The study aimed at qualitatively exploring working adult's perceptions of the implementation of robotics, artificial intelligence (AI), and automation (RAIA) on their job security, job satisfaction, and employability. By means of a cross-sectional and exploratory design, the researchers conducted 21 semi-structured interviews with a diverse sample. The heterogeneous sample came from numerous industries for instance consulting, accounting and finance, and hospitality and varied seniority levels. The thematic analysis led to the emergence of five high-level themes and several sub-themes. The findings indicate that (a) "human touch" and "soft skills" remain irreplaceable and cannot be replicated by RAIA, (b) employees need to perceive RAIA as an opportunity and not a threat, (c) employees might experience a job satisfaction dilemma, and (d) organizations have to be well prepared pre-and post-industrial change. The findings could be used by industrial and organizational psychologists, human resource practitioners, and strategic information technology decision-makers when managing RAIA-related technological changes in organizations. Employees' suggestions and perceptions could be considered to mitigate the consequences of technological changes in organizations. Both employees and employers need to change their perspective toward RAIA technology, work with a flexible, open mind, and embrace the potential impact of RAIA advancements on job roles and responsibilities. Employees will have to follow a path of continuous learning and keep up with technology.

The authors wish to acknowledge his tremendous contribution to the early drafts of this paper, and also to the field of organizational psychology and the design of sociotechnical systems, to which he dedicated his career. AbstractThe socio-technical systems approach to design is well documented. Recognising the benefits of this approach, organizations are increasingly trying to work with systems, rather than their component parts. However, few tools attempt to analyse the complexity inherent in such systems, in ways that generate useful, practical outputs. In this paper, we outline the 'System Scenarios Tool' (SST), which is a novel, applied methodology that can be used by designers, end-users, consultants or researchers to help design or re-design work systems.The paper introduces the SST using an example of its application, and describes the potential benefits of its use, before reflecting on its limitations. Finally, we discuss potential opportunities for the tool, and describe sets of circumstances in which it might be used. 3 Practitioner SummaryThe paper presents a novel, applied methodological tool, named the "Systems Scenarios Tool". We believe this tool can be used as a point of reference by designers, endusers, consultants or researchers, to help design or re-design work systems. Included in the paper are two worked examples, demonstrating the tool's application.

As a discipline, design science has traditionally focused on designing products and associated technical processes to improve usability and performance. Although significant progress has been made in these areas, little research has yet examined the role of human behaviour in the design of socio-technical systems (e.g., organizations). Here, we argue that applying organizational psychology as a design science can address this omission and enhance the capability of both disciplines. Specifically, we propose a method to predict malfunctions in socio-technical systems (PreMiSTS), thereby enabling them to be designed out or mitigated. We introduce this method, describe its nine stages, and illustrate its application with reference to two high-profile case studies of such malfunctions: (1) the severe breakdowns in patient care at the UK's Mid-Staffordshire NHS Foundation Trust hospital in the period 2005-2009, and (2) the fatal Grayrigg rail accident in Cumbria, UK, in 2007. Having first identified the socio-technical and behavioural antecedents of these malfunctions, we then consider how the PreMiSTS method could be used to predict and prevent future malfunctions of this nature. Finally, we evaluate the method, consider its advantages and disadvantages, and suggest where it can be most usefully applied.

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