EJSS_26_2_03

European Journal of Social Sciences ISSN 1450-2267 Vol.26 No.2 (2011), pp. 159-175 © EuroJournals Publishing, Inc. 2011 http://www.europeanjournalofsocialsciences.com

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Service Quality in Hospitality Services:

Gap Model and Factor Analysis

R. Renganathan

Professor, School of Management, SASTRA University, Thanjavur, India

E-mail: [email protected] Tel: 91- 97861 72972

Abstract

The aim of this study is to analyze the hotel guests’ expectations and perceptions of hotel services and the role of demographic variables in evaluating the Service quality and also to ascertain how Factor analysis can be used to identify number of factors underlying SERVQUAL components (items). Statistical methods like descriptive analysis, reliability analysis, multiple regressions and exploratory factor analysis were used to evaluate the service quality. The findings of the research showed that with regard to individual SERVQUAL dimensions, gap values are positive for tangibles, reliability, assurance and gap values are negative for responsiveness and empathy. With regard to Factor analysis, data on hotel guests’ perceptions divided SERVQUAL items into four main factors, with Eigen values greater than 1.0 and data on expectations divided SERVQUAL items into three main factors, with Eigen values greater than 1.0.It is recommended to the managers’ of the hotels to understand the expectations of their guests’ and make their service personnel to respond as per their guests’ expectations and also to be compassionate enough to serve their guests’ appropriately. Managers can utilize the guests’ feedback to understand their perception towards the various hospitality aspects of the hotels. Keywords: Service quality, Guests’ expectation, Guests’ perception, Factor analysis

1. Introduction Traveling for business and for pleasure has become the order of the day. The hospitality industry is one of the fastest growing industries today. In the Liberalised, Privitised and Global (LPG) environment organizations are forced to excel others in providing quality service to their customers. Retaining the loyal customer is very important for the survival and growth of any organization. Loyal customer may act as an unpaid brand ambassador. In order to thrive and excel in the competitive environment hospitality organizations have to maintain quality as per the expectations of customers. Hospitality managers have to understand the quality and the value of services being offered to their guests’ as expected by them. The positive consequences of companies achieving high levels of customer satisfaction and service quality are well documented (Rust & Zahorik, 1993; Zeithaml, Berry and Parasuraman, 1996). Indeed, service quality and customer satisfaction issues are important as companies attempt to differentiate their services and compete effectively in the marketplace (Parasuraman, Zeithaml & Berry [PZB], 1988; Brown & Swartz, 1989). Most research in the area of service quality has been based upon the model developed by Parasuraman, Zeithaml and Berry (1985, 1988), which incorporates a comparison of customer expectations and perceptions of service performance.

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Purchase decisions of the hospitality customers are based on the factors like price, benefit and satisfaction. Nightingale (1985) contends that customer satisfaction leads to loyalty and a flourishing business. Hadyn Ingram (1999) contends that satisfying the guest leads to repeat customer, personal recommendations and a favorable image, and this is hard won but easily lost. Consumer satisfaction and loyalty, secured through high quality products and services providing value for money, for the consumer, are essential for long-term survival, let alone long-term success (Zeithaml et al., 1990; Robledo, 2001).

When tourists move from one place to another, they expect that hospitality service is well taken care of. The demand for travel and tourism in India is expected to increase at a compound annual growth rate (CAGR) of 11.8 per cent between 2005 and 2010, to reach US$ 144.4 billion (INR 6,778.2 billion).The demand for travel and tourism is expected to reach US$ 431.7 billion (INR 24,252.4 billion) by 2020 (Travel and tourism economic impact: India, 2010). The Indian hotel industry, being a direct beneficiary of the growth in the economy and the tourism industry, has also recorded strong growth over the past few years. (Travel and tourism economic impact: India, 2010).

Hospitality industry in India is highly fragmented and unorganized. Hotels play a vital role in the tourism industry. The lodging sector, which is a critical component of tourism sector, is still in the growth of stage of its life cycle, Vinnie Jauhari (2001). Hotels in India are broadly classified into two categories —approved and unapproved. The Department of Tourism (DoT) grants approval and classifies hotels into seven categories —Heritage hotels, 5-star deluxe, 5-star, 4-star, 3-star, 2-star and 1-star. The DoT reclassifies hotels every three years and provides reclassification to 5-star deluxe, 5-star and 4-star hotels. (Ministry of Tourism, Government of India)

The aim of this study is to analyze the hotel guests’ expectations and perceptions of hotel services and the role of demographic variables in evaluating the Service quality and also to ascertain how Factor analysis can be used to identify number of factors underlying SERVQUAL components (items).SERVQUAL gap model questionnaire developed by Parasuraman et al. (1985) were used to elicit responses from the hotel guests who stayed in three hotels from February 2011 to April 2011 at Vellore (Tamilnadu/ India).As a oldest city in South India, Vellore is well known for ancient temples, well administered colleges and best hospital facilities. Christian Medical College in Vellore is an internationally renowned institution. SPSS was used for data analysis. Statistical techniques like descriptive analysis, reliability analysis, multiple regressions and exploratory factor analysis were used to analyze the guests’ perceptions and expectations with regard to various SERVQUAL dimensions – tangibles, reliability, responsiveness, assurance and empathy.

1.1. Conceptual Framework

The present study examines the relationship between guests’ perceptions and expectations based upon the demographic variables towards SERVQUAL dimensions. The relationships among the variables are shown in Figure 1.

Figure 1: Conceptual framework

Demographic variables SERVQUAL Dimensions

Age Gender

Monthly income Highest qualification

Tangibles Reliability Responsiveness Assurance Empathy

Guests’

Perception

Guests’ Expectation

G a

p


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2. Literature Review 2.1. Perspectives on Service Quality

Quality is a subjective notion and there is no generally agreed definition for quality. The word quality means different things to people according to the context. David Garvin (1988) identifies five perspectives on quality.

1. The transcendent view of quality is synonymous with innate excellence: a mark of uncompromising standards and high achievement.

2. The product-based approach sees quality as precise and measurable variable. Differences in quality, it argues, reflect differences in the amount of ingredient or attribute possessed by the product.

3. User-based definitions start with the premise that quality lies in the eyes of the beholder. 4. The manufacturing-based approach is supply based and is concerned primarily with

engineering and manufacturing practices. 5. Value based definitions define quality in terms of value and price. By considering the

tradeoff between performance (or conformance) and price, quality comes to be defined as ‘affordable excellence’. David Garvin (1988)

It is commonly said that what is not measured is not managed. Without measurement, managers can’t be sure whether service quality gaps exist (Christopher Lovelock et al. 2006). And, of course, measurement is needed to determine whether goals for improvement are being met after changes have been implemented (Christopher Lovelock et al. 2006). In general it is difficult to measure and quantify service quality. The main purpose of measuring service quality is to ensure whether service is provided as per the expectations of the customers. 2.2. The SERVQUAL (Service Quality) Model

Much of the contemporary theories consider service quality from the viewpoints of both provider and customer, and Parasuraman et al. (1985) propose a model, which enables perceptual gaps to be identified. In 1991, these authors developed this framework into the SERVQUAL scale, which enables actual service delivery to be measured. Zeithaml et al. (1990) suggest that the criteria used by customers in molding their expectations and perceptions fit in five dimensions of service quality:

• Tangibles: physical evidence, appearance of physical facilities, personnel, and communication materials.

• Reliability: ability to perform the promised service dependably and accurately.

• Responsiveness: willingness to help customers and provide prompt service.

• Assurance: knowledge and courtesy of employees and their ability to convey trust and confidence.

• Empathy: provision of individualized caring attention to customers. The SERVQUAL model of service quality claims that the consumer evaluates the quality of a

service experience as the outcome of the difference (gap) between expected and perceived service (Zeithaml et al. 1990). The model also highlights the main requirements for delivering high service quality and is a useful framework to assess the quality of hotels. The SERVQUAL model of service quality identifies five gaps that cause unsuccessful delivery. These five quality gaps are the result of inconsistencies in the quality management process (Zeithaml et al. 1990):

• Gap 1.The management perception gap. Manager’s perceptions of customer’s expectations may be different from actual customer’s needs and desires, suggesting that management perceives the quality expectations inaccurately.

• Gap 2.The quality specification gap. Divergences in service quality specifications might signify that, even if customer needs are known, they may not be translated into appropriate service specifications.


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• Gap 3.The service delivery gap. This is referred to as the service performance gap and denotes that quality specifications are not met by the performance in the service production and delivery process.

• Gap 4.The market communication gap. This gap indicates that promises given by market communication activities are not consistent with the service delivered.

• Gap 5.The perceived service quality gap. This gap results when the perceived service falls short of the expectations of customers (Zeithaml et al. 1990). Brogowicz et al. (1990) contend that this gap is the most important, because it compares actual to perceived service delivery. Zeithaml and Bitner (2003) stated that in order to manage service quality, it was important to manage the gaps between expectations and perceptions on the part of the management, employers and customers. Service quality scores (Q) can be measured by subtracting the customer’s perception score (P) from the customer’s expectations score (E). This can be denoted by the equation: Q= P-E (Zeithaml et

al.1990). The SERVQUAL model has come in for criticism from, for example, Johns (1996) argues that

it may be too cumbersome for general use, but provides a useful service tool, which can: point the way forward for more rigorous quality monitoring. Despite criticism, SERVQUAL has been used to measure service quality in a variety of contexts, including Hospitals Emin Babakus et al. (1991), Hotels Al-Rousan et al. (2010), Banks Newman, K. (2001), and Airline services Zhao, J. (2000).Other researchers refuted the criticism when they proposed that practitioners require a generic model to ensure reliability, which allows both cross-industry and cross-functional comparisons to be made Pitt et

al., 1997

Figure 2: Gap model of Service quality

Sources: A.Parasuraman, Valarie A.Zeithaml, and Leonard L.Berry, “A Conceptual model of service quality and its

implications for future research, “Journal of Marketing (Fall 1985).44.

2.3. Use of SERVQUAL Instrument in the Hospitality Industry

The SERVQUAL model consists of 22 statements for assessing consumer (guest) perceptions and expectations regarding the quality of a service. Respondents (Guests) are asked to rate their level of agreement or disagreement with given statements on a five point Likert scale. Consumers’ (Guests’) perceptions are based on the actual service they receive, while consumers’ (guests’) expectations are based on past experiences and information received. Several researchers used SERVQUAL instrument to examine consumers’ expectations of hospitality organizations. Researchers arrived at the most important dimensions of the service, that is, assurance, reliability and tangibles although they did rank


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them in different orders (Saleh and Ryan, 1991; Fick and Richie, 1991; Bojanic and Rosen, 1994). Saleh and Ryan (1991) examined the hotel managers’ perception of consumer expectations and found that most important dimensions of reliability, tangibles and assurance correlated with order, but not the degree of consumer expectations. In their research in the hotel sector, Gabbie and O’Neill (1997) reported that the higher expectations of consumer related to the dimensions of reliability and assurance while the dimensions of tangibility and empathy were lowest in their rankings. According to the Servqual model quality is determined by evaluating the relationship between the expected and the actual and a reflection of the deviations. In this model the guest is usually the center of the evaluation (Buttle, 1996).

Kotler (2003) noted that demographic characteristics were one of the most popular and well-accepted bases for segmenting consumers. Demographic information is often the most accessible and cost effective way to identify a target market Schiffman and

Kanuk (2000). Demographics are easier to measure than any other segmentation variables; they are invariably included in psychographics and sociocultural studies because they add meaning to the findings Schiffman and Kanuk (2000). Demographic variables are the most popular bases for distinguishing customer groups (Kotler 2003). Consumer wants, preferences and usage rates are often associated with demographic variables and also demographic variables are easy to measure (Kotler 2003). Several researchers identified that tourists’ images differed according to different demographic characteristics (Baloglu, 1997; MacKay & Fesenmaier, 1997; Walmsley & Jenkins, 1993). Skogland and Siguaw (2004) proposed that demographic variables positively influenced customer satisfaction. Literature suggests that hotel managers should not overlook the importance of the effect of demographic factors on customer perceptions of behavioural intentions, satisfaction, service quality, value, image, and the dimensions of service quality (Al-Sabbahy & Ekinci, 2004; Shergill & Sun, 2004; Skogland & Siguaw, 2004).

3. Objectives of the Study This study mainly focuses on guests’ perception regarding the service provided by the service personnel as expected. This study also analyses the significance of expectations and perceptions of hotel guests. Further this study also analyzes the role of demographic variables of the guests in evaluating the Service quality and also to ascertain how Factor analysis can be used to identify number of factors underlying SERVQUAL components (items). Following Gabbie and O’Neill’s (1997) findings, that the hotels in their study placed too strong an emphasis on the more tangible elements of customer service, this study is aimed at testing the importance placed on tangibles and intangibles by customers (guests) in star hotels. Johns (1996) argues that service quality management hinges on matching hotel intentions with perceptions of customers.

4. Methodology 4.1. Data Collection and Sample

Questionnaires were designed according to the SERVQUAL model of measuring the gaps between guests’ expectations and perceptions. Hotel guests’ perceptions were measured with a self-administered questionnaire. Primary and secondary data were used for this study. Secondary data was collected from websites and various journals. Primary data was collected with the help of SERVQUAL model questionnaire from 252 guests who stayed in and around Vellore (Tamilnadu/ India). In particular, the questionnaire design was primarily used to explore one major gap (Gap 5. The perceived

service quality gap) out of five gaps suggested by Parasuraman et al. (1985) For the purpose of this research data were collected from the guests who stayed in three hotels

of Vellore (Tamilnadu/India) with the help of SERVQUAL questionnaire. Out of the total 400 questionnaires distributed, researcher was able to collect only 252 questionnaires fully completed in all


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aspects which amount to 63% of response rate. The guests who stayed at the hotels were requested to complete the survey questionnaire. The questionnaire is regarding the guests’ expectations, before actually experiencing the service from the hotels and their perception after stay. No consideration in this survey was taken with respect to ratings of hotels, the type of guest i.e. local or tourists, or the frequency of their visits. 4.2. Hypothesis of the Study

Null hypothesis H0: None of the independent variables (Age, gender, monthly income, highest qualification) are

significant predictors of the dependent variables**. Alternative hypothesis Ha: At least one independent variable (Age, gender, monthly income, highest qualification) is a

significant predictor of the dependent variables**. ** (Average value of ‘Tangibles-perception’; Average value of ‘reliability-perception’;

Average value of ‘responsiveness-perception’; Average value of ‘assurance-perception’; Average value of ‘empathy-perception’; Average value of ‘Tangibles-expectation’; Average value of ‘reliability- expectation’, Average value of ‘responsiveness- expectation’, Average value of ‘assurance- expectation’, and Average value of ‘empathy- expectation’.) 4.3. Data Analysis

Collected data were analyzed with the help of software package SPSS. Statistical technique like descriptive analysis, reliability analysis, multiple regressions and exploratory factor analysis were used to evaluate the service quality.

5. Results and Discussion 5.1. Profile of the Respondents

Table 1 shows the demographic profile of the hotel guests involved in this study. In order to elicit responses from the hotel guests a total of 400 questionnaires were distributed and 252 questionnaires were returned which amounts to overall response rate of 63%. As per the Table 1 out of 252 hotel guests 57.54% were male and 42.46 were female. In terms of level of education more than 45% hotel guests had post graduate degree, 32.94% of the hotel guests had under graduate degree, and 32.94% of the hotel guests had diploma qualification. With regard to guests’ monthly income, 30.8% guests income were in the range of 21-000- 30,000,28.80% guests’ income were in the range of 11,000-

20,000 followed by 15.2% of the guests’ income were in the range of 31,000- 40,000 and 13.49% of the hotel guests’ monthly income were below 10,000. 5.2. Reliability Analysis for SERVQUAL Dimensions

Table 2 shows the component and total reliabilities of SERVQUAL scores. The findings show that the reliability coefficients for all dimensions are above 0.70 (George and Mallery, 2003, p.231) which indicates a high level of internal consistency for the scale. Corrected item-total correlations were also studied; that is, the scores for an item and the summated scores of the rest of the items comprising a subscale (e.g., the subscale measuring the reliability dimension of service quality) were correlated.

The coefficient alpha values for the expectations subscales were .863, .965, .930, .979, and .976 for tangibles, reliability, responsiveness, assurance, and empathy, respectively. Coefficient alpha values for the perceptions subscales were .822, .806, .973, .885, and .733 for tangibles, reliability, responsiveness, assurance, and empathy, respectively. When using Likert-type scales it is imperative to calculate and report Cronbach’s alpha coefficient for internal consistency reliability for any scales or


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subscales one may be using. The analysis of the data then must use these summated scales or subscales and not individual items (components) (Joseph A. Gliem, Rosemary R. Gliem 2003). Of the individual perception items, two items had correlation with the total scores that was lower than the .35 cut-off value suggested by Saxe and Weitz (1982). These items are, “Operating hours are convenient to all customers”(Empathy 5-E5) had a correlation of .233 and “Employees give personal attention to customers” (Empathy 2-E2) had a correlation of .346 with the total scores and the rest of the item-total correlations for the perceptions scale ranged from .434 to .978 (Appendix A). None of the item-total correlations for the expectation items were less than the .35 cut-off value. Item-total correlations in the expectations subscales ranged from .478 to .971.Item analysis results for both the perceptions and expectations scores are presented in Appendix A. Appendix A also contains item means and standard deviations. Table 1: Demographic profile of the hotel guests (n=252)

S.NO. Characteristics Categories Number of respondents Percentage (%)

1 Age

Below 25 years 42 16.67 26-36 years 110 43.65 37-47 years 45 17.86 48-58 years 55 21.83

2 Gender Male 145 57.54 Female 107 42.46

3 Educational Qualification

Under graduation 83 32.94 Post graduation 114 45.24 Diploma 45 17.85 Others 10 03.97

4 Monthly Income

Below 10,000 34 13.49 11,000- 20,000 72 28.80 21-000- 30,000 77 30.80 31,000- 40,000 38 15.20 41,000- 50,000 31 12.40

Table 2: Result of reliability analysis for SERVQUAL dimensions

Dimensions Number of Attributes Cronbach’s Alpha

Expectation (Desired) Perception (Actual)

Tangibles 4 0.863 0.822 Reliability 5 0.965 0.806 Responsiveness 4 0.930 0.973 Assurance 4 0.979 0.885 Empathy 5 0.976 0.733

5.3. Guests’ Perception Versus Guests’ Expectation

Table 3 shows the weighted average values of guests’ perceptions of hotel service with their own expectations with regard to hotel’s service quality. As per Table 3, it can be inferred that average total expectations values are more than average total perceptions values resulting in negative total SERVQUAL gap. With regard to individual SERVQUAL dimensions, gap values are positive for tangibles, reliability, assurance and gap scores are negative for responsiveness (-.4375) and empathy(-.0516).Hotel guests are satisfied with reference to tangibles, reliability and assurance, further with regard to responsiveness and empathy guests’ perceptions of service quality does not exceed their expectations, which is an indicator of dissatisfaction. The results of the application of SERVQUAL model in the hotels of Vellore (Tamilnadu/India) show that the expectations of the hotel guests are higher than their perception for the SERVQUAL dimensions responsiveness and empathy.


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Table 3: GAP 5- Guests’ Perception versus guests’ Expectation (Sample size n = 252)

Dimension

Mean score for guests’

actual Perception

(P)

Mean score for guests’

Expectation

(E)

Difference Gap score

(P-E)

Tangibles 3.5952 3.5089 +0.0863

Reliability 3.5230 3.4897 +0.0333

Responsiveness 3.4712 3.9087 -0.4375

Assurance 3.7410 3.4603 +0.2807

Empathy 3.4349 3.4865 -0.0516

Total (Average score) 3.5530 3.5708 -.01776

5.4. Multiple Regression

5.4.1. Significance of the Model

Standard Multiple-regression analysis was used with the four factors as independent variables to test the model for guests’ perception and guests’ expectation. (see Appendix B).Appendix B shows the multiple regression with age, gender, monthly income, highest qualification of the hotel guests’ as independent variables and average values of hotel guests’ perceptions and expectations with regard to various SERVQUAL dimensions tangibles, reliability, responsiveness, assurance and empathy as dependent variables.

Numbers in columns labeled ()d- (Significance F- the term “Sig.” in SPSS refers to a significance test, which is another way of saying ‘statistical hypothesis test) are p-values which give the results of a hypothesis test. In this case, the p values refers to a test of the entire model (i.e., the entire collections of independent variables) as a whole. In the Appendix B for guests’ perceptions and expectations - ‘d’ values show the goodness of fit of the model. ‘Significance - F’ indicates whether the model as a whole is significant. The lower this number, the better the fit. Typically, if “Significance F” is greater than 0.05, we conclude that our model could not fit the data. If “Significance F”. is greater than .1 then the model is not significant (a relationship could not be found).

The form of hypothesis test is H0: None of the independent variables (Age, gender, monthly income, highest qualification) are

significant predictors of the dependent variables**. Ha: At least one independent variable (Age, gender, monthly income, highest qualification) is a

significant predictor of the dependent variables**. ** (Average value of ‘tangibles-perception’; Average value of ‘reliability-perception’; Average

value of ‘responsiveness-perception’; Average value of ‘assurance-perception’; Average value of ‘empathy-perception’; Average value of ‘tangibles-expectation’; Average value of ‘reliability- expectation’, Average value of ‘responsiveness- expectation’, Average value of ‘assurance- expectation’, and Average value of ‘empathy- expectation’.)

As per the Appendix B, all the significant values (in Appendix B-‘( )d’) are less than .05 (5% significance level), for the guests’ perceptions and expectations except for guests’ perception for the dimension ‘assurance’ (.065). We have to reject the null hypothesis (H0) and accept alternative hypothesis (Ha) - At least one independent variable is a significant predictor of the dependent variables. Even guests’ perception for the dimension ‘assurance’ (.065) is significant at 90% significance level. 5.4.2. Adjusted R-Square

Adjusted R-square measures the proportion of the variance in the dependent variable,that is explained by variations in the independent variables. As per the Appendix B, with regard to perception of the guests’ values of the “Adjusted R-Square” ()c- 2.5% of the variances were explained for the dimension tangibles , 4.4% of the variances were explained for the dimension reliability, 8.4% of the variances were explained for the dimension responsiveness, 1.9% of the variances were explained for the


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dimension assurance and for the dimension empathy 4.2% of the variances were explained. As per the Appendix B, with regard to expectation of the guests’ values of the “Adjusted R-Square” ()c- 3.3% of the variances were explained for the dimension tangibles , 8.3% of the variances were explained for the dimension reliability, 8.6% of the variances were explained for the dimension dimension responsiveness , 4.9% of the variances were explained for the dimension assurance and for the dimension empathy 5.6% of the variances were explained. 5.4.3. R-Square

R-square is the percentage of variance in the dependent variable explained by the collection of independent variables. R-square measures the proportion of the variation in the dependent variable (average values of ‘tangibles perception’, average values of ‘reliability perception’, average values of ‘responsiveness perception’, average values of ‘assurance-perception’, average values of ‘empathy-perception’) that was explained by variations in the independent variables. As per the Appendix B, with regard to perception of the guests’ values of the “R-Square”- 4.1% of the variations were explained for the dimension tangibles, 5.9% of the variations were explained for the dimension reliability, 9.8% of the variations were explained for the dimension responsiveness, 3.5% of the variations were explained for the dimension assurance and for the dimension empathy 5.8% of the variations were explained. As per the Appendix B, with regard to expectation of the guests’ values of the “R-Square”- 4.9% of the variations were explained for the dimension tangibles, 9.8% of the variations were explained for the dimension reliability, 10.1% of the variations were explained for the dimension responsiveness, 6.4% of the variations were explained for the dimension assurance and for the dimension empathy 7.1% of the variations were explained.

“Sige.” (Significance) are the p values for each of the independent variables and only applies to a single independent variable but not to the entire group.

The form of hypothesis test is H0: Independent variable (Age, gender, monthly income, highest qualification) is not a

significant predictor of the dependent variable**. Ha: Independent variable is a significant predictor of the dependent variable. If P<.05 reject the null hypothesis (H0) and conclude that the independent variable is a

significant predictor of the dependent variable. ** (Average value of ‘tangibles-perception’; Average value of ‘reliability-perception’; Average

value of ‘responsiveness-perception’; Average value of ‘assurance-perception’; Average value of ‘empathy-perception’; Average value of ‘tangibles-expectation’; Average value of ‘reliability- expectation’, Average value of ‘responsiveness- expectation’, Average value of ‘assurance- expectation’, and Average value of ‘empathy- expectation’.)

As per the Appendix B, with regard to perception of the guests’ for the dimension reliability, based on the guests’ gender the value is .015; for the dimension responsiveness, based on the guests’ age, the value is .000, based on the guests’ gender, the value is .000, based on the guests’ highest qualification, the value is .004; and for the dimension empathy, based on the guests’ gender, the value is .025. We have to reject the null hypothesis (H0) and accept alternative hypothesis (Ha). It can be inferred that guests’ gender is the significant predictor for the dimension reliability; guests’ age, gender, and highest qualification are significant predictors for the dimension responsiveness; and guests’ gender is the significant for the dimension empathy. Even, with regard to perception of the guests’ for the dimension tangibles, based on their gender the value is .052, and for the dimension reliability, based on their monthly income the value is .076 and for dimension assurance based on their age the value is .073. These are significant at 10% level of significance. As per the Appendix B, with regard to expectation of the guests’ for the dimension tangibles, based on their age the value is .003, based on their gender the value is .005; for the dimension reliability, based on their age, the value is .000, based on their gender, the value is .000, based on their highest qualification, the value is .031; for the dimension responsiveness, based on their highest qualification, the value is .000; for the dimension


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assurance, based on their age, the value is .000, based on their gender, the value is .017, based on their highest qualification, the value is .007 and for the dimension empathy, based on their age, the value is .000, based on their gender, the value is .005, based on their highest qualification, the value is .031. We have to reject the null hypothesis (H0) and accept alternative hypothesis (Ha). It can be inferred that guests’ age and gender are significant predictors for the dimension tangibles; guests’ age, gender, and highest qualification are the significant predictors for the dimension reliability; guests’ highest qualification is the significant predictor for the dimension responsiveness; guests’ age, gender, and highest qualification are the significant predictors for the dimension assurance; guests’ age, gender and highest qualification are the significant predictors for the dimension empathy. These are significant at 5% level of significance. 5.5. Exploratory Factor Analysis

Factor analysis is used to identify a smaller number of factors underlying larger number of observed variables. Table 4 shows Kaiser-Meyer-Olkin (KMO) and Bartlett's Test. The KMO ranges from 0-1, with higher values indicating greater suitability. Ideally this value is to be greater than 0.7.According to Kaiser a KMO measure of 0.9-1.0 is marvelous, 0.8-0.9 meritorious, 0.7-0.8 middling, 0.6-0.7 mediocre, 0.5-0.6 miserable, (Marcus J.Schmidt, Svend Hollensen, 2006).Table 4 shows with regard to guests’ perception, Kaiser-Meyer-Olkin Measure of Sampling Adequacy (MSA) is 0.734 and Bartlett’s Test of Sphericity is significant [Chi-Square χ2 (10) =1090, p<0.001].With regard guests’ expectation, Kaiser-Meyer-Olkin Measure of Sampling Adequacy (MSA) is 0.878 and Bartlett’s Test of Sphericity is significant [Chi-Square χ2 (10) =2182, p<0.001] and therefore the factor analysis is suitable for this data set. Table 5 contains the rotated factor loadings, which are the correlations between the variable and the factor. Because these are correlations, possible values range from -1 to +1. For a good factor solution, a particular variable should load high on one factor and low on all other factors in the rotated factor matrix (Ajay S.Gaur et al., 2006). Table 4: Kaiser-Meyer-Olkin (KMO) and Bartlett's Test

Guests’ perception Guests’ expectation

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .734 .878

Bartlett's Test of Sphericity Approx. Chi-Square 1090.462 2182.320

Degrees of freedom 10 10

Significance .000 .000

The retention decision of each item is based on factor loadings which are greater than or equal

to 0.40 as recommended, (Hair et.al., 1998, p.111). Table 5 summarizes the variance explained by the factor analysis solution and gives an indication about the number of useful factors. Factor analysis results for the perceptions data and expectations data appear in Table 5. Data on perceptions divided SERVQUAL items into four main factors with Eigen values greater than 1.0, accounting for 81.1%. Whereas the exploratory factor analysis extracted five factors, which accounted for 65.1 per cent of variance in the data (Suzana Markovi´c et al., 2010).Loading factors explains the importance of the specific factor for the customers. The first factor accounted for 31.9% of the variability, second factor accounted for 25.3% of the variability, third factor accounted for 18.3% of the variability and fourth factor accounted for 5.6% of the variability. SERVQUAL items were divided into four main factors. As per the Table 5, it can be inferred that the bold items under component 1 started with Tangibles 3(T3) with value .947 followed by assurance 2(A2) with value .917, assurance 3(A3) with value .904 and ended with reliability 4(R4) with value .465(considering the factor loading values recommended 0.4 and above, Hair et.al., 1998 p. 111)). The bold items under component 2 started with reliability

2(R2) with value .892 and ended with tangibles 1 (T1) with value .663.


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The bold items under component 3 started with responsiveness (RP2, RP3) with values .953, .953 and ended again with responsiveness with value .883. The bold items under component 4 had only one item empathy E5 with value .855(considering the factor loading values recommended 0.4 and above, Hair et.al. (1998 pg.111)). It can be concluded that the first factor extracted from rotated factor matrix accounted for 31.9% out total variance explained 81.1%. As per the Table 5 it can be inferred that the most important factor based upon the guests’ perception was tangibles, assurance, followed by reliability as second factor, responsiveness as third factor and ended with empathy.

` Factor analysis results for the expectations data also appear in Table 5. Data on expectations divided SERVQUAL items into three main factors, with Eigen values greater than 1.0, accounted for 88.3% of the variation. The first factor accounted for 54.6% of the variability, second factor accounted for 17.1% of the variability, and third factor accounted for 16.6% of the variability. As per the Table 5, we can infer that the bold items under component 1 started with tangibles 4 (T4), reliability (R4) assurance (A1), assurance (A3), with values .933 ended with tangibles (T3) with value .410. Bold items under second component started with responsiveness (RP3) with value .967, followed again by responsiveness (RP1), with value .920 and ended with tangibles (T3) with the value .524. Bold items under third component started with reliability (R1, R2, R3) with values .818 each and ended with tangibles (T1) with value .446.It can be concluded that the first factor extracted from rotated factor matrix accounted for 54.6% out total variance explained 88.3%. As per the Table 5 it can be inferred that the most important factor based upon the guests’ expectation was tangibles, reliability and

assurance, followed by responsiveness as second factor and reliability as third factor. The communalities of 22 items for the perception of the guests’ ranged from 0.577 to 0.964 and

for the expectation of the guests’ ranged from 0.480 to 0.982 indicating that a large amount of variance has been extracted by the factor solution. Whereas the communalities of 29 items for the perception of the guests’ ranged from 0.447 to 0.793 indicating that a large amount of variance has been extracted by the factor solution, (Suzana Markovi´cs et al., 2010). Only one item (Guests’ expectations for the item- employees are neat-appearing-.480(T3)) was below the suggested value of 0.50, (Hair et al., 2006). Whereas only one item (hotel guests’ perception for the item-‘typical service quality for hotel category’) was below the suggested value of 0.50, (Suzana Markovi´c et al., 2010). Table 5: Rotated Factor Matrixa Rotated Factor Matrixb

Component Component (Perception) Component (Expectation) Communalities

1 2 3 4 1 2 3 Perception Expectation

1. T1** .498 .663 .240 .060 .710 .128 .446 .749 .719

2. T2**

.784 .097 .146 .087 .659 .166 .594 .654 .816 3. T3

** .947 -.201 -.039 -.012 .410 .524 .193 .940 .480 4. T4

** .479 .729 .321 .083 .933 .102 .316 .871 .980 5. R1

** .346 .786 .064 -.202 .554 .080 .818 .782 .982 6. R2

** -.118 .892 .053 -.179 .554 .080 .818 .845 .982 7. R3

** .889 .171 .054 .012 .554 .080 .818 .823 .982

8. R4** .465 .791 .229 -.096 .933 .102 .316 .904 .980

9. R5**

.768 .366 .002 -.218 .892 .147 .255 .771 .882 10. RP1 .074 .125 .931 -.059 -.074 .920 -.042 .891 .855

11. RP2** .086 .220 .953 -.013 .038 .892 .066 .964 .801

12. RP3** .086 .220 .953 -.013 .066 .967 .073 .964 .945

13. RP4** .123 .266 .883 -.023 .208 .839 .066 .866 .751

14.A1**

.860 -.090 .195 .246 .933 .102 .316 .846 .980 15. A2

** .917 .306 .074 -.040 .891 .134 .302 .941 .904

16. A3**

.904 .289 .040 -.048 .933 .102 .316 .905 .980 17. A4

** .501 .522 .128 .192 .891 .134 .302 .577 .904

18. E1** .335 .670 .306 .269 .881 .119 .257 .727 .856

19. E2**

.693 .237 .084 .155 .932 .049 .268 .568 .942 20. E3

** -.029 .751 .273 .264 .911 .058 .262 .709 .901 21. E4

** -.039 .789 .237 .276 .921 .054 .289 .756 .934


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Table 5: Rotated Factor Matrixa Rotated Factor Matrixb - continued

22. E5

.102 .117 -.123 .855 .868 .099 .317 .770 .865 Eigen value 10.059 4.148 2.433 1.182 15.026 3.371 1.023 17.822 19.42 Variance explained 31.9% 25.3% 18.3% 5.6% 54.6% 17.1% 16.6% 81.1% 88.3%

Extraction method: Principal Axis Factoring. Rotation method: Varimax with Kaiser Normalization. Rotation converged in a.5 iterations .b. 4 iterations (Tangibles: T1-T4; Reliability: R1-R5; Responsiveness: RP1-RP4; Assurance: A1-A4; Empathy: E1-E5) **1. Hotel has modern-looking equipment (T1); 2.The physical facilities are visually appealing (T2); 3.Employees are neat-appearing (T3);4.Materials associated with the service are visually appealing (T4); 5.When the hotel promises to do something by a certain time, it does so (R1); 6.When a customer has a problem, the restaurant shows a sincere interest in solving it (R2); 7.The hotel performs the service right the first time (R3); 8.Services are provided at the time the restaurant promises to do (R4); 9.The records are error-free (R5); 10.Employees tell customers when services will be performed (RP1);11.Employees give prompt service to customers (RP2); 12.Employees are willing to help customers (RP3);

13.Employees are never too busy to respond to customer’s requests (RP4); 14.The behaviour of employees instill confidence in customers (A1); 15.Customers feel safe in their transactions(A2); 16. Employees are consistently courteous (A3); 17.Employees have the knowledge to answer customer’s questions (A4); 18. Hotel gives individual attention to the customer (E1); 19.Employees give personal attention to customers (E2); 20. Hotel understands specifics needs of its customers (E3); 21. Hotel has customer’s interest at heart (E4); 22.Operating hours are convenient to all customers.

6. Results and Implications for Managers On the basis of the statistical analysis, findings and suggestions are presented as follows:

Component and reliabilities of SERVQUAL dimension scores indicated high level of internal consistency for the SERVQUAL scale. Hotel guests are satisfied in terms of SERVQUAL dimensions tangibles, reliability, assurance and dissatisfied in terms of SERVQUAL dimensions responsiveness and empathy. The findings of this study reveal that among the five SERVQUAL dimensions, with regard to hotel guests’ perception mean score was high for ‘assurance’. The findings of this study reveal that among the five SERVQUAL dimensions, with regard to hotel guests’ expectation, mean score was high for ‘responsiveness’. It is suggested to the managers of the hotels to encourage and motivate their contact personnel in order to help their hotel guests and to provide timely service as expected by the guests. Managers have to prepare their contact personnel to properly communicate with their guests, to understand about the guests’ expectation and also to motivate contact personnel to provide easy access to their guests whenever they require.

As per the multiple regression analysis, it can be interpreted that, demographic variables like age, gender, income, highest qualification, are the significant predictors of the hotel guests perceptions and expectations for the SERVQUAL dimensions tangibles, reliability, responsiveness, assurance and empathy.

With regard to Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphercity, factor analysis is suitable for this data set. Factor analysis data on perception divided SERVQUAL components (items) into four main factors. Under the first factor, 4 tangibles items out of 4 items, 3 reliability items out of 5 items, 4 assurance items out of 4 items, 1 empathy item out of 5 items are above the cut-off of 0.40 to identify high factor loadings. Under the second factor, 2 tangibles items out of 4 items, 3 reliability items out of 5 items, 3 empathy items out of 5 items are above the cut-off of 0.40 to identify high loadings. Under the third factor, 4 responsiveness items out of 4 items are above the cut-off of 0.40 (considering the factor loading values recommended 0.4 and above, Hair et.al., 1998 p.111) to identify high factor loadings and fourth factor may be dropped because it comprises of 1empathy item out of 5 items. Data on expectation divided SERVQUAL components (items) into three main factors. Under the first factor, 4 tangibles items out of 4 items, 5 reliability items out of 5 items, 4 assurance items out of 4 items, 5 empathy items out of 5 items are above the cut-off of 0.40 to identify high loadings. Under the second factor, 1 tangibles item out of 4 items, 4 responsiveness items out of 4 items are above the cut-off of 0.40 to identify high factor loadings and under the third factor, 2 tangibles out 4 of items, 3 reliability items out of 5 items are above the cut-off


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of 0.40 (considering the factor loading values recommended 0.4 and above, Hair et.al., 1998 p.111) to identify high factor loadings. We can very well see the differences in the factor loadings for the hotel guest’s perceptions and expectations for SERVQUAL dimensions tangibles, reliability, responsiveness, assurance and empathy. Hotel managers can understand the components which lead to customer satisfaction and dissatisfaction and they can act accordingly.

7. Limitations and Directions for Future Research Opinions expressed by the guests stayed in different hotels were not considered separately. No consideration in this survey was taken with respect to ratings of hotels, the type of guest i.e. local or tourists (domestic tourists or inbound tourists), or the frequency of their visits. Since study area Vellore (Tamilnadu/ India) is well known for hospital facilities, future research can be carried out from the perspective of medical tourism. Expectations of the locals, inbound tourists and domestic tourists can be studied separately and also can be compared. In order to compare the guests’ perceptions with hotel staffs’ perceptions for the various SERVQUAL dimensions, future research can include hotel staffs’ perceptions of service performance.

8. Conclusion Understanding the customer expectations is imperative for the survival and growth of any industry. As far as the hotel industry is concerned, in order to satisfy the hotel guests, managers have to concentrate on service quality, even though it is a subjective category. In order to understand the guests’ expectations, managers of the hotel industry can very well apply the SERVQUAL model in their hotels and can ascertain the quality of their services. Hotels can get a good brand image and patronage only when they consistently meet or exceed the expectations of their guests. The findings of this study reveal that dimensions of service quality are very important for the managers of the hotel industry to understand the perceptions and expectations of their guests. The findings of the study reveal that guests stayed in the hotels view responsiveness as the most important satisfactory attribute. It is suggested to the managers of the hotels to encourage and motivate their contact personnel in order to help their hotel guests and to provide timely service as expected by the guests. This will ultimately satisfy the guests and enhance their credibility.

In general satisfied guests may disseminate positive word of mouth and thereby act as an unpaid brand ambassador. Hotel managers may adopt different ways (for example mystery shopping) to monitor and understand the needs of their guests, (Renganathan. R, 2011).

This study is really useful to the managers of the concerned hotels to understand the importance given by the guests for the tangibles and intangible services offered to them during their stay in their hotels.

Acknowledgement The researcher would like to thank Ms. B.R.Aswini, an MBA student of SASTRA University (2009-2011 batch), Thanjavur, for the help and support in collection of data.

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Appendix A: Perceptions Scale, Expectations Scale, Item- Total Correlations,

Reliabilities, Item Means and Standard Deviations (N = 252)

Items in each

dimensions Corrected Item-total Correlation Mean Standard deviation

Tangibles** Perception Expectation Perception Expectation Perception Expectation

1.T1 .725 .801 3.40 3.41 .632 .582 2.T2 .798 .828 3.54 3.42 .639 .583 3. T3 .460 .478 3.98 3.72 .744 .700 4.T4 .649 .804 3.46 3.49 .607 .554

Reliability**

5.R1 .869 .941 3.55 3.49 .607 .554 6. R2 .522 .941 3.11 3.49 1.208 .554 7. R3 .434 .941 3.68 3.49 .582 .554 8.R4 .869 .877 3.51 3.49 .589 .554 9. R5 .616 .811 3.77 3.50 .614 .575

Responsiveness **

10.RP1 .899 .840 3.43 3.92 .549 .774 11.RP2 .978 .809 3.48 3.86 .554 .906 12. RP3 .978 .929 3.48 3.96 .554 .792 13.RP4 .878 .799 3.49 3.89 .554 .674

Assurance**

14.A1 .677 .969 3.68 3.49 .721 .554 15.A2 .895 .895 3.75 3.43 .553 .550 16A3 .896 .971 3.77 3.51 .543 .554

17. .A4 .597 .947 3.75 3.51 .567 .554 Empathy

**

18.E1 .713 .876 3.39 3.46 .632 .552 19. E2 .346 .971 3.79 3.51 .474 .554 20. E3 .616 .954 3.22 3.51 .825 .554 21. E4 .670 .946 3.27 3.49 .870 .554 22. E5 .233 .907 3.51 3.44 .806 .551

**1.Hotel has modern-looking equipment (T1);2.The physical facilities are visually appealing (T2); 3.Employees are neat-appearing (T3);4.Materials associated with the service are visually appealing (T4); 5.When the hotel promises to do something by a certain time, it does so (R1); 6.When a customer has a problem, the restaurant shows a sincere interest in solving it (R2); 7.The hotel performs the service right the first time (R3); 8.Services are provided at the time the restaurant promises to do (R4); 9.The records are error-free (R5); 10.Employees tell customers when services will be performed (RP1);11.Employees give prompt service to customers (RP2); 12.Employees are willing to help customers (RP3);

13.Employees are never too busy to respond to customer’s requests (RP4); 14.The behaviour of employees instill confidence in customers (A1); 15.Customers feel safe in their transactions(A2); 16. Employees are consistently courteous (A3); 17.Employees have the knowledge to answer customer’s questions (A4); 18. Hotel gives individual attention to the customer (E1); 19.Employees give personal attention to customers (E2); 20. Hotel understands specifics needs of its customers (E3); 21. Hotel has customer’s interest at heart (E4); 22.Operating hours are convenient to all customers (E5)

Appendix B: Multiple Regression

Guests’ perception Coefficientsa

Dimension Model Unstandardized

Coefficients (B)

Standardized

Coefficients

(Beta)

t Sig.e F R R

2

Average of ‘Tangibles-perception’

(Constant) Age Gender Monthly Income Highest qualification

3.488 .011 .164 -.023 -.038

.020 .153 -.052 -.106

16.238 .216 1.953 -.704

-1.414

.000 .829 .052 .482 .158

2.632 (.035)

d .202b .041

(.205)c

Average of ‘Reliability-perception’


3.336 .031 .220 -.061 -.012

.053 .191 -.129 -.030

14.598 .577 2.460 -1.782 -.407

.000

.564

.015

.076

.684

3.906 (.004)

d .244b .059

(.044)c


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Appendix B: Multiple Regression - continued

Average of ‘Responsiveness-perception’


4.199 -.246 -.292 .035 .076

-.443 -.272 .079 .213

20.140 -4.973 -3.571 1.114 2.918

.000

.000

.000

.266

.004

6.728 (.000)d .313b .098

(.084)c

Average of ‘Assurance-perception’


3.942 -.090 -.007 .025 -.020

-.166 -.007 .058 -.058

18.772 -1.800 -.089 .794 -.768

.000

.073

.929

.428

.443

2.241 (.065)d .187b .035

(.019)c

Average of ‘Empathy-perception’


3.328 -.015 .182 -.016 -.031

-.028 .175 -.037 -.088

16.093 -.310 2.253 -.511

-1.181

.000

.757

.025

.610

.239

3.775 (.005)d .240b .058

(.042)c

Guests’ expectation Coefficientsa

Dimension Model

Unstandardize

d Coefficients

(B)

Standardized

Coefficients

(Beta)

t Sig.e F R R

2

Average of ‘Tangibles-expectation’


4.159 -.146 -.231 -.006 .020

-.274 -.224 -.014 .057

20.211 -2.996 -2.864 -.187 .757

.000

.003

.005

.852

.450

3.153 (.015)d .220b .049

(.033)c

Average of ‘Reliability- expectation’


4.241 -.238 -.300 .041 .056

-.436 -.284 .095 .158

20.680 -4.893 -3.739 1.335 2.167

.000

.000

.000

.183

.031

6.704 (.000)d .313b .098

(.083)c

Average of ‘Responsiveness expectation’


4.321 -.020 -.067 .025 -.146

-.027 .046 .041 -.301

15.346 -.298 -.604 .583

-4.133

.000

.766

.546

.560

.000

6.908 (.000)d .317b .101

(.086)c

Average of ‘Assurance- expectation’


3.931 -.203 -.203 .047 .073

-.360 -.186 .105 .201

18.233 -3.967 -2.399 1.451 2.706

.000

.000

.017

.148

.007

4.204 (.003)d .252b .064

(.049)c

Average of ‘Empathy- expectation’


4.028 -.208 -.232 .054 .057

-.377 -.218 .124 .160

19.174 -4.173 -2.823 1.719 2.166

.000

.000

.005

.087

.031

4.748 (.001)d .267b .071

(.056)c

a. Dependant variables: Average of ‘Tangibles-perception’, Average of ‘reliability-perception’, Average of ‘responsiveness-perception’, Average of ‘assurance-perception’, Average of ‘empathy-perception’, Average of ‘Tangibles-expectation’, Average of ‘reliability- expectation’, Average of ‘responsiveness- expectation’, Average of ‘assurance- expectation’, Average of ‘empathy- expectation’.

b. Predictors: (Constant), Age, Gender, Monthly income, Highest qualification. c. Adjusted R square. d. Predictors: (Constant), Age, Gender, Monthly income, Highest qualifications.

“Sige.” (Significance) are the p values for each of the independent variables.

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