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Connected vehicle-based variable speed limit (CV-VSL) systems in fog area use multi-source detection data to indicate drivers to make uniform change in speed when low visibility conditions suddenly occur. The purpose of the speed limit is to make the driver's driving behavior more consistent, so as to improve traffic safety and relieve traffic congestion. The on-road dynamic message sign (DMS) and on-board human–machine interface (HMI) are two types of warning technologies for CV-VSL systems. This study aims to analyze drivers’ acceptance of the two types of warning technologies in fog area and its influencing factors.

This study developed DMS and on-board HMI for the CV-VSL system in fog area on a driving simulator. The DMS and on-board HMI provided the driver with weather and speed limit information. In all, 38 participants participated in the experiment and completed questionnaires on drivers’ basic information, perceived usefulness and ease of use of the CV-VSL systems. Technology acceptance model (TAM) was developed to evaluate the drivers’ acceptance of CV-VSL systems. A variance analysis method was used to study the influencing factors of drivers’ acceptance including drivers’ characteristics, technology types and fog density.

The results showed that drivers’ acceptance of on-road DMS was significantly higher than that of on-board HMI. The fog density had no significant effect on drivers’ acceptance of on-road DMS or on-board HMI. Drivers’ gender, age, driving year and driving personality were associated with the acceptance of the two CV-VSL technologies differently. This study is beneficial to the functional improvement of on-road DMS, on-board HMI and their market prospects.

Previous studies have been conducted to evaluate the effectiveness of CV-VSL systems. However, there were rare studies focused on the drivers’ attitude toward using which was also called as acceptance of the CV-VSL systems. Therefore, this research calculated the drivers’ acceptance of two normally used CV-VSL systems including on-road DMS and on-board HMI using TAM. Furthermore, variance analysis was conducted to explore whether the factors such as drivers’ characteristics (gender, age, driving year and driving personality), technology types and fog density affected the drivers’ acceptance of the CV-VSL systems.

The purpose of this paper is to present a review of research developments on the ecosystem of driving forces for electronic procurement (e-procurement) on project procurement and to propose directions for future research for an effective adoption and sustained usage.

A systematic literature review was conducted in three phases to identify and examine literature. A total of 68 papers were retrieved and were thoroughly reviewed to identify the drivers for e-procurement.

A total of 61 drivers were identified and subsequently developed into a categorization framework for synthesized understanding which reveals existing interrelationships. Although literature has consensus on some selected drivers, few studies have identified drivers relating to sustainability. Gaps were identified from the existing literature and directions for future research were proposed.

Since this is a literature review, future research could conduct further investigations focusing on the research gaps identified. The framework developed presents a basis for further research to explore the drivers in various socio-economic environments.

This study provides valuable insights for improving the understanding of practitioners on the complex network of drivers for e-procurement. These findings stimulate discussions on benefits required for assessment in e-procurement adoption by practitioners.

This study provides the first comprehensive review of the drivers for e-procurement adoption in the construction industry, which was lacking in the existing body of knowledge.

The objective of this paper is the development of a voluntary compliance model that is applied to the current and substantive governmental regulatory electronic on‐board recorder (EOBR) proposal in the workplace.

This study is designed to qualitatively evaluate the responses of commercial drivers to the federal government's proposed EOBR safety technology. Results are based on the analysis of published comments made by commercial drivers to a US government online database, the Federal Docket Management System.

Four major thematic categories emerged from the commercial driver comments are analyzed, including government control over workplace behavior; financial impact; technology readiness; and attribution issues –, e.g. misplaced responsibility for safety problems (generally on other drivers or outside parties). This paper links these concerns to a procedural justice model and discusses how procedural justice concerns can contribute to voluntary compliance and employment turnover intentions. Important commercial driver, motor carrier, and public policy implications are thus revealed.

This study represents the first development of a voluntary compliance model that is applied to the proposed EOBR mandate. The deployment of this voluntary compliance model may lead to greater compliance rates.

The objective of the study is to identify and analyse drivers of smart manufacturing using integrated grey-based approaches. The analysis facilitates industry practitioners in the identification of preference of drivers through which smart manufacturing can be implemented. These drivers are explored based on existing literature and expert opinion.

Modern manufacturing firms have been adopting smart manufacturing concepts to sustain in the global competitive landscape. Smart manufacturing incorporates integrated technologies with a flexible workforce to interlink the cyber and physical world. In order to facilitate the effective deployment of smart manufacturing, key drivers need to be analysed. This article presents a study in which 25 drivers of smart manufacturing and 8 criteria are analysed. Integrated grey Technique for Order Preference by Similarity to Ideal Solution (grey TOPSIS) is applied to rank the drivers. The derived ranking is validated using “Complex Proportional Assessment – Grey” (COPRAS-G) approach.

In total, 25 drivers with 8 criteria are being considered and an integrated grey TOPSIS approach is applied. The ranking order of drivers is obtained and further sensitivity analysis is also done.

In the present study, 25 drivers of smart manufacturing are analysed. In the future, additional drivers could be considered.

The study presented has been done with inputs from industry experts, and hence the inferences have practical relevance. Industry practitioners need to focus on these drivers in order to implement smart manufacturing in industry.

The analysis of drivers of smart manufacturing is the original contribution of the authors.

What happens to nonelite workers’ meaning, belonging, and identity when work is “on-demand”? On-demand organizations, such as Uber and TaskRabbit, have ambiguous boundaries and locations of workers. This qualitative study investigated how organizational and societal boundary discourse and the organization of the work itself, constructed sometimes conflicting worker roles that influenced how ride-hailing workers understood the boundaries of the on-demand organization and their location with respect to it. The roles of app–user and driver–partner constructed ride-hailing workers as outside the boundaries of the organization, while the driver–bot role constructed them as (nonhuman) elements of organizational technology. While the driver–partner role had positive and empowering identity, meaning, and belongingness associations, its conflict with the other roles blocked these positive associations, and led to cynicism and fatalism. We reflect on the possible impacts of the on-demand economy on society, workers, and the practice of work, particularly for nonelite workers.

Technological advances regarding artificial intelligence (AI) are affecting the transport sector. Although fully autonomous delivery, or self-driving trucks, are not operating currently, various AI applications have become fixed components of cargo vehicles. Since many research approaches primarily concentrate on the technical aspects of assistance systems (ASs), the economic question of how to improve efficiency is seldom addressed. Therefore, the purpose of this paper is to apply an efficiency analysis to measure the performance of truck drivers supplying retail stores.

For this comparative study, 90 professional truck drivers in three groups are compared with (1) trucks without AS, (2) trucks with AS that cannot be turned off and (3) trucks with AS that can be turned off. First, we build a model investigating the impact of performance expectation, effort expectation, social influence and facilitating conditions on the behavioural intention to use AS. Second, we explore the impact of truck drivers' behavioural intention on actual technology use, misuse and disuse; operationalize these constructs; and merge them with our behavioural constructs to create one econometric model.

The human–AI system was found to be the most efficient. Additionally, behavioural intention to use ASs did not lead to actual usage in the AI-alone observation group, but did in the human–AI group. Several in-depth analyses showed that the AI-alone group used AS at a higher level than the human–AI group, but manipulations through, for example, kickdowns or manual break operations led to conscious overriding of the cruise control system and, consequently, to higher diesel consumption, higher variable costs and lower efficiency of transport logistical operations.

Efficiency analysis with data envelopment analysis is, by design, limited by the applied input and output factors.

This study represents one of the first quantitative efficiency analyses of the impact of digitalization on transport performance (i.e. truck driver efficiency). Furthermore, we build an econometric model combining behavioural aspects with actual technology usage in a real application scenario.

Emerging technologies have the capacity to transform industries offering substantial benefits to users. Given the increasing demand for advanced logistics services, third-party logistic service providers (LSPs) face greater pressure to deploy and realise these technologies, especially given the demands and operational challenges created during the COVID-19 crisis. Drawing upon the diffusion of innovation (DOI) theory and technology–organisation–environment (TOE) framework, this paper goes beyond just identifying drivers and barriers to technology adoption to understanding how LSPs and industry experts perceive these drivers and barriers and simultaneously confront and undertake actions to implement them.

An exploratory study was conducted in three phases: (1) in-depth interviews with twelve stakeholders in the Australian logistics industry; (2) five in-depth interviews conducted with stakeholders during the COVID-19 crisis and (3) a focus group discussion session. All interviews were analysed using content analysis and revealed several drivers for the deployment of emerging technologies, including internal organisational factors that drive supply chain (SC) network optimisation.

The analysis of the three phases identified several drivers for the deployment of emerging technologies in logistics, including internal organisational factors that drive SC network optimisation. Also identified were external drivers including the impact of the COVID-19 crisis, along with barriers and specific actions that were considered and implemented by LSPs for sustainable operations, particularly in a post-COVID-19 environment.

This study explores organisational and industry drivers for the implementation of emerging technologies. Explicitly, it extends the extant research by highlighting organisational and industry drivers and enablers that influence adoption and deployment of emerging technologies. Second, it advances the existing perspectives on LSPs in the Australian context on the development and implementation of technology strategies. The paper offers insights around implementation of technologies, directly obtained from industrial application for managers and practitioners.

Though academic study on the subject is still in its early stages, there is growing interest in using blockchain technology for transforming the supply chain. The academic literature is divided and yet only includes studies evaluating how the supply chain has changed organizations. To comprehend the new phenomena, this study aims to investigate the factors of blockchain technology in driving supply chain transformation. To be more precise, the authors developed from the literature the most prevalent criteria for determining if supply chain transformations are ready to be scaled up.

This study followed a combination of two multi-criteria decision making methods evaluation based on distance from average solution and complex proportional assessment) methodology in this research: planning, investigating, executing out, establishing a rating of the criteria and evaluating it.

The study shows that the “organizational driver” and the “technology driver” are the factors most important to the transformation of the supply chain, whereas the “financial driver” and the “regulatory driver” are less important. This study also makes some managerial recommendations to address the factors impeding the supply chain’s transformation. Each factor’s significance was explored, and a proposed study agenda was also presented.

Although the main forces behind the transformation of the supply chain have been recognized, further research into statistical correlation is required to confirm how the various elements interact.

This research aids decision-makers in comprehending the key forces behind supply chain transformation. Managers and decision-makers might better predict and allocate the necessary resources to start the road toward digitization and make well-informed choices once these aspects have been investigated and understood.

In light of the pandemic’s effects on the world and the increase in businesses embracing the digital economy, the supply chain transformation is more important than ever. Beyond blockchain deployment and the pilot studies on digital transformation, there is a gap. The topics and factors this study uncovered will operate as a framework and recommendations for more theoretical investigation and practical applications.

This paper systematically reviewed research work on drivers of teamwork, which will reinforce construction work teams to enhance workers’ safety performance. This study adds to the existing but limited understanding of teamwork drivers on construction workers’ safety performance. This paper presents scholars and industry-based professionals with critical initiatives that have to be implemented in organisations to get positive results in safety while working in teams with an emphasis on systems drivers of teamwork on safety performance at the organisational level, which will help in providing information on the functioning of the teams and contribute towards improved safety performance of team workers.

This study aims to systematically examine the existing body of knowledge on drivers of teamwork by analysing 53 publications from the years 1997–2021. The Scopus search engine was used to conduct a systematic review and germane publications were collated.

According to the findings of the review, since 1997, there has been a burgeoning concern in the research of drivers of teamwork and its impact on workers’ safety performance. After performing a systematic review, 37 drivers of teamwork were identified. The top five drivers are effective communications, team workers’ relations, leadership, shared knowledge and information, and team training. Moreover, it was noted that the United States and Australia have been the international regions of focus for most of the research in the area of drivers of teamwork from the years 1997–2021. The 37 drivers of teamwork are distributed into six major socio-technical components: people drivers; culture drivers; metrics drivers; organisational and management practices and procedures drivers; infrastructure drivers and technology drivers.

The results reported present research scholars and professional practitioners with an overview of the drivers of teamwork that could be implemented in the construction industry to streamline potential implementations and improve safety performance of construction workers.

A list of teamwork drivers has been developed to ratify potential empirical research in the area of construction safety. The results would contribute to the existing but restricted understanding of drivers of teamwork in the construction industry.

The purpose of this paper is to uncover the importance of several variables centered around partnership in renewable energy (RE) projects. The concept developed earlier is applied to the project environment to identify interrelations between external and internal drivers, project partnership, resources and project success. A framework consisting of logical chain to project success is proposed.

The study derived the concepts and variables of RE projects’ partnership from the academic literature. The methodology of the case study was used to gain insights in relation to the variables in the logical chain of the proposed framework, suggesting that certain external/internal drivers direct diverse stakeholders to a partnership.

RE technologies are not commodities and require involvement of different stakeholders, who directly or indirectly are impacted by implementation of the RE projects. For the project to be a success, it is critical to involve the stakeholders early in the process and induce partnership synergy, through which dynamic capabilities and implementation mechanisms are capitalized upon as resources to achieve project success.

There appears to be no specific framework directly linking partnership synergy and project success; their relationships are only indirectly inferred. Focusing on project-level activities of public and private stakeholders the paper proposes a framework that conceptualizes relationships among external/internal drivers, partnership, resources and project success/performance. This could be a promising future research direction, helping technology project stakeholders maximize their returns by realizing full advantage of collaboration and partnership