JBRMR 2056-6271 https://doi.org/10.24052/JBRMR/ 55 Volume 19 Issue 01 2024-10-01

595 https://jbrmr.com/details&cid=595 2024-11-02 Purpose: This paper examines the behavioural determinants of luxury shopping behaviour, focusing on the effect of past experiences and nostalgic memories and how interaction with others influences buying behaviours in light of the social cognitive theory perspective. Design/methodology/approach: A qualitative method was applied to textual data collected from in-depth in-person interviews with 24 female Japanese shoppers. The software applications NVivo and User Local were used for text mining, and an original analytical framework focusing on the values of luxury consumption (‘conspicuous’, ‘social’, ‘hedonic’ and ‘learning from others and self’) was proposed and discussed. Results: Consumers identified positive past experiences and nostalgic memories as aspects of luxury consumption and as impactful factors influencing their future purchasing behaviours. When purchasing luxury goods requiring considerable expenditure, consumers reaffirmed the value of luxury goods and the meaning of owning luxury goods in a social context by learning from others who shared their experiences, which helped them to make a decision about their next purchasing opportunity. Limitations/implications of the study: In order to deepen the findings and understand their implications, work is needed to broaden the scope of the analysis and generalise the output of this study to various consumer segments. Practical implications: The findings provide a valid argument that aids luxury goods retailers’ development of effective marketing strategies. In particular, we found that luxury goods shoppers highly value hospitality and service at stores and that they learn and use ‘fond memories’ of past purchases that relate to their own (ascertained from talking and exchanging with others in a social context) to inform their own purchase decisions. 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596 https://jbrmr.com/details&cid=596 2024-11-02 As consumers become more aware of their purchasing decisions' social and environmental impacts, fashion businesses in the UK increasingly turn to corporate social responsibility (CSR) strategies to gain a competitive advantage. However, there is a dearth of research on which CSR areas are most attractive to UK fashion customers and how these practices influence their buying decisions. This paper addresses this gap by examining the influence of CSR on consumer purchase intention in the UK fashion industry. Using a positivist lens, the study employs a quantitative method to investigate the leading CSR dimensions that affect purchase decisions and assess the strength of these effects. A deductive approach is used to test established hypotheses based on data collected from a sample of UK fashion consumers. Abrar, M., Sibtain, M.M. and Shabbir, R. (2021). Understanding purchase intention towards eco-friendly clothing for generation Y & Z. 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597 https://jbrmr.com/details&cid=597 2024-11-02 This article looks at the influence of automation and artificial intelligence (AI) on unemployment in the United Kingdom from a management standpoint. The study seeks to determine the extent to which automation and AI contribute to unemployment and how management methods can offset these consequences. This research is now in its basic phases, with a full literature evaluation and initial data gathering underway. Early findings indicate considerable sectoral disparities in the impact of automation and AI on employment. This work in progress describes the research objectives, suggested methodology, and expected contributions to academic debate and practical management techniques. Acemoglu, D. and Restrepo, P., 2020. Robots and Jobs: Evidence from US Labor Markets. Journal of Political Economy, 128(6), pp.2188-2244. Autor, D.H., 2015. Why Are There Still So Many Jobs? The History and Future of Workplace Automation. Journal of Economic Perspectives, 29(3), pp.3-30. Bessen, J.E., 2020. AI and Jobs: The Role of Demand. NBER Working Paper, No. 24235. Brynjolfsson, E. and McAfee, A., 2021. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W.W. Norton & Company. Frey, C.B. and Osborne, M.A., 2017. The Future of Employment: How Susceptible Are Jobs to Computerization? Technological Forecasting and Social Change, 114, pp.254-280. Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P. and Dewhurst, M., 2017. Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation. McKinsey Global Institute. Chui, M., Manyika, J. and Miremadi, M., 2016. Where Machines Could Replace Humans—and Where They Can’t (Yet). McKinsey Quarterly, July. Autor, D.H. and Salomons, A., 2018. Is Automation Labor Share-Displacing? Productivity Growth, Employment, and the Labor Share. Brookings Papers on Economic Activity, 2018(1), pp.1-63. Kotter, J.P., 2012. Leading Change. Harvard Business Review Press. Davenport, T.H. and Kirby, J., 2016. Only Humans Need Apply: Winners and Losers in the Age of Smart Machines. Harper Business. Kagermann, H., Wahlster, W. and Helbig, J., 2013. Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0. National Academy of Science and Engineering. Goldin, C. and Katz, L.F., 2021. The Race between Education and Technology. Harvard University Press. O'Neil, C., 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing Group. Webb, M., 2020. The Impact of Artificial Intelligence on the Labor Market. Stanford Institute for Human-Centered Artificial Intelligence. Mokyr, J., 2021. A Culture of Growth: The Origins of the Modern Economy. Princeton University Press. Acemoglu, D. and Restrepo, P., 2021. Demographics and Automation. Review of Economic Studies, 88(2), pp.648-678. Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., Ko, R. and Sanghvi, S., 2020. The Future of Work in Europe: Automation, Workforce Transitions, and the Shifting Geography of Employment. McKinsey Global Institute.   https://jbrmr.com/cdn/article_file/2024-11-02-12-41-52-PM.pdf Policy Dapo Baderinwa Automation Artificial Intelligence Unemployment Management Strategies Workforce Planning Continuous Learning Policy Interventions Economic Development United Kingdom 1320951 164 0