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The Role of Service Quality and Expectations in Explaining Customer Complaints Silke I. Januszewski∗ University of California, San Diego This version: August 2004
Abstract Customer complaints are related to the experienced quality of a product or service. If product quality is unobservable ex ante, customer complaints may be driven by expectations as well as by the actually experienced quality. I test different hypotheses about the effects of actual quality and of expected quality on customer complaints in the U.S. airline industry. I find that there are fewer complaints when the actual quality of service is higher. In addition, higher expected quality leads to more complaints especially when the quality is worse than expected. The results imply that complaints are largely explained by disappointed expectations.
JEL code: L15, L93, M31
∗
I would like to thank Nancy Rose and Glenn Ellison for their advice, Richard Schmalensee and participants at the 2003 Berkeley-Stanford IO Fest for helpful comments, and the Alfred P. Sloan Foundation for financial support through the MIT Global Airline Industry Program.
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Introduction
Customer complaints express the dissatisfaction of consumers with a product or service which they purchased. It is important for firms to understand the sources of customer dissatisfaction in order to better provide remedies and prevent future dissatisfaction. A greater degree of dissatisfaction reduces the likelihood of repeat purchases from existing customers. Customer complaints may also reduce demand from new potential customers if the complaints become public information or if they are passed on by word of mouth. For example, Behn and Riley (1999) find that passenger complaints in the airline industry are negatively correlated with future revenues and profits. Srinivasan et al. (2002) find a negative effect of some measures of service quality, such as on-time arrivals, number of mishandled bags, and number of ticket oversales, on executive compensation. In this paper, I test three different hypotheses about the determinants of customer complaints. The first is that complaints are simply determined by the level of quality that customers experience. In this case, the lower the service quality the more complaints consumers file. Additional differences may exist across firms or over time which will be controlled for in the empirical estimation by including firm and year fixed effects. A second hypothesis is that consumers form rational expectations about the level of service quality to be received if the true quality is unobservable ex ante, and that customer complaints are driven by disappointed expectations. In this case, a lower level of service quality increases complaints as under the first hypothesis. Additionally, consumers’ prior expectations also influence the likelihood with which they file a complaint. In particular, consumers are more likely to be disappointed and complain if they expected a higher level of service quality. Therefore, the effect of expected quality on customer complaints should
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have the opposite sign as the effect of the actual delivered quality, i.e. we would expect a positive effect. Previous research by Boulding et al. (1999) has found that prior expectations can influence a consumer’s perception of the product or service quality that she received. Consumers may perceive the quality of a product to be higher if they expected to receive high quality prior to the purchase. As a result, higher expected quality may not lead to more complaints as under the second hypothesis but rather improve the consumer’s perception of the received quality and reduce the number of complaints. Under this third hypothesis, the predicted effect of expected quality on complaints is negative as is the predicted effect of actual quality. I test these hypotheses on data from the U.S. airline industry. The data set contains passenger complaints filed with the United States Department of Transportation (DOT) in the years 1988–2000. The data are reported for separate complaint categories by airline and month. For two of the complaint categories, the DOT also reports measures of actual service quality aggregated by airline and month. I use these data to study how actual service quality affects the number of complaints. In addition, I compute measures of expected service quality based on a rational expectations model and test how these expectations affect the complaint behavior of airline passengers. The rational expectations approach employed here is new to the literature on customer satisfaction and complaints. Existing studies derive consumers’ expectations from survey data which are often collected ex post and may therefore be biased by the actual consumption experience. Still, my findings are consistent with results from previous studies. I find that there are more customer complaints when actual service quality is lower, as predicted by the hypotheses outlined above. The expected level of service quality is found to have a
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positive effect on customer complaints, i.e. there are more complaints when passengers expected to receive higher quality. This result supports the second hypothesis which states that customer complaints are driven by disappointment relative to expectations. The result also suggests that prior expectations do not influence consumers’ perceptions so much as to cause complaints to be decreasing in expected quality. The results that I find are robust across a variety of different specifications. The size of the estimated effects of both actual and expected performance is quite large relative to the average number of complaints received. For example, the mean number of complaints about flight problems increases by 8.6 percent for a one percentage point increase in delayed flights and decreases by 6.1 percent for a one percentage point increase in expected flight delays. The mean number of complaints about baggage handling increases by 14.8 percent for each additional mishandled bag per thousand passengers and decreases by 13.2 percent for each additional expected mishandled bag per thousand passengers. The results imply that customer complaints are not only driven by the actual level of quality experienced but also by prior expectations, where expectations are derived from a rational expectations model. Customers with higher expectations are more likely to complain for a given level of quality. This finding links customer satisfaction not only to current quality but also to past levels of quality: Higher quality in the past leads to higher expectations and more complaints if the previous level of quality is not maintained. The remainder of the paper is organized as follows: Section 2 reviews previous studies on the relationship between actual quality, expected quality and customer satisfaction or complaints in the literature. Most of these studies come from the marketing literature as this topic has received little attention from economists so far. Section 3 describes the data
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used in this study. Section 4 outlines the model that is tested and describes the estimation of the rational expectations variable. Section 5 presents the results and section 6 concludes.
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Previous studies on customer satisfaction and complaints
Customer complaints have been studied extensively in the marketing literature, but have received little attention from economists (see Kolodinsky, 1995, for an assessment). The marketing literature uses three paradigms to explain the relationship between actual or perceived product quality, consumers’ expectations, and consumer satisfaction. The paradigm that is most closely related to this study is the so-called expectations disconfirmation paradigm. It assumes that dissatisfaction depends on prior expectations and on the disconfirmation of those expectations which occurs if the actual or perceived quality of a product is worse than expected (Oliver, 1980). A second paradigm which has been brought forward to explain customer complaints is equity theory which assumes that satisfaction is related to the customer’s perception of having received ’fair’ treatment. Lastly, attribution theory argues that satisfaction with a success or failure of an outcome is related to the attributed cause of the outcome, its variability, and its controllability by the firm. Satisfaction, in turn, influences complaint behavior. Maute and Forrester (1993) and Kolodinsky (1995) show that complaints are negatively related to the degree of satisfaction. Richins (1982) finds that, in addition, the decision to complain is influenced by individuals’ attitudes. In empirical tests of the expectations-disconfirmation paradigm, a number of studies have found that disconfirmation of prior expectations as well as prior expectations themselves in-
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crease customer satisfaction (see, for example, Oliver, 1980, and the references cited therein, p.461, Bearden and Teal, 1983, Oliver and deSarbo, 1988, and de Ruyter et al., 1993). In those studies, disconfirmation is defined such that better-than-expected performance increases satisfaction and worse-than-expected performance decreases satisfaction. Anderson and Sullivan (1993) find that the perceived quality and the disconfirmation of expectations positively affect consumer satisfaction, while Johnson et al. (1995) show that both expected and perceived quality increase customer satisfaction. These studies have in common that they test the effect of some combination of actual or perceived quality, s, expected quality, E(s), and disconfirmation, d, on satisfaction. However, if disconfirmation is defined mathematically as the difference between actual and expected quality, d = s − E(s), then the effects cannot be tested independently. In contrast to this literature, I will concentrate on the direct effects of actual and expected quality. For comparison, I will also report some results including a disconfirmation variable. In addition to a direct effect of prior expectations on satisfaction, expectations may also influence customer satisfaction indirectly by affecting the consumer’s perception of a product’s quality. This may especially be the case if the product quality is difficult to assess. We can view such effects in the context of confirmatory bias which implies that individuals give greater weight to information which confirms their prior beliefs than to information which contradicts their prior beliefs. Both the marketing literature and the behavioral economics literature have found evidence of such confirmatory bias (see, e.g. Boulding et al., 1999, and Rabin, 1998, for a review). In the context of customer satisfaction, confirmatory bias would lead consumers to be more likely to be satisfied with the quality of a product or service if they expected its quality to be high.
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3
Data
This study uses data for the airline industry which are collected by the U.S. Department of Transportation (DOT). Airline passengers can register air travel service problems with the DOT, independent of whether they have already filed a complaint with the airline itself or not. The DOT publishes the number of complaints it receives in its monthly Air Travel Consumer Report. The report aggregates complaints by airline and by the month in which the complaint is filed. The majority of complaints concerns incidents that occurred in the same month or one month prior to the filing of the complaint. There is no direct benefit to the passenger from filing a complaint with the DOT. In particular, the DOT does not mediate individual customer complaints. Registering a complaint mainly serves the purpose of informing the public about the incident. The complaints are registered in eleven different categories. Among these categories are flight problems, which encompass delays, cancellations and other schedule disruptions, and baggage, for claims concerning lost, damaged or delayed baggage. For these two categories, monthly data on each airline’s performance are also available in the Air Travel Consumer Report.1 The reported statistics are the number of mishandled baggage reports filed per thousand passengers and the on-time performance which is defined as the percentage of flights delayed more than fifteen minutes. As the complaint data, the performance data are aggregated by month and carrier for all routes. In addition, the performance data are reported only for major airlines. These are all U.S. airlines with at least one percent of total domestic scheduled-service revenues. 1
Performance data are also available for a third complaint category, ticket oversales. However, those data are only reported on a quarterly basis and for a much shorter time series. I therefore do not include that category in the analysis.
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The DOT has collected and published the information on passenger complaints, on-time performance and mishandled bags since 1988. This study includes data from January 1988 to September 2000 for nine major carriers: American Airlines, America West, Continental Airlines, Delta Airlines, Northwest Airlines, Southwest Airlines, TWA, United Airlines, and US Airways. Since 1995, more detailed, flight-level data on arrival times are available. These data allow to construct other measures of on-time performance. I will use the percentage of flights with long delays of over forty-five minutes as an alternative to the on-time statistics reported by the DOT. The results of my estimations are quite robust to which of these measures I use. Flight cancellations have only been reported since the second half of 1998. I do not include them in this study. Both the aggregated and the detailed on-time arrival data only report flight-level delays which may deviate from the average delays experienced by passengers. Bratu and Barnhart (2002) find for a set of proprietary data from one major airline that flights which transport a greater number of passengers are on average more delayed. In addition, the flight-level average does not account for passengers who miss connecting flights. Bratu and Barnhart demonstrate in simulations that the statistics published by the DOT tend to underestimate the true delays experienced by passengers. However, they also show that the published ontime statistics are positively correlated with the true delay so that the DOT statistics can be used as proxy for the true experienced delay. Previous studies on the relationship between expectations, actual quality and customer satisfaction or complaints have employed data from consumer surveys and questionnaires which ask participants ex post to report their ex ante expectations. It is possible that
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the responses to questions about prior expectations and disconfirmation are affected by the actual consumption experience. For example, a consumer of a high quality product may overstate her ex ante expectation of the product’s quality. For this reason, I instead use the concept of rational expectations, i.e. the expectations are defined to be the best prediction of actual performance given all available information at the time of purchase. I test the results for robustness using different prediction models to generate the expectations. Table 1 presents summary statistics on complaints and on service quality for the sample used in this study. The mean number of complaints about flight problems is 20.03 per month, with a standard deviation of 31.31. The minimum number of complaints is zero and the maximum is 507 complaints for a single airline in a single month. Complaints are effectively truncated at zero, and I will take account of that in the estimation by using Tobit regressions. The mean percentage of flights delayed over fifteen minutes is 21.22 with a minimum of 4.6 and a maximum of 63.9 during Northwest’s pilot strike in September 1998. For delays over 45 minutes, the mean is 6.5 percent with a minimum of 0 and a maximum of 24.45 percent. For mishandled bags, the number of complaints per month has a mean of 8.25 and a standard deviation of 9.06. Here, the minimum is again zero, and the maximum is 93 complaints. The average number of mishandled bags per thousand passengers is 5.51 with a minimum of 2.67 and a maximum of 18.96. On average, there are over 47,000 departures per airline and month with over 4 million enplaned passengers. Table 2 shows means and standard deviations by airline. There is substantial variation in complaints across airlines. Southwest Airlines receives the fewest complaints in absolute numbers and per passenger in both categories, whereas TWA receives the most complaints per passenger. The standard deviations within airline, reported in parentheses, are quite
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large. Flight delays and the number of mishandled bags also vary across airline, but much less so than the complaints. This suggests that airline-specific effects are important for the complaint behavior. I will account for this in the empirical estimation by including airline fixed effects.
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Model and Estimation
I model customer complaints as a function of actual performance, s, and expected performance, E(s), allowing for a constant effect β0 which can account for airline-specific or time-specific effects. Differences across airlines can arise, for example, if these firms differ in their complaint handling. Variations in complaint behavior across time may arise if awareness effects, such as newspaper reports, affect the filing of complaints. In the estimation, I will proxy for these effects by including year dummies.
Complaints = β0 + β1 s + β2 E(s)
(1)
This formulation allows us to test different hypotheses about individuals’ complaint behavior. The first hypothesis is that complaints purely depend on the quality of service received by the consumer. This would be true, for example, if individuals decide to complain only when their net benefit from filing a complaint is positive and if this net benefit was purely a function of the received quality but not of prior expectations. Under this hypothesis, the predicted effect of expected quality on the number of complaints, β2 , is zero and the predicted effect of the level of quality on complaints, β1 , is negative. The second hypothesis is that individuals decide to complain if they are disappointed
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with a service, i.e. if the received quality was lower than expected. This hypothesis implies that the number of complaints is decreasing in the level of quality and increasing in the expected level of quality. Under this hypothesis, β1 is positive and β2 is negative. A related hypothesis is based on the expectations-disconfirmation paradigm described in section 2. This hypothesis predicts a negative effect of prior expectations as well as of the deviation of actual quality from expectations, or the disconfirmation. In the formulation of equation 1, the predicted direct effect of actual quality, β1 is negative while the direct effect of expectations, β2 is ambiguous. Lastly, we can test the hypothesis that confirmatory bias determines the effect of expectations on customer complaints. Under this hypothesis, high expected quality leads to high perceived quality and therefore fewer complaints. The predicted coefficient for the level of quality is negative as is the predicted coefficient for expected quality. Only a fraction of dissatisfied consumers complain, either due to differences in the costs of complaining across individuals or due to different attitudes towards complaining as described, for example, in Richins (1982). Variations in actual and expected quality may affect the number of dissatisfied consumers as well as the likelihood that a dissatisfied consumer will complain. I am unable to separately identify these two effects with the data used in this study. The DOT data on the quality of on-time arrivals and baggage handling are average values for each airline and month, not transaction-level data. I will therefore test the predictions of the hypotheses outlined above based on average experienced and expected quality. The rest of this section will proceed to describe the estimation of passengers’ expectations before I present the results on customer complaints in section 5.
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4.1
Estimating expectations
In order to generate the passengers’ expectations for on-time arrival and baggage-handling, I estimate an econometric model for both variables and assume that the predictions from this model are equal to the passengers’ expectations. The percentage of delayed flights and the number of mishandled bags per passenger are estimated as separate functions of the number of the airline’s departures and enplaned passengers, periods of bankruptcy and strikes, and month and carrier fixed effects. The error term is assumed to be first-order autoregressive allowing for correlation of current performance with past performance. Higher orders of serial correlation were tested but are rejected by the data. The estimation equation is the following:
sjt =αj + αm + αn + γ1,j departuresjt + γ2,j passengersjt + γ3,j bankruptcyjt
(2)
+ γ4,j strikejt + νjt
where s stands for delays or baggage handling, and j and t indicate the carrier and the time period, respectively. αj , αm , and αn are carrier, month, and year fixed effects, respectively. νjt is a first-order autoregressive error term. The number of the airline’s total departures and enplaned passengers are included as proxies for the size of operations and for the load factor which, in turn, affect on-time performance and baggage handling. The bankruptcy and strike variables are dummy variables which are equal to one if airline j experiences a bankruptcy or a strike in period t. Periods of bankruptcy and strike are rare but can severely disrupt operations. I estimate several specifications based on equation 2. The results are reported in table
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3 for flight delays and in table 4 for baggage handling as the dependent variable. The first specification, reported in column 1, constrains the slopes for all the explanatory variables to be common across airlines. The second specification, reported in column 2, assumes common slopes for the number of passengers and of departures but airline-specific slopes for the effects of bankruptcies and strikes. This assumes common effects of the size of operations and the load factor but allows for the fact that bankruptcies and strikes, which can be quite idiosyncratic events, can have different impacts on the on-time and baggage handling performance. Finally, the specification in column 3 allows for airline-specific slope coefficients of all explanatory variables as in equation 2. The results in column 1 of table 3 show that when the slopes are constrained to be common across airlines, the percentage of flights delayed over 15 minutes is increasing in the number of enplaned passengers. This reflects the fact that planes tend to be more delayed in high-demand periods due to congestion effects. The number of total domestic departures has a negative effect on the percentage of flight delays. Airlines tend to add departures at small and medium-sized airports which are less congested than the larger ones, and the variation in departures within carriers is driven by this effect. Periods of bankruptcy are estimated to have a negative coefficient in this estimation but statistically the effect cannot be distinguished from zero. Periods of restructuring under bankruptcy protection may have a composition effect that leads to fewer flight delays if carriers in bankruptcy reduce the number of flights out of congested airports. Strikes are estimated to increase the percentage of delayed flights. The effect is quite large with flight delays estimated to be 9 percent higher during strike periods. The month fixed effects are also quite large. The omitted month is January which together with the month of December has the longest delays. The shortest
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delays are estimated to be in the spring and in the fall which are the months with the fewest weather disruptions. The coefficient for the first-order autoregressive term, which is not reported in the table, is estimated to be 0.69. The second column of table 2 shows the results for the specification with interaction terms for each carrier with the bankruptcy and strike variables, allowing for carrier-specific effects of these variables. The coefficients for the interaction terms are not reported but are available upon request. None of the bankruptcy effects are statistically different from zero. The largest strike effect is for Northwest Airlines which experienced a five-month long flight attendant strike in 2000. This effect is estimated to have increased flight delays by over 19 percentage points. The other strikes, for American Airlines and US Airways, did not have effects that were significantly different from zero. This is likely because these strikes were much shorter. The other coefficients in this specification are similar to the effects reported in column 1. The last column of table 3 contains the results for the specification with carrier interactions for the number of passengers, the number of departures, and bankruptcy and strike periods. The interaction terms are jointly significant for all but the bankruptcy variables. The main effects are similar to the ones reported before. However, the regression fit as measured by the R-squared, is very low. Most of the results presented in section 5 will be based on expectations predicted from the second specification. I will also report some results based on the more flexible third specification. All three specifications yield very similar results when estimating the effects of expected performance on customer complaints. Table 4 shows the same estimations for the other measure of service quality, the number of mishandled bags per thousand passengers. Here, the only statistically significant effects
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in the first two specifications are the month fixed effects. In column 3, the interaction terms between carrier fixed effects and the number of enplaned passengers are jointly significant. Most of the coefficients on the number of passengers are positive, as they were in the previous table for flight delays. The number of departures has no statistically significant effect on mishandled baggage. We also cannot reject that the effects of bankruptcies and strikes are equal to zero. In the case of strikes, this may be due to the fact that all strikes during this period were by pilots or flight attendants who are not directly involved in baggage handling. The autoregressive terms are all estimated to have coefficients of 0.78. As in the case of flight delays, I form predicted values for mishandled baggage in each period based on the specifications in column 2 and 3. The main results in section 5 will be based on the second specification and I will present robustness checks based on the third specification. The results for passenger expectations predicted from all three specifications are very similar and I cannot reject statistically that they are equal.
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Results
I estimate the effect of the expected and of the actual quality of service on the number of complaints per million passengers in order to test the hypotheses outlined in section 4 above. The estimation uses Tobit regressions to account for the censoring of the dependent variable at zero. I begin with a specification which only estimates the effect of actual quality on the number of complaints and then add the expected quality as another regressor. As a measure of expected quality, I use the predicted values from the estimations of the previous section. All reported standard errors are bootstrapped because the expected quality is a predicted value from another estimation. I first report a specification without year and carrier fixed 15
effects. Next, I add year fixed effects only. The third specification includes year and carrier fixed effects. I include year and carrier fixed effects in the regressions in order to control for variations in the propensity to complain over time and across carriers. Variations over time may occur, for example, due to unobserved changes in the costs of filing a complaint over time. In addition, there may be awareness effects. In periods in which, for example, news media provide extensive coverage of service quality problems in the airline industry, passengers may be more aware of service quality and be more likely to complain. In my data, there is a positive correlation between the number of newspaper reports about airline service quality in the previous year and the number of complaints, after controlling for actual and expected quality. Carrier fixed effects are included to control for systematic differences in the number of complaints across carriers that go beyond the effect of expectations. These differences may arise, for example, due to differences in complaint handling. Table 5 reports the results for flight delays. The dependent variable is the number of complaints about flight problems per million passengers. Column 1 shows results of a Tobit regression with the actual percentage of delayed flights as the only regressor. The estimated effect is 0.431 and is statistically highly significant. Columns 2 and 3 of table 5 add year effects and year and carrier effects, respectively, to the regression. The coefficient estimate on actual delays decreases only slightly to 0.374 and 0.340, respectively. Next, I include the expected percentage of flight delays as a regressor to test the hypothesis that prior expectations influence the complaint behavior of airline passengers. Column 4 of table 5 shows the results without year and carrier fixed effects. The coefficient on actual delays is 0.454, only a small change compared to the specification which excluded the
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expected value. The expected value itself is estimated to have a negative effect of –0.101, which is substantially smaller than the effect of the actual performance. When year and carrier fixed effects are not included, the effect of expectations is only marginally significant. Once we control for those fixed effects, the coefficient on the expected value increases in magnitude to –0.299 and is quite precisely estimated. The coefficient on actual delays remains very robust across the different specifications. The results from this specification imply that a one percentage point increase in flight delays increases the number of complaints by 0.420 per million passengers or 1.7 additional complaints per month for the average airline, an 8.5 percent increase in the average number of complaints in this category. A one percentage point increase in expected flight delays decreases the number of complaints by 0.299 per million passengers or 1.2 additional complaints per month for the average airline, a decrease of 6.1 percent relative to the average number of complaints in this category. The last column of table 5 shows the regression with the expected value of flight delays based on the third specification, with full carrier interactions, that was presented in section 4.1. This is to show that the results of the complaint regressions are quite robust to using different specifications for predicting the expected values. The coefficients change only by a small amount compared to column 6, and none of the differences is statistically significant. In sum, table 5 shows that passenger complaints are increasing in the actual number of delays and decreasing in the number of expected delays. This is true in specifications which have no other control variables as well as in specifications which include year and carrier fixed effects. Next, we turn to the other measure of service quality, the number of mishandled bags. Results for this variable are reported in table 6. This table follows the table 5 in its organiza-
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tion. I start by presenting results including only the actual level of quality as a regressor and proceed to add the expected value to test whether it has a significant effect on the number of complaints, presenting results with and without year and carrier fixed effects. The quality variable in these regressions is reported as mishandled bags per thousand passengers. The dependent variable is the number of complaints about baggage problems per one million passengers. Qualitatively, the results in this complaint category are very similar to the ones for flight delays. The actual number of mishandled bags has a positive effect on the number of complaints. The coefficient estimate is 0.429 in the specification without fixed effects, and 0.199 when year and carrier effects are included. When we include the expected number of mishandled bags, the results show that complaints are increasing in the actual number of mishandled bags and decreasing in the expected number of mishandled bags. The coefficients on actual mishandled bags increase slightly compared to the previous specifications which did not include the expected value. The coefficients on both the actual value and the expectation decrease in magnitude when year and firm fixed effects are included. In the specification with both fixed effects, reported in column 6, the estimated effects are 0.300 for actual mishandled bags and –0.266 for expected mishandled bags. This means that one additional mishandled bag per one thousand passengers increases the number of complaints by 0.300 per million passengers or 1.2 complaints per month for the average airline. This is a 14.8 percent increase compared to the average number of complaints about mishandled baggage in a month. An increase in the expected number of mishandled bags by one per thousand passengers decreases the number of complaints by 0.266 per million passengers or 1.1 complaints per month for the average airline, a decrease of 13.2 percent relative to the average number of complaints in this cat-
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egory. The last column of table 6 shows that the results are robust to using the alternative measure of expectations which is based on the specification with full carrier interactions. Overall, the results show that for both complaint categories the number of complaints per passenger is decreasing in the actual quality of service – the inverse of flight delays or mishandled baggage – and increasing in the expected level of service quality. This provides evidence in favor of the hypothesis that customer complaints are driven by disappointment. Lower actual quality causes customers to complain more often. However, complaints are not driven by actual quality alone. The expected level of quality, as predicted by a model of rational expectations, also has an effect on the likelihood of filing a complaint. The higher the quality that customers expected to receive, the greater the potential for disappointment. Consistent with this hypothesis, the number of complaints increases with the expected level of quality. The rest of this section will present some further results and robustness checks. Tables 7 and 8 contain results from a restricted version of the regressions already presented. Following the hypothesis that complaints are driven by disconfirmation, I regress the number of complaints on the difference between the actual quality and the expected quality, s − E(s). This effectively imposes the constraint that β2 = −β1 in equation 1 above. The results in tables 7 and 8 show that complaints are increasing in this difference. For flight delays, the coefficients range between 0.381 and 0.438 in the three specifications without fixed effects, with year fixed effects and with year and carrier effects. For mishandled bags, in table 8, the estimated effects are between 0.300 and 0.615. Next, I decompose these effects into a negative difference between actual and expected flight delays or mishandled baggage, i.e. a better-than-expected performance, and a positive
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deviation from the expectation, i.e. a worse-than-expected performance. Interestingly, for flight delays I find that worse-than-expected performance has a large effect and increases the number of complaints, whereas better-than-expected performance has no statistically significant effect on complaints. This is consistent with the fact that complaints are a truncated measure of customer satisfaction: Only dissatisfied consumers complain while consumers who experience better-than-expected performance do not register their satisfaction level. For flight delays, we observe this effect for all three specifications, i.e. with and without controlling for year and carrier fixed effects. In the category of mishandled baggage, the effects of better-than-expected performance and worse-than-expected performance are both significantly positive when we do not include carrier fixed effects. Better-than-expected performance is defined as the difference between the actual and the expected number of mishandled bags, s − E(s), if s < E(s) i.e. it is a negative number. A positive coefficient on this variable therefore means that passengers file fewer complaints when there are fewer mishandled bags than expected. The coefficients on better-than-expected performance are smaller than the ones on worse-than-expected performance, suggesting that the latter have a stronger effect on the complaint behavior. When carrier effects are included, however, we find a positive and statistically significant effect only for worse-than-expected performance while the effect of better-than-expected performance can statistically not be distinguished from zero, as in the case of flight delays. These results are consistent with previous studies which have found that ”disconfirmation” affects customer satisfaction. Here, I define disconfirmation as the difference between actual and expected performance and find that worse-than-expected performance increases the number of complaints. The fact that these results are consistent is interesting because
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the existing studies in the marketing literature derive consumers’ expectations from surveys whereas here I use a rational expectations approach to construct a measure of expected performance. Some robustness checks for the results on flight delays are presented in table 9. These results are from estimations on a sample which includes data from 1995 to 2000 only. The DOT changed its reporting of on-time arrivals at the beginning of 1995. Before this date, flights which were delayed due to mechanical problems were not included in the DOT’s statistics but after 1994 on-time arrival rates were computed based on all flights. The purpose of these estimations is to test whether the estimated effects for on-time arrivals changed significantly after 1994. All specifications reported in this table include year and carrier fixed effects. Column 1 starts with an estimation which includes actual flight delays only. The next column also includes expected flight delays as an explanatory variable. Both estimations show a positive effect of actual flight delays on complaints and a negative effect of expected flight delays on complaints, as before. The coefficient estimates are slightly larger than for the sample which includes all years from 1989 to 2000 but the differences are not statistically significant. Column 3 shows that when the difference between actual and expected complaints is used as the explanatory variable, the effect is again positive. It is also a little bit larger than the one computed for the entire sample. The same is true when this effect is split up into positive and negative deviations from the expectation. Overall, the effects for this later sample have point estimates which are larger in magnitude than the estimates for the entire sample but the differences in the estimates are not statistically significant. After 1994, the DOT also started to collect more detailed data on flight delays which allow
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to construct other measures of aggregate on-time arrivals as an alternative to the summary statistic published by the DOT, the percentage of flights delayed over 15 minutes. Column 5 of table 9 shows the results of a specification which uses the percentage of flights that are over 45 minutes delayed rather than over 15 minutes delayed. This formulation can capture the effect of very long flight delays on complaints. As for the shorter delays, I find that the number of complaints about flight problems is increasing in the actual percentage of long delays and decreasing in the expected percentage of long delays. The coefficient estimates are 0.817 for actual delays and –0.367 for expected delays. Further robustness checks which were performed but are not reported here include specifications which regress the number of complaints in the current month on the actual and the expected service quality of the previous month. The reason for these robustness checks is that complaints are reported for the month in which they are filed which includes incidents from the current month as well as from past months. The majority of these incidents are from the current and from the previous month. The results for specifications using the actual and the expected service quality from the previous month rather than from the current month are similar to the results presented here. None of the estimated effects change sign. The coefficient estimates are generally smaller in magnitude, indicating that the data from the previous month have a smaller effect on customer complaints than data from the current month.
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Conclusion
This paper examines the effects of actual service quality and of expected service quality on customer complaints in the U.S. airline industry. I find that customer complaints about flight 22
delays and about mishandled baggage are increasing in the level of actual service quality and decreasing in the level of expected quality. The effects are robust across a number of different specifications. When the impact of actual quality and of expectations is decomposed into the effects of worse-than-expected and better-than-expected performance, I find that the former is most important in explaining passenger complaints. In most specifications, better-thanexpected performance has no significant effect on the number of complaints. The use of the concept of rational expectations rather than of survey data in the estimation is new to this literature. My findings support the hypothesis that customer complaints are largely driven by disappointed consumers who received worse service than they expected. We also learn from the results that if consumers should rationally expect lower quality from a given airline or in a given time period, they will not complain unless they the received quality is even worse than expected. The findings support the notion that consumers form rational expectations of the service quality to be received and that these expectations influence customer satisfaction. Since expectations are partly based on past performance, the results further imply that the customer satisfaction in any given period is determined not only by the current quality level but also by past levels of performance. In particular, a decrease in service quality relative to the past will result in more customer complaints and may reduce demand for this airline. The effect of prior expectations on customer satisfaction is another channel in addition to reputation effects through which past quality can affect current demand. Future research should try to link customer complaints and quality closer to the demand for a product or service. In order to investigate this link, one could use transactions-level data on customer complaints, quality, quantity, and price. The data set used in this study
23
cannot be used for this purpose because, while prices and some information on service quality are available at the level of individual routes, passenger complaints for each airline are only reported in aggregated figures for the entire United States. However, this study has been able to establish the relationship between expected and actual service quality and customer complaints.
24
References [1] Anderson, E.W. and M.W. Sullivan (1993), “The Antecedents and Consequences of Customer Satisfaction for Firms”, Marketing Science 12(2), 125-143. [2] Bearden, W.O. and J.E. Teel (1983), ”Selected Determinants of Consumer Satisfaction and Complaint Reports”, Journal of Marketing Research 20. [3] Behn, B.R. and R.A. Riley (1999), “Using Nonfinancial Information to Predict Financial Performance: The Case of the U.S. Airline Industry”, Journal of Accounting, Auditing and Finance, 29-55. [4] Boulding, W., A. Kalra, and R. Staelin (1999), “The Quality Double Whammy”, Marketing Science 18(4), 463-484. [5] Bratu, S., and C. Barnhart (2002), “A Study of Passenger Delay for a Major Hub-andSpoke Airline”, mimeo, MIT Center for Transportation and Logistics. [6] De Ruyter, K., J. Bloemer, and P. Peeters (1997), “Merging service quality and service satisfaction. An empirical test of an integrative model”, Journal of Economic Psychology 18, 387-406. [7] Folkes, V.S., S. Koletsky, and J.L. Graham (1987), “A Field Study of Causal Inferences and Consumer Reaction: The View from the Airport”, Journal of Consumer Research 13(4), 534-539. [8] Johnson, M.D., E.W. Anderson, and C. Fornell (1995), “Rational and Adaptive Performance Expectations in a Customer Satisfaction Framework”, Journal of Consumer Research 21, 695 - 707. 25
[9] Kolodinsky, J. (1995), ”Usefulness of Economics in Explaining Consumer Complaints”, Journal of Consumer Affairs 29(1), 29-54. [10] Maute, M.F. and W.R. Forrester (1993), “The structure and determinants of consumer complaint intentions and behavior”, Journal of Economic Psychology 14, 219-247. [11] Oliver, R.L. (1980), “A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions”, Journal of Marketing Research 42, 460-467. [12] Oliver, R.L. and W.S. DeSarbo (1988), “Response Determinants in Satisfaction Judgments”, Journal of Consumer Research 14, 495-507. [13] Rabin, M. (1998), “Psychology and Economics”, Journal of Economic Literature 36(1), 11-46. [14] Richins, M. (1982), ”An Investigation of Consumers’ Attitudes Towards Complaining”, in Advances in Consumer Research IX, A. Mitchell (ed.), Ann Arbor, MI: Association for Consumer Research, 502-506. [15] Srinivasan, D., A. Sayrak, and N.J. Nagarajan (2002), “Executive Compensation and Non-financial Performance Measures: A Study of the Incentive and Value Relevance of Mandated Non-financial Disclosures in the U.S. Airline Industry”, mimeo, University of Pittsburgh. [16] U.S. Department of Transportation, Air Travel Consumer Report, various issues.
26
601 1215 1215 1215 1215
Delays over 45 minutes, in percent
Complaints about mishandled bags
Mishandled bags per 1000 passengers
Departures (thousands)
Passengers (million)
21.49 (6.17) 6.84 (2.87) 14.56 (11.76) 5.32 (1.33) 5.61 (0.75)
Delays over 15 minutes, in percent
Delays over 45 minutes, in percent
Complaints about mishandled bags
Mishandled bags per 1000 passengers
Passengers (million)
2.91 (0.32)
5.33 (2.23)
4.14 (3.84)
7.64 (3.00)
21.66 (8.79)
12.01 (19.27)
America West
4.08
47.02
5.51
8.25
6.50
21.22
20.03
Mean
7.16 (1.17)
5.16 (1.45)
8.03 (6.58)
6.45 (2.43)
21.60 (4.96)
16.51 (14.45)
Continental Airlines
1.95
19.86
1.76
9.06
3.04
7.18
31.31
Std. Dev.
1.44 (0.18)
5.38 (1.44)
8.21 (7.52)
5.37 (1.98)
22.51 (5.46)
19.24 (20.78)
Delta Airlines
0.38
13.83
2.67
0
0
4.6
0
Min
3.42 (0.58)
5.97 (1.64)
6.99 (6.95)
6.32 (2.43)
19.28 (7.61)
21.50 (19.60)
Northwest Airlines
9.34
86.892
18.96
93
24.45
63.9
507
Max
1.80 (0.26)
4.12 (0.70)
2.34 (1.95)
5.04 (2.00)
16.80 (6.32)
2.37 (2.49)
Southwest Airlines
Standard deviations in parentheses. Source: Department of Transportation, Air Travel Consumer Report , various issues. Data are averages over the years 1988-2000, except arrival within 45 minutes of schedule which is only available since 1995.
32.63 (35.37)
Complaints about flight problems
Carrier
American Airlines
1215
Delays over 15 minutes, in percent
Table 2: Descriptive Statistics by Carrier
1215
Observations
Complaints about flight problems
Variable
Table 1: Descriptive Statistics
5.45 (0.91)
6.24 (2.00)
9.74 (8.91)
6.68 (3.50)
21.70 (7.56)
16.24 (14.41)
TWA
4.70 (0.62)
6.43 (1.55)
13.27 (13.87)
8.45 (4.03)
24.30 (7.92)
36.49 (65.75)
United Airlines
4.27 (1.61)
5.61 (1.92)
6.93 (6.67)
5.71 (3.07)
21.61 (6.66)
23.25 (29.12)
US Airways
Table 3: Regressions of Percentage of Flights Delayed over 15 minutes on Explanatory Variables. (1) (2) (3) Enplaned passengers (millions)
1.642
1.489
0.556
(0.455)
(0.454)
(0.611)
-0.224
-0.202
-0.150
(0.057)
(0.057)
(0.132)
-1.924
-2.937
-3.244
(1.259)
(3.790)
(3.725)
9.011
2.649
1.999
(1.566)
(2.218)
(2.275)
-3.577
-3.396
-4.560
(0.486)
(0.485)
(0.501)
-5.403
-5.311
-7.447
(0.638)
(0.633)
(0.664)
-9.060
-8.972
-10.869
(0.694)
(0.689)
(0.702)
-9.299
-9.187
-10.911
(0.740)
(0.735)
(0.736)
-5.337
-5.104
-7.849
(0.806)
(0.802)
(0.836)
-6.384
-6.155
-8.892
(0.816)
(0.813)
(0.852)
-5.973
-5.748
-8.873
(0.813)
(0.810)
(0.864)
-10.064
-9.988
-11.224
(0.721)
(0.717)
(0.700)
-9.104
-8.895
-10.180
(0.701)
(0.698)
(0.694)
-7.442
-7.184
-8.840
(0.640)
(0.636)
(0.644)
-0.412
-0.261
-1.717
(0.506)
(0.502)
(0.512)
Carrier fixed effects
Yes
Yes
Yes
Bankruptcy*Carrier interactions
No
Yes
Yes
Strike*Carrier interactions
No
Yes
Yes
Departures*Carrier interactions
No
No
Yes
Passengers*Carrier interactions
No
No
Yes
1458
1458
1458
0.1834
0.1927
0.0601
Departures (thousands) Bankruptcy Strike Month=February Month=March Month=April Month=May Month=June Month=July Month=August Month=September Month=October Month=November Month=December
Observations R-squared Standard errors in parentheses
Table 4: Regressions of Mishandled Bags per 1000 Passengers on Explanatory Variables. (1) (2) (3) Enplaned passengers (millions) Departures (thousands) Bankruptcy Strike Month=February Month=March Month=April Month=May Month=June Month=July Month=August Month=September Month=October Month=November Month=December Carrier fixed effects Bankruptcy*Carrier interactions Strike*Carrier interactions Departures*Carrier interactions Passengers*Carrier interactions Observations R-squared Standard errors in parentheses
-0.036
-0.038
-0.030
(0.094)
(0.095)
(0.124)
0.009
0.007
0.009
(0.013)
(0.013)
(0.031)
-0.223
-0.319
-0.365
(0.287)
(0.743)
(0.750)
0.037
0.136
0.113
(0.310)
(0.436)
(0.463)
-1.337
-1.342
-1.410
(0.097)
(0.098)
(0.107)
-1.419
-1.415
-1.670
(0.128)
(0.128)
(0.142)
-2.291
-2.289
-2.491
(0.139)
(0.140)
(0.149)
-2.290
-2.282
-2.495
(0.151)
(0.151)
(0.159)
-1.437
-1.435
-1.753
(0.165)
(0.165)
(0.183)
-1.294
-1.283
-1.633
(0.168)
(0.169)
(0.188)
-1.116
-1.106
-1.495
(0.169)
(0.170)
(0.193)
-2.017
-2.009
-2.136
(0.146)
(0.146)
(0.150)
-2.042
-2.046
-2.222
(0.142)
(0.142)
(0.148)
-1.759
-1.760
-1.918
(0.124)
(0.125)
(0.132)
0.807
0.803
0.645
(0.096) Yes No No No No 1470 0.2243
(0.097) Yes Yes Yes No No 1470 0.2357
(0.104) Yes Yes Yes Yes Yes 1470 0.0691
no no 1188
Year effects Carrier effects Observations
yes no 1188
--
0.374 (0.062)
(2)
yes yes 1188
--
0.340 (0.052)
(3)
no no 1188
-0.101 (0.064)
0.454 (0.064)
(4)
no no 1197
--
0.429 (0.059)
yes no 1197
--
0.376 (0.053)
(2)
yes yes 1197
--
0.199 (0.050)
(3)
no no 1197
-0.704 (0.121)
0.616 (0.078)
(4)
yes no 1197
-0.641 (0.091)
0.577 (0.067)
(5)
yes no 1188
-0.141 (0.078)
0.408 (0.073)
(5)
Results from Tobit regressions. Bootstrapped standard errors are in parentheses. Mishandled bags are per thousand passengers. * The expectation is the predicted value from the regression with full carrier interactions (column 3 in tables 3 and 4).
Year effects Carrier effects Observations
Expected mishandled bags
Actual mishandled bags
(1)
Table 6: Dependent variable is the number of complaints about mishandled baggage per million passengers
Results from Tobit regressions. Bootstrapped standard errors are in parentheses. Flight delays are in percent.
--
0.431 (0.072)
Expected delays
Actual delays
(1)
Table 5: Dependent variable is the number of complaints about flight problems per million passengers
yes yes 1197
-0.266 (0.090)
0.300 (0.058)
(6)
yes yes 1188
-0.299 (0.069)
0.420 (0.076)
(6)
yes yes 1197
-0.333 (0.062)
0.332 (0.056)
(7)*
yes yes 1188
-0.265 (0.091)
0.422 (0.071)
(7)*
yes no 1197
0.522 (0.091)
0.608 (0.125)
(5)
yes no 1188
-0.023 (0.050)
0.713 (0.127)
(5)
Results from Tobit regressions. Bootstrapped standard errors are in parentheses. Mishandled bags are per thousand passengers.
yes yes 1197
no no 1197
yes no 1197
year effects carrier effects Observations
no no 1197
0.472 (0.099)
0.300 (0.059)
(4)
Negative deviation from expectation (better than expected)
0.577 (0.057)
(3)
0.710 (0.131)
0.615 (0.074)
(2)
Positive deviation from expectation (worse than expected)
(Actual - expected) mishandled bags
(1)
Table 8: Dependent variable is the number of complaints about mishandled baggage per million passengers
Results from Tobit regressions. Bootstrapped standard errors are in parentheses. Flight delays are in percent.
yes yes 1188
no no 1188
yes no 1188
year effects carrier effects Observations
no no 1188
-0.093 (0.066)
0.412 (0.069)
(4)
Negative deviation from expectation (better than expected)
0.381 (0.071)
(3)
0.876 (0.131)
0.438 (0.081)
(2)
Positive deviation from expectation (worse than expected)
(Actual - expected) delays
(1)
Table 7: Dependent variable is the number of complaints about flight problems per million passengers
yes yes 1197
-0.006 (0.097)
0.463 (0.114)
(6)
yes yes 1188
0.035 (0.057)
0.688 (0.112)
(6)
yes yes 486
Results from Tobit regressions. Bootstrapped standard errors are in parentheses. Flight delays are in percent.
yes yes 486
yes yes 486
-0.367 (0.079)
Expected delays over 45 minutes
year effects carrier effects Observations
0.817 (0.239)
(5)
Actual delays over 45 minutes
-0.121 (0.098)
0.558 (0.118)
(4)
Negative deviation from expectation (better than expected)
yes yes 486
-0.502 (0.115)
0.565 (0.093)
(3)
0.892 (0.153)
yes yes 486
0.460 (0.125)
(2)
Positive deviation from expectation (worse than expected)
(Actual - expected) delays
Expected delays
Actual delays
(1)
Table 9: Robustness checks for flight delays. Only observations after 1995. Dependent variable is the number of complaints about flight problems per million passengers
