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.     

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.     

Big data? Big deal: Searching for big data’s performance effects in HR

Big data continues to gather increasing interest in the business press as well as within the management literature. While this interest has spilled over into the realm of human resources (HR) management, solid evidence of its positive performance impacts is lacking. I explore three possibilities for this lack of evidence: (1) HR possesses big data but largely lacks the ability to use it; (2) HR does not actually possess big data; and (3) big data is generating value for HR and positively affects organizational performance, but the winners in the race to utilize big data in HR are not publicizing their successes. Following this, I discuss current forms of big data implementation, highlighting an evolutionary progression of implementations in various settings and emphasizing the importance of balancing deductive with inductive analytical approaches. Finally, I discuss conditions under which big data may hold greater value for the HR function, and I suggest ways managers and organizations can make the most of big data.     

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.     

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.     

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.     

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.     

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.     

Leadership succession planning for today’s digital transformation economy: Key factors to build for competency and innovation

Digital transformation is rapidly changing the competitive landscape and the war on talent for today’s organizations. As part of this economy, organizations and their HR units must continuously reevaluate leadership structures and practices that exploit core competencies while allowing for innovation (i.e., leadership ambidexterity) and incorporate big data with predictive analytics. In this vein, understanding how HR executives can create better solutions around this problem remains sparse. Specifically, what frameworks can HR executives apply to identify potential alignment failures in leadership succession planning in light of newer emerging markets? What internal decision-making traps need to be recognized? Finally, what specific forms of data and evidence must test these plans for relevance and recharge and renew the talent-to-strategy pipeline? In this article, we examine these questions by reviewing the gaps in the literature and identifying through our four-step model how organizations can incorporate ambidexterity-building as a leadership succession planning practice.     

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.     

知识动态能力视角的电商平台大数据分析价值链战略

电子商务领域信息技术的开放性提高了服务创新的可见性,为适应多维度要素交互的平台竞争环境,需要基于电商平台丰富的数据资源,通过大数据分析提高战略规划的动态性、灵活性和响应敏捷性,形成一种能够快速集合与组织资源的模式,实现业务价值传递的可持续,满足竞争对抗与互动以及时间轴动态演化的需要。鉴于此,基于大数据分析、知识管理、动态能力、业务流程理论和指向性网络调查数据,构建大数据分析价值链战略研究模型,探讨大数据分析、动态能力、流程级创新与核心竞争力及战略绩效之间的关联。研究结果表明,大数据分析能实现有效的内生源和外生源知识管理,帮助企业形成动态能力,构建核心竞争力,进而提高战略绩效;大数据分析能提高企业组织的灵活性,可作为企业在竞争中赖以生存发展的战略投资;外生源知识管理和内生源知识管理均可单独运作产生知识动态能力,但外生源知识管理的作用更显著有效,更值得重视;知识共享是流程级创新的潜在障碍,与合作商进行知识共享需要选择合适的路径;动态能力既可直接影响流程级创新与核心竞争力,也可调节知识资产对竞争力的影响。总之,大数据分析能够通过影响动态能力和流程级创新来提高核心竞争力,且动态能力在知识管理与流程级创新及核心竞争力(战略绩效)间具有中介作用,电商平台应客观认识大数据分析潜在价值,将之纳入信息技术战略,通过梳理大数据分析→动态能力→核心竞争力→战略绩效的价值链过程,形成战略协同,最终提高知识创新的边际绩效。     

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.     

Re-engineering the purchasing function

Purchasing is increasingly being viewed as a key strategic function with numerous articles describing programmes or technologies that can be implemented to help a firm achieve competitive advantage. However, what is lacking is research that focuses on the individuals who will be responsible for implementing these initiatives. This article considers the development of the purchasing function within a multinational aerospace company and applies Maister's Professional Service Firm model in an operational manner to outline the changing roles and responsibilities of the procurement function. This framework not only illustrates how planning and organising issues can be addressed using the concepts developed by Maister, but also provides an illustration of how one model propounded by management educators can provide a foundation for enhancing organisational performance.     

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.     

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.     

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.     

Choosing the Right Metrics to Maximize Profitability and Shareholder Value

There is an ever-present need for managers to justify marketing expenditures to the firm. This can only be done when we can establish a direct link between marketing metrics and future customer value and firm performance. In this article, we assess the marketing literature with regard to marketing metrics. Subsequently, we develop a framework that identifies key metrics that firms should focus on that can give a firm a better picture of how they got to where they are now and insights towards how they can continue to grow into the future. We then identify several organizational challenges that need to be addressed in order for firms to build the capabilities of collecting the right data, measuring the right metrics, and linking those metrics to customer value and firm performance. Finally, we offer guidelines for future research with regard to marketing metrics to help firms establish successful marketing strategies, measure marketing effectiveness, and justify marketing expenditures to top management.     

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.     

The Impact of Top Mana(g)ement Team Process on Corporate Social Responsibility and Firm Performance

This article aims to explore how top management team (TMT) process affects strategic corporate social responsibility (CSR), and in turn, how strategic CSR influences firm performance. In addition, this article examines whether CSR mediates the relationship between TMT process and firm performance. The sample consists of 203 hotels from the tourism and hospitality industry in the southeast China. TMT processes assessed are communication and cohesion. Results indicate that (1) corporate social responsibility is positively related to firm performance, (2) top management team process (communication and cohesion) is positively related to corporate social responsibility, and (3) corporate social responsibility fully mediates the relationship between top management team process and firm performance. Results highlight upper echelons mechanisms that underpin the TMT process–firm performance relationship. This study contributes to understanding how TMT process affects firm performance both directly and indirectly, through strategic CSR.     

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.