Intention-Based Critical Factors Affecting Willingness to Adopt Novel Coronavirus Prevention in Pakistan: Implications for Future Pandemics

Asymmetric effects of fine particulate matter and stringency policy on COVID-19 intensity

This study aims to examine the influence of environmental performance (PM2.5) on COVID-19 intensity . For this purpose, we employ the newly introduced Hidden Panel Cointegration test and Nonlinear Panel Autoregressive Distributed Lag model. Results indicate the asymmetric linkages between PM2.5 and COVID-19 intensity, as the positive shock in PM2.5 raises the COVID-19 intensity by 21%, whereas the negative shock in PM2.5 decreases COVID-19 intensity by 12% in long-run. On the contrary, the positive shock in stringency measures decreases COVID-19 intensity by 42.8%, while the negative shock in stringency policy increases COVID-19 intensity by 66.7%. These findings imply that higher pollution increases the COVID-19 severity while higher stringency measures slow down people's movement and reduce COVID-19 intensity. However, a sudden negative shock in lockdown increases people's interaction, leading to a higher spread of the virus. These results suggest that governments should adopt stringent action plans to contain the transmissibility of COVID-19.

Renewable energy, economic freedom and economic policy uncertainty: New evidence from a dynamic panel threshold analysis for the G-7 and BRIC countries

This study aims to demonstrate the impact of renewable energy consumption (REC) on environmental degradation using the EKC hypothesis testing for the BRIC and G-7 countries. Two EKC models were created and tested, with Model 2 including REC and other independent variables such as economic freedom (EF) and economic policy uncertainty (EPU), which affect the level of renewable energy consumption and CO2 emissions. Empirical findings indicate that the EKC hypothesis is verified faster in the REC-EF-EPU-based EKC model (Model 2) than in the EF-EPU-based EKC model (Model 1) for G-7 countries since the turning point takes place earlier in Model 2 than in Model 1 with REC. This suggests that renewable energy consumption accelerates the reduction of CO2 emissions. Moreover, this earlier turning point results in lower environmental cleaning costs, less time vesting, and saving resources and money for G-7 countries. However, the study found no evidence supporting the EKC hypothesis for the BRIC countries.

Linking environmental corporate social responsibility to firm performance: The role of partnership restructure

In this study, we integrate the signal institutional theory and stakeholder theory to examine partnership restructure as a critical mechanism linking environmental corporate social responsibility (ECSR) to corporate financial performance. Keeping in line with most prior studies, we first argue that a positive relationship exists between ECSR and firm performance. Then we propose that partnership restructure mediates the nexus between ECSR and firm performance because ECSR may motivate firms to change their partners in the better interests of the firms. In addition, we propose that the firms' industry power will exaggerate while dysfunctional competition will weaken the positive nexus between ECSR and partnership restructure. Evidence based on a survey covering 206 manufacturing firms in China offers good support for our predictions. This last section offers research contributions and implications for the managers based on the findings.

Unleashing the dynamic and nonlinear relationship among new-type urbanization, foreign direct investment, and inclusive green growth in China: an environmental sustainability perspective

As an important way for China to integrate into the international circulation, foreign direct investment (FDI) can not only increase China's capital accumulation but also directly affect inclusive green growth by promoting the diffusion and transfer of green technologies and activating the domestic market. Based on China's provincial panel data from 2007 to 2019, this paper discusses the following issues: first, Global Malmquist-Luenberger (GML) productivity index is used to measure and analyze the current situation of inclusive green growth in 30 provinces of China; second, we set a fixed effect panel model to test the relationship between FDI and inclusive green growth in China. Third, based on PVAR (panel vector autoregressive) model, the dynamic impacts of FDI and new urbanization on inclusive green growth are tested. Finally, a regression model with the new urbanization level as the threshold variable is constructed to test the threshold effect of FDI on China's green inclusive growth. Accordingly, each region needs to formulate FDI introduction policies according to the local new urbanization level, so as to give full play to the positive role of FDI in inclusive green growth.

The effect of mandatory environmental regulation on green development efficiency: evidence from China

The existing literature finds that mandatory environmental regulation (MER) can significantly reduce environmental pollution. However, much less is known about how the implementation of MER affects green development efficiency (GDE). Based on the Air Pollution Control Action Plan which was enforced in 2013 in China's most developed regions as an exogenous shock, we find that first, MER has a significant negative effect on the improvement of GDE by reducing regional scale efficiency. Second, MER mainly reduces the GDE of cities with stronger regulation intensities and with larger economic volumes. Third, MER also has a negative impact on regional green total factor productivity by changing technical progress. We suggest that when implementing MER, governments should enhance regional and global cooperation, promote green technology, and use comprehensive policy tools to stimulate firms' green innovation.

Analyzing the determinants of sustainability of China Pakistan Economic Corridor (CPEC) projects: an interpretive structural modelling (ISM) approach

China Pakistan Economic Corridor (CPEC) is a game changer initiative of South Asian Pacific Rim. It has great importance for almost all Asian countries. Its success is expected to dictate the economic development of the stakeholders. The aim of this study is to evaluate the essential determinants deriving the sustainability of CPEC projects. The design of the study comprises of the review of literature, data collection, and analysis. Population under study is the folk of stakeholders of CPEC. Sampling envisages on purposive sampling design, i.e., 14 experts from within the stakeholders. Primary data is collected in the field setting through a survey questionnaire appropriate for the study. ISM is used for modelling and MICMAC for analysis and classification using inductive approach. The findings of the literature survey show that there are 23 prime determinants of sustainability of CPEC projects. The results of ISM show that 13 determinants are at Level-I, nine at Level-II, and one determinant namely "economic globalization" is at Level-III being the most critical and driving determinant. The findings of MICMAC show that only one determinant is classified in independent quadrant, and all the remaining determinants are in linkage quadrant, whereas, no determinant is shown in autonomous and/or dependence quadrant. But most of the determinants have potential to be classified in dependent and independent quadrants. It is intimately evident that the results of MICMAC corroborate the results of ISM. It is useful for folk of the stakeholders by way of developing an understanding about the multitude of determinants, intra-determinant relations, prioritizing the determinants for policy decisions, and/or for building future studies. This study has some limitations, e.g., the study uses qualitative approach and answers what and how questions that do not quantify the relations or tell the cause of indicated relations.

Which factors influence the decisions of renewable energy investors? Empirical evidence from OECD and BRICS countries

The importance of using renewable energy (RE) sources has increased significantly in recent times, especially considering the growing concerns about climate change problems and rising fossil fuel prices, which pose a significant threat to the national economies. Therefore, empirical studies that can be used both domestically and internationally in harmony can be created in line with rising investments in RE. However, there has no more analysis of RE investments from the viewpoint of investors in the literature up to this point, and it is crucial to highlight the best investor practices when deploying RE. This research provides theoretical and empirical support for the factors influencing RE investments; used in this analysis are newly constructed panel data on 34 OECD countries and the 5 BRICS countries that range from 2000 to 2020. Specifically, the generalized moment method (GMM), robustness check, fixed and random effects models, panel unit testing, and other panel regression techniques were employed in the study to analyze the determinants of RE investment. The main findings of this paper suggest that economic growth, RE policy, and R&D expenditures all have a statistically significant and positive relationship with RE capacity. Furthermore, RE investment is inversely relative to energy use, electricity use, and carbon (CO₂) emissions. As a result, rigorous governmental or state regulation (policy, R&D) is essential for RE investment.

The impact of smart city pilots on corporate total factor productivity

The existing literature on smart city pilots mainly focuses on the city level and rarely addresses the firm level. This paper assesses the impact of smart city pilot policy (SCP) on firms' total factor productivity (TFP) and explores the impact of SCP under different heterogeneities as well as the mechanisms of action of the SCP. The LP approach is used in this paper to measure firms' TFP, and the impact of SCP is analyzed by the DID model with firms' panel data from 2009 to 2019 as research objects. First, it was found that the SCP can significantly increase the TFP of firms (0.041). Second, through heterogeneity analysis, we found that SCP can strengthen the monopoly position of monopolistic firms and state-owned enterprises. Moreover, the SCP can also alleviate the development imbalance of TFP between firms in coastal and non-coastal areas. In addition, SCP can significantly improve TFP of heavy polluting enterprises. Finally, we find that the important ways for SCP to improve firms' TFP is increasing investment in technological innovation, talent agglomeration, attracting financing, improving resource allocation efficiency, and digital transformation. The study provides unique insights for policy makers and business managers in China and other emerging countries to enhance TFP and achieve corporate sustainable development.

Sustainable impact of COVID-19 on education projects: aspects of naturalism

History records show that pandemics and threats have always given new directions to the thinking, working, and learning styles. This article attempts to thoroughly document the positive core of coronavirus 2019 (COVID-19) and its impact on global social psychology, ecological stability, and development. Structural equation modeling (SEM) is used to test the hypotheses and comprehend the objectives of the study. The findings of the study reveals that the path coefficients for the variables health consciousness, naturalism, financial impact and self-development, sustainability, compassion, gregariousness, sympathy, and cooperation demonstrate that the factors have a positive and significant effect on COVID-19 prevention. Moreover, the content analysis was conducted on recently published reports, blog content, newspapers, and social media. The pieces of evidence from history have been cited to justify the perspective. Furthermore, to appraise the opinions of professionals of different walks of life, an online survey was conducted, and results were discussed with expert medical professionals. Outcomes establish that the pandemics give birth to creativity, instigate innovations, prompt inventions, establish human ties, and foster altruistic elements of compassion and emotionalism.

Risk management in sustainable supply chain: a knowledge map towards intellectual structure, logic diagram, and conceptual model

The global spread of COVID-19, international trade protectionism, geopolitical conflicts, and climate change presents challenges and risks to sustainable supply chains (SSCs). In recent years, scholarly interest in sustainable supply chain risk management (SSCRM) has continued to rise. A helpful literature review is necessary to enable supply chain practitioners to apply empirical findings from academic research or conceptual frameworks to their operations to maintain the stability and competitiveness of sustainable supply chains. The knowledge map of SSCRM is explored in this study using both quantitative and qualitative analysis. A total of 793 articles were retrieved to reveal the knowledge map of SSCRM. Scientometric and context analysis are combined in quantitative analysis to identify the intellectual structure of risk management research related to SSC. Then, a critical review is conducted in qualitative analysis to summarize and analyze the motivations, strategies, approaches, and tools of SSCRM. Combining the quantitative and qualitative analysis results, a conceptual model is constructed for SSCRM from three aspects: (1) risk identification, (2) risk assessment, and (3) risk mitigating and responding. Finally, future research directions are suggested based on the conceptual model for guiding the theories and practice of SSCRM. This study can work as a roadmap for providing appropriate risk management policies and toolkits to SSC, which could advance theoretical thinking on how to mitigate SSC risks.

Determining the COVID-19 effects on spillover between oil market and stock exchange: a global perspective analysis

This paper investigates volatility spillovers between the global crude oil market and the stock markets of the global oil stock markets (Russian, Canada, China, Kuwait, and the USA) pre and after the COVID-19 pandemic. We use wavelet Granger causality methods to study the volatility spillovers between global oil stock markets, mainly from January 1, 2019, to March 31, 2021. Our Results (1) shows that WTI and Brent oil prices had a negative mean return before COVID-19 but a positive mean return during the pandemic spread. Other Results (2) find the positive, significantly lowest, and highest frequency during the COVID-19 outbreak for all selected countries. The results also show that the link between oil WTI & Brent prices and stock markets return in the lowest (33-66 days) and highest frequency range (4-16) before the Covid-19 epidemic, especially in the first quarter of 2020. Before the COVID-19 period, the Russian oil stock market is seriously prejudiced with oil prices on a modest scale, but not after the pandemic's start. This study also perceives direction opposite between the COVID-19 period. The Canadian and United States America oil and stock markets influence the lowest scale in the previous COVID-19 sample for the U.S. market. Moreover, this paper exposed that oil marketing highest oil futures in their portfolios than stock shares for all times. We found that oil price shocks had a more significant impact on the stock markets of the United States and Canada than on the stock markets of other countries.

Retesting the EKC hypothesis through transmission of the ARMEY curve model: an alternative composite model approach with theory and policy implications for NAFTA countries

We investigate the validity of the environmental Kuznets curve (EKC) hypothesis for the NAFTA countries. In this investigation, we approach this hypothesis from a different methodology and propose employing the ARMEY curve hypothesis since the mathematical-functional propositions of both hypotheses were constructed on the same inverted U-shaped relationships. Thus, for the first time, it can be interpreted that the EKC hypothesis is empirically tested through a transmission mechanism of the ARMEY curve hypothesis in a single composite model. Therefore, this approach makes our study different from all empirical studies in the relevant literature. We apply the Augmented Mean Group (AMG) estimator to this aim. Empirical findings indicate that the ARMEY curve hypothesis was verified only for the USA. However, this new approach proposed in this study cannot test the EKC hypothesis through the ARMEY curve model for any NAFTA country since this approach requires verification of the ARMEY curve hypothesis and a significant composite model for the same NAFTA country. If our composite model was significant, it might make it possible to numerically determine a maximum real GDP per capita level that would minimize or maximize CO₂ emission levels for the USA. Therefore, this study introduces-proposes this new methodology as an alternative way of testing the EKC hypothesis in the relevant literature for future empirical studies.

Innovation Research in Tourism and Hospitality Field: A Bibliometric and Visualization Analysis

New studies have constantly been emerging in the field of tourism. However, it is not clear to what extent which study contributes to the literature and tourism application areas. There are a few bibliometric studies that illustrate the intellectual structure of the tourism innovation field. Therefore, the purpose of this study is to determine the performance and intellectual structure of research studies conducted on innovation in the field of tourism. For this purpose, a bibliometric analysis of 387 research studies, which were published over the period 1975–2021 and cited in SCI-EXPANDED, SSCI, A&HCI indexes of WoS, is performed. Co-citation analysis indicates that innovation research studies in tourism can be categorized into three groups: (1) conceptualization and types of innovation in tourism, (2) review research on innovation in tourism, and (3) methodological studies. According to the co-word analysis, sharing economy, open innovation, sustainability, and technology are the most popular topics of recent times. A steady increase is observed in the number of citations to articles in the field of tourism innovation after 2008. The research study has crucial impacts in terms of guiding the researchers who would study in this field and providing the intellectual appearance of the field.

The asymmetric nexus between air pollution and COVID-19: Evidence from a non-linear panel autoregressive distributed lag model

The emergence of a new coronavirus (COVID-19) has become a major global concern that has damaged human health and disturbing environmental quality. Some researchers have identified a positive relationship between air pollution (fine particulate matter PM2.5) and COVID-19. Nonetheless, no inclusive investigation has comprehensively examined this relationship for a tropical climate such as India. This study aims to address this knowledge gap by investigating the nexus between air pollution and COVID-19 in the ten most affected Indian states using daily observations from 9th March to September 20, 2020. The study has used the newly developed Hidden Panel Cointegration test and Nonlinear Panel Autoregressive Distributed Lag (NPARDL) model for asymmetric analysis. Empirical results illustrate an asymmetric relationship between PM2.5 and COVID-19 cases. More precisely, a 1% change in the positive shocks of PM2.5 increases the COVID-19 cases by 0.439%. Besides, the estimates of individual states expose the heterogeneous effects of PM2.5 on COVID-19. The asymmetric causality test of Hatemi-J's (2011) also suggests that the positive shocks on PM2.5 Granger-cause positive shocks on COVID19 cases. Research findings indicate that air pollution is the root cause of this outbreak; thus, the government should recognize this channel and implement robust policy guidelines to control the spread of environmental pollution.

Interventions for the Current COVID-19 Pandemic: Frontline Workers' Intention to Use Personal Protective Equipment

BACKGROUND

Frontline workers (FLWs) are at a higher risk of COVID-19 infection during care interactions than the general population. Personal protective equipment (PPE) is regarded as an effective intervention for limiting the transmission of airborne viruses. However, research examining FLWs' intention to use PPE is limited.

OBJECTIVES

This study addresses this research gap and also contributes by expanding the conceptual mechanism of planned behavior theory by incorporating three novel dimensions (perceived benefits of PPE, risk perceptions of the epidemic, and unavailability of PPE) in order to gain a better understanding of the factors that influence FLWs' intentions to use PPE.

METHOD

Analysis is based on a sample of 763 FLWs in Pakistan using a questionnaire survey, and the structural equation modeling approach is employed to evaluate the suppositions.

RESULTS

Study results indicate that attitude, perceived benefits of PPE, and risk perceptions of the epidemic have positive influence on FLWs' intention to use PPE. In comparison, the unavailability of PPE and the cost of PPE have opposite effects. Meanwhile, environmental concern has a neutral effect.

CONCLUSIONS

The study results specify the importance of publicizing COVID-19's lethal impacts on the environment and society, ensuring cheap PPE, and simultaneously enhancing workplace safety standards.

Assessment of public intention to get vaccination against COVID-19: Evidence from a developing country

OBJECTIVES

Widespread acceptance of the COVID-19 vaccine will be the next important step in fighting the novel coronavirus disease. Though the Pakistani government has successfully implemented robust policies to overcome the COVID-19 pandemic; however, studies assessing public intention to get COVID-19 vaccination (IGCV) are limited. The aim of this study is to deal with this literature gap and has also expanded the conceptual framework of planned behaviour theory. We have introduced three new considerations (risk perceptions of the pandemic, perceived benefits of the vaccine, and unavailability of vaccine) to have a better understanding of the influencing factors that encourage or discourage public IGCV.

METHODS

Results are based on a sample collected from 754 households using an inclusive questionnaire survey. Hypotheses are tested by utilizing the structural equation modelling approach.

RESULTS

The results disclose that the intention factors, that is, attitude, risk perceptions of the pandemic, and perceived benefits of the vaccine, impart positive effects on public IGCV. In contrast, the cost of the vaccine and the unavailability of the vaccine have negative effects. Notably, environmental concern has an insignificant effect.

CONCLUSIONS

Research findings emphasize the importance of publicizing the devastating impacts of COVID-19 on society and the environment, ensuring vaccination availability at an accessible price while simultaneously improving public healthcare practices.

Data-Driven Dynamic Adjustment and Optimization Model of Emergency Logistics Network in Public Health

Background and Aim

In the long-term prevention of the COVID-19 pandemic, parameters may change frequently for various reasons, such as the emergence of mutant strains and changes in government policies. These changes will affect the efficiency of the current emergency logistics network. Public health emergencies have typical unstructured characteristics such as blurred transmission boundaries and dynamic time-varying scenarios, thus requiring continuous adjustment of emergency logistics network to adapt to the actual situation and make a better rescue.

Practical Significance

The infectivity of public health emergencies has shown a tendency that it first increased and then decreased in the initial decision-making cycle, and finally reached the lowest point in a certain decision-making cycle. This suggests that the number of patients will peak at some point in the cycle, after which the public health emergency will then be brought under control and be resolved. Therefore, in the design of emergency logistics network, the infectious ability of public health emergencies should be fully considered (ie, the prediction of the number of susceptible population should be based on the real-time change of the infectious ability of public health emergencies), so as to make the emergency logistics network more reasonable.

Methods

In this paper, we build a data-driven dynamic adjustment and optimization model for the decision-making framework with an innovative emergency logistics network in this paper. The proposed model divides the response time to emergency into several consecutive decision-making cycles, and each of them contains four repetitive steps: (1) analysis of public health emergency transmission; (2) design of emergency logistics network; (3) data collection and processing; (4) adjustment and update of parameters.

Results

The result of the experiment shows that dynamic adjustment and update of parameters help to improve the accuracy of describing the evolution of public health emergency transmission. The model successively transforms the public health emergency response into the co-evolution of data learning and optimal allocation of resources.

Conclusion

Based on the above results, it is concluded that the model we designed in this paper can provide multiple real-time and effective suggestions for policy adjustment in public health emergency management. When responding to other emergencies, our model can offer helpful decision-making references.

System dynamics analysis of COVID-19 prevention and control strategies

The COVID-19 pandemic now affects the entire world and has many major effects on the global economy, environment, health, and society. Focusing on the harm COVID-19 poses for human health and society, this study used system dynamics to establish a prevention and control model that combines material supply, public opinion dissemination, public awareness, scientific and technological research, staggered work shifts, and the warning effect (of law/policy). Causal loop analysis was used to identify interactions between subsystems and explore the key factors affecting social benefit. Further, different scenarios were dynamically simulated to explore optimal combination modes. The main findings were as follows: (1) The low supervision mode will produce a lag effect and superimposed effect on material supply and impede social benefit. (2) The strong supervision mode has multiple performances; it can reduce online public opinion dissemination and the rate of concealment and false declaration and improve government credibility and social benefit. However, a fading effect will appear in the middle and late periods, and over time, the effect of strong supervision will gradually weaken (but occasionally rebound) and thus require adjustment. These findings can provide a theoretical basis for improving epidemic prevention and control measures.

Evaluating Green Technology Strategies for the Sustainable Development of Solar Power Projects: Evidence from Pakistan

Energy is the main element for a modern lifestyle that must be considered in economically reliable and sustainable development dialogues. The financial performance of solar power projects has become the main issue, especially in developing countries such as Pakistan, where it has gained the special attention of government and regulatory authorities. The present study evaluates green technology strategies for the sustainable development of solar power projects in Pakistan. We examine the moderating role of cost and riskiness of the methods between the nexus of capital budgeting techniques and the financial performance of solar power projects. The analysis is performed on data collected from 44 respondents (chief financial officers and chief executive officers) by accompanying an inclusive questionnaire survey. Partial least squares structural equation modeling (PLS-SEM) is used to assess the formulated suppositions. The results reveal that green technology strategies positively impact the sustainable development of solar power projects. The profitability index is a good source of higher financial performance of the solar power projects. The results further demonstrate that the cost and riskiness of the methods significantly moderate the nexus of capital budgeting techniques and the financial performance of solar power projects. These findings provide a valuable manual for policymakers, government institutions, and regulators to select the appropriate green technology strategy to increase cleaner production and sustainable development of solar power projects.

Does temperature matter for COVID-19 transmissibility? Evidence across Pakistani provinces

The outbreak of novel coronavirus (COVID-19) has become a global concern that is deteriorating environmental quality and damaging human health. Though some researchers have investigated the linkage between temperature and COVID-19 transmissibility across different geographical locations and over time, yet these studies are scarce. This study aims to bridge this gap using daily temperature and COVID-19 cases (transmissibility) by employing grey incidence analysis (GIA) models (i.e., Deng's grey incidence analysis (DGIA), the absolute degree GIA (ADGIA), the second synthetic degree GIA (SSDGIA), the conservative (maximin) model) and correlation analysis. Data on temperature are accessed from the NASA database, while the data on COVID-19 cases are collected from the official website of the government of Pakistan. Empirical results reveal the existence of linkages between temperature and COVID-19 in all Pakistani provinces. These linkages vary from a relatively stronger to a relatively weaker linkage. Based on calculated weights, the strength of linkages is ranked across provinces as follows: Gilgit Baltistan (0.715301) > Baluchistan (0.675091) > Khyber Pakhtunkhwa (0.619893) > Punjab (0.619286) > Sindh (0.601736). The disparity in the strength of linkage among provinces is explained by the discrepancy in the intensity of temperature. Besides, the diagrammatic correlation analysis shows that temperature is inversely linked to COVID-19 cases (per million persons) over time, implying that low temperatures are associated with high COVID-19 transmissibility and vice versa. This study is among the first of its kind to consider the linkages between temperature and COVID-19 transmissibility for a tropical climate country (Pakistan) using the advanced GIA models. Research findings provide an up-to-date glimpse of the outbreak and emphasize the need to raise public awareness about the devastating impacts of the COVID-19. The educational syllabus should provide information on the causes, signs, and precautions of the pandemic. Additionally, individuals should practice handwashing, social distancing, personal hygiene, mask-wearing, and the use of hand sanitizers to ensure a secure and supportive atmosphere for preventing and controlling the current pandemic.

Does temperature matter for COVID-19 transmissibility? Evidence across Pakistani provinces

The outbreak of novel coronavirus (COVID-19) has become a global concern that is deteriorating environmental quality and damaging human health. Though some researchers have investigated the linkage between temperature and COVID-19 transmissibility across different geographical locations and over time, yet these studies are scarce. This study aims to bridge this gap using daily temperature and COVID-19 cases (transmissibility) by employing grey incidence analysis (GIA) models (i.e., Deng's grey incidence analysis (DGIA), the absolute degree GIA (ADGIA), the second synthetic degree GIA (SSDGIA), the conservative (maximin) model) and correlation analysis. Data on temperature are accessed from the NASA database, while the data on COVID-19 cases are collected from the official website of the government of Pakistan. Empirical results reveal the existence of linkages between temperature and COVID-19 in all Pakistani provinces. These linkages vary from a relatively stronger to a relatively weaker linkage. Based on calculated weights, the strength of linkages is ranked across provinces as follows: Gilgit Baltistan (0.715301) > Baluchistan (0.675091) > Khyber Pakhtunkhwa (0.619893) > Punjab (0.619286) > Sindh (0.601736). The disparity in the strength of linkage among provinces is explained by the discrepancy in the intensity of temperature. Besides, the diagrammatic correlation analysis shows that temperature is inversely linked to COVID-19 cases (per million persons) over time, implying that low temperatures are associated with high COVID-19 transmissibility and vice versa. This study is among the first of its kind to consider the linkages between temperature and COVID-19 transmissibility for a tropical climate country (Pakistan) using the advanced GIA models. Research findings provide an up-to-date glimpse of the outbreak and emphasize the need to raise public awareness about the devastating impacts of the COVID-19. The educational syllabus should provide information on the causes, signs, and precautions of the pandemic. Additionally, individuals should practice handwashing, social distancing, personal hygiene, mask-wearing, and the use of hand sanitizers to ensure a secure and supportive atmosphere for preventing and controlling the current pandemic.

Asymmetric impact of temperature on COVID-19 spread in India: Evidence from quantile-on-quantile regression approach

The emergence of new coronavirus (SARS-CoV-2) has become a significant public health issue worldwide. Some researchers have identified a positive link between temperature and COVID-19 cases. However, no detailed research has highlighted the impact of temperature on COVID-19 spread in India. This study aims to fill this research gap by investigating the impact of temperature on COVID-19 spread in the five most affected Indian states. Quantile-on-Quantile regression (QQR) approach is employed to examine in what manner the quantiles of temperature influence the quantiles of COVID-19 cases. Empirical results confirm an asymmetric and heterogenous impact of temperature on COVID-19 spread across lower and higher quantiles of both variables. The results indicate a significant positive impact of temperature on COVID-19 spread in the three Indian states (Maharashtra, Andhra Pradesh, and Karnataka), predominantly in both low and high quantiles. Whereas, the other two states (Tamil Nadu and Uttar Pradesh) exhibit a mixed trend, as the lower quantiles in both states have a negative effect. However, this negative effect becomes weak at middle and higher quantiles. These research findings offer valuable policy recommendations.

Understanding the Antecedents and Consequences of Service-Sales Ambidexterity: A Motivation-Opportunity-Ability (MOA) Framework

Drawing on the motivation, opportunity and ability (MOA) framework, we investigate the influence of charismatic leadership on salespeople’s service and sales activities—termed service-sales ambidextrous (SSA) behavior, which subsequently turn into service recovery performance outcomes. The primary aim of this research is to strengthen salespeople’s service quality in parallel to their selling activities while recovering a service failure. We validate the model using a sample of 344 business-to-business salespeople using partial least square structural equation modeling (PLS-SEM) technique. Study results show that charismatic leadership is positively and significantly related to service-sales ambidexterity. Likewise, service-sales ambidexterity has a positive and significant relationship with service recovery performance and adaptive selling behavior. Moreover, we found a significant relationship between adaptive selling behavior and service recovery performance. The results further specify that salesperson motivation, opportunity and ability to engage in SSA significantly moderate the relationship between charismatic leadership and service-sales ambidexterity. The results suggest the need for training programs that provide the salesperson with opportunities to understand the simultaneous implementation of selling strategies while also providing customer services.

Do Perceived Risk, Perception of Self-Efficacy, and Openness to Technology Matter for Solar PV Adoption? An Application of the Extended Theory of Planned Behavior

Solar PV (photovoltaic) technology has gained considerable attention worldwide, as it can help reduce the adverse effects of CO2 emissions. Though the government of Pakistan is adopting solar PV technology due to its environmental friendliness nature, studies focusing on consumer’s acceptance of solar PV are limited in the country. This research aims to close this knowledge gap by looking into the various considerations that may influence consumers’ willingness to adopt (WTA) solar PV for household purposes. The study further contributes by expanding the conceptual framework of the theory of planned behavior by including three novel factors (perceived risk, perception of self-efficacy, and openness to technology). The analysis is based on questionnaire data collected from 683 households in Pakistan’s provincial capitals, including Lahore, Peshawar, Quetta, Gilgit, and Karachi. The proposed hypotheses are investigated using the state-of-the-art structural equation modeling approach. The empirical results reveal that social norms, perception of self-efficacy, and belief about solar PV benefits positively influence consumers’ WTA solar PV. On the contrary, the perceived risk and solar PV cost have negative effects. Notably, the openness to technology has an insignificant effect. This study can help government officials and policymakers explore cost-effective, risk-free technologies to lessen the environmental burden and make the country more sustainable. Based on research results, study limitations, as well as prospective research directions, are also addressed.