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.     

Emerging Market Retail: Transitioning from a Product-Centric to a Customer-Centric Approach

In an environment with digital disruptions, retailers must adopt a customer-centric approach to survive and compete effectively. Retailers need to be agile and forward-looking in adopting the relevant analytics and performance metrics to bring a customer-centric approach across upstream and downstream activities in the retail value chain. However, retailers in emerging markets (EMs) need clarity on the specific analytics and performance metrics in the value chain that will enable them to transition from their current product-centric state to the desired customer-centric state. Employing a triangulation approach (i.e., literature review, marketplace evidence, and managerial interviews) in the fragmented retail landscape of EMs, this study provides an organizing framework that explains: (i) the need for a customer-centric approach across the retail value chain, (ii) the specific performance metrics that need to be adopted across upstream and downstream activities in the retail value chain to enable EM retailers to achieve their desired customer-centric state, and (iii) the role of analytics in providing insights to achieve these performance metrics and improving monetary and non-monetary firm performance outcomes. We also provide firm-specific and macro-level conditions that can influence the EM retailers’ adoption of relevant analytics and explain the different paths retail formats can follow to adopt analytics. We present a strategy matrix that enables retail managers to identify the appropriate analytics to be adopted at different retail value chain stages to achieve desired performance metrics. We also highlight future research opportunities in retailing in EMs.     

“I can get no e-satisfaction”. What analytics say? Evidence using satisfaction data from e-commerce

This study mines customer satisfaction (CS) segments using almost 270 thousand responses from a CS survey which ran in 140 e-commerce stores of a European country. To achieve this, it develops and applies a business analytics (BA)-informed framework. Then, it presents examples of how one e-commerce store exploited the extracted CS segments to build automated marketing actions for its customers, ranging from social media sharing strategies for the satisfied segments, to discounts for the less satisfied. This study contributes to customer satisfaction and segmentation literature. The extracted insights can be utilized to support decision making, ranging from targeted advertising for specific customer segments, to benchmarking for companies in similar industries.     

Do Retailers Benefit from Deploying Customer Analytics?

Prior research has documented a general positive relationship between the deployment of customer analytics and firm performance. In this research we focus on the retailing industry, an industry characterized by tight margins that lead to careful scrutiny of all business investments. Using survey data from 418 top managers based in the Americas, Europe Middle East and Africa (EMEA) and Asia, we show that of the eight industries in the study, firms in the retail industry have the most to gain from deploying customer analytics. However, we also find that not only do many retailers not perceive this potential gain, they do not invest in customer analytics at an economically appropriate level. Thus we identify a gap between perception and reality concerning the potential for customer analytics in the retail industry that has both theoretical and practical implications.     

Applications of business analytics in healthcare

The American healthcare system is at a crossroads, and analytics, as an organizational skill, figures to play a pivotal role in its future. As more healthcare systems capture information electronically and begin to collect more novel forms of data, such as human DNA, how will we leverage these resources and use them to improve human health at a manageable cost? In this article, we argue that analytics will play a fundamental role in the transformation of the American healthcare system. However, there are numerous challenges to the application and use of analytics: the lack of data standards, barriers to the collection of high-quality data, and a shortage of qualified personnel to conduct such analyses. There are also multiple managerial issues, such as how to get end users of electronic data to employ it consistently to improve healthcare delivery and how to manage the public reporting and sharing of data. In this article, we explore applications of analytics in healthcare, barriers and facilitators to its widespread adoption, and ways in which analytics can help us achieve the goals of the modern healthcare system: high-quality, responsive, affordable, and efficient care.     

Correcting analytics maturity myopia

Companies face increasing pressure to compete in the practice of analytics and strive for analytics maturity to sustain their competitive advantage. A single-minded, narrow focus on gaining analytics maturity, however, leads to analytics maturity myopia. Based on our studies of analytical capabilities and numerous conversations with executives and managers, we offer a scorecard for organizations to identify the presence of analytics maturity myopia and propose a framework for organizations to correct this issue. The proposed framework partially explains the mixed and conflicting results regarding the relationship between analytics maturity and business value found in the literature. Specifically, we recommend that companies focus on three factors that are critical to realizing value from analytics initiatives: (1) a balanced view of value to different stakeholders, (2) a continuous expansion of the business ecosystem beyond current stakeholders to identify and pursue new opportunities, and (3) use of an emergent strategy to take advantage of unexpected opportunities and develop organizational agility.     

The Role of Big Data and Predictive Analytics in Retailing

The paper examines the opportunities in and possibilities arising from big data in retailing, particularly along five major data dimensions—data pertaining to customers, products, time, (geo-spatial) location and channel. Much of the increase in data quality and application possibilities comes from a mix of new data sources, a smart application of statistical tools and domain knowledge combined with theoretical insights. The importance of theory in guiding any systematic search for answers to retailing questions, as well as for streamlining analysis remains undiminished, even as the role of big data and predictive analytics in retailing is set to rise in importance, aided by newer sources of data and large-scale correlational techniques. The Statistical issues discussed include a particular focus on the relevance and uses of Bayesian analysis techniques (data borrowing, updating, augmentation and hierarchical modeling), predictive analytics using big data and a field experiment, all in a retailing context. Finally, the ethical and privacy issues that may arise from the use of big data in retailing are also highlighted.     

Data Science,Predictive Analytics,and Big Data in Supply Chain Management: Current State and Future Potential

While data science, predictive analytics, and big data have been frequently used buzzwords, rigorous academic investigations into these areas are just emerging. In this forward thinking article, we discuss the results of a recent large‐scale survey on these topics among supply chain management (SCM) professionals, complemented with our experiences in developing, implementing, and administering one of the first master's degree programs in predictive analytics. As such, we effectively provide an assessment of the current state of the field via a large‐scale survey, and offer insight into its future potential via the discussion of how a research university is training next‐generation data scientists. Specifically, we report on the current use of predictive analytics in SCM and the underlying motivations, as well as perceived benefits and barriers. In addition, we highlight skills desired for successful data scientists, and provide illustrations of how predictive analytics can be implemented in the curriculum. Relying on one of the largest data sets of predictive analytics users in SCM collected to date and our experiences with one of the first master's degree programs in predictive analytics, it is our intent to provide a timely assessment of the field, illustrate its future potential, and motivate additional research and pedagogical advancements in this domain.     

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.     

Teaching information technology alongside business analytics: Case study

Business analytics can be described as the statistical analysis of data to make decisions and meaningful conclusions. As the demand to advance the curriculum of undergraduate business education increases, courses in business analytics aim to provide students with fundamental skills in critical thinking. Educators have found that spreadsheet applications that include statistical features are easy to use and facilitate student learning. The authors analyzed student performance in an introductory business analytics course that used Microsoft Excel as a statistical tool by comparing scores from this introductory course with those from an information technology course in which only Excel skills were learned.     

Solving the problems of new product forecasting

An important consideration in solving the problems of new product forecasting entails distinguishing new product forecasting from the process of forecasting existing products. Particular differences between the two can be identified across the dimensions of data, analytics, forecast, plan, and measurement. For example, new product forecasting features little to no data with which to begin the process, whereas data are available and accessible in forecasting existing products. The minimal data situation requires a qualitative approach that lays out assumptions to provide transparency; in contrast, quantitative techniques are predominantly used when forecasting existing products. Different assumptions help construct a range of new product forecast outcomes on which company contingencies can be planned versus a singular point forecast for an existing product. And the measure of forecast accuracy, which is a common metric in forecasting existing products, must give way to meaningfulness so that the new product forecast is actionable. Recognizing new product forecasting as a cross-functional, company-wide process helps resolve the problems of new product forecasting. While incapable of remedying all problems, a properly understood and organized new product forecasting effort can help the company better prepare, execute, and support a new product launch, affording a greater propensity to achieve new product success.     

An investigation of skill requirements for business and data analytics positions: A content analysis of job advertisements

Presently, analytics degree programs exhibit a growing trend to meet a strong market demand. To explore the skill sets required for analytics positions, the authors examined a sample of online job postings related to professions such as business analyst (BA), business intelligence analyst (BIA), data analyst (DA), and data scientist (DS) using content analysis. They present a ranked list of relevant skills belonging to specific skills categories for the studied positions. Also, they conducted a pairwise comparison between DA and DS as well as BA and BIA. Overall, the authors observed that decision making, organization, communication, and structured data management are key to all job categories. The analysis shows that technical skills like statistics and programming skills are in most demand for DAs. The analysis is useful for creating clear definitions with respect to required skills for job categories in the business and data analytics domain and for designing course curricula for this domain.     

Revisiting customer analytics capability for data-driven retailing

Customer analytics is one of the most dominant strategic weapons in today's competitive retail environment. In spite of its strategic importance, there is scant attention to investigating customer analytics capabilities in the retail context. Drawing on a systematic literature review and thematic analysis, this study proposes a multidimensional customer analytics capability model by identifying relevant dimensions and sub-dimensions in retail settings. The principal contribution of this study is that the model links a customer analytics perspective to a resource-based view (RBV)-capability of the retailers by proposing six customer analytics capability dimensions and twelve sub-dimensions in the spectrum of market orientation and technology orientation. The customer analytics capability dimensions depict three crucial themes of marketing, such as value creation (offering capability and personalization capability), value delivery (distribution capability and communication capability), and value management (data management capability and data protection capability). By incorporating this capability dimensions, practitioners will likely be able to engage customers and enhance customer equity.     

Differentiating the workforce: The performance effects of using contingent labor in a context of high-performance work systems

In this study we focus on the impact of contingent labor on the outcomes of high-performance work systems (HPWS). Building on the emerging research on the social mechanisms linking HRM to organizational effectiveness, we argue that a higher incidence of contingent labor diminishes the productivity payoff associated with the use of HPWS for managing standard employees. We test these arguments using a sample of 229 British firms of different industries. The results support our arguments and help develop a more holistic and critical viewpoint in the analysis of workforce differentiation.     

Social media analytics for enterprises: Typology,methods, and processes

This article provides an overview of social media analytics for managers that seek to utilize the practice for social media intelligence. Currently, managers are challenged to analyze an abundance of social media data but lack a framework within which to do so. Toward this end, this article presents a simple typology of social media analytics for enterprises. It also discusses various analytics methods for social media data. Then, this article discusses management processes of social media analytics for enterprises. An illustration of social media analytics is provided with real-world consumer review data. Finally, four challenges are discussed.     

Job allocations as cultural sorting in a culturally diverse organizational context

This study addresses the inadequacy of the career and diversity literatures in explaining the dynamics of job allocations in a culturally diverse organizational context. In order to better understand this topic, we conducted a qualitative study involving personal interviews with 50 Emiratis and international employees who worked in managerial positions in the United Arab Emirates (UAE). The purpose of the study was to explore the unique challenges associated with managing a culturally diverse workforce and explore the role of culture in allocating jobs among employees with diverse cultural backgrounds. Despite the perceived value of having a culturally diverse workforce, our findings indicate a lack of effective diversity management strategies for dealing with challenges and issues associated with a high level of cultural diversity in the participating organizations. This study provides insights into the impact of cultural/nationality-based stereotypes on job allocations as well as on organizational outcomes. Based on the findings, we discussed implications for research and practice and as well as for policy makers.     

Business analytics: Why now and what next?

Business analytics is a revolution that is impossible to miss. At its core, business analytics is about leveraging value from data. Instead of being referred to as the ‘sludge of the information age,’ data has recently been deemed ‘the new oil.’ While data can be employed for purposes such as detecting new opportunities, identifying market niches, and developing new products and services, it is also notoriously amorphous and hard to extract value from. In this Guest Editors’ Perspective, we first present a structural framework for deriving value from business analytics. Extracting value from data requires aligning strategy and desirable behaviors to business performance management in conjunction with analytic tasks and capabilities. We then introduce three special articles that provide in-depth insights regarding how business analytics is being employed in the management of healthcare, accounting, and supply chains.     

Managing millennials’ personal use of technology at work

Growing up with the internet and unparalleled access to technology, millennials (individuals born during 1981–1995, also known as Gen Y and Gen Me) extensively use various technologies for non-work-related reasons while at work. Both popular media and scholarly research have portrayed this issue negatively and have supported monitoring and restricting personal use of technology. However, if organizations are to attract and retain millennials—now the largest generation in the U.S. workforce—it is crucial to understand their characteristics and what drives them. Drawing on research on generational differences, organizational control, and cyberloafing, this article explains how unique characteristics of millennials lead them to engage in personal use of technology at work and how organizations might address this issue. Specifically, I contrast two one-sided approaches (deterrence and laissez-faire) that can lead to dysfunctional outcomes when used in excess and recommend more viable solutions. These solutions include establishing a workplace technology use policy based on shared understanding, fostering both relaxation and urgency mentalities, and training both millennials and their managers.     

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.     

How data analytics is transforming agriculture

Two discussions about the interaction between data analytics and competitive analysis have been taking place in the past decade: one focusing on micro-level firm capabilities and the other on macro-level industry competitiveness. We seek to integrate the micro- and macro-level analyses via the lenses of firms in agricultural input markets. Agriculture is undergoing a tremendous transformation in the collection and use of data to inform smarter farming decisions. Precision agriculture has brought a heightened degree of competition for input supply firms, forcing greater interactions among friends and foes.