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Prior research has revealed that a large share of managers is reluctant towards the use of artificial intelligence (AI) in decision-making. This aversion can be caused by several factors, including individual drivers. The purpose of this paper is to better understand the extent to which individual factors influence managers’ attitudes towards the use of AI and, based on these findings, to propose solutions for increasing AI adoption.

The paper builds on prior research, especially on the factors driving the adoption of AI in companies. In addition, data was collected by means of 16 expert interviews using a semi-structured interview guideline.

The study concludes on four groups of individual factors ranked according to their importance: demographics, familiarity, psychology and personality. Moreover, the findings emphasized the importance of communication and training, explainability and transparency and participation in the process to foster the adoption of AI in decision-making.

The paper identifies four ways to foster AI integration for organizational decision-making as areas for further empirical analysis by business researchers.

This paper offers four ways to foster AI adoption for organizational decision-making: explaining the benefits and training the more adverse categories, explaining how the algorithms work and being transparent about the shortcomings, striking a good balance between automated and human-made decisions, and involving users in the design process.

The study concludes on four groups of individual factors ranked according to their importance: demographics, familiarity, psychology and personality. Moreover, the findings emphasized the importance of communication and training, explainability and transparency and participation in the process to foster the adoption of AI in decision-making.

This study is one of few to conduct qualitative research into the individual factors driving usage intention among managers; hence, providing more in-depth insights about managers’ attitudes towards algorithmic decision-making. This research could serve as guidance for developers developing algorithms and for managers implementing and using algorithms in organizational decision-making.

The purpose of this study is to identify, analyze and explain the ethical implications that can result from the datafication of service.

This study uses a midrange theorizing approach to integrate currently disconnected perspectives on technology-enabled service, data-driven business models, data ethics and business ethics to introduce a novel analytical framework centered on data-driven business models as the general metatheoretical unit of analysis. The authors then contextualize the framework using data-intensive insurance services.

The resulting midrange theory offers new insights into how using machine learning, AI and big data sets can lead to unethical implications. Centered around 13 ethical challenges, this work outlines how data-driven business models redefine the value network, alter the roles of individual actors as cocreators of value, lead to the emergence of new data-driven value propositions, as well as novel revenue and cost models.

Future research based on the framework can help guide practitioners to implement and use advanced analytics more effectively and ethically.

At a time when future technological developments related to AI, machine learning or other forms of advanced data analytics are unpredictable, this study instigates a critical and timely discourse within the service research community about the ethical implications that can arise from the datafication of service by introducing much-needed theory and terminology.

The purpose of this paper is to introduce the papers in this special issue on humans, algorithms and data. The authors first set themselves the task of identifying the main challenges arising from the adoption and use of algorithms and data analytics in management, accounting and organisations in general, many of which have been described in the literature.

This paper builds on previous literature and case studies of the application of algorithm logic with artificial intelligence as an exemplar of this innovation. Furthermore, this paper is triangulated with the findings of the papers included in this special issue.

Based on prior literature and the concepts set out in the papers published in this special issue, this paper proposes a conceptual framework that can be useful both in the analysis and ordering of the algorithm hype, as well as to identify future research avenues.

The value of this framework, and that of the papers in this special issue, lies in its ability to shed new light on the (neglected) connections and relationships between algorithmic applications, such as artificial intelligence. The framework developed in this piece should stimulate scholars to explore the intersections between “technical” as well as organisational, social and individual issues that algorithms should help us tackle.

Entrepreneurs are increasingly relying on artificial intelligence (AI) to assist in creating and scaling new ventures. Research on entrepreneurs’ use of AI algorithms (machine learning, natural language processing, artificial neural networks) has focused on the intra-organizational implications of AI. The purpose of this paper is to explore how entrepreneurs’ adoption of AI influences their inter- and meta-organizational relationships.

To address the limited understanding of the consequences of AI for communities of entrepreneurs, this paper develops a theory to explain how AI algorithms influence the micro (entrepreneur) and macro (system) dynamics of entrepreneurial ecosystems.

The theory’s main insight is that substituting AI for entrepreneurial ecosystem interactions influences not only entrepreneurs’ pursuit of opportunities but also the coordination of their local entrepreneurial ecosystems.

The theory contributes by drawing attention to the inter-organizational implications of AI, explaining how the decision to substitute AI for human interactions is a micro-foundation of ecosystems, and motivating a research agenda at the intersection of AI and entrepreneurial ecosystems.

This study aims to examine hotel guests’ acceptance of technology agency – the extent to which they would let artificial intelligence (AI)-based systems make decisions for them when staying in hotels. The examination was conducted through the prism of several antecedents of acceptance of technology agency, including perceived ethics, benefits, risks and convenience orientation.

A thorough literature review provided the foundation of the structural model, which was tested using confirmatory factor analysis, followed by structural equation modeling. Data were collected from 400 US hotel guests.

The most important determinant of acceptance of technology agency was perceived ethics, followed by benefits. Risks of using AI-based systems to make decisions for consumers had a negative impact on acceptance of technology agency. In addition, perceived loss of competence and unpredictability had relatively strong impacts on risks.

The results provide a conceptual foundation for research on systems that make decisions for consumers. As AI is increasingly incorporated in the business models of hotel companies to make decisions, ensuring that the decisions are perceived as ethical and beneficial for consumers is critical to increase the utilization of such systems.

Most research on AI in hospitality is either conceptual or focuses on consumers’ intentions to stay in hotels that may be equipped with AI technologies. Occupying a unique position within the literature, this study discusses the first time AI-based systems that make decisions for consumers. The value of this study stems from the examination of the main concept of technology agency, which was never examined in hospitality.

The exponential growth of artificial intelligence (AI) technologies, coupled with advanced algorithms and increased computational capacity, has facilitated their widespread adoption in various industries. Among these, the financial technology (FinTech) sector has been significantly impacted by AI-based decision-making systems. Nevertheless, a knowledge gap remains regarding the intricate mechanisms behind the micro-decision-making process employed by AI algorithms. This paper aims to discuss the aforementioned issue.

This research utilized a sequential mixed-methods research approach and obtained data through 18 interviews conducted with a single FinTech firm in France, as well as 148 e-surveys administered to participants employed at different FinTechs located throughout Europe.

Three main themes (ambidexterity, data sovereignty and model explainability) emerge as underpinnings for effective AI micro decision-making in FinTechs.

This research aims to minimize ambiguity by putting forth a proposition for a model that functions as an “infrastructural” layer, providing a more comprehensive illumination of the micro-decisions made by AI.

This research pioneers as the very first empirical exploration delving into the essential factors that underpin effective AI micro-decisions in FinTechs.

Artificial intelligence (AI) has received much attention due to its promethean-like powers to transform the management and delivery of public sector services. Due to the proliferation of research articles in this context, research to date is fragmented into research streams based on different types of AI technologies or a specific government function of the public sector (e.g. health, education). The purpose of this study is to synthesize this literature, identify challenges and opportunities, and offer a research agenda that guides future inquiry.

This paper aggregates this fragmented body of knowledge by conducting a systematic literature review of AI research in public sector organisations in the Chartered Association of Business Schools (CABS)-ranked journals between 2012 and 2023.

The search strategy resulted in the retrieval of 2,870 papers, of which 61 were identified as primary papers relevant to this research. These primary papers are mapped to the ten classifications of the functions of government as classified by the Organisation for Economic Co-operation and Development (OECD), and the reported challenges and benefits aggregated.

This study advances knowledge by providing a state-of-the-art of AI research based the OECD classifications of government functions, reporting of claimed benefits and challenges and providing a research agenda for future research.

This paper aims to identify, synthesise and critically examine the extant academic research on the relation between big data analytics (BDA), corporate accountability and non-financial disclosure (NFD) across several disciplines.

This paper uses a structured literature review methodology and applies “insight-critique-transformative redefinition” framework to interpret the findings, develop critique and formulate future research directions.

This paper identifies and critically examines 12 research themes across four macro categories. The insights presented in this paper indicate that the nature of the relationship between BDA and accountability depends on whether an organisation considers BDA as a value creation instrument or as a revenue generation source. This paper discusses how NFD can effectively increase corporate accountability for ethical, social and environmental consequences of BDA.

This paper presents the results of a structured literature review exploring the state-of-the-art of academic research on the relation between BDA, NFD and corporate accountability. This paper uses a systematic approach, to provide an exhaustive analysis of the phenomenon with rigorous and reproducible research criteria. This paper also presents a series of actionable insights of how corporate accountability for the use of big data and algorithmic decision-making can be enhanced.

This paper discusses how NFD can reduce negative social and environmental impact stemming from the corporate use of BDA.

To the best of the authors’ knowledge, this paper is the first one to provide a comprehensive synthesis of academic literature, identify research gaps and outline a prospective research agenda on the implications of big data technologies for NFD and corporate accountability along social, environmental and ethical dimensions.

As algorithms permeate nearly every aspect of digital life, artificial intelligence (AI) systems exert a growing influence on human behavior in the digital milieu. Despite its popularity, little is known about the roles and effects of algorithmic literacy (AL) on user acceptance. The purpose of this study is to contextualize AL in the AI environment by empirically examining the role of AL in developing users' information processing in algorithms. The authors analyze how users engage with over-the-top (OTT) platforms, what awareness the user has of the algorithmic platform and how awareness of AL may impact their interaction with these systems.

This study employed multiple-group equivalence methods to compare two group invariance and the hypotheses concerning differences in the effects of AL. The method examined how AL helps users to envisage, understand and work with algorithms, depending on their understanding of the control of the information flow embedded within them.

Our findings clarify what functions AL plays in the adoption of OTT platforms and how users experience algorithms, particularly in contexts where AI is used in OTT algorithms to provide personalized recommendations. The results point to the heuristic functions of AL in connection with its ties in trust and ensuing attitude and behavior. Heuristic processes using AL strongly affect the credibility of recommendations and the way users understand the accuracy and personalization of results. The authors argue that critical assessment of AL must be understood not just about how it is used to evaluate the trust of service, but also regarding how it is performatively related in the modeling of algorithmic personalization.

The relation of AL and trust in an algorithm lends strategic direction in developing user-centered algorithms in OTT contexts. As the AI industry has faced decreasing credibility, the role of user trust will surely give insights on credibility and trust in algorithms. To better understand how to cultivate a sense of literacy regarding algorithm consumption, the AI industry could provide examples of what positive engagement with algorithm platforms looks like.

User cognitive processes of AL provide conceptual frameworks for algorithm services and a practical guideline for the design of OTT services. Framing the cognitive process of AL in reference to trust has made relevant contributions to the ongoing debate surrounding algorithms and literacy. While the topic of AL is widely recognized, empirical evidence on the effects of AL is relatively rare, particularly from the user's behavioral perspective. No formal theoretical model of algorithmic decision-making based on the dual processing model has been researched.

To remain competitive, efficient and productive, organisations need to ensure that their employees continuously learn and develop. This is even more challenging and critical in times characterised by volatility, uncertainty, complexity and ambiguity (VUCA). Hence, several technological applications have been introduced with the promise to make organisational training and development (T&D) more efficient and targeted through digitisation and automation. However, digitising and automating processes in the sensitive field of T&D also poses challenges and perils for employees and organisations as a whole.

Structured by the T&D process of (1) assessment/planning, (2) design/implementation and (3) evaluation, the authors present different digitisation and automation possibilities and discuss the specific opportunities and challenges they pose. Subsequently, the authors identify and discuss overarching themes of opportunities and challenges of technology use in T&D via a meta-review.

This synthesis revealed three central topics that decision-makers in T&D should carefully consider when it comes to the implementation of technological applications: opportunities and challenges of (1) data collection, (2) decision-making and (3) the value of human contact.

This review integrates previously fragmented research on specific technologies applied to specific T&D functions and provides researchers and practitioners with a fuller picture of the opportunities and challenges of technology applied in T&D.