A comparative study of ordinary and fastidious

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Findings revealed that the two groups do not value different dimensions of service ... entropy; hotel; prioritisation; customer satisfaction .... valid results questionable when the customers change (Davidson, 2003). .... xij. ∑m i=1 x2 ij. . ,nij : the normalised component of the decision matrix. ..... Chin, J. B., & Tsai, C. H. (2013).
Total Quality Management, 2017 Vol. 28, No. 3, 331 –350, http://dx.doi.org/10.1080/14783363.2015.1082420

A comparative study of ordinary and fastidious customers’ priorities in service quality dimensions ∗

Reza Dabestania , Arash Shahina, Hadi Shirouyehzadb and Mohammad Saljoughiana a

Department of Management, University of Isfahan, Isfahan, Iran; bDepartment of Industrial Engineering, Faculty of Engineering, Islamic Azad University, Najafabad Branch, Najafabad, Isfahan, Iran The current study aims to prioritise service quality dimensions (SQDs) for hotels through a two-step procedure: firstly, by forming congruent groups of customers based on their level of expectations as well as perceptions of service; and secondly, by prioritising SQDs for each group and analysing the results. To reach that aim, the customers of three four-star hotels were targeted and asked to report on the quality of the service they expected to receive prior to its delivery as well as their feelings towards the service after they received it. Then, using data envelopment analysis we discriminated the majority of customers, those who were satisfied more easily (ordinary customers), from the ones who were harder to please (fastidious customers). Finally, we exploited Technique for Order Preference by Similarity to an Ideal Solution and Shannon Entropy to prioritise SQDs for each group. The approach taken in this paper, which first distinguishes two groups of fastidious from ordinary customers based on their expectations and perceptions, is rather noble. Findings revealed that the two groups do not value different dimensions of service quality in a similar way. The results of this study can provide insightful guidance for the hoteliers to customise their service and exploit their resources more efficiently. Keywords: service quality dimensions; data envelopment analysis; TOPSIS; Shannon entropy; hotel; prioritisation; customer satisfaction

1. Introduction Constituting more than 80% of the GDP of the largest economy, just about 40% of that of the second economic giant, and about more than half of the GDP of south Asian countries, service sector has become a promising industry (Olorunniwo, Hsu, & Udo, 2006; Ghani & Kharas, 2010). As a major section of the service industry, hospitality has received a lot of attention in the recent decades (Mok, Sparks, & Kandampully, 2013). World Tourism Organization (2013) reported that the number of international tourists hit a new record in 2012 when more than a billion passengers visited another country. This organisation also predicted that the growth rate in 2012 (4%) will be repeated in 2013. The striking facts mentioned above, along with numerous studies conducted in this area, accentuate the essentiality of customer satisfaction in this business (Abdullah & Rozario, 2009). Anderson, Fornell, and Mazvancheryl (2004) claimed that customer satisfaction is ‘a valuable, forward-looking indicator of future net cash flows’ (p. 174). It also influences customer spending (Fornell, Rust, & Dekimpe, 2010) and customer loyalty (Iglesias, Singh, & Batista-Foguet, 2011). Moreover, Shirouyehzad, HosseinzadehLotfi, Shahin, Aryanezhad, and Dabestani (2012a) asserted that customer satisfaction ‘has a major impact on customer loyalty, price sensitivity, cross buying and positive word of ∗

Corresponding author. Email: [email protected]

# 2015 Taylor & Francis

332 R. Dabestani et al. mouth’ (p. 291). When the customer is satisfied, it means he/she – who has received the service – has judged the service. This standpoint of customer satisfaction gave birth to the concept of service quality (SQ) (Erto & Vanacore, 2002). SQ is a very appealing topic for researchers, because it can influence customer satisfaction (Chou, Liu, Huang, Yih, & Han, 2011), increase a firm’s revenue (Seth, Deshmukh, & Vrat, 2005), differentiate the company from its competitors (Kandampully & Suhartanto, 2003), raise market share (Chow & Luk, 2005), boost firm performance (Deng, Chen, & Pei, 2008), and lead to sustainable competitive advantage (Ladhari, 2009). SQ is mostly defined as ‘the comparison customers make between their expectation and their perception of the service received’ (Wang, Wang, & Zhao, 2007, p. 52). This definition covers a wide range of variables in service, from its intangible aspects to tangible ones (Chou et al., 2011). One of the most well-known models of SQ, namely SERVQUAL, was suggested by Parasuraman et al. in 1988 (Amin, Yahya, Ismayatim, Nasharuddin, & Kassim, 2013; Quinn, Lemay, Larsen, & Johnson, 2009). In SERVQUAL it is hypothesised that the quality of service is the distance customers feel between what they expect to receive and what they actually receive (Ganguli & Roy, 2013; Wang et al., 2007). SERVQUAL is defined as ‘a concise, multiple-item scale that organizations can use to better understand the service expectations and perceptions of consumers, and as a result, improve service’ (Parasuraman, Zeithaml, & Berry, 1988, p. 30). This scale considers five major aspects of SQ: tangibles, which is customers’ physical experience of the service: those aspects that can be seen, smelled, touched, heard, etc.; reliability, which is the similarity between the service promised and the service delivered; responsiveness, which is the staff’s willingness to help customers; assurance, which is the degree to which customers can rely on the personnel and their knowledge; and empathy, which is the attention each individual customer receives. These aspects are also known as service quality dimensions (SQDs) (Azˇman & Gomisˇcˇek, 2014; Ladhari, Ladhari, & Morales, 2011). Some related studies are summarised and compared in Table 1. The model was further revised, modified, and extended to be adjusted with the other situations. For instance, Shahin (2007) developed a scale for SQ for hotels and airports which comprised 12 dimensions and 30 sub-dimensions (Table 2). To explore SQ and its impact in different situations, different techniques were proposed so far. For example, Deng et al. (2008) consolidated importance – performance analysis (IPA) with back-propagation neural network and discovered that the three most important factors to be mentioned by the managers are promptness in the provision of service, the capability of coping with customers’ demands, and giving attention to each individual customer. IPA was joined with fuzzy neural network in Deng and Pei’s (2009) study of critical service dimensions. The results revealed that the relationship between performance and importance attributes is non-linear. Data envelopment analysis (DEA), as a mathematical programming method, was also exploited by Pulina, Detotto, and Paba (2010). in their case of Italian hotels to evaluate the efficiency of the services provided there. In their study, the hotels were categorised based on their size. Using DEA, they suggested ways to improve the performance of the less efficient hotels. Despite the fact that the literature on SQ is quite bulky, scientists have yet failed to come up with a holistic picture of the concept of SQ and the interrelationship between its dimensions (Mok et al., 2013). Also, the results obtained lack conformity (Wang, Chen, & Chen, 2012). Scholars hypothesise three possible reasons for that: firstly, the definition and classifications of SQ are subjective and intangible in nature, which makes them difficult to be thoroughly and uniformly perceived (Wu, Liao, Hung, & Ho, 2012). Secondly, the means and techniques to measure SQ are widely varied, which makes the results difficult to compare and contrast (Wang et al., 2012). Thirdly,

Total Quality Management 333 Table 1.

Some related studies on evaluating SDQs.

Authors name

Methodology

Research characteristics

† Assessing differences between clusters † Using statistical tests † Using confirmatory factor analysis † Limited number of criteria Badri (2001) Integrated AHP and goal † Limited number of attributes programming model † One-way relationships between attributes and alternatives † Provising a systematic approach which is more complete and non-biased † Developing some objective functions and quality measures Chow and Luk (2005) Analytic hierarchy process † Limited number of criteria † One way connection between criteria and alternatives † Sensitivity analysis a † Of SQDs † Providing a benchmarking approach in which a firm can compare its position with competitors Chowdhary and Cluster analysis † Preparing list of important SQ concerns Prakash (2007) † Cluster analysis based on each SQD Deng et al. (2008) Artificial neural network and † Using multi collinearity and non-linear importance –performance values analysis † Determining the importance of each criterion Hsu and Pan (2009) Monte Carlo – AHP † Providing more reliable results than AHP-based model † Considering one-way relationships between criteria and alternatives Deng and Pei (2009) Fuzzy neural network and IPA † Utilising non-normal, multi-collinearity and † Non-linear values † Acquiring the importance of each attribute † Implicitly † Using linguistic variables Shirouyehzad et al. DEA-based approach † Unlimited number of criteria (2012a) † Comparative analysis of dimensions † Consistency in decision-making † Providing a systematic and easy-to-use approach † Sensitivity analysis of criteria † Addressing service elements that should be improved † Providing more reliable results than some MCDM methods like AHP Shirouyehzad et al. DEA and TOPSIS † Prioritising SQDs based on the (2012b) efficiency of hotels † Ranking alternatives † Identifying the elements which required to be improved Athanassopoulos (2000)

Customer segmentation

(Continued)

334 R. Dabestani et al. Table 1.

Continued.

Authors name

Methodology

Calabrese and Scoglio Exploratory factor analysis (2012)

Kuo, Chen, and Deng (2012)

IPA-Kano model

Chin and Tsai (2013)

Analytic hierarchy process

Research characteristics † Assessing SQ by means of firm-specific quality dimension † Identifying critical success factors through employees’ knowledge † Using exploratory factor analysis † Categorising and diagnosing SQ attributes † Providing specific strategies for each category † Acquiring the advantage of both IPA and Kano † Unlimited number of criteria † Considering PZB SQ model and innovation † Calculating the relative weights among the dimensions and indicators

customers’ understanding and expectation of the service are a function of a wide range of variables such as their personality and prior exposure to similar service, which makes even valid results questionable when the customers change (Davidson, 2003). In this study, an attempt is made to compensate for the last-mentioned shortcoming by first dividing our sample into two groups of ‘regular’ and ‘fastidious’ and then prioritising the SQDs for each group. In other words, one of the major reasons underlying the cloudy and mixed results obtained in studies concerning SQD is the subjective nature of inexpert individuals’ judgements, which stems from their previous exposures to similar services. Therefore, we proposed that to alleviate the unwanted influence of customers’ background, they can be assigned into two groups: one with relatively more expectations and less perception (fastidious) and the other with relatively less expectations and more perceptions (ordinary). To reach that aim, we needed to exert a tool which can divide the population into two statically significant groups. This tool had to be able to accomplish this task taking into account both inputs and outputs (in our case, expectations and perceptions) of each decision-making unit (DMU; in our case, each customer). A very well-known tool capable of meeting this is DEA. We suggest that our work can have both theoretical and practical implications: from the theoretical perspective, the results can partly explain why the findings in the literature are inconsistent, and from the practical perspective, it can help customise the service, which results in higher customer satisfaction. The current study is an attempt to give a clearer picture of the dimensions that have a more important role in improving the quality of service from customers’ perspective. However, it is hypothesised that since people differ widely in terms of their personality traits, and also considering the fact that the only source of data is the reports of these individuals on their attitudes, it might be oversimplification to just subtract each customer’s expectation score from his/her perception and then calculate the gap value for each SQD, accordingly. To avoid this trap, customers’ data about their expectations and perceptions were used as well as their attitudes towards the service to segregate them into two homogeneous groups regarding the level of their expectations and perception of the service. In the methodology of this paper, DEA method is used to identify the members of each group: the subjects at the frontier (which are considered efficient DMUs in DEA) are considered regular, since their perception of the service is high and their expectation is low, and

Total Quality Management 335 Table 2.

Factor analysis of customers’ expectations of SQDs.

SQDs (1) Reliability

(2) Responsiveness

(3) Security and confidentiality (4) Access and approachability (5) Communication (6) Understanding the customer (7) Credibility

(8) Tangibles

(9) Courtesy (10) Price (11) Competence (12) Flexibility

SQ sub-dimensions (1) Performance (2) Accuracy and dependability (3) Consistency (4) Completeness (1) Willingness to help customer (2) Readiness, promptness (timeliness and speed) (3) Comfort (1) Physical security (2) Financial security (3) Safety (1) Ease of contact (2) Timely access (1) Word-of-mouth communication (2) Giving information (1) Comprehension (2) Individual attention (1) Trustworthiness and believability (2) Honesty (3) Reputation of service (1) Appearance (2) Tools or equipment used to provide the service (3) Availability of physical facilities (1) Politeness, respect and consideration (2) Empathy (1) Discountable for money (2) Valuable for money (1) Skills, (2) Knowledge and professionalism of personnel (1) Specification and volume flexibility (2) Service delivery speed

Factor loading

KMO Bartlet

Cronbach Alpha

0.837 0.274 0.589 0.637 0.697 0.563

0.552

0.687

0.551

0.775

0.548

0.620

0.500

0.840

0.500

0.840

0.500

0.792

0.679

0.702

0.626

0.656

0.500

0.707

0.500

0.748

0.500

0.636

0.500

0.621

0.656 0.635 0.753 0.336 0.863 0.863 0.513 0.513 0.629 0.629 0.699 0.644 0.914 0.696 0.554 0.540 0.783 0.783 0.606 0.606 0.734 0.734 0.636 0.636

the rest are regarded as fastidious. Afterwards, Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Shannon Entropy are exploited to weight and prioritise SQDs for each group. Finally, the results in each group are analysed in order to discover the likely similarities and differences between the two groups. It is suggested that using this way, service providers can better adjust and customise their services, and hence make their attempts in satisfying customers more effective.

2.

Data envelopment analysis

DEA is a non-parametric method which evaluates the efficiency of each DMU and compares the result with other DMUs. This tool has been extensively used in recent years and

336 R. Dabestani et al. has successfully defined benchmarks for the organisations active in the service sector of the industry (Soteriou & Stavrinides, 1997). A primary advantage of DEA to other models is that unlike other methods which require an exact definition of the relationship between every input and output, DEA regards each DMU as a ‘black box’. Therefore, no presumption of the relationship between inputs and outputs is necessary (Hwang & Chang, 2003). Charnes, Cooper, and Rhodes (1978) first introduced this method and asserted that DMUs might have one or more inputs and outputs. The main DEA model is as follows: s ur yrk r=1 m i=1 vi xik

max.

s.t.

s ur yrj r=1 ≤ 1; m i=1 vi xij

ur , vi ≥ 0;

(1)

j = 1, . . . , n,

r = 1, . . . , s;

i = 1, . . . , m.

Here, xij equals the ith input value, and yrj equals the rth output value; moreover, vi and ur are, respectively, the weights assigned to ith input and rth output. Also, k is the DMU whose efficiency is being evaluated (Charnes et al., 1978). In DEA, an efficiency frontier is defined based on the existing data, and if a DMU is located in it, the DMU is considered efficient; otherwise, it is assumed inefficient (Porter, 1999). Regarding the fact that solving a fractional model is rather complex, Charnes et al. (1978) suggested the Charnes, Cooper and Rhodes model, in which the return to scale is given constant. However, this ratio is variable in most cases in the real settings. Therefore, Banker, Charnes, and Cooper (1984) developed the Banker, Charnes and Cooper (BCC) model, in which return to scale is considered a variable. The BCC model in outputoriented mode is shown below: min Z =

m 

vi xip + w

i=1

s.t. s 

ur yrp = 1,

r=1 m  i=0

vi xij −

s 

ur yrj + w ≥ 0

j = 1 . . . n,

r=1

Ur ≥ 1 r = 1 . . . s, Vi ≥ 1

i = 1 . . . m,

(2)

Total Quality Management 337 The dual of the model above is equivalent to the modified, output-oriented BCC, which is presented below:  max Z = u − 1

m 

Si +

S 

i=1

 Sr

(3)

r=1

s.t. n 

lj xij + si = xip ,

j=1 n 

lj yrj − sr = uyrp ,

j=1 n 

lj = 1,

j=1

lj ≥ 0(j = 1, 2, . . . , n). Here, u is the efficiency value, and lj is the weight assigned to jth DMU. Also, m, n, and s are the number of inputs, DMUs, and outputs, respectively. Finally, xij and yrj are the values of the ith input and rth output of the jth DMU. If the score of a given DMU is equal to 1.00, the DMU is considered efficient, and for the values below 1.00, it is marked inefficient (Khodabakhshi & Asgharian, 2009).

3. TOPSIS TOPSIS is a practical decision-making tool for prioritising and choosing the best solution (Tan, 2011). TOPSIS belongs to a group of methods called multi-criteria decision-making (MCDM), which are used to find the best solution from a limited set of alternatives (Chen & Hwang, 1992). Of course, the results might show inconsistency with the best solution for some criteria, but they are – holistically speaking – optimised (Garcı´a-Cascales & Lamata, 2012). TOPSIS defines two ideal solutions of positive and negative, and then urges the ideal solution to have the shortest possible distance from the ideal positive and the longest possible distance from the negative ideal solution (Behzadian, KhanmohammadiOtaghsara, Yazdani, & Ignatius, 2012; Langkumaran & Kumanan, 2009). Opricovic and Tzeng (2004) illustrate this method in the following steps: Step 1: forming the normalised decision matrix: xij nij =  m 2 , nij : the normalised component of the decision matrix. i=1 xij Step 2: Calculating the weighted normalised decision matrix: V = ND × Wn×n , V : the weighted normalised component of decision matrix.

338 R. Dabestani et al. Step 3: Determining the positive and negative ideal solutions: + ′ ′ V + = {v+ i , . . . , vn } = {(max vij |i [ I ), (min vij |i [ I )}, − ′ ′ V − = {v− i , . . . , vn } = {(min vij |i [ I ), (max vij |i [ I )}

in which I′ is the advantage criteria, and I′′ is the cost criteria. Step 4: Computing the separation measures, utilising the n-dimensional Euclidean distance. The distance between each element of the alternative from that of the ideal solution is calculated through the following formulas:   n 2 + Si =

(Vij − VJ+ ) i = 1, 2, . . . , m, j=1

  n − Si =

(Vij − VJ− )2

i = 1, 2, . . . , m

j=1

in which Vj+ is the positive ideal option, and Vj− is the negative deal option. Step 5: Calculating the relative closeness to the ideal solution. The relative closeness of the alternative aj with respect to cl+ i is defined as: cl+ i = 4.

S− i +. S− i + Si

Entropy technique

A simple yet powerful method used to assign weights to criteria – based on the dispersion and variance – is Shannon Entropy (Zeleny, 1996). The process is as follows (Wang & Lee, 2009): 1. Normalising the criteria Xij . Pij =  j Xij 2. Calculating Ej indicator for each criterion using the formula below Ej = −k



Pij Ln(Pij ) K =

1 Ln m

m = number of alternatives.

3. Calculating Dj indicator Dj = 1 − Ej .

Total Quality Management 339 4. Calculating each indicator’s final weight Dj Wj =  D j .

5. Research methodology The current study aims to categorise the customers as ‘Fastidious’ and ‘Ordinary’, and then identify and prioritise the SQDs for each group. Traditionally, gap analysis can be utilised to recognise dissatisfied from satisfied customers: the higher the value of the gap, the more dissatisfied the customer. The dissatisfaction can be caused by either the inferior quality of the service or the fastidious nature of the customer. When the majority of population is rather satisfied with the service, it is logical to conclude that the ones who show the most dissatisfaction are fastidious and the rest are ordinary. Based on this logic and using DEA, we assigned the customers to the mentioned groups. Afterwards, we applied gap analysis as well as TOPSIS and Entropy to prioritise the SQDs for each group of ordinary and fastidious. The stages we went through are as follows: Step 1: Measuring customers’ expectations At the first step, the customers clarified how important each aspect of the service is to them. To do so, they filled in a questionnaire containing 30 questions, each of which measured one sub-dimension of SQ (as mentioned in Table 2). We used a 5-point Likert scale and asked the customers to check ‘1’ for the aspects they regarded completely unimportant and ‘5’ for the ones they regarded completely important. Step 2: Measuring customers’ perceptions In this stage, the customers were asked to evaluate the quality of the service they had received. Again, a questionnaire containing 30 questions was exploited to record their judgement on the service, regarding all of the SQ sub-dimensions (as mentioned in Table 2), through a five-point Likert scale. The customers checked ‘1’ if they were completely dissatisfied with the questioned aspect of the service and ‘5’ if completely satisfied. Step 3: DEA modelling In this step, DEA was used to differentiate between the regular and fastidious customers in four-star hotels. To solve the problem using DEA, three elements should be defined, verified, and measured: DMU, its input(s), and output(s). For this purpose, customers in each hotel were considered as DMUs. Customers judge and perceive the service they receive based on their prior exposure and experience of that service. In other words, the customer receives a service, and his/her perception of this service makes his/her attitude as to how to expect the service to be. Moreover, his/her satisfaction of the service (which probably leads to his/her future action as to whether to get back to the hotel or recommend it to others) makes his perceptions. Thus, we hypothesised that it is logical to assign customers’ expectations as the input and their perceptions as the output of the DMU. As mentioned above, DEA assigns DMUs into two groups, namely, efficient and inefficient. Based on the definition, efficient DMUs receive more input but produce less output compared to inefficient ones. Therefore, customers with lower level of expectation and higher level of perceptions (as the efficient DMUs) were classified as regular customers. On the contrary, customers with higher level of expectations and lower level of perceptions (as the inefficient DMUs) were considered as fastidious customers. The DEA model is demonstrated in Figure 1.

340 R. Dabestani et al.

Figure 1. Inputs and outputs of proposed DEA model.

Step 4: Measuring SQ gaps After measuring the customers’ expectations and perceptions of SQ, SQ gap for fastidious and ordinary customers were calculated using the formula below: Gap = Expectation − Perception. Step 5: Prioritisation of SQ dimension Using TOPSIS and Entropy, we prioritised the SQDs for each of the two categories of fastidious and ordinary. The data acquired from Step 4 (gap values) were exploited as the inputs for the two techniques.

6. Case study and findings The case in this study comprised Iranian customers of 3 four-star hotels located in Isfahan, Iran. It is important to note that there are just 4 four-star hotels in Isfahan city and three of them accepted to cooperate in this study. The sample volume was calculated using Cochran sampling formula, with a sampling error equal to 5%, a confidence level of 0.95, and a degree of variability equal to 2.5. Therefore, 384 questionnaires were distributed in the hotels and 334 of them were returned, denoting an 87% response rate. Since the level of selected hotels and room rental fee are similar, it is possible to combine the data together. The facilities of these hotels are somehow similar. Each of the respondents answered two questionnaires (containing 30 questions), which were aimed at measuring their expectations and perceptions of the SQDs. The questionnaires were designed based on the dimensions and sub-dimensions of the SQD presented in Tables 2 and 3. A brief introduction of the participating hotels is presented below:

6.1.

Aseman Hotel

Aseman Hotel is located in the centre of Isfahan and in the vicinity of its river, Zayanderood. The hotel is a 13-storey building and can accommodate customers in 90 rooms. Slightly more than half of the respondents in this hotel were between 25 and 35 years of age and have a bachelor’s degree. Also, a majority of the guests (68.2%) are married males and on a business trip (63.6%) (Appendix 1).

6.2.

Ali Qapu Hotel

Ali Qapu Hotel is a four-star hotel close to the historical monuments of Isfahan. It has 104 rooms, which are either deluxe or suites. Unlike Aseman Hotel, the age distribution in this hotel is more even, and a majority of the passengers are females (53%) and on their vacation (46.9%). Also, 69.2% are married and just about half have a bachelor’s degree (Appendix 2).

Total Quality Management 341 Table 3.

Factor analysis of customers’ perceptions of SQDs.

SQDs (1) Reliability

(2) Responsiveness

(3) Security and confidentiality (4) Access and approachability (5) Communication (6) Understanding the customer (7) Credibility

(8) Tangibles

(9) Courtesy (10) Price (11) Competence (12) Flexibility

SQ sub-dimensions (1) Performance (2) Accuracy and dependability (3) Consistency (4) Completeness (1) Willingness to help customer (2) Readiness, promptness (timeliness and speed) (3) Comfort (1) Physical security (2) Financial security (3) Safety (1) Ease of contact (2) Timely access (1) Word-of-mouth communication (2) Giving information (1) Comprehension (2) Individual attention (1) Trustworthiness and believability (2) Honesty (3) Reputation of service (1) Appearance (2) Tools or equipment used to provide the service (3) Availability of physical facilities (1) Politeness, respect and consideration (2) Empathy (1) Discountable for money (2) Valuable for money (1) Skills, (2) knowledge and professionalism of personnel (1) Specification and volume flexibility (2) Service delivery speed

Factor loading

KMO Bartlet

Cronbach Alpha

0.576 0.373 0.532 0.519 0.616 0.591

0.661

0.655

0.617

0.783

0.614

0.635

0.500

0.630

0.500

0.633

0.500

0.650

0.581

0.656

0.546

0.650

0.500

0.694

0.500

0.621

0.500

0.780

0.500

0.803

0.644 0.696 0.577 0.418 0.565 0.565 0.641 0.641 0.750 0.750 0.527 0.850 0.528 0.704 0.786 0.600 0.767 0.767 0.680 0.680 0.820 0.820 0.843 0.843

6.3. Piruzi Hotel Piruzi Hotel is not far from Ali Qapu Hotel in the downtown of Isfahan. Respondents’ personal data showed that about 44% are in their youth and have a bachelor’s degree. Moreover, about 80% of the participants are male and 59% are on business trips (Appendix 3). Before performing any calculation on the data, the reliability of our questionnaires had to be approved. Hence, we ran a factor analysis to exclude the unreliable parts of the questionnaire. In order for every item to be reliable, its factor loading value should be equal to or above 0.500 and its Cronbach’s alpha equal to or above 0.600 (Govindarajan & Kopalle, 2006). All questions successfully passed both criteria except for the ones corresponding to ‘Accuracy and Dependability’ and ‘Safety’. The former is a subset of Reliability, which

342 R. Dabestani et al. scored 0.274 in Factor Loading, and the latter belongs to Security and Confidentiality, with a Factor Loading of 0.336, which fell below the passing score. Therefore, all the data except for the ones mentioned above were accredited for further process. More information on the procedure is provided in Tables 2 and 3. After the reliability test, the data were exploited for the calculations explained in section five. The results are presented below: Step 1: Measuring customers’ expectations of four-star hotels The customers were unanimous in selecting ‘flexibility’, ‘reliability’, and ‘price’ as their three most important expectations of service in all the three hotels. However, the customers’ least important expectation was ‘understanding the customer’ in Aseman and Piruzi hotels, but ‘communication’ in Ali Qapu Hotel. The second least important SQD was price (¼4.121), access, and approachability (¼4.379), and communication for the customers of Aseman, Ali Qapu, and Piruzi, respectively. Further detail is provided in Table 4. Step 2: Measuring customers’ perceptions of four-star hotels Again, there was consensus between all the customers in all the three hotels on perceiving ‘reliability’ and ‘courtesy’ as the two most satisfactory aspects of the delivered service. Moreover, the customers of Aseman and Piruzi hotels were most dissatisfied with the price, while the residents of Ali Qapu were most unhappy with ‘tangibles’. Table 5 holds a more comprehensive account of the information gained in this step. Step 3: DEA modelling In this step, based on the model presented in Section 3 (DEA) and the methodology of the paper, customers were classified as efficient (ordinary) and inefficient (fastidious). Thirty-two subjects from the whole population were identified as fastidious and the rest were classified as ordinary. In another word, according to the DEA model, ordinary customers have higher perceptions and lower expectation in comparison with fastidious customers. In fact, fastidious customers have lower perceptions and higher expectations in comparison with ordinary customers. The advantage of using DEA is that the proposed

Table 4.

Customers’ expectations of hotels from SQDs. Aseman Hotel

No. 1 2 3 4 5 6 7 8 9 10 11 12

SQDs Access and approachability Communication Competence Courtesy Credibility Flexibility Price Reliability Responsiveness Security and confidentiality Tangibles Understanding the customer

Ali Qapu Hotel

Piruzi Hotel

Mean

Standard error

Mean

Standard error

Mean

Standard error

4.333

0.652

4.379

0.608

4.273

0.646

4.220 4.394 4.394 4.308 4.500 4.121 4.576 4.374 4.354

0.542 0.558 0.558 0.443 0.496 0.576 0.438 0.466 0.618

4.364 4.500 4.485 4.503 4.508 4.523 4.627 4.451 4.424

0.515 0.430 0.455 0.460 0.468 0.434 0.427 0.493 0.405

4.212 4.333 4.508 4.508 4.462 4.553 4.546 4.426 4.436

0.621 0.405 0.484 0.354 0.432 0.589 0.500 0.390 0.600

4.409 4.091

0.492 0.656

4.449 4.447

0.447 0.422

4.415 4.182

0.466 0.426

Total Quality Management 343 Table 5.

Customers’ perceptions of hotels from SQDs. Aseman Hotel

No. 1 2 3 4 5 6 7 8 9 10 11 12

SQDs Access and approachability Communication Competence Courtesy Credibility Flexibility Price Reliability Responsiveness Security and confidentiality Tangibles Understanding the customer

Ali Qapu Hotel

Piruzi Hotel

Mean

Standard error

Mean

Standard error

Mean

Standard error

3.871

0.563

4.174

0.757

3.886

0.613

3.864 3.765 3.992 3.950 4.008 3.242 4.106 3.939 3.970

0.737 0.634 0.797 0.543 0.747 0.860 0.470 0.650 0.601

4.250 4.417 4.424 4.462 4.500 4.296 4.503 4.379 4.285

0.423 0.417 0.458 0.391 0.588 0.696 0.506 0.525 0.575

3.462 3.568 4.091 3.670 3.777 2.712 3.888 3.589 3.824

0.730 0.868 0.607 0.677 0.644 1.117 0.618 0.619 0.669

3.955 3.630

0.684 0.801

4.079 4.432

0.758 0.479

3.530 3.477

1.827 0.875

method can classify customers based on efficiency score. Moreover, in this method, the customers are classified based on 12 SQDs. Step 4: Measuring SQ gaps SQ Gap was calculated in this step for both groups. Table 6 provides the average gap for the ordinary customers: the largest gap for the residents of Aseman and Piruzi hotels was reported for ‘security and confidentiality’, while in Ali Qapu Hotel ‘Reliability’ was accounted for the largest gap. Moreover, the results reveal that the narrowest gap for the ordinary guests of Aseman and Piruzi hotels pertains to ‘responsiveness’, while the narrowest gap belongs to ‘understanding the customer’. For fastidious customers, however, the story is different: the results are calculated for each individual and illustrated in Table 7. In order to compare the mean of SQ gap in the two groups (fastidious and ordinary), the following hypothesis test is suggested: Table 6.

SQ gap values of ordinary customers in four-star hotels.

No.

SQDs

Aseman

Aliqapu

Piruzi

1 2 3 4 5 6 7 8 9 10 11 12 Wj

Access and approachability Communication Competence Courtesy Credibility Flexibility Price Reliability Responsiveness Security and confidentiality Tangibles Understanding the customer

0.3824 0.3265 0.2878 0.3627 0.1961 0.2549 0.1698 0.3269 0.0980 0.8137 0.4314 0.3431 0.168559

0.0873 0.2222 0.2349 0.1746 0.0635 0.0249 0.0365 0.3222 0.0317 0.2063 0.0556 0.0159 0.684853

0.6019 0.8077 0.3923 0.3173 0.6250 0.5577 0.6423 0.7423 0.2692 1.5769 0.6442 0.6827 0.146588

Note: Wj, weight.

344 R. Dabestani et al.

Table 7.

SQ gap values of fastidious customers in four-star hotels. Fastidious customers

No

SQDs

#1

#2

#3

#4

#5

...

#28

#29

#30

#31

#32

1 2 3 4 5 6 7 8 9 10 11 12 Wj

Access and approachability Communication Competence Courtesy Credibility Flexibility Price Reliability Responsiveness Security and confidentiality Tangibles Understanding the customer

0.75 1 0.33 0.5 0 0 1 1.67 1.5 3 1.5 0.5 0.050

1.25 1.67 0.67 1.5 1.5 2.5 2.67 2 3 2 3 1.5 0.009

0.5 0 1 0.5 1 1.5 0.33 1.33 1 0.5 1 0 0.038

0.5 0.33 0.67 0.5 1 1 0.67 1.33 1 0 1 0 0.034

1.5 0.67 1 0.5 0 0.5 0.33 0.33 0 1 0.5 1 0.040

... ... ... ... ... ... ... ... ... ... ... ... ...

1 1 0.6 1 0.5 0 1 0.3 1.5 1 1 0.5 0.022

1.3 1.3 1.3 1 1 2 2 1.6 1 3 1 1 0.009

1 1 1.3 0.5 1 2 2 2 0 3.5 1 0.5 0.033

0.5 0 2 0 1.5 1.5 1.7 1.6 1 3.5 1 0.5 0.044

0 1 2 0 1.5 1.5 1.7 1.6 1 3.5 1.5 0.5 0.039

Note: Wj, weight.

Total Quality Management 345 Table 8. T independent hypothesis results – comparing SQ gaps between ordinary and fastidious customers. t-Test for equity of means

No. 1 2 3 4 5 6 7 8 9 10 11 12

SQDs Access and approachability Communication Competence Courtesy Credibility Flexibility Price Reliability Responsiveness Security and confidentiality Tangibles Understanding the customer

t

Sig. (twotailed)

Mean difference

3.867

0.000

0.357

4.516 2.958 0.587 3.048 3.322 6.676 2.527 3.711 11.950

0.000 0.003 0.558 0.003 0.001 0.000 0.012 0.000 0.000

6.477 5.923

0.000 0.000

Standard error difference

95% confidence interval of the difference Lower

Upper

0.092

0.175

0.539

0.378 0.323 0.061 0.354 0.418 0.660 0.470 0.425 1.584

0.083 0.109 0.105 0.116 0.126 0.098 0.186 0.114 0.132

0.213 0.107 20.145 0.125 0.170 0.465 0.103 0.199 1.322

0.543 0.539 0.269 0.584 0.667 0.855 0.838 0.651 1.845

0.667 0.644

0.103 0.108

0.464 0.429

0.871 0.859

In order to compare the mean of SQ gap in the two groups, the hypothetical test is suggested: H0 : m 1 = m 2 , H1 : m 1 = m 2 . According to Table 8, the significance level of all of the SQDs except the fourth dimension in the t-test is below 0.05, which suggest that the SQ gap level of customers in the second group (ordinary customers) is significantly lower than SQ gap level in the first group (fastidious customers). This shows the validity of the classification.

Table 9. No. 1

Prioritisation of SQD for fastidious customers. SQDs

cl+ i

cl+ i

Rank

Price

0.1910

8

Reliability Responsiveness Security and confidentiality Tangibles Understanding the customer

0.7788 0.1877 0.4323

1 10 4

0.2070 0.1104

6 11

Rank No

0.1954

7

7

2 3 4

Access and approachability Communication Competence Courtesy

0.0773 0.1880 0.4032

12 9 5

8 9 10

5 6

Credibility Flexibility

0.5234 0.6019

3 2

11 12

SQDs

346 R. Dabestani et al. Table 10. Prioritisation of SQD for ordinary customers in four-star hotels. No.

SQDs

cl+ i

Rank

1 2 3 4 5 6 7 8 9 10 11 12

Access and approachability Communication Competence Courtesy Credibility Flexibility Price Reliability Responsiveness Security and confidentiality Tangibles Understanding the customer

0.1753 0.5976 0.7048 0.5273 0.1430 0.0032 0.0159 0.9745 0.0266 0.7340 0.0689 0.0027

6 4 3 5 7 11 10 1 9 2 8 12

Step 5: Prioritisation of SQDs In this step, the gap values were fed into the TOPSIS and Entropy to separately prioritise the dimensions of SQ for each of the two groups. The results are summarised in Tables 9 and 10 for fastidious and ordinary customers, respectively. As it is shown in Table 9, ‘Reliability’, ‘flexibility’, and ‘credibility’ are at the top list for fastidious customers, while ‘reliability’, ‘security and confidentiality’, and ‘competence’ owned this rank for ordinary customers. Additionally, ‘communication’ and ‘understanding the customer’ were at the bottom of the list for fastidious customers, while ‘understanding the customer’ and ‘flexibility’ were the least prioritised for ordinary customers. Further detail is provided in Tables 9 and 10. 7.

Discussion and conclusions

Customer satisfaction has dramatic impacts on the performance of firms (Shirouyehzad et al., 2012a; Tontini & Picolo, 2010). Perhaps this is the reason why the most prominent means of evaluating SQ measures customer satisfaction and regards it as the indicator of SQ (Nadiri & Hussain, 2005). Following a similar line of thought, we aimed to evaluate SQDs in this study; but we did so through a DEA – MCDM-based approach. The primary advantage of the proposed model is that in order to have more precise results, it differentiates between two major groups and then investigates their preferences. In this model, a DEA model was proposed in which customers’ expectations were considered as the inputs, and their perceptions were regarded as the outputs of the DMUs – which were customers in this study. It was further hypothesised that as the DMUs with higher inputs and lower outputs are inefficient, the customers with higher expectations and lower perception are fastidious. Similarly, it was concluded that the rest of the population could be considered ordinary. In other words, we exploited DEA to assign the customers into two groups of efficient and inefficient: efficient customers were the ones with lower expectations but higher perceptions compared to the rest of the customers, and were called ordinary in this study. Conversely, inefficient customers are those with higher expectations and lower perceptions in comparison with the other customers, and were called fastidious. Afterwards, using the concept of SQ Gap and decision-making techniques such as TOPSIS and Entropy, we prioritised SQDs for both ordinary and fastidious groups. The results revealed that ‘Reliability’, ‘flexibility’, and ‘credibility’ are at the top list for fastidious customers, while ‘reliability’, ‘security and confidentiality’, and

Total Quality Management 347 ‘competence’ owned this position for ordinary customers. Additionally, ‘communication’ and ‘understanding the customer’ were at the bottom of the list for fastidious customers, while ‘understanding the customer’ and ‘flexibility’ were the least prioritised for ordinary customers. The results suggest that in order to increase customer satisfaction, hoteliers should focus primarily on the five most important SQDs, namely, ‘reliability’, ‘flexibility’, ‘credibility’, ‘security and confidentiality’, and ‘competence’. The only dimension of SQD that was in the three most important factors of both lists and was also ranked first was ‘reliability’. This can imply that the most important dimension of SQ is reliability, and paying specific attention to it can result in very positive outcomes. One of the main advantages of this study compared to Buyukozkan, Cifci, and Guleryuz (2011), Chow and Luk (2005), and Shirouyehzad, Lotfi, Shahin, Aryanezhad, and Dabestani (2012b) is that the current study has addressed and taken care of one major shortcoming in the cited works, in which important SQDs were identified and classified, but the results were possibly influenced or even distorted by a minority who had totally different expectations or perceptions. In this paper, the customers were categorised first, and then the crucial dimensions were identified and compared for each group. Still from another standpoint, classifying the customers and then realising their needs and expectations can help hoteliers better customise their offered service. Finally, like any other research, this study has its own limitations. The biggest restriction this study suffered from was the number of participants: Data were collected from 334 customers. Hence, any sort of generalisation should be made cautiously. Additionally, only three four-star hotels in one city were investigated. This restriction happened because there are just 4 four-star hotels available in Isfahan, and three of them accepted to participate in this study. We also could not use the information from five-star hotels since there is just one of them in Isfahan. Moreover, three-star hotels were out of the table since our experts in hospitality believed that the criteria assessed in our questionnaires cannot be measured in those hotels. This can raise the possibility that the results are influenced by the social and cultural issues of that specific region. It is recommended that further research be conveyed under different regional, demographical settings and with a higher number of participants. Disclosure statement No potential conflict of interest was reported by the authors.

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350 R. Dabestani et al. Appendix 1. Sample demographic characteristics – Aseman Hotel

Age 15 –25 25 –35 35 –45 45 –55 55 and over Marriage status Single Married Purpose of the journey Job related Entertainment Others

N

%

11 35 14 6 0

16.7 53 21.2 9.1 0

21 45

31.8 68.2

42 22 2

63.6 33.3 3

Gender Male Female Education level High school Diploma Associates Bachelors Masters and higher Customers’ revenue (rials) Less than 3,000,000 3,000,000 –5,000,000 More than 5,000,000

N

%

45 21

68.2 31.8

0 8 11 35 12

0 12.1 16.7 53 18.2

5 11 50

7.6 16.7 75.8

N

%

31 35

47 53

1 7 10 35 13

1.5 10.6 15.2 53 19.7

5 21 40

7.6 31.8 60.6

N

%

37 29

56.1 43.9

2 14 10 29 11

3 21.2 15.2 43.9 16.7

8 18 40

12.1 27.3 60.6

Appendix 2. Sample demographic characteristics – Ali Qapu Hotel

Age 15 –25 25 –35 35 –45 45 –55 55 and over Marriage status Single Married Purpose of the journey Job related Entertainment Others

N

%

10 19 11 15 11

15.2 28.8 16.7 22.7 16.7

20 45

30.8 69.2

13 30 21

20.3 46.9 32.8

Gender Male Female Education level High school Diploma Associates Bachelors Masters and higher Customers’ revenue (rials) Less than 3,000,000 3,000,000 –5,000,000 More than 5,000,000

Appendix 3. Sample demographic characteristics – Piruzi Hotel

Age 15 –25 25 –35 35 –45 45 –55 55 and over Marriage status Single Married Purpose of the journey Job related Entertainment Others

N

%

7 29 22 6 2

10.6 43.9 33.3 9.1 3

13 53

19.7 80.3

16 39 11

24.2 59.1 16.7

Gender Male Female Education level High school Diploma Associates Bachelors Masters and higher Customers’ revenue (rials) Less than 3,000,000 3,000,000 –5,000,000 More than 5,000,000

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