Cross-national differences in big data analytics adoption in the retail industry

Big data analytics (BDA) has emerged as a significant area of research for both researchers and practitioners in the retail industry, indicating the importance and influence of solving data-related problems in contemporary business organization. The present study utilised a quantitative-methods approach to investigate factors affecting retailers' adoption of BDA across three countries. A survey questionnaire was used to collect data from managers and decision-makers in the retail industry. Data of 2278 respondents were analysed through structural equation modelling. The findings revealed that security concerns, external support, top management support, and rational decision making culture have a greater effect on BDA adoption in developed countries UK than in UAE and Egypt. However, competition intensity and firm size have a greater effect on BDA adoption in UAE and Egypt than in UK. Finally, human variables (competence of information system's staff and staff's information system knowledge) have a greater effect on BDA adoption in Egypt than UK and UAE. The findings indicate that a “one-size-fits-all” approach is insufficient in capturing the heterogeneity of managers across countries. Implications for practice and theory were demonstrated.     

The provenance of loyalty card data for urban and retail analytics

The deployment of loyalty card and other consumer data in geographic research brings opportunities to explore and understand patterns of purchasing behaviour in unprecedented detail. However, valid generalisation requires thorough evaluation of their potential bias. We argue that, in competitive markets where consumers can choose to shop across competing retailers, loyalty card data from just one of these may not represent a ‘complete’ view of all purchases, and that this ‘completeness’ must be controlled for when assessing bias. To this end, we undertake a UK wide analysis of loyalty card data assembled by a major UK grocery retailer and provide guidelines for their effective deployment in the domains of urban and retail analysis. We assess, for the first time, the ‘completeness’ of circa 500 million customer transactions recorded by a major customer loyalty programme in representing the overall purchasing patterns of circa 16 million consumers across the entire UK, and develop a method by which to do this. Moreover, no operator has complete national store coverage, and so we suggest ways of accommodating this when conducting analysis using loyalty card data. We illustrate the importance of these issues before providing recommendations for the wider use of consumer loyalty card data.     

Implementing big data strategies: A managerial perspective

Despite considerable recent advances in big data analytics, there is substantial evidence that many organizations have failed to incorporate them effectively in their own decision-making processes. Advancing the existing understandings, this article lays out the steps necessary to implement big data strategies successfully. To this end, we first explain how the big data analytics cycle can provide useful insights into the characteristics of the environments in which many organizations operate. Next, we review some common challenges faced by many organizations in their uses of big data analytics and offer specific recommendations for mitigating them. Among these recommendations, which are rooted in the findings of strategy implementation research, we emphasize managerial responsibilities in providing continued commitment and support, the effective communication and coordination of efforts, and the development of big data knowledge and expertise. Finally, in order to help managers obtain a fundamental knowledge of big data analytics, we provide an easy-to-understand explanation of important big data algorithms and illustrate their successful applications through a number of real-life examples.     

The intermediating role of big data analytics capability between responsive and proactive market orientations and firm performance in the retail industry

Big data analytics capability (BDAC) is the key resource for competitive advantage in the drastically changing market. Although some studies have investigated the impacts on firm performance, there is limited understanding of how firms enhance their BDAC. This study draws on organisational culture and investigates the effects of responsive and proactive market orientations on BDAC and firm performance. The results show that both responsive and proactive market orientations increase BDAC. Further, BDAC fully mediates the relationship between these two market orientations and firm performance. Our findings suggest that BDAC researchers should focus on market orientations that enhance BDAC.     

Business curriculum redesign: Integrating data analytics

Data analytics is an integral part of planning and decision making in business. Priorities have shifted to hiring skilled employees to support a company’s analytics requirements. The authors discuss the background of big data and data analytics, demand for trained professionals, and information on the development of a data analytics curriculum. Curriculum design models are explored and emphasis placed on university curriculum redesign. This is a perspective piece that also addresses interdisciplinary collaboration, accreditation, and related challenges.     

On realising the utopian potential of big data analytics for maximising return on marketing investments

ABSTRACT

This commentary explores the Big Data transition of the ?eld of Marketing. The potential value of Big Data Analytics for both ?rms and customers is investigated and impediments for Marketing are identi?ed. It is concluded that despite the threats and obstacles, exciting challenges and opportunities for creating value are to be explored and exploited by marketing scholars and practitioners.     

The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources

Big data analytics have transformed research in many fields, including the business areas of marketing, accounting and finance, and supply chain management. Yet, the discussion surrounding big data analytics in human resource management has primarily focused on job candidate screenings. In this article, we consider how significant strategic human capital questions can be addressed with big data analytics, enabling HR to enhance overall firm performance. We also examine how new data sources that help assess workforce performance in real time can assist in the identification and development of the knowledge stars that contribute to firm performance disproportionately as well as help reinforce firm capabilities. But in order for big data analytics to be successful in the HR field, regulatory and ethical challenges must also be addressed; these include privacy concerns and, in Europe, the General Data Protection Regulation (GDPR). We conclude by discussing how big data analytics can facilitate strategic change within HR and the organization as a whole.     

Addressing barriers to big data

Increasingly, big data is viewed as the most strategic resource of the 21st century, similar in importance to gold and oil. While sitting on these vast pools of data, many organizations are simply not ready to take advantage of this new strategic resource. Embracing big data requires addressing a number of barriers that fall into the domains of technology, people, and organization. A holistic, socio-technical approach is required to overcome these barriers. This article introduces the specific tactics we recommend for addressing big data barriers, which involve changes to technology infrastructure, a focus on privacy, promotion of big data and analytic skills development, and the creation of a clear organizational vision related to big data.     

Fighting off the big guys: comparing competitive retail services strategies in industrialized and developing world settings

As global trends regarding increased mergers and acquisitions continue, small local retailers must learn how to fend off these strong attackers. We assess key services strategies when small-local retailers compete against large-national retailers in industrialized (USA) and developing world (black South African township) retail settings. Of key interest is how theories generated from industrialized world research apply in developing world settings. We found that small retailers fend off large national retailers using virtually identical strategies regardless of the level of economic development.

Delineating retail conurbations: A rules-based algorithmic approach

Retail conurbations may be defined as market areas with high intra-market movement. A limited range of approaches has been used to delineate such retail conurbations. This paper evaluates a simplified version of an existing zone design method used to define labour market areas, the Travel-To-Work-Area algorithm (TTWA), for application in a retail context. Geocoded loyalty card spend data recorded by Boots UK Limited, a large health and beauty retailer, were used to develop retail conurbations (newly termed Travel-To-Store-Areas (TTSAs)) for several UK regions using this algorithm. The output TTSA boundaries displayed significantly greater intra-zone flows compared to existing retail conurbation delineation approaches. There is thus scope for researchers and analysts to broaden the zone design approaches used to develop retail conurbations.     

A process for evaluating retail store efficiency: a restricted DEA approach

A managerial process is developed for assessing the efficiency of 552 individual stores for a multi-store, multi-market retailer employing Data Envelopment Analysis (DEA). Incorporating assurance regions into a DEA model allowed for a more complete specification of inputs and outcomes than usually found in DEA applications. This procedure permitted the researchers to capture top management's strategic thinking. Practical usefulness of the process' results is illustrated with respect to two management issues: evaluating store managers and identifying critical success factors (CSFs).     

Big-box retailing and the urban retail structure: the case of the Toronto area

This paper examines the growth and impact of power centres and “big-box” retailers through the 1990s in the Greater Toronto Area (GTA). The data reveal that 72% of big boxes were added in the previous nine years and that twice as many opened in the suburban fringe as compared to the equally populated central area. Of all big-box locations, 59% are on industrial lands and close to expressways. Of all postal codes in the GTA, 11% account for 47% of the big-box locations and a 19% versus 7% of 1989–1995 sales growth. Average annual sales per store in the heavily dominated big-box areas is $2+million versus $1.4 million in non-box areas and market shares for the dominant big-box retailers range between 19 and 33% in their respective retail categories. In eight retail sectors in direct competition with big-box retailers, there is an overall decline of −7% in share of total stores. Between 1993 and 1997, the proportion of retail employment within 2 km of a big-box increased from 28 to 43%. This increase is accounted for by additional retail and service firms.     

Big data: Dimensions,evolution, impacts,and challenges

Big data represents a new technology paradigm for data that are generated at high velocity and high volume, and with high variety. Big data is envisioned as a game changer capable of revolutionizing the way businesses operate in many industries. This article introduces an integrated view of big data, traces the evolution of big data over the past 20 years, and discusses data analytics essential for processing various structured and unstructured data. This article illustrates the application of data analytics using merchant review data. The impacts of big data on key business performances are then evaluated. Finally, six technical and managerial challenges are discussed.     

Location effects: Geo-spatial and socio-demographic determinants of sales dynamics in brick-and-mortar retail stores

For brick-and-mortar retail operators, store location is an essential prosperity factor, affecting the volume and structure of sales. Understanding the complexity of location effects on sales dynamics and utilizing such information may be the key element of corporate success in a competitive market environment. In general, store locations can be characterized by representative sets of geo-spatial and socio-demographic features. Nowadays, multiple sources of location-related data are available from public authorities and other open sources. However, using such data may be a complex task: distinct location factors can have divergent effects on sales of different types of products. Hence, our objective is to quantify the effects of different measures of location on sales dynamics over a wide range of product categories. For this purpose, we introduce a methodology combining econometric modeling and cluster analysis. The presented empirical analysis is performed using data on 479 brick-and-mortar shops of a major drugstore chain operating in Czechia (2019 data are used to avoid distortions due to COVID-19). Besides estimating location effects on sales at the product-category level, we identify and evaluate groups (clusters) of product categories with similar sales dynamics. Both the methodology proposed and the empirical results presented can be utilized by different retail chains to assess and plan brick-and-mortar store locations. Also, the research presented can be instructive for academic researchers and other stakeholders in the fast-moving consumer goods sector.     

Connecting big data management capabilities with employee ambidexterity in Chinese multinational enterprises through the mediation of big data value creation at the employee level

Drawing from the knowledge-based dynamic capabilities (KBDCs) view, this study examines the association of big data management capabilities with employee exploratory and exploitative activities at the individual level. Furthermore, it also investigates the mediating role of big data value creation in the association of big data management capabilities with exploratory and exploitative activities. The partial least square method was employed to analyse the hypotheses using data collected from 308 employees of 20 Chinese multinational enterprises. The existing literature gives scant attention to the role of big data management capabilities at the individual level. The main contribution of this study is that it conceptualises big data management as the ability to utilise external knowledge (generated from global users) under the resource constrained environment of an emerging economy. Furthermore, this study builds upon the existing literature on KBDC to explain big data management capabilities as antecedents to ambidexterity at the individual employee level.     

A relational performance model for developing innovation and long-term orientation in retail franchise organizations

A relational performance model is developed to show how innovation and long-term orientation can be evaluated and monitored within franchised retail service organizations. Using principles of justice from social exchange theory as a guide, our performance model measures how franchisee entrepreneurial passion (EP) and perceptions of fairness help to promote innovation and long-term commitment across retail franchise organizations. We test our model using data collected from one large U. S. retail service organization ($5B+ annual revenue) and its independent franchise operators. We discover that targeting distributive justice (or fairness) helps to leverage the EP of the independent operators for promoting innovation, while both procedural and distributive elements of the organizational justice climate help to enhance franchisee's long-term commitment. Moreover, we find that retail franchise operators perceive organizational justice differently than do corporate district managers, which suggests several important implications for both research and practice.     

Combining sell-out data with shopper behaviour data for category performance measurement: The role of category conversion power

Retailers need to manage a series of complex decisions relating to numerous products. To reduce this complexity, they have introduced category management practices, which consider groups of similar products (categories) that can be managed separately as single business units (SBUs). Although the concept that the store offer should be organised as a category mix and that this strategy allows for better overall store management is already consolidated, retailers still struggle to adopt an approach to the store performance measurement starting from a category level perspective. Nowadays, the available methods for measuring categories’ performance are quite limited. The current trend sees the measurement of category performance mainly based on sell-out data that are ill-equipped to fully address category management issues. Retailers should broaden their field of analysis not only by focusing on the product/sales perspective but also by including other methodologies such as shopper behaviour analysis. In this regard, the use of technology offers the retail sector new perspectives for those analysis. Therefore, we intend to contribute to the ongoing debate on the retail analytics topic by presenting a shopper behaviour analytics system for category management performance monitoring. More in detail, we could derive a new key performance indicator, category conversion power (CCP), aimed at analysing and comparing the single categories organised within the store. The research is based on a unique dataset obtained from a real-time locating system (RTLS), which allowed us to collect behavioural data togheter with sell-out data (from POS scanner). We argue that retailers could exploit this new analytical method to gain more understanding at the category level and therefore make data-driven decisions aimed at improving performance at the store level.     

Effects of perceived retail crowding: a meta-analytic study

ABSTRACT

We performed a meta-analysis to understand the crowding construct in the retail context. The systematic review identified 535 studies, of which 69 were present in the analysis conducted, generating 504 quantitative relationships. Our meta-analysis indicated that the perception of crowding generates many negative feelings, a few positive feelings, minimal perceived value, low attractiveness of the environment and low short- and long-term consequent behaviours. At the methodological level, it was found that student samples and experiments tend to produce a stronger impact on the relationship between crowding and satisfaction. It was also found that the effect of crowding perception on customer satisfaction differed according to cultural influence. Finally, the results did not demonstrate an influence of the type of density (human vs spatial) on the relationship between retail crowding and satisfaction.     

Big data dreams: A framework for corporate strategy

The phenomenon of big data—large, diverse, complex, and/or longitudinal data sets—is having a stark influence on organizational strategy making. An increase in levels of data and technological capabilities is redefining innovation, competition, and productivity. This article contributes to both practical strategic application and academic research in the strategic management domain by presenting a framework that identifies how big data improves functional capabilities within organizations, shapes entirely new industries, and is a key component of innovative and disruptive strategies used by learning organizations to diversify and break down barriers of traditionally defined industries. This framework provides an appropriate basis for internal corporate strategy discussions that surround big data investments by explaining how firms create value through various approaches. In addition, we offer guidance for how firms might derive their own big data approach through the merits of aligning data strategy aspirations with data strategy authenticity.     

Uncovering the message from the mess of big data

User-generated content, such as online product reviews, is a valuable source of consumer insight. Such unstructured big data is generated in real-time, is easily accessed, and contains messages consumers want managers to hear. Analyzing such data has potential to revolutionize market research and competitive analysis, but how can the messages be extracted? How can the vast amount of data be condensed into insights to help steer businesses’ strategy? We describe a non-proprietary technique that can be applied by anyone with statistical training. Latent Dirichlet Allocation (LDA) can analyze huge amounts of text and describe the content as focusing on unseen attributes in a specific weighting. For example, a review of a graphic novel might be analyzed to focus 70% on the storyline and 30% on the graphics. Aggregating the content from numerous consumers allows us to understand what is, collectively, on consumers’ minds, and from this we can infer what consumers care about. We can even highlight which attributes are seen positively or negatively. The value of this technique extends well beyond the CMO's office as LDA can map the relative strategic positions of competitors where they matter most: in the minds of consumers.