Yue Guo

Social media has evolved into a socio-technical infrastructure that shapes public attention, social interaction, and information governance. Understanding how user engagement behaviors, such as retweets, comments, and likes, collectively influence information diffusion is important for forecasting digital dynamics. Using large-scale data from Sina Weibo, this study develops a hybrid Hawkes–Transformer framework that combines the interpretability of self-exciting point processes with the predictive capacity of deep learning. The model captures both interactions within a post and competition across parallel posts within the same trending topic. Empirical results show that retweets strongly amplify diffusion through self-excitation, while comments can suppress diffusion by diverting user attention. In addition, parallel cascades tend to fragment rather than reinforce information flow. By incorporating Hawkes-estimated parameters as structured inputs into a Transformer model, the proposed approach improves predictive performance while retaining interpretability. These findings provide insights into how attention is distributed and competed for in social media environments, with implications for understanding algorithmic visibility and managing information diffusion.

This paper studies enforcement when the enforcer is itself evaluated by the apparent performance of the system it polices. We call such an actor an evaluated enforcer. In research misconduct governance, universities and research organizations are expected to detect and correct misconduct, yet their own evaluations depend on publications, grants, awards, and rankings that strict enforcement can damage. We develop a static enforcement model with heterogeneous researchers, an academic institution, endogenous detection, performance absorption, reputation costs, and a costly public-monitoring extension. Four results follow. First, observed misconduct need not track true misconduct: stronger enforcement can lower true misconduct yet raise observed cases as detection improves, so case counts are not sufficient statistics for integrity. Second, performance absorption—undiscovered misconduct counting toward measured output—lowers the institution's privately optimal enforcement. Third, reputation rules induce selective blindness when active disclosure is penalized like passive exposure. Fourth, private enforcement can fall below the social optimum. Endogenizing public monitoring as costly external information production shows that it can weaken low-enforcement incentives by raising detection and exposure risk, but is not unambiguously beneficial: monitoring is costly and can become excessive once integrity is high. Policy therefore faces a double boundary—too little correction sustains low enforcement, while excessive correction induces defensive over-enforcement. The paper contributes to the economics of enforcement by identifying evaluated enforcers whose incentives are distorted by the evaluation systems in which they are embedded.

This study examines the effect of firms’ participation in platform-endorsed review solicitation programs on consumers’ online review generation. We leverage a natural experiment on TripAdvisor, which launched a review solicitation program that allows hotels to collect reviews directly from guests after their stays with the aid of certified connectivity partners. Applying a two-stage difference-in-differences approach to a panel data set of online reviews for a matched set of hotels across TripAdvisor and Expedia, we find that hotels’ participation in the review solicitation program results in a 34.3% increase in review volume, a 0.151 increase in review rating, but a 16.9% decrease in review length. Review solicitation, however, generates a notable negative spillover effect on the volume of organic reviews. Specifically, the volume of organic reviews is reduced by 15.5% after hotels start soliciting reviews. We provide evidence that the motivational crowding-out effect plays an important role in driving this negative spillover. Further analyses reveal that the effects of review solicitation are heterogeneous with respect to hotels of different types and consumers with different demographic and behavioral characteristics. Finally, using a novel structural topic model, we detect a significant shift in review content from specific and concrete topics to general and abstract topics. Our findings suggest that review platforms and firms should be cautious about the unintended negative consequences of review solicitation on consumers’ review generation. This paper was accepted by Hemant Bhargava, information systems. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72371192, 72132008, 71872061, and 72061127002] and the Humanities and Social Science Fund of Ministry of Education of China [Grant 22YJA630021]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01006 .

A brand manager can interpret a rival’s product recall as an opportunity to preempt sales and/or signal superior quality by raising their brand’s ad spending. Conversely, they may interpret the recall as a threat that may harm the substitute brand’s image and/or lead buyers to draw unfavorable comparisons between the substitute brand and the recalling brand. Such interpretation nudges the manager to suppress their brand’s ad spending. The authors test the interpretations empirically in the context of 62 substitute car models’ responses to a model’s recall. They assess the response over 31 weeks and 308 geographical regions, leading to 591,976 model-week-region observations. Regression discontinuity in time (RDiT) analysis reports that, on average, a substitute brand responds by lowering its ad spending by 50%, suggesting that threat interpretation dominates opportunity interpretation. A decomposition of spending by type suggests that substitute brands increase their spending on price advertising by 25%, decrease spending on quality advertising by 71%, while making no adjustment to brand advertising. This nuanced analysis suggests that substitutes attempt sales preemption, avoid quality signaling, and are not worried about brand spillover. Supplementary, a follow-up analysis reports that this advertising strategy strengthens the positive spillover effect of a brand’s recall on its substitute brands’ sales volume. The key findings hold for another major automobile recall event in the same market. The findings contribute to the literature on the management of quality perception while informing substitute brands’ managers on their response to a brand’s quality failure and whether the response helps or hurts the substitutes’ sales. Further, the findings build an empirical foundation for future analytic investigation on strategic interactions among brands when a quality defect occurs.

Many retail investment platforms have introduced artificial intelligence (AI) analysts to help individual investors gather insights and evaluate investment products. Yet, there is scant empirical effort examining the business impact of such an AI tool on individual investors' financial market investments. To fill this gap, we collaborate with Ant Fortune (an affiliate of Alibaba) , the largest retail investment platform in China, and explore a unique dataset wherein an AI analyst was introduced to facilitate individual investors’ investment activity. Our analyses provide the first empirical evidence showing that individual investors who use the AI analyst perform more manual investment sessions and invest more than those who do not use the AI tool. Interestingly, our further analyses document both positive and negative effects across heterogeneous investor groups. Specifically, we demonstrate that, on the one hand, the use of an AI analyst could drive more investment activity from individual investors who lack adequate financial knowledge, indicating a positive financial inclusion effect. On the other hand, we find that AI analyst use reduces investment activity from investors who are less familiar with financial technology, suggesting a negative resistance effect of AI applications. More importantly, using an AI analyst alters investors’ risk tolerance, increasing their investment in products carrying increased volatility. Despite the positive effects of using AI analysts for investment returns, information overload causes moderate negative impact for investors with less finance knowledge. Our results help platform managers understand whether and how they should implement AI analysts to promote individual investors’ standing in financial markets.

Purpose: The space industry has experienced rapid development over the last few years. Activities such as building things in space, learning about our Earth and exploring outer space are satisfying people's fantasies and achieving humanity's ambitions. Such activities have also generated many issues that belong to several information systems (IS) research domains. In this article, the authors discuss the challenges and opportunities associated with the space economy.Design/methodology/approach: The authors discuss why the emerging space economy opens a new frontier of e-commerce and data analytics. Linking three important IS research areas (i.e. digital commerce, data analytics and information security) to the space economy, this study motivates scholars to pay close attention to this promising new frontier for IS research.Findings: The authors identify new research opportunities within several IS research contexts (digital commerce, data analytics and information security). The authors highlight the potential for opening a robust, interdisciplinary field in the IS domain that could provide valuable insights for practitioners and academics.Originality/value: Because of the unique characteristics of the space economy, this article presents some promising avenues, research opportunities and implications for several IS fields (digital commerce, data analytics, decision science, information sharing and information security and new business models). Indeed, many opportunities are interdisciplinary in scope, with overlaps occurring between IS and other disciplines.

Increasing number of firms are adopting enterprise social media (ESMs) for their day-to-day communication. Despite the spending made on these IT tools, participation levels on ESM are not high. Und...

This study examines the effect of firms’ participation in review solicitation programs on consumers’ generation and perception of online reviews. We leverage a natural experiment on TripAdvisor, which launched a review solicitation program that allows hotels to collect reviews directly from guests after their stays. Applying a difference-in-differences approach to a panel dataset of online reviews for a matched set of hotels across TripAdvisor and Expedia, we find that hotels’ participation in the review solicitation program results in a 34.2% increase in review volume, a 0.145 increase in review rating, but a 16.2% decrease in review length. A closer inspection reveals that the effects on review valence and length are driven by the influx of more positive and shorter solicited reviews rather than by changes in the valence and length of organic reviews. The influx of solicited reviews, however, has a negative spillover effect on the volume of organic reviews due to a motivational crowding-out effect. Specifically, the volume of organic reviews is reduced by 15.3% after hotels start soliciting reviews. Further analyses show that solicitation disclosure hurts the perceived helpfulness of reviews, and this negative effect is alleviated by reviewer reputation. Our findings suggest that review platforms and firms should be cautious about the unintended negative consequences of review solicitation on consumers’ review generation and perception.

Purpose: This study explores the factors influencing the value of enterprise social media (ESM) from the perspective of compatibility. Establishing a theoretical model based on compatibility theory, the authors examine the effects of two dimensions of compatibility and the mediating effects of employees' intrinsic motivations. ESM is an important tool that helps companies to enhance knowledge sharing and cross-department collaboration. Thus, it is important to understand factors that can facilitate the role of ESM in improving enterprise operating performance.Design/methodology/approach: The authors conducted a survey among 353 users of a leading ESM platform and empirically investigated how compatibility influences ESM value through employees' intrinsic motivations. Structural equation modeling (SEM) was applied to study the relationship among compatibility, employees' intrinsic motivations and ESM value.Findings: The empirical research results indicate that compatibility of self-interest with group interest influences the value of ESM, and intrinsic motivations toward collaboration and toward knowledge management partially mediate the effects of the two dimensions of compatibility on ESM value.Research limitations/implications: First, the empirical analysis relies on data from surveying employees of Chinese companies. Therefore, one direction for future research is to reexamine the model using data from other countries. Second, the effects of compatibility identified in the study may vary among different ESM platforms. In addition, the findings may change for organizations having different sizes.Practical implications: This finding suggests that managers should pay close attention to potential conflicts of interest when implementing ESM to enhance group communication and collaboration. This study also highlights the importance of compatibility of new working processes with experience in practice. In addition, intrinsic motivations towards both cooperation and knowledge management in ESM are important factors influencing the value creation of ESM. Therefore, to cultivate employees' intrinsic motivation, managers and organizations need to facilitate the formation of a collaborative atmosphere and habits of cooperative adoption.Originality/value: Although previous studies show that compatibility is a strong belief salient to technology acceptance and continuance usage behavior, the operational definition of compatibility developed by prior studies has generally been limited to the technology perspective and the individual level. However, the primary benefit of ESM is enabling online team collaboration and knowledge sharing across various departments. Thus, the level of compatibility between employees' self-interests and group interests may influence their intrinsic motivations toward ESM usage. From this perspective of individual–group interest conflicts, the authors propose a conceptual research model based on the theory of compatibility in innovation diffusion.