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Socio-Economic Planning Sciences 44 (2010) 100–112

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Socio-Economic Planning Sciences

journal homepage: www.elsevier .com/locate/seps

Improving customer experience in tourism: A frameworkfor stakeholder collaboration

Ram Gopalan a,*, Bindu Narayan b

a Fox School of Business and Management, Temple University, A517 Alter Hall, Philadelphia, PA 19122, USAb Mu Sigma Business Solutions, Sunningdale, Embassy Golf Links Business Park, Intermediate Ring Road, Bangalore 560 071, India

a r t i c l e i n f o

Article history:Available online 26 November 2009

Keywords:TourismCustomer experience measurementService qualityBenchmarking

* Corresponding author.E-mail addresses: ram.gopalan@temple.edu (R

mu-sigma.com (B. Narayan).

0038-0121/$ – see front matter � 2009 Elsevier Ltd.doi:10.1016/j.seps.2009.11.001

a b s t r a c t

Tourism represents a service industry replete with unique complexities since a tourist’s overall experi-ence is modulated by multiple stakeholders, e.g., immigration officials at airports, policy makersresponsible for investment in transportation infrastructure and managers at various tourist attractions.Effective management of customer satisfaction in this service sector entails cross-functional collabora-tion and a transparent measurement scheme that clearly delineates the impact of each stakeholder’sactions on overall customer experience. In this paper, we propose a simple conceptual framework forstakeholder collaboration in tourism. A four-phase customer experience measurement process isdeveloped to prioritize resource allocation and to increase tourists’ advocacy levels for a destination. Theproposed measurement framework has wide applicability and can also be exercised in the context ofother public sector services, e.g., mass transit systems. We illustrate the process using an empirical casestudy at Chennai, a tourist destination in India and provide a number of substantive insights that arevalid for this destination.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Policy makers confront a plethora of options when allocatingresources to improve public services. In a mass transit system,funds can be allocated to modernizing the vehicles in the system,adding new rail lines, or improving the safety at specific transitpoints. In fighting the war against terrorism, a government canincrease the size of police forces in heavily populated urban areas,invest in cutting-edge technology or improve international co-ordination with agencies from other countries. In the tourismsector, policy makers can improve transportation infrastructure,advertise tourist destinations in other countries or simplify andexpedite immigration procedures. While implementing publicpolicy to improve such services, it is useful to have a frameworkthat synthesizes the influence of multiple stakeholders.

In this paper, we develop a four-phase customer experiencemeasurement process for the tourism sector. Specifically, we adoptthe perspective of a policy maker responsible for ‘‘selling’’ a specifictourist destination to the public at large. We study the followingissues:

. Gopalan), bindu.narayan@

All rights reserved.

1. For tourists at a specific destination, what set of environmentalvariables S influence a customer’s perception of servicedelivery? Which subset of variables S0 is particularly relevant ina given locale?

2. For the subset S0, is it possible to derive a ranking of the impactof each variable in S0 on overall customer experience? Whichvariable has the greatest impact in terms of causing dissatis-faction? Which environmental factor or variable is perceivedthe most positively?

3. How can we benchmark the performance of the chosen desti-nation with an ‘‘idealized’’ peer destination? What is the extentof discrepancy in performance with the peer and on what set ofkey dimensions is there the greatest discrepancy?

4. Do the individual characteristics of the person experiencing theservice influence overall perception of customer experience? Ifso, in what manner?

Our goal is to develop a robust measurement process forobtaining substantive insights and to facilitate continuousimprovement of the customer’s experience. The framework isstrategic in nature and draws heavily upon methodologies readilyavailable in the marketing literature, e.g., perceptual maps. While itassists decision makers in assessing the relative impact of variouspolicy levers, it does not provide a detailed prescription as to theexact amount of resource allocation needed. It should also be noted

R. Gopalan, B. Narayan / Socio-Economic Planning Sciences 44 (2010) 100–112 101

here that while the framework is illustrated in the context oftourism, it is broad in scope and potentially applicable to theimprovement of many other public sector services, e.g., mass transitsystems.

The paper contains three additional sections. Section 2 providesa literature review on the hospitality industry and the measure-ment of customer satisfaction. This section also introduces theconceptual framework used in our study. In Section 3, we discussthe implementation of the four-phase customer experiencemeasurement process. The final section discusses implications forpolicy makers, the limitations of the current research design anddirections for future research.

2. Literature review and conceptual framework for thecurrent study

Management scientists have developed numerous models forresource allocation in the public sector, in areas such as the fightagainst drug trafficking (Baveja et al. [1,2], Caulkins [4]), planningemergency disaster relief (Gong and Batta [10]) and improvingpolice patrol operations (D’Amico et al. [7]). In our study, weaddress resource allocation in tourism, a sector which is rapidlygrowing in importance, particularly for developing economies. Wefirst briefly review the literature pertaining to measuring servicequality in tourism.

2.1. Measurement of the service experience in the tourism andhospitality industry

In their survey article, Pizam and Ellis [23] have listed variouselements of a service encounter in the hospitality industry,including ‘material product’, ‘behavior and attitude of employees’and the ‘environment’. They state that, ‘‘unlike material productsor pure services, most hospitality experiences are an amalgam ofproducts and services. Therefore it is possible to say that satis-faction with a hospitality experience such as a hotel stay ora restaurant meal is a sum total of satisfactions with the individualelements or attributes of all the products and services that makeup the experience’’. Indeed, the above statement can be extrapo-lated to the delivery of all public services, and our study attemptsto evaluate the relative importance of the various service elementsthat influence the customer experience in hospitality and tourism.In a similar spirit, Kotler [28] (page 253) defines Customer ValueAnalysis (CVA) as a mental heuristic or model that customersmight use to assess the relative benefits and costs associated withvarious product offerings. In the CVA framework, in order toascribe a total value to a product, customers either associatebenefits and costs to individual attributes of a product, or associatebenefits and costs with various actions that need to be performedwhile using the product (e.g., maintenance or product disposal).Therefore our work can also be viewed as an alternate mechanismto execute Customer Value Analysis in the context of hospitalityand tourism.

Ekinci et al. [8] define ‘tangible’ and ‘intangible’ elements in theservice quality of accommodations. For a tourist, the quality ofaccommodations is more tangible when compared to the discom-fort caused by air pollution. Nevertheless, both types of elementscontribute to the ultimate satisfaction with and level of advocacyfor a destination. Moreover, critical service elements are oftendestination-specific. Pizam et al. [25], in their study of Cape Cod,Massachusetts, identified the following eight components of touristsatisfaction: beach opportunities, cost, hospitality, eating anddrinking facilities, accommodation facilities, environment, andextent of commercialization. Heung and Cheng [12] assessed HongKong tourists’ satisfaction with shopping and identified the

following four key dimensions from fifteen shopping attributes:Tangibles Quality, Staff Service Quality, Product Value, and ProductReliability. It is clear from the above discussion that the serviceelements that determine overall customer experience will varyfrom destination to destination. Many of the inferences in this workare substantive and pertain mostly to the specific destinationstudied, but the measurement process itself is portable andrepeatable, and can be implemented at any destination.

We now review the major paradigms used in measuringcustomer satisfaction with any product or service (not necessarilytourism).

2.2. Paradigms in customer satisfaction research

Oliver [20] has proposed the expectancy-disconfirmation modelin measuring customer satisfaction, where (dis)satisfaction isdriven by an expectancy not being met, e.g., a customer ata restaurant expected the food to be of the highest quality, but wasdisappointed by the actual standards demonstrated. The expec-tancy-disconfirmation model may be appropriate for productcategories where expectancies can be clearly formed prior toexperiencing the actual service. At many tourist destinations, someanticipation of the experience can be formulated from informationsources such as travel brochures and websites, but a customer maynot really know what to expect until she has arrived at the desti-nation. This is particularly true for first-time visitors. Cronin andTaylor [6] proposed SERVPERF, an alternate customer satisfactionmodel. In this model, customers develop a level of satisfaction witha product based upon its actual performance, without any a prioriexpectancies of performance. We will refer to this paradigm ofcustomer satisfaction measurement as a performance-basedassessment. A variant of performance-based assessment, referredto as Importance-Performance analysis, was proposed by Martillaand James [17]. They measured not only the performance on keyservice dimensions, but also their relative importance to thecustomer, e.g., a customer may be satisfied with the quality of thefood, but dissatisfied with the decor of a restaurant. Nevertheless,the customer may be satisfied overall with the restaurant visit,since the food quality was far more important to the customer thanthe decor. Finally, Parasuraman et al. [21] proposed the SERVQUALscale to measure satisfaction with services and identified manydimensions of service satisfaction, such as the responsiveness ofemployees, their empathy and reliability. The application of theSERVQUAL scale to tourism may prove difficult as a tourist’sexperience is formulated after encountering multiple serviceproviders in multiple locations. An employee at an amusementpark may be responsive, but a custom’s official could have provedan irritant. Hudson et al. [13] have compared the four mainmethods of measuring customer service quality – Importance-Performance Analysis (IPA), SERVQUAL, SERVQUAL weighted byImportance, and SERVPERF and found that there was no significantdifference between the four methodologies.

Meyer and Schwager [18] develop an important distinctionbetween Customer Experience Management (CEM) and CustomerRelationship Management (CRM), stressing the importance of thequality of the customer experience, which is more affective innature. There are many examples of products that customersencounter that they are satisfied with, but nevertheless theirexperience with overall product usage has left something to bedesired. In tourism, the quality of the overall customer experiencealso influences the advocacy level for the destination. Therefore, inour work, we have attempted to include a measurement of factorsthat could easily distort a customer’s experience.

We now proceed to a discussion of the measurement frameworkfor the current study.

R. Gopalan, B. Narayan / Socio-Economic Planning Sciences 44 (2010) 100–112102

2.3. Stakeholder collaboration framework for current study

Our goal in this paper is to identify and measure the key metricsfor the entire tourist experience, from the point of entry at theimmigration desk (if an international tourist) to final departurefrom a destination. Our study assesses the delivery of services totourists at two Asia-Pacific destinations, Chennai (in India) andSingapore. We specifically chose tourism as it represents a complexand extended service and draws upon a large network consisting ofthe airline and hotel industries, tour and travel operators, desti-nation managers and entertainment complexes. Tourism alsorequires the active participation of the local community and theoverall co-ordination of the government. Each of these entitiesconstitutes a separate stakeholder, with different roles. Destina-tions are marketed by the respective governments; it is the touroperators who acquire specific customers; hoteliers provideaccommodation; and within each destination, local attractions aremanaged by separate destination managers, e.g., a private enter-prise managing an amusement park, or a local government bodymanaging a museum. The ‘customer experience’ in tourism consistsof an assorted bundle of experiences, starting with the immigrationdesk and customs clearance at the airport. Even the condition ofroads and attitude of local people have to be considered as influ-ences, apart from the quality of hotel accommodation and theenjoyment derived from sightseeing.

Moreover, a notable feature of international tourism, accordingto a WTO Press Release (Monte Carlo, Monaco, 2004), is that thereis a gradual shift in the preference for destinations from Europe andNorth America to East and South Asia. The Asia-Pacific regionconsists of about 50 countries, most of them being developingeconomies. In many parts of this region, the infrastructure is poor.At the same time, the region contains exemplary countries likeSingapore. An average tourist’s out-of-room expenditure is barely$40 a day in India, while in Singapore, it is $250. While it is knownanecdotally that Singapore offers better infrastructure, we under-took this study to systematically measure and prioritize thecomponents of service delivery that cause dissatisfaction to touristsvisiting India. The main site for the study is Chennai, one of theimportant tourist destinations within India. In a presentation madeby SIHRA, an association responsible for promoting tourism andhospitality [33], Chennai is identified as the most important hubgranting access to many other tourism-related activities in the stateof Tamil Nadu, e.g., temples such as the one in the town of Tir-uvannamalai, beach resorts such as Kanya Kumari and wildlifesanctuaries such as Mudumalai. In the same presentation, thetourism industry is forecasted to grow at a robust 4.2% per annum.

We now proceed to the conceptual framework which is outlinedin Fig. 1. In this framework, the actions (or inaction) of a largenumber of stakeholders influence a number of environmentalvariables, either tangible or intangible, that directly create a tour-ist’s experience. At any given destination, we could have a large setS of environmental variables that distort a tourist’s experience.Usually, a small subset S0 of S will prove particularly relevant ata given locale. The environmental variables combine to createa tourist’s experience and when tourists leave a destination, theyeither turn into advocates (or promoters) of a destination, or theyturn into detractors. Our measurement framework focuses onidentifying key environmental variables that create the greatestgaps between promoters and detractors. We also benchmark theperformance of the chosen destination with an ‘‘idealized’’ peer, todevelop standards of performance for the variables that create thebiggest gaps between promoters and detractors. The results of suchperiodic measurement are to be shared at a cross-functionalstakeholder forum, where collaborative decisions can be made toguide resource allocation. We emphasize that the measurement

should be executed iteratively and validated periodically withtracking studies.

2.4. Comparison of the proposed framework with other decision-making paradigms in the public sector

The principal point of difference with existing decision-makingparadigms is that we attempt to synthesize and articulate theoverall experience of a large number of customers of a system viaan empirical, survey-based measurement. Our methodology maytherefore be viewed as a framework that exclusively expresses thecustomer’s viewpoint vs. that of other stakeholders such as policymakers. However, it remains important for all stakeholders tounderstand the customer’s perspective prior to committingresources to major policy decisions. The remainder of this sectioncompares the proposed framework with well-known public sectorresource allocation paradigms.

2.4.1. Input–output analysisCorrea and Parker [30] have applied input–output analysis to

the management of a hospital. The same technique is also prevalentin the analysis of many economic systems, e.g., municipal govern-ments, public libraries and even sectors of the economy such asagriculture. The conceptual basis for input–output analysis isa system consisting of several interdependent internal componentsopen to an external environment, e.g., in the study of an economicsector, consumers, investors, governments and foreign countriescould constitute the external components, while labor, capital andimports could be considered the primary inputs. Our goal in thispaper is to simply focus on the improvement of the customerexperience at a specified tourist destination and not to developa macro-economic input–output model of the tourism sector asa whole. Moreover, in the context of tourism, since several inde-pendently operating agencies (e.g. the police force and immigrationdepartments) affect the overall experience, accurate stipulation ofthe interdependencies for an input–output model might provedifficult.

2.4.2. Analytic hierarchy processVargas [14] explains the AHP approach and provides an over-

view. The application of AHP however requires structuring anoverall goal, with a clear delineation of criteria and sub-criteria thataffect the ultimate goal. The relative importance of various criteriamay be elucidated by asking decision makers to stipulate reason-able weights. This process entails a large number of pair-wisecomparisons. Moreover, if there is no objective basis for weightassignment, decision makers may not agree as to the relativeweights for various criteria. Our work may be viewed as a modulethat needs to be executed well before a highly structured meth-odology like the AHP can be applied. The measurement frameworkpresented in this paper is useful at a very early stage of problemidentification. At the time that our methodology is applied, eventhe criteria and sub-criteria that may eventually form inputs to anAHP process maybe far from clear. We also do not attempt to assignany relative weights to the various environmental variables Sinfluencing customer experience, but merely rank them in order ofimportance.

2.4.3. Data envelopment analysisData Envelopment Analysis (DEA) compares organizational

units for their relative efficiencies, e.g., various public schools ina state. If a given organizational unit is capable of producinga specified output, then other organizational units would be able todo the same if they were to operate efficiently (Ramanathan [31]).DEA is an effective approach when estimating the relative

Stakeholder,e.g.,

TransportationInfrastructure

Stakeholder,e.g., Immigration

Procedures

Stakeholder,e.g., Law

Enforcement

Stakeholder,e.g., Destination

Manager

EnvironmentalVariable,

e.g., Road Conditions

EnvironmentalVariable,

e.g., Speed

EnvironmentalVariable,

e.g., SafetyPerceptions

EnvironmentalVariable,

e.g., Availability ofSigns

CustomerExperience

PeriodicExperience

Measurement &Audit,Peer

Benchmarks

Customerbecomes aPromoter

Customerbecomes aDetractor

Stakeholder Forum for Collaboration & Resource Allocation to Improve

Customer Experience

GapAnalysistoIdentifySignificantDrivers

Universal Set S of AllEnvironmental Variables that Influence Customer Experience

Fig. 1. A conceptual framework for stakeholder collaboration to measure and improve customer experience.

R. Gopalan, B. Narayan / Socio-Economic Planning Sciences 44 (2010) 100–112 103

efficiency of a Decision Making Unit (DMU), but it cannot be used todetermine the absolute efficiency of the unit. In the context oftourism, DEA may be potentially applied to compare the efficiencyof agencies (at multiple destinations) whose responsibility it is topromote tourism. In our work, we focus on analyzing the customerexperience at a single destination. Moreover, an important chal-lenge in the management of tourism is that the customer experi-ence is modulated my multiple stakeholders or decision-makingunits, even within a single destination.

2.4.4. Multi-criteria optimization and goal programmingMulti-objective optimization (Steur [32]) considers two or more

conflicting objectives together, subject to a common set ofconstraints. In the context of multi-criteria decision-making, deci-sion makers look for pareto-optimal solutions rather than a single‘‘best’’ solution that optimizes all objective functions. Goalprogramming is a branch of multi-objective optimization wheresome or all of the objectives can be treated as constraints. Whenconsidering the customer experience in tourism, it is difficult toeven express an objective function (and a corresponding feasibleregion) numerically in terms of decision variables. While multiplecriteria do affect the customer’s experience, it may prove a difficulttask to translate them into a multi-objective optimization model.Our paper adopts a different frame of reference and may be viewedas a piece of empirical research, where customer opinions aresolicited through depth interviews and survey instruments. Whileit is data intensive, the ultimate goal is to focus on obtainingdescriptive insights into a tourist’s environment, and not onprescriptive model building which is the focus of most multi-criteriaoptimization frameworks.

3. Research methodology, data collection and results

Fig. 2 provides a flow diagram describing the measurementframework that was implemented. There are four phases in the

measurement framework: (i) qualitative research to identify thecritical drivers of customer experience (the set S0) (ii) questionnairedevelopment and Promoter–detractor Gap Analysis (PGA) to identifythe key environmental variables (elements of S0) that create thebiggest experience gaps between promoters and detractors (iii)perceptual mapping to benchmark the destination with an ideal-ized peer and (iv) an assessment of individual tourists’ character-istics that could influence perception of the experience.

3.1. Phase I: qualitative research

The purpose of the qualitative research phase is to (i) enumerateall dimensions of service delivery that affect the overall serviceexperience (the set S) and (ii) to further refine the set S to a muchsmaller subset S0 of critical drivers of satisfaction at the chosendestination. Qualitative research is interpretive and draws upona whole slew of methods such as focus groups, in-depth interviews,case-based research and observational studies (Carson et al. [3]).Our qualitative research phase consisted only of in-depth inter-views, conducted with the help of an interview guideline. Weconducted in-depth interviews with twelve people. Five differenttypes of respondents were contacted during this phase: govern-ment officials in the department of tourism, tour operators, resortor hotel managers, tourists themselves and academicians.

Griffin and Hauser [11] have pointed out that the process ofinterviewing nine respondents in-depth for 1 h (9 person hours) isable to uncover 90% of customer needs in service delivery. Toachieve the same depth of understanding with focus groups, wewould require six 8-person focus groups, each lasting 2 h (48people and 96 person hours). Moreover, in-depth interviewing is aninexpensive research technique, and for this study, both theresearchers conducted the interviews themselves. However, onelimitation of our study is that we documented the interview notesand analyzed them manually, even though software is available tocode data from qualitative interviews (Weitzman and Miles [34]).

PHASE I: Qualitative Research

• Use depth interviews to identify all service dimensions S• Reduce to a manageable subset S’ of service dimensions for the purpose of measurement

PHASE II: Promoter-Detractor Gap Analysis (PGA)• Develop questionnaire to measure service performance for the set S’

• Execute PGA to identify areas with the largest service gaps between promoters & detractors• Explore specific service attributes of importance with the Probe and Learn sub-phase

PHASE III: Peer Benchmarking with a Perceptual Map• Develop a perceptual map based on a factor analysis of the data• Plot the performance of the destination vs. an “idealized” peer on the map. Compare service delivery to idealized peer

PHASE IV: Individual Respondent Effect

• Influence of Individual Respondent Characteristics on Perception of Service Experience

Repeat

Measurement

with

Periodic

Audits

Fig. 2. The four-phase service quality measurement process.

R. Gopalan, B. Narayan / Socio-Economic Planning Sciences 44 (2010) 100–112104

We adopted a convergent interview technique, whereby theinterview guide for successive respondents was progressivelyrefined based upon what was learnt from prior interviews. Ifneeded, the interviewers probed respondents on specific topics. Wealso performed a content analysis of the data, which is essentiallycoding all of the respondent’s comments into definitive categoriesfor further analysis.

Initially, 25 separate categories or drivers of service deliverywere identified. We selected eleven out of the 25 drivers as havinggenuine impact on the customer perception of service delivery atthe chosen destination, based upon the (i) strength of emphasisduring interviews associated with these particular drivers and (ii)consistent agreement as to these categories as determinants of theservice experience, based on the perspective of multiple respon-dents. We note here that one limitation of our methodology is thatthere is a degree of subjective judgment in this reduction from 25 to11 drivers. The 11 categories or drivers of satisfaction were immi-gration procedures at ports, cleanliness of the destination, condi-tion of roads, safety of the destination, traffic congestion on roads,language barriers, friendliness of local people, freedom to designand deviate from a fixed itinerary, services offered by the touroperator, comfort of stay at the hotel and the enjoyment derivedfrom sight seeing.

We noted earlier, in our review of the customer satisfactionliterature, that the importance-performance analysis paradigmattempts to ascribe different weights to various service qualitydrivers. When viewed in the light of this paradigm, our qualitativeresearch phase can be interpreted as a funneling mechanism thatfirst identifies a universal set S of service dimensions that affect theservice experience in any way, followed by selection of a muchsmaller subset S0 of service delivery dimensions that are moreimportant (i.e. have relatively larger weights) at the specific locale

considered. Usually, the set S is invariant across all tourist desti-nations, but the set S0 could vary by region, being the result ofvarious influences, including local cultural norms and preferences.In our measurement framework, once all elements of the set S’ havebeen identified, we do not differentiate between the individualelements of S0 in terms of importance.

We also note that there are many other degrees of freedom forthe researcher even in the qualitative research phase. In particular,the frame of reference is important. In this study, we used the cityof Chennai as the frame of reference and benchmarked Singapore’sperformance based upon this frame of reference. An alternateresearch design might have included the qualitative research phaseat both Singapore (the idealized peer) and Chennai, uncoveringsome important drivers of satisfaction at Singapore which may notbe relevant in Chennai at all. Since it was more important for us tounderstand the drivers of satisfaction locally, we limited our qual-itative research phase to the city of Chennai.

3.2. Phase II: quantitative survey, promoter–detractor gap analysis(PGA) and the probe and learn sub-phase

We conducted a quantitative survey in Phase II, to accuratelymeasure the destination’s performance on the 11 metrics identifiedfrom Phase I.

3.2.1. Survey instrument, data collection methodology andreliability tests

The questionnaire consisted of 5 sections, pertaining to detailsabout the tour, expectation ratings for the key drivers, performanceratings for the key drivers, satisfaction and loyalty ratings andfinally, a demographic profile of the respondent. All measurementswere based upon a 5-point Likert scale. While we directly asked the

R. Gopalan, B. Narayan / Socio-Economic Planning Sciences 44 (2010) 100–112 105

respondents to indicate their level of satisfaction with the experi-ence, we also asked them if they would recommend the destinationto their friends.

Reichheld [26] suggests that it is better to measure whethera customer is willing to recommend a product, rather than asking ifthe customer is simply satisfied with the product. He observes thatin most industries, the percentage of a firm’s customers who werewilling to ‘recommend’ or advocate a product correlated directlywith differences in growth rates among competitors. The propen-sity of a customer to ‘Recommend’ is perhaps the strongest indi-cator of customer loyalty (Reichheld [26]). Following this approach,we have used the propensity to ‘Recommend’ the destination as themain dependent variable in measuring customer satisfaction.However, we also collected information on both overall satisfactionand repurchase intentions, i.e., if the tourist would consider visitingthe destination again. We validated the questionnaire using theface validity method, whereby we sought the opinion of fiveexperts. The questionnaire was modified based on their feedback.We also tested the reliability of our scale using the internalconsistency method. The value of Cronbach’s alpha for the scale is0.755, which is higher than the recommended threshold of 0.7(Nunnally [19]).

The target population consisted of tourists, both domestic andinbound, visiting Chennai. Sampling units were individuals. In thecase of couples, families or groups visiting Chennai, every one in thegroup was a prospective respondent, since we were assessingindividual perceptions, not group perceptions. We chose threetourist destinations in Chennai to carry out the data collection (Thiscould be a limitation of the study. Even though the study attemptsto measure the entire tourist experience, specific respondents stillneed to be approached at specific locales, biasing the constitution ofthe respondent pool). Hence, our sampling frame consisted ofdomestic and inbound tourists visiting these three locations. One ofthe researchers administered the interviews, assisted by threegraduate students. We collected data from 160 respondents, viaconvenience sampling, using the mall-intercept technique. Oursample consisted of 128 domestic tourists and 32 inbound (inter-national) tourists.

The mean performance ratings for the majority of drivers wereabove 3 (with 3 corresponding to ‘Neutral’). This is an indicationthat respondents were more or less satisfied with the performanceon the key drivers of satisfaction. We also found that the perfor-mance gap scores (¼Expected Performance�Actual Performance)were not reflective of the overall satisfaction level of the respon-dents. Hence, we did not use the expectancy-disconfirmationframework of Oliver [20] and we focused purely on performance-based measures for service quality.

3.2.2. PGA: promoter–detractor gap analysisReichheld [26] uses a 10-point scale to divide customers into

three logical clusters based on their propensity to recommenda product. Promoters are the customers with the highest rates ofrepurchase and referral, who provide ratings of nine or ten on thescale; ‘‘Passively satisfied’’ are the customers who provide a ratingof seven or eight and ‘‘Detractors’’ score a product from zero to six.Reichheld introduced the concept of a Net-Promoter Primer Score,which is the difference between the percentage of promoters andthe percentage of detractors and he emphasizes that for companiesaiming to achieve world-class loyalty, this score has to be improved.Ideally, the ideal Net-Promoter Primer score should be above 75%.

The goal of our Promoter–Detractor Analysis phase is to leverageReichheld’s study of customer advocacy and develop a scheme toprioritize policy levers for action. However, our work consistentlyused a 5-point Likert scale to measure satisfaction and advocacyand is therefore significantly different from the scale used by

Reichheld [26]. In our work, we classify respondents into ‘Recom-menders or Promoters’ (rating of 4 or 5 on the item ‘Will yourecommend the destination to others?’) and ‘Non-Recommendersor Detractors’ (rating of 1, 2 or 3). Every respondent must be eithera promoter or a detractor and their status provides a synopsis oftheir overall service experience. There were 100 (62.5%) promotersand 60 (37.5%) detractors in our sample. Hence, the Net-PromoterPrimer score for this destination is only 25%.

In the qualitative research phase, the set S0 consisted of 11important policy levers, e.g., improving the condition of roads.Using Promoter–Detractor Gap Analysis (Fig. 3), we were able toidentify the primary levers in S0 that can be used to convertdetractors into promoters. For a service dimension to proveimportant in influencing advocacy, there must not only be a strongcorrelation between the performance rating on the servicedimension and the advocacy rating, but promoters must providea much higher average rating than detractors (for this servicedimension) in terms of absolute value.

To facilitate further discussion, we define the sets A, B and C asbelow:

A¼ {Service dimensions: Both promoters and detractors ratethe performance on this dimension as ‘high’}B¼ {Service dimensions: Promoters rate the performance onthis dimension as ‘high’, but detractors rate the performance onthis dimension as ‘low’}C¼ {Service dimensions: Both promoters and detractors rate theperformance on this dimension as ‘low’}

We note that the elements in the sets A, B and C will very muchdepend upon the assigned threshold ‘performance target’ whichdelineates a rating as either ‘high’ or ‘low’. The decision maker(s)must therefore use considerable indigenous judgment to performthis classification. In Fig. 3, the link between A_d and A_p indicatesthat for service dimension A, both promoters and detractorsprovided high ratings, a situation that might satisfy policy makers.Likewise, the link between C_d and C_p indicates that bothpromoters and detractors provided low ratings for the servicedimension C. This might mean that either this dimension did notinfluence advocacy at all, or that the service environment is simplyunable to improve this dimension in any way. Ideally, we arelooking for policy levers of the type linking B_d to B_p, i.e.,promoters provide a high rating, but detractors provide a low ratingfor B. The suggested prioritization scheme for policy makers is thatthey should first work on improving service dimensions in the setB, to accrue more consistent performance ratings from allcustomers and benefit from the low-hanging fruit. Then, dimen-sions in the set C should be closely examined to understand if anydrastic improvements can be designed to improve the serviceexperience on these dimensions. Finally, policy makers can focus onattributes in the set A, to improve upon the good ratings evenfurther.

To help classify each service dimension into one of the threetypologies (i.e. A, B or C), we also performed a univariate analysis asfollows. We identified those drivers for which there is a statisticallysignificant difference with respect to the average performanceratings provided by promoters and detractors. The results for thetests on all the key drivers are provided in Table 1. We identified siximportant levers, which can be used for converting detractors topromoters. The ‘Language’ barrier is the attribute which detractorsfind the most unsatisfactory, in comparison to promoters. The nexthighest difference in means occurs for cleanliness of the destina-tion. If we define extrinsic policy levers as those that can only beimproved by the government and intrinsic policy levers as those thatcan be improved by tour operators or destination managers, we

HighAverageRating

LowAverageRating

Detractors(Service Critics)

Promoters(Service Advocates)

A_d

A_p

B _d

C _p

Target performance desiredfor High vs. Lowclassification

Improvementsare feasible whenpromoters rate a service dimension as highand detractors rate thesame service dimension aslow.

Improvementsmay be feasiblewhen both promotersand detractors rate aservice dimension as low.

Both promoters and detractors rate theservice dimension as high.

B _p

C _d

Fig. 3. Promoter–detractor gap analysis (PGA).

R. Gopalan, B. Narayan / Socio-Economic Planning Sciences 44 (2010) 100–112106

discover three extrinsic and three intrinsic conversion levers thatare critical at this destination.

The critical extrinsic conversion levers are as follows.

1. Language (Highest difference in means)2. Cleanliness (2nd highest difference in means)3. Condition of roads (4th highest difference in means)

The critical intrinsic conversion levers are as follows.

1. Services of tour operator (3rd highest difference in means)2. Site seeing (5th highest difference in means)3. Choice of itinerary (6th highest difference in means)

Moreover, at this destination, the Promoter–Detractor analysisindicates that the following intrinsic and extrinsic components areeither less critical from the standpoint of improving advocacy, orrated poorly by both promoters and detractors (e.g., trafficcongestion).

1. Comfort of stay (intrinsic)2. Friendliness of local people (extrinsic)3. Safety (extrinsic)4. Congestion on roads (extrinsic)

To summarize, Promoter–Detractor Gap Analysis is a simple toolto evaluate the impact of each service dimension in the set S0 onconsumer advocacy and to provide a prioritization of all policylevers in the set. If such information can be shared at a suitable

Table 1Significance test for difference of means between promoter and detractor ratings.

Service quality drivers Difference betweenpromoters anddetractors

t-Value Significance(2-tailed)

Language 0.832 3.738 0.000Cleanliness 0.809 4.621 0.000Service of tour operator 0.633 3.322 0.002Condition of roads 0.585 3.553 0.001Site seeing 0.577 4.431 0.000Choice of itinerary 0.442 2.882 0.005Friendliness of local people 0.218 1.585 0.115Congestion of roads 0.203 1.061 0.291Safety 0.187 1.568 0.119Comfort of stay 0.120 0.873 0.384

stakeholder forum (Fig. 1), it will provide an objective starting pointfor the contentious discussion on resource allocation. The roles ofindividual stakeholders in modulating the overall customer expe-rience also becomes clear as a result of the gap analysis.

3.2.3. Probe and learn: exploring the impact of language onadvocacy

Probe and learn is a process used by some firms to manage theirnew product development projects (Lynn et al. [16]). In thisscheme, firms may rapidly develop a product prototype andexplore marketing of this product in a limited way, to see if it will besuccessful. If not, another prototype may be developed, or yetanother market explored. The underlying theme is one of rapidexperimentation, with poor avenues being discarded quickly andpromising avenues explored in greater detail.

Once the PGA phase is complete, it may be appropriate to usethe probe & learn methodology, by studying some significantservice dimensions in greater detail, to see whether any additionalinsights for managing the public service can be obtained. This sub-phase is similar to the measurement and management of criticalvalue-adding processes within a Six Sigma framework, as outlinedby Reidenbach and Goeke [27].

Since language leads to the most significant difference betweenPromoters and Detractors, we probed this attribute in-depth.Tourists interact with tour operators, hoteliers, cab drivers, shopowners, policemen and local people. While English is widelyspoken in Chennai, the comfort level of cab drivers, shop owners,policemen and local people with the English language is still rela-tively low.

We further classified domestic tourists from four southern states(Tamil Nadu, Kerala, Karnataka and Andhra Pradesh) as ‘South-erners’ and the remaining domestic tourists as ‘Northerners’. Manynortherners are also not familiar with Tamil, the vernacular spokenlanguage in Chennai. A section of northerners are not conversant inEnglish as well. At the same time, Hindi, which is the language thatmany North Indians speak, is not a popular language in Chennai.Most residents of this city speak either Tamil or English, but veryrarely are they fluent in Hindi.

We made an assumption that all southerners have some degreeof comfort with the Tamil language, which the northerners do notpossess. We also assumed that international inbound tourists donot speak Tamil. Based on these assumptions, we partitioned ourdata set into ‘South Indians’, ‘North Indians’ and ‘Inbound Tourists’,thereby defining a new variable termed the ‘Origin’ of therespondents.

Table 2Factor analysis of service quality drivers: the rotated component matrix.

Service quality drivers Factor 1 Factor 2 Factor 3

Condition of roads 0.776 0.134 0.142Site seeing 0.733 0.133 - 0.005Safety 0.656 0.180 - 0.007Cleanliness 0.592 0.373 0.331Choice of itinerary 0.522 �0.215 �0.387Service of tour operator 0.005 0.753 0.009Friendliness of local people 0.155 0.579 0.009Comfort of stay 0.263 0.569 �0.321Congestion of roads 0.213 �0.298 0.698Language �0.104 0.243 0.652

R. Gopalan, B. Narayan / Socio-Economic Planning Sciences 44 (2010) 100–112 107

We classified all respondents who gave a rating of 4 or 5 for thestatement, ‘‘Language barrier was not a problem’’ as ‘‘Comfortablewith language’’ and all others as ‘‘Uncomfortable with language’’.We performed a cross-tabulation of ‘Origin’ vs. ‘Comfort withlanguage’, for Promoters and Detractors separately. We found thatin both the ‘Promoter’ and ‘Detractor’ categories, the percentage ofNorth Indians who were not comfortable with the language wasmore than 50% (53.3% and 77.8%, respectively). Only 32.4% of SouthIndians in the ‘Promoter’ category and 71% of South Indians in the‘Detractor’ category were uncomfortable with language. We foundthe association between ‘Origin’ and ‘Comfort with language’significant at the 10% significance level, in both the categories.Inbound (international) tourists do not seem to have the sameproblem with language that northerners do, possibly because theyusually have a better support system. A possible policy outcomebased upon this type of probe and learn analysis could be to providespecial language-based support to tourists based upon their origin,e.g., provide interpreters at key venues.

3.3. Phase III: benchmarking with an ‘‘idealized’’ peer

The third phase of the methodology consists of two sub-components (i) first, performing a factor analysis of the performancedata to identify key dimensions of service quality (i.e., summarizethe information contained in the set S0 into a much smaller numberof factors) (ii) derive a perceptual map based upon the factor anal-ysis, to benchmark the destination with an ‘‘idealized’’ peer.

3.3.1. Factor analysis and perceptual mappingA perceptual map is a tool used by marketing managers to

position a brand. In developing this map, managers ask a sample ofcustomers to rate their own brand and several competing brands ona set of attribute dimensions. For instance, if the product is tooth-paste, customers may be asked to rate several toothpaste brands onattributes such as price, ability to whiten teeth, attractiveness of thepackaging and so on. Usually any brand will possess a very large setof attributes based upon which customers could evaluate the brand.Therefore, a factor analysis of the attribute ratings is performed toreduce the attributes to a smaller, more manageable number ofdimensions. The underlying factors provide brands with a mecha-nism to position themselves vis-a-vis each other, e.g., if price andquality emerge as the principal factors, one brand may positionitself as high-price and high quality, while another brand mayadopt the position of a low-cost commodity product. Usually, ina brand perceptual map, a manager will try to adopt a position forhis brand that is significantly distinct from other brands, i.e., build‘distance’ for his brand from other brands on the perceptual map.

The perceptual mapping process can be used in the bench-marking of public services as follows. The attribute dimensions thatcustomers use to rate a public service are derived in the qualitativeresearch phase and quantitative ratings for performance areobtained during the second phase. We carry out a factor analysis ofthe performance ratings on the satisfaction drivers to summarizethe attributes into a smaller number of meaningful dimensions. Wecan then position the destination of interest (e.g., Chennai) ona perceptual map and compare this position with ratings obtainedfor an idealized peer. For this study, we chose Singapore to be thebenchmark. The main distinction to be noted with the perceptualmapping process for public services is that policy makers can usethe learning from the map to ‘close’ the gap with the idealized peer,rather than adopt a distinctive position.

In our factor analysis of the attribute performance data, wedropped ‘ease of immigration procedures’ from the list of keydrivers, as only 20% of the sample consisted of inbound tourists. Thefactor analysis yielded three factors (refer Table 2). The first factor

(F1) is essentially an extrinsic factor as it is a combination of threeextrinsic components (condition of roads, safety, cleanliness) andtwo intrinsic components (sight seeing, choice of itinerary). Thesecond factor (F2) is essentially an intrinsic factor as it comprises ofone extrinsic component (friendliness of local people) and twointrinsic components (service of tour operator, comfort of stay). Thethird factor (F3) is exclusively an extrinsic factor and includes twoextrinsic components (congestion of roads, language). As recom-mended in Malhotra [35], we evaluated the appropriateness ofusing factor analysis to study the correlation structure of theperformance ratings by using (i) the KMO measure of samplingadequacy which for our sample was 0.689 (any value above 0.5 isacceptable as per reference [35]) and (ii) Bartlett’s test of sphericitywhich yielded a chi-square value of 139.496, with 45 degrees offreedom, and a p-value of 0.000 (significant at the 5% level). Thesetwo test statistics together provide strong support to indicate thatthe factors in this model do represent a meaningful dimensionalreduction of the performance data.

We then computed the factor scores for the 160 respondentsand carried out a multiple regression analysis with the three factorsas the independent variables and propensity to recommend thedestination as the dependent variable. The regression model wasfound to be significant at the 5% level, with an R2 value of 17.2%. Allthe three factors have a significant influence on ‘Recommend’(Table 3). Of the three factors, the first one (F1), with a predomi-nance of extrinsic components, is the most significant one. Webenchmarked the performance of Chennai vs. Singapore by usinga perceptual map consisting of average scores for intrinsic andextrinsic service dimensions as the two principal axes (see Figs. 4and 5).

3.3.2. Benchmarking with SingaporeWe also conducted a second survey among tourists who visited

Singapore and obtained a sample consisting of 40 respondents(Fig. 4). A statistically significant difference exists in the perfor-mance of the two destinations with respect to almost all extrinsiccomponents. Tourists who visited Singapore are much more satis-fied with ‘Immigration procedures’, ‘Cleanliness’, ‘Condition ofroads’, ‘Language barrier’, ‘Congestion of roads’ and ‘Safety of thedestination’. Singapore tourists are also more satisfied (relative toChennai tourists) with respect to certain intrinsic components,such as ‘Comfort of stay’, ‘Service of tour operator’ and ‘Sight seeing’(significant at the 0.05 level). There is no significant differencebetween Singapore and Chennai tourists (at the 5% level) for thedrivers ‘choice of itinerary’ and ‘friendliness of local people’. Even‘site seeing’ does not represent a driver with a significant differenceif the significance level is dropped to 1%.

If rank ordered by the p-values, the first six drivers, for whichthere is the most significant difference between Chennai andSingapore, are all extrinsic components. A tourist visiting Singaporeis therefore relatively much more satisfied with the extrinsic

Table 3Linear regression to validate factors’ impact on advocacy.

Variables inthe model

Standardizedcoefficients (Beta)

t-Value Significance

(Constant) 3.756 50.987 0.000Factor 1 0.330 4.567 0.000Factor 2 0.241 3.346 0.001Factor 3 0.144 1.998 0.047

R. Gopalan, B. Narayan / Socio-Economic Planning Sciences 44 (2010) 100–112108

components. When we rank ordered the eleven key drivers inChennai based on the reported performance, we found that onlyone among the top four drivers is extrinsic (Site seeing, Choice ofitinerary, Safety (extrinsic), Comfort of stay; in descending order).

Moreover, while 97.5% of Singapore tourists are satisfied, only82.5% of Chennai tourists are satisfied with the overall serviceexperience. The percentage of Singapore tourists who are willing to‘recommend’ is 90%, versus 62.5% in Chennai. The percentage ofSingapore tourists willing to revisit is 70%, whereas, in Chennai, thepercentage is only 38.8%. We also tested for the significance of thedifferences in these proportions and found them to be significant atthe 5% level.

A competitive perceptual map provides more clarity for thiscomparison. We plotted the mean values of extrinsic and intrinsiccomponents on a two-dimensional graph (Fig. 5). We also plottedthe ratings provided by promoters and detractors at these twodestinations on this map. The perceptual map clearly makes thepoint that the gap between Chennai and Singapore is much largerwith respect to extrinsic components, i.e., Chennai has the inherentpotential to be competitive with Singapore as a destination ifevaluated purely from the perspective of the attractions offered.However, the poor infrastructure and associated environmentalconsiderations induce tourists to advocate this destination withless enthusiasm.

0

1

2

3

4

5

6

Immigr

ation

(*)

Cleanli

ness

Condit

ion of

road

s

Lang

uage

Conge

stion

of ro

ads

Saf

C

ENVIRONM

Averag

e R

atin

g o

n a 5-p

oin

t L

ikert S

cale

Fig. 4. A comparison of ratings provided by tourists in Chennai and Singapore. *For the variFor all remaining drivers, n¼ 160 (Chennai) and n¼ 40(Singapore). All differences in ratings‘Choice of itinerary’ and ‘Friendliness of local people’, which were not significant.

The perceptual mapping technique can be made part of anongoing continuous improvement process. A longitudinal view,tracking the ‘distance’ between Chennai and Singapore over time,for both intrinsic and extrinsic components, can provide insights topolicy makers on how best to close the gap. Stakeholders can alsoreplicate the perceptual map for specific pairs of service dimen-sions (rather than factor scores or extrinsic component averages) toidentify and implement tactical ‘improvement targets’ for certainattributes.

We note here that researchers in tourism have studied inter-national destinations (e.g., Singapore (Pawitra and Tan [22]), Turkey(Yuksel and Yuksel [29])) and also documented cross-culturalaffects on the perceptions formed by tourists (e.g., Kozak [15],Chaudhary [5]). In particular, we must point out that whencomparing the results from the two surveys at Chennai andSingapore, we have to be mindful of the differences created by thedifferent cultures of the respondents at these two cities. At leastsome of the difference may come from a systematic tendency ofrespondents in one country (vs. the other) to interpret Likert scalesdifferently, e.g., many Asian cultures are known to avoid conflictand they may have a tendency to provide ‘‘grade inflated’’ scores,especially during a personal interview.

3.4. Influence of individual respondent characteristics onperception of service experience

The objective of this terminal phase was to examine whetherthe perception of the service experience varied based upon specificcharacteristics of the individual experiencing the service. The probe& learn sub-phase has already provided a preamble to such analysiswhen the ‘origin’ of tourists was used to probe the servicedimension of ‘language barriers’. In this fourth phase, we alsostudied the influence of many other demographic characteristics

ety

omfor

t of s

tay

Service

of to

ur op

erator

Site se

eing

Choice

of iti

nerar

y

Friend

lines

s of lo

cal p

eople

ENTAL VARIABLE

ChennaiSingapore

able ‘immigration’ alone, the sample size was n¼ 32 (Chennai) and n¼ 40 (Singapore).between Chennai and Singapore were statistically significant at the 5% level, except for

Chennai overall

Singapore overall

Ch.Promoters

Ch.Detractors

Si.PromotersSi.Detractors

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

X-axis: Intrinsic components (mean value)

Y-axis: Extrinsic components (mean value)

Fig. 5. Competitive perceptual map for benchmarking Chennai vs. Singapore.

R. Gopalan, B. Narayan / Socio-Economic Planning Sciences 44 (2010) 100–112 109

such as age, gender, education, and occupation. The individual’s tripcharacteristics were also considered, e.g., the duration of stay,number of prior visits to the same destination, reasons for the visit,group size, and the nature of the entity managing the tour (e.g.,whether the trip was organized by the tourist herself, her ‘friendsand relatives’, a tour operator, a hotelier or others).

We discovered noticeable patterns only with respect to age,duration of stay and the number of prior visits. We found significantdifferences in the propensity to recommend between respondentswho belonged to the ‘<35’ age group and those who belonged tothe ‘>35’ age group (Fig. 6). Hence, we further examined thedifference in the average ratings for the key drivers of satisfactionfor these two age groups. Fig. 7 summarizes our findings. Statisti-cally significant differences exist only with respect to three drivers– safety, cleanliness and comfort of stay, and these three dimen-sions must be managed carefully for younger tourists.

There also seems to be a ‘honeymoon effect’ on recommenda-tion levels with respect to the number of prior visits and duration ofstay. The propensity to recommend initially increases with the firstfew visits. It reaches a maximum for a second or third time visitor

0

20

40

60

80

100

120

AGE < 35 AGE > 35Age Category

Pro

po

rtio

n o

f P

ro

mo

ters/D

etracto

rs

PromotersDetractors

Fig. 6. Influence of individual tourist’s age on advocacy.

and then reduces thereafter. The percentage of first-time visitorswilling to recommend is 64.3%, the percentage of second or thirdtime visitors willing to recommend is 76.2% and the percentage offourth (or higher) time visitors willing to recommend is only 51.6%.This association is significant at the 5% level. We note here thatPizam and Milman [24] have also predicted satisfaction for first-time visitors to a destination.

Similarly, the propensity to recommend initially increases withthe duration of stay and is a maximum when a tourist stays at thisdestination for two to three weeks. (The percentage of visitorswho have stayed for one week or less and willing to recommendis 61.1%, the percentage of visitors who have stayed for two orthree weeks and willing to recommend is 78.6%, the percentage ofvisitors who have stayed for one month or more and willing torecommend is 64.3%.) But this effect did not prove to be statis-tically significant.

4. Policy implications, limitations of the current frameworkand future research directions

4.1. Summary and policy implications

We have developed a four-phase measurement framework thatcan be used to assess the key drivers of satisfaction with any serviceexperience and illustrated the methodology using tourism as anexample. In the first phase, we derived eleven drivers of tourists’satisfaction using a qualitative research methodology. The firstchallenge for public policy is that management of each of thesedrivers rests with different entities, e.g., the condition of roads ismanaged by the public works department, while language-relatedissues are often managed by local destination managers. It is veryevident that the key drivers of satisfaction in tourism (at thisdestination) cannot be controlled and managed by a single entityand that inter-agency cooperation is of paramount importance.Specifically, an integrated and interdisciplinary network for infor-mation sharing and cross-functional collaboration, consisting of thedepartments of transport, police, public works, tour operators,hoteliers and destination managers must be developed to managetourism effectively (see Fig. 1).

We also classified the key drivers of the service experience intoextrinsic components that can be influenced by the government andintrinsic components that can be controlled locally by tour opera-tors and destination managers. Using Promoter–Detractor GapAnalysis in Phase II, we prioritized intrinsic and extrinsic

• Condition of roads• Choice of itinerary • Site seeing

• Congestion of roads

All Tourists

Younger (Age < 35) tourists are moreconcerned about these dimensions.

Older (Age > 35) tourists are moreconcerned about this dimension.

Age does not influenceperception of these dimensions

• Friendliness of local people• Service of tour operator • Language• Safety• Cleanliness• Comfort of stay

Statisticallysignificantdifferencesbetween < 35 & > 35

Fig. 7. Influence of tourist’s age on perceptions of service dimensions. Note: ‘Ease of immigration procedures’ has not been categorized as it pertains only to inbound tourists(international arrivals).

R. Gopalan, B. Narayan / Socio-Economic Planning Sciences 44 (2010) 100–112110

components for managerial action. We identified three intrinsicand three extrinsic conversion levers which can be used to convertdetractors to promoters. The three important intrinsic conversionlevers (Services of tour operator, Site seeing, Choice of itinerary) canbe improved upon by the tour operator. However, only the localgovernment can control overall cleanliness and the condition ofroads (two of the extrinsic conversion levers). We emphasize thatthese findings are substantive and valid only for the destinationstudied.

The probe and learn sub-phase identified the destinationmanager as yet another important stakeholder who can playa pivotal role in improving the Net-Promoter Primer score. Thebarrier presented by the local language and the quality of site seeingcan be improved upon by the destination manager, e.g., by providingaccess to interpreters or by posting signboards (in appropriatelanguages) at suitable spots. However, international tourists do notseem to have the same problem with language as domestic tourists.This could be a situation very peculiar to India, where residents ofeach state speak a different language. An inbound tourist usually hasa better support system and is also well versed in English, a languagethat is widely spoken in India. In terms of policy, analyzing andcategorizing the tourist population by their ‘place of origin’ is anextremely useful device in the study of customer satisfaction.

In Phase III, benchmarking with Singapore was undertaken. Theeleven drivers, upon exploratory factor analysis, were summarizedinto three factors. We found that the first factor, with a predomi-nance of extrinsic components, has the most significant impact onadvocacy of the destination. The implication of this finding is that,to acquire loyal customers, extrinsic components have to be tightlycontrolled and managed, especially in developing economies. Wecompared Chennai’s performance on the eleven key drivers ofsatisfaction vis-a-vis Singapore. The central finding from thebenchmarking study is that both satisfaction level and propensityto recommend are higher among Singapore tourists vis-a-visChennai tourists. Singapore tourists are relatively more satisfiedwith the extrinsic components, while Chennai tourists are moresatisfied with the intrinsic components. Hence, there seems to bea vital link between tourists’ satisfaction, extrinsic components andoverall customer loyalty. We also summarized this finding ina perceptual map, which clearly illustrates that Chennai needs toimplement programs to close the gap with respect to extrinsiccomponents.

In terms of implementing policy for resource allocation, if desti-nations are able to systematically collect such customer satisfactiondata and share them at a suitable stakeholder forum, it providesa transparent and objective basis for beginning the discussion.

In the final and terminal phase, it was found that the individualdemographics of the tourist and the characteristics of the tour itselfhave an influence on advocacy. Tourists who are 35 years (or older)are more likely to recommend a destination than tourists who areless than 35 years old. The implication of this finding is thatstakeholders may have to device specific strategies to converttourists in the ‘<35’ segment into promoters. There is also evidenceof a ‘honeymoon’ effect, with respect to the number of prior visits tothe same destination. The propensity to recommend a destinationinitially increases with the number of prior visits. A tourist tends toadvocate a destination strongly during his second or third visit,after which advocacy levels seem to decrease. Tour operators arebetter served if they can effectively use second or third time visitorsas links in viral marketing, but beyond a threshold number of visits,tourists may become cloyed with the destination and may not besuitable advocates.

4.2. Limitations of the current study

Customer satisfaction measurement in a service environmentmodulated by multiple stakeholders (and when service is experi-enced by multiple customer segments) presents unique challenges.It is difficult to even list all relevant customer segments (e.g. back-packers vs. high-end tourists) and to identify their service needsclearly. Obtaining a good sampling frame in tourism is also chal-lenging. It is much easier to measure satisfaction with a tangiblematerial product, e.g. a Honda Accord because a sampling frame canbe easily created for customers of this tangible product byapproaching a series of dealerships. It is not so in the case of tourismwhere customers are motivated to visit destinations for vastlydifferent reasons such as recreation, medical tourism or adventure.

Every study pertaining to tourism provides substantive insightsthat are difficult to generalize across multiple destinations. Ourstudy focuses only on two Asia-Pacific destinations and it may notbe appropriate to extrapolate the findings to other tourism contextsdue to cultural effects and other environmental factors. Forinstance, ‘safety’ may be a more important extrinsic component inthe case of a destination like Kashmir, while ‘language’ may still

R. Gopalan, B. Narayan / Socio-Economic Planning Sciences 44 (2010) 100–112 111

represent a very important service dimension in China. The clas-sification of the dimensions of service delivery into intrinsic andextrinsic components, while appropriate at this destination, maynot be applicable to other environments. Moreover, in the deliveryof some public services, certain service attributes may not beclearly defined or even measurable.

Finally, the respondents for this study were interviewed viaa mall-intercept technique and it was not possible to control forthe proportion of respondents within certain strata, e.g., inboundtourists constituted only 20% of the sample. In benchmarking witha peer destination in another country, cultural factors willcertainly come into play and influence the interpretation of allmeasurement scales. Another practical limitation on bench-marking is that while any tourism bureau could easily deploya solid study in its own home turf, it may not be possible to querycustomers at another destination (possibly in another country)with an equal degree of freedom. Due to similar operationalrestrictions, the size of our respondent sample was also limited inSingapore. Finally, the study is descriptive and does not provideguidelines as to the exact amount of resource allocation neededfor each service dimension.

4.3. Future research directions

Future research could explore the interrelationships betweenvarious customer segments and the service attributes or productcharacteristics that are of importance to each segment, e.g., some ofthe following issues.

� How can a policy maker envision, define or derive distinctcustomer segments for a given tourist destination (e.g. low-endbackpackers vs. medical tourists)? How do low-budget back-packers evaluate a service experience when compared to high-end tourists? What is the relative importance of each serviceattribute to each customer segment?� Alternate paradigms to measuring satisfaction and enhancing

the customer experience may also be feasible and must beexplored, e.g., Fornell and Wernerfelt [9] stress the importanceof managing the customer complaint process.� A very important research dimension in tourism is product

design. The cost of the product (in tourism) could vary greatlybased upon tour duration, amenities provided and the valueperception of different customer segments. Customizing theproduct based upon the service perceptions and price sensi-tivity of various customer segments provides a unique andchallenging opportunity. Good product designs can greatlyimprove customer experience.� Policy makers must also note that any customer value map

derived from this methodology may only be stable for a limitedperiod of time. They must address issues relating to periodicrevalidation of the customer value model, and be willing tochange the underlying dimensions of the competitive percep-tual map as customer tastes and preferences evolve.

Our study merely proposes a transparent measurement frame-work to assess tourists’ advocacy level for a destination. Evenassuming that a particular destination was successful in executingthis framework (and creating a forum for stakeholder collabora-tion), it would require a high degree of active involvement from thestakeholders to act upon the results of the study. Resource alloca-tion decisions may still be made on the basis of lobbying or politics,essentially ignoring or discarding the findings from such periodicmeasurements. On a more positive note, we emphasize that themethodology is flexible and potentially applicable to a wide varietyof public sector systems besides tourism.

Acknowledgments

We wish to place on record our deep gratitude to Professor P.Vijayaraghavan, T.T.K. Chair of Marketing, Department of Manage-ment Studies, IIT Madras, for his insightful critiques and sugges-tions. We also thank the referees and editor for a number ofcomments that significantly improved this manuscript.

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[35] Malhotra N. Marketing research, an applied orientation. 3rd ed. India: PearsonEducation; 1999.

R. Gopalan, B. Narayan / Socio-Economic Planning Sciences 44 (2010) 100–112112

Ram Gopalan is an Assistant Professor in the Department of Marketing & Supply ChainManagement, Fox School of Business, Temple University, Philadelphia. He completedhis Bachelor of Technology degree in Mechanical Engineering from the Indian Instituteof Technology Madras, an M.S. in Industrial Engineering from the State University ofNew York, Buffalo and a Ph.D. in Operations Research from Massachusetts Institute ofTechnology, Boston. His research has appeared in Operations Research, Computers andOperations research, Transportation Research E, Journal of the Operational ResearchSociety and a number of other academic journals.

Bindu Narayan holds a Ph.D. Degree from the Department of Management Studies,Indian Institute of Technology Madras. She has a Bachelor’s degree of Technology inElectronics & Telecommunication Engineering from Kerala University, India. She didher Masters in Business Administration, with specialization in marketing, from CochinUniversity of Science & Technology. She has 7 years of experience in industry andacademia. She conducts research in services marketing and customer satisfactionmeasurement. She is currently a senior analyst with Mu-Sigma Corporation in Ban-galore, India.