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With Industry 4.0 and the extensive rise of smart technologies, we are seeing remarkable transformations in work practices and workplaces. Scholars report the phenomenal progress of smart technologies. At the same time, we can hear the rhetoric emphasising their potential threats. This study focusses on how and where intelligent machines are leveraged in the workplace, how humans co-working with intelligent machines are affected and what they believe can be done to mitigate the risks of the increased use of intelligent machines.

We conducted in-depth interviews with 15 respondents working in various leadership capacities associated with intelligent machines and technologies. Using NVivo, we coded and churned out the themes from the qualitative data collected.

This study shows how intelligent machines are leveraged across different industries, ranging from chatbots, intelligent sensors, cognitive systems and computer vision to the replica of the entire human being. They are used end-to-end in the value chain, increasing productivity, complementing human workers’ skillsets and augmenting decisions made by human workers. Human workers experience a blend of positive and negative emotions whilst co-working with intelligent machines, which influences their job satisfaction level. Organisations adopt several anticipatory strategies, like transforming into a learning organisation, identifying futuristic technologies and upskilling their human workers, regularly conducting social learning events and designing accelerated career paths to embrace intelligent technologies.

This study seeks to understand the emotional and practical implications of the use of intelligent machines by humans and how both entities can integrate and complement each other. These insights can help organisations and employees understand what future workplaces and practices will look like and how to remain relevant in this transformation.

The purpose of this paper is to investigate the operation of multidisciplinary team (MDT) meetings within a forensic hospital in England, UK.

Mixed methods, including qualitative face to face interviews with professionals and service users, video observations of MDT meetings and documentary analysis. Data were collected from 142 staff and 30 service users who consented to take part in the research and analysed using the constant comparison technique of grounded theory and ethnography.

Decisions taken within MDT meetings are unequally shaped by the professional and personal values and assumptions of those involved, as well as by the power dynamics linked to the knowledge and responsibility of each member of the team. Service users’ involvement is marginalised. This is linked to a longstanding tradition of psychiatric paternalism in mental health care.

Future research should explore the nuances of interactions between MDT professionals and service users during the meetings, the language used and the approach taken by professionals to enable/empower service user to be actively involved.

Clear aims, responsibilities and implementation actions are a pre-requisite to effective MDT working. There is a need to give service users greater responsibility and power regarding their care.

While direct (video) observations were very difficult to achieve in secure settings, they enabled unmediated access to how people conducted themselves rather than having to rely only on their subjective accounts (from the interviews).

This paper aims to compare the nature of three different business and management education (BME) research streams (online/blended learning, entrepreneurship education and experiential learning), along with their citation sources to draw insights on their support and legitimacy bases, with lessons on improving such support and legitimacy for the streams and the wider BME research field.

The authors analyze the nature of three BME research streams and their citation sources through tests of differences across streams.

The three streams differ in research foci and approaches such as the use of managerial samples in experiential learning, quantitative studies in online/blended education and literature reviews in entrepreneurship education. They also differ in sources of legitimacy recognition and avenues for mobilization of support. The underlying literature development pattern of the experiential learning stream indicates a need for BME scholars to identify and build on each other’s work.

Identification of different research bases and key supporting literature in the different streams shows important core articles that are useful to build research in each stream.

Readers will understand the different research bases supporting the three research streams, along with their targeted audience and practice implications.

The discovery of different support bases for the three different streams helps identify the network of authors and relationships that have been built in each stream.

According to the authors’ knowledge, this paper is the first to uncover differences in nature and citation sources of the three continuously growing BME research streams with recommendations on ways to improve the support of the three streams.

This study aims to provide a picture of the current state of art in the use of big data for decision-making processes for the management of health-care organizations.

A systematic literature review was carried out. The research uses two analyses: descriptive analysis, describing the evolution of citations; keywords; and the ten most influential papers, and bibliometric analysis, for content evaluation, for which a cluster analysis was performed.

A total of 48 articles were selected for bibliographic coupling out of an initial sample of more than 5,000 papers. Of the 48 articles, 29 are linked on the basis of their bibliography. Clustering the 29 articles on the basis of actual content, four research areas emerged: quality of care, quality of service, crisis management and data management.

Health-care organizations believe strongly that big data can become the most effective tool for correctly influencing the decision-making processes. Thus, more and more organizations continue to invest in big data analytics, and the literature on this topic has expanded rapidly. This study seeks to provide a comprehensive picture of the different streams of literature existing, together with gaps in research and future perspectives. The literature is mature enough for an analysis to be made and provide managers with useful insights on opportunities, criticisms and perspectives on the use of big data for health-care organizations. However, to date, there is no comprehensive literature review on the big data analysis in health care. Furthermore, as big data is a “sexy catchphrase,” more clarity on its usage may be needed. It represents an important tool to be investigated and its great potential is often yet to be discovered. This study thus sheds light on emerging issues and suggests further research that may be needed.

As online course enrollments increase, it is important to understand how common course features influence students' behaviors and performance. Asynchronous online courses often include a discussion forum to promote community through interaction between students and instructors. Students interact both socially and cognitively; instructors' engagement often demonstrates social or teaching presence. Students' engagement in the discussions introduces both intrinsic and extraneous cognitive load. The purpose of this study is to validate an instrument for measuring cognitive load in asynchronous online discussions.

This study presents the validation of the NASA-TLX instrument for measuring cognitive load in asynchronous online discussions in an introductory physics course.

The instrument demonstrated reliability for a model with four subscales for all five discrete tasks. This study is foundational for future work that aims at testing the efficacy of interventions, and reducing extraneous cognitive load in asynchronous online discussions.

Nonresponse error due to the unincentivized, voluntary nature of the survey introduces a sample-related limitation.

This study provides a strong foundation for future research focused on testing the effects of interventions aimed at reducing extraneous cognitive load in asynchronous online discussions.

This is a novel application of the NASA-TLX instrument for measuring cognitive load in asynchronous online discussions.