conceptualisation of service quality for hybrid services: a hierarchical approach

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Conceptualisation of service qualityfor hybrid services: a hierarchicalapproachShirshendu Ganguli a & Sanjit Kumar Roy ba Marketing & Strategy Department , IFHE University, IBSHyderabad , Hyderabad , Indiab Marketing and Advertising Department , Coventry UniversityBusiness School , Coventry , UKPublished online: 08 Jul 2013.

To cite this article: Shirshendu Ganguli & Sanjit Kumar Roy (2013) Conceptualisation of servicequality for hybrid services: a hierarchical approach, Total Quality Management & BusinessExcellence, 24:9-10, 1202-1218, DOI: 10.1080/14783363.2013.814293

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Conceptualisation of service quality for hybrid services:a hierarchical approach

Shirshendu Gangulia and Sanjit Kumar Royb∗

aMarketing & Strategy Department, IFHE University, IBS Hyderabad, Hyderabad, India;bMarketing and Advertising Department, Coventry University Business School, Coventry, UK

The purpose of the paper is to identify the dimensions of hybrid service quality (HSQ)and to test a hierarchical model of HSQ. The hybrid service-quality dimensions (i.e.service provision, employee competence, image, price, tangibles, ease of subscription,information security and quality, technology usage convenience, technologyreliability) were identified using exploratory factor analysis. The reliability andvalidity of the factors were tested using first-order confirmatory factor analysis. Next,second-order confirmatory factor analysis was used to identify three second-orderfactors (i.e. interaction quality, technology quality and auxiliary quality). Finally,third-order confirmatory factor analysis was used to test the third-order hierarchicalmodel. This study contributes to the literature by identifying the dimensions of a newcategory of services which we call ‘hybrid services’ and testing a hierarchical modelof HSQ. These dimensions will aid service managers to focus on the various aspectsof hybrid services in order to influence customers’ behavioural outcomes positively.

Keywords: hybrid services; service quality; hierarchical model; higher-order factoranalysis

Introduction

The conceptualisation and measurement of service quality is an important field of research

in the history of services marketing literature. Service quality is considered to be a success-

ful method of differentiation in the service-oriented business (Parasuraman, Zeithaml, &

Berry, 1988). As technology has penetrated the service delivery mode in today’s business

scenario, the proliferation of information technology has transformed the way in which

services are produced, delivered and consumed. Service automation started long back

(Collier, 1983). The use of technology in service delivery has helped in the provision of

better services to customers with increasing convenience (Sachan, Ali, & Gupta, 2007).

As human–human interactions during service delivery are increasingly being replaced

by human–technology interactions (Bitner, Brown, & Meuter, 2000), it is not necessary

for customers to be present at the company premises and interact with humans (employ-

ees). In services like e-retail, online gaming and self-service technology (SST) like kiosks,

vending machines, human–human interactions are completely replaced by human–tech-

nology interaction. On the other hand, there are still conventional services like restaurants

and barber shops which continue to rely on human–human interactions in order to deliver

their services. However, there is a rise and growth of a new category of services, which can

be termed as hybrid services (for example, banking, telecommunication, utility services,

stock trading, airlines and Internet services). The distinguishing characteristic of this

service category is that customers’ interactions with a firm are a mix of human and

# 2013 Taylor & Francis

∗Corresponding author. Email: [email protected]

Total Quality Management, 2013

Vol. 24, No. 10, 1202–1218, http://dx.doi.org/10.1080/14783363.2013.814293

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technology interactions. In these services, in spite of technology being used for service

delivery, a great deal of human interactions is still needed in the service delivery

process. A series of human interactions occur during initial service subscription, during

service consumption and/or during problem solving or any other form of customer

service. The services as discussed above can be classified (based on interactions happening

during service delivery) as shown in Table 1.

This study is based on the third category of services called hybrid services, which can

be defined as services which are delivered to the customers using both human and technol-

ogy channels, with both options available to the customers and customers using them as

per their convenience and availability. There are studies on service quality of conventional

services like SERVQUAL by Parasuraman et al. (1988) and by other researchers (e.g.

Kim, Kim, & Lee, 2011; Tsai, Hsu, & Chou, 2011). On the other hand, there are

studies which have examined the technology-based services like E-S-QUAL by Parasura-

man, Zeithaml, and Malhotra (2005) and other researchers (e.g. Herington & Weaven,

2009). The case of hybrid services as a research topic is relatively unexplored, and as

observed by Nasr, Eshghi, and Ganguli (2012), this offers the scholars and practitioners

an opportunity for a new avenue of theoretical and practical research.

An ongoing debate in the service-quality literature is about the dimensionality of service

quality (Brady & Cronin, 2001). According to the Nordic school (Gronroos, 1984), service

quality is conceptualised as comprising functional and technical qualities. However, accord-

ing to the American school of thought Parasuraman et al. (1988) define service quality using

the characteristics of service encounter, i.e. reliability, assurance, responsiveness, empathy

and tangibles. There is no agreement on the number of dimensions of service quality. In fact,

service quality can be seen as a complex process at different levels of abstractions and it was

modelled as a hierarchical construct (Bai, Lai, Chen, & Hutchison, 2008; Brady & Cronin,

2001). Hence, while exploring the dimensionality of service quality in case of hybrid ser-

vices, it is also important to look at hybrid service quality (HSQ) as a construct which is hier-

archical in nature. Thus this study has twofold objectives: first to identify the dimensions of

HSQ and secondly to test a hierarchical model of HSQ.

This study contributes to the services marketing literature by identifying the service-

quality dimensions and building a hierarchical model of service quality for hybrid ser-

vices. The rest of the paper is organised as follows: we first present a review of the

service-quality literature both for the conventional services and the technology enables

services. Next, we provide the research methods used followed by data analysis and

results. Finally we provide a discussion and conclusion section.

Literature review

Conceptualisation of service quality

In services marketing, service quality is defined as an overall assessment of service by the

customers. Service quality is perceived by the practitioners as key dimensions that

Table 1. Classification of services.

Type of service delivery Services classified as

Human-based service delivery Conventional services (e.g. restaurants)Technology-based service delivery Technology-based services (e.g. e-retail)Both technology- and human-based delivery Hybrid services (e.g. banking)

Total Quality Management 1203

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customers use when evaluating the services. Parasuraman et al. (1988, p. 16) consider

service quality as the ‘global judgment, or attitude, relating to the superiority of the

service’. Parasuraman, Zeithaml, and Berry (1985) define service quality from the custo-

mers’ perspective, which results when they compare their prior expectations with the

actual performance of the service. These authors argue that to properly conceptualise

the service-quality construct, both the process and output of service production are impor-

tant. Shostack (1984) proposed the use of blueprinting to test the service quality. Similarly,

Bitner, Ostrom, and Morgan (2008) examined the service delivery process through the

service blueprinting model, which highlights the customer’s role in the service process

and how companies can innovate and include human-to-human and human-to-technology

interfaces at the firm boundaries. Hence, conceptualisation of perceived service

quality ought to include both the process as well as the service outcomes, because these

reflect a firm’s ability to serve the customer needs as well as to sustain its competitive

advantage.

Measurement of service quality in conventional services

The focus of service-quality measurement for conventional services is primarily on the

human interactions (i.e. between employees of the service company and the customers).

Gronroos’s (1984) service-quality model consists of functional quality (i.e. customer’s

interactions with the firm), technical quality (i.e. the service delivery) and corporate

image. In the context of human interactions, SERVQUAL is perhaps the most widely

known scale for measuring service quality (Parasuraman et al., 1988). It focuses mainly

on the human interactions during the service delivery and consumption. SERVQUAL con-

sists of five dimensions: reliability, tangibility, responsiveness, assurance and empathy.

Cronin and Taylor (1992) proposed an alternative scale called SERVPERF while criticis-

ing the SERVQUAL scale. This scale includes all the SERVQUAL scale dimensions, but

uses only service performance (perception) as a measure of perceived service quality

instead of the gap (between expectation and perception) approach of SERVQUAL. In

Chinese mobile phone service industry, the SERVQUAL model was tested by Lai, Hutch-

inson, Li, and Bai (2007). They found one more dimension named convenience in addition

to the existing dimensions of SERVQUAL. Saravanan and Rao (2007) identified six

service-quality dimensions in the automobile service sector namely, the human aspects

of service delivery, core service, social responsibility, non-human aspects, tangibles and

service marketing. Lonial, Menezes, Tarim, Tatoglu, and Zaim (2010) evaluated SERVQ-

UAL scale in case of the hospital services in Turkey. Findings suggest that the factor struc-

ture remains invariant when applied in the different cultural context. Tsai et al. (2011)

developed a multi-criteria model to evaluate the difference (gap) between airport passen-

gers’ perceptions and their expectations and propose strategies to minimise this gap. Kim

et al. (2011) examined the differences between the perceived service quality of full service

carriers and low-cost carriers. Findings suggest that the low-cost carriers ranked lower on

the service-quality dimensions as compared to the full service carriers.

Research has also found customers’ price perceptions, corporate image and pro-

motional activities are also used as the indicators of service quality (Al-Hawari,

Hartley, & Ward, 2005). Several other dimensions of service quality other than the

SERVQUAL dimensions are also identified in literature. For example, problem solving

(Dabholkar, Thorpe, & Rentz, 1996), policy, waiting time, social factors (Brady &

Cronin, 2001), core quality, relational quality and service features (Levesque & McDou-

gall, 1996), knowledge and accessibility (Olorunniwo & Hsu, 2006).

1204 S. Ganguli and S.K. Roy

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Service quality is also conceptualised as a hierarchical construct. Dabholkar et al.’s

(1996) service-quality scale for retail stores found a model of hierarchical factor structure

with five second-order dimensions which are physical aspects, reliability, personal inter-

action, policy and problem solving and five first-order dimensions appearance and conven-

ience; promises and doing it right; courteous/helpful and inspiring confidence. Fodness

and Murray (2007) developed a hierarchical model of service quality for airport services

consisting of three second-order dimensions, i.e. function, interaction and diversion and

first-order dimensions effectiveness and efficiency, productivity, decor and maintenance.

Brady and Cronin (2001) tested a hierarchical model of service quality in a multi-service

scenario which consists of three primary dimensions: interaction quality, physical environ-

ment quality and outcome quality. Each of the primary second-order factors consists of

first-order factors, e.g. attitude, behaviour and expertise; ambient conditions, design and

social factors; waiting time, tangibles and valence. Similarly, Bai et al. (2008) examined

a multi-level multidimensional model of perceived service quality for four public utility

services. The model has three primary dimensions namely, outcome, environment and

interaction quality and eight sub-dimensions namely stability, recovery, hard environment,

soft environment, standardisation, easiness, guarantees, and customer relations. However,

Garcia and Caro (2010) criticised the use of hierarchical reflective models based on the

issue of unobserved heterogeneity in customer perceptions and proposed a new technique

to measure perceived service quality using latent class cluster analysis. Table 2 provides a

tabular synthesis of the service-quality dimensions of conventional services from selected

publications.

Measurement of service quality in technology-enabled services

The conventional method of measuring service quality may not be relevant in case of tech-

nology-enabled services like Internet-based services, call centres, kiosks and SST. Bitner

et al. (2000) found that technology is an enabler for service encounter satisfaction, where

technology effectiveness is driven by customised service offerings, recovery from service

failure and spontaneous customer. Our review of literature shows that some conventional

dimensions of service quality like tangibles lost their relevancy (Li, Tan, & Xie, 2003) in

case of technology-enabled services. Similarly, new dimensions of service quality are

identified for technology-enabled services such as automated search, communication

among customers, information acquisition, content, mass customisation, and ease of use

(Peterson, Balasubramanian, & Bronnenberg, 1997). Parasuraman et al’s. (2005) E-S-

QUAL resulted in four quality dimensions (e.g. efficiency, fulfilment, system availability

and privacy) for electronic services. Cristobal, Flavian, and Guinaliu (2007) found web

design, customer service, assurance and order management as dimensions for e-service

quality.

A hierarchical model of e-service quality was proposed by Fassnacht and Koese (2006)

which consisted of three basic dimensions, e.g. environment quality, delivery quality and

outcome quality and nine first-order factors which are graphic quality, clarity of layout,

attractiveness of selection, information quality, ease of use, technical quality, reliability,

functional benefit, and emotional benefit. Al-Hawari et al. (2005) found five dimensions

for automated service quality in banking which are ATM quality, telephone-banking

quality, Internet banking quality, core service and price. Tsao and Tseng (2011) examined

the impact of e-service-quality dimensions on online shopping behaviour. The authors

found that e-service-quality dimensions impact web brand equity positively, which in

turn impacts perceived value and behavioural intention positively. Service-quality


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Table 2. Service quality in conventional services.

Author(s) (year) Objectives of the studyApplication field/

industry Service-quality dimensions

Gronroos (1984) To understand – (a) how service quality is perceived byconsumers and (b) determine in what way service quality isinfluenced

Multiple serviceindustries consideredin the sample

† Technical quality† Functional quality† Corporate image† Perceived service† Expected service

Parasuraman et al.(1988)

To develop a scale for assessing the service quality in services andretailing organisations

Four different serviceindustries considered

SERVQUAL† Tangibles† Reliability† Responsiveness† Assurance† Empathy

Cronin and Taylor(1992)

To (i) investigate the conceptualisation and measurement of SQand (ii) the relationships between CS, SQ and purchaseintentions

Four different serviceindustries considered

SERVPERF† Tangibles† Reliability† Responsiveness† Assurance† Empathy

Dabholkar et al.(1996)

To develop a scale of service quality in retail services context Retail store chains inUSA

Hierarchical factor structure with five second-order dimensions –physical aspects, reliability, personal interaction, policy andproblem solving

Levesque andMcDougall(1996)

To investigate the determinants of CS and future intentions Retail banking sector Three factors – core quality, relational quality and features

Brady and Cronin(2001)

Tested a hierarchical model of service quality Multi-service scenario Three primary dimensions: interaction quality, physicalenvironment quality and outcome quality

Olorunniwo andHsu (2006)

To operationalise the service measurement scale and determinethe nature of the SQ construct and its relationship with the CSand behavioural intentions

Retail banking sector Responsiveness, tangibility, reliability, knowledge andaccessibility dimensions contribute significantly to servicequality

Fodness andMurray (2007)

To develop a conceptual model of service quality Airport services Three second-order dimensions, i.e. function, interaction anddiversion and first-order dimensions of effectiveness andefficiency, productivity, decor and maintenance

Tsai et al. (2011) Developed a multi-criteria model to evaluate the difference (gap)between customer perceptions and their expectations andpropose strategies to minimise this gap

Airport passengers Dimensions of service quality: physical environment; interactionand outcome

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dimensions identified for SST-based services are ease of use, fun, usefulness, perform-

ance, solving intense need, saving time and money, avoiding service persons, technology

anxiety and robustness (Meuter, Ostrom, Bitner, & Roundtree, 2003). Service-quality

dimensions identified for the call centre services are adaptiveness, assurance, empathy,

authority, customer orientation, anticipating customer requests, offering explanations/jus-

tifications, educating customers, and offering personalised information (Dean, 2004).

Table 3 provides a tabular synthesis of the service-quality dimensions of technology-

enabled services from selected publications.

Our review of the service-quality literature reveals that a large body of knowledge

focuses on measuring service quality in conventional as well as technology-enabled ser-

vices. However, less attention is given to the measurement of service quality of hybrid

services.

Methods

Measurement instrument

An initial measurement instrument was derived from desk research and expert and prac-

titioner consultation. The instrument consists of 18 measurement items related to technol-

ogy-enabled service quality and 36 measurement items related to conventional service

quality. The items of technology quality were about the ease of use, reliability, security,

information quality, speed and convenience of using technology and sourced from Dab-

holkar and Bagozzi (2002), Fassnacht and Koese (2006), Herington and Weaven

(2009), Parasuraman et al. (2005), and Yang, Jun, and Peterson (2004). The items of con-

ventional service quality were about subscription ease (Kim, Park, & Jeong, 2004), tangi-

bles (Parasuraman et al., 1988), image (Aydin & Ozer, 2005), price (Al-Hawari et al.,

2005), reliability (Kumar, Kee, & Charles, 2010), employee behaviour (Brady, Cronin,

& Brand, 2002), relational quality (Levesque & McDougall, 1996), service facilities

(Woo & Fock, 1999) and customer service (Dean, 2004). Our measurement instrument

also had four items measuring customer satisfaction (CS) (adapted from Cronin, Brady,

& Hult, 2000; Olorunniwo & Hsu, 2006) and three items of customer loyalty (adapted

from Parasuraman et al., 2005; Zeithaml, Berry, & Parasuraman, 1996), which were

used for testing the nomological validity of the measurement model.

The banking industry fits the description of hybrid services. It has transcended from

being a conventional service to becoming a hybrid service as a result of the introduction

of technology-based delivery channels like ATM, Internet and phone banking. Due to the

influx of technology in banking, the whole idea of banking is now focused on convenience

and perceived ease of use. The banking system should be able to meet new challenges

which are being posed by the adoption of technology in service delivery. At the same

time, there are a number of reasons and occasions during which human–human inter-

actions happen in banking services, e.g. customer visiting a branch for some queries or

problems. Hence the retail banking service sector was chosen for data collection.

Sampling and data collection

Initially, a draft version of the questionnaire was shared with 50 students of an MBA ser-

vices marketing module in a B-school in Southern India, who were asked to provide

general comments on the design and ease of understanding of the individual questions.

The comments were favourable. The questionnaire was also shown to six colleagues in

the marketing departments and four practitioners (bank managers) for further comments.


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Table 3. Service quality in technology-enabled services.

Author(s) (year) Objectives of the studyApplication field/

industry Service-quality factors/dimensions

Bitner et al.(2000)

Examined technology is an enabler for service encountersatisfaction

Multiple servicescenario

Technology effectiveness is driven by customised serviceofferings, recovery from service failure and spontaneouscustomer

Li et al. (2003) To analyse service-quality dimensions in the web-basedInformation age

Web-based servicesin differentcountries

Service-quality dimensions are – responsiveness,reliability, assurance, empathy, information quality andintegrated communication (web based plus traditional)

Peterson et al.(1997)

To provide a framework for understanding possibleimpacts of Internet on marketing to consumers

Internet-basedservices

Dimensions of service quality are – automated search,communication among customers, informationacquisition, content, mass customisation, and ease of use

Parasuramanet al. (2005)

To develop a multiple item scale for assessing electronicservice quality

Online retail services E-S-QUAL scaleFour dimensions: efficiency, fulfilment, privacy and

system availability.E-recovery service quality scale

(ERecSQUAL)Three dimensions: responsiveness, contact and

compensationCristobal et al.

(2007)To build a model of e-service quality and test effect of the

quality on CSInternet users in

Barcelona, SpainFour dimensions – web design, customer service, assurance

and order managementFassnacht and

Koese (2006)To build hierarchical model of E-service Quality Three different types

of electronicservices

Overall quality with the three dimensions: environmentquality, delivery quality and outcome quality

Al-Hawari et al.(2005)

To develop a model for automated service quality Banking services inQueensland

The dimensions are ATM service quality, telephone-banking service quality, Internet banking service quality,price and core service quality

Meuter et al.(2003)

Explore usage patterns and benefits of using SSTs SSTs Ease of use, fun, usefulness, performance, solving intenseneed, saving time and money, avoiding service persons,technology anxiety and robustness

Dean (2004) To establish how customers’ expected service level iscompared to minimum level they consider adequate andrelation between perceived customer orientation andservice quality expectations in call centres

Call centres inAustralia

Adaptiveness, assurance, empathy, authority, customerorientation, anticipating customer requests, offeringexplanations/justifications, educating customers, andoffering personalised information

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With minor presentational amendments, the measurement instrument was then piloted on a

quota sample of 200 members of the population with the help of a market research agency

using the computer assisted telephone interview method. Responses were collected on a

seven-point scale anchored at ‘strongly disagree’ to ‘strongly agree’. The pilot sample

consisted of about 54% males. The sample size allowed the initial pilot measures to be

tested for unidimensionality and reliability. As suggested by Churchill (1979) and Para-

suraman et al. (1988), Cronbach’s alpha and item-to-total correlations were checked

and an item was deleted if the value of alpha increased considerably on deleting the

item from the items loading onto a factor and if the value of item-to-total correlation of

the item was considerably low as compared to all other factor loadings. We also

checked for the meaningful factor structure and cross-loadings if any. Based on the itera-

tive process, eight measurement items were deleted. The items deleted are measuring ‘pro-

motions’, ‘geographical presence’, ‘operation hours’, ‘technology failure’, ‘technology

usage risk’, ‘technology-recognising by name’, ‘prompt service’ and ‘specific needs’.

After deleting these items from the analysis, exploratory factor analysis was again used

on the remaining 46 items resulting in nine factors with eigenvalues greater than one.

The factors were labelled as service provision, employee competence, image, price, tan-

gibles, ease of subscription, information security & quality, technology usage convenience

and technology reliability. These factors accounted for about 70% of the total variance.

Subsequent to the piloting of the measurement instrument, a large-scale data collection

was conducted in the last quarter of 2011, resulting in the main data-set used in the study.

A sample was selected from a suitable database of retail bank customers (a good mix of

public sector and private banks) and the questionnaire was administered online through

a web interface in conjunction with a well-known market research agency specialising

in online data collection. The market research company in question used quotas to

ensure that the resultant sample was broadly nationally representative. In all 1200 ques-

tionnaires were sent, of which 950 were returned. Of the 950 responses, 750 were

usable, resulting in a 62.5% response rate. Two-hundred questionnaires were not used

because they were not filled in properly by the respondents. Respondents were instructed

to choose their most frequently used bank (i.e. main bank) and state their level of agree-

ment with the series of statements given in the questionnaire using a seven-point Likert-

type scales anchored at ‘strongly disagree’ to ‘strongly agree’. Fifty-two per cent of the

respondents were males, with the average age of the respondents being 36 years. Seven

per cent of the respondents were customers of the bank for less than 1 year, 41% for

more than 1–3 years and rest for more than 3 years. Ninety-eight per cent had savings

accounts, with 46% using Internet banking. All respondents were using some form of tech-

nology channels like ATM, Internet banking, telephone-banking and mobile banking.

Sixteen per cent were heavy users of banking services (more than 20 times per month);

44% were medium users (10–20 times per month) and the rest were light users (fewer

than 10 times per month).

Data analysis and results

Common method variance

Common method bias test was carried out to mitigate the risk of common methods bias in

our sample. Harman’s one-factor test was conducted through entering all the measurement

variables in an exploratory factor analysis using SPSS. The sample would have a common

methods bias problem if a single construct explains more than 50% of the extracted


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variance (Podsakoff, Mackenzie, Lee, & Podsakoff, 2003). The results indicated that the

factor obtained only attained 18.3% of extracted variance for the sample.

Exploratory factor analysis

Data analysis was done in four stages. First, the exploratory factor analysis (using principal

component analysis and oblimin rotation) was used to identify the underlying dimensions

of service quality for hybrid services. For this, the sample was split into two approximately

equal sub-samples: sample 1 (n ¼ 380) and sample 2 (n ¼ 370). Exploratory factor analy-

sis was performed on 46 measurement items of HSQ using SPSS 18.0. We obtained nine

factors using the criteria eigenvalues greater than 1.0.

The factors were labelled service provision (SP), employee competence (EC), image

(IMG), price (PRI), tangibles (TAN), ease of subscription (ES), information security

and quality (ISQ), technology usage convenience (TUC), and technology reliability

(TR). Total variance explained by the factors is about 72%. Cronbach alpha values of

all the extracted factors were greater than the recommended value of .70 (Hair, Black,

Babin, Anderson, & Tatham, 2006). Results of the factor analysis are shown in Table 4,

which is similar to those obtained in the pilot study.

First-order confirmatory factor analysis

Next, first-order confirmatory factor analysis (using AMOS 18.0) was used on sample 2 to

confirm the factor structure of the service-quality dimensions and establish their reliability

and validity. The measurement items loaded significantly onto the first-order hybrid service-

quality factors. The factor structure obtained was similar to those obtained in the exploratory

factor analysis stage. The model fit indices for the measurement model were acceptable, i.e.

(x2 ¼ 2946.4, df ¼ 1044, p , .001; x2/df ¼ 2.82; comparative fit index (CFI) ¼ .93;

Tucker Lewis index (TLI) ¼ .92; incremental fit index (IFI) ¼ .93; normed fit index

(NFI) ¼ .91; and root mean square error approximation (RMSEA) ¼ .06).

Composite reliability of all the factors were above the recommended value of .70

(Bagozzi & Yi, 1988). Fornell and Larcker’s (1981) method was used to examine the con-

vergent and discriminant validity of the factors obtained. The factor loadings of all the

measurement items are greater than .5 and the average variance extracted (AVE) values

of the dimensions are greater than .50, which supports the convergent validity. The

AVE values of all the quality factors were greater than the square of the inter-construct

correlations, which indicated the discriminant validity of the measurement model.

Higher-order confirmatory factor analysis

According to Brown (2006, p. 320), higher-order (second-order) is a theory-driven pro-

cedure whereby the researcher imposes a more parsimonious structure on the interrelation-

ships among the factors obtained in the lower-order (first-order) confirmatory factor

analysis. The author further states that higher-order model is useful when the lower-order

factors are distinctive and share a significant variance. A higher-order confirmatory factor

analysis tests a theory-based account of the interrelationships between the lower-order

factors and the higher-order factors that have direct effects on the lower-order factors.

We concur with Dabholkar et al. (1996) and Brady and Cronin (2001) and propose that

HSQ is a multidimensional and multi-level construct. We examined the factor correlations

among the factors estimated previously in our first-order models to determine which


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Table 4. Exploratory factor analysis results.

Factor/measurement itemFactor

loadingsCronbach’s

alpha

Service provisionBank’s customer service provide explanation (x1) .78 .95Customer service representatives are supportive (x2) .79Customer service representatives offer personalised information

(x3).74

Customer service representatives answered my call promptly(x4)

.70

My bank is sympathetic and reassuring (x5) .75My bank employees are knowledgeable to resolve my problems

(x6).61

My bank resolves my complaints quickly (x7) .69My bank offers a fair compensation for its mistakes (x8) .52Employee competenceMy bank employees are trustworthy (x9) .69 .96My bank employees are competent (x10) .73My bank employees are easily approachable (x11) .81My bank employees are courteous, polite and respectful (x12) .84My bank employees are willing to help customers (x13) .81My bank employees are pleasant and friendly (x14) .80My bank employees are caring (x15) .76ImageMy bank has a good reputation (x16) .62 .92My bank offers a wide range of services (x17) .54My bank provides a variety of service options (x18) .54My bank fulfils its promises (x19) .61My bank performs all services right, the first time (x20) .57My bank performs its services reliably, consistently and

dependably (x21).66

My bank’s statements and other documents are accurate (x22) .44My bank’s statements and other documents are easy to

understand (x23).40

PriceMy bank clearly explains its service charges (x24) .72 .89The fees that my bank charges are acceptable and reasonable

(x25).78

My bank fees are competitive (x26) .77TangiblesMy bank employees are neat in appearance (x27) .52 .81My bank’s physical facilities are visually appealing (x28) .79My bank’s printed materials (e.g. brochures) are visually

appealing (x29).77

Ease of subscriptionIt is easy to open a new bank account with my bank (x30) .88 .84∗ (corr.

coeff)It is convenient and hassle free to open a new bank account withmy bank (x31)

.85

Technology usage convenienceMy bank’s technology is accessible beyond regular business

hours (x32).70 .88

My bank’s technology gives me more freedom of mobility (x33) .71It is more convenient to use technology (x34) .76

.80

(Continued)


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factors might be grouped together as parts of the higher-order factors, and whether the

empirical correlations we obtained also made conceptual sense to combine the factors

obtained in the first stage of analysis. We observed that the following three groupings cor-

related with one another: (1) interaction quality (employee competence and service pro-

vision), (2) technology quality (information security & quality, technology usage

convenience, technology reliability), and (3) auxiliary quality (image, price, tangibles,

and ease of subscription). We modelled these three groupings as three second-order con-

structs as depicted in Figure 1 and describe them in more detail in Table 5. The model fit

indices for the second-order measurement model were acceptable (x2 ¼ 3048.4, df ¼

1068, p , .001; x2/df ¼ 2.85; CFI ¼ .94; TLI ¼ .93; IFI ¼ .94; NFI ¼ .92; and

RMSEA ¼ .06).

Based on the recent recommendations in the service-quality literature (Bai et al., 2008;

Brady & Cronin, 2001) and the emerging interpretations of formative and reflective

measurement, we represent HSQ using the reflective indicators of hierarchical organised

factors. The three second-order factors loaded onto a single third-order factor, which we

labelled as HSQ. Then we estimated the empirically driven but conceptually justifiable

hierarchical model of HSQ. All the relationships from the third-order hybrid service-

quality factor to the three underlying second-order factors and from them to their sub-com-

ponents are strong, positive and significant, as hypothesised (see Figure 1 and Table 6).

The model fit was adequate (x2 ¼ 3048.4, df ¼ 1068, p , .001; x2/df ¼ 2.85; CFI ¼

.94; TLI ¼ .93; IFI ¼ .94; NFI ¼ .92; and RMSEA ¼ .06). Since the correlations

between the first-order factors were high and the second-order model did not result in a

significant decrease in the model fit, it can be concluded that the hypothesised second-

order model provided a good account of the correlations among the first-order factors.

Similarly, there was no significant decrease in the model fit indices of the third-order

factor model as a result of the correlations between the second-order factors. Hence, the

hypothesised third-order model provided a good account of the correlations among the

second-order quality factors.

Table 4. Continued.

Factor/measurement itemFactor

loadingsCronbach’s

alpha

My bank’s technology allows me to complete transactionsquickly (x35)

My bank’s technology saves me a lot of time (x36) .77Information security and qualityI feel safe using my bank’s technology (x37) .65 .89My personal information is not misused by my bank (x38) .68My bank’s technology is personalised (x39) .61My bank’s technology provides the precise information I need

(x40).80

My bank’s technology provides sufficient information (x41) .76My bank’s technology provides the reports I need (x42) .67Technology reliabilityThe technology provided by my bank is easy to use (x43) .83 .90The technology provided by my bank is user friendly (x44) .84The technology provided by my bank works accurately (x45) .64My bank’s technology is reliable (x46) .73

∗Significant at p , .01.


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Nomological validity

Next, to test the nomological validity of the higher-order factor model, we estimated a

structural equation model with the relationship between the higher-order hybrid service-

quality factor and two other constructs conceptualised in the literature as the consequences

of service quality, i.e. CS and customer loyalty. Previous literature has shown that service

quality has a significant effect on both CS and customer loyalty (McDougall & Levesque,

2000; Zeithaml et al., 1996). CS was measured using a four-item scale adopted from Aydin

and Ozer (2005) and McDougall and Levesque, (2000). Customer loyalty was measured

by using a three-item scale adopted from Ganesh, Arnold, and Reynolds (2000) and

Zeithaml et al. (1996). Both the constructs were measured using a seven-point Likert-

type scale anchored at ‘strongly disagree’ to ‘strongly agree’. The model fit indices of

the structural model are acceptable. In the structural model the impact of the higher-

order hybrid service-quality factor on CS (b ¼ .37; p , .01) and loyalty are positive

and significant (b ¼ .35; p , .01). Thus, we suggest that the higher-order hybrid

service-quality model possesses nomological validity.

Figure 1. Hierarchical hybrid service-quality model.


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Discussion

We began this article by arguing that there exists a category of service called ‘hybrid ser-

vices’. A significant body of literature on service quality in the last three decades has

focused on the conceptualisation and measurement of service quality (Fodness &

Murray, 2007; Parasuraman et al., 1988). Broadly, two streams of conceptualisation

exist, one which considers service quality from an overall perception perspective and

the second from the lens of a multidimensional perspective. We argue that with the emer-

gence of this new category of services termed ‘hybrid services’ there is a need to explore

and identify the dimensions of such services so as to manage them properly.

The results presented here are an effort in this direction. We provide empirical evi-

dence that HSQ is a multidimensional hierarchical construct. Firstly, this research contrib-

utes to the existing body of knowledge on service quality by identifying the dimensions of

hybrid services. Secondly, we contribute to this body of literature by testing a hierarchical

model of HSQ. Collectively the results of this research contribute to the discipline in

various areas.

First, the findings suggest that customers’ perceptions of HSQ are reflected through

three primary dimensions, which are interaction quality, technology quality and auxiliary

quality. The first dimension is about the interactions of the service provider with the cus-

tomers and hence it is called interaction quality (Bai et al., 2008; Brady & Cronin, 2001).

The second dimension is about the quality of service delivered using the technological

tools and hence it is named as technology quality (Gronroos, 1984). The third dimension

is about the supplementary services which are not the core services but help in service

delivery and consumption. Hence it is termed as auxiliary service quality (Levesque &

McDougall, 1996). Second, empirical results also indicate that the three primary dimen-

sions are driven by nine sub-dimensions. The sub-dimensions are service provision,

employee competence, image, price, tangibles, ease of subscription, information security

Table 5. Second-order confirmatory factor analysis results.

Second-order factors First-order factors Loadings Variance explained (%)

Interaction quality Employee competence .84 59.3Service provision .90 81.0

Technology quality Information security and quality .94 88.4Technology usage convenience .80 64.0Technology reliability .75 56.2

Auxiliary quality Image .95 90.2Price .73 53.3Tangibles .73 53.3Ease of subscription .72 51.8

Note: All the factor loadings are significant at p , .001.

Table 6. Third-order confirmatory factor analysis results.

Third-order factor Second-order factors Loadings Variance explained (%)

Overall hierarchical service quality Interaction quality .93 86.5Technology quality .71 50.4Auxiliary quality .95 90.2

Note: All the factor loadings are significant at p , .001.


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and quality, technology usage convenience, technology reliability. Customers base their

evaluation of primary dimensions on the assessment of the corresponding sub-dimensions.

These findings suggest that customers evaluate service quality of hybrid services at an

overall abstract level, a dimensional level and at the sub-dimensional level. This provides

an insight into how customers evaluate HSQ. More precisely, the findings of this study

suggest that hybrid service managers should be concerned with improving the quality of

the services they provide across the three primary dimensions. This can be achieved

through managing the sub-dimensions identified in this study. For example, managers

could actually improve the perceptions of technology quality by focusing their resources

on making the online banking environment more secure and reliable and making banking

technology more convenient. In this regard, educating customers might play an important

role (Rafaeli, Ziklik, & Doucet, 2008).

Similarly, customers’ perceptions of interaction quality can be enhanced by making

sure that employees who interact with the customers are competent enough to deal with

the customers’ requirements and requests (Yang et al., 2004). Moreover, a focus on excel-

lent customer services both online and offline will enable the perceptions of interaction

quality to be higher in the minds of consumers (Dabholkar et al., 1996). Perceptions of

the quality of hybrid services can also be enhanced by focusing on supplementary (auxili-

ary) services like price cues (Al-Hawari et al., 2005), presence of tangibles, reputation of

the service firms (Aydin & Ozer, 2005; Parasuraman et al., 1988) and relative ease of sub-

scribing to the service (Lai et al., 2007; Tsao & Tseng, 2011). On the basis of these find-

ings, our hierarchical conceptualisation of HSQ seems appropriate.

With the hierarchical structure of hybrid services being tested in this study, prac-

titioners will be able to measure HSQ at three levels as highlighted above. They can

measure service quality at any of these levels depending upon the kind of information

needed and hence the decision to be taken. Thus this measurement of HSQ allows prac-

titioners to implement strategies related to measuring and managing such hybrid services.

A fair understanding and measurement of these dimensions can be used by the hybrid

service businesses to compare their perceived service performance vis-a-vis their

competitors.

Our model of service quality will help the managers of hybrid service providers in

understanding the assessment of quality of service experiences from the customers’ view-

point. This is useful for the service manager of a hybrid service because he can measure the

overall perceptions of service quality on these dimensions to get a broad indication of the

firm’s service-quality performance. Our framework can guide the managers of hybrid ser-

vices in their endeavours to enhance the service experience of customers. It can also be

used to categorise customers based on the sub-dimensions. In fact, proper profiling of

customers can be done by using discriminant analysis, which will help to identify the

quality factors which separate the satisfied customers from the unsatisfied ones. This

will help the hybrid service businesses to identify the areas of core competency and

service deficiencies too.

Limitations and future research

Although this research contributes to the literature and the findings have important man-

agerial implications, it is not without limitations. First, the cross-sectional nature of the

study is a limitation. There is a need to repeat the study using a longitudinal research

design. Secondly, the sampling strategy chosen in this study is non-probability sampling.

So the results of the study should be generalised with caution. The generalisability of the


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findings can be greatly enhanced by replicating the study in the case of other hybrid ser-

vices, i.e. telecommunications, retailing and insurance services. The findings of the study

can be extended to conduct a longitudinal study to examine how customers’ perceptions

and evaluation of hybrid service-quality changes over time. A logical extension of this

research is to empirically assess the effect of these quality dimensions on different

stages of customer loyalty, i.e. cognitive, affective, conative and action loyalty to have

a better understanding of how loyalty develops in customers’ minds in case of hybrid ser-

vices. Future research can also examine the interaction effect among the second-order

HSQ factors and examine their impact on the overall HSQ and related constructs like

CS and customers’ attitude towards hybrid services. We also recommend that future

researchers use qualitative inquiry to better explicate the HSQ dimensions and then

follow it up with the quantitative confirmatory approach.

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conceptualisation of service quality for hybrid services: a hierarchical approach

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