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How to embed artificial intelligence (AI) in human resource management (HRM) is one of the core challenges of digital HRM. Despite regulations demanding humans in the loop to ensure human oversight of AI-based decisions, it is still unknown how much decision-makers rely on information provided by AI and how this affects (personnel) selection quality.

This paper presents an experimental study using vignettes of dashboard prototypes to investigate the effect of AI on decision-makers’ overreliance in personnel selection, particularly the impact of decision-makers’ information search behavior on selection quality.

Our study revealed decision-makers’ tendency towards status quo bias when using an AI-based ranking system, meaning that they paid more attention to applicants that were ranked higher than those ranked lower. We identified three information search strategies that have different effects on selection quality: (1) homogeneous search coverage, (2) heterogeneous search coverage, and (3) no information search. The more applicants were searched equally often (i.e. homogeneous) as when certain applicants received more search views than others (i.e. heterogeneous) the higher the search intensity was, resulting in higher selection quality. No information search is characterized by low search intensity and low selection quality. Priming decision-makers towards carrying responsibility for their decisions or explaining potential AI shortcomings had no moderating effect on the relationship between search coverage and selection quality.

Our study highlights the presence of status quo bias in personnel selection given AI-based applicant rankings, emphasizing the danger that decision-makers over-rely on AI-based recommendations.

Virtual reality (VR) has been explored as a training and testing environment in a range of work contexts, and increasingly so in transport. There is, however, a lack of research exploring the role of VR in the training of tram drivers, and in providing an environment in which advances in tram technology can be tested safely. This study aimed to test a novel haptic tram master controller within a tram-based Virtual environment (VE).

The master controller is the primary mechanism for operating a tram, and its effective manipulation can significantly influence the comfort and well-being of passengers, as well as the overall safety of the tram system. Here, the authors tested a haptically enhanced master controller that provides additional sensory information with 16 tram drivers. The feasibility and user acceptance of the novel technology were determined through surveys.

The results indicate that the haptic master controller is seen as beneficial to the drivers suggesting that it could enhance their driving and demonstrate good acceptance. The VE has provided a potential training environment that was accepted by the drivers and did not cause adverse effects (e.g. sickness).

Although this study involved actual tram drivers from a local tram company, the authors acknowledge that the sample size was small, and additional research is needed to broaden perspectives and gather more user feedback. Furthermore, while this study focused on subjective feedback to gauge user acceptance of the new haptic technology, the authors agree that future evaluations should incorporate additional objective measures.

The insights gained from this VE-based research can contribute to future training scenarios and inform the development of technology used in real-world tram operations.

Through this investigation, the authors showed the broader possibilities of haptics in enhancing the functionality and user experience of various technological devices, while also contributing to the advancement of tram systems for safer and more efficient urban mobility.

A maturity model for Industry 4.0 (I4.0 MM) with influencing factors is designed to address maturity issues in adopting Industry 4.0. Standardisation in I4.0 supports manufacturing industry transformation, forming reference architecture models (RAMs). This paper aligns key factors and maturity levels in I4.0 MMs with reputable I4.0 RAMs to enhance strategy for I4.0 transformation and implementation.

Three steps of alignment consist of the systematic literature review (SLR) method to study the current published high-quality I4.0 MMs, the taxonomy development of I4.0 influencing factors by adapting and implementing the categorisation of system theories and aligning I4.0 MMs with RAMs.

The study discovered that different I4.0 MMs lead to varied organisational interpretations. Challenges and insights arise when aligning I4.0 MMs with RAMs. Aligning MM levels with RAM stages is a crucial milestone in the journey toward I4.0 transformation. Evidence indicates that I4.0 MMs and RAMs often overlook the cultural domain.

Findings contribute to the literature on aligning capabilities with implementation strategies while employing I4.0 MMs and RAMs. We use five RAMs (RAMI4.0, NIST-SME, IMSA, IVRA and IIRA), and as a common limitation in SLR, there could be a subjective bias in reading and selecting literature.

To fully leverage the capabilities of RAMs as part of the I4.0 implementation strategy, companies should initiate the process by undertaking a thorough needs assessment using I4.0 MMs.

The novelty of this paper lies in being the first to examine the alignment of I4.0 MMs with established RAMs. It offers valuable insights for improving I4.0 implementation strategies, especially for companies using both MMs and RAMs in their transformation efforts.