The Challenges of Service Quality Measurements
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The Challenges of Service Quality Measurements
The engine of economic growth in Singapore has always been powered by the goods producing industries. For the year from 2005 to 2007, this sector contributed an average of 60.7% per annum to the Gross Domestic Product (GDP) as compared with an average of 39.3% per annum from the services producing industries (Department of Statistics, 2008).
However, further investigation revealed that there was a significant slow down in the goods producing industries in recent years. The average growth rate for the same period in the goods producing industries had been 6.5% per annum as compared with 10.6% per annum in the services producing industries. The slowdown in the goods producing industries was more significant between the period 2006 and 2008 when the growth rate fell from 10.8% (in 2006) to 1.8% (in 2007) and further slipped into -5.3% in the three quarter of 2008. In contrast, the growth in the services producing industries had been gaining momentum in the same period – 8.6% (in 2006) to 12.8% (in 2007) and achieving 7% in the three quarter of 2008. These figures indicated that the services producing industries had been outpacing its goods producing industries and the gap had been widening, from -2.2% (in 2006), 11% (in 2007) and 12.3% (in third quarter 2008) (Department of Statistics, 2008). Thus, the figures indicated that services producing industries will one of the main contributors to Singapores GDP.
Similar to the goods producing industries, “delivering superior service quality appears to be a prerequisite for success, if not survival” in the services producing industries (Parasuraman, A., Zeithaml, V.A. and Berry, L.L., 1988, pp. 13). However, quality in the services producing industries can be “an elusive and indistinct construct” (Parasuraman, Zeithaml and Berry, 1985, pp. 41). Therefore, as industries try to metamorphose, Singapore companies need to understand that there are fundamental differences between service operations and manufacturing operations (Hope, C. and Muhlemann, A., 1997).
In order to establish a good benchmark for the services producing industries, there is a need to determine the attributes that will affect the service quality so that service providers can allocate their production resources more efficiently and efficiently. However, the determination of services attributes and evaluation of service quality is complex (Parasuraman et al., 1985) and strongly correlate with other consumers physiological factors such as values, national culture, gender, etc. (Hofstede, Neuijen, Ohayv and Sanders, 1990; Oh, 1999; Andrews, Kiel, Drennan, Boyle, and Weerawardena, 2007).
Thus, this research aims to evaluate the service quality evaluation process to determine the important attributes and whether consumers from different physiological background rank these attributes differently.
Summary of Literature Review
As early as the 1980s, scholars had agreed that there are fundamental differences between the quality evaluations of goods producing industries and the service producing industries (Lockyer, 1986). This is due to the unique services characteristics of SHIP, namely Simultaneity, Heterogeneity, Intangibility and Perishability (Parasuraman et al., 1985).
Simultaneity meant that service is produced and consumed at the same time and this make evaluation of service quality difficult because firstly, the supporting facility which accommodates the customer must meet different customers preferences; secondly, the high level of contact demands quality delivery mode but customers themselves are sometime unpredictable; thirdly, quality control is difficult as pre-test the provision of service, like one would do with a product before releasing the output to consumer, is not possible (Siew, 2008a).
Heterogeneity means each service provided of a customer differs from that service provided to an earlier customer. Service quality measurement becomes challenging because – firstly, the service and delivery procedures cannot be standardised; secondly, there is no best mode of delivery of service as the same customer getting the service at different times can lead to different evaluation outcome since customers do not have a consistent benchmark for service quality (Siew, 2008a).
The intangibility challenges to service quality evaluation includes firstly, the inability to write clear specification or procedures for the service and delivery of the service, hence training of servers to provide consistently good service becomes difficult; secondly, without written benchmark, there is a great deal of subjectivity in assessing the service and service quality due to varying expectations (Siew, 2008a).
Perishability of services meant that outputs must be consumed as it is produces and cannot be inventoried. This increases the cost of operation as the services producing industries have to staff up to make sure customers are not turned away because of lack of service. Long queues and absence of service providers when customers are present can create negative perceptions about service quality (Siew, 2008a).
As a result, conventional methodologies used to evaluate quality in the goods producing industries are inadequate for evaluating services quality because of the elusive, unique and often indistinct characteristics of services (Parasuraman et al., 1985). Since 1984, there are as many as 23 service quality evaluation models to resolve the problem. Generally, they can be classified into three categories:
Customer perception models – as tangible measurement is difficult, many scholars had used perception of services as a tool to evaluate service quality. Generally, the models first determine a set of perception criteria that will determine service quality evaluation and measures these criteria on a predetermined scale. From these scales, service quality is then determined. These perception criteria included the customers tolerance level (Johnston, 1995a), the experience and ideal values of individuals (Mattson, 1992), desires, expectations and perceived performance (Spreng and Mackoy, 1996; Oh, 1999).
Service attributes models – the common technique in this category is to bundle the intangible of service quality with more tangible aspects of service behaviours and attributes. The models then evaluate these more tangible measures to determine the level of service qualities. These tangible aspects of services include degree of labour intensity, interaction and customization