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Challenges associated with ensuring the accuracy and reliability of cost estimation of highway construction bid items are of significant interest to state highway transportation agencies. Even with the existing research undertaken on the subject, the problem of inaccurate estimation of highway bid items still exists. This paper aims to assess the accuracy of the cost estimation methods employed in the selected studies to provide insights into how well they perform empirically. Additionally, this research seeks to identify, synthesize and assess the impact of the factors affecting highway unit prices because they affect the total cost of highway construction costs.

This paper systematically searched, selected and reviewed 105 papers from Scopus, Google Scholar, American Society of Civil Engineers (ASCE), Transportation Research Board (TRB) and Science Direct (SD) on conceptual cost estimation of highway bid items. This study used content and nonparametric statistical analyses to determine research trends, identify, categorize the factors influencing highway unit prices and assess the combined performance of conceptual cost prediction models.

Findings from the trend analysis showed that between 1983 and 2019 North America, Asia, Europe and the Middle East contributed the most to improving highway cost estimation research. Aggregating the quantitative results and weighting the findings using each study's sample size revealed that the average error between the actual and the estimated project costs of Monte-Carlo simulation models (5.49%) performed better compared to the Bayesian model (5.95%), support vector machines (6.03%), case-based reasoning (11.69%), artificial neural networks (12.62%) and regression models (13.96%). This paper identified 41 factors and was grouped into three categories, namely: (1) factors relating to project characteristics; (2) organizational factors and (3) estimate factors based on the common classification used in the selected papers. The mean ranking analysis showed that most of the selected papers used project-specific factors more when estimating highway construction bid items than the other factors.

This paper contributes to the body of knowledge by analyzing and comparing the performance of highway cost estimation models, identifying and categorizing a comprehensive list of cost drivers to stimulate future studies in improving highway construction cost estimates.

The purpose of this study is to test whether equity theory (ET) – which posits that individuals compare their outcome/input ratio to the ratio of a “comparison other” and classify individuals as Benevolent, Equity Sensity, and Entitled – applies to the modern workplace of global virtual teams (GVT), where work is mostly intellectual, geographically dispersed and online, making individual effort nearly impossible to observe directly.

Using a sample of 1,343 GVTs comprised 6,347 individuals from 137 countries, this study tests three ET’s predictions in the GVT context: a negative, linear relationship between Benevolents’ perceptions of equity and job satisfaction in GVTs; an inverted U-shaped relationship between Equity Sensitives’ perceptions of equity and job satisfaction in GVTs; and a positive, linear relationship between Entitleds’ perceptions of equity and job satisfaction in GVTs.

Although the second prediction of ET is supported, the first and third have statistically significant opposite signs.

The research has important ramifications for management studies in explaining differences in organizational behavior in GVTs as opposed to traditional work settings.

The authors conclude that the main novelty with ET in GVTs is that GVTs are an environment stingy with satisfaction for “takers” (Entitleds) and generous in satisfaction for “givers” (Benevolents).

Research on electric sports (eSports) has experienced significant growth in recent years as a consequence of increasing connectivity, institutionalization, and technological advances. However, the interdisciplinary nature of the eSports as a field and the burgeoning growth in eSports articles have rendered it necessary to conduct a systematic review of extant literature to take stock of the knowledge accumulated. To this end, we aim to undertake a comprehensive review of extant literature that takes stock of published research to derive opportunities for future research in the realm of eSports. In so doing, we contribute to the advancement of the field by mapping out the knowledge trajectory of eSports research and elucidating areas that have remained underexplored thus far.

To conduct systematic review of the eSports literature, we employed a framework that included six essential steps: protocol, search, appraisal, synthesis, analysis, and report. This comprehensive approach enables us to meticulously investigate the existing body of literature on eSports and identify key trends and topics addressed within the field. By conducting the multidisciplinary systematic literature review, we thoroughly assess the current state of eSports literature and subsequently outline potential research avenues that can contribute to eSports fields.

This study draws on a six-phase framework – member preparation, team formation, character selection, team coordination, team performance, and team reflection – to illustrate the roles played by different levels of analysis unit (i.e. characters, players, and teams) and three distinct yet interconnected stages (i.e. inputs, process, and outputs) within eSports games as well as the research opportunities it brings.

We conducted a rigorous systematic review of the eSports literature by using quantitative citation analysis and qualitative content analysis. Furthermore, we adopted team dynamic view of eSports to identify potential research avenues for future research that contribute to advancing our understanding of the eSports tournaments.

Computer-assisted instruction (CAI) has been used to combat reading challenges, namely reading accuracy and rate for learners with intellectual, developmental and learning disabilities (IDLD). Whilst most reading CAI effectiveness has been studied in English, other transparent languages have less evidence. This study provides a systematic review and meta-analysis of CAI effectiveness for transparent language reading for K-3 learners with IDLD.

This study systematically reviews academic peer-reviewed studies from 2010 to 2023 with either randomised controlled treatment (RCT) or single-case treatments. Articles were searched from the ACM Digital Library, Google Scholar, IEEE Xplore, ERIC, PsychINFO and Science Direct databases, references and systematic review articles. Reading component skills effect sizes were computed using the random effect sizes model.

11 RCT studies of reading CAI for transparent languages with 510 learners with IDLD were found. A random effect sizes (Cohen’s d) of CAI on individual reading component skills were d = 0.24, p-value = 0.063 and confidence interval (CI) 95% (−0.068–0.551) for phonics and phonemic awareness d = 0.41, p-value = 0.000 and CI 95% (0.175–0.644). Given an average intervention dosage of 1.8 h weekly for a maximum of 16 weeks, CAI had better retention with d = 1.13, p-value = 0.066 and CI 95%(−0.339–2.588). However, these results must be interpreted with a concern of only using published studies.

The study contributes to quantitative CAI effectiveness for transparent language reading components for learners with IDLD.

The current and modern electrical distribution networks, named smart grids (SGs), use advanced technologies to accomplish all the technical and nontechnical challenges naturally demanded by energy applications. Energy metering collecting is one of these challenges ranging from the most basic (i.e., visual assessment) to the expensive advanced metering infrastructure (AMI) using intelligent meters networks. The AMIs’ data acquisition and system monitoring environment require enhancing some routine tasks. This paper aims to propose a methodology that uses a distributed and sustainable approach to manage wide-range metering networks, focused on using current public or private telecommunication infrastructure, optimizing the implementation and operation, increasing reliability and decreasing costs.

Inspired by blockchain technology, a collaborative metering system architecture is conceived, managing massive data sets collected from the grid. The use of cryptography handles data integrity and security issues.

A robust proof-of-concept simulation results are presented concerning the resilience and performance of the proposed distributed remote metering system.

The methodology proposed in this work is an innovative AMI solution related to SGs. Regardless of the implementation, operation and maintenance of AMIs, the proposed solution is unique, using legacy and new technologies together in a reliable way.

In this systematic review of the literature on women’s entrepreneurship education, this paper aims to examine the current state of the field. The authors analyze publication trends, identify major themes and propose an agenda for future research.

The authors review 363 articles published between 1993 and 2023, to develop a synthesized overview of women’s entrepreneurship education, complete with insights into the journals that have provided the most coverage of this topic, as well as how it has emerged over time.

The authors tracked the evolution of research themes and collaboration networks over a 30-year period. Results show there has been significant growth in research on women’s entrepreneurship education, as evidenced by a surge of publications on the topic and the total number of citations.

The authors categorized and analyzed six thematic clusters within the literature: entrepreneurial intention, ethical perspectives, gender-specific barriers, gender stereotypes, rural entrepreneurship and entrepreneurial self-efficacy. Building on these thematic clusters, this study discusses future research directions to advance the field.

This study aims to examine how the theory of planned behaviour and technology acceptance theory can be used to understand the adoption of non-pharmaceutical interventions (NPIs). The relationships between attitudes, behavioural intentions towards using NPIs, actual use of NPIs and word-of-mouth (WOM) were examined and compared between early and late adopters.

A survey was conducted to test the hypotheses with partial least squares structural equation modelling (n = 278).

The results indicate that relationships between attitudes, intentions and behavioural intentions were positive and significant in the whole data set – and that there were differences between the early and late adopters. WOM had no substantial relationship with actual usage and early adopters’ behavioural intentions.

This research gives a better sense of how WOM impacts attitudes, behavioural intentions and actual usage among early and late adopters of NPIs and highlights the effectiveness of WOM, especially among late adopters of NPIs. Furthermore, using the TAM allows us to make specific recommendations regarding encouraging the use of NPIs. A new three-stage communications model is introduced that uses early adopters as influencers to reduce the NPI adoption time by late adopters.

Teleworking, working from home and flexible work have gained popularity over the last few years. A shift in policies and practices in the workplace is required owing to the COVID-19 pandemic accelerating current trends in work-from-everywhere (WFE) research. This article presents a systematic literature review of WFE research from 1990 to early 2023 to understand the transformation of the field.

The Web of Science database was used to conduct this review based on rigorous bibliometric and network analysis techniques. The prominence of the research studied using SPAR-4-SLR and a collection of bibliometric techniques on selected journal articles, reviews and early access articles. Performance and keyword co-occurrence analysis form the premise of cluster analysis. The content analysis of recently published papers revealed the driving and restraining forces that help define and operationalize the concept of WFE.

The major findings indicate that the five established and accelerated trends from cluster analysis are COVID-19 and the pandemic, telework(ing), remote working, work from home and well-being and productivity. Driving and restraining forces identified through content analysis include technological breakthroughs, work–life integration challenges, inequality in the distribution of jobs, gender, shifts in industry and sector preferences, upskilling and reskilling and many more have been published post-COVID in the restraining forces category of WFE.

A key contribution of this pioneering study of “work from everywhere” is the linking of the bibliometric trends of the past three decades to the influencing and restraining factors during the pandemic. This study illustrates how WFE could be perceived differently post-COVID, which is of great concern to practitioners and future researchers.

A wide range of publications on WFE and multiple synonyms can create confusion if a systematic and effective system does not classify and associate them. This study uses both bibliometric and scientometric analyses in the context of WFE using systematic literature review (SLR) methods.

This systematic review aims to assess the studies collected by identifying factors influencing the acceptance of e-learning systems before and during the current propagation of the COVID-19 pandemic.

This study undertook a literature review on the in-depth revision of studies published before 2021. The reviewed research papers meet the inclusion and exclusion criteria. A total of 97 out of 214 articles met the inclusion criteria and were subsequently used in this review.

The findings revealed that the survey questionnaire is the most common data collection instrument used regardless of the research objectives. 2019 was a remarkable year because of the emergence of the COVID-19 pandemic.

This systematic review relied on specific databases (ScienceDirect, Emerald, IEEE and Google Scholar) to search for the articles included in this paper. However, these databases may not comprehensively represent all papers published on e-learning using the technology acceptance model (TAM).

This paper suggests a guide for managers and scholars in educational institutions and acts as a roadmap for practitioners and academics in the educational field and policymakers. This research spotlights the significant factors influencing the acceptance and adoption of e-learning.

This research assessed articles that examined the TAM in e-learning and classified them according to their methodology, country of dissemination, context and distribution within the year of publication. This paper contributes to the body of knowledge in a way that will benefit stakeholders in an educational setting.

Artificial intelligence (AI) is experiencing growth and prosperity worldwide because of its convenience and other benefits. However, AI faces challenges related to consumer resistance. Thus, drawing on the user resistance theory, this study explores factors that influence consumers’ resistance to AI and suggests ways to mitigate this negative influence.

This study tested four hypotheses across four studies by conducting lab experiments. Study 1 used a questionnaire to verify the hypothesis that AI’s “substitute” image leads to consumer resistance to AI; Study 2 focused on the role of perceived threat as an underlying driver of resistance to AI. Studies 3–4 provided process evidence by the way of a measured moderator, testing whether AI with servant communication style and literal language style is resisted less.

This study showed that AI’s “substitute” image increased users' resistance to AI. This occurs because the substitute image increases consumers’ perceived threat. The study also found that using servant communication and literal language styles in the interaction between AI and consumers can mitigate the negative effects of AI-substituted images.

This study reveals the mechanism of action between AI image and consumers’ resistance and sheds light on how to choose appropriate image and expression styles for AI products, which is important for lowering consumer resistance to AI.