1. Chapter 1 INTRODUCTION The development and growth of services industry is unquestionable in most countries (Mathe & Peras, 1994). Considering the Indian economy, the services sector accounts for more than 54.2% of the nation´s GDP (2007). Moreover, for service-based economies’, growth is accomplished by increasing competition and technological advances, thus reinforcing service quality as an important and sustaining competitive advantage (Bharadwaj, Varadarajan & Fahy, 1993). Numerous studies have shown that provision of high-quality services is directly related to increase in profits, market share, and cost savings (Devlin and Dong, 1994). With competitive pressures and the increasing necessity to deliver patient satisfaction, the elements of quality control, quality of service, and effectiveness of medical treatment have become vitally important (Friedenberg, 1997). There are many researchers who have defined service quality in different ways. While Cronin and Taylor, 1994 view service quality as ‘a form of attitude representing a long-run overall evaluation’, Parasuraman, Zeithaml and Berry (1985, p. 48) defined service quality as ‘a function of the differences between expectation and performance along the quality dimensions’. The quality of service—both technical and functional—is a key ingredient in the success of service organizations (Grönroos, 1984). Technical quality in health care is defined primarily on the basis of the technical accuracy of the diagnosis and procedures. Functional quality, in contrast, relates to the manner of delivery of health-care services. In the health care environment, alternatives of delivery systems, increasing knowledgeable consumers and technology costs are demanding from hospital managers a closer and better understanding of their service quality (O´Connor & Shewchuk, 1989). SERVQUAL (Parasuraman, Zeithaml & Berry, 1988) and SERVPERF (Cronin &Taylor, 1992) scales have been employed to evaluate service quality in a multitude of industries. This study is an attempt to evaluate the practical usefulness and the psychometric proprieties of these multiple-item scales in accessing patients’ perceptions of the hospital service environment. The purpose of this study is to measure consumer perceptions of the quality of services provided by a large hospital in New Delhi. To accomplish this, updated service quality measurement literature was revised. A theoretical background supports the methodological and statistical procedures employed to evaluate service quality at the hospital, service quality dimensionality and predictive validity of the SERVPERF scale. 1.1 INDUSTRY PROFILE Sector structure/Market size Healthcare, which is a US$ 35 billion industry in India, is expected to reach over US$ 75 billion by 2012 and US$ 150 billion by 2017, according to Technopak Advisors in their report – ‘India Healthcare Trends 2008’. The sector offers immense potential to healthcare players as the country witnesses a rise in the incidence of lifestyle-related and other diseases. A growing elderly population and rise in income levels are also pushing for better facilities in the country. To meet this growing demand, the country needs US$ 50 billion annually for the next 20 years, says a Confederation of Indian Industry (CII) study. India needs to add 3.1 million beds by 2018 to the existing 1.1 million, and requires immediate investments of US$ 82 billion, as per the Technopak Advisors report. According to a latest report by McKinsey, driven by strong local demand, Indian healthcare market is expected to continue growing close to previously projected rates of 10 to 12 per cent. With average household consumption expected to increase by more than seven per cent per annum, the annual healthcare expenditure is projected to grow at 10 per cent and also the number of insured is likely to jump from 100 million to 220 million. Medical Tourism In 2007, India treated 450,000 foreign patients ranking it second in medical tourism. According to a study by McKinsey and the CII, medical tourism in India could become a US$ 2 billion industry by 2012 (from US$ 350 million in 2006). Credit Suisse estimates medical tourism to be growing at between 25-30 per cent annually. The key selling points of the medical tourism industry are its cost effectiveness and its combination with the attractions of tourism. Treatment cost is lowest in India – 20 per cent of the average cost incurred in the US, Singapore, Thailand and South Africa. Chapter 2 LITERATURE REVIEW Service quality has become an important research topic in view of its significant relationship to costs (Crosby, 1979), profitability (Buzzell and Gale, 1987; Rust and Zahorik, 1993; Zahorik and Rust, 1992), customer satisfaction (Bolton and Drew 1991; Boulding et al, 1993), customer retention (Reichheld and Sasser, 1990), and service guarantee (Kandampully and Butler, 2001). Service quality has also become recognized as a driver of corporate marketing and financial performance (Buttle, 1996). Service quality affects customer satisfaction. A popular definition of service quality proposed by Berry et al. (1988) is ‘conformance to customer specifications’—that is, it is the customer’s definition of quality that matters, not that of management. Evans and Lindsay (1999) proposed the view that customer satisfaction results from the provision of goods and services that meet or exceed customer needs. Although it is widely acknowledged that there is a need for quality indicators of patient satisfaction with medical care, very little research in this area exists (Berman-Brown and Bell, 1998). Service quality is described as “the ability of a service in providing customer satisfaction related to other alternatives” (Bojanic, 1991. P.28). According to the disconfirmation paradigm in the services marketing literature (Sasser, Olsen & Wyckoff, 1978; Lewis & Booms, 1983; Lehtinen & Lehtinen, 1985; Gummesson & Grönroos, 1988; Brown & Swartz, 1989; Grönroos, 1990; Parasuraman, Zeithaml & Berry, 1994), service quality is the gap between consumer’s perceptions of service performance and level of expectations. The outcome of this process is: negative disconfirmation (performance rated below level of expectations), positive disconfirmation (performance evaluated above level of expectations) or confirmation (performance equal to expectations level). In the health care environment, consumer´s perceptions can be considered the patient´s evaluation of specific hospital service attributes relative to his/her expectations. Expectations, in the service quality literature, are not viewed as normative expectations (Miller, 1977; Swan & Trawick, 1980; Prakash, 1984) or what the patient believes should be offered, but rather are seen as what the patient believes would be offered in a health care service product. Several studies indicate that a lower priority is placed on patients’ non-clinical expectations of service quality. Carson et al. (1998) have stated that some professionals contend that consumers’ perception of quality service in health care is distorted due to the inability of patients to judge the technical competence of the medical practitioner with any accuracy. Since patients are often unable to assess the technical quality of medical services accurately, functional quality is usually the primary determinant of patients’ perceptions of quality (Donabediam, 1980, 1982). There is growing evidence to suggest that this perceived quality is the single most important variable influencing consumers’ perceptions of value, and that this, in turn, affects their intention to purchase products or services (Bolton and Drew, 1988; Zeithaml, 1998). John (1996) believes that medical courses cover technical details and knowledge in detail, but expect students to develop customer-service skills as they become more experienced. The focus of medical practitioners on technical prowess and knowledge is understandable, given the highly complex nature of the profession and the immense resources that are devoted to the education and training of doctors (Baldwin et al. 2002). 2.1. SERVICE QUALITY MEASUREMENT SCALES 22Q=∑ (Px – Ex),x=1What’s not measured cannot be managed. Services marketing literature presents disconfirmation-based (SERVQUAL - Parasuraman, Zeithaml & Berry, 1988) and performance-based (SERVPERF – Cronin & Taylor, 1992) scales that are useful for hospital managers to understand patient’s judgment about the quality of service received. Both scales have been widely applied to evaluate service quality in different service industries, such as retail banking, appliance repair and maintenance, credit card and long-distance telephone providers (Parasuraman, Zeithaml & Berry, 1988; Parasuraman, Berry & Zeithaml, 1991); hospitals (Carman, 1990; Babakus & Mangold, 1992); professional medical services (Brown & Swartz, 1989; Swartz & Brown, 1989; Soliman, 1992; Walbridge & Delene, 1993); public recreation programs (Crompton & Mackay, 1989); placements center, tire stores, dental school patient clinics and acute care hospitals (Carman, 1990); dry cleaning and the fast food industry (Cronin & Taylor, 1992). The SERVQUAL (Service Quality) scale consists of 22 pairs of statements that purportedly measure consumer’s expectations and perceptions of service performance. By summing the gap scores (performance-minus-expectations) for each of the items, service quality is measured by the following equation: where Q represents consumer’s perceived service quality, P and E the ratings corresponding to performance and expectations of “x” statement, respectively. Using a 7 point Likert scale, with ends anchored by the labels “Strongly Disagree” (value 1) and “Strongly Agree” (value 7), the gap score P-E results in a value ranging from -6 (lowest quality) to +6 (highest quality). Through numerous qualitative studies, a set of five dimensions have evolved, which have been consistently ranked by customers to be most important for service quality, regardless of service industry. These dimensions are defined as follows: Tangibles: appearance of physical facilities, equipment, 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: the caring, individualized attention the firm provides its customers The gap between performance and expectations, which forms the cornerstone of the disconfirmation paradigm, has been challenged by other studies (Carman, 1990; Bolton & Drew, 1991; Babakus & Boller, 1992; Babakus & Mangold, 1992; Brown, Churchill & Peter, 1993; Boulding et. al., 1993; Peter, Churchill & Brown, 1993). Cronin and Taylor (1992), based on other studies (Churchill & Surprenant, 1982; Woodruff, Cadotte & Jenkins, 1983), state that only consumer´s perceptions is needed to evaluate service quality and that performance-based scales are a better and more efficient way of measuring service quality provided by organizations. According to the same authors, some features (such as pleasure, for example) could not be measured by the simple arithmetic difference between perceptions and expectations, while performance could fully capture consumers perceptions of service quality. Also, expectation measurement is questionable in those cases that consumers do not have them well formed (Carman, 1990). The disconfirmation-based multiple-items scale is challenged by the SERVPERF (Service Performance) scale. According to Cronin and Taylor (1992, 1994) and Teas (1993, 1994), the gap score P-E has several psychometric pitfalls such as conceptualization and operationalization of service quality based on customer satisfaction literature and that P-E specification could be problematic when a service attribute is a classic ideal point attribute rather than a vector attribute. Based on their findings, Cronin and Taylor (1992) suggest that the use of the performance battery of SERVQUAL scale would be a richer construct in measuring the multifaceted nature of service quality. This scale, called SERVPERF (Service Performance), would measure service quality of an organization using the following equation: 22Q=∑ Px,x=1 , where Q represents consumers’ perceived service quality and P consumers’ performance rating of “x” statement. Using a 7 point Likert scale, with ends anchored by the labels “Strongly Disagree” (value 1) and “Strongly Agree” (value 7), the authors conclude that SERVPERF has a higher performance than SERVQUAL in measuring service quality; and it reduces by 50% the number of items that must be measured (from 44 items to 22 items). Empirical studies evaluating validity, reliability, and methodological soundness of service quality scales clearly point to the superiority of the SERVPERF scale. Chapter 3 THE RESEARCH PROBLEM The study aims to study the service quality at a hospital in New Delhi through SERVPERF scale and identify important service dimensions for performance improvement. The objectives of this study are: Reliability, Dimensionality and Validity Statistics for the SERVPERF scale used for the hospital Report the Customer Perceptions for the five dimensions of SERVPERF/SERVQUAL study instrument. Report the Customer Perceptions for Services provided by the doctors at the hospital. Study the relation of age to customer perceptions of service quality. Assess whether the Cost of Services at the large hospital in New Delhi are justified. 3.1. THE RESEARCH DESIGN The study is based on exploratory and descriptive research design with the objective of measuring the perceptions of service quality at a large Hospital in New Delhi through SERVPERF scale. The study uses both primary and secondary information. 3.1.1. RESEARCH METHODOLOGY An initial exploratory research was done by this researcher using secondary data and case studies to identify the important factors for customer perceptions of service quality that may be included in the questionnaire. A detailed questionnaire was designed on the basis of the exploratory study for the purpose of this descriptive study. Primary data on the customers’ perceptions of quality of services provided by the Hospital in New Delhi, was collected through the structured questionnaire. The questionnaire for the primary survey was based on the SERVPERF scale, first proposed by Cronin & Taylor, 1992. It had 37 questions on a 7-point Likert scale. Out of these, 22 questions form the SERVPERF scale. Further analysis of the data is done for achievement of the research goals through the use of Statistical software. 3.1.2. SAMPLE DESIGN AND SELECTION The study presents the ‘customer’ perception on healthcare services quality, on a sample population. The respondents were either the patients themselves or their relatives. The information was collected through a structured questionnaire. According to Kinnersley P, Stott N, Peters T, Harvey I and Hackett P (1996), the response rates in the hospital completion group were relatively low (57%) expressing both refusal to participate or inability to respond, and reluctance to answer a questionnaire immediately after consultation because of long waiting time beforehand, or because a relative, an ambulance or a taxi is waiting to take the patient home. Further, patients completing the questionnaire immediately after consultation in the hospital, satisfaction estimates were higher than in case of home completion, in spite of procedures to preserve anonymity and confidentiality at hospital. Two studies conclude that patients express less satisfaction when the questionnaire is completed at home rather than in the medical facility. This could be interpreted as an over-estimation of patient satisfaction in case of completion in the facility, patients being more prone to express their real opinion when they have more time to consider the consultation and are safely back home. It could be concluded that completion at home may be better than immediately after consultation. For the purpose of this study, the respondents were at their homes or workplace but not in the hospital. This is to avoid bias due to the above mentioned reason. Convenience Sampling has been used due to monetary and man-power constraints. 3.1.3. DATA COLLECTION Before an attempt was made to collect the information from the sample, exploratory desk research was conducted to see the literature and other library material available on the subject. Various studies were reviewed to have a thorough knowledge before considering how to collect the information from the respondents. After having the background knowledge a structured questionnaire was prepared to obtain answers pertinent to the objectives of the study. For the purpose of the study, a primary survey was done among the literate population of India who have availed the services provided by the hospital in New Delhi at some point of time. The methods of primary data collection are: Web based Questionnaire Paper based Questionnaire Telephonic interviews to answer the Questionnaire Particular care was taken that the paper based questionnaires were not handed to any people who have just returned from the hospital to avoid bias in the study. An additional question regarding Overall Service Quality rating was added after the pilot run of the questionnaire. 48 people responded to the survey and the data was coded and cleaned. 5 respondents had a filled incorrectly and had to be removed. The remaining 43 responses are used in this research. The Overall Service Quality, being added at a later stage, has been answered by 31 out of the 43 respondents. 3.2. DATA ANALYSIS Various statistical techniques were used on the primary data collected from the respondents. All the data was coded to numeric codes. Since most of the data used in the analysis is interval type, parametric statistical tools have been extensively used. In some analyses, nominal data is compared with the interval data and appropriate care is taken to use the specific statistical tools specializing in these types of comparisons. SPSS v16.0 was the main statistical tool used in this analysis. 3.3. LIMITATIONS OF THE STUDY Any study based on consumer survey through a pre-designed questionnaire suffers from the basic limitation of the possibility of difference between what is recorded and what is the truth, no matter how carefully the questionnaire has been designed and field investigation has been conducted. This is because the consumers may not deliberately report their true preferences and even if they want to do so, there are bound to be differences owing to problems in filters of communication process. The error has been tried to be minimized by conducting interviews personally yet there is no full proof way of obviating the possibility of error creeping in. The following limitations of the study should be taken into the account. As the study was to be completed in a short time, the time factor acted as a considerable limit on the scope and the extensiveness of the study. The information provided by respondents may not be fully accurate due to unavoidable biases. Since this is a one person survey, the number of respondents is relatively small for this kind of study. However, this could not be helped due to constraints on time and money. The technique for collecting the data is Convenience Sampling due to monetary and manpower constraints. Chapter 4 RESULTS AND DISCUSSION This chapter contains the analysis and discussion of the primary data collected from the respondents. 4.1. DEMOGRAPHICSThe demographic profile of the respondents is presented in Table 1.The largest group of respondents (51.2%) was aged 31-40 years. The next largest group (23.3%) was aged 21-30 years.Most of the respondents (69.8%) are married / living with partners. Only a small 7% are divorced / separated / widowed.65.1% of the respondents are employed, while 18.6% of them are students.In terms of education, the largest group of respondents was Postgraduate/Masters degree holders (76.7%).Personal Annual income was measured in Indian Rupees (INR). When personal income was examined, 30.2% of respondents had an annual income of Rs. 6 – 10 Lakhs. They were closely followed by those earning Rs. 10 – 20 Lakhs per annum (20.9%) and then those in the income category of Rs. 3 - 6 Lakhs (18.6%). Only 14% respondents belong to the annual income bracket of less than 3 Lakhs. This suggests that the cost of services of the hospital in New Delhi, is a deterrent to a large population of India to avail the services of the hospital.Table 1. Demographic Profile of the RespondentsAge Group ( in years)FrequencyPercent21-301023.3%31-402251.2%41-5024.7%51-6049.3%61-70511.6%Total43100%Current Marital StatusFrequencyPercentUnmarried1023.3%Married / Living with Partner3069.8%Divorced/Separated/Widowed37.0%Current Employment StatusFrequencyPercentStudent818.6%Employed2865.1%Unemployed37.0%Retired49.3%EducationFrequencyPercentUniversity Graduate614.0%Postgraduate / Masters3376.7%Doctorate / Ph.D / M.Phil49.3%Annual Income (in Rupees)FrequencyPercentLess than 3 Lakhs614.0%3 - 6 Lakhs818.6%6 – 10 Lakhs1330.2%10 – 20 Lakhs920.9%Above 20 Lakhs716.3% 4.2. SERVPERF SCALE ATTRIBUTES Before we discuss the SERVPERF scores, we need to verify the reliability, dimensionality and the validity of the scale. 4.2.1. Reliability of the SERVPERF Scale Reliability refers to the property of a measurement instrument that causes it to give similar results for similar inputs. Cronbach's alpha (Cronbach, 1951) is a measure of reliability. More specifically, alpha is a lower bound for the true reliability of the survey. Mathematically, reliability is defined as the proportion of the variability in the responses to the survey that is the result of differences in the respondents. That is, answers to a reliable survey will differ because respondents have different opinions, not because the survey is confusing or has multiple interpretations. The computation of Cronbach's alpha is based on the number of items on the survey (k) and the ratio of the average inter-item covariance to the average item variance. Under the assumption that the item variances are all equal, this ratio simplifies to the average inter-item correlation, and the result is known as the Standardized item alpha (or Spearman-Brown stepped-up reliability coefficient). The reliability coefficients (Cronbach’s alpha) for the individual dimensions of SERVPERF ranged from 0.809 to 0.929 (Table 2). None of the reliability alphas was below the cut-off point of 0.60, which is generally considered to be the criterion for demonstrating internal consistency of a scale (Nunnally, 1978). Table 2. Reliability Scores for the SERVPERF ScaleLatent VariableItem MeansCronbach’s AlphaTangibles5.209.809Reliability4.702.907Responsiveness4.703.791Assurance4.866.926Empathy4.553.929Global4.791.972Service Quality by Doctors4.922.934 4.2.2. Dimensionality of the SERVPERF Scale A confirmatory factor analysis (CFA) should be conducted to check the uni-dimensionality of a scale (Stevens 1996; Anderson and Gerbing, 1991), thus ascertaining that each item in the model represents the same measure (Ahire et al., 1996). Before we can use Factor Analysis, we check the KMO and Bartlett's Test for Sampling Adequacy. Since p<0.05 (Table 3), the survey data is adequate for factor analysis. Following the method used by Cronin and Taylor (1992), the 22 scale items were treated as uni-dimensional and a factor analysis was performed. As expected, almost all the items loaded on a single factor in the unrotated component matrix (Table 5). The reliability, as assessed by Cronbach’s alpha, was 0.972 (Table 2), a further indication that the scale can be treated as uni-dimensional. 4.2.3. Validity of the SERVPERF Scale According to Churchill Jr. (1987), validity is an important psychometric propriety in evaluating scales fitness towards measuring abstract constructs. 4.2.3.1. Face validity Because the service-quality constructs were determined from the literature, their selection can be defended on face value, and face validity is thus ensured (Kaplan and Sacuzzo, 1993). 4.2.3.2. Content validity Because the measurement instrument was adapted from the SERVQUAL measure, which has been widely used among researchers and has achieved consensus for the variables under consideration, content validity can be confidently asserted (Bohrnstedt, 1983). 4.2.3.3. Construct validity To address construct validity of the scale, a factor analysis was conducted. The data set was subjected to varimax rotation and this resulted in four factors with Eigen values greater than 1.00 (Table 5 and Table 6). Parasuraman, Zeithaml and Berry (1991), in their revised service-quality model, proposed that service quality can be measured using the five dimensions as reflective indicators. However, in the present study, factor analysis did not identify any meaningful dimensions that conform to the dimensions recommended by the SERVQUAL instrument. This could be due to the highly correlated nature of the five dimensions of service quality. Several earlier studies also failed to validate the SERVQUAL instrument based on responses (Lam, 1997; Babakus and Mangold, 1992). Thus, for further analysis in this paper, the original dimensions on the SERVQUAL instrument are used as the apriori dimensions. Table 3. KMO and Bartlett's Test for the SERVPERF Scale VariablesKaiser-Meyer-Olkin Measure of Sampling Adequacy..868Bartlett's Test of SphericityApprox. Chi-Square1104.858df231.000Sig..000 Table 4. Total Variance Explained for the SERVPERF Scale VariablesComponentInitial EigenvaluesExtraction Sums of Squared LoadingsRotation Sums of Squared LoadingsTotal% of VarianceCumulative %Total% of VarianceCumulative %Total% of VarianceCumulative %113.98763.57963.57913.98763.57963.5797.73835.17435.17421.5146.88070.4591.5146.88070.4594.74521.56956.74331.3045.92676.3851.3045.92676.3853.42215.55672.29841.0214.64181.0261.0214.64181.0261.9208.72781.0265.7633.46984.4956.6392.90687.4017.5762.61790.0188.3911.77791.7959.3291.49593.29010.2671.21494.50411.2461.11895.62212.2221.01096.63213.163.74197.37314.134.60797.98115.125.56898.54916.095.43198.98117.058.26299.24318.051.23199.47419.041.18899.66220.031.14199.80221.026.11899.92022.018.080100.000Extraction Method: Principal Component Analysis. Table 5. Unrotated Component Matrix for the SERVPERF Scale VariablesComponent1234The physical facilities in the hospital are visually appealing.712.362-.322-.273The hospital has modern looking equipment.781.324-.291-.140Materials associated with the service (such as pamphlets or statements) are visually appealing.593.465.511-.144Personnel in the hospital are neat in appearance.745-.212.176.043When the hospital promises to do something by a certain time it does so..780.135-.272.148The hospital provides its services at the time it promises to do so..841-.223.084.257When you have a problem, the hospital shows a sincere interest in solving it..841-.315.046-.098The hospital insists on error-free records..832-.175-.318-.258The hospital gets things right the first time..743.032-.472.169The personnel in the hospital tell you exactly when services will be performed..506.206-.020.739Personnel in the hospital give you prompt service..830-.060-.040.116Personnel in the hospital are always willing to help you..828-.292.259.197Personnel in the hospital are never too busy to respond to your requests..681.488-.057-.012The behaviour of personnel in the hospital instills confidence in you..865-.018-.054-.074You feel safe in your dealings with the hospital..893-.249.097-.020Personnel in the hospital are consistently courteous with you..812.020.410-.071Personnel in the hospital have the knowledge to answer your questions..855-.325.055-.234The hospital has personnel who give you personal attention..852.279.121.098The hospital gives you individual attention..887.142-.005.034The hospital has your best interests at heart..866-.141-.071.016The hospital has operating hours convenient to all its patients..753.283.353-.162The personnel of the hospital understand your specific needs..914-.285-.125-.104Extraction Method: Principal Component Analysis. Table 6. Rotated Component Matrix for the SERVPERF Scale VariablesComponent1234The physical facilities in the hospital are visually appealing.224.811.328.001The hospital has modern looking equipment.295.778.330.137Materials associated with the service (such as pamphlets or statements) are visually appealing.182.166.884.085Personnel in the hospital are neat in appearance.696.165.290.193When the hospital promises to do something by a certain time it does so..410.619.177.376The hospital provides its services at the time it promises to do so..744.225.218.421When you have a problem, the hospital shows a sincere interest in solving it..825.300.209.070The hospital insists on error-free records..678.651.068-.050The hospital gets things right the first time..424.695-.039.373The personnel in the hospital tell you exactly when services will be performed..168.183.162.870Personnel in the hospital give you prompt service..615.408.250.317Personnel in the hospital are always willing to help you..812.080.304.344Personnel in the hospital are never too busy to respond to your requests..137.594.518.254The behaviour of personnel in the hospital instills confidence in you..620.499.316.154You feel safe in your dealings with the hospital..818.299.288.167Personnel in the hospital are consistently courteous with you..624.155.634.137Personnel in the hospital have the knowledge to answer your questions..851.329.241-.058The hospital has personnel who give you personal attention..422.446.567.362The hospital gives you individual attention..526.514.429.290The hospital has your best interests at heart..698.440.217.219The hospital has operating hours convenient to all its patients..398.292.740.082The personnel of the hospital understand your specific needs..831.474.139.093Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.a. Rotation converged in 8 iterations. 4.2.3.4. Predictive Validity Predictive validity was examined by regressing Overall Service Quality (dependent variable) on the 22 SERVPERF statements scores (independent variables). The Coefficient of Determination (R2) values (significant at p<0.05) provide the ability of the model to explain the variance in the Overall Service Quality. According to the results, the R2 value is 0.941 (Table 7), which provides strong evidence of the performance ability in explaining the variance in the Overall Service Quality ratings, with an adjusted R2 value of 0.824 (Table 7). The coefficients for the variables are shown in Table 8. Table 7. Model Summary for the SERVPERF Scale regressed against Overall Service QualityModelRR SquareAdjusted R SquareChange StatisticsF ChangeSig. F Change1.970a.941.8248.002.001 Table 8. Coefficients for the SERVPERF Scale regressed against Overall Service QualityModelUnstandardized CoefficientsStandardized CoefficientsBStd. ErrorBeta(Constant)-.048.605The physical facilities in the hospital are visually appealing-.158.158-.234The hospital has modern looking equipment-.009.129-.014Materials associated with the service.430.124.740Personnel in the hospital are neat in appearance.141.160.210When the hospital promises to do something by a certain time it does so..179.125.332The hospital provides its services at the time it promises to do so.-.272.157-.503When you have a problem, the hospital shows a sincere interest in solving it.-.215.142-.461The hospital insists on error-free records..217.197.418The hospital gets things right the first time..330.116.653The personnel in the hospital tell you exactly when services will be performed..163.075.289Personnel in the hospital give you prompt service.-.148.166-.254Personnel in the hospital are always willing to help you.-.049.146-.099Personnel in the hospital are never too busy to respond to your requests..495.121.882The behaviour of personnel in the hospital instills confidence in you.-.701.178-1.334You feel safe in your dealings with the hospital.-.110.165-.225Personnel in the hospital are consistently courteous with you..372.168.700Personnel in the hospital have the knowledge to answer your questions..370.190.661The hospital has personnel who give you personal attention..021.181.046The hospital gives you individual attention.-.448.245-.905The hospital has your best interests at heart..220.154.420The hospital has operating hours convenient to all its patients.-.156.114-.266The personnel of the hospital understand your specific needs..231.269.431 4.3. SERVPERF SCORES The SERVPERF scores for the Hospital are given in Table 9. Each score is out of a maximum of 7. It provides an overview of the mean scores and standard deviation for the measures of perceptions according to the SERVPERF scale. The scale questions are classified by their latent variables, namely, Tangibles, Reliability, Responsiveness, Assurance and Empathy. Tangibles has a mean score of 5.209 (Table 2), the highest among all the latent variables. ‘Materials associated with the services (such as pamphlets or statements)’ has a comparatively low score of 4.81 (Table 9). This aspect of the services can be improved with relative ease for the hospital. However, the ‘Personnel in the hospital are neat in appearance’ has a mean score of 5.49, the highest among all the service attributes. Nowadays, the customers are highly discerning and they expect hospitals to provide clean and hygienic environment with adequate civic amenities. The hospital needs to take regular measures to upgrade facilities. The dimension, Reliability, has a mean score of 4.702 (Table 2). This is a worrisome sign for the hospital since the customers perceive the hospital to be low on reliability. This aspect of their services has to be improved a great deal if they want to retain their customers. The hospital should, ideally, provide its services at the time it promises to do so. However, it should definitely provide the services it promises. But the scores for the variables are 4.84 and 4.91 respectively (Table 9). This shows that the customers do not have faith in the services promised by the hospital. The lowest rated service attribute is ‘The hospital gets things right the first time’ with a mean score of 4.53 (Table 9). Responsiveness has a mean score of 4.703 (Table 2), again a warning sign for the hospital since this points towards a lack of service functionality in an, essentially, a service organization. ‘Personnel in the hospital are never too busy to respond to your requests’ has a mean score of 4.65 (Table 9). This service attribute may be difficult to overcome due to many reasons, like staff shortage. However, this attribute has to be taken seriously by the hospital to improve the perceptions of service quality on the whole and to maintain competitive advantage. Assurance is another service quality dimension, which scores 4.866 (Table 2). The ‘Personnel in the hospital are consistently courteous with you’ attribute has a mean score of 5.00 (Table 9), which is the hallmark or a service organization. Patients expect hospital personnel to be polite and with improved socio-economic status, this expectation becomes more explicit. If the personnel are not perceived to be polite this can be a significant dissatisfier. They need to be counseled and sensitized on this account. With proper feedback, there is tremendous scope of improvement. ‘Personnel in the hospital have the knowledge to answer your questions’ draws a mean score of 4.98 (Table 9), which is an encouraging sign for the hospital since the customers believe that the hospital personnel are professionally capable of solving their queries. Empathy scores are poor 4.553 (Table 2). This is despite the high score of 5.02 (Table 9) for having convenient operating hours. The medical profession is a grim profession and empathy towards patients is a cornerstone of the profession. This shows lack of training of staff in empathic behaviour. The hospital needs to train its staff in empathy towards its customers if they are to survive the competition from other hospitals. The perception of the hospital having the customers’ best interests has a mean score of 4.16 (Table 9), which is not surprising. However, the customer perception of the hospital personnel giving them individual attention has a mean score of 4.63 (Table 9), which, again, is worrying. The personnel have to give the patients individual attention for improving service quality. The calculated mean Global Service Quality on the SERVPERF scale for the hospital is 4.791 (Table 2). However, the mean Overall Service Quality on the survey is 4.26 with a standard deviation of 0.828 (Table 17). This means that the customers may regard individual service quality attributes higher than when asked for Overall Service Quality. Table 9. SERVPERF ScoresLatent VariableHospital Service Quality – SERVPERF ScaleMeanStd. Dev.TangiblesThe physical facilities in the hospital are visually appealing5.231.172The hospital has modern looking equipment5.301.225Materials associated with the service (such as pamphlets or statements) are visually appealing4.811.385Personnel in the hospital are neat in appearance5.491.242ReliabilityWhen the hospital promises to do something by a certain time it does so.4.841.446The hospital provides its services at the time it promises to do so.4.911.461When you have a problem, the hospital shows a sincere interest in solving it.4.561.666The hospital insists on error-free records.4.671.569The hospital gets things right the first time.4.531.533ResponsivenessThe personnel in the hospital tell you exactly when services will be performed.4.671.393Personnel in the hospital give you prompt service.4.701.372Personnel in the hospital are always willing to help you.4.791.567Personnel in the hospital are never too busy to respond to your requests.4.651.395AssuranceThe behaviour of personnel in the hospital instills confidence in you.4.631.496You feel safe in your dealings with the hospital.4.861.552Personnel in the hospital are consistently courteous with you.5.001.574Personnel in the hospital have the knowledge to answer your questions.4.981.422EmpathyThe hospital has personnel who give you personal attention.4.631.746The hospital gives you individual attention.4.471.579The hospital has your best interests at heart.4.161.479The hospital has operating hours convenient to all its patients.5.021.389The personnel of the hospital understand your specific needs.4.491.518 4.4. CUSTOMER PERCEPTIONS OF QUALITY OF SERVICES PROVIDED BY DOCTORS The customer perceptions on the quality of services provided by the doctors at the Hospital were also recorded on the same 7 – point Likert scale as the SERVPERF (Table 10). 4.4.1. Scale Attributes – Reliability and Dimensionality A factor analysis was performed (Table 11, 12 and 13). All the items loaded on a single factor in the unrotated component matrix (Table 12). The reliability, as assessed by Cronbach’s alpha, was 0.934 (Table 2). Hence, the scale can be treated as uni-dimensional. 4.4.2. Scale Score Analysis The mean score was 4.922 (Table 2), which is significantly above the mean SERVPERF score of 4.791 (Table 2) or the surveyed Overall Service Quality mean score of 4.26 (Table 17). This score shows that the customers have more faith in the doctors at the Hospital, New Delhi, than they have in the hospital itself. The waiting time acceptability has a mean score of 4.40 (Table 10), which points towards inefficiency in handling the rosters. The hospital needs to augment services and streamline the number of patients seen per physician, so as to decrease waiting time and improve service quality. The mean score for adequate time spent during consultation has a mean score of 4.88 (Table 10), which shows that the patients are happy with this attribute. The patients are also happy with the doctors in terms of their communication and taking their opinions into account. There is need to sensitize doctors about patient expectations, which if heeded to, can improve the service quality without any additional input in terms of resources The patients agree with the doctor’s instructions (mean score of 5.37 (Table 10)) which is the highest rating in the whole scale and encouraging for both, the doctors and the hospital. The waiting time, however, has a mean score of 4.40 (Table 10), which can be improved. Table 10. Perceptions of Quality for Services provided by Doctors on the Survey ScaleSurvey QuestionsMeanStd. DeviationSaw the doctor at the appointed time 4.771.586Waiting time acceptable 4.401.514The doctor spent adequate time with me4.881.219The doctor explained what he/she was doing during the consultation4.881.366The doctor took my opinion into account4.931.470The doctor explained his/her decisions5.051.495I got the information I wanted4.841.174Agree with doctor's instructions5.371.215The doctor wanted to know if I had pain.5.191.277 4.4.3. Scale Factor Analysis A Factor analysis of the scale was conducted. The KMO and Bartlett's Test for Sampling Adequacy showed that the sample size was adequate (Table 11). The data set was subjected to varimax rotation and this resulted in two factors with Eigen values greater than 1.00 (Table 13). Based on this analysis, the factors were reduced to two dimensions (Table 14): Time Consciousness Service Consciousness Table 11. KMO and Bartlett's Test for the Doctors Service Quality VariablesKaiser-Meyer-Olkin Measure of Sampling Adequacy..841Bartlett's Test of SphericityApprox. Chi-Square325.988df36.000Sig..000 Table 12. Component Matrix for the Doctors Service Quality VariablesComponent12Saw the doctor at the appointed time .844-.375Waiting time acceptable .766-.549The doctor spent adequate time with me.834-.397The doctor explained what he/she was doing during the consultation.886-.043The doctor took my opinion into account.894.200The doctor explained his/her decisions.829.302I got the information I wanted.779.220Agree with doctor's instructions.793.401The doctor wanted to know if I had pain..681.276Extraction Method: Principal Component Analysis.a. 2 components extracted. Table 13. Rotated Component Matrix for the Doctors Service Quality VariablesComponent12Saw the doctor at the appointed time .390.837Waiting time acceptable .218.917The doctor spent adequate time with me.369.847The doctor explained what he/she was doing during the consultation.640.614The doctor took my opinion into account.806.435The doctor explained his/her decisions.824.316I got the information I wanted.732.346Agree with doctor's instructions.861.218The doctor wanted to know if I had pain..695.238Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.a. Rotation converged in 3 iterations. Table 14. Doctors Service Quality VariablesTime ConsciousnessService ConsciousnessSaw the doctor at the appointed time The doctor explained what he/she was doing during the consultationWaiting time acceptable The doctor took my opinion into accountThe doctor spent adequate time with meThe doctor explained his/her decisionsI got the information I wantedAgree with doctor's instructionsThe doctor wanted to know if I had pain 4.5. RELATION OF AGE AND OVERALL SERVICE QUALITY According to Hall JA, Dornan MC (1990), it was observed that older patients have a higher opinion of care provided than others. For several authors, this contributes to construct validity of satisfaction questionnaires (Grogan S, Conner M, Norman P, Willits D, and Porter I, 2000). Null Hypothesis H0: There is no relation between Age and Overall Service Quality perception. Alternate Hypothesis H1: There is a relation between Age and Overall Service Quality perception. Table 15 shows that the Pearson Chi-Square test between Age and Overall Service Quality has a significance of 0.011 (p<0.05). Hence the null hypothesis is rejected. However, since this is a nominal by interval (one variable is categorical and the other is quantitative) comparison, an Eta test must be performed. Eta (η) is a measure of association that ranges from 0 to 1, with 0 indicating no association between the row and column variables and values close to 1 indicating a high degree of association. Eta is appropriate for a dependent variable measured on an interval scale and an independent variable with a limited number of categories. Asymmetric η with the column variable Y as dependent is Where, Table 16 shows the Eta value as 0.646, which shows a moderately strong association between Age and Overall Service Quality perception. The means of Overall Service Quality perception were plotted against Age in Figure 1. From the graph, it does look like the perception of Overall Service Quality does increase with age. Further study with a larger dataset needs to be done to find the relation of age and overall service quality perception. Table 15. Overall Service Quality * Age Group (in years) - Chi-Square TestsValuedfAsymp. Sig. (2-sided)Pearson Chi-Square26.065a12.011Likelihood Ratio25.71012.012Linear-by-Linear Association11.6571.001N of Valid Cases34a. 19 cells (95.0%) have expected count less than 5. The minimum expected count is .06. Table 16. Overall Service Quality * Age Group (in years) - Directional MeasuresValueNominal by IntervalEtaOverall Service Quality Dependent.646Age Group ( in years) Dependent.623 4.6. ANALYSIS OF OTHER QUESTIONS ON THE SURVEY SCALE The customers rate the sign-postings of the consultation rooms at hospital at a mean score of 5.16 (Table 17), which is encouraging sign. The ease of making an appointment by phone is rated at 5.07 (Table 17), which is an important factor in improving the services. Also, the possibility of obtaining an appointment on convenient day and hour is rated highly at 5.05 (Table 17). Both these perception point to a customer confidence that they can and will receive services at any time they are required. This is critical attribute for a tertiary care hospital, especially when emergency services are provided. The administrative procedures are rated at 4.88 (Table 17). This shows that the customers’ perception towards administrative procedures, like completing papers, is not favourable and should be streamlined to improve efficiency and speed of processing. The cost of services provided is rated poorly at a mean score of 4.19 (Table 17). This shows that the customers do not agree with the current prices of the services provided by the hospital. The hospital should look into ways for cutting costs and reducing prices for their services to maintain competitive advantage among the hospitals in the Delhi region. The Overall Service Quality has not been rated well either. Its mean score is 4.26 (Table 17). These scores show that the prices for the services are high for the quality of services provided. The hospital either has to improve its service quality to justify for the prices they charge or it has to reduce the prices for its services. Table 17. Other Questions on the Survey ScaleSurvey QuestionsMeanStd. DeviationEase to make an appointment by phone5.071.334Possibility of obtaining an appointment on convenient day and hour5.051.495Inside the hospital the consultation room was clearly sign-posted'5.161.413Administrative procedures (completing papers and paying) fast and easy4.881.467The cost of services provided is justified.4.19.906Overall Service Quality4.26.828 4.7. CONCLUSION This is an initial study that is limited in scope. It can for the basis of further studies on the hospital to gauge improvements in service performance and, thus, help in maintaining competitive advantage. The research results offer important insights. According to Parasuraman, Zeithaml and Berry (1991), dimensions may overlap in some degree because of the complex nature of service quality. The heterogeneity degree in customer’s evaluation explains the number of service dimensions found in other replicated SERVQUAL papers - from two (Babakus e Boller, 1992) to eight (Carman, 1990). Moreover, the number of factors across services industries may not be the same, due to different data collection and analysis procedures adopted by studies replicating SERVQUAL and SERVPERF models. Following this rationale, our factor solution doesn’t disagree with Parasuraman, Zeithaml and Berry´s (1988) five factor solution. However, they point out organizational needs towards development and improvement of scales applicable to the hospital industry. This finding calls our attention for the need of developing alternative procedures in measuring service quality, such as proposed by Peter, Churchill and Brown (1993) and Devlin, Dong and Brown (1993). Because perceived quality is an important measure in influencing consumers’ value perception and, in turn, in affecting consumers’ intention to purchase products or services (Bolton & Drew, 1988; Zeithaml, 1998), the findings of the present study are of importance for the administrators of the hospital with respect to the non-clinical aspects of service quality. This survey can be considered as a pro-active marketing activity designed to improve functional service quality and as a result, increasing customer / patient satisfaction, which is the desired outcome. It is a cost-effective method for assessing service quality and provides insights on deficiencies and potential improvements. This survey should be used as a marketing strategy since it shows that the hospital is a responsive organization. An implication of this study is that top management must place emphasis on change management. Managers in the hospital must use whatever benchmark information that is available to identify potential improvement areas and then use best practices in the industry to improve in these areas. As a long-term solution, it is recommended that the managers at the hospital should implement, on a continuous basis, an information system that can support efficient management decisions. This is necessary because it is only by comparing data with other hospitals that they can gauge their own performances against those of others. It can reasonably be said that in the current competitive healthcare scenario, customer perception surveys can be an important market information and research tool in the hands of contemporary hospital administrators. As this study concludes, customers’ perception of service quality remains a challenge. REFERENCES Fogarty, G., Catts, R., & Forlin, C. (2000), ‘Identifying shortcomings in the measurement of service quality’, Journal of Outcome Measurement, 4(1), 425-447 Isabelle Gasquet, Sylvie Villeminot, Carla Estaquio, Pierre Durieux, Philippe Ravaud and Bruno Falissard(2004), ’Construction of a questionnaire measuring outpatients' opinion of quality of hospital consultation departments’, Health and Quality of Life Outcomes 2004, 2:43 Cronin, J. Joseph & Taylor, Steven A., ‘Measuring Service Quality: A Reexamination and Extension’, Journal of Marketing, 56(3), p.55-68, Jul. 1992. Hall JA, Dornan MC (1990), ‘Patient socio-demographic characteristics as predictors of satisfaction with medical care: a meta-analysis’, Soc Sci Med 1990, 30:811-818 Grogan S, Conner M, Norman P, Willits D, Porter I (2000), ‘Validation of a questionnaire measuring patient satisfaction with general practitioner services’. Qual Health Care 2000, 9:210-215. Cornelia Prejmerean, and Simona Vasilache (2009), ‘Study regarding Customer Perception of Healthcare Service Quality in Romanian Clinics based on their profile’, The Bucharest Academy of Economic Studies, Romania Kinnersley P, Stott N, Peters T, Harvey I, Hackett P, ‘A comparison of methods for measuring patient satisfaction with consultations in primary care’, Fam Pract 1996, 13:41-51. Brysland, A. and Curry, A. (2001),
Service improvements in public services using SERVQUAL
, Managing Service Quality, Vol.11, No.6, pp. 389-401 Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1988),
SERVQUAL: a multi-item scale for measuring consumer perceptions of the service quality
, Journal of Retailing, Vol. 64, No. 1, pp. 12-40. Bharadwaj, S. G.; Varadarajan, P. R. & Fahy, J., ‘Sustainable Competitive Advantage in Service Industries: A Conceptual Model and Research propositions’, Journal of Marketing, 57(4), p.83-9, Oct. 1993. Devlin, Susan J. & Dong, H. K., ‘Service Quality from the Customers' Perspective’, Marketing Research, 6(1), p.5-13, 1994. Friedenberg, R.M. (1997), ‘The next medical revolution should be quality’, Radiology, 204 (1), 31A-34A. Grönroos, C. (1984), ‘A service quality model and its marketing implication’, European Journal of Marketing 18 (4), 36-44. Mathe, Herve & Perras, Cynthia, ‘Successful Global Strategies for the Service Companies’, Long Range Planning, 27(1), p.36-49, Feb. 1994. O'Connor, Stephen & Shewchuk, Richard, ‘The Influence of Perceived Hospital Service Quality on Patient Satisfaction and Intentions to Return’, Academy of Management Best Papers: Forty-Ninth Annual Meeting of the Academy of Management, Washington, Aug. 1989. p.95-9. APPENDIX 1 – LIST OF TABLES List of TablesPage NumberTable 1. Demographic Profile of the Respondents13Table 2. Reliability Scores for the SERVPERF Scale14Table 3. KMO and Bartlett's Test for the SERVPERF Scale Variables16Table 4. Total Variance Explained for the SERVPERF Scale Variables16Table 5. Unrotated Component Matrix for the SERVPERF Scale Variables17Table 6. Rotated Component Matrix for the SERVPERF Scale Variables18Table 7. Model Summary for the SERVPERF Scale regressed against Overall Service Quality19Table 8. Coefficients for the SERVPERF Scale regressed against Overall Service Quality19Table 9. SERVPERF Scores21Table 10. Perceptions of Quality for Services provided by Doctors on the Survey Scale22Table 11. KMO and Bartlett's Test for the Doctors Service Quality Variables23Table 12. Component Matrix for the Doctors Service Quality Variables23Table 13. Rotated Component Matrix for the Doctors Service Quality Variables24Table 14. Doctors Service Quality Variables24Table 15. Overall Service Quality * Age Group (in years) - Chi-Square Tests26Table 16. Overall Service Quality * Age Group (in years) - Directional Measures26Table 17. Other Questions on the Survey Scale27 APPENDIX 2 – LIST OF FIGURES List of FiguresPage NumberFigure 1. Graph of Overall Service Quality for different age groups26