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Poor maintenance management leads to non-negligible economic, environmental and social impacts and obstacles to the sustainable manufacturing paradigm. Studies evaluating maintenance impacts on sustainability underline growing interest in the topic, but reports on the industrial field are lacking. Therefore, this paper investigates the industrial environment and the indicators that manufacturing companies use for measuring their maintenance impacts.

In this pilot survey study, several stakeholders of production enterprises in the south of Italy were interviewed to unveil the spread of the measurement of maintenance impacts on sustainability and the indicators used by those companies.

The interview results showed a low level of awareness among stakeholders about maintenance impacts on sustainability. Maintenance stakeholders are mainly focused on technical and economic factors, whereas environmental, quality and safety stakeholders are becoming more aware of maintenance impacts on environmental and social factors. However, both groups need guidelines to define sustainability indicators to assess such impacts.

This exploratory study allowed us to investigate the current situation in industrial organisations and achieve the first variegated and diversified vision of the awareness of company stakeholders on maintenance impacts on the sustainability of several business functions. This paper provides a valuable contribution to “maintenance and sustainability” research area in production contexts and sheds light on non-negligible maintenance impacts on sustainability, providing preliminary insights on the topic and an effective basis for defining future research opportunities. Moreover, this study enables increased awareness among internal and external manufacturing company stakeholders on the role of maintenance in sustainable production.

As a new concept for humane entrepreneurship (HumEnt) evolves, many new research questions arise. At the exploratory stage, the authors found it relevant to examine and discuss the perception of the fundamental assumptions of the HumEnt concept and activities undertaken in this area by business practice.

To thoroughly understand the studied phenomenon, a combination of quantitative and qualitative methods was used. An exploratory survey was obtained from 126 purposefully selected enterprises in Poland; then, a single case study was analysed.

The conducted analysis showed differences between the activities of enterprises and the perception of the HumEnt concept among employees that are especially noticeable at different levels of the management hierarchy.

The multifaceted nature of the results obtained is limited by the inability to infer international differences, to capture trends over time and to generalise to the total population of enterprises.

Although the surveyed companies recognise the importance of the HumEnt concept, it is not tantamount to its execution. The research results may be valuable, especially for smaller enterprises, where the business practice may require support in applying the HumEnt approach.

The research explored both the actual state confirmed by the actions taken and the perception of the importance of individual elements of HumEnt. A knowing–doing gap has been demonstrated between these planes. Moreover, thanks to a two-stage study, practices were selected that can be successfully implemented also in small and medium-sized enterprises.

This paper will examine the unsafe work practices that are plaguing the construction industry. Statistics show that four out of five of all workplace accidents are attributed to unsafe behaviour. Research studies have sought to understand worker self-protection. For example, it is difficult to make predictions of conditions that influenced worker’s behaviour to act unsafely or safely in a given work situation. It is evident there is a gap in the literature in this area of research, most notably failing to understand the underlying “why” factors. The aim of the study is to identify and examine the proximate set of contributing factors most likely to have an influence on workers’ decisions about participation in unsafe behaviour.

To perform the study, questionnaires were adopted, and 225 construction workers from 9 construction companies participated in the study.

Results showed that both underlying organisational factors and individual factors could affect the risk aversion among construction workers. The paper also highlights measures to create a safe work environment to minimise unsafe behaviour among construction workers. Results from the study are important to help organisation to systematically plan for a good working environment.

As the results were based only from the questionnaires, a deeper understanding behind the workers’ responses was not probed.

Construction companies should work at several organisational levels at the same time. It is necessary to include levels such as individual, group, workplace and management levels, thus taking a system perspective on risk behaviour and safety.

This study aims to present the concept of aircraft turbofan engine health status prediction with artificial neural network (ANN) pattern recognition but augmented with automated features engineering (AFE).

The main concept of engine health status prediction was based on three case studies and a validation process. The first two were performed on the engine health status parameters, namely, performance margin and specific fuel consumption margin. The third one was generated and created for the engine performance and safety data, specifically created for the final test. The final validation of the neural network pattern recognition was the validation of the proposed neural network architecture in comparison to the machine learning classification algorithms. All studies were conducted for ANN, which was a two-layer feedforward network architecture with pattern recognition. All case studies and tests were performed for both simple pattern recognition network and network augmented with automated feature engineering (AFE).

The greatest achievement of this elaboration is the presentation of how on the basis of the real-life engine operational data, the entire process of engine status prediction might be conducted with the application of the neural network pattern recognition process augmented with AFE.

This research could be implemented into the engine maintenance strategy and planning. Engine health status prediction based on ANN augmented with AFE is an extremely strong tool in aircraft accident and incident prevention.

Although turbofan engine health status prediction with ANN is not a novel approach, what is absolutely worth emphasizing is the fact that contrary to other publications this research was based on genuine, real engine performance operational data as well as AFE methodology, which makes the entire research very reliable. This is also the reason the prediction results reflect the effect of the real engine wear and deterioration process.

On shield tunnel construction (STC) site, human error is widely recognized as essential to accident. It is necessary to explain which factors lead to human error and how these factors can influence human performance. Human reliability analysis supports such necessity through modeling the performance shaping factors (PSFs). The purpose of this paper is to establish and validate a PSF taxonomy for the STC context.

The approach taken in this study mainly consists of three steps. First, a description of the STC context is proposed through the analysis of the STC context. Second, the literature which stretch across the PSF methodologies, cognitive psychology and human factors of STC and other construction industries are reviewed to develop an initial set of PSFs. Finally, a final PSF set is modified and validated based on STC task analysis and STC accidents cases.

The PSF taxonomy constituted by 4 main components, 4 hierarchies and 85 PSFs is established for human behavior modeling and simulation under the STC context. Furthermore, by comparing and evaluating the performance of STC PSF and existing PSF studies, the proposed PSF taxonomy meets the requirement for qualitative and quantitative analysis.

The PSF taxonomy can provide a basis and support for human behavior modeling and simulation under the STC context. Integrating PSFs into a behavior simulation model provides a more realistic and integrated assessment of human error by manifesting the influence of each PSFs on the cognitive processes. The simulation results can suggest concrete points for the improvement of STC safety management.

This paper develops a taxonomy of PSFs that addresses the various unique influences of the STC context on human behaviors. The harsh underground working conditions and diverse resources of system information are identified as key characteristics of the STC context. Furthermore, the PSF taxonomy can be integrated into a human cognitive behavior model to predict the worker’s behavior on STC site in future work.

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.

Container terminal operation is one of the most risky industries. Many of the accidents that occurred were found to be caused by human errors. However, it seems relatively little research has been conducted to examine the influence of leader-member exchange (LMX) relationship on employee safety behavior. Therefore, the purpose of this study is to examine the effects of leader-member exchange and safety climate on employees’ safety organizational citizenship behaviors (SOCB) in the container terminal context based on the social exchange theory.

A structural equation modeling was used with confirmatory factor analysis, and survey data are collected from 265 employees in major container terminals in Taiwan.

Results indicated that LMX is positively associated with safety climate, whereas safety climate positively influences employees’ safety citizenship behavior. Specifically, results indicated that safety climate mediates the effect of LMX on employees’ SOCB.

This study was limited to LMX dimensions adapted from the studies of Li and Liao (2014) and Vidyarthiv et al. (2014). Future research could examine the linkages between LMX, ethical climate, safety performance and supervisor leadership influence. Furthermore, this research focused specifically on employees from the container terminal operators in Taiwan. It would be valuable to collect data from employees from other countries to obtain a balanced view of the relationship between LMX, team-member exchange (TMX), safety climate and employee SOCB in container terminal operations.

This research provides a useful implication for container terminal operators to enhance LMX qualities and employee safety behavior through organizational participation, employee-helping behaviors and informing workers to obey safety rule and regulation.

Given the prevalence of accidents and unsafe behavior in container terminal operations, this research sought to examine the relationships among LMX, safety climate and employee SOCB in the container terminal context. Theoretically, this study highlights the importance of LMX and safety climate in explaining the SOCB of employees.

To investigate the influence of logistics and transportation workers’ perceptions of their management’s simultaneous safety and operations focus (or lack thereof) on related worker safety and operational perceptions and behaviors.

This multi-method research consisted of two studies. Study 1 aimed to establish correlational relationships by evaluating the impact of individual-level worker perceptions of operationally focused routines (as a moderator) on the relationship between worker perceptions of safety-related routines and workers’ self-reported safety and in-role operational behaviors using a survey. Study 2 aimed to establish causal relationships by evaluating the same conceptual relationships in a behavioral-type experiment utilizing vehicle simulators. After receiving one of four pre-task briefings, participants completed a driving task scenario in a driving simulator.

In Study 1, the relationship between perceived safety focus and safety behavior/in-role operational behavior was strengthened at higher levels of perceived operations focus. In Study 2, participants who received the balanced pre-task briefing committed significantly fewer safety violations than the other 3 treatment groups. However, in-role driving deviations were not impacted as hypothesized.

This research is conducted at the individual (worker) level of analysis to capture the little-known perspectives of logistics and transportation workers and explore the influence of balanced safety and operational routines from a more micro perspective, thus contributing to a deeper understanding of how balanced routines might influence worker behavior when conducting dynamic tasks to ensure safe, effective outcomes.

This paper aims to present a summary of the performance measurement and evaluation plan of the Wyoming connected vehicle (CV) Pilot Deployment Program (WYDOT Pilot).

This paper identified 21 specific performance measures as well as approaches to measure the benefits of the WYDOT Pilot. An overview of the expected challenges that might introduce confounding factors to the evaluation effort was outlined in the performance management plan to guide the collection of system performance data.

This paper presented the data collection approaches and analytical methods that have been established for the real-life deployment of the WYDOT CV applications. Five methodologies for assessing 21 specific performance measures contained within eight performance categories for the operational and safety-related aspects. Analyses were conducted on data collected during the baseline period, and pre-deployment conditions were established for 1 performance measures. Additionally, microsimulation modeling was recommended to aid in evaluating the mobility and safety benefits of the WYDOT CV system, particularly when evaluating system performance under various CV penetration rates and/or CV strategies.

The proposed performance evaluation framework can guide other researchers and practitioners identifying the best performance measures and evaluation methodologies when conducting similar research activities.

To the best of the authors’ knowledge, this is the first research that develops performance measures and evaluation plan for low-volume rural freeway CV system under adverse weather conditions. This paper raised some early insights into how CV technology might achieve the goal of improving safety and mobility and has the potential to guide similar research activities conducted by other agencies.