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Supplier selection along with continuous evaluation of their performance is a crucial activity in healthcare supply chain management for effective utilization of scarce resources while providing quality service at an affordable price, and minimizing chances of stock-out, avoiding serious consequences on the illness or fatality of the patients. Presence of both qualitative and quantitative evaluation criteria, set of potential suppliers and participation of different stakeholders with varying interest make healthcare supplier selection a challenging task which can be effectively solved using any of the multi-criteria decision making (MCDM) methods.

To deal with various qualitative criteria, like cost, quality, delivery performance, reliability, responsiveness and flexibility, this paper proposes integration of grey system theory with a newly developed MCDM tool, i.e. mixed aggregation by comprehensive normalization technique (MACONT) to identify the best performing supplier for pharmaceutical items in a healthcare unit from a pool of six competing alternatives based on the opinions of three healthcare professionals.

While assessing importance of the six evaluation criteria and performance of the alternative healthcare suppliers against those criteria using grey numbers, and exploring use of three normalization procedures and two aggregation operations of MACONT method, this integrated approach singles out S5 as the most compromised healthcare supplier for the considered problem. A sensitivity analysis of its ranking performance against varying values of both balance parameters and preference parameters also validates its solution accuracy and robustness.

This integrated approach can thus efficiently solve healthcare supplier selection problems based on qualitative evaluation criteria in uncertain group decision making environment. It can also be deployed to deal with other decision making problems in the healthcare sector, like supplier selection for healthcare devices, performance evaluation of healthcare units, ranking of physicians etc.

Increasing public consciousness and demand for sustainable environment make selection of a safe location for effective disposal of healthcare waste (HCW) a challenging issue. This problem becomes more complicated due to involvement of multiple decision makers having varying knowledge and interest, conflicting quantitative and qualitative evaluation criteria, and presence of several alternative locations.

To efficiently resolve the problem, the past researchers have already coupled different multi-criteria decision-making tools with uncertainty models and criteria weight measurement techniques, which are time-consuming and highly computationally complex. Based on involvement of a group of experts expressing their opinions with respect to relative importance of criteria and performance of alternative locations against each criterion, this paper proposes application of ordinal priority approach (OPA) integrated with grey numbers to solve an HCW disposal location selection problem.

The grey OPA can simultaneously estimate weights of the experts, criteria and locations relieving the decision makers from complicated computational steps. The potentiality of grey OPA in solving an HCW disposal location selection problem is demonstrated here using an illustrative example consisting of three experts, six criteria and four alternative locations.

The derived results show that it can be employed to deal with real-time HCW disposal location selection problems in uncertain environment providing acceptable and robust decisions. It relieves the experts from pair-wise comparisons of criteria, normalization of data, identification of ideal and anti-ideal solutions, aggregation of information and so on, while arriving at the most consistent decision with minimum computational effort.

In many of today’s manufacturing industries, such as automobile, aerospace, defence, die and mould making, medical and electrical discharge machining (EDM) has emerged as an effective material removal process. In this process, a series of discontinuous electric discharges is used for removing material from the workpiece in the form of craters generating a replica of the tool into the workpiece in a dielectric environment. Appropriate selection of the tool electrode material and combination of input parameters is an important requirement for performance enhancement of an EDM process. This paper aims to optimize an EDM process using single-valued neutrosophic grey relational analysis using Cu-multi-walled carbon nanotube (Cu-MWCNT) composite tool electrode.

This paper proposes the application of grey relational analysis (GRA) in a single-valued neutrosophic fuzzy environment to identify the optimal parametric intermix of an EDM process while considering Cu-MWCNT composite as the tool electrode material. Based on Taguchi’s L9 orthogonal array, nine experiments are conducted at varying combinations of four EDM parameters, i.e. pulse-on time, duty factor, discharge current and gap voltage, with subsequent measurement of two responses, i.e. material removal rate (MRR) and tool wear rate (TWR). The electrodeposition process is used to fabricate the Cu-MWCNT composite tool.

It is noticed that both the responses would be simultaneously optimized at higher levels of pulse-on time (38 µs) and duty factor (8), moderate level of discharge current (5 A) and lower level of gap voltage (30 V). During bi-objective optimization (maximization of MRR and minimization of TWR) of the said EDM process, the achieved values of MRR and TWR are 243.74 mm³/min and 0.001034 g/min, respectively.

Keeping in mind the type of response under consideration, their measured values for each of the EDM experiments are expressed in terms of linguistic variables which are subsequently converted into single-valued neutrosophic numbers. Integration of GRA with single-valued neutrosophic sets would help in optimizing the said EDM process with the Cu-MWCNT composite tool while simultaneously considering truth-membership, indeterminacy membership and falsity-membership degrees in a human-centric uncertain decision-making environment.

This research intends to analyse the trend in educational technology (EdTech) over the last 20 years using systematic scientific mapping and bibliometric analysis and how it relates to the Indian context. Considering the anticipated growth in this field over the previous three years post-pandemic, an existing literature analysis is required. This study aims to map the existing intellectual structure in EdTech applications to extend the knowledge base further in this field. This study also intends to research how the Indian education sector compares in terms of the research output for the EdTech sector, considering the increased government focus on online learning as per the education policy in 2020. The study's findings will pave the way for sustainable research that will be extended in the future.

Bibliometric analysis is conducted on the manuscripts extracted from Web of Science databases for the last 20 years (from 2003 to 2023). This study uses a descriptive research approach for bibliometric analysis as, by nature, this is an exploratory investigation, and no physical or existing experiment can be performed on the quantification, characteristic or productivity of EdTech applications. VoS Viewer and R software are extensively considered for a detailed bibliometric analysis.

E-learning, blended learning and distance education emerged as the most frequently used keywords. The results reveal that technology adoption, higher education, technology and modelling are the most researched topics in this field.

This research is limited to the last 20 years' database obtained from the Web of Science database and limited to educational, management and operation databases only.

The paper intends to analyse the global scenario of EdTech research and ensures that the paper will effectively connect with researchers, educators, policymakers and practitioners from different parts of the world. The results derived from the bibliometric analysis, cluster analysis and identification of key authors, journals and countries can contribute towards the improved contribution in this area.

The paper discusses the research in EdTech over the last two decades and effectively tries to bridge the gap in global research. Integrating systematic scientific mapping and bibliometric analysis is an innovative way to assess the growth and impact of EdTech. Considering the post-pandemic scenario and the government's emphasis on online learning, these are consistent with current developments.

Introduction: Artificial intelligence (AI) and digitisation offer substantial human potential and profit margins, making them promising retail solutions. Retail leaders have successfully integrated comprehensive uses into their daily operations, while competitors heavily invest in new projects. The Indian retail sector is undergoing a significant transformation, which can be attributed to factors such as growing income, demographic characteristics, and enhanced consumerism, as well as the rapid development of new technologies such as digitisation and AI, which is changing both consumers’ and retailers’ buying behaviour.

Purpose: This study aims to determine the influence of AI on elements that drive digitisation in the retailing sector, as well as the factors that lead to organised retailers adopting digitisation and its impact on their business.

Methodology: The study employs a standardised questionnaire distributed to organised stores via an online link, and the data are analysed with SmartPLS software 3.0.

Finding: The retail sector is driven by elements that promote digitalisation in food and groceries retailing, such as simplicity of operation, adoption of digital payment, quicker internet connection, retailer consumer interface, and the involvement of AI.

Research implication: AI has significant consequences for retailing, which serves as the interface between marketers and customers.

Theoretical implication: The study’s findings reflect the perspectives of retailers, store managers, and entrepreneurs on how digitalisation and AI are crucial for the creation and growth of long-term competitive advantages in retail.

The COVID-19 pandemic has recently had an impact on the stock market all over the globe. A thorough review of the literature that included the most cited articles and articles from well-known databases revealed that earlier research in the field had not specifically addressed how the BRIC stock markets responded to the COVID-19 pandemic. The data regarding COVID-19 were collected from the World Health Organization (WHO) website, and the stock market data were collected from Yahoo Finance and the respective country’s stock exchange. A random forest regression algorithm takes the closing price of respective stock indices as target variables and COVID-19 variables as input variables. Using this algorithm, a model is fit to the data and is visualised using line plots. This study’s findings highlight a relationship between the COVID-19 variables and stock market indices. In addition, the stock market of BRIC countries showed a high correlation, especially with the Shanghai Composite Stock Index with a correlation value of 0.7 and above. Brazil took the worst hit in the studied duration by declining approximately 45.99%, followed by India by 37.76%. Finally, the data set’s model fit, which employed the random forest machine learning method, produced R² values of 0.972, 0.005, 0.997, and 0.983 and mean percentage errors of 1.4, 0.8, 0.9, and 0.8 for Brazil, Russia, India, and China (BRIC), respectively. Even now, two years after the coronavirus pandemic started, the Brazilian stock index has not yet returned to its pre-pandemic level.

This study investigates organizations' non-adoption intention towards the enterprise metaverse. The innovation resistance theory (IRT) is used as an underpinning theory to examine the impact of various risks on non-adoption intention towards the enterprise metaverse.

A total of 294 responses were collected to examine the proposed hypotheses. A structural equation modelling technique was used to investigate the hypotheses using SPSS AMOS and PROCESS MACRO.

The results of this study reveal that performance, security and psychological risks are significantly associated with non-adoption intention towards enterprise metaverse. Further, distrust significantly meditates the association between performance risk, social risk, technological dependence risk, security risk and psychological risk and non-adoption intention towards enterprise metaverse. Moreover, the results of moderated-mediation hypotheses indicate that the mediating effect of distrust on the association among performance risk, social risk, psychological risk and non-adoption intention towards enterprise metaverse is higher for individuals having high technostress compared to individuals having low technostress.

The study's findings will enrich the metaverse literature. Further, it provides a deeper understanding of enterprise metaverse adoption from a B2B perspective using the underpinnings of IRT. The study helps organizations understand the risks associated with the adoption of the enterprise metaverse.

This study aims to focus on delineating the drivers of intention to adopt mobile banking (m-banking) and its actual use among Islamic banking customers by extending the UTAUT2 model with the trust factor. The study also examined the moderating roles of age, gender and experience in the model.

An explanatory research design was used, and an online survey was conducted to collect responses from Islamic banking customers. A total of 329 completed responses were used to analyze the data. The partial least squares method was used for data analysis, and a multi-group analysis was applied for moderation-related analysis.

Trust positively and significantly influences the behavioral intention to adopt m-banking among Islamic banking customers. In addition, social influence, effort expectancy, hedonic motivation and habits significantly influence behavioral intentions among Islamic banking customers.

This study provides an extended UTAUT2 model that has never been tested in the context of Islamic m-banking. In addition, this study is expected to be the first scholarly research on Islamic banking in the Maldives.

This chapter aims to examine the role of consumer demographics over the chosen parameters of online shopping. Online shopping had emerged as an important platform for the consumers during the phase of pandemic spread in India which even included several phases of lockdowns. The state of pandemic commenced at a severe note leading to restrictive movement, social distancing, observing least contact with objects, and several other limitations. Due to this, many businesses had moved to online selling in order to target greater sales. This study was conducted in order to provide insights to various businesses, experts, and academic researchers in this domain to find out the role of demographical and behavioral differences of different consumer segments. It could serve as a robust study providing information about the current consumer behavior at the time of pandemic spread toward online shopping. This would help marketing experts explore the different opportunities and challenges involved in this new scenario formed due to COVID-19. Apart from adding value to the existing literature, this study leads a way to future research.

Social media users can now create, exchange, modify and consume socially generated experiences which can enhance social influence toward mobile banking (MB). This study aims to provide understanding of how social actor interactions through social networking platforms (SNPs) can create social influence for MB adoption and present a research framework that can help to understand which social actors have higher social influence toward MB adoption in conventional and Islamic banks.

SNP users have different levels of perceptions and experiences about the usability and credibility of MB. Therefore, their experiences are subjective realties which can generate socially constructed knowledge. To understand these subjective realties, a social constructivist approach is adopted. Data were collected from interviews with 60 individuals from diverse occupational backgrounds.

Identification element of social influence explained that the shared reviews and recommendations of opinion leaders, industry experts, celebrities and friends were highly positive for conventional banks; therefore, there is high word-of-mouth for MB of conventional banks. Internalization of social influence highlighted that people are more likely to accept the wisdom of the crowd and close friends, which can generate their engagement and connection with MB. Finally, the compliance factor of social influence explained that people can only adopt MB when they perceive high usability and credibility.

This study has provided understanding to the marketers of how social actors on SNPs can play a role in the creation, exchange, modification and consumption of socially generated influence that can impact the MB adoption intention for conventional and Islamic banks.

Although many theories and models have been presented about the marketing strategies and antecedents of MB adoption, the extensive use of SNPs has changed marketing strategies. For example, this study has found that social media users are highly influenced by the social reviews and recommendations they receive from their close friends. Therefore, socially generated influence on SNPs can create an adoption intention toward MB.