7BUS2002- 0901-2023 Research Business Methods
7BUS2002- 0901-2023 Research Business Methods
1. Contemporary business issue, research aim, research objectives and research question
1.1 Research background and importance of the research
Artificial intelligence (AI) breakthroughs have caused a significant upheaval in the retail industry in recent years. Tesco, a significant participant in the UK retail sector, has led the way in implementing AI technologies to improve a number of company functions (LinkedIn, 2023). Interest in the possible effects of AI integration on customer experience has increased. AI applications have the power to change how customers interact with Tesco’s products. These applications range from efficient supply chain management to personalised recommendations. It is essential to comprehend the effects of AI integration on Tesco customers’ experiences for a number of reasons. As stated by Prause et al. (2021), Tesco’s strategic use of AI contains valuable insights for competitors and peers trying to negotiate the changing retail landscape, as the sector becomes more digitised. In order to ensure that Tesco’s AI initiatives are in line with customer preferences and expectations, it is imperative that it conducts an investigation into the influence on the customer experience.
From the above image, it can be stated that when asked how their organisation evaluated the use of AI-driven personalization, data accuracy was the most important factor, according to 47% of global business executives (Chevalier, 2023). The speed of real-time information and maintaining clients or repeat business, both mentioned by 44% of the respondents, came in close second (Chevalier, 2023). Then, 42% of respondents said that saving time for the company was another sign of success. By offering a thorough analysis of the particular effects of AI integration at Tesco, the research fills a significant vacuum in the existing literature and adds insightful information to the conversation around AI in retail. For instance, Tesco’s continued competitiveness and growth depend on its ability to understand how AI affects customer experience at a detailed level, since it is the cornerstone of every retail company’s success. Essentially, this study contributes to the scholarly discourse on artificial intelligence in retail while simultaneously providing Tesco with actionable recommendations for navigating the AI terrain effectively to improve consumer satisfaction.
1.2 Aim
The aim of this research is to systematically investigate and analyse the impact of artificial intelligence (AI) integration on customer experience within the retail ecosystem, with a specific focus on Tesco UK.
1.3 Objectives
- To analyse the current AI applications across the retail operations of Tesco UK
- To identify the impact of AI in order to detect the customer preferences and decisions in the modern retail for Tesco
- To identify the major challenges faced by the company while incorporating AI in order to enhance the customer experience by Tesco
- To recommend the strategies through which Tesco can cover the AI challenges and enhance the customer experience level
1.4 Research questions
- What are current AI applications across the retail operations of Tesco UK?
- What is the impact of AI in order to detect the customer preferences and decisions in modern retail for Tesco?
- What are the major challenges faced by the company while incorporating AI in order to enhance the customer experience by Tesco?
- What will be the strategies through which Tesco can cover the AI challenges and enhance the customer experience level?
2. Literature review
2.1 Current AI applications across the retail operations
Artificial intelligence (AI) solutions are now essential to many different operational domains in the modern retail environment. In the words of Robinson et al. (2020), artificial intelligence (AI)-powered chatbots and virtual assistants are being used more frequently in customer-facing interactions to improve service responsiveness and efficiency. AI’s predictive analytics helps supply chain management by optimising inventory levels and streamlining operations to increase operational effectiveness. Furthermore, demand forecasting relies heavily on AI algorithms, which help businesses predict market trends and adjust their product offerings accordingly. As mentioned by Jain and Aggarwal (2020), AI is used in marketing and sales to optimise customer experience and increase sales through customised recommendations, targeted advertising, and dynamic pricing schemes. All things considered, the introduction of AI applications into retail operations represents a paradigm change, encouraging flexibility, data-driven judgement, and increased customer involvement in a quickly changing market landscape.
2.2 Impact of AI in order to detect the customer preferences and decisions in the modern retail
Artificial intelligence (AI) has a significant influence on decision-making and preference detection in modern retail. As opined by Ma and Sun (2020), large-scale statistics are analysed by AI-driven algorithms, which enable businesses to identify complex patterns in customer behaviour. Artificial Intelligence (AI) recognises personal preferences through monitoring online and in-store interactions. This allows for the personalization of marketing campaigns and product recommendations. As stated by Kopalle et al. (2023), Algorithms for machine learning are always changing; they instantly adjust to the shifting tastes and trends of their users. Retailers are better able to anticipate and satisfy customer expectations thanks to this dynamic responsiveness, which promotes a more customised purchasing experience. As opined by Dash et al. (2019), AI also helps to optimise inventory management by ensuring that products are in line with current demand. Therefore, the smooth integration of AI in retail contributes to enhanced consumer loyalty and long-term corporate success by improving operational efficiency and creating a more personalised and fulfilling customer journey.
2.3 Theoretical underpinning
Technology acceptance model
The Technology Acceptance Model (TAM) serves as the theoretical foundation for the effects of artificial intelligence (AI) on consumer preferences and decisions in contemporary retail (Wang et al. 2023). According to TAM, users’ acceptance of technology is dependent on how beneficial and simple they believe it to be. Perceived usefulness in the retail context of artificial intelligence is demonstrated by personalised recommendations that improve the shopping experience by matching products to individual tastes. The smooth integration of AI technologies into the customer journey minimises complexity and reflects ease of use. When users believe AI-driven features are helpful and easy to use, they are more likely to accept them. As stated by Zhou et al. (2020), the TAM’s emphasis on attitudes and behavioural intentions offers a prism through which to view how customers’ decisions are influenced by favourable perceptions of AI’s utility. Thus, utilising TAM in the retail domain provides a strong framework to dissect the psychological foundations of AI adoption, clarifying how perceived utility and convenience of use jointly influence consumer choices and preferences in the ever-changing, technologically-driven retail setting.
2.4 Issues identified in literature
The research on AI in contemporary retail highlights a number of important problems that want consideration. As per the views of Cheng et al. (2021), due to AI’s reliance on vast amounts of client data, privacy problems become increasingly prevalent and raise issues with data security and ethical use. Another serious issue is algorithmic bias, which occurs when AI systems reinforce or even create preexisting societal biases. The literature also emphasises how difficult it can be to strike the correct balance between intrusion and customisation, since too specific recommendations can make customers uncomfortable. As said by de Fine Licht et al. (2020), one issue with transparency is the opacity of AI decision-making processes, which makes it difficult for consumers to comprehend how decisions are made. In addition, there are real-world obstacles including the high expense of putting AI solutions into practice and the requirement for intensive staff training. Comprehending and resolving these concerns is essential to actualizing AI’s full potential in retail while maintaining moral, just, and customer-focused procedures.
2.5 Relevance of the literature with the research questions
The extant literature bears significant relevance to the research inquiries, furnishing a basis for comprehending the complexities of artificial intelligence applications in the retail domain and the corresponding consequences and obstacles. According to the first research question, studies on the use of AI in various retail operations now provide Tesco with insights into possible areas for integration. The research into Tesco’s customer-centric AI applications can also be guided by the theoretical frameworks and empirical data provided by the literature examining the effect of AI on identifying customer preferences and decisions. The third study topic is similar to the difficulties faced by businesses implementing AI in the retail industry, as covered in the literature. Recurring themes include privacy problems, algorithmic bias, and transparency issues, which are consistent with the requirement to pinpoint the main obstacles Tesco faced in enhancing customer experience through AI. Lastly, the literature provides insightful viewpoints on approaches to deal with obstacles in integrating AI for a better customer experience. To address the fourth study question, Tesco-specific strategies will be developed using insights from the body of existing information. As a result, the literature offers a theoretical and contextual framework that directly influences and directs the investigation of the chosen research questions.
3. Research design and methodology
3.1 Research design
Research design is the blueprint outlining the systematic structure and strategy for conducting a study, encompassing the overall plan for data collection, analysis, and interpretation (Sharma et al. 2023). This study will implement a descriptive research approach with the goal of methodically examining how Tesco, UK, uses artificial intelligence (AI) to inform organisational decision-making when delivering personalised services. A thorough examination of contemporary AI applications, organisational decision-making procedures, and the resulting customised consumer experiences is made easier with the use of descriptive research. It makes it possible to record trends, patterns, and insights related to Tesco’s AI integration. The adoption of a descriptive design makes sense since it allows for a thorough analysis of the phenomenon being studied and provides a comprehensive picture of the environment of AI-driven organisational decision-making and how it affects customer-centric services.
3.2 Research philosophy
Research philosophy is the set of beliefs, assumptions, and principles guiding the researcher’s approach to knowledge generation and the nature of reality in a study (Khatri, 2020). This study, which is guided by interpretivism as its research methodology, will comprehend the intricate interactions that Tesco, UK, faces between organisational decision-making, artificial intelligence (AI), and personalised customer experiences. As suggested by Van der Walt (2020), Interpretivism acknowledges the subjective character of human experience and aims to understand the interpretations people make of various situations. The study’s objective of revealing the subtle insights into how AI impacts organisational decision-making and shapes personalised services is in line with interpretivism, which embraces multiple perspectives and uses qualitative methods to create a comprehensive understanding of the interrelationships in Tesco’s dynamic retail landscape.
3.3 Data gathering methods
Interviews offer a detailed investigation of individual perspectives, experiences, and ideas, making them perfect for gathering primary data on Tesco, UK’s use of AI for organisational decision-making. Because of its interactive format, interviews provide extensive qualitative data collection and in-depth questioning that are crucial for comprehending the intricate relationships between AI, organisational decision-making processes, and customer personalization. Furthermore, interviews enable the investigation of participants’ implicit knowledge, providing a thorough and contextually rich comprehension of the complex interactions among technology, decision-making, and customer experiences within the particular Tesco setting.
3.4 Sampling strategy
Choosing a subset of research participants from a larger group is known as a sampling technique (Lohr, 2021). For this study, purposeful sampling will work best since it enables deliberate participant selection based on experience and engagement with Tesco’s AI implementation, guaranteeing pertinent insights. Purposive sampling improves the study’s depth by focusing on decision-makers, AI implementers, and customer experience strategists. This allows for the capture of a variety of viewpoints that are essential to comprehending organisational dynamics. In this research five managers from Tesco will be selected across the different retail locations in order to conduct the interview. By ensuring that the chosen participants have the necessary training and work experience, this approach maximises the value of the information gathered and offers a thorough grasp of how AI affects Tesco’s organisational decision-making and customer experiences.
3.5 Data analysis methods
The methodical review and interpretation of gathered data is known as data analysis. The best method for this research is to use interview content analysis since it will enable the methodical examination of themes, patterns, and subtleties in the qualitative interview data. As stated by Kleinheksel et al. (2020), by finding recurring themes and revealing underlying meanings, content analysis will ensure a methodical and rigorous approach to obtaining insights. This method is in line with the nuanced and context-dependent character of the research objectives as it allows for a thorough knowledge of how AI influences organisational decision-making and creates personalised consumer experiences at Tesco through the categorization of replies.
4. Expected outcome
The study’s anticipated results have the potential to significantly increase Tesco’s worth. First and foremost, Tesco will be able to optimise its current AI infrastructure, guaranteeing compliance with industry best practises and improving operational efficiency, with the use of insights on the AI applications now used across retail operations. Tesco can better customise services and products to match customer expectations by having a more nuanced view of consumer behaviour as a result of knowing how AI affects customer preferences and decisions. By recognising the main obstacles to AI integration, Tesco will be better able to prevent problems before they arise and integrate AI technologies more easily. Tesco will be able to refine its AI approach to improve consumer experiences with the help of the suggested solutions to overcome these obstacles, which will function as practical recommendations. The study’s overall goals are to give Tesco a thorough and strategic roadmap that will enable it to fully utilise AI for organisational decision-making and the provision of individualised services, ultimately enhancing long-term success, customer happiness, and sustained competitiveness in the ever-changing retail market.
References
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Chevalier, S., 2023. Success measurement in using AI-driven personalization worldwide 2023. Available at: https://www.statista.com/statistics/1415821/success-measurement-in-using-ai-driven-personalization-worldwide-2023/ (Accessed: 26 November 2023)
Dash, R., McMurtrey, M., Rebman, C. and Kar, U.K., 2019. Application of artificial intelligence in automation of supply chain management. Journal of Strategic Innovation and Sustainability, 14(3), pp.43-53.
de Fine Licht, K. and de Fine Licht, J., 2020. Artificial intelligence, transparency, and public decision-making: Why explanations are key when trying to produce perceived legitimacy. AI & society, 35, pp.917-926.
Jain, P. and Aggarwal, K., 2020. Transforming marketing with artificial intelligence. International Research Journal of Engineering and Technology, 7(7), pp.3964-3976.
Khatri, K.K., 2020. Research paradigm: A philosophy of educational research. International Journal of English Literature and Social Sciences (IJELS), 5(5).
Kleinheksel, A.J., Rockich-Winston, N., Tawfik, H. and Wyatt, T.R., 2020. Demystifying content analysis. American journal of pharmaceutical education, 84(1).
Kopalle, P.K., Pauwels, K., Akella, L.Y. and Gangwar, M., 2023. Dynamic pricing: Definition, implications for managers, and future research directions. Journal of Retailing.
Linkedin, 2023. How Tesco is Redefining Retail Experience with Generative AI. Available at:https://www.linkedin.com/pulse/how-tesco-redefining-retail-experience-generative-ai-turnb (Accessed: 25 November 2023).
Lohr, S.L., 2021. Sampling: design and analysis. CRC press.
Ma, L. and Sun, B., 2020. Machine learning and AI in marketing–Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), pp.481-504.
Prause, L., Hackfort, S. and Lindgren, M., 2021. Digitalization and the third food regime. Agriculture and human values, 38, pp.641-655.
Robinson, S., Orsingher, C., Alkire, L., De Keyser, A., Giebelhausen, M., Papamichail, K.N., Shams, P. and Temerak, M.S., 2020. Frontline encounters of the AI kind: An evolved service encounter framework. Journal of Business Research, 116, pp.366-376.
Sharma, L.R., Jha, S., Koirala, R., Aryal, U. and Bhattarai, T., 2023. Navigating the Research Landscape: A Guide to the Selection of the Right Research Design. International Research Journal of MMC (IRJMMC), 4(1), pp.64-78.
Van der Walt, J.L., 2020. Interpretivism-constructivism as a research method in the humanities and social sciences–more to it than meets the eye. International Journal of Philosophy and Theology, 8(1), pp.59-68.
Wang, C., Ahmad, S.F., Ayassrah, A.Y.B.A., Awwad, E.M., Irshad, M., Ali, Y.A., Al-Razgan, M., Khan, Y. and Han, H., 2023. An empirical evaluation of technology acceptance model for Artificial Intelligence in E-commerce. Heliyon, 9(8).
Zhou, J., Zhang, B., Tan, R., Tseng, M.L. and Zhang, Y., 2020. Exploring the systematic attributes influencing gerontechnology adoption for elderly users using a meta-analysis. Sustainability, 12(7), p.2864.
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