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The increasing prevalence and availability of big data represent a potentially revolutionary development for human resource management (HRM) scholars. Despite this, the current literature provides eclectic and often contradictory guidance for scholars attempting to conceptualize big data and subsequently incorporate it into relevant theoretical frameworks. The authors attempt to bridge this gap by discussing key considerations relevant to understanding and integrating big data into the existing theoretical landscape. Building on a novel, integrative definition of big data, the authors propose a parsimonious theoretical framework utilizing the established dimensions of complexity and dynamism as meta-attributes to bring order to the various attributes that have been proposed as central to defining big data (e.g., volume, variety, velocity, and variability). Throughout, the authors highlight numerous theoretical and empirical opportunities and considerations that this perspective holds for future HRM scholarship.

A recent Supreme Court of Canada (SCC) ruling resulted in stricter rules being placed on how police organizations can obtain confessions through a controversial undercover operation, known as the Mr. Big technique. The SCC placed the onus on prosecutors to demonstrate that the probative value of any Mr. Big derived confession outweighs its prejudicial effect, and that the police must refrain from an abuse of process (i.e. avoid overcoming the will of the accused to obtain a confession). The purpose of this paper is to determine whether a consideration of the social influence tactics present in the Mr. Big technique would deem Mr. Big confessions inadmissible.

The social psychological literature related to the compliance and the six main principles of social influence (i.e. reciprocity, consistency, liking, social proof, authority, scarcity) was reviewed. The extent to which these social influence principles are arguably present in Mr. Big operations are discussed.

Mr. Big operations, by their very nature, create unfavourable circumstances for the accused that are rife with psychological pressure to comply and ultimately confess. A consideration by the SCC of the social influence tactics used to elicit confessions – because such tactics sully the circumstances preceding confessions and verge on abuse of process – should lead to all Mr. Big operations being prohibited.

Concerns regarding the level of compliance in the Mr. Big technique call into question how Mr. Big operations violate the guidelines set out by the SCC ruling. The findings from the current paper could have a potential impact of the admissibility of Mr. Big confessions, along with continued use of this controversial technique.

The current paper represents the first in-depth analysis of the Mr. Big technique through a social psychological lens.

Using analytics for firms' competitiveness is a vital component of a company's strategic planning and management process. In recent years, organizations have started to capitalize on the significant use of big data for analyses to gain valuable insights to improve decision-making processes. In this regard, leveraging and unleashing the potential of big data has become a significant success factor for steering firms' competitiveness, and the related literature is increasing at a very high pace. Thus, the authors propose a bibliometric study to understand the most important insights from these studies and enrich existing conceptual models.

In this study, the authors use a bibliometric review on articles related to the use of big data for firms' competitiveness. The authors examine the contributions of research constituents (authors, institutions, countries and journals) and their structural and thematic relationships (collaborations, co-citations networks, co-word networks, thematic trends and thematic map). The most important insights are used to enrich a conceptual model.

Based on the performance analysis results, the authors found that China is by far the most productive country in this research field. However, in terms of influence (by the number of citations per article), the most influential countries are the UK, Australia and the USA, respectively. Based on the science mapping analysis results, the most important findings are projected in the common phases of competitive intelligence processes and include planning and directions concepts, data collection concepts, data analysis concepts, dissemination concepts and feedback concepts. This projection is supplemented by cross-cutting themes such as digital transformation, cloud computing, privacy, data science and competition law. Three main future research directions are identified: the broadening of the scope of application fields, the specific case of managing or anticipating the consequences of pandemics or high disruptive events such as COVID-19 and the improvement of connection between firms' competitiveness and innovation practices in a big data context.

The findings of this study show that the most important research axis in the existing literature on big data and firms' competitiveness are mostly related to common phases of competitive intelligence processes. However, concepts in these phases are strongly related to the most important dimensions intrinsic to big data. The use of a single database (Scopus) or the selected keywords can lead to bias in this study. Therefore, to address these limitations, future studies could combine different databases (i.e. Web of Science and Scopus) or different sets of keywords.

This study can provide to practitioners the most important concepts and future directions to deal with for using big data analytics to improve their competitiveness.

This study can help researchers or practitioners to identify potential research collaborators or identify suitable sources of publications in the context of big data for firms' competitiveness.

The authors propose a conceptual model related to big data and firms' competitiveness from the outputs of a bibliometric study.

The purpose of this paper is to analyze the impact of smart city initiatives and big data on supply chain management (SCM). More specifically, the connections between smart cities, big data and supply network characteristics (supply network structure and governance mechanisms) are investigated.

An integrative framework is proposed, grounded on a literature review on smart cities, big data and supply networks. Then, the relationships between these constructs are analyzed, using the proposed integrative framework.

Smart cities have different implications to network structure (complexity, density and centralization) and governance mechanisms (formal vs informal). Moreover, this work highlights and discusses the future research directions relating to smart cities and SCM.

The relationships between smart cities, big data and supply networks cannot be described simply by using a linear, cause-and-effect framework. Accordingly, an integrative framework that can be used in future empirical studies to analyze smart cities and big data implications on SCM has been proposed.

Smart cities and big data alone have limited capacity of improving SCM processes, but combined they can support improvement initiatives. Nevertheless, smart cities and big data can also suppose some novel obstacles to effective SCM.

Several studies have analyzed information technology innovation adoption in supply chains, but, to the best of our knowledge, no study has focused on smart cities.

The purpose of this paper is to propose a new conceptual framework for big data analytics (BDA) in the healthcare sector for the European Mediterranean region. The objective of this new conceptual framework is to improve the health conditions in a dynamic region characterized by the appearance of new diseases.

This study presents a new conceptual framework that could be employed in the European Mediterranean healthcare sector. Practically, this study can enhance medical services, taking smart decisions based on accurate data for healthcare and, finally, reducing the medical treatment costs, thanks to data quality control.

This research proposes a new conceptual framework for BDA in the healthcare sector that could be integrated in the European Mediterranean region. This framework introduces the big data quality (BDQ) module to filter and clean data that are provided from different European data sources. The BDQ module acts in a loop mode where bad data are redirected to their data source (e.g. European Centre for Disease Prevention and Control, university hospitals) to be corrected to improve the overall data quality in the proposed framework. Finally, clean data are directed to the BDA to take quick efficient decisions involving all the concerned stakeholders.

This study proposes a new conceptual framework for executives in the healthcare sector to improve the decision-making process, decrease operational costs, enhance management performance and save human lives.

This study focused on big data management and BDQ in the European Mediterranean healthcare sector as a broadly considered fundamental condition for the quality of medical services and conditions.

Big data is a key component to realise the vision of smart factories, but the implementation and usage of big data analytical tools in the smart factory context can be fraught with challenges and difficulties. The purpose of this paper is to identify potential barriers that hinder organisations from applying big data solutions in their smart factory initiatives, as well as to explore causal relationships between these barriers.

The study followed an inductive and exploratory nature. Ten in-depth semi-structured interviews were conducted with a group of highly experienced SAP consultants and project managers. The qualitative data collected were then systematically analysed by using a thematic analysis approach.

A comprehensive set of barriers affecting the implementation of big data solutions in smart factories had been identified and divided into individual, organisational and technological categories. An empirical framework was also developed to highlight the emerged inter-relationships between these barriers.

This study built on and extended existing knowledge and theories on smart factory, big data and information systems research. Its findings can also raise awareness of business managers regarding the complexity and difficulties for embedding big data tools in smart factories, and so assist them in strategic planning and decision making.

As various different and even contradictory concepts are proposed to depict a firm's capabilities related to big data, and extant relevant research is fragmented and scattered in several disciplines, there is currently a lack of holistic and comprehensive understanding of how big data alters value creation by facilitating firm capabilities. To narrow this gap, this study aims to synthesize current knowledge on the firm capabilities and transformation of value creation facilitated by big data.

The authors adopt an inductive and rigorous approach to conduct a systematic review of 185 works, following the “Grounded Theory Literature-Review Method”.

The authors introduce and develop the concept of big data competency, present an inductive framework to open the black box of big data competency following the logic of virtual value chain, provide a structure of big data competency that consists of two dimensions, namely, big data capitalization and big data exploitation, and further explain the evolution of value creation structure from value chain to value network by connecting the attributes of big data competency (i.e. connectivity and complementarity) with the transformation of value creation (i.e. optimizing and pioneering).

The big data competency, an inclusive concept of firm capabilities to deal with big data, is proposed. Based on this concept, the authors highlight the significant contributions that extant research has made toward our understanding of how big data alters value creation by facilitating firm capabilities. Besides, the authors provide a future research agenda that academics can rely on to study the strategic management of big data.

With the advent of the fourth industrial revolution and the intelligent economy, this conceptual chapter explores the evolution of educational governance from one based on governing by numbers and evidence-based governance to one constituted around governance by data or data-based educational governance. With the rise of markets and networks in education, Big Data, machine data, high-dimension data, open data, and dark data have consequences for the governance of national educational systems. In doing so, it draws attention to the rise of the algorithmization and computerization of educational policy-making. The author uses the concept of “blitzscaling”, aided by the conceptual framing of assemblage theory, to suggest that we are witnessing the rise of a fragmented model of educational governance. I call this governance with a “big G” and governance with a “small g.” In short, I suggest that while globalization has led to the deterritorializing of the national state, data educational governance, an assemblage, is bringing about the reterritorialization of things as new material projects are being reconstituted.

The purpose of this paper is to examine how managers orchestrate, bundle and leverage resources from big data for value creation in emerging economies.

The authors grounded the theoretical framework in two perspectives: the resource management and entrepreneurial orientation (EO). The study utilizes an inductive, multiple-case research design to understand the process of creating value from big data.

The findings suggest that EO is vital through which companies based in emerging economies can create value through big data by bundling and orchestrating resources thus improving performance.

This is one of the first studies to have integrated resource orchestration theory and EO in the context of big data and explicate the utility of such theoretical integration in understanding the value creation strategies through big data in the context of emerging economies.