The impact of culture on consumers’ perceptions of service recovery efforts

The increasing globalization of business provides a compelling reason for understanding the cultural context of consumer behavior. The research reported here examines the impact of culture on consumers’ perceptions of service recovery efforts. In particular, we studied in an experimental setting, across East-West cultures, the combined effects of explanation and compensation in shaping customers’ attributions and post-recovery perceptions in a medium contact service—a restaurant setting. Our findings show that the differential sensitivity of East Asian and American consumers to situational constraints influence their attributions for service failures, and thus moderate their satisfaction with service recovery process. More specifically, the results suggest that a causal explanation for service failure decreases the likelihood of US consumers falling prey to the fundamental attribution error. Conversely, among East Asians, an explanation had minimal influence in attributional processes. Finally, our results indicate that attributional processes influence customer perceptions of employee effort, which in turn is linked to post-recovery satisfaction.     

Predictable forward performance processes: Infrequent evaluation and applications to human-machine interactions

We study discrete-time predictable forward processes when trading times do not coincide with performance evaluation times in a binomial tree model for the financial market. The key step in the construction of these processes is to solve a linear functional equation of higher order associated with the inverse problem driving the evolution of the predictable forward process. We provide sufficient conditions for the existence and uniqueness and an explicit construction of the predictable forward process under these conditions. Furthermore, we find that these processes are inherently myopic in the sense that optimal strategies do not make use of future model parameters even if these are known. Finally, we argue that predictable forward preferences are a viable framework to model human-machine interactions occurring in automated trading or robo-advising. For both applications, we determine an optimal interaction schedule of a human agent interacting infrequently with a machine that is in charge of trading.     

Analytical solvability and exact simulation in models with affine stochastic volatility and Lévy jumps

We investigate analytical solvability of models with affine stochastic volatility (SV) and Lévy jumps by deriving a unified formula for the conditional moment generating function of the log-asset price and providing the condition under which this new formula is explicit. The results lay a foundation for a range of valuation, calibration, and econometric problems. We then combine our theoretical results, the Hilbert transform method, various interpolation techniques, with the dimension reduction technique to propose unified simulation schemes for solvable models with affine SV and Lévy jumps. In contrast to traditional exact simulation methods, our approach is applicable to a broad class of models, maintains good accuracy, and enables efficient pricing of discretely monitored path-dependent derivatives. We analyze various sources of errors arising from the simulation approach and present error bounds. Finally, extensive numerical results demonstrate that our method is highly accurate, efficient, simple to implement, and widely applicable.