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.     

How talent capability can shape service analytics capability in the big data environment?

We know very little about how big data-driven service analytics capabilities (SAC) are built in data-driven service organizations and the potential role of talent capability in facilitating overall SAC and the impact of both on firm performance (FPER). Drawing on the dynamic capabilities (DC) approach, this study investigates the link between SAC and FPER examining the mediating role of talent capability and the moderating influence of a firm’s strategic alignment. On the basis of two Delphi studies and survey data from 267 service analysts in the US and France, the findings show that even though SAC are built on technology, talent and information capabilities, their overall impact on firm performance is mediated by the level of talent capability of service analytics managers. The findings also confirm the critical moderating impact of strategic alignment between dynamic talent capability and firm performance in the big data environment.     

The effects of firm size and firm performance on CEO pay in Canada: A Re-Examination and Extension

Using insights from academic and practitioners' perspectives and recent data, this paper extends the literature by using pay variables that are typically used by practitioners, including those not studied in previous academic research. Consistent with previous findings, firm size, measured by three-year average revenues, has strong effects on CEO pay. However, the relationship is not the same for firms of different sizes. Revenue elasticity is strong among small companies and disappears for medium and large companies. Firm performance, measured by accounting-based measures (return on assets and return on equity), and market-based measures (total shareholder return and shareholder value), have little effects on CEO cash compensation, but strong positive effects on equity compensation. Implications for research and practice are discussed.     

Big data analytics and firm performance: Effects of dynamic capabilities

Drawing on the resource-based view and the literature on big data analytics (BDA), information system (IS) success and the business value of information technology (IT), this study proposes a big data analytics capability (BDAC) model. The study extends the above research streams by examining the direct effects of BDAC on firm performance (FPER), as well as the mediating effects of process-oriented dynamic capabilities (PODC) on the relationship between BDAC and FPER. To test our proposed research model, we used an online survey to collect data from 297 Chinese IT managers and business analysts with big data and business analytic experience. The findings confirm the value of the entanglement conceptualization of the hierarchical BDAC model, which has both direct and indirect impacts on FPER. The results also confirm the strong mediating role of PODC in improving insights and enhancing FPER. Finally, implications for practice and research are discussed.     

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.     

Developing firms' growth approaches as a multidimensional decision to enhance key stakeholders' wellbeing

Firms facing a dynamic marketplace associated with the increasing adoption of new-age technology, the growth of big data and analytics, and the increased importance of stakeholders seek guidance on the investment of scarce resources across these three primary dimensions. The key questions facing firms are: How can we capitalize on the three primary dimensions (the increasing new-age technology adoption, the growth of big data and analytics, and the increased importance of key stakeholders) and overcome their associated challenges to achieve sustainable firm growth? This study aims to revive the thinking based on the growth strategy literature in the current environment, by presenting a 4E growth strategy matrix comprising of entrenching, empowering, enterprising, and enriching growth strategies. We propose that a firm can invest resources to maximize on one, two, or all three primary dimensions to produce the four growth strategies. We present an organizing framework based on insights from our triangulated research approach. Each of the growth strategies discussed will result in building certain types of firm capabilities, which will then lead to serving the needs and well-being of the key stakeholders. We also present an extensive research agenda that emerges from the four growth strategies.     

How career plateau beliefs and leader interpersonal unfairness harm job performance in dysfunctional organizational settings

Building on social exchange theory and attribution theory, this study unpacks the relationship between employees' perceptions of organizational politics and job performance, considering the mediating effect of career plateau beliefs and the moderating effect of leader interpersonal unfairness. The findings provide empirical support for the theoretical predictions. An important reason for which perceptions of dysfunctional organizational politics reduce job performance is that employees develop beliefs that opportunities for their career development are limited. This mediating role of career plateau beliefs is particularly salient to the extent that employees are exposed to organizational leaders who treat them with disrespect. Organizations can mitigate the risk that highly politicized decision-making processes lead to negative performance outcomes by stimulating fair interpersonal relationships.     

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.     

The ‘big data’ myth and the pitfalls of ‘thick data’ opportunism: on the need for a different ontology of markets and consumption

ABSTRACT

The twin pillars of big data and data analytics are rapidly transforming the institutional conditions that situate marketing research. In response, many proponents of culturalist paradigms have adopted the vernacular of ‘thick data’ to defend their vulnerable position in the marketing research field. However, thick data proselytising fails to challenge several outmoded ontological assumptions that are manifest in the big data myth and it situates socio-cultural modes of marketing thought in a counterproductive technocratic discourse. In building this argument, I first discuss the relevant historical continuities and discontinuities that have shaped the big data myth and the thick data opportunism. Next, I argue that culturally oriented marketing researchers should promote a different ontological frame— the analytics of marketplace assemblages—to address how big data, or more accurately its socio-technical infrastructure, produces new kinds of emergent and hybrid market structures, modes of social aggregation, consumption practices, and prosumptive capacities.     

Resentful and religious: How religiosity can mitigate the detrimental effects of workplace embitterment on helping behaviors

This study aims to establish how employees' experiences of workplace embitterment may direct them away from voluntary efforts to help coworkers, mediated by emotional exhaustion and moderated by religiosity. Three rounds of survey data, collected from employees and their supervisors in various Pakistani organizations, reveal that a sense of being emotionally overburdened by work links rancorous feelings due to negative work events with tarnished helping behavior, mitigated by employees' ability to draw on their religious faith. As an original contribution, this research addresses the effect of an actually felt negative emotion (workplace embitterment), instead of a source of emotional hardship, on employees' propensity to halt extra-role work efforts; it also describes how the personal resource of religiosity influences this process.     

Implementing social media marketing strategically: an empirical assessment

The purpose of this study is to examine how firms implement social media systematically to drive strategic marketing actions. To this end, the study conceptualises social media implementation as a multidimensional, organisational construct composed of social media strategy, active presence, customer engagement initiatives and social media analytics. Using primary data, the study operationalises the social media implementation construct and tests its effect on firm performance isolated into social media performance and marketing performance. The results indicate that all except the active presence dimension of social media implementation are positively related to social media performance. The results further indicate that social media performance is positively related to marketing performance. The study contributes to the literature by offering a novel conceptualisation and empirical validation of the social media implementation construct.     

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.     

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.     

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.     

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.     

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.     

Big data in an HR context: Exploring organizational change readiness,employee attitudes and behaviors

This research highlights a contextual application for big data within a HR case study setting. This is achieved through the development of a normative conceptual model that seeks to envelop employee behaviors and attitudes in the context of organizational change readiness. This empirical application considers a data sample from a large public sector organization and through applying Structural Equation Modelling (SEM) identifies salary, job promotion, organizational loyalty and organizational identity influences on employee job satisfaction (suggesting and mediating employee readiness for organizational change). However in considering this specific context, the authors highlight how, where and why such a normative approach to employee factors may be limited and thus, proposes through a framework which brings together big data principles, implementation approaches and management commitment requirements can be applied and harnessed more effectively in order to assess employee attitudes and behaviors as part of wider HR predictive analytics (HRPA) approaches. The researchers conclude with a discussion on these research elements and a set of practical, conceptual and management implications of the findings along with recommendations for future research in the area.     

The power of prediction: predictive analytics,workplace complements,and business performance

Anecdotes abound suggesting that the use of predictive analytics boosts firm performance. However, large-scale representative data on this phenomenon have been lacking. Working with the Census Bureau, we surveyed over 30,000 American manufacturing establishments on their use of predictive analytics and detailed workplace characteristics. We find that productivity is significantly higher among plants that use predictive analytics—up to $918,000 higher sales compared to similar competitors. Furthermore, both instrumental variables estimates and the timing of gains suggest a causal relationship. However, we find that the productivity pay-off only occurs when predictive analytics are combined with at least one of three workplace complements: significant accumulation of IT capital, educated workers, or workplaces designed for high flow-efficiency production. Our findings support claims that predictive analytics can substantially boost performance, while also explaining why some firms see no benefits at all.

     

Target Costing,Business Model Innovation,and Firm Performance: An Empirical Analysis of Chinese Firms

Market orientation theory was used to relate the implementation of target costing systems and business model innovation to firm performance using a sample of 189 electronics and information industry manufacturers in China. As expected, the implementation of target costing was positively associated with both business model innovations and firm performance. Further, the diversity of product development teams was also crucial. It positively moderated the association between target costing and business model innovation. Finally, the business model innovation was positively related to firm performance. Copyright © 2012 ASAC. Published by John Wiley & Sons, Ltd.