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The purpose of this study is to explore the trends in publications made to International Journal of Quality and Reliability Management (IJQRM) for time period ranging from 2002–2022. In this study authors performed bibliometric analysis on the publications extracted from Scopus database using citations, authors, keywords and bibliographic coupling methods. The analysis helped authors highlighting the achievements of the journal towards the development of field of “quality” and “reliability” management and explore future avenues to be researched upon in the domain.

In this paper outputs derived through VoS viewer and bibliographic data of Scopus were used to examine various tabular and pictorial aspects of the authors, sources, documents, countries and keywords analysis.

The analysis results revealed that International Journal of Quality and Reliability Management (IJQRM) has got incredible 33,344 citations since 2002 with average citation count per document at 22.69. Sohal A.S. from Monash University, Australia has topped the list of most influential authors in terms of citations per document (107.20 citations/doc) published with IJQRM. However, Antony J. with massive 39 publications in total with 2074 citations is the most prominent contributor for the journal. The author has gained 6% of the total journal citations. The contributions from India, United Kingdom (UK), United States of America (USA), Iran, Australia, Portugal, Brazil, Spain, Sweden, Malaysia and Saudi Arabia are more noteworthy.

The study has implications for future researchers and journal editorial team to explore possibilities in tapping various uncovered and upcoming research areas related to the scope of journal. The findings of the study may prompt research initiatives linked to diverse fields in sync with changes happening across globe such as: Artificial Intelligence, Robotics, Data Science etc.

The study is an attempt to investigate the long standing heritage offered by IJQRM to the world of quality and reliability management. The authors tried to unearth some hidden facts about the IJQRM and attempted to showcase the past and explore future directions to the researchers and editorial team.

The primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.

This research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.

This study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.

This analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.

This study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

The study aims to identify salespeople’s challenges while selling newly launched products in the fast-moving consumer goods (FMCG) sector by examining the holistic environment in which they perform their selling tasks. Furthermore, it develops a hierarchical model mapping the interrelationships between identified challenges to explore their dependence and driving power through qualitative research techniques.

The current study is exploratory and inductive in its research design. It used focus-group discussion (FGD), semistructured interviews and thematic content analysis (TCA) to identify new-product selling challenges in the FMCG sector. The identified factors were then worked into a hierarchical model using total interpretive structural modeling (TISM) to analyze their relationship. The factors were further classified into clusters based on their driving and dependence power, with the help of the Matrice d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) technique

The TISM and MICMAC results identified salespeople’s most critical new-product selling challenges in the FMCG sector: product innovation, product differentiation, customer perception and market turbulence. An enhanced organizational focus on these factors will ensure that salespeople get adequate input to tackle the challenges they face while selling newly launched FMCG products.

The study was confined to identifying challenges in the FMCG sector alone but offered scope for application in other sectors.

This study will help organizations to identify and close gaps in the new-product selling process, thereby improving the performance of salespeople and contributing to a new product’s success. The study findings have a bearing on various stages of product development, management and life cycle. They also highlight the need for greater synergy between an organization’s sales force and other departments.

To the best of the authors’ knowledge, this research is unique in identifying new-product selling challenges in the FMCG sector. It also delineates the complex Web of interrelationships between them and classifies the identified factors based on their driving and dependence on power. The research results can help in organizational decision-making and sales practices, empowering salespeople in their new-product selling tasks.

In a virtual interconnected digital space, the metaverse encompasses various virtual environments where people can interact, including engaging in business activities. Machine learning (ML) is a strategic technology that enables digital transformation to the metaverse, and it is becoming a more prevalent driver of business performance and reporting on performance. However, ML has limitations, and using the technology in business processes, such as accounting, poses a technology governance failure risk. To address this risk, decision makers and those tasked to govern these technologies must understand where the technology fits into the business process and consider its limitations to enable a governed transition to the metaverse. Using selected accounting processes, this study aims to describe the limitations that ML techniques pose to ensure the quality of financial information.

A grounded theory literature review method, consisting of five iterative stages, was used to identify the accounting tasks that ML could perform in the respective accounting processes, describe the ML techniques that could be applied to each accounting task and identify the limitations associated with the individual techniques.

This study finds that limitations such as data availability and training time may impact the quality of the financial information and that ML techniques and their limitations must be clearly understood when developing and implementing technology governance measures.

The study contributes to the growing literature on enterprise information and technology management and governance. In this study, the authors integrated current ML knowledge into an accounting context. As accounting is a pervasive aspect of business, the insights from this study will benefit decision makers and those tasked to govern these technologies to understand how some processes are more likely to be affected by certain limitations and how this may impact the accounting objectives. It will also benefit those users hoping to exploit the advantages of ML in their accounting processes while understanding the specific technology limitations on an accounting task level.

E-tourism is instilling in the tourism industry with the advancement in the technological infrastructure all over the world and fetching tremendous tourists’ attention. The dynamic changes in the technological aspects unveil varied developments in the tourism industry. This paper attempts to reveal the developments in the field of e-tourism by a systematic review of the literature using bibliometric analysis.

In total,146 research articles were retrieved from the Web of Science data during the period of 2004 – 2020, for further analysis using VOSviewer and Biblioshiny package of R Studio.

Useful insights resulted in the form of most cited papers, contribution in e-tourism research by different authors, countries, institutions, journals and so on, co-occurrence analysis and cluster analysis for major trends or themes of e-tourism. This study solicits an elaborated review of e-tourism research and unveils the future directions for the researchers.

This study adds substantial value to the research of e-tourism by analysing the bibliometric data of the last 16 years, that is, from 2004 – 2020, procured from the Scopus by analysing the significant trends developed in the e-tourism research. It also adds value by indicating the emerging areas of e-tourism.

This will not be an overstatement to state that the micro, small and medium enterprise (MSME) industry is crucial and the vital driver of the world economy. It covers different fields and dimensions such as defense products, electrical components and low-cost products. The sector plays a vital role in rendering work with low capital expenditure and is one of the emerging pillars of the Indian economy. Given the significance of this sector in contributing towards India's gross domestic product (GDP), it becomes appropriate to resolve all the issues related to MSME on a primary basis for ensuring required support. The recent global pandemic of COVID-19 has impacted this sector to a great extent. This research study targets the MSME industry and points out the directly linked enablers adding to improve the sector's resiliency and sustainability. Therefore, identification and the interrelationship between the MSME enablers need to be studied, which helps make a preliminary list that deals with their impedance benefaction towards resiliency increment.

The writers have done a comprehensive literature analysis of the enablers for the MSME sector to enable effectively and efficiently during emergencies and pandemics. An endeavor has been made on the enablers to order them by utilizing the modified Total Interpretative Structure Modelling (m-TISM) technique. Authentication of this research work highlights the significance of enablers and their position in a hierarchical structure. Further, MICMAC investigation on the recognized enablers is performed to arrange them in the four quadrants on their dependence and driving power.

The authors have attempted to predict the significance of the MSME sector and its essential contribution to the development of India's economy. The result of m-TISM in the current research work revealed the essential commitment of a hierarchical design dealing with the MSME considering the viewpoint of future development. The well-planned traditional design in the MSME helps establish better government policies and programs and transport infrastructure.

Every research study has a few restrictions. Likewise, the boundaries of the current study are that inputs collated for fostering the models are from a few specialists that may not mirror the assessment of the whole MSME sector.

The MSME sector is the developing sector in the current day, and it is needed to keep supporting the sector for the country's development. The current study has set out the functional establishment to improve MSME practicality. In addition, the research highlights the accountability of the MSME authorities to go with the identified enablers having solid driving power for successful usage of the available resources. This will help the MSME development and add value to practitioners and policymakers in the future.

The growth of this sector is essential for the development of the economy and the development of a nation. The current study presents a unique structure that gives a superior comprehension of the enablers. It will help play a crucial role in developing the MSME area. The structure model developed with the assistance of m-TISM and MICMAC examine the identified enablers with inputs from experts in the field. The hierarchy developed from the study recognized the enablers located on their commitment of suitability development of the MSME field.

This study aims to develop a synthetic knowledge repository consisted of interrelated Web Ontology Language.

The ontology composes the main framework to categorize data of product life cycle with eco-design mode (PLC-EDM) and automatically infer specialists’ knowledge for data confirmation, eventually assisting the utilizations and generation of strategies toward decision-making

(i) utilization of a novel model with ontology mode for information reuse cross the different eco-design applications; (ii) generation of a sound platform toward life cycle evaluation; and (iii) implementation of the PLC-EDM model along the product generation process.

It cannot substitute an evaluation tool of life cycle. Certainly, this model does not predict the “target and range” and/or the depiction of the “utility module” that are basic activities in life cycle assessments as characterized through the international organization for standardization regulations.

As portion of this framework, a prototype Web application is presented which is applied to produce, reuse and verify knowledge of product life cycle.

By counting upon the ontology, the information conducted by the utilization is certainly semantically represented to promote the data sharing among various participants and tools. Besides, the data can be verified against possible faults by inferring over the ontology. Hence, a feasible way to a popular topic in the domain of eco-design applications extension in the industry.

The goals are: to lean on rigid modeling principles; and to promote the interoperability and diffusion of the ontology toward particular utilization demands.

The authors address the role that income plays in allowing individuals to resist dominant institutional norms and engage in entrepreneurship.

This is a conceptual article that develops propositions about the relationship of institutional logics and income level with opportunity entrepreneurship.

The authors suggest that high-income individuals are less impacted than low-income individuals by institutional logics that do not support opportunity entrepreneurship. More specifically, the positive effects of a national business system that reflects and replicates market logics within a society have a greater impact on the proclivity to pursue opportunity entrepreneurship of low-income individuals than those with high incomes.

Policymakers addressing poverty need to understand that examining the overall societal impact of institutions is not enough. Weak institutions have a disproportionately negative impact on low-income individuals. In addition to critical resources, the accessibility of market logics is key.

This study is the first in the entrepreneurship domain to theorize how and why institutions matter more for low-income individuals. This occurs via two mechanisms: (1) market logic accessibility and (2) the degree to which institutionalized market logics decrease opportunity cost. In so doing, this study contributes to the literature on embedded agency within the institutional logic perspective.

This study intends to recognize and prioritize the work-family balance (WFB) enablers for working sole mothers by employing total interpretive structural modelling (TISM) and “Matrice-d’impacts-croisés-multiplication-appliqués-à-un-classment” method (MICMAC).

This paper utilizes the integrated approach in two stages. In initial stage, strategic literature review and expert mining technique have been conducted to recognize and validate WFB enablers. In subsequent stage, TISM has been applied to observe the contextual relationships among WFB enablers in the direction to construct a TISM-based structural model. Furthermore, MICMAC technique has been employed to categorize the WFB enablers based on their driver and dependence power.

This paper has identified novel 13 key enablers of WFB among working sole mothers and constructed a unique TISM-based hierarchical model. Moreover, WFB enablers have been categorized into four clusters using MICMAC analysis. In the developed TISM model, working sole mother-related WFB personal enablers are primarily at the upper level, family-related WFB enablers are in the center and work-related WFB enablers are in the lowest level.

The developed framework on WFB enablers among working sole mothers can provide a resolution to difficulties faced by sole mothers in managing WFB by providing a pathway to enhance their performance by improving the organizational effectiveness through improving WFB policies.

Based on the best of authors' awareness, this study first incorporates the TISM-MICMAC technique to recognize and prioritize the WFB enablers for working sole mothers.

Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research.

This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis.

This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues.

This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted.

This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.