included eight compensation levels (i.e., 100%, 110%, 120%, 130%, 140%, 150%, 160%, and 170% of the purchase price). Because the prior study showed there was no further increase in customer loyalty for overcompensations beyond the level of 168% (which was identified as the optimum), we did not include compensation sizes beyond this particular level in the present study. Procedure. Participants read a scenario in which they imagined that they had bought a new espresso machine at a nearby store for the price of $100. In the present study we only included one product price, because in the previous study the compensation–loyalty relationship was not affected by the magnitude of the product failure. Participants imagined themselves that they came home and then realized that their espresso machine did not function properly. They thus decided to go back to the store to complain about this malfunctioning. Participants were asked to evaluate eight responses by which the store could react to this situation; each of these reactions presented a specific compensation level. Importantly, the different compensation levels were presented in a random order. We used the same four items as in Study 1 to measure customer loyalty. In Measures. addition, to measure whether the compensation level manipulation was successful, we also probed perception of the magnitude of the compensation. Therefore, we asked participants’ participants for each of the compensation do you find this levels: “To what extent compensation (1 = 7 = not at all, very much). large?” Results Similar to Study 3, most analyses were conducted using Mplus software. We again treated the independent variable, level of compensation, as a quantitative variable and we estimated the functional form of its effect by considering both a linear and a quadratic trend, using a regression approach. Because every participant responded to eight different compensation levels, we used a multilevel regression model with observations (level 1) nested
included eight compensation levels (i.e., 100%, 110%, 120%, 130%, 140%, 150%, 160%, and 170% of the purchase price). Because the prior study showed there was no further increase in customer loyalty for overcompensations beyond the level of 168% (which was identified as the optimum), we did not include compensation sizes beyond this particular level in the present study. Procedure. Participants read a scenario in which they imagined that they had bought a new espresso machine at a nearby store for the price of $100. In the present study we only included one product price, because in the previous study the compensation–loyalty relationship was not affected by the magnitude of the product failure. Participants imagined themselves that they came home and then realized that their espresso machine did not function properly. They thus decided to go back to the store to complain about this malfunctioning. Participants were asked to evaluate eight responses by which the store could react to this situation; each of these reactions presented a specific compensation level. Importantly, the different compensation levels were presented in a random order. Measures. We used the same four items as in Study 1 to measure customer loyalty. In addition, to measure whether the compensation level manipulation was successful, we also probed participants’ perception of the magnitude of the compensation. Therefore, we asked participants for each of the compensation levels: “To what extent do you find this compensation large?” (1 = not at all, 7 = very much). Results Similar to Study 3, most analyses were conducted using Mplus software. We again treated the independent variable, level of compensation, as a quantitative variable and we estimated the functional form of its effect by considering both a linear and a quadratic trend, using a regression approach. Because every participant responded to eight different compensation levels, we used a multilevel regression model with observations (level 1) nested
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