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The authors analyse the determinants of loan defaults in micro, small and medium enterprises (MSME) loans in India from the survival duration perspective to draw inferences that have implications for lenders and policymakers.

The authors use the Kaplan–Meier survivor function and the Cox Proportional Hazard model to analyse 4.29 lakhs MSME loan account data originated by a large bank having a national presence from 1st January 2016 to 31st December 2020.

The estimated Kaplan–Meier survival function by various categories of loan and socio-demographic characteristics reflects heterogeneity and identifies the trigger points for actions. The authors identify the key identified default drivers. The authors find that the subsidy amount is more effective at the lower level and its effectiveness diminishes significantly beyond an optimum level. The simulated values show that the effects of rising interest rates on survival rates vary across industries and types of loans.

The identified points of inflection in the default dynamics would help banks to initiate actions to prevent loan defaults. The default drivers identified would foster more nuanced lending decisions. The study estimation of the survival rate based on the simulated values of interest rate and subsidy provides insight for policymakers.

This study is the first to investigate default drivers in MSME loans in India using micro-data. The study findings will act as signposts for the planners to guide the direction of the interest rate to be charged by banks in MSME loans, interest subvention and tailoring subsidy levels to foster sustainable growth.

This study aims to understand the motivations behind using Airbnb as a collaborative housing platform brand, their impact on satisfaction and engagement, and how the latter affects brand electronic Word of Mouth (eWOM) and brand loyalty. It also analyzes the potential moderating effect of the use intensity of collaborative housing platforms for the proposed causal relationships.

An online survey was addressed to active users of Airbnb with a final sample of 405 users. Data were analyzed using a covariance-based structural equation modeling (SEM) to test the causal model and a multigroup SEM to test the moderator effect of the intensity of use.

The results show that convenience affects brand satisfaction and hedonic motivation influences brand engagement, and both affect return intention and eWOM. In contrast, sustainability motivations seem unrelated both with satisfaction and engagement. Furthermore, the mechanism by which people reach loyalty in terms of return intention varies according to their usage intensity. Thus, the engagement–intention way is stronger for more intensive brand users. In contrast, the satisfaction–intention mechanism is significantly stronger for those with more sporadic use.

The management of collaborative housing platforms should promote tools and actions that favor enjoyment and fun since they increase engagement. On the other hand, it should be interesting to communicate the idea of the convenience of the applications, such as availability for a great offer, ease of contracting, or saving time, since this type of motivation directly correlates with customer satisfaction.

The motivational and behavioral heterogeneity demonstrated in this research can make it easier for people to be reached through different communication strategies and arguments both by the collaborative housing platforms and by public agencies with interests in city tourism management.