Research Spotlights

Fashionable Consumer Technology, IT Fashion, and Consumer Behavior (p. 1293)

Varun Grover, Xinhui Zhan, Heshan Sun, Dan (Claire) Jiang

This research examines the intersection of fashion and consumer technology, introducing the concept of “IT fashion” to describe how individual perceptions of technology as fashionable influence consumer behaviors. The paper introduces novel constructs associated with IT fashion and fashionable consumer technology, particularly perceived societal-level IT fashion (PSITF, reflecting a person’s perception of society’s views on a technology) and perceived fashionableness of IT (PFIT, capturing a person’s perception of the technology’s fashionableness). Through two empirical studies, we demonstrate that IT fashion significantly impacts consumer behaviors by enhancing the symbolic values associated with technology use. These findings suggest that marketers and technology developers should not only focus on the utilitarian aspects of technology but also on its aesthetic and symbolic appeals to boost consumer acceptance and market success. The paper contributes to policy by highlighting the need for a nuanced understanding of the sociocultural dimensions of technology adoption, encouraging stakeholders to consider these factors in strategy development and communication.

Probing Digital Footprints and Reaching for Inherent Preferences: A Cause-Disentanglement Approach to Personalized Recommendations (p. 1314)

Cong Wang, Yansong Shi, Xunhua Guo, Guoqing Chen

This study introduces DISC (Disentangling consumers’ Inherent preferences, item Salience effect, and Conformity effect), a novel personalized recommendation approach that leverages disentangled representation learning and causal graph modeling to provide interpretable and effective recommendations. By analyzing consumer behavior across various shopping stages, DISC identifies and differentiates the inherent factors that influence purchasing decisions. DISC cuts through biases to pinpoint consumers’ inherent preferences driving purchases, empowering platforms with the ability to deliver tailored recommendations that resonate deeply with users. Through extensive experiments on real-world data sets, DISC significantly outperforms existing methods, demonstrating its superiority in both in-sample prediction and generating recommendations that align with consumers’ true interests. With its robust performance and theoretical underpinnings, DISC holds promising implications for e-commerce platforms seeking to enhance recommendation accuracy, interpretability, and user engagement.

Win by Hook or Crook? Self-Injecting Favorable Online Reviews to Fight Adjacent Rivals (p. 1333)

Le Wang, Xin (Robert) Luo, Liangfei Qiu, Feng Xu, Xueying Cui

Literature has long assumed that unethical behaviors are fueled by competition. This study challenges this prevailing notion by introducing a conceptual distinction between rivalry and competition. We show that rivalry-induced deterrence triggers the formation of mutual forbearance in self-injecting favorable reviews. Mutual forbearance collapses when one party unilaterally engages in self-injecting behaviors, prompting the other party to retaliate by increasing their self-injecting activities. The presence of an additional rival leads to a 3.2% decrease in self-injecting intensity. Furthermore, self-injecting intensity increases by 0.095% for every 1% rise in that of the rivals and increases by 0.041% for a 1% rise in that of the nonrival competitors. Given the apparent role of rivalry in reducing self-injecting behaviors, platforms should strategically consider how system designs and policies can cultivate firms’ feelings of rivalry and avoid non-rival competition. We recommend platforms consider including evaluations from peer hotels as an alternative index for recommending hotels to potential consumers. Introducing peer evaluations to the current online review systems could attenuate the bias of consumer ratings and facilitate the formation of rivalry, which hinders self-injecting behaviors.

Skill-Biased Technical Change, Again? Online Gig Platforms and Local Employment (p. 1354)

Xue Guo, Zhi (Aaron) Cheng, Paul A. Pavlou

This study explores the impact of online gig platforms like TaskRabbit on the employment of incumbent service workers, focusing on the housekeeping sector. It highlights how TaskRabbit’s entry correlates with a decrease in middle-skilled roles such as supervisors due to automation, whereas low-skilled jobs like janitors remain stable because of their manual nature. Notably, many middle-skilled workers transition to self-employment within the sector instead of facing layoffs. Amidst competing narratives about the gig economy’s influence on labor markets, this research offers critical insights into how gig platforms redistribute the workforce and create new employment opportunities, suggesting significant implications for policymakers and practitioners. Policymakers should recognize the shift from traditional roles to self-employment facilitated by these platforms, which enhances local entrepreneurship. Meanwhile, it is crucial for them to ensure fair working conditions, wages, and benefits. Practitioners can see that gig platforms not only improve operational efficiency but also enable middle-skilled workers to launch their own businesses, indicating a potential strategy for new platforms looking to increase their service offerings and client base. Traditional companies, in response, need to adapt their job designs, incentive structures, and company culture to align with the changing needs for flexibility and autonomy in the gig economy.

An Experimental Evaluation of Gender Differences in Responses to Major-Donor Funding Schemes for Crowdfunded Social Ventures (p. 1375)

Sofia Bapna, Gordon Burtch

Social ventures (organizations that seek to address social or societal problems) fundraising through crowdfunding compete for contributions. Thus, they often involve major donors to influence the contributions of smaller donors. We examine what type of major-donor contribution scheme is most likely to attract smaller donors. In particular, we examine whether and to what degree male and female donors differ in their response to alternative major-donor contribution schemes that are commonly used in practice. When informed that a major donor has kicked off a crowdfunding campaign with an initial contribution, women are 50% more interested in contributing relative to when no major donor was mentioned and 35% more interested in contributing relative to when they were informed that the major donor’s contribution is conditional on the combined donations from others reaching a certain threshold. The initial contribution or seed money from the major donor leads to perceptions, among women, that the organization conducting the project is of high quality, is likely to reach its funding goal, and is likely to achieve its implementation goals. Unlike females, males appear to be agnostic to major donor involvement. Our study suggests that social ventures will be more successful at attracting contributions from female donors if a major donor kicks off the campaign with an initial contribution.

Untangling the Performance Impact of E-marketplace Sellers’ Deployment of Platform-Based Functions: A Configurational Perspective (p. 1397)

Jicheng Zeng, Yulin Fang, Huifang Li, Youwei Wang, Kai H. Lim

Platform-based functions (PBFs) serve as indispensable tools for enhancing sellers’ performance within the e-marketplace. However, the complexity of their interaction and the determination of optimal configurations pose ongoing challenges. Employing fuzzy-set qualitative comparative analysis on a robust data set comprising 3,300 apparel sellers, our study delves into the intricate interplay of PBFs, revealing the nuanced combinations that exert significant influence on sales outcomes. In our investigation, we underscore the pivotal role of two key elements: the meticulous assessment of seller reputation and the strategic alignment of product positioning. These factors emerge as crucial determinants in shaping effective PBF configurations. Surprisingly, our research uncovers unforeseen drawbacks associated with pricing and marketing functions, particularly for reputable sellers offering high-priced products. Beyond theoretical elucidation, our findings offer pragmatic, actionable insights. We provide concrete strategies for sellers and platform operators to optimize their approaches, fostering a more vibrant and competitive e-marketplace ecosystem.

Return of the Movie Night? Analyzing the Impact of Netflix Subscriptions on Offline Movie Spending (p. 1418)

Sihan Fang, Hyeokkoo Eric Kwon, Anandasivam Gopal, Yongjin Park

The advent of streaming services, like Netflix and Prime Video by Amazon, has led to the expectation that demand for offline movie consumption is likely to die (i.e., consumers will simply stop going to the movies). We study this question in our work, and instead, we find that although streaming services provide value to customers in terms of comfort, convenience, and price, they do not lead to a reduction in offline movie consumption. Instead, we show that the offline movie consumption actually increases because streaming services do not fully substitute for the social aspects of going to the movies or the pleasure that is received from watching movies on the “big screen.” Our work thus speaks to the policies implemented in some countries where streaming platforms have been restricted given the concerns about disruption of the offline movie industry. We believe that these policies are potentially misguided because they assume perfect substitution between the two services. Our work also helps inform managers of movie theaters and streaming platforms by providing arguments for the specific types of service offerings that are more likely to be enjoyed at home versus at the movie theater. As we show in the paper, social and hedonic needs are more likely to be met at the movie theater rather than through streaming services. We reiterate the sentiments expressed by Steven Spielberg from 2019: “I hope all of us really continue to believe that the greatest contributions we can make as filmmakers are to give audiences the motion picture theatrical experience. I am a firm believer that movie theaters need to be around forever.”

To Partner or Not to Partner? The Partnership Between Platforms and Data Brokers in Two-Sided Markets (p. 1437)

Xin Zhang, Lihong Cheng, Yugang Yu, Yong Tan

Data have become a vital competitive asset for online advertising platforms, such as Facebook and Snapchat. These platforms often partner with data brokers to enhance targeting capabilities, which raises important consumer privacy concerns. This study develops a game-theoretic model to analyze the economic dynamics of partnerships between competing platforms and a data broker in a two-sided market. Our findings reveal that heightened consumer privacy concerns can encourage platforms to collaborate with data brokers rather than discourage them, as these concerns may strategically soften price competition on the advertiser side. However, when both platforms partner with a data broker, a prisoner’s dilemma may arise, even if the broker significantly improves targeting capabilities. From a policy perspective, such partnerships can harm consumer surplus in a purely ad-sponsored model, where users are not charged. Conversely, they may enhance consumer surplus in a mixed revenue model that combines advertising with subscriptions. These insights suggest that policymakers should consider regulations on platform-data broker partnerships to protect consumer interests, particularly regarding privacy protections that platforms may not prioritize under competitive pressures.

Player-vs.-Player Game Design and Pricing: A Tournament Design Perspective (p. 1461)

Haowen Deng, Yifan Dou, Zenan Wu, Cheng Zhang

This paper examines how game developers can effectively design player-versus-player (PvP) games while balancing revenue and player engagement. In many PvP games, the ability to purchase powerful items creates a “pay-to-win” environment, often frustrating nonpaying players. To address this issue, this paper proposes a tournament design model that allows developers to create different game versions with varying advantages, influencing players’ chances of winning. The findings indicate that this approach enhances developers’ pricing flexibility and mitigates competition between versions. By offering multiple free versions alongside paid options, developers can engage players who may be unwilling to spend money, thereby translating their willingness to play into other players’ willingness to pay. Furthermore, the model shows that tournament design can significantly increase profits, particularly among players with limited budgets. Ultimately, this strategy benefits both developers and players, fostering a win-win outcome for the gaming community. This work elucidates the rationale of the freemium model that offers differentiated free versions in games with PvP battles.

An Explainable Artificial Intelligence Approach Using Graph Learning to Predict Intensive Care Unit Length of Stay (p. 1478)

Tianjian Guo, Indranil R. Bardhan, Ying Ding, Shichang Zhang

We propose and test a novel graph learning-based explainable artificial intelligence (XAI) approach to address the challenge of developing explainable predictions of patient length of stay (LoS) in intensive care units (ICUs). Specifically, we address a notable gap in the literature on XAI methods that identify interactions between model input features to predict patient health outcomes. Our model intrinsically constructs a patient-level graph, which identifies the importance of feature interactions for prediction of health outcomes. It demonstrates state-of-the-art explanation capabilities based on identification of salient feature interactions compared with traditional XAI methods for prediction of LoS. We supplement our XAI approach with a small-scale user study, which demonstrates that our model can lead to greater user acceptance of artificial intelligence (AI) model-based decisions by contributing to greater interpretability of model predictions. Our model lays the foundation to develop interpretable, predictive tools that healthcare professionals can utilize to improve ICU resource allocation decisions and enhance the clinical relevance of AI systems in providing effective patient care. Although our primary research setting is the ICU, our graph learning model can be generalized to other healthcare contexts to accurately identify key feature interactions for prediction of other health outcomes, such as mortality, readmission risk, and hospitalizations.

Should Ad Exchanges Subsidize Advertisers to Acquire Targeting Data? (p. 1502)

Wangsheng Zhu, Shaojie Tang, Vijay Mookerjee

Large volumes of online impressions are sold daily via real-time auctions to deliver targeted advertisements to consumers. Advertisers use data to learn about user preferences and select the most appropriate ad for each user, which also help them optimize their bids in an ad auction. Although ad exchanges may provide some user data to advertisers, they are usually limited, and advertisers often acquire data from various sources to improve targeting performance. The acquisition of such data can significantly influence the revenue of the ad exchange, which motivates ad exchanges to take actions that reduce advertisers’ data acquisition costs and encourage them to buy data. In this study, we propose three subsidy frameworks to increase ad exchange revenue by inducing more advertisers to acquire data: all subsidized, winner subsidized, and loser subsidized. Using a stylized model, we analyze the impact of subsidy provisions on the platform’s net revenue. Our results show that winner subsidized can be better or worse than all subsidized depending on the cost of data acquisition, its beneficial impact on ad selection, and the distribution of impression values.

Identity Work in Interdependent Professional Groups: The Role of a Target Identity in Enterprise Systems Implementation (p. 1522)

Johann Dietz, Kai Spohrer, Hartmut Hoehle

Powerful new technologies in the workplace are significantly changing what employees do and how they depend on others. Key tasks that professionals regard as core to their job are automated, forcing entire professional groups to reconsider their identity within their organization. In doing so, professional groups can either feel threatened and resist new technology or develop enhanced role identities that they pursue using the technology. Our study of financial accountants and management accountants shows how managers can help employees develop enhanced role identities and become more satisfied with their jobs. Managers should ensure that each professional group has early and concrete experiences with how new technology helps them perform new, value-added tasks that are consistent with their sense of who they are as professionals in the organization. However, management must also consider how different organizational functions and professional groups will work together in light of new technology. During the technology implementation process, managers and consultants should foster intensive exchange and critical discussion among interdependent professional groups to facilitate an open renegotiation of which professional group takes on which of the existing and new organizational tasks.

Resale Royalty in Non-Fungible Token Marketplaces: Blessing or Burden for Creators and Platforms? (p. 1543)

Murat M. Tunc, Hasan Cavusoglu, Zhiqiang (Eric) Zheng

Resale royalties, first introduced in the 1920s to support artists through a share of future resales, have now adopted by nonfungible token (NFT) marketplaces for digital art trading. Although these royalties are often viewed as beneficial for creators, our research reveals unexpected consequences. Using data from a major NFT marketplace, we find that NFTs with higher royalty rates sell for significantly lower prices and take longer to sell. Surprisingly, creators do not recoup these initial losses through royalty payments within four years. We discover that higher up-front minting costs lead creators to set higher royalty rates. We reveal a delayed gratification effect where creators with higher royalties accept lower up-front prices in hopes of future royalty income. We also find an overconfidence effect where confident creators, measured by their past sales and follower count, are more likely to lower initial prices. Our research contributes to the ongoing debate about royalty enforcement in NFT marketplaces and offers empirical evidence to inform platforms and creators. Platform managers should carefully consider both reducing up-front minting costs and implementing royalty rate limits to improve market liquidity. Creators should be cautious about setting high royalty rates as they may not provide the expected financial benefits.

And No One Gets the Short End of the Stick: A Blockchain-Based Approach to Solving the Two-Sided Opportunism Problem in Interorganizational Information Sharing (p. 1565)

Lukas Florian Bossler, Arne Buchwald, Kai Spohrer

In interorganizational information sharing, opportunism has two sides: information poaching by the information recipient and information manipulation by the information provider. The threat of such opportunism can entirely preclude business relationships. Prior countermeasures either failed to reliably prevent opportunistic behavior or addressed only one of the two sides. To overcome these shortcomings, we develop three design principles of an information system that facilitates reliable interorganizational information sharing without revealing the underlying data. We instantiate the design in a blockchain system for wear-based leasing contracts for machine tools. We demonstrate and evaluate the efficacy and utility of our design through in-depth interviews with business and technology experts and a survey of 85 machine tool users and 77 lessors in the manufacturing industry. Our study conceptualizes information poaching and information manipulation as two sides of the same problem and shows empirically that opportunities for beneficial business relationships arise when both are addressed together. Our design principles provide a blueprint for a shared information system that prevents both information poaching and information manipulation, thereby enabling new interorganizational information sharing relationships based on data that would otherwise be considered too sensitive to share or too untrustworthy to rely on.

How Product Display Orientation Affects Customers’ Choice Satisfaction in Online Purchase: A Choice Closure Perspective (p. 1587)

Yanli Jia, Yulin Fang, Jun Ouyang

The current research offers practical guidance for e-commerce websites on effectively displaying products to improve customer satisfaction with their choices and reduce order cancellations, a challenge that has long perplexed online retailers. Specifically, a horizontal (versus vertical) display of comparable products on an e-commerce website is more positively related to customer choice satisfaction by promoting a higher level of choice closure. Our findings suggest that if e-commerce platforms are designing a new interface, they may consider adopting a horizontal display as a beneficial starting point. However, when a vertical design has been adopted for certain reasons, online retailers can incorporate cues of finality (e.g., putting a “the end” mark at the end of a product display) to help customers finalize their choices and achieve choice satisfaction, thereby mitigating potential downsides associated with vertical display layouts.

Mergers Between On-Demand Service Platforms: The Impact on Consumer Surplus and Labor Welfare (p. 1612)

Xiaogang Lin, Tao Lu, Xin Wang, Gang Kou

Mergers between on-demand service platforms have recently become prevalent, resulting in antitrust debates. On the one hand, mergers reduce market competition, raising antitrust concerns, such as potential increases in service prices. On the other hand, merged firms typically enable agents to serve all the customers from previously separate platforms, thereby gaining an advantage from resource pooling. To offer guidelines for resolving these debates, we investigate the impact of a merger between two on-demand service platforms. Our results show that because of the merger-enabled pooling benefit and a cross-side network effect (i.e., customers benefit from more agents and vice versa), a merger can result in a win-win-win outcome, where the platform profits, consumer surplus, and labor welfare are all improved after a merger. The win-win-win outcome is more probable if the premerger market is less saturated or if the market is more differentiated on the customer/agent side. Therefore, antitrust agencies might consider being more lenient toward a merger if the market has room to grow or if the two merging platforms have differentiated features.

Two-Sided Impacts of Service Provider’s Identity Disclosure in e-Customer Service Platforms: Evidence from Two Field Experiments (p. 1631)

Sunghun Chung, Jaehwuen Jung, Jooyoung Park, Chul Ho Lee, Yasin Ceran

Digital technologies have revolutionized customer service, enabling efficient online complaint resolution. In our research, we investigate the impact of disclosing service provider identities on performance, customer satisfaction, and biases within a customer complaint management platform. In the first large-scale field experiment involving 75,041 customers, 1,280 service providers, and 672 companies, we found that revealing provider identities enhanced performance by reducing anonymity and promoting accountability. This effect was most pronounced among inexperienced providers with greater discretion in handling complaints. Customers benefited from improved service quality and reported higher satisfaction with resolutions. In the second study involving 2,710 customers, we explored the role of ethnic identity in customer satisfaction. Our findings showed that customers reported greater satisfaction with providers from majority ethnic backgrounds, whereas minority customers exhibited lower satisfaction when paired with providers of the same ethnicity. Four additional studies with 1,211 participants delved into the psychological mechanisms driving these effects. Our results highlight the nuanced role of transparency and ethnic cues in shaping customer perceptions. This research offers actionable insights for firms aiming to optimize service interactions by balancing transparency, accountability, and inclusivity in complaint management policies.

Lost Time in Crowdsourcing Contests (p. 1652)

Ho Cheung Brian Lee, Anant Mishra

Crowdsourcing contests are a popular way to seek innovative business solutions, but the availability of multiple contests can cause “lost time,” the delay between a contest’s start and a solver’s first visit. This study examines how lost time affects solvers’ submission behavior and its impact on matching inefficacy, measured by solver disqualification, which occurs when a solver fails to meet the seeker’s baseline submission criteria. Using mediation analysis, we show that lost time can increase matching inefficacy because solvers spend less time on their initial submissions and submit fewer solutions. Interestingly, we identify latecomer disadvantage as an important mechanism driving solvers’ behavioral responses to lost time, suggesting that simply increasing contest duration does not eliminate the issue. Instead, modifying submission behaviors through incentive structures—such as early bird bonuses—and continuous feedback may help alleviate the negative effects of lost time. Additionally, offering grace periods to late entrants can counterbalance the disadvantage of joining later. These strategies, informed by our findings, can enhance solver engagement and improve the effectiveness of crowdsourcing platforms in achieving desirable outcomes.

Growing Platforms by Adding Complementors Without a Contract (p. 1670)

Raveesh Mayya, Zhuoxin Li

Online platforms often face challenges in sustaining growth, especially in competitive markets such as food delivery. This paper examines a novel strategy in which platforms list nonpartnered restaurants, allowing consumers to order from them via third-party deliverers. Whereas these restaurants gain visibility without paying commissions, concerns arise about potential harm because of lack of control over menus and pricing. We analyze the impact of this strategy using data from Grubhub and a California policy change that banned nonpartnered listings. We find that being listed as nonpartnered boosts takeout revenue for these restaurants, particularly independent ones. Additionally, there’s a positive spillover effect on partnered restaurants. However, regulatory delisting reverses these gains, highlighting the delicate balance between platform growth strategies and regulatory actions. For platform owners, this study underscores the potential of noncontracted partnerships as a growth strategy, especially if there are third-party enablers on the platform such as deliverers. However, it also cautions against potential disruptions from regulatory changes, urging businesses to adapt their strategies accordingly. For restaurant owners, our finding emphasizes the importance of adapting to changes by enhancing operational readiness to capitalize on increased visibility. They should advocate for regulations that enhance their choices and overall transparency, not inadvertently decrease them.

An Investigation of p-Hacking in E-Commerce A/B Testing (p. 1691)

Alex P. Miller, Kartik Hosanagar

Concerns about the integrity of statistical analyses have risen in recent years, with particular attention given to “p-hacking”—a process whereby analysts conduct several statistical tests until they achieve a statistically significant result. Although extensively studied in academic settings, less is known about its prevalence in industrial contexts. In this study, we investigate whether p-hacking occurs in e-commerce A/B testing. We analyzed nearly 2,300 A/B tests conducted by hundreds of firms using a large A/B testing platform. Such platforms typically offer continuous monitoring of test results, a feature that facilitates real-time decision making but also enables potential p-hacking through selective stopping or continuation of experiments. Contrary to concerns raised by earlier research on academic practices, we found no significant evidence of p-hacking in our sample. These findings suggest that the industrial application of experimentation may be less susceptible to p-hacking than academic research. We discuss several possible factors explaining the divergent results, highlighting the potential role of organizational learning and the importance of economic incentives. Our study contributes to the broader discussion on research integrity and underscores the importance of considering contextual factors in assessing statistical malpractice.

Walrasian Pricing for Combinatorial Markets with Compact-Bidding Languages: An Application to Truckload Transportation (p. 1718)

Mohsen Emadikhiav, Robert Day

Combinatorial auctions offer several economic advantages but also face multiple technical challenges, including bid generation, the need to solve a combinatorial allocation problem, and determining reasonable prices. These challenges are even more pronounced in a combinatorial exchange, where bidders can simultaneously buy and sell combinations of goods. Motivated by truckload transportation markets, we explore new mechanisms for finding Walrasian equilibrium prices in combinatorial auctions and exchanges where bidders can use a compact-bidding language to express their potentially complex preferences. With the goal of improving economic efficiency and reducing the environmental impact of the trucking industry, we identify significant potential gains by developing a method for integrated allocation and price determination based on an industry-specific bidding language. We also demonstrate our proposed mechanism’s adaptability and flexibility by considering a number of practical constraints.

Team Makes You Better: Evidence from Online Medical Consultation Platforms (p. 1738)

Xiaofei Zhang, Jingchuan Pu, Yixin Lu, Feng Guo

Our study of online medical consultation platforms shows that, when physicians work in virtual teams, they perform better even in their individual patient consultations. This improvement occurs primarily through peer effects: physicians are motivated to work harder when they can observe their teammates’ performance. The findings from our study offer valuable insights for the design and operation of online medical consultation (OMC) platforms. Specifically, whereas the rise of OMC platforms has significantly improved the connectivity among patients and physicians by reducing or even eliminating certain frictions in patient–physician encounters, there remain major challenges as to how to effectively match patients and physicians and facilitate value creation for both sides given the overall supply shortage of physicians. Our analysis indicates that team consultation may complement rather than cannibalize individual consultation. Whereas our research focuses on the healthcare sector, these insights apply broadly to other contexts (e.g., open-source software development and massive open online course platforms) in which professionals can form voluntary teams. The key is to carefully balance individual work with teamwork, providing the right tools and incentives for collaboration.

A Natural Disaster Reshapes Prosocial Microlending (p. 1760)

Yi Ding, Haifeng Xu, Bernard C. Y. Tan

A natural disaster may result in a situation where lenders can hardly find appropriate loans from the disaster-affected region. When faced with the mismatch between the supply and demand of microloans, lenders can either simply choose not to make any contributions or divert their prosocial intentions towards a different group of beneficiaries. Using a natural experiment based on the Ebola outbreak in Africa in 2014, our research shows that a natural disaster increases (over the short-term) the average contribution size and decreases (over the long-term) the average fundraising time per dollar for prosocial microloans by borrowers from regions closer to the affected region. In contrast, a natural disaster decreases (over the long-term) the average contribution size and increases (over the long-term) the average fundraising time per dollar for prosocial microloans by borrowers from regions farther away from the affected region. This redistribution of prosocial microlending is an unintended consequence of a natural disaster that inflicts long-term economic hardship in some regions. Policymakers and researchers should closely monitor the redistribution of prosocial microlending resulting from a natural disaster so that prompt action can be taken to alleviate potential negative consequences that may arise.

Do Reductions in Search Costs for Partial Information on Online Platforms Lead to Better Consumer Decisions? Evidence of Cognitive Miser Behavior from a Natural Experiment (p. 1780)

Dorothy Lianlian Jiang, Shun Ye, Liang Zhao, Bin Gu

Online platforms increasingly utilize technologies like artificial intelligence (AI)-empowered tools to reduce consumers’ search costs and simplify decision making. However, these tools often target specific types of information, leading to what we term "search cost reduction for partial information." Although designed to assist consumers, our study highlights their unintended consequence: these tools can induce "cognitive miser" behavior, where consumers focus on easily accessible information while neglecting other critical details. This behavior can ultimately result in poorer decision making. Using a natural experiment on Yelp, we evaluated the impact of its AI-powered image categorization feature, introduced in 2015 to reduce the search costs of review images. Through a difference-in-differences design and text analysis of consumer complaints, we found that this feature negatively affected decision quality. These findings carry important implications for platform managers and policymakers. Although search cost reduction tools can improve efficiency, they also risk biasing consumer attention toward easily accessible information at the expense of holistic decision making. Online platforms could mitigate these effects by complementing AI-empowered search cost reduction features with tools that emphasize information requiring greater cognitive effort, thereby ensuring balanced consumer awareness. We recommend that platform designers carefully evaluate the broader impacts of such tools to better support consumer decision making.

The Death of a Technical Skill (p. 1799)

John J. Horton, Prasanna Tambe

For managers, we show that opportunities for skill development strongly influence matching in online information technology (IT) markets. Employers cannot easily circumvent labor scarcity by adopting older technologies, as workers avoid projects with declining future skill value absent substantial wage premiums. However, older workers, having shorter career horizons, are less sensitive to declining skill value, suggesting potential benefits in matching them with legacy technologies. For policy makers, our research demonstrates that labor market tightness persists across both new and old technologies in online IT markets. This challenges the notion that employers can engage in labor arbitrage by avoiding cutting-edge technologies. Policy frameworks therefore need flexibility to address skill shortages wherever they emerge. Additionally, our findings highlight the need for more granular data collection on technical skill evolution beyond broad occupational categories. Our online context provides unique insights into how corporate decisions about technology standards cascade into labor markets. The findings underscore the importance of policies promoting continuous learning and adaptability, while suggesting that age-diverse hiring practices could help address both skill shortages and age discrimination concerns in technical fields.

Push It Cross the Finish Line—Designing Online Interfaces to Induce Choice Closure at the Postdecision Prepurchase Stage (p. 1821)

Younghwa Lee, Andrew N. K. Chen, Weiquan Wang

Shopping cart abandonment, where people leave items in their carts without completing the purchase, has been a persistent issue in e-commerce for many years. One reason shoppers abandon their carts is because they don’t feel their decision to buy is final. Our study focuses on interface interventions that can help make decisions feel final. By drawing upon theories about why people feel uneasy with their choices, we examine how these interface interventions can ease negative feelings and help people commit to their decisions and complete their purchase. We conducted a series of controlled experiments to test interface interventions that provide users with direct support (i.e., coupons) and social support (i.e., social endorsements) for their choices. Our findings show that these interventions help reduce negative feelings and make decisions feel more final (i.e., a sense of choice closure), ultimately leading to greater customer satisfaction with their decisions.

Follow Your Heart or Listen to Users? The Case of Mobile App Design (p. 1846)

Subrahmanyam Aditya Karanam, Ashish Agarwal, Anitesh Barua

Firms strive to improve their products over time to compete effectively in the market. Typically, firms enhance products to stay competitive by adding novel features, imitating competitors, or leveraging customer input through social media. In the case of mobile apps, user feedback in the form of reviews includes suggestions of novel features or features that are already present in competing apps. Leveraging the information contained in reviews and version release notes of iOS apps, we develop a deep learning–based natural language processing approach to identify four types of app features: developer-initiated novel, developer-initiated imitative, user-suggested novel, and user-suggested imitative. We evaluate the impact of these feature categories on app demand. We observe that developer-initiated novel and user-suggested imitative features significantly boost app demand. Conversely, user-suggested novel features can negatively impact demand, especially when they are implemented contextually distant from user suggestions, though contextually close implementations have a positive effect. Although we observe that the aggregate impact of developer-initiated imitative features is insignificant, features that are slightly modified do have a positive effect on demand. The primary contribution of our study is to investigate user reviews as a source of ideas for new features and to evaluate their performance impacts relative to those of developer-initiated features.

Leveraging Context: Re-Thinking Research Processes to Make “Contributions to Theory” (p. 1871)

Viswanath Venkatesh

Researchers constantly face challenging new phenomena that warrant study. Although we do a remarkable job of studying these phenomena, we continue to miss a big opportunity to learn from these findings in ways that can help future work. This article calls for researchers to re-think common major research processes to abstract principles that can lead to more cumulative knowledge. Specifically, this paper suggests change related to four research activities—defining the problem, anchoring to prior research, seeking novelty, and reporting practices.

Customer Engagement Prediction on Social Media: A Graph Neural Network Method (p. 1887)

Tengteng Ma, Yuheng Hu, Yingda Lu, Siddhartha Bhattacharyya

With the rapid prevalence and massive user growth of social media platforms, efficiently targeting potential customers on these platforms has grown in importance for companies. Enhancing the likelihood that a social media user will engage with brand posts holds profound implications for online marketing strategy design. However, predicting customer engagement on social media comes with its own set of challenges. In this work, we design a graph neural network model called the graph neural network with attention mechanism for customer engagement (GACE) to predict customer engagement (like/comment/share) of brand posts. We exploit large-scale content consumption information from the perspective of heterogeneous networks and learn latent customer representation by developing a graph neural network model. We examine GACE using a large-scale Facebook data set, and the comprehensive results show significant performance improvement over state-of-the-art baselines. Furthermore, we conduct an interpretability analysis, which sheds some light on the explanation of the proposed model. To illustrate the practical significance of our work, we provide examples to quantify the economic value of improved predictive power using a cost-revenue analysis in the context of targeted marketing.

Flow of the Game: A Hidden Markov Model of Player Engagement in Online Mobile Games (p. 1898)

Jiaying Deng, Stephanie Lee, Yong Tan

Mobile gaming is a prominent component of the entertainment industry, yet high attrition rates and low engagement are notorious problems faced by most mobile game publishers. This paper investigates the effects of in-game challenge-related factors and reward ads on player engagement in mobile games. Using a Hidden Markov Model, we analyze how perceived challenge, fluctuation of perceived challenge, and reward ads impact players’ engagement states. Our findings indicate that whereas moderate levels of perceived challenge enhance engagement, excessive challenge level has diminishing returns. Furthermore, fluctuations in perceived challenge help sustain engagement by providing a balance between stimulation and cognitive restoration. Third, reward ads provide access to scaffolds, offering temporary support that helps players move to a higher engagement state, especially during challenging phases of the game. For game developers, incorporating dynamic challenge adjustments can improve player experience and keep players engaged. Additionally, reward ads offer a strategic opportunity for game publishers to monetize their games while maintaining engagement, with a stronger impact when players perceive a higher level of challenge. These insights provide valuable practices for designing mobile games that effectively balance user engagement and monetization strategies, ensuring sustained growth in a competitive landscape.

The Impact of Mobile Data Cost on Consumer Price Sensitivity: A Study of a Hotel Booking App (p. 1912)

Xiaopeng Luo, Cheng He, Yu Jeffrey Hu, Xitong Li, Yuan Cheng

Mobile data cost arises from the perception that mobile data are more expensive than Wi-Fi because of additional charges incurred when exceeding fixed data quotas. Understanding mobile data cost is a global and industry-wide concern as the perception of this cost is common in both developed and developing countries, and even subtle behavioral shifts because of this cost can have significant impacts given the prevalence of mobile shopping. Using hotel search data, we find that consumers are approximately twice as price sensitive when using mobile data compared with Wi-Fi, leading to an average revenue reduction of $17 per hotel booking. Additionally, consumer search effort decreases across decision stages when using mobile data rather than Wi-Fi, showing that mobile data users pursue satisfactory options without extensive search effort, obtain less product information, and therefore rely more on price information, becoming more price sensitive. This study provides implications for mobile shopping environments in which limited mobile data plans are common, suggesting that firms should consider consumers’ connectivity types in marketing strategies, offer data subsidies to encourage more thorough searches, and develop customized recommendations and app versions for mobile data users and that policymakers should monitor potential price discrimination to protect consumer interests.

From Anonymity to Accountability: How Virtual Identity Disclosure Changes the Quantity and Quality of “Likes” (p. 1926)

Bingjie Qian, Tat Koon Koh, Xiaoquan (Michael) Zhang

An integral component of user participation in online communities is giving “likes” to content posted by others. Meanwhile, online users are often allowed to create a virtual identity unrelated to their real-world identity. The objective of this study is to identify the motivations behind users’ giving “likes” when their virtual identity (i.e., username) is hidden or shown. Specifically, we examine the impact of an exogenous policy change in an online community that made usernames publicly visible. Our results show that users “liked” fewer but higher-quality articles after the policy change, consistent with their protective self-presentation motivation. This study emphasizes the significance of virtual identity, arguing that a virtual identity devoid of real-world information should not be equated with anonymity. It also identifies “liking” as a key channel of self-presentation and underscores the importance of protective self-presentation. For platforms, understanding users’ motivations to give “likes” and the effects of virtual identity disclosure can help refine community policies to encourage quality content engagement. For content creators, our findings suggest they can enhance content engagement by aligning their offerings with the self-presentation goals of their audience.