MG5645 Understanding Business Management
1. Introduction and Background
The retail sector has undergone significant transformations in recent years due to the advent of emerging technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI). The application of IoT and AI in the retail industry has enabled businesses to improve customer experiences, enhance operational efficiency, and increase revenue. However, the effective adoption and implementation of these technologies require competent business management practices. This research aims to explore the impact of business management on promoting IoT and AI businesses in the UK retail sector, with a focus on a specific company.
1.1 Background and Rationale
The increasing competition in the retail sector, coupled with changing customer preferences and behaviors, has necessitated the adoption of emerging technologies such as IoT and AI. These technologies can provide retailers with valuable insights into customer behavior, preferences, and needs, which can be leveraged to enhance customer experiences and improve operational efficiency. However, the effective adoption and implementation of these technologies require competent business management practices (Basri, 2020.).
The potential contribution of this research lies in providing insights into the impact of business management practices on promoting IoT and AI businesses in the UK retail sector. By exploring the role of business management practices in enhancing the effectiveness and efficiency of IoT and AI adoption and implementation, this research can contribute to both theory and practice.
The problem statement for this research is the gap in the literature regarding the impact of business management practices on promoting IoT and AI businesses in the UK retail sector. While previous studies have explored the impact of IoT and AI on the retail industry, there is limited research on how effective business management practices can enhance the adoption and implementation of these technologies in the sector. Therefore, this research is required to fill this gap in the literature and provide valuable insights into the role of business management practices in promoting IoT and AI businesses in the UK retail sector.
1.2 Research aim and objectives
The aim of this research is to explore the impact of business management on promoting IoT and AI businesses in the UK retail sector, with a focus on a specific company.
The specific objectives of this research are:
- To identify the key business management practices that promote the adoption and implementation of IoT and AI technologies in the UK retail sector
- To explore the impact of effective business management practices on the adoption and implementation of IoT and AI technologies in the UK retail sector.
- To investigate the challenges associated with the adoption and implementation of IoT and AI technologies in the UK retail sector.
- To provide recommendations for effective business management practices that can enhance the adoption and implementation of IoT and AI technologies in the UK retail sector.
No hypothesis is included in this research. The research questions are derived from the gaps and current thinking in the literature, and the objectives are formulated to answer these research questions. The SMART objectives are specific, measurable, achievable, relevant, and time-bound, and are aligned with the aim of the research
2. Literature Review and Evaluation
The retail sector has been undergoing rapid transformation in recent years with the increasing integration of the Internet of Things (IoT) and Artificial Intelligence (AI) technologies into business management. This integration has created new opportunities for retailers to better understand their customers and their preferences, improve operational efficiency, and offer personalized services to their customers. The purpose of this research is to examine the impact of business management practices on the promotion of IoT and AI in the retail sector (Soni et al., 2019).
. The study aims to identify the gaps in the existing literature on this topic and contribute to theory and practice.
The integration of IoT and AI technologies in the retail sector has become an important area of research in recent years. Several studies have highlighted the potential benefits of these technologies for the retail sector. For example, according to a study by Lee and Kwon (2018), IoT and AI technologies can help retailers to optimize their supply chain management, improve inventory management, and enhance customer experience by offering personalized services. Similarly, Mäntymäki et al. (2018) suggested that IoT and AI technologies can help retailers to develop new business models and improve their operational efficiency.
Despite the potential benefits of IoT and AI technologies, there are several challenges associated with their implementation in the retail sector. One of the major challenges is the lack of understanding of these technologies by retailers. According to a study by (Caglio et al. 2018), many retailers lack the necessary skills and knowledge to implement these technologies effectively. Another challenge is the high cost associated with the implementation of these technologies. According to a study by (Chen et al. 2019), the cost of implementing IoT and AI technologies can be a major barrier for retailers, especially for small and medium-sized enterprises (SMEs).
In order to address these challenges, several studies have proposed different strategies and frameworks to promote the adoption of IoT and AI technologies in the retail sector. For example, (Chen et al. 2019) proposed a framework for the adoption of IoT and AI technologies in the retail sector that involves three stages: awareness, readiness, and implementation. Similarly, (Lee and Kwon 2018) suggested that retailers should focus on developing a customer-centric approach to IoT and AI implementation in order to enhance customer experience.
Despite the growing interest in the integration of IoT and AI technologies in the retail sector, there is a lack of research that examines the impact of business management practices on the promotion of these technologies. This research aims to fill this gap in the literature by examining the impact of business management practices, such as leadership, strategic planning, and organizational culture, on the promotion of IoT and AI technologies in the retail sector. By identifying the key factors that influence the adoption of these technologies, this study aims to contribute to the development of effective strategies and frameworks for the promotion of IoT and AI technologies in the retail sector (Zhang et al., 2020).
After reviewing the literature, it is clear that the integration of IOT and AI technologies in the retail sector has the potential to revolutionize the industry by providing personalized experiences for customers, optimizing supply chain management, and enhancing overall efficiency. Various studies have highlighted the benefits of these technologies in enhancing customer experience and driving sales in the retail sector. For example, (Park et al.2019) found that the use of IOT technologies in a retail store can improve customer satisfaction by providing real-time product information and personalized recommendations.
Additionally, AI-powered chatbots can provide 24/7 customer service and support, reducing the need for human customer service representatives and ultimately saving time and money for retailers. According to a study by Capgemini Research Institute (2021), 62% of consumers in the UK are willing to interact with AI-powered chatbots if it means faster and more convenient customer service.
However, there are also challenges associated with the integration of IOT and AI in the retail sector. Security and privacy concerns are major issues, as the collection and processing of large amounts of customer data can leave retailers vulnerable to cyber-attacks and data breaches. Furthermore, the implementation of these technologies can be costly and requires significant investment in infrastructure and training (Di et al., 2020).
Despite these challenges, there is a growing trend of retailers adopting IOT and AI technologies to remain competitive in the market. The literature suggests that effective implementation and management of these technologies can lead to increased profitability, improved customer satisfaction, and enhanced overall efficiency in the retail sector (Verma et al., 2021).
3. Research Methodology
In this section, the research methodology that will be adopted for this study will be presented, evaluated, and justified. The methodology will be designed to achieve the research aim and objectives. The following components will be discussed in detail:
- Research philosophy: The research philosophy that will be adopted for this study will be interpretivism (Akram et al., 2021.). This philosophy emphasizes the subjective interpretation of social phenomena and acknowledges the influence of the researcher’s own experiences and perspectives on the research.
- Research approach and strategy: The research approach that will be used for this study will be inductive, as it will involve collecting and analyzing data to develop new theories and insights into the impact of business management on promoting IOT and AI in the retail sector. The research strategy will be case study research, as it will involve an in-depth investigation of a UK-based company that has successfully implemented IOT and AI in its retail operations (Victoria., 2022).
- Research method: This study will use a mixed-method research approach, combining both qualitative and quantitative methods to collect and analyze data. The qualitative method will involve conducting semi-structured interviews with key stakeholders in the selected UK-based company to gain a deeper understanding of the impact of business management on promoting IOT and AI in the retail sector. The quantitative method will involve analyzing secondary data, such as financial reports and market research reports, to identify the impact of IOT and AI on the company’s financial performance and market position (Cubric., 2020).
- Research Design: The research design that will be adopted for this study will be a multiple-case design, as it will involve investigating multiple cases of UK-based companies that have implemented IOT and AI in their retail operations. The methods of data collection will include primary data collection methods, such as semi-structured interviews with key stakeholders in the selected companies, and secondary data collection methods, such as analyzing financial reports and market research reports. The data collected will be analyzed using a thematic analysis approach to identify patterns and themes in the data.
- Sampling strategy: The sampling strategy that will be used for this study will be purposive sampling. This strategy will involve selecting UK-based companies that have successfully implemented IOT and AI in their retail operations and have demonstrated significant improvements in their financial performance and market position. The selection criteria will include the size of the company, the industry sector, and the level of investment in IOT and AI.
- Data collection: The data collection methods that will be used for this study will include conducting semi-structured interviews with key stakeholders in the selected UK-based companies, as well as collecting secondary data from financial reports and market research reports (Donthu and Gustafsson., 2020.).
- Data analysis: The data collected will be analyzed using a thematic analysis approach, which involves identifying patterns and themes in the data. The primary data collected from the semi-structured interviews will be transcribed and coded, and the codes will be analyzed to identify common themes and patterns. The secondary data collected from financial reports and market research reports will be analyzed using statistical analysis methods to identify the impact of IOT and AI on the company’s financial performance and market position (Sima et al., 2020).
The choices made in the components of the research methodology are justified by the need to achieve the research aim and objectives. The mixed-method approach allows for a more comprehensive analysis of the impact of business management on promoting IOT and AI in the retail sector. The use of multiple-case design and purposive sampling will enable the selection of UK-based companies that have successfully implemented IOT and AI in their retail operations, thereby providing a more accurate representation of the impact of IOT and AI on the retail sector. The thematic analysis approach will enable the identification of common patterns and themes in the data, providing a deeper understanding of the impact of IOT and AI on the retail sector (Di Vaio et al., 2020).
In terms of data collection, primary data will be collected through semi-structured interviews with key stakeholders in the selected UK-based retail company, including top-level management, department heads, and employees directly involved in the implementation and management of IoT and AI systems. The sample size will be determined through purposive sampling, ensuring that individuals with relevant experience and knowledge are included in the study. Secondary data will be collected through a comprehensive review of relevant literature and company reports (Cao., 2021).
Data analysis will involve both qualitative and quantitative methods. Qualitative data from interviews will be transcribed and analyzed using thematic analysis, allowing for the identification of recurring patterns and themes related to the research questions. Quantitative data, such as financial performance metrics and customer satisfaction ratings, will be analyzed using descriptive statistics and inferential analysis to identify patterns and trends (Ahmad et al., 2022).
4. Planning
In this section, we will discuss the planning and ethical considerations for the proposed research. It is essential to consider ethical issues to ensure that the research is conducted ethically and with integrity. Therefore, this research will follow Brunel University’s ethical guidelines for research.
Ethical considerations:
One of the primary ethical considerations for this study is obtaining informed consent from the participants. Informed consent will be obtained from all participants, and they will be made aware of the study’s purpose, their right to withdraw at any time, and how the data will be used. Additionally, the anonymity and confidentiality of the participants will be ensured by using codes instead of personal information.
Another ethical consideration is the use of existing data from the chosen company. To ensure the protection of sensitive company information, permission will be sought from the management of the company. The data collected from the company will be kept confidential and used only for research purposes.
Potential limitations of the study:
One of the potential limitations of the study is the time constraint. The proposed research is a comprehensive study that requires extensive data collection and analysis. Therefore, the study’s scope may be limited due to time constraints (Mahroof., 2019.). Additionally, the availability of data may be a limitation, and the researcher may not have access to all the required data. However, efforts will be made to mitigate these limitations by using multiple sources of data and conducting a thorough analysis.
Gantt chart
| “Task” | “Week 1” | “Week 2” | “Week 3” | “Week 4” | “Week 5” | “Week 6” |
| “Introducing the topic of conducting research” | ||||||
| “Literature Review” | ||||||
| “Methodology” | ||||||
| “Collecting secondary source of data” | ||||||
| “Data analysis for research” | ||||||
| “Final Recommendation” | ||||||
| “Implication of Conclusion” | ||||||
| “Final submission” |
Table 1: Gantt chart
(Source: self-developed)
In order to ensure the study is completed on time, a Gantt chart will be used to provide a detailed timetable of all aspects of the research. The chart will outline the various stages of the research process, including ethics application, literature review, background research, data collection, data analysis, and writing deadlines. The Gantt chart will help the researcher to manage their time efficiently and ensure that all aspects of the research are completed within the required timeframe (Guha et al., 2021).
This section has outlined the ethical considerations and potential limitations for the proposed research. Additionally, a Gantt chart has been provided to ensure that the research is completed within the required timeframe. The researcher will follow ethical guidelines to ensure that the research is conducted with integrity and respect for the participants’ rights (Mondol., 2021).
In conclusion, this research proposal aims to investigate the impact of business management on promoting IoT and AI in the retail sector in a UK-based company. By filling the gaps in the literature and using relevant theoretical frameworks, the study will provide valuable insights into how businesses can effectively implement IoT and AI technologies in the retail sector. The proposed methodology, including the use of case studies and interviews, is well-suited to achieving the research aims and objectives. Additionally, the study has taken into account important ethical considerations and potential limitations, while providing a detailed Gantt chart to ensure efficient and effective planning. Overall, this research has the potential to contribute to both theory and practice in the fields of business management and technology implementation in the retail sector.
References
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Akram, U., Fülöp, M.T., Tiron-Tudor, A., Topor, D.I. and Căpușneanu, S., 2021. Impact of digitalization on customers’ well-being in the pandemic period: Challenges and opportunities for the retail industry. International Journal of Environmental Research and Public Health, 18(14), p.7533.
Basri, W., 2020. Examining the impact of artificial intelligence (AI)-assisted social media marketing on the performance of small and medium enterprises: toward effective business management in the Saudi Arabian context. International Journal of Computational Intelligence Systems, 13(1), p.142.
Cao, L., 2021. Artificial intelligence in retail: applications and value creation logics. International Journal of Retail & Distribution Management.
Cubric, M., 2020. Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study. Technology in Society, 62, p.101257.
Di Vaio, A., Boccia, F., Landriani, L. and Palladino, R., 2020. Artificial intelligence in the agri-food system: Rethinking sustainable business models in the COVID-19 scenario. Sustainability, 12(12), p.4851.
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Guha, A., Grewal, D., Kopalle, P.K., Haenlein, M., Schneider, M.J., Jung, H., Moustafa, R., Hegde, D.R. and Hawkins, G., 2021. How artificial intelligence will affect the future of retailing. Journal of Retailing, 97(1), pp.28-41.
Mahroof, K., 2019. A human-centric perspective exploring the readiness towards smart warehousing: The case of a large retail distribution warehouse. International Journal of Information Management, 45, pp.176-190.
Mondol, E.P., 2021. The Impact of BlockChain and Smart Inventory System on Supply Chain Performance in the Retail Industry. International Journal of Computations, Information and Manufacturing (IJCIM), 1(1).
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Soni, N., Sharma, E.K., Singh, N. and Kapoor, A., 2019. Impact of artificial intelligence on businesses: from research, innovation, market deployment to future shifts in business models. arXiv preprint arXiv:1905.02092.
Verma, S., Sharma, R., Deb, S. and Maitra, D., 2021. Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), p.100002.
Victoria, V., 2022. IMPACT OF PROCESS VISIBILITY AND WORK STRESS TO IMPROVE SERVICE QUALITY: EMPIRICAL EVIDENCE FROM DUBAI RETAIL INDUSTRY IMPACT OF PROCESS VISIBILITY AND WORK STRESS TO IMPROVE SERVICE QUALITY: EMPIRICAL EVIDENCE FROM DUBAI RETAIL INDUSTRY. International Journal of Technology, Innovation and Management (IJTIM), 2(1).
Zhang, Y., Xiong, F., Xie, Y., Fan, X. and Gu, H., 2020. The impact of artificial intelligence and blockchain on the accounting profession. Ieee Access, 8, pp.110461-110477.
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