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Developing zones of tolerance formanaging passenger rail service

qualityRobert Y. Cavana and Lawrence M. Corbett

Victoria Management School, Victoria University of Wellington,Wellington, New Zealand, and

Y.L. (Glenda) LoTranspower New Zealand Ltd, Wellington, New Zealand

Abstract

Purpose The purpose of this article is to develop and empirically test an extension to thethree-column format SERVQUAL instrument to evaluate passenger rail service quality.

Design/methodology/approach This article combines the literatures of service quality and railtransport quality to develop the conceptual framework. Three new transport dimensions (comfort,connection, and convenience) are added to the original five SERVQUAL dimensions (assurance,empathy, reliability, responsiveness, and tangibles). The instrument was tested on a passenger line inWellington, New Zealand. Valid responses to 340 questionnaires were statistically analyzed.

Findings High Cronbach alpha values supported the reliability of the instrument. Content andconstruct validity are demonstrated also. Regression analysis identified assurance, responsivenessand empathy as the quality factors that had significant effects on overall service quality. In addition,customers indicated that reliability and convenience were also very important factors. Service qualityzones of tolerance were identified for each dimension and attribute.

Research limitations/implications There are not many published studies to confirm or compare

the results of the three-column SERVQUAL instrument, either in the general service literature or in therail passenger literature. Although the five original SERVQUAL dimensions have been tested quiteextensively, the three new rail transport dimensions require further development and testing,particularly since the sample was drawn from a single passenger line in New Zealand. Moredevelopment and empirical testing are required to refine this measure.

Practical implications Based on the eight dimensions, the practical use of the zones of tolerancefor identifying areas of quality shortfall and managing quality are illustrated in this paper.

Originality/value This paper provides one of the few empirical applications of the three-columnSERVQUAL instrument and extends it to make it more suitable for evaluating rail passenger servicequality.

Keywords Customer servicesquality, SERVQUAL, Railtransport, Transportation, Surveys, NewZealand

Paper type Research paper

IntroductionThe rapid growth of service sectors all over the world and the deregulation of manyservice industries have led researchers with an interest in quality issues to theimportance of acquiring more understanding about service quality. It is recognizedthat high quality service is essential for firms that want to be successful in theirbusiness (Parasuraman et al., 1988; Rust and Oliver, 1994). It leads to customer loyalty(Lewis, 1994), higher profitability (Gundersen et al., 1996) and lower cost (Grant, 1998).

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0265-671X.htm

Rail servicequality

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Received April 2003Revised June 2005

International Journal of Quality &Reliability Management

Vol. 24 No. 1, 2007pp. 7-31

q Emerald Group Publishing Limited0265-671X

DOI 10.1108/02656710710720303
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However, the existing knowledge about product quality is insufficient to deal withservice quality (Parasuraman et al., 1985). This is because of the intangibility,heterogeneity and inseparability characteristics of service industry outputs(Parasuraman et al., 1985, 1988; Lewis, 1994). Service quality is recognized by a

number of authors as both abstract and elusive (Parasuraman et al., 1985, 1988;Carman, 1990; Cronin and Taylor, 1992; Lewis, 1994).

Parasuraman et al. (1985, 1988, 1991a, b, 1994a, b) have developed an instrument,called SERVQUAL, to measure service quality in organizations:

SERVQUAL has emerged as perhaps the most popular standardized questionnaire tomeasure service quality (Frost and Kumar, 2001, p. 372).

The three-column format SERVQUAL instrument is the latest development byParasuraman et al. (1994a), and it is claimed that this can be used for managers fordiagnostic purposes and it offers the opportunity for using the perception itemsseparately for predictive purposes. Despite the potential diagnostic value, there have beenvery few reported empirical studies using this instrument. Recently Caruana et al. (2000)

have undertaken a study to assess the usefulness of the revised SERVQUAL instrument.However, their study focuses on experiments with different combinations of the one, twoand three column SERVQUAL instruments, rather than on the diagnostic usefulness ofthe instrument for managers. This suggests more empirical work is required using thethree-column SERVQUAL instrument. This is the research gap we wish to address in thispaper. In addition, we selected the rail passenger industry for study since there are veryfew applications of the SERVQUAL instrument reported based on the rail transportindustry. Also one of the co-authors previously worked in the company selected for thestudy, and had access to data and company support for the project.

Hence, the purpose of this paper is to provide an empirical application of thethree-column format SERVQUAL instrument developed by Parasuraman et al. (1994a).

In addition, the paper extends the standard five dimensional SERVQUAL instrument toinclude an additional three dimensions specifically related to measuring quality in therail passenger industry. We then describe the testing of this enhanced SERVQUALinstrument and the results of applying it on a rail passenger line (called Rail Co. in thisresearch) in Wellington, New Zealand. Finally we discuss the insights into the factorsthat affect passengers overall perception of quality based on the results of this study.

Background to the SERVQUAL modelIn the early 1980s, Gronroos (1982) indicated that service quality included technicalquality and functional quality. Technical quality referred to the results that thecustomer could get after a service. Functional quality referred to those processesduring which the service was delivered to the customer. Lehtinen and Lehtinen (1982)

stated that there were three components for service quality: physical quality, corporatequality and interactive quality. Physical quality involved the physical issues of theservice. Corporate quality involved the image of the organization. Interactive qualityreferred to the interaction among people including contact staff and customers.Accordingly, service delivery contains two components: the outcomes of a service andthe processes during service delivery (Parasuraman et al., 1985). From this stream ofresearch work, Parasuraman et al. (1985) developed the well-known five-gap model ofservice quality (SERVQUAL) based on their exploratory research. Parasuraman et al.

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(1985) originally identified ten dimensions for perceived service quality. These weresubsequently reduced to the following five dimensions (Parasuraman et al., 1988, p. 23):tangibles, reliability, responsiveness, assurance and empathy.

This model was further refined by expanding the expectation to two levels: desired

and adequate (Parasuraman et al., 1991a, 1994a, b). Desired service is defined as thelevel of service representing a blend of what customers believe can be and should beprovided. Adequate service is the minimum level service customers are willing toaccept (Parasuraman et al., 1994a, p. 202). There is a region called zone of tolerancethat separates the desired service level and the adequate service level. Anyperformance rated within the zone would be considered satisfactory (Parasuramanet al., 1994a). This expanded model is the latest along this stream of research work.This model is also described as the three-column model owing to the way the authorslay out the questions in the instrument.

The general SERVQUAL instruments (one or two-column formats) have been testedand used by many researchers in the field of service quality in various researchsettings. Examples include: care hospital (Bowers et al., 1994; Carman, 1990; Lam,1997), bank (Cronin and Taylor, 1992; Llosa et al., 1998; Parasuraman et al., 1988,1991b), airline (Fick and Ritchie, 1991; Young et al., 1994; Frost and Kumar, 2001), hotel(Fick and Ritchie, 1991), restaurant (Fick and Ritchie, 1991) and public services (Orwiget al., 1997).

However, there have been very few applications of the three-column SERVQUALinstrument. Walker and Baker (2000) used this framework to investigate the zone oftolerance as it related to consumer experience with health clubs. They claimed thattheir results suggested service marketers can make better use of resources byinvesting directly in improving performance to meet/exceed adequate expectationlevels on essential service dimensions rather than pursuing an ideal standard onless-essential dimensions (p. 433). De Carvalho and Leite (1999) reported on a study

using the framework with postal agencies in Brazil. Also, while there are a number ofstudies of rail passenger service quality (e.g. Disney, 1998, 1999; Hanna and Drea, 1998;Drea and Hanna, 2000; Tripp and Drea, 2002), there is very little published literaturethat reports on the use of SERVQUAL in the assessment of railway passenger servicequality.

Service quality in public transport industryAllen and DiCesare (1976) considered that quality of service for public transportindustry contained two categories: user and non-user categories. Under the usercategory, it consists of speed, reliability, comfort, convenience, safety, special servicesand innovations. For the non-user-category, it is composed of system efficiency,pollution and demand. Silcock (1981) conceptualized service quality for public

transport industry as the measures of accessibility, reliability, comfort, convenienceand safety. Pullen (1993, p. 261) defined quality of service for local public transportindustry as a concept that involves those attributes of the service which affect itsfitness for purpose and the attributes, and indeed fitness for purpose, require detaileddefinition in relation to local objectives and circumstances.

Traditionally, the performance indicators for public transport industry are dividedinto two categories: efficiency and effectiveness. Under the efficiency category, themeasures are concerned with the processes that produce the services while the

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measures in the effectiveness category are used to determine how well the servicesprovided are with respect to the objectives that are set for them (Pullen, 1993). Qualityof service is one of the performance indicators under the effectiveness category. It iscomposed of accessibility, reliability, comfort, convenience and safety. These quality

measures are shown in Appendix 1 (Silcock, 1981). Allen and DiCesare (1976) classifiedthe performance measures into three categories: quantity of service, quality of serviceand cost/revenue of which are further divided into user and non-user measures.Passengers waiting time, lost mileage and characteristics of each journey mode (timeof arrival, time spend, time of arrival at the destination) are commonly used measuresin the industry to measure quality of service (Pullen, 1993). More recently, outputquality measures that have been used for the rail system in Britain include trainperformance (delays per passenger train), train overcrowding, asset condition (brokenrails per train mile), and safety or accident risk (signals passed at danger per train mile)(Pollitt and Smith, 2002).

Following the deregulation of public transportation in New Zealand and othercountries, operators became more aware of the need to understand what theircustomers wanted. Psychometric measures caught the attention of many transportundertakings. The level of transport service (LOTS) was developed to measure thequality of service based on travel speed and comfort. The measures are shown inPullen (1993) (see Appendix 2). The rating scale is from A (excellent) to F (not suitable).Each attribute was assigned a weight that reflects the importance of each attribute.The weighted score reflects the performance of that attribute. The overall performance(i.e. overall LOTS value) can be obtained to reflect the quality of service.

Other measurement systems are discussed in Du Plessis (1984), McKnight et al.(1986), Forsyth and Smyth (1986), Miller (1995).

Hanna and Drea (1998), and Drea and Hanna (2000) have studied quality of servicein part of the Amtrak passenger rail system in the US. Their research focus was on the

attributes of service quality that influenced the transport choice of the surveyrespondents, e.g. rail vs automobile. The attributes used in the first paper were:comfort, cost, timing (ability to travel when I want), location (ability to travel where Iwant) and in-transit productivity (ability to work while traveling). In the later papercost, convenience getting to the station, parking availability, Amtrak comfort, seatcomfort, ride, seating area cleanliness, and courtesy of on-board staff were the servicequality attributes tested.

Tripp and Drea (2002) also used a survey of Amtrak passengers to assess the directand indirect relationship between pre-core/peripheral and core service performancecomponents and their impact on the likelihood of repeat purchase (p. 433). They foundthat the core experiences on-board that determined the customers attitude to theservice provider an subsequently their intention to use the train again. These attributes

included announcements, seat comfort, ride, cleanliness of seating area, courtesy ofon-board staff, rest rooms and cafe car conditions.

The conceptual modelPerceived quality is subjective, enduring and less situation-specific. It is an attitude toreflect customers judgment of the excellence of service. Parasuraman et al. (1994a, b)demonstrate that the conceptualization of service quality can be based on adisconfirmation paradigm (i.e. the gap between performance and expectations). The

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gap model of service quality and the concept of transport service quality showedconsistency that service quality should be measured on a multidimensional basis.Some transport service quality literature pointed out that different methods could beused for measuring service quality. It depends on the type of users, purposes for using

the measures and the environment in which the services were provided. From thispoint-of-view, SERVQUAL is an instrument that could be used to fulfill the purpose ofmeasuring perceived service quality from the customers perspective in this industry.

On the other hand, inconsistency is also found when comparing these two. Theyhave different dimensions in conceptualization of the service quality. SERVQUAL ismuch more service oriented. Those commonly used in the public transport industry aremore industry-based. SERVQUAL is much more humanistic, or customer-related,while most of the measures used in public transport industry are much moremechanistic, or have a technical focus, or use more objective measures, as discussed inthe previous section. This led to the criticism that SERVQUAL could not tell the wholestory. In Genestre and Herbigs (1996) study, it was shown that by adding product

quality to SERVQUAL, several strong products related factors are identified. In theairline industry, Young et al. (1994) added the industry-based measures to SERVQUALmeasures, and the predictive power to satisfaction was significantly increased.

In summary, in order to measure the quality of service thoroughly, the attributesused in SERVQUAL, the public transport industry, and the railway service sectorshould be grouped together to form a pool of items for measurement. The basis of theconceptual framework to be used in this research is an extended SERVQUAL modelthat incorporates the relevant attributes mentioned above.

The value of adopting this model is:. The diagnostic value is very high. The broad areas that are not doing well and

their importance to the evaluation can be unfolded..

As seen from Table I, while SERVQUAL has the ability to capture somedimensions, elements that are not captured can be incorporated in the additionalmeasures.

. The gap model has already been gone through a complete building process since1985 and was fully tested afterwards.

From the literature review, it is clear that we need a set of attributes that could beclassified into different dimensions to measure service quality. Also these attributesand dimensions will need to be more context-specific than the basic SERVQUALdimensions. The traditional measures in public transportation industry lackinformation about the underlying perception of customers, while the SERVQUALmodel is too service-oriented and lacks information about the service offering.

Therefore the combination of the dimensions from these two different aspects ofmeasuring service quality could increase the understanding of the quality constructfor the railway service sector. Table I summarizes our findings about thedimensions or aspects used by the SERVQUAL instrument, and the publictransportation operators and researchers. It is these dimensions that we willincorporate in our extended model. We propose that three more dimensions that areimportant to the railway passenger service are added. They are: convenience,comfort and connection.

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The conceptual framework for rail passenger service quality, based Parasuraman et al.(1994a) expanded three-column model, is provided in Figure 1. In the expanded model,the expected service is further divided into two levels: desired level and adequate(minimum acceptable) level.

Figure 1.Conceptual framework formeasuring railwaypassenger service quality

Sources of information

Reliabilitya SERVQUAL; Allen and DiCesare (1976); Corry (1997); Miller (1995);Nieuwenhuis (1997); Silcock (1981)

Responsivenessa SERVQUALAssurancea SERVQUALSafety Silcock (1981)Safety and security Allen and DiCesare (1976)Empathya SERVQUALTangiblesa SERVQUALConvenienceb Allen and DiCesare (1976); Catling (1996); Corry (1997); Moodie (1997);

Silcock (1981)Speedb,c Allen and DiCesare (1976); Arentz (1969); Pullen (1993)Duration Moodie (1997)Quick Miller (1995)Comfortb Allen and DiCesare (1976); Catling (1996); Francois (1997); Moodie (1997);

Pullen (1993); Silcock (1981)

Notes: aSERVQUAL dimensions; bProposed new dimensions; cSpeed dimension renamed connectionafter exploratory factor analysis (Lo, 1999)

Table I.Summary of dimensions

for rail passenger servicequality

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MethodologyOperational definitionsThe indirect measure (difference score) of the perceived quality service construct isoperationalized as the difference between the perceived performance and the

expectation (Parasuraman et al., 1994a):. Measure of service adequacy (MSA) is defined as MSA P2 Ea:. Measure of service superiority (MSS) is defined as MSS P2 Es.

where:. P the ratings of the perceptions of service performance.. Ea the ratings of the minimum acceptable level of expected service.. Es the ratings of the desired level of expected service.

Advantages of using indirect measurements, as used in our research are:.

Understanding customer expectation about service quality can help managers tofind out the shortfalls, which are required to be improved (Parasuraman et al.,1988, 1994a), and to examine the dynamics of service quality over time(Parasuraman et al., 1993). Managers can understand the expectation for eachimportant area and develop strategies to increase customer satisfaction bylowering their expectation (Carman, 1990).

. The potential for inflated ratings in indirect measurement, which may lead toerroneous inferences, is much lower than that in direct measurement(Parasuraman et al., 1994a).

. The response error is low (Parasuraman et al., 1994a).

Item selectionAfter identifying the dimensions, the next step was to consolidate the different itemsthat are used to measure the corresponding dimensions from a variety of literature. It isa very difficult task at this stage. Since a large part of the conceptual framework(Figure 1) for this research comes from SERVQUAL, questions in the SERVQUALinstrument were used as the starting point in designing the questions for the fiveoriginal dimensions. If SERVQUAL is used as a basic skeleton for measuring servicequality in a specific firm, items for each dimension can be reworded or added to makethe instrument more suitable to be used (Carman, 1990; Cronin and Taylor, 1992;Parasuraman et al., 1988, 1991b). According to the guidelines in using the SERVQUAL(Parasuraman et al., 1991b), minor wording can be changed and content-specific itemscan be added provided that the question is general and not transaction-specific. In

addition it is better to retain all the items in the SERVQUAL instrument unless it isproved to be unnecessary (Carman, 1990) because deletion of items will influence theintegrity of the scales (Parasuraman et al., 1991b).

Three new dimensions were added to complement the measure of quality of railwaypassenger service. They are convenience, comfort and connection as shown in Figure 1.

Based on the findings in the two small pre-tests and the suggestions from themanagement of the Rail Co. and a statistics consultant, the battery of questions used inthis research is shown in Table II.

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Measure of importance of dimensionsThe SERVQUAL instrument rated the importance of features by the allocation ofpoints relative to other features. The total points should be added up to 100 across thefeatures. Since the questionnaire was to be filled in on trains by passengers, therequirement for passenger to sum up eight numbers on train is not an easy task. Inaddition, we wanted to use a scale that could help respondents to answer the questionsmore quickly, Therefore we used an alternative nine point bi-polar numerical scale

Dimension Items

Assurance 1. Courtesy staff on train4. Being informed if there are delays

10. Personal safety at station16. Personal safety on train29. Courtesy staff at ticket office30. Having the knowledge to answer your questions37. Providing you with information about Tran Metro

Empathy 31. Dealing with you in a caring fashion when you make inquiries32. Understanding your needs when you make inquiries38. Having your best interests at heart

Reliability 23. Maintaining the frequency of trains as scheduled in timetables24. Providing on time train services34. Dependability in handling your service problems35. Performing services right the first time

Responsiveness 3. Willingness to help you28. Prompt service36. Availability of staff in handling your requests

Tangibles 2. A neat, professional appearance staff on train6. Clarity of information given in timetables7. Clarity of timetables given at stations

12. Cleanliness of station13. Modern appearance station18. Cleanliness of train19. Overall appearance train33. A neat, professional appearance staff in ticket office

Comfort 17. Availability of seating train

20. Comfortable seats on train21. Comfortable temperature on train22. Smoothness of ride on train27. Traveling time on train

Connection 11. Adequacy of parking facilities14. Ease of access to your home station15. Ease of access to the nearest station at your working place/school25. Frequency of trains that meet your needs26. Trains running at suitable times so you can catch connecting transport services

Convenience 5. Ease of access to travel information8. Ease of buying tickets9. Convenient office hours at ticket office

Table II.Battery for the railpassenger three-columnSERVQUAL instrument

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with 1 being not at all important and 9 being extremely important (Cavana et al.,2001).

In determining relative importance, the regression analysis or the direct ratingapproach could be used (Brandt, 1997). The scale described in this section adopts the

latter approach. For this research, the former was basically used to determine therelative importance of the quality dimensions. The direct rating approach aims atproviding additional information for understanding how passengers evaluate therelative importance.

Summary of questionnaire designFor the initial stage in questionnaire design, the questions in the SERVQUALinstrument were used. They were subjected to minor wording modification andaddition of some items that were relevant to the urban railway passenger sector underthe eight dimensions outlined in Table II. This resulted in 46 questions. Following twosmall tests on the question design, we reduced the number of questions to 38. Thereasons were:

. The original questions in SERVQUAL are so generic that they might not beappropriate when used in the railway industry. The respondents either did notgive answers to the questions or wrote a question mark besides the questions.

. In order to reduce the time needed to fill in the questionnaire so that a betterresponse rate and better quality of response could be obtained, some questionsthat are believed to have some duplications with other items in the questionnairewere deleted. In addition, some wordings were changed in the questions to makethe questions clearer to raise respondents interest in completing thequestionnaire.

The draft questionnaire was piloted with a sample of 20 passengers who got onto the

selected Wellington train service that departed at 6:30 a.m. on a week day.The questionnaire was designed to ask information about passengers expectation

in terms of minimum and desired service levels, their perception of services providedby the Rail Co., their value for money rating, overall quality rating, overall satisfactionrating, their trip information and some demographic data. Finally, an open-endedquestion was asked such that comments and suggestions from passengers could beobtained. Further details of the questionnaire are provided in Appendix 2.

Sampling and data collectionOne passenger rail service in Wellington was surveyed for the purposes of thisresearch. The rail company has six lines into and out of Wellington. Based on theresources available, we surveyed only one line of these train services. Randomsampling from the passengers on one line is sufficient to determine the underlyingfactors that will affect their perception of passenger services. The population studied inthis research was all passengers who traveled by the Rail Co. trains during thesurveyed period and had their home station on Upper Hutt line.

The sample was stratified by peak and off peak users. A total of 800 questionnaireswere distributed. The number of returned questionnaires was 429. Among the returnedquestionnaires, 208 (48.5 percent) were collected on trains and 221 (51.5 percent) weremailed back by the respondents. Thus the response rate was 53.6 percent. Among the

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429 returns, 340 (79.3 percent) of them were usable. The non-usable questionnaireswere mainly due to the presence of many missing items, many response errors andpassengers whose home stations were not on the selected line. The number ofnon-usable questionnaires was 89 (20.7 percent).

Reliability and validity of the survey instrumentWe then subjected our data collection instrument to reliability and validity analysis.

DimensionalityAs previously described, the items used for measuring the three new dimensions werebased on the literature review. MSS is a measure for excellent quality and is consistentwith the original definition of the gap model. Therefore exploratory factor analysis onMSS was performed to investigate the factor structure of the three newly added items(Lo, 1999). In order to estimate the number of factors, principal component analysiswas performed. With the selection criteria that the eigenvalue should be greater than 1,

three factors resulted. This shows consistency in the number of factors that wepostulated for the new items. In addition, due to the findings from previous research,that intercorrelations are found among the dimensions in measuring perceived qualityof services, the three-factor solution was subjected to oblique rotation in order tofacilitate the interpretation of the result (Parasuraman et al., 1988).

All 38 questions under the eight dimensions were retained for reliabilityassessment. In order to investigate the distinctiveness of these dimensions, theintercorrelations among them were computed and are shown in Table III. The averageintercorrelations for the three newly added dimensions were 0.62, 0.53, and 0.56,respectively. This shows that the newly added dimensions are quite distinctive bythemselves. However, the five SERVQUAL dimensions, which have a higher averageintercorrelation, could suffer from some multicollinearity in this passenger services

model (Tabachnick and Fidell, 1989). This suggests that the multiple regressionanalyses, using the eight dimensions as independent variables, performed hereaftershould be interpreted carefully.

ReliabilityReliability is defined as the extent to which an experiment, test, or any measuringprocedure yields the same results on repeated trials (Carmines and Zeller, 1979, p. 11).

Dimension (1) (2) (3) (4) (5) (6) (7) (8)

(1) Assurance 1.00(2) Empathy 0.78 1.00

(3) Reliability 0.78 0.74 1.00(4) Responsiveness 0.79 0.79 0.75 1.00(5) Tangibles 0.76 0.64 0.63 0.66 1.00(6) Comfort 0.65 0.56 0.65 0.58 0.66 1.00(7) Connection 0.56 0.48 0.57 0.50 0.55 0.55 1.00(8) Convenience 0.63 0.53 0.53 0.56 0.57 0.48 0.53 1.00

Note: Numbers reported are Pearson correlations; all values are significant at p , 0.01, correlationsonly shown below the diagonal

Table III.Correlations among theeight dimensions in theconceptual model

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In this research, the internal consistency method was adopted for estimatingreliabilities. It has the advantage of being a single test administration. Besides, thismethod has also been used for instrument assessment in various research studies onperceived quality of service (Carman, 1990; Babakus and Boller, 1992; Cronin and

Taylor, 1992; Bowers et al., 1994; Lam, 1997; Llosa et al., 1998; Parasuraman et al.,1991b).

Cronbachs alpha was used to assess internal consistency. Table IV shows theCronbachs alpha values for the eight dimensions. For measuring MSA, the range isfrom 0.71 to 0.86. For measuring MSS, the range is from 0.74 to 0.85. The acceptablerange for Cronbachs alpha in measuring constructs with narrow to moderately broadconceptual scope is from 0.55 to 0.90 (Van de Ven and Ferry, 1984). The result showsthat the items are reliable in measuring the corresponding concepts. In addition, theresults are consistent with those reported by Parasuraman et al. (1988).

Validity

The other important criterion that is used to determine the goodness of an instrumentis validity. Validity is to measure the extent to which a scale fully and unambiguouslycaptures the underlying unobservable, construct it is intended to measure(Parasuraman et al., 1988, p. 28). However, Asubonteng et al. (1996) note that:

The validity of a measure of service quality is difficult to test as a proven criterion is notavailable.

They indicate (based on Peter and Churchill, 1986) that there are several differentforms of validity that can serve as a criteri for assessing the psychometric soundness ofa scale: discriminate validity, face validity and convergence and concurrent validity.

We will discuss the face, convergent and discriminant validity of this research.These tests are similar to those carried out by Parasuraman et al. (1994a, pp. 212-214)

when comparing the validity of the three-column format with the one and two columnformat data collection instruments.Face validity. In assessing the face validity of the instrument for this research, it was

necessary to see how the pools of items were selected. The items were derived from theliterature review about quality of services in service industries, quality of service andcustomer satisfaction on public transportation, existing documents of the passengersurveys carried out by the Rail Co. and suggestions from management personnel in theRail Co. Moreover, two small pre-tests were carried out during the development of the

Measure of service adequacy (MSA) Measure of service superiority (MSS)Dimension Cronbachs alpha Cronbachs Alpha

Assurance 0.83 0.84Empathy 0.85 0.85Reliability 0.85 0.83Responsiveness 0.71 0.75Tangibles 0.85 0.81Comfort 0.86 0.82Connection 0.78 0.74Convenience 0.82 0.77

Table IV.Reliability coefficients by

quality dimensions forthe rail passenger quality

instrument

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instrument. The respondents were train users and they generally could understand thecontent of questions. Therefore the instrument can be considered to have face validity.

Convergent validity. Convergent validity is established when the scores obtained bytwo different instruments measuring the same concept are highly correlated (Cavana

et al., 2001, p. 213). For this research, convergent validity was assessed by regressingthe overall service quality ratings and overall value for money ratings on the scores forthe eight dimensions. The R2 values for the overall service quality regression are 0.39and 0.16 for MSS and MSA respectively. The R2 values for the overall value for moneyregression are 0.22 and 0.06 for MSS and MSA respectively. Both of the R2 values forMSS are generally high and the results are consistent with the findings reported inParasuraman et al. (1994a). However, these values for MSA are not high. AlthoughParasuraman et al. (1994a) also reported a R2 value of 0.10 for MSA scores in the autoinsurer company, the validity of this instrument in measuring service adequacy shouldbe treated with caution. The perceptions-only scale is found to be higher than the othertwo scales (R2 of 0.51 for the overall service quality regression and anR2 of 0.30 for theoverall value for money regression). It is consistent with the findings from someprevious studies (e.g. Babakus and Boller, 1992) that the predictive power is higher forperception-only scale.

Discriminant validity. Discriminant validity is found when based on theory, twovariables are predicted to be uncorrelated, and the scores obtained by measuring themare indeed empirically found to be so (Cavana et al., 2001, p. 213). In this research,discriminant validity was assessed by comparing the R2 obtained from the overallvalue for money regression with that obtained from the overall quality regression.Theoretically, the extended rail passenger SERVQUAL instrument is used to measurequality, therefore it should relate better to the overall quality construct than the overallvalue construct, and the variance explained in the quality regression should be higherthan that in the value regression. The R2 values provided above, indicate that the

instrument possesses discriminant validity.Summary of validity tests. These tests indicate that extended rail passengerSERVQUAL instrument outlined in this paper, does provide face, convergent anddiscriminate validity. Hence it is sufficiently valid to continue with the exploratoryanalysis of diagnosing the zones of tolerance for managing rail passenger servicequality.

Analysis and resultsOne of the main purposes of this research was to understand the quality of railwaypassenger service. In order to achieve the goal, the following research questions wereaddressed:

.

Is there any linear relationship between each of the eight dimensions and theperception of overall quality?. What is the relative importance of the eight dimensions?. What are the shortfalls of the service in the broad areas (i.e. dimensions)?. What are the shortfalls of the attributes in the poorly performed broad areas?

Parasuraman et al. (1988, p. 47) defined service quality as global judgment or attituderelating to the superiority or excellence of service when they developed the

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SERVQUAL instrument. We used the MSS scores of the eight dimensions as theindependent variables in the following analysis to answer the research questions.

Correlations

Since perceived service quality in the conceptual model is measured at two levels:desired level and adequate (minimum acceptable) level, we correlated both the MSSand MSA scores with overall service quality. As shown in Table V, there arestatistically significant relationships between the perceived service quality of each ofthe eight dimensions and the perception of overall service quality at the 99 percentconfidence level. They are therefore positively correlated with the perception of overallquality. The correlations ranged from 0.36 to 0.61 for MSS scores and 0.19 to 0.37 forMSA scores.

Relative importance of quality factorsIn order to identify the relative importance of the eight dimensions in the overall

quality, standard multiple regression analysis was performed since the eightdimensions are postulated to be correlated. The MSS scores were regressed on theoverall quality measure for reasons mentioned above. However, a limitation of theregression approach to assessing importance is that it tells us nothing about the causalrelationships it simply provides the best statistical relationship (correlations)between the variables. Although statistically useful, the results must be interpretedwith caution.

Nevertheless, the R, R2 and adjusted R2 are provided in Table VI. The multiplecorrelation coefficient is statistically significant at the 99 percent confidence level.However, due to the high correlation among the dimensions, the amount of varianceattributable to unique sources is only 0.052. The shared variability shows that theamount of variance that the eight dimensions jointly contribute to R2 is 0.351.

The relative importance of the quality factor is also summarized in Table VI.Parasuraman et al. (1988) used standardized slope coefficients to determine the relativeimportance. The higher the standardized slope coefficient value, the more importantthe dimension is, provided this coefficient is statistically significant.

Three dimensions were found to be statistically significant at the 10 percent level orbetter. Assurance is the most important followed by responsiveness and empathy. Theother five dimensions are not statistically significant in this multiple regression

Measure of service adequacy (MSA) Measure of service superiority (MSS)Dimension Pearsons correlation coefficient Pearsons correlation coefficient

Assurance 0.37 0.61

Empathy 0.33 0.55Reliability 0.26 0.50Responsiveness 0.37 0.57Tangibles 0.26 0.47Comfort 0.25 0.45Connection 0.19 0.36Convenience 0.26 0.42

Note: All values are significant at p, 0.01

Table V.Correlations between thedimensions of perceived

service quality (MSS andMSA) and the perception

of overall quality

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analysis. The relatively small and/or negative magnitudes of the regressioncoefficients for the comfort, reliability, tangibles, convenience and connectiondimensions should be interpreted carefully because all of them have statisticallysignificant simple correlations with overall service quality. Owing to the fact that theeight dimensions are highly correlated (Table III), multi-collinearity will be present andthe signs (negative values) and values of the coefficients will not be meaningful.However the overall regression model can still be used for prediction purposes.

In part II of the questionnaire (see appendix B), respondents were asked to rate theeight dimensions (with 1 being not at all important and 9 being extremelyimportant). Since the three new dimensions were slightly reconfigured, the meaning ofthese three dimensions in the instrument will no longer exactly match the meaning ofthe reconfigured ones. However, since the differences in meanings are not substantial,the findings could still be used for comparison and are shown in Table VII. In realitythe ranking of importance by the direct method is likely to be more robust than theranking using regression analysis (since the presence of multi-collinearity will distortthe regression coefficients and weights).

Measure of service superiority (MSS)

DimensionStandardized slope

coefficientSignificance level

of slopeSquare of semipartial

correlation (sr2)

Assurance 0.410 * * * 0.000 0.035Comfort 0.084 0.191 0.003Connection 20.007 0.902 0.000Convenience 0.028 0.635 0.000Empathy 0.135 * 0.087 0.005Reliability 20.068 0.394 0.001Responsiveness 0.163 * * 0.048 0.007Tangibles 20.061 0.398 0.001

R 0:634 * * *

R2 0:403Adjusted R2 0:388Unique variability sum of sr2 0:052Shared variability 0:351

Notes: *p , 0.1; * *p , 0.05; * * *p , 0.01

Table VI.Relative importance ofthe eight dimensions in

predicting overall quality

Dimension Mean score Standard error

Reliability 8.43 0.048

Convenience 8.13 0.056Responsiveness 8.06 0.054Assurance 7.81 0.066Comfort 7.66 0.064Empathy 7.63 0.073Speed/connectiona 7.51 0.082Tangibles 7.02 0.081

Notes: a Speed is used in the initial conceptual model and connection in the final conceptual model

Table VII.Passengers ratings ofimportance of the qualitydimensions

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Based on the direct rating method, customers indicated that reliability, convenience,responsiveness and assurance were the most important factors that affect passengersperception of service quality. However, all features were regarded as relativelyimportant.

The zones of toleranceSince two levels of expectation (adequate and desired) have been measured in thisresearch, managers and analysts can learn whether their customers service perceptionsfall within the zone of tolerance (the space between adequate service and desiredservice) or outside the zone. Managers can see where customers perceptions fall relativeto the zone of tolerance for individual service quality items and dimensions, and comparetheir own customer data to competitor customer data. These insights are possible only ifcustomers expectations are measured separately (Parasuraman et al., 1993).

We investigated the presence of the zone of tolerance in service quality for eachdimension. There was a significant difference (p , 0:01) between minimum (or

adequate) and desired levels of service for each dimension. We then tested thesignificance of the gaps between the minimum and perceived service levels (MSA), andthe desired and perceived service levels (MSS). There was no significant gap at the 95percent level for empathy and tangibles between the minimum level and perceivedquality. On the other hand there were significant gaps at the 99 percent level for alldimensions between desired and perceived quality. Figure 2 shows these results.Perceived service for each dimension except Assurance is statistically within the zoneof tolerance but near the adequate (minimum) level.

The Rail Co. is not doing well for the most important dimension, Assurance. It fallsoutside the zone of tolerance and statistically below the minimum (adequate) level ofexpected service. Similar plots for four types of companies: computer manufacturer,retain chain, auto insurer and life insurer are in Parasuraman et al.s (1994a) study.

The individual zones of tolerance for each dimension in Figure 2 can be examinedmore closely by considering the zone of tolerance for each of the attributes (items) thatmake up the dimension. Since Assurance falls below the minimum acceptable expectedlevel of service, we have provided the detailed analysis for each attribute for thisdimension in Figure 3.

Figure 3 shows that passengers were worried about their personal safety at railwaystations and were not satisfied about the information given by Rail Co. when there weredelays. A complete analysis of the remaining dimensions is contained in Lo (1999).

These results were discussed fully with managers of the Rail Co. in Wellington, andthey were found to be most interesting and useful. They provided valuable insights forquality improvements, hence confirming their value as a diagnostic tool, only madefeasible by the collection of data through the rail passenger extended three-column

format SERVQUAL instrument initially developed by Parasuraman et al. (1994a).The general implications of these zones of tolerance are discussed in the

conclusions under managerial implications

Conclusions and recommendationsThis paper has developed an extension to the three-column SERVQUAL model(Parasuraman et al., 1994a) to evaluate the quality of passenger rail services. We havecombined the literatures of service quality and rail transport quality to develop our

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conceptual framework. We reported on the results of testing the model on a passengerline in Wellington, New Zealand. Three new dimensions (comfort, convenience andconnection) were added to the original five SERVQUAL dimensions (assurance,empathy, reliability, responsiveness and tangibles) and were modified afterexploratory factor analysis.

The high Cronbachs alpha values for each dimension support that the items arereliable in measuring the underlying concepts. The high reliability in measuring thequality of rail passenger services is also evidenced by the high value in the total-scalereliability. In addition, Pearsons correlations between the ratings of each of the eightdimensions and the overall quality rating show that all of them are significantly related

to it.The model for measuring perceived quality contains eight factors. Therefore in

predicting the overall service quality, all of the eight factors were entered in the regressionequation. This regression method identified three significant factors in predicting overallservice quality that were Assurance, Responsiveness and Empathy. Based on the directmethod of rating the importance of these factors by the passengers surveyed, Reliabilityand Convenience were also identified as very important factors or dimensions of quality.However, the passengers regarded all the factors as relatively important.

Figure 2.Service qualityperceptions relative tozones of tolerance bydimension

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Limitations

Key limitations of this research include:

. Although the SERVQUAL model has been tested for a long period of time invarious industries, the expanded three-column SERVQUAL instrument is

relatively new. There are not many published empirical studies to support thismodel.

. The applications of the SERVQUAL model in rail passenger services are notfound in published academic literature. It makes the validation process verydifficult.

. The final adapted model is developed based on the adoption of SERVQUALmodel and the incorporation of three new dimensions into the original model toform a larger and more comprehensive framework. The five SERVQUAL

constructs have already undergone a series of empirical verification andvalidation. However, the three new dimensions are only theoretical constructs.

Readers should take caution to this point.

. This is the first time that this instrument has been tested empirically. Thereforereplications of the testing are necessary before it can be used commercially.

. The findings are based on a random sample from one line of train services inWellington, New Zealand.

Figure 3.Quality perceptionsrelative to zones of

tolerance for the assurancedimension

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. This model helps to identify only the broad area of service shortfalls. Thedetailed causes of dissatisfaction, and the actions that should be done to correctthem, should only be revealed by carrying out other research or surveys to getinsights to the specific problem.

Comparison of the model used in this research with other models and the findings fromairline services and other service industries indicate that this model generally performsas well as other models. The service attributes used in this study were derived from theliterature and our factor analysis. They are similar to most of the attributes used inother rail passenger studies, e.g. Hanna and Drea (1998), Drea and Hanna (2000), andTripp and Drea (2002) that used literature searches and focus groups. Their Cronbachalpha values are similar to those in this study. As stated earlier, there are few studiesthat have used the three-column SERVQUAL instrument, The Walker and Baker(2000) study is one such study but it involved customers of health clubs, and only usedthe standard five SERVQUAL dimensions. This finding is sufficient enough to supportthe application of our model in rail passenger services since one of the principal

objectives of the research was to apply the adapted SERVQUAL model to identifylimitations in existing measurements of SERVQUAL as it relates to this serviceindustry.

Managerial implicationsThe traditional quality measures on transportation industry alone are not sufficient tohelp managerial decision-making. The quality standard set by referencing thesemeasures are mostly from managements point-of-view. The psychometric measuresthat are based on the customer perspective do not seem to have caused much attention.Although many transport operators have carried out some passenger surveys in orderto capture passengers perceived service quality and/or passenger satisfaction, these

measures seem to be treated as unidimensional and lack conceptual clarity. Forexample, service quality and satisfaction are used interchangeably in much of thetransport literature. In view of this weakness, the SERVQUAL framework from themarketing literature is brought into the transport industry for studying themeasurement of service quality. It provides managers the opportunity of looking at theindicators of service quality from other perspectives.

An important feature of the extended three-column SERVQUAL model is itsdiagnostic value in terms of zone of tolerance and expectation management. It helpsmanagers to analyze the effectiveness of the service quality and identify those problemareas that need to be improved.

The perceived quality rating relative to the minimum and desired level of expectedservices help managers to develop their long-term and short-term strategy planning.

The short-term improvement plan may include those aspects that are below the zone oftolerance. The long-term improvement plan may be formulated by referencing therelative position of the perceived quality pointer within the zone of tolerance and byconsidering the width of the zone of tolerance (Kettinger and Lee, 1997). The narrowerthe zone, the more attention is needed.

In reality, it is unlikely to fulfill all the ideal service quality requirements desired bytheir customers. Therefore it is necessary for the managers to manage customerexpectation by the adequacy level of expectation so as to widen the zone of tolerance

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(Kettinger and Lee, 1997). In addition, the managers could optimize the utilization ofthe available resources by positioning their company towards the lower end of the zoneof tolerance for the appropriate dimensions.

Practical implicationsThe administration problem pointed out by Parasuraman et al. (1994a) was present.The response rate for this research was 54 percent. Among the returnedquestionnaires, 21 percent were non-usable. This was mainly due to missing andinvalid data within each questionnaire. The rather high percentage of non-usablereturns reflects the complexity of the questionnaire. Therefore the suggestionsrecommended by Parasuraman et al. (1988) in handling this problem should bere-considered in future research from practical point-of-view. Data should be collectedlongitudinally since monitoring and continuous improvements are the major goals ofquality management.

The results on the direct ratings of the importance about the dimensions are notconfirmed by the results generated by statistical analysis. This discrepancy was alsofound in Parasuraman et al.s (1991b) study. Brandt (1997) noted that this may be dueto methodology differences in measuring the concept of relative importance. However,before the above explanation is accepted, this discrepancy should be investigated. Asfor this research, the possible reasons may be:

. The bipolar nine-point numerical scale is not good enough to capture theconcepts of relative importance relating to service quality.

. The questions for the features in part II are not clear enough to capture all themeanings of the attributes that constitute that feature. It implies that thequestions should be re-examined more carefully in order to get a fullyrepresentation meaning of the underlying meaning of that dimension.

This part of the information is important and useful especially when this instrument isgoing to be used commercially. It helps to reduce the necessity to use complexstatistical techniques during data analysis. Moreover, it is easier for managers tounderstand and interpret the surveys results (Brandt, 1997).

Recommendations for future researchThe original SERVQUAL instrument focused mainly on a dimensional view of servicequality evaluation. It did not include any concepts on object-based evaluation. Sincethis research aims at adding value to the application of the SERVQUAL model in a railpassenger sector, a compromise step was taken in view of the complex nature of railpassenger service which involves several services encounters during service delivery.Only those questions that were important to assess the performance of both staff ontrains and staff at the ticket office were asked separately. Otherwise they were askedpertaining to one type of staff who were believed to have greater performance influenceon that attribute. This step was taken to avoid long questionnaires, which could causeadministration problems.

However for rail passenger services, inclusion of objects in quality evaluationsseems to be appealing since this structure can reduce variability due to objects. It canalso provide a richer and more accurate picture of the nature and structure of theservice quality. Future research can investigate the possibility of bringing this

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multidimensional-multiobject model as suggested by Singh (1991) in measuring railpassenger service quality in practice.

In addition, this exploratory research study also reveals the followingrecommendation on the refinement of this initial instrument:

. More studies should be carried out to clarify the discrepancy of results fromregression analysis and direct rating in obtaining the relative importance ofservice quality dimension before the instrument is used commercially.

. The wording of the questions and the adequacy of the questions in thequestionnaire should be reexamined based on the results from the open-endedquestions which ask respondents to give their comments and suggestions relatedto quality of service.

. The order of the questions should be rearranged and sub-headings should beremoved to avoid the possible methodology artifact if factor analysis is going tobe used in development of the instrument.

. More empirical tests should be carried out to refine the measures. Morevalidation of the model through replication should be performed in future.

Finally, the zones of tolerance provide information about what areas and attributesthat are need to be improved but not how to improve them. Future research on finding,examining and measuring the determinants of expectation would add value inmonitoring service quality.

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Appendix 1

Characteristics Measures

Travel speed SpeedDelay

Comfort SpaceHorizontal accelerationVertical accelerationJerkTemperatureVentilationNoise

Source: Pullen (1993)

Table AII.LOTS classification of

quality measures

Aspects Performance measures

Accessibility Percentage of population within 14

mile of a routePercentage of public transport dependent within 1

4mile of a route

Percentage of employment served by bus services

Reliability Number of buses taking x minutes longer than schedulePercentage of buses one minute early to four minutes lateAverage waiting time of passengers

Excess waiting time of passengersComfort Maximum number passengers/total available seats averaged over each route at

maximum load point (i.e. load factors)Peak-hour floor area/passenger averaged over each route at the minimum load point(i.e. floor area)VentilationVehicle jerk

Convenience Number of transfers/number of passengers (i.e. route directness)Hours of serviceStop spacingBus stop provisionVehicle step heightInformation services

Ratio of bus journey time: time by car

Safety Number of accidents/veh-kmNumber of crimes/veh-km

Source: Silcock (1981)

Table AI.Silcocks classification of

quality measures

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Appendix 2. Overview of the rail passenger three-column SERVQUALquestionnaire(Adapted from Parasuraman et al., 1994a)

Part IA total of 38 items that are used to measure quality of passenger service are asked. For each item,passengers were asked to give three different attitude ratings. They are the minimum level ofexpected service, the desired level of expected service and their perception of the Rail Co.sperformance. A 9-point scale is used with 1 being the lowest level and 9 being the highest level.For rating the perception of the companys performance, an option for no opinion is provided(Figure A1).

Part IIThe major information obtained in this part are: perception of passengers importance of eightmajor features about quality of service, overall quality rating, overall perception of value formoney about the service and overall satisfaction with the services provided.

For measuring the importance of the eight features, respondents were asked to give a ratingfrom 1 to 9 with 1 being not at all important and 9 being extremely important.

For measuring the overall quality of service provided, respondents were asked to give arating from 1 to 9 with 1 being extremely poor, 5 being neutral and 9 being extremely good.

For measuring the overall value for money, respondents were asked to give a rating from 1 to9 with 1 being poor value, 5 being neutral and 9 being excellent value.

For measuring overall satisfaction, respondents were asked to give a rating from 1 to 9 with 1being terrible, 5 being neutral and 9 being delighted.

Part IIIInformation about the trip on which the questionnaire was distributed is asked in this part. Itincludes the boarding station, getting off station and boarding time. Questions about how

regularly the respondent uses the train and her home station are also included.

Part IVDemographic information, including gender, age group and occupational group.

Figure A1.Sample of part I of thequestionnaire used in thisresearch

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Part VAn open ended question about respondents comments and suggestions related to the quality ofpassenger service.

About the authorsRobert Y. Cavana is a reader in decision sciences at the Victoria Management School, VictoriaUniversity of Wellington, New Zealand. Previously he was Corporate Economist at NZ RailwaysCorporation. He holds an MCom (Hons) in economics from the University of Auckland and a PhDin management science from University of Bradford, England. His research interests includesystems thinking, system dynamics, transport and logistics, resource management andsustainable development. He has co-authored two books: Systems Thinking and Modelling:Understanding Change and Complexity (Pearson Education, Auckland, 2000) and Applied

Business Research: Qualitative and Quantitative Methods (Wiley, 2001). He is a chartered memberof the Institute of Logistics and Transport in New Zealand and a managing editor of SystemDynamics Review. Robert Y. Cavana is the corresponding author and can be contacted at:[email protected]

Lawrence M. Corbett is an associate professor at the Victoria Management School, Victoria

University of Wellington, New Zealand. He holds a BE in chemical and materials engineeringfrom the University of Auckland, New Zealand, and an MBA from Cranfield University,England. He has industrial experience as an engineer and operations manager in themetallurgical industry in New Zealand, Australia and England. He has published in Journal ofOperations Management, International Journal of Production Research, International Journal ofOperations & Production Management, International Journal of Quality & Reliability

Management, and Production and Inventory Management. His research interests are qualitymanagement, manufacturing strategy, and the evolution of competitive advantage.

Y.L. (Glenda) Lo is a graduate of Victoria University of Wellington, New Zealand with aMasters degree in Management Studies (Decision Sciences) and Victoria University ofTechnology, Australia with a 1st class Honours degree in Computer Science, Australia. She hasworked in the field of industrial and educational research and software quality control in HongKong. From 2001 until 2004, she was teaching computing and commercial subjects at Institute of

Vocational Education (Chai Wan) in Hong Kong. Currently she is working as a Business Analystat Transpower NZ Ltd, Wellington, New Zealand.

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