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The human element in airline service quality: contact personnel and the customer Sunil Babbar Department of Information Technology & Operations Management, Barry Kaye College of Business, Florida Atlantic University, Boca Raton, Florida, USA, and Xenophon Koufteros Information & Operations Management, Mays Business School, Texas A&M University, College Station, Texas, USA Abstract Purpose – The purpose of this paper is to examine empirically the dimension of personal touch and its elements of individual attention, helpfulness, courtesy, and promptness as determinants of customer satisfaction for passenger airlines. Design/methodology/approach – Survey data from 437 individuals and a hierarchical approach to structural equation modeling are used to systematically evaluate four alternative measurement models. A second-order measurement model of personal touch appeared to represent the data very well and can be supported from a theoretical point of view. Findings – Personal touch is found to statistically and substantively affect passenger satisfaction explaining about 54 percent of the variance. In other words, collectively, individual attention, helpfulness, courtesy, and promptness are found to have a significant effect on airline passenger satisfaction. Research limitations/implications – This research examines the satisfaction of customers of US passenger airlines. Future research should overcome this limitation by extending the survey to include experiences on international flights and with non-US airlines. The results are biased more towards the responses of “younger” passengers and those who flew primarily in the economy level of service. Practical implications – The findings of this research have important strategic and managerial implications for passenger airlinesandserve to validate the corporate culture and customer service quality driven models of exemplary airlines such Southwest Airlines, JetBlue Airways, and Scandinavian Air Systems. Originality/value – The paper provides a very thorough review of the literature and is the first to examine empirically the affect of personal touch displayed by contact employees on the satisfaction of customers of passenger airlines. Keywords Customer relations, Customer satisfaction, Customer services quality, Airlines, Interpersonal skills, United States of America Paper type Research paper Introduction The number of passenger boardings on flights of US airlines has risen from about 452 million in 1991 to more than 738 million in 2005 (Bureau of Transportation Statistics, 2006). US passenger airlines currently employ a combined 405,000 full-time equivalent employees. Many of these employees are customer contact employees who shape the experience of airline passengers through the quality of service they provide. The current issue and full text archive of this journal is available at www.emeraldinsight.com/0144-3577.htm IJOPM 28,9 804 Received 16 June 2007 Revised 10 April 2008 Accepted 20 May 2008 International Journal of Operations & Production Management Vol. 28 No. 9, 2008 pp. 804-830 q Emerald Group Publishing Limited 0144-3577 DOI 10.1108/01443570810895267 Interactions between customers and the employees of an airline probably influence the customers’ perceptions of the airline (Gursoy et al., 2005). Interestingly, because service quality is more visible, passengers may use service quality as a basis for judging the overall quality of an airline (Rhoades and Waguespack, 1999). Most visible to service customers is the service that contact employees provide. Since the 1980s, customer contact, generally defined as the interface between the customer and the service provider, has been recognized as a key difference between services and manufacturing operations (Chase, 1981; Soteriou and Chase, 1998). In keeping with the recent definition of Zomerdijk and Vries (2007), we define customer contact as a direct encounter between a customer and a service provider that takes place at the same time but not necessarily in the same place, and has the potential for interaction between the two. A contact employee is someone who engages in such encounters with customers as part of the service delivery process. An astute understanding of the critical role customer contact employees play in shaping the experience of customers can be invaluable to service providers. In this study, we empirically examine the impact of service provided by customer contact personnel of US passenger airlines on customer satisfaction with the airline. In particular, we identify human- or people-related factors; individual attention, helpfulness, courtesy, and promptness that shape the element of “personal touch” in the service provided by contact employees and examine their role as determinants of customer satisfaction with the airline. In the following sections, we review the literature, state our hypothesis, outline systematically the research methods we employ, present our findings, discuss managerial implications, and identify limitations of our research. Literature review There are indeed fundamental differences between goods and services. With goods being tangible, their designers are able to utilize well-established physical principles to understand and shape their performance (Taura and Yoshikawa, 1994). Services, on the other hand, are typically intangible and involve relatively greater customer contact (Soteriou and Chase, 1998). Given the intangible nature of services, it becomes hard to set standards (Harvey, 1998). While the question of how best to delight customers continues to challenge both manufacturing and service organizations, the building blocks of service and their relationship to service quality are less obvious than for manufactured goods (Soteriou and Chase, 1998). It is generally recognized that the “softer” or intangible service component is harder to design and manage (Vandermerwe, 1994). Yet, more often than not, it is this soft component that tends to carry a heavier weight in the customers’ assessment of their quality of experience with a firm’s product. Because of the intangible nature of service, how a service is delivered becomes an important determinant of service quality (Gro ¨ nroos, 1984). This attests to the importance of the role of contact employees and the need to better understand and measure the extent of their influence on customer satisfaction. Customer contact is a key consideration in the design and delivery of services including airline service. The roots of thinking on customer contact in the context of service delivery can be traced back to Whyte’s (1946) published essay “When workers and customers meet”. Since that time, many have noted the relevance of customer contact as a key differentiator of services and manufacturing Airline service quality 805 (Chase, 1978, 1981; Parasuraman et al., 1985; Kellogg and Chase, 1995; Harvey, 1998; Prajogo, 2006) and it is now an important dimension in most service taxonomies (Mersha, 1990; Soteriou and Chase, 1998). Mills et al. (1983) consider the interface between the firm and the customer to be a seminal element of consideration for service firms. Such an interface has also been referred to as “contact theory” in the literature (Kellogg and Chase, 1995) with a number of frameworks having incorporated the concept of customer contact (Mills and Margulies, 1980; Schmenner, 1986). Contact with the customer provides service-delivering firms with valuable opportunities for responding to the needs of customers while simultaneously marketing their products to them (Chase et al., 1984; Delene and Lyth, 1989; Chase and Hayes, 1991). It also gives contact employees the opportunity to build customers’ trust and confidence and to help sustain a relationship with them well into the future (Mills and Margulies, 1980). A review of the literature reveals a progression of thinking on what constitutes customer contact. Early on, customer contact was defined simply as the physical presence of the customer in the system (Chase, 1978). Later, Schmenner (1986) defined customer contact as an interaction between the customer and service provider and customization of the service that places demands on the design of the service delivery system. More recently, Mersha (1990) has defined customer contact as customer presence, either face-to-face or else mediated through technology, with low- or high-levels of interaction between the customer and service provider. Zomerdijk and Vries (2007) echo Mersha’s sentiment in defining customer contact as a direct encounter between a customer and a service provider that takes place at the same time but not necessarily in the same place, and has the opportunity for interaction. The importance of the role of customer contact employees in creating and providing high-quality service has been underscored by numerous researchers (Zeithaml et al., 1985; Hartline and Ferrell, 1996; Cook et al., 2002). Jan Carlzon, responsible for turning Scandinavian Air Systems (SAS) into a customer-driven company, first introduced the concept he referred to as “moment of truth” (Peters and Austin, 1985). Carlzon defines a moment of truth as “an episode in which a customer comes into contact with any aspect of the company, however remote, and thereby has an opportunity to form an impression” (Collier, 1994). In his book, Moments of Truth, Carlzon (1987, p. 3) writes, “Last year each of our ten million customers came in contact with approximately five SAS employees”: These 50 million “moments of truth” are the moments that ultimately determine whether SAS will succeed or fail as a company. They are the moments when we must prove to our customers that SAS is their best alternative. By homing in on these moments of truth and changing the company’s outlook from one of flying “airplanes” to one of flying “people”, he brought to the forefront the critical role which contact employees play in determining customer satisfaction. The management of moments of truth can indeed shape the quality of service rendered (Gro ¨ nroos, 1990). Behaviors and attitudes of contact employees can significantly shape the customers’ experience with the service provided and their assessment of its quality (Parasuraman et al., 1985, 1988; Haywood-Farmer, 1988; King and Garey, 1997; Winsted, 2000; Yoon et al., 2001). It has been suggested that, in the case of services, employees represent the firm and define its product (Shostack, 1977), and actions IJOPM 28,9 806 which the employees take can shape the customers’ perceptions of the firm itself (Zeithaml and Bitner, 2000). The affect theory of social exchange (Lawler, 2001), which posits relationships as a source of emotions, attests to the important role which contact employees play in shaping the customers’ assessment of the service firm itself. As Sierra and McQuitty (2005) explain, this theory purports that emotions are directed at the group context and not limited to the service agent. Accordingly, a favorable encounter with a service employee positively impacts a customer’s impression of the entire service firm. A customer’s emotional response to a service can often be attributed to the customer’s experience with the service employees (van Dolen et al., 2001) and can influence the customer’s future purchase intentions (Berry, 2000). Service encounters resulting in positive customer emotions increase the probability of customer loyalty and repeat purchase (Czepiel, 1990; Lawler et al., 2000). Customers have been found to evaluate quality of service based on the level of concern and civility (Winsted, 2000) and listening and understanding demonstrated by employees (Chandon et al., 1997). Further, individual attention (Vandermerwe, 1994), cheerfulness (Harvey, 1998), friendliness (Brown and Sulzer-Azaroff, 1994) and courtesy (Goodwin and Smith, 1990) have also been recognized as determinants of customer satisfaction with the service experience. King and Garey (1997) have examined the concept of relational quality in service encounters. According to them, relational quality refers to customers’ perceptions and evaluations of service employees’ communications and behaviors such as respect, courtesy, warmth, empathy and helpfulness, and involves customers’ feelings and emotional states through interactions with employees. Research hypothesis Despite the significance of the service the airline industry provides, there is limited empirical research and theory development in the area of airline service quality. Often, data from the US Department of Transportation is used as a basis for commentary about service quality and the relative positions of airlines (Rhoades et al., 1998; Rhoades and Waguespack, 1999; Gursoy et al., 2005; Bowen and Headley, 2006). A number of authors have presented conceptual commentary on the culture of particular airlines (Ekdahl et al., 1999; Laszlo, 1999; Ford, 2004; Smith, 2004). Others have examined service breakdowns through critical incidents (Edvardsson, 1992) and service failure (Bejou and Palmer, 1998) in airlines and presented frameworks for managing the air travel experience (Carlzon, 1987; Le Bel, 2005). In general, US airlines are found wanting in the quality of service they render to their customers (Oneal, 1991; McClenahen, 1991; Rhoades et al., 1998). It thus comes as no surprise that customer satisfaction with US passenger airlines is extremely low even today (Yu, 2007). As noted earlier, customer contact employees can play an important role in shaping customer satisfaction. Despite this, the nature of the relationship between service elements of individual attention, helpfulness, courtesy, and promptness of contact employees and the satisfaction of airline customers remains to be empirically examined. Our extensive review of the literature provides the theoretical underpinning for the relationship we hypothesize and empirically examine. Accordingly, we state the following: Airline service quality 807 H1. Individual attention, helpfulness, courtesy, and promptness of contact employees constitute elements of “personal touch” and positively affect the level of satisfaction of airline customers. Research methods In order to test our hypothesis, we sought empirical evidence that supports the posited relationship. To gain such empirical evidence we first took measurements for each construct. Instrument development To develop our measurement instrument, we reviewed the extensive literature on service quality. Such literature spans many disciplines such as marketing, management information systems, operations management, hospitality management, and health care management. It is quite clear that the literature is influenced by the seminal work on SERVQUAL and its subsequent refinements. The SERVQUAL instrument (Parasuraman et al., 1988, 1991) separately measures the expected level of service and the experienced level of service. Service quality scores are based on the difference between the two measures. While the SERVQUAL instrument has been used extensively, its tenure has been controversial and has lead to modifications and the emergence of other competing instruments (SERVPERF – Cronin and Taylor, 1992; TOPSIS – Mukherjee and Nath, 2005). van Dyke et al. (1997) and van Dyke et al. (1999) have done an excellent job in summarizing some of the difficulties surrounding SERVQUAL and so we refrain from engaging in a similar debate of the consequential issues pertaining to SERVQUAL’s performance. Given some of the insights the SERVQUAL instrument provides, we used it as one basis to draw and adapt items from, in formulating our constructs. We concentrate here on the interpersonal interactions that occur during service delivery. Such interactions can have the greatest impact on service quality perceptions (Brady and Cronin, 2001). According to Brady and Cronin, three distinct factors constitute customer perceptions of interaction quality. They include perceptions of employee attitudes, employee behaviors, and employee expertise. We sought measures that would capture such factors. Based on the literature, constructs of interest were identified and defined. Items were adopted, adapted, and created to reflect each construct. We asked each of our survey respondents to identify the airline he/she uses most often for domestic (USA) flights and then to rate that airline relative to what their expectations were for that specific airline. Subsequent to item generation, to refine our instrument, we subjected the constructs and items to a formal pre-test study. We provided each construct (i.e. individual attention, helpfulness, courtesy, promptness, and satisfaction) with its definition and list of items to 12 college of business faculty members from several large universities in the USA representing multiple disciplines such as marketing, service operations, information systems, and strategy. The participants evaluated each item based on the definition for each construct and provided additional comments as they relate to the coverage of the content domain of each construct. The feedback we received was useful and helped us reword several items and simplify the language. Pilot study The issue of proper measurement is important and should not be taken lightly. Before embarking on confirmatory factor analytic techniques it is prudent to examine IJOPM 28,9 808 the measurement model through exploratory methods. All of the methods employed are shown in Figure 1. The items were entered in a survey that was administered to 170 respondents who were mostly business students. Each construct with its block of items was first factor analyzed separately. This was done to assure the internal rule of unidimensionality. Only one factor emerged for each case and the loadings appeared to be adequate. We specified principal axis factoring as the method of extraction and direct oblimin (oblique rotation) as the choice of rotation. In other words, five separate analyses were used, one for each construct. For each case, one clear factor emerged and the loadings were fairly substantive; the lowest was 0.595 while the overwhelming majority were above 0.80. The Cronbach’s a reliabilities were acceptable and ranged from 0.784 for individual attention to 0.946 for satisfaction. Next, we subjected the entire set of items across all the constructs to an exploratory factor analysis using maximum likelihood estimation as the method of extraction (to be as compatible with our confirmatory approach as possible) and oblique rotation, as we would expect the factors to be correlated. Subjecting all the items across all the constructs to exploratory factor analysis is more stringent than analyzing each construct separately as both the internal and external rules of unidimensionality are addressed simultaneously. An attempt to run exploratory factor analysis across constructs (excluding satisfaction which is treated as a dependent variable in our model) failed to discriminate between constructs and produce four distinct factors. In fact, one general factor emerged that explained 55 percent of the variance. This was not totally unexpected as prior empirical research has reached similar findings. A significant number of studies (Arnau and Thompson, 2000; Brady and Cronin, 2001; Kilbourne et al., 2004; Lai, 2006) have posited and tested a second-order factor structure where service quality is conceptualized at the second-order latent level and the individual dimensions as first-order latent variables. Overall, after the pilot study we added three items and modified two items to improve the coverage of content domain and readability. Description of large-scale sample The final survey was administered to students (n ¼ 437) at a large university in the Southeastern USA responses for the large-scale study were collected throughout the course of 2006 (spring, summer and fall semesters, respectively). Respondents were all students majoring in business. Although we acknowledge that our respondents may not be representative of the US “population”, students do form an eligible respondent group as they too fly on airlines just like the many other residents of the USA. As we will present below, the respondents had a fairly decent level of flying experience. Students also come from all walks of life with quite a bit of variation in reported household incomes. A great number of respondents are considered “non-traditional” students as they work and go to school at the same time. As is depicted in Table I, about a third of the respondents typically fly four or more times a year and 60 percent of respondents at least twice a year. About 7.1 percent fly more than once a month. These results suggest that overall, respondents have a decent level of flying experience. Respondents were quite evenly split between male and female. Female respondents accounted for 56.4 percent of the total sample and male respondents 43.6 percent. In general, the respondents were relatively young with about 90.7 percent being 40 years or less in age. The majority of respondents were white (52.4 percent), Airline service quality 809 Figure 1. Measurement and structural model methods Examining Unidimensionality: Internal Perspective Exploratory Factor Analysis for Each Construct - Principal Axis Factoring - Oblimin Rotation Examining Unidimensionality: External & Internal Perspective Exploratory Factor Analysis Across the Antecedent Constructs - Maximum Likelihood Estimation - Oblimin Rotation Examining Reliability - Cronbach’s alpha Hierarchical Models: Construction of Four Measurement Models for Antecedent Contructs Model 1: One Latent First-Order Factor Model 2: Four Latent Uncorrelated (Orthogonal) First-Order Factors Model 3: Four Latent Correlated First-Order Factors Model 4: Four Latent First-Order Factors & one Second-Order Factor Selection of Measurement Model - Four Latent First-Order Factors & one Second-Order Factor - Convergent Validity (Magnitude of Coefficients & T-values) Modifications to Instrument - Change of Wording - Addition of Items Construction and Assessment of Structural Model - Fit Indices - Path Coefficients Comparison of Four Measurement Models - Fit Indices Examination of Separate Measurement Model for each Construct - Model Fit (Fit Indices) - Convergent Validity (Magnitude of Coefficients & T-values) - Discriminant Validity (AVE vs Squared Correlation) - Composite Reliability - Average Variance Extracted Exploratory Methods, n = 170 Confirmatory Methods, n = 437 IJOPM 28,9 810 Demographic Scale I typically fly Less than once a year Once a year Twice a year Four times a year Once a month Once a week More than once a week Frequency 78 96 123 107 28 2 1 Valid percent 17.9 22.1 28.3 24.6 6.4 0.5 0.2 I am Male Female Frequency 190 246 Valid percent 43.6 56.4 My age is , ¼ 20 years 21-30 years 31-40 years 41-50 years 51-60 years . 60 years Frequency 32 317 46 31 9 1 Valid percent 7.3 72.7 10.6 7.1 2.1 0.2 I fly most often for Leisure Business Frequency 379 57 Valid percent 86.9 13.1 My ethnic background is Black Native American Hispanic White Asian/Pacific Islander Other Frequency 64 2 95 226 30 14 Valid percent 14.8 0.5 22.0 52.4 7.0 3.2 My annual household income is , $20,000 $20,001-40,000 $40,001-60,000 $60,001-100,000 . $100,000 Frequency 79 110 71 101 63 Valid percent 18.6 25.9 16.7 23.8 14.9 The class I most often fly in is Economy Business First class Frequency 409 16 10 Valid percent 94.0 3.7 2.3 Table I. Demographics Airline service quality 811 followed by hispanic (22.0 percent), and black (14.8 percent). The respondents and perhaps their immediate family appeared to be relatively affluent; over 38.7 percent reported annual household incomes of over $60,000 and 14.9 percent reported incomes of over $100,000. The overwhelming majority of respondents (86.9 percent) reported that they fly primarily for leisure whereas 13.1 percent reported flying primarily for business. The respondents typically fly in economy class of service (94 percent). There are no substantive differences between our respondents and the non-respondent university student body in terms of several demographics. The average age of students at the university is 25 for undergraduates and 34 for graduate students. The university has a population that includes 61 percent female students and 39 percent male students. About 57 percent of all students indicate “White” as their ethnicity, 17 percent black, and 16 percent hispanic. Confirmatory methods We followed Anderson and Gerbing’s (1988) two-step approach for structural equation modeling, which suggests that researchers should first obtain an adequate measurement model, and then test the structural model in a second step. Confirmatory factor analysis with a covariance matrix as input and maximum likelihood estimation was first performed on each of the five constructs separately (Figure 1). We first calculated a covariance matrix of the 30 items of interest in this study using PRELIS 2.0 (Jo ¨ reskog and So ¨ rbom, 2002), and screened the data for possible violations of statistical assumptions, such as skewness, kurtosis (peakedness), and possible outliers. No violations of the assumptions were identified. To assess convergent validity, the individual item loadings and their respective t-tests can be examined. t-Values greater than j2j are considered to be significant at the 0.05 level, and t-values greater than j2.576j suggest significance at the 0.01 level (Koufteros, 1999). It is generally recognized that to support model fit, a consensus among the following is sought: a x 2 /df , 2; a non-normed fit index (NNFI) . 0.90 and a comparative fit index (CFI) . 0.90; and a standardized root mean square residual (RMR) below 0.05 (Byrne, 1998; Hu and Bentler, 1999; Koufteros and Marcoulides, 2006). Reliability of a given construct within the context of structural equation modeling can be assessed through the composite reliability estimate (Koufteros, 1999). The estimate of average variance extracted (AVE) (Fornell and Larcker, 1981) is complementary to the measure of composite reliability. Estimates of AVE greater than 0.50 and composite reliabilities above 0.80 are acceptable (Koufteros, 1999). Discriminant validity can be assessed by comparing the AVE with the squared correlation between constructs (Fornell and Larcker, 1981). Discriminant validity is exhibited when the AVE is higher than the squared correlation between two constructs. In other words, this occurs when the “within variance” is higher than the “between variance.” First- and second-order measurement models A hierarchical approach was used for further measurement model development. The hierarchical approach was deemed necessary given the findings from our pilot study and prior research that posited the existence of a higher-order structure for service quality related constructs (Kettinger and Lee, 1994; Kettinger et al., 1995; Myerscough, 2002; Somers et al., 2003; Rodgers et al., 2005; Anitsal and Paige, 2006; Lai, 2006). IJOPM 28,9 812 A hierarchical approach is a systematic process that evaluates alternative models that can potentially describe relationships between observed and latent variables. This process includes the construction of four models (Figure 1). The first model is hypothesized to include one first-order latent variable with 21 observed indicators. Model 2 hypothesizes four first-order uncorrelated (orthogonal) factors. Model 3 is similar to Model 2 except that factor correlations are specified. Finally, Model 4 (Figure 2) includes one second-order factor and four first-order factors with corresponding observed variables. Our methods are reflective of the body of literature that posits and tests higher-order models (Rindskopf and Rose, 1988; Arnau and Thompson, 2000; Somers et al., 2003; Lai, 2006). Gerbing et al. (1994) suggest that we should recognize that the first-order factors are the constituent facets of the constructs of interest. In other words, they are the building blocks of the constructs. In our specific case, individual attention, helpfulness, courtesy, and promptness are the facets of personal touch which is operationalized as a second-order factor. A measurement model is selected based on fit indices and theoretical considerations. An examination of convergent validity in the context of the selected measurement model is also due (tests for discriminant validity tests and assessment of composite reliability and AVE remain the same as reported in the previous analysis above). Structural model Once a measurement model has been selected, the next step is to test the substantive hypothesis. A structural model is evaluated and if the model fits the data adequately, the t-values of the structural coefficients (i.e. g s and b s) can be used to test the research hypothesis. The magnitudes of the structural coefficients as well as the statistical significance associated with such coefficients are useful in the assessment of potential relationships between variables. The analysis can be complemented by an evaluation of R 2 . Results The five posited measurement models appear to be supported by various fit indices. There is evidence of potent relationships between observed and latent variables (t-values) and fit indices are suggestive of well-fitting models (Table II). Descriptive statistics are presented in Table III. Each construct had a composite reliability and an AVE that met generally acceptable criteria (Table IV). While the constructs appear to enjoy convergent validity and manifeststrong model fit, it is apparent from Table IV that there is lack of evidence suggestive of discriminant validity between the four antecedents to passenger satisfaction. The correlations between constructs are strong, leading to high-squared correlations (i.e. between variance). Such squared correlations are higher than AVEs (within variance) for all comparisons. For discriminant validity support to be tenable, AVE for each construct has to be higher than the squared correlation between two constructs under comparison. Our findings provide credence to the arguments advanced by other researchers that the model ought to be specified at a higher level, i.e.a second-order model can represent the datamore effectively. Although other researchers in the service quality field have posited second-order measurement models and attest to respectable model fit, it is imperative that alternative models be specified, tested, and compared as there is quite a bit of disparity in findings across the literature. The first model (Model 1) (Table V) specifies that all 21 items are Airline service quality 813 . the airline industry provides, there is limited empirical research and theory development in the area of airline service quality. Often, data from the US. factors are the constituent facets of the constructs of interest. In other words, they are the building blocks of the constructs. In our specific case, individual