Investigating the impact of resourcecapabilities on customer loyalty: a structuralequation approach for the UK hotels using

online ratingsUsha Ramanathan and Ramakrishnan Ramanathan

Department of Business Systems, University of Bedfordshire Business School, Luton, UK

AbstractPurpose – In this paper, the authors aim to examine the impact of resource capabilities on customer loyalty of UK hotels. Understanding this impactwill help organisations to improve customer satisfaction in order to obtain improved customer loyalty.Design/methodology/approach – The authors use a relatively innovative data source, namely online ratings. They measure resource capabilities of afirm using customer ratings in terms of various operational criteria. Similarly, customer loyalty is measured using guests’ ratings on their intention to usethe same service (stay again in the same hotel) and their intention to recommend the service to friends. The authors employ structural equationmodelling to test research hypotheses.Findings – The authors’ results indicate that there is a significant positive influence of resource capabilities on customer loyalty. They further find thatthe significant influence of resource capabilities on customer loyalty does not differ across hotels with various star ratings.Research limitations/implications – The authors looked at the online guest ratings available on a particular website, but it is only one of the manywebsites offering online hotel reservations, and not all customers that made hotel reservations using this e-booking facility would be inclined to leavefeedback after their stay in the hotel. This limitation can be partially overcome by pooling similar data from a number of online hotel booking sites.Practical implications – The most important managerial implication is that good resource capabilities of firms translate well into customer loyalty.Thus, managers should ensure good performance in terms of various hotel attributes – cleanliness, quality of room, facilities, and customer service –and also ensure that customers perceive good value for their money while staying in the hotel.Originality/value – The authors applied structural modelling framework to verify the resource capability – performance link in the context of hotels.They used a relatively novel data source – online guest ratings of hotels – to understand the relationships between resource capabilities and customerloyalty.

Keywords Resource capabilities, Customer loyalty, Hotel performance variables, Online guest ratings, Hotels, UK

Paper type Research paper

An executive summary for managers and executive

readers can be found at the end of this article.

1. Introduction

In the past few decades, hotel and tourism sectors indeveloped countries have taken a tremendous structuralimprovement to compensate declining profits in other areaslike manufacturing (Kerr, 2003). The tourism and hotelsectors have been registering growth despite the problems ofrecession and terrorism (Nunes and Spelman, 2008). Thehotel sector is especially competitive with the emergence of avariety of hotels and apartments throughout the world. It hasbeen argued that, in order to stay ahead of competition, hotelsneed to utilise their resource capabilities to improve theiroperational performance in terms of various criteria(Knutson, 1988; McCleary et al., 1993). The theory of

service-profit-chain (Kamakura et al., 2002; Heskett et al.,

1994) can be used to show that performance of a firm in the

service sector requires high degree of customer satisfaction/

loyalty and customer loyalty is achieved by greater levels of

operational performance, which in turn requires resource

capabilities, including manpower.The resource-based-view (RBV) of the firm has been

extensively used to understand the links between resource

capabilities, operational performance and financial

performance of firms in various industrial sectors (Ortega

and Villaverde, 2008; Song et al., 2007; Wu et al., 2006; Wong

and Karia, 2010). For example, Nath et al. (2010) have

shown marketing capabilities and operational capabilities have

close relationships with overall performance of firms.

However, to our knowledge, only very few studies have

tested this link for the hotel industry (e.g. Jogaratnam and

Tse, 2004; Daghfous and Barkhi, 2009). In this paper, we

attempt to study the links between resource capabilities and

performance (measured using customer loyalty) in the hotel

sector. We draw on the RBV for the purpose.Thus, testing the relationship between resource capabilities

and performance in the context of hotels is a contribution of

this study. In addition, we also study how star ratings of hotels

influence the capability-performance link. This is another

contribution. A third contribution of this study is the use of a

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

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Journal of Services Marketing

27/5 (2013) 404–415

q Emerald Group Publishing Limited [ISSN 0887-6045]

[DOI 10.1108/JSM-12-2011-0186]

404

relatively novel internet-based data source (www.laterooms.

com).Rest of this paper is organised as follows. Section 2 outlines

the theoretical background of this research from the literature.

Section 3 explains the conceptual framework and researchhypotheses. Section 4 describes the data. Structural equation

models (SEM) are developed and their results are presentedin section 5. Section 6 discusses the results of SEM models

and the conclusions of this research to assist managerialdecision making. Finally, Section 7 specifically mentions

managerial implications of our results. This section alsoincludes some limitations of this research and scope for future

research.

2. Literature review

We review previous literature related to the concepts relevantfor this paper in this section. We first draw on the resource

based view (RBV) of the firm to understand how resource

capabilities are linked to customer loyalty, and review relatedprevious studies. We then review studies attempting resource

capabilities and performance linkage in the specific context ofhotels. We further review relevant literature on customer

loyalty. A brief review is also provided on our analysismethodology, namely structural equation modelling. Finally,

since our study uses online-ratings as our data source, webriefly review similar studies using online-ratings in the last

sub-section.

2.1 Literature linking resource capabilities and

performance

We believe that the resource based view (RBV) of a firmprovides the theoretical underpinning to understand how

resource capabilities are linked to customer loyalty (which we

employ as the measure of firm performance). The RBVrecognises that the basis for a competitive advantage of an

organisation lies primarily in the application of the bundle ofvaluable resources at the firm’s disposal (Wernerfelt, 1984;

Rumelt, 1974). This theoretical paradigm seeks to explainfirm behaviour and the subsequent outcomes (Barney and

Clark, 2007). A number of studies have tested the resource-based view of the firm in different contexts. For example,

using RBV as their theoretical backdrop, Nath et al. (2010)have recently shown that marketing capabilities and

operations capabilities of logistics firms significantly

influence firm performance. The marketing literature has anumber of studies that proposed and proved that marketing

capability generally was significantly related to financialperformance of firms (e.g. Ortega and Villaverde, 2008; Song

et al., 2007). More specifically, Ortega and Villaverde (2008)have shown that marketing capability had strong impact on

performance for firms that invest on better assets to innovatein a dynamic business environment. Similar observations are

also available in the strategic management literature whichsuggests that firm capabilities (marketing, operations,

technological, supply chain, and/or their interactions) have

significant impact on performance depending on the way inwhich firms align themselves with their business environment

(McDaniel and Kolari, 1987; Song et al., 2005; Wu et al.,2006; Conant et al., 1990). Wong and Karia (2010) have used

RBV to explain the competitive advantages of logistic serviceproviders by identifying strategic logistics resources and

characteristics.

The RBV has been used heavily in the hotel literature as

well. Espino-Rodrıguez and Padron-Robaina (2005) andEspino-Rodrıguez and Gil-Padilla (2005) have used RBV to

link outsourcing capabilities of hotels to organisationalperformance. Dahlstrom et al. (2009) have used the RBVperspective to investigate the relationship between service

offerings and governance structures in the Norwegian hotelindustry. Barros and Mascarenhas (2005) argued using RBV

that if the resources in a hotel chain were not easilyexchangeable between units in the chain, different uniqueassets would exhibit different levels of efficiency. Using data

envelopment analysis (DEA), the authors have then estimatedthe relative efficiency levels of Portuguese hotels.

2.2 Review of the hotel literature linking resource

capabilities to performance

There is relatively smaller number of articles in the hotelliterature that directly link resource capabilities to

performance. For example, Jogaratnam and Tse (2004) havefound that performance of hotels varied based on their

operations capabilities. More recently, Daghfous and Barkhi(2009) have linked total quality management, supply chainmanagement and customer relationship management

capabilities of four- and five-star hotels in the UAE using anexploratory survey and statistical analysis. However, the hotelliterature that deals with service quality has a number of

previous studies that attempt to link resource capabilities tofirm loyalty indirectly using service quality criteria. The

service quality literature usually evaluates performance ofhotels in terms of several criteria or dimensions. For example,the SERVQUAL approach of measuring service quality uses

five important dimensions: reliability, assurance, tangibles,empathy and responsiveness (Parasuraman et al., 1988). If itis assumed that resource capabilities are directly related tocustomer evaluations of service quality in terms of thesedimensions, then these studies address the resource capability

– performance link. This is a plausible assumption because ifa firm has higher resource capability in terms of say reliability(by investing appropriate resources in improving

performance), then it will receive a higher rating in terms ofthis dimension.Before reviewing the hotel literature on service quality, we

provide further arguments on the use of service quality ratings

to measure resource capability. First of all, we wish tohighlight the difference between resources and capabilities.Resources represent the assets used by a firm as inputs to

organisational processes; and capabilities are the firm’sabilities to combine, develop, and use the resources in orderto create competitive advantage (Kaleka, 2002). Thus

resources are not specific to a firm, while capabilities are theunique and inimitable knowledge accumulated by the regular

use of resources. In this study, we interpret resource capabilityas the capability to use available resources. However, it shouldbe noted that the service quality literature is familiar with

multiple gaps in influencing perceived service quality(Parasuraman et al., 1985). Three (management

perceptions, translation of perceptions to specifications andservice design) of the five gaps discuss why and how a firmmight not be able to deliver good service quality even if the

firm has the required resource capabilities. In our study, weassume that the impact of these gaps is negligible so thatresource capabilities are directly related to customer

evaluations of service quality.

Investigating the impact of resource capabilities on customer loyalty

Usha Ramanathan and Ramakrishnan Ramanathan

Journal of Services Marketing

Volume 27 · Number 5 · 2013 · 404–415

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A number of studies in the hotel literature focused mainly

on measuring service quality. For example, Su and Sun(2007) have used content analysis and SERVQUAL

dimensions for analysing Taiwan’s criteria of hotel servicequality. Hsieh et al. (2008) have used the SERVQUALinstrument to measure service quality of Hot Spring hotels in

Taiwan and then derived weights for service qualitydimensions. Extending the SERVQUAL instrument for a

rural context, Albacete-Saez et al. (2007) have developedmeasures and scales to assess quality of service in rurallylocated tourism lodgings. Using multivariate statistics

(confirmatory factor analysis), they have found fivedimensions as useful: personnel response, complementary

offer, tourist relations, tangible elements and empathy.Many studies that attempted to link service quality

dimensions to performance of hotels did not directlymeasure performance in terms of financial values, butusually measured using the notions of customer satisfaction

and customer loyalty. This literature is reviewed in the nextsection.

2.3 Studies linking resource capabilities (service

quality) and customer satisfaction/loyalty

There are studies in the hotel literature that have explored thelink between perceived service quality and customer

satisfaction/loyalty (e.g. Dube and Renaghan, 1999 a, b;Iacobucci et al., 1995; Oh, 1999; Gonzalez et al., 2007;Hutchinson et al., 2009). In their two part study, Dube and

Renaghan (1999a, b) have examined guests’ perceptions ofbuilding customer loyalty and the contribution of various

hotel criteria in delivering promised benefits. They haveconducted a primary survey of frequent business and leisuretourists in the US to understand the relative contribution of

115 functional practices to customer loyalty. Iacobucci et al.(1995) have studied consumers’ understanding and use of the

words quality and satisfaction using critical incidentmethodology.Gonzalez et al. (2007) have used factor analysis and

regression on the data from a primary survey and have foundevidence for a positive relationship between perceived service

quality and satisfaction. They have also found that customersatisfaction and perceived service quality positively influenced

behavioural intentions. Wilkins et al. (2007) have used surveyof 63 items relating to hotel performance to understand thedeterminants of service quality in the first class and luxury

hotel segments in Queensland, Australia. Using exploratoryprincipal component factor analysis, they have found that the

63 items reduced to seven components. They have furtherfound that the seven components could be grouped into threedimensions – service experience, physical product, and,

quality of food and beverages using confirmatory factoranalysis. Chao (2008) has empirically analysed the impact of

four different attributes of service quality (personnel,operational, merchandise and physical) on customer loyalty.There are also studies that attempted to identify the relative

importance of various performance criteria of service qualityin influencing customer satisfaction (Knutson, 1988;

McCleary et al., 1993). Cleanliness is generally reported asthe most important criterion (Weaver and McCleary, 1991).

Quality of rooms with comfortable beds and good towels hasalso been found important (Knutson, 1988; Weaver andMcCleary, 1991; Weaver and Oh, 1993). Other criteria such

as customer service (Knutson, 1988; Weaver and McCleary,

1991; Weaver and Oh, 1993), and, safety and security

(Knutson, 1988; Weaver and McCleary, 1991) have also been

found important.The use of customer loyalty as a proxy for firm performance

is not unique to studies in the hotel literature. In the e-commerce context, Heim and Sinha (2001) and Jiang and

Rosenbloom (2005) have used customer loyalty as a proxy forperformance. In this paper, we use customer loyalty to

measure firm performance.

2.4 Influence of star rating

The function offered by hotels is essentially a service function,which is only “experienced” by customers during their stay

and makes the assessment of quality difficult (Heineke and

Davis, 2007). In order to guide potential guests on the natureof facilities and service that can be expected in hotels, the star

rating of hotels is generally used. This rating usually variesbetween 2 and 5. In general, the higher the star rating, the

higher is the expected level of service and facilities in a hotel.Several research studies have attempted to test the

correctness of star ratings by comparing the ratings with the

facilities available in hotels and the service experienced byhotel guests. There have been conflicting findings. For

example, using data obtained from a primary questionnairesurvey and customer reviews from Tripadvisor.com, Briggs

et al. (2007) have argued that star grading schemes andassociated standards are largely driven by physical facilities of

the hotels, and that these schemes do not adequately take intoaccount customer service orientation of hotels. As per Orfila-

Sintes and Mattsson (2009), different levels of hotel quality

do not really have an impact on hotel operations as such, andthe difference between high and low quality accommodation

is in the quality of the extra services and tangibles. Fernandazand Bedia (2004) have also found that, in general, hotel star

rating systems do not correspond to levels of service quality.Thus the literature shows that hotels with different star ratings

do have differences in their offerings. We take a closer look at

the impact of star ratings of hotels on the link betweenresource capabilities and performance. We employ structural

equation modelling (SEM) for the purpose, and hence weprovide a brief description of SEM below.

2.5 Structural equation modelling

Structural equation modelling (SEM) is a multivariate

statistical analysis methodology developed to examine aseries of dependence relationships simultaneously, and is

particularly useful in testing theories involving multiple and

simultaneous inter-dependence relationships (Anderson andGerbing, 1988; Hair et al., 2006). It is generally an extension

of multivariate regression analysis and path analysis (Jiang andRosenbloom, 2005). Recent articles such as Ulengin et al.(2010) and Anderson and Vastag (2004) provide reviews ofthe applications of SEM for analysing relationships among

various possible and existing factors of business. Thismethodology is receiving increasing attention among

researchers, especially in the hotel literature.Oh (1999) has used structural equation methodology and

has found evidence for a holistic model of service quality,

customer value, and customer satisfaction. Ha and Fanda(2008) have established, among other things, that perceived

value is positively related to satisfaction of customers, andsatisfaction is positively related to customer loyalty and to

their repurchase intentions using structural equation

Investigating the impact of resource capabilities on customer loyalty

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modelling in the context of travel agencies in the UK. Yee et al.(2008) have used structural equation modelling to understandthe relationships among quality, customer satisfaction, andfirm profitability in high contact service industries in HongKong. Their study has found evidence for significantrelationships among employee satisfaction, service qualityand customer satisfaction, and these are also linked to firmprofitability. Interestingly, they have also found that firmprofitability influenced employee satisfaction. Gallarza andSaura (2006) confirmed the existence of a quality-value-satisfaction-loyalty chain in explaining the behaviour of touristcustomers using SEM. Recently, Qin and Prybutok (2009)have used SEM to identify the links among service quality,customer satisfaction and behavioural intentions in fast-foodrestaurants in the US. In this research we use SEM to identifythe impact of resource capabilities on customer loyalty and tounderstand the moderating effect of star ratings of hotels onthe relationships between resource capabilities of hotels andcustomer loyalty. We use a relatively novel data on online-ratings for our analysis. Hence, we provide a brief discussionof studies that used online ratings in the next sub-section.

2.6 Empirical studies that used online ratings

The growing popularity of the internet and the increasingavailability of user-generated content have provided a richcollection of data. The data are not restricted only to the hotelindustry but several sectors such as the e-commerce(e.g. Heim and Sinha, 2001), movies (Duan et al., 2008),tourism (e.g. Lu and Stepchenkova, 2012) or restaurants(e.g. Zhang et al., 2010) have also seen tremendousaccumulation of customer data. Several studies have takenadvantage of such voluminous secondary data and used themto understand customer behaviour. Most of these empiricalstudies are based on the availability of significant amount ofonline customer ratings, customer feedback or customerreviews (Wang and Archer, 2007; Yang, 2006; Ye et al., 2011).Some have used e-bay feedback (Dellarocas and Wood, 2008;Finch, 2007), some have used online ratings (Heim andSinha, 2001; Ye et al., 2011) while some have used onlineconsumer reviews (Chen and Xie, 2008; Ye et al., 2009).Though the number of studies using online-data has beenincreasing steadily, there are not many in the hotel industry.Among the studies that used online data in the hotel industry,Ye et al. (2009, 2011) have focussed on how the number ofbookings for Chinese hotels varied depending on customerreviews using data from ctrip.com. We in this paper have usedanother UK based internet source, namely www.laterooms.com.

3. Conceptual framework and researchhypotheses

As mentioned earlier, we draw on the resource based view(RBV) of a firm to understand the relationships between afirm’s resources and capabilities, and customer loyalty. Basedon the studies reported in section 2.2 and 2.3, there isevidence that resource capabilities in hotels are related tocustomer loyalty. Thus, using the resource-based view, weposit that there will be a significant link between resourcecapabilities and customer loyalty. This forms our mainhypothesis:

H1. Resource capabilities of hotels positively influencecustomer loyalty.

In this research, we do not measure resource capabilities

directly but capture using a proxy. We assume that the abilities

of firms in utilising available resources are manifested via the

levels of ratings in terms of various operational criteria. Thus,

a firm that is able to utilise its available resources to improve

its performance in terms of, say, customer service, will be able

to achieve good ratings in terms of this performance criterion.

As discussed in sections 2.2 and 2.3, this assumption has been

used in previous hotel literature.In this study, we use more than one measure of customer

loyalty. The measures we use are customers’ own intention to

stay again in same hotel, and customers’ willingness to

recommend the hotel to friends. We further include a

secondary hypothesis to understand the relationships between

these two measures of customer loyalty. Specifically, we test

the hypothesis that customers’ intention to stay again is

positively related to their willingness to recommend to friends.

This hypothesis is motivated by studies in the hotel literature

that have proved customer satisfaction positively influences

behavioural intentions of guests (Olorunniwo and Hsu, 2006;

Olorunniwo et al., 2006; Qin and Prybutok, 2009; Sui and

Baloglu, 2003). Thus, our second hypothesis is the following:

H2. Customers’ intentions of future stay significantly

influence their recommendation to friends.

Finally, we include our last hypothesis on the moderating

roles of star ratings of hotels on the link between resource

capabilities and firm performance. A number of previous

studies highlight that efficient firms will be able to convert

resource capabilities to performance better than inefficient

firms (e.g. Nath et al., 2010). However, there are no studies

that proved that efficiency of a hotel is related to star ratings.

On the contrary, as per the literature survey discussed in

Section 2.4, many of the previous studies have found that star

ratings of hotels did not influence service quality. Adapting

these findings to our results, we posit that the relationship

between resource capabilities and customer loyalty will not be

moderated by star ratings of hotels:

H3. The relationship between resource capabilities of

hotels and customer loyalty will not be moderated by

star ratings of hotels.

Our conceptual framework is shown in Figure 1. Thus, we

investigate the link between resource capabilities and

customer loyalty, but these are measured via proxies.

Resource capabilities are measured using guest ratings of

hotel performance in terms of various criteria, while customer

loyalty is measured using customer ratings in terms of his/her

own intention to stay in the hotel again and in terms of his/her

willingness to recommend the hotel to friends.

4. Data description

In this section, we discuss our data source and present

summary details of the data. We have used secondary data

from a relatively novel data source – online guest ratings

available at one of the websites specialising discounted hotel

reservations online. It is one of the many websites offering

online hotel reservations, and not all customers that made

hotel reservation using this e-booking facility will be inclined

to leave feedback after their stay in the hotel. However, the

available ratings are assumed reflecting their true opinion on

experience of their stay at hotels. We use these true opinions

Investigating the impact of resource capabilities on customer loyalty

Usha Ramanathan and Ramakrishnan Ramanathan

Journal of Services Marketing

Volume 27 · Number 5 · 2013 · 404–415

407

of customers to test the relationship between the hotel

resource capabilities and customer loyalty.

4.1 Data source

We have chosen a relatively novel data source to collect our

data. Our data have been collected from the website of

LateRooms (www.laterooms.com). “LateRooms” is an online

hotel booking system including over 18000 properties across

the UK and Europe. About 40 per cent of the total number of

accommodations available through late rooms is in the UK

and Ireland.The LateRooms database comprises many options,

properties from hotels to self-catering apartments, from

budget hotels to 5 * hotels, and booking options from ordering

online to telephone booking. Hotel star classifications either

based on self-classification by the hotels themselves, assessed

by the Automobile Association (AA) of the UK, VisitBritain,

VisitWales or VisitScotland.As per the press-pack of the website (LateRooms, 2008), it

has over 650,000 registered users and 100,000 daily visitors.

Many of the visitors are unique visitors – the company

estimates that it has 2.5 million unique visitors to its site every

month. The company estimates that about 45 per cent of

bookings are made in their website for business travel whilst

55 per cent are made by leisure customers. About 75 per cent

of bookings are made for independent properties while the

rest is booked for branded hotels. A majority of its customers

prefer 3 * (44 per cent) hotels, followed by 4 * (27 per cent)

while about 2 per cent of its customers booked for 5 * hotels.One of the attractive features of LateRooms is the

availability of user reviews. Users can read from over

190,000 true hotel reviews, written by customers who have

booked through LateRooms and actually stayed at the hotel.

Guests utilising the service of a hotel (booked through

LateRooms) are subsequently asked by LateRooms to rate the

performance of the hotel. The rating is captured in terms of

five different factors namely customer service, cleanliness of

hotel, quality of room, value for money, quality of food, and

family friendliness of the hotel using a Likert type scale

varying from low (1) to high (6) with an additional options for

non-availability (NA). In addition, customer is asked whether

he/she would stay in the hotel again (yes/no) and whether he/

she would recommend the hotel to a friend (yes/no). The

ratings from all the reviewers of a hotel are then summarised

such that the hotel is rated using the 1-6 Likert scale ratings

for the five factors. The answers to the two yes/no questions is

summarised as percentages (per cent of reviewers that said

they would stay in the hotel again and per cent of the

reviewers that said they would recommend the hotel to a

friend).In this paper, for the purpose of analysis, we have used

these online ratings by hotel guests. As already mentioned in

section 2, we assume that these online ratings can be used as

proxies for resource capabilities of hotels. It seems it is the

first time information from such hotel reviews (LateRooms)

are used to explore the relationships among various

operational quality attributes and customer loyalty or

satisfaction. However, the use of online ratings for exploring

such relationships is not new, with several studies existing in

the context of e-commerce websites (bizrate.com,

epubliceye.com, etc.) (e.g. Heim and Sinha, 2001 and Jiang

and Rosenbloom, 2005).

4.2 Data

We have collected ratings for hotels/guesthouses throughout

the UK during August-September 2008. Though our data

have considered not only hotels but also other properties such

as guesthouses, we use the term hotels generically to denote

all these properties in this paper. We have selected only those

hotels that received ratings from at least 30 customers and the

ratings are average scores based on the ratings of individual

customers. Ratings for a total of 664 hotels were used in the

study. About 44 per cent of these hotels are part of hotel

chains while the remaining comprised independent hotels.

The hotels received ratings from a total of 24,544 customers,

of which 16,739 are leisure customers and 7,805 are business

customers.Guests’ rating on seven performance variables namely

cleanliness, room quality, family friendliness, customer

service, value for money, stay and recommend to friend are

used in our analysis. Table I reports the descriptive analysis of

the data. All the seven variables have positive significant

correlation with each other (p , 0.01). Mean ratings of the

first five variables are in the range of 4.19 to 5.00 (6 being the

maximum rating). Average score of stay again and

Figure 1 The conceptual framework

Investigating the impact of resource capabilities on customer loyalty

Usha Ramanathan and Ramakrishnan Ramanathan

Journal of Services Marketing

Volume 27 · Number 5 · 2013 · 404–415

408

recommend to friend are 82.99 and 81.46 per cent

respectively.

5. Empirical analysis

We have used structural equation modelling (SEM) to explore

the relationships among various attributes of customers’

opinion in respect to validate our research hypotheses. SEM is

a recent statistical analysis approach that can be used to

analyse path relationships (Hair et al., 2006). It is being

increasingly used to study online customer ratings (e.g. Jiang

and Rosenbloom, 2005). We adopt Anderson and Gerbing’s

(1988) two stage procedure for testing our conceptual

framework and research hypotheses.

5.1 Model constructs

The first stage deals with the development of structural

models. In this stage, we have used Amos 6 for conducting

confirmatory factor analysis (CFA). This is mainly for

developing and testing the unidimensional nature of our

latent constructs, namely resource capabilities and customer

loyalty. We have further used Amos 6 in the second stage to

validate our measurement model linking resource capabilities

and customer loyalty as specified in our conceptual

framework. This two stage procedure will help to minimise

“interpretational confounding,” which has been observed in

the single stage SEM procedures (Burt, 1976; Anderson and

Gerbing, 1988).

5.1.1 Stage 1: measurement modelsTable II provides the results of the confirmatory factor

analysis. The five performance variables (cleanliness, room

quality, family friendliness, customer service and value for

money) converged into a single factor. In line with our

assumption that these ratings stand proxies for resource

capabilities, we term this group to represent the “resource

capability”. Table II shows that the results of this CFA are

acceptable (Nunnally, 1978; Fornell and Larcker, 1981; Hair

et al., 2006). All the performance variables have high factor

loadings (above the recommended minimum of 0.60). The

reliability of this factor (resource capability), shown by

Cronbach’s alpha and composite reliability, is good (above the

recommended minimum of 0.7). This factor is able to explain

65.3 per cent of variations in the constituent variables, which

is more than the recommended minimum of 50 per cent.Similarly, results the CFA of the other two loyalty-related

variables (stay again and recommend to friends) are also

satisfactory. Both these variables converged to a separate

factor. We term this group to represent the “customer

loyalty”. The two variables have very high factor loadings

(0.972). The high reliability of this hotel performance factor is

shown by very high Cronbach’s alpha and composite

reliability. The percent variable extracted is also very high.

5.1.2 Stage 2: structural modelHaving verified the unidimensionality of our latent constructs,

we have used SEM in the second stage to verify our theorised

structural relationships between resource capabilities and

customer loyalty. Figure 2 provides the results of the theorised

relationships. In SEM, the model fit is assessed using a variety

of indicators, more prominent being the Chi-square and

degree of freedom, the comparative fit index (CFI), the

goodness-of-fit index (GFI), the incremental fit index (IFI),

the Trucker Lewis Index (TLI) and root mean square error of

approximation (RMSEA) (Hair et al., 2006; Baumgartner and

Homburg, 1996; Bentler et al., 2001). Our conceptual model

performs well in terms of these indicators. GFI and CFI for

our structural model are 0.97 and 0.983 respectively,

comfortably exceeding the recommended minimum level of

0.9. RMSEA is at the recommended maximum of 0.08. Other

fit indices (IFI and TLI) also exceed the recommended

minimum of 0.90. The ratio of Chi-square to degrees of

Table I Descriptive statistics of data

Variables Mean Std dev. 1 2 3 4 5 6 7

1. Cleanliness 5.00 0.652 1

2. Room quality 4.66 0.760 0.707 * 1

3. Family friendliness 4.19 0.835 0.429 * 0.441 * 1

4. Customer service 4.77 0.668 0.631 * 0.593 * 0.510 * 1

5. Value for money 4.56 0.670 0.576 * 0.673 * 0.466 * 0.575 * 1

6. Stay again 83% 14% 0.635 * 0.694 * 0.458 * 0.603 * 0.683 * 1

7. Recommend to friends 81% 15% 0.686 * 0.732 * 0.487 * 0.666 * 0.704 * 0.888 * 1

Notes: *Correlation is significant at the p , 0.01 level (2-tailed) and n=664

Table II Results of Confirmatory factor analysis

Latent construct Observed variables Factor loading Cronbach’s alpha Composite reliability Percentage of variance extracted

Resource capabilities Cleanliness 0.838 0.865 0.85 65.30

Room quality 0.857

Family friendliness 0.683

Customer service 0.823

Value for money 0.821

Customer loyalty Stay again 0.972 0.944 0.93 94.67

Recommend to friends 0.972

Investigating the impact of resource capabilities on customer loyalty

Usha Ramanathan and Ramakrishnan Ramanathan

Journal of Services Marketing

Volume 27 · Number 5 · 2013 · 404–415

409

freedom is slightly higher than the recommended fit of 4, butwhen all the measures are taken together, our structuralmodel generally indicates a good level of fit.

5.2 Results

Figure 2 shows that the five performance variables(cleanliness, room quality, family friendliness, customerservice and value for money) have significantly high positiveloadings with their corresponding factor “resourcecapabilities”. Similarly, the other two observed variables(stay again and recommend to friends) also have highsignificant positive loading (l) of 0.88 and 0.84 with“customer loyalty.” In the structural model, resourcecapabilities have a significant positive relationship (0.93)with customer loyalty. This high positive loading validates ourfirst hypothesis that resource capabilities of a firm positivelyinfluence customer loyalty.In the structural model, the path from “stay again” to

“recommend to friends” is positive and significant (0.13).This proves our second hypothesis that customers’ intentionto come back to the same hotel again positively translates intorecommendation to friends (refer to Figure 2).Thus, our structural model has validated the research

hypotheses that the resource capabilities positively influencecustomer loyalty and customers’ intentions of future staysignificantly influence their intention to recommend tofriends.In order to verify our third hypothesis (moderating role of

star ratings of hotels), we divided our data into two groups –hotels with star ratings 4 and above, and hotels with starratings 3 and below. We applied the same structural model tothe two groups. Results are shown in Table III. The last tworows of the table show the results for the two groups. First ofall, the structural models for the two groups perform well interms of various SEM model fit indicators. CFI, GFI, IFI andTLI are all well above the recommended minima of 0.9 in

terms of these indicators, while RMSEA is either below or atthe recommended maximum of 0.08. The last column of thetable shows that resource capabilities have positiverelationship (with path coefficients 0.90 and 0.95) withcustomer loyalty for the two groups. The relationship betweenresource capabilities and customer loyalty is equallysignificant for both groups. This result shows that therelationship between resource capabilities and customerloyalty is not moderated by star ratings of hotels, and thussupports of third hypothesis.

6. Discussion and conclusions

Our results strongly support the resource-based view of thefirm in the context of hotels. Resource capabilities, asmeasured by good performance of hotels in terms of variousoperational measures, strongly influence customer loyalty(measured using ratings on intentions to stay again andintentions to recommend the hotel to friends). This result isconsistent with previous studies like Conant et al. (1990),McDaniel and Kolari (1987), Song et al. (2005), Wu et al.(2006) and Wong and Karia (2010) that studied thecapability-performance links. Our results also supportsimilar findings in the hotel literature, such as those byDube and Renaghan (1999a, b), Gonzalez et al. (2007) andChao (2008), that linked performance of hotels in terms ofvarious criteria to customer satisfaction/loyalty. Our findingsgenerally point to the fact that hotels need to ensure goodperformance in terms of various service attributes in order towin customers.Our findings point to the need for improving resource

capabilities in order to register good performance. Thus,hotels should ensure that they have the required capabilities toperform various services. The capability to maintain overallcleanliness of hotels will result in good customer rating interms of this attribute. This rating has a significantly high

Figure 2 Structural equation model

Investigating the impact of resource capabilities on customer loyalty

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410

loading (0.75 as shown in Figure 2) in contributing to the

overall capabilities, which, in turn, will result in better hotelperformance in winning customers. Similarly, the capabilities

of a hotel in ensuring good quality of rooms has still higher

loading (0.82) in contributing to overall hotel capabilities.

Similarly, other performance variables (family friendliness,customer service and value for money) also contribute well to

the overall hotel capabilities, which in turn have significant

impact on guests’ intentions to stay again and to recommendto friends.Thus, all the five performance variables contribute to

resource capabilities of hotels. Of these, room quality andvalue for money have high loadings, providing the greatest

influence on overall hotel capabilities. The importance of

these variables has been stressed in the hotel literature. For

example, Knutson (1988), Weaver and McCleary (1991),Weaver and Oh (1993), and Mohsin and Lockyer (2010) have

shown that room quality with comfortable beds and good

towels is considered very important by hotel guests.Similarly, the importance of “value for money” has been

stressed in several studies on hotel performance (Oh, 1999;

Mattila and O’Neill, 2003; Gallarza and Saura, 2006; Chenand Schwartz, 2008). Al-Sabbahy et al. (2004) have classified

“value for money” as an influencing factor of customers’

future choice behaviour. Chen and Schwartz (2008) stressed

the importance of value when guests book a room on theinternet and showed that the patterns of changes in room

rates observed by guests while searching for a deal affects their

propensity to book. Using a three year longitudinal study of asingle hotel, Mattila and O’Neill (2003) have found price as a

significant predictor of overall guest satisfaction and three key

guest-satisfaction components: guest room cleanliness,

maintenance, and attentiveness of staff.Our findings support the second hypothesis. Thus, guests’

intention to stay in the same hotel again seems to have a

significant positive influence their intention to recommend thehotel to friends. This finding has marketing significance.

Thus, good performance of a hotel in terms of various

performance variables will not only have a positive impact onguests’ intention to stay again, but will also get translated to

the intention of guests to recommend to their friends. Loyal

customers bring business back to the hotel not only through

their future stay but also by recommending the hotel to theirfriends. As mentioned in the literature survey, there seems to

be no study that specifically looked at this link, but there are a

number of studies in the marketing literature that highlightedthe positive relationships between satisfaction and behavioural

intentions (Olorunniwo and Hsu, 2006; Olorunniwo et al.,2006; Qin and Prybutok, 2009). Our finding is generally in

agreement with this literature.Our findings support our third hypothesis. Thus, the link

between resource capabilities and customer loyalty does not

depend on star ratings of hotels. Since we measured resourcecapabilities using guest ratings in this study, this finding could

imply that hotel guests rated performance with due regard tothe star category. That is, when guests expect a high level ofservice in a five star hotel and receive a certain level of service,

they provided a rating keeping in mind their expectations andthe actual service received. This finding is generally consistentwith the literature that highlighted that star ratings of a hoteldo not moderate their service levels and performance (Briggs

et al., 2007; Orfila-Sintes and Mattsson, 2009; Fernandaz andBedia, 2004)

7. Summary, managerial implications andlimitations of the study

We have used a relatively novel data source – online guestratings of hotels – to understand the relationships between

resource capabilities and customer loyalty. Using structuralequation model, we found support for three hypotheses. Wehave found that resource capabilities of hotels, measured in

terms of ratings in terms of different performance variables,have a significant positive influence on customer loyalty(measured using guests’ intention to stay again and to

recommend to friends). We have also found that guests’intention to stay again positively translates to their intentionto recommend the hotel to friends. Finally, we have found

that the positive relationship between resource capabilitiesand customer loyalty is not affected by the star ratings ofhotels. These findings are consistent with similar results in theliterature.We believe that our results have managerial implications.

The most important implication is that good performance of

hotels in terms of performance variables positively translatesto customer loyalty. Thus, managers should ensure goodperformance in terms of various hotel attributes – cleanliness,

quality of room, facilities, and customer service, and alsoensure that customers perceive good value for their moneywhile staying in the hotel. Second, a happy customer that isready to stay in the hotel again is generally inclined to

recommend the hotel to friends. From a mangers’perspective, this will help bring future business. The abovehighlighted practical implications of this research can help the

managers to plan the available resources effectively to win thecustomers. Managerial decision making becomes simple if themangers are aware of impact of resource capabilities in

customer loyalty.In spite of these interesting findings, our study has some

limitations. Though we have used a novel data source, ourdata might have limited the generalisability of our results. Welooked at the online guest ratings available in a particularwebsite, but it is only one of the many websites offering online

hotel reservations, and not all customers that made hotel

Table III Summary of model fit

Models n x2 (df) GFI CFI IFI TLI RMSEA

Loading l

Resource capabilities ! Customer loyalty

Whole data 664 71.46 (13) 0.970 0.983 0.983 0.972 0.08 0.93 *

4 star and above 402 45.60 (13) 0.968 0.983 0.983 0.972 0.07 0.90 *

3 star and below 262 36.35 (13) 0.961 0.984 0.984 0.974 0.08 0.95 *

Note: *Represents significant at p , 0.01

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reservation using this e-booking facility will be inclined to

leave feedback after their stay in the hotel. This limitation can

be partially overcome by pooling similar data from a number

of online hotel booking sites (e.g. www.tripadvisor.com, www.

booking.com, www.expedia.co.uk, www.travelocity.co.uk,etc.). More focused results are possible by conducting a

targeted primary questionnaire survey.

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Corresponding author

Usha Ramanathan can be contacted at: usharamnath@

gmail.com or usha.ramanathan@beds.ac.uk

Executive summary and implications formanagers and executives

This summary has been provided to allow managers and executivesa rapid appreciation of the content of this article. Those with aparticular interest in the topic covered may then read the article intoto to take advantage of the more comprehensive description of theresearch undertaken and its results to get the full benefits of thematerial present.

Back home after a stay at a hotel for either a business orpleasure trip and the last thing you might want to do is fill in

an online survey about the room, value for money, restaurant

facilities, receptionist’s welcome, fluffy or rough towels,

comfort of the bed or whatever. Or indeed, call up awebsite to leave your feedback. Maybe you will put it off for

another time, and then forget all about it. Or, if there wassomething particularly annoying or unpleasant about the

hotel perhaps you will, after all, spend some time passing onyour opinion if only to get it off your chest.Getting a comprehensive picture of customers’ attitudes to

a hotel’s services in this way in an attempt to understand how

those attitudes impact on loyalty and recommendation issomewhat limited. But it is important for hotel managements

to find out not merely what guests liked (or disliked) aboutthe facilities but, importantly, what will bring them back for

another stay and recommend the place to friends orcolleagues.In “Investigating the impact of resource capabilities on

customer loyalty: a structural equation approach for the UK

hotels using online ratings” Usha Ramanathan andRamakrishnan Ramanathan conclude that the resource

capabilities of hotels have a significant positive influence oncustomer loyalty (measured using guests’ intention to stay

again and to recommend to friends). They also found thatguests’ intention to make a return visit positively translates to

their intention to recommend the hotel and that the positiverelationship between resource capabilities and customer

loyalty is not affected by the star ratings of hotels.Cleanliness, room quality, family friendliness, customer

service, value for money, stay and recommend to friend wereall used in the analysis. Therefore, good achievements by

hotels in terms of such performance variables positivelytranslates to customer loyalty. As a result, managers should

ensure good performance in terms of various hotel attributes– cleanliness, quality of room, facilities, and customer service,

and also ensure that customers perceive good value for theirmoney while staying in the hotel. Second, a happy customer

that is ready to stay in the hotel again is generally inclined torecommend the hotel to friends.Findings point to the need for improving resource

capabilities in order to register good performance. Thus,

hotels should ensure that they have the required capabilities toperform various services. The capability to maintain overall

cleanliness will result in good customer rating in terms of thisattribute. Similarly, the capabilities of a hotel in ensuring good

quality of rooms contribute significantly to overall hotelcapabilities. Similarly, other performance variables (family

friendliness, customer service and value for money) alsocontribute well to the overall hotel capabilities, which in turn

have significant impact on guests’ intentions to stay again andto recommend to friends.The importance of room quality and value for money have a

great influence on overall hotel capabilities. And, as with

previous research, guest satisfaction, room cleanliness,maintenance, and attentiveness of staff. As predicted, an

intention to stay in the same hotel again seems to have asignificant positive influence guests’ intention to recommend

the hotel to friends. This finding has marketing significance asthe good performance of a hotel in terms of variousperformance variables will not only have a positive impact

on guests’ intention to stay again, but will also get translatedinto valuable “word-of-mouth” recommendation.Interestingly, in this study the authors used secondary data

from a relatively novel source – online guest ratings available

at one of the websites specializing discounted hotel

Investigating the impact of resource capabilities on customer loyalty

Usha Ramanathan and Ramakrishnan Ramanathan

Journal of Services Marketing

Volume 27 · Number 5 · 2013 · 404–415

414

reservations online. It is one of the many websites offeringonline hotel reservations, and not all customers that made ahotel reservation using this e-booking facility will be inclinedto leave feedback after their stay.The link between resource capabilities and customer loyalty

does not, according to the study findings, depend on starratings of hotels. This may be because guests took intoaccount the star category when rating the hotel: if they stayedat a five-start hotel they expected five-star treatment, but notif they were in a fewer-star alternative.The resource-based view of a firm recognises that the basis

for an organisation’s competitive advantage lies primarily in

the application of the bundle of valuable resources at the

firm’s disposal. The suggestion is that firm capabilities

(marketing, operations, technological, supply chain, and/or

their interactions) have significant impact on performance

depending on the way in which firms align themselves with

the business environment.

(A precis of the article “Investigating the impact of resource

capabilities on customer loyalty: a structural equation approach for

the UK hotels using online ratings”. Supplied by Marketing

Consultants for Emerald.)

Investigating the impact of resource capabilities on customer loyalty

Usha Ramanathan and Ramakrishnan Ramanathan

Journal of Services Marketing

Volume 27 · Number 5 · 2013 · 404–415

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