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After having drawn lessons from the recent COVID-19 pandemic for retailers in the previous chapters, in this last chapter we provide an outline on retailing over a longer time horizon. We start with projections of how the phygitalization trend in retailing will further evolve and what role data plays as a basis for a competitive advantage – on the condition of smart and ethical use. Besides looking at customers (downstream), we address the upstream in the value delivery network, focusing on how to succeed in balancing between efficiency and sustainability in the retail supply chain. Retailers face huge challenges. This chapter contributes to setting the scene for retailers to thrive in the brand-new post-pandemic aftermath.

The purpose of this paper is to explore various limitations of conventional mining systems in extracting useful buying patterns from retail transactional databases flooded with Big Data. The key objective is to assist retail business owners to better understand the purchase needs of their customers and hence to attract customers to physical retail stores away from competitor e-commerce websites.

This paper employs a systematic and category-based review of relevant literature to explore the challenges possessed by Big Data for retail industry followed by discussion and implementation of association between MapReduce based Apriori association mining and Hadoop-based intelligent cloud architecture.

The findings reveal that conventional mining algorithms have not evolved to support Big Data analysis as required by modern retail businesses. They require a lot of resources such as memory and computational engines. This study aims to develop MR-Apriori algorithm in the form of IRM tool to address all these issues in an efficient manner.

The paper suggests that a lot of research is yet to be done in market basket analysis, if full potential of cloud-based Big Data framework is required to be utilized.

This research arms the retail business owners with innovative IRM tool to easily extract comprehensive knowledge of useful buying patterns of customers to increase profits. This study experimentally verifies the effectiveness of proposed algorithm.

Retail management has acquired the attention of scholars and practitioners, with many international and prestigious journals specifically relating to the topic. Also, top-tier journals in other disciplines have proposed special issues on the new advances in retailing, with emphasis on the role of new and smart technologies. On the one hand, the research in retailing seems to be prolific; on the other hand, the interest in retail education (from a research and university perspective) seems to be more limited. The purpose of this paper is to capture the (mis)match between the leading universities' offerings and job demand in the UK. In this way, the paper identifies opportunities for educators and researchers to educate future career-ready professionals in retailing and improve research in retail education.

The research evaluates the offer of UK retail education in terms of programmes/courses, focusing on the Russell Group universities for the academic year 2020/2021 (September starts) and the demand of certain skills and competences by the largest retailers in the UK. The study utilizes secondary data based on the courses/programmes specifically related to the retail sector and on the job opportunities through the leading UK grocery retailers.

The findings reveal the extent of the gap between the university educational offerings and the requirements from retailers.

To the best of authors’ knowledge, this paper is the first attempt to capture and compare multiple evidence bases related to academic curriculums and employers' requirements for specific retail competencies.

In the most abstract way, artificial intelligence (AI) allows human work to be shifted toward technological systems that are currently not fully capable. Following this, the domain of retail can be sketched as a natural fit for the application of AI tools, which are known for their high proportion of human work and concurrent low profit margins. This paper aims to explore the current dissemination of the application of AI within the industry. The value-added core tasks of retail companies are examined to determine the possible utilization and the market adoption within the globally largest retail companies is given.

The paper uses two different approaches to identify the scientific state-of-the-art: a search on the major scientific databases and an empirical study of the ten largest international retail companies and their adoption of AI technologies in the domains of wholesale and retail.

The application within the different value-added core tasks varies greatly depending on the area. In summary, there are numerous possible applications in all areas. Especially, in areas where future forecasts are needed within the task areas (such as marketing or replenishment), the use of AI, today, is both scientifically and practically highly developed. In contrast, the market adoption of AI is highly variable. The pioneers have integrated extensive applications into everyday business, while the challengers are investing heavily in new initiatives. Some others, however, show neither active use nor any effort to adopt such technology.

To the best of the author’s knowledge, this is one of the first research contributions to analyze the areas of application and the impact of AI structured along the value-added core processes of retail companies.

Designing effective business analytics (BA) platforms that visualise data, provide deep insights and support data-driven decision-making is a challenging task. Understanding the elements shaping BA platform design is crucial for success. The purpose of this study is to explore the impact of visualisation on usability (UI) and user experience (UX) while emphasising the importance of insights understanding in BA platform design.

This paper presents a case study following a startup’s journey as it undergoes two redesign phases for its BA platform. A combination of quantitative and qualitative methods is used to assess UX/UI and insights understanding of the platform. Indicatively this included semi-structured interviews, observations, think-aloud techniques and surveys to monitor runtime per task, number of errors, users’ emotions and users’ understanding.

Our findings suggest that modifications in aesthetics and information visualisation positively influence overall usability, UX, and understanding of platform insights – a critical aspect for the success of the startup.

Our goal is not to make a methodological contribution, but to illustrate how companies, constrained by time and pressure, navigate platform changes without meticulous design and provide learnings on important elements while designing BA platforms.

This paper concludes with suggested methods for assessing BA platforms and recommends practical practices to follow. These practices include recommendations on important elements for BA platform users, such as navigation and interactivity, user control and personalisation, visual consistency and effective visualisation.

This study contributes to practice as it presents a real-life case and offers valuable insights for practitioners.

The effect of the transition toward digital technologies on today’s businesses (i.e. Industry 4.0 transition) is becoming increasingly relevant, and the number of studies that have examined this phenomenon has grown rapidly. However, systematizing the existing findings is still a challenge, from both a theoretical and a managerial point of view. In such a setting, the knowledge management (KM) discipline can provide guidance to address such a gap. Indeed, the implementation of fundamental digital technologies is reshaping how firms manage knowledge. Thus, this study aims to critically review the existing literature on Industry 4.0 from a KM perspective.

First, the authors defined a structuring framework to highlight the role of Industry 4.0 transition along with absorptive capacity (ACAP) processes (acquisition, assimilation, transformation and exploitation), while specifying what is being managed, that is data, information and/or (actual) knowledge, according to the data-information-knowledge (DIK) hierarchy. The authors then followed the systematic literature review methodology, which involves the use of explicit criteria to select publications to review and outline the stages a process has to follow to provide a transparent and replicable review and to analyze the existing literature according to the theoretical framework. This procedure yielded a final list of 150 papers.

By providing a clear picture of what scholars have studied so far on Industry 4.0 transition, in terms of KM, this literature review highlights that among all the studied digital technologies, the big data analytics technology is the one that has been explored the most in each phase of the ACAP process. A constructive body of research has also emerged in recent years around the role played by the internet of things, especially to explain the acquisition of data. On the other hand, some digital technologies, such as cyber security and smart manufacturing, have largely remained unaddressed. An explanation of the role of these technologies has been provided, from a KM perspective, together with the business implications.

This study is one of the first attempts to revise the literature on Industry 4.0 transition from a KM perspective, and it proposes a novel framework to read existing studies and on which to base new ones. Furthermore, the synthesis makes two main contributions. First, it provides a clear picture of the different digital technologies that support the four ACAP phases in relation to the DIK hierarchy. Accordingly, these results can emphasize what the literature has looked at so far, as well as which digital technologies have gained the most attention and their impacts in terms of KM. Second, the synthesis provides prescriptive considerations on the development of future research avenues, according to the proposed research framework.

The use of innovative technologies by firm employees is a key factor in ensuring the competitiveness of firms. However, researchers and practitioners have been concerned about the willingness of technology end users to use innovative technologies. This study, therefore, aims to determine the factors affecting the intention to use marketing analytics technology.

This study surveyed 213 firm employees. The quantitative data collected was analysed using partial least squares structural equation modelling.

The results reveal that performance expectancy, facilitating conditions, attitudes and perceived trust have a positive and significant effect on intentions to use marketing analytics. Effort expectancy, social influence and personal innovativeness in information technology were found not to predict intentions to use marketing analytics.

This study has practical implications for firms seeking to enhance the use of marketing analytics technology in developing countries.

This study contributes to the use of UTAUT, perceived trust, personal innovativeness and user attitude in predicting the intentions to use marketing analytics technology.

This study examines the relationship between servant leadership and its dimensions on firm performance, with big data playing the role of a mediator.

Survey responses used for analysis in this study have been taken from business managers associated reputed private sector organizations in India. A conceptual model is proposed grounded to the Conservation of Resource Theory (COR). Structural equation modeling has been used to test the proposed model.

Servant leadership significantly relates to firm performance, whereby Big Data is seen to play the role of a mediator. The results also indicate that none of the dimensions of servant leadership independently affect firm performance.

The study adds to extant research by examining the mediating mechanism of Big Data in servant leadership and firm performance. It also suggests that each dimension of servant leadership gets reflected in overall servant leadership. Here it is important to note that Big Data analytics partially mediate the effectiveness of servant leadership.