Digital technologies as enablers of supply chain sustainability in an emerging economy

Factors That Influence the Intention of Smallholder Rice Farmers to Adopt Cleaner Production Practices: An Empirical Study of Precision Agriculture Adoption

Sustainable agriculture is crucial for achieving SDG2 and building a resilient climate-food system. This study provides a nuanced understanding of factors that influence the adoption of precision agriculture technology by Vietnamese smallholder rice farmers as a sustainable agricultural solution. The study's objectives are: (1) to provide a nuanced understanding of factors that influence adoption of precision agriculture technology by Vietnamese smallholder rice farmers; and (2) to identify factors associated with agricultural practice in Vietnam that may impact the adoption of precision agriculture technology. The study conducted 35 semi-structured interviews with smallholder rice farmers and agriculture experts. Data were analyzed using iterative thematic analysis. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was used for empirical analysis. The UTAUT constructs of Performance expectancy, Effort expectancy, Government support, and Social influence were found to be determinants of adoption. Trialability and Observability impact Effort expectancy and Performance expectancy. We also discovered that the performance of agricultural cooperatives and support of lead firms play a crucial role in facilitating agricultural technology adoption by Vietnamese smallholder rice farmers. The results confirm the applicability of UTAUT in defining smallholders' behavioural intention and predicting agricultural technology adoption. The study also provides constructive feedback on the UTAUT model by highlighting the effect of agricultural cooperatives' performance as innovation intermediaries and of the support of lead firms.

Post-COVID-19's impact on green supply chain management and sustainable E-commerce performance: the moderating role of big data analytics

This study investigates the relationship between COVID-19 and the adoption of green supply chain management practices (GSCM) in the Pakistani e-commerce industry. It also assesses the impact of these practices on ecological sustainability across three dimensions and explores the role of big data analytics (BDA) in enhancing them after the pandemic. The research utilized partial least squares structural equation modeling to evaluate data and test hypotheses. The research sample was composed of 390 managers operating within Pakistan's e-commerce industry. The study's preliminary findings reveal that COVID-19 has positively influenced the adoption of GSCM practices, which are moderated by BDA. Implementing GSCM has a positive impact on the perceived environmental and social resilience of e-commerce enterprises, but no significant effect on their perceived economic resilience. Additionally, GSCM acts as a mediator in the relationship between the impact of COVID-19 and the perceived environmental and social resilience of e-commerce firms, but not for economic resilience. This research focuses on Pakistan's e-commerce industry and investigates how COVID-19 affects the adoption of eco-friendly supply chain practices. It measures the impact of these practices on ecological sustainability using three dimensions. The study also examines how BDA can improve the adoption of GSCM in e-commerce, offering new insights into sustainability during and post-pandemic. The results recommend that the e-commerce industry can use BDA and GSCM practices to improve e-commerce sustainable performance. This is initial research that integrates COVID-19 impact, BDA, GSCM practices, and e-commerce sustainability in a single framework that was overlooked previously.

Spatio-temporal heterogeneity of the coupling between digital economy and green total factor productivity and its influencing factors in China

The synergy of the digital economy and green total factor productivity (TFP) is the foundation for achieving beneficial outcomes for both the economy and environment. This synergy is also the catalyst for high-quality development and sustainable economic growth in China. The study applied a modified Ellison-Glaeser (EG) index, super-efficiency slacks-based measure (SBM) with a Malmquist-Luenberger (ML) index, coupling coordination degree, and other models to explore the spatiotemporal heterogeneity of the coupling between the digital economy and green TFP from 2011 to 2020 and also analyzed the influencing factors of the coupling. The results show that the coupling between the digital economy and green TFP showed an overall upward trend from imbalance to synergy during the study period. The distribution of the synergistic coupling expanded from point-like to band-like, and there was a significant spreading pattern from the east to the center or west China. The number of cities in a transition state decreased significantly. Spatial jumps, a coupling linkage effect, and evolution in time were prominent. Additionally, the absolute difference among cities expanded. Although coupling in the west experienced the fastest growth rate, the coupling in the east and resource-based cities showed significant benefits. Coupling did not reach an ideal coordinated state, and a neutral interaction pattern remains to be formed. Industrial collaboration, industrial upgrading, government support, economic foundation, and spatial quality all positively impacted the coupling; technological innovation had a lagged effect; and environmental regulation has not reached its full potential. Further, the positive effects of government support and spatial quality performed better in the east and in non-resource-based cities. Due to the optimization of industrial structure, the coupling of the west and resource-based cities achieved better dividends; however, spatial quality needs further improvement. Therefore, the efficient coordination of China's digital economy and green TFP requires a scientific, reasonable, localized, and distinctive approach.

Circular Economy Research in the COVID-19 Era: a Review and the Road Ahead

The onset of the Coronavirus Disease 2019 (COVID-19) pandemic has resulted in a major crisis that has severely impacted numerous economic, environmental, and social aspects of human life. During the pandemic, the potential of the circular economy (CE) has gained increasing attention as a prospective remedy for numerous sustainability problems. This systematic literature review charts CE research in the COVID-19 era. To this end, 160 journal articles were selected from the Scopus database. The performance indicators of the literature were determined and described through a bibliometric analysis. Moreover, the conceptual structure of CE research was identified via a keyword co-occurrence network. Based on bibliographic coupling, the focus of CE research in the COVID-19 era revolves mainly around five thematic areas, including: (1) waste management; (2) digitalization and sustainable supply chain management; (3) the impact of COVID-19 on food systems; (4) sustainable development goals, smart cities, and bioeconomy; and (5) closed-loop supply chains. Overall, this review contributes to enriching the literature by determining the main thematic areas and future research directions that can help to advance the transition to the CE and reduce the impact of COVID-19 and similar disasters in the future.

Highlighting artificial intelligence roles in business area Amid the COVID-19 crisis

Purpose: The fast development of technology and data has fueled the use of artificial intelligence (AI) in the business area, but there has been no comprehensive review to guide and assess this evolution, especially in the context of Covid-19 crisis. Our objective is to highlight the nature and scale of AI research in the business area, during the COVID-19 Pandemic.

Methods: We performed a scoping review and searched two literature databases (Scopus and MDPI) for terms related to AI and Covid-19 by focusing on scientific papers published in the field of business. We used multiple tools (Endnote, Covidence) for titles and abstracts selection, followed by full-text screening. The studies must include research on artificial intelligence and Covid-19, and then be published in English-language, between March 2020 and March 2022.

Results: 31 studies met eligibility criteria (of 391 studies selected). Most of the published articles refer to conceptual analysis or quantitative works, the rest of the articles used a literature review except 4 articles published using a qualitative method of analysis. In addition, we observe an evolution of the total number of publications for the 31 articles included in the analysis. Conclusions: Studying AI in the business field amid the covid-19 crisis is at an early stage of maturity, especially with the use of new AI technologies.). For the field to progress, more studies are needed in the next few years.