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Pakistan ranks third globally in terms of newborn deaths occuring within the first 24 hours of life. With a neonatal mortality rate of 42.0%, it carries the highest burden compared to neighboring countries such as Bangladesh (17%), India (22.7%) and Afghanistan (37%). While there has been a decline in neonatal mortality rates in Pakistan, the pace of this decline is slower than that of other countries in the region. Hence, it is crucial to conduct a comprehensive examination of the risk factors contributing to neonatal mortality in Pakistan over an extended period. This study aims to analyze the trends and determinants of neonatal mortality in Pakistan over three decades, providing valuable insights into this persistent issue.

The study focused on neonatal mortality as the response variable, which is defined as the death of a live-born child within 28 days of birth. Neonates who passed away during this period were categorized as “cases,” while those who survived beyond a specific timeframe were referred to as “noncases.” To conduct a pooled analysis of neonatal mortality, birth records of 39,976 children born in the five years preceding the survey were extracted from four waves (1990–2018) of the Pakistan Demographic and Household Survey. The relationship between risk factors and the response variable was examined using the Cox Proportional Hazard Model. Neonatal mortality rates were calculated through the direct method using the “syncmrates” package in Stata 15.

During the extended period in Pakistan, several critical protective factors against neonatal mortality were identified, including a large family size, improved toilet facilities, middle-aged and educated mothers, female children, singleton live births, large size at birth and longer birth intervals. These factors were found to reduce the risk of neonatal mortality significantly.

This study makes the first attempt to analyze the trends and patterns of potential risk factors associated with neonatal mortality in Pakistan. By examining a large dataset spanning several years, the study provides valuable insights into the factors influencing neonatal mortality.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-09-2022-0604

The study aims to present an insight into the research landscape of Library and Information Science (LIS) by India using a bibliometric visualization tool. The study analyses the research growth and trends, highly cited articles, productive publication titles, institutional and country collaboration.

The data were downloaded from the Web of Science Core Collection for a period of 20 years and analysed through VOSviewer, a data visualization software.

The results indicate that the overall annual contributions are increasing, although with uneven and slow growth from 2001 to 2014. However, the highest contributions and impact is witnessed over the past few years. All the top 10 cited papers are related to the area of information processing and management. The visualization technique made it clear that the area of research has made a transition from traditional concepts of library and information to novel ones involving big data, machine learning, altmetrics, etc. Also, the Indian Institute of Technology System, the Council of Scientific and Industrial Research and the Indian Institute of Management System have made the highest contributions. Furthermore, India shares maximum collaborations with the USA, followed by England and China.

The findings of this study would help readers to gain understanding about the contribution of India for the development of the LIS. It would also help researchers to identify the hotspots and left out areas of research in the Indian context that require further investigation, thus would help in policy decisions and future research. Furthermore, researchers will be sensitized about the network visualizations that can also help them to get connected with the peers. The study can also help the journals to recognize the trending topics, which will provide the researchers with the opportunities to work on the same. Funding agencies can also be benefitted by the findings of the current study as they will be informed about the research areas which need to be funded.

There are not many research studies that highlight the research trends in the area of LIS from India and visualize the collaboration among institutions and countries. The study tries to showcase the research trends and collaborative frameworks in the field of LIS in terms of network visualization.

This paper aims to identify the factors that influence students' mental health, particularly in the context of MBA students passing through an emotional phase of the placement season.

A conceptual model through literature has been proposed. To test the proposed model of this study, a survey was conducted among the students of three MBA institutes of national reputation in India. The study's hypotheses were investigated using partial least squares-structural equations modeling and analysis of variance. To corroborate the findings of the survey data, a qualitative study in the form of open-ended interviews with five students was conducted.

The study revealed that female students, non-engineering graduates and students from non-family business backgrounds undergo stress, anxiety and depression higher than their classmates. Cumulative grade point average and bank loans do not significantly affect students' stress, anxiety and depression during the placement season. It was found that the increase in the levels of mindfulness scores led to a significant negative impact on stress, anxiety and depression among the students.

There is a gap in the literature that addresses the mental health of MBA students during campus job placements and the role of mindfulness in mitigating stress, anxiety and depression in these students. This research attempts to fill these research gaps.

This study aims to identify the enablers of human resource (HR) digitalization and HR analytics. This paper also aims to build a relationship map using interpretive structural modeling.

A systematic literature review is used to identify the key enablers of HR digitalization and HR analytics. Ten expert opinions have been taken from the key officials of IT firms located in New Delhi North Central Region.

This study is focused on the enablers of HR analytics. It is found that change management (CM) in the organization is the key enabler of implementing HR digitalization and analytics in an organization. However, other elements like learning culture, training and development, E-learning management and HR transformation (HRT) play a vital role in implementing HR analytics. It is also found that implementing artificial intelligence for HR practices is the ultimate goal for every organization.

Management teams in IT firms should focus on the continuous learning process in the organization. The CM should be expedited for digitalization and adoption of HR analytics. Managers must go through the ramification of HRT, which possesses diligence in HR analytics and artificial intelligence.

This study explicitly talks about the enablers of HR digitalization and HR analytics. It also explores the relationship between the enablers. This study also describes the driving and dependence power of all the enablers.

The purpose of this study is to identify the prominent research constituents in the domain of integration of Lean manufacturing and Industry 4.0 techniques and analyze the intellectual structure among them.

A bibliometric analysis of articles based on Donthu et al. (2021a) has been adopted to conduct a systematic review of the integration of Lean manufacturing and Industry 4.0 using the Scopus database.

The co-citation analysis and bibliographic coupling depicted three clusters and themes around which the research related to the integration of Lean manufacturing and Industry 4.0. Publications related to the topic have majorly focused on the development of conceptual models and frameworks for integrating Lean manufacturing and Industry 4.0, analyzing the compatibility between the two techniques for better implementation of one another and the techniques' combined impact on operational performance.

Most of the review studies related to the domain of integration of Lean manufacturing and Industry 4.0 have adopted a systematic literature review methodology. The present study has tried to infer the intellectual framework of the research being conducted in the said domain using the bibliometric analysis to identify the prominent research constituents in the field and examine the intellectual relationship between them.

The objective of this study is to investigate the barriers hindering the integration of lean manufacturing (LM) practices within the furniture industry of Bangladesh. The traditional operational paradigms in this sector have posed substantial challenges to the effective implementation of LM. In this study, the barriers of implementing LM in the furniture business are examined, aiming to provide a systematic understanding of the barriers that must be addressed for a successful transition.

The research reveals that “Fragmented Industry Structure,” “Resistance to Lean Practices” and “Inadequate Plant Layout and Maintenance”, emerged as the foremost barriers to LM implementation in the furniture industry. Additionally, “Insufficient Expert Management,” “Limited Technical Resources” and “Lack of Capital Investment” play significant roles.

The outcomes of this study provide valuable insights into the furniture industry, enabling the development of strategies for effective LM implementation. One notable challenge in lean implementation is the tendency to revert to established practices when confronted with barriers. Therefore, this transition necessitates informed guidance and leadership. In addition to addressing these internal challenges, the scope of lean implementation should be broadened.

This study represents one of the initial efforts to systematically identify and assess the barriers to LM implementation within the furniture industry of Bangladesh, contributing to the emerging body of knowledge in this area.

This paper aims to critically analyse the circular economy (CE) models adopted in hospitality and tourism to tackle societal challenges in the current Anthropocene era, exploring the driving values and sustainability approaches.

A systematic literature review was carried out with an increasing level of analysis to delve more in-depth into the ability of the CE models to respond to current societal challenges. The review involved a stepwise analysis, starting with a thematic analysis of 151 papers to create a conceptual structure map of circular hospitality and tourism literature. Based on originality and representativeness, 22 papers were selected and analysed according to Hoffman and Jennings’ (2018; 2021) institutional approach to sustainability, which entails specific archetypes to address the Anthropocene society: market rules, technology fix and cultural re-enlightening.

The hospitality and tourism industry has made progress towards implementing CE models. However, CE models in market role and technology fix archetypes may fail to address the current challenges of the Anthropocene era. New culture-led approaches within the cultural re-enlightening archetype, such as the regenerative land practices, a-growth and place-making, and circular society, can foster a more radical shift towards strong sustainability.

The paper highlights the urgent need for a cultural shift towards radical and strong sustainability, identifying future research directions. Policymakers have a crucial role in shaping sustainability practices consistent with an ecological culture centred on acknowledging planetary boundaries.

Despite the increasing interest in CE models, it is still debated how the hospitality and tourism industry can continue to thrive while avoiding harmful impacts on the environment and local communities. The study critically reflects on the current contribution of CE models towards strong sustainability in hospitality and tourism.

Operational excellence (OpEx) is a direction toward learning and developing an excellent culture in all aspects of an organization. To reach this culture, revolutionizing activities using industry 4.0 (i4.0) technologies might be a significant empowering tool. This study aims to identify the challenges and benefits of both concepts and investigate their interrelationship to be considered in applying industry 4.0 technologies toward operational excellence.

The challenges and benefits of OpEx and i4.0 are identified and finalized by reviewing the literature. The causal relations between the considered factors are extracted using the fuzzy DEMATEL (Decision Making Trial and Evaluation Laboratory) method. Then, the analytical network process (ANP) is applied to determine the importance and weight of the factors (challenges and benefits of OpEx and i4.0) according to the constructed network.

The findings illustrated a strong network structure between the factors. First, the causal factors included OpEx and i4.0 challenges, while the OpEx challenges also affected the i4.0 challenges. Both group challenges had a significant effect on OpEx and i4.0 benefits. This means that challenges are the causal factors to be considered in the alignment of i4.0 toward OpEx. Among the OpEx challenges, lack of strategic planning and proper infrastructure were the main influential factors. In contrast, lack of government support and undeveloped business models were identified as the main challenges of i4.0.

OpEx and i4.0 concepts are reviewed and their pros and cons are studied. Previous studies determined an interaction among these concepts. However, from a practical viewpoint, the relation between the challenges and benefits of i4.0 and OpEx was studied for the first time for their alignment.

The Denial of Service (DoS) attack is a category of intrusion that devours various services and resources of the organization by the dispersal of unusable traffic, so that reliable users are not capable of getting benefit from the services. In general, the DoS attackers preserve their independence by collaborating several victim machines and following authentic network traffic, which makes it more complex to detect the attack. Thus, these issues and demerits faced by existing DoS attack recognition schemes in cloud are specified as a major challenge to inventing a new attack recognition method.

This paper aims to detect DoS attack detection scheme, termed as sine cosine anti coronavirus optimization (SCACVO)-driven deep maxout network (DMN). The recorded log file is considered in this method for the attack detection process. Significant features are chosen based on Pearson correlation in the feature selection phase. The over sampling scheme is applied in the data augmentation phase, and then the attack detection is done using DMN. The DMN is trained by the SCACVO algorithm, which is formed by combining sine cosine optimization and anti-corona virus optimization techniques.

The SCACVO-based DMN offers maximum testing accuracy, true positive rate and true negative rate of 0.9412, 0.9541 and 0.9178, respectively.

The DoS attack detection using the proposed model is accurate and improves the effectiveness of the detection.