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Work-family conflict is an important topic which had an evolution, starting from a static definition, where work and family domains were divided, to a more dynamic and complex balance. COVID-19 has influenced society and created a significant distress among families and working activity, and this topic has been characterised by a major interest, considering some old definitions where this balance was considered problematic but not as an enriching opportunity.

The authors used SCOPUS to find all records mentioning work-family conflict, by considering book, article and review, excluding conference paper and considering only records written in English language. After a duplicated and not pertinent record removal, the authors obtained a number of 675 records. The authors considered 437 records from SCOPUS to create a cluster map.

Using SCOPUS and VOSviewer the authors have clustered 5 different areas, which are regrouped in next clusters considering keywords with most co-occurrence and significancy: Work-life balance and burnout gender cluster job stress and performance social and family support job satisfaction.

Cluster map is origined only by SCOPUS database.

This work aims to find a state of art about this topic, creating hypothesis where this problem has been exacerbated by 2020 due to important society modifications created by COVID-19, where recent evolution of work-family balance has been complicated by papers which come back to consider this balance as problematic.

This paper analyzes employees’ perceptions of data collection processes for human resource analytics (HRA). More specifically, we study the effect that information sharing practices have on employees’ attributions (i.e. benevolent vs malevolent) through the perceived legitimacy of data collection and monitoring processes. Moreover, we investigate whether employees’ emotional reaction (i.e. fear of datafication) depends on their perceived legitimacy and attributions.

The research is based on a sample of 259 employees operating for an Italian consulting firm that developed and implemented HRA processes in the last 3 years. The hypothesized model has been tested using structural equation modeling (SEM) on Stata 14.

This paper demonstrates the mediating role of perceived legitimacy in the relationship between information sharing practices and employees’ benevolent and malevolent attributions about data collection and monitoring processes for HRA practices. Results also reveal that perceived legitimacy predicts employees’ fear of datafication, with benevolent attributions that partially mediate this relationship.

This research indicates that employees perceive, try to make sense of and emotionally react to HRA processes. Moreover, we reveal the crucial role of information sharing practices and perceived legitimacy in determining employees’ attributions and emotional reactions to data collection and monitoring processes.

Combining human resource (HR) attributions, HR system strength, information processing and signaling theories, this work explores employees’ perception, attributive processes and emotional reactions to data collection processes for HRA practices.

Firms have already begun integrating artificial intelligence (AI) as a replacement for conventional performance management systems owing to its technological superiority. This transition has sparked a growing interest in determining how employees perceive and respond to performance feedback provided by AI as opposed to human supervisors.

A 2 x 2 between-subject experimental design was employed that was manipulated into four experimental conditions: AI algorithms, AI data, highly experienced human supervisors and low-experience human supervisor conditions. A one-way ANOVA and Welch t-test were used to analyze data.

Our findings revealed that with a predefined fixed formula employed for performance feedback, employees exhibited higher levels of trust in AI algorithms, had greater performance expectations and showed stronger intentions to seek performance feedback from AI algorithms than highly experienced human supervisors. Conversely, when performance feedback was provided by human supervisors, even those with less experience, in a discretionary manner, employees' perceptions were higher compared to similar feedback provided by AI data. Moreover, additional analysis findings indicated that combined AI-human performance feedback led to higher levels of employees' perceptions compared to performance feedback solely by AI or humans.

The findings of our study advocate the incorporation of AI in performance management systems and the implementation of AI-human combined feedback approaches as a potential strategy to alleviate the negative perception of employees, thereby increasing firms' return on AI investment.

Our study represents one of the initial endeavors exploring the integration of AI in performance management systems and AI-human collaboration in providing performance feedback to employees.

Looking at the limits of artificial intelligence (AI) and controlling based on complexity and system-theoretical deliberations, the authors aimed to derive a future outlook of the possible applications and provide insights into a future complementary of human–machine information processing. Derived from these examples, the authors propose a research agenda in five areas to further the field.

This article is conceptual in its nature, yet a theoretically informed semi-systematic literature review from various disciplines together with empirically validated future research questions provides the background of the overall narration.

AI is found to be severely limited in its application to controlling and is discussed from the perspectives of complexity and cybernetics. A total of three such limits, namely the Bremermann limit, the problems with a partial detectability and controllability of complex systems and the inherent biases in the complementarity of human and machine information processing, are presented as salient and representative examples. The authors then go on and carefully illustrate how a human–machine collaboration could look like depending on the specifics of the task and the environment. With this, the authors propose different angles on future research that could revolutionise the application of AI in accounting leadership.

Future research on the value promises of AI in controlling needs to take into account physical and computational effects and may embrace a complexity lens.

AI may have severe limits in its application for accounting and controlling because of the vast amount of information in complex systems.

The research agenda consists of five areas that are derived from the previous discussion. These areas are as follows: organisational transformation, human–machine collaboration, regulation, technological innovation and ethical considerations. For each of these areas, the research questions, potential theoretical underpinnings as well as methodological considerations are provided.

This paper aims to identify ethical challenges of using artificial intelligence (AI)-based accounting systems for decision-making and discusses its findings based on Rest's four-component model of antecedents for ethical decision-making. This study derives implications for accounting and auditing scholars and practitioners.

This research is rooted in the hermeneutics tradition of interpretative accounting research, in which the reader and the texts engage in a form of dialogue. To substantiate this dialogue, the authors conduct a theoretically informed, narrative (semi-systematic) literature review spanning the years 2015–2020. This review's narrative is driven by the depicted contexts and the accounting/auditing practices found in selected articles are used as sample instead of the research or methods.

In the thematic coding of the selected papers the authors identify five major ethical challenges of AI-based decision-making in accounting: objectivity, privacy, transparency, accountability and trustworthiness. Using Rest's component model of antecedents for ethical decision-making as a stable framework for our structure, the authors critically discuss the challenges and their relevance for a future human–machine collaboration within varying agency between humans and AI.

This paper contributes to the literature on accounting as a subjectivising as well as mediating practice in a socio-material context. It does so by providing a solid base of arguments that AI alone, despite its enabling and mediating role in accounting, cannot make ethical accounting decisions because it lacks the necessary preconditions in terms of Rest's model of antecedents. What is more, as AI is bound to pre-set goals and subjected to human made conditions despite its autonomous learning and adaptive practices, it lacks true agency. As a consequence, accountability needs to be shared between humans and AI. The authors suggest that related governance as well as internal and external auditing processes need to be adapted in terms of skills and awareness to ensure an ethical AI-based decision-making.