The out-of-region bias: Distance estimations basedon geographic category membership

Caglar Irmak & Rebecca Walker Naylor &

William O. Bearden

Published online: 16 July 2010# Springer Science+Business Media, LLC 2010

Abstract This research addresses estimations of distance, an important factor innumerous consumer decisions (e.g., store choice, willingness to pay for travel). Ourcentral hypothesis is that when two places are located in the same geographiccategory, individuals estimate them to be closer to each other than when they arelocated in different categories, a bias we term “the out-of-region bias.” In study 1,we demonstrate that participants are more willing to visit a store located in the city inwhich they live rather than a store located in a neighboring city because theymistakenly think that a store in their city is closer to home. In studies 2a and b, weshow that the out-of-region bias leads consumers to express higher willingness topay for an airline ticket that involves travel across versus within regions becausethey inaccurately estimate greater distance between cities that are located in differentregions (vs. the same region). Study 2a also demonstrates that the out-of-region biasis ameliorated in the presence of a map, indicating that the bias is a conceptual (vs.perceptual) bias, and study 2b rules out an alternative explanation for our resultsbased on consumers’ tendency to exaggerate north–south distances.

Keywords Distance estimates . Geographic reasoning . Spatial categorization .Conceptual bias . Store choice

Mark Lett (2011) 22:181–196DOI 10.1007/s11002-010-9120-3

C. Irmak (*) :W. O. BeardenMoore School of Business, University of South Carolina, 1705 College Street, Columbia, SC 29208,USAe-mail: caglar.irmak@moore.sc.edu

W. O. Beardene-mail: bbearden@moore.sc.edu

R. W. NaylorFisher College of Business, The Ohio State University, 2100 Neil Avenue, 538 Fisher Hall,Columbus, OH 43210, USAe-mail: naylor_53@fisher.osu.edu

1 Introduction

Consumers often need to make distance estimations. For example, before goingshopping, a consumer might estimate the distance of different stores from his/herhome and pick the one that he/she thinks is closest. Similarly, long distance traveling(e.g., from one region of a country to another) often requires estimation of traveldistance and time in order to better plan the trip and to make decisions about howmuch one is willing to pay for travel. Making distance estimations accurately helpsconsumers minimize travel time and travel costs, two key goals in a society that isboth increasingly pressed for time and concerned about rising energy costs. Moreefficient travel decisions help consumers and businesses use energy resources moreefficiently, an issue important not only to consumers and companies but also topublic policy makers.

Despite their importance across a variety of consumer decisions, distanceestimations are prone to biases that may lead consumers to make suboptimaldecisions (Krishna 2007). For example, Raghubir and Krishna (1996) identified thedirect-distance bias, demonstrating that consumers employ direct distance estima-tions rather than path distance estimations when deciding which route to takebetween two points. As a result, they tend to prefer a route that is shorter in terms ofthe direct distance between the two end-points over an alternative route that isshorter in terms of path distance, thereby increasing the total distance traveled.Consumers have also been shown to rely on travel time rather than travel distancewhen deciding which route to take, mainly because travel duration is moreaccessible in memory (Kang et al. 2003). Finally, consumers estimate longer traveltime for locations that are north of the origin point compared to locations that aresouth of the origin point due to the metaphoric link between northbound travel anduphill (i.e., more difficult) travel and southbound travel and downhill (i.e., easier)travel (Nelson and Simmons 2009).

In the present research, we add to the body of research on distance estimations inmarketing by exploring the impact of spatial categorization on consumers’estimations of the distance between two geographic locations. In doing so, wedemonstrate another bias that influences consumers’ distance estimations, a bias weterm the “out-of-region bias.” The out-of-region bias refers to the fact that peopletend to estimate two locations (e.g., retail stores) within one geographic region (e.g.,a city) to be closer to one another than two locations in separate regions (i.e., twodifferent cities). Just as other biases can lead to suboptimal decision making, the out-of-region bias can lead consumers to make inaccurate distance estimations when twolocations in different regions are actually closer to one another than are two locationsin the same region. Although extant research in psychology on geographic reasoninghas demonstrated that geographic categorization can influence location and distanceestimates (Friedman 2009; Friedman and Brown 2000a, b; Friedman et al. 2002a, b;Friedman and Montello 2006), this research has primarily been conducted on global-scale location and distance estimates and has not examined the impact ofgeographic-category-based distance estimates on consumption decisions. Thus, oneof the key contributions of our work is to demonstrate that the out-of-region biasimpacts not only distance estimations (even at the local level) but also subsequentconsumption decisions (e.g., store choice and willingness to pay for travel). Hence,

182 Mark Lett (2011) 22:181–196

our findings point to the importance of the previously unexamined role of spatialcategories in predicting consumer spatial behavior, delineating trade areas, andlocating retail and service facilities (Huff 1963, 2003; Huff and Rust 1984), as wellas pricing travel services within and across perceived geographic regions.

2 Theoretical background

Past research on spatial inferences has shown that spatial categorization can lead tosystematic errors in distance estimations between locations (Krishna 2007).Individuals have been shown to judge two pairs of equidistant points to be closerto each other when the points are within the same spatial category than when theyare in different spatial categories (Allen and Kirasic 1985; Burris and Branscombe2005; Coren and Girgus 1980). Much of this past research on the effects of spatialcategorization on distance estimations has focused on the effect of visual groupingcues (i.e., boundaries that show two points or locations either in the same or indifferent perceptual groups). One recent study demonstrated, for example, that whenshown two different pairs of cities—one consisting of two American cities and theother consisting of a Canadian and an American city (with the members of both pairsequidistant from each other)—on a map that depicts national borders, people judgethe crow-fly distance between the Canadian and American cities to be significantlygreater than the distance between the two American cities (Burris and Branscombe2005). This distortion in online distance estimates is explained by the visual illusioncaused by crossing a boundary used to distinguish the self from not-self. Because anindividual’s home nation is a part of the extended self and crossing the boundarybetween that nation and other nations may serve as a symbol of the self/not-selfdistinction, individuals perceive locations beyond their national boundary to befarther away than locations within their national boundary (Burris and Rempel2004).

Biases of this type that occur in the presence of visual cues due to onlineperceptual distortions/illusions are referred to as perceptual biases (Raghubir andKrishna 1996). Recent work in psychology on geographic reasoning has insteadexplored conceptual biases that can be attributed to geographic category information,which is stored in long-term memory (Friedman 2009; Friedman and Brown 2000a, b;Friedman et al. 2002a, b; Friedman and Montello 2006). Information about geographic“categories,” or regions, can be stored in the long-term memory at multiplelevels, from very broad levels of categorization (e.g., continents) to very finelevels of categorization (e.g., neighborhoods within a given city or town). Sincegeography is a “numerically sparse” domain for most people, in that they donot know the exact distances between locations (Friedman and Brown 2000a,197), judgments of numeric locations (i.e., latitude), or numeric distances areestimates as opposed to fact retrieval. Information about geographic categoriesserves as an input to these estimates and can bias the estimates, as categorizationmaximizes the similarity between two items that are in the same category andminimizes similarity between two items in different categories (Rosch 1973). Suchbiases based on categorization have been shown in research on risk perceptions.For example, Carvalho et al. (2008) demonstrated that perceived similarity

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between two locations can lead to enhanced perceptions of risk (i.e., if a food-borne illness occurs in a culture perceived to be similar to one’s own, the risk to theself is perceived to be higher). In other words, if two cultures are categorized asbeing similar, the psychological distance between them is reduced. The out-of-region bias operates in a similar manner for judgments of physical distance; if twolocations are categorized as being in the same region, the perceived physicaldistance between them is reduced, leading to potentially suboptimal consumerdecisions. As such, biases based on geographic categorization are conceptualbiases that can occur even in the absence of visual cues since information aboutcategory structure is often stored in memory (and, hence, affects judgments evenwhen visual information about geographic categories is not provided).

Past research exploring the biasing effects of geographic categorization hasdemonstrated that reliance on category-level information can lead to systematicerrors in estimating the latitudes where geographic entities are located (Brown 2002;Friedman 2009; Friedman and Brown 2000a, b; Friedman et al. 2002b). Forexample, although Northern US cities (e.g., Chicago) are located at approximatelythe same latitude as Southern European cities (e.g., Rome), study participants judgethem to be on different latitudes (Tversky 1981). This bias occurs becauseindividuals divide the Old and New World into conceptually distinct subregions(e.g., the Northern and Southern USA, North-central, and Mediterranean Europe),and they rely on category-level information in estimating the latitude of cities indifferent parts of the world (e.g., assuming, incorrectly, that cities in the NorthernUSA and North-central Europe are at approximately the same latitude) (Friedmanand Brown 2000b). Friedman et al. (2002a, b) and Friedman and Montello (2006)demonstrate similar effects of geographic categories on bearing and distanceestimates, respectively.

This prior research has focused on global-scale estimates (i.e., latitude anddistance estimates of cities around the world), and the biases observed have beenattributed to people’s beliefs about geographic regions and where these regions arelocated with respect to global landmarks like the equator, oceans, poles, etc.(Friedman 2009; Friedman and Brown 2000a, b; Friedman and Montello 2006). Forexample, in trying to identify the latitude of a city in South America, someone mightassume that all of South America is south of the equator (which is, of course, nottrue) and use this category-level information to generate an incorrect locationestimate for the city in question. We argue that the out-of-region bias can affectdistance estimates even without comparisons to global-level landmarks.

Study 1 is designed to demonstrate the potentially negative influence of the out-of-region bias on nearby distance-related consumption decisions. As such, westudy whether the out-of-region bias leads to suboptimal decisions in choosing alocal retail store to visit. To do so, we present participants with two branches ofthe same retail chain, which are located either in the city in which participants liveor in an adjacent city (where cities represent different geographic categories). Wepredict that individuals will estimate the store in their city to be closer to theuniversity campus in their home city than the store in the adjacent city and, as aresult, will be more likely to choose to visit the store located in their city over theone located in the adjacent city (even though the store in the adjacent city isobjectively closer).

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3 Study 1

3.1 Method

One hundred and fifty-nine undergraduate students at the University of SouthCarolina participated in this study to fulfill a course requirement. Participants wereasked to imagine that they wished to visit a Home Depot store. They were then toldthat the Home Depot website indicated that Home Depot has a store at 285 ForumDrive1 in Columbia, SC, the city in which participants lived, and a store located at5600 Sunset Boulevard in Lexington, SC, a neighboring city located west ofColumbia. Half of the participants first estimated travel time and distance between thecampus and the two stores and were then asked, if they did indeed need to go to one ofthese Home Depot stores from the campus, which one they would choose to visit. Theother half of the participants first chose the store they would visit and then estimatedthe distance between the campus and the two stores. The presentation order of the twostores was also counterbalanced such that, when making distance estimates,approximately half the participants first estimated the distance between the campusand the store in Columbia, SC, while the other half first estimated the distance betweenthe campus and the store in Lexington, SC. Thus, the study used a 2 (Choice Order:Before distance estimation vs. After distance estimation) ! 2 (Order of DistanceEstimations: Store in Columbia distance estimation first vs. Store in Lexingtondistance estimation first) ! 2 (Store Location City: Columbia vs. Lexington) mixeddesign, where Choice Order and Order of Distance Estimations were between-subjectsfactors and Store Location City was a within-subjects factor.

Finally, participants were asked how often they shop at Home Depot and how far(in miles) they currently live from the campus on which the study took place.

3.2 Results and discussion

We conducted a mixed ANOVA with Choice Order and Order of DistanceEstimations as between-subjects independent variables and Store Location City asa within-subjects independent variable and the distance estimation betweencampus and the store as the dependent variable. The results showed a main effectof Store Location City (F(1, 154)=31.30, p<.0001), indicating that the store inColumbia, SC was estimated to be significantly closer to the campus (M=12.94 miles) than the store in Lexington, SC (M=17.35 miles), despite the fact thatthe store in Lexington is, in fact, objectively closer (14.09 miles, 15 min traveltime) to the campus than the store in Columbia (20.47 miles, 31 min travel time;see www.HomeDepot.com). Further, there was not a significant main or interactiveeffect of Choice Order or Order of Distance Estimation on any of the dependentvariables (all p>.10).

We then ran the same mixed ANOVA with driving time estimations as thedependent variable. Results were parallel to those for distance estimations.Specifically, participants estimated that the driving time from the campus to thestore in Columbia is shorter (M=18.1 min) than it is to the store in Lexington (M=

1 The addresses provided to participants are real and were taken from the Home Depot website.

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24.1 min, F(1,154)=40.58, p<.0001). Thus, both travel distance and travel time areunderestimated for the within-region store and are overestimated for the out-of-region store (Fig. 1).

Investigation of the binary Store Choice measure revealed that 65% of theparticipants chose to go to the Home Depot store in Columbia, the city in which thecampus is located, while the remaining 35% chose to go to the store in Lexington,the neighboring city. A t test comparing the choice rate of the store in Columbia(65%) and a 50% chance of choosing either store demonstrates that participants’ rateof choosing the store in their home city was greater than chance, as expected (t(158)=3.89, p<.0001). Further, a logistic regression predicting Store Choice with thedifference in estimated distance to the stores in Lexington and Columbia fromcampus indicated that the greater the estimated difference in distance, the higher thelikelihood of choosing the store in Columbia (!=.20, Wald’s "2 (1, 158)=26.63,p<.0001). A similar pattern of results was observed when the difference inestimated driving time was used as the independent variable (!=.18, Wald’s "2 (1,158)=29.52, p<.0001). When both the difference in distance and driving timeestimations were used as predictor variables, in line with Kang et al. (2003), the

Fig. 1 Study 1: actual andestimated travel distance andtime

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difference in driving time estimations remained significant (!=.14, Wald’s "2 (1,158)=6.37, p<.05), while the difference in distance estimations was nonsignificant(!=.061, Wald’s "2 (1, 158)=.91, p>.30). These results indicate that the effect ofdistance estimations on choice was mediated by driving time estimations, whichwas confirmed by a Sobel test (z=2.93, p<.01). Finally, when distance of theparticipant’s home from campus or their shopping frequency at Home Depot wereseparately added to each of these models as covariates, neither of these additionalvariables were significant in either model (all p>.20).

Study 1 thus demonstrates that consumers estimate retail locations located in thesame city to be closer to each other compared to retail locations located in twodifferent cities. As a result, when consumers make travel decisions based on the cityin which a retail outlet is located, they may make suboptimal decisions, travelinggreater distances than necessary to reach the retailer. Given that most of theresearch on geographic reasoning has looked at global-scale estimates (e.g.,North American cities vs. European cities), our work makes a contribution inthat it documents the influence of geographic categories at a local level. Ourfindings also point to the importance of spatial categories in modeling consumerspatial behavior for predicting retailers’ market areas (Huff and Rust 1984).Although the Huff model has been successfully used by many retailers since itsintroduction (Huff 1963, 2003), our findings demonstrate that geographiccategorization of market areas may create nonlinearity in consumer spatialbehavior; thus, integrating geographic category as a parameter in the model mayhelp increase its predictive power.

The next study builds on study 1 in several ways. First, we use a different typeof geographic category, regions within the USA (e.g., Southeast vs. Northeast),instead of city. Second, we measure participants’ perception of the similaritybetween the home and target geographic locations (in terms of culture, climate, andregional similarity) in order to demonstrate that the process behind distanceestimations is indeed based on geographic categorization and not other types ofsimilarity. We also wish to demonstrate that the out-of-region bias is a conceptualbias based on information stored in memory versus a perceptual bias based onvisual cues. Whereas perceptual biases are exacerbated by visual cues (e.g.,increasing awareness of or attention paid to the stimuli in question; Raghubir andKrishna 1996), conceptual biases are attenuated by visual cues, as visual cuesprovide objective and accurate information. In order to demonstrate that the out-of-region bias is indeed a conceptual bias that can be attenuated in the presence ofvisual cues, we will demonstrate, in study 2a, that the effects of the bias areattenuated when study participants are asked to make distance estimations betweenregions in the presence of a map showing the locations in question. All mapprojections onto a flat surface distort the surface of the earth in some fashion(Snyder 1993), resulting in inaccurate distance estimations due to the tendency tooverestimate the size of high latitude areas (e.g., Saarinen et al. 1996). The mappresented to the participants in our study was especially designed to depict equaldistance between the cities in question (thus presenting visual information thatconflicts with the distance estimates consumers would make if relying solely ongeographic category information), while at the same time reflecting the actualcrow-fly distance between the cities.

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4 Study 2a

Three different cities were used as stimuli in study 2a: Columbia, SC, a Southeasterncity, New Orleans, LA, another Southeastern city, and New York City, NY, aNortheastern city. New Orleans and New York are approximately the same crow-flydistance (approximately 600 miles) from Columbia, SC, the home city for the studyparticipants. We predict that when two cities are in the same region (i.e., theSoutheastern region of the USA), they will be inferred to be closer to each other thanwhen they are in two different regions (i.e., the Southeastern and the Northeasternregion of the USA), although the actual distance between cities is almost identical.

4.1 Pretest

In order to ensure that individuals perceive the cities to be in the geographiccategories we expected, we conducted a pretest with 80 participants from the samepopulation as the participants from the main study. Ninety-five percent of the pretestparticipants agreed that their home city (Columbia) is in the Southeast. Similarly,96% believed that New York City is in the Northeast, 78% thought that New Orleansis in the Southeast, and78% thought that both Columbia and New Orleans are in theSoutheast.

4.2 Method

A total of 194 undergraduate students at the University of South Carolinaparticipated in this study to fulfill a course requirement. All participants were askedto estimate the straight line (crow-fly) distance in miles between Columbia, SC (theirhome city) and New Orleans, LA (another Southeastern city), as well as betweenColumbia, SC and New York City, NY (a Northeastern city). The presentation orderof the two cities was counterbalanced such that approximately half the participantsfirst estimated the distance between Columbia and New Orleans, and the other halffirst estimated the distance between Columbia and New York City. Approximatelyhalf of the participants made their distance estimations in the presence of a map ofthe USA that depicts many US cities including Columbia, New Orleans, and NewYork City (see Fig. 2). The straight-line distances between New Orleans andColumbia and between New York City and Columbia were both 3.6 cm on thescreen of the computers on which the study was conducted. The other half of theparticipants did not see the map when they made their estimations.

In addition to making estimates of the distance between the two cities,participants were also asked how long they thought it would take to fly (assuminga direct flight) between the two cities and how much they were willing to pay for adirect flight. Previous research (Kang et al. 2003) has shown that the order ofestimations of time and distance may affect accuracy, so, in study 2, we controlledfor this factor. Thus, the study used a 2 (Map Condition: Present vs. Absent) ! 2(Order of Estimation: Distance first vs. Time first) ! 2 (Order of Target City: NewOrleans distance estimation first vs. New York City distance estimation first) ! 2(Target City: New Orleans vs. New York City) mixed design, where the first threefactors were between-subjects factors and the last factor was a within-subjects factor.

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In order to understand the underlying process of individuals’ distance estimations,we also asked participants to provide ratings of similarity between New Orleans andColumbia, as well as between New York City and Columbia in terms of (1) theirlocation (region) in the USA (i.e., whether they were located in the same region), (2)their culture, (3) their climate, and (4) their overall similarity (1 = “Not at allsimilar,” 7 = “Very similar”). Based on prior research on categorization and itsrelation to perceived similarity (Goldstone 1994), these measures represent theextent to which participants utilize spatial categorization, cultural categorization,climate categorization, or a more general categorization system in the distanceestimation process.

After these measures were collected, in order to control for participants’ priorexperience-based knowledge about the distances between the cities used as stimuli,participants were also asked how many times they had been to New Orleans and toNew York City, how many times in the last 12 months they had traveled out of state/traveled by an airplane/made a long-distance (further than 250 miles) trip, and howfar their hometown (i.e., the location of their permanent residence) is from thecampus.

4.3 Results and discussion

We first conducted a mixed ANOVAwith Order of Target City, Order of Estimation,and Map Condition as between-subjects independent variables and Target City as awithin-subjects independent variable. Distance estimation between cities served asthe initial dependent variable. The results showed a main effect of Target City (F(1,

Fig. 2 Map used in study 2a

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186)=11.23, p<.01), indicating that Columbia was estimated to be significantlyfarther from New York City (M=1,108 miles) than it is from New Orleans (M=747 miles). Further, although there were no significant main or interactive effects ofOrder of Target City and Order of Estimation on the results (all p>.10), there was asignificant interactive effect of Map Condition and Target City (F(1, 186)=4.15,p<.05), indicating that the difference in participants’ estimated distance betweenColumbia and New York City and between Columbia and New Orleans was greaterwhen a map was absent versus present (Map absent: MColumbia–New York City=1,324 miles, SDColumbia–New York City=2,797, MColumbia–New Orleans=742 miles,SDColumbia-New Orleans=388, p<.001, Cohen’s d=.40; Map present: MColumbia–New York City=893 miles, SDColumbia–New York City=605, MColumbia–New Orleans=751 miles,SDColumbia–New Orleans=472, p>.35, Cohen’s d=.26; see Fig. 3). In addition, whentravel frequency variables or distance of hometown from campus is separately added tothe analysis as covariates, none of these additional variables was significant, and theydid not interact with the independent variables (all p>.20). The findings that the out-of-region bias is mitigated in the presence of a map and that distance of a participant’shometown has no effect on distance estimations are not consistent with Burris andBranscombe (2005) documentation of visual illusions (in the presence of a map)created when self-relevant boundaries are crossed. Since the out-of-region bias did notemerge in the presence of a map, it may be that geographic regions within the samenational boundary (i.e., the Southeastern vs. Northeastern USA) may not create thesame self/not-self distinction as do national borders, a question we leave to futureresearch (Table 1).

We next ran two additional mixed ANOVAs with flight duration estimations andwillingness to pay as the dependent variables. Results were parallel with the resultsfor distance estimations. Specifically, participants estimated that a direct flightbetween Columbia and New York City will take more time (M=2.97 h) than a flightbetween Columbia and New Orleans (M=2.58 h, F(1, 186)=8.42, p<.01), and theywere willing to pay more for a plane ticket to New York City (M=$238) than to New

0

500

1000

1500

AbsentPresent

Map

Est

imat

ed D

ista

nce

(mile

s)

Columbia to New Orleans

Columbia to New York

Fig. 3 Study 2a: distanceestimates by target city and mapcondition

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Orleans (M=$202, F(1, 186)=56.54, p<.0001). Again, there were no significant effectsof Order of Target City and Order of Estimation (all p>.34). The interactive effect ofMap and Target City for flight duration estimation and willingness to pay showed thesame pattern as the results for distance estimation. Although the interaction effects didnot reach significance (F(1, 186)=1.88, p=.13 and F(1, 186)=1.16, p=.39 for flightduration estimation and willingness to pay, respectively), the difference in participants’estimated flight duration between Columbia and New York City and between Columbiaand New Orleans was greater when a map was absent versus present (Map absent:MColumbia–New York City=3.19 h, SDColumbia–New York City=1.34,MColumbia–New Orleans=2.59 h,SDColumbia–New Orleans=1.31, p<.01, Cohen’s d=.34; Map present: MColumbia–New York City=2.76 h, SDColumbia–New York City=1.44, MColumbia–New Orleans=2.58 h, SDColumbia–New Orleans=1.47, p>.32, Cohen’s d=.21). The difference in participants’ willingness to pay for a flightbetween Columbia and New York City and between Columbia and New Orleans was,however, not influenced by the presence of a map; participants were willing to paysignificantly more for a flight to NYC than a flight to New Orleans irrespective of the Mapcondition (Map absent: MColumbia–New York City=$232, SDColumbia–New York City=113,MColumbia–New Orleans=$202, SDColumbia–New Orleans=102, p<.01, Cohen’s d=.30; Mappresent: MColumbia–New York City=$242, SDColumbia–New York City=93, MColumbia–New Orleans=$204, SDColumbia–New Orleans=81, p<.01, Cohen’s d=.44). These findings suggest that theout-of-region bias may exist even in the presence of a map for certain measures (e.g.,willingness to pay).

Next, to test whether the difference in distance estimations for the two TargetCities mediated the effect of Target City on willingness to pay, we ran regressionmodels based on procedures established by Judd et al. (2001) for testing mediationin within-subjects designs. Specifically, we created a “difference in distanceestimations” variable by subtracting participants’ distance estimation betweenColumbia and New York City from their distance estimation between Columbiaand New Orleans. In a similar fashion, we created a “difference in willingness topay” variable using participants’ willingness to pay for flights between Columbiaand New York City and Columbia and New Orleans. Finally, we created a“summation of distance estimations” variable by adding participants’ distanceestimations between Columbia and New York City and between Columbia and NewOrleans. This last variable is used for testing whether the distance estimations had a

Table 1 Study 2a results

Map absent Map present

Columbia–New York City

Columbia–New Orleans

Columbia–New York City

Columbia–New Orleans

Crow-fly distance estimations (miles) 1,324a 742b 893b 751b

Flight duration estimations (hours) 3.19a 2.59b 2.76ab 2.58b

WTP for a plane ticket (USD) 232a 202b 242a 204b

Regional similarity ratings 1.60a 2.82b 1.69a 3.01b

Significance levels (across rows only; cells with the same letters are not significantly different from oneanother): cells labeled a and b are significantly different from each other at the p<.05 level

Mark Lett (2011) 22:181–196 191191

moderating, rather than mediating, effect on willingness to pay (Judd et al. 2001).Then, we ran a regression model predicting difference in willingness to pay bydifference in distance estimations and summation of distance estimations. The resultsrevealed only difference in distance estimations as a significant predictor ofdifference in willingness to pay (!=.37, t(193)=4.67, p<.0001), while thesummation of distance estimations was not a significant predictor (p>.20), indicatingthat the effect of Target City on willingness to pay was mediated (but not moderated)by distance estimations (Judd et al. 2001).

Finally, we investigated the mediating effects of perceived similarity on distanceestimations. To do so, we first conducted a mixed ANOVA with Order of TargetCity, Order of Estimation, and Map Condition as between-subjects independentvariables and Target City as a within-subjects independent variable. Difference inrated regional similarity of the cities served as the dependent variable. The resultsrevealed a main effect of Target City (F(1, 186)=119.54, p<.0001), indicating thatColumbia and New Orleans were rated as significantly more regionally similar toeach other than were Columbia and New York City (MColumbia–New Orleans=2.92,MColumbia–New York=1.67). Then, we created measures of “difference in similarity” bysubtracting participants’ rated similarity of Columbia and New Orleans from that ofColumbia and New York City for each of the four items (i.e., region, culture,climate, and overall similarity). Next, we created “summation of similarity” variablesby adding participants’ rated similarity between Columbia and New York City andbetween Columbia and New Orleans for each of the four types of similarity. In orderto test the potential mediating role of each of these similarity measures, we ran fourseparate regression models predicting difference in distance estimations bydifference in and summation of regional, cultural, climate, and overall similaritymeasures. The results revealed difference in regional similarity as the only significantpredictor of difference in distance estimations (!=.019, t(193)=2.03, p<.05); thesummation of similarity measures were not significant (all p>.10). Although the foursimilarity measures were correlated (Cronbach’s #=.78), none of the other threesimilarity measures had significant effects on difference in distance estimations (allp>.30). Taken together with the previous results, showing significant effects ofTarget City on both distance estimations and perceptions of regional similarity,these results indicate that regional similarity (but not other types of similarity)mediates (but does not moderate) the effect of Target City on distance estimations(Judd et al. 2001).

The results of study 2a thus demonstrate that the out-of-region bias leadsconsumers to express higher willingness to pay for an airline ticket that involvestravel across versus within regions because they inaccurately estimate greaterdistance between cities that are located in different regions (vs. the same region).Study 2a also demonstrates that when individuals are provided with a map on whichto base their judgments, the out-of-region bias is ameliorated, indicating that the biasdoes not occur due to online perceptual judgments but is instead driven bygeographic category information stored in long-term memory.

There is, however, a possible alternative explanation for the results of study 2a.Friedman and Montello (2006) note that people tend to exaggerate north-southdistances. Similarly, Nelson and Simmons (2009) argue that a lifetime of exposure tothe metaphor that “north is up” and “south is down” leads people tend to think that

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locations north of the origin point take longer to get to than locations south of theorigin point due to the metaphoric link between northbound travel and uphill traveland southbound travel and downhill travel. Because New York City is located northof Columbia, SC and New Orleans, LA is located southeast of Columbia, the factthat participants estimated New York City to be further away from Columbia couldbe driven by the fact that the New York City to Columbia distance is a north-southversus an east-west distance (like the New Orleans to Columbia distance). The goalof study 2b was, therefore, to replicate the results of study 2a using two locationsnorth of Columbia to rule out this alternative explanation.

5 Study 2b

Although the primary purpose of study 2b was to rule out an alternative explanationfor the results of study 2a, a secondary goal was to increase the external validity ofour findings by using a different kind of geographic category, national boundaries.To do so, we demonstrate that the inaccuracy in distance estimations and willingnessto pay for airline tickets also exists for two cities that are both located north of theorigin city when one of the cities is located in a different country (i.e., Canada andthe USA).

5.1 Method

Forty-four undergraduate students at the University of South Carolina participated inthis study to fulfill a course requirement. The design and procedure of the study weresimilar to those of study 2a with one important difference: we used Toronto, Canadaand Minneapolis, Minnesota instead of New York City and New Orleans as the twotarget cities. Although both cities are located north of Columbia, SC, becauseMinneapolis is in the USa and Toronto is in Canada, we expect participants toestimate Toronto to be significantly farther from Columbia compared to Minneap-olis. In fact, Toronto is 672 miles and Minneapolis is 1,003 miles away fromColumbia, yielding a conservative test of our hypothesis. Approximately half theparticipants estimated the distance from Columbia to the target cities, and the otherhalf estimated the distance from the target cities to Columbia. This manipulation ofreference city (i.e., whether the participant’s home city or the target city was theinitial reference point) helped us to control for potential direction effects inparticipants’ distance estimations. The study thus employed a 2 (Order: Minneapolisdistance estimation first vs. Toronto distance estimation first) ! 2 (Reference City:Participants’ Home City vs. Target City) ! 2 (Target City: Minneapolis vs. Toronto)mixed design, where the first two factors were between-subjects factors and the lastfactor was a within-subjects factor.

5.2 Results and discussion

We first conducted a mixed ANOVA with Reference City and Order as between-subjects independent variables and Target City as a within-subjects independentvariable and the distance estimation between cities as the dependent variable. The

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results showed a main effect of Target City (F(1, 40)=21.32, p<.0001), indicatingthat Columbia was estimated to be significantly farther from Toronto (M=1,589 miles) than it was from Minneapolis (M=1,153 miles), despite the fact thatToronto is actually located over 300 miles closer to Columbia than is Minneapolis.There were no significant main or interactive effects of Reference City or Order (allp>.30).

We then ran two additional mixed ANOVA’s with the same factors with (1) flightduration estimations and (2) willingness to pay for a direct flight as the dependentvariables. Results were parallel with the results for distance estimations. Specifically,participants estimated that a direct flight between Columbia and Toronto will takemore time (M=4.39 h) than a flight between Columbia and Minneapolis (M=3.42 h,F(1, 40)=19.41, p<.0001). Participants were also willing to pay more for a planeticket to Toronto (M=$324) than for a plane ticket to Minneapolis (M=$235, F(1,40)=32.61, p<.0001). Again, there were no significant main or interactive effects ofReference City or Order (all p>.10). Mediation analyses using the same proceduredescribed in study 2a revealed that distance estimations again mediate the effect ofTarget City on willingness to pay estimates (Judd et al. 2001).

6 General discussion

Across three studies, we demonstrate that individuals are subject to the out-of-regionbias in making distance estimations. When a location is in a different region from theregion in which an individual’s origin point is located, individuals estimate the targetlocation to be farther from the origin point than a place that is located in the sameregion as the origin point, even if the actual distances indicate the opposite.Specifically, in study 1, we demonstrate that participants are more willing to visit astore located in the city in which they live rather than a store located in aneighboring city because they mistakenly think that a store in their city is closer tohome than a store in a neighboring city. In studies 2a and 2b, we show that the out-of-region bias leads consumers to express higher willingness to pay for an airlineticket that involves travel across versus within regions because they inaccuratelyestimate greater distance between cities that are located in different (vs. the same)regions. Study 2a also demonstrates that when individuals are provided with a mapon which to base their judgments, the out-of-region bias is ameliorated, indicatingthat the bias is a conceptual (vs. perceptual) bias. Study 2b demonstrates thatconsumers’ inaccurate distance estimates cannot be explained by an intuition thatdestinations north of the origin point are farther away than destinations south of theorigin point (Nelson and Simmons 2009) or by the tendency to exaggerate north–south distances (Friedman and Montello 2006).

Although prior research on geographic reasoning in the psychology literature hasdemonstrated that geographic categorization can lead to biased location and distanceestimates (Brown 2002; Friedman 2009; Friedman and Brown 2000a, b; Friedman etal. 2002a, b; Friedman and Montello 2006), this research has primarily beenconducted on a global scale and has not examined the impact of geographic-category-based distance estimates on decisions of relevance to consumers. Thus, ourwork makes a contribution in that it demonstrates that the out-of-region bias impacts

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not only distance estimations (even at the local level) but also subsequentconsumption decisions (e.g., store choice and willingness to pay for travel). Wealso demonstrate that the effects of the out-of-region bias are driven by entrenchedcategory structures stored in long-term memory that influence consumers’geographic reasoning in the absence of maps or other visual cues.

Future research in this area can investigate other memory-based processes thatmight affect distance estimations. For example, it would be interesting to explorewhether locations visited on a single trip (e.g., shopping trips on which consumersvisit multiple retail locations) are estimated to be closer to one another in retrospect.Based on the findings of this research, we expect individuals to estimate two places(e.g., retail outlets, cities, countries) they have visited on a single trip to be closer toone another than two places they have visited on two separate trips because placesvisited on the same trip are more likely to be remembered together and, hence,classified in the same category. Future research may also explore the effect of othertypes of categorization structures that may be stored in long-term memory (e.g.,goal-based; Ratneshwar et al. 2001) or the use of multiple categorization structures(Gregan-Paxton et al. 2005) in making not only distance estimations but also inchoosing routes between two points. For example, inspired by Soman and Shi(2003) work on the effect of path characteristics and perceptions of progress, futureresearch could examine how categorization of a route by virtual progress influencesdistance estimations and route choice.

Given the ubiquity of distance estimations in consumers’ everyday life, ourresearch also has important practical implications. From the consumer’s perspective,making accurate distance estimations is important for several reasons. First, accuratedistance estimates help consumers minimize travel time, thereby providing moretime for other activities. Second, accurate distance estimates are helpful toconsumers who wish to minimize the cost of travel in the face of ever-increasinggas prices. Finally, accurate distance estimates also lead to more efficient usage ofresources (e.g., fossil fuels), an issue important to many individual consumers, aswell as to consumer welfare from a broader societal perspective. From the marketer’sperspective, it is important to know that consumers’ retailer preferences areinfluenced by the way consumers spatially categorize locations. Selecting thelocation of retail stores based on the actual distance between the retail location and acertain neighborhood may not be sufficient; retailers may instead need to considerthe way consumers categorize geographic space when deciding on new storelocations. As such, our findings suggest that geographic categorization may be animportant factor to add to the Huff model (Huff 1963, 2003; Huff and Rust 1984),which determines consumers’ shopping probability in a retail store based on twofactors, attractiveness of the retailer (e.g., its square footage) and distance ofconsumer location to the store. Likewise, airlines may use the findings of thisresearch in making pricing decisions. Our research shows that consumers are willingto pay more for flights across spatial regions (e.g., flights from the Southeast to theNortheast) rather than within regions (e.g., flights within the Southeast). Suchcategorizations may be incorporated in ticket prices, such that airlines may adjustprices for flights within, rather than across, geographic regions regardless of thetravel distance. Airlines may also wish to provide consumers with maps whenbooking airline tickets in order to mitigate the influence of the out-of-region bias.

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References

Allen, G. L., & Kirasic, K. C. (1985). Effects of the cognitive organization of route knowledge onjudgments of macrospatial distance. Memory & Cognition, 13, 218–227.

Brown, N. R. (2002). Real-world estimation: Estimation modes and seeding effects. In B. H. Ross (Ed.),The psychology of learning and motivation: Advances in research and theory. Vol 41 (pp. 321–359).San Diego: Academic.

Burris, C. T., & Branscombe, N. R. (2005). Distorted distance estimation induced by a self-relevantnational boundary. Journal of Experimental Social Psychology, 41, 305–312.

Burris, C. T., & Rempel, J. K. (2004). “It’s the end of the world as we know it”: Threat and the spatial-symbolic self. Journal of Personality and Social Psychology, 86, 19–42.

Carvalho, S. W., Block, L. G., Sivaramakrishnan, S., Manchanda, R. V., & Mitakakis, C. (2008). Riskperception and risk avoidance: The role of cultural identity and personal relevance. InternationalJournal of Research in Marketing, 25, 319–326.

Coren, S., & Girgus, J. S. (1980). Principles of perceptual organization and spatial distortion: The gestaltillusions. Journal of Experimental Psychology: Human Perception and Performance, 6, 404–412.

Friedman, A. (2009). The role of categories and spatial cuing in global-scale location estimates. Journal ofExperimental Psychology. Learning, Memory, and Cognition, 35, 94–112.

Friedman, A., & Brown, N. R. (2000a). Updating geographical knowledge: Principles of coherence andinertia. Journal of Experimental Psychology. Learning, Memory, and Cognition, 26, 900–914.

Friedman, A., & Brown, N. R. (2000b). Reasoning about geography. Journal of Experimental Psychology:General, 129, 193–219.

Friedman, A., & Montello, D. R. (2006). Global-scale location and distance estimates: Commonrepresentations and strategies in absolute and relative judgments. Journal of ExperimentalPsychology. Learning, Memory, and Cognition, 32, 333–346.

Friedman, A., Brown, N. R., & McGaffey, A. P. (2002a). A basis for bias in geographical judgments.Psychonomic Bulletin & Review, 9, 151–159.

Friedman, A., Kerkman, D. D., & Brown, N. R. (2002b). Spatial location judgments: A cross-nationalcomparison of estimation bias in subjective North American geography. Psychonomic Bulletin &Review, 9, 615–623.

Goldstone, R. L. (1994). The role of similarity in categorization: Providing a groundwork. Cognition, 52,125–157.

Gregan-Paxton, J., Hoeffler, S., & Zhao, M. (2005). When categorization is ambiguous: Factors that facilitatethe use of a multiple category inference strategy. Journal of Consumer Psychology, 15, 127–140.

Huff, D. L. (1963). A probabilistic analysis of shopping center trade areas. Land Economics, 39, 81–90.Huff, D. L. (2003). Parameter estimation in the Huff model. ArcUser, October–December, 34–36.Huff, D. L., & Rust, R. T. (1984). Measuring the congruence ofmarket areas. Journal of Marketing, 48, 68–74.Judd, C. M., Kenny, D. A., & McClelland, G. H. (2001). Estimating and testing mediation and moderation

in within-subject designs. Psychological Methods, 6, 115–134.Kang, Y., Herr, P. M., & Page, C. M. (2003). Time and distance: Asymmetries in consumer trip knowledge

and judgments. Journal of Consumer Research, 30, 420–429.Krishna, A. (2007). Biases in spatial perception: A review and integrative framework. In M. Wedel & R.

Peters (Eds.), Visual marketing: From attention to action (pp. 162–192). Mahwah: Erlbaum.Nelson, L., & Simmons, J. P. (2009). On southbound ease and northbound fees: Literal consequences of

the metaphoric link between vertical position and cardinal direction. Journal of Marketing Research,46, 715–724.

Raghubir, P., & Krishna, A. (1996). As the crow flies: Bias in consumers’ map-based distance judgments.Journal of Consumer Research, 23, 26–39.

Ratneshwar, S., Barsalou, L. W., Pechmann, C., & Moore, M. (2001). Goal-derived categories: The role ofpersonal and situational goals in category representations. Journal of Consumer Psychology, 10, 147–157.

Rosch, E. (1973). Natural categories. Cognitive Psychology, 4, 328–350.Saarinen, T., Parton, M., & Billberg, R. (1996). Relative size of continents on world sketch maps.

Cartographica, 33, 37–47.Snyder, J. P. (1993). Flattening the earth: Two thousand years of map projections. Chicago: University of

Chicago Press.Soman, D., & Shi, M. (2003). Virtual progress: The effect of path characteristics on perceptions of

progress and choice. Management Science, 49, 1229–1250.Tversky, B. (1981). Distortions in memory for maps. Cognitive Psychology, 13, 407–433.

196 Mark Lett (2011) 22:181–196