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Inscrutable machine learning (ML) models are part of increasingly many information systems. Understanding how these models behave, and what their output is based on, is a challenge for developers let alone non-technical end users.

The authors investigate how AI systems and their decisions ought to be explained for end users through a systematic literature review.

The authors’ synthesis of the literature suggests that AI system communication for end users has five high-level goals: (1) understandability, (2) trustworthiness, (3) transparency, (4) controllability and (5) fairness. The authors identified several design recommendations, such as offering personalized and on-demand explanations and focusing on the explainability of key functionalities instead of aiming to explain the whole system. There exists multiple trade-offs in AI system explanations, and there is no single best solution that fits all cases.

Based on the synthesis, the authors provide a design framework for explaining AI systems to end users. The study contributes to the work on AI governance by suggesting guidelines on how to make AI systems more understandable, fair, trustworthy, controllable and transparent.

This literature review brings together the literature on AI system communication and explainable AI (XAI) for end users. Building on previous academic literature on the topic, it provides synthesized insights, design recommendations and future research agenda.

Research into the interpretability and explainability of data analytics and artificial intelligence (AI) systems is on the rise. However, most recent studies either solely promote the benefits of explainability or criticize it due to its counterproductive effects. This study addresses this polarized space and aims to identify opposing effects of the explainability of AI and the tensions between them and propose how to manage this tension to optimize AI system performance and trustworthiness.

The author systematically reviews the literature and synthesizes it using a contingency theory lens to develop a framework for managing the opposing effects of AI explainability.

The author finds five opposing effects of explainability: comprehensibility, conduct, confidentiality, completeness and confidence in AI (5Cs). The author also proposes six perspectives on managing the tensions between the 5Cs: pragmatism in explanation, contextualization of the explanation, cohabitation of human agency and AI agency, metrics and standardization, regulatory and ethical principles, and other emerging solutions (i.e. AI enveloping, blockchain and AI fuzzy systems).

As in other systematic literature review studies, the results are limited by the content of the selected papers.

The findings show how AI owners and developers can manage tensions between profitability, prediction accuracy and system performance via visibility, accountability and maintaining the “social goodness” of AI. The results guide practitioners in developing metrics and standards for AI explainability, with the context of AI operation as the focus.

This study addresses polarized beliefs amongst scholars and practitioners about the benefits of AI explainability versus its counterproductive effects. It poses that there is no single best way to maximize AI explainability. Instead, the co-existence of enabling and constraining effects must be managed.

Information and communication technologies (ICTs) are reshaping many industries, often by reshaping how information is shared. However, while the effects and uses of ICT are often associated with organizations (and industries), their use occurs at the individual level. To explore the relationships between individual uses of ICT and changes to organization and industry structures, we examined the residential real estate industry. As agents, buyers and sellers increase their uses of ICT, they also change how they approach their daily work. The increasing uses of ICT are simultaneously altering industry structures by subverting some of the realtors’ control over information while also reinforcing the existing contract‐based structures. This structurational perspective and our findings help to explain why information intermediaries persist when technology‐based perspectives would suggest their disappearance.

The purpose of this paper is to provide theoretical and practical insight into the process of crossover with the proposition that affect intensity is an important explanatory mechanism of crossover.

This paper provides an empirical and conceptual overview of the construct of crossover, and addresses key gaps in the literature by proposing a process of discrete emotional crossover. It is proposed that individual differences in affect intensity may moderate and/or explain the crossover of discrete emotions in the workplace.

This paper responds to the call of various researchers within the crossover field by putting forth a unique explanation for the occurrence of crossover. This explanation draws significantly on emotions theory and research.

This paper is unique in its presentation of affect intensity as a moderator of the crossover process and in its discussion of the crossover of discrete emotions such as joy and fear rather than the crossover of emotional or psychological states.

This study aims to explore the collaborative potential of Microsoft Copilot and Anthropic Claude AI as an assistive technology in education and library services. The research delves into technical architectures and various use cases for both tools, proposing integration strategies within educational and library environments. The paper also addresses challenges such as algorithmic bias, hallucination and data rights.

The study used a literature review approach combined with the proposal of integration strategies across education and library settings.

The collaborative framework between Copilot and Claude AI offers a comprehensive solution for transforming education and library services. The study identifies the seamless combination of real-time internet access, information retrieval and advanced comprehension features as key findings. In addition, challenges such as algorithmic bias and data rights are addressed, emphasizing the need for responsible AI governance, transparency and continuous improvement.

Contribute to the field by exploring the unique collaborative framework of Copilot and Claude AI in a specific context, emphasizing responsible AI governance and addressing existing gaps.

The study explores the readiness of government agencies to adopt artificial intelligence (AI) to improve the efficiency of disaster relief operations (DRO). For understanding the behavior of state-level and national-level government agencies involved in DRO, this study grounds its theoretical arguments on the civic voluntarism model (CVM) and the unified theory of acceptance and use of technology (UTAUT).

We collected the primary data for this study from government agencies involved in DRO in India. To test the proposed theoretical model, we administered an online survey questionnaire to 184 government agency employees. To test the hypotheses, we employed partial least squares structural equation modeling (PLS-SEM).

Our findings confirm that resources (time, money and skills) significantly influence the behavioral intentions related to the adoption of AI tools for DRO. Additionally, we identified that the behavioral intentions positively translate into the actual adoption of AI tools.

Our study provides a unique viewpoint suited to understand the context of the adoption of AI in a governmental context. Companies often strive to invest in state-of-the-art technologies, but it is important to understand how government bodies involved in DRO strategize to adopt AI to improve efficiency.

Our study offers a fresh perspective in understanding how the organizational culture and perspectives of government officials influence their inclinations to adopt AI for DRO. Additionally, it offers a multidimensional perspective by integrating the theoretical frameworks of CVM and UTAUT for a greater understanding of the adoption and deployment of AI tools with organizational culture and voluntariness as critical moderators.

This study investigates the effect of auditing industry specialization (AIS) on the relative derivatives use for earnings management.

The sample chosen in this study comprises 30,599 firm-year observations of Chinese public companies from 2005 to 2018. The sample is divided into two time periods (2005–2013 and 2014–2018) according to the year when IFRS 9 was implemented (IFRS 9, first discussed by the International Accounting Standards Board in March 2008, is based on an expected credit loss model for determining new and existing expected credit losses on financial assets. The definition was completed in July 2014 and implemented in 2018). AIS was gauged with respect to audit firms and individual auditors, and measured by market share in number and scale of clients. Linear regression is adopted to test hypotheses. Moreover, two-stage least square model (2SLS) is used to eliminate the concern of possible endogeneity.

When gauged with respect to client scale, the scale-based AIS constrained the level of derivatives use for earnings management in the first period (2005–2013) while increased the level in the second period (2014–2018). The findings sustain for the analysis of audit firms and that of individual auditors, and for different definitions of AIS.

The positive AIS-IN relation after the adoption of IFRS 9 implies the sacrifice audit independence. This could be indebted to the government policy that favors local audit firms to be comparable to international Big 4 audit firms, and therefore results in competition among local auditors/audit firms in securing number rather than quality of clients.

The data of AIS in China are collected using a Python web crawler.

Governments are increasingly turning to artificial intelligence (AI) algorithmic systems to increase efficiency and effectiveness of public service delivery. While the diffusion of AI offers several desirable benefits, caution and attention should be posed to the accountability of AI algorithm decision-making systems in the public sector. The purpose of this paper is to establish the main challenges that an AI algorithm might bring about to public service accountability. In doing so, the paper also delineates future avenues of investigation for scholars.

This paper builds on previous literature and anecdotal cases of AI applications in public services, drawing on streams of literature from accounting, public administration and information technology ethics.

Based on previous literature, the paper highlights the accountability gaps that AI can bring about and the possible countermeasures. The introduction of AI algorithms in public services modifies the chain of responsibility. This distributed responsibility requires an accountability governance, together with technical solutions, to meet multiple accountabilities and close the accountability gaps. The paper also delineates a research agenda for accounting scholars to make accountability more “intelligent”.

The findings of the paper shed new light and perspective on how public service accountability in AI should be considered and addressed. The results developed in this paper will stimulate scholars to explore, also from an interdisciplinary perspective, the issues public service organizations are facing to make AI algorithms accountable.

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.

The purpose of this paper is to address a theoretical and methodological framework to validate a model for explaining social responsibility in cooperative societies.

A qualitative methodology based on the assessment and agreement of an expert panel has been used. More exactly, a Delphi technique will help achieve agreement about the set of indicators previously defined and to get a single and agreed definition.

The results consist of a consensus scale for each variable of the proposed model. This unanimity in the opinions about the final result will be the basis for further quantitative treatment of the proposed conceptual model.

Limitations derive from the initial state of the study and the need to practical analysis.

Cooperative societies could have a way to analyze their position related to social responsibility. In general, contributions to social responsibility have improved, in particular, in the field of these entities.

The paper contributes to properly measure the variables of the conceptual model. The main variable of analysis, called Orientation to Social Responsibility in Cooperatives (OSRCOOP), is not directly observable, and it is necessary to measure it through a set of indicators. Likewise, with the other strategic variables with which OSRCOOP is related to the model proposed (member satisfaction, innovation, quality of service and cooperative outcome or performance).