Big data technology: developments in current research and emerging landscape

In this study, big data studies (01/2015–6/2018) are reviewed and several highly cited papers are identified, which indicates a growing interest in the area of big data. The papers and proceedings from international peer-reviewed journals and ranked conferences were reviewed. We employed Principal component analysis and citation and co-citation analysis to identify themes of research emanating from these studies. Citation and co-citation analysis reveals that there is cross-functional nature of big data research, which permeates different business sectors and is influenced by themes in engineering and information management.     

Enabling supply chain analytics for enterprise information systems: a topic modelling literature review and future research agenda

ABSTRACT

This paper provides a literature review of the research within the framework of 1) analytics,2) supply chain management, and 3) enterprise information systems, and relate the findings to competitive enablers. The findings are used to construct a future research agenda. The methodology is a systematic two-stage approach, based on a Smart Literature review framework using topic modelling. The research agenda proposes future research within the themes of 1) context, 2) cross-functional analytics, 3) cross-planning level analytics, 4) implementation and assimilation of analytics in EIS, 5) analytics and big data for SCM, 6) managerial aspects of analytics, and 7) data and system heterogeneity.     

Internet-of-things enabled supply chain planning and coordination with big data services: Certain theoretic implications

Recent advances in information technology have led to profound changes in global manufacturing. This study focuses on the theoretical and practical challenges and opportunities arising from the Internet of Things (IoT) as it enables new ways of supply-chain operations partially based on big-data analytics and changes in the nature of industries. We intend to reveal the acting principle of the IoT and its implications for big-data analytics on the supply chain operational performance, particularly with regard to dynamics of operational coordination and optimization for supply chains by leveraging big data obtained from smart connected products (SCPs), and the governance mechanism of big-data sharing. Building on literature closely related to our focal topic, we analyze and deduce the substantial influence of disruptive technologies and emerging business models including the IoT, big data analytics and SCPs on many aspects of supply chains, such as consumers value judgment, products development, resources allocation, operations optimization, revenue management and network governance. Furthermore, we propose several research directions and corresponding research schemes in the new situations. This study aims to promote future researches in the field of big data-driven supply chain management with the IoT, help firms improve data-driven operational decisions, and provide government a reference to advance and regulate the development of the IoT and big data industry.     

HR and analytics: why HR is set to fail the big data challenge

The HR world is abuzz with talk of big data and the transformative potential of HR analytics. This article takes issue with optimistic accounts, which hail HR analytics as a ‘must have’ capability that will ensure HR's future as a strategic management function while transforming organisational performance for the better. It argues that unless the HR profession wises up to both the potential and drawbacks of this emerging field and engages operationally and strategically to develop better methods and approaches, it is unlikely that existing practices of HR analytics will deliver transformational change. Indeed, it is possible that current trends will seal the exclusion of HR from strategic, board‐level influence while doing little to benefit organisations and actively damaging the interests of employees.     

Are firms ready to use big data analytics to create value? The role of structural and psychological readiness

In this study, we leverage Information Technology (IT) readiness literature and resource-based view (RBV) to investigate the impact of firm structural and psychological readiness on firm value creation, as mediated by big data analytics usage. The proposed research model is empirically validated using survey data from 179 senior IT managers. The findings demonstrate the importance of both structural (i.e. IT infrastructure capability, tools functionality, employee analytical capability, and bigness of data) and psychological readiness (i.e. IT proactive climate) in enhancing firm value creation through big data analytics usage. These results provide interesting theoretical and practical insights.     

PySAL: the first 10 years

ABSTRACT

This paper examines the field of regional science from the perspective of wider developments surrounding open-source software and the rising open-science movement. Regional science has been fairly isolated from these currents and a number of possible explanations for that isolation are identified. Opportunities that the emerging fields of data science and analytics afford for regional science are identified, and exemplar efforts leading the charge in engaging with these opportunities are highlighted.     

Inductive reasoning: The promise of big data

Theory is a cornerstone of organizational research. Recently, however, some organizational scientists have argued that there is an overemphasis on theory development in our prominent publication outlets, calling for a rejuvenation of empirically driven research. To bring empirical research back to the forefront, the organizational sciences need a shock to the system: the advent of big data analytics in organizations provides just such a shock. The purpose of the following paper is to advocate for big data analytics as tools that can be used to support inductive research methods in the organizational sciences. We then highlight areas of organizational research and practice in which big data analytics can have an impact, provide readers with a tempered perspective on big data in the organizational sciences, and suggest a number of ways that researchers, reviewers, and editors can prepare themselves for the introduction of big data research in the organizational sciences.     

The Intellectual Structure Of Research Into PPPs

Abstract

Public–private partnerships (or PPPs) encompass a broad spectrum of public sector infrastructure and service initiatives. Recently, some scholars have undertaken literature review studies of the various definitions of the concept of PPPs and its research traditions, identifying several distinct PPP research approaches. This article aims to: (1) enhance the findings of these literature reviews; (2) identify the cited works and authors (intellectual structure) in the published research on PPPs; (3) define the subfields that constitute the intellectual structure of PPP research fields. The methodology is based on the bibliometric techniques of citation and author co-citation analysis applied to published research on PPPs included in the Social Science Citation Index.     

A semantic similarity analysis of Internet of Things

ABSTRACT

Internet of Things (IoT) has gradually become one of the most popular topics among both academia and industry, and it is considered as a crucial part of future Internet. However, very few objective and systematic review was conducted to address high-value articles and summarize the intellectual components from journals for examination and identification of the intellectual core and structure of IoT. Therefore, this study conducted a co-citation analysis for IoT by using 68 high-value articles retrieved within 1457 source papers from Web of Science online database. By using factor analysis, ten critical factors were identified, which includes (1) frameworks and challenges of IoT; (2) current situation of IoT in different applications; (3) interactions of IoT; (4) security issues of IoT; (5) application domains of IoT; (6) data management of IoT; (7) IoT in product lifecycle management; (8) enabling technologies of IoT; (9) IoT in smart cities; (10) IoT in recommender systems. Hierarchical Custer Analysis and Multidimensional Scaling were used to graphically illustrate the intellectual elements of IoT. In the current findings, fundamental elements of IoT including architectural framework, enabling technologies, network communication, data management, and IoT interactions were discussed with a series of challenges for wider and deeper IoT applications.     

Using crowdsourced data to analyze transport crime

ABSTRACT

Anecdotal evidence suggests that harsh social conditions in the road haulage industry are having an impact on transport crime. This paper analyses transport crime, and demonstrates how to use a combination of official statistics and crowdsourced data in the process. A hierarchical regression analysis was applied to investigate the relations among different factors in order to predict transport crime threats. A secondary data set on transport crime from the Swedish Police was combined with primary crowdsourced data from volunteer observations of trucks in Sweden from both high-wage and low-wage countries. The findings imply that transportation is more vulnerable to antagonistic threats in geographical areas where the low-wage hauliers operate more frequently. For policymakers and practitioners, these findings provide useful guidance for the planning of security measures. To the authors’ knowledge, this paper is the first exploratory study of its kind that uses a combination of official statistics and crowdsourced data.     

What do we know about business strategy and environmental research? Insights from Business Strategy and the Environment

Business Strategy and the Environment (BSE) is a premier journal dedicated to interdisciplinary research that advances business practice leading to improvements in environmental performance. Using big data analytics, this review examines the intellectual structure and the drivers of research impact of BSE in the scholarly domain. The bibliometric results suggest three major findings. First, the top three countries contributing to BSE are the United Kingdom, the United States, and China. Second, BSE's research manifests through five thematic clusters, namely, business strategy and sustainability; corporate governance and sustainability reporting; green marketing and pro-environmental behavior; innovation and environmental policy; and environmental management systems. Finally, BSE's research impact in terms of citations is significantly influenced by author affiliation (United States); article age (older), appearance (lead article and special issue), length (longer), and method (mix methods); title length (shorter title); and number of keywords (more keywords) and references (more references). Implications for BSE's readers and future contributors are discussed.     

Reinforcing collectiveness in entrepreneurial interactions within start-up teams: a multiple-case study

ABSTRACT

Using a qualitative multiple-case study approach and data from four high-technology team startups, we elaborate a theory on organizing entrepreneurial actions as team efforts and the kinds of interactions that reinforce collectiveness amongst entrepreneurial teams. Through systematic thematic analysis, we find that entrepreneurial action reinforces collectiveness through and during (a) the joint analysis and planning of entrepreneurial opportunities and strategies, (b) the joint decision-making and realization of opportunities and (c) the evaluation, feedback and sanction of entrepreneurial action. We analyse the dimensions through Giddens’s ideas on the duality of structures and agencies. We identify interactions that reflect a joint elaboration of opportunities, open and continuous sharing of knowledge and feelings, equality and democracy, joint effort and credit, informality and lack of bureaucracy, and feedback and helping. Our insights could be applied to create collectively entrepreneurial teams and to design education and training activities at a macro level to enable regional development.     

The wisdom of crowds: The potential of online communities as a tool for data analysis

Online communities have become an important source for knowledge and new ideas. This paper considers the potential of crowdsourcing as a tool for data analysis to address the increasing problems faced by companies in trying to deal with “Big Data”. By exposing the problem to a large number of participants proficient in different analytical techniques, crowd competitions can very quickly advance the technical frontier of what is possible using a given dataset. The empirical setting of the research is Kaggle, the world?s leading online platform for data analytics, which operates as a knowledge broker between companies aiming to outsource predictive modelling competitions and a network of over 100,000 data scientists that compete to produce the best solutions. The paper follows an exploratory case study design and focuses on the efforts by Dunnhumby, the consumer insight company behind the success of the Tesco Clubcard, to find and lever the enormous potential of the collective brain to predict shopper behaviour. By adopting a crowdsourcing approach to data analysis, Dunnhumby were able to extract information from their own data that was previously unavailable to them. Significantly, crowdsourcing effectively enabled Dunnhumby to experiment with over 2000 modelling approaches to their data rather than relying on the traditional internal biases within their R&D units.     

Statistical Inference,Learning and Models in Big Data

The need for new methods to deal with big data is a common theme in most scientific fields, although its definition tends to vary with the context. Statistical ideas are an essential part of this, and as a partial response, a thematic program on statistical inference, learning and models in big data was held in 2015 in Canada, under the general direction of the Canadian Statistical Sciences Institute, with major funding from, and most activities located at, the Fields Institute for Research in Mathematical Sciences. This paper gives an overview of the topics covered, describing challenges and strategies that seem common to many different areas of application and including some examples of applications to make these challenges and strategies more concrete.     

Individual adoption of HR analytics: a fine grained view of the early stages leading to adoption

Abstract

High performing organizations are using analytics for evidence-based decision-making. However, the human resource (HR) function in many organizations has been slow to adopt this innovation. This study applies innovation theory, informed by the Theory of Planned Behavior (TPB), to examine the individual’s decision to adopt HR Analytics in an effort to identify why the adoption rate is lagging. We examined early stages of the individual decision process beginning from Stage 1 (knowledge) and leading to Stage 3, (the decision) to adopt or not to adopt the innovation. We found several points in the process that can act as barriers or facilitators. Organizations and champions of this innovation wishing to facilitate HR analytics adoption can take action to remove as many of these barriers to the individual’s decision as possible. Further research should focus on the best way to remove these barriers.     

The evolution of the intellectual structure of operations management—1980–2006: A citation/co-citation analysis

Citation analysis combined with a network analysis of co-citation data from three major operations management (OM) journals is used to reveal the evolution of the intellectual structure of the OM field between 1980 and 2006. This spans the entire time since the beginning of research journals specific to the field. Employing a bibliometric citation/co-citation analysis to investigate the foundations of the discipline enables a robust, quantitative approach to uncovering the evolution of research in OM. The study finds that the intellectual structure of the field made statistically significant changes between the 1980s, the 1990s, and the 2000s and evolved from a pre-occupation with narrow, tactical topics toward more strategic, macrotopics, including new research methods and techniques. A factor analysis identifies the 12 top knowledge groups in the field and how they change over the decades. Illustrations of the structure of the co-citations representing the field are generated from a spring-embedded algorithm that is an improvement over the standard multi-dimensional scaling (MDS) approach to illustrating the knowledge groups.     

Searching for big data: How incumbents explore a possible adoption of big data technologies

Big data is often described as a new frontier of IT-enabled competitive advantage. A limited number of exemplary firms have been used recurrently in the big data debate to serve as successful illustrations of what big data technologies can offer. These firms are well-known, data-driven organizations that often, but not always, are born digital companies. Comparatively little attention has been paid to the challenges that many incumbent organizations face when they try to explore a possible adoption of such technologies. This study investigates how incumbents handle such an exploration and what challenges they face. Drawing on a four-year qualitative field study of four large Scandinavian firms, we are able to develop a typology of how incumbents handle the exploration of and resistance to adopting big data technologies. Directly affecting the incumbents’ exploration are two aspects that separate the adoption of big data technologies from that of other technologies. First, being an elusive concept, big data technologies can mean different things to different organizations. This makes the technologies difficult to explain before an investing body, while it simultaneously opens up possibilities for creative definitions. Second, big data technologies have a transformative effect on the organization of work in firms. This transformative capability will make managers wary as it might threaten their position in the firm, and it will create ripple effects, transforming other systems besides those directly connected to the technology.     

Big data for cyber physical systems in industry 4.0: a survey

ABSTRACT

With the technology development in cyber physical systems and big data, there are huge potential to apply them to achieve personalization and improve resource efficiency in Industry 4.0. As Industry 4.0 is the relatively new concept originated from an advanced manufacturing vision supported by the German government in 2011, there are only several existing surveys on either cyber physical systems or big data in Industry 4.0. In addition, there are much less surveys related to the intersection between cyber physical systems and big data in Industry 4.0. However, cyber physical systems are closely related to big data in nature. For example, cyber physical systems will continuously generate a large amount of data which requires the big data techniques to process and help to improve system scalability, security, and efficiency. Therefore, we conduct this survey to bring more attention to this critical intersection and highlight the future research direction to achieve the fully autonomy in Industry 4.0.     

Fuzzy model estimation of the risk factors impact on the target of promotion of the software product

ABSTRACT

This article studies the application of fuzzy logic to the risk analysis of a new software product development and marketing in specific case of a small size IT company. Identification and analysis of external and internal risk factors show that this type of business activity could be evaluated as high-risk enterprise. So, the purpose of the paper is to develop robust method to evaluate probability of occurrence of major risk events and their impact on the company financial health. The fuzzy logic is used to estimate degrees of threat of each relevant risk factor due to lack of reliable statistical data. The novelty of proposed approach is the inclusion into the model the risk event time.