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.     

Big business in the Russian empire: A European perspective

This paper presents an inventory of the largest private companies in the Russian Empire in 1914, and their comparison to the largest contemporary British, German, and French companies identified by Youssef Cassis as ‘big business’. It focusses on three questions. First, how big was big business in Russia from a European perspective? Second, how did the structure of big business in Russia compare to that of other large European economies? And finally, how did foreign entrepreneurship appear in Russian big business? Drawing on new empirical evidence, it contributes to the discussion on the ‘backward’ and ‘peripheral’ character of the Russian economy before the First World War.     

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.     

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.     

Integrating Analytics Through the Big Data Information Chain: A Case From Supply Chain Management

The benefits of good decision making by a distributor often have broad leverage across a supply chain, and data science provides a comprehensive framework for making this possible. We present a case study of an ongoing partnership between the authors and corporate managers at a distributor of heating, ventilating, and air‐conditioning products. We describe in detail the “vertical integration” of our analytical tools through a long chain of data scientific activities, backward to raw data, and forward to visually appealing output, in an organization with legacy information technology infrastructure. The models are applied to a large‐scale data set, and spreadsheet‐based decision support tools that include useful visualization capabilities for the firm are illustrated. We also offer this case as a blueprint for building a collaborative research relationship between academia and industry.     

Detours on the Path to a European Big Data Economy

As envisioned by the European Commission, national boundaries are supposed to disappear under the Digital Single Market. Many small and medium-sized companies (SMEs) are not aware of the significant potentials and promises of big data for their business activities and models.     

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.     

Strategic choices of firms in expanding overseas business

Based on a case study of Pfizer’s production scope evolution in China from 1993 to 2002, we propose that MNCs usually have to face dual pressures of keeping integration among global businesses on the one hand, and remaining responsive to local market conditions on the other. When there is large discrepancy between the two, due to the bounded rationality of the decision-makers, the development paths of the subsidiaries are likely to exhibit recursive pattern, instead of the sequential mode as suggested in the extant literature. On the other hand, we argue that companies can make strategic choices to facilitate the development of their overseas subsidiaries. By the adjustment of the strategic parameters pertaining to the content and processes of their development paths, companies can accumulate knowledge about the local market and strike a balance between the dual pressures they have to face. The implication of our study for the indigenous Chinese companies in their international expansion is that when facing unfamiliar business environment in overseas market, paying attention to the idiosyncratic local market condition and keeping strategic flexibility are pivotal to their success. __________ Translated from Guanli shijie 맜理世界 (Management World), 2005(10): 123–138     

Global strategies of European firms

In this research four internationally operating business units of European multinational corporations are studied from a global strategy content perspective. In opposition to large parts of the existing global strategy literature, the observed pattern of strategic behavior exclusively related to neither prevailing generic strategies nor to any of the different content-oriented approaches to global strategy. Based on the empirical evidence, a taxonomy of four types of global strategies is identified. The taxonomy proposes to distinguish global strategies along two key dimensions: internal source of competitive advantage generation (upstream, downstream) and external focus of competitive advantage exploitation (country centered, country transcending).     

Internationalisation of Japanese professional business service firms

This paper explores the internationalisation of Japanese professional business service (PBS) firms. The UK was chosen as the geographical area of study due to the large number of Japanese foreign direct investments over the last 20 years. A comparative study of Japanese and Western service providers was conducted in the UK in spring 2002 to ascertain if there are any significant differences in their ways of doing business with Japanese clients. The preliminary results show that Japanese firms tend to be very small in the UK in terms of both size and market share. They seem also to be tightly connected with Japanese businesses operating in the UK.     

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.     

Entrepreneurial capacities as antecedents of business performance in Brazilian firms

We investigate the relationship between entrepreneurial capacities and firm performance. More specifically, we investigate the effects of radical innovation and learning orientation on business performance. We test the effects of entrepreneurial orientation on learning orientation and radical innovation. The results suggest that radical product innovation and companies' orientation to learn have a positive effect on organizational performance. Additionally, we find that entrepreneurial orientation positively influences a firm's capability to learn and innovate. Our findings also show a direct effect of learning orientation on radical innovation, which means that companies with a higher proclivity to learn are more likely to create products and processes, representing a major departure from the state of current knowledge. Copyright © 2014 ASAC. Published by John Wiley & Sons, Ltd.     

Recent developments in European business ethics

In the first part of the paper, factual information is given about developments in European business ethics since it started on a more or less institutionalized basis, five or six years ago. In the second part some comments are presented on the meaning of the developments and the possible causes. Attention is given to resemblances and differences between American and European business ethics. In the short last part some suggestions are proposed about tasks business ethics will face in the next decade.Henk J. L. van Luijk is Professor of Ethics at Nijenrode, The Netherlands School of Business, Breukelen, The Netherlands, and at the University of Groningen, The Netherlands. He is chairman of the Executive Committee of EBEN, The European Business Ethics Network. His special field is business ethics. He is the author of three books on various philosophical subjects. In his special field he published several articles, mainly in Dutch philosophical and professional journals.     

Crucial issues in successful European business

Time has come to put business ethics explicitly on the agenda of those who bear responsibility in the business world and of the scholars working in the field of business administration sciences. Reflection should bear on specific issues of concern, but also on processes of decision in business practices and on patterns of thought at work inside issues and processes. What the issues are, which processes and patterns need scrutiny, is to be decided upon in the course of the public normative debate in which ethicists, as experts of conceptual analysis and argumentation, and corporate executives as experts in decision-making each take an equal and fair share, as people who are able and willing to proffer an argued personal standpoint in an open debate.Henk J. L. van Luijk is Professor of Ethics at Nijenrode, The Netherlands School of Business, Breukelen, The Netherlands, and at the University of Groningen, The Netherlands. He is chairman of the Executive Committee of EBEN, The European Business Ethics Network. His special field is business ethics. He is the author of three books on various philosophical subjects. In his special field he published several articles, mainly in Dutch philosophical and professional journals.     

Assessing contribution of research in business to practice

This editorial offers some thoughts on wider criteria of evaluation than journal and article impact metrics. The editorial suggests that the measurement of journal and article impact metrics simply confirms the status quo rather than the promotion of resonance between practice, research and theory. The editorial proposes a more holistic recognition of impact, influence and usefulness that elevates the importance of three further dimensions of application, context and involvement. The editorial considers the value of scientific research to business practitioners and students concluding that an ingenuous and on-going scheme of exchange between scholars and practitioners would lead to cross-fertilization of ideas and experience and enhance empathy, learning and understanding. The alternative pursuit of scientific “respectability” by scholars in the most myopic academic sense has become less and less useful to students as well as the business community leaving the suspicion of business schools obsessed with making money whilst pretending to pursue knowledge and produce future citizens to make the world a better place.     

Multinational activity of European firms and heterogeneity

This paper offers an empirical assessment of the multinational activity of European firms. It takes the predictions of models of firm heterogeneity and FDI activity as a reference to explore the characteristics of multinational firms from 30 European countries. We use a data set, based on ORBIS, which links information of parent–affiliate pairs of firms. Our results show that more productive firms have greater multinational activity in terms of both scope, the number of foreign markets where they invest, and scale, the volume of local sales by subsidiaries active in foreign markets. The estimation of gravity equations shows that country characteristics that encourage multinational activity successively induce the entry of less productive parent firms. We confirm this asymmetry for the GDPs of the home and host countries, distance, contiguity and other standard gravity equation variables.     

How much prestige is enough? Assessing the value of multiple types of high-status affiliates for young firms

Young, unproven firms can signal their worthiness, or potential, through affiliations with various types of prestigious parties. Drawing from signaling theory, we present a formal consideration of the implications of multiple numbers and types of prestigious affiliates for IPO valuations. We argue that different types of prestigious affiliates – prestigious executives, directors, venture capital firms, and underwriters – convey different signals of IPO worth, depending on the extent to which they provide certification or substantive benefits. Based on a sample of 257 software IPOs, we find considerable support for our expectations. The benefits of prestigious executives and directors accumulate in a linear, more is better fashion; in contrast, the payoffs from VC and underwriter prestige accumulate in a curvilinear fashion. We discuss the theoretical implications of these findings and propose an agenda for future research.     

Data mining in business services

Data mining applies traditional statistical tools as well as artificial intelligence algorithms to the analysis of large datasets. Data mining has proven very effective in many fields, including business. This paper reviews applications of data mining relevant to the service industry, and demonstrates primary business functions and data mining methods. Typical industry data mining process is described, analytic tools are reviewed, and major software tools noted.     

Taming Uncertainty in Big Data

While the classic definition of Big Data included the dimensions volume, velocity, and variety, a fourth dimension, veracity, has recently come to the attention of researchers and practitioners. The increasing amount of user-generated data associated with the rise of social media emphasizes the need for methods to deal with the uncertainty inherent to these data sources. In this paper we address one aspect of uncertainty by developing a new methodology to establish the reliability of user-generated data based upon causal links with recurring patterns. We associate a large data set of geo-tagged Twitter messages in San Francisco with points of interest, such as bars, restaurants, or museums, within the city. This model is validated by causal relationships between a point of interest and the amount of messages in its vicinity. We subsequently analyze the behavior of these messages over time using a jackknifing procedure to identify categories of points of interest that exhibit consistent patterns over time. Ultimately, we condense this analysis into an indicator that gives evidence on the certainty of a data set based on these causal relationships and recurring patterns in temporal and spatial dimensions.