Higher Education Service Quality Analysis

‘College image’ is highly associated with ‘service quality’ thus the variables introduced by

Carney (1994) were developed under the context of college image, most of the variables noted are also relevant to the measurement of service quality. Athiyaman (1997) used eight quality characteristics to scrutinize professional higher education services, namely, teaching students well, availability of academic and non-academic staff for student consultation, well equipped 69 library, computer facilities, recreational facilities, size of classrooms, level and complexity of subject matter and student workload. In addition to that the author observed that consumer satisfaction is alike to attitude, but it is short-term and results from an assessment of

Service quality in education has been described in many ways (Fuller, 1986; Hughes, 1988), some emphasize on the quality of inputs to the professional higher education systems, whereas others stress the quality of processes and outcomes (Table No.2.3). Furthermore, the expectations of various stakeholders on professional higher education may also differ. It is often very difficult for an educational institution to meet all the expectations or requirements at the same time. As a result, it is generally observed that the education quality of an institute is good for some stakeholders and bad to others

(Chenh, 1995). The prime objective of this chapter is to identify, summarize and evaluate the key literature in the field of service quality rather than to take a stance on the on-going debate on service-quality research. Although the area of higher education is extensively spread and varies as per the educational systems of different countries, still, there are few common professional higher education sectors such as Engineering education, Hospitality education, MBA education. In

It includes the initial education for becoming an engineer and any advanced education and specializations that follow. In primary and secondary level schools where technology education is part of the curriculum often serves as the base for engineering education at the university level

(Douglas, Iverson & Kalyandurg, 2004). In an attempt to investigate the management and performance of engineering educational systems, Ho et al. (2001) developed a performance evaluation model for engineering education systems. The concept of balanced scorecard was revealed to construct a performance evaluation model (Kaplan and Norton, 1990). Lopes and

Lanzer (2002) highlighted the issue of performance evaluation-productivity and quality-of academic departments at a University. Data Envelopment Analysis (DEA) was applied to replicate a process of cross assessment between the departments. Martin (2003) also applied DEA methodology for evaluating the performance of Zaragoza University’s departments (Spain). The dimensions that were included in the study concerned with both the teaching and the research activity of the departments. Therefore, this pointed out those departments that more