management theories assignment on the use of big data for market analysis
Task: how can businesses management theories assignment strategies be used to determine retail markets using big data analysis
Chapter 1 – Introduction
The industry where goods and services are sold directly to the consumers is known as retail industry. Grocery stores, stores that sell electronic gadgets are all part of retail industry. Retail industry has witnessed growth in the past decade because advanced technology has impacted the industry in various positive ways. Big data is a technical aspect that have impacted industries worldwide. Data that are large, complex and have varieties are known as big data. Big data is generally used by leaders across the world to understand the patterns and chaos of wide range of information. As mentioned earlier on this management theories assignment, big data has explored in several industries and it is expected to foster future innovation in various sectors across the world (Hasan, Popp and Oláh, 2020). The primary objective of big data analytics is to analyse the large volume of data. Big data analytics help in involvement of Artificial Intelligence in big datasets (Hariri, Fredericks, and Bowers, 2019). This is how big data analytics hold data from different sectors. There is no exception in case of retail industry.
Retail industry has witnessed the importance of big data analytics in the current business environment. It is said that big data analytics is one such technological resource that has given rise to a new concept named Retail analytics. The process where big data is used for analyzing the change in needs and demands of customers and for optimizing pricing strategies is known as retail analytics.Stores that are using this analytical tool are known as smart stores and their dependence on retail analytics have helped them to optimize profit in various ways (Gregorczuk, 2022). Moreover, big data analytics is generally used in retail industries to help customers with product information. Detailed information about products is helping consumers to make informed decisions. This is further having a positive impact on the profitability of the firm. Big data analytics have also helped retail industry to overcome one of the recent crises i.e., spread of Corona virus. The spread of this virus has given rise to the data volume. These data are further used for making decisions (Sheng, et.al., 2021). The change in customer needs identified on this management theories assignmentis also analysed with the help of this data volume. This is how retail industry and data analytics have become interconnected with each other. Finally, insertion of big data analytics in retail industry has given rise to a new strategy that is known as omni-channel strategy. When goods and services by retail leaders are sold in both offline and online channels it is known as omni-channel strategy. Customers shopping from online platforms are analysed and their purchasing behaviours are monitored with the help of this big data analytics. This is how retail leaders in UK are gaining competitive advantage and increase their share in the market even after the devastating impact of the pandemic.
1.2 Research Rationale
The objective of this part of the management theories assignmentresearch study is to provide justificationfor choosing this research topic. Customers are the most important stakeholder of any industry. There is no exception in case of retail industry. In this volatile business environment, acquiring customers is one of the most difficult tasks and responsibilities. Industry leaders across nations have adopted various strategies to acquire customers because customers primarily control the financial performance of an organization. Engagement and interest of customers in the core activities and products/services of an organization is determined by the industry leaders before acquiring them (Zheng, Li and Na, 2022). Apart from volatility in the international market, intense competition is another important factor that is faced by retail industry leaders across the world. Retail leaders are therefore, adopting unique strategies to drive the interest of the customers from other competitors. Retail market is huge and heterogenous. Using data analytics can help retail leaders segment customers in smaller groups that are homogeneous in nature (Yoseph, et.al., 2020). Thus, it can be said that this management theories assignmentresearch study is important because it help customers understand the process in which customers can be segmented conveniently and quickly.
1.3 Problem Statement
Providing high-quality services to the customers of the retail industry is one of the major challenges faced by organizational leaders of this industry. In the 21st century the needs and demands of customers are changing. It is the core responsibility of a retail leader to serve customers in both online and offline stores. While serving customers, inventory managers often find it difficult to understand whether they should keep the online store open even if products are running out of stock (Hole, Pawar, and Khedkar, 2019).This problem has been identified as omni-channel problem. Furthermore, with the increase in adoption of omni-channel strategies within the retail industry, the concept of omni-channel marketing has gained popularity. Industry leaders of the retail industry often find it difficult to practice omni-channel marketing because they have limited information about this marketing strategy. Ineffective marketing strategies might prevent managers of the retail industry to retain customers. This is how these firms might lose competitive advantage and their market share can also decrease.Retail leaders are also witnessing a rise in the bargaining power of the customers because of increasing competition. Moreover, retail leaders have also encountered a shift in the purchasing behavior of customers. Due to the outbreak of the pandemic, customers are not focusing on buying leisure items from retail stores. Rather they are intrigued towards buying products that are necessary for their living. Thus, understanding the psyche of customers post the pandemic is another problem that is faced by retail industry leaders. This management theories assignmentproblem is also having an impact on effectiveness and productivity of organizations across the world.
1.4 Research Scope
The objective of this part of the management theories assignmentresearch study is to focus on parameters of the research study and the extent to which this research topic is explored by the researcher. In this research study, the researcher primarily focuses on utilization of big data in acquisition of customers of the retail industry. In this research study, the researcher primarily focuses on the UK industry and how retail operators in this industry are using big data for customer acquisition. This research study gives basic and in-depth information about change in the needs and demands of customers. Moreover, after the outbreak of the pandemic, the purchasing behavior of customers of the retail industry. People are cautious when it comes to spending money. Big data can help retail leaders how the psychological needs of customers are changing.
1.5 Research Aims
This management theories assignmentresearch study aims at discussing ways in which retail businesses in UK are using big data for acquisition of customers.
1.6 Research Objectives
• To explore importance of customer acquisition in the retail industry.
• To explore concepts associated with big data and how it is relevant in the current business environment.
• To critically analyze the impact of big data on UK’s retail industry.
• To explore various ways in which big data is being leveraged by the retail industry leaders to acquire customers.
• To recommend actions that can further help retail industry leaders in effective utilization of big data.
1.7 Research Questions
• Why acquisition of customers in the retail industry is important?
• What is big data and how it is relevant in the current business environment?
• How Big data impacts UK’s retail industry?
• How big data is being utilized by the retail leaders of UK in order to acquire customers? • How retail leaders can further improve their strategies and leverage big data for customer acquisition?
1.8 Structure of the research study
Themanagement theories assignment research study starts with an introduction. In this introduction chapter, the background of the research topic and problems that are associated with the topic have been discussed in details. Apart from that the research scope has also been explained in this chapter. The objective behind mentioning the research scope is to analyze the parameters that are associated with utilization of big data analytics in the retail industry. At the end of the of this chapter, the aim and objectives of the research have also been mentioned. The researcher focuses on accomplishing these research objectives in the following part of the study. The second chapter comprises of the Literature review. The objective of thismanagement theories assignment chapter is to present work of scholars across the world. The viewpoint of scholars on utilization of big data in retail industry have been presented in details in this section. Thus, it can be said that the objective behind conducting a literature review is to provide theoretical understandings to the reader. The next chapter is research methodology. The objective of this chapter is to discuss about various tools and techniques that are adopted by the researcher to collect and interpret data that further accomplishes the research objectives. Then comes the 4th chapter. This chapter hold significant importance in the research study. In this chapter, the collected data is interpreted using analytical tools. Finally, the management theories assignmentresearch ends with conclusion and recommendation. Each research objective is linked with conclusion and at the end of the report few recommendations are provided to the industry leaders that can further help them in effective utilization of big data.
Chapter 2 – Literature Review
The objective of this chapter of the management theories assignmentresearch study is to present work of several scholars across the nations. Works that are associated with big data analytics, its impact on retail industry and how it is used to acquire customers of the retail industry are presented. Moreover, the purpose behind presenting the Literature Review section is to provide in-depth information of the selected topic. Readers can also understand the contradicting viewpoint that are associated with big data analytics with the help of the Literature review.
2.2 Overview of UK retail industry
UK is one of the leading nations in the world. The country is categorized under, developed nations, and have experienced growth in various over the last decade. There is no exception in the retail industry. According to the management theories assignmentstudy conducted by Hasan, (2021), retail industry plays an important and critical role in the service industry of UK. This industry is known for offering products or services directly to its customers. Thus, retail industry is marked as an indicator of strength of customer’s spending. The author states that performance of the UK retail industry also have an impact on the growth of whole UK economy. Retail leaders in UK are therefore adopting various strategies to optimize profit and reduce costs in the industry. These leaders are also focused on international expansion. Thus, it can be said that activities and performance of UK retail industry holds structured significance. However, as opined by Seidu, et.al.,(2021), the retail industry in UK currently at its lowest point because of several uncertainties in the international market. The intention of online shopping has increased and the shift in this purchasing behaviour has increased the demand of warehouse space. Moreover, Brexit has become one of the biggest contributors behind the change in the UK retail industry. Such changes are expected to have an impact on the process of procurement. Thus, it is the responsibility of the industry leaders to forecast problems, analyse data and take relevant actions to avoid any kind of discrepancies within the sector.
2.3 Customer Acquisition
The process of getting customers who are potential and can contribute to the sales of the firm is known as customer acquisition. According to the management theories assignmentstudy conducted by CORDOVA-BUIZA, et.al., (2022), unlike organizational leaders of traditional firms, current leaders are emphasizing on digital marketing. The objective behind adopting this strategy is to get attention of customers who are suitable for the products/services of the customer. Thus, it can be said that digital marketing strategies are used by retail leaders to acquire customers.
2.4 Importance of Customer Acquisition in Retail industry
According to the study conducted by Vashishtha and Sharma, (2016), retailers in the current scenario are making tireless efforts to experience growth within the industry. Retail leaders know acquisition of potential customers can only bring significant growth to the customers. The sales strategies that are adopted by retailers important and it is meant to attract customers that can further contribute to the growth of the company. The author also states that customer acquisition are assets of the retail industry and they should be managed well for the profit optimization of the company. In the current business environment, organizational leaders are hiring customer relationship managers. These managers and their team analyse the needs and preferences of customers. After the analysis customers are segmented and targeted. This is how customer acquisition is done.
In addition to this, according to the study conducted by Lehrke, et.al., (2018), customers are acquired with the help of digital marketing strategies. Retail leaders in the UK and other industry thinks that if customers can be acquired rightfully by retail leaders, then it can enhance the presence of brand within the industry. The author also states that retail leaders who are focusing on acquiring customers are adopting various strategies by using advanced technologies. These technologies are not only helping them to attract or retain customers but they are also responsible for creating seamless customer experiences. Thus, from the above discussion, acquisition of customers in the retail industry increases the value of the brand, optimize profit, and help companies to gain competitive advantage.
2.5 Big Data and Customer acquisition
In the above part of the Literature review section, it has been mentioned that various unique strategies and technologies are taken into consideration while acquiring customers in the retail industry. Big data analytics is one such technology. According to the management theories assignmentstudy conducted by Liu, et.al., (2020), big data analytics generally consist of two different concept. One is data that are hold by a company and the second aspect tools and techniques that are used to analyse the data. The author states that big data analytics is helping retail leaders to acquire customers by analysing their purchasing behaviour. The purchasing behaviour analysis is further helping these retail leaders to set the price of the product, ensure profit optimization and improve the performance of the firm. The author states that there are steps that are leveraged by retail leaders while acquiring customers using big data analytics. Big data analysis focuses on data mining. It also comprises of neural networks that further helps in social media analysis. In the current scenario, social media are used by a huge percentage of people across the world. Social media analysis help in getting insights about customers. Customers who have similar interests and desires are further clustered and this clustering of customers help in customer segmentation. Thus, from the above analysis it can be found that,big data analytics help in acquisition of customers by providing basic insights about customers. These basic insights and behavioural pattern of customers are further used by retail leaders to identify prospects and profitable customers. However, as opined by Kitchens, et.al., (2018), customer analytics is an integral part of big data analytics. Customer analytics is generally used by retail leaders to understand the behaviour of customers at a time. The author states that in the past decade understanding the behaviour of customers have become important because it is responsible for customer acquisition.With the advancement in technology, web traffic analytics, online reviews are also concepts that are discussed by the leaders of the retail industry. These analytics are used by the retailers to understand the change in the buying pattern of customers. The author also states that customer-data analytics consist of data that can be marked as relationship-oriented. These relationship-oriented data further help in deeper analysis of customers. Finally, these analysis help retail leaders to acquire or retain customers in various ways.
Furthermore, according to the management theories assignmentstudy conducted by Shabbir and Gardezi, (2020), big data analytics reduces the cost of customer acquisition by a certain percentage. The author states that with the decrease in the rate of customer acquisition, the profitability of the firm along with organization’s efficiency increases. The author states that big data analytics not only help in effective acquisition of customers but it is also responsible for enhancing organization’s performance. Big data analytics help retail leaders to access large volume of data from sources that are diverse. Analytical techniques are also used to analyse semi-structured data. These data are further used efficiently by these leaders. Finally, it can be said that big data analytics helps managers to get perceptions about businesses. These perceptions are further used for acquiring customers and retaining them.
Thus, from the above discussion,big data analytics holds several significances within the retail industry. Big data analytics is responsible for mining data that are large in volume, unstructured and complex. These data are further mined to get fruitful information about customers and their purchasing pattern. Once, the purchasing pattern of customers are analysed it further help the retail leaders to acquire customers and retain them. Thus, it can be said that big data analytics is responsible for enhancing the brand value of the firm along with their products or services.
2.6 Critical analysis of big data on retail industry
Retail executives have started recognizing opportunities related to usage of big data. As pointed out by Mercier, Richards& Shockley (2013), consumers are now increasingly using data and technology for having control over their shopping experience. This technology adoption and multi-channel shopping experiences have emerged as norms in the retail industry, thereby making data critical for the businesses. Big data enables the retailers in managing, integrating and understanding vast array of data arising from the coordination between multi-channel shopping interaction and new data competencies. Here, the authors have further pointed out data has become crucial for the entire value chain of a retail business including activities of merchandising, assortment planning, managing inventory, distribution, marketing, sales, service and others.
Fig: Big Data in Retail Industry
(Source: Timofeeva, 2019)
According to Timofeeva (2019), big data helps in the storage, processing and analysis of vast information that further contributes towards sustainable development of the organizations. While the adoption of this technology is still midlevel in the retail industry, it is expected that the retail industry will experience rapid growth in its implementation in the coming years. Huge amount of data circulates in the retail industry involving retailers, shoppers, suppliers, marketers and other actors. Here, the application of Big data along with artificial intelligence (AI) can be beneficial; in transforming such vast amounts of data into resultant knowledge and meaningful information. These management theories assignmenttechnologies can help in enhancing shopping experience, effective personalization, dynamic customer service, improving security, clearing supply chain, dynamic pricing, developing new products and services and predicting demand. Thus, the use of big data can generate positive impact on the retail industry. Furthermore, Cheahand Wang (2017) have opined that big data along with internet and IoT devices can help in offering business opportunities to traditional retail companies by enabling them to connect with their users and engage them in unique ways. In this regard, companies are required to follow three key principles. They should make use of big data value chain for determining the market demand that helps in gaining profitability. Here, data can be gathered from both public and private domains. The companies should leverage big data for developing new business models based on the new insights gained from information about market demand. Lastly, they should depend heavily on market data and operational data from various sources for modifying their business models. These include IoT device data, customer usage data and production data that have been gathered from the different sources. According to Sun, et al. (2016), retail firms are increasingly considering big data for gaining competitive advantage in the market. However, there are various factors that influence the organizational adoption of big data in the firms. These factors can either act as motivators or obstacles in adopting and implementing the big data technology in the operations of retail firms. The authors have pointed out that such factors can be classified under three categories, namely, innovation characteristics, environment characteristics and organization characteristics. Innovation characteristics determining big data adoption include relative advantage, observability, cost of adoption, complexity, trialability and compatibility. Environment characteristics include security, privacy and ethical concerns, market turbulence, partner readiness, regulatory environment and institution based trust. Lastly this management theories assignment shows organization characteristics influencing the adoption are human resources, management support, technology resources and readiness, decision-making, change efficiency, business resources, firm size, business strategy and organization structure.
Fig: Big Data impact on Customer Profiling
(Source: Chauhan, Mahajan and Lohare, 2017)
As pointed out by Chauhan, Mahajanand Lohare (2017), large amounts of data are generated in the retail industry because of various customer interactions. Retailers are using big data analytics here for obtaining a unified picture of customers’ habits, tastes and preferences across various stores or online channels. These data are further used for making strategic decisions by utilizing the valuable insights gained about consumer behaviour. It helps in contributing positively towards the growth and development of retail businesses. Here, the authors have further opined that big data has the capability of emphasizing on unstructured data gathered from social media, sensors and other devices, customer transactions, e-commerce transactions, medical records and others (Chauhan, Mahajan and Lohare, 2017). Customer profiling is done using big data that enables the retailers in enhancing customer experience and obtain their loyalty towards brands. Thus, retailers collect massive amounts of data with the help of this technology and ultimately help in ensuring customer-centricity in their operations and services.
According to the management theories assignment research big data in the retail industry helps in data analysis through volume, varity and velocity. The use of this technology helps in providing improved customer services for attracting more customers and retaining existing customers. Analysis through big data can offer greater customization by collecting data about customers in real time form various multiple sources. Furthermore, it helps in letting customers know about the real-time location, status and availability of the orders, thereby keeping them satisfied through clear communication. Big data further helps in managing fraud by improving detection rates and ensuring safer environment for the retail businesses. Here, predictive analytics can also play a significant role for identifying unforeseen events before their occurrence. In addition, it allows retailers to use dynamic pricing for handling the increased competition in the market.
As pointed out by Silva, Hassaniand Madsen (2019), big data has also been used in the fashion industry for discovering and developing trends. Here, big data analytics performed on the purchasing behaviour of customers from their past shopping experiences can enable retail firms in understanding the fashion qualities or elements to which shoppers respond positively. This can also be applied for identifying popular designers amongst the consumers who are yet to get signed with major fashion brands. Big data also helps in analysing and understanding customer demands, which are further translated into tangible fashion designs. It also helps in getting the right pricing , discounting, stock, size and colour for customers. Thus, this enables the fashion retailers in remaining at the forefront of their competition in the market.
However, there are several challenges related to the usage and deployment of big data in the retail industry identified on this management theories assignment. According to Del Vecchio, et al. (2018), there are three key challenges of using big data. These include deciding the particular data to use when it is gathered from outside the organization, handling analytics technology and securing right capabilities for doing this activity and using the detailed insights for transforming the business operations. This shows that the use of big data involves reducing issues related to understand different types of open innovation strategies while collecting data from various sources. Furthermore, Yinand Kaynak (2015) have shown the challenges of big data with respect to volume, velocity, variety, value and veracity. These are involved with the dimensions of e-commerce because of the presence of high volume data with high velocity and high variety of information available. Here, big data makes them more cost effective, innovative and enhanced insight by processing such large volumes of data. Besides, Dekimpe (2020) has also pointed out various challenges associated with using big data in the retail industry. These include difficulty in balancing between technical complexity and managerial relevance, listening and understanding the data and analyzing how much to listen to and take from such large volumes of data. Thus, along with generating benefits, big data can also be challenging for the retail industry.
2.7 management theories assignmentLiterature Gap
The objective of this part of the management theories assignmentliterature review section is to discuss about missing information within this section. Due to time and budget constraints, the scholarly works of few authors have been considered for presenting the literature review section. There are many other authors who have worked on this topic. Since works of all authors have not been explored in this chapter therefore, it can be marked as one of the primary literature gaps. Moreover, the researcher has considered research work which is not less than 10 years old. Therefore, the researcher has not presented the factors associated with big data analytics before 10 years. This can be marked as another example of insufficient information. Thus, it can be said that more insights and in-depth analysis of the research could have made the research more authentic and reliable.
The objective of the management theories assignmentliterature review was to find out ways in which retail leaders use big data analytics for the acquiring customers within the industry. The research study concludes that retail industry in the UK industry holds significant importance because it is responsible for the growth of the country’s economy. Moreover, this industry is also facing intense competition in recent times. Thus, retail leaders are primarily focused on gaining competitive advantage and increase the share of the firm in the international market. Thus, to gain competitive advantage leaders are focused on acquiring customers. The literature review section concludes that big data analytics is an analytical tool that is helping customers to get insights about customers. The searching and buying pattern of customers are analyzed by using web traffic analytical tool.
Moreover, social media activities of customers are also tracked by big data analytical tool and these activities are further used to get insights about customers. These insights are further used to analyze the purchasing pattern of customers. The purchasing pattern of customers can help retail leaders gain knowledge about tastes and preference of customers. All these data are finally used to acquire customers. Furthermore, while analyzing the topic it has been found that big data analytics is an advanced analytical tool that helps in reducing the acquisition cost of consumers. Despite, having several advantages retail leaders might face security issues while churning data for acquiring customers. Finally, it can be said that big data analytical tools are the future of retail industry because it will significantly contribute to its growth.
Chauhan, P., Mahajan, A. and Lohare, D., 2017. Role of Big Data in retail customer-centric marketing. National Journal of Multidisciplinary Research and Development, management theories assignment2(3), pp.484-488.https://svv-research-data.s3.ap-south-1.amazonaws.com/17270002-2-3-83-600.pdf
Cheah, S. and Wang, S., 2017.Big data-driven business model innovation by traditional industries in the Chinese economy. Journal of Chinese Economic and Foreign Trade Studies, management theories assignment10(3), pp. 229-251.https://www.researchgate.net/profile/Sarah-Cheah-3/publication/319223739_Big_data-driven_business_model_innovation_by_traditional_industries_in_the_Chinese_economy/links/5cda35f6299bf14d959504d3/Big-data-driven-business-model-innovation-by-traditional-industries-in-the-Chinese-economy.pdf
CORDOVA-BUIZA, F., URTEAGA-ARIAS, P.E. and CORAL-MORANTE, J.A., 2022.Relationship between Social Networks and Customer Acquisition in the Field of IT Solutions.IBIMA Business Review, pp.1-10.https://ibimapublishing.com/articles/IBIMABR/2022/631332/631332.pdf Dekimpe, M.G., 2020. Retailing and retailing research in the age of big data analytics. International Journal of Research in Marketing, 37(1), pp.3-14.https://www.sciencedirect.com/science/article/pii/S016781161930062X
Del Vecchio, P., Di Minin, A., Petruzzelli, A.M., Panniello, U. and Pirri, S., 2018. Big data for open innovation in SMEs and large corporations: Trends, opportunities, and challenges. Creativity and Innovation Management, management theories assignment27(1), pp.6-22.http://tarjomefa.com/wp-content/uploads/2018/05/9104-English-TarjomeFa.pdf
Gregorczuk, H., 2022. Retail Analytics: Smart-Stores Saving Bricks and Mortar Retail or a Privacy Problem?. Law, Technology and Humans, 4(1), pp.63-78.https://lthj.qut.edu.au/article/download/2088/1225
Hariri, R.H., Fredericks, E.M. and Bowers, K.M., 2019. Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data, 6(1), pp.1-16.https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0206-3?cv=1&ref=https://githubhelp.com
Hasan, F., 2021. Corporate social responsibility and agency cost: Evidence from the UK retail industry. International Journal of Research in Finance and Management, management theories assignment4(2), pp.105-115.https://www.researchgate.net/profile/Fakhrul-Hasan-3/publication/356944700
Corporate_social_responsibility_and_agency_cost_Evidence_from_the_UK_retail_industry/links/61bb28f14b318a6970e57595/Corporate-social-responsibility-and-agency-cost-Evidence-from-the-UK-retail-industry.pdf Hasan, M., Popp, J. and Oláh, J., 2020.Current landscape and influence of big data on finance. Journal of Big Data, 7(1), pp.1-17.https://journalofbigdata.springeropen.com/articles/10.1186/s40537-020-00291-z
Hole, Y., Pawar, M.S. and Khedkar, E.B., 2019, November. Omni channel retailing: An opportunity and challenges in the Indian market. In Journal of Physics: Conference Series (Vol. 1362, No. 1, p. 012121). IOP Publishing.https://iopscience.iop.org/article/10.1088/1742-6596/1362/1/012121/pdf
in retail.[pdf] IBM.management theories assignmentAvailable at:
Kitchens, B., Dobolyi, D., Li, J. and Abbasi, A., 2018.Advanced customer analytics: Strategic value through integration of relationship-oriented big data. Journal of Management Information Systems, 35(2), pp.540-574.http://ahmedabbasi.com/wp-content/uploads/J/Kitchens_BigData_JMIS.pdf
Lehrke, S., Lewis, M., Bliznakov, K. and Weber, J., 2018.The digital energy retailer. GAS, 9, p.13.https://web-assets.bcg.com/img-src/BCG-The-Digital-Energy-Retailer-Apr-2018_tcm9-189309.pdf Liu, Y., Soroka, A., Han, L., Jian, J. and Tang, M., 2020. Cloud-based big data analytics for customer insight-driven design innovation in SMEs. International Journal of Information Management, 51, p.102034.https://orca.cardiff.ac.uk/id/eprint/126966/1/Liu%20Y%20-%20Cloud-based%20big%20data%20_updated.pdf
Mercier, K., Richards, B. & Shockley, R., 2013.Analytics: The real-world use of big data management theories assignment
Seetharaman, A., Niranjan, I., Tandon, V. and Saravanan, A.S., 2016.Impact of big data on the retail industry. Journal of Corporate Ownership & Control, 14(1),
Seidu, R.D., Young, B.E., Madanayake, U.H. and Clark, H., 2021. The UK retail industry and its effect on construction sectors. Journal of Emerging Trends in Economics and Management Sciences, management theories assignment 12(1), pp.27-33.https://www.scholarlinkinstitute.org/jetems/articles/The%20UK%20Retail%20Industry%20new.pdf
Shabbir, M.Q. and Gardezi, S.B.W., 2020. Application of big data analytics and organizational performance: the mediating role of knowledge management practices. Journal of Big Data, 7(1), pp.1-17.https://journalofbigdata.springeropen.com/articles/10.1186/s40537-020-00317-6
Sheng, J., Amankwah Amoah, J., Khan, Z. and Wang, X., 2021. COVID 19 pandemic in the new era of big data analytics: Methodological innovations and future research directions. British Journal of Management, management theories assignment32(4), pp.1164-1183.https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1467-8551.12441
Silva, E.S., Hassani, H. and Madsen, D.Ø., 2019. Big Data in fashion: transforming the retail sector. Journal of Business Strategy, 41(4), pp. 21-27.https://openarchive.usn.no/usn-xmlui/bitstream/handle/11250/2630855/LOCKEDUNTIL20210801_2019MadsenBig.pdf sequence=4&isAllowed=y
Sun, S., Cegielski, C.G., Jia, L. and Hall, D.J., 2018.Understanding the factors affecting the organizational adoption of big data. Journal of computer information systems, 58(3), pp.193-203.https://www.researchgate.net/profile/Shiwei-Sun-7/publication/308955269_Understanding_the_Factors_Affecting_the_Organizational_Adoption_of_Big_Data/links/5b1859dc0f7e9b68b424a351/Understanding-the-Factors-Affecting-the-Organizational-Adoption-of-Big-Data.pdf
Timofeeva, A., 2019. Big data usage in retail industry. Izvestia Journal of the Union of Scientists-Varna. Economic Sciences Series, management theories assignment8(2), pp.75-82.https://journals.mu-varna.bg/index.php/isuvsin/article/download/6307/5535
Vashishtha, S. and Sharma, S., 2016. New customer acquisition by a retailer: a conceptual paper. International Journal of Applied Business and Economic Research, 14, pp.485-497.https://www.researchgate.net/profile/Swati-Vashishtha-2/publication/311951912_New_customer_acquisition_by_a_retailera_conceptual_paper/links/5864a3b508ae329d6203abbd/New-customer-acquisition-by-a-retailera-conceptual-paper.pdf
Yin, S. and Kaynak, O., 2015. Big data for modern industry: challenges and trends [point of view]. Proceedings of the IEEE, management theories assignment103(2), pp.143-146.https://ieeexplore.ieee.org/stamp/stamp.jsp arnumber=7067026
Yoseph, F., Ahamed Hassain Malim, N.H., Heikkilä, M., Brezulianu, A., Geman, O. and PaskhalRostam, N.A., 2020.The impact of big data market segmentation using data mining and clustering techniques. Journal of Intelligent & Fuzzy Systems, 38(5), pp.6159-6173.https://www.researchgate.net/profile/Fahed-Yoseph-2/publication/339919049_The_impact_of_big_data_market_segmentation_using_data_mining_and_clustering_techniques/links/5eab70a0a6fdcc70509de68a/The-impact-of-big-data-market-segmentation-using-data-mining-and-clustering-techniques.pdf
Zheng, R., Li, Z. and Na, S., 2022. How customer engagement in the live-streaming affects purchase intention and customer acquisition, E-tailer's perspective. Journal of Retailing and Consumer Services, 68, p.103015.https://www.sciencedirect.com/science/article/pii/S0969698922001084management theories assignment