TESCO Study

EVALUATION OF MARKET ORIENTATION USING ARTIFICIALLY INTELLIGENT DRIVEN MARKET PRACTICES WITH SPECIAL REFERENCE TO THE CASE STUDY OF TESCO

Executive Summary

The study will be intended to assess the impact of AI-based marketing activities on the market orientation of Tesco, the largest UK-based retailer. In the context of growing AI penetration into customer analytics, personalisation, and decision-making, it is now necessary to grasp the strategic influence of AI on market responsiveness. The research embraces positivist philosophy, deductive research and descriptive quantitative research, where the survey of 60 customers of Tesco will be conducted in a structured way, in which primary data will be collected. The identification of customers’ perception patterns of AI-enabled operational services and the correlation between these perceptions and the market orientation component would be performed through statistical analysis. The research is substantiated using proven theories like Market Orientation Theory and Technology Acceptance Model. By the results, it is predicted that AI will help Tesco to be more proficient in learning about the customers and offering them products tailored to their needs and in keeping up with the market trends. The findings of the research will add to the available literature and provide practical information to retailers that need to maximise AI-based methods.

Table of Contents

Introduction. 4

Literature Review.. 5

Methodology. 7

Critical Evaluation. 9

Limitations. 10

Expected Research Outcomes. 10

Time Plan (Gantt Chart) 11

References. 12

Introduction

Background

Market orientation has been identified as one of the most important strategic orientations that helps organisations know what their customers want, how the market is changing and how they can sustain a competitive edge. In the recent past, the pace of implementing Artificial Intelligence (AI) has altered the usual marketing process since decisions are no longer based on intuition but on facts (Stone et al., 2020). In the retail market of the UK, AI-powered applications, including predictive analytics, automatic customer segmentation, and personalised recommendations and demand forecasting, are starting to gain popularity in the effort to make the market more responsive. Among the most prosperous supermarket chains in the UK, Tesco has been prolific in implementing AI-enabled technologies in supply chain operations, pricing, customer analytics and loyalty programmes. With the increasing competition, it is crucial to establish the relationship between the AI-based marketing practices and the market orientation of Tesco to determine the manner in which the enterprise is able to create customer value and operational effectiveness. Problem Statement

In spite of the increasing application of AI in retail, the academic literature concerning the impact of AI-based marketing practices on market orientation, among the biggest retailers in the UK, including Tesco, is limited (Haque et al., 2024). Available literature only addresses the use of AI or market orientation individually, leaving an unknown gap in comprehending the interplay of the two. Thus, the article requires empirical research assessing the role of AI tools in the responsiveness and customer-oriented strategies of Tesco within the market. Research Questions

● What is the AI-driven marketing in the case of Tesco?

● What is the level at which AI practices increase the market orientation at Tesco?

● Which perceptions do customers develop about these practices enabled by AI?

Research Aim and Objectives

Aim:

This study aims to assess the impact of the AI-based practice on the market orientation of Tesco.

Objectives:

● To determine the prominent AI-based marketing technologies applied by Tesco.

● To determine the connection between AI use and market orientation.

● To examine the customer reactions to artificial intelligence retail practice.

Research Rationale

The increasing use of AI tools in retail emphasises the importance of realising that they have strategic value other than driving operational efficiency (Cao, 2024). The study is significant as it addresses the role of AI in helping Tesco make customer-oriented decisions and place the company at a competitive advantage. The research can be used by retailers to develop superior AI-driven approaches that increase customer interaction levels, enhance market mastery, and increase organisational advantages in the long run.

Literature Review

Key Studies and Papers

The application of market orientation is a well-recognised strategic determinant of organisational performance in any competitive market like the retail industry. The initial background literature in this field, by Truong et al., (2022), theorised the use of market perspective as an act of creating, sharing, and reacting to market knowledge. Likewise, Mukhtar et al., (2023), highlighted the customer orientation, competitor orientation as well as inter-functional coordination as dimensional aspects that help firms to provide high value. The classical definitions reveal that it is important to decipher the customer needs and react appropriately to changes in the market factors that are becoming more diversified with the appearance of digital and AI technologies.

AI tools have transformed the way organisations gather market intelligence and analyse it in the retail industry. Survey research, including Hicham et al., (2023), claim that marketing activities with AI help with accuracy in decision making, lower the time of response and personalise customers. Moreover, Naheed et al., (2025) point out that AI systems effectively help in market-oriented operations through the identification of customer trends, robotic interactions, and the anticipation of customer behaviour. These tools play a big role in the responsiveness and customer-oriented nature of an organisation and are in tandem with the main guiding principles of market orientation.

In the framework of UK supermarkets, Tesco has become one of the leaders in the implementation of AI and sophisticated analytics. A study conducted by Kumar et al., (2024) indicates that Tesco could improve demand forecasting and retention of customers due to the introduction of machine learning into its inventory management and loyalty programmes. In addition, according to Abdulquadri (2025), the level of customer satisfaction increases in the case of AI-enabled personalisation applied to loyalty-based platforms like the Tesco Clubcard, by allowing the use of personalised product suggestions and unique promotions. All these studies point to the idea that AI is a strategic facilitator of market orientation via enhancing the data-driven insights and real-time decision-making.

Theoretical Framework

The study relies on the market orientation theory that signifies how organisations create and react on market intelligence to ensure completion. Intelligence collection and responsiveness can be regarded as the current means of enhancing the use of AI-based instruments to collect data and become more responsive.

Further, the research is also using the Technology Acceptance Model (TAM), which gives attention to the perceived usefulness, ease of use regarding the adoption of new technologies (Manda and Salim, 2021). TAM is applicable since staff and consumers of Tesco are exposed to AI-driven systems and their adoption determines the success or failure of these systems in facilitating market orientation.

Conceptual Framework

Figure 1: Conceptual Framework

(Source: Self-Created)

Gap in Literature

Despite the current literature admitting the emerging role of AI in marketing activities, there are still multiple gaps. First, the majority of studies involve the adoption of AI as a whole but do not evaluate it in terms of its contribution to market orientation. Second, there is a lack of studies that consider the strategic implications of AI in the UK supermarket market, although Tesco is one of the innovators in digital retail technologies. Moreover, the attitudes of customers towards AI-used strategies are under-researched, which also leads to the lack of full insight into the impact that this type of technologies has on the results of market orientation. As such, this paper attempts to fill the above gaps by exploring a direct connection of AI marketing practices with the overall market orientation of Tesco with empirical quantitative data.

Methodology

Research Philosophy

This paper applies the positivist research philosophy, which focuses on objectivity, measurability, and the application of scientific methods to produce factual and generalisable knowledge. Positivism is also suitable for this research due to the fact that the objective is to determine the association between marketing activities involving AI and market orientation based on observable and measurable reactions of Tesco customers. According to this philosophy, the reality is considered to be external and independent of human perception, which gives the researcher an opportunity to acquire numerical facts and examine them statistically, without subjective interpretation. Research Approach

The deductive research approach is utilised since the study starts with a set of theories about the market orientation and AI adoption and those theories are tested with the help of the primary data (Polisetty et al., 2024). Deduction is an appropriate one since the researcher comes up with hypotheses out of existing literature and compares whether the practical results are subject to assumptions or disapprove the theoretical assumptions. Such a strategy will provide orderly and rational development of theory into the data analysis and will make it possible to assess the role of AI-based practice in the market orientation of Tesco directly. Research Design

The research design of the study is descriptive, which seeks to present in a systematic manner the nature, perceptions and behaviours of Tesco customers in connection with AI-based marketing practices. A descriptive design will work effectively since the study aims at indicating and recording the status quo patterns instead of the manipulation of factors or the setting of cause and effect. It will allow the researcher to provide a precise account of the ways in which customers view the services of Tesco that are operated via AI and to the degree to these perceptions can be associated with the ideas of market orientation. Research Strategy

Quantitative research strategy is followed, which is centred on gathering and studying of numerical data. Quantitative method relates to positivism and deduction as it enables the researcher to measure the perceptions of customers, discover patterns and statistically examine the relationships (Younus and Zaidan, 2022). It also keeps the objectivity and replicability since all the participants are to answer the same questionnaire in the format of structured questions. This approach is especially differentiated into studying a high number of respondents and drawing generalisable conclusions regarding the use of AI in Tesco market practices.

Data Collection Method

A structured survey by the use of Google Forms will be used to collect primary data. The survey will have a questionnaire in the form of multiple-choice closed-ended questions which will assess the level of familiarity of customers with AI-driven practices, perceived usefulness of these technologies and their impact on the market orientation of Tesco. With internet distribution, the different respondents are easily accessed and data collection is made easy. Close-ended questions are easy to reply to, more consistent and they are more amenable to statistical analysis. This survey is appropriate in an effort to gather quantifiable information using a sample of 60 participants and this will yield credible quantitative data as per research objectives. Sampling Framework

The sampling population will be the Tesco customers or those who have had experience of shopping at Tesco. The players will be chosen with the help of a non-probability convenience sampling method, as they will be chosen, depending on their presence and readiness to answer the questionnaire online. It is an effective strategy because of time-constrained scholarly studies and guarantees a convenient sample of the population. The sample size will be 60 participants, as it will be sufficient to outline the descriptive tendencies and create initial ideas about the connection between the AI-based practices and the market orientation in Tesco. Data Analysis Method

The statistical methods to be used in the analysis of collected data will consist of descriptive statistics such as frequency distribution, percentages, and graphical summaries. Such methods will be used to determine trends in the reactions of clients and facilitate result interpretation on the contribution of AI-based practices towards the market orientation of Tesco.

Ethical Considerations

The respondents will give their informed consent, will remain anonymous and they can withdraw at any moment. No personal and sensitive information is going to be collected. Reliability and Validity

Consistent, structured questions will be used to maintain some degree of reliability, whereas validity will be reinforced by the correspondence between the survey items and the research goals and proven theories.

Critical Evaluation

The selected methodology offers a systematic and objective study of the impact of AI-driven market practices on the market orientation of Tesco, but numerous shortcomings should be admitted as the priority. A positivist philosophy and quantitative design are appropriate to ensure scientific rigour, but they disregard the subjective customer experiences that would be depicted through the use of a qualitative approach. Although the deductive approach enables testing of the theories, it limits the opportunities to explore new conducts that can be produced in relation to the AI-related behaviours in retail. Equally, the descriptive design can be applied to record current perceptions but not to determine cause and effects, which can make it difficult to understand how AI directly influences the market orientation. Convenience sampling method, though it is convenient, is prone to bias and can only be generalised on a limited number of 60 respondents.

In terms of literature, the research that is already done gives solid basis on market orientation and adoption of AI, but is predominantly based on conceptual arguments instead of the retail-specific studies. Numerous works highlight the potential of AI to achieve strategic solutions but do not have direct evidence related to the opinions of customers, which is the goal of the given research. It is also appropriate to rely on classical theories like the Market Orientation Theory and TAM, but they might be inadequate to understand the dynamic aspect of AI-based decision-making. All in all, even though the methodology adopted is appropriate, a mixed-methods strategy can have made the study more deep and relevant to the context.

Limitations

This paper has a number of methodological weaknesses. Convenience sampling could lead to the fact that the sample was not representative of the entire Tesco customer and the results are less likely to be extrapolated across the whole population. Statistical depth is also restricted by the relatively small size of the sample of 60. A descriptive quantitative design would record the perceptions, but will not be able to tell causal between AI-driven practices and market orientation. Also, dependence on survey answers that are self-reported can cause bias in responses, which will negatively influence the accuracy of the data collected.

Expected Research Outcomes

It is anticipated that the research will confirm that the application of AI in marketing will greatly increase the market orientation of Tesco by increasing customer understanding, personalisation, and market trends responsiveness. The results might indicate that customers think that the implementations of AI (e.g., personalised recommendations and automated analytics) can be used to improve their shopping experience. The paper will also find the major areas where the AI will help achieve better decisions in Tesco. In general, the results are supposed to offer information that will enable newer and improved strategies of AI in the retail industry.

Time Plan

Tasks / Activities

Week 1

Week 2

Week 3

Week 4

Week 5

Week 6

Week 7

Week 8

Week 9

Week 10

Week 11

Week 12

Topic Finalisation

Literature Review

Development of Conceptual Framework

Designing Survey Questionnaire

Ethical Review / Approval

Data Collection (Google Forms)

Data Analysis (Statistical)

Drafting Findings & Discussion

Writing Full Report

Editing & Proofreading

Final Submission

Table 1: Gantt chart

(Source: Self-Created)

References

Abdulquadri, A.O., 2025. Personalization and User Autonomy in Self-Service Experiences. In Practical Applications of Self-Service Technologies Across Industries (pp. 443–474). IGI Global Scientific Publishing.

Cao, L., 2021. Artificial intelligence in retail: applications and value creation logics. International Journal of Retail & Distribution Management, 49(7), pp.958–976.

Haque, A., Akther, N., Khan, I., Agarwal, K. and Uddin, N., 2024, October. Artificial intelligence in retail marketing: Research agenda based on bibliometric reflection and content analysis (2000–2023). In Informatics (Vol. 11, №4, p. 74). MDPI.

Hicham, N., Nassera, H. and Karim, S., 2023. Strategic framework for leveraging artificial intelligence in future marketing decision-making. Journal of Intelligent Management Decision, 2(3), pp.139–150.

Kumar, P., Choubey, D., Amosu, O.R. and Ogunsuji, Y.M., 2024. AI-enhanced inventory and demand forecasting: Using AI to optimize inventory management and predict customer demand. World J. Adv. Res. Rev, 23(1), pp.1931–1944.

Manda, E.F. and Salim, R., 2021. Analysis of the influence of perceived usefulness, perceived ease of use and attitude toward using technology on actual to use Halodoc application using the technology acceptance model (TAM) method approach. Int. Res. J. Adv. Eng. Sci, 6(1), pp.135–140.

Mukhtar, U., Grönroos, C., Hilletofth, P., Pimenta, M.L. and Ferreira, A.C., 2023. Inter-functional value co-creation as an antecedent of supply chain performance: a study based on the coordination theory. Journal of business & industrial marketing, 38(11), pp.2324–2340.

Naheed, S., Pinto, R. and Pirola, F., 2025. Analysing the Capabilities of Generative AI to Determine Its Role in Customer Experience Management for Effective Product Development. IFAC-PapersOnLine, 59(10), pp.1492–1497.

Polisetty, A., Chakraborty, D., G, S., Kar, A.K. and Pahari, S., 2024. What determines AI adoption in companies? Mixed-method evidence. Journal of Computer Information Systems, 64(3), pp.370–387.

Stone, M., Aravopoulou, E., Ekinci, Y., Evans, G., Hobbs, M., Labib, A., Laughlin, P., Machtynger, J. and Machtynger, L., 2020. Artificial intelligence (AI) in strategic marketing decision-making: a research agenda. The Bottom Line, 33(2), pp.183–200.

Truong, H.B., Jesudoss, S.P. and Molesworth, M., 2022. Consumer mischief as playful resistance to marketing in Twitter hashtag hijacking. Journal of Consumer Behaviour, 21(4), pp.828–841.

Younus, A.M. and Zaidan, M.N., 2022. The influence of quantitative research in business & information technology: An appropriate research methodology philosophical reflection. American Journal of Interdisciplinary Research and Development, 4(1), pp.61–79.