The rapid advancements in information and communication technology during the third industrial revolution of the late 20th century has marked the beginning of a new era in the retail sector with the introduction of E-commerce. The dawn of the new century witnessed industry 4.0, revolutionizing all areas of online business by bringing in novel opportunities and possibilities. Despite the progress in technology, the determination of correct pricing on online selling platforms still remains a very complex task. The adoption of big data technology has enabled online sellers to make real-time price changes of high magnitude and proximity. However, with increasing awareness among buyers regarding modern pricing strategies, it is necessary to examine probable changes in consumer behavior when exposed to dynamic pricing scenarios. This study investigates the factors that influence consumer behavior, and their prospective online purchase decisions in a dynamic pricing context, through an exploratory factor analysis approach. A primary research survey was conducted, and 178 samples were finalized for data analysis through a series of web surveys completed by respondents in India. This study identifies, measures and classifies 27 research items into variables, namely shopping experience, privacy concerns, awareness about dynamic pricing, buying strategy, fair price perceptions, reprisal intentions and intentions for self-protection. These seven factors could be used to explain consumer behavior in a dynamic pricing situation.

This study investigates discretionary earnings management practices, tracing the changes over the years in selected top performing and highly liquid listed Indian firms. It empirically measures the impact of corporate governance, financial legislation and global reporting standards on the firms’ earnings management practices. The study analyses a sample of 712 firm-year data comprising 89 listed Indian companies across 7 different sectoral indices of the National Stock Exchange of India (NSE) over 8 years (2011–2018). The Modified Jones model was used to compute Discretionary Accruals to measure Earnings Management based on data obtained using Bloomberg terminals. Statistical results and plots generated in Stata offer evidence that instances of earnings management have significantly reduced after the enactment of the Companies Act 2013 and the adoption of Indian Accounting standards which are converged with the IFRS. Findings suggest that services firms are engaging in relatively higher levels of earnings management compared to manufacturing firms. This study reveals the positive impact of improved corporate governance, regulation, and enforcement by significantly reducing the levels of earnings management among listed firms in India.

A non-zero sum bimatrix game may yield numerous Nash equilibrium solutions while solving the game. The selection of a good Nash equilibrium from among the many options poses a dilemma. In this article, three methods have been proposed to select a good Nash equilibrium. The first approach identifies the most payoff-dominant Nash equilibrium, while the second method selects the most risk-dominant Nash equilibrium. The third method combines risk dominance and payoff dominance by giving due weights to the two criteria. A sensitivity analysis is performed by changing the relative weights of criteria to check its effect on the ranks of the multiple Nash equilibria, infusing more confidence in deciding the best Nash equilibrium. JEL Codes: C7, C72, D81