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Drawing upon the stimulus-organism-response model, this study aims to explore the impact of soft aspects of service quality on revisit intention through the mechanism of perceived empathy.

For the examination of the hypothesized relationships, the study adopts structural equation modelling to analyse the data of 562 respondents (i.e. 281 family members and 281 inpatients).

The empirical results suggest that service quality increased family member empathy perception, which, in turn, improved inpatients’ revisit intentions.

Past studies have focused on the roles of overall service quality. The authors have extended the literature by examining the specific but important aspect of service quality and its effects on emotional response. Importantly, the study explains that the affective reactions of a patient’s family, fastened with perceived empathy, have a central role in influencing the patients’ subsequent reactions. Moreover, the prior studies collected the data either from hospital employees or patients. However, in the present study, the authors used a unique sample (family members as well as patients) to have a deeper understanding. Thus, the study enhances the literature on the stimuli-response (i.e. service quality – revisit intentions) relationship in the context of service marketing in general and health care in specific. Important academic and managerial contributions and recommendations for future research are discussed.

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.