Search results

The objective is to ascertain and amalgamate the extant data about farmers' plans for climate change adaptation and mitigation. The chapter provides comprehensive insights to help farmers and policymakers make informed and relevant decisions.

We conducted five-step scoping review procedure with key databases: Scopus, ProQuest, Science Direct, Springer Link and PLOS ONE. The keywords including climate change AND Agriculture AND production AND rice AND farmer AND farming AND mitigation AND adaptation AND resilience AND Indonesia focusing on publications from 2013 to 2023. We employed PRISMA-Scr protocol and data were thematically extracted using NVivo 12 Plus tool.

The search yielded 878 records, of which 116 were screened in full text, and 37 were included in the review. Key findings indicate that adaptation strategies and management planning involving social capital and social networks are essential for anticipating and responding to climate change-induced disasters. The collaboration between farmers, farmer groups, government and NGOs is pivotal for climate change mitigation.

The research implications highlight the need for targeted policy interventions and capacity-building initiatives to enhance farmers' knowledge and skills in climate change adaptation, fostering collaboration among various stakeholders.

The originality and value of the paper lie in the comprehensive review of the adaptive measures and strategies employed by farmers in Indonesia to mitigate the impacts of climate change on agriculture. This contribution is significant as it not only addresses local challenges but also connects them to broader global discussions on food security and climate adaptation.

This study investigates the impact of quality, technology and trust on customers’ purchase intentions and word-of-mouth in S-commerce.

Data were collected from 1,162 respondents in India using a quantitative methodology and convenience sampling. The conceptual model and hypotheses were examined using partial least squares structural equation modeling (PLS-SEM).

The findings illustrate that perceived value is influenced by quality, technology and trust. Comprehending perceived value is essential for influencing customers’ buying choices and their inclination to promote favorable word-of-mouth regarding S-commerce websites. In addition, system quality was the most significant predictor among the nine predictors of perceived value for S-commerce sites.

This study represents an initial investigation to elucidate the influence of quality, technology and trust on customers’ purchase intentions and word-of-mouth behavior during Indian consumers’ transition to social commerce, thereby enabling marketers to enhance their business and customer retention strategies.

This study aims to analyse and understand customer sentiments and perceptions from neobanking mobile applications by using advanced machine learning and text mining techniques.

This study explores a substantial large data set of 330,399 user reviews available in the form of unstructured textual data from neobanking mobile applications. This study is aimed to extract meaningful patterns, topics, sentiments and themes from the data.

The results show that the success of neobanking mobile applications depends on user experience, security features, personalised services and technological innovation.

This study is limited to textual resources available in the public domain, and hence may not present the entire range of user experiences. Further studies should incorporate a wider range of data sources and investigate the impact of regional disparities on user preferences.

This study provides actionable ideas for neobanking service providers, enabling them to improve service quality and mobile application user experience by integrating customer input and the latest trends. These results can offer important inputs to the process of user interaction design, implementation of new features and customer support services.

This study uses text mining approaches to analyse neobanking mobile applications, which further contribute to the growing literature on digital banking and FinTech. This study offers a unique view of consumer behaviour and preferences in the realm of digital banking, which will add to the literature on the quality of service concerning mobile applications.

The objective of the article is to extend the applicability of conventional technology acceptance models by thoroughly and specifically analysing the influencing elements and their relationship with the adoption of fintech services. The numerical approach was employed. A total of 573 responses were included in the study. In this study, the sample data were statistically analysed using Smart-PLS 4.0. The study findings indicate that user trust in fintech services is positively influenced directly by perceived ease of use, brand reputation and government support while perceived risk and utility are not significantly correlated with user confidence in fintech services. In addition, user attitudes about fintech services are positively impacted by brand reputation, government support and perceived usefulness. Users' perceptions of risk and usability do not significantly affect how they feel about fintech services. User attitudes regarding fintech services are positively indirectly influenced by perceived utility, brand reputation, and government support, as well as by a considerable amount of trust. Users' attitudes regarding fintech services are not significantly impacted by the indirect impacts of perceived usefulness and risk. The results of this study give financial service providers an empirical foundation and a user assessment for implementing cutting-edge, user-centred services. Additionally, it offers helpful information for financial service providers, fintech company marketing tactics and the accomplishment of strategic objectives.

Supply chains are facing several challenges due to disruptions and changing situations such as COVID-19 and the need for increased levels of resilience is more important than ever. This paper focuses on exploring the impact of artificial intelligence (AI) on supply chain resilience (SCR) through a review of the existing literature. To address the gap of AI on SCR, this study focused on answering the following two research questions: (1) What is the role of AI technologies in SCR? (2) What are the key ethical and social implications of AI that arise in the process of enhancing SCR?

This study collected relevant data available in the existing literature from peer-reviewed journals and articles on supply chain and AI. The study employed a systematic literature review (SLR) and qualitative thematic analysis to identify the key themes that generate relevant findings.

The study’s findings highlight that AI’s role in enhancing SCR is important in several areas, such as improved demand and supply forecasts, accurate problem-solving, increased efficiency of tasks and improved customer services, amongst others. However, AI does not come without limitations. Although it improves the resilience of supply chains, it also leads to ethical and social implications related to job displacement, privacy and security, biases and transparency.

The study offers intriguing insights into closing the disparity between theory and practice, utilising a systematic approach to demonstrate how AI impacts the resilience level of supply chains.

This study presents the positive impact that AI technologies have on enhancing the resilience of supply chains. Although there are challenges and ethical and social implications because of AI implementations, supply chains benefit from the use of AI and big data.

The purpose of this research is to evaluate the utility of clickstream data and machine learning algorithms in predicting student performance and enhancing online learning experiences. By leveraging clickstream data and machine learning algorithms, the study aims to predict student performance accurately, enabling timely and personalized interventions. This approach seeks to reduce high failure and dropout rates in online courses, ultimately enhancing educational outcomes and preserving the reputation of educational institutions.

This study utilizes clickstream data from the Open University Learning Analytics Data set (OULAD) to predict student performance in virtual learning environments. The approach involves extracting and organizing data into weekly and monthly interactions. Various machine learning models, including traditional methods (Logistic Regression, Naive Bayes, K-Nearest Neighbors, Random Forest, XGBoost) and advanced time-series models (LSTM-XGBoost, GRU), are employed to analyze the data. The GRU model demonstrated the highest accuracy, offering insights into student engagement and learning patterns.

The study reveals that integrating clickstream data with machine learning models provides a robust framework for predicting student performance in virtual learning environments. Among the methods tested, the GRU algorithm outperformed six baseline models, achieving an accuracy of 90.13%. These findings underscore the effectiveness of using advanced time-series models to monitor and improve student engagement and success rates in online education.

This research introduces a novel approach to student performance prediction by combining traditional and advanced time-series machine learning models with clickstream data. The study’s originality lies in its comprehensive analysis of both weekly and monthly student interactions, providing educators with a powerful tool for early intervention. The findings contribute to the growing body of literature on learning analytics, offering practical solutions to enhance online education’s effectiveness and reduce dropout rates.

Organizational pursuit of sustainability in multi-tier supply chain systems operating in unpredictable environments is often associated with the emergence of paradoxical tensions. This study aims to summarize and synthesize existing literature on managing various paradoxical tensions in supply chains (i.e. sourcing, making, delivering and reverse logistics) as organizations pursue sustainability transformation. It also strives to motivate new academic research inquiry into developing responses to sustainability paradoxes.

The study draws on a systematic literature review of 73 papers from the Web of Science database selected at the intersection of paradox, sustainability and logistics/ supply chain management (SCM). Applying paradox theory as a guiding lens, we investigate organizational strategies, practices and capabilities described in the literature to navigate sustainability paradoxes in supply chains.

The results assert that the success of sustainability transformation will depend on an organizational ability to recognize, accept and navigate paradoxical tensions in one's supply chain. This requires developing the dynamic capabilities of paradoxical leadership, strategic agility, innovativeness, collaboration with contextualization and governance. Successful sustainability transformation is not reliant on finding an optimal, final design but rather the continuous balancing of tensions inherent within or across the organizations that make up one's supply chain.

The research offers an integrative conceptual framework to guide organizations in navigating sustainability paradoxes in supply chains, embracing strategic, practice and capability levels. It also outlines opportunities for future research inquiries connected to this framework that are needed to build additional insight for addressing paradoxical tensions related to the pursuit of sustainable supply chain management.

This study takes a dynamic capabilities approach to navigating paradoxical tensions in pursuit of sustainable supply chain management.

In the context of promoting sustainable development in SMEs, the present study aims to investigate the relationship among solution dimensions based on the Industry 4.0 (I4.0) concept.

The study employs a comprehensive methodology that includes a systematic literature review, workshop, grounded theory and interpretive structural modeling. Various dimensions concerning I4.0 sustainability are tested and evaluated using a questionnaire design followed by hypothesis formulation. Further, grounded theory is used to extract the key solution dimensions that capture the essence of I4.0 implementation in SMEs. Finally, the solution dimensions for I4.0 sustainability are modeled using the ISM approach to understand the structural interdependencies among them, and Matrice d'Impacts Croisés Multiplication Applied to a Classification (MICMAC) analysis is done to understand the driving and dependence power among these dimensions.

The study identified 14 solution dimensions for the implementation of I4.0 in SMEs for sustainable development. Out of the 14 solution dimensions, human resource training programs (D4) appear at level 11, followed by top management commitment (D1), strategic collaborations (D3) and coordination among key stakeholders (D5) at level 2 in the hierarchical interpretive structural modeling (ISM) model. Also, these dimensions have an effect size of more than 0.50 which indicates a substantial correlation between the sustainability dimensions and Industry 4.0 implementation in SMEs.

The study contributes to the overall goal of fostering sustainability within the SME sector, which can pave the way for various stakeholders for the successful implementation of I4.0 sustainable solution dimensions.

The objective of this investigation is to discover the impacts of energy security (ES) pursuit on climate risk (CR). Notably, we also evaluate the role of institutional quality in signifying the effects of ES on CR.

Six metrics to assess the availability, acceptability, developability and sustainability of energy security are used while we consider nine dimensions of climate risk. We employ several econometric methods, namely the panel-corrected standard error (PCSE) and feasible generalized least squares (FGLS), to a global sample of 65 countries during the 2014–2021 period. Furthermore, long-term impacts are estimated using the dynamic fixed effects (DFE) estimate, which is applied using the autoregressive distributed lag (ARDL) technique.

The estimation outcomes demonstrate that the pursuit of ES mitigates CR. By using a variety of econometric methodologies and using additional explanation factors, we obtain reliable conclusions. The outcomes propose that the favourable impacts of ES initiatives are more likely to exist in the long run. Notably, ES impacts are more substantial in nations with well-designed institutional systems.

Our study is critical since the findings offer pertinent implications for policymakers and authorities in designing appropriate policies regarding energy security initiatives on the path towards climate resilience and environmental sustainability.

Our work adds to the body of literature by considering various cross-country characteristics, with a focus on variations in the degree of energy security. Our study adds to the previous paper on the effects of climate modification by pointing out the way those heterogeneities shape the magnitude of harm attributed to climate shifts. Specifically, we examine how these differences influence climate modification’s effects on the threat to power safety and the effectiveness of policy strategies. By using this method, we can offer more individualized policy implications that consider variations across nations in the formulation of policies.

This study aims to analyze the status and application/use of human–computer interaction (HCI) in libraries by conducting a systematic literature review (SLR).

A Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) approach was used to search Scopus, Web of Science and Google Scholar databases. The search criteria included research studies published in English language between 2010 and 2021, which were 4,167 citations. Out of 4,167 citations, a total of 50 studies were selected for the final analysis.

The results showed a positive attitude of librarians toward HCI applications in libraries worldwide. The results depict that one-third (30%) of the studies were conducted in the USA, followed by four (8%) studies in China. Out of 50 studies, a portion of 15 (30%) studies were based on digital libraries, followed by seven (14%) studies on academic libraries and five (10%) studies on libraries and their websites. HCI was used for searching and retrieving information, users’ interaction, authentication, online help/support, feedback, library web access, web OPAC, virtual access to resources, indigenous repository and virtual services. The most productive year was 2015, and journal of The Electronic Library had more articles on HCI than other journals.

The findings of this study could assist policymakers and library authorities in reconciling the HCI application in libraries for providing effective and efficient access and services to end-users.

This study is unique as no comprehensive study has been conducted on the use of HCI in librarianship using the SLR method.