Search results

The current research elucidates the role of empathy in design of artificial intelligence (AI) systems in healthcare context, through a structured literature review, analysis and synthesis of academic literature published between 1990 and 2024.

This study aims to advance the domain of empathy in AI by adopting theory constructs context method approach using the PRISMA 2020 framework.

The study presents a current state-of-the-art literature to review the connections between empathy and AI and identifying four clusters showing the emerging trajectories in the field of AI and empathy in healthcare setting.

Despite a rise in empirical research, the potential pathways enhancing AI accountability by incorporation of empathy is unclear. The research aims to contribute to the existing literature on AI and empathy in the healthcare sector by carving out four distinct clusters depicting the future research avenues.

The study aims to identify the practical borders of AI legal personality and accountability in human-centric services.

Using a framework tailored for AI studies, this research analyses structured interview data collected from auditors based in Poland.

The study identified new constructs to complement the taxonomy of arguments for AI legal personality: cognitive strain, consciousness, cyborg paradox, reasoning replicability, relativism, AI misuse, excessive human effort and substitution.

The insights presented herein are primarily derived from the perspectives of Polish auditors. There is a need for further exploration into the viewpoints of other key stakeholders, such as lawyers, judges and policymakers, across various global contexts.

The findings of this study hold significant potential to guide the formulation of regulatory frameworks tailored to AI applications in human-centric services. The proposed sui generis AI personality institution offers a dynamic and adaptable alternative to conventional legal personality models.

The outcomes of this research contribute to the ongoing public discourse on AI’s societal impact. It encourages a balanced assessment of the potential advantages and challenges associated with granting legal personality to AI systems.

This paper advocates for establishing a sui generis AI personality institution alongside a joint accountability model. This dual framework addresses the current uncertainties surrounding human, general AI and super AI characteristics and facilitates the joint accountability of responsible AI entities and their ultimate beneficiaries.

This article centres on the pertinence of redefining accounting for tomorrow, particularly for facilitating the attainment of the UN Sustainable Development Goals (SDGs) and, thereby, for shaping a better world. In aspiring for accounting to reach its full potential as a multidimensional technical, social and moral practice, this paper aims to focus on ideas, initiatives and proposals for realising accounting’s future potential and responsibilities.

The study deploys a further developed “strategic implementation framework”, initially proposed by Carnegie et al. (2023), with an emphasis on accounting serving “the public interest” so as “to enable the flourishing of organisations, people and nature” (Carnegie et al., 2021a, p. 69; 2021b). It depicts strategies towards the future of accounting and the world.

Significant opportunities are identified for accounting and accountants, working closely with a diversity of stakeholders, to become alert to and cognisant of the nature, roles, uses and impacts of accounting. The evidence presented notes a predominant inattention of accounting and accountants to the SDGs despite the deteriorating state of our social and natural environment.

Whilst this article examines other articles in this special issue (SI), there is no substitute for carefully reading, reflecting on and deliberating upon these articles individually.

The time for accounting to focus on creating a better world can no longer be extended. Accounting’s full potential will not be realised by remaining in a narrow and complacent, technicist state.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

The purpose of this multi-voiced paper is to propose a prosocial paradigm for the field of performance management and management control systems. This new paradigm suggests cultivating prosocial behaviour and prosocial groups in organizations to simultaneously achieve the objectives of economic performance and sustainability.

The authors share a common concern about the future of humanity and nature. The authors challenge the influential assumption of economic man from neoclassical economic theory and build on evolutionary science and the core design principles of prosocial groups to develop a prosocial paradigm.

Findings are based on the premise of the prosocial paradigm that self-interested behaviour may outperform prosocial behaviour within a group but that prosocial groups outperform groups dominated by self-interest. The authors explore various dimensions of performance management from the prosocial perspective in the private and public sectors.

The authors call for theoretical, conceptual and empirical research that explores the prosocial paradigm. They invite any approach, including positivist, interpretive and critical research, as well as those using qualitative, quantitative and interventionist methods.

This paper offers implications from the prosocial paradigm for practitioners, particularly for executives and managers, policymakers and educators.

Adoption of the prosocial paradigm in research and practice shapes what the authors call the prosocial market economy. This is an aspired cultural evolution that functions with market competition yet systematically strengthens prosociality as a cultural norm in organizations, markets and society at large.

Financial technologies form the heart of considerable disruptive innovation. Fintech is the emerging financial infrastructure for modern business. Big data are the feedstock for artificial intelligence (AI) that drives many fintech sectors – start-up finance, commodities and investment instrumentation, payment systems, currencies, exchange markets/trading platforms, market-failure response forensics, underwriting, syndication, risk assessment, advisory services, banking, financial intermediaries, transaction settlement, corporate disclosure, and decentralized finance. This chapter demonstrates how analyzing big data, largely processed through cloud computing, drives fintech innovations, scholarship, forensics, and public policy. Despite their apparent virtues, some fintech mechanisms can externalize various social costs: flawed designs, opacity/obscurity, social media (SM) influences, cyber(in)security, and other malfunctions. Fintech suffers regulatory lag, the delay following the introduction of novel fintechs and later assessment, development, and deployment of reliable regulatory mechanisms. Big data can improve fintech practices by balancing three key influences: (1) fintech incentives, (2) market failure forensics, and (3) developing balanced public policy resolutions to fintech challenges.

This study investigates the effect of education on crop insurance knowledge within the context of noncompliance experiences. In addition, the study delves into the role of government endorsement in education, which is instructive for the implementation of future insurance promotions.

The study designs a randomized controlled trial (RCT) conducted in Jiangsu Province, China. A total of 518 sample farmers were randomly assigned to two experiments: The Education Experiment and the government’s Endorsement Experiment, respectively. After conducting a set of rigorous exogeneity tests, econometric analysis was conducted using baseline survey data and post experiment data.

Our results revealed that insurance education served as an effective tool in improving farmers’ insurance knowledge, especially their understanding of insurance mechanisms. However, this effect can be mitigated by the noncompliant insurance experience of farmers. Moreover, government-endorsed education proved to be more efficient in improving farmers’ insurance knowledge, thus highlighting the significance of building trust between insureds and insurers.

This study contributes to the literature by demonstrating that using a simple education tool, such as, brochures, can effectively improve farmers insurance knowledge. In addition, insurance mechanisms are now more urgently in need of universalization than policy information. Furthermore, by conducting the RCT, this study obtains unbiased causal inference on the effect of education on insurance knowledge and underscores the role of government endorsement in this process. In addition, the study illustrates the tradeoff between insurers’ efforts in enhancing education and regulating noncompliant insurance misconducts, which compromises education efforts. Overall, this study provides insights into the marketing strategies of insurers and government propaganda aimed at stimulating farmers’ incentives to purchase insurance.

This paper aims to contribute to the literature on corporate and leadership responsibility by proposing a relational business model for shared responsibility.

First, a literature review on corporate and leadership responsibility is presented and discussed. This is followed by an overview of existing public and private regulations and future perspectives that enforce and/or foster corporate and leadership responsibility. Based on the concepts of relational economics, relational leadership and proactive regulation, the theoretical foundations of a relational business model are derived. In addition, a decision model for the empirical application of the relational business model in ethical dilemma situations is developed and presented.

Theoretical elaboration of a relational business model and an associated relational decision-making approach.

This study contributes to a new way of doing business in terms of shared responsibility. Furthermore, corporate responsibility and leadership responsibility are usually researched as two distinct fields, with the former referring to the meso level and the latter to the micro level. A relational approach, which views leadership as a relational phenomenon, contributes to bridging both concepts.