An internal customer service quality data development analysis model for bank branches.

Introduction

Much has been said about the importance of achieving high levels of service quality (SQ)[1]. SQ is considered by many as the key to gaining competitive advantage and its importance has been documented in a number of studies[2-4]. Today, the field of service quality management is experiencing a number of advances and, in the banking industry alone, it is difficult to identify a single bank which has not initiated some kind of service quality improvement programme. Nevertheless, no universally accepted methodology exists on how to improve service quality.

Data envelopment analysis (DEA) is a benchmarking technique which has been gaining increasing popularity during the last few years. Developed by Charnes, Cooper and Rhodes in 197815], DEA has successfully been used to provide bank branch benchmarks, when multiple outputs are produced with multiple inputs[6-9]. However, even though some DEA models were presented in the literature which address issues of service quality[10,11], most DEA models developed to assess bank branch performance do not include service quality as an output.

In this paper, we present results from an ongoing study on the efficiency of bank branches. The broader study examines issues of operating efficiency and profitability, and the results have been reported elsewhere (see [9,12,13]). In this project we add the quality dimension in order to complete the capabilities-service quality-performance triad, as described by Roth and Jackson[14]. More specifically, we focus on the problem of improving service quality at the branch level. To this end, we develop a DEA model that can be used to provide direction for improvement to branches which do not use their resources in the most efficient way to produce service quality. The paper does not aim to develop service quality measures but rather to show how such measures can be incorporated into a model which can provide useful suggestions towards service quality improvement.

Schneider and Bowen[15] report strong correlation between internal and external customer perceptions of service in bank branches. The model focuses on internal customer perceptions of service quality which are sometimes easier to measure.

The rest of the paper is structured as follows. First, we present a brief description of the DEA methodology. A DEA model to provide internal customer service quality benchmarks of bank branches is then developed. We next demonstrate the applicability of the DEA model in a banking environment. Model results and limitations are then discussed, followed by concluding remarks.

Background on data envelopment analysis

Data envelopment analysis is a mathematical programming technique developed by Charnes et al.[5] to evaluate the relative efficiency of public-sector not-for-profit organizational units where accounting and financial ratios are of little value, multiple outputs are produced with multiple inputs, and the input-output transformation relationships are not known. DEA compares the observed outputs and inputs for all units of an organization, identifies the relatively best practice or yardstick units to define an efficient frontier, and then measures the degree of inefficiency of the other units relative to this frontier.

Consider, for example, Figure 1, which illustrates seven decision making units (DMUs). Each DMU consumes a single input (x) to produce a single output (y). DEA establishes an envelopment surface which defines the frontier of efficient units, against which inefficient units are identified. Referring to Figure 1, one such possible envelopment surface can be constructed by DMUs 1,3,4,6 and 7. Clearly, DMUs 2 and 5 in this example are inefficient. They could improve their operations either by reducing their input or by augmenting their …