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This study aims to investigate the impact of artificial intelligence (AI) on customer loyalty in the luxury fashion market. It explores how AI-driven tools influence customer trust, satisfaction, commitment and engagement, which in turn affect loyalty. By examining these relationships, the study provides insights into the acceptance and effectiveness of AI technologies in enhancing customer loyalty within the luxury fashion sector.

This study employs structural equation modelling (SEM) to analyse data collected from 406 luxury consumers in Iran. The data was gathered using a targeted sampling procedure, leveraging DigiKala’s e-commerce platform. A comprehensive literature review informed the measurement items, and a seven-point Likert scale was used. The methodology includes confirmatory factor analysis (CFA) to assess the reliability and validity of the constructs, followed by hypothesis testing through SEM.

The study reveals that AI significantly enhances customer loyalty in the luxury fashion market by positively influencing trust, satisfaction, commitment and engagement. Satisfaction and engagement were found to be key mediators between AI and loyalty, while trust had no direct impact on loyalty. The results underscore the importance of AI-driven personalized experiences in fostering stronger customer relationships and loyalty.

This study is one of the first to explore the impact of AI on customer loyalty in the luxury fashion market, using a comprehensive model that includes trust, satisfaction, commitment and engagement as mediators. It extends the stimulus-organism-response (SOR) and technology acceptance model (TAM) frameworks, offering valuable insights for luxury brands on how AI can be leveraged to enhance customer relationships and loyalty.

The purpose of this paper is to find out how consumers constantly trade off the potential extra cost of mass customisation with the additional time they have to wait to receive their customised products.

The authors examine this issue by using conjoint analysis to estimate the trade-offs using a sample of Australian consumers. The authors use cluster analysis to form market segments in the three product categories examined.

The segments demonstrate that there are groups of customers who are quite willing to trade-off price with waiting time. The results have significant implications for Australian manufacturers who are contemplating moving into mass customisation.

Many researchers have investigated the issue of a customer’s readiness to buy a customised product. In particular, they have examined whether customers are willing to pay extra for a mass-customised product, whether they would spend some time to design it, as well as wait to receive it. There has been no study that has examined all three factors simultaneously. The results of this study can help manufacturers form a better understanding of customer willingness for purchasing mass-customised products.

The purpose of this study is to apply and extend the predictors within the theory of planned behaviour (TPB) to understand consumers' behaviour toward recycling end-of-life garments among Australian consumers. The predictors explored within this study include attitude, perceived behavioural control, subjective norms, self-identity, general recycling behaviour eco-literacy, self-efficacy, intentions to recycle and behaviour to recycle end-of-life garments.

Data were collected from a sample of consumers across all eight recognised states/territories in Australia through survey questionnaires. A total of 481 usable responses were analysed using structural equation modelling.

Results show positive relationships between the factors explored with all hypotheses supported. The findings of this study have theoretical and managerial implications. They (1) provide an insight into the significant factors that influence consumers' recycling behaviour amongst Australian fashion consumers; (2) bridge the gap in the explanatory nature of TPB by extending this theory; (3) call to develop marketing campaigns to educate consumers on the impact of fashion waste; (4) suggest the need for provision of household textile collection bins at a national level and (5) highlight the need for policy reform on garment recycling enabled by the Australian government.

This study is part of the limited studies that focus on the recycling of consumer fashion waste within the Australian context. Little research has also applied the TPB to end-of-life fashion products with a focus on recycling. In addition, no study to the authors' knowledge has, in combination, explored self-efficacy, self-identity, general recycling behaviour and eco-literacy as predictors of intentions to recycle end-of-life garments.

This study aimed to examine consumers’ impulse purchases of luxury products in the metaverse, specifically by exploring how consumers’ motivational and emotional experiences affect virtual luxury product purchases in the metaverse.

An online survey was administered to a total of 230 users of various metaverse platforms in China. The data were analysed using partial least squares structural equation modelling (PLS-SEM) disjoint two-stage approach.

The findings indicated that motivational experience (goal importance and goal interest) and positive emotion (fantasy, feeling and fun) have positive effects on impulse buying of luxury products in the metaverse, but none for negative emotions (loneliness and isolation).

This study indicated that understanding and leveraging consumers’ motivational experiences and positive emotions can drive their impulse buying behaviour of luxury products in the metaverse, hence providing virtual and brand retailers with a testbed for their products before they launch in the physical market.

This study enriches our comprehension of consumers’ metaverse luxury purchases by delving into the impacts of motivational and emotional experiences on impulse buying behaviours.