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Transportation Research Part E 48 (2012) 743–754

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Linkages between customer service, customer satisfaction and performance in the airline industry: Investigation of non-linearities and moderating effects Adams B. Steven ⇑, Yan Dong, Martin Dresner Robert H. Smith School of Business, University of Maryland, College Park, MD 20742, United States

a r t i c l e

i n f o

Article history: Received 28 April 2011 Received in revised form 27 September 2011 Accepted 8 December 2011

Keywords: Customer service Customer satisfaction Firm performance Market structure Market power Non-linear relationship Moderated relationship Optimal performance

a b s t r a c t The paper investigates the linkages between customer service, customer satisfaction, and firm performance in the US airline industry. In particular, the moderating effects of market concentration and firm dominance on the service–satisfaction–performance relationship are examined. Our major finding is that market concentration dampens the relationship between customer satisfaction and airline profitability. Although the same moderating relationship was not found for market power, these results, combined, indicate that airlines can increase profits in concentrated markets without providing for the same, concomitant increases in customer satisfaction as airlines operating in more competitive markets. From a public policy perspective, our results point to the importance of regulators monitoring airline actions, such as mergers and alliances, that serve to increase the concentration of markets, but may result in lower levels of customer satisfaction. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction There has been considerable research done on investigating the relationship between customer service, customer satisfaction and firm performance. A firm should be able to increase customer satisfaction by improving its customer service, ultimately leading to better firm performance. A number of research papers in Accounting, Operations Management, Marketing, and Transportation and Logistics have found these links (e.g., Banker et al., 2000; Behn and Riley, 1999; Anderson et al., 1994, 1997; Anderson and Mittal, 2000; Capon et al., 1990; Dresner and Xu, 1995). However, other research has found an inconsistent relationship between customer service, satisfaction and performance (e.g., Anderson and Mittal, 2000; Johnston et al., 1990; Johnston, 1995; Mersha and Adlakha, 1992; Ittner and Larcker, 1998; Arthur Andersen, 1994). The first objective of this paper is to investigate moderating variables that may influence the linkage between customer satisfaction and performance. In particular, we examine how this relationship may be moderated by market structure variables; notably market power and market concentration. We hypothesize that when a market is less competitive, either due to firm dominance or to high concentration in a market, then the link between customer satisfaction and firm performance will be weak. The rationale for this conjecture is that when a market is less competitive, there are fewer options open to customers. Even dissatisfied customers will purchase products or services from firms operating in these markets. Therefore, there will be fewer incentives for firms to improve their customer service in order to increase satisfaction. A second objective of this study is to examine potential non-linearities in the relationship between customer service and customer satisfaction. Most of the empirical work has modeled a linear relationship between service and satisfaction (e.g., ⇑ Corresponding author. Tel.: +1 240 595 7721. E-mail address: [email protected] (A.B. Steven). 1366-5545/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.tre.2011.12.006

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Sim et al., 2010; Yee et al., 2008, 2010; Homburg et al., 2005; Nagar and Rajan, 2005; Behn and Riley, 1999; Riley et al., 2003). However, there could very well be diminishing marginal returns to the provision of customer service. Therefore, the impact of customer service on satisfaction could depend on the level of services being provided. At low service levels, increases in services could produce significant changes in satisfaction levels. But at higher service levels, changes in satisfaction could be less significant due to diminishing marginal returns. Thus, we allow for nonlinear relationships in our model. Following the work of Dresner and Xu (1995), we use the airline industry as the market setting for our research. The airline industry provides an excellent setting for the study of the service–satisfaction–performance relationships because of the publically available data on all three types of variables. In addition, airlines compete on defined origin–destination routes, so market structure variables can be computed from public data. The remainder of this paper is organized as follows. Section 2 provides a brief literature review and presents our hypotheses. In Section 3, the research model is developed and the data and variables described. Our results are presented in Section 4 and discussed in Section 5. Section 6 concludes the paper. 2. Literature review and hypotheses development 2.1. Customer service and customer satisfaction The relationship between service and satisfaction has received considerable interest among scholars. In the Transportation and Logistics field, Dresner and Xu (1995) examined the link between customer service and customer satisfaction using data from the airline industry. They found that three measures of customer service – mishandled baggage, ticket over-sales, and on-time performance, were all positively related to customer complaints, their measure for customer satisfaction. In particular, reducing mishandled baggage and ticket over-sales (leading to fewer bumped passengers) and increasing on-time flight performance, all contributed to fewer customer complaints recorded by the US Department of Transportation. Park et al. (2004), using data on the Korean airline industry, found a similar relationship between airline service quality and customer satisfaction. Comparable results have also been found in other industries. For example, using subjective data from the retail industry, Babakus et al. (2004) found that perceived service quality leads to customer satisfaction. A similar finding was made by Yee et al. (2008, 2010) using a survey of 206 service shops based in Hong Kong. Most of the empirical work in the Transportation and Logistics field has assumed a linear relationship between customer service and customer satisfaction (e.g., Sim et al., 2010; Yee et al., 2008, 2010; Homburg et al., 2005; Nagar and Rajan, 2005; Behn and Riley, 1999). However, the relationship is likely to be nonlinear due to diminishing marginal returns to customer service. It stands to reason that increasing customer service leads to higher satisfaction, but that diminishing marginal returns eventually sets in. This nonlinear view has been supported in a number of studies (e.g., Anderson and Mittal, 2000; Matzler et al., 2004; and more recently, Slevitch and Oh, 2010; Finn, 2011). Therefore, our first hypothesis is as follows: H1. Satisfaction increases with customer service, but at a diminishing marginal rate. 2.2. Customer satisfaction and firm performance Although a few studies have found no significant relationship (Arthur Andersen, 1994), or even a negative relationship between customer satisfaction and financial performance (Ittner and Larcker, 1998), the preponderance of the literature suggests that higher customer satisfaction contributes to higher performance; for example through lower marketing costs or due to lower price elasticity of demand. Along these lines, in their study of the Swedish market, Anderson et al. (1994), using 1989–1990 company-level market share data, suggest that the provision of high customer satisfaction positively impacts future financial returns. Customer satisfaction can improve profitability because it influences the repurchase behavior of customers (e.g., Stank et al., 1999; Verhoef, 2003). Thus, customer satisfaction leads to customer loyalty, which in turn contributes to the profitability of a firm (Anderson et al., 1994; Mittal and Kamakura, 2001). In addition, satisfied customers may be willing to pay premium prices for products, thus also contributing to increased profitability (Homburg et al., 2005). Perhaps the most relevant literature to this study is Dresner and Xu (1995) and Behn and Riley Jr. (1999). Dresner and Xu (1995), in addition to examining the impact of customer service on customer satisfaction as noted above, also looked at the impact of satisfaction on profitability in the airline industry. Their finding suggests that increased satisfaction contributes to higher profits, even after controlling for the additional costs involved in providing that higher level of satisfaction. Supplementing Dresner and Xu (1995), Behn and Riley Jr. (1999), incorporate a number of operating measures into their model in order to determine how nonfinancial airline information, including customer satisfaction, relates to financial performance. Using an instrumental variables approach, similar to Dresner and Xu (1995), they find a positive link between customer satisfaction and operating income. Furthermore, in two airline industry studies, Yee et al. (2008, 2010) also find a significant positive relationship between customer satisfaction and firm performance. The findings from the literature suggest that higher levels of customer satisfaction will lead to improved firm performance. Thus our second hypothesis is the following: H2. Higher customer satisfaction leads to higher profitability.

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While the studies cited above provide evidence for a positive relationship between customer satisfaction and firm performance, very few papers have considered how the competitive environment may affect the relationship between these two variables. Most of the work in this area has examined either how market structure may be correlated with customer service or customer satisfaction, or how customer service or satisfaction may directly influence profitability. Mazzeo (2003), for instance, investigated route-level concentration in the airline industry and linked it to on-time performance. He found that flight delays are more prevalent on concentrated routes. Mayer and Sinai (2003) found that airport concentration is related to the length of airline delays. Carriers that dominate a hub airport may schedule many of their flights to land or leave at the same time, thus leading to schedule delays. Forbes (2008) found that prices fall, on average, with increases in delays, and that this response to delays is larger in more competitive markets. In this study, we argue that the impact of customer satisfaction on firm performance as measured by profitability depends on the degree of competition in a market. In particular, in less competitive markets, the link will be weak since firms operating in these markets may be able to operate profitably even if they provide low levels of customer satisfaction. Along these lines, using data from the retail grocery industry, Banker and Mashruwala (2007) found that the interaction between the degree of competition in a market and the level of customer satisfaction significantly affects financial performance of firms in the industry. We add to Banker and Mashruwala’s (2007) work by using the Herfindahl–Hirschman Index (HHI) to measure the level of concentration in a market, rather than the dummy variables measuring the presence of competitors used in the previous study. This leads to the third hypothesis: H3. The effect of customer satisfaction on performance is stronger when the market is less concentrated. A second, related measure of competition is firm market share. Holding the level of competition constant, it is possible that the dominance of a firm in a market may also impact the link between satisfaction and performance. Hofer et al. (2008) separated the impact of concentration and dominance in their examination of factors that affect premium prices air carriers are able to charge at their hub airports. Although the authors found that dominance and concentration are correlated, they also concluded that these factors both have a significant influence on the prices charged by hub carriers. In particular, a dominant firm may be able to achieve higher profitability than a less dominant carrier, even it if provides lower levels of customer satisfaction. In the airline industry, a dominant carrier may have customers ‘‘locked in’’ to their operations through frequent flier plans. Customers may choose to fly with these dominant carriers, even if service and satisfaction are at lower levels than what is provided by less dominant competitors. We, therefore, propose Hypothesis 4 as follows: H4. The effect of customer satisfaction on performance is stronger for a less dominant firm. 3. Model development and data 3.1. Model development The basis for our model is that customer service leads to customer satisfaction and that customer satisfaction leads to profitability. In addition, we incorporate a number of control variables that may affect firm profitability. A representation of the model is shown in Fig. 1. Following Dresner and Xu (1995), customer service can affect profitability indirectly through customer satisfaction. As discussed above, higher levels of customer service are expected to lead to greater satisfaction (H1), which, in turn, should

Stage length, size, operating cost, firm effects, time effects, Oversal

Concentration H3 H1

Customer Service

H2

Satisfaction

H4

Market share

The dashed arrows indicate control variables Fig. 1. Service, satisfaction and financial relationship.

Profitability

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contribute to higher profitability (H2). Our two market structure variables impact the satisfaction–profitability relationship (H3 and H4), but also may have direct impacts on profitability, in that firms operating in concentrated markets, or firms that dominate their markets, may be more profitable than other firms, ceteris paribus. There are other factors that may impact profitability, in addition to customer satisfaction. Most notably, an airline’s operating cost will have an impact on profitability. Therefore, we use an airline’s operating cost as a control variable that has a direct impact on an airline’s profits. Other firm-specific variables that may affect profitability include average stage length and airline size. Since a disproportionate percentage of an airline’s operating costs on a route are incurred at the end points of a flight (i.e., take-off and landing fees, fuel consumed during take-off and landing, terminal costs) operating costs do not vary proportionally with distance. Short haul flights have higher costs per flight-mile than do long haul flights. Thus, carriers with a shorter haul route network will have higher operating costs per available seat-mile than carriers with a longer haul network, ceteris paribus. However, on the other hand, short haul flights tend to have higher yields than long haul flights. So, a short haul carrier will also be expected to have disproportionately higher revenues than a long haul carrier. Therefore, the net impact of stage length on operating profits cannot be determined a priori. Firm size may also affect profitability, although the impact is difficult to predict, a priori.1 In addition, one of our customer service variables, ticket over-sales, may have a direct link to profitability. Although ticket over-sales is hypothesized to lead to decreased satisfaction and decreased profitability, airlines overbook their flights for a reason; that is to generate revenues. Therefore, over-sales may also have a direct positive impact on profitability in that they generate revenues for carriers. Finally, we include a series of airline-specific dummy variables to control for other omitted firm-specific influences, and time-specific dummies to control for seasonal effects and other time-dependent factors that influence airline profitability.

3.2. The empirical model Due, in part, to the widely available, detailed public data on the airline industry, this industry has been used by previous researchers to examine the service–satisfaction–performance relationship (e.g., Anderson et al., 2009; Sajtos et al., 2010; Sim et al., 2010; Dresner and Xu 1995). Drawing from the literature, our customer service measures include an airline’s on-time performance, a measure for lost or mishandled bags, and involuntary boarding denials due to ticket over-sales. A fourth important service attribute that has been omitted from this line of research is a variable measuring flight cancellations. When a flight is cancelled, customers incur costs in terms of additional travel time and inconvenience; for example in switching to another flight. We, therefore, include a measure for flight cancellations as a fourth service attribute. Following Dresner and Xu (1995), Behn and Riley Jr. (1999) and Sim et al. (2010), a variable measuring customer complaints to the US Department of Transportation is used as proxy for customer satisfaction. An origin–destination route-based measure of HHI measures competition, while route-based market shares are used to measure market power/dominance. Finally, available seat-miles operated by an airline during a quarter are used to operationalize the size variable. Detailed variable definitions, methods of calculation, and sources of data for the variables are provided below. The empirical model is provided in Eqs. (1) and (2). We first estimate complaints, and then the estimated value for complaints is used in the second equation in a two stage least square (2SLS) methodology that corrects for the endogeneity between the complaints and the other customer service measures: 2

2

2

2

Compl ¼ a0 þ a1 Ontime þ a2 Ov ersal þ a3 Lbag þ a4 Cancel þ u1 Ontime þ u2 Ov ersal þ u3 Lbag þ u4 Cancel þ

15 X j¼5

aj Airline þ

40 X

ak Quarter

ð1Þ

k¼16

Profit ¼ b0 þ b1 Complfit þ b2 Stagelength þ b3 HHI þ b4 Mktshare þ b5 OperCost þ b6 Av seat þ b7 Ov ersal þ b8 Complfit  Mktshare þ b9 Complfit  HHI þ

21 X i¼10

bi Airline þ

46 X

bt Quarter

ð2Þ

t¼22

Hypotheses 1 is tested using Eq. (1). The coefficients for the first order terms for the four customer service variables test the relationships between service and satisfaction, while the coefficients for the second order terms test the type of relationship, either linear or nonlinear. To find support for H1, specifically, the coefficient for Ontime should have a negative sign and the coefficient for Ontime2 a positive sign. On the other hand, the coefficients for Oversal, Lbag and Cancel should all have positive signs, while the coefficients for their squared terms are expected to have negative signs. Hypothesis 2 is tested by b1, while the coefficients for the interaction terms in Eq. (2) between Complfit and HHI and between Complfit and Mktshare test Hypotheses 3 and 4. To find support for these hypotheses, the coefficients for both interaction terms should have the opposite signs to the sign of the coefficients for the Complfit variable, which is expected to be negative, thus indicating that in markets with high degrees of concentration and market power, the Complfit–Profit relationship is weaker. 1 Oum and Zhang (1997) and Creel and Farell (2001), for instance, found increasing returns to scale for airlines, while other studies (e.g., Caves et al., 1984) found constant returns to scale.

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3.3. Variables and data Table 1 provides detailed descriptions of the variables in our model. Our study uses a panel data set of 26 quarters from the first quarter of 2003 to the first quarter of 2009. The data come from five different sources: the Air Travel Consumer Report (ATCR), a report published monthly by the Department of Transportation (DOT), the DOT’s DB1B database, the Air Carrier Financial Report (form 41 financial data) and T100 database, and the On-Time Performance database, also from the DOT. Data are collected on the 12 largest US airlines that have consistently appeared in the Air Travel Consumer Report, as well as in the other databases used over the 25 quarters. In total, the sample includes 300 observations. Table 2a and b provide descriptive statistics for the variables in the study as a whole (Table 2) and by specific airline (Table 3). Total number of passengers between an origin–destination city-pair can be estimated from the DB1B database; that is, the 10% sample of all US domestic origin and destination tickets. Data from the DBIB database are used to calculate the HHI and market share variables. Since our analysis is at the firm level, and the database is at the route level, data are averaged across all routes operated by an airline. We first calculate quarterly market shares and HHI figures for each origin–destination city pair route for an airline. We then calculate route weights for each carrier, defined as the ratio of an airline’s passengers on a specific route to its total quarterly enplanements. Firm level HHI and market share are then calculated using these weights. On average, 63,800 origin–destination routes per quarter were used to calculate the competition and market share variables. It is interesting to note from Table 2 that at the origin–destination route level, on average, competition is equivalent to three airlines per route with equal market share. As can be seen in Table 2, airlines are generally not profitable, with an average profit ratio of only 1.0, the breakeven level. From Table 3, it is also interesting to note that among the four customer service variables, the greatest variation across the means of the airlines is in mishandled baggage and ticket over-sales. There are fewer variations in on-time performance and flight delays. 4. Results Table 4 provides the results of the customer satisfaction model (Eq. (1)). All first order terms for the customer service variables have the expected signs and are significant at the .05 level or better. All four squared terms have the expected signs, with three of the four statistically significant. Hypothesis 1 is thus supported for three of the four service attributes, implying that satisfaction increases with service performance at a decreasing rate. The one exception is flight cancellations that has a linear relationship with satisfaction. Table 5 provides the results for the profit equation (Eq. (2)). The fitted complaints variable is significant and negative as expected. The interaction variable between fitted complaints and HHI is positive and significant, indicating that the effect of

Table 1 Variable definitions. Dependent variables Complaints (Compl)

Profit (Profit) Independent variables On-time flight percentage (Ontime) Ticket over-sales (Oversal) Lost bags (Lbag) Flight cancellations (Cancel) Airline fixed effects (Airline) Quarterly fixed effects (Quarter) Fitted complaints (Complfit) Competition (HHI)

Market share (Mktshare) Operating cost (OperCost) Available seat miles (Avseat) Stage length (Stagelength)

The number of complaints per 100,000 passengers received by the US Department of Transportation in a quarter (i.e., 3 month period, January–March being the first quarter of a year) for an airline and serves as the measure for our satisfaction variable The ratio of operating revenue to operating cost for an airline in a quarter. This ratio was designated as a ‘‘profitability ratio’’ by Dresner and Xu (1995) The overall percentage of flights arriving within 15 min of scheduled time by a carrier per quarter. Higher values for this variable are expected to reduce the number of complaints The number of passengers per 100,000 passengers who are involuntarily denied boarding by a carrier per quarter. The greater the number of over-sales, the greater the expected number of complaints The number of mishandled bags per every 100,000 passengers in a quarter per carrier. These include damaged baggage, pilfered baggage and lost baggage. This variable is expected to be positively related to complaints The overall number of flights cancelled by an airline in a quarter per every 100,000 flights. This measure is also expected to lead to lower satisfaction levels by raising the number of complaints Dummy variables included in the model to capture fixed firm effects Dummy variables included in the model to capture time-related effects across quarters The estimated dependent variable from the satisfaction equation. It is expected to have a negative relationship with profitability The Herfindahl–Hirschman index, a measure of market concentration. This index is defined as the sum of the squared market shares of all airlines on an origin–destination route averaged across routes in the dataset to produce a firm level measure. This variable measures the level of competition faced by a carrier across its operating markets The market share at the origin–destination route level across all routes in the dataset averaged to the firm level The ratio of the quarterly operating cost for a carrier to the carrier’s available seat miles. This variable is expected to have a negative impact on profitability The available seat miles per quarter per carrier. This is a size measure to capture the potential effects of size on profitability The average length of haul of a carrier in a quarter

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Table 2 Descriptive statistics. Variable

Mean

Std. dev.

Min

Max

Complaints (per 100,000 passengers) Profit (operating revenue/operating cost) Ontime (%) Lost bags (per 100,000 passengers) Ticket over-sales (per 100,000 passengers) Flight cancellations (per 100,000 flights) HHI Market share Available seat miles (tens of billions of miles) Cost (billions of dollars) Revenue (billions of dollars)

0.931 1.006 77.460 5.331 1.091 1.489 0.330 0.440 1.240 1.670 1.636

0.591 0.094 6.890 3.172 1.290 1.052 0.092 0.110 0.834 1.160 1.100

0.110 0.797 57.000 2.080 0.000 0.077 0.180 0.240 0.127 0.156 0.172

3.960 1.245 92.000 12.750 11.790 6.377 0.550 0.776 2.910 4.710 3.770

Table 3 Mean values of the customer service and concentration variables by carrier. Carrier

Airtran Alaska American Airlines Atlantic Southeast Continental Delta Hawaiian JetBlue Northwest Southwest US Airways United Airlines a

Mean values of variables Lost bags

Ticket over-sales

On-time performance

Cancellations

Complaints

Market share

Available seatmilesa

3.38 4.53 5.72 14.09 4.14 6.06 3.00 3.82 4.23 4.47 6.51 4.86

0.46 1.10 0.66 4.14 1.55 1.59 0.13 0.02 0.80 0.97 0.90 0.69

75.93 75.06 75.06 71.20 76.60 77.41 93.13 75.67 76.73 81.47 76.96 76.30

1.06 1.83 2.08 2.89 0.85 1.64 0.44 0.87 1.27 0.95 1.86 1.77

0.88 0.60 1.13 0.73 0.97 1.25 0.66 0.53 1.00 0.20 1.74 1.37

0.35 0.61 0.44 0.31 0.35 0.40 0.55 0.49 0.47 0.64 0.41 0.34

0.43 0.49 2.70 0.17 1.27 2.15 0.19 0.61 1.23 2.21 1.05 2.08

Tens of billions of miles.

Table 4 Regression results with complaints as the dependent variable. Estimate Intercept 6.829 Ontime 0.161 2 Ontime 0.001 Lbag 0.109 2 Lbag 0.003 Oversal 0.166 Oversal2 0.013 Cancel 0.135 Cancel2 0.009 Firm and time dummy variables included R2 0.7925 P-value