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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

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 research aims to explore the research data ethics governance framework and collaborative network to optimize research data ethics governance practices, to balance the relationship between data development and utilization, open sharing, data security and to reduce the ethical risks that may arise from data sharing and utilization.

This study explores the framework and collaborative network of research data ethics policies by using the UK as an example. 78 policies from the UK government, university, research institution, funding agency, publisher, database, library and third-party organization are obtained. Adopting grounded theory (GT) and social network analysis (SNA), Nvivo12 is used to analyze these samples and summarize the research data ethics governance framework. Ucinet and Netdraw are used to reveal collaborative networks in policy.

Results indicate that the framework covers governance context, subject and measure. The content of governance context contains context description and data ethics issues analysis. Governance subject consists of defining subjects and facilitating their collaboration. Governance measure includes governance guidance and ethics governance initiatives in the data lifecycle. The collaborative network indicates that research institution plays a central role in ethics governance. The core of the governance content are ethics governance initiatives, governance guidance and governance context description.

This research provides new insights for policy analysis by combining GT and SNA methods. Research data ethics and its governance are conceptualized to complete data governance and research ethics theory.

A research data ethics governance framework and collaborative network are revealed, and actionable guidance for addressing essential aspects of research data ethics and multiple subjects to confer their functions in collaborative governance is provided.

This study analyzes policy text using qualitative and quantitative methods, ensuring fine-grained content profiling and improving policy research. A typical research data ethics governance framework is revealed. Various stakeholders' roles and priorities in collaborative governance are explored. These contribute to improving governance policies and governance levels in both theory and practice.

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.

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.

The study aims to investigate how enterprise information systems strategies-enabled strategy-making (ISS-SM) influences organizational agility (OA) via the mediated role of IT-enabled dynamic capabilities (ITDC) under environmental dynamism (ED). The study also investigates natural country moderation associated with the business context of the countries where the respondents are located might influence these relationships.

The study aims to investigate how enterprise ISS-SM influences OA via the mediated role of ITDC under ED. The study also investigates natural country moderation associated with the business context of the countries where the respondents are located that might influence these relationships.

The results demonstrate that ISS-SM influences ITDC to gain OA independent of the ED level. Indian and Brazilian firms show no different effects in the relationship of the research model. However, post hoc analysis revealed that strong ISS-SM on OA is fully mediated by ITDC under higher ED with a substantial coefficient of determination, more prominent for Indian firms characterized by young-age and middle-size firms, agribusiness and government sectors.

The fundamental to enabling practice and praxis of the strategy-as-practice approach to OA gains mediated through ITDC in different business context conditions.

The research contributes to extending the literature on the enterprise information systems strategy and information technologies capabilities.

In a context where human–machine interaction is growing, understanding the limits between automated and human-based methods may leverage qualitative research. This paper aims to compare human and machine analyses, highlighting the challenges and opportunities of both approaches.

This study applied qualitative secondary analysis (QSA) with machine learning-based text mining on qualitative data from 25 interviews previously analyzed with traditional qualitative content analysis.

By analyzing both techniques' strengths and weaknesses, this study complements the results from the original research work. The previous human model failed to point to a particular aspect of the case, while the machine analysis did not recognize the sequence of time in the interviewee's discourse.

This study demonstrates that combining content analysis with text mining techniques improves the quality of the research output. Researchers may, therefore, better handle biases from humans and machines in traditional qualitative and quantitative research.