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The purpose of this paper is to lay out the conceptual issues arising alongside the rise of sensory technologies in workplaces designed to improve wellness and productivity.

This is a text based conceptual paper. The authors’ approach is to throw light on some of the emerging issues with the introduction of wearable self-tracking technologies in workplaces.

The paper indicates that scholars will need to put ethical issues at the heart of research on sensory tracking technologies in workplaces that aim to regulate employee behaviour via wellness initiatives.

The study explores the legal issues around data protection and potential work intensification.

Privacy and personal data protection, workplace discipline are discuss in this paper.

This is an original paper. Since there is very little scholarly research in this area, it is important to begin to consider the implications of sensory technology in workplaces linked to wellness initiatives, given the probable impact it will have on work design and appraisal systems.

Most scholarly and governmental discussions about artificial intelligence (AI) today focus on a country’s technological competitiveness and try to identify how this supposedly new technological capability will improve productivity. Some discussions look at AI ethics. But AI is more than a technological advancement. It is a social question and requires philosophical inquiry. The producers of AI who are software engineers and designers, and software users who are human resource professionals and managers, unconsciously as well as consciously project direct forms of intelligence onto machines themselves, without considering in any depth the practical implications of this when weighed against human actual or perceived intelligences. Neither do they think about the relations of production that are required for the development and production of AI and its capabilities, where data-producing human workers are expected not only to accept the intelligences of machines, now called ‘smart machines’, but also to endure particularly difficult working conditions for bodies and minds in the process of creating and expanding the datasets that are required for the development of AI itself. This chapter asks, who is the smart worker today and how does she contribute to AI through her quantified, but embodied labour?

This chapter reviews the ongoing processes of marketisation in secondary school teaching and its further embedment through commodification of teachers’ performance. We track developments through documentary evidence from Government statements and other agency reports and unstructured interviews with teachers’ union representatives in the South West of England. Following Carter and Stevenson (2012) we begin by introducing the labour process debate concerning teachers’ productive labour to provide the backdrop for the argument that teachers’ work is increasingly commodified and judged along neoliberalised requirements. Commodification has taken place through measurement of abstract standards constructed by associating individual teachers with their pupils’ achievements, as well as subjective assessment of teacher behaviour judged against newly introduced ‘Teacher Standards’. We argue that this attempted quantification of teacher output is constructed, in Marxist terms, to accommodate to the ‘socially necessary labour time’ and to indirectly maximise work ‘output’ for individual teachers through a process of standardisation of processes involved in task completion. We attempt to define new ways of measuring teachers’ work through the lens of abstract labour and link such processes to workplace alienation. In such fashion, teachers are subject to work intensification, increased monitoring and surveillance, further standardisation of work and weakening of creative autonomy leading to intensified alienation from the professional nature of the job.

Algorithmic monitoring has been widely applied to the practice of platform economy as a management means. Despite its benefits, negative effects of algorithmic monitoring are gradually emerging.

Based on moral disengagement theory, this research aims to investigate how algorithmic monitoring might affect gig workers’ attitudes and behaviors. Specifically, we explored the effect of algorithmic monitoring on gig workers’ unethical behavior. A three-wave survey was conducted online, and the sample consisted of 318 responses from Chinese gig workers.

The results revealed that algorithmic monitoring positively affected unethical behavior through displacement of responsibility, and the individualistic orientation of gig workers moderated this relationship. However, the relationship between moral justification and algorithmic monitoring was not significant.

This research contributes to the algorithmic monitoring literature and examines its impact on gig workers’ unethical behavior. By revealing the underlying mechanism and boundary conditions, this research furthers our understanding of the negative influences of algorithmic monitoring and provides practical implications.

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.