Application of Monte Carlo AHP in ranking dental quality attributes

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Expert Systems with Applications Expert Systems with Applications 36 (2009) 2310–2316 www.elsevier.com/locate/eswa

Application of Monte Carlo AHP in ranking dental quality attributes Tsuen-Ho Hsu a, Frank F.C. Pan b,* a

Department of Marketing and Distribution Management, National Kaohsiung First University of Science and Technology, Taiwan b Department of Healthcare Administration, Tajen University, 20, Wei-Hsin Road, Yenpu, Pingtung, Taiwan 907, Taiwan

Abstract Previous studies on the healthcare service quality have either ignored the multi-dimensional nature of healthcare services or naively generalized their research findings to dental services. This study reports the development of a comprehensive model that measures dental service quality in a hierarchical method. Examining the quality structure of dental services by using an analytic hierarchy process (AHP), we then add Monte Carlo simulation to precisely identify the priorities of the leading attributes. The empirical testing with a dental clinic group is then presented and demonstrates its usefulness in identifying real orders of quality attributes. Results from this model provide stronger confidence for the management of dental clinics than traditional AHP, and have significant cost-saving and revenue-increasing contributions. This model extends the applications of both AHP and the Monte Carlo simulation in service industry management, and proves its ability in clearly prioritizing critical attributes, in that it greatly sharpens the effectiveness of the decision-making process. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Analytical hierarchy process (AHP); Dental practice; Monte Carlo simulation; Service quality

1. Introduction Although medical and healthcare services have several distinct features, service quality remains at the core of success of this industry (Gummesson, 1987). By improving service quality, product, general, and healthcare service providers can enhance their performance in responding to customers’ preferences, leading to repetitive purchases (O’Connor, Shewchuk, & Bowers, 1991; Woodside, Frey, & Daly, 1989) and ultimate loyalties (De Ruyter, Wetzels, & Bloemer, 1998; Juran, 1989; Levitt, 1972). While service quality is closely linked with a hospital’s financial performance (Li & Collier, 2000) and serves as an offset to competitors’ price wars (Rust, Danaher, & Varki, 2000), activities associated with quality improvement that healthcare service providers have performed contribute little to the organization’s competitive advantage. This challenge

* Corresponding author. Tel.: +886 8 7624002x611; fax: +886 8 7378687. E-mail addresses: [email protected] (T.-H. Hsu), [email protected] (F.F.C. Pan).

0957-4174/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2007.12.023

is exaggerated at a time when support from the government is decreasing. Dental care service is the first medical section in Taiwan to apply the Global Budget System for government insurance reimbursement, regulated by the National Health Insurance Bureau (NHIB). Reimbursement for individual dental services is limited to an amount that is pre-determined jointly by the respective dentist associations and the NHIB. As service quality consistently affects customer satisfaction (Lin, 2007) and, accordingly, business performance in terms of profitability (Fornell, 1992) and stockholder value (Fornell, Mithas, Morgeson, & Krishnan, 2006; Srivastava, Shervani, & Fahey, 1998), dental clinics must secure and retain sufficient patients for survival and better financial outcomes (Li & Collier, 2000) and meet what target customers perceive to be valuable. Using correctly measured information is one of the most viable approaches to obtaining reliable market information for strategic decisions. Most studies have used traditional analytical hierarchy process (AHP) to solve the problems of these traditional methods (Cheickna & Wen, 2002; Karbhari, 1994) and are constrained by being unable to prioritize those criteria


T.-H. Hsu, F.F.C. Pan / Expert Systems with Applications 36 (2009) 2310–2316

with similar weights. The current research develops a model that adopts Monte Carlo simulation (Enrick, 1987; Rosenbloom, 1996) in an analytic hierarchical process to identify customers’ quality perceptions with higher confidence. As a tool, the simulation technique provides tests or experiments for alternatives in real life (Szymankiewicz, McDonald, & Turner, 1988) and has received growing acceptance as a viable and practical technique (Chan and Chan, 2005). Simulation helps to better forecast outcomes, thereby minimizing risks for decision-making (Chan and Chan, 2005). Parsimoniously, the model we propose in this research is able to differentiate those criteria that were previously weighted similarly. We perform empirical testing with a sample of patients of a large dental service chain in metropolitan Kaohsiung, Taiwan. The empirical results confirm the model’s capability in correctly listing the critical quality attributes by comparing the ranking results from conventional AHP and Monte Carlo simulation models. 2. Service quality for dental practice 2.1. Service quality in the hierarchical structure Measurements used to assess service quality have mainly stemmed from the disconfirmation paradigm (Churchill & Surprenant, 1982). Later, the Nordic method (Gronroos, 1984) followed the same paradigm to measure perceived quality but emphasized the functional and technical aspects. The PZB model with SERVQUAL (Parasuraman, Zeithaml, & Berry, 1985; Zeithaml, Berry, & Parasuraman, 1988) was then developed and used to measure various gaps between consumers and service providers regarding the five dimensions in order to determine consumer satisfaction. For example, Brown and Swartz (1989) showed that several gaps exist between customers and providers in healthcare services. Other researchers advanced this paradigm by viewing the concept of service quality as having a multi-dimensional, hierarchical structure (Dabholkar, Thorpe, & Rentz, 1996; Rust & Oliver, 1994). These two research streams substantially contribute to the development of the integration model of service quality (Brady & Cronin, 2001), suggesting that current researchers should measure focal service in a hierarchical way (Dabholkar et al., 1996). 2.2. Uniqueness of dental practice services Other than those commonalities shared by all service businesses, healthcare services have several distinctions, such as higher uncertainty regarding the service results (Arrow, 1963) and the fact that service providers dominate the service procedure due to a knowledge asymmetry (Phelps, 1997). In contrast to general medical treatment, dental service providers interact with their patients for a longer duration (24 min on average for dental practice and 7 min for physician visits) and offer more treatment alternatives than general medical treatment.

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In the literature, five dimensions are generally employed to determine the patients’ quality perceptions toward a particular dental service provider (Newsome and Wright, 1999): technical competence, interpersonal factors, convenience, costs, and facilities. Although patients are not eligible to judge, but trust the dentist’s technical proficiency (Tolpin, 1985), a dentist’s technical competence remains an essential factor when making a service alternative selection (Andrus & Buchheister, 1985; Janda, Wang, & Rao, 1996). Inseparability between the service receiver and provider is inherent in almost every service exchange, and the quality of such an interaction relationship, thus, has a significant impact on the mutual communication and quality perception of the focal service (Andrus & Buchheister, 1985; Lahti, Verkasalo, Hausen, & Tuutti, 1996). On the other hand, lacking the capability to judge a service provider’s technical competence, patients seek to satisfy their need to judge by referring to other possible cues that are embedded in their interactions with service staff, i.e. interaction factors. Thus, the interaction or interpersonal factor is an important factor that results in patients’ satisfaction and repetitive purchasing (Holt & McHugh, 1997). Other than technical competence and interpersonal factors as important determinants of service quality, the present research reveals additional information on quality attributes related to dental practice through an innovative procedure. 3. Analytic hierarchical process and Monte Carlo simulation As a multi-criteria decision-making method, AHP is widely used as one of the major methods in solving a wide variety of problems that involve complex criteria across different levels where the interaction of criteria is common (Saaty, 1977, 1980). A plethora of studies have shown that AHP provides fair to excellent solutions for selecting the optimal alternative, allocating resources, measuring performance, and designing systems (Saaty, 1980; Saaty & Vargas, 1982), including business policy and strategic processes (e.g. Emshoff & Saaty, 1982; Wu & Wu, 1991), marketing (e.g. Bult & Foekens, 1993; Dyer, Forman, & Mustafa, 1992; Wind & Saaty, 1980), human resource management (e.g. Lootsma, 1980; Yamaki & Sekitani, 1999), and public affairs (e.g. Duke & Aull-Hyde, 2002; Saad, 2001). It is noteworthy that AHP applications in service quality are few, despite service quality being the core of the increasingly important service industry. AHP is coarse in finalizing the rankings of competing candidates when used to identify major contributors to the particular problems in question. The main difficulty associated with AHP application is centered on the decision regarding the priorities of all alternatives involved in the decision-making process. Traditionally, Eigen values from AHP computation have been used as bases for ranking, yet the absence of the probability of individual alternatives tends to confuse decision-makers, particularly for the alternatives in proximity. Previous researchers (e.g. Rosenbloom, 1997) have suggested adding Monte Carlo simula-


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tion in conventional AHP to enhance the screening capability when there is a need to identify the optimal among the leading alternatives. Partly drawn on the work of Brady and Cronin (2001), who advocated the service quality model with hierarchical dimensions, and in response to the demand of clear information for better quality decision-making, we propose to add simulation in service quality to form a MCAHP model. Doing so may help to advance AHP application and ease its vulnerability. 4. Monte Carlo AHP model

relies on Monte Carlo simulation, specifically the triangular distribution method, to rank the importance of each attribute to service quality. Simulation values are entered into AHP computation and the frequency of ranking of each alternative is documented. As suggested, we perform 1000 simulations to gain reference values (sets that appear with CR > 0.1 are excluded). The outcomes are then used to estimate the probabilistic optimality, in which a clear message can be delivered to the management for relevant decisions (see Rosenbloom, 1997 for detailed steps of the Monte Carlo method). We illustrate an empirical test using this model in the following section.

4.1. Concept of Monte Carlo AHP model 5. Empirical application Saaty’s AHP lacks probability explanations to distinguish adjacent alternatives in final ordering. In response to this specific problem, Rosenbloom (1997) suggested that, in the distribution of 1/9 and 9, where aj,i = 1/ai,j and ai,i = 1, the pairwise values could be viewed as random variables ai,j. This means that every paired matrix will be symmetrically complementary. The value of a random variable aj,i will be dependent on ai,j. Therefore, it is reasonable to assume that {ai,j|i>j} is independent, and the final scores S1, S2, . . ., Sn will be stochastic as well. In the case of Si > Sj, alternative i is superior to alterative j at a certain level of error (a). To obtain the probability information for ai,j in the context of multiple decision-makers, we assume that the probability of evaluations made by all experts regarding ai,j are equal. This will convert every ai,j into a discrete random variable. In the case of one decision-maker, on the other hand, the judgment made regarding each paired uncertainty will become a continuous random variable (Rosenbloom, 1997). We use the triangular distribution of Monte Carlo simulation for its efficiency to represent three values of the highest, modest, and lowest likelihood of each evaluation, shown as Fig. 1. 4.2. Procedures of the Monte Carlo AHP model The model in this research follows the initial steps of Saaty’s AHP, which uses the geometric mean in computing the importance of each alternative. However, this model

Fig. 1. Triangular distribution in Monte Carlo simulation.

We borrowed the multidimensional and hierarchical concept of service quality from Brady and Cronin (2001), as stated in the earlier section of this paper, to build a hierarchy of dental services for the focal clinic, and show such empirical testing of the Monte Carlo AHP model in the following sections. The sample comes from a major dental clinic chain in Kaohsiung, Taiwan: the ABC (always best choice) clinics. The clinic chain has six dental facilities in the greater Kaohsiung metropolitan area. The average number of treatment chairs for each facility is eight, which is the largest in scale in this particular healthcare area, with some exceptions in medical centers. Services offered include general dental services and specialty services such as oral surgeries, peridontics, implants, full mouth rehabilitation, periodontal plastic surgeries, and so on. 5.1. Hierarchy of dental service quality Researchers have generally categorized dental services into three types (Janda et al., 1996). The first cluster of services includes core dental services, while the second cluster includes those which support or facilitate core procedures, and the third service cluster is intangible and includes factors associated with the dentist and the clinic. Major characteristics included in the first cluster are service quality, the dentist’s professional competence, attitudes of the dentists and assistants, hours of operation, waiting time, appointment necessity, fees charged and payment plans, method of pain control, and the availability of emergency services. Attributes categorized in the second cluster include auxiliary factors such as location (for easy access), the parking facility, physical environment, and atmosphere. Factors affecting the customer’s behavior, including reputation and word-of-mouth regarding a specific dentist, and other factors which may drive a patient’s visiting behavior are included in the third cluster. Criteria involved in the initial hierarchy for dental service quality are those presented in relevant studies on healthcare or dental service quality (e.g. Brady & Cronin, 2001; Carman, 2000; Janda et al., 1996; Linder-Pelz, 1982; McAlexander, Kaldenberg, & Koenig, 1994). Some



additional elements are added, as suggested by dental experts, such as friendliness (Crane & Lynch, 1988; Gopalakrishna & Mummalaneni, 1993), treatment continuity (Gopalakrishna & Mummalaneni, 1993), and image or reputation (Clow, Fischer, & O’Bryan, 1995; Janda et al., 1996), and have been modified by consulting the director of each respective clinic of the group. As a result, we delete the following: ‘service quality’ to avoid possible confusion among respondents; ‘advertising’ since the laws ban such a campaign; and ‘payment plan’ since the National Health Insurance is almost fully responsible for all routine dental services. We further add the following: ‘clinic layout and route’ as a distinctive factor other than ‘physical environment and atmosphere’; ‘familiarity with dentist or assistant’ in the dimension of physical environment quality since patients are highly reluctant to visit dentists who are unfamiliar; and ‘easing of emergent pain’, which has generally been ignored by non-dental healthcare services yet is characterized as a major motive to visit a dentist in Taiwan. The modified hierarchy brings a new factor that is similar to the social factor in Brady and Cronin’s (2001) model. Experts included in modifying such a hierarchy are dentists with experience ranging from 7 to 20 years (with a mean of 11 years). The final structure of the modified hierarchy is shown in Fig. 2. 5.2. Sample We approached those patients who visited the same dentist three or more times in the past 12 months for volunteer

participation in this study. Some simple dental hygiene questions were used to screen qualified respondents. Patients who passed such screening could be viewed as experts regarding a certain dentist or clinic since they are more knowledgeable than the general population is. We collected data on 303 out of 400 patients approached in three different clinical shifts (morning, afternoon, and night) on each business day in November 2006. A total of 115, or 42.5%, of the responses passed the expert screening, with 59.30% being female, and the age distribution as follows: 35.6% aged 21–30 years old, 27.40% aged 31–40 years old, and 13.70% aged 41–50 years old. Around 60% of customers hold a bachelor’s degree, and more than 90% received at least a high school education. Respondents had few or no difficulties in completing the questionnaire. 5.3. Results We used the Expert Choice software program to compute the results. Table 1 shows the results from the traditional AHP. Fifty-two cases were eliminated because the associated CR were larger than 0.1 (Saaty, 1980). Pain relief ranks first (0.19) in the patient’s perception of service quality, as indicated in Table 1, followed by professional competence (0.09), pain control (0.08), service charges, appointment requirement, and dentist attitudes (all with a weight of 0.07). The ranking problem appears again in the final stage of measuring patients’ perceptions, shown in the right column of Table 1. As plenty of scholars criticized, almost all weights were located in the limited and adjacent area between 0.05 and 0.08, with the exception of Dentists’ reputation Professional competence

Interaction Quality

Dentists’ attitudes Pain control Time took for treatment Acquaintance with dentist

Dental Service

Environment & Atmosphere

Environment Quality

Quality

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Easy access location Clinic layout and route Parking facility Waiting for treatment Easy appointment

Result Quality Fees for services Pain relief

Fig. 2. Expert-based hierarchies for dental service quality.


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Table 1 Weights and ranks of attributes, by Saaty AHP Criteria

Interaction quality

Environment quality

Result quality

Weights (C)

Attributes

0.35

Attitudes Pain control Treatment duration Professional competence Reputation Environment & atmosphere Layout & route Location Parking Acquaintance Waiting for treatment Easy appointment Service charge Pain relief

0.22

0.43

Table 3 Model comparisons in attribute ranking

Weights (A)

Adj. wgts. (C*A)

Rank

0.217 0.242 0.161

0.08 0.08 0.06

4 4 8

0.276

0.10

2

0.104 0.216

0.04 0.05

12 9

0.155 0.207 0.187 0.235 0.202

0.03 0.05 0.04 0.05 0.09

14 9 12 9 3

0.174

0.07

6

0.173 0.452

0.07 0.19

6 1

Attributenmodels

Saaty AHP

Monte Carlo AHP

Remark

Attitudes Control Duration Competence Reputation Environment Layout Location Parking Acquaintance Waiting Appointment Fee Pain relief

4 4 8 2 12 9 14 9 12 9 3 6 6 1

7 5 8 6 14 10 11 9 13 12 2 3 4 1

O h h O h h N h h O N N N h

(N) Ascended, (O) descended, (h) unchanged.

pain relief, which had a value of 0.19. Managers under such a scenario would be confused by not receiving clear information for decision-making. We further performed Monte Carlo simulation and report the results in Table 2. In Table 3, new rankings appear in a quite different way compared to the traditional AHP. Saaty’s AHP and MCAHP provide roughly consistent results in signaling the direction of quality attributes, yet Monte Carlo simulation further identifies those superior attributes which would otherwise be viewed as the same by conventional AHP. For example, traditional AHP reveals that pain control and service provider’s attitudes are at the same level, whereas MCAHP reflects the fact that pain control is far more important. Another example is the case of the importance of ease in making an appointment over service charges. There are many variations in ranking between these two approaches. The most noteworthy change may be that the traditional AHP ranks professional

competence as the second most important contributor to patients’ quality perceptions, whereas the Monte Carlo AHP replaces it with the attribute of waiting for treatment. Improvement decisions based on different AHP approaches would result in different efficiencies and effectiveness in contributing to patient’s quality perceptions. With this Monte Carlo AHP, we have also identified the impacts of other attributes raised in previous studies. Easy access or convenient location, and physical facilities with a pleasant atmosphere contribute the least to perceptions of quality, whereas the associated cost has medium impacts (e.g. Holt & McHugh, 1997; Janda et al., 1996). 6. Implications and conclusion 6.1. Practical implications and limitations Instead of placing ‘dentist competence’ as the second most important attribute in dental practice services, as revealed by traditional AHP, the MCAHP model ranked the same factor as sixth in the list of service quality determinants. The new model reveals that the ‘appointment

Table 2 Ranking of perceived quality attributes, by Monte Carlo AHP (MCAHP) Attributenrank

1

2

3

4

5

6

7

8

9

10

11

12

13

14

Attitudes Control Duration Competence Reputation Environment Layout Location Parking Acquaintance Waiting Appointment Fee Relief

0 1 0 0 0 0 0 0 0 0 402 35 9 553

0 18 12 0 0 0 0 0 0 0 417 209 66 278

0 97 0 72 0 0 0 0 0 0 131 395 181 124

0 235 0 217 0 0 0 0 0 0 31 174 315 28

6 413 0 369 0 0 0 3 0 0 15 79 104 11

101 235 0 217 0 0 0 9 0 0 4 91 230 5

707 1 33 6 0 9 3 141 1 0 0 17 81 1

147 0 446 0 0 57 23 316 3 1 0 0 7 0

34 0 315 0 0 155 84 389 8 12 0 0 3 0

3 0 132 0 0 398 251 106 33 73 0 0 4 0

2 0 56 0 2 265 379 33 88 175 0 0 0 0

0 0 144 0 0 1 249 1 1 603 0 0 1 0

0 0 4 0 121 23 90 0 487 275 0 0 0 0

0 0 0 0 862 0 1 0 111 26 0 0 0 0

Note: Sum of frequency of each line and column are 1000 times.



requirement’ is the third contributor to quality, and is more important than ‘dentist competence’. Incorporated with such deviations are many minor discrepancies, as traditional AHP may also suggest which exceed the scope of healthcare professionals’ perceptions, and thus deserves close monitoring and associated actions. Deviations include ‘pain control’ over ‘service provider’s attitude’, the ‘fee charged’ over the fifth, and so on. This clearly shows the clinic managers that a quality improvement strategy (Beach & Burns, 1995) will not be useful unless the decisions are made following the correct information, based on reliable measurements. It is also interesting to note that none of the additives firmly suggested by the directors of the clinic received substantial attention from the patient group. For example, patients ranked ‘familiarity’ 9th under the traditional AHP and 12th under the MCAHP method in evaluating its importance to service quality. This reminds the management that a quality perception gap between patients and service providers is apparent. Aligning service providers’ perceptions with what patients want by listening carefully to the patients is vital for any dental business. The quality perceptions of particular groups may be useful when attempting to segment a market. For this purpose, ‘dentist attitudes’ and ‘pain relief’ are the major determinants of service quality, specifically for less frequent patients, and ‘easy access’ and ‘parking facility’ are important to newly enrolled patients. The data collected from dental patients has confirmed the applicability of MCAHP to include the strength of conventional AHP but avoid the ranking confusion. We propose that applying this model in other professional services that require one-on-one encounters, such as those offered by lawyers, general medical services, nursing homes, retirement residences, beauty salons, individual tourist agents, treasure management services, VIP financial services, and many others would be possible. The generalizability of the sample could be a major limitation of the current study. This research has purposefully excluded certain groups, such as senior citizens and patients in extreme pain, as they are unable to perform pair comparison as the AHP requires. The second limitation is that the data were collected from one particular clinic chain and not randomly across all dental clinics. Although the literature suggests that a geographical difference may have no impact on the variance of service quality perception, special care may be taken when generalizing this finding. The National Health Insurance System, as the major denominator for all dental clinics in Taiwan, may further guarantee a satisfactory level of generalizability of this model. 6.2. Conclusion The cluster of core services includes the main criteria in customers’ quality perceptions, for these functionally satisfy patients’ needs. Drawn on Brady and Cronin’s (2001) hierarchical service quality, we integrate Monte

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Carlo simulation into traditional AHP in order to develop a new model of service quality measurement. Both traditional AHP and Monte Carlo AHP methods retain ‘pain relief’, ‘waiting time’ and ‘pain control’ in the list of the five leading contributors regarding customers’ quality perceptions. The MCAHP model replaces ‘providers’ attitudes’ and ‘dentist competence’ with ‘appointment requirement’ and ‘fee charged’ as five new startup factors for quality. This research contributes to the literature in several ways. First, the survey identifies specific determinants of patients’ perceived quality that are distinct from general and other healthcare services. Second, we established and confirmed the usefulness of the model, applying Monte Carlo simulation in the traditional AHP. We provided evidence that the MCAHP model is not only reliable in identifying the top quality attributes, but is also useful when prioritizing similar or competing alternatives is required. Characterized with intolerable pain, dental practice was generally perceived as torture by patients. Accordingly, patients’ expectations and perceptions are distinctive from other healthcare services as well as from dentists’ perceptions. With the goal of offering quality dental services and gaining customer preferences, clinic managers shall be alert to these special requirements by listening to the patients and involving patients in improvement projects. References Andrus, D., & Buchheister, J. (1985). Major factors affecting dental consumer satisfaction. Health Marketing Quarterly, 3(1), 57–68. Arrow, K. J. (1963). Uncertainty and the economics of medical care. American Economic Review, 53, 941–973. Beach, L. R., & Burns, L. R. (1995). The service quality improvement strategy. International Journal of Service Industry Management, 6(5), 5–15. Brady, M. K., & Cronin, J. J. Jr., (2001). Some new thoughts on conceptualizing perceived service quality: A hierarchical approach. Journal of Marketing, 65(3), 34–49. Brown, S. W., & Swartz, T. A. (1989). A gap analysis of professional service quality. Journal of Marketing, 53(2), 92–98. Bult, J. R., & Foekens, E. W. (1993). An analytic approach to marketing decisions. International Journal of Research in Marketing, 10(4), 407–409. Carman, J. M. (2000). Patient perceptions of service quality: Combining the dimensions. Journal of Services Marketing, 14(4), 337–352. Chan, F. T. S., & Chan, H. K. (2005). Design of a PCB plant with expert system and simulation approach. Expert System with Application, 28(3), 408–423. Cheickna, S., & Wen, H. J. (2002). A conceptual framework for evaluation of information technology investments. International Journal of Technology Management, 24(2/3), 236–261. Churchill, G. A., & Surprenant, C. (1982). An investigation into the determinants of customer satisfaction. Journal of Marketing Research, 19(4), 491–504. Clow, K. E., Fischer, A. K., & O’Bryan, D. (1995). Patient expectations of dental services: Image affects expectations, and expectations affect perceived service quality. Journal of Health Care Marketing, 15(3), 23–31. Crane, F. G., & Lynch, J. E. (1988). Consumer selection of physician and dentist: An examination of choice criteria and cue usage. Journal of Health Care Marketing, 8(1), 16–19.


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