Search Results (985)

This paper aims to define a model for measuring the localness of a company in an innovative and reliable way, motivated by the growing consumer interest in purchasing local products and supporting local economies. The proposed Expenses Localness Indicators (ELI) model uses existing data from information systems to define Localness Indicators, and incorporates Localness Levels based on geographic and economic criteria. It can be applied to any type of financial entity and overcomes the difficulty of defining localness in e-commerce companies or digital businesses in general. Previous studies have examined the impact of localness and investigated its effectiveness as a branding strategy for managers, mainly through product traceability. The ELI model uses as data the expenses of a company paid to other financial entities. The Expenses Localness Indicators are determined based on the distribution of these payments combined with the localness of the paid financial entities. These Indicators represent the degree of localness as a percentage, ranging from 0% (non-local) to 100% (fully local), and may vary over time. The results of the presented examples indicate that a company’s localness increases as it spends more of its expenses on local financial entities and vice versa. Specific strategies have been tested using synthetic data that demonstrate the correct functioning of the model’s indicators. The ELI model could be used to provide reliable and certifiable information to consumers who want to know where their money goes when they buy products. Implementing the proposed model on a large scale would require acceptance by as many companies and states as possible. However, by making the necessary adjustments, the model could be applied on a smaller scale, supported by consumers and local governments interested in uncovering knowledge about localness. It could also be established as a valid indicator of localness to provide information that researchers, government agencies and professionals can use to promote local entrepreneurship. Full article

Based on para-social interaction (PSI) theory and social identity perspective, this study explores the mechanisms driving consumers’ impulse buying in social live shopping. It examines how live content design, namely information comprehensiveness (INFCOM) and interactivity (INT), affects consumer cognition and affective experiences, namely perceived usefulness (PU), PSI, and sense of belonging (SOB), to generate the influence of the urge to buy impulsively (UBI), and further explores the moderating role of the consumer–broadcaster para-social relationship (PSR) between live content design and consumer experience. Findings indicate that in an accumulative social live shopping environment, comprehensive information and strong interactivity enhance consumer social identity, reduce shopping hesitations and obstacles, and encourage UBI. Forming a close consumer–broadcaster relationship is crucial for promoting social identity and increasing UBI. Even without interactive engagement, consumers who feel a close connection with the broadcaster still experience interaction and SOB. PSR influences impulse buying by enhancing consumer perceptions and thereby promoting UBI. This study advances the understanding of impulse buying from a social identity perspective and suggests that merchants and livestream designers can improve quality and sales by providing comprehensive product information and incorporating diverse interactive elements in live broadcasts. Full article

Despite the growing popularity of digital artworks that use nonfungible tokens (NFTs), systematic frameworks for analyzing the content characteristics driving NFT artworks’ creation, sale, and collection remain underdeveloped. Drawing on key insights from a diffusion of innovations, social identity, and value-based adoption theories, this study constructed a conceptual model that identified six key factors: uniqueness, profitability, prestige, community engagement, collectability, and compatibility. These factors’ effects on consumer purchasing behavior were investigated using perceived value as a mediator. Empirical data were collected from 300 Chinese participants and analyzed using multiple regression analysis. The significant direct effects of profitability, community engagement, collectability, and compatibility on purchasing behavior were identified. Uniqueness and prestige were found to exert indirect effects mediated by perceived value. Furthermore, a fuzzy-set qualitative comparative analysis uncovered configurations of content characteristics sufficient for driving high purchasing behavior. It highlighted low community engagement as a necessary condition for low purchasing behavior and underscored multiple attributes’ synergistic interplay in shaping consumer decisions. By integrating these attributes into the conceptualization of NFT content characteristics and synthesizing theoretical insights, this study enhances the understanding of consumer behavior. Recommendations are provided for NFT creators and platforms to improve content quality, cater to diverse preferences, and enhance user experiences, thereby promoting adoption and sustainable growth. Full article

This study introduces the Channel-wise Attention with Relative Distance (CARD) model for NFT market prediction, addressing the unique challenges of NFT valuation through a novel deep learning architecture. Analyzing 26,287 h of transaction data across major marketplaces, the model demonstrates superior predictive accuracy compared to conventional approaches, achieving a 33.5% reduction in Mean Absolute Error versus LSTM models, a 29.7% improvement over Transformer architectures, and a 30.1% enhancement compared to LightGBM implementations. For long-term forecasting (720-h horizon), CARD maintains a 35.5% performance advantage over the next best model. Through SHAP-based regime analysis, we identify distinct feature importance patterns across market phases, revealing how liquidity metrics, top trader activity, and royalty dynamics drive valuations in bear, bull, and neutral markets respectively. The findings provide actionable insights for investors while advancing our theoretical understanding of NFT market microstructure and price discovery mechanisms. Full article

The integration of advanced technologies, such as the Metaverse, has the potential to revolutionize the retail industry and enhance the shopping experience. Understanding consumer behavior and leveraging machine learning predictions based on analysis can significantly enhance user experiences, enabling personalized interactions and fostering overall engagement within the virtual environment. In our ongoing research effort, we have developed a consumer behavior framework to predict interesting buying patterns based on analyzing sales transaction records using association rule learning techniques aiming at improving sales parameters for retailers. In this paper, we introduce a validation analysis of our predictive framework that can improve the personalization of the shopping experience in virtual reality shopping environments, which provides powerful marketing facilities, unlike real-time shopping. The findings of this work provide a promising outcome in terms of achieving satisfactory prediction accuracy in a focused pilot study conducted in association with a prominent retailer in Saudi Arabia. Such results can be employed to empower the personalization of the shopping experience, especially on virtual platforms such as the Metaverse, which is expected to play a revolutionary role in future businesses and other life activities. Shopping in the Metaverse offers a unique blend of immersive experiences and endless possibilities, enabling consumers to interact with products and brands in a virtual environment like never before. This integration of cutting-edge technology not only transforms the retail landscape but also paves the way for a new era of personalized and engaging shopping experiences. Lastly, this empowerment offers new opportunities for retailers and streamlines the process of engaging with customers in innovative ways. Full article

Companies use emoticons in the content of their brand fan pages as a means to enhance their relationships with consumers. Few studies have been conducted on how emoticons work on Facebook brand fan pages. In addition, previous research on emoticons does not provide any obvious mechanism for emoticons’ effects, and their findings also have certain limitations as a result that reveal mixed results. This study was designed to clarify the mechanism for emoticons’ effects. Two studies were conducted in total. In Study 1, we conducted a one-way ANOVA on 82 subjects recruited through Amazon Mechanical Turk (MTurk) and PROCESS macro model 4 for the mediation analysis. We confirmed that emoticons lowered the perceived functional value of brand fan pages and increased the perceived hedonic value. In addition, we found that the influence of emoticons on consumer attitudes toward brand fan page was only mediated by the hedonic value. In Study 2A, which examined the influence of product type and brand status, we conducted a 2 (emoticons) × 2 (product type) × 2 (brand status) ANOVA on 233 subjects recruited through Amazon MTurk, and contrast analysis and PROCESS macro model 6 were used for the interaction effect analysis and mediation analysis. We found that the positive effect of emoticons only occurred in utilitarian products with high brand status and hedonic products with low brand status. Study 2B, conducted using an Instagram version, yielded results identical to those of Study 2A. Finally, this study’s theoretical and practical implications are discussed. Full article

This paper explores optimal strategies for manufacturers, streamers, and retailers in a dual-channel environment, focusing on three commission structures and two power structures. Our analysis identifies steady states where dynamic commissions converge, enhancing profitability and stability for all parties. We find that less dominant partners prefer commission structures that reinforce existing power structures. Profitability is influenced by dynamic commissions: under manufacturer dominance, dynamic wholesale price and commission rate increase profitability for manufacturers and retailers while decreasing streamers’ profits. In contrast, under streamer dominance, a dynamic commission rate enhances streamers’ profits but reduces those of manufacturers and retailers. This evaluation highlights the shared interests between manufacturers and retailers. Taking the spillover effect into account, commission strategies should consider hassle cost, initial commission rate, and spillover impact. Product selection strategies show consistent trends, with moderate hassle cost and a disutility factor ranging from moderate to high, regardless of the spillover effect. Full article

In recent years, the fashion industry has undergone a significant transformation driven by digital innovations, particularly with the emergence of the digital retail theater (DRT). A DRT integrates augmented reality (AR), virtual reality (VR), and 3D modeling to create immersive shopping experiences that bridge the physical and digital worlds. This study specifically focuses on apparel DRTs and investigates the key factors influencing U.S. consumers’ intention to adopt this technology. Drawing on the unified theory of acceptance and use of technology (UTAUT) and perceived risk theory, we developed and tested an integrative research model. Primary data were collected through a structured online survey administered via Amazon Mechanical Turk (MTurk). A total of 400 valid responses were obtained from U.S. consumers. Data were analyzed using multiple regression analysis to examine the hypothesized relationships. The results indicate that effort expectancy (ease of use), facilitating conditions (technical infrastructure), physical risk (concerns about potential harm), and time/convenience loss risk significantly influence consumers’ intention to adopt apparel DRTs. Surprisingly, performance expectancy and social influence were not significant predictors of DRT adoption. These findings provide valuable insights for apparel retailers, emphasizing the importance of user-friendly designs, robust technical infrastructure, and minimizing perceived risks to foster DRT adoption. Full article

This research aims at examining the progress of retail demand forecasting and customer classification via regression models and RFM analysis in the retail chain industry. Entailing actual retail sales data, this work utilizes three regression models:—MLP Regressor, Ridge Regressor, and KNN Regressor to forecast sales. Of them, the MLP Regressor yielded the least Mean Squared Error (MSE = 2.66 × 10) and the best coefficient of determination (R² = 0.9398) stressing its ability to identify deviations from linearity in the sales data. Also, RFM analysis, augmented by K-Means clustering, successfully categorized customers into actionable segments: loyal customers, champions, at-risk, and hibernating. Exploratory data analysis (EDA) findings indicated dramatic changes in sales and revenue, activities, and customer interactions, and products. The combined application of these approaches offers operational solutions in product acquisition, marketing communication, and revenue enhancement. The study advances current research by integrating predictive regression models with RFM segmentation, offering a dual-framework that enhances retail demand forecasting and customer behavior analysis, thereby bridging a critical gap in data-driven decision-making. However, bearing in mind that the lack of demographic data and limited feature variety may constrain the model’s ability to capture personalized customer behaviors, the findings provide a foundation for integrating more diverse datasets and advanced learning approaches for improved retail analytics. Full article

Information asymmetry between manufacturers and online retailers regarding consumer preferences for store brands profoundly influences operational strategy. By leveraging information technology, online retailers can collect valuable consumer data, creating a strategic dilemma: whether to share this information with manufacturers and, if so, with which manufacturer (national or third-party). This study aims to explore an online retailer’s strategic decisions regarding sharing information with manufacturers, filling a gap in the literature on store brands and consumer preferences. Using game theory, we analyze the interactions among an online retailer, a national manufacturer, and a third-party manufacturer, incorporating the Hotelling model to capture consumer preference and product differentiation. Our findings reveal that information sharing does not consistently benefit the online retailer or manufacturers. Notably, without side payment, the online retailer is unwilling to share information with either manufacturer, and manufacturers do not always gain more from receiving such information—a result that challenges conventional wisdom. However, when side payment is introduced, the online retailer’s willingness to share information depends on key factors: the probability of low brand loyalty (low-type) consumers, the proportion of comparison shoppers, the side payment, and the degree of information uncertainty. These findings provide innovative insights for operations managers, highlighting the critical role of information management in shaping strategic decisions and enhancing the efficacy and financial outcomes of information sharing in the context of store brands. Full article

This research explores consumer behavior in e-shopping apps, specifically focusing on how the consumers use e-carts and why they abandon them. A model based on the Regulatory Focus Theory was developed to explain the predicted relationships. The study used a self-administered survey to gather 274 qualifying questionnaires from Pakistani online buyers. The partial least square structural equation modeling (PLS-SEM) technique was used to analyze the data. Empirical findings elaborate that the consumers’ self-suppression motivation to engage in e-shopping encourages e-cart use and decreases e-cart abandonment. Conversely, consumers’ self-expansion motivation increases e-cart abandonment. Also, visiting clearance pages increases cart abandonment. Moreover, when acting as a mediator it increases e-cart abandonment for both the self-suppression and self-expansion motivations. Furthermore, the moderating effects of product involvement were found to influence e-cart use rather than e-cart abandonment. Theoretical contributions and managerial implications for digital marketers are provided. Full article

In online medical consultations, patients convey their medical condition through self-disclosure, and the linguistic features of this disclosure, as signals, may significantly impact doctors’ diagnostic behavior and service quality. Based on signaling theory, this paper collects consultation data from a large online medical platform in China, employs text mining and classification techniques to extract relevant variables, and applies econometric models to empirically examine the effect of patients’ self-disclosure linguistic features on the quality of online medical services. The results indicate that the completeness and readability of patients’ self-disclosure have a significant positive impact on the quality of doctors’ services, while the expertise and positive sentiment of the disclosure have a significant negative effect. From the perspective of signaling theory, this study reveals the mechanism through which patients’ self-disclosure linguistic features influence doctors’ online consultation behavior, providing an important theoretical foundation for promoting online doctor–patient interaction and enhancing patient well-being. Full article

This paper proposes an innovative, intelligent shopping cart system with an interdisciplinary approach using Bluetooth low energy (BLE) beacons. The research integrates online and offline retail strategies by presenting campaigns and ads to the customers during in-store navigation. In a testing environment, BLE beacons are strategically positioned to monitor the purchasing process and deliver relevant insights to retailers. The technology anonymously logs customers’ locations and the duration of their browsing at each sales shelf. Through the analysis of client movement heatmaps, retailers may discern high-traffic zones and modify product placement to enhance visibility and sales. Additionally, the system provides an additional revenue model for store owners through location specific targeted ads displayed on a tablet mounted on the cart. Unlike previous BLE-based tracking solutions, this research bridges the gap between customer movement analytics and real-time targeted advertising in retail settings. The system achieved an accuracy of 82.4% when the aisle partition length was 3.00 m and 91.7% when the aisle partition length was 6.00 m. This system, which can generate additional income for store owners by generating 0.171 USD in a single test simulation as a result of displaying ads to three test customers in a two-partitioned aisle layout, offers a new and scalable business model for modern retailers. Full article

The increasing popularity of short videos has presented sellers with fresh opportunities to craft video advertisements that incorporate diverse modal information, with each modality potentially having a different influence on consumer engagement. Understanding which information is most important in attracting consumers can provide theoretical support to researchers. However, the dimensionality of the multimodal features of short video advertisements is often higher than the available data, posing specific difficulties in data analysis. Therefore, designing a multimodal analysis framework is needed to comprehensively extract and reduce the dimensionality of the different modal features of short video advertisements, thus analyzing which modal features are more important for consumer engagement. In this study, we chose TikTok as the research subject, and employed deep learning and machine learning techniques to extract features from short video advertisements, encompassing visual, acoustic, title, and speech text features. Subsequently, we introduced a method based on mixed-regularization sparse representation to select variables. Ultimately, we utilized multiblock partial least squares regression to regress the selected variables alongside additional scalar variables to calculate the block importance. The empirical analysis results indicate that visual and speech text features are the key factors influencing consumer engagement, providing theoretical support for subsequent research and offering practical insights for marketers. Full article

The different combinations of streaming media modes and product types influence the sales performance of streaming e-commerce. However, which combination is more effective in boosting product sales is unclear. Drawing on the cognitive fit theory, we collected sales data from 564 short videos and live streams on TikTok to investigate how the interaction of streaming media mode and product type impacts streaming e-commerce sales quantity. This study reveals that short video e-commerce works better at selling search products. In contrast, live-streaming e-commerce excels at boosting experience products, particularly expensive ones. Furthermore, the interaction effect between streaming e-commerce mode and product type is more significantly affected by low-priced products. This research contributes to understanding streaming e-commerce and offers valuable insights for e-commerce stakeholders. Full article