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Post-pandemic sovereign authorities in several economies have nudged primary education institutions to adopt platform-based teaching. The shift to platform technology attempts to ensure continuity in the teaching–learning process. In the context of predominantly digitally mediated teaching process, this shift may exacerbate disparities and social injustice by limiting access to primary education in resource-constrained developing economies. The purpose of this study is to explore the efficacy of such a digital framework provided by government and private partners and the challenges faced by the teachers in absence of proper scaffolding.

Using an integrative theoretical framework that is composed of capability theory, technology adoption theories and the scaffolding framework, this paper analyses the challenges faced by primary school teachers when adapting to platform-based teaching. Social media analytics along with text analytics using Natural Language Processing and latent Dirichlet allocation-based topic modelling approach to extract latent topics or themes used by users during their tweets related to e-teaching.

The findings of this study highlight that adopting a platform-based and hybrid approach improves access to education and flexibility and highlights the importance of scaffolds in achieving desired learning outcomes. EdTech companies can play a significant role through private-public partnership models to offer technical scaffold. Collaborative efforts between educational institutions and EdTech service providers are essential for ensuring the long-term sustainability of platform-based teaching and learning.

After the pandemic, there has been no published literature available which examined the role of scaffolds and EdTech companies in ensuring digital ecosystem for better teaching–learning outcome through platforms.

The purpose of this article is to investigate and analyze the interactions between economic and sustainable development elements in the context of remanufacturing in India.

To comprehend the hierarchical and contextual link among factors impacting remanufacturing in India, the study used interpretive structural modeling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) methodologies.

The integrated ISM-DEMATEL approach identifies optimal utilization of the resources as the most crucial factor influencing remanufacturing in India, followed by reducing landfills, conserving energy and low cost. The study also reveals that optimal utilization of resources, reduction of landfills, conservation of energy and incorporated advanced technology impacts most of the factors but get affected by a few factors.

Industry practitioners and policymakers should consider the remanufacturing process to achieve sustainable and economic development. The government and other stakeholders can use the ISM framework and cause-and-effect diagram to classify the impact factors and their impact on the Indian economy and environment.

This study supports the process to save the landfills and curbing pollution, conserve energy and optimize utilization of the resources, generate employment and supporting the development of the economy. Remanufacturing will undoubtedly contribute to the development of an environment and economy in India that benefits both.

ISM and DEMATELs strategy offers a tiered model and a cause-and-effect relationship between the variables affecting remanufacturing in India.

Peer-to-peer (P2P) secondhand resale platforms (SRP) are competitive places where different value systems beyond market values interact. This study aims to investigate the conflicts that may arise in interactions between users on SRP and the extent to which these conflicts are (ir)resolved, by drawing on economies of worth theory.

The study takes a qualitative and interpretative approach to examine 22 active users on P2P resales platforms such as Vinted, including in-depth interviews. Following the Straussian view of grounded theory, the study uses constant comparison (open, axial and selective coding) to analyze data on SRP users’ experiences.

Drawing on the economies of worth theory, the study shows that SRP users rely on four different value systems or “worlds” when using the platforms (market, domestic, green and civic worlds) that come into conflict, at either an interactional (three conflicts identified) or an individual (two conflicts identified) level. The findings reveal that these conflicts are temporarily resolved at the interactional level and in a sustainable way at the individual level.

This study sheds further light on the relationship between consumers on SRP by offering a more nuanced perspective on these exchanges than market-oriented exchanges. It also analyzes the data through the economies of worth theory, which is an appropriate lens to better understand social interactions and conventions. Finally, the study offers recommendations on how managers can improve buyers’ and sellers’ experiences on these platforms and, thus, foster their satisfaction.

The goal of this study is to better understand the driving force behind the use of artificial intelligence (AI) in pharmaceutical manufacturing firms (PMFs) that are recognized as developing countries in the Middle East and North Africa (MENA) region that are listed by the Chambers of the Industries of Jordan, the Kingdom of Saudi Arabia, Morocco, and Algeria. Furthermore, the effect of adopting and using AI in managing raw materials (RMs), products, parts, and components for PMFs through supply chains (SCs).

A self-administrated questionnaire survey was used to gather data from 95 out of 511 participating managers (e.g. manufacturing, supplying, IT, operational, and logistical managers) utilizing a quantitative technique with a random sample size. In fact, 18.8% of the 89 different manufacturing firms (MFs) in the MENA area responded, with five to six managers from each company. The raw data was analyzed using partial least squares structural equation modeling (PLS-SEM).

The study’s findings show that the readiness to embrace artificial intelligence (AI) in the production management supply chain performance (PMSCP) of pharmaceutical manufacturing firms in the Middle East and North Africa (MENA) is positively and significantly influenced directly and indirectly by sustainable strategic supplier reliability (SSSR), shipping process dependability (SPD), technological factors (TFs), and infrastructure transformational development capability (ITDC).

As the studied countries are growing economies, such study findings might offer insightful consequences for stakeholders and policymakers regarding the significance of using artificial intelligence system adoptions in pharmaceutical manufacturing enterprises in the MENA region. The managers may also concentrate on the strong positive direct and indirect links between SSSR, SPD, TFs, and ITDC preparedness to accept AI adoption and its applications and systems in supply chain and production management departments and the consequences of informational and product delivery.

Although the importance and variety of emotions have been emphasized in existing literature, studies on discrete emotions remain limited. This study aims to propose a method for more precise recognition and calculation of emotions in massive amounts of online data on attraction visitor experiences and behaviour, by using discrete emotion theory.

Using HowNet’s word similarity calculation technique, this study integrated multiple generic dictionaries, including the sentiment vocabulary ontology database of the Dalian University of Technology, the National Taiwan University Sentiment Dictionary and the Boson Dictionary. Word2vec algorithm filters emotion words unique to hospitality and tourism in 1,596,398 texts from Sogou News, Wikipedia and Ctrip reviews about attractions, and 1,765,691 reviews about attractions in China.

The discrete sentiment dictionary developed in this study outperformed the original dictionary in identifying and calculating emotions, with a total vocabulary extension of 12.07%, demonstrating its applicability to tourism.

The developed new dictionary can be used by researchers and managers alike to quickly and accurately evaluate products and services based on online visitor reviews.

To the best of the authors’ knowledge, this study is the first to construct a sentiment dictionary based on discrete emotion theory applicable to hospitality and tourism in the Chinese context. This study extended the applicability of affective psychology to hospitality and tourism using discrete emotion theory. Moreover, the study offers a methodological framework for developing a domain-specific sentiment dictionary, potentially applicable to other domains in hospitality.

The aim is to analyze the European eco-management because the global warming has become a topical issue impacting the whole world. Individual countries are trying to minimize all the catalysts of global warming, such as carbon emissions. This paper addresses this issue and analyzes the performance of European eco-management for the purpose of future energy investments being environmentally.

This paper develops a fuzzy decision-making model to study the performance indicators of selected countries based on EMAS III standard. It employs interval type-2 fuzzy DEMATEL to evaluate the performance factors and TOPSIS methodology to assess five selected European countries' performance in relation to eco-friendly, emission and renewable energy.

Eco-friendly energy plays the most critical role in this respect followed by emissions and renewable energy which constitute significant factors. The novelty of this study is identifying significant criteria regarding environmental and energy efficiency of investments and making performance assessments of European countries with a new fuzzy decision-making model. Both expert opinions and datasets are used for the analysis. This paper supports previous research about energy efficiency investments in Europe.

The innovative feature of this study is identifying significant criteria regarding environmental and energy efficiency of investments and assessing the performance of European countries with a new fuzzy decision-making model. The fact that the analysis only concerns the European region is an important limitation. In future analyses, other groups of countries can be examined. Innovations can be made regarding the method applied. In this context, analyses can be done utilizing different fuzzy numbers. Finally, the importance of the criteria can be calculated with other methods such as SWARA.

The paper fills the gap in performance analysis of European eco-management for environmentally friendly and efficient energy investments is done in this manuscript.

Analysis of European eco-management performance was done for environmentally friendly and efficient energy investments. A fuzzy decision-making model is constructed. The paper fills the gap in performance analysis of European eco-management for environmentally friendly and efficient energy investments.

Today, collaborative consumption platforms support business activities. In particular, the sharing economy (SE) increases social and economic dynamism and reduces environmental impact by conserving resources. Therefore, this paper investigates the sustainable value drivers of sharing economy practices.

We explore multiple case studies as a research approach to answer the research question. Multiple sources of evidence were used to triangulate the data. The SE practices influence several sectors; thus, we conducted a sector analysis investigating the healthcare, food and fashion industries. Analysing different sectors provides valuable theoretical and practical insights, facilitating decision-making and fostering innovation. Sectoral differences emphasize how different sustainable SE models are configured.

The findings highlight that SE allows for the development of sustainable business models attesting to: (1) value capture in the food industry, (2) value delivery in the healthcare industry and (3) value creation in the fashion industry. The study opens avenues for future research.

The originality of this study is in the new lens from which the phenomenon is observed.

This review study aims to explore the prevalent paradoxes in digital transformation (DTN) of business and provide insights on how businesses can effectively navigate them.

The study conducts a systematic literature review, utilizing findings from a bibliometric analysis. A sample of 229 articles published in top-tier journals, retrieved from the Scopus database, is reviewed to identify nine clusters representing different sectors and paradoxes in DTN.

The review identifies and summarizes studies addressing the paradoxes that arise during DTN in various sectors. Scholars have analyzed the growing need for digital innovations and the benefits they bring, but this study aggregates high-quality research to address the gap in understanding prevalent paradoxes.

This study provides valuable insights into the paradoxes of DTN and offers guidance to businesses on effectively managing these challenges. It contributes to the existing literature by consolidating and presenting key research findings in this domain.

This study explores the effect of local budget retention rate changes (RER) on total factor productivity (TFP) and its components in Vietnam.

The study employs a two-system generalized method of moments (GMM) estimator and data from 2012 to 2019 across all 63 provinces/cities of Vietnam.

The study finds that local budget retention rates significantly influence public investment, affecting scale and allocation efficiency. The reallocation of budgets between regions and from the central government to local levels incurs certain costs, often resulting in economically robust provinces experiencing reductions in their retention rates.

Recognizing the challenges of immediate structural budget changes due to cultural and historical factors, the study suggests a more gradual policy approach. It emphasizes the importance of policy predictability, as abrupt reductions in the retention rate lead to higher costs than gradual reductions, thus implementing budget policies with a clearer timeline. This study provides insight into local budget allocation regimes and their impact on productivity in transitioning countries.

First, the study provides fresh evidence of the impact of retention rate changes on TFP and its components in Vietnam. Second, the study provides insights into the mechanisms of the nexus of increased budget spending, capital efficiency and, most importantly, attaining improvement in education. We also offer further insights into inefficient budget allocation agents in Vietnam, especially in large cities, which should alert scholars to explore this topic further in the future.