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This paper aims at measuring the factors affecting early-stage entrepreneurial activity by opportunity vs necessity motives in India using theory of planned behaviour.

The study is based on the Adult Population Survey (APS) of Global Entrepreneurship Monitor (GEM), covering 4,165 respondents in 2018. The data has been analysed using descriptive statistics, chi-square test, analysis of variance and logistics regression. The theory of planned behaviour has been used to identify the determinants of early-stage entrepreneurial activity by opportunity vs necessity motives.

About 13.1% of the respondents reported early-stage entrepreneurial activity, of which opportunity motives were reported by 6.5% respondents, necessity motives by 5.4% respondents and the remaining 1.2% respondents reported other motives. Further, the mean difference in early-stage entrepreneurial activities by motives shows the domination of opportunity-driven entrepreneurial activities. Finally, marginal effects of all determining variables and three components of the theory of planned behaviour, i.e. attitude towards entrepreneurship (ATE), perceived subjective norm (PSN) and perceived behavioural control (PBC), have been estimated on opportunity vs necessity motives of early-stage entrepreneurial activities.

This paper contributes theoretically and practically to the existing body of knowledge by predicting the factors affecting opportunity vs necessity motives of early-stage entrepreneurial activities by applying the theory of planned behaviour. Considering the current focus of the government on promoting entrepreneurship, this piece of research can be valuable in adopting a motive-based approach in implementing entrepreneurial initiatives.

This paper provides unique insights into developing a policy framework for promoting new ventures based on the perceived motives of the entrepreneurs.

This study aims to investigate the behavior of different types of stone columns, including the short and floating columns, as well as the ordinary and the geosynthetic encased stone columns (OSC and GESC). The effectiveness of the geosynthetic encasement and the impact of the installation using the lateral expansion method on the column performance is evaluated through a three-dimensional (3D) unit cell numerical analysis.

A full 3D numerical analysis is carried out using the explicit finite element code PLAXIS 3D to examine the installation influence on settlement reduction (ß), lateral displacement (U_x) and vertical displacement (U_z) relative to different values of lateral expansion of the column (0% to 15%).

The findings demonstrate the superior performance of GESC, particularly short columns outperforming floating counterparts. This enhanced performance is attributed to the combined effects of geosynthetic encasement and increased lateral expansion. Notably, these strategies contribute significantly to decreasing lateral displacement (U_x) at the column’s edge and reducing vertical displacement (U_z) under the rigid footing.

In contrast to previous studies that examined the installation effect of OSC contexts, this paper presents a comprehensive investigation into the effect of geosynthetic encasement and the installation effects using the lateral expansion method in very soft soil, using 3D numerical simulation. The study emphasizes the significance of the consideration of geosynthetic encasement and lateral expansion of the column during the design process to enhance column performance.

Despite the significant growth in Islamic economies and the increasing number of Muslim youths inclining digital services, empirical-based research addressing the adoption of digital Islamic services is still scarce. Particularly, as a new term in the Islamic finance industry, ZakaTech has recently emerged as a modern term describing novel technologies adopted by zakat (compulsory levy on all believing and practicing high-net-worth Muslims) institutions; yet, it has largely been neglected in the literature. Therefore, this paper aims to propose an integrated model that scrutinizes the factors of unified model of acceptance and use of technology (UTAUT) of ZakaTech, combined with social cognitive theory (SCT), especially in a time of COVID-19 social distancing.

The UTAUT–SCT model was validated via SmartPLS structural equation modeling by using a valid sample of 510 users (individual zakat payers) from Saudi Arabia.

The results demonstrated the suitability of the integrated UTAUT–SCT model in predicting zakat payers’ intention to use ZakaTech services. This proposed model has 70% explanatory power to explain variance in intention. All UTAUT constructs are statistically significant, except for effort expectancy. Social isolation caused by the pandemic and trust in e-zakat system exerted a significant influence on the inclination to uptake ZakaTech services.

To the best of the authors’ knowledge, this research is among the first research that studies Muslims’ adoption of ZakaTech during COVID-19. Particularly, this study could add value to FinTech acceptance literature by empirically examining an integrated framework of UTAUT–SCT in a context as modern and unique as ZakaTech.

The apparel industry of Bangladesh is rethinking lean manufacturing (LM) deployment because of the challenges imposed by the COVID-19 pandemic. Due to COVID-19, LM implementation in the apparel industry has become more difficult. Thus, the purpose of this study is to explore the barriers to implementing LM practices in the apparel industry of Bangladesh in the context of COVID-19 pandemic.

For evaluating the barriers, an integrated framework that combines the Delphi method and fuzzy total interpretive structural modeling (TISM) has been designed. The application of fuzzy TISM has resulted in a structured hierarchical relationship model of the barriers with driving and driven power.

The findings reveal that “lack of synchronization of lean planning with strategic planning”, “lack of proper understanding of lean concept” and “low priority from the top management” are the three top most important barriers of LM implementation in apparel industry.

These findings will help the apparel industry to formulate strategy for implementing the LM practices successfully. The proposed model is expected to contribute to the sustainable development goals (SDGs) such as Responsible Consumption and Production (SDG 12); Decent Work and Economic Growth (SDG 8); Industry, Innovation and Infrastructure (SDG 9) via resilient strategies.

This study is one of few initial efforts to investigate LM implementation barriers during the COVID-19 epidemic in a real-world setting.

Food SMEs is the backbone of local and world economy. Even while food SMEs are aware of the potential advantages of implementing supply chain analytics (SCA), only a small number of companies use data-based decision-making. This is because of technophobia. In light of this, the purpose of this study is to investigate the factors that have an impact on SCA adoption which in turn influence the sustainable performance of firms.

The data were collected from 221 managers working in food-related SMEs in China by using a questionnaire-based survey. The framework of this study was validated using a rigorous statistical procedure using the technique, namely, partial least squares structural equation modelling.

The findings of this study suggest that all modified UTAUT components (i.e. performance expectancy, effort expectancy, social influence, facilitating conditions and technophobia) significantly influence SCA adoption. Moreover, the existing study highlights and confirms the significance of adopting SCA to improve sustainable performance.

This research is novel, as it extends and investigates the theoretical framework based on UTAUT theory in SCA context and its impact on sustainable organizational performance. In addition, the factor of technophobia is tested in SCA context. This study has several contributory managerial implications for food SMEs.

Mega-infrastructure development plans pave the way for policies to upgrade the infrastructure, environmental management and different aspects of locals’ well-being. These developmental dynamics can positively affect rural tourism including heritage sites destinations. The quality of life of local people can be linked to this positive developmental change through long-term and sustainable economic revitalization projects. In terms of this process, developing large-scale infrastructure and incorporating tourism sustainability can improve the quality of life-related to different dimensions that are critical to the community's well-being. Therefore, researchers have attempted to address this issue.

Data were collected between September and October 2020. The study sample size was the residents of Zhabagly village, including Zhabagaly, Abaiyl and 115 Railway settlements. Moreover, the residents were older than 18 years. A systematic random sampling technique was utilised to reach the targeted sample size and the researchers received 243 responses from the locals. Structural equation modelling (SEM) was used for analysis.

The findings from the structural equation modelling suggest that sustainable tourism increases due to the positive effect of mega-infrastructure development and positively impacts the locals' quality of life. Notably, no direct effect of mega-infrastructure development on quality of life reveals the pivotal role of sustainable tourism. Therefore, during the COVID-19 period, the dimensions of sustainable tourism – economic, market, socio-cultural and environmental – played a role in securing the positive impact of mega-infrastructure development on the locals’ quality of life.

This research highlighted the fact that when infrastructure projects are implemented to their full potential, they will generate sustainable tourism activities, provide eco-adventure activities, relax, treat signatories and boost the economy of all stakeholders. The study used AMOS to test the hypotheses. Qualitative research methods, including interviews with citizens, government officials and tourism managers, require further study.

The infrastructural development on a mega-scale means building an upscaling tourism ecosystem. This ecosystem is marked by the availability of drinking water, waste and energy management facilities that support the elevation of living material, community, health, safety and emotional well-being. It reflects the policy-level implications for future Belt and Road initiatives (BRIs). The tourism industry's resilience during COVID-19 has practical lessons for other industries.

Large-scale infrastructure construction must create favourable conditions for the rapid development of tourism. The availability of clean water, waste and energy management facilities contributes to the food production, social cohesion, physical and mental health and general well-being of the ecosystem. This is one of the few studies that used sustainable tourism as a mediator between the impact of large infrastructure projects and their impact on the quality of life of locals during the COVID-19 pandemic. Aksu-Zhabagly, a World Heritage Site in Kazakhstan, was the site of this field study.

Monetary policy has major impacts on macroeconomic indicators of the country. Accordingly, uncertainty regarding monetary policy shifts can cause challenges and risks for businesses, financial markets and investors. Thus, the purpose of this study is to investigate how real estate market volatility responds to monetary policy uncertainty.

The GARCH-MIDAS model is applied in this study to investigate the nexus between monetary policy uncertainty and real estate market volatility. This model was fundamentally instituted to accommodate low-frequency variables.

The results of this study reveal that increased monetary policy uncertainty highly affects the volatility in real estate market during the peak period of COVID-19 as compared to full sample period and COVID-19 recovery period; hence, a significant decline is evident in real estate market volatility during crisis.

This study is particularly focused on peak and recovery period of COVID-19 considering the geographical region of Greece, Japan and the USA. This study provides a complete perspective on the nexus between monetary policy uncertainty and real estate markets volatility in three distinct economic views.

In the light of high reliance on digital technology to mitigate the consequences of the coronavirus disease 2019 (COVID-19) pandemic and its containment measures, this study investigates the factors influencing firms' decision to adopt digital technologies during COVID-19 in four Middle East and Northern African (MENA) countries, namely, Egypt, Jordan, Morocco and Tunisia.

The study used the International Labour Organization (ILO)/Economic Research Forum (ERF) COVID-19 - MENA Monitor Enterprise Survey (CMMENT), comprising 5,480 firms, surveyed during 2020–2021. The empirical model is estimated using the linear probability model (LPM) to address the problem of unobserved heterogeneity between firms, countries, and time.

The results revealed that firm characteristics, such as firm size and foreign ownership, encourage digital transformation in the business sector. Moreover, firms that face challenges during the pandemic, comply with the containment measures, and receive government assistance are more likely to adopt digital solutions. Furthermore, the results indicated that firms operating in services sector have a higher likelihood to adopt digital technology. Disaggregating the total sample into several sub-samples, the results are robust across countries and technology types, supporting the initial hypothesis that COVID-19 encourages digital transformation in the MENA region.

The study has numerous contributions. First, to the best of the authors' knowledge, this is the sole study that uses micro data collected during the COVID-19 to examine the factors influencing firms' decision to adopt and invest in digital solutions in the MENA countries. Second, the paper employs the LPM estimator to address the issue of unobserved heterogeneity between firms, countries and time. Finally, the paper offers some practical recommendations for accelerating digital transformation in MENA region.

During the coronavirus disease 2019 (COVID-19) pandemic, ubiquitous social media has become a primary channel for information dissemination, social interactions and recreational activities. However, it remains unclear how social media usage influences nonpharmaceutical preventive behavior of individuals in response to the pandemic. This paper aims to explore the impacts of social media on COVID-19 preventive behaviors based on the theoretical lens of empowerment.

In this paper, survey data has been collected from 739 social media users in China to conduct structural equation modeling (SEM) analysis.

The results indicate that social media empowers individuals in terms of knowledge seeking, knowledge sharing, socializing and entertainment to promote preventive behaviors at the individual level by increasing each person's perception of collective efficacy and social cohesion. Meanwhile, social cohesion negatively impacts the relationship between collective efficacy and individual preventive behavior.

This study provides insights regarding the role of social media in crisis response and examines the role of collective beliefs in the influencing mechanism of social media. The results presented herein can be used to guide government agencies seeking to control the COVID-19 pandemic.