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This study aims to measure the global research landscape on Pharmacovigilance (PV) indexed in the Scopus database for six years period spanning from 2015 to 2020. The study examines growth rate, author productivity and prominent authors, institutions and countries.

The research literature on PV published globally and indexed in the Scopus core collection database was retrieved using the search string “pharmacovigilance” as per the Medical List of Subject Heading. A total of 1,272 documents were retrieved based on the query set. Quantitative and visualization technologies were used for data analysis and interpretation. Network visualization maps including research collaboration of authors, journals, institutions and countries were created by using the VOSviewer program.

A total of 1,272 global scientific research output on PV were retrieved with an annual average growth rate of 21.70% and with a proportion of 3.84% compound annual growth rate. The relative growth rate for the world’s PV publications decreased gradually from the rate of 0.71 to 0.20. On the other hand, the doubling time (DT) of PV publications displays an increasing trend from the rate of 0.97 to 3.46. The mean relative growth and DT of PV publication for six years is 0.32 and 1.87, respectively. The global publications on PV registered a total of 6,387 citations with an average citation impact of 5.02 citations per paper. The average number of authors per paper was 0.76 and the average productivity per author was 1.33. The most productive journal was Drug Safety with a total of 74 papers. France has the highest number of publications with a total of 251 papers accounting for 19.73%. Uppsala Monitoring Center, Sweden dominated in research productivity on PV with a proportion of 1.89% papers. There is a considerable research output in the areas of medicine accounting for 77.43% of total publications.

The outcome of the study may help regulators, drug manufacturers, medical scientists and health-care professionals to identify the progress in PV research. In addition, it will also help to identify the prolific authors, journals, institutions and countries/territories in the development of research.

PV across the globe has become an important public health issue concerning to regulators, drug manufacturers and health-care professionals, therefore, it is feasible to know the research landscape to safeguard of public health.

The investigation is the first attempt to the scientometric assessment of PV research output at the global level.

This study aims to investigate the research productivity in terms of publications count of the top four premiers Indian Institute of Management (IIM) institutions and to explore the current research trends.

Bibliometric techniques were employed to assess the performance in terms of research productivity of authors affiliated with IIMs. The Elsevier Scopus database was selected as a tool to extract the prospective publications data limiting the time frame for 2010–2021. The IIM-Ahmedabad, IIM-Bangalore, IIM-Calcutta and IIM-Lucknow have been selected for the study. The harvested data were analyzed by using the standard bibliometric indicators and scientometric parameters to measure the research landscape such as average growth rate, compound average growth rate, relative growth rate, doubling time, degree of collaboration, collaborative index, collaborative coefficient and modified collaborative coefficient. VOSviewer 1.6.17, BibExcel and Microsoft Excel were used for data analysis and visualization.

The research productivity of selected four IIMs has shown an upward trend during the study period from 2010–2021 and accrued 4,397 publications with an average of 366 publications per year. The authorship patterns demonstrate the collaborative trends as most of the publications were produced by the multiple-authors (81.03%). IIM-Ahmedabad has produced the maximum number of publications (32.20%). The research productivity of IIMs has come out in collaboration with the 125 nations across the world and the USA, the UK, Canada, Germany and China are the front runners with IIMs in the collaborative network. The high magnitude and density of collaboration are evident from the calculated mean values of the degree of collaboration (0.82). The mean values of the collaborative index (2.64), collaborative coefficient (0.51) and modified collaborative coefficient (0.51) demonstrated a positive trend, but indicate the fluctuation in the collaborative pattern as time proceeds.

The study is limited to the publications data indexed in the Scopus database, therefore the outcome may not be generalized across other databases available in the public domain like Web of Science (WoS), PubMed, Dimensions and Google Scholars.

The findings of the study may aid academics and library professionals in identifying research trends, collaboration networks and evaluating other academic and research institutions by using the current advancement in data analysis.

The present study is the first effort to evaluate the research productivity of IIMs. The expanding literature will make an important contribution to identifying patterns and evaluating current research trends on a worldwide scale.

With the aid of bibliometric analysis, this study aims to show the state-of-the-art of research on the digital divide and identifies new areas for further investigation.

Performance analysis and science mapping were used in the study to analyse a sample of 3,571 studies that were published between 2018 and 2022. The “Title-Keyword-Abstract” search option was used to collect the anticipated publications data from the Scopus database. The gathered data were analysed using the common bibliometric indices to evaluate the research landscape. The science mapping tactics made use of the VOSviewer and Biblioshiny software.

The performance and science mapping analysis shows that recent research on the digital divide has not been sufficiently exposed and examined. The analysis discovered emerging topics, prolific authors and nations, affiliations, a network of collaboration among authors, countries and institutions, bibliographic coupling and keyword co-occurrence.

This work presents a state-of-the-art that has significant theoretical and practical ramifications for the existing digital divide literature. The methodologies and database used in the current study are more extensive.

This study aims to measure the global research landscape of the National Institute of Pharmaceutical Education and Research (NIPER) of India on a set of quantitative and qualitative metrics in terms of research output toward exploring research trends and give an overview of collaborative practices by researchers of NIPERs.

The present study has selected the Scopus database as a tool to retrieve potential publications of studied NIPERs during the last 12 years (2010–2021). NIPER-Mohali, NIPER-Hyderabad, NIPER-Ahmedabad, NIPER-Guwahati and NIPER-Kolkata have been selected for the study. The study has adopted a comprehensive search strategy to extract 3,926 publications data. VOS viewer 1.6.17, BibExcel and Microsoft Excel were used for data analysis and visualization.

The global scientific research output of NIPERs accrued 3,926 publications with an average of 327 publications per year. The retrieved publications fetched a total of 67,772 citations with an average citation impact of 17.26. There observed a steady growth of publications from 168 to 509 registered with an average growth rate of 18.44%. The mean relative growth rate and doubling time of research output are 0.26 and 2.94. The authorship patterns explore collaborative trends as most of the publications were published by multiple authors (99.39%). NIPERs have expanded their outreach to collaborate with the USA, Malaysia, Saudi Arabia, Australia and the UK to collaborate on research and regulatory reforms exhibits in the USA as a major contributor.

The present study is the first effort to evaluate the global research productivity of NIPERs and assess the current research trends on a set of quantitative and qualitative metrics to provide some insights into the complex dynamics of research productivity. The study’s outcome may help to identify the current research progress of NIPERs at the global level.

From last few decades, logistics management (LM) constitutes a global concern among organization’s supply chain (SC) to improve their business effectiveness. The purpose of this paper is to uncover and analyze the critical factors (CFs) related to the implementation of effective LM concept and benchmark the SC performance.

The most common (16) CFs were identified and selected through literature and use of the Delphi method. Subsequently, the selected most common CFs were analyzed to distinguish their causal relations using the fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) technique under unclear surroundings. A case example of Indian Logistics Company is also discussed to reveal the practical applicability of the research.

Provision of the effective information communication and technological developments in the system and Management dedication, support and involvement CFs are found to have the top most influences in the effective implementation of LM. This paper also groups the CFs into cause and effect relationship which provides valuable insights for analyzing the factors in successful implementation of LM.

This work attempts to understand the different CFs, their relative position and the importance rating in the system, due to which, managers can differentiate the factor which greatly affects the concepts of implementing LM, and thus, improvements can be made accordingly.

First, this work offers 16 CFs to LM implementation from a SC scenario. Second, in the context of contributing to the theory, the combined Delphi and fuzzy DEMATEL-based model is provided that helps in managing the logistic related issues effectively.

This research is about embedding service-based supply chain management (SCM) concepts in the education sector. Due to Canada's competitive education sector, the authors focus on Canadian universities.

The authors develop a framework for evaluating and forecasting university performance using data envelopment analysis (DEA) and artificial neural networks (ANNs) to assist education policymakers. The application of the proposed framework is illustrated based on information from 16 Canadian universities and by investigating their teaching and research performance.

The major findings are (1) applying the service SCM concept to develop a performance evaluation and prediction framework, (2) demonstrating the application of DEA-ANN for computing and predicting the efficiency of service SCM in Canadian universities, and (3) generating insights to enable universities to improve their research and teaching performances considering critical inputs and outputs.

This paper presents a new framework for universities' performance assessment and performance prediction. DEA and ANN are integrated to aid decision-makers in evaluating the performances of universities.

The findings suggest that higher education policymakers should monitor attrition rates at graduate and undergraduate levels and provide financial support to facilitate research and concentrate on Ph.D. programs. Additionally, the sensitivity analysis indicates that selecting inputs and outputs is critical in determining university rankings.

This research proposes a new integrated DEA and ANN framework to assess and forecast future teaching and research efficiencies applying the service supply chain concept. The findings offer policymakers insights such as paying close attention to the attrition rates of undergraduate and postgraduate programs. In addition, prioritizing internal research support and concentrating on Ph.D. programs is recommended.

This hypothetical case study aims to revisit the classical model given by Henri Fayol whereby he put forward a set of 14 principles to guide managers in decision-making across organizations. The case study showcases the dilemma in which the top manager of an automobile company finds himself when some of the very basic principles – on which the whole discipline of management is founded – are ignored. It will also serve as an aid for faculty members in B-Schools to teach students the significance of basic management principles postulated many years back which stand relevant even in contemporary times.

This case study is based on a hypothetical scenario in the corporate world. Different incidents in a fictitious automobile manufacturing firm are presented and the corresponding principles given by Henri Fayol are inferred.

This case study highlights that decision-making gets complicated if fundamental principles of management are not complied with. The decision taken during each and every situation which has been discussed in this case study is contrary to the correct course of action as propounded by Fayol. Modern-day managers must acknowledge the relevance and importance of these principles for achieving success in business.

This case study underscores that even in this volatile business environment where most of the management practices are technology-driven, we cannot disregard the most elementary rules of management. The managers working at different levels in the organizational hierarchy may be guided to make the right decisions in situations similar to the ones described.

Business models (BMs) are becoming increasingly crucial for value creation in the healthcare sector. The study explores the conceptualization and application of BM concepts within the healthcare sector and investigates their evolution in emerging economies (EEs) and developed economies (DEs). This study aims to uncover these two contexts' shared characteristics and unique variances through a comparative analysis.

The paper systematically investigates and consolidates the literature on healthcare by employing the antecedents, decisions and outcomes (ADO) framework and finally examines 71 shortlisted articles published between 2003 and 2022.

The recognition of the BM within healthcare is increasing, both in EEs and DEs. EEs prioritize value creation and capture through cost efficiency, while DEs focus on innovation. Key theories employed include a resource-based view, the network theory and the theory of innovation. Case studies are commonly used as a methodology. Further research is needed to explore the decisions and outcomes of BMs.

The study adopts stringent filtration and keyword criteria, potentially excluding relevant research. Future researchers are encouraged to broaden their selection criteria to encompass a more extensive range of relevant studies.

Beyond comparing and highlighting gaps in BMs between EEs and DEs, benchmarking DE's healthcare business models (HBMs) helps healthcare organizations in EEs align their practices, mitigate risks and establish efficient healthcare systems tailored to their specific contexts. The study adopts stringent filtration and keyword criteria, potentially excluding relevant research. Future researchers are encouraged to broaden their selection criteria to encompass a more extensive range of relevant studies.

The study analyzes HBMs using an SLR framework perspective and provides practical implications for academicians and practitioners to enhance their decision-making.

The purpose of this paper is to ascertain and analyze the interactions among different barriers of 5S implementation in manufacturing organizations.

In this paper, 15 barriers affecting the implementation of 5S in manufacturing organizations have been identified from literature analysis and discussion with academic and industrial experts. Afterwards, identified barriers were validated by using nation-wide questionnaire-based survey. Then, interpretive structural modeling (ISM) approach has been utilized to find out the interaction among the identified barriers in order to develop hierarchy-based model.

The research identifies several key barriers which have high driving power and weak dependence power. In this concern, these barriers entail extreme care and handling for successful implementation of 5S. Financial constraints, lack of top management commitment, and no proper vision and mission are found to be the key barriers.

The developed ISM model is based on experts’ opinion. This developed hierarchy-based model requires further validation by using structural equation modeling approach or by performing detailed case studies.

In this paper, ISM-based structural model has been recommended for Indian manufacturing organizations, which is a novel exertion in the area of 5S implementation.

This paper aims to describe the use of a meta-learning framework for recommending cost-sensitive classification methods with the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?”

This paper describes the use of a meta-learning framework for recommending cost-sensitive classification methods for the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” The framework is based on the idea of applying machine learning techniques to discover knowledge about the performance of different machine learning algorithms. It includes components that repeatedly apply different classification methods on data sets and measures their performance. The characteristics of the data sets, combined with the algorithms and the performance provide the training examples. A decision tree algorithm is applied to the training examples to induce the knowledge, which can then be used to recommend algorithms for new data sets. The paper makes a contribution to both meta-learning and cost-sensitive machine learning approaches. Those both fields are not new, however, building a recommender that recommends the optimal case-sensitive approach for a given data problem is the contribution. The proposed solution is implemented in WEKA and evaluated by applying it on different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. The developed solution takes the misclassification cost into consideration during the learning process, which is not available in the compared project.

The proposed solution is implemented in WEKA and evaluated by applying it to different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system.

The paper presents a major piece of new information in writing for the first time. Meta-learning work has been done before but this paper presents a new meta-learning framework that is costs sensitive.