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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 study aims to examine the conceptualization of online abuse (OA) and identifies theories, countries of research, top-cited articles, methodologies, antecedents, mediators, outcomes and moderators of OA and future research opportunities. Two research questions are addressed. How have the past studies on OA progressed regarding theories, context, characteristics and methodology? What future research opportunities can be done in this area?

This study systematically reviews, synthesizes and integrates OA literature using the well-recommended preferred reporting items for systematic reviews and meta-analyses (PRISMA) rules. The literature on OA was synthesized based on the Theory–Context–Characteristics–Methodologies (TCCM) framework given by Paul and Rosado-Serrano.

Through an examination of TCCM used in OA research, the review presents an all-inclusive and up-to-date overview of the research in this arena and sets a future research agenda to spur scholarly research. This systematic literature review has analyzed top-quality sample papers, published in the past decade. As a result, it contributes to a better understanding of this relationship by analyzing the different types of use and the value added to the shopping experience.

This study provides groundwork for researchers and promotes a deeper understanding of OA.

The purpose of this study is to examine characteristics of retracted publications from Indian authors and inspect a relationship between journal impact factor (JIF) and the number of authors (NoA).

The authors examined the general characteristics of retracted publications and investigated the correlation between JIF and NoA from Indian authors from January 1, 2017, to December 31, 2022. Data were mined from retraction watch http://retractiondatabase.org/ (n = 1,459) and determined the year of publication, year of retraction, authors, journals, publishers and causes of the retractions. A journal citation report was extracted to gather the JIFs.

About one-third of retracted papers were published in 2020; 2022 has the highest retraction rate (723); studies with two authors represent about one-third (476) of the published articles; Journal of Ambient Intelligence and Humanized Computing (354) has the highest number of retractions; Springer published the most retracted papers (674); and the majority of the journal (1,133) is indexed in journal citation reports, with impact factor extending from 0.504 to 43.474. Retraction due to legal reasons/legal threats was the most predominant reason for retraction.

This study reflects growth in author collaborations with a surge in the JIF. This study recommends that quick retraction is essential to reduce the adverse effects of faulty research.

This paper examines and empirically validates the artificial intelligence-enabled human resource management (AI-enabled HRM) dimensions and sustainable organisational performance (SOP) relationship. It also examines the mediation and moderation of employee engagement (EE) and fusion skills (FS).

The indirect effects of AI-enabled HRM dimensions on SOP were found using structural equation modelling (SEM), bootstrapping and FS’s moderation effect by AMOS 22.

Results showed that AI-enabled HRM dimensions indirectly affected SOP through EE as a full and partial mediator with no moderation effects of FS.

This is the first study to link AI-enabled HRM dimensions, EE and SOP and determine how FS moderates EE and SOP.

The purpose of this study is to examine the relationships between tourists’ motivations, perception of tourism impacts, place attachment (PA) and loyalty toward Jallianwala Bagh, a dark heritage site in Punjab, India.

A mixed-method approach comprising qualitative and quantitative methods was used. Semi-structured interviews and the Delphi method helped generate a 34-item survey instrument. A sample size of 869 respondents was obtained, split into two subsets for exploratory factor analysis and confirmatory factor analysis.

Seven out of nine hypotheses were supported. Motivated tourists perceived higher positive tourism impacts but lower negative tourism impacts. Higher perceptions of positive tourism impacts increased both PA and loyalty to the destination. Surprisingly, the perception of negative tourism impacts did not significantly affect tourist loyalty, contrary to some previous research.

This study informs stakeholders about tourists’ cognitive and affective responses at a dark tourism site, aiding in the planning and development of sustainable tourism strategies.

By understanding the tourists’ motivations and perceptions, stakeholders can manage tourism impacts more effectively, ensuring that tourists’ experiences align with sustainable practices.

This study enriches the understanding of the tourists’ complex interactions with dark heritage sites. It introduces a new angle by examining how motivations, PA and perceptions of tourism impacts influence tourist loyalty, especially in the context of dark tourism.

Managing weeds and pests in cropland is one of the major concerns in agriculture that greatly affects the quantity and quality of the produce. While the success of preventing potential weeds and pests is not guaranteed, early detection and diagnosis help manage them effectively to ensure crops’ growth and health

We propose a diagnostic framework for crop management with automatic weed and pest detection and identification in maize crops using residual neural networks. We train two models, one for weed detection with a labeled image dataset of maize and commonly occurring weed plants, and another for leaf disease detection using a labeled image dataset of healthy and infected maize leaves. The global and local explanations of image classification are obtained and presented

Weed and disease detection and identification can be accurately performed using deep-learning neural networks. Weed detection is accurate up to 97%, and disease detection up to 95% is made on average and the results are presented. Further, using this crop management system, we can detect the presence of weeds and pests in the maize crop early, and the annual yield of the maize crop can potentially increase by 90% theoretically with suitable control actions

The proposed diagnostic models can be further used on farms to monitor the health of maize crops. Images obtained from drones and robots can be fed to these models, which can then automatically detect and identify weed and disease attacks on maize farms. This offers early diagnosis, which enables necessary treatment and control of crops at the early stages without affecting the yield of the maize crop

The proposed crop management framework allows treatment and control of weeds and pests only in the affected regions of the farms and hence minimizes the use of harmful pesticides and herbicides and their related health effects on consumers and farmers.

This study presents an integrated weed and disease diagnostic framework, which is scarcely reported in the literature

Heritage tourism is an increasingly popular form of tourism that allows individuals to connect with the past and immerse themselves in cultural and historical narratives. Hence, the purpose of this study is to explore the intricate relationships among vicarious nostalgia (VNOS), memorable tourism experiences (MTEXs) and their collective influence on tourists’ behavioral intentions (BINTs). Additionally, this study examines the moderating effect of social return (SN) in the context of heritage tourism.

Data were gathered using a self-administered questionnaire from 259 tourists visiting heritage sites in Jaipur. The proposed model was tested using structural equation modeling.

The results confirmed that VNOS had a significant positive impact on BINT in the context of heritage tourism. The causal relationship between VNOS and BINT was fully mediated by MTEX. The results further verified that the presence of SN strengthens the association between MTEXs and BINT.

This research will guide the firms associated with heritage tourism to target specific cohorts interested in heritage tourism. Policymakers may find it easier to create unique offerings and packages that appeal to visitors interested in historical sites and produce memorable travel experiences. One key implication is to create “social media friendly spaces” at different locations of the sites. To increase tourism, managers may use the findings from this research to create plans for the ethical promotion and protection of cultural and natural heritage sites.

Overall, this research advances the understanding of the role of VNOS in heritage tourism by elucidating its cognitive and emotional aspects and their subsequent influence on the memorability of tourist experiences and BINT s. Additionally, by considering the moderating effect of SN, this study provides a comprehensive view of how these factors collectively shape tourists’ decisions and actions in the context of heritage destinations. This research has been conducted in the heritage city of Jaipur (North-Western India), which, surprisingly – despite its popularity as a heritage tourism site – has not been sufficiently explored in the scholarly research.

Considering the swift adoption of innovative sustainability practices in businesses to accomplish sustainable development goals (SDGs), research on corporate sustainability has increased significantly over the years. This research intends to analyze the published literature, emphasizing the existing, emerging and future research directions on achieving the SDGs through corporate sustainability.

This research analyzed the growing trends in corporate sustainability by incorporating 2,038 Scopus articles published between 1999 and 2022 using latent Dirichlet allocation (LDA) topic modeling, bibliometrics and qualitative content analysis techniques. The bibliometric data were analyzed using performance and science mapping. Thereafter, topic modeling and content analysis uncovered the topics included under the corporate sustainability umbrella.

The findings indicate that investigation into corporate sustainability has considerably increased from 2015 to date. Additionally, the majority of studies on corporate sustainability are from the United States of America, the United Kingdom and Germany. Besides, the USA has the most collaboration in terms of co-authorship. S. Schaltegger was considered the most productive author. However, P. Bansal was ranked as the top author based on a co-citation analysis of authors. Further, bibliometric data were evaluated to analyze leading publications, journals and institutions. Besides, keyword co-occurrence analysis, topic modeling and content analysis highlighted the theoretical underpinnings and new patterns and provided directions for further research.

This study demonstrates various existing and emerging themes in corporate sustainability, which have various repercussions for academicians and organizations. This research also examines the lagging themes in the current domain.