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How Influences Restaurant Travel Behavior Question 1: How do people weigh information from a Yelp listing? We used an online stated-preference survey to test how people weigh various factors in choosing a restaurant to visit. Respondents were asked to choose among 3 restaurants for a hypothetical lunchtime meal in an unfamiliar downtown. They would be dining alone, walking to the destination, and using Yelp as their sole source of information. We presented between 6 and 18 sequential sets of restaurant review mockups and asked respondents to choose their favorite from each set. The key factors we varied were: (a) number of stars, (b) number of reviews, and (c) distance. To add realism, restaurant names, photographs, and opening times were varied randomly from a curated set of similar options. Restaurant genre and price were held constant. Below is an example of a choice set shown to survey participants: Model Specification and Result We used a revealed-preference survey to collect data on whether respondents had dined out in different neighborhoods in the East Bay. Respondents were given a list of neighborhoods both near and far from the UC Berkeley campus, including downtown Oakland, Rockridge, and El Cerrito, and asked whether they had been to a restaurant there. Respondents were also provided the option of looking at a map of the neighborhood and a list of popular restaurants there, to refresh their memory. These questions were only asked of respondents who indicated that they were UC Berkeley students. Below is an example of the neighborhood information shown to our survey participants: V visited =β 1 Distance Home + β 2 Deviation + β 3 ActivityDensity β 4 F requentY elpU ser + β 5 Y earsInBay + β 6 F requentDiner + β 7 W hite + β 8 F emale + β 9 Albany + β 10 DowntownOakland+ β 11 MidtownCerrito + β 12 NorthBerkeley + β 13 N orthside+ β 14 P iedmont + β 15 Richmond + β 16 Rockridge+ β 17 SouthBerkeley + β 18 T emescal V N otV isited =0 CHANGING ACTIVITY PATTERNS IN THE DIGITAL AGE: Motivation: App-based recommendation systems like Yelp are changing the way people explore their neighborhoods. By expanding users’ choice sets for shops, restaurants, recreation, and other place-based activities, Yelp nudges people to visit new areas that they were not previously familiar with. Over time, people who regularly use Yelp may develop broader or more diverse travel patterns than people who don’t. Question 2: How does Yelp change people’s activity footprint? Summary statistics Number of observations = 1362 Number of estimated parameters = 8 L(β 0 ) = -1496.310 L( ˆ β ) = -1272.567 -2[L(β 0 ) -L( ˆ β )] = 447.485 ρ 2 = 0.150 ¯ ρ 2 = 0.144 V choice i =β 1 (Star i * F requentY elpU ser )+ β 2 (Star i * InfrequentY elpUser )+ β 3 (Distance i * HasCar )+ β 4 (Distance i * HasNoCar )+ β 5 (Review i * LowStar i * F requentY elpU ser )+ β 6 (Review i * LowStar i * InfrequentY elpUser )+ β 7 (Review i * HighStar i * F requentY elpU ser )+ β 8 (Review i * HighStar i * InfrequentY elpUser ) * FrequentYelpUser --- one uses Yelp more than 40% of the time one dines out in the past month * HasCar --- one has car access at home * LowStar --- the number of stars is less than 3.5 Parameter Description Estimate Std.error t-stat p-value Stars Frequent Yelp users 1.03 0.0923 11.20 0.00 Infrequent Yelp users 1.18 0.0821 14.32 0.00 Distance (miles) Has Car Access -1.78 0.236 -7.56 0.00 Does Not Have Car Access -1.02 0.256 -4.00 0.00 Reviews <3.5 Stars Frequent Yelp users -0.0236 0.278 -0.09 0.93 Infrequent Yelp users -0.245 0.219 -1.12 0.26 >=3.5 Stars Frequent Yelp users 0.516 0.0601 8.58 0.00 Infrequent Yelp users 0.319 0.0453 7.05 0.00 Findings and Takeaways Parameter Description Estimate Std.error t-stat p-value Distance from home (km) -0.0186 0.00709 -2.62 0.01 Distance deviation from commute (km) -0.220 0.0387 -5.69 0.00 Activity density (100s of destinations) 0.0627 0.0211 2.97 0.00 Neighborhood Albany 0.266 0.447 0.59 0.55 Downtown Oakland -1.49 1.48 -1.01 0.31 Midtown Cerrito 0.511 0.534 0.96 0.34 North Berkeley 1.85 0.417 4.43 0.00 Northside 1.22 0.321 3.82 0.00 Piedmont 0.511 0.406 1.26 0.21 Richmond 2.54 0.928 2.74 0.01 Rockridge 0.251 0.317 0.79 0.43 South Berkeley 1.81 0.565 3.20 0.00 Temescal 0.501 0.332 1.51 0.13 Frequent Yelp User 0.725 0.196 3.69 0.00 Years lived in Bay Area 0.526 0.178 2.95 0.00 Dines Out More than Once/Week 0.437 0.184 2.38 0.02 Race = White 0.822 0.179 4.60 0.00 Sex = Female -0.459 0.174 -2.64 0.01 Model Specification and Result *Deviation --- the extra distance one needs to travel to a neighborhood ([school to neighborhood] + [neighborhood to home] – [school to home]) *ActivityDensity --- the total number of activities in the neighborhood *Neighborhoods are dummy variables and Elmwood is considered the base. Findings and Takeaways Summary statistics Number of observations = 1019 Number of estimated parameters = 18 L(β 0 ) = -706.317 L( ˆ β ) = -488.287 -2[L(β 0 ) -L( ˆ β )] = 436.061 ρ 2 = 0.309 ¯ ρ 2 = 0.283 Kelan Paul Samuel Yiyan CE 264 Stoy Sohn Maurer Ge Final Project4 The basic results of this survey are just as expected: people prefer restaurants that are closer, have higher ratings, and have a greater number of reviews. Here are some of the more interesting findings: 1) An average respondent is willing to walk 8 blocks (0.4 miles) to a restaurant with higher Yelp rating, or 6 blocks (0.3 miles) to one whose rating is based on 100 extra reviews. 2) Frequent Yelp users evaluate listings differently from other survey respondents. They place a higher value on the number of reviews (60% higher, significant at the 1% level) and a lower value on the number of stars (12% lower, significant at the 10% level) 3) Respondents with access to cars at home would not be willing to walk as far as other survey respondents, to reach an equivalent restaurant. 4) The volume of reviews only matters if they are mostly positive. For restaurants with a rating below 3.5 stars, the number of reviews had no statistically significant effect on respondents’ choices. Other demographic characteristics of the respondents did not substantially change the model, so have been left out from this specification. These results are useful for restaurant owners promoting a destination on Yelp, but also for city planners! People’s location and travel choices largely depend on the information they have. People will travel farther to places that have a community recommendation (i.e., higher rating), or even to places which they have more reliable information about (i.e., more reviews). Planners seeking to nudge people’s travel patterns to be more environmentally sustainable may be able to make progress simply by providing better information about alternatives. For example, a Yelp or Google Maps plugin that provides results based on transit proximity or convenience for trip chaining could help people develop more compact travel footprints with little personal cost. There are 208 survey respondents in total and 155 are usable for Stated Preference Survey part. There are 208 survey respondents in total and 98 are usable for Revealed Preference Survey part. This model estimates the probability that a survey respondent has gone to restaurants in a particular neighborhood, based on characteristics of the neighborhood, demographics of the respondent, and the spatial proximity to home and school. (All survey respondents are Berkeley graduate students.) 1) As expected, people are more likely to visit neighborhoods that are near their home or convenient to their daily commute. People who dine out more often and have lived in the Bay Area longer have been to more neighborhoods. Places with more destinations (proxied by business and point-of-interest listings) have more visitors. 2) But, even after accounting for all these factors, respondents who frequently use Yelp are substantially more likely to have been to any given neighborhood. Frequent Yelp users have a spatial activity footprint equivalent to someone who has lived in the Bay Area for an additional 16 months, on average. These results suggest that people who use Yelp explore more areas of the city than people who don’t. This could partially be explained by people who like exploring also choosing to use apps like Yelp more, but it seems likely that Yelp listings nudge people to visit locations that they wouldn’t otherwise know about. One challenge associated with the finding is that information availability might lead to larger environmental footprints. This provides further evidence that information availability is an important factor in determining people’s location choice sets and therefore their travel behavior and that smartphones are altering how people engage with cities. Future research could look into whether these larger choice sets result in more “optimal” location choice and travel behavior in people’s day-to-day lives, or if they simply represent a preference for variety
