Community leader, by leveraging highly sticky private traffic, has become crucial force in the market expansion of community group-buying platforms. Subsidizing community leader's private traffic can effectively enhance the conversion rate from traffic to orders. Prior research has predominantly focused on platform subsidy strategies for public traffic, with limited attention to private traffic, particularly in comparative analyses of subsidies for both types of traffic. By adopting a unique perspective on the value of private traffic, we develop a gametheoretic model to investigate how private traffic influences the operational strategies of community groupbuying platforms, with particular emphasis on new entrant. Our findings reveal that an entry platform tends to subsidize the user group with greater traffic upon market entry. Although such subsidies incur higher subsidy costs, the benefits are more substantial. Under both subsidy strategies, the incumbent platform generally responds by lowering prices, resulting in a decline in its profits. Notably, other market parameters, such as the maximum changing cost, exert asymmetric positive or negative moderating effects on the value of private traffic, collectively influencing platform market performance and profitability. These results provide novel insights into future research on private traffic and the market entry of community group-buying platforms.

The widespread adoption of online question and answer (Q&A) systems by e-commerce platforms has sparked interest in understanding their potential impacts. Nevertheless, little is known about how various types of consumer questions influence product popularity. Using a dataset from JD.com, this study categorizes consumer questions along the content and temporal dimensions and investigates how different types of questions influence product popularity. The findings reveal that questions concerning core, auxiliary, and peripheral attributes are generally associated with higher product popularity. By comparison, post-purchase questions, particularly those focused on core attributes, are associated with lower product popularity. Further analysis suggests that factors such as question length, product price, and product type moderate these relationships. Specifically, for high-priced search products, longer post-purchase core attribute questions are linked to stronger negative associations with product popularity, whereas longer post-purchase peripheral attribute questions show more positive associations. Questions regarding auxiliary attributes do not show similar patterns. These findings provide crucial theoretical and practical insights for e-commerce platforms and merchants in optimizing Q&A strategies.

Tailoring doctor recommendations to patients' needs and preferences is the core of online consultation platforms. Existing studies encountered challenges in capturing patients' personalized preferences towards different attributes of doctors in doctor selection, owing to the sparsity of individuals' medical consultation records. This study proposes a patient preference disaggregation analysis method to learn the preference models of patients from user-generated content and generate personalized doctor recommendations. A compensatory value function is employed to represent the preference model of a patient towards different attributes of doctors, indicating the compensatory mechanisms among these attributes. An optimization model is established to learn parameters in the value function from historical decision examples, incorporating regularization to prevent overfitting. These examples consist of doctor attribute performance derived from sentiment analysis of online reviews, factoring in reliability and popularity based on comprehensive indicators like diagnosis volume and ratings. Patients are segmented into cohorts (e.g., male, female, minor, severe cases), with preference models developed for each. An individual-cohort matching model estimates personalized preferences for tailored recommendations. Validation on the Dxy.com platform confirms that different patient cohorts have distinct attribute preferences, and our method enhances the patient medical experience through personalized recommendations.

To tackle the issue of numerous suspicious reviews, some platforms have adopted crowdsourcing by inviting consumers to become public assessors. Drawing on cognitive load theory, this study examines the impact of purchase verification (PV) badges and review typesetting on voting behaviors, as well as their moderating roles in the relationship between review depth and voting behavior. Through five experiments, we demonstrate that review depth and typesetting influence voting behavior through serial mediations involving perceived reviewer effort and review helpfulness. PV badges influence voting through review credibility and review helpfulness. Both PV badges and review typesetting act as negative moderators between review depth and voting behavior. This pioneering study highlights the critical roles of PV badges and typesetting in public assessment systems, offering valuable insights for platform management.

Purpose Prefabricated construction technology (PCT) is increasingly adopted worldwide because of its low-carbon advantage, lean nature and efficiency. Previous studies have focused on the factors influencing PCT diffusion and have qualitatively examined their interactions. This study quantitatively investigated the inter-level coupling among multi-dimensional factors that influence PCT diffusion from both micro- and macro-perspectives. Design/methodology/approach About 13 critical factors influencing PCT diffusion were identified through literature analysis and semi-structured interviews. The interpretive structural modeling was used to decompose the hierarchical structure of these factors. Subsequently, system dynamics analysis clarified the positive and negative feedback loops and the qualitative couplings at the micro-level between these factors. A coupling coordination model was applied to quantitatively evaluate the coupling between factors and the coordination among macro-level subsystems. Findings Policy intervention, which is the fundamental driver for PCT diffusion, requires full application to promote diffusion directly or indirectly through its influence on other levels. Subsystems incorporating “inter-organizational technology collaboration” and “inter-organizational information sharing” perform well, highlighting the benefits of multi-stakeholder synergy. Overall, managing the key factors within each subsystem by level contributes to the advancement of the PCT diffusion system with non-additive effects. Research limitations/implications However, this study is a static analysis based on the current PC development stage, without considering the impact of space-time evolution, which will be explored in future research. Practical implications For organizations, giving full play to the synergy of multiple stakeholders is an effective way to promote PC. As the stakeholders responsible for the diffusion of PCT, organizations are suggested to take corporate social responsibility while chasing profits and form an open and win-win culture. The challenges brought by the industry transformation will be jointly overcome through mobilizing their initiatives and establishing scales and types of technology alliances and demonstration bases. This will significantly support individual organizational growth and contribute to broader industry advancements. Social implications Regarding the government, strengthening policy favor for technology superiority and technology maturity and highlighting effective interactions with leading organizations will nonlinearly improve the coupling coordination level of PCT diffusion. Additionally, policymakers should continually evaluate and adjust policies based on the performance of technology investment, maturity, industry development and market competition. By tracing the underlying causes of policy issues and their impacts on technology investment and industry development, policymakers can make informed adjustments to existing measures. This dynamic approach will help address emerging challenges and optimize policy measures so as to improve the overall effectiveness of PCT promotion. Originality/value This study systematically elucidates the hierarchical coupling and coordination of multidimensional factors from both qualitative and quantitative aspects as well as from macro- and micro-perspectives. Moreover, it clarifies the internal logic of PCT diffusion and provides evidence-based suggestions for policy improvement and practical management.

Purpose This study investigates the impact of social media posts on panic buying behavior while also examining the moderating role of the posts’ attention level and exploring the mediating effects of perceived credibility, perceived risk and perceived scarcity. Design/methodology/approach Building upon the elaboration likelihood model and scarcity theory, this study examines the impacts of numerical presentation and posting account type on panic buying behavior and explores their underlying mechanisms. To test our hypotheses, we conducted two online randomized experiments and employed t -tests and regression analysis as the main analytical methods. Findings Our findings highlighted the significance of incorporating precise numerical information and utilizing institutional accounts to enhance users’ perceptions of scarcity and credibility, thereby inducing panic buying behavior. Moreover, posts from institutional accounts were also found to amplify users’ perceived risks. Additionally, we discovered that the impacts of both numerical presentation and posting account type were negatively moderated by the posts’ attention level. Originality/value While previous research has acknowledged the impact of social media posts on panic buying behavior, it has overlooked the identification of specific attributes within these posts that may exert influence as well as neglected to consider the mediating role of user attitudes. This study contributes to the existing literature by elucidating the underlying mechanisms through which three distinct characteristics of social media posts impact panic buying behavior, thereby providing practical insights for effectively managing social media content and mitigating panic buying.

This paper investigates the incentive mechanism for dual-channel healthcare service supply chains, where doctors simultaneously undertake both offline and online medical tasks, based on the common agency theory. Considering the geographical distance between online patients and public hospitals, we construct common agency, game-theoretic models under two scenarios: without spillover effects and with spillover effects. Through analytical solutions, we derive the equilibrium outcomes for both scenarios and conduct comparative and numerical analyses. The findings reveal that as follows: (1) Compared to the scenario without spillover effects, the incentive intensity for offline healthcare increases when spillover effects are considered, and doctors exert higher effort levels in offline healthcare. (2) The incentive intensity for online healthcare may decrease, yet doctors’ effort levels in the online channel do not decline accordingly and may even increase; (3) Non-economic incentives (e.g., online reputation) exhibit a substitution effect on economic incentives; (4) Online reputation not only influences decision-making in the online healthcare channel but also affects decisions in the offline channel through spillover effects. These findings provide valuable insights for public hospitals and online healthcare platforms to optimize incentive structures and for doctors to allocate efforts effectively across dual-channel healthcare services.

Accurate forecasting of duty-free shopping demand plays a pivotal role in strategic and operational decision-making processes. Despite the extensive literature on sustainability, operations management, and consumer behavior in the context of duty-free shopping, there is a noticeable absence of an integrated end-to-end solution for precise demand forecasting. Furthermore, existing forecasting models often encounter limitations in effectively leveraging multi-source data as reliable indicators for duty-free shopping demand. To address these gaps, our study introduces a pioneering deep-learning architecture known as the Attention-Aided Interaction-Driven Long Short-Term Memory-Convolutional Neural Network Model (AI-LCM). Designed to capture intricate cross-correlations within multi-source data, encompassing search queries, COVID-19 impact, economic factors, and historical data; this model represents a significant methodological advancement. Rigorous evaluation against state-of-the-art benchmarks conducted on robust real-world datasets confirms the superior forecasting performance exhibited by our AI-LCM model. We elucidate the manifold implications for various stakeholders while illustrating the extensive applicability of our model and its potential to inform data-driven decision-making strategies.

PurposeThis study aims to investigate the impact of news quality on users’ risk perceptions toward online news and its subsequent influence on perceived believability and user engagement in sharing news. Additionally, we explore the moderating effects of fake news awareness and social tie variety.Design/methodology/approachDrawing upon the social amplification of risk framework, this study investigates the relationship between news quality and users’ news-sharing behaviors, along with its underlying mechanism. An online questionnaire involving 399 eligible participants was employed for hypotheses testing, and the structural equation model served as the main analytical method.FindingsThe influence of news quality on users’ news-sharing behavior is sequentially mediated by risk perception and perceived believability. Individuals with a heightened awareness of fake news or a diverse social tie are more inclined to perceive greater risks associated with news-sharing behavior and question news authenticity.Originality/valueThis study contributes to the existing literature on users’ news-sharing behaviors by examining the influence of risk perception on the relationship between news quality, perceived believability and users’ news-sharing behavior. Additionally, it explores the moderating effects of fake news awareness and social tie variety. Our findings offer valuable insights into comprehending user inclinations towards news sharing and mitigating the dissemination of fake news.

PurposeThis study aims to investigate the impact of physician efforts in online reviews on outpatient appointments, while also examining the moderating effect of physician title.Design/methodology/approachThis study employs the heuristic-systematic model (HSM) to analyze the impact of physician efforts on outpatient appointments. Subsequently, a fixed effect model is employed to examine the research model using an 89-week panel dataset (from April 16, 2018 to December 29, 2019) comprising appointment and online review information pertaining to 8,157 physicians from a prominent online health community in China.FindingsThe findings suggest that physicians with lower professional titles exhibit a significantly higher inclination to enhance heuristic information (e.g. attracting helpful votes) compared to those with higher professional title. All physicians can enhance their outpatient appointments by dedicating efforts towards improving systematic review information, but physician title would weaken the relationship. Moreover, the effect of increasing review volume is considerably more substantial than that of increasing review length, which also surpasses the influence of providing managerial response.Originality/valueUnlike previous studies that primarily focus on patients’ perspectives, this paper represents one of the pioneering effects to examine physicians’ engagement in online reviews.

PurposeThe objective of this study is to investigate the interaction effect between incentive type (financial and compassionate incentives) and the ethicality of merchant strategy on consumer willingness to post positive reviews, while also examining potential variations in consumer responses based on consumption experience, shopping frequency and social class.Design/methodology/approachBuilding upon construal level theory, we hypothesized the moderating influence of the ethicality of merchant strategy and examined the three-way interaction among consumers’ demographic characteristics (i.e. consumption experience, shopping frequency and social class), incentive type and the ethicality of merchant strategy. To empirically test our hypotheses, we conducted four experiments and employed ANOVA for data analysis.FindingsThe ethicality of merchant strategies moderates the association between incentive type and consumer willingness to post positive reviews, with compassionate incentives eliciting more pronounced moral judgments toward merchant strategies compared to financial incentives. The moderating effect of the ethicality of merchant strategy on the relationship between incentive type and consumer willingness to post positive reviews is particularly strong among consumers who have favorable consumption experiences, engage in frequent shopping and belong to lower social classes.Originality/valueThis study contributes to the existing literature on online reviews by examining the impact of compassionate incentives on consumer review behaviors, analyzing the ethicality of merchant strategies within the realm of online reviews and investigating variations in consumer responses to merchant strategies regarding consumption experience, shopping frequency and social class.

This study focuses on a broadly adopted but largely underexplored cashback strategy, the praise cashback strategy (PCS), which aims to entice consumers to keep purchases and post positive online reviews. The literature acknowledges the impact of cashback strategies on consumers' purchase and review behaviors; however, it does not focus on how they affect consumers' return behaviors. Moreover, there are few studies on the effect of cashback strategies on consumer surplus and social welfare in the presence of strategic consumers, especially in relation to analyzing the proportion of fake reviews. By incorporating consumers' review and return behaviors into a newsvendor model, we establish a sequential procedure for merchants to determine pricing, inventory, and PCS decisions, and then examine the conditions under which merchants prefer to adopt a PCS and its impact. Our results reveal that the adoption of a PCS is a typical prisoner's dilemma for merchants in markets where PCSs are prevalent. Only when the PCS phenomenon is pervasive in the market, will merchants adopt a PCS with a small cashback amount. A PCS market negatively impacts consumer surplus and social welfare; however, merchants and consumers may benefit from a small cashback amount. Contrary to the common belief that a PCS undermines the organic evolution of online reviews and reduces return rates, we show that a PCS only elicits a few fake reviews, while merchants may face higher return rates from adopting it. These results benefit future studies on cashback strategies and fake reviews and provide evidence that supports the scientific governance of PCSs.

Express companies and users prefer contactless pickup and delivery methods such as smart lockers, particularly during the pandemic. Extant studies have examined the determinants of user intentions in using smart lockers but ignored the analysis of the process of their switching from traditional home pickup or delivery to contactless smart lockers. Moreover, neither the moderating role of user characteristics or senders' perspectives have been investigated. To address these gaps, we developed a push-pull-mooring framework and collected two online questionnaires to investigate senders' switching intentions and compare the differences between senders and receivers. The results of the PLS-SEM analysis identify the push effect of low perceived value of home pickup, pull effect of the attractiveness of smart lockers on switching intention and the mooring effects of inertia. The attractiveness of smart locker fully mediates the relationship between low perceived value and switching intention in sending condition and play a suppressing effect in receiving condition. The inertia of using home pickup negatively affects switching intention and strengthens the positive impact of the low perceived value of home pickup on switching intention. This study sheds new light on users’ switching intention to contactless pickup and delivery methods and assists express companies in understanding user intentions and making scientific decisions.

Purpose The importance of online reviews on online hotel booking has been widely acknowledged. However, not all online reviews affect consumers equally. Compared with common online reviews, key online reviews (KORs) have a greater influence on consumers' decisions and online hotel booking. This study takes the first step to investigate the factors affecting the identification of KORs and the role of KORs in online hotel booking. Design/methodology/approach To test the research hypotheses, this study develops a crawler to obtain 551,600 online reviews of 650 hotels in ten representative large cities in China. This study first uses a binary logistic regression to identify KORs by combining review content quality and reviewer characteristics and then uses a log-regression model to investigate the role of KORs in online hotel booking. Findings This study mined the factors affecting the identification of KORs by analyzing review contents and reviewer characteristics. Our results revealed that KORs play a mediating role in the effects of review content and reviewer characteristics on online hotel booking. Originality/value This study focuses on KORs, which have received limited attention in research but are important to practitioners. Specifically, this study investigates the antecedents and consequences of KORs. Our results enable hotel managers to manage online reviews effectively, particularly KORs.

This book explores the population development challenges in China, proposes effective measures to address these challenges by adopting various quantitative methods and simulates China’s population development under three different family planning policies