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The purpose of the study is to estimate the willingness to pay (WTP) for major chemical fertilizers and revisit the fertilizer subsidy policy in Nepal.

We surveyed 619 households from six districts and assessed farmers’ WTP for urea, diammonium phosphate (DAP) and muriate of potash (MOP) during the fertilizer crisis. Our study elicited the WTP for fertilizers when fertilizers were not available on the market. A modified payment card approach was used to elicit farmers’ WTP.

The study found that farmers who buy fertilizer from agrodealers, buy from gray markets, have bank accounts, are willing to take a risk, have strong or medium economic conditions and incur higher travel costs have a higher WTP for fertilizers. Farmers in sampled areas, on average, are willing to pay 31 percent more for urea, 13 percent more for DAP and 19 percent more for MOP than the government recommended fertilizer price.

The design of the payment card and the estimation techniques used to fit the valuation function are likely to influence WTP.

Overall, literature on households’ WTP for fertilizers in developing countries is scarce. Our study contributes to the knowledge of WTP for fertilizers.

The application of correct contractor selection strategies leads to the selection of a qualified contractor and, as a result, the on-time delivery of the project with the desired quality and within the predetermined budgetary constraints. For this reason, evaluating and qualifying contractors before reviewing the proposed prices has been considered an important issue. One factor that disrupts the project completion process and the failure to achieve pre-planned goals effectively is the occurrence of contractors' disputes and claims in projects. To this end, the present study explores claim-reduction strategies for selecting effective contractors in an uncertain environment to reduce possible problems.

The two-step grey data envelopment analysis (GDEA) approach was used to measure efficiency as a powerful tool in selecting efficient contractors during tenders. This approach can extend the applications of multi-criteria decision-making (MCDM) models. In other words, given some uncertainties, the unavailability of some data, and the problems with the DEA model, the two-step GDEA model was used to rank the contractors. The data confirmed the satisfactory outcomes from the selected model.

The preliminary assessment of contractors is a pre-tendering process and a step in categorizing contractors, excluding contractors lacking required qualifications, and selecting efficient contractors. At first, it will help the employer to exclude inexperienced and unqualified contractors, save resources and time, reduce threats, replace opportunities with threats, and reduce material and non-material costs during the completion of the project until the projects are put into operation. Consequently, this approach reduces claims to a minimum level and increases the organization's effective material and non-material profit.

Oil and gas plans and projects have a significant, sensitive, and decisive role in the economic, social, political, cultural, infrastructural, and all-round development of Iran; This is while most of the financial resources needed to implement the development and programs across the country come from oil revenues. Studies have indicated that despite the importance of these plans and projects, many of them are not completed successfully, and this causes irreparable losses to the country's economy and development in various fields.

1. The findings of this study can be used by organizations to select more effective contractors to assign projects and plans to them.

2. The preliminary assessment of contractors is a pre-tendering process and a step in categorizing contractors, excluding contractors who lack required qualifications, and finally selecting efficient contractors.

3. At first, it will help the employer to exclude inexperienced and unqualified contractors, save resources and time, reduce threats, replace opportunities with threats, and reduce material and non-material costs during the completion of the project until the projects are put into operation.

4. This approach also gives credit to the employer during the execution period and contributes to assessing unqualified contractors and reducing the temptation to hand over the project to an unqualified contractor but with a lower bid price.

5. Consequently, this approach reduces claims to a minimum level and increases the effective material and non-material profit of the organization.

6. Moreover, it provides an extra-organizational evaluation for contractors, motivating them to upgrade their capabilities and optimally allocate material and non-material resources, especially human resources.

The findings of this study can be used by organizations to select more effective contractors to assign projects and plans to them.

The preliminary assessment of contractors is a pre-tendering process and a step in categorizing contractors, excluding contractors who lack required qualifications, and finally selecting efficient contractors.

At first, it will help the employer to exclude inexperienced and unqualified contractors, save resources and time, reduce threats, replace opportunities with threats, and reduce material and non-material costs during the completion of the project until the projects are put into operation.

This approach also gives credit to the employer during the execution period and contributes to assessing unqualified contractors and reducing the temptation to hand over the project to an unqualified contractor but with a lower bid price.

Consequently, this approach reduces claims to a minimum level and increases the effective material and non-material profit of the organization.

Moreover, it provides an extra-organizational evaluation for contractors, motivating them to upgrade their capabilities and optimally allocate material and non-material resources, especially human resources.

Traditional multivariable grey prediction models define the background-value coefficients of the dependent and independent variables uniformly, ignoring the differences between their physical properties, which in turn affects the stability and reliability of the model performance.

A novel multivariable grey prediction model is constructed with different background-value coefficients of the dependent and independent variables, and a one-to-one correspondence between the variables and the background-value coefficients to improve the smoothing effect of the background-value coefficients on the sequences. Furthermore, the fractional order accumulating operator is introduced to the new model weaken the randomness of the raw sequence. The particle swarm optimization (PSO) algorithm is used to optimize the background-value coefficients and the order of the model to improve model performance.

The new model structure has good variability and compatibility, which can achieve compatibility with current mainstream grey prediction models. The performance of the new model is compared and analyzed with three typical cases, and the results show that the new model outperforms the other two similar grey prediction models.

This study has positive implications for enriching the method system of multivariable grey prediction model.

Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.

First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.

The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.

Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

In this study, a new neural differential grey model is proposed for the purpose of accurately excavating the evolution of real systems.

For this, the proposed model introduces a new image equation that is solved by the Runge-Kutta fourth order method, which makes it possible to optimize the sequence prediction function. The novel model can then capture the characteristics of the input data and completely excavate the system's evolution law through a learning procedure.

The new model has a broader applicability range as a result of this technique, as opposed to grey models, which have fixed structures and are sometimes over specified by too strong assumptions. For experimental purposes, the neural differential grey model is implemented on two real samples, namely: production of crude and consumption of Cameroonian petroleum products. For validation of the new model, results are compared with those obtained by competing models. It appears that the precisions of the new neural differential grey model for prediction of petroleum products consumption and production of Cameroonian crude are respectively 16 and 25% higher than competing models, both for simulation and validation samples.

This article also takes an in-depth look at the mechanics of the new model, thereby shedding light on the intrinsic differences between the new model and grey competing models.

The overall goal of science is to build a valid and reliable body of knowledge about the functioning of the world and how applying that knowledge can change it. As personnel and human resources management researchers, we aim to contribute to the respective bodies of knowledge to provide both employers and employees with a workable foundation to help with those problems they are confronted with. However, what research on research has consistently demonstrated is that the scientific endeavor possesses existential issues including a substantial lack of (a) solid theory, (b) replicability, (c) reproducibility, (d) proper and generalizable samples, (e) sufficient quality control (i.e., peer review), (f) robust and trustworthy statistical results, (g) availability of research, and (h) sufficient practical implications. In this chapter, we first sing a song of sorrow regarding the current state of the social sciences in general and personnel and human resources management specifically. Then, we investigate potential grievances that might have led to it (i.e., questionable research practices, misplaced incentives), only to end with a verse of hope by outlining an avenue for betterment (i.e., open science and policy changes at multiple levels).

The damping accumulated discrete MGM(1, m) power model is proposed for the problem of forecasting the share of agricultural output value and the share of employment in China.

In this study, the damping accumulated discrete MGM(1, m) power model was developed based on the idea of discrete modelling by introducing a damping accumulated generating operator and power index. The new model can better identify the non-linear characteristics existing between different factors in the multivariate system and can accurately describe and forecast the trend of changes between data series and each of them.

The validity and rationality of the new model are verified through numerical experiment. It is forecasted that in 2023, the share of agricultural output value in China will be 7.14% and the share of agricultural employment will be 21.98%, with an overall decreasing trend.

The simultaneous decline in the share of agricultural output value and the share of employment is a common feature of countries that have achieved agricultural modernisation. Accurate forecasts of the share of agricultural output value and the share of employment can provide an important scientific basis for formulating appropriate agricultural development targets and policies in China.

The new model proposed in this study fully considers the importance of new information and has higher stability. The differential evolutionary algorithm was used to optimise the model parameters.

The purpose of this study was to establish the cause-effect relationship between determinants of trust in the buyer–supplier integration and the procurement performance of large manufacturing firms in Tanzania.

The study surveyed 52 firms from Temeke Municipality, Tanzania using questionnaire subjected to one procurement manager and one stores manager tallying a sample size of 104 respondents. Explanatory design was employed due to the presence of cause–effect relationship and the null hypotheses were tested using binary logistic regression technique at p values < 0.05 and ExpB > 1.

Mutual goals, geographical vicinity among partners, and supplier reliability are significant for the procurement performance of the manufacturing firms in Tanzania, whereas interpersonal and inter-organizational trusts and perceived buyers’ confidence are of no significant impact.

Buyer–supplier integration is a recently embraced and paramount practice for the manufacturing firms in Tanzania. Therefore, longitudinal study would further add value. The presence of the causality from the tested hypothesis appeals for the necessity of progress tracking.

Causality has been established, and a framework has been developed for the performance of large manufacturing firms using trust of buyer–supplier integration.

There shall be creation of more employment opportunities and timely availability of materials from large manufacturing firms in Tanzania.

Anchored on transaction cost economics and resource dependency theories, the study disclosed the root cause of procurement performance in the context of manufacturing firms in Tanzania whilst considering trust as a resource advantage of buyer–supplier integration.

This study aims to develop a cause-effect relationship between criteria that contribute to water security using the Intuitionistic Fuzzy-Decision-Making Trial and Evaluation Laboratory (IF-DEMATEL) method. Differently from the typical DEMATEL which utilizes crisp numbers, this modification introduces intuitionistic fuzzy numbers (IFNs) to enhance judgments in a group decision-making environment. In particular, the linguistic variables used in IF-DEMATEL are defined using the concept of three-tuple of IFNs.

Data with the linguistic variable “influence” were collected from a group of experts in water security via personal unstructured interviews. Seven water security criteria are considered in this study. Computational software was employed to execute the computational procedures of the IF-DEMATEL method. It is anticipated that by taking into account the hesitation degree of IFNs will reflect the scenario in real life, which could lead to precise decision-making.

The results show that “Over-Abstraction”, “Saltwater Intrusion” and “Limited Infrastructures” are the cause criteria that contribute to water security. In addition, the relationship map of influence shows that “Water Pollution” and “Rapid Urbanization” are the most vulnerable criteria as these two criteria are most easily affected by other criteria in a unidirectional relation.

It is anticipated that these findings will serve as useful references for water security management and policymakers.

The present study makes a noteworthy contribution to the modification of DEMATEL where three-tuple of intuitionistic fuzzy numbers are considered in the computations. The present study also provides additional evidence with respect to factors that contribute to water security.