This study was conducted to investigate whether there were differences in eco-friendly food, home meal replacement (HMR) purchases, and eating-out behavior according to the level of agri-food consumer competence. The data for the study were extracted from main food consumers (n=3,321) in the 2022 Food Consumption Behavior Survey. The competence index was divided into awareness-attitude-practice items, and three groups were classified by competence level. The results showed an agri-food consumer competency score of 70.62, with the highest score for awareness (73.96), followed by practice (69.28) and attitude (66.18). The frequency of purchasing eco-friendly food was higher in the excellent group compared to other groups, and quality and price satisfaction was higher with higher competency (p<0.001). Regarding HMR, the results showed that the shortage group had the lowest HMR consumption rate, and satisfaction decreased as competence decreased (p<0.001). The main reason for eating-out was to enjoy food in all groups (59.0%), followed by a lack of cooking time in the excellent group (15.7%) and hassle with food preparation in the moderate and shortage groups (17.3%, 16.6%) (p<0.001). In short, agri-food consumption competency showed differences by contents and components, and differences in food purchases and eating-out behavior by competency level were found.

This study analyzes consumer fashion purchase patterns from a big data perspective. Transaction data from 1 million transactions at two Korean fashion brands were collected. To analyze the data, R, Python, the SPADE algorithm, and network analysis were used. Various consumer purchase patterns, including overall purchase patterns, seasonal purchase patterns, and age-specific purchase patterns, were analyzed. Overall pattern analysis found that a continuous purchase pattern was formed around the brands’ popular items such as t-shirts and blouses. Network analysis also showed that t-shirts and blouses were highly centralized items. This suggests that there are items that make consumers loyal to a brand rather than the cachet of the brand name itself. These results help us better understand the process of brand equity construction. Additionally, buying patterns varied by season, and more items were purchased in a single shopping trip during the spring season compared to other seasons. Consumer age also affected purchase patterns; findings showed an increase in purchasing the same item repeatedly as age increased. This likely reflects the difference in purchasing power according to age, and it suggests that the decision-making process for purchasing products simplifies as age increases. These findings offer insight for fashion companies’ establishment of item-specific marketing strategies.

The wide application of digital media technology in fashion shows has become the epitome of the development and innovation of today's fashion industry, enabling designers to break through the constraints of time and space, changing the performance of today's fashion shows, and making them present unprecedented new features. With the development of information technology, the integration of emerging digital technology and the fashion industry is accelerating. So far, separate studies have been carried out in various academic fields on the combination of Metaverse and NFT, but the current status and nature of relevant research are still incomplete. Furthermore, the current research on virtual fashion shows and NFT in China's apparel industry is limited. The purpose of this study is to investigate the influence of digital fashion marketing stimulation on consumer brand attitudes using the stimulation-organ-response (SOR) framework model. By analyzing 77 cases of virtual fashion shows in China, this study obtained antecedent variables and designed a research model. An online sample of 300 Chinese Gen Z consumers was collected and analyzed using SPSS and FSQCA. This research hopes to provide valuable information for the sustainable development of China's fashion industry, and to help Chinese fashion brands confirm the future market development direction of Metaverse and NFT.

Webpage cookies collect and authorize access to users’ online footprints and regulate the data authorization for access, sharing, and usage. Data authorization, which is built based on, but exceeding cookies protocol, enables personalized recommendations under the framework of data-driven content-user matching in a way against customer privacy invasiveness and data breaches. However, gaps exist in how users’ desire for a personalized experience and the site’s perceived ethics contribute to the site-trust and cookies acceptance of categories at each type of site and how businesses’ reward incentives and cookie-based controls may intensify the willingness to contribute to the user data donation continuously.

While companies and brands have always collected and used customer data for multiple purposes, the advent of smart devices, Internet of Things (IoT), and big data has made it much easier to access and utilize consumers’ personal information. For consumers, however, such ease of access to their personal data and frequent cases of data breach have increased their concerns about data privacy (Harris & Associates, 1996; Milne et al., 2004). Nevertheless, consumers continue to share their personal information with companies and brands in the digital environment (Turow et al., 2015).

Since delivery food has become a new dietary culture, this study examines consumer awareness through big data analysis. We present the direction of delivery food for healthy eating culture and identify the current state of consumer awareness. Resources for big data analysis were mainly articles written by consumers on various websites; the collection period was divided into before and after COVID-19. Results of the big data analysis revealed that before COVID-19, delivery food was recognized as a limited product as a meal concept, but after COVID-19, it was recognized as a new shopping list and a new product for home parties. This study concludes by suggesting a new direction for healthy eating culture.

As visual marketing gains a more critical role in marketing communications, consumer eye-tracking data has been utilized to assess the effectiveness of those marketing efforts (Croll, 2016; Glazer, 2012). With eye-tracking data, researchers can capture consumers’ visual attention effectively and may predict their behavior better than with traditional memory measures (Wedel & Pieters, 2008). However, due to the complexity of data: its volume, velocity and variety, known as 3Vs of Big Data, marketing scholars have been slow in fully utilizing eye-tracking data. These data properties may pose a challenge for researchers to analyze eye-tracking data, especially gaze sequence data, with traditional statistical approaches. Commonly, researchers may analyze gaze sequences by computing average probabilities of gaze transitions from a particular area of interest to another area of interest. When the variance of gaze sequence data in the sample is small, this method would uncover a meaningful “global” trend, a trend consistent across all the individuals. However, when the variance is large, this method may not enable researchers to understand the nature of the variance, or the “messiness” of data. In this paper, first, to overcome this challenge, we propose an innovative method of analyzing gaze sequence data. Utilizing the singular value decomposition, our proposed method enables researchers to reveal a “local” trend, a trend shared by only some individuals in the sample. Second, we illustrate the benefits of our method through analyzing gaze sequence data collected in an advertising study. Finally, we discuss the implications of our proposed method, including its capability of uncovering a hidden “local” trend in “messy” gaze sequence data.

Many measurement methods for understanding consumers’ acceptable price range have been developed. Among these, Price Sensitivity Meter (PSM) is one of the most popular. It has been regarded as a convenient research method because of the ease of data collection and data processing. In particular, PSM requires only four questions to determine the price range. Nevertheless, it also has some problems from a theoretical viewpoint. The purpose of the present research is to develop a new price research method for measuring consumers’ acceptable price range. In particular, applying survival analysis to data prepared for PSM, Japanese consumers’ price acceptance ranges for several categories were estimated.