Our study investigated what factors contribute to the perceived usability of Virtual Reality technology by small firms operating in a conservative industry. For this purpose, we interviewed the developers and users of a newly developed Virtual Reality-equipped heavy machinery. We conducted eleven semi-structured interviews and used thematic analysis to identify the main themes that influenced the perceived usability of the technology by the consumers. This was significant as it contributed to their decisions about purchasing and continuing to use the technology. Consequently, it impacted the developers of the technology in their competitive capability in a conservative and homogenous industrial arena. The dimension and depth of perceived usability by this specific groups are discussed, and the practical implications are provided.

This paper provides a stagewise overview of the important issues that play a role in technology adoption and use in organizations. In the current literature, there is a lack of consistency and clarity about the different stages of the technology adoption process, the important issues at each stage, and the differentiation between antecedents, after-effects, enablers, and barriers to technology adoption. This paper collected the relevant issues in technology adoption and use, mentioned dispersedly and under various terminologies, in the recent literature. The qualitative literature review was followed by thematic analysis of the data. The resulting themes were organized into a thematic map depicting three stages of the technology adoption process: pre-change, change, and post-change. The relevant themes and subthemes at each stage were identified and their significance discussed. The themes at each stage are antecedents to the next stage. All the themes of the pre-change and change stages are neutral, but the way they are managed and executed makes them enablers or barriers in effect. The thematic map is a continuous cycle where every round of technology adoption provides input for the subsequent rounds. Based on how themes have been addressed and executed in practice, they can either enhance or impair the subsequent technology adoption. This thematic map can be used as a qualitative framework by academics and practitioners in the field to evaluate technological changes.

This research investigated the organizational challenges related to the development and implementation of virtual reality (VR) technology for operation in a conservative heavy machinery industry. The incorporation of a VR solution for heavy machinery equipment enhanced the safety and convenience of operation under dangerous work conditions. However, the development and implementation processes faced challenges. Furthermore, the adoption of the solution by users was perceived to be slower than anticipated. We aimed to explore the main challenges that the developer organization faced and how it also influenced user organizations. Due to the exploratory nature of the research, qualitative analysis was chosen, interviews were conducted, and thematic analysis was applied. The themes and subthemes were identified and discussed. The results showed the existence of challenges related to technology maturity, managerial challenges regarding communication and support coordination, workload, and multiple stakeholder management. The findings emphasize the importance of attending to the existing and potential organizational challenges before and throughout technological innovation. Theoretical and managerial implications are discussed, and a future research agenda is suggested.

In this paper, we investigate how managers enhance sense-making during technological changes in safety-critical industries. Implementation of new technologies is not a linear and predictable process. Technologies evolve faster than we can fully prepare for. This means that new challenges are introduced into the sociotechnical system in which the technology is embedded, leading to shifts in paradigms, interpretation, and business practices. These new technology-induced changes have been less the focus of previous work on sense-making compared to the more traditional paradigms and core practices. In this paper, we focus on the managerial role in these changes. We examine the relevant literature on technological change implementation and organizational sense-making in the past three years and use thematic analysis to capture the main themes plausible within the scope of the paper. Several themes and sub-themes are derived and presented. The resulting themes shed light on what the managers need to be aware of and do to support and enhance the sense-making process during technological changes, which leads to empowerment of the employees to do their own sense-making and maintain reliable operations.

Virtual reality (VR) offers novel ways to develop skills and learning. This technology can be used to enhance the way we educate and train professionals by possibly being more effective, cost-efficient, and reducing training-related risks. However, the potential benefits from virtual training assume that the trained skills can be transferred to the real world. Nevertheless, in the current published scientific literature, there is limited empirical evidence that links VR use to better learning. The present investigation aimed to explore the use of VR as a tool for training procedural skills and compare this modality with traditional instruction methods. To investigate skill development using the two forms of training, participants were randomly divided into two groups. The first group received training through an instructional video, while the second group trained in VR. After the training session, the participants performed the trained task in a real setting, and task performance was measured. Subsequently, the user's experienced sense of presence and simulator sickness (SS) was measured with self-report questionnaires. There were no significant differences between groups for any of the performance measures. There was no gender effect on performance. Importantly, the results of the present study indicate that a high sense of presence during the VR simulation might contribute to increased skill learning. These findings can be used as a starting point that could be of value when further exploring VR as a tool for skill development.

The aim of this research was to explore trainees' perceptions and evaluation of Virtual Reality fire extinguisher training. Virtual Reality technology is being adopted by many industries for various purposes including safety training for safety critical industries. The future direction of Virtual Reality training requires an understanding of trainees' evaluation of it; this fact motivated this research. Data were collected from 85 participants using a questionnaire after the training. Observation notes were taken to provide a better understanding of the context. Qualitative research with a thematic analysis was used to analyze the data. The results of this analysis revealed that the most salient themes reflect on issues surrounding the realism of the Virtual Reality simulation, namely different emotional and bodily experiences during the training, while the benefits of the training (health, safety, environmental advantages, efficiency and convenience, repeatability and variety of scenarios) make it a good supplement. Nevertheless, improved realism is needed to make it more effective and enhance transfer and acceptance. This study encourages the consideration of important matters (such as realism and emotions) when using Virtual Reality for fire training. It also describes the positive perceptions of this type of training (repeatability of training, safety and environmental concerns).

This paper discusses the challenges with collecting positivistic empirical data (objective, observable, reliable, replicable, experimental, and true) in human reliability analysis (HRA), and illustrates it by presenting the difficulties in collecting empirical data on the performance-shaping factor (PSF) complexity. The PSF complexity was chosen to illustrate the difficulties with empirically collecting data because it is included in many HRA guidelines and it has been discussed as an important PSF to understand error rates in large accident scenarios. This paper discusses the challenges with collecting empirical data from a pure positivistic paradigm with experiments, as well as from literature reviews and data from event reports, training, and operations. The paper concludes that because of all the challenges with the positivistic empirical data collections methods in HRA, we should discuss whether experts' judgements could be a better approach to obtain HRA data and error rates. In a postpositivistic view, qualitative data or experts' judgments could also be looked at as empirical data if the data were collected in a systematic and transparent way.

In the development of the Petro-HRA method [5], a human reliability analysis (HRA) developed for the petroleum industry, a number of factors believed to effect human performance were reviewed and considered for inclusion in the method's performance shaping factor (PSF) taxonomy. The method was created for prospective risk analysis of post-initiator events and it was created as a method that focused on including the most important PSFs, rather than attempting to include all aspects of human performance. This paper assess whether fatigue should be among the PSFs included. This article presents: (1) how fatigue is included in current human reliability methods; (2) fatigue and its underlying aspects; (3) how these aspects affect performance and; (4) the consideration of including fatigue as a PSF in Petro-HRA. Four possible PSFs based on the causes of are suggested: Sleep deprivation, Shift-length, Non-day shift, and Prolonged task performance. However, due to the relative low impacts of the PSFs and the Petro-HRA's focus on only the strongest PSFs, the final method did not include any of the suggested fatigue PSFs.

With the ultimate purpose of assessing risk within augmented reality-equipped socio-technical systems, in our previous work, we systematically organized and extended state-of-the-art taxonomies of human failures to include the failures related to the extended capabilities enabled by AR technologies. The result of our organization and extension was presented in form of a feature diagram. Current state-of-the-art taxonomies of faults leading to human failures do not consider augmented reality effects and the new types of faults leading to human failures. Thus, in this paper, we develop our previous work further and review state-of-the-art taxonomies of faults leading to human failures in order to: 1) organize them systematically, and 2) include the new faults, which might be due to AR. Coherently with what done previously, we use a feature diagram to represent the commonalities and variabilities of the different taxonomies and we introduce new features to represent the new AR-caused faults. Finally, an AR-equipped socio-technical system is presented and used to discuss about the usefulness of our taxonomy.

Dynamic Probabilistic Risk Analysis (PRA) methods couple stochastic methods (e.g., RAVEN) with safety analysis codes (e.g., RELAP5-3D) to determine risk associated to complex systems such as nuclear plants. Compared to classical PRA methods, which are based on static logic structures (e.g., Event-Trees, Fault-Trees), they can evaluate with higher resolution the safety impact of timing and sequencing of events on the accident progression. Recently, special attention has been given to nuclear plant sites which consist of multiple units and, in particular, on the safety impact of system dependencies, shared systems and common resources on core damage frequencies. In the literature, classical PRA methods have been employed to model multi-unit sites in a limited number of cases while Dynamic PRA methods have never been applied to analyze a full multi-unit model. This paper presents a PRA analysis of a multi-unit plant using Dynamic PRA methods. We employ RAVEN as stochastic tool coupled with RELAP5-3D. The site under consideration consists of three units (each unit is composed by a reactor and its associated spent fuel pool) while the considered initiating event is a seismic induced station blackout event. This paper describes in detail how the multi-unit site has been constructed and, in particular, how unit dependencies and shared resources are modeled from both a deterministic and stochastic point of view.

The authors have recently developed a microworld, a simplified process control simulator, to simulate a nuclear power plant. The microworld provides an environment that can be readily manipulated to gather data using a range of participants, from students to fully qualified operators. Because the microworld represents a simplified domain, it is possible to have more precise experimental control compared with the complex and confounding environment afforded by a full-scope simulator. In this paper, we discuss collecting human reliability data from a microworld. We review the generalizability of human error data from the microworld compared to other data sources like full-scope simulator studies and compare advantages and disadvantages of microworld simulator studies to support human reliability data collection needs.

In recent decades, simulators have become an increasingly accepted part of training in sectors like aviation, medicine, and the petroleum industry. Some countries like the Netherlands, the UK, and Finland have accepted simulators as a part of driver’s education, but in Norway the use of simulators is both limited and restricted. This experimental study aimed to determine whether simulator-based training in night driving could be beneficial compared to traditional Norwegian training. Two equal-sized groups of learner drivers completed both simulator training and traditional training, and both training sessions were followed by a multiple-choice test mapping the learner drivers’ theoretical knowledge on the topic. The results show that theoretical learning outcome is higher from simulator training compared to traditional training, indicating that an increased use of simulators could be beneficial in driver training.

Goals-Operators-Methods-Selection rules (GOMS) was originally developed as a task analytic tool for modeling behavioral primitives in human users of human-computer interfaces. GOMS was recently adapted for Human Reliability Analysis (HRA), producing the GOMS-HRA method. The GOMS-HRA method provides a taxonomy of task level primitives in human activities that correspond to human error probabilities and task timing. The GOMS-HRA method has been used in computation-based HRA (CoBHRA), due to its calibration to the subtask level of human performance, the optimal decomposition level for dynamic risk modeling. GOMS-HRA has also been linked to procedures, and it is possible to map procedure steps (called procedure level primitives) to task level primitives. This paper introduces another important development to the GOMS-HRA framework-the task level errors, which represent the use of the GOMS-HRA taxonomy for predicting human error types. While many HRA methods map task types or task primitives to error rates, the prediction of error is often a generic error type. In reality, each task level primitive has predilections to certain types of errors. To model human error dynamically requires the determination of the types of errors that can occur.

Successful, round-the-clock operations in high-risk and complex organizations rely on the proper transfer of critical information through skilled team communication and a reliable system for handing over the operations to the next shift. Handovers, by nature, pose a risk to processes when information is lost or corrupted between the sender and the receiver. This paper reviews some of the large-scale accidents that have occurred in the past 25 years, whose investigations reveal a failure in handover as one of the underlying causes of the accident. The paper then discusses the results of a qualitative study on the handover activity at the Norwegian User Support and Operations Center's (N-USOC). The N-USOC has a control room for experiments on plant breeding in closed growth systems inside the International Space Station (ISS). This study provides an invaluable insight into the HO variability of a specific team in the use of two different control room consoles. Finally, the paper expounds on why there is a difference in the HO of two consoles by the same operators and why thorough planning is vital to efficient and safe operations.

A lack of empirical data is often been presented as a large challenge for HRA, which begs the question: why is this so difficult? HRA methods were not developed as objective quantitative test methods, but more as qualitative evaluation methods because objective data did not exist. Since HRA methods include substantial qualitative evaluation of the meaning of the elements in HRA methods, such as definitions of the performance shaping factors as well as their strength, these elements cannot be objective measured. This paper also discusses other challenges with collection data from event reports, literature reviews, experiments and databases. The conclusion in this paper is that a decision should be made about how we should look at HRA methods: as qualitative evaluation methods or objective quantitative test methods. Quantitative and qualitative methods have different approaches to evaluate the quality of the methods making it difficult to be something in between.

As tools like full-scale simulators and microworlds become more readily available to researchers, a fundamental question remains the extent to which full scenarios and simulators are necessary for valid and generalizable results. In this paper, we explore the continuum of scenarios and simulators and evaluate the advantages and disadvantages of each for human performance studies. The types of scenarios presented to participants may range from microtasks to complex multi-step scenarios. Microtasks usually involve only brief exposure to the human-system interface but may thereby facilitate ready data collection through repeated trials. In contrast, full scenarios present a sequence of actions that may require an extended period of time. The tradeoffs center on the fidelity of the situations and the requirements for the type of human performance data to be collected. The type of simulator presented to participants may range from a part-task simulator, to a simplified microworld, or to a full-scope high-fidelity simulator. The simplified simulators present greater opportunity for control but lose much of the context of real-world use found in full-scope simulators. We frame scenarios and simulators in the context of micro- vs. macro-cognition and provide examples of how the different experimental design choices lend themselves to different types of studies.

Human error is attributed as the cause of 50–90% of all accidents and incidents. One of the method-types that try to estimate or predict human error is human reliability analysis (HRA). This paper explores how microworlds - graphically rich and complex rule governed virtual worlds that users immersed themselves in – can be used in HRA. The main focus is research microworlds, but also microworlds made for recreational purposes (i.e. video games) are discussed.