Exploring factors influencing construction waste reduction: A structural equation modeling approach

Liu, Jingkuang; Yi, Yanqing; Wang, Xuetong

doi:10.1016/j.jclepro.2020.123185

Cited by 203 publications

(82 citation statements)

References 63 publications

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“…The standard path coefficient refers to the degree of correlation between two variables. Supposing that the path coefficient is significant, the hypothetical relationship is supported [114].…”

Section: Tools For Analysismentioning

confidence: 97%

Consumer Intention to Participate in E-Waste Collection Programs: A Study of Smartphone Waste in Indonesia

2021

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Indonesia is a developing country with a low-level e-waste management system based on a limited number of informal initiatives. E-waste requires proper management procedures, which involve the design of a reverse logistics management network. Consumers play a critical role in such a network, because the network runs when they willingly participate as suppliers of waste. This paper applies the Theory of Planned Behavior framework and extends it using Reverse Logistics drivers, the Value Belief Norm Theory, and facility accessibility to explain consumer intention to participate in e-waste collection programs. A survey was conducted on smartphone users in Indonesia, with a total of 324 valid questionnaires. The results showed that government drivers, facility accessibility, and personal attitudes significantly influence consumer intentions. Environmental concern has a positive influence on consumer intentions through the variables of the Theory of Planned Behavior and perceived behavioral control through government drivers. This study shows the need for integration, because the variables reinforce each other. However, neither economic drivers nor subjective norms significantly influence consumer intentions. This finding distinguishes Indonesia from other countries, especially developed countries, in that e-waste collection programs have not become part of the culture in Indonesia. For this reason, Indonesia needs regulations, as the most influential variable, to regulate the implementation of such a program.

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Section: Tools For Analysismentioning

confidence: 97%

Consumer Intention to Participate in E-Waste Collection Programs: A Study of Smartphone Waste in Indonesia

2021

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“…The user will give a MinSup and a MinConf, for items sets X and Y , if the confidence degree of the rule X = > Y is not lower than MinCon, we called this rule as a mining association rules. From semantic angle, the confidence degree expresses the correct degree of the rule (Liu et al 2020 ); support degree expresses what percent’s objects we can induce from this rule, vise the impatience of this rule to all data. For example, among 200 student grade records there are 30 records which expresses that capability of learning is A, and among these 30 records there are 15 records which expresses the grade of extracurricular activities is C. So the rule that the learning grade is A implies that the extracurricular activities grade is C has the confidence degree C = 15/30 = 0.5 and the support degree S = 15/200 = 0.075.…”

Section: Proposed Qoe-based Prediction Modelmentioning

confidence: 99%

A new QoE-based prediction model for evaluating virtual education systems with COVID-19 side effects using data mining

Tan

Lin

2021

Soft Comput

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Today, emerging technologies such as 5G Internet of things (IoT), virtual reality and cloud-edge computing have enhanced and upgraded higher education environments in universities, colleagues and research centers. Computer-assisted learning systems with aggregating IoT applications and smart devices have improved the e-learning systems by enabling remote monitoring and screening of the behavioral aspects of teaching and education scores of students. On the other side, educational data mining has improved the higher education systems by predicting and analyzing the behavioral aspects of teaching and education scores of students. Due to an unexpected and huge increase in the number of patients during coronavirus (COVID-19) pandemic, all universities, campuses, schools, research centers, many scientific collaborations and meetings have closed and forced to initiate online teaching, e-learning and virtual meeting. Due to importance of behavioral aspects of teaching and education between lecturers and students, prediction of quality of experience (QoE) in virtual education systems is a critical issue. This paper presents a new prediction model to detect technical aspects of teaching and e-learning in virtual education systems using data mining. Association rules mining and supervised techniques are applied to detect efficient QoE factors on virtual education systems. The experimental results described that the suggested prediction model meets the proper accuracy, precision and recall factors for predicting the behavioral aspects of teaching and e-learning for students in virtual education systems.

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“…As discussed by many scholars, along with well-known models (e.g., decision-making [6][7][8][9]), the artificial intelligence techniques have provided a high capability in the estimation of non-linear and intricate parameters [10][11][12]. Plenty of scientific efforts (e.g., concerning environmental subjects [13][14][15][16][17][18][19][20][21][22][23], gas consumption modeling [24,25], sustainable developments [26], pan evaporation and soil precipitation simulation [26][27][28][29][30][31], energy-related estimations [32][33][34][35][36][37][38][39], water supply assessment [16,[40][41][42][43][44][45][46][47][48][49], computer vision and visual processing [50]…”

Section: Introductionmentioning

confidence: 99%

Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings

Moayedi

Mosavi

2021

Sustainability

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The significance of accurate heating load (HL) approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are formulated through synthesizing a multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), the dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

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Exploring factors influencing construction waste reduction: A structural equation modeling approach

Cited by 203 publications

References 63 publications

Consumer Intention to Participate in E-Waste Collection Programs: A Study of Smartphone Waste in Indonesia

Consumer Intention to Participate in E-Waste Collection Programs: A Study of Smartphone Waste in Indonesia

A new QoE-based prediction model for evaluating virtual education systems with COVID-19 side effects using data mining

Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings

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