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.
An overview of the variables used for this particular study are described in figure 7, variable overview 3.1.5 Variable overview Variable Type Variable name Explanation Method Coding Dwell time Independent DWELLTIME Time spent in store Time from entering store until leaving the store Minutes and seconds notation Dwell time in seconds Independent DWELLSEC Time spent in store in seconds Dwell time transformed into seconds notation Seconds notation Buy Dependent BUY Purchase incidence More than 30 seconds at checkout is registered as a purchase 0 = No 1 = Yes Employee contact Independent CONTACT If approached by employee, time of approach Time spent in store before being approach by an employee (if approached, otherwise NONE) Minutes and seconds notation Employee contact in seconds Independent CONTACTSEC If approached by employee, time of approach in seconds Employee contact transformed into seconds notation Seconds notation Employee contact categories Independent Employee contact in 20 second intervals Employee contact transformed into intervals of 20 seconds 0 = No approach, 1 = 1 – 20 seconds, 2 = 21 – 40 seconds, etc. Employee contact dummy Independent Employee-customer contact yes or no Dummy variable from employee contact, 1 if a time was noted, 0 if there was contact 0 = No 1 = Yes Region variables Independent A, B, C, D, E, (G: Fitting room, H: store window) Region visited yes or no Dummy variable 0 = No 1 = Yes Fitting room dummy Independent FITROOM Visited fitting room yes or no Dummy variable 0 = No 1 = Yes Figure 7, variable overview 3.1.6 Descriptives Figure 8a and figure 8b both give some insight on what the dataset looks like. In figure 8a we can see how DWELLSEC and CONTACTSEC are distributed. We see for example that the average dwell time is 927 seconds. The average time for customers to be approached by an employee is almost 91 seconds. (appendix 14, descriptives) Variable N Min Max Mean Standard deviation DWELLSEC 9786 1 2758 927,07 561,373 CONTACTSEC 6958* 1 179 90,63 51,960
5.3.1 Limitations First, in all of the models that where ran in this research a lot of unexplained variance still existed meaning that the purchasing process of customers is far more complex than just looking at whether there was employee-customer interaction or even by looking at the timing of the approach. The actual purchase decision of a customer is a result of a complex psychological and behavioral process that possibly can never be fully explained. Second, this research is written under the assumption that the effect of irritation and intrusiveness is causing customers to deflect when they are approached too early and are interrupted in achieving their shopping goals within their shopping trip. However there could very well be other reasons than the feelings of irritation and intrusiveness causing these customers to deflect. In the field of advertising for instance the ‘liking’ and the ‘effectiveness’ are said to be two completely different things meaning that a disliked advertisement does not necessarily mean it is not effective and vice versa (Greyser 1973), and that no firm conclusions should be made about the effectiveness of advertisements because it is argued that they can still be effective despite the irritation it causes (Aaker and Bruzzone 1985). A better understanding of the psychological and behavioral process underlying the reduction in purchasing likelihood due to an early approach could therefore benefit from different types of data and approaches, for example self-stated data through questionnaires and/or lab and field experiments. Third, the results of model 2a might be slightly biased. Model 2a provided some alarming values for parameters that might indicate a biased model in terms of multicollinearity. Therefore, the results of this model, producing the inverted U-curve, should be interpreted with care. Fourth, this research is based on observations gathered by tracking the radio frequencies of customers’ mobile phones. Customers without mobile phones, typically a very small fraction of the population, are therefore not observed. Fifth, this thesis is written under the assumption that all customers and therefore all customer segments react in the same manner in respect to approach timing.