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This study aims to profile mobile users based on their need for cognitive closure (NFC) (preference for order, preference for predictability, discomfort with ambiguity, close-mindedness and decisiveness) and identify differences among the groups regarding their perceptions of personalized preferences and privacy concerns.

Based on the data from 285 participants, the authors seek to identify and profile unique consumer segments (mobile users) generated based on their NFC. Second, once the segments are established, the authors analyze how the segments differ across their personalized preferences and privacy concerns.

The data generated three distinct consumer segments: equivocal users, structured users and eclectic users. Across the segments, there were differences in their mobile personalization (experience, value and actions) and preference for information privacy (perceived risks and fabrication of personal information).

United States (US)-based sample may restrict the generalizability of this research. Thus, future research should include participants from other geographic regions to increase external validity.

Retail managers can apply this knowledge to implement appropriate personalization strategies for these distinct target groups.

Segmenting clusters based on differences in consumption trait (NFC) provides key insights to retailers looking to deliver personalized customer experience, particularly in a mobile shopping context.

This study aims to examine YouTube comments relevant to sustainable fashion posted on fashion haul videos over the past decade (2011–2021). It is guided by two research questions: (1) How have sustainable fashion-related comments posted on YouTube fashion haul videos changed over time? and (2) What themes are relevant to sustainable fashion in the comments posted on fashion haul videos?

A data set of comments from 110 fashion haul videos posted on YouTube was refined to only include comments with keywords related to sustainable fashion. Leximancer, a machine learning technique, was employed to identify concepts within the data and co-occurrences between concepts. Linguistic Inquiry and Word Count software was employed to assess the prevalence of concepts and identify sentiment over time.

Over the decade, the authors identified increased comments and conversations relevant to sustainable fashion. For instance, conversations surrounding sustainable fashion were linked to “waste” and “addicted” between 2011 and 2013, which evolved to include “environment” and “clothes” between 2014 and 2016, to “buy” and “workers” between 2017 and 2019 and “sustainable” between 2020 and 2021, demonstrating the changes in conversation topics over time.

With increasing engagement from YouTube viewers on sustainable fashion, retail-affiliated content that promotes sustainable fashion is proposed as one approach to engage viewers and promote sustainable practices in the fashion industry, whereby content creators can partner with retailers to feature products and educate viewers on the benefits of sustainable fashion.

The findings suggest that consumers are becoming more aware of and responsive to sustainable fashion. The originality of this research stems from identifying the source of this interest.

This study aims to investigate the impact of coronavirus disease 2019 (COVID-19) on the relationship between buyer firms' corporate social responsibility (CSR) activities toward business partners, such as suppliers, and firm value. The study further explores the role of ownership structure in shaping this relationship.

The authors employ a difference-in-differences (DID) regression method to distinguish between the periods before and during the COVID-19 crisis. The authors utilize data from firms listed on the Korean stock market between 2013 and 2020.

The results show that CSR activities for suppliers have a positive impact on the value of buyer firms. Furthermore, this positive relationship is amplified during the COVID-19 period. In addition, the study finds that the positive relationship is more prominent in samples with higher ownership by controlling shareholders or foreign investors.

Overall, this study makes a valuable contribution to the existing literature by examining the positive effects of CSR activities on firm value and by shedding light on the role of ownership structure in influencing these effects. Additionally, the study emphasizes the significance of CSR activities for business partners in mitigating supply chain disruptions during the COVID-19 pandemic.

The aim of this study is to investigate the psychographic factors of shopping enjoyment, market mavenism and consumer innovativeness and their influence on hedonic and utilitarian values, attitudes and patronage intentions of the small concept luxury department (SCLD) store.

The researchers utilized a quantitative method. An online survey was created and distributed over a three-week period to a representative sample of US luxury consumers. Structural equation modeling (SEM) was used to test the hypothesized relationships among variables.

The theory of reasoned action (TRA) theoretical framework provided an effective model to investigate the perspectives and behaviors of luxury consumers within the SCLD. The findings of this study suggest the SCLD is an effective store format for consumers who are characterized as market mavens, are considered innovative and enjoy shopping.

The findings from this research are beneficial to luxury department store retailers looking to invest resources into a new experiential format to appeal to their consumer base.

The research extends the literature in a rapidly evolving area of retail formats and consumer perception by exploring psychographic factors, including shopping enjoyment, market mavenism and consumer innovativeness, and their impact on the beliefs regarding retail formats' hedonic and utilitarian value.

The purpose of this study is to solve the problem of an unknown initial position in a multi-robot raster map fusion. The method includes two-dimensional (2D) raster maps and three-dimensional (3D) point cloud maps.

A fusion method using multiple algorithms was proposed. For 2D raster maps, this method uses accelerated robust feature detection to extract feature points of multi-raster maps, and then feature points are matched using a two-step algorithm of minimum Euclidean distance and adjacent feature relation. Finally, the random sample consensus algorithm was used for redundant feature fusion. On the basis of 2D raster map fusion, the method of coordinate alignment is used for 3D point cloud map fusion.

To verify the effectiveness of the algorithm, the segmentation mapping method (2D raster map) and the actual robot mapping method (2D raster map and 3D point cloud map) were used for experimental verification. The experiments demonstrated the stability and reliability of the proposed algorithm.

This algorithm uses a new visual method with coordinate alignment to process the raster map, which can effectively solve the problem of the demand for the initial relative position of robots in traditional methods and be more adaptable to the fusion of 3D maps. In addition, the original data of the map can come from different types of robots, which greatly improves the universality of the algorithm.