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.     

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.     

A strategic approach to workforce analytics: Integrating science and agility

Workforce analytics is a major emerging trend in human resource management. Yet, despite the enthusiasm, there exists a misunderstanding of how organizations can successfully use workforce analytics to achieve important organizational outcomes. This article proposes ways to overcome this execution dilemma and achieve organizational success with workforce analytics through the integration of agile development with scientific research. We use a number of company examples to outline five key parts of an agile workforce analytics process: (1) prioritizing issues, (2) integrating deductive and inductive approaches, (3) preparing and validating data, (4) applying multiple methods in concert to support decisions, and (5) transforming insight into action to improve business outcomes.     

Exploring a Measurement of Analytics Capabilities

Changes in the volume and velocity of data have led many organizations to consider assessing and improving analytics capabilities. The purpose of this research is to describe a methodology developed to assess organizations’ analytics capabilities and explore the empirical value of data collected using this methodology. The measurement for analytics capabilities was developed by IBM during 2009–11 marketing efforts. To assess the data’s empirical value, we investigate whether measurements of analytics capabilities are internally consistent, associated with decisions to invest in analytics software and hardware, and able to explain firm profitability. In analyzing consistency, we find a natural sequence in the development of analytics capabilities. Exploring decisions to invest in analytics, we discover that firms with higher levels of capabilities are more likely to invest, as are firms that are larger and located in more profitable industries. However, we find no relationship between analytics capabilities and firm profitability.     

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.     

Click Here for a Data Scientist: Big Data,Predictive Analytics,and Theory Development in the Era of a Maker Movement Supply Chain

Predictive analytics is impacting many diverse areas, ranging from baseball and epidemiology to forecasting and customer relationship management. Manufacturers, retailers, software companies, and consultants are creatively discovering new applications of big data using predictive analytics in supply chain management and logistics. In practice, predictive analytics is generally atheoretical; however, we develop a 2 × 2 model to explain the role of predictive analytics in the theory development process. This 2 × 2 model shows that in our discipline we have traditionally taken one path to theory development, but that predictive analytics can be a salient component of a comprehensive theory development process. The model points to a number of research questions that need to be addressed by our research community. These questions are not just highly relevant to the academic community but also in urgent need of answers to help practitioners execute the right strategies with greater precision and efficiency. We also discuss how one disruptive trend, the maker movement, changes the nature of who the producers are in the supply chain, making big data even more valuable. As we engage in higher levels of dialogue we will be able to make meaningful progress addressing these vital research topics.     

Thirsty in an Ocean of Data? Pitfalls and Practical Strategies When Partnering With Industry on Big Data Supply Chain Research

Increased volume, velocity, and variety of data provides new opportunities for businesses to take advantage of data science techniques, predictive analytics, and big data. However, firms are struggling to make use of their disjointed and unintegrated data streams. Despite this, academics with the analytic tools and training to pursue such research often face difficulty gaining access to corporate data. We explore the divergent goals of practitioners and academics and how the gap that exists between the communities can be overcome to derive mutual value from big data. We describe a practical roadmap for collaboration between academics and practitioners pursuing big data research. Then we detail a case example of how, by following this roadmap, researchers can provide insight to a firm on a specific supply chain problem while developing a replicable template for effective analysis of big data. In our case study, we demonstrate the value of effectively pairing management theory with big data exploration, describe unique challenges involved in big data research, and develop a novel and replicable hierarchical regression‐based process for analyzing big data.     

Data Science,Predictive Analytics,and Big Data: A Revolution That Will Transform Supply Chain Design and Management

We illuminate the myriad of opportunities for research where supply chain management (SCM) intersects with data science, predictive analytics, and big data, collectively referred to as DPB. We show that these terms are not only becoming popular but are also relevant to supply chain research and education. Data science requires both domain knowledge and a broad set of quantitative skills, but there is a dearth of literature on the topic and many questions. We call for research on skills that are needed by SCM data scientists and discuss how such skills and domain knowledge affect the effectiveness of an SCM data scientist. Such knowledge is crucial to develop future supply chain leaders. We propose definitions of data science and predictive analytics as applied to SCM. We examine possible applications of DPB in practice and provide examples of research questions from these applications, as well as examples of research questions employing DPB that stem from management theories. Finally, we propose specific steps interested researchers can take to respond to our call for research on the intersection of SCM and DPB.     

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.     

Applying the Ignatian Pedagogical Paradigm to the Creation of an Accounting Ethics Course

This article explains how and why the Ignatian Pedagogical Paradigm (IPP), a 450-year-old approach to education, can serve as a framework for a modern principles-based ethics course in accounting. The IPP takes a holistic view of the world, combining five elements: context, experience, reflection, action, and evaluation. We describe the components of the IPP and discuss how they align with suggestions from prior research for providing principles-based ethics instruction in accounting. We conclude by describing how we used the IPP as a framework to create a graduate-level accounting ethics course.     

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.     

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.     

Comparing consumer-produced product reviews across multiple websites with sentiment classification

ABSTRACT

Online consumer reviews have been extensively studied. However, existing literature analyzing online consumer review data mostly relies on a single data source, resulting in potentially biased analytics conclusions. Many websites encourage consumers to post reviews of their purchased products, so that new consumers can evaluate these reviews for the same product across different websites to help them make purchasing decisions. Confusions often arise in this process, because there often exist substantial discrepancies in customer reviews across different retailers on the same product. Clarifying such confusions can help consumers reduce concerns to make up their mind for their purchases, therefore benefiting both consumers and retailers. Through text analytics and sentiment analysis, we comparatively examine the underlying patterns of online consumer reviews of three large retailers including Sears, Home Depot, and Best Buy for a same product. Afterward, we combine online consumer reviews from these large retailers and conduct an overall text analytics and sentiment analysis. The overall results are further compared with the results from individual retailers. The findings show that the sentiment of the online consumer reviews could vary substantially so relying on a single data source to make purchase decision is not a wise idea. Based on the results, we further devise a framework to comparatively examine and integrate multiple data sources for social media analytics of online consumer reviews. This study offers important managerial implications and identifies several new research directions for social media analytics.     

Implementing Big Data Analytics in Small Firms: A Situated Human Practice Approach

This study identifies and addresses an important gap in the nascent literature on big data analytics, using a longitudinal case study to investigate the implementation and application of big data analytics into a small firm specialized in transport logistics. Our research is rooted in Practice Theory, considering the implementation of new technologies in organizations as a result of multiple social negotiations, interpretations, and interactions. Our findings indicate the importance and centrality of human factors in decision-making and operational implementation, technology representing only a means to a clearly specified and collectively assumed objective. Big data analytics adoption and use in the case-study firm represents a gradual process, with each stage justified by the need to solve the problems caused by heavy and unpredictable road traffic. This approach validates the entrepreneurial effectuation model, which defines a firm's strategy as a fragmented but continuous effort to find and implement effective solutions to the market challenges encountered.     

Comparing Business Intelligence and Big Data Skills

While many studies on big data analytics describe the data deluge and potential applications for such analytics, the required skill set for dealing with big data has not yet been studied empirically. The difference between big data (BD) and traditional business intelligence (BI) is also heavily discussed among practitioners and scholars. We conduct a latent semantic analysis (LSA) on job advertisements harvested from the online employment platform monster.com to extract information about the knowledge and skill requirements for BD and BI professionals. By analyzing and interpreting the statistical results of the LSA, we develop a competency taxonomy for big data and business intelligence. Our major findings are that (1) business knowledge is as important as technical skills for working successfully on BI and BD initiatives; (2) BI competency is characterized by skills related to commercial products of large software vendors, whereas BD jobs ask for strong software development and statistical skills; (3) the demand for BI competencies is still far bigger than the demand for BD competencies; and (4) BD initiatives are currently much more human-capital-intensive than BI projects are. Our findings can guide individual professionals, organizations, and academic institutions in assessing and advancing their BD and BI competencies.     

The marketing–finance interface: A new integrative review of metrics,methods, and findings and an agenda for future research

The marketing–finance interface is an important research field in marketing, helping demonstrate the accountability of marketing within companies and building a necessary interdisciplinary bridge to finance and accounting research. Since the first comprehensive review article by Srinivasan and Hanssens (2009), the marketing–finance field has broadened considerably, as has research in finance and accounting. This updated systematic review of extant and new research integrates research in marketing, finance, and accounting into an overarching marketing–finance research framework. We discuss new methodological developments and offer solutions to recent technical debates on the event-study method and Tobin's q. Motivated in part by a survey of marketing–finance researchers, the article identifies and synthesizes four key emerging research areas: digital marketing and firm value, tradeoffs between “doing good” and “doing well,” the mechanisms of firm-value effects, and feedback effects. The article closes with a future research agenda for this dynamic research field and offers key conclusions.     

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.     

制度环境、会计准则与会计信息价值相关性

文章以2006年至2007年已经完成股权分置改革的上市公司为研究对象,从地区制度环境这一会计规则的外部执行机制入手,运用会计信息决策有用性的经验模型检验了2007年会计准则变迁的资本市场经济后果,研究发现：受证券市场环境的影响,会计准则变迁后会计信息的价值相关性显著降低了;制度环境对会计制度变迁效果影响符合投资者保护的“替代假说”相一致,即在会计信息价值相关性的影响研究中财务会计系统发挥了“替代机制”的作用。文章增进了会计准则变迁理论的实证研究积累。