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This study aims to know whether board composition is effective in improving firm performance and particularly to determine whether this relationship varies across different levels of performance, that is, companies with very low performance, low performance, moderate performance, high performance and very high performance.

The authors use a data set covering 213 Indian companies registered on S&P Bombay Stock Exchange 500 Index over the period 2001 to 2019 by using Tobin's Q as a performance parameter. The study applies the quantile regression technique and compares the results with fixed effect generalized least squares (GLS) regression.

The findings reveal that board size positively affects the company's performance across all quantiles. Independent directors negatively impact the performance of companies across all quantiles. However, the strength of these relationships increases with increase in performance, thereby supporting agency theory and stewardship theory, respectively. The effect of executive directors on the performance of the companies varies across quantiles. The effect is adverse at moderate and high quantiles only.

The findings provide some grounds for regulators to exercise caution while designing board composition guidelines, keeping in mind the unique internal environment of each company which ultimately affects their performance levels. Similarly, Indian companies are also suggested to compose their boards keeping in mind their performance levels.

The study contributes towards the debate on the board composition and firm performance relationship by adding to the agency theory and stewardship theory that all the companies cannot have the similar board composition. Rather its composition depends upon the performance levels of the companies.

This study measured influencer characteristics, consumer emotions, self-construal, and behavioural intentions of Gen Z consumers for selecting fast-food restaurants in India. A consumer behaviour model was conceptualized based on established theories.

A cross-sectional design was employed for hypothesis testing. Influencer characteristic perceptions, consumer emotions, self-construal, and behavioural intentions were measured for Gen Z consumers in Tier 1 cities in India using structural equation modelling.

Influencer characteristics significantly influenced behavioural intentions, consumer emotions, and self-construal in Gen Z consumers. Self-construal was also a significant predictor of behavioural intentions. Consumer emotions had a negative effect on behavioural intentions. Self-construal was a mediator between influencer characteristics and behavioural intentions and between consumer emotions and behavioural intentions. However, consumer emotions did not mediate the relationship between influencer characteristics and behavioural intentions.

Marketers can leverage these insights to design influencer campaigns that resonate with the emotions and self-construal of Gen Z consumers. Microinfluencers with characteristics that align with the target demographic’s emotions and self-perception can be strategically chosen.

Only a limited number of studies have investigated the influence of social media marketing on consumer behaviour within the fast-food industry, specifically with Gen Z consumers. This study sheds new light on the behavioural intention of Gen Z consumers predicted through influencer characteristics, consumer emotions, and self-construal through a conceptual model. The results support choosing microinfluencers and investing in them judiciously to promote fast-food businesses.

Systematic literature review (SLR) papers have gained significant importance during the last years as many reputed journals have asked for literature review submissions from the authors. However, at the same time, authors are experiencing a high number of desk rejections because of a lack of quality and its contribution to the existing body of knowledge. Therefore, the purpose of this paper is to offer guidance to researchers who intend to communicate SLR papers in top-rated journals. We attempt to offer a guide to buddy researchers who plan to write SLR papers. This purpose is achieved by clearly stating how the traditional review method is different from SLR, when and how can each type of literature review method be used, writing effective motivation of a review paper and finally how to synthesize the available literature. We have also presented a few suggestions for writing an impactful SLR in the last. Overall, this chapter serves as a guide to various aspirants of SLR paper to understand the prerequisites of an SLR paper and offers deep insights to bring in more clarity before writing an SLR paper, thereby reducing the chances of desk rejection.

The purpose of this paper is to show the relevance of teachers’ extra role behaviour towards improving students’ learning efficacy status. This study examines the intervening role of art-based teaching pedagogies, i.e. involvement of different forms of art during the traditional teaching session between extra role behaviour and students’ learning efficacy.

The statistical test results showed that teachers’ extra role behaviour is significant for improving and strengthening students’ learning efficacy. Further, the moderation analysis showed that if art is integrated with teachers’ extra role behaviour, the effect on learning efficacy of students will increase. Art-based teaching pedagogies suggest involvement of art in teaching practices. Multiple regression analysis was conducted to evaluate the direct effect of extra role behaviour on students’ learning efficacy with the intervening role of art-based teaching pedagogies.

Results indicated a linear effect of teachers’ extra role behaviour on students’ learning efficacy and that art-based teaching pedagogies had an indirect effect (mediation) on students’ learning efficacy.

The study will bridge the gap between academic initiatives taken and its overall implementation in primary and secondary schools.

This study aims to construct an appropriate framework by incorporating essential components from the most renowned theories to investigate the variables that impact behavioural intentions towards embracing cashless transactions (CLT).

A survey was conducted to ascertain the users’ intention to adopt CLT in Chennai, Tamil Nadu, India. Further, this study used a “partial least squares-based structural equation modelling” technique to analyse the relationships between latent factors.

The results of the proposed model revealed that 11 independent variables together explain the intention to use CLT with a 60.5% explanatory power. Further, perceived usefulness is the most influential factor in predicting users’ willingness to adopt CLT, followed by social influence, perceived costs, attitude, trust and device barriers. Finally, the findings of moderator effects indicate that income and experience interact positively and strongly with behavioural intention to adopt CLT. It indicates that high-income, experienced users are more likely to convert their intentions into actions.

This study integrated critical elements from the major theories, such as Theory of Reasoned Action, Technology Acceptance Model, Decomposed Theory of Planned Behaviour, the unified theory of acceptance and use of technology (UTAUT) model and UTAUT2, to investigate the adoption of CLT. As a result, 11 crucial factors were identified from the existing literature that impacts CLT adoption without overlapping. Consequently, the model presented in this study provides a more profound understanding than previous research regarding why individuals adopt CLT systems. Accordingly, these results could aid policymakers in addressing people’s concerns and facilitating a seamless transition to a cashless society.

The role of mindfulness in influencing green behaviors has been recognized in literature though it has not been explored sufficiently in the context of organic food. This study makes an attempt to explore the role of mindfulness in influencing young consumers' purchase intention (PI) toward organic food in India.

A total of 348 useable responses were collected through an intercept survey at organic food stores using a purposive sampling approach. Data analysis was carried out through structural equation modeling.

Mindfulness emerged as a significant predictor of behavioral intention. Further, the specific indirect effects of mindfulness through attitude, perceived behavioral control (PBC), drive for environmental responsibility (DER) and label reference willingness (LRW) were also significant.

This research is one of the initial efforts to link mindfulness with PI for organic food. The results could help the government and marketers tap onto the potential of mindfulness with regard to environment-friendly products and frame appropriate strategies for stimulating the demand for organic food in India

This paper aims to develop and validate a cashless transaction adoption model (CTAM) that integrates all essential elements to investigate the adoption of “cashless transactions (CLT)”.

The researchers surveyed 375 respondents from each of Bengaluru’s eight zones in India. In addition, using the respondents’ replies, a “partial least squares-based structural equation modelling (PLS-SEM)” technique was used to analyse the relationship between the components.

The results of CTAM reveal that 12 independent variables explain 84.7% of the variation in behavioural intention to adopt CLT. In addition, performance expectancy is the strongest predictor of users’ intentions to embrace CLT, followed by perceptions of the economy’s security and economic offence reduction, social influence, perceived trustworthiness, the expected level of effort and innovativeness. Furthermore, in terms of impediments, perceived risk and cost are the negative influence factors that affect behavioural intention to adopt CLT.

The research successfully developed and validated a comprehensive CTAM that integrates essential elements to investigate the adoption of CLT. Consequently, this research, for the first time, elucidates the precise role of “Perceived Economic Offense Reduction (PEOR)”, “Perceived Economic Benefit (PEB)” and “Perceived Economy’s Security (PES)” in influencing individuals’ behavioural intentions towards adopting CLT. Accordingly, this CTAM offers a more in-depth explanation than any other research for understanding why individuals embrace CLT systems.

Introduction: Artificial intelligence (AI), the engineering of brilliant machinery, performs intelligent human intelligence tasks, such as learning and problem-solving. Insurance is a financial protection policy either for individuals or entities to reimburse losses from the insured company. The role of AI in insurance always helps enhance customer services and understand their behaviour.

Purpose: This chapter aims to determine the role of AI in the insurance industry in India. The insurance industry is expanding very fast, and to further increase its horizons, the part of the technology of AI is essential. However, this sector has initiated using AI technology and is expanding its scope to benefit the customers.

Methodology: The authors selected research papers of the last five years to review and determine how the technology changed during the period and how an increase in AI benefits the industry and facilitates delivering the best services, and understanding the customer’s needs and behaviour.

Findings: It has been found that the industry is moving very fast and adopting the AI technology methods to enhance customer services, betterment for growing India, and serve insurance services to the nation efficiently.

This study aims to provide insight into the operational factors of big data. The operational indicators/factors are categorized into three functional parts, namely synthesis, speed and significance. Based on these factors, the organization enhances its big data analytics (BDA) performance followed by the selection of data quality dimensions to any organization's success.

A fuzzy analytic hierarchy process (AHP) based research methodology has been proposed and utilized to assign the criterion weights and to prioritize the identified speed, synthesis and significance (3S) indicators. Further, the PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) technique has been used to measure the data quality dimensions considering 3S as criteria.

The effective indicators are identified from the past literature and the model confirmed with industry experts to measure these indicators. The results of this fuzzy AHP model show that the synthesis is recognized as the top positioned and most significant indicator followed by speed and significance are developed as the next level. These operational indicators contribute toward BDA and explore with their sub-categories' priority.

The outcomes of this study will facilitate the businesses that are contemplating this technology as a breakthrough, but it is both a challenge and opportunity for developers and experts. Big data has many risks and challenges related to economic, social, operational and political performance. The understanding of data quality dimensions provides insightful guidance to forecast accurate demand, solve a complex problem and make collaboration in supply chain management performance.

Big data is one of the most popular technology concepts in the market today. People live in a world where every facet of life increasingly depends on big data and data science. This study creates awareness about the role of 3S encountered during big data quality by prioritizing using fuzzy AHP and PROMETHEE.