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Our study explored and mapped cisgender female consumers' motivation and shopping experience for cross-sexual fashion, i.e. people shopping for clothes that are not designed or marketed for their biological sex.

Using a qualitative method, this study explored and mapped consumers' motivation and shopping experience for cross-sexual fashion. Thirteen cisgender female millennials were interviewed about their memories and perceptions of their pre-purchase, purchase and post-purchase experiences.

The findings defined the model of cross-sexual shopping behaviour in cisgender women with the following: (1) two pre-purchase schemes, i.e. fashioned gender schema and nonconformity motivation; (2) one pivotal and main purchase factor, i.e. time invested in the experience itself; and (3) two post-purchase schemes, i.e. use for comfort and use for protection. Practical marketing approaches in advertising and in-store experiences were identified in order to better target cross-sexual consumers.

Unisex fashion (or degendered fashion) has pioneered a fashion trend considered a growing trend in younger generations. To our knowledge, this study is the first research exploring regendered fashion (i.e. going beyond the cisgender and same-sex purchase approach of fashion consumerism) through the lens of cross-sexual consumer behaviour.

There is a growing interest in the use of HR-based Industry 4.0 technologies for equality, diversity, and inclusion (EDI) issues yet the emerging trends of Industry 4.0 in EDI implementations and interventions are not fully covered. This chapter investigates the emerging themes regarding EDI and Industry 4.0 interaction through Google-based big data that show the actual interest in Industry 4.0 and EDI. Drawing on a web analytics method that tracks the real click behaviours of web users through querying combined sets of keywords, the study explores the trends and interactions between Industry 4.0 technologies and EDI-related HR practices. Our search engine results page (SERP) analyses find a high volume of queries and a significant interest between EDI elements and artificial intelligence (AI) only. In contrast to the suggestions of the extant literature, no significant user interest in other Industry 4.0 applications for EDI implementations was observed. The authors suggest that other Industry 4.0 technologies such as machine learning (ML) and natural language processing (NLP) for EDI implementations are in their early stages.