Determining the Dimensions of Service Quality in Banking Industry: Examining the Gronrooss Model in Iran
INTRODUCTION
The economic share of services currently accounts for more than half the sum of all GNPs worldwide (Akan, 2005). In todays market place, many service companies need to mobilize their internal energies in order to meet the challenges of a changing environment. One of these challenges includes customers demands as the quality of service improves (Wong and Sohal, 2002).
Service quality is increasingly becoming a major strategic variable (Robledo, 2001; Terziovski and Dean, 1998). And This construct has received increased scrutiny during the last few decades (Svensson, 2004).
In the 1980, large organizations became more interested in the development of service quality measures (
In the 1980, large organizations became more interested in the development of service quality measures ( Dedeke, 2003 ). Much of the research has focused on measuring service quality using the SERVQUAL instrument ( Kang, 2006 Ladhair, 2008 ).
While, the SERVQUAL technique has attracted a lot of attention for its conceptualization of quality measurement issues, it has also attracted criticism (ONeill et al., 1998). One criticism of SERVQUAL has been the point that the instrument mainly focuses on the service delivery process.
That is, while the contemporary studies on service quality seemingly focused on the process of service delivery, additional aspects to be considered have already been suggested, especially by European scholars (Kang and James, 2004).
For example, Gronroos noted that the quality of a service as perceived by customers has three dimensions: functional (or process) dimension, technical (or outcome) dimension and image (Kang and James, 2004).
Thus, the European perspective versus American perspective suggests that service quality considers two more components, technical quality and image. No efforts have been made to test the European perspective to determine the dimensions of services quality in banking industry of Iran. The purpose of this study is to extend our understanding of service quality by empirically examining the conceptualization of service quality suggested in the European perspective (i.e., Gronrooss model) (Kang and James, 2004).
Service quality: Researchers have tried to develop conceptual models to explain the service quality and to measure consumers perceived service quality in different industries (Seth et al., 2005).
A good operational example of a standardized framework for understanding service quality is the SERVQUAL instrument developed by Parasuraman. The researchers discovered five general dimensions with focus group interviews which they labeled: Areliability, Aresponsiveness, Atangibles, Aassurance and Aempathy (Wong and Sohal, 2002).
Service quality is an important factor for success in the banking sector.Thus, some bank managers emphasize the various dimensions of service quality (Glaveli et al., 2006).
Bahia and Nantel consequently developed a specific new scale for perceived service quality in retail banking. This Bank Service Quality (BSQ) model is an extension of the original 10 dimensions of the model of Parasuraman. In addition, Bahia and Nantel incorporated additional items such as courtesy and access, as proposed by Carman and items representing the marketing mix of the A7Ps (product/service, place, process, participants, physical surroundings, price and promotion) from the Boom and Bitner framework (Petridou et al., 2007).
Aldlaigan and Buttle have developed and validated a new 21 item scale that describes customers service quality perceptions comprising four dimensions: service system quality, behavioral service quality, service transactional accuracy and machine service quality (Petridou et al., 2007).
In comparing BSQ with SERVQUAL, Bahia and Nantel argued that the main advantage of BSQ for banks is related to its content validity. For example, the services portfolio dimension and the price dimension of BSQ are absent from SERVQUAL (Petridou et al., 2007).
The current study doesnt emphasis the various dimensions of service quality and assesses a Gronrooss comprehensive three-dimensional model in the banking industry of Iran.
The research model: Over 20 years ago, Gronroos first proposed that customers overall evaluations of service quality were a result of their assessment of two dimensions, which he termed functional and technical service quality. He proposed that customers compared their expectations to their experience of service quality in forming their judgment (Gronroos, 1984) and defined service quality as follows: (Fig. 1).
The Gronrooss model proposes that service quality consists of technical and functional dimensions and that a service organizations image functions as a filter in the perception of service quality (Gronroos, 1994). The model also proposes that there are direct relationships between service quality perception and the technical and functional quality dimensions, in addition to the indirect effects of technical and functional quality on service quality perception. Finally, the model suggests that service quality leads to customer satisfaction (Kang and James, 2004).
The research model was tested using a structural equation modeling approach. LISREL 8.52 was used to estimate the parameters and assess the fit of the model (Fig. 2).
The LISREL methodology development started in 1970, when Karl Joreskog presented a first LISREL model at a conference. The first generally, available LISREL program was published in 1975. The name LISREL is an acronym for Linear Structural Relations. The qualifier linear is to restrictive for the current version of the LISREL program, but the name LISREL has become synonymous with structural equation modeling or SEM.
Fig. 1: Gronroos’s service quality model (Gronroos, 1984)
Fig. 2: Research model
Since, there are two approaches available in the structural equation modeling approach, it would be necessary to address each approach and the one that the study employs. In this research, all responses about questions utilized an appropriate Likert scale and a path analysis with latent variables-the common factor of the sets of item-scores was used.
