This study investigates the effects of experiential marketing by categorizing fashion pop-up store experiences according to the strategic experiential modules (SEMs): sensory, emotional, cognitive, behavioral, and relational. It analyzes how these experiential factors influence shopping flow, impulse buying, and word-of-mouth intentions. A survey was conducted with 400 participants, equally distributed by gender and age group (20 and 30-year-old). Valid responses from 320 participants were analyzed using factor analysis, reliability testing, correlation analysis, and regression analysis in SPSS. Findings revealed four key elements of experiential marketing: sensory/emotional, relational, cognitive, and behavioral. Sensory/emotional, relational, and cognitive factors positively affected shopping flow, which enhanced impulse buying and word-of-mouth intentions. However, behavioral factors did not have a significant effect. These results underscore the impact of experiential marketing on pop-up store customer behavior and highlight the understudied area of word-of-mouth marketing. The study specifically targeted consumers most likely to visit pop-up stores, ensuring practical significance by providing data to develop strategies for increasing experiential marketing efficiency. Additionally, the results identify the critical elements of experiential marketing in pop-up stores and examine how they interact with shopping flow and impulse buying. This research contributes valuable insights into optimizing consumer engagement in pop-up retail environments, emphasizing the importance of sensory and relational experiences in driving consumer behavior and addressing gaps in existing marketing literature.

In recent years, with the advancement of virtual reality (VR) technology, research in related fields has gradually increased. As personal head-mounted display devices become more prevalent in the market, this study explores the phenomenon of integrating VR technology with online shopping from the consumer's perspective. The study focuses on consumers' acceptance of VR technology in online shopping and analyzes the types of virtual environments most likely to stimulate consumer purchase intention. Based on the SOR (Stimulus-Organism-Response) and TAM (Technology Acceptance Model) theories, a TAM-SOR integrated model was constructed. Taking into account influencing factors in the current online shopping environment, the model was built and tested using SPSS and AMOS to validate the hypotheses. Structural equation modeling and mediation effect analyses on the collected samples indicate that external stimulus variables in a VR shopping environment—such as flow experience, spatial presence, and entertainment—have a significant positive impact on purchase intention. Additionally, perceived ease of use and perceived usefulness serve as chain mediators, enabling external stimulus variables to further influence consumer purchase intention through these mediating variables.

This study aims to establish an online shopping mall marketing strategy based on big data analysis methods. The customer cluster analysis method was utilized to analyze customer purchase patterns and segment them into customer groups with similar characteristics. Data was collected from orders placed over one year in 2023 at ‘Jeonbuk Saengsaeng Market’, the official online shopping mall for agricultural, fish, and livestock products of Jeonbuk Special Self-Governing Province. K-means clustering was conducted by creating variables such as ‘TotalPrice’ and ‘ElapsedDays’ for analysis. The study identified four customer groups, and their main characteristics. Furthermore, regions corresponding to customer groups were analyzed using pivot tables. This facilitated the proposal of a marketing strategy tailored to each group’s characteristics and the establishment of an efficient online shopping mall marketing strategy. This study is significant as it departs from the traditional reliance on the intuition of the person in charge to operate a shopping mall, instead establishing a shopping mall marketing strategy through objective and scientific big data analysis. The implementation of the marketing strategy outlined in this study is expected to enhance customer satisfaction and boost sales.

This study aims to help companies with efficient investment and marketing strategies by empirically verifying the impact on satisfaction and purchase intention for artificial intelligence-based digital technology supported shopping assistants introduced in e-commerce. Frequency, factor, SEM, and multiple group analysises were conducted using SPSS 26.0 and Amos 26.0. As a result, first, motivated consumer innovativeness elements of AI shopping assistant were derived into a total of four categories: functional, hedonic, rational, and reliable. Second, in the order of hedonic and rational, satisfaction with the AI shopping assistant was significantly affected, and in the order of rational and functional, purchase intention was significantly affected. The satisfaction with the AI shopping assistant did not affect the purchase intention. Third, in the case of hedonic, the AI-preferred group had a more significant effect on satisfaction than the human-preferred group, and in the case of rational, there was no difference by group in purchase intention. Thus, it was found that consumers prefer AI shopping helpers for e-commerce because they can shop reasonably and are functionally convenient. Therefore, when introducing AI shopping assistants, it is essential to include content that can compare and analyze fundamental information, such as product prices, as well as search functions and payment system compatibility that facilitate shopping.

The online shopping market is expanding, with online shopping malls now subdivided into personal computer(PC) and mobile versions. Meanwhile, various efforts to promote online sales are being carried out in a bid to improve performance, and detailed research is required to inform such strategies. The purpose of this study was to classify online shopping mall types into PC fashion malls and mobile fashion malls with the aim of assessing sales promotion satisfaction and investigating the relationship between sales promotion satisfaction and consumers’ behavioral intentions. Data were collected by a survey firm in June 2023, and 248 copies of the data were used for analysis. SPSS 28.0 was used to process the data, and frequency analysis, factor analysis, reliability analysis, and regression analysis were performed. The satisfaction factors for various sales promotions used by PC and mobile fashion shopping malls were empirically subdivided in consideration of consumer perspectives, and potentially effective marketing strategies were presented. Differences were observed in the type of satisfaction with sales promotion between PC fashion shopping malls and mobile fashion shopping malls and in the effect of sales promotion satisfaction on behavioral intention. Based on the study’s findings, effective sales promotion strategies that can increase satisfaction and enhance behavioral intention may be developed and implemented through the use of various and different sales promotion strategies in PC and mobile fashion shopping malls.

Previous studies offered inconsistent empirical results for the influence of customer participation on service satisfaction. One possible explanation for this inconsistency is that existing conceptualizations of customer participation do not clearly differentiate the distinct roles of customer participation in service. To address this gap, Dong and Sivakumar (2015) have proposed an updated classification for customer participation based on “output specificity,” which refers to the degree to that the nature of the output is influenced by the person who provides the resource. The output of the customer participation can either be “specific” or “generic”. The “specific output” is defined as the expected service outcome can be idiosyncratic depending on whether the service is provided by the customer or the employee. In contrast, “generic output” refers to expected service outcome is well defined regardless of whether it is delivered by the service provider or the customer. How output specificity of customer participation influences service satisfaction still lacks of empirical examination.

The Covid-19 pandemic has caused unprecedented crises to societies and economies around the world and has brought drastic changes in the way consumers behave. Fashion business is one of the industries that has been significantly affected by Covid-19 as many consumers reduced their discretionary spending during the pandemic. While the world is entering the post-pandemic era and recovering from the pandemic, it is important to uncover and reflect on the reasons behind varying patterns of consumers’ coping behaviors associated with fashion shopping. However, current research on consumer fashion behavior during the pandemic primarily focuses on a particular type of shopping behavior, without addressing varying patterns of fashion consumption behaviors. In addition, most of these studies attributed such changes in behaviors to motivations toward protection against health-adverse threats based on the Protection Motivation Theory, which mostly focuses on protective behaviors and has limited power in understanding varying internal reasons toward various coping behaviors. Considering the varying adaptive and maladaptive patterns of fashion consumption behaviors observed in the market, it is important to address the psychological mechanism behind varying adaptive and maladaptive patterns of fashion consumption behaviors. Thus, drawing from the Stimulus-Organism-Response (SOR) framework, this study aims to investigate how cognitive appraisal of threats affects the affective/emotional state of consumers and consequently their intention to engage in various coping behaviors in the context of fashion shopping. Specifically, this study aims to investigate how individuals’ cognitive appraisals on risks and uncertainty induce varying emotional feelings (i.e., fear, anxiety, and hope), which further leads to their decisions to engage in problem vs. emotion-focused coping through fashion shopping during the pandemic.

In the rapidly changing online shopping environment, resulting from the impact of the COVID-19 pandemic, companies are implementing sales promotions, such as offering various discount coupons, to increase consumers' product purchase volume. They are also attracting consumers through low-price appeals, with the expectation of improving sales. However, sales promotions issued by companies have numerous usage conditions, and consumers need to make appropriate efforts to meet the stated conditions. Previous research on promotions and consumer behavior has primarily focused on analyzing monetary promotions (such as full discounts) or non-monetary promotions (such as reward points) individually, with little attention paid to a comparative analysis of the two. Additionally, the type of promoted product can impact consumer behavior.

Although the phenomenon of lead categories is well-documented in the marketing literature, our understanding of this important store choice factor remains limited. Lead categories are defined as those product categories that are so important for the shopping trip that they influence the consumer’s store choice decision. The purposes of this paper are to offer theoretical bases that explain why lead categories form and to understand how overall images of product quality, selection, and price affect lead category formation. The authors use theories of anchoring effects and automatic cognitive processing to offer theoretical explanations regarding why consumers form lead categories and how overall images of product quality, selection, and price affect lead category formation. Using survey data collected from consumers at two grocery stores, the authors find that positive overall product quality and selection images facilitate lead category formation and that an overall low-price image hinders it.

This study proposes a new collaborative filtering model that integrates Restricted Boltzmann Machines. The proposed two-stage model is applied to household-level supermarket purchase data. Results show that our model fits the data better and outperforms existing collaborative filtering methods in predicting shopping patterns. The proposed model also improves interpretations of market complexity and common causes of coincidence associated with customers’ multi-category purchases.

Previous studies offered inconsistent empirical results for the influence of customer participation on service satisfaction. One possible explanation for this inconsistency is that existing conceptualizations of customer participation do not clearly differentiate the distinct roles of customer participation in service. To address this gap, Dong and Sivakumar (2015) have proposed an updated classification for customer participation based on “output specificity,” which refers to the degree to that the nature of the output is influenced by the person who provides the resource. The output of the customer participation can either be “specific” or “generic”. The “specific output” is defined as the expected service outcome can be idiosyncratic depending on whether the service is provided by the customer or the employee. In contrast, “generic output” refers to expected service outcome is well defined regardless of whether it is delivered by the service provider or the customer. How output specificity of customer participation influences service satisfaction still lacks of empirical examination.

This study examines the impact of others' reviews (reviews, product ratings) on consumer responses (helpfulness & buying intention) in an online shopping platform. We propose that review features, such as review message construal and review inconsistency between review message valence and rating, determine review credibility as product-related information, which in turn influences helpfulness of review and buying intention toward the product. Specifically, low- level construal review messages will be perceived as more credible than high-level construal review messages, which affect helpfulness and buying intention. In addition, the effect of review message construal will be moderated by review inconsistency. The effect of the review message construal will be enhanced in the condition of review consistency (positive content-high rating & negative content-low rating), but it will be disappeared or attenuated in the condition of inconsistency (positive content-low rating, negative content-high rating).

Online shopping has grown exponentially in the last decade, benefiting both consumers and companies. Among several advantages, online shopping lets consumers compare values and prices from different stores on multiple mobile devices in real time. In addition, for young people, it is a way of expressing their identity and independence.

Prejudice refers to ideas, beliefs, feelings, and attitudes that people have about other less familiar groups as a whole or individuals within those groups, based on their perceived group membership (e.g., race, class, gender, and religion. Prejudice has become increasingly of major importance to scientific thinking about relations between groups. However, little is known about how prejudice affects consumer buying behavior, especially regarding shopping activities that involve crossing between suppliers.