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This study examines the relationship between coupon design and repurchase without using coupons of cherry pickers, defined as consumers who rely heavily on promotions and exhibit low brand loyalty. Drawing on theories of discount dependency, reinforcement, and extinction, we investigate whether specific coupon attributes can shift cherry pickers from promotion-driven purchases toward sustained engagement. Using data from 987 coupons distributed by a leading Asian cosmetics e-commerce platform between January and June 2024, we estimate regression models incorporating both main and interaction effects of coupon design factors. The results reveal that segment-targeted coupons reinforce discount dependency and reduce voluntary repurchase without coupons, while indirect rewards positively influence sustained platform visits. Immediate rewards stimulate short-term purchases but weaken long-term engagement. Expiration dates and minimum purchase requirements emerge as critical constraints, significantly interacting with discount depth to produce both positive and negative outcomes. Overall, three hypotheses were fully supported, five partially supported, and one rejected, highlighting the complexity of coupon effects. The findings contribute theoretically by demonstrating the interactive role of coupon attributes in shaping consumer behavior, and practically by suggesting that firms should prioritize indirect, delayed, and moderately constrained coupons to transform cherry pickers into loyal customers and achieve sustainable platform growth.

The rapid expansion of e-commerce has intensified the significance of online reviews as key informational cues that help mitigate uncertainty in digital transactions. Although prior research has widely explored the role of reviews, relatively few studies have addressed how their quantitative attributes function across different product contexts. This study investigates the impact of positive and negative reviews on consumer purchase behavior, emphasizing a comparison between absolute review counts and proportional measures, and assessing whether these effects differ for search versus experience goods. Drawing on purchase data from a leading South Korean e-commerce platform, both linear and threshold regression models were applied to capture potential nonlinearities. The findings reveal that negative reviews exert a considerably stronger influence on purchase behavior than positive reviews, consistent with Prospect Theory and the concept of negativity bias. Furthermore, absolute review volume showed significant effects, whereas proportional indicators were not statistically meaningful, suggesting that consumers react more strongly to the number of reviews rather than their relative distribution. Product category also moderated these relationships, with review counts having greater impact for search goods than for experience goods. Threshold regression highlighted nonlinear patterns, such as diminishing returns from positive reviews and heightened deterrent effects of negative reviews when their volume is low. Overall, this study extends electronic word-of-mouth research by integrating volume, asymmetry, and product heterogeneity into a unified framework, while offering practical guidance for improving review management and consumer trust in digital marketplaces.

Generative AI (Gen-AI) is getting importance in enhancing business competitiveness and overcoming environmental and economic concerns. However, its adoption and readiness assessment is subjected to various complexities. Therefore, the presented study aims for exploring various readiness factors impacting Gen-AI adoption. With the help of the Technology–Organization–Environment (TOE) theoretical framework, the presented study provided the framework of 14 readiness factors (RF) to support the adoption. The readiness factors including quality control automation, talent availability, data security and governance, ecosystem access were explored using literature review and further confirmed using expert opinion. Also, seven criteria including connectivity, Cloud–Edge Orchestration, Semantic consistency, decentralization were identified. Analytic Hierarchy Process (AHP) was used to compute criteria weights. Then, readiness factors were mapped with seven criteria, and COPRAS method was used to prioritize the readiness factors. The top readiness factors investigated as digital infrastructure, ethical & regulatory preparedness, data readiness quality and maturity, and organizational readiness. In the next step, sensitivity analysis was conducted by changing criteria weights and analyzed that relative ranking of readiness factors remains same. This research results are highly useful for industry managers and practitioners in adopting Gen-AI and opens a revolutionary potential for conducting further research in this direction.

Online reviews crucially shape consumer purchasing decisions, with visual cues receiving increasing attention. However, the specific impact of human faces within these visuals remains underexplored. This study investigates how user-generated photos (UGPs) and facial characteristics affect review helpfulness. Using 152,320 Amazon clothing reviews, we analyze the role of UGPs, face presence, quantity, quality, and expression across different valences. Our findings reveal that while both UGPs and human faces enhance review helpfulness, general UGPs are more effective in positive reviews, whereas human faces are more impactful in negative ones. Furthermore, results do not support the “more is better” assumption, showing that reviews with multiple faces are generally less helpful than single-face ones, a pattern reversed only when facial clarity is high, or the review is negative. Finally, non-smiling faces enhance the helpfulness of negative reviews, supporting the role of consistency. Theoretically, this study enriches media richness theory by highlighting the fit between content type and review valence, provides evidence that information-processing constraints limit the benefits of visual quantity, and extends internal consistency to expression-rating alignment. In practice, we offer guidance to reviewers on selecting credible images and to platforms on optimizing algorithms to prioritize high-value visual cues.

As the fashion industry grows, there is a critical need for effective digital marketing training for new employees. This pilot study aims to enhance novice training by analyzing expert’s behavior and judgments. Particularly, it aims to identify challenges faced by novices and examines the strategies professionals employ to overcome them. By utilizing job-task analytics, this study revealed expert decision-making and revealed judgment process. Semi-structured interviews and observation from eye tracking technology are used in data collection from fashion industry practitioners as a target group. The results showed that novices have a different area of attention pattern than experts due to lack of experience. These insights provide insights a useful guide for novices to become experts in digital marketing field.

This research explores the impact of COVID-19 mandatory social distancing policies on online knowledge-sharing behavior. As a key social distancing measure, these policies restrict face-to-face interaction, potentially encouraging individuals to seek virtual alternatives for social connection. While some may compensate for reduced in-person contact by engaging more in online communities, the psychological burden of isolation could also suppress prosocial behaviors like contributing knowledge. To examine this dynamic, we utilize a staggered difference- in-differences (DID) approach to assess user activity on a major Questions and Answers (Q&A) platform. Our analysis reveals that statewide mandatory social distancing policies led to a notable increase in quality-adjusted knowledge contributions. These insights shed light on how policy responses to crises shape prosocial behavior in digital spaces and contribute to research on crisis adaptation and online knowledge exchange.

In open source software (OSS) ecosystems, where interdependent packages evolve together, developers place high importance on common values such as collaborative openness and a strong commitment to software quality (e.g., compatibility). Recent surveys highlight a new landscape of OSS development, reflecting shifts towards social aspects, such as peer assistance and collaborative teamwork. However, the existing literature offers limited theoretical and empirical insights into how these motivations and OSS common values influence ecosystems beyond individual OSS projects. To address this gap, this study proposes a practice-based motivation framework that integrates self-determination theory (SDT) with the social practice view. Our research model posits that OSS developers are driven to participate in social practices governed by specific codes of conduct that align with the pursuit of OSS common values. We test our hypotheses using data from large-scale survey of 932 OSS package developers. The findings contribute to OSS research by identifying key social practices that direct developers’ efforts toward achieving OSS common values. By emphasizing the social dimensions of OSS development, this study provides a more comprehensive understanding of the dynamics shaping OSS ecosystems.