Discriminatory attitudes between ridesharing passengers
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- Findingown data
Generic social dominance orientation strongly influences discriminatory attitudes in ridesharing.
“We also find that one's generic social dominance orientation strongly influences his/her discriminatory attitudes in ridesharing, supporting the claim that behavior in shared mobility platforms reflects long-standing social dominance attitudes.”
Quote in the paperSubstantial claim- Rests on an assumption: The six Likert-scale preference questions validly measure discriminatory attitudes in ridesharing.
- Findingown data
The Likert-scale preferences are consistently correlated with one another.
“Additionally, the correlations presented in Table 4 demonstrate consistent correlation among all Likert-scale preferences, justifying various factor formulations.”
Quote in the paperSubstantial claim - Findingown data
Respondent race has no significant effect on discriminatory attitudes.
“A respondent's race does not have a significant effect on discriminatory attitudes, but white respondents that live in majority white counties are more likely to hold discriminatory attitudes with regard to race (no effect is observed regarding class preferences).”
Quote in the paperSubstantial claim- Rests on an assumption: The six Likert-scale preference questions validly measure discriminatory attitudes in ridesharing.
- Findingown data
Higher-income respondents are more likely to hold class-related discriminatory attitudes.
“Conversely, higher-income respondents appear more likely to hold discriminatory attitudes regarding class, but no effect is observed regarding racial preferences.”
Quote in the paperSubstantial claim- Rests on an assumption: The six Likert-scale preference questions validly measure discriminatory attitudes in ridesharing.
- Findingown data
Respondents in counties with a larger 2016 Republican presidential vote share are more likely to hold race-related discriminatory attitudes.
“The same is true of respondents that live in counties in which a larger share of the electorate voted for the Republican candidate in the 2016 presidential election.”
Quote in the paperSubstantial claim- Rests on an assumption: The six Likert-scale preference questions validly measure discriminatory attitudes in ridesharing.
- Findingown data
For higher-income respondents, no effect is observed on racial preferences.
“Conversely, higher-income respondents appear more likely to hold discriminatory attitudes regarding class, but no effect is observed regarding racial preferences.”
Quote in the paperSubstantial claim- Rests on an assumption: The six Likert-scale preference questions validly measure discriminatory attitudes in ridesharing.
- Findingown data
Women with children show a positive association with discriminatory attitudes toward fellow passengers of different class and race in shared rides.
“We find that discriminatory attitudes toward fellow passengers of differing class and race in the shared ride are positively correlated with respondents that are male or are women with children.”
Quote in the paperSubstantial claim- Rests on an assumption: The six Likert-scale preference questions validly measure discriminatory attitudes in ridesharing.
- Findingown data
Male respondents show a positive association with discriminatory attitudes toward fellow passengers of different class and race in shared rides.
“We find that discriminatory attitudes toward fellow passengers of differing class and race in the shared ride are positively correlated with respondents that are male or are women with children.”
Quote in the paperSubstantial claim- Rests on an assumption: The six Likert-scale preference questions validly measure discriminatory attitudes in ridesharing.
- Findingown data
For respondents in counties with a larger 2016 Republican presidential vote share, no effect is observed on class-related preferences.
“The same is true of respondents that live in counties in which a larger share of the electorate voted for the Republican candidate in the 2016 presidential election.”
Quote in the paperSubstantial claim- Rests on an assumption: The six Likert-scale preference questions validly measure discriminatory attitudes in ridesharing.
- Findingown data
For white respondents in majority white counties, no effect is observed on class-related preferences.
“A respondent's race does not have a significant effect on discriminatory attitudes, but white respondents that live in majority white counties are more likely to hold discriminatory attitudes with regard to race (no effect is observed regarding class preferences).”
Quote in the paperSubstantial claim- Rests on an assumption: The six Likert-scale preference questions validly measure discriminatory attitudes in ridesharing.
- Findingown data
White respondents in majority white counties are more likely to hold race-related discriminatory attitudes.
“A respondent's race does not have a significant effect on discriminatory attitudes, but white respondents that live in majority white counties are more likely to hold discriminatory attitudes with regard to race (no effect is observed regarding class preferences).”
Quote in the paperSubstantial claim- Rests on an assumption: The six Likert-scale preference questions validly measure discriminatory attitudes in ridesharing.
- Findingown data
Income shows a significant direct effect on the class factor.
“Income has a significant direct effect on class preferences, but no significant correlation with race preferences.”
Quote in the paperSubstantial claim - Findingown data
Living in counties with a larger share of the 2016 GOP vote has no effect on class preferences.
“Finding 2: For respondents that live in counties in which a larger share of the electorate voted for the Republican candidate in the 2016 presidential election, there is an effect on race preferences but no effect on class preferences.”
Quote in the paperSubstantial claim - Findingown data
Education does not significantly affect class or race preferences.
“Other characteristics, such as age and education, have no significant effect on class or race preferences.”
Quote in the paperSubstantial claim - Findingown data
Being Asian has a significant effect on F_SDO.
“Finding 7: Whether a respondent is Asian has a significant effect on F_SDO but no direct effect on F_Race or F_Class.”
Quote in the paperSubstantial claim - Findingown data
Being Asian has no direct effect on F_Race or F_Class.
“Finding 7: Whether a respondent is Asian has a significant effect on F_SDO but no direct effect on F_Race or F_Class.”
Quote in the paperSubstantial claim - Findingown data
Among the factors, F_SDO shows the strongest association with discriminatory attitudes in shared rides.
“Finding 5: Table 5 shows that F_SDO has the strongest effect on discriminatory attitudes in ridesharing.”
Quote in the paperSubstantial claim - Findingown data
For F_Class, the male effect is mainly indirect through F_SDO.
“In contrast, the effect of being male on F_Class is dominated by the indirect effect via F_SDO.”
Quote in the paperNot a substantial claim - Findingown data
In the survey, higher-income Uber or Lyft users reported more discriminatory attitudes.
“Instead, our findings indicate that users of Uber or Lyft with higher income reported greater discriminatory attitudes in our survey.”
Quote in the paperSubstantial claim - Findingown data
Using transit as one's main travel mode has a significant negative effect on social dominance orientation.
“Finding 4: Whether a respondent primarily uses transit as a travel mode has a significant negative effect on a respondent's social dominance orientation.”
Quote in the paperSubstantial claim - Findingown data
The combination of race and environment has a significant effect on discriminatory attitudes.
“Finding 3: A respondent's race per se does not have a significant effect on discriminatory attitudes, but the combination of race and environment does.”
Quote in the paperSubstantial claim - Findingown data
The county's white population share by itself is not significant in the models.
“However, the measure of an area's whiteness (percent white population in a county) alone is not significant when included in the models (not shown in the tables above).”
Quote in the paperSubstantial claim - Findingnone given
Across SEM 1 and SEM 2, coefficient signs remain the same whether or not they are significant.
“In general, the sign of these coefficients does not vary between SEM 1 and 2, regardless of significance.”
Quote in the paperNot a substantial claim - Findingown data
Each factor loading has a p-value less than 0.01.
“Each of these factor loadings has a p-value of less than 0.01, and can be interpreted as indicating a strong association to the underlying latent factors, with minimal variation among the items.”
Quote in the paperSubstantial claim - Findingown data
SEM Models 1 and 2 show significant direct and indirect effects linking sociodemographic characteristics to the latent variables.
“In particular, the results of SEM Models 1 and 2 reveal significant direct and indirect effects among the sociodemographic characteristic and latent variables under consideration.”
Quote in the paperSubstantial claim - Findingown data
When included in the model, travel behavior variables show no significant direct effect on discriminatory attitudes.
“However, no travel behavior variables have a significant direct effect on discriminatory attitudes when included in the model, suggesting that discrimination is specific more to the individual than to the mode of choice.”
Quote in the paperSubstantial claim - Findingown data
For class preferences, no effect appears for white respondents in majority white counties.
“Specifically, white respondents that live in majority white counties are more likely to hold discriminatory attitudes with regard to race (no effect is observed regarding class preferences).”
Quote in the paperNot a substantial claim - Findingown data
A person's general social dominance orientation strongly shapes their discriminatory attitudes in shared rides.
“The second finding is that one's generic social dominance orientation strongly influences his/her discriminatory attitudes in ridesharing, supporting the claim that behavior in shared mobility platforms can reflect long-standing social dominance attitudes.”
Quote in the paperSubstantial claim - Findingown data
For male respondents, the total effect on F_Race is 0.42, made up of a 0.30 indirect effect through F_SDO and a 0.12 direct effect.
“For example, being male has a total effect of 0.42 on F_Race, including the indirect effect of 0.30 via F_SDO and the direct effect of 0.12.”
Quote in the paperSubstantial claim - Findingown data
Every coefficient reported in Table 5 reaches the 0.99 confidence level.
“Table 5 also summarizes the standardized coefficients for the explanatory variables included in the structural equations that support the two models (all coefficients are significant at the 0.99 confidence level).”
Quote in the paperNot a substantial claim - Findingown data
White people in counties where whites are the majority are more likely to express race-based discriminatory attitudes.
“Specifically, white respondents that live in majority white counties are more likely to hold discriminatory attitudes with regard to race (no effect is observed regarding class preferences).”
Quote in the paperSubstantial claim - Findingown data
A respondent's race alone has no significant effect on discriminatory attitudes.
“Finding 3: A respondent's race per se does not have a significant effect on discriminatory attitudes, but the combination of race and environment does.”
Quote in the paperSubstantial claim - Findingown data
The coefficient of Percent GOP on F_Race is 0.623.
“The coefficient of Percent GOP on F_Race is 0.623.”
Quote in the paperNot a substantial claim - Findingown data
The study finds considerable variation among users in discriminatory attitudes toward shared rides.
“This paper explores the phenomenon of discriminatory attitudes in the shared ride and demonstrates substantial variation across users.”
Quote in the paperSubstantial claim - Findingown data
In SEM 1, male respondents have a coefficient of 0.341 on F_Class, predicting a male's F_Class score 0.341 standard deviations higher than a female's.
“In SEM 1, for example, the coefficient of male respondents on F_Class is 0.341, indicating that the model predicts a male respondent's F_Class factor score to be 0.341 standard deviations higher than a female respondent's.”
Quote in the paperNot a substantial claim - Findingown data
Living in counties with a larger share of the 2016 GOP vote has an effect on race preferences.
“Finding 2: For respondents that live in counties in which a larger share of the electorate voted for the Republican candidate in the 2016 presidential election, there is an effect on race preferences but no effect on class preferences.”
Quote in the paperSubstantial claim - Findingown data
The F_Race and F_Class factors are strongly correlated.
“Finding 6: The factors F_Race and F_Class are highly correlated with one another.”
Quote in the paperNot a substantial claim - Findingown data
Income is not significantly correlated with race preferences.
“Income has a significant direct effect on class preferences, but no significant correlation with race preferences.”
Quote in the paperSubstantial claim - Findingown data
Age does not significantly affect class or race preferences.
“Other characteristics, such as age and education, have no significant effect on class or race preferences.”
Quote in the paperSubstantial claim - Study design and conductown data
Each of the three factors has a reasonably strong Cronbach's alpha.
“Accounting for reverse-worded preferences and the number of items included in each factor, each of these three factors has a reasonably strong Cronbach's alpha measure.”
Quote in the paperSubstantial claim - Interpretationown data
Behavior in shared mobility platforms reflects long-standing social dominance attitudes.
“We also find that one's generic social dominance orientation strongly influences his/her discriminatory attitudes in ridesharing, supporting the claim that behavior in shared mobility platforms reflects long-standing social dominance attitudes.”
Quote in the paperSubstantial claim- Rests on an assumption: The six Likert-scale preference questions validly measure discriminatory attitudes in ridesharing.
- Study design and conductown data
Compared with TNC users, the respondents were fairly representative in gender and race.
“Compared to the population of TNC users, the respondents were fairly representative with regard to gender and race.”
Quote in the paperNot a substantial claim - Study design and conductown data
The four race-based preference items have a Cronbach's alpha of 0.842.
“A simultaneous linear regression of the four race-based preferences (i3, i4, i5, and i6), which have a Cronbach's alpha of 0.842.”
Quote in the paperNot a substantial claim - Premisenone given
Much sharing-economy research concentrates primarily on racial discrimination from whites to blacks.
“Additionally, much research on the sharing economy concentrates primarily on racial discrimination from whites to blacks.”
Quote in the paperSubstantial claim - Premisecitation
Experimental evidence indicates that Uber and Lyft platform design allows individual drivers to discriminate against riders.
“Seminal research by Ge et al. (2016), for example, provided experimental evidence that the design of Uber and Lyft's platforms allows for discrimination from individual drivers to riders (Ge et al., 2016).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Ge et al., 2016
Not checked in the free run.
- Premisecitation
Prior studies provide evidence of driver-to-passenger discrimination in ridehailing services from TNCs such as Lyft and Uber.
“Prior studies have provided evidence for driver-to-passenger discrimination in the context of ridehailing services provided by transportation network companies (TNCs) like Lyft and Uber.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Ge et al., 2016
Not checked in the free run.
- Study design and conductown data
The two class-based preference items have a Cronbach's alpha of 0.682.
“A simultaneous linear regression of the two class-based preferences (i1 and i2), which have a Cronbach's alpha of 0.682.”
Quote in the paperNot a substantial claim - Interpretationreasoning
The factors reveal underlying latent variables manifested by the observed indicators.
“In keeping with the logical similarity of the questions being grouped, it is reasonable to expect that these factors reveal 1 underlying latent variables manifested by the respective set of observed indicators.”
Quote in the paperSubstantial claim - Study design and conductown data
The eight social dominance orientation items have a Cronbach's alpha of 0.899.
“A simultaneous linear regression of eight additional Likert-scale questions measuring respondents' preference for social hierarchies and the domination of higher-status groups over lower-status groups in general.These statements have a Cronbach's alpha of 0.899.”
Quote in the paperNot a substantial claim - Premisereasoning
Existing research has largely focused on driver-to-rider discrimination.
“As demonstrated above, existing research has largely focused on the effect of discrimination from drivers to riders, in part due to the long history of such discrimination in transportation and other sectors.”
Quote in the paperSubstantial claim - Study design and conductown data
The sample was less representative in age, education, and income.
“The sample was less representative with regard to age (with an overrepresentation of those in the 25 to 34 year old age band), education (with an underrepresentation of graduate degrees), and income (with an overrepresentation of those in the $35,000 to $74,999 income band).”
Quote in the paperNot a substantial claim - Study design and conductreasoning
The relationships examined here are not causal.
“As noted in Section 3.3 the relationships explored in this research do not indicate causal relationships.”
Quote in the paperSubstantial claim - Study design and conductown data
The model fit is strong enough to support the hypothesized structure without post-hoc modifications.
“As such, the measures of fit are strong enough to obviate the need for post-hoc model modifications and the hypothesized models appear to fit the data well enough to support the hypothesized structure.”
Quote in the paperSubstantial claim - Interpretationown data
People who endorse social dominance orientation items are more likely to report discriminatory preferences for shared rides.
“This suggests that individuals who agree with social dominance orientation questions are also more likely to hold discriminatory preferences in the context of shared rides.”
Quote in the paperSubstantial claim - Premisecitation
The Waze carpool app introduced a feature letting riders choose drivers by ratings, mutual friends, gender, and other filters.
“In March 2018, the Waze carpool app (which connects commuters with fellow amateur drivers) launched a new feature that allows riders to select drivers based on ratings, mutual friends, gender, and other custom filters (Perez, 2018).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Perez, 2018
Not checked in the free run.
- Premisecitation
Brown's Los Angeles County study found that riders are less likely to share rides in racially or ethnically diverse neighborhoods.
“in her study of Lyft use in Los Angeles County, Brown found that riders are less likely to share rides in racially or ethnically diverse neighborhoods (Brown, 2018, p. 3).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Brown, 2018
Not checked in the free run.
- Study design and conductnone given
Because of social desirability bias, stated preferences probably understate discriminatory attitudes.
“As noted earlier, one major limitation of our research is that stated preferences are likely to under represent discriminatory attitudes due to social desirability bias.”
Quote in the paperSubstantial claim - Interpretationown data
Actions on shared mobility platforms can mirror enduring social dominance attitudes.
“The second finding is that one's generic social dominance orientation strongly influences his/her discriminatory attitudes in ridesharing, supporting the claim that behavior in shared mobility platforms can reflect long-standing social dominance attitudes.”
Quote in the paperSubstantial claim - Interpretationreasoning
A 30 percentage point increase in Percent GOP would increase predicted F_Race by 0.19 standard deviations.
“Because Percent GOP is presented as a decimal between 0 and 1, this means that an increase of 30 percentage points in Percent GOP would increase a respondent's predicted factor score by 0.19 standard deviations.”
Quote in the paperNot a substantial claim - Interpretationreasoning
White riders from less diverse areas may be more inclined to hold discriminatory views.
“Taking the share of a population that is white as a suitable proxy for the overall diversity of an area in the United States, this finding indicates that white riders living in less diverse communities may be more likely to harbor discriminatory attitudes.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The white share of a population is a suitable proxy for the overall diversity of an area in the United States.
- Interpretationown data
Discriminatory attitudes are tied more to the person than to the travel mode chosen.
“However, no travel behavior variables have a significant direct effect on discriminatory attitudes when included in the model, suggesting that discrimination is specific more to the individual than to the mode of choice.”
Quote in the paperSubstantial claim - Interpretationown data
If Uber or Lyft let riders state preferences about each other, some users might discriminate by race and class.
“Our results suggest that if Uber or Lyft were to allow riders to express preferences about one another, some users may discriminate against others based on race and class.”
Quote in the paperSubstantial claim - Interpretationreasoning
Some riders would likely use a feature that lets them state potentially discriminatory preferences about fellow passengers.
“It is reasonable to expect that some riders would also take advantage of a feature that allows them to indicate potentially discriminatory preferences for fellow passengers.”
Quote in the paperSubstantial claim
- May combine two claims. 16
- Classified as probably not a substantial claim. 11
- The quote only partly states this claim. 1
May combine two claims.
Findingown dataWomen with children report more discriminatory responses toward both race and class preferences, and the difference is significant.
“Finding 1: Male respondents and women with children have significantly more discriminatory responses to both race and class preferences.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataFactor loadings range from 0.75 to 0.86 for F_Class and F_Race and from 0.70 to 0.92 for F_SDO.
“These factor loadings, presented next to the appropriate item, range from 0.75 to 0.86 for F_Class and F_Race and from 0.70 to 0.92 for F_SDO.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIn the survey, 26.6% of respondents wanted to see a fellow passenger's profile photo and 33.4% wanted name, gender, and age before entering the ride.
“Indeed in our survey, many respondents indicated a preference for seeing fellow passenger's profile photo (26.6 percent) or name, gender, and age (33.4 percent) before entering the ride.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataF_SDO's effect is 0.73 on F_Race and 0.69 on F_Class.
“The impact of F_SDO on F_Race and F_Class respectively is also similar (0.73 and 0.69 respectively).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataFor income, the direct effect is significant for F_Class but not for F_Race.
“The opposite is true of income, which has a significant direct effect on F_Class, but not on F_Race.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe county's percent GOP vote has a significant total effect on F_Race but not on F_Class.
“In particular, whether a respondent is white in a majority white county and the percent GOP vote in the respondents' county have significant total effects on F_Race, but not F_Class.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataBeing white in a majority white county has a significant total effect on F_Race but not on F_Class.
“In particular, whether a respondent is white in a majority white county and the percent GOP vote in the respondents' county have significant total effects on F_Race, but not F_Class.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataMen report more discriminatory responses toward both race and class preferences, and the difference is significant.
“Finding 1: Male respondents and women with children have significantly more discriminatory responses to both race and class preferences.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductown dataOf 1,222 qualified respondents, 112 failed attention tests, leaving a final analysis sample of 1,110, of whom 841 had used dynamic ridesharing.
“Of 1,222 qualified respondents who completed the survey, 112 failed the attention tests.4 The final sample size of the analysis was 1,110 respondents, 841 of whom had previously used dynamic ridesharing.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Premisenone givenAdditional passengers increase overall trip time and create potential for passenger frustration.
“Of course, additional passengers increase the overall trip time for riders, creating the potential for frustration on the part of passengers.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
InterpretationreasoningRider-rider discrimination is plausible in uberPOOL and Lyft Shared rides.
“With uberPOOL and Lyft's Shared rides, rider-rider discrimination is also plausible.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown dataThe survey respondents were relatively young, male, white, and educated compared with the American population.
“As shown in Table 1, the survey respondents were relatively young, male, white, and educated in comparison to the American population.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Study design and conductnone givenFive additional attention and logical consistency tests were applied, and responses failing two or more were omitted.
“For this research, we also applied five further tests of attention and logical consistency to the completed responses (e.g., a respondent should not strongly agree with one preference and also with the opposite preference).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningThe researchers screened quick and thoughtless survey behavior with two basic attention check questions.
“As such, the researchers screened such behavior through two basic attention check questions”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Interpretationown dataFigure 1 and Tables 5 and 6 support the hypotheses stated earlier.
“The results presented in Figure 1 and Tables 5 and 6 confirm the hypotheses explained above.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe factor loadings indicate a strong association to the underlying latent factors with minimal variation among items.
“Each of these factor loadings has a p-value of less than 0.01, and can be interpreted as indicating a strong association to the underlying latent factors, with minimal variation among the items.”
Quote in the paperSubstantial claimMay combine two claims. The quote only partly states this claim.
Study design and conductcitationBoth hypothesized SEMs have good fit indices: CFI 0.97 and 0.963, TLI greater than 0.95, and RMSEA 0.059-0.072 for SEM 1 and 0.065-0.072 for SEM 2.
“Both hypothesized models have reasonably strong indices of fit, as indicated in Table 5. Comparative fit indices (CFIs) of 0.97 and 0.963 respectively indicate that the models exhibit good fit according to several general guidelines and recommendations (Bentler, 1990, MacCallum et al., 1996, Hu and Bentler, 1999).Similarly, the models also both have high Tucker-Lewis indices (TLIs) of greater than”
Quote in the paperSubstantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationZhang and Zhao created preference-based matching methods that include passenger preferences.
“Zhang and Zhao, for example, developed preference-based matching methods that go beyond efficiency criteria to incorporate passenger preferences.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Zhang and Zhao, 2018
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.
PremisecitationSome personal preferences are not socially acceptable, so planners should be wary of misusing preference-based matching.
“But as the authors of this paper point out, not all individual preferences are socially respectable and planners must be cautious of the potential misuse of such preference-based matching algorithms (Zhang and Zhao, 2018, p. 2).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Zhang and Zhao, 2018
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationMany riders hold discriminatory attitudes toward passengers of different social class and race.
“Among other preliminary findings, the research from Sarriera et al. indicated that many riders harbor discriminatory attitudes towards passengers of different social class and race.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Sarriera et al.
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductreasoningBecause race and ethnicity are hard to separate in measurement and modeling, the study combines them.
“While each of these four questions focuses either on race or ethnicity, in practice it is difficult to differentiate the two phenomenon in measurement and modeling, so they are combined for the purposes of this paper and referred to as racial or race preferences.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductnone givenThe study measured stated attitudes about discrimination in shared rides.
“This paper measured respondents' stated attitude about discrimination in the context of a shared ride.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenA limitation is that Likert-based scales are analyzed as continuous rather than interval data.
“While researchers often perform SEM and confirmatory factor analysis using Likert-scale responses, one limitation of such analysis is that it treats Likert-based scales as continuous data rather than continuous interval data.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningAsian identity may shape general social dominance attitudes but does not account for discrimination in shared rides.
“This finding suggests that this sociodemographic characteristic may influence social dominance attitudes in general, but does not explain discrimination in the shared ride context.”
Quote in the paperSubstantial claim
The paper relies on this without support.
No studies have provided evidence of rider-rider discriminatory attitudes in dynamic ridesharing.
“However, no studies to date have provided evidence of rider-rider discriminatory attitudes in the context of dynamic ridesharing.”
The paper relies on this without support.
No studies have considered variation of discriminatory attitudes by ridesharing user characteristics.
“Additionally, no studies have yet considered the variation of discriminatory attitudes in accordance with ridesharing user characteristics.”
The paper relies on this without support.
The six Likert-scale preference questions validly measure discriminatory attitudes in ridesharing.
“Six attitudinal questions within this last category are of special interest to this paper because they assess the existence of and potential for discrimination in ridesharing services through stated preferences.”
The authors state this as an assumption.
The white share of a population is a suitable proxy for the overall diversity of an area in the United States.
“Taking the share of a population that is white as a suitable proxy for the overall diversity of an area in the United States”
The paper relies on this without support.
Lyft Shared users paired with an additional passenger can see the name of that passenger when matched with a ride already in progress.
“Lyft Shared users who are paired with an additional passenger can view the name of their fellow passengers when they are matched with a ride already in progress.”
The paper relies on this without support.
Lyft Shared passengers may obtain further information about one another before entering the ride through Lyft's Facebook integration.
“Through Lyft's Facebook integration, it is also possible that Lyft Shared passengers can gain further information about one another before entering the ride.”
The paper relies on this without support.
The app gives this information well before the ride arrives, so a passenger can cancel if they judge the other passenger unsuitable.
“Because the app provides this information well before the ride arrives, the passenger is able to cancel the ride if he deems the other passenger unsuitable.”
The paper relies on this without support.
Currently, ridesharing matching algorithms focus on efficiency and ignore passengers' personal characteristics and preference for social interaction.
“Currently, ridesharing matching algorithms are efficiency-oriented and ignore passengers' personal characteristics and preference for social interaction, but this may not always be the case.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Significance claim conflicts with the coefficient table
MinorThe text says all explanatory-variable coefficients are significant at the 0.99 confidence level, but Table 5 includes coefficients without significance markers and others marked only at the 0.1 level.
“Table 5 also summarizes the standardized coefficients for the explanatory variables included in the structural equations that support the two models (all coefficients are significant at the 0.99 confidence level).”
“Demographics Male 0.341 * * * 0.423 * * * 0.056 0.121 * 0.416 * * * Has children -0.18 -0.145 -0.152 -0.114 -0.042”
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- Authors
- Middleton, S., & Zhao, J.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Zhao, Jinhua
- ORCID
- Zhao, Jinhua
- Venue
- Transportation
- Published
- 2019-06-12
- Volume
- 47
- Issue
- 5
- Pages
- 2391-2414
- ISSN
- 0049-4488 (ISSN-L)
- 1572-9435
- OpenAlex
- W2951598729
- Cited by
- 30
- References
- 46
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 6 · 2023: 2 · 2022: 3 · 2021: 8 · 2020: 1 · 2019: 1
- FWCI
- 6.87
- Type
- book chapter
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Transportation (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- MIT IDSS Seed Fund
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Transportation and Mobility Innovations
- Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
- Social Sciences › Sociology and Political Science › Social and Intergroup Psychology
- Keywords
- ride-sharing, discriminatory attitudes, ride-hailing, shared mobility, social dominance orientation, racial discrimination, classism, Uber, Lyft, gender differences, income, 2016 US presidential election
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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