The influence of emotional intelligence and behavioural biases on mutual fund churning frequency: Evidence from India.

Behavioural finance invalidates the rationalistic assumptions of the efficient market hypothesis by proposing a realistic explanation for overreaction and underreaction. These phenomena are caused by investors making financial decisions based on their emotions without realizing them. This study attempts to establish the effect of investors' emotional intelligence on behavioural biases, namely, herding, overconfidence bias, and disposition effects, and its consequences for the churning frequency of mutual fund portfolios. This quantitative cross-sectional study was undertaken to collect data from 499 mutual fund investors using a self-administered questionnaire. We found that the disposition effect has a negative impact and that overconfidence bias positively affects the churning frequency of mutual fund portfolios. Furthermore, emotional intelligence, particularly its subconstructs, affects herding bias and overconfidence bias, ultimately impacting the churning frequency of investors. An investor with a higher level of self-motivation is likely to have a disposition effect and herding bias. Investors with disposition impact may be encouraged to reorganize their portfolio if there are any schemes that have been providing negative returns for more than two years or that are not projected to offer significant returns in the future. By understanding how they stack up on each emotional intelligence metric, investors and financial advisors may focus on avoiding the biases that could jeopardize long-term portfolio returns.

Students' continuance intention to use MOOCs: empirical evidence from India.

In recent years, there has been an increasing interest in understanding the Massive open online courses (MOOCs) due to its gaining popularity. Even though the number of online platforms and programs has grown during the COVID-19 pandemic, there is still a high rate of dropout and non-completion. In this work, the expectation-confirmation model is combined with MOOC features such as perceived openness, perceived reputation, and other factors i.e., perceived enjoyment, and perceived computer self-efficacy to investigate the learner's continued intention to use MOOC. A survey was undertaken and the data was collected from 383 students pursuing their degrees (undergraduate and post-graduate) in Karnataka state, India. The collected data were analyzed with structural equation modelling in Smart PLS 3. The study confirms a significant influence of confirmation and perceived usefulness on satisfaction, and direct significant influence of perceived computer self-efficacy, satisfaction, and perceived usefulness on continuance intention. Also, the results demonstrated the significant influence of confirmation on perceived enjoyment and usefulness and the effect of computer self-efficacy on usefulness. The findings in this study indicate that the MOOC platforms should focus on confirming learner expectations and the usefulness of courses to ensure student satisfaction and continuance of courses.

An empirical examination of customer advocacy influenced by engagement behaviour and predispositions of FinTech customers in India.

Background: The extensive adoption and usage of emerging technologies furthered by the global coronavirus disease 2019 (COVID-19) pandemic, has reduced direct face to face communications. New FinTech (financial technology) apps and technologies are flooding the Indian digital payments market and competitors are striving hard to attract and retain their customers. Especially when customer engagement behaviours (CEBs) are digital in nature, it is essential to gauge the intrinsically driven customer motivations which drive a positive CEB. The objective of this paper was to empirically test the influence of customer-based antecedents such as emotions, moral identity, self-concept, communal focus, perceived cost and perceived benefits on non-transactional experiential customer engagement behaviours (CEBs) and CEB's effect on customer advocacy in the FinTech industry. Methods: Data from 380 financial app users in south India were gathered by administering a survey that captured customer predispositions, CEBs, and customer advocacy. Structural equation modelling (SEM) using smart PLS (partial least squares) 3.0 was applied to test the theoretical model. Results: Results indicate that CEB fully mediates the relationship between self-concept and customer advocacy. The positive CEBs get formed through customer predispositions leading to referral/advocacy behaviours. Conclusions: This paper provides directions for FinTech practitioners, marketers, technologists, and academicians to devise marketing strategies customized to customer needs and factors. This is one of the first research studies to demonstrate and empirically validate the CEB model for the FinTech industry during the COVID-19 pandemic.