After re-specification of the contact variables the second model is focused on the moderating effect of approach timing in seconds. This model includes the same variables as the first model but in this model the dummy variable for employee-customer contact is replaced by a variable measuring after how many seconds customer contact took place and the quadratic function of customer contact in seconds. Again after re-specification of the variables the third model is focused on the moderating effect of approach timing in 20 second intervals. In this model the employee-customer contact in seconds and it’s quadratic function are replaced by 20 second interval variables allowing for more practical implications than the optimal second approach and also acts as a robustness check for the previous model. Since the outcome variable, purchase incidence (whether someone made a purchase or not), is a binary distributed variable all of the models are tested with the binary logistic regression. Results For the first model, focused on the direct effect of employee-customer interaction on purchasing likelihood, I found that when employee-customer interaction takes place during the shopping trip of the customer this negatively influences the purchasing likelihood of this customer. In fact, when employee-customer interaction takes place the purchasing likelihood of the customer drops from 11,38% to 9,74%. In order to explore the optimal timing of approach in greater detail, I estimated a second model allowing for a quadratic effect of approach timing on a customers’ purchasing likelihood. In other words, in this model I focused on the moderating effect of approach timing in seconds. The results of my maximum likelihood estimation of this binary logistic regression indeed suggested – as hypothesized – a parabola (Figure S2) which indicates an inverted-U relationship between approach timing (in seconds) between an employee and a customer in a fashion retail store and that customers’ purchasing likelihood.
After re-specification of the contact variables the second model is focused on the moderating effect of approach timing in seconds. This model includes the same variables as the first model but in this model the dummy variable for employee-customer contact is replaced by a variable measuring after how many seconds customer contact took place and the quadratic function of customer contact in seconds. Again after re-specification of the variables the third model is focused on the moderating effect of approach timing in 20 second intervals. In this model the employee-customer contact in seconds and it’s quadratic function are replaced by 20 second interval variables allowing for more practical implications than the optimal second approach and also acts as a robustness check for the previous model. Since the outcome variable, purchase incidence (whether someone made a purchase or not), is a binary distributed variable all of the models are tested with the binary logistic regression. Results For the first model, focused on the direct effect of employee-customer interaction on purchasing likelihood, I found that when employee-customer interaction takes place during the shopping trip of the customer this negatively influences the purchasing likelihood of this customer. In fact, when employee-customer interaction takes place the purchasing likelihood of the customer drops from 11,38% to 9,74%. In order to explore the optimal timing of approach in greater detail, I estimated a second model allowing for a quadratic effect of approach timing on a customers’ purchasing likelihood. In other words, in this model I focused on the moderating effect of approach timing in seconds. The results of my maximum likelihood estimation of this binary logistic regression indeed suggested – as hypothesized – a parabola (Figure S2) which indicates an inverted-U relationship between approach timing (in seconds) between an employee and a customer in a fashion retail store and that customers’ purchasing likelihood.
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