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This study aims to investigate the relationship between the adoption of human resource (HR) analytics and managerial decision-making (DM), with attitude toward artificial intelligence (AI) as a potential moderator.

This study was conducted in three phases. In Phase I, a comprehensive scale to measure the “Adoption of HR analytics” was conceptualized and developed. In Phase II, the scale was validated and operationalized. Finally, in Phase III, a survey of 377 managers was conducted, and a conceptual model was validated using structural equation modeling.

This study reveals that the adoption of HR analytics (HRA) and a positive attitude toward AI significantly influence DM. The findings suggest that the structural factors play the most important role in the adoption of HRA, followed by individual factors, value and system support.

These findings hold valuable implications for managers seeking integration of HRA and AI within organizational systems and processes. HR practitioners can evaluate their organization’s readiness for HRA, enabling them to build a future-proof workforce with the necessary skills. It can help managers make the adoption of AI-enabled HRA a reality. The study also helps to remove inhibitions and concerns of HR managers and employees related to AI.

This paper addresses the methodological, practical knowledge and evidence gap in the area of adoption of HRA and DM. It sheds light on the “future of work” in HR, highlighting a potential shift toward human-AI collaboration.

The purpose of this study is to apply bibliometric techniques to critically examine the contributions of Professor (Dr) Jiju Antony in the domain of quality management research over a period close to 30 years (i.e. 1995 to 2023).

The study incorporates a bibliometric analysis approach using VoSviewer software package to critically examine the contributions and trends in publications on Scopus indexed publications of Antony, followed by an industry wide evaluation of contributions made. Thematic analysis of the bibliographic data was imported in comma-separated values (CSV) format by selecting Antony (last name) and Jiju (first name) in the author search tab in Scopus. The search was made on September 09, 2023 and bibliographic records of 429 documents were considered for the analysis. The analysis was carried out in terms of most frequent keywords used, sources with most frequent publications, thematic analysis of most cited works and global collaborations.

Antony has focused his research interest largely in the domain of quality management, publishing his research in top rated journals in the domain with a broad network of international collaborators. Antony has received 18,802 and 40,947 citations in Scopus and Google Scholar, respectively. This signifies the impact that Antony has created through his research publications. His major contributions are on the topics of six sigma, lean six sigma, continuous improvement, critical success factors and quality management practice implementations in various organizations. Diverse methodologies both qualitative and quantitative were utilized to conduct his research. However, his body of work is not without criticism. Such criticism includes the limited scope of work, with narrow focus on ISO 9001 and QMS standards, Total Quality Management (TQM). Critique also highlights the necessity for more depth, following insufficient exploration of distinctions between TQM and operational excellence (OPEX) methodologies like lean, six sigma, kaizen and agile. Antony’s work has yet to consider a diverse range of industry sectors, in terms of implementation of quality management principles, geographical location, the impact of national culture on corporate performance and explore data quality influence on decision-making. Notably, there is clear opportunity to consider the service sector in future research.

To best of the authors’ knowledge, there are few previous studies conducted using bibliometric analysis for analyzing the work of an individual. Therefore, the present study aims to set a trend whereby bibliometric analysis can be used to recognize and critically asses the contributions of other researchers in their respective domains.

Purpose: Green consumerism is on the rise in the 21st century, impelling businesses to prioritise environmental awareness and expand eco-products to keep up with the growing demand. This research examines how social media (SM) and moral obligations (MO) affect consumer views and their propensity to make eco-friendly choices.

Methodology: Data were gathered from 508 participants using an adaptive questionnaire. The proposed model was tested using ‘structural equation modelling’.

Findings: The results show that electronic word-of-mouth (EWOM) and the intent to acquire green goods favourably impact consumer behaviour. MO positively influences attitudes and intentions to make green purchases (GPI), with attitudes acting as a mediator between MO and GPI.

Implications: This research is of utmost importance for marketers wanting to enhance their SM communication strategies to influence consumers’ opinions of green products and raise the possibility that they would make environmentally conscious purchases.

Purpose: Health insurance and big data analytics have become increasingly intertwined in recent years, offering both opportunities and challenges for the industry. Thus, the primary aim is to utilize bibliometric analysis for comprehensive literature reviews in health insurance and big data analytics.

Design/methodology/approach: Scopus, chosen for its broad coverage, is utilized to extract 493 manuscripts meeting the inclusion criteria set (year and language) for a 25-year period. The tools employed in the study include VOSViewer and Biblioshiny package (R-programming).

Findings: An emerging trend has been observed in the field of health insurance and big data analytics for 25 years. The US has been observed as the topmost leading country to contribute to the subject under study. The Ministry of Science and Technology of Taiwan is at the top first rank of top leading institutions contributing 20 documents to the field of health insurance and big data analytics. Moreover, thematic mapping and word cloud is done to find the most relevant keywords in the study. Furthermore, co-occurrence analysis revealed the relationship of keywords for health insurance and big data mining.

Implications: The implications of the research extend beyond academic insights and have practical implications for stakeholders involved in healthcare policy, practice, and research.

Originality/Value/Implications: The novelty in the manuscript has been brought in by focusing on one of the many types of insurance, i.e., health. Moreover, big data analytics in relation to health insurance for such a range of time period serves as the original presentation of the work with regards to the matter under study.

The purpose of this study is to formulate a new class of vehicle routing problem with an objective to minimise the total cost of raw material collection and derive a new approach to solve optimization problems. This study can help to select the optimum number of suppliers based on cost.

To model the raw material vehicle routing problem, a mixed integer linear programming (MILP) problem is formulated. An interesting phenomenon added to the proposed problem is that there is no compulsion to visit all suppliers. To guarantee the demand of semiconductor industry, all visited suppliers should reach a given raw material capacity requirement. To solve the proposed model, the authors developed a novel hybrid approach that is a combination of block and edge recombination approaches. To avoid bias, the authors compare the results of the proposed methodology with other known approaches, such as genetic algorithms (GAs) and ant colony optimisation (ACO).

The findings indicate that the proposed model can be useful in industries, where multiple suppliers are used. The proposed hybrid approach provides a better sequence of suppliers compared to other heuristic techniques.

The data used in the proposed model is generated based on previous literature. The problem derives from the assumption that semiconductor industries use a variety of raw materials.

This study provides a new model and approach that can help practitioners and policymakers select suppliers based on their logistics costs.

This study provides two important contributions in the context of the supply chain. First, it provides a new variant of the vehicle routing problem in consideration of raw material collection; and second, it provides a new approach to solving optimisation problems.

The paper focuses on bridging the existing literature gap on the role of leadership in influencing employee engagement considering the advancement in technologies. With this, the author explores how the three critical elements of service-based companies' business environment-artificial intelligence (AI) success, employee engagement, and leadership are interlinked and are valuable for raising the engagement level of employees.

A purposive sampling strategy was used to select the employees working in the respective companies. The survey was distributed to 150 senior management employees but responses were received from only 56 employees making the response rate 37.33%. Consequently, an empirical examination of these 56 senior management employees belonging to service-based companies based in Delhi NCR using a survey questionnaire was conducted.

The PLS-SEM (partial least squares structured equation modelling) revealed that AI has a positive role in affecting employee engagement levels and confirmed the mediation of leadership. The magnitude of the indirect effect was negative leading to a reduction in total effect magnitude; however, as the indirect effect model has a higher R square value, the inclusion of a mediating variable made the model more effective.

This study contributes to extending the existing knowledge of the academicians about the relationship theory of leadership, AI implementation in organizations, AI association with leadership and AI impact on employee engagement. The author extends the theoretical understanding by showing that more integration of AI-supported leadership could enable organizations to enhance employee experience and motivate them to be engaged. Despite its relevance, due to the limited sample size, focus on a specific geographic area (Delhi NCR) and the constraint of only using quantitative analysis, the findings open the scope for future research in the form of qualitative and longitudinal studies to identify AI-supported leadership roles.

The study findings are beneficial majorly for organizations to provide them with more in-depth information about the role of AI and leadership style in influencing employee engagement. The identified linkage enables the managers of the company to design more employee-tailored strategies for targeting their engagement level and enhancing the level of productivity of employees. Moreover, AI-supported leadership helps raise the productivity of employees by amplifying their intelligence without making technology a replacement for human resources and also reducing the turnover rate of employees due to the derivation of more satisfaction from existing jobs. Thus, given the economic benefit and societal benefits, the study is relevant.

The existing studies focused on the direct linkage between AI and employee engagement or including artificial intelligence as a mediating variable. The role of leadership is not evaluated. The leadership enables supporting the easy integration of AI in the organization; therefore, it has an important role in driving employee engagement. This study identifies the contribution of leadership in organizations by providing the means of enhancing employee satisfaction without hampering the social identity of the company due to the integration of AI.

The purpose of this study is to investigate the influence of credibility and information quality (IQ) of online food vloggers on consumer attitude and purchase intention towards street food consumption. It also examines the relationship between consumer attitude and purchase intention influenced by online food vlogger reviews in North India.

In order to evaluate the framework, primary data were gathered from 389 street food consumers located in Jammu, Chandigarh and Delhi – cities situated in northern India. The collected data were then subjected to analysis using the partial least squares-structural equation modeling (PLS-SEM) techniques.

The results indicate that the perceived value of street food, influenced by the credibility of food vloggers and the quality of information provided, has a positive impact on consumer attitude and purchase intention towards street food consumption. A positive impact of consumer attitude on the purchase and consumption of street food was also observed.

This research offers a thorough investigation into the elements that influence consumers' opinions regarding vloggers endorsements. The findings reveal that consumer's attitudes towards vloggers recommendations are mainly influenced by the quality of information provided, followed by credibility and the intention to make a purchase. Furthermore, this research is of significance to practitioners and academics interested in comprehending consumer behavior in the realms of tourism and food-related endeavors.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2024-0158

The main objective of the study is to address the lack of sustainability assessments of smart connected health systems in the academic literature by presenting an assessment model to determine the alignment of these systems with the 17 Sustainable Development Goals (SDGs) proposed in the 2030 Agenda.

An evaluation model based on decision analysis is proposed that includes three phases: alignment framework, information gathering and assessment. This model measures the alignment of the connected health system with each of the 17 SDGs, identifying the goals and criteria associated with each SDG that the system achieves to satisfy.

The analysis reveals that the system has achieved more than 24% of the targets among the 17 SDGs. In addition, it identifies four sustainability challenges that the system potentially addresses in relation to the SDGs, providing valuable guidance for researchers and practitioners interested in sustainable health technology development.

The study's results have significant implications for policymakers and stakeholders in the health and technology sectors.

The originality of this study lies in its comprehensive approach to assessing the sustainability of connected health systems in the context of the SDGs, filling an important gap in the existing literature.

The current research aims to investigate the impact of coronavirus 2019 (COVID-19) evolution, government stringency measures and economic resilience on stock market volatility in the Middle East and North African (MENA) emerging markets. Other macroeconomic factors were also taken into account.

Based on financial data from 10 selected MENA countries, we tested an integrated framework that has not yet been explored in prior research. The exponential generalized autoregressive conditional heteroskedasticity (E-GARCH) was adopted to analyze data from March 2020 to February 2022.

Our research illustrates the direct and indirect effects of the virus outbreak on stock market stability and reports that economic resilience could alleviate the volatility shock. This finding is robust across the various proxies of economic resilience used in this study. We also argue that the negative impact of the pandemic on equity market variation gets more pronounced in countries with higher level of stringency scores.

Policymakers ought to strengthen their economic structures and reinforce the economic governance at the national level to gain existing and potential investors’ trust and ensure lower stock market volatilities in times of crisis. Our study also recommends some key economic factors to consider while establishing efficient policies to tackle unexpected shocks and prevent financial meltdowns.

Our findings add to the evolving literature on the reaction of economic and financial markets to the sanitary crisis, particularly in developing countries where research is still scarce. This study is the first of its kind to investigate the stock market reaction to stringency measures in the understudied MENA region.