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The rise of social media has led to the emergence of influencers and influencer marketing (IM) domains, which have become important areas of academic inquiry. However, despite its prominence as an area for study, several significant challenges must be addressed. One significant challenge involves identifying, assessing and recommending social media influencers (SMIs). This study proposes a semantic network model capable of measuring an influencer's performance on specific topics or subjects to address this issue. This study can assist managers in identifying suitable SMIs based on their estimated reach.

Data from popular YouTube influencers and publicly available performance measures (views and likes) are extracted. Second, the titles of the past videos made by the influencer are used to develop a semantic network connecting all the videos to other videos based on similarity measures. Third, the nearest neighbor approach extracts the neighbors of the target title video. Finally, based on the set of neighbors, a range prediction is made for the views and likes of the target video with the influencer.

The results show that the model can predict an accurate range of views and likes based on the suggested video titles and the content creator, with 69–78% accuracy across different influencers on YouTube.

The current study introduces a novel and innovative approach that exploits the textual association between a SMI's previous content to forecast the outcome of their future content. Although the findings are encouraging, this research recognizes various constraints that upcoming researchers may tackle. Forecasting views of posts concerning novel subjects and precisely adjusting video view counts based on their age constitute two primary limitations of this study.

Managers interested in hiring influencers can employ the suggested approach to evaluate an influencer's potential performance on a specific topic. This research aids managers in making informed decisions regarding influencer selection, utilizing data-based metrics that are simple to comprehend and explain.

The study contributes to outreach evaluation and better estimating the impact of SMIs using a novel semantic network approach.

Blockchain technology has been labeled as the most disruptive technological innovation of the current decade due to its impact on almost every major industry. Based on privacy calculus theory and prior adoption literature on emerging technologies, this research investigates the impact of blockchain technology in the consumer technology segment. It elaborated on the mechanism through which blockchain technology influences users’ willingness to share information with technology products enabled by blockchain.

Taking a heterogeneous pool of users, this study conducted multiple experiments with the application of blockchain (vs. regular database) technology to high (vs. low) sensitive data to study the impact of blockchain perception on users’ information-sharing tendencies.

Through a mediated moderation analysis, the result shows that the use of blockchain technology enhances the sense of security among users. However, the impact of this heightened sense of security only develops a higher willingness to share information when the data is highly sensitive.

The research reflects on the perception of blockchain technology and the leading impact on willingness to share information with firms. This could be a critical criterion for determining investment in blockchain technologies for consumer products, particularly based on the sensitivity of the data the consumer is sharing.

This research focuses on the perception of blockchain technology among consumers and its impact on consumers’ decision-making related to their data sharing. People have a higher sense of safety when it comes to blockchain-enabled products. However, we find that it would not be the same for all contexts, and the sensitivity of the data collected would have an impact on this relationship and consumers’ data-sharing decisions.

The objective of this work is twofold: firstly, to develop a model for assessing healthcare service quality (HSQ), and secondly, to evaluate the effectiveness of machine learning algorithms in predicting the quality of healthcare services.

In this study, a comprehensive literature review has been performed to identify key quality dimensions in the healthcare services domain. Delphi’s method has been used to confirm the criticality of these dimensions based on experts’ opinions and proposed a novel CIRMQUAL model. Factor analysis techniques have been used to further validate the CIRMQUAL model. Using the data collected through a questionnaire survey, a number of machine learning models have been developed to predict the customer satisfaction level based on the service quality (SQ) performance of a healthcare unit on different dimensions of the CIRMQUAL model.

The study developed a CIRMQUAL model with 14 dimensions (quality of care, safety and security, skill and conduct, staff attitude, tangibles, quality of the atmosphere, patient rights, follow-up, communication, cost of treatment, availability of resources, accessibility, waiting time and services), and these dimensions have been clubbed into four major dimensions, i.e. clinical quality, infrastructural quality, relationship and managerial quality. Furthermore, the application of machine learning algorithms has demonstrated significant accuracy in predicting SQ, highlighting its ability to improve healthcare services and the satisfaction level of patients.

Managers of healthcare units work hard to identify and address the pain points of the patients and improve the working of the healthcare units being managed by them. The availability of many scales with numerous dimensions adds to their confusion in selecting a suitable scale. The current work addresses this confusion and provides four clear areas for assessing the quality of healthcare units. By using this scale, managers can assess the quality of services provided by them, identify the dimensions of low performance, plan and take suitable corrective actions to improve the performance of their healthcare units.

A comprehensive SQ model, i.e. CIRMQUAL has been proposed as a new scale to assess SQ in healthcare units. The model has been developed after analyzing the dimensions used by many researchers available in the literature. This model can be used by future researchers to assess the SQ in healthcare units. Moreover, an attempt has been made to use artificial intelligence-based techniques for predicting customer satisfaction. Such attempts are in the initial stage for healthcare sector. Future researchers can take this concept forward and test the applicability of different machine learning techniques in different functional areas of healthcare.

Good health is of utmost importance for all human beings. In spite of the expenditure of substantial time and efforts by various stakeholders, the service delivery doesn’t match the expectations of patients. Many times, the service providers are not aware of this dissatisfaction and specific aspects of service delivery that need to be improved to reduce dissatisfaction. The model proposed will help the service providers in this regard and the service providers will be able to take focused steps. Such initiatives will definitely improve patient’s satisfaction and their social well-being.

This work is unique because it uses a novel technique to redefine the quality of services in healthcare by using a dual methodology. The research presents a model that includes various factors and it is specially developed to evaluate the quality of services in healthcare settings. This study advances the area’s progress by implementing computational tools for accurate evaluation of HSQ. The healthcare decision-makers may use this novel perspective to evaluate and enhance the quality of service.

The scope of this study is to explore and summarize the pool of dimensions, models and measurement techniques of service quality used in healthcare services and to propose a comprehensive conceptual model for practitioners and researchers.

This research employs a comprehensive review of available literature by using multiple keywords on different electronic repositories using the recommendations of the PRISMA approach for the selection of articles. A critical analysis of available studies helped in compiling a list of core service quality dimensions in healthcare services.

This paper presents a comprehensive account of different dimensions and their measurement items used by various researchers to assess service quality in healthcare systems. Most of the researchers have used SERVQUAL model either in its original or modified form while the others have proposed and used totally different dimensions to assess the service quality in healthcare. Many dimensions are just an existing dimension of SERVQUAL that has undergone a name change while others are completely new. The dimensions used by many researchers have items drawn from more than one dimension of SERVQUAL model. The availability of so many dimensions and models adds to the confusion that researchers and practicing managers experience when determining the appropriate model to be used in their work. To mitigate this confusion, there is a need to develop a comprehensive model; the current work is an attempt to meet this need. Through our analysis, we identify four major service quality dimensions: clinical quality, infrastructural quality, relationship and managerial quality and propose a model named CIRMQUAL.

After exploring all available models in the domain of healthcare, this research presents the best possible areas to enhance the quality of healthcare services. It also enhances the research insights for academicians and working professionals by developing and proposing a comprehensive model for measuring healthcare service quality. The proposed model covers almost all of the service quality dimensions used by other researchers and will make the choice of dimensions/model easy for the future researchers/practitioners interested in measuring and improving the quality of services offered by their healthcare units. Such a comprehensive model has not been developed by any researcher thus far.

The purpose of this paper is to empirically investigate linkage among productivity, quality, and business performance in home-based brassware units in India.

This study involved action research of home-based brassware units applying procedures for three-stage least-squares (3SLS) regression analysis, with data obtained through questionnaire survey based on convenience sampling.

This study has supported the established belief that quality leads to productivity, and subsequently productivity leads to better business performance for home-based brassware units in India. The consistent and logical result of this study using 3SLS regression analysis has provided empirical understanding of the appropriate linkage among productivity, quality, and business performance.

This study has limitations of findings, as it studied the home-based brassware units in the Indian context only.

This study implies that marketable home-based brassware products are to be produced by taking into account boundaries of production within the framework of goals and value created by motivation and dependability for monitoring the business performance. Identifying an appropriate linkage among productivity, quality, and business performance may project a holistic evaluation of the policy development related to home-based brassware units.

This is an original study to test empirical linkages among productivity, quality, and business performance using 3SLS regression analysis particularly for home-based brassware units in India.

Does the simultaneous vertical and horizontal alignment of HR systems positively impact innovation? The authors use the “innovation value chain” model to explore the interplay between the central strategic human resource management concepts of vertical and horizontal fit of HR systems and their role in positively impacting product innovation management.

The authors use the findings from a case study of a large multinational organization in the high-tech sector for the present study. In the first phase, the authors analyse responses to 20 qualitative interviews with senior business and HR executives at the organization using the grounded theory approach. In the second phase, the authors analysed six years of performance ratings and salary data for 4,500–5,500 employees.

Phase 1 of the study established the importance of innovation management as a strategic priority and the role of vertical and horizontal fit of HR systems and practices in positively impacting innovation management. Phase 2 reinforced the findings from Phase 1 by demonstrating the vertical and horizontal fit of the performance and compensation management processes towards furthering innovation management. Our study findings suggest that both forms of fit boost innovation management and interact to reinforce each other mutually, magnifying their respective positive effects towards improving innovation management.

While past studies have generally focused on the isolated role of either the HR system or that of a bundle of HR practices on innovation, the present study empirically demonstrates the simultaneous role of vertical and horizontal fit of HR systems and practices in furthering innovation management. The authors use interviews with senior executives and objective performance and salary data to provide the first experimental evidence of the mechanism of the interplay between the two forms of fit.

The previous researchers have identified human capital, relational capital and structural capital as knowledge assets in knowledge-driven organizations. The current study is an attempt to identify and validate the knowledge assets in construction projects. The study also aims to understand the interrelation of these knowledge assets and their impact on project performance through the development of a conceptual model.

The study is divided into three phases. In phase I, the constructs of “knowledge assets” and “project performance” in construction projects are identified using the exploratory factor analysis. In phase II, these constructs are validated using confirmatory factor analysis. Two separate surveys are conducted for phase I and phase II, respectively. In phase III, the authors develop two conceptual models based on the literature review and two construction project cases in India. The models examine the inter-relationship of knowledge assets and measures their impact on project performance. The models are empirically tested using the responses of the second survey through a structural equation model.

The study extracts four knowledge asset constructs and one performance construct which are named human capital, structural capital, relational capital, human capital capacity building process and project performance, respectively. The study finds that both the conceptual models are statistically excellent fit. The results of the models suggest that relational capital and structural capital have a direct positive impact on project performance, whereas human capital has an indirect effect on project performance mediated through relational capital, structural capital and human capital capacity building process.

The items for knowledge asset constructs and measurement of project performance are moderated by experts, working in construction projects in India, hence the process may contain subjective bias. Further, two construction project cases were selected by authors in the study that originate from India.

The study has implications for the project executors (contractors) as well as for project owners. The contractors must maintain healthy relations with all the stakeholders in a project like a client, suppliers, architects, etc. They must develop systems that are people-friendly to avoid the problems of time and cost overruns in projects. The owners must also maintain healthy relations. This can result in a win-win situation for both parties and can lead to superior project performance.

The study develops and empirically tests two conceptual models that explain the interrelations of knowledge assets and how it benefits the construction project performance in India. Therefore, the generalization of the results is difficult; however, the results can be replicated in projects with similar settings.

The purpose of this paper is to identify the negative aspects of blockchain technology and to shed the light on most productive years, countries, authors, sources and frequent keywords.

A Web of Science bibliographic data set containing 209 journal articles was evaluated using descriptive and network analytics. A two-step process is adopted in this study; descriptive analysis is initially carried out using RStudio to determine the most productive years, nations, sources and authors, and using co-occurrence of keyword analysis in VOSviewer, the most influential keywords are determined.

The findings reveal that 2022 is the most prolific year in terms of number of publications. It is discovered that China tops the list for having published the most articles. Similarly, the most productive authors are Kumar A and Abhishek K.

To the best of the authors’ knowledge, this bibliometric analysis is unique in that it takes a thorough approach to examine the negative aspects of blockchain technology and identify research trends and offer insights that might guide future research and practical solutions.

Purpose: The study aims to explore the wider acceptance of blockchain technology and growing faith in this technology among all business domains to mitigate the chances of fraud in various sectors.

Design/Methodology/Approach: The authors focus on studies conducted during 2015–2022 using keywords such as blockchain, fraud detection and financial domain for Systematic Literature Review (SLR). The SLR approach entails two databases, namely, Scopus and IEEE Xplore, to seek relevant articles covering the effectiveness of blockchain technology in controlling financial fraud.

Findings: The findings of the research explored different types of business domains using blockchains in detecting fraud. They examined their effectiveness in other sectors such as insurance, banks, online transactions, real estate, credit card usage, etc.

Practical Implications: The results of this research highlight (1) the real-life applications of blockchain technology to secure the gateway for online transactions; (2) people from diverse backgrounds with different business objectives can strongly rely on blockchains to prevent fraud.

Originality/Value: The SLR conducted in this study assists in the identification of future avenues with practical implications, making researchers aware of the work so far carried out for checking the effectiveness of blockchain; however, it does not ignore the possibility of zero to less effectiveness in some businesses which is yet to be explored.