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Gender bias in artificial intelligence (AI) should be solved as a priority before AI algorithms become ubiquitous, perpetuating and accentuating the bias. While the problem has been identified as an established research and policy agenda, a cohesive review of existing research specifically addressing gender bias from a socio-technical viewpoint is lacking. Thus, the purpose of this study is to determine the social causes and consequences of, and proposed solutions to, gender bias in AI algorithms.

A comprehensive systematic review followed established protocols to ensure accurate and verifiable identification of suitable articles. The process revealed 177 articles in the socio-technical framework, with 64 articles selected for in-depth analysis.

Most previous research has focused on technical rather than social causes, consequences and solutions to AI bias. From a social perspective, gender bias in AI algorithms can be attributed equally to algorithmic design and training datasets. Social consequences are wide-ranging, with amplification of existing bias the most common at 28%. Social solutions were concentrated on algorithmic design, specifically improving diversity in AI development teams (30%), increasing awareness (23%), human-in-the-loop (23%) and integrating ethics into the design process (21%).

This systematic review is the first of its kind to focus on gender bias in AI algorithms from a social perspective within a socio-technical framework. Identification of key causes and consequences of bias and the breakdown of potential solutions provides direction for future research and policy within the growing field of AI ethics.

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-08-2021-0452

In the paper, “TV‐trackmeter”, a stereoscopic measuring system developed by Tecnomare, is presented, some recent innovations and upgrading are described, and its reliable use in hostile environments proved. The latest release of the device implements highlighted featuring capabilities such as 3D measuring, automatic mapping, false colour depth‐maps, geometric modelling, multi‐point tracking, recording/retrieving of stereo pair images, and use of new and more powerful hardware. A theoretical introduction to the operating mode of a stereoscopic device, followed by an error propagation analysis is included. A brief description is also given of the accuracy of the device, i.e. pose detection (position and attitude estimation) of the scene objects. An evaluation of the tracking speed capability is provided. Some examples are shown of trials carried out within a nuclear power plant and underwater. Two further applications for this system are described.

There is a growing need for methodological plurality in advancing operations management (OM), especially with the emergence of machine learning (ML) techniques for analysing extensive textual data. To bridge this knowledge gap, this paper introduces a new methodology that combines ML techniques with traditional qualitative approaches, aiming to reconstruct knowledge from existing publications.

In this pragmatist-rooted abductive method where human-machine interactions analyse big data, the authors employ topic modelling (TM), an ML technique, to enable constructivist grounded theory (CGT). A four-step coding process (Raw coding, expert coding, focused coding and theory building) is deployed to strive for procedural and interpretive rigour. To demonstrate the approach, the authors collected data from an open-source professional project management (PM) website and illustrated their research design and data analysis leading to theory development.

The results show that TM significantly improves the ability of researchers to systematically investigate and interpret codes generated from large textual data, thus contributing to theory building.

This paper presents a novel approach that integrates an ML-based technique with human hermeneutic methods for empirical studies in OM. Using grounded theory, this method reconstructs latent knowledge from massive textual data and uncovers management phenomena hidden from published data, offering a new way for academics to develop potential theories for business and management studies.

This study aims to provide a picture of the current state of art in the use of big data for decision-making processes for the management of health-care organizations.

A systematic literature review was carried out. The research uses two analyses: descriptive analysis, describing the evolution of citations; keywords; and the ten most influential papers, and bibliometric analysis, for content evaluation, for which a cluster analysis was performed.

A total of 48 articles were selected for bibliographic coupling out of an initial sample of more than 5,000 papers. Of the 48 articles, 29 are linked on the basis of their bibliography. Clustering the 29 articles on the basis of actual content, four research areas emerged: quality of care, quality of service, crisis management and data management.

Health-care organizations believe strongly that big data can become the most effective tool for correctly influencing the decision-making processes. Thus, more and more organizations continue to invest in big data analytics, and the literature on this topic has expanded rapidly. This study seeks to provide a comprehensive picture of the different streams of literature existing, together with gaps in research and future perspectives. The literature is mature enough for an analysis to be made and provide managers with useful insights on opportunities, criticisms and perspectives on the use of big data for health-care organizations. However, to date, there is no comprehensive literature review on the big data analysis in health care. Furthermore, as big data is a “sexy catchphrase,” more clarity on its usage may be needed. It represents an important tool to be investigated and its great potential is often yet to be discovered. This study thus sheds light on emerging issues and suggests further research that may be needed.

Enabled by increased (“big”) data stocks and advanced (“machine learning”) analyses, the concept of human resource analytics (HRA) is expected to systematically improve decisions in human resource management (HRM). Since so far empirical evidence on this is, however, lacking, the authors' study examines which combinations of data and analyses are employed and which combinations deliver on the promise of improved decision quality.

Theoretically, the paper employs a neo-configurational approach for founding and conceptualizing HRA. Methodically, based on a sample of German organizations, two varieties (crisp set and multi-value) of qualitative comparative analysis (QCA) are employed to identify combinations of data and analyses sufficient and necessary for HRA success.

The authors' study identifies existing configurations of data and analyses in HRM and uncovers which of these configurations cause improved decision quality. By evidencing that and which combinations of data and analyses conjuncturally cause decision quality, the authors' study provides a first confirmation of HRA success.

Major limitations refer to the cross-sectional and national sample and the usage of subjective measures. Major implications are the suitability of neo-configurational approaches for future research on HRA, while deeper conceptualizing and researching both the characteristics and outcomes of HRA constitutes a core future task.

The authors' paper employs an innovative theoretical-methodical approach to explain and analyze conditions that conjuncturally cause decision quality therewith offering much needed empirical evidence on HRA success.

The purpose of this study is to explore the potential and growth of big data across several industries between 2016 and 2020. This study aims to analyze the behavior of interest in big data within the community and to identify areas with the greatest potential for future big data adoption.

This research uses Google Trends to characterize the community’s interest in big data. Community interest is measured on a scale of 0–100 from weekly observations over the past five years. A total of 16 industries were considered to explore the relative interest in big data for each industry.

The findings revealed that big data has been of high interest to the community over the past five years, particularly in the manufacturing, computers and electronics industries. However, over the 2020s the interest in the theme decreased by more than 15%, especially in the areas where big data typically had the greatest potential interest. In contrast, areas with less potential interest in big data such as real estate, sport and travel have registered an average growth of less than 10%.

To the best of the author’s knowledge, this study is original in complementing the traditional survey approaches launched among the business communities to discover the potential of big data in specific industries. The knowledge of big data growth potential is relevant for players in the field to identify saturation and emerging opportunities for big data adoption.

This paper aims to propose a research model with eight constructs, i.e. BDA leadership, BDA talent quality, BDA security quality, BDA privacy quality, innovation, financial performance, market performance and customer satisfaction.

The research model focuses on whether (1) Big Data Analytics (BDA) leadership influences BDA talent quality, (2) BDA talent quality influences BDA security quality, (3) BDA talent quality influences BDA privacy quality, (4) BDA talent quality influences Innovation and (5) innovation influences a firm's performance (financial, market and customer satisfaction). An instrument was designed and administered electronically to a diverse set of employees (N = 188) in various organizations in the USA. Collected data were analyzed through a partial least square structural equation modeling.

Results showed that leadership significantly and positively affects BDA talent quality, which, in turn, significantly and positively impacts security quality, privacy quality and innovation. Moreover, innovation significantly and positively impacts firm performance. The theoretical and practical implications of the findings are discussed. Recommendations for future research are provided.

The study provides empirical evidence that leadership significantly and positively impacts BDA talent quality. BDA talent quality, in turn, positively impacts security quality, privacy quality and innovation. This is important, as these are all critical factors for organizations that collect and use big data. Finally, the study demonstrates that innovation significantly and positively impacts financial performance, market performance and customer satisfaction. The originality of the research results makes them a valuable addition to the literature on big data analytics. They provide new insights into the factors that drive organizational success in this rapidly evolving field.

One of the leading factors that shape product and service delivery are data collected in databases and other repositories maintained by companies. The transformation of such data into knowledge and wisdom may constitute a new source of income. This paper aims to explore how small/medium-sized enterprises (SMEs) advance their business models (BMs) around data to handle data-driven products and how this contributes to their innovativeness and performance.

To investigate the phenomenon, the as-is BM of a multinational SME was mapped and its limitations were revealed through a qualitative study. The BM canvas was used. Then the data-driven approach was innovated within the facility management (FM) industry, where a high volume of operational and sensor-based data being collected creates added value in terms of new data-based products.

A data-driven business model (DDBM) blueprint for the FM industry that supports the need to complement service-driven operations with the data-driven approach is delivered. Enhanced BM equips a facility manager with additional managerial tools that enable decreasing property utilization costs and opens up new opportunities for generating revenue. This paper drafts the way to evolve from service to data-driven business and point out the attitudes that managers should adopt to promote and implement DDBM.

The DDBM constitutes a guideline that supports FM organizations in focusing their activities and resources on generating business value from data and monetizing data-driven products.

The research expands knowledge regarding BMs and their evolution. The gap regarding the DDBM innovation within the FM industry is filled.

Industry 4.0 (I4.0) not only turns traditional industrial activities upside down but also demonstrates its potential to enhance industrial competitiveness and productivity. In this context, technological advancement and I4.0 is a strategy to be pursued. This study aims to consider different I4.0 technologies by analysing Indian small and medium enterprises (SMEs).

Key factors and promising I4.0 technologies were selected using literature analysis and experts’ panel. The appropriate I4.0 technology for Indian SMEs is recommended using the fuzzy complex proportional assessment (COPRAS) method.

Results reveal that ability to expand IT infrastructure, change in the organization’s structure and the capacity to analyse key performance indicators as three crucial key factors in I4.0 implementation. In particular, the smart factory is identified as a better I4.0 for Indian SMEs.

This work has analysed Indian SMEs, but it is appropriate for other developing economies with limited technical resources, financial resources and inadequate skill sets. This work identifies a gap in the current literature, and the findings proposed by this work are oriented to assist decision makers, industrial managers and practitioners in selecting I4.0 technology and enhancing the industrial infrastructure. At the same time, cooperation between the government and industrial community is required to develop programmes for imparting the knowledge of I4.0 among SMEs. The framework used in this study will arm the industrial management in adopting I4.0.

The article aims to elucidate how embracing Tropicália's conceptual framework can foster a more fluid and adaptive approach to organizing, transcending traditional boundaries and embracing diversity, innovation and creativity. The analysis encompasses various facets of organizational dynamics, including holdership, professional praxis, organizational ambiance, knowledge dissemination and diversity promotion. By examining Tropicália's reverberations in these areas, this article seeks to provide insights and perspectives that can contribute to the literature on organizational theory and practice, offering a rejuvenated and contemporaneous approach to the art of organizing.

This article explores the conceptual architecture of Tropicália, a Brazilian cultural and artistic movement, and its potential impact on contemporary organizational structures. By embracing Tropicália's essence, organizations can cultivate an adaptable and diverse ethos, free from traditional constraints. This analysis encompasses holdership as sustenance, professional praxis, organizational ambiance, knowledge dissemination and diversity promotion. Tropicália's potential to foster engagement, fuel innovation and shape an inclusive culture is examined. This article contributes a contemporary perspective to organizational theory, emphasizing the importance of integrating Tropicália's intellectual fabric for navigating the modern business landscape and fostering creativity and innovation.

The findings of this study highlight the potential impact of Tropicália on contemporary organizational practices. By embracing Tropicália's conceptual framework, organizations can foster a more fluid and adaptive approach to organizing, transcending traditional boundaries and embracing diversity, innovation and creativity. Tropicália's immersive and transformative esthetic experiences can create dynamic and inclusive organizational environments that encourage individual agency and stakeholder engagement. The analysis encompasses implications for holdership and management practices, organizational culture, collaboration and knowledge sharing, diversity and inclusion, innovation and creativity. Tropicália has the potential to foster employee engagement, drive innovation and create a more inclusive and adaptive organizational culture.

This article provides originality and value by exploring the potential ramifications of Tropicália on contemporary organizational esthetics. It offers a fresh and contemporary perspective on the art of organizing by drawing upon the unique conceptual framework of Tropicália. By embracing the principles of Tropicália, organizations can cultivate an organizational ethos that goes beyond traditional boundaries, fostering adaptability, diversity and innovation. The analysis encompasses aspects of organizational practices, including holdership, professional praxis, organizational culture and diversity and inclusiveness. The findings contribute to the existing literature on organizational theory and praxis, offering a rejuvenated perspective on organizing in the modern business landscape.