Showing papers in "Information Systems Research in 2023"
TL;DR: In this paper , the authors developed a framework to classify negative reviews and managerial responses and examined how the fit between the nature of review and the way managers responded impacts the customers' complaining behavior in the future.
Abstract: Online reviews are very instrumental in driving customer behaviors. This coupled with the fact that negative reviews seem to have a stronger effect on customer behaviors raises the stakes for managers to effectively respond to such reviews in order to protect their brand. However, given the exponential growth in the volume of reviews, a strategic approach that enables managers to focus their efforts in responding to negative reviews is needed. This paper develops a framework to classify negative reviews and managerial responses and examines how the fit between the nature of review and the nature of managerial response impacts the customers’ complaining behavior in the future. We focus on the mix of rational and emotional cues in exploring the appropriateness of managerial responses to negative reviews. Using text analysis (e.g., natural language processing and deep learning) and using large sale review and response data from TripAdvisor, we extract and code the variables in our model. The findings provide specific and actionable guidelines for responding to negative reviews in online forums. First, managers should respond to negative reviews in order to safeguard the brand and improve firm reputation. Second, managers should be aware that they can respond both rationally and emotionally to negative reviews. Whereas emotional responses have been the preferred mode in most firms, our theorizing and findings clearly indicate response with rational cues is also particularly important in dealing with complaints. When complaints pertain to primarily the procedures in the service delivery process such as speed and flexibility, managers should respond with rational cues that explain the reasons for the service failure and the steps taken to address such failures and reinforce the value of the service provided by the firm. When customers complain only about the nature of their interactions with the hotel or also file grievances about the services not aligning with their needs, managers should respond with more emotional cues such as apologizing or appreciating the customer for patronage and being attentive to the empathy and emotional gratification needs of customers. When customers complain that they were discriminated against, they were not getting what they deserve, or the service did not meet their requirements, managers should respond with both rational cues that explain the discrepancy between actions and expected outcomes and providing some compensation and emotional cues that satisfy the customers’ need for emotional gratification. Such customized and calibrated responses that are appropriate for the nature of the complaint would be critical in shaping the views of other customers in the online review forum. Firms, in their efforts to deal with the growing volume of reviews, have increasingly automated the response process using template responses. Our findings suggest that a more deliberate approach of carefully tailoring the responses to negative reviews is likely to be beneficial in online review forums. Firms could use a data-driven approach of extracting and classifying the nature of complaints according to our proposed framework. Recent advances in machine learning algorithms allow for such classification with greater precision. Instead of drafting each response from scratch, managers can use machine-written skeletons in their responses to target some specific reviews. Firms could then generate responses that are tailored to the nature of the complaints. Such approaches to generate tailored responses could allow firms to deal with the large review volumes in a more effective manner.
4 citations
TL;DR: In this paper , the authors trace the intellectual roots and foundations of the growing use of "digital" as a conceptual label, identify when the label use is warranted as well as outlines implications that the moniker holds for future scholarship, policy, and practice.
Abstract: We live in a time when digital technologies reshape most aspects of business and social life. This challenges received assumptions about modes of operation in organizations. As a result, scholars and practitioners increasingly use the label “digital” to signify that something has changed to the extent that a plethora of long-established management concepts are expressed in a new formulaic form of “digital x,” and x can stand for innovation, strategy, transformation, infrastructure, etc. In the information systems discipline and beyond, “digital” has emerged as an oft-used conceptual label to characterize age-long phenomena hitherto described by the IT (or x) label. There is a sense among academic and practitioner communities that digital and IT are not mere synonyms, but beyond the hype, something fundamentally different is being signaled when the “digital” label is invoked. This paper traces the intellectual roots and foundations of the growing use of “digital” as a conceptual label, identifies when the label use is warranted as well as outlines implications that the moniker holds for future scholarship, policy, and practice. In particular, the paper offers actionable guidance that enables more reflective use of the term “digital” as we move forward.
3 citations
TL;DR: In this article , a systematic comparison of methods for individual treatment assignment is presented, and the results show that large A/B tests can provide substantial value for learning treatment-assignment policies, rather than simply choosing the variant that performs best on average.
Abstract: This study presents a systematic comparison of methods for individual treatment assignment. We group the various methods proposed in the literature into three general classes of algorithms (or metalearners): learning models to predict outcomes (the O-learner), learning models to predict causal effects (the E-learner), and learning models to predict optimal treatment assignments (the A-learner). We discuss how the metalearners differ in their level of generality and their objective function, which has critical implications for modeling and decision making. Notably, we demonstrate that optimizing for the prediction of outcomes or causal effects is not the same as optimizing for treatment assignments, suggesting that, in general, the A-learner should lead to better treatment assignments than the other metalearners. We then compare the metalearners in the context of choosing, for each user, the best algorithm for playlist generation in order to optimize engagement. This is the first comparison of the three different metalearners on a real-world application at scale (based on more than half a billion treatment assignments). In addition to supporting our analytical findings, the results show how large A/B tests can provide substantial value for learning treatment-assignment policies, rather than simply choosing the variant that performs best on average.
3 citations
TL;DR: In this article , the authors examine how social sharing of consumers' brand purchases (via posting selfies on social media platforms) affects brand competition in a market where different types of consumers, characterized by their distinct personal characteristics (i.e., personalities, hobbies, and lifestyles) and brand preferences (i., being loyal to one of two horizontally differentiated brands or neither), all desire accurate public perception of their true type.
Abstract: We examine how social sharing of consumers’ brand purchases (via posting selfies on social media platforms) affects brand competition in a market where different types of consumers, characterized by their distinct personal characteristics (i.e., personalities, hobbies, and lifestyles) and brand preferences (i.e., being loyal to one of two horizontally differentiated brands or neither), all desire accurate public perception of their true type. Our analysis shows that social sharing enhances the profit of the advantaged brand that attracts a larger size of loyal consumers but can hurt the profit of the disadvantaged brand that attracts a smaller size of loyal consumers. That is, in a horizontally differentiated market, social sharing may further strengthen the competitive status of the advantaged brand. Interestingly, the disadvantaged brand may become more likely to suffer from social sharing if it follows the conventional wisdom to expand the loyal segment. When the public can learn a consumer’s true type from other information sources (e.g., the consumer’s online blog), social sharing of consumers’ brand purchases brings a smaller profit gain to the advantaged brand. Our theoretical findings shed light on how brands can devise competitive strategies to leverage the power of social media.
2 citations
TL;DR: In this article , the authors show that state-of-the-art explainability methods evoke mental model adjustments that are subject to confirmation bias, allowing misconceptions and mental errors to persist and even accumulate.
Abstract: Although future regulations increasingly advocate that AI applications must be interpretable by users, we know little about how such explainability can affect human information processing. By conducting two experimental studies, we help to fill this gap. We show that explanations pave the way for AI systems to reshape users' understanding of the world around them. Specifically, state-of-the-art explainability methods evoke mental model adjustments that are subject to confirmation bias, allowing misconceptions and mental errors to persist and even accumulate. Moreover, mental model adjustments create spillover effects that alter users' behavior in related but distinct domains where they do not have access to an AI system. These spillover effects of mental model adjustments risk manipulating user behavior, promoting discriminatory biases, and biasing decision making. The reported findings serve as a warning that the indiscriminate use of modern explainability methods as an isolated measure to address AI systems' black-box problems can lead to unintended, unforeseen problems because it creates a new channel through which AI systems can influence human behavior in various domains.
2 citations
TL;DR: In this paper , Wu et al. studied members of a popular online community in Taiwan and found that both connectivity and communality influence member commitment, but connectivity has a stronger influence than communality on knowledge contribution and individual growth.
Abstract: Online communities (OCs) have historically focused on building knowledge repositories, but as OCs add more synchronous communication, it is important to understand how different communication capabilities influence user commitment, individual growth, and knowledge contribution. We studied 452 members of a popular OC in Taiwan and found that both connectivity (direct user-to-user interaction) and communality (knowledge repositories) influence member commitment, but connectivity has a stronger influence than communality on knowledge contribution and individual growth. We also found that four media capabilities (transmission velocity, parallelism, symbol sets, and reprocessability) have strong influence on both connectivity and communality. These findings suggest that managers of OCs should add software capabilities that help OC members find like-minded members, enable instant messaging among members, and provide richer communication beyond simple text messages.
1 citations
TL;DR: In this article , the authors use high-resolution mobile phone data with geolocation information and propose a novel technical framework to study how social influence propagates within a phone communication network and affects the offline decision to attend a performance event.
Abstract: We use high-resolution mobile phone data with geolocation information and propose a novel technical framework to study how social influence propagates within a phone communication network and affects the offline decision to attend a performance event. Our fine-grained data are based on the universe of phone calls made in a European country between January and July 2016. We isolate social influence from observed and latent homophily by taking advantage of the rich spatial-temporal information and the social interactions available from the longitudinal behavioral data. We find that influence stemming from phone communication is significant and persists up to four degrees of separation in the communication network. Building on this finding, we introduce a new “influence” centrality measure that captures the empirical pattern of influence decay over successive connections. A validation test shows that the average influence centrality of the adopters at the beginning of each observational period can strongly predict the number of eventual adopters and has a stronger predictive power than other prevailing centrality measures. Our centrality measure can be used to improve optimal seeding strategies in contexts with influence over phone calls, such as targeted or viral marketing campaigns.
1 citations
TL;DR: In this article , the authors identify a unique challenge for catastrophe insurance that sets it apart from other lines of insurance and leverages machine learning to study the fairness of ratemaking methods.
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.
1 citations
TL;DR: In this paper , the authors present a motive control theory of insiders' computer abuse (ICA) and develop targeted sanctions for committing ICA to control the harmful influence of financial motives.
Abstract: Reports indicate that employees are willing to share sensitive information under certain circumstances, and one-third to half of security breaches are tied to insiders. These statistics reveal that organizational security efforts, which most often rely on deterrence-based sanctions to address the insider threats to information security, are insufficient. Thus, insiders’ computer abuse (ICA)—unauthorized and deliberate misuse of organizational information resources by organizational insiders—remains a significant issue for industry. We present a motive–control theory of ICA that distinguishes among instrumental and expressive motives and internal and external controls. Specifically, we show that organizational deterrents (e.g., sanctions) do not create motives for ICA, but weaken existing motives (e.g., financial benefits). Conversely, financial benefits and psychological contract violations create motives to perform ICA, and insiders’ self-control diminishes the influence of these motives. The implications for practice are threefold: (1) organizations should make efforts to reduce psychological contract breach for employees by increasing the congruence between expectations and reality to reduce expressive motives for ICA; (2) organizations should seek maintain personnel with adequate self-control to diminish the impact of harmful ICA motives should they arise; and (3) organizations should develop targeted sanctions for committing ICA to control the harmful influence of financial motives.
1 citations
TL;DR: This paper examined the impact of news content sentiment on digital news readership and social media sharing and found that individuals are likely to read news articles with negative headline sentiment on the news website but tend to share articles with positive article sentiment on Twitter.
Abstract: This study examines the impact of news content sentiment on digital news readership and social media sharing. Using econometric analyses and models estimated with rich clickstream data on online news readership and social media sharing data collected from Twitter, we find a differential effect of sentiment on news readership and sharing behaviors. Specifically, individuals are likely to read news articles with negative headline sentiment on the news website but tend to share articles with positive article sentiment on Twitter. Upon decomposition of news article sentiment, we find a contrasting positive author sentiment effect and a negative news topic valence effect on news readership. Interestingly, we uncover that an increase in a Twitter user’s followers leads to an increase in the Twitter user’s propensity to share positive-sentiment news articles. Overall, our findings affirm the coopetitive but complementary relationship between news websites and social media platforms. Our results also guide publishers to better craft their news content and manage social media presence to improve audience engagement and readership outcomes while preserving the agenda-setting ability of news media. Importantly, given the dichotomy between news reading and sharing behaviors, predicting individual behaviors based on social media opinions may need to be viewed with prudence.
1 citations
TL;DR: This article proposed a supervised deep topic modeling approach for text analysis that leverages the auxiliary data associated with text, such as ratings in consumer reviews or categories of posts in online forums, to enhance the discovery of latent topics in text.
Abstract: This study proposes a novel supervised deep topic modeling approach for effective text analysis. This approach leverages the auxiliary data associated with text, such as ratings in consumer reviews or categories of posts in online forums, to enhance the discovery of latent topics in text. The proposed approach can effectively improve topic modeling performance in several ways. First, the learned latent topics are more meaningful and distinguishable, which helps text data exploration. Second, the latent topics discovered by the novel supervised deep topic model are more accurate, which improves the performance of downstream econometrics and predictive analytics that utilize latent topics as inputs. Given the prevalence of auxiliary data in real-world text analysis tasks and the wide adoption of topic modeling in business research and practice, the study offers an effective solution for extracting insights from text data.
TL;DR: Henfridsson et al. as discussed by the authors examined the relationship between generativity and user base growth in the context of a digital platform and found that the dominant narrative of generativity engendering growth, although generally supported by their analysis, obscures the fact that the inverse is also true; that is, growth can lead to expansion of product boundaries (inverse generativity) and that generativity can be bounded; that growth can stabilize ecosystem boundaries (bounded generativity).
Abstract: The assumption that generativity engenders unbounded growth has acquired an almost taken-for-granted position in information systems and management literature. Against this premise, we examine the relationship between generativity and user base growth in the context of a digital platform. To do this, we synthesize the literature on generativity into two views, social interaction (expansion of ecosystem boundaries) and product view (expansion of product boundaries), that jointly and individually relate to user base growth. Both views help us explain how opening a platform relates to the emergence and resolution of conflicting expectations in a platform ecosystem that result in new functions and expanded use. We adopt a panel vector autoregressive approach combining data from six large transaction platforms that engaged with open-source developer communities. We found that the dominant narrative of generativity engendering growth, although generally supported by our analysis, obscures the fact that the inverse is also true; that is, growth can lead to expansion of product boundaries (inverse generativity) and that generativity can be bounded; that is, growth can stabilize ecosystem boundaries (bounded generativity). Against this background, we propose an extended generativity theory that presents generativity and growth in an integrative view and raises awareness about the limitations of the “unbounded growth” claim. We conclude that there is value in separating the two views of generativity conceptually and analytically, along with their relationship to user base growth, and we call for research on the pathways through which generativity produces growth. History: Ola Henfridsson, Senior Editor; Robert Wayne Gregory, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.1209 .
TL;DR: In this article , the authors consider how the introduction of P2P lending platforms, and the resulting access to capital, influences local abortion rates, a medical procedure characterized by significant financial barriers and social stigma.
Abstract: Access to short-term capital remains a pressing problem for many people, especially those facing medical emergencies or needing immediate care. Peer-to-peer lending platforms have the ability to resolve these capital constraints by providing access to small to medium sums of money in an environment that is private and protective of personal information. In this study, we consider how the introduction of P2P lending platforms, and the resulting access to capital, influences local abortion rates, a medical procedure characterized by significant financial barriers and social stigma. We find that the entry of the P2P platform LendingClub is associated with an increase in the rate at which women choose to not carry to term. We argue that the availability of capital through these platforms, when combined with privacy protections, is able to reduce the financial barriers women face when accessing abortion services. This observed effect is stronger in more religious areas and areas with lower levels of education, indicating that social frictions and stigma may be higher in these areas, and also showing where providing an additional channel for funding is more influential.
TL;DR: In this paper , the authors investigated how market valuations of a firm's investments to develop intra-firm and interfirm information technology (IT) capabilities are conditional on regulatory context.
Abstract: Situated in the U.S. electric utility industry in a period of significant market restructuring, our study investigates how market valuations of a firm’s investments to develop intrafirm and interfirm information technology (IT) capabilities are conditional on regulatory context. We find that firms are rewarded by investing in intrafirm IT capabilities in a more deregulated context, and by investing in interfirm IT capabilities in a more uncertain regulatory context. When deregulation expands customer choice, intrafirm IT capabilities create value by enabling greater efficiency and service reliability through coordination of a firm’s internal activities. When regulatory uncertainty increases for key aspects such as price control, value chain configuration, and information control, interfirm IT capabilities create value by enabling greater flexibility through reduction of external transaction costs with customers and suppliers. When allocating resources to develop IT capabilities, executives need to consider that market valuation of IT capabilities development is not static, but dynamic with changes in market structure and regulatory uncertainty. Regulators also need to consider that the regulatory context that they shape through their deliberations and decisions has a substantial impact on the market valuation of investments by firms to develop different types of IT capabilities.
TL;DR: In this article , the authors examined the effect of the decoy effect in the context of recommendations and found that in the personalized context, including a decoy minimizes the demand for the target option and pushes consumers to opt out of purchase.
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.
TL;DR: In this paper , the authors analyze the code of over 1,300 systems as they underwent 19 million changes over 25 years and find that the modularity of their underlying code as a system evolves provides a powerful antidote to system atrophy.
Abstract: Information systems age ungracefully. Once-modern systems aging into unmaintainable, buggy, meltdown-prone albatrosses is a widespread phenomenon that has received limited research attention. The received wisdom is that degenerative deterioration can be combated with refactoring or architectural improvements to their existing code. We conceptualize this phenomenon as system atrophy, and corroborate its existence by analyzing the code of over 1,300 systems as they underwent 19 million changes over 25 years. Such atrophy in systems has bread-and-butter consequences for organizations that rely on them. We show that it stunts the evolution of systems, makes them more bug-prone, and disengages developers. Atrophy in existing systems also makes it for organizations to implement other new systems because there are harder to integrate with them and cannibalize resources left over after their costlier upkeep. We then develop the idea that little increments in the modularity of their underlying code as a system evolves provide a powerful antidote to such atrophy. However, this antidote gradually loses its potency as a system ages further. Contrary to the popular belief, architectural improvements slow down atrophy but do not stop it. Our findings suggest that organizations must plan to eventually phase out these information systems, rather than just hoping to maintain them. For practice, we offer new insights on managing the tradeoff between evolution and atrophy; and how organizations can extract more useful life from aging systems.
TL;DR: In this article , Susarla was funded by an R01 grant from the National Library of Medicine, through [Grant R01LM013443] through the National Institutes of Health (NIH).
Abstract: Funding: A. Susarla was funded by an R01 grant from the National Library of Medicine, through [Grant R01LM013443].
TL;DR: In this paper , a multimethod approach is used to examine how direct communication contributes to matching quality in the context of a peer-to-peer platform for long-term real estate rental properties.
Abstract: On digital platforms, a challenging issue is to ensure the matching quality of two-sided transactions between providers and buyers. This study uses a multimethod approach to examine how direct communication contributes to matching quality in the context of a peer-to-peer platform for long-term real estate rental properties. We found that longer direct phone communication between the renter (customers) and the host (providers) enables the renter to choose a relatively more unique property within her consideration set. Also, the relationship between direct phone communication and ex post transaction satisfaction is stronger when a relatively more unique alternative is chosen. Direct communication enables consumers to collect additional information that supplements online observable product features and supports choice decisions. It allows consumers to anchor less on the centers of their consideration sets and select those unique alternatives. Consumers can better leverage the breadth of their consideration sets and overcome the limits of online information, eventually resulting in desirable matching outcomes. For platform owners, our study highlights the importance of aligning two critical practices for improving matching quality: supplying customers with useful online information and supporting direct communication.
TL;DR: In this article , the authors investigate whether including a discussion of a candidate's weakness in a recommendation may be an effective way to increase the perceived credibility of the recommender and thereby improve the candidate's chance of receiving an interview.
Abstract: Companies are using online professional networks at an increasing rate to find qualified candidates to interview for job openings. Although recommendations published on these sites can provide valuable information and influence hiring decisions, the information may suffer from credibility issues due to the medium by which it is shared. In this study, we investigate whether including a discussion of a candidate’s weakness in a recommendation may be an effective way to increase the perceived credibility of the recommender and thereby improve the candidate’s chance of receiving an interview. We surveyed hiring managers and recruiters to collect data to measure the impact different recommendations have on their decisions. Our findings show that including a discussion of weakness in a recommendation increases the perceived credibility of the recommender, which has a positive effect on the candidate’s likelihood of being interviewed. However, when the discussion of weakness counters common gender-based expectations, it is harmful. When the discussion of weakness is consistent, it is helpful. Furthermore, we find that the physically attractive candidates (as shown in their profile picture) are harmed regardless of the weakness discussed. We investigate this further and find that additional discussion of the candidate’s strengths can reduce the negative impact of the discussion of weakness, but only if the strengths are consistent with common gender-based expectations.
TL;DR: In this article , the authors identify the importance of task characteristics and incentive alignment in successful collaboration and suggest using task characteristics to determine the workflow that will benefit from a collaborative approach, and emphasize the role of management's active involvement in aligning incentives between team members and the project or company's goals.
Abstract: Effective teamwork is crucial in modern-day business, especially in knowledge work. However, building and maintaining effective teams is a challenging task for firms. Whereas previous literature emphasizes the significance of team composition, dynamics, and senior management’s role, the role of task characteristics and incentive alignment in effective collaboration is largely ignored. Our study addresses this gap by identifying the importance of task characteristics and incentive alignment in successful collaboration. Through three large-scale field experiments, we find that tasks with high difficulty and urgency are suitable for collaboration, whereas collaboration can be detrimental to tasks that don’t require urgent completion. We also find that aligning individual incentives with organizational goals is critical to successful collaboration. Our research offers practical guidance to organizations implementing information systems for collaborative problem solving. We suggest using task characteristics to determine the workflow that will benefit from a collaborative approach. Furthermore, we emphasize the importance of management’s active involvement in aligning incentives between team members and the project or company’s goals.
TL;DR: In this article , the influence of IT peers on the choice a management student makes to pursue a career in the IT industry was examined, and it was found that having peers who have worked in IT reduces the likelihood of receiving and accepting an offer in IT industry.
Abstract: The productivity of the information technology (IT) industry depends on the supply of high-quality human capital, especially of managers who contribute to operational, finance, sales and marketing, and leadership roles. This study examines the influence of IT peers on the choice a management student makes to pursue a career in the IT industry. Analyzing student networks at a leading business school in India, we find that having peers who have worked in IT reduces the likelihood of receiving and accepting an offer in the IT industry. If a student has no IT experience, however, IT peers ameliorate this effect to a certain degree. Our study has important implications for the IT industry and for management schools. First, managers trying to exploit peer-to-peer learning as a way to train workers ought to be aware that negative messages could be transmitted along with positive ones, leading to productivity loss. Second, we highlight that peer influences are important when managers in emerging economies choose their occupations and industries. Managers seeking to attract more talent to the IT industry should keep this channel in mind while designing recruitment strategies.
TL;DR: In this article , the effect of customer-generated images (CGIs) on postpurchase satisfaction was investigated and it was found that CGIs lead to a decline in subsequent ratings compared with product ratings not exposed to CGIs.
Abstract: Customer-generated images (CGIs) are images posted by customers on e-commerce platforms, and they usually appear in the review sections together with review text and ratings provided by customers having purchase experiences. Despite their prevalent adoption by e-commerce platforms, the effect of CGIs on customers’ postpurchase satisfaction remains unclear. We find that CGIs lead to a decline in subsequent ratings compared with product ratings not exposed to CGIs. Furthermore, high CGI review ratings and high aesthetic quality exacerbate the negative effect, whereas reviewers’ face disclosure in CGIs can alleviate the negative effect. Through cross-product analyses, we find that the negative effect is more prominent for experience goods (e.g., women’s dresses) than for search goods (e.g., lightning cables). Results from a laboratory experiment show that participants experience significantly higher expectation and negative disconfirmation when reading CGI reviews with high ratings, whereas the uncertainty reduction effect is insignificant, which collectively explains the decline of subsequent product ratings from a theoretical perspective. These findings suggest that platforms and retailers should be aware of the potential negative effect of CGIs on the rating dynamics and take appropriate measures to circumvent it.
TL;DR: In this article , the authors classify feedback intervention into two types: outcome and process, and empirically examine the effects of the two types of feedback on the convergence and diversity of submissions following feedback interventions.
Abstract: As more businesses are turning to crowdsourcing platforms for solutions to business problems, determining how to manage the sourcing contests based on their objectives has become critically important. Aside from static design parameters, such as the reward, a lever organizations can use to dynamically steer contests toward desirable goals is the feedback offered to contestants during the contest. In this study, first, using the psychology literature on the theory of feedback intervention, we classify feedback into two types: outcome and process. Second, using data from almost 12,000 design contests, we empirically examine the effects of the two types of feedback on the convergence and diversity of submissions following feedback interventions. We find that process feedback, providing goal-oriented information to contestants, fosters convergent thinking, leading to submissions that are similar. Outcome feedback, on the other hand, encourages divergent thinking, producing a greater variety of solutions to a problem. Furthermore, the effects are strengthened when the feedback is provided earlier in the contest rather than later. Based on our findings, we offer insights on how practitioners can strategically use an appropriate form of feedback to either generate greater diversity of solutions or efficient convergence to an acceptable solution.
TL;DR: Wang et al. as mentioned in this paper adopted the computational design science paradigm to develop a novel fall prevention framework, which includes the hidden Markov model with generative adversarial network (HMM-GAN) that extracts temporal and sequential patterns from sensor signals and recognizes snippet states and a logistic regression that utilizes the snippet states to determine whether and when to trigger protective devices to prevent fall injuries.
Abstract: Whereas modern medicine has enabled humans to live longer and more robust lives, recent years have seen a significant increase in chronic care costs. The prevention of threats to mobility is critical for chronic disease management. Researchers and physicians often analyze data from wearable motion sensor–based information systems (IS) to prevent falls. However, prior studies on fall prevention often achieve suboptimal performance because of their limited capacities in modeling data distributions. In this study, we adopt the computational design science paradigm to develop a novel fall prevention framework, which includes the hidden Markov model with generative adversarial network (HMM-GAN) that extracts temporal and sequential patterns from sensor signals and recognizes snippet states and a logistic regression that utilizes the snippet states and determines whether and when to trigger protective devices to prevent fall injuries. We evaluate the proposed framework against prevailing fall-prevention models and the HMM-GAN component against state-of-the-art sensor analytics models on large-scale data sets. Through an in-depth case study, we demonstrate how the proposed framework can lead to significantly reduced potentially catastrophic falls. Besides practical health information technology contributions, HMM-GAN offers methodological contributions to the IS knowledge base for scholars designing novel IT artifacts for healthcare applications.
TL;DR: In this paper , the authors used functional MRI (fMRI) to identify the neural correlates of the privacy calculus and leveraged brain insights to design and test a neurally informed behavioral intervention to help people protect their privacy.
Abstract: People are increasingly aware that their information is being tracked online. Although people generally self-report privacy to be important to them, in practice they often disclose far more private information than their self-reported privacy preferences. This “privacy paradox” could be better understood by uncovering the neural processes underlying the privacy calculus: weighing the risks against the benefits of disclosure. We assess the neural processes shaping privacy tradeoffs to characterize the neural mechanisms underlying privacy tradeoffs to design behavioral interventions that help people make better decisions that align with their privacy preferences. In Study 1, we used functional MRI (fMRI) to identify the neural correlates of the privacy calculus. In Study 2, we leveraged brain insights to design and test a neurally informed behavioral intervention to help people protect their privacy. Our results show that altering the timing when information is presented precisely at the time of decision (specifically within a second) directs attention to privacy risks versus benefits, therefore discouraging participants from disclosing their private information. Identifying the neural processes of privacy helps elucidate the privacy calculus and sheds light on the privacy paradox and guides the design of neurally informed behavioral interventions to help people protect their privacy.
TL;DR: Tan et al. as discussed by the authors investigated the impact of mobile payment adoption on bank customer credit card activities and the change of this impact after the mobile payment expansion, and they found that mobile payment not only increases credit card activity at the focal bank through both off-line and online channels, but also enhances customer loyalty to the bank by reducing churn.
Abstract: The rapid growth in the adoption of mobile payments has already begun to reshape bank payment practices. Utilizing a unique data set from a leading bank in Asia that records credit card transactions of its customers before and after the launch of Alipay mobile payment, the largest mobile payment platform in the world, this study aims to understand the impact of mobile payment adoption on bank customer credit card activities and the change of this impact after the mobile payment expansion. To do so, we employ the difference-in-differences method coupled with matching to estimate the effects. We find that mobile payment adoption not only increases customer credit card activities at the focal bank through both off-line and online channels, but also enhances customer loyalty to the bank by reducing churn. Specifically, the total credit card transaction amount and frequency of our focal bank increased by 9.4% and 10.7%, respectively. Moreover, we examine the change in the treatment effect after the mobile payment expansion and find an increase in adopters’ credit card activities and a reduction in their churn after the expansion. Next, we discuss the underlying mechanisms and show that mobile payment acts as a substitute for physical card payment in the off-line channel, and this supports the key underlying mechanism of the reduced transaction cost. However, a certain complementary effect exists between personal computer and mobile payments, likely driven by the coadoption of the two. Finally, we provide empirical evidence on conditions that facilitate the use of mobile payments, following the unified theory of acceptance and use of technology. History: Yong Tan, Senior Editor; Tianshu Sun, Associate Editor. Funding: This work was supported by National Natural Science Foundation of China [Grants U1811462, 72071102]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2021.0156 .
TL;DR: In this article , the authors explore how different types of heuristics differ from one another and demonstrate that borrowers select heuristic based on their motives, leading to varying consequences.
Abstract: People often use heuristics as mental shortcuts when making financial decisions. Although the literature typically considers heuristics as behavior biases, we explore how different types of heuristics differ from one another. Through peer-to-peer lending data, we observe that borrowers who use limited attention when applying for loans tend to choose round loan amounts, simplifying the decision-making process but compromising accuracy. This round-number heuristic decreases their funding success rate and increases the probability of default. On the other hand, some borrowers select loan amounts in “lucky numbers” that superstitious lenders favor. This lucky-number heuristic caters to the lenders’ preference, thus increasing the borrowers’ funding success rates and reducing the likelihood of default. Our paper demonstrates that borrowers select heuristics based on their motives, leading to varying consequences. We also show that heuristics are not all the same, and people’s choice of heuristics provides insight into their characteristics and can predict decision outcomes. For instance, factoring in heuristic usage information improves default prediction accuracy in our setting. Our findings can be beneficial to practitioners in refining the underwriting and screening of borrowers and loans.
TL;DR: In this paper , the authors assess the viability of selectively targeting large drug vendors operating on darknet sites and find that the arrest of a major drug vendor reduced subsequent transaction levels by 39% and the number of remaining vendors by 56% on Silk Road 2.0.
Abstract: Practice and Policy-Oriented Abstract Law enforcement bodies have largely responded to the increase in darknet activities through site shutdowns, which involve significant investment of policing resources. Despite these efforts, new darknet sites continue to show up after the site takedowns. We offer a new look at this issue by assessing the viability of selectively targeting large drug vendors operating on darknet sites. We find that the arrest of a major drug vendor reduced subsequent transaction levels by 39% and the number of remaining vendors by 56% on Silk Road 2.0. This deterrent effect also spilled over to drug vendors located in countries beyond the prosecutorial jurisdiction of the arrested vendor. We further find that small darknet drug vendors were most deterred by the arrest and vendors selling dangerous drugs were relatively more deterred. Our study findings hold policy-relevant implications to government agencies and law enforcement. Whereas site shutdowns can disrupt these markets momentarily, the selective targeting of large-scale drug vendors should be given serious consideration and used to a broader extent. The design of future enforcement strategies should also account for the finding that darknet markets are made up of both small-scale drug dealers new to the drug trade and large-scale drug syndicates.