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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.

Role congruity theory and gender stereotypes research suggests men are expected to engage in agentic behavior and women in communal behavior as leaders, and that role violation results in backlash. However, extant gender and leadership research does not directly measure expectations–behavior incongruence. Further, researchers have only considered one condition of role incongruence – display of counter-role behavior – and have not considered the outcomes of failing to exhibit role-congruent behavior. Additionally, few studies have examined outcomes for male leaders who violate gender role prescriptions. The present study aims to address these shortcomings by conducting a novel empirical test of role congruity theory.

This experimental study used polynomial regression to assess how followers evaluated leaders under conditions of incongruence between follower expectations for men and women leaders’ behavior and leaders’ actual behavior (i.e. exceeded and unmet expectations). Respondents read a fictional scenario describing a new male or female supervisor, rated their expectations for the leader’s agentic and communal behavior, read manipulated vignettes describing the leader’s subsequent behavior, rated their perceptions of these behaviors, and evaluated the leader.

Followers expected higher levels of communal behavior from the female than the male supervisor, but no differences were found in expectations for agentic behavior. Regardless of whether expectations were exceeded or unmet, supervisor gender did not moderate the effects of agentic or communal behavior expectations–perceptions incongruence on leader evaluations in polynomial regression analyses (i.e. male and female supervisors were not evaluated differently when displaying counter-role behavior or failing to display role-congruent behavior).

In addition to providing a novel, direct test of role congruity theory, the study highlighted a double standard in gender role-congruent behavior expectations of men and women leaders. Results failed to support role congruity theory, which has implications for the future of theory in this domain.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

No study has investigated the effects of different parameters on publication bias in meta-analyses using a machine learning approach. Therefore, this study aims to evaluate the impact of various factors on publication bias in meta-analyses.

An electronic questionnaire was created according to some factors extracted from the Cochrane Handbook and AMSTAR-2 tool to identify factors affecting publication bias. Twelve experts were consulted to determine their opinion on the importance of each factor. Each component was evaluated based on its content validity ratio (CVR). In total, 616 meta-analyses comprising 1893 outcomes from PubMed that assessed the presence of publication bias in their reported outcomes were randomly selected to extract their data. The multilayer perceptron (MLP) technique was used in IBM SPSS Modeler 18.0 to construct a prediction model. 70, 15 and 15% of the data were used for the model's training, testing and validation partitions.

There was a publication bias in 968 (51.14%) outcomes. The established model had an accuracy rate of 86.1%, and all pre-selected nine variables were included in the model. The results showed that the number of databases searched was the most important predictive variable (0.26), followed by the number of searches in the grey literature (0.24), search in Medline (0.17) and advanced search with numerous operators (0.13).

The results of this study can help clinical researchers minimize publication bias in their studies, leading to improved evidence-based medicine.

To the best of the author’s knowledge, this is the first study to model publication bias using machine learning.

Completing Right-of-Way (ROW) acquisition process on schedule is critical to avoid delays and cost overruns on transportation projects. However, transportation agencies face challenges in accurately forecasting ROW acquisition timelines in the early stage of projects due to complex nature of acquisition process and limited design information. There is a need of improving accuracy of estimating ROW acquisition duration during the early phase of project development and quantitatively identifying risk factors affecting the duration.

The quantitative research methodology used to develop the forecasting model includes an ensemble algorithm based on decision tree and adaptive boosting techniques. A dataset of Georgia Department of Transportation projects held from 2010 to 2019 is utilized to demonstrate building the forecasting model. Furthermore, sensitivity analysis is performed to identify critical drivers of ROW acquisition durations.

The forecasting model developed in this research achieves a high accuracy to predict ROW durations by explaining 74% of the variance in ROW acquisition durations using project features, which is outperforming single regression tree, multiple linear regression and support vector machine. Moreover, number of parcels, average cost estimation per parcel, length of projects, number of condemnations, number of relocations and type of work are found to be influential factors as drivers of ROW acquisition duration.

This research contributes to the state of knowledge in estimating ROW acquisition timeline through (1) developing a novel machine learning model to accurately estimate ROW acquisition timelines, and (2) identifying drivers (i.e. risk factors) of ROW acquisition durations. The findings of this research will provide transportation agencies with insights on how to improve practices in scheduling ROW acquisition process.

The purpose of this paper is to examine the degree of persistence in four precious metal prices (i.e. gold, palladium, platinum and silver) during the last four US recessions.

Using daily price data for gold, palladium, platinum and silver running from July 2, 1990, to March 21, 2022, and dating of business cycles in the USA provided by NBER (2022), the paper uses fractional integration to test the degree of persistence of precious metal prices.

The empirical analysis shows the unrelenting prominence of gold in relation to other precious metals (palladium, platinum and silver) as a hedge against market uncertainty in the post-pandemic new era.

Two are the main contributions of the paper. Firstly, the authors contribute to the commodity markets and finance literature on precious metal price modelling. Secondly, the authors also contribute to the literature on commodity markets and business cycles with a special focus on recessionary periods.

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.

The study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.

The study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.

This study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.

This study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.

To the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.

This study examines how technical drivers as well as social drivers influence organic communication and top management involvement (TMI) in new product development (NPD) projects. Technical drivers are of strategic importance and product innovativeness and social drivers are of intrinsic and extrinsic relevance. Organic communication is defined as continuous, bidirectional and informal communication between top management and the NPD teams. Further, arguing that TMI must be studied as a multifaceted construct, it is conceptualized to occur as guidance, active motivation and providing resources and creating a tolerant climate. Subsequently, the effect of TMI and organic communication on NPD performance is investigated.

The data set, collected via surveys from top managers and project managers involved in 86 NPD projects in 85 firms, is analyzed using PLS structural equation modeling.

The authors show that the strategic importance of the project has a positive influence on TMI through active motivation, providing resources and creating a tolerant climate for innovation, but does not have an effect on guidance. Results also show that active motivation and organic communication improve budget and schedule adherence, whereas providing guidance and stimulating a tolerant climate have detrimental effects. In summary, the results show that only active motivation enhances all types of performance while stimulating a tolerant climate appears to have the opposite effect. The results revealed that organic communication between top management and the NPD team has a strong positive effect on all elements of TMI (providing guidance, actively motivating the NPD team, providing resources and creating a tolerant climate). In other words, when top management communicates with the NPD team throughout the project in an informal way and listens to them in addition to engaging in a one-way communication, they are more likely to be seen by the team as being deeply involved in the project.

Executives must walk a managerial tightrope to actively motivate and to assist in providing resources, yet they must not be overbearing with direct guidance and must limit their tolerance for failures.

Involvement of key organizational actors such as top management and the link to project performance has attracted significant attention in research. However, nuanced empirical insights into the dyad of top management and project teams has so far been absent. The study’s findings detail the effect of technical and social drivers of top management involvement in new product development projects. Most notably, (1) the effect of motivation and stimulating a tolerant climate on performance, and (2) the effect of organic communication on top management involvement. Moreover, this study is unique in that it empirically examines TMI from both top management and team perspectives.

Recent library and information science literature suggests transgender and nonbinary populations are not treated, served and represented on an equal or equitable basis as cisgender populations are in libraries. This article aims to assess the prevalence of bias and inclusion efforts in Illinois libraries.

This quantitative, cross-sectional, descriptive study utilizes a critical queer theory lens and includes a Likert scale survey with a demographic question on gender identity to measure four constructs and determine if there is a relationship between gender identity and bias, inclusion efforts, and knowledge of transgender and nonbinary user needs.

Results suggest respondents' biases reinforce structural cisgenderism in Illinois libraries and may account for the unequal conditions trans and nonbinary populations experience. Additionally, there is a correlation between cisgender-identifying Illinois LIS professionals and biased attitudes and behaviors, use of inclusive practices, and knowledge of transgender and nonbinary user needs.

This study contributes quantitative data, analysis and practical implications to a body of predominantly qualitative library literature on transgender and gender diverse experiences in libraries.

The purpose of this study is to critically review consumer knowledge in marketing and propose a future research agenda. Despite the many works that have examined this variable, given its strong influence on behaviour, it has generally been studied in association with other constructs, and no studies have focused on it in a specific way. Its definition, measurement and approaches to its role and usefulness are superficial and underdeveloped. After structuring and analysing the existing literature, the authors establish, (I) which aspects are of little use to the discipline, and (II) which research lines have the most potential and should be developed and studied in greater depth, to advance and complete the existing consumer knowledge framework.

A search was undertaken for documents in the main databases in which the term “consumer knowledge” appears in a marketing or consumer context, and a critical and reflexive approach was taken to analyse the main contributions and to structure them by content blocks.

Five main content blocks were identified. A set of research gaps were detected, mainly related to the lax conceptualisation of the topic, measurement problems and the scarcity of more useful works connected with business management, and several research lines are proposed that complement the existing framework to make it more complete and operational.

This paper offers a critical review and proposes a research agenda for one of the most used but little studied variables in the field of marketing, which may help academics and professionals in the discipline to continue developing useful theories and models.

El objetivo de este trabajo es revisar críticamente el conocimiento del consumidor en marketing y proponer una agenda de investigación futura. A pesar de los numerosos trabajos que han examinado esta variable, dada su fuerte influencia en el comportamiento, generalmente se ha estudiado en asociación con otros constructos, y ningún estudio se ha centrado en ella de manera específica. Su definición, medición y aproximaciones sobre su papel y utilidad son superficiales y poco desarrollados. Después de estructurar y analizar la literatura existente, establecemos (I) qué aspectos tienen poco uso para la disciplina y (II) qué líneas de investigación tienen más potencial y deben ser desarrolladas y estudiadas con mayor profundidad; para avanzar y completar el marco existente sobre conocimiento del consumidor.

Se realizó una búsqueda de documentos en las principales bases de datos en las que aparece el término “conocimiento del consumidor” en un contexto de marketing o consumo, y se adoptó un enfoque crítico y reflexivo para analizar las principales contribuciones y estructurarlas por bloques de contenido.

Se identificaron cinco bloques principales de contenido. Se detectó un conjunto de huecos de investigación, principalmente relacionados con la laxa conceptualización del tema, problemas de medición y la escasez de trabajos más útiles conectados con la gestión empresarial; y se proponen varias líneas de investigación que complementan el marco existente para hacerlo más completo y operativo.

Este documento ofrece una revisión crítica y propone una agenda de investigación para una de las variables más utilizadas pero poco estudiadas en el campo del marketing, lo que puede ayudar a académicos y profesionales en la disciplina a continuar desarrollando teorías y modelos útiles.

本文旨在对市场营销中的消费者知识进行批判性审视, 并提出未来的研究议程。虽然已有许多研究检验了该变量, 但由于其对行为产生强大影响, 通常会与其他结构变量一起研究, 而没有以特定方式专注于该变量。对其定义、测量以及其作用和用途的方法仍旧存在研究空白。通过对现有文献进行结构化分析后, 确定了以下两个方面：（I）哪些方面对该学科意义不大, （II）哪些研究方向最具研究潜力, 并且应该进一步深入发展和研究, 以推进和完善现有的消费者知识框架。

通过主要数据库检索市场营销或消费者背景下涉及“消费者知识”一词的文献, 采取批判性和反思性方法来分析其主要贡献, 并通过内容块对其进行结构化。

识别了五个主要内容块, 并发现存在一定程度的研究空白, 主要涉及该主题的概念松散化、测量问题以及与商业管理相关的有效研究的稀缺性。此外, 本文提出了几个研究线索, 这些线索为现有框架补充了信息, 使其更加完整且具备更强的操作性。

本文对市场营销领域中广泛使用但研究较少的变量进行了批判性评述, 并提出了相关研究议程。这一工作有助于学术界和专业人士继续发展实用的理论和模型。