A pathway to involve consumers for exchanging electronic waste: a deep learning integration of structural equation modelling and artificial neural network

Do information publicity and moral norms trigger waste-sorting intention among households? A sequential mediation analysis

Introduction

The quick pace of technological advancement and urbanization has led to a significant increase in waste production, severely damaging environmental quality and human health. The sorting of waste is a viable option to reduce environmental hazards and attain high recovery rates in the cities. This research extended the theory of planned behavior (TPB) by integrating information publicity (IP) and moral norms (MNs).

Methods

A conceptual model has been developed to explore the predictors of waste-sorting intention of households. The data from 361 Pakistani households have been collected using the purposive sampling method and analyzed via PLS-SEM.

Results and discussion

The study's results revealed that IP is important in creating awareness and establishing moral norms regarding waste sorting among households. The findings further confirm that MN, attitude (ATD), subjective norms (SNs), and perceived behavioral control (PBC) sequentially mediate between IP and WSI. The findings of the current study provides useful practical implications to the practitioners and academicians to combat environmental pollution.

Enhancing consumption of sustainable transportation: Determinants and outcome of consumer engagement with bike-sharing service

Bike-sharing service has become a popular sustainable means of transportation due to its direct impact on traffic congestion, energy consumption, the environment, and people's quality of life. Existing literature suggests that sustainable consumption can be promoted by engaging consumers with green products. This study examined drivers and the outcome of consumer engagement with bike-sharing services based on the technology acceptance model (TAM). A survey was conducted to collect the data from the users of the bike-sharing service in Kuala Lumpur. Structural equation modelling was used to analyse the data and find the relationship between variables. The empirical analyses showed that perceived ease of use and perceived usefulness of the bike-sharing service positively impact all facets of consumer engagement with bike-sharing service, which subsequently influences the continuance usage intention of bike-sharing service. The findings of this study offer useful insights that could enhance the consumption of bike-sharing service. This study also offers some guidelines to transportation practitioners, policymakers, and urban planners regarding promoting healthy and sustainable travel behaviour among urban commuters through bike-sharing service.

Determination of factors affecting customer satisfaction towards "maynilad" water utility company: A structural equation modeling-deep learning neural network hybrid approach

The Maynilad Water Services Inc. (MWSI) is responsible for supplying water to the west zone of Metro Manila. The utility provides service to 17 cities and municipalities which frequently experience water interruptions and price hikes. This study aimed to identify the key factors affecting customer satisfaction toward MWSI by integrating the SERVQUAL dimensions and Expectation Confirmation Theory (ECT). An online questionnaire was disseminated to 725 MWSI customers using the snowball sampling method to obtain accurate data. Ten latent were analyzed using Structural Equation Modeling and Deep Learning Neural Network hybrid. It was found that Assurance, Tangibles, Empathy, Expectations, Confirmation, Performance, and Water consumption were all factors affecting MWSI customers' satisfaction. Results showed that having an affordable water service, providing accurate water bills, on-time completion of repairs and installations, intermittent water interruptions and professional employees contribute to the general satisfaction. MWSI officials may utilize this study's findings to assess further the quality of their services and design effective policies to improve. The employment of DLNN and SEM hybrid showed promising results when employed in human behavior. Thus, the results of this study would be beneficial when examining satisfaction to utilities and policies among service providers in different countries. Moreover, this study could be extended and applied among other customer and service-focused industries worldwide.