Search results

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

The authors investigated cocoa farmers' access to subsidized fertilizer in Ghana and implications on productivity.

Primary data were sourced from 435 cocoa farmers. Cragg hurdle and two-step Tobit model with continuous endogenous regressors/covariates were applied for the drivers of cocoa farmers' participation in fertilizer subsidy programme and productivity. Propensity score matching (PSM), inverse-probability weights (IPW) and augmented inverse-probability weights (AIPW) were applied for productivity impact assessment of fertilizer subsidy.

All the farmers were aware of fertilizer subsidy for cocoa production in Ghana. Farmers became aware of fertilizer subsidy through extension officers, media and other farmers. Half of cocoa farmers benefitted from fertilizer subsidy. Averagely, cocoa farmers purchased 292 kg of subsidized fertilizer. Many socio-economic, farm-level characteristics and institutional factors determine cocoa farmers' participation in fertilizer subsidy programme, quantity of subsidized fertilizer obtained and productivity. Beneficiaries of fertilizer subsidy recorded higher cocoa productivity than non-beneficiaries. Hence, fertilizer subsidy for cocoa production in Ghana leads to a gain in productivity.

There should be more investments in fertilizer subsidy so that all cocoa farmers benefit and obtain the required quantities.

The authors provide new evidence on cocoa productivity gain or loss emanating from fertilizer subsidy by combining different impact assessment techniques for deeper analysis: PSM, IPW and AIPW.

The author presents new estimates of the probability weighting functions found in rank-dependent theories of choice under risk. These estimates are unusual in two senses. First, they are free of functional form assumptions about both utility and weighting functions, and they are entirely based on binary discrete choices and not on matching or valuation tasks, though they depend on assumptions concerning the nature of probabilistic choice under risk. Second, estimated weighting functions contradict widely held priors of an inverse-s shape with fixed point well in the interior of the (0,1) interval: Instead the author usually finds populations dominated by “optimists” who uniformly overweight best outcomes in risky options. The choice pairs used here mostly do not provoke similarity-based simplifications. In a third experiment, the author shows that the presence of choice pairs that provoke similarity-based computational shortcuts does indeed flatten estimated probability weighting functions.

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.

In order to improve the estimation accuracy of soil organic matter, this paper aims to establish a modified model for hyperspectral estimation of soil organic matter content based on the positive and inverse grey relational degrees.

Based on 82 soil sample data collected in Daiyue District, Tai'an City, Shandong Province, firstly, the spectral data of soil samples are transformed by the first order differential and logarithmic reciprocal first order differential and so on, the correlation coefficients between the transformed spectral data and soil organic matter content are calculated, and the estimation factors are selected according to the principle of maximum correlation. Secondly, the positive and inverse grey relational degree model is used to identify the samples to be identified, and the initial estimated values of the organic matter content are obtained. Finally, based on the difference information between the samples to be identified and their corresponding known patterns, a modified model for the initial estimation of soil organic matter content is established, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.

The results show that the methods of logarithmic reciprocal first order differential and the first-order differential of the square root for transforming the original spectral data are more effective, which could significantly improve the correlation between soil organic matter content and spectral data. The modified model for hyperspectral estimation of soil organic matter has high estimation accuracy, the average relative error (MRE) of 11 test samples is 4.091%, and the determination coefficient (R²) is 0.936. The estimation precision is higher than that of linear regression model, BP neural network and support vector machine model. The application examples show that the modified model for hyperspectral estimation of soil organic matter content based on positive and inverse grey relational degree proposed in this article is feasible and effective.

The model in this paper has clear mathematical and physics meaning, simple calculation and easy programming. The model not only fully excavates and utilizes the internal information of known pattern samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation of soil organic matter. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.

The paper succeeds in realizing both a modified model for hyperspectral estimation of soil organic matter based on the positive and inverse grey relational degrees and effectively dealing with the randomness and grey uncertainty in spectral estimation.

This paper investigates the overall system risk for a foodgrains supply chain capturing the interrelationship among the risk factors and the effect of risk mitigation strategies.

This paper first calculates the weight of risk factors using an integrated approach of failure mode, effects analysis and fuzzy VIKOR technique. Next, the weights are utilized as input for the weighted fuzzy Petri-net (WFPN) approach to calculate the system risk.

Two different WFPN models are developed based on the relationships among the risk factors, and both models demonstrate a higher risk value for the overall system.

The proposed methodology will help practitioners or managers understand the complexity involved in the system by capturing the interrelationship behaviour. This study also considers the concurrent effect of risk mitigation strategies for calculating the overall system risk, which helps to improve the system’s performance.

Fall accidents can cause casualties and economic losses in the construction industry. Fall portents, such as loss of balance (LOB) and sudden sways, can result in fatal, nonfatal or attempted fall accidents. All of them are worthy of studying to take measures to prevent future accidents. Detecting fall portents can proactively and comprehensively help managers assess the risk to workers as well as in the construction environment and further prevent fall accidents.

This study focused on the postures of workers and aimed to directly detect fall portents using a computer vision (CV)-based noncontact approach. Firstly, a joint coordinate matrix generated from a three-dimensional pose estimation model is employed, and then the matrix is preprocessed by principal component analysis, K-means and pre-experiments. Finally, a modified fusion K-nearest neighbor-based machine learning model is built to fuse information from the x, y and z axes and output the worker's pose status into three stages.

The proposed model can output the worker's pose status into three stages (steady–unsteady–fallen) and provide corresponding confidence probabilities for each category. Experiments conducted to evaluate the approach show that the model accuracy reaches 85.02% with threshold-based postprocessing. The proposed fall-portent detection approach can extract the fall risk of workers in the both pre- and post-event phases based on noncontact approach.

First, three-dimensional (3D) pose estimation needs sufficient information, which means it may not perform well when applied in complicated environments or when the shooting distance is extremely large. Second, solely focusing on fall-related factors may not be comprehensive enough. Future studies can incorporate the results of this research as an indicator into the risk assessment system to achieve a more comprehensive and accurate evaluation of worker and site risk.

The proposed machine learning model determines whether the worker is in a status of steady, unsteady or fallen using a CV-based approach. From the perspective of construction management, when detecting fall-related actions on construction sites, the noncontact approach based on CV has irreplaceable advantages of no interruption to workers and low cost. It can make use of the surveillance cameras on construction sites to recognize both preceding events and happened accidents. The detection of fall portents can help worker risk assessment and safety management.

Existing studies using sensor-based approaches are high-cost and invasive for construction workers, and others using CV-based approaches either oversimplify by binary classification of the non-entire fall process or indirectly achieve fall-portent detection. Instead, this study aims to detect fall portents directly by worker's posture and divide the entire fall process into three stages using a CV-based noncontact approach. It can help managers carry out more comprehensive risk assessment and develop preventive measures.

The study evaluates the effects of GVC participation on firm productivity and profitability. Hence this study aims to find evidence whether there is a clear difference between the productivity and profitability effects of simple and complex backward and forward participations for Turkish firms.

The authors employ a firm level data from the Türkiye's both first and second top 500 industrial enterprises from 1993 to 2019. In addition, the authors calculate country-sector level both backward and forward GVC participation indices with their simple and complex sub-indices for each year from 1990 to 2015 from the Full Eora data of the Eora Global Supply Chain Database. The authors estimate the model with OLS and fixed effects. To understand the role of the 2008 global crisis, the authors also undertake estimations for the pre-crisis and post-crisis. The authors also divide the data by R&D intensity of sectors.

While backward GVC participation lowers both labor productivity and profitability growth, forward GVC participation promotes both. Moreover, simple and complex backward participation have similarly negative effects on productivity and profitability growth, simple and complex forward participation have the completely opposite effects though. The authors then provide substantial evidence for the differing effects of participation on productivity and profitability growth between pre-crisis and post-crisis periods. Interestingly, backward participation has a negative impact for both hi-tech and low-tech firms while forward participation boosts the productivity growth only for low-tech firms, probably due to the relatively more upstream position of low-tech firms.

To the best of the knowledge, no previous study has yet examined the profitability effects of GVC for firms. Second, in addition to overall backward and forward GVC participation rates, the authors also calculate and utilize simple and complex GVC measures in the estimations. Third, to reveal whether the global financial crisis leads to a shift in the productivity and profitability effects of GVCs, the authors separately run the regressions for the pre- and post-crisis periods. Fourth, the authors then investigate the argument that hi-tech sectors/firms could benefit more from joining GVCs compared to firms in low-tech technology sectors.

The agricultural extension system in Indonesia has experienced its ups and downs in line with the sociopolitical dynamics of the country. This study examines the impact of access to agricultural extension on the adoption of technology and farm income of smallholder farmers in Banten, Indonesia.

This study uses a quasi-experimental research design to estimate the impact outcomes at the farm level, with methods that form part of the counterfactual framework.

Estimation results show that farming experience, off-farm income, irrigation, group membership, mobile phones and livestock ownership significantly affect extension access. The results of this main study show the important role of extension access to technology adoption and agricultural income. These studies found consistently positive and statistically significant effects of access to extension services on technology adoption and farm income.

The consistent positive and significant effect of extension access implies that public investment by the government in agricultural extension can optimize the potential impact on technology adoption and agricultural income, which also affects the distribution of the welfare of rural smallholder farmers.

Agricultural extension as a key to increasing technology adoption. However, the impact of access to agricultural extension in Indonesia has received less attention in terms of adoption and farm income.

Achieving a zero-carbon city requires a long-term strategic perspective. The authors propose a decision-making model which would take into account the economic, environmental and social impacts for prioritizing the zero-carbon measures for sustainable urban transportation.

An integrated intuitionistic fuzzy gained and lost dominance score (IF-GLDS) model is introduced based on intuitionistic fuzzy Yager weighted aggregation (IFYWA) operators and proposed weight-determining IF-SPC procedure. In addition, a weighting tool is presented to obtain the weights of decision experts. Further, the feasibility and efficacy of developed IF-SPC-GLDS model is implemented on a multi-criteria investment company selection problem under IFS context.

The results of the developed model, “introducing zero-emission zones” should be considered as the first measure to implement. The preference of this initiative offers sustainable transport in India to achieve a zero-carbon transport by having the greatest impact on the modal shift from cars to sustainable mobility modes with a lower operational and implementation cost as well as having greater public support. The developed model utilized can be relocated to other smart cities which aim to achieve a zero-carbon transport. Sensitivity and comparative analyses are discussed to reveal the robustness of obtained result. The outcomes show the feasibility of the developed methodology which yields second company as the suitable choice, when compared to and validated using the other MCDA methods from the literature, including TOPSIS, COPRAS, WASPAS and CoCoSo with intuitionistic fuzzy information.

A new intuitionistic fuzzy symmetry point of criterion (IF-SPC) approach is presented to find weights of criteria under IFSs setting. Then, an IF-GLDS model is introduced using IFYWA operators to rank the options in the realistic multi-criteria decision analysis (MCDA) procedure. For this purpose, the IFYWA operators and their properties are developed to combine the IFNs. These operators can offer a flexible way to deal with the realistic MCDA problems with IFS context.