The role of corporate social responsibility and government incentives in installing industrial wastewater treatment plants: SEM-ANN deep learning approach

Abstract

Contaminated industrial wastewater is one of the severe causes of health diseases. The wastewater treatment trend in developing countries is less, and governments are not strictly pursuing the wastewater management protocols adopted by firms. To study the intention to install wastewater treatment plants at the firm level and provide policy suggestions to the developing countries’ governments, we have collected a cross-sectional dataset from manufacturing firms. We have presented an integrated model based on the theory of planned behaviour and tested our proposed model by implying SEM-ANN deep learning techniques. Results revealed that Environmental knowledge, installation cost, firm reputation, and corporate social responsibility positively influence firm management’s intention to install wastewater treatment plants, but awareness of the risk associated with contaminated wastewater has a negative influence. On the other hand, Government incentives do not influence the process. In addition, our study has found that firm size moderates the relationship between explained and exploratory variables. Our study provided valuable insight into the available literature and provided some policy suggestions to developing countries' governments to cope with water scarcity and health issues caused by contaminated industrial wastewater.

Identifying factors influencing industry 4.0 adoption for sustainability

“…RQ1 and RQ2 are answered with the help of β coefficients obtained by SEM, while RQ3 is resolved with the help of ANN sensitivity analysis. The 2-stage hybrid analysis technique reinterprets conventional statistical techniques by fusing linear and nonlinear methodologies, strengthening the previously accumulated knowledge (Mustafa et al ., 2023).…”

The Impact of Cloud Computing Adoption on Firm Performance Among SMEs in Palestine

“…ANN delivers greater accuracy than SEM Song, 2022). The usage of ANN method helps scholars to estimate the significance of the variables (Shahzad et al, 2020), and to verify and complete the SEM outcomes (Alhasnawi et al, 2023;Mustafa et al, 2023;). ANN is an immensely similar dispersed computer that comprises uncomplicated processing components, which possess a usual predisposition to store new information and make it obtainable for usage (Shahzad et al, 2020).…”

“…Further, ANN provides stronger and superior forecasting precision than the typical regression methods such as SEM (Hamdan et al, 2022b;. Thus, it is reasonable to claim that ANN and SEM methods can complete one another (Mustafa et al, 2023;Alhasnawi et al, 2023). ANN encompasses three roles: network architecture, transfer functions, and learning rule; also, it has multi-layer perception subgroup which is largely employed by scholars, including three tiers such as hidden neuron, output and input (Mustafa et al, 2023;Alhasnawi et al, 2023).…”

“…To answer the above research questions, this study uses a two-phase systematic method through combining SEM and machine learning framework that many academicians recommend for assessing technology adoption behaviors (Mustafa et al, 2023;Hamdan et al, 2022b;Song, 2022). The potency in SEM's path examination is used to answer QS1 and QS2, whereas the machine learning approach responds to QS3.…”

“…Due to its deep learning capacity, the use of ANN construction using at least two hidden tiers may produce more accurate non-linear relations in the framework (Kalinić et al, 2021). Scholars have revealed that the use of ANN produces outcomes with higher robustness and greater accuracy than SEM; thus, it is reasonable to state that ANN and SEM examinations complete one another (Mustafa et al, 2023;Alhasnawi et al, 2023). Hence, the paper seeks to fulfill the current literature gap by employing a deep learning two-phase method to examine linear and non-linear relationships.…”