The present book chapter contributes to examining the intersection of gaming and sustainable marketing, focusing on how gaming strategies can be effectively used to foster consumer behavior that is environmentally friendly and engaging, and is exploring new ways to encourage consumers to adopt sustainable practices. Utilizing aspects of game technology and design to encourage and engage users, gamification has emerged as a viable strategy for inducing behavioral change in a variety of contexts. This chapter reviews the conceptual framework, foundation, and application of game theory to sustainable marketing.This chapter provides insights into effective game plans that promote sustainable consumer practices such as recycling, energy conservation, and responsible consumption. Furthermore, the chapter discusses the various challenges and ethical concerns associated with sustainable marketing games. Finally, the chapter attempts to raise awareness of how games can be used as a means to advance sustainability goals and create a more environmentally conscious society.

In recent years, there has been an enormous increase in the number of companies and of customers for almost every industry. The increment in the number of companies has also provided the choices to the customer but in turn it has also created new challenges. Thus, the companies must work not only to improve their products or services but to sustain customers in the competitive world. Churn prediction is the prediction of customers who are at a potential risk of discontinuing the product or service of the company. Thus, in today’s competitive world, churn prediction is more relevant. In the present work, we have employed various machine learning models for an early prediction of churns, to mitigate the potential risk of losing the customers. The authors have chosen ensemble models for this task. Finally, the models are trained on the dataset. The results for various models are compared using accuracy, precision, recall, and F1 score. Moreover, it is also observed that for our dataset XGBoost outperformed over other models

Purpose: This study aims to examine the factors influencing user satisfaction with unified payment interface (UPI)-based payment systems during the COVID-19 pandemic in India. The study also aimed to examine whether the user satisfaction with UPI-based payment systems during the COVID-19 pandemic will transform into their continuance intention post-COVID-19 pandemic. Design/methodology/approach: The study was performed in three phases, i.e. pre-testing (for developing questionnaire), pilot study (using exploratory factor analysis to ensure unidimensionality) and the main study. The main study was based on the feedback from a sample of 369 internet users who first used the UPI-based payment system during the COVID-19 pandemic. Data generated were analysed using the structural equation modelling approach. Findings: The study findings suggest that the users who are satisfied with UPI-based transactions during the COVID-19 pandemic are likely to continue their use of this payment mode in future. Factors such as post-adoption perceived value, perceived usefulness and post-adoption perceived risk were observed to be key constructs in explaining user satisfaction and continued intention for UPI-based payment systems. Originality/value: The study is one of the pioneering studies, in the sense that it investigated the continuance intention of UPI-based payment systems, which, surprisingly, did not gain much attention from past researchers.

The study is grounded on the premise that the food-leftover sharing practice can simultaneously contribute to resolving the problems of food shortage and food wastage. The primary aim of the study was to identify the factors influencing consumer intentions to share food leftovers. The study was conducted in four stages. First, a comprehensive literature analysis was done to identify the relevant factors and underlying relationships among them. Second, a research instrument was designed, and pretesting was conducted to check the appropriateness of the research instrument with the study context. Third, a pilot study was performed on the 194 respondents to check the dimensionality of the study scales. Four, the final data was collected and analyzed from a sample of 331 (collected through offline and online modes) using structural equation modeling in AMOS 24. The pilot and final study data were collected from New Delhi (An Indian metropolitan city). The study findings suggest that the TPB constructs i.e. subjective norms, attitude, and perceived behavior control influence consumer intentions to share food leftovers. Additionally, religiosity, moral obligation, and environmental concern were observed to be the predictors of attitude toward the practice of food-leftover sharing. This study offers food leftover sharing as a solution to food waste generation and hence, aligned with the sustainable development goals (SDG -12) i.e., responsible consumption and production.