilities, on average, do improve outcomes in terms of patient experiential quality, suggesting that analytics can reduce uncertainty, but we also found evidence for the moderating role of equivocality. Specifically, as task analyzability decreases (i.e., increasing equivocality), clinical healthcare analytics becomes less effective in improving experiential quality. However, when equivocality is high because of differentiation, there is a positive relationship between clinical healthcare analytics and experiential quality but only in larger hospitals. From a managerial perspective, this study has implications for boundary conditions of data analytics in organizations. Collapse

Topics: healthcare analytics technology adoption electronic health record health information system artificial intelligence

Methods: theory development post-hoc analysis experimental group longitudinal research machine learning

Theories: organizational information processing theory

The Effects of Digitally Delivered Nudges in a Corporate Wellness Program

2023 | Journal of the Association for Information Systems | Citations: 1

Authors: Babar, Yash; Curley, Shawn; Ke, Zhihong; Liu, De; Sheffler, Zachary

Abstract: We investigate how two digitally delivered nudges, namely light social support ... Expand

Abstract: We investigate how two digitally delivered nudges, namely light social support (nonverbal cues such as kudos or likes) and motivational messaging, affect employees' self-reported physical activity in an online, corporate wellness program. Within this unique field setting, using data from several years, we found evidence that both types of nudges provide benefits beyond the effect of cash incentives. However, the effects vary by individual, depending on whether the employee is actively engaging in physical activity, and by time, depending on how long the employee has been in the wellness program. We found light social support to be less effective over time, while motivational messages were found to be more effective with the duration in the program and generally more effective for physically inactive users. Our findings have implications for the design of wellness systems, suggesting different approaches depending on an employee's current activity level and tenure in the program.De Liu is a Xian Dong Eric Jing Professor of Information and Decision Sciences at the Carlson School of Management, University of Minnesota. He received his PhD from the University of Texas at Austin, and his master's and bachelor's degrees from Tsinghua University. His recent research interests include gamification, AI /augmented reality, internetbased auctions and market mechanisms, online reviews, and crowdfunding. His research has appeared in leading journals such as MIS Quarterly, Management Science, Information Systems Research, Journal of Marketing, Journal of Market Research, and Production and Operations Management. He has served as an associate editor for Information Systems Research and Journal of Organizational Computing and Electronic Commerce. He is also the academic director of the Business Analytics programs at the Carlson School of Management.Yash Babar is an assistant professor in the Department of Operations and Information Management at the University of Wisconsin-Madison. He received his PhD from the University of Minnesota in information and decision sciences and his bachelor's degree from BITS Pilani Goa. He researches the socioeconomic impacts of information systems exploring online-offline interactions in digital platforms like the gig economy, online communities, social media, and mobile fitness applications. His work has appeared in Information Systems Research and MIS Quarterly and at several international IS conferences. Collapse

Topics: mobile phone web forum intrinsic motivation user behavior

Methods: longitudinal research difference in differences Poisson regression propensity score matching robustness check

Theories: self determination 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

The motivation paradox: Understanding contradictory effects of awards on contribution quantity versus quality in virtual community

2023 | Information & Management | Citations: 0

Authors: Banerjee, Shankhadeep; Bhattacharyya, Samadrita; Bose, Indranil

Abstract: We use the lens of the intrinsic/extrinsic motivation theory to holistically mod ... Expand

Abstract: We use the lens of the intrinsic/extrinsic motivation theory to holistically model the effects of intangible awards on member contribution in virtual communities. Using multiple studies on Yelp, we validate that the influence of award stimuli on contribution outcomes become significantly moderated by the aspect of contribution (quantity versus quality) and the novelty of stimuli (time and repetition of awards) to interestingly form divergent (downward versus upward) yet theoretically explicable curvilinear trajectories of contribution. This study contributes to IS literature on virtual communities and social-cognitive literature on work motivation and offers insights to community managers to design better incentive systems. Collapse

Topics: online community intrinsic motivation electronic health blockchain online review

Methods: theory development ANOVA parametric test regression analysis method experimental group

Theories: motivation theory self determination theory cognitive evaluation theory

Expl(AI)ned: The Impact of Explainable Artificial Intelligence on Users’ Information Processing

2023 | Information Systems Research | Citations: 0

Authors: Bauer, Kevin; von Zahn, Moritz; Hinz, Oliver

Abstract: Because of a growing number of initiatives and regulations, predictions of moder ... Expand

Abstract: Because of a growing number of initiatives and regulations, predictions of modern artificial intelligence (AI) systems increasingly come with explanations about why they behave the way they do. In this paper, we explore the impact of feature-based explanations on users’ information processing. We designed two complementary empirical studies where participants either made incentivized decisions on their own, with the aid of opaque predictions, or with explained predictions. In Study 1, laypeople engaged in the deliberately abstract investment game task. In Study 2, experts from the real estate industry estimated listing prices for real German apartments. Our results indicate that the provision of feature-based explanations paves the way for AI systems to reshape users’ sense making of information and understanding of the world around them. Specifically, explanations change users’ situational weighting of available information and evoke mental model adjustments. Crucially, mental model adjustments are subject to the confirmation bias so that misconceptions can persist and even accumulate, possibly leading to suboptimal or biased decisions. Additionally, mental model adjustments create spillover effects that alter user behavior in related yet disparate domains. Overall, this paper provides important insights into potential downstream consequences of the broad employment of modern explainable AI methods. In particular, side effects of mental model adjustments present a potential risk of manipulating user behavior, promoting discriminatory inclinations, and increasing noise in decision making. Our findings may inform the refinement of current efforts of companies building AI systems and regulators that aim to mitigate problems associated with the black-box nature of many modern AI systems. History: Alessandro Acquisti, senior editor; Jason Chan, associate editor. Funding: This work was supported by the Deutsche Forschungsgemeinschaft (DFG) (Projek 449023539), Volkswagen Foundation (ML2MT), and LeibnizInstitute for Financial Research SAFE. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.1199. Collapse

Topics: explainable artificial intelligence artificial intelligence shapley additive explanation local interpretable model agnostic explanation decision making

Methods: F-test regression analysis method experiment field study robustness check

Incentivizing Effort Allocation Through Resource Allocation: Evidence from Scientists’ Response to Changes in Funding Policy

2023 | Organization Science | Citations: 0

Authors: Blomfield, Michael; Vakili, Keyvan

Abstract: Prior research in management and economics has predominantly focused on how mana ... Expand

Abstract: Prior research in management and economics has predominantly focused on how managers or policymakers can shape workers’ allocation of effort using output-based or effort-based incentives. In many settings, however, managers may seek to influence workers’ effort choices through resource allocation—that is, changing the cost of securing resources for different projects or activities. In this paper, we develop a formal model to investigate how a worker changes the allocation of a fixed amount of effort across different projects in response to changes in the cost of securing resources for each project. Our model shows how cutting resources available to one project, under certain circumstances, can inadvertently reduce the share of effort allocated to other projects and vice versa. We use the insights from the model to explore the effectiveness of funding strategies designed to influence the research direction of academic scientists. We specifically examine how U.S. scientists working in stem cell research responded to a 2001 policy change that restricted access to federal funding for research in the human embryonic stem cell (hESC) area. In line with our model’s predictions, we find that cutting resources for hESC research inadvertently reduced U.S. scientists’ output in non-hESC areas of stem cell research—an effect that is strongest among the highest-ability scientists. Our findings highlight the complexities of incentivizing effort allocation using resource-based incentives. In particular, we show how altering resource-based incentives in one area can have unforeseen spillover effects on effort allocation in other areas. Funding: Financial support from the London Business School is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.1565. Collapse

Topics: IT career human resource management individual characteristic

Methods: longitudinal research robustness check ordinary least square autocorrelation analysis experimental group

Theories: theory of economic development

Editor's Comments: Tuum Est

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Burton-Jones, Andrew

Abstract: ... Expand

Abstract: Collapse

Topics: knowledge sharing artificial intelligence digital platform

Methods: difference in differences

Theories: garbage can theory

Social Group Buying as a Marketing Strategy

2023 | HICSS | Citations: 0

Authors: Chen, Huiyan; Peng, Jing; Guan, Mengcheng; Li, Jianbin

Abstract: Social group buying (SGB) is a novel form of group buying that encourages custom ... Expand

Abstract: Social group buying (SGB) is a novel form of group buying that encourages customers to purchase deeply discounted products together with friends. Over the past few years, SGB has become a popular marketing strategy for online sellers to acquire new customers. Using a dataset from an e-commerce platform, we investigate whether and how SGB affects the sales of sellers. We find that enrolling a few products into SGB has a positive spillover effect on the sales of the sellers’ other products, and the effect varies substantially across different types of sellers. Specifically, the positive spillover effect is larger for smaller sellers and more diversified sellers. Moreover, we find that the spillover effect exhibits similar heterogeneity at the brand level, except that it can be negative for large brands and non-diversified brands. This finding suggests that sellers may gain from SGB at the expense of large or non-diversified brands. Collapse

Topics: electronic commerce advertising management marketing management

Methods: difference in differences mathematical model longitudinal research