ANÁLISIS DE SENTENCIAS

The in-restaurant time window of influence was studied using a direct covert observational approach, with structured data collection, to examine family fast-food ordering (for parties with at least one child between the ages of 2-12 years, in a fast-food restaurant, in

Toronto, Canada. To examine the window of influence, the researcher visually followed a single transaction from the customers’ entry into the restaurant to when they received their food order. A structured observational instrument, with a closed-ended coding scheme, was used for quantification of key behaviours. Figure 6.1 illustrates the three stages measured.

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Figure 6.1 Graphic illustration of the three observed stages: Stage 1 (in-restaurant, pre- order): the time between the customer entering the restaurant and starting to place the food order. Stage 2 (order): the time taken to order food with the restaurant employee. Stage 3 (food delivery): the time from food order completion to customer receipt of food.

6.3.1 Restaurant selection

The fast-food restaurant was selected after visiting a larger sample of restaurants in Toronto Canada, in order to identify a restaurant that offered a seating arrangement that allowed for inconspicuous observation of customer orders. The selected restaurant also offered an environment with a high frequency of family visits, and a demographic that was representative of an average Canadian family neighbourhood, based on the publicly available sociodemographic data about the neighbourhood in which the restaurant was located. The local households were representative of a middle class Canadian income for that location based on census data (Statistics Canada, 2016b).

The restaurant used for the field research had a single customer entrance, and easy visibility of the order counter to enable unobstructed observation. The restaurant had seating for approximately 100 customers, free Wi-Fi, as well as free and easily accessible parking.

The selected restaurant also had an indoor play structure for children. Based on the author’s knowledge of the inner workings of the corporation, there are a limited number of indoor play structures built. These are only built in areas with a high density of families. It is for this reason that a restaurant with a play structure was chosen, to increase the likelihood of observing families dining.

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6.3.2 Training

Prior to the study launch, the protocol was refined and tested by the author with 30 consumer observations, in three different restaurants. This was followed by a 10-hour training period for the research assistant by the author, including familiarization, testing, and refinement of the data capture form (this training data has not been used). The research assistant for the portion of the in-restaurant observational work was paid for her work and was a 3rd _{year Ryerson University student (Olena Gurba).}

6.3.3 Researcher’s field position

At each visit, the researcher ordered a beverage or a snack and took a seat where she could observe the families ordering, while making notes in her notebook. The activities were intended to be subtle and not out of place for the environment, and the researcher noted that they did not feel noticed by the customers or by the restaurant staff. The restaurant staff were not aware of the presence of a researcher or of an ongoing study. This was to minimize restaurant employee bias.

6.3.4 Time of day

Field work was completed during the hours of 11am to 1pm on Fridays, Saturdays and Sundays, over 24 days between June and August of 2016 (holiday weekends were excluded from observations). Lunchtimes were selected for the highest potential volume of family visits. Two hundred families were observed ordering fast food over 65 hours of field time.

The times selected were the busiest times for visits with children - resulting in the longest line- ups and therefore the most time in line. Observations were made during the lunch times of Fridays, Saturdays, and Sundays, which are peak dining occasions in fast- food restaurants. Conducting the research during peak dining times for a timing study, provided a high-end estimate for the window of influence. During quieter times in the restaurant, family ordering would likely be faster. This highlights the importance of focusing on

nudging techniques that are not time consuming due to the short-time window even during the longest wait times.

6.3.5 Selection of observable participants

The researcher visually followed a single transaction from when the adult/child party first entered the restaurant to when they received their food orders. After a transaction was complete, the researcher observed the next adult/child group that entered the restaurant.

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Family transactions were defined as the observed presence of at least one adult and at least one child. With this observational method, it was not possible to ascertain if the adult(s) in the party were the parent(s) of the child.

6.3.6 Data collection

Data collection had two components. A timing component, measuring the three

observable stages to the food ordering process, and a second component that recorded the observable traits of the subjects and their inter-subject interactions.

A digital timer was used to record how long families waited in line prior to ordering, how long they spoke to the restaurant employee during the ordering process, and how long they waited to receive their meal.

The research focused on adult/child food ordering at the counter, and not on the ordering of parties without children or customers using the ordering kiosks. A structured

observation instrument with a closed-ended coding scheme was used.

In addition to the three stages of the ordering transaction, the researcher recorded customer demographics (number of people in the transaction; gender; gender of the person placing the order; race; appearance; estimated age range – adults: under 30, 30- 50, 50+; children: infant, <6, 6-12, 12+); observable behaviour of family members (presence and usage of smart phones; parent-child interactions).

While researcher observations of customer characteristics are subject to error, for example misidentifying the race of a customer, in the case of subject bias due to observable customer characteristics, it could be argued that visual evidence can be equally misidentified by restaurant staff (in terms of service bias) (Myers et al., 2010).

While the researcher was unable to see the details of the food order, or overhear the full ordering conversation, the researcher was able to record whether a child’s meal bundle was purchased, as this was easy to identify by the distinctive packaging of the child meals.

Figure 6.2 shows the data capture form used by the researcher. A close-ended coding scheme was used. This was based on predetermined observable behaviours and was developed through pre-testing of the data capture form.

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6.3.7 Data analysis of the time frame of the food ordering process

The information from the collection forms was entered and analysed in relation to the time frame of the food ordering process as well as the parent/child interactions during the ordering process. Descriptive statistics, cross-tabulations, t-tests, and one-way ANOVAs were performed to identify possible differences between subgroups, using the statistical software package IBM SPSS Version 23.0, and to examine average wait time, order time and time to receive food.