Mitigating the environmental concerns through total quality management and green manufacturing practices.

This study seeks to explore the intricate relationship between total quality management (TQM) and environmental performance (EP), incorporating the mediating role of green manufacturing practices (GMPs). Additionally, the study examines the moderating impact of environmental strategy on the connections between GMPs and EP. Data were collected through a questionnaire distributed to managers of manufacturing small and medium-sized enterprises (SMEs) and were subjected to analysis using structural equation modeling. The results reveal a positive and significant impact of TQM on EP. Furthermore, the findings suggest that GMPs partially mediate the association between TQM and EP, while the anticipated moderating effect of environmental strategy between GMPs and EP is also supported in this study. These outcomes hold valuable implications for enhancing the environmental performance of SMEs through the integration of TQM and GMPs. It is important to note that this research exclusively focuses on manufacturing SMEs; therefore, future studies should extend their examination of this concept to other industries. Additionally, the study's findings provide a valuable roadmap for SME administrators aiming to elevate their environmental performance.

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

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.

A data-driven Markov process for infectious disease transmission.

The 2019 coronavirus pandemic exudes public health and socio-economic burden globally, raising an unprecedented concern for infectious diseases. Thus, describing the infectious disease transmission process to design effective intervention measures and restrict its spread is a critical scientific issue. We propose a level-dependent Markov model with infinite state space to characterize viral disorders like COVID-19. The levels and states in this model represent the stages of outbreak development and the possible number of infectious disease patients. The transfer of states between levels reflects the explosive transmission process of infectious disease. A simulation method with heterogeneous infection is proposed to solve the model rapidly. After that, simulation experiments were conducted using MATLAB according to the reported data on COVID-19 published by Johns Hopkins. Comparing the simulation results with the actual situation shows that our proposed model can well capture the transmission dynamics of infectious diseases with and without imposed interventions and evaluate the effectiveness of intervention strategies. Further, the influence of model parameters on transmission dynamics is analyzed, which helps to develop reasonable intervention strategies. The proposed approach extends the theoretical study of mathematical modeling of infectious diseases and contributes to developing models that can describe an infinite number of infected persons.

A moderated mediation model to predict the adoption intention of renewable wind energy in developing countries.

Developing countries are deprived of resources, and their economies are dwindling. Energy shortfall is one of the leading issues in developing countries that not only shatter economies but is the leading cause of depletion of natural resources and environmental pollution. There is an urgent need to shift to renewable energy sources to save economies and natural resources and to save our ecological system. Aiming for this, we have collected a cross-sectional data set to study the household intentions of shifting to wind energy and analysed the moderated mediation interactions of the variable to better understand socio-economic and personal factors. 840 responses were analysed using smart-PLS 4.0; results revealed that cost value and social influence directly relate to renewable energy adoption. Environmental knowledge directly influences attitude towards the environment, and health consciousness influences perceived behavioural control. Results also revealed that social influence strengthens the indirect relationship between awareness of renewable energy and its adoption, while it weakens the indirect relationship between health consciousness and renewable energy adoption.

What motivates online community contributors to contribute consistently? A case study on Stackoverflow netizens.

Online Question and answer (Q&A) communities are the common and famous platforms to learn and share knowledge and are very useful for every knowledge seeker. Less knowledge contribution is a critical issue for the sustainability and future of these platforms. The motivation of inactive users to participate in Q&A communities is a real challenge. Based on the social cognitive and social exchange theory, we have studied the knowledge contribution patterns of active and consistent StackOverflow users over the last eleven years. We have used a difference generalized method of moments estimator to estimate the proposed model. Results revealed that reciprocation of knowledge and social interaction positively, whereas knowledge seeking of active and consistent users negatively influences knowledge contribution. Peer recognition and repudiation have partially positive and negative effects on users' knowledge contribution. This research offers theoretical and practical suggestions to encourage people to contribute their knowledge to online Q&A communities.

Designing a clean environmental framework for Pakistan: analyzing the nonlinear impact of aggregate demand drivers on CO2 emissions.

Aggregate demand in every country is one of the main components of macroeconomics, and Pakistan is no exception. This paper uses a fresh econometric framework to analyze the nonlinear impact of aggregate demand drivers on CO2 emissions in Pakistan by using a nonlinear ARDL approach over the data period from 1975 to 2019. The linear findings reveal that consumption and government expenditure indicates deteriorating effects on carbon emissions in long run in Pakistan. However, nonlinear findings revealed that positive change in government expenditure and trade has positive effects on carbon emissions. The positive change in investment has a negative significant and meaningful effect on the environment in Pakistan and asymmetric findings are also country-specific. Therefore, this study offers a few important policy implications for theorists, academics, and policymakers of Pakistan as well developing economies.

An SEM-ANN approach to evaluate public awareness about COVID, A pathway toward adaptation effective strategies for sustainable development.

This study was conducted to evaluate public awareness about COVID with aimed to check public strategies against COVID-19. A semi structured questionnaire was collected and the data was analyzed using some statistical tools (PLS-SEM) and artificial neural networks (ANN). We started by looking at the known causal linkages between the different variables to see if they matched up with the hypotheses that had been proposed. Next, for this reason, we ran a 5,000-sample bootstrapping test to assess how strongly our findings corroborated the null hypothesis. PLS-SEM direct path analysis revealed HRP -> PA-COVID, HI -> PA-COVID, MU -> PA-COVID, PM -> PA-COVID, SD -> PA-COVID. These findings provide credence to the acceptance of hypotheses H1, H3, and H5, but reject hypothesis H2. We have also examined control factors such as respondents' age, gender, and level of education. Age was found to have a positive correlation with PA-COVID, while mean gender and education level were found to not correlate at all with PA-COVID. However, age can be a useful control variable, as a more seasoned individual is likely to have a better understanding of COVID and its effects on independent variables. Study results revealed a small moderation effect in the relationships between understudy independent and dependent variables. Education significantly moderates the relationship of PA-COVID associated with MU, PH, SD, RP, PM, PA-COVID, depicts the moderation role of education on the relationship between MU*Education->PA-COVID, HI*Education->PA.COVID, SD*Education->PA.COVID, HRP*Education->PA.COVID, PM*Education -> PA.COVID. The artificial neural network (ANN) model we've developed for spreading information about COVID-19 (PA-COVID) follows in the footsteps of previous studies. The root means the square of the errors (RMSE). Validity measures how well a model can predict a certain result. With RMSE values of 0.424 for training and 0.394 for testing, we observed that our ANN model for public awareness of COVID-19 (PA-COVID) had a strong predictive ability. Based on the sensitivity analysis results, we determined that PA. COVID had the highest relative normalized relevance for our sample (100%). These factors were then followed by MU (54.6%), HI (11.1%), SD (100.0%), HRP (28.5%), and PM (64.6%) were likewise shown to be the least important factors for consumers in developing countries struggling with diseases caused by contaminated water. In addition, a specific approach was used to construct a goodness-of-fit coefficient to evaluate the performance of the ANN models. The study will aid in the implementation of effective monitoring and public policies to promote the health of local people.

An integrated model of UTAUT2 to understand consumers' 5G technology acceptance using SEM-ANN approach.

It has been a decade since the first extensive study on the internet's adoption and use was conducted. Circumstances have changed in the last decade internet has become an essential need for every human being. Socio-psychological, economic, and personal factors play a significant role in shaping human behaviour. But their role in shaping consumer behaviour toward 5G is still unexplored. In order to determine the impact of socio-psychological elements on 5G technology adoption intention, the study integrated curiosity, perceived value, functional value, and environmental awareness into UTAUT2 and analyzed how they interact. Instead of relying on linear models, this study employed a dual-stage SEM-ANN approach because customers' decision-making process to adopt new technology is complex. Valid responses from 840 respondents were collected, investigated, and ranked using the deep learning ANN approach. All predictors were found statistically significant except social influence. ANN sensitivity analysis revealed that newly integrated predictors (environmental awareness, curiosity) are surprisingly the most important predictors, followed by facilitating conditions and perceived satisfaction. SEM-ANN hybrid two-step deep learning approach explained 83.6% variance higher than the baseline model (UTAUT2). The study improved UTAUT2 by adding new variables and expanding its canvas to predict user technology adoption. This will show how consumers react to 5G services and help telecoms grow into new markets.

Does Public Awareness Matter to Achieve the UN's Sustainable Development Goal 6: Clean Water for Everyone?

United Nations set a Sustainable Development Goal to provide clean water for everyone (SDG 6). The successful implementation of SDG 6 is still miles to go. Public awareness's role as a key factor in achieving Sustainable Development Goal 6 is vital but received less attention from researchers in the past. To understand the role of public awareness and other relevant factors in achieving SDG 6, we have collected a cross-sectional dataset from a developing country and applied a partial least square structural equation modelling approach. The results revealed that willingness to pay for clean water, social influence, and facilities provided by the government, public awareness, and knowledge about contaminated water positively influence the households' intentions to use clean water. We also found that public awareness partially mediates relationships. Study results have useful policy implications for governments, NGOs, and other stakeholder organizations working on achieving SDG 6 in developing countries.

Digital Students' Satisfaction With and Intention to Use Online Teaching Modes, Role of Big Five Personality Traits.

During the COVID-19 pandemic, online teaching modes were found vital to continue students' learning process, but sustainable implementation of online teaching models is an area of concern for policymakers. Psychiatrists are also eager to know students' behavior toward learning and modes of teaching during COVID-19. We have drawn a model based on the big five personality traits to study students' satisfaction with online teaching modes and their adoption intentions toward online teaching modes. We have collected data from 718 bachelor's and master's level students from four different universities. We have applied the SEM-ANN dual-stage approach to test personality traits' influence and ranked them based on their normalized importance. The results revealed that agreeableness, conscientiousness, neuroticism, and openness positively influence students' satisfaction with online teaching models, but that extraversion negatively influences their satisfaction. Agreeableness, extraversion, and neuroticism positively impact, but openness negatively influences. Conscientiousness does not affect adoption intention. Furthermore, agreeableness is the most significant, and conscientiousness is the least important factor for students to adopt online teaching modes. The findings of the study have useful perceptiveness for educational policymakers, academics, and psychiatrists.

Éclaircissement to Understand Consumers' Decision-Making Psyche and Gender Effects, a Fuzzy Set Qualitative Comparative Analysis.

Consumers' decision-making is complex and diverse in terms of gender. Different social, psychological, and economic factors mold the decision-making preferences of consumers. Most researchers used a variance-based approach to explain consumer decision-making that assumes symmetric relationship between variables. We have collected data from 468 smartwatch users and applied a fuzzy set qualitative comparative analysis (fsQCA) to explain and compare male and female consumers' decision-making complexity. fsQCA assumes that an asymmetric relationship between variables can exist in the real world, and different combinations of variables can lead to the same output. Results explain that different variables have a core and secondary level of impact on consumer decision-making. Hence, we can not claim that certain factors are significant or insignificant for decision-making. fsQCA results revealed that cost value, performance expectancy, and social influence play a key role in consumers' buying decisions. This study has contributed to the existing literature by explaining consumer decision-making by applying configuration and complexity theories and identifying unique solutions for both genders. A major contribution to theoretical literature was also made by this research, which revealed the complexity of consumer purchasing decisions made for new products.

Does Health Consciousness Matter to Adopt New Technology? An Integrated Model of UTAUT2 With SEM-fsQCA Approach.

Every emerging technology has its pros and cons; health-conscious users pay more importance to healthy and environment-friendly technologies. Based on the UTAUT2 model, we proposed a comprehensive novel model to study the factors influencing consumers' decision-making to adopt the technology. Compared to prior studies that focused on linear models to investigate consumers' technology adoption intentions and use behavior. This study used a Structural Equation Modeling-fuzzy set qualitative comparative analysis (SEM-fsQCA) approach to account for the complexity of customers' decision-making processes in adopting new technology. We collected valid responses from 830 consumers, analyzed them, and evaluated them using a deep learning SEM-fsQCA technique to capture symmetric and asymmetric relations between variables. We have extensively incorporated a health-consciousness attitude as a predictor and mediator to understand better the decision-making toward technology adoption, specifically 5G technology. All the factors tested in our model are statistically significant except the economic factors. Health-consciousness attitude (HCA) and behavioral intention (BI) found significant predictors and valid mediators in the process of 5G technology adoption. FsQCA provided six configurations to achieve high 5G adoption. The findings have significant practical ramifications for telecom corporations, advertisers, government officials, and key policymakers. Additionally, the study added substantial theoretical literature to technology adoption, particularly the adoption of 5G technology.

Mediating Role of Green Supply Chain Management Between Lean Manufacturing Practices and Sustainable Performance.

Manufacturing companies in today's industrial world are seeking to use the new manufacturing process methods. The primary goal of corporations is to achieve optimum production while deploying minimal capital. The fundamental purpose of this study is to examine the influence of various lean manufacturing practices on the sustainability performance of companies and the mediating role of green supply chain management (GSCM). The data was gathered using questionnaires from 250 Pakistani manufacturing firms and analyzed using AMOS 25. Results demonstrate that process and equipment, product design, supplier relationships, and customer relationships significantly affect sustainable performance. It is also recognized that Green Supply Chain Management mediates the interaction between HR processes, product design, supplier relationship, customer relationship, and environmental performance. The findings of this study will enable managers and decision-makers of manufacturing companies to increase sustainable efficiency and reduce waste through the use of lean manufacturing and GSCM implementation.