Abstract

This research examines how data analysis has impacted fast food franchises in the United Kingdom using McDonald's as a case study. The research looks at the usefulness of data analysis for three different kinds of company outcomes: improved operational efficiency, consumer insights, and strategic decision-making.

The findings demonstrate that chain restaurants may improve operations, better understand their patrons, and make more informed strategic decisions using data analysis.

Despite using secondary sources, the study provided a complete examination of the problem. Other primary data collection and comparative studies may benefit data analysis in the fast food industry.

Introduction

The ubiquitous availability of data analysis has ushered in a new era of prosperity for fast-food franchises in the United Kingdom, and McDonald's is a shining example of this. Extracting valuable insights from vast databases is essential for operational success and maintaining a competitive edge (Namin, 2017). Modern analytical techniques help chains of restaurants better understand their patrons, internal processes, and market status.

Their analytical abilities enable them to make informed decisions, increase productivity, enhance the customer experience, and create more targeted marketing strategies. Using McDonald's as a case study, this poster gives an in-depth investigation of the part data analysis plays in the success of UK fast-food franchisees.

Literature review

A substantial corpus of study on the impact of data analysis on fast food franchises, particularly in the UK, illustrates the revolutionary potential of data-driven insights.

According to Limakrisna and Ali (2016), researchers and academics have delved deeply into the phenomenon to unearth its various facets and provide insight into how it influences productivity, consumer satisfaction, and long-term planning.

A recurring theme in the study literature is data analysis for understanding customer preferences and behaviour (Juliana et al., 2022). Based on their collected information,

McDonald's and other fast-food businesses may discover much about their patrons' preferences, routines, and habits. Businesses can better their menus, advertisements, and general customer service by adequately analysing this data to understand the preferences of their consumers.

Additionally, studies have emphasised the practical benefits of data analysis in chain restaurants (Slack et al., 2021). Restaurants can streamline operations, reduce costs, and increase efficiency by analysing sales volumes, transaction times, and inventory levels. By modifying worker numbers, developing more accurate demand forecasts, and more effectively allocating resources, firms may use this information to enhance service quality and customer satisfaction.

The literature also emphasises the need for data analysis for chain restaurants, particularly when making strategic decisions. Businesses may use predictive analytics to anticipate changes in their sector, identify new opportunities as they present themselves, and adjust their strategies as necessary. Chains may increase their competitiveness and long-term success by using sales data, industry trends, and outside factors to inform decisions about what to offer, where to expand, and how to advertise the restaurant.

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Methodology

With McDonald's as the case study, this study employs a scientific method to examine how data analysis has impacted fast food franchisees in the UK. The study uses secondary sources, including scholarly articles, business reports, earlier research, and other applicable books, journals, and websites. By examining the work of earlier scholars and industry experts, the research leverages secondary data to provide a comprehensive picture of the problem (Corti, 2018).

A systematic literature review process is employed to ensure the correctness and legitimacy of the findings (Ajayi, 2017). In order to find papers and research on data analysis in the context of fast food chains, the researcher first thoroughly searches several academic resources, including esteemed journals and scholarly archives.

The researcher can gather results from a wide range of sources and ensure that all pertinent points of view and research techniques are covered by employing broad search terms like "data analysis," "chain restaurants," and "United Kingdom."

It is common practice to evaluate the sources' validity, usefulness, and dependability in light of the study's stated objectives (Ruggiano & Perry, 2019). This inquiry aims to identify relevant findings, theoretical frameworks, and empirical data that provide light on how data analysis affects fast food chains (Sileyew, 2019). The main objective of thematic analysis is to identify repeating themes, patterns, and gaps in the text.

This research uses a methodical and exacting strategy based on secondary data analysis to examine the effects of analytics on UK fast-food businesses. While primary research would produce the most thorough results, researching pertinent secondary literature allows for a more thorough examination of the problem by drawing on previously undiscovered facts and viewpoints.

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Analysis

This research, which uses McDonald's as its case study and only secondary sources, explores how data analysis affects fast food franchisees in the United Kingdom. The findings of this thorough literature review of scholarly works, business reports, and case studies offer significant insights into the various effects of data analysis in this context.

Customer insights, which are emerging as a critical application area, are significantly impacted by data analysis. By examining the information they collect, restaurant chains may discover a lot about their patrons' preferences, purchasing patterns, and demographics (Le et al., 2022).

The results show that this information enables restaurants to enhance their menus, focus their advertising, and offer more personalised service, which results in happier and more devoted customers (Rajput and Gahfoor, 2020).

In fast food chains, data analysis significantly impacts operational effectiveness. By examining sales data, transaction times, and inventory levels, businesses may streamline processes and allocate resources more effectively (Isnaini, Nurhaida, & Pratama, 2020). The outcomes are better overall performance, lower costs, and increased productivity.

Analysing data has a significant impact on strategic decision-making as well. According to the research, chain restaurants may better plan their menus, business plans, and advertising by using predictive analytics and historical data to forecast market trends and demand changes (Chiu and Hsieh, 2016).

By employing this data-driven strategy, chain restaurants may more effectively adapt to changing consumer tastes and exploit new market opportunities.

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Future works

Although this study has provided essential insights into the effects of data analysis on UK restaurant chains using secondary sources, there are still opportunities for more research. In the future, researchers may decide to do primary research through surveys or interviews with restaurant chain management and personnel.

This preliminary data collection will aid in illuminating the particular challenges, strategies, and outcomes of data analysis in this context. Over time, the implications of evolving data-analysis techniques on restaurant chains require long-term investigation.

Comparative studies comparing the data analysis techniques employed by various fast-food franchises may help acquire a more thorough knowledge of the problem.

Conclusion

This article uses secondary sources to demonstrate how data analysis has transformed the UK fast food business using McDonald's as a case study. The study concludes that data analysis gives chain restaurants essential customer insights, improves operational effectiveness, and contributes to strategic decision-making.

Data-driven insights will help increase customer happiness, save expenses, and keep a competitive edge in a fast-paced sector. Future research must rely on secondary sources because primary data collection and comparative studies would considerably increase our understanding of the problem.

The findings demonstrate how critical data analysis is to expanding and improving restaurant chains.

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References

Ajayi, V.O., 2017. Primary sources of data and secondary sources of data. Benue State University, 1(1), pp.1-6.
Chiu, J.Z. and Hsieh, C.C., 2016. The impact of restaurants’ green supply chain practices on firm performance. Sustainability, 8(1), p.42.
Corti, L., 2018. Data collection in secondary analysis. The SAGE handbook of qualitative data collection, pp.164-181.
Isnaini, D.B.Y., Nurhaida, T. and Pratama, I., 2020. Moderating effect of supply chain dynamic capabilities on the relationship of sustainable supply chain management practices and organizational sustainable performance: A study on the restaurant industry in Indonesia. International Journal of Supply Chain Management (IJSCM), 9(1), pp.97-105.
Juliana, J., Nagoya, R., Bangkara, B., Purba, J. and Fachrurazi, F., 2022. The role of supply chain on the competitiveness and the performance of restaurants. Uncertain Supply Chain Management, 10(2), pp.445-452.
Le, T.M.H., Nguyen, V.K.L., Le, T.T.H., Nguyen, T.T.H. and Vu, K.N., 2022. Customer satisfaction and fast-food restaurants: an empirical study on undergraduate students. Journal of Foodservice Business Research, pp.1-22.
Limakrisna, N. and Ali, H., 2016. Model of customer satisfaction: Empirical study at fast food restaurants in bandung. International Journal of Business and Commerce, 5(6), pp.132-146.
Namin, A., 2017. Revisiting customers' perception of service quality in fast food restaurants. Journal of Retailing and Consumer Services, 34, pp.70-81.
Rajput, A. and Gahfoor, R.Z., 2020. Satisfaction and revisit intentions at fast food restaurants. Future Business Journal, 6, pp.1-12.
Ruggiano, N. and Perry, T.E., 2019. Conducting secondary analysis of qualitative data: Should we, can we, and how?. Qualitative Social Work, 18(1), pp.81-97.
Sileyew, K.J., 2019. Research design and methodology (pp. 1-12). Rijeka: IntechOpen.
Slack, N.J., Singh, G., Ali, J., Lata, R., Mudaliar, K. and Swamy, Y., 2021. Influence of fast-food restaurant service quality and its dimensions on customer perceived value, satisfaction and behavioural intentions. British Food Journal, 123(4), pp.1324-1344.

DEVELOPMENT LOG 

Week 1

I intended to provide a succinct but comprehensive explanation of the study when I wrote the abstract and introduction for the poster presentation on the effects of data analysis on chain restaurants in the United Kingdom. In order to present fresh insights on the transformational impact of data analysis for fast food chains like McDonald's, I synthesised material from academic journals, trade magazines, and other research due to the need for more sources.

This week, I have been working diligently on the initial stages of the poster presentation. This article aims to demonstrate the value of data analysis in the fast-food industry through an analytical and well-structured narrative. I have devoted much time to reading scholarly articles, industry reports, and related publications to gather relevant ideas and data-supported evidence. This approach has made it easier for me to understand the subject's nuances, define critical ideas, and identify recent advancements.

I have been reading academic articles on the effects of data analysis on the hospitality industry, emphasizing fast-food chains, to be ready for this week's lectures (Namin, 2017). These articles have established the theoretical framework for how data analysis could enhance internal processes, decision-making, and customer service.

According to white papers and trade periodicals, I have read, the most prosperous restaurant chains today use cutting-edge data-driven practices and technology (Isnaini, Nurhaida, & Pratama, 2020). Thanks to these books, I now have some fresh perspectives and intriguing study topics

I encountered a need for more UK-based studies when performing my research. Finding research on the UK market was more challenging, but I found several international studies on the effects of data analysis in chain restaurants. However, I made sure to do a thorough literature study in order to find relevant information for the UK context.

During my research, I came across some work unrelated to our topic but nevertheless essential and thought-provoking. I came found a study (Le et al., 2022) that examined the impact of data analytics on menu pricing strategies in the restaurant industry. Even though this subject was unrelated to my research, I learnt a lot about how data analysis may inform pricing decisions and increase bottom-line revenues. I have developed a deeper understanding of data analysis and how it affects the hotel industry by researching similar topics.

As I go, I am considering how to look into the discrepancy between theoretical research and practical implementations in the fast-food industry. Investigating case studies or doing primary research to get real-world data would be beneficial for comprehending the implications of data analysis on certain UK restaurant businesses. Learning more about the challenges and moral dilemmas associated with data collection and processing in this environment would be fascinating.

As I continue to work on the poster presentation, I can not wait to dig more into data analysis challenges in the fast food industry. Data analysis significantly impacts organizations in many ways, including the consumer insights it offers, the efficiency with which they manage their operations, and the strategic decisions they make. I intend to improve the content of my poster presentation to make it more engaging and educational for a larger audience and further explore the links between many fields of study.

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Week 2

I made it a point to be as detailed as possible while still keeping things under control when preparing the literature review for my poster presentation on the effects of data analysis on UK fast-food businesses. It is vital to demonstrate the current level of expertise by examining the pertinent literature before demonstrating the benefit of data analysis in the context of fast food chains.

I have been focusing this week on reading a lot of academic papers, reports, and related publications to learn more about the impacts of data analysis in the fast-food industry. I have been working on organising the literature review by identifying recurring themes and assessing the accuracy of the inferences made from the sources I have selected. By examining and synthesizing the pertinent literature, I intend to show how data analysis may change many aspects of restaurant chains, including how they serve customers, how effectively they operate, and what strategic decisions they make.

I have been reading academic works explaining the importance of data analysis in the restaurant industry to prepare for this week's lectures. These studies have provided theoretical justification and empirical evidence to support the assertions stated in the literature review.

I have also studied white papers and industry studies covering the most advanced data-driven procedures and technologies well-known restaurant companies use. These papers have improved my understanding of the practical uses of data analysis and piqued my interest in the potential implications of the area.

Condensing the extensive literature study into a valuable and logical piece was difficult. It was challenging to balance breadth and depth and choose and organise the most important discoveries. However, by synthesising the key concepts and arguments from the chosen sources, I have performed a thorough literature review that provides a solid framework for the study (Corti, 2018).

Despite being beyond the purview of our work, I have found several fascinating studies on the moral implications of data analysis in the restaurant industry. After reading these studies, I have been thinking about the potential consequences of data privacy, security, and transparency in the setting of fast food chains (Julian et al., 2022).

Although not extensively discussed in the literature review, these elements improve our comprehension of data analysis's broader impact on the industry.
I must balance thorough research with succinctness as I continue working on the poster presentation.

By ensuring that the literature review provides the reader with the necessary background information to understand the research's relevance and implications in the following sections, I also intend to establish a solid foundation for the remainder of the poster presentation.

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Week 3

I spent much time creating a comprehensive description of my research methodology for my poster presentation, which outlined the data analysis findings on UK fast food businesses. The study must be complete, transparent, and reproducible while relying solely on secondary sources, which is where the methodology comes into play (Corti, 2018).

This week, I have devoted much work detailing the steps for acquiring and assessing secondary sources and establishing the research methodology. I have emphasised the need for a thorough search strategy to locate pertinent research articles, business reports, and case studies. The sources' relevance, authority, and freshness are only a few of the selection factors I have covered in depth. My goal is that by outlining the methodology, the study will have a strong foundation, and the results will be more reliable.

I have been reading academic articles regarding data analysis research techniques and their applications to the hotel industry to prepare for this week's lectures. These studies have clarified various secondary source research techniques, including literature reviews and meta-analyses (Ajayi, 2017). I have also read articles that address some of the issues specific to secondary research, such as the potential for bias, the constraints of the data that are accessible, and the necessity of a complete synthesis of the available data. These studies have been essential in refining and strengthening the methodology section.

How to bring more precision and clarity to the technique section is one issue I have been thinking about. Although secondary sources provide a solid foundation for the study, it is crucial to communicate the methods used to choose, assess, and synthesise the sources (Ruggiano and Perry, 2019). I have been considering how to better explain the strategy by going into greater detail about the search keywords and strategies I used and the standards I applied to include or reject results. The reader may judge the reliability of the study and its findings with the use of these details.

I am competent in gathering pertinent data to support the research objectives and completing a comprehensive examination of several secondary sources during the duration of this study. I now thoroughly understand the value of data analysis in the fast-food industry due to my diligence in reading and assessing the material provided. By combining the data from several sources, I could also identify trends, emerging themes, and general patterns.

The sources I used to research this essay introduced me to some intriguing related work that examines the application of data analysis in industries like retail and e-commerce. These challenges, albeit outside the scope of this study, offer insight into cutting-edge data-driven strategies that could impact fast-food companies. Thanks to my increased understanding of these interrelationships, I now have a broader perspective and suggestions for new research directions.

Making the research process as concise and transparent as feasible without losing clarity is something I am thinking about as I refine the methodology section. Striking a balance between oversimplified explanations and those that lack the fundamental level of information is beneficial for methodology sections. I will think about how to explain the procedure in an understandable but not unduly theoretical way.

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Week 4

I have been working on the analytical part of the poster presentation on the effects of data analysis on chain restaurants in the UK, concentrating on a comprehensive examination of the selected secondary sources to extract relevant insights and provide informed conclusions. It is crucial to evaluate and synthesise the findings of the literature review in order to comprehend the significance of data analysis to the fast food industry as a whole.

This week, I have been working to organise and make sense of the material I acquired from secondary sources. By focusing on key findings, identifying trends, and making connections between many sources, I have been gathering my study on the effects of data analysis on various aspects of fast food chains. By thoroughly analyzing the information available and providing insightful conclusions based on my results, I hope to contribute to the body of knowledge on this topic (Corti, 2018).

I have been reading academic articles on analytical frameworks and approaches for data analysis, emphasizing the hotel industry (Ajayi, 2017), to prepare for this week's lectures. Understanding how to analyze and correctly interpret the data has greatly benefited from reading these publications. Additionally, I have studied case studies and market studies that illustrate data analysis initiatives for fast food chains. These resources have given insightful knowledge on how data-driven efforts affect KPIs and their execution.

I have been thinking about how to organise the analysis part best. It could be challenging to condense your findings into understandable and convincing arguments due to the volume of information available (Ruggiano & Perry, 2019). I am actively looking at different strategies, including structuring the analysis topically or chronologically, to make it flow effortlessly and be simple to follow. I wish to increase the poster presentation's overall impact and persuasiveness by providing a well-organized analysis.

I have done pretty well identifying data trends and patterns throughout the analysis process. This has helped me fully see how crucial data analysis is to the fast food industry. I have been able to draw connections and generate important ideas by critically evaluating the information from many sources and incorporating it into my overall research. My data analysis is now more complete and accurate thanks to integrating analytical frameworks and models from the literature.

During my study, I came across some pertinent writing exploring data analysis's importance in industries like hospitality and tourism. Although they have no immediate bearing on the project, these shed insight into the broader implications of data analysis in the hotel business (Sileyew, 2019). Studying these links has increased my understanding of chain restaurants' potential and challenges when seeking to employ data analysis to their advantage.

As I enhance the analysis section, one of the things on my mind is how to strike a decent balance between offering sufficient detail and limiting the article to a manageable length. To offer a thorough analysis while maintaining within the word restriction, careful study of the most important results and their supporting evidence is necessary. This paper offers a brief and precise argument for the importance of data analysis to the fast food industry.

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Week 5

My current focus is on the Future Work and Conclusion sections of my poster presentation on the effects of data analysis on UK chain restaurants. These sections provide a concise summary of the most significant results, their implications, and an outlook on the research.

The Future Work section, which explores potential future avenues for data analysis in the fast food industry, took the better part of the week to draw out. Because it has the potential to increase operational effectiveness and customer happiness, data-driven decision-making is something I am considering (Chiu and Hsieh, 2016). I also consider using cutting-edge analytical methods and technology like machine learning and artificial intelligence. By exposing these options, I aim to inspire future academics and business leaders to learn more about the subject.

I have been reading academic works that examine new trends and challenges in the data analysis of the hotel business in line with this week's lectures. These studies have demonstrated the dynamic nature of data analytics and stressed the importance of staying current with changes in the industry.

I have also seen case studies and papers demonstrating how data analysis is used effectively in industries like e-commerce and retail. Although they have no direct bearing on my research, they demonstrate how new concepts may be incorporated into the fast food sector.

I have been considering how to offer practical advice in the Future Work section while still allowing for application in real-world settings (Rajput and Gahfoor, 2020). Chain restaurants in the UK have particular difficulties because of their limited financial and technological resources. By considering these elements, I will provide applicable and consistent recommendations with the industry's current situation.

I will quickly discuss the study's relevance and summarize its key findings in this section. This article demonstrates how data analysis can completely transform the chain restaurant industry by influencing strategic choices, increasing operational effectiveness, enhancing the customer experience, and gaining a competitive edge.

I will briefly summarise the significant concerns raised during the poster presentation to underline the significance of data analysis and its role in predicting the future of chain restaurants.

Throughout my research, I successfully synthesized material from many secondary sources and came to insightful conclusions. By doing a thorough literature study and making links between numerous studies, I have developed a complete understanding of the impacts of data analysis on restaurant chains. Additionally, I have developed a robust analytical framework to assess the data and draw insightful conclusions.

As the assignment's due date drew near, I wondered whether there were any possible disadvantages to conducting all of the research utilising secondary sources. Even if secondary sources are excellent at filling in the gaps, they come with limitations, such as potential biases or a need for more information pertinent to the particular context of UK chain restaurants. I would appreciate any guidance you could provide me on how to get around this limitation and increase the reliability of the findings. 

References

Ajayi, V.O., 2017. Primary sources of data and secondary sources of data. Benue State University, 1(1), pp.1-6.
Chiu, J.Z. and Hsieh, C.C., 2016. The impact of restaurants’ green supply chain practices on firm performance. Sustainability, 8(1), p.42.
Corti, L., 2018. Data collection in secondary analysis. The SAGE handbook of qualitative data collection, pp.164-181.
Isnaini, D.B.Y., Nurhaida, T. and Pratama, I., 2020. Moderating effect of supply chain dynamic capabilities on the relationship of sustainable supply chain management practices and organizational sustainable performance: A study on the restaurant industry in Indonesia. International Journal of Supply Chain Management (IJSCM), 9(1), pp.97-105.
Juliana, J., Nagoya, R., Bangkara, B., Purba, J. and Fachrurazi, F., 2022. The role of supply chain on the competitiveness and the performance of restaurants. Uncertain Supply Chain Management, 10(2), pp.445-452.
Le, T.M.H., Nguyen, V.K.L., Le, T.T.H., Nguyen, T.T.H. and Vu, K.N., 2022. Customer satisfaction and fast-food restaurants: an empirical study on undergraduate students. Journal of Foodservice Business Research, pp.1-22.
Limakrisna, N. and Ali, H., 2016. Model of customer satisfaction: Empirical study at fast food restaurants in bandung. International Journal of Business and Commerce, 5(6), pp.132-146.
Namin, A., 2017. Revisiting customers' perception of service quality in fast food restaurants. Journal of Retailing and Consumer Services, 34, pp.70-81.
Rajput, A. and Gahfoor, R.Z., 2020. Satisfaction and revisit intentions at fast food restaurants. Future Business Journal, 6, pp.1-12.
Ruggiano, N. and Perry, T.E., 2019. Conducting secondary analysis of qualitative data: Should we, can we, and how?. Qualitative Social Work, 18(1), pp.81-97.
Sileyew, K.J., 2019. Research design and methodology (pp. 1-12). Rijeka: IntechOpen.
Slack, N.J., Singh, G., Ali, J., Lata, R., Mudaliar, K. and Swamy, Y., 2021. Influence of fast-food restaurant service quality and its dimensions on customer perceived value, satisfaction and behavioural intentions. British Food Journal, 123(4), pp.1324-1344.