Search results

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.

This study examines the investment horizon influence, mediated by market optimism, on earnings management based on accruals and real activities. Based on short-termism, the authors argue that earnings management increases in optimistic periods to boost corporate profits.

The authors analyzed non-financial Brazilian publicly traded firms from 2010 to 2020 by estimating industry-fixed effects of groups of short- and long-horizon firms to compare their behavior on earnings management practices during bullish moments. For robustness, the authors used alternate measures and trade-off analyses between earning management practices.

The findings indicate that, during bullish moments, companies prioritize managing their earnings through real activities management (RAM) rather than accruals earnings management (AEM), depending on their time horizon. The results demonstrate the trade-off between earnings management practices.

This study presents limitations when using proxies for earnings management and investor sentiment.

Investors and regulators should closely monitor companies' operations, especially during bullish market conditions to prevent fraud.

The study addresses investor sentiment mediation in the earnings management discussion, introducing the short-termism approach.

Globalization and increased outsourcing have contributed to increased supply chain complexity, exposing firms to greater vulnerability in the areas of product safety and supply chain security. Meanwhile, stakeholders pressure firms to ensure that their products are safe, and their supply chains are secure. Drawing from stakeholder theory, this paper aims to explore how the supply chain characteristics of distance and power affect the adoption of consumer protection (CP) practices, which ensure product safety and supply chain security.

Using primary survey data from a sample of Canadian manufacturing firms, this research examines the relationships among supply chain characteristics, adoption of CP practices and firm performance.

Analysis supported the use of two practices related to product safety (consumer education and product design) and three practices for supply chain security (packaging, tracking and authenticity). Greater cultural distance between the focal firm and its suppliers was positively associated with investments in safer design practices, while increased geographical distance between the focal firm and the customer was significantly related to increased consumer education. Moreover, as power of a focal firm relative to its suppliers increased, so too did investments in supply chain security. Finally, CP practices were related to improved operational performance along multiple dimensions.

This research focuses on the critical role of two key stakeholder groups in improving product safety and supply chain security: suppliers and customers. The authors add to the theoretical discussion of product safety and supply chain security by identifying critical differences between suppliers and customers for the focal firm. Second, the research informs the managerial community of the potential benefits of investments in CP practices.

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.

In this paper, the authors tested if promoting the workforce's happiness (through high performance work policies or HPWP) and well-being in European Public companies relates to their profitability (return on equity, ROE), market risk (beta) and stock price return. Also, the authors tested if investors have a performance benefit if they buy a portfolio screened with companies with HPWP.

The authors proxied the quality of the HPWP efforts in the first method with the Refinitiv workforce score. They used this data in an unbalanced panel of eastern, western, northern and southern Europe companies from 2011 to 2022. The panel data also included the ROE, the market risk (beta) and the stock price return of these companies. The authors estimated the corresponding regressions with the panel data and tested the relationship between the workforce score and these three variables. In a second method, they simulated the weekly performance of a portfolio that invested only in European companies with high standards in their HPWP and compared its performance against a conventional market portfolio (with no HPWP screening).

In the first method, the authors found no significant relationship between the workforce score and the ROE, beta, or stock price return in the panel regression, controlling for random effects. In the second one, they found no over or underperformance in the HPWP portfolio against the European market one in the second method.

The results suggest that there is no risk or cost for European Public companies and investors alike if they promote, with better HPWP, the happiness and well-being of their workforce. The findings suggest that if European companies promote HPWP, there will be no adverse impact on their profits, market risk, or stock price performance. Also, investors will not lose performance (against a conventional market portfolio) if they screen their portfolios with this type of workforce-friendly companies.

Increase the scarce literature on the test of the workforce score with company profitability (ROE), stock market price variation and stock market risk level.

The Government’s investment in infrastructure projects is considerably high, especially in bridge construction projects. Government authorities must establish an initial forecasted budget to have transparency in transactions. Early cost estimating is challenging for Quantity Surveyors due to incomplete project details at the initial stage and the unavailability of standard cost estimating techniques for bridge projects. To mitigate the difficulties in the traditional preliminary cost estimating methods, there is a requirement to develop a new initial cost estimating model which is accurate, user friendly and straightforward. The research was carried out in Sri Lanka, and this paper aims to develop the artificial neural network (ANN) model for an early cost estimate of concrete bridge systems.

The construction cost data of 30 concrete bridge projects which are in Sri Lanka constructed within the past ten years were trained and tested to develop an ANN cost model. Backpropagation technique was used to identify the number of hidden layers, iteration and momentum for optimum neural network architectures.

An ANN cost model was developed, furnishing the best result since it succeeded with around 90% validation accuracy. It created a cost estimation model for the public sector as an accurate, heuristic, flexible and efficient technique.

The research contributes to the current body of knowledge by providing the most accurate early-stage cost estimate for the concrete bridge systems in Sri Lanka. In addition, the research findings would be helpful for stakeholders and policymakers to propose policy recommendations that positively influence the prediction of the most accurate cost estimate for concrete bridge construction projects in Sri Lanka and other developing countries.

The aim is to study the impact of schooling on between-groups wage inequality beyond the lens of the standard approach in the literature.

Simple econometric theory is used to make the main point of the paper. Supporting empirical evidence is also presented.

Disregarding the persistence of current earnings implies a bias in the estimation of the wage return to schooling both at labour-market entry and in the rest of the working life.

The use of current earnings as a dependent variable in wage-schooling models may be problematic and requires specific handling.

The impact of schooling on the between-groups dimension of wage inequality may be different than previously thought.

The paper is the first to show that, when current earnings are used as a dependent variable, the identification of a wage-schooling model with the standard (time-invariant external instrument-variable) approach may lead to misleading conclusions.

Industrialisation has contributed to global environmental problems, especially in developed countries, but increasingly so in developing ones as well. The rising public concern for the natural environment is compelling business entities to revise their business models towards green lean (GL) management. Most manufacturing firms have realised that GL implementation is a critical factor that drives their success. Therefore, keeping in view the above said aspects, the purpose of this paper is to empirically assess the complementary impact of GL practices on environmental performance.

Data from a sample of 124 Indian manufacturing industries are analysed using a structural equation modelling technique.

Evidence suggests that GL practices such as top management commitment, government support, human resource management, health and safety of employees and public pressure and legislature have significantly positive effect on environmental performance of manufacturing industries.

The sample is limited to Indian manufacturing industries situated in northern region, with a low response rate.

Successful implementations of GL practices can lead to improved environmental performance. Manufacturing industries within emerging economies like India can improve on their GL practices by incorporating these findings into their business models, while research could be guided to focus their inquiries on this and related genres of scholarly work.

To the best of the authors’ knowledge, this study is one of the first to empirically assess the complementary impact of GL practices on environmental performance within the Indian context.

During the previous two decades, “Green Supply Chain Management” (GSCM) has been gaining the attention of researchers and practitioners from various fields (e.g. operations, logistics and supply chain management). Its significance is constantly growing, and various studies are conducted in order to capture its overall organizational contribution. The present study attempts to bring together various organizational aspects that have never been collectively investigated before in the relevant literature. Under that rationale, a robust conceptual framework is developed and empirically tested. This framework includes 17 factors that are classified in three dimensions: (1) drivers of GSCM practices, (2) GSCM practices and (3) firm performance (GSCM outcomes).

The examination of the proposed conceptual framework was performed using a newly developed structured questionnaire that was distributed to a sample of Greek manufacturing organizations. Supply Chain managers and Chief Executive Officers (CEOs) were used as key respondents, due to their knowledge and experience. After the completion of the three-month research period (last quarter of 2019), 292 useable questionnaires were returned. The empirical data were analyzed using the “Structural Equation Modeling” technique. The study is empirical (based on primary data), explanatory (examines cause and effect relationships), deductive (tests research hypotheses) and quantitative (includes the analysis of quantitative data collected with the use of a structured questionnaire).

Empirical results point out that internal environmental management, green innovative practices and environmental proactivity are GSCM practices with the most significant impact on firm performance. Moreover, the mediating role of GSCM practices in the relationship between GSCM drivers and firm performance is also highlighted. Finally, it was found that GSCM practices can explain 35% of the variance in firm performance and the drivers of GSCM practices can explain 78% of the variance of these practices.

The proposed three-dimensional conceptual framework of this empirical study and its underlining rationale has rarely been adopted in the relevant literature. Moreover, the study investigates which GSCM practices have an impact on firm performance, thus offering value to practitioners of the field. Also, it is one of the few similar studies that have been conducted on a European country.

The primary objective of this study is to investigate the association between the dimensions of intellectual capital (IC) and sustainability practices (SP) within the context of manufacturing medium and large (ML) firms in Uganda. The study aims to shed light on whether and how different dimensions of IC contribute to the adoption and implementation of SP by these firms.

This study utilized a cross-sectional and quantitative approach, collecting data through a questionnaire survey from a sample of manufacturing ML firms. The collected data underwent analysis to identify patterns and relationships using the SmartPLS structural equation modeling (SEM) technique.

The findings demonstrated that the three categories of IC (human, structural and relational capital) influence the SP of ML manufacturing enterprises in Uganda. This suggests that IC is a critical component of SP.

Manufacturing enterprises should use their IC to create strategies for sustainable solutions, such as creating new, ecologically and socially responsible products and services and improving current ones to lessen their environmental effect.

This research advances knowledge of SP by revealing if all aspects of IC are significant for the SP of manufacturing enterprises in Uganda.