We propose an instrumental-variable (IV) approach to estimate the causal effect of service satisfaction on customer loyalty, by exploiting a common source of randomness in the assignment of service employees to customers in service queues. Our approach can be applied at no incremental cost by using routine repeated cross-sectional customer survey data collected by firms. The IV approach addresses multiple sources of biases that pose challenges in estimating the causal effect using cross-sectional data: (i) the upward bias from common-method variance due to the joint measurement of service satisfaction and loyalty intent in surveys; (ii) the attenuation bias caused by measurement errors in service satisfaction; and (iii) the omitted-variable bias that may be in either direction. In contrast to the common concern about the upward common-method bias in the estimates using cross-sectional survey data, we find that ordinary-least-squares (OLS) substantially underestimates the casual effect, suggesting that the downward bias due to measurement errors and/or omitted variables is dominant. The underestimation is even more significant with a behavioral measure of loyalty–where there is no common methods bias. This downward bias leads to significant underestimation of the positive profit impact from improving service satisfaction and can lead to under-investment by firms in service satisfaction. Finally, we find that the causal effect of service satisfaction on loyalty is greater for more difficult types of services.
Beverage consumption occurs many times a day in response to a variety of needs that change throughout the day. In making their choices, consumers self-regulate their consumption by managing short run needs (e.g., hydration and mood pickup) with long-term goals (e.g., health). Using unique intra-day beverage consumption, activity and psychological needs data, we develop and estimate a model of high frequency consumption choices that accounts for both intra-day changes in short run needs and individual level unobserved heterogeneity in the degree of self-regulation. A novel feature of the model is that it allows for dynamics of consumption and stockpiling at the level of product attributes. The model is used to evaluate introduction of new products in the beverage category and gain insight into the linkage between self-regulation and excess consumption. Broadly, the modeling framework of balancing short run needs with long-term goals has wide ranging applications in choices where long term effects are gradual (e.g., nutrition, exercise, smoking and preventive health care).