In recent years there has been a remarkable distribution of the omnichannel environment, which has emerged as a significant factor in the fashion industry. Today's society is characterized by customers who possess extensive knowledge and easy access to information online, with the added convenience of constantly available e-commerce. As consumer awareness has escalated, it has become imperative for businesses to understand how customers' needs, pain points, and desires impact their customer journey in an omnichannel environment. Consequently, the aim of this research is to gain a comprehensive understanding of how Generation Z customers' needs, pain points, and desires influence their behavior throughout the customer journey within the fashion industry's omnichannel landscape. This study aims to contribute new and crucial insights to the existing body of knowledge, given the lack of recent research on this topic from the consumer perspective. To achieve these research objectives, empirical data was collected through a qualitative multi-method study comprising five focus groups and five semi-structured interviews, with a total of 25 participants. The participants consisted of individuals of Swedish origin who were born in, and belong to, Generation Z. The collected data was afterwards analyzed and discussed, which meant comparing and contrasting the results with previous research on the topic. The conclusion of this thesis reveals remarkable observations about Generation Z in Sweden, where this study has identified the most common combinations of different channels in omnichannel atmospheres. Overall, this study highlights a clear preference among female participants to use the services provided by e-commerce, while male participants show a propensity for physical, traditional commerce. In addition, this report sheds new light on the information search and evaluation process, which constitutes the initial stage of the consumer's decision-making process. Finally, needs, pain points and desires based on each step in the customer journey are identified, as well as how the problems can be resolved with initiatives from the companies.

This cumulative dissertation consists of four papers that investigate retailer switching and identify potential and threat of cross-channel retail services (CCRS) for retail businesses. The first, conceptual paper compares different theories and models of retail quality. It derives the concept of retailer aspects as a framework to measure retailer quality and further discusses how CCRS can serve as a tool to enhance those retailer aspects. The second paper discusses results from a consumer panel survey and quantifies the influence of satisfaction with retailer aspects on retailer switching during webrooming behavior. The findings of this second paper present the two retailer aspects assurance of delivery and competitive product pricing as key determinants for retailer switching. The third paper defines CCRS and outlines a conceptual classification scheme for CCRS assessments – the CCRS Pentagon. The fourth paper quantifies the impact of CCRS adoption and retailer aspect preferences on CCRS-induced retailer switching based on a second consumer panel survey. Results from this second study affirm the existence of CCRS-induced retailer switching. The paper concludes that CCRS can serve as a retailer’s lock-in mechanism, but that the availability of CCRS in retail environments also threatens retail businesses. In sum, the dissertation provides academic contributions and suggestions for further academic research as well as practical implications and management tools for application in retail businesses.:I. Introduction II. Enhancing Multi-Channel Retail Quality through Cross-Channel Services III. What Drives Competitive Webrooming? The Roles of Channel and Retailer Aspects IV. Cross-Channel Retail Services: A Service Classification Along The Retail Function V. Service-induced Retailer Switching – Power of Cross-Channel Retail Services VI. Conclusion Appendix A: Estimated parameters in baseline-category logit model Appendix B: Conditional marginal effects and conditional probability Appendix C: T-Tests for retailer aspects per service example