Search results

Human resource analytics (HRA) has developed as a new business trend and challenge, stressing the strategic relevance of human resource management (HRM) to senior management executives. HRA is a process that uses statistical techniques, to link HR practices to organizational performance. The purpose of this study is to carry out recent development in HRA, bibliometric analysis and content analysis to present a comprehensive account of HRA to fill the gap in the evolution and status of its research.

The study is based on the recent advances in HRA in terms of it evolution and advancement by analyzing and drawing conclusions 480 articles retrieved from the Web of Science (WoS) database from 2003 to March 2022. The methodology is divided into four steps: data collection, analysis, visualization and interpretation. The study performed a rigorous bibliometric assessment of HRA using the bibliometric R-package and VOS viewer.

The findings based on the literature survey, and bibliometric analysis, reveal the path-breaking articles, the prominent authors, most contributing institutions and countries that have contributed to the HRA scholarship. The results show that the number of publications has significantly increased from 2015 onwards, reaching a maximum of 101 journals in 2021. The USA, China, India, Canada and the United Kingdom were the most productive countries in terms of the total number of publications. Human Resource Management Journal, Human Resource Management, International Journal of Manpower, and Journal of Organizational Effectiveness-People and Performance are the top four academic outlets in the field of HRA. Additionally, the study identifies four clusters of HRA research and the knowledge gaps in HRA scholarship.

The present study is based on the articles retrieved from the WoS. The study underpins HRA research to understand the trends and presents a structured account. However, the study is not free from limitations. It is recommended that future research could be undertaken by combining WoS and Scopus databases to have a more detailed and comprehensive view. This study indicates that the field is still in its infancy stage. Hence, there is a need for more arduous research on the topic to help develop a better understanding of this field.

The findings of knowledge clusters will drive future researchers to augment the field. The evolution of the four clusters and their subsequent development will fill the gaps in the literature. This study enriches the HRA literature and the findings of this study may assist academicians, researchers and managers in furthering their research in the identified research clusters

This paper inspects in detail the seasonality (deterministic) in container freight rates, and compares seasonality patterns in different freight rate indices. A deterministic seasonality unit root test is performed to achieve set objectives. This study concludes that all the indices (tested in this paper) exhibit significant deterministic seasonality. For January and August, there is no seasonal effect observed in all five series. At the same time, all the indices except Exports from Europe Rate Index (EEI) exhibit significant seasonal patterns in February, September, and December. All five indices exhibit significant seasonality during May, and the coefficient sign shows a drop in the freight rates. During March, October, and November; it is observed that only EEI exhibit significant seasonal patterns. The results could be beneficial for carriers and agents who are involved in the containerised freight transport business. Also, shippers could get a clear idea about the freight rates' nature across various trade routes.

During the past decade, the necessity to integrate manufacturing and sustainability has increased mainly to reduce the adverse effect on the manufacturing industry, transforming traditional manufacturing into smart manufacturing by adopting the latest manufacturing technology as part of the Industry 4.0 revolution. Smart manufacturing has piqued the interest of both academics and industry. Manufacturing is a foundation of products and services required for human health, safety, and well-being in modern society and from an organizational standpoint. This paper uses bibliometric analysis better to understand the relationship between smart manufacturing and sustainability scholarship and provide an up-to-date account of current industry practices.

This paper used the bibliometric analysis method to analyze and draw conclusions from 839 articles retrieved from the Scopus database from 1994 to February 2022. The methodology is divided into four steps: data collection, analysis, visualization, and interpretation. The current study aims to comprehend smart manufacturing and sustainability scholarship using the bibliometric R-package and VOSviewer software.

The study provides fascinating insights that may assist scholars, industry professionals, and top management in conceptualizing smart manufacturing and sustainability in their organizations. The results show that the number of publications has significantly increased from 2015 onwards, reaching a maximum of 317 journals in 2021 with an increasing publication annual growth rate of 21.9%. The United Kingdom, India, the United States of America, Italy, France, Brazil and China were the most productive countries in terms of the total number of publications. Technological Forecasting and Social Change, Journal of Cleaner Production, International Journal of Production Research, Production Planning and Control, Business Strategy and the Environment Technology in Society, and Benchmarking: An International Journal emerged as the top outlets.

The research in the area of smart manufacturing and sustainability is underpinned by this study, which aims to understand the trends in this field over the last two decades in terms of prolific authors, most influential journals, key themes, and the field's intellectual and social structure. However, according to the research, this field is still in its early stages of development. As a result, a more in-depth analysis is required to aid in the development of a better understanding of this new field.

The paper focuses on integrating smart manufacturing and sustainability through increased interest from 2015 onwards through the literature review. Specific policies should be formulated to improve the manufacturing sector's competence. Furthermore, these findings can guide researchers who want to delve deeper into smart manufacturing and sustainability.

During the past decade, the green workplace has attracted the attention of academics and industry alike. As a result, new research areas like green human resource management (GHRM) started emerging. Green workplace means greening of traditional business functions, namely, human resource management, marketing, operations and supply chain. GHRM can play a strategic role in enabling organizations to implement and formulate sustainable strategies and help them achieve corporate sustainability. This paper helps understand the GHRM scholarship and provides an up-to-date account of GHRM practices.

In this paper, the authors have adopted the bibliometric analysis method to analyse and draw inferences from 247 articles retrieved from the Web of Science (WoS) database for the time horizon of almost two decades: 2005 to June 2021. The methodology's schematic view comprises four steps: data collection, data analysis, data visualization and interpretation. The present study intends to understand GHRM scholarship using Bibliometric R-package and VOSviewer software.

The study offers some interesting insights that may help scholars, line managers and top management to conceptualize the GHRM practices in their organization, which is currently limited to a few HR practices (hiring, training and reward), geographies, developed countries and industries. These can be extended to other HR practices (e.g. industrial relations). In addition, this paper contributes to the GHRM literature in identifying the keywords, authors, journals, intellectual, conceptual and social structure.

The study underpins the research in the area of GHRM in order to understand the trends on this topic over the past 17 years in terms of prolific authors, most impactful journals, key themes and the field's intellectual and social structure. The study indicates that this field is still in its development stage. Hence, there is a need for more arduous research on the topic to help develop a better understanding of this new field.

The paper helps understand green practices in human resource functions, focusing on corporate sustainability goals. It helps the line managers and top management make GHRM a central function for achieving sustainability goals. In addition, these findings can be a road map for the researchers to investigate the field of GHRM further.

The current industrial revolution is powered by data, which is also referred as Industry 4.0. The Industry 4.0 has attracted significant attention from academia and the industry professionals. The supply chain integration (SCI) has played a significant role in enhancing supply chain performance and organizational performance. This study explores the relationship between Industry 4.0 and SCI via an extensive literature review to understand the various levels of integration with the supply chain processes and to identify missing links, through a framework, and suggest further research directions.

In this paper, we have used systematic literature review approach to identify the building blocks of the conceptual framework, which is the main contribution of the present study. We have used Scopus database to search literature using keywords.

The study offers some interesting insights that may help scholars to advance theoretical debates. Moreover, the study also provides interesting direction to the practitioners engaged in supply chain management in data-driven environment. In this study, we have proposed a conceptual framework for the adoption of Industry 4.0 and SCI.

In this study we have proposed a conceptual framework. However, the framework is yet to be empirically tested. Hence, we caution readers to evaluate the findings of the present study in context to its limitations. This is an attempt to develop a conceptual framework which may be tested using longitudinal data.

The present work helps in integrating two independent subjects', i.e. Industry 4.0 and SCI. The theoretical framework presented here integrates Industry 4.0 and SCI which can be useful to the practitioners and policymakers engaged in implementing Industry 4.0.

The following statistical study shows the results of an investigation of supply chain innovation practices of Indian Cement Manufacturers, which are a significantly large and quickly advancing industry in India. Aside from being a study of adoption of supply chain innovation practices (SCIP) in an emerging super power (India) the benefits of supply chain innovation (SCI) are discussed including the major advances in supply chain management. The statistical study compared innovation supply chain practices (like JIT, WMS, CPFR, and others) in 125 large cement firms to their financial performance measures (ROI, market share, EBIDTA, customer satisfaction, productivity). The resulting correlations showed very high levels of reliability and adequacy, and were uses to make conclusions on which supply chain innovation practices had a positive impact on company financial performance. The conclusions are somewhat astounding with the interesting finding that some innovation practices do not support firm performance and maybe should be avoided; while others ICS practices are shown to support firm performance to a high degree.

This paper aims to explore the challenges faced by start-ups during COVID-19 and highlight solutions for catering to the new-normal consumer behaviour. The study accounts for 15 deep-tech start-ups sailing through the pandemic and their responsiveness. This study brings forth insights and experiences from the Indian start-up founders and CEOs during COVID-19.

This study uses a qualitative approach and is exploratory and phenomenological. A purposive sample of 15 young start-ups founded between the years 2013–2020 by founders aged between 24 and 41 was selected for the study. The recorded telephonic interview was collected from the founders from April 2021 to September 2021. The thematic analysis of the study evolves from Braun and Clarke (2006) using the MAXQDA 2020.

The study emphasizes upon challenges faced by start-ups, crisis management of start-ups and the relevance of technology-based start-ups during challenging times. This research provides a qualitative framework to establish the role of the technology acceptance model (TAM) towards the adaptability, responsiveness and resilience demonstrated by the start-ups. The findings also highlight the solutions to address challenges faced by start-ups and road to recovery.

The study has great relevance and lessons for budding entrepreneurs during crisis management. The study has implications for corporations and governments in terms of setting up incubators and accelerators to support budding entrepreneurs.

The study is unique in highlighting the relevance and importance of TAM for start-ups during crisis management like COVID-19. The study thrusts upon the need of technology acceptance for better crisis management.

The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint.

A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint.

The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior.

The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation.

Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective.

The COVID-19 pandemic situation is increasing day by day and has affected the lifestyle and economy worldwide. Due to the absence of specific treatment, the only way to control a pandemic is by stopping its spread. Early identification of affected persons is urgently in demand. Diagnostic methods applied in hospitals are time-consuming, which delay the identification of positive patients. This study aims to develop machine learning-based diagnosis model which can predict positive cases and helps in decision-making.

In this research, the authors have developed a diagnosis model to check coronavirus positivity based on an artificial neural network. The authors have trained the model with clinically assessed symptoms, patient-reported symptoms, other medical histories and exposure data of the person. The authors have explored filter-based feature selection methods such as Chi2, ANOVA F-score and Mutual Information for improving performance of a classification model. Metrics used to evaluate performance of the model are accuracy, precision, sensitivity and F1-score.

The authors got highest classification performance with model trained with features ranked according to ANOVA FS method. Highest scores for accuracy, sensitivity, precision and F1-score of predictions are 0.93, 0.99, 0.94 and 0.93, respectively. The study reveals that most relevant predictors for COVID-19 diagnosis are sob severity, cough severity, sob presence, cough presence, fatigue and number of days since symptom onset.

Treatment for COVID-19 is not available to date. The best way to control this pandemic is the isolation of positive persons. It is very much necessary to identify positive persons at an early stage. RT-PCR test used to check COVID-19 positivity is the time-consuming, expensive and laborious method. Current diagnosis methods used in hospital demand more medical resources with increasing cases of coronavirus that introduce shortage of resources. The developed model provides solution to the problem cheaper and faster decreases the immediate need for medical resources and helps in decision-making.