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The domains of Industry 4.0 and Smart Farming encompass the application of digitization, automation, and data-driven decision-making principles to revolutionize conventional sectors. The intersection of these two fields has numerous opportunities for industry, society, science, technology and research. Relatively, this intersection is new, and still, many grey areas need to be identified. This research is a step toward identifying research areas and current trends.

The present study examines prevailing research patterns and prospective research prospects within Industry 4.0 and Smart Farming. This is accomplished by utilizing the Latent Dirichlet Allocation (LDA) methodology applied to the data procured from the Scopus database.

By examining the available literature extensively, the researchers have successfully discovered and developed three separate research questions. The questions mentioned above were afterward examined with great attention to detail after using LDA on the dataset. The paper highlights a notable finding on the lack of existing scholarly research in the examined combined field. The existing database consists of a restricted collection of 51 scholarly papers. Nevertheless, the forthcoming terrain harbors immense possibilities for exploration and offers a plethora of prospects for additional investigation and cerebral evaluation.

This study examines the Industrial Revolution's and Smart Farming's practical effects, focusing on Industry 4.0 research. The proposed method could help agricultural practitioners implement Industry 4.0 technology. It could additionally counsel technology developers on innovation and ease technology transfer. Research on regulatory frameworks, incentive programs and resource conservation may help policymakers and government agencies.

The paper proposes that the incorporation of Industry 4.0 technology into agricultural operations can enhance efficiency, production and sustainability. Furthermore, it highlights the significance of creating user-friendly solutions specifically tailored for farmers and companies. The study indicates that the implementation of supportive legislative frameworks, incentive programmes and resource conservation methods might encourage the adoption of smart agricultural technologies, resulting in the adoption of more sustainable practices.

This study examines the Industrial Revolution's and Smart Farming's practical effects, focusing on Industry 4.0 research. The proposed method could help agricultural practitioners implement Industry 4.0 technology. It could additionally counsel technology developers on innovation and ease technology transfer. Research on regulatory frameworks, incentive programs and resource conservation may help policymakers and government agencies.

Based on a thorough examination of existing literature, it has been established that there is a lack of research specifically focusing on the convergence of Industry 4.0 and Smart Farming. However, notable progress has been achieved in the field of seclusion. To date, the provided dataset has not been subjected to analysis using the LDA technique by any researcher.

The objective of this article is to identify the relationships between the enablers in the implementation of Green Lean Six Sigma (GLSS) in the Mexican manufacturing industry (MMI).

To create the survey instrument, the authors did an extensive literature research, which they then applied in the MMI to find the relationships between enablers and their impact on the positive effects of implementing GLSS projects. Using exploratory and confirmatory factor analyses (EFA and CFA), the data were empirically and statistically corroborated. Furthermore, the authors validated the hypotheses that support the research using the structural equation modeling (SEM) approach in SPSS Amos.

The findings reveal that leadership has a positive impact on social and economic benefits (EcB), as well as an indirect impact on the environmental benefits (EB) of GLSS projects, with organizational involvement (OI) and performance measurement (PM) functioning as mediators.

This study represents an empirical reference for practitioners and researchers pursuing high-quality, low-cost, environmentally and socially sustainable products or processes through the implementation of GLSS projects in the manufacturing industry.

This study provides a statistically validated model using the SEM technique to represent the relationships between GLSS enablers in the MMI.

This study explores the relationship between Tourism Systems and Smart Cities, aiming to identify what prevents public managers from including these systems in Smart City strategies. This separation neglects that increasing tourism attractiveness may also impact city resources, infrastructures and inhabitants.

To this end, we developed a critical literature review consulting three databases: Scopus, Web of Science and EBSCOhost. A total of 73 articles were selected and analyzed through thematic analysis.

Through this critical review, we develop a framework of barriers to integrating a tourism system in Smart City strategies composed of four main barrier themes and 11 barrier factors. Findings show the need for innovative research and public managers to go beyond considerations related to technological challenges and instead focus on other barriers hindering integration, such as the lack of participatory governance and knowledge of tourism systems' current and future impact.

This study offers a critical identification of barriers impeding the inclusion of tourism systems in Smart City strategies, providing a useful guideline for public managers aiming to follow an innovative approach to smart development where tourism can still be a tool to enhance the attractiveness of the territory while considering its current and future impact on the city.

The purpose of this study is to examine the determinants impacting consumer behaviour in organic food consumption in Bangladesh. This study aims to identify the key factors facilitating organic food consumption and establish a framework by analysing their contextual relationships.

The study used interpretive structural modelling (ISM), relying on expert perspectives from experienced academicians and marketing professionals. A Matrice d'Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) analysis was performed to assess the driving forces and interdependencies among these determinants.

The MICMAC analysis grouped determinants influencing organic food purchases into four categories. The dependent factors, like attitude and food safety, showed moderate driving forces and high dependence. Linkage determinants, such as environmental concern and price, exerted considerable influence with moderate dependence. Independent variables, especially knowledge about organic food, had a strong impact with relatively low dependence.

This study’s insights offer valuable guidance for managers in the organic food industry, providing strategies to address consumer behaviour. Prioritising education on environmental benefits, transparent pricing, collaborating on policies, ensuring food safety and understanding determinants impacting purchase intent can aid in designing effective marketing strategies and product offerings aligned with consumer needs, ultimately promoting sustainability.

To the best of the authors’ knowledge, this study is the first to investigate the interconnections and relative significance of determinants influencing organic food purchases, using the ISM approach and MICMAC analysis. It delves into the previously unexplored territory of understanding the relationships and hierarchical significance of these determinants in shaping consumer behaviour towards organic food purchases.

This paper uses the complex proportionality assessment (COPRAS) method to examine the driving factors of Industry 4.0 (I4) technologies for lean implementation in small and medium-sized enterprises (SMEs).

Adopting I4 technology is imperative for SMEs seeking to maintain competitiveness within the manufacturing sector. A thorough understanding of the driving factors involved is required to support the implementation of I4. For this objective, the multi-criteria decision-making (MCDM) tool COPRAS was used to efficiently analyze and rank these driving elements based on their importance. These factors can help small and medium-sized firms (SMEs) prioritize their efforts and investments in I4 technologies for lean implementation.

This study evaluates and prioritizes the nine I4 factors according to the perceptions of SMEs. The ranking offers significant insights into the factors SMEs consider more accessible and effective when adopting I4 technologies.

The author's original contribution is to examine I4 driving factors for lean implementation in SMEs using COPRAS.