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Linking customer satisfaction, employee appraisal, and business performance: an evaluation methodology in the banking sector

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Abstract

The linkage among customer satisfaction, employee evaluation, and business performance data is very important in modern business organizations. Several previous research efforts have studied this linkage, focusing mainly on the financial or business performance in order to analyze the efficiency of an organization. However, recent studies have tried to consider other important performance indicators, which are able to affect business operations and future growth (e.g., external and internal customer satisfaction). In the case of the banking industry, studying the relations among the aforementioned variables is able to give insight in the performance evaluation of bank branches and the viability analysis of the banking organization. This paper presents a real-world study for measuring the relative efficiency of a set of bank branches using a Data Envelopment Analysis (DEA) approach. In particular, a multistage DEA network model is proposed, using a set of performance indicators that combine customer satisfaction, employee evaluation, and business performance indices. The main aim of the presented study is to evaluate the relative efficiency of each customer service delivery step, in the environment of a bank branch. The results are also able to estimate the contribution of the assessed performance indicators to the branch’s overall efficiency, and to determine potential improvement actions.

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Appendices

Appendix A: The MUSA method

The MUSA (MUlticriteria Satisfaction Analysis) method is a multicriteria preference disaggregation approach, which provides quantitative measures of customer satisfaction considering the qualitative form of customers’ judgments. The main objective of the MUSA method is the aggregation of individual judgments into a collective value function, assuming that client’s global satisfaction depends on a set of n criteria or variables representing service characteristic dimensions.

The method assesses global and partial satisfaction functions Y and \(X_{i}^{*}\) respectively, given customers’ judgments Y and X i . The method follows the principles of ordinal regression analysis under constraints using linear programming techniques. The MUSA method infers an additive collective value function Y and a set of partial satisfaction functions \(X_{i}^{*}\). The main objective is to achieve the maximum consistency between the value function Y and the customers’ judgments Y. Introducing a double-error variable, the ordinal regression equation has the following form:

$$ \tilde{Y}^{*} = \sum_{i = 1}^{n} b_{i} X_{i}^{*} - \sigma^{ +} + \sigma^{ -} \quad \mbox{with }\sum_{i = 1}^{n} b_{i} = 1 $$
(A.1)

where \(\tilde{Y}^{*}\) is the estimation of the global value function Y , b i is the weight of the i-th criterion, and σ + and σ are the overestimation and the underestimation errors, respectively.

The global and partial satisfaction Y and \(X_{i}^{*}\) are monotone functions normalized in the interval [0,100]. Thus, in order to reduce the size of the mathematical program, removing the monotonicity constraints for Y and \(X_{i}^{*}\), the following transformation equations are used:

$$ \left\{ \begin{array}{l@{\quad}l} z_{m} = y^{*m + 1} - y^{*m}&\mbox{for } m = 1,2,\ldots,\alpha - 1 \\ [2pt] w_{ik} = b_{i}x_{i}^{*k + 1} - b_{i}x_{i}^{*k} & \mbox{for } k = 1,2,\ldots,\alpha_{i} - 1\mbox{ and }i = 1,2,\ldots,n \end{array} \right. $$
(A.2)

where y m is the value of the y m satisfaction level, \(x_{i}^{*k}\) is the value of the \(x_{i}^{k}\) satisfaction level, and α and a i are the number global and partial satisfaction levels.

According to the aforementioned definitions and assumptions, the basic estimation model can be written in a linear program formulation, as follows:

$$ \left\{ \begin{array}{l} \displaystyle[ \min]F = \sum_{j = 1}^{M} \sigma_{j}^{ +} + \sigma_{j}^{ -} \\ \mbox{subject to} \\ \displaystyle\sum_{i = 1}^{n} \sum_{k = 1}^{x_{i}^{j} - 1} w_{ik} - \sum_{m = 1}^{y^{j} - 1} z_{m} - \sigma_{j}^{ +} + \sigma_{j}^{ -} = 0 \quad \mbox{for }j = 1,2, \ldots,M \\ \noalign{\vspace*{2pt}} \displaystyle\sum_{m = 1}^{\alpha - 1} z_{m} = 100 \\ \noalign{\vspace*{2pt}} \displaystyle\sum_{i = 1}^{n} \sum_{k = 1}^{\alpha _{i} - 1} w_{ik} = 100 \\ z_{m},\ w_{ik},\ \sigma_{j}^{ +},\ \sigma_{j}^{ -} \quad \forall m,i,j,k \end{array} \right. $$
(A.3)

where M is the size of the customer sample, and \(x_{i}^{j}\), y j are the j-th level on which variables X i and Y are estimated.

The preference disaggregation methodology includes also a post optimality analysis stage in order to overcome the problem of model stability. The final solution is obtained by exploring the polyhedron of multiple or near optimal solutions, which is generated by the constraints of the previous linear program. This solution is calculated by n linear programs (equal to the number of criteria) of the following form:

$$ \left\{ \begin{array}{l} \displaystyle[ \max ]F' = \sum_{k = 1}^{\alpha _{i} - 1} w_{ik} \quad \mbox{for }i = 1, 2, \ldots, n \\ \mbox{under the constraints} \\ F \le F^{*} + \varepsilon \\ \mbox{all the constraints of LP (A.3)} \end{array}\right. $$
(A.4)

where ε is a small percentage of F . The average of the solutions given by the n LPs (A.4) may be taken as the final solution. In case of non-stability, this average solution is less representative.

The assessment of a performance norm may be very useful in customer satisfaction analysis. The average global and partial satisfaction indices are used for this purpose and are assessed through the following equations:

$$ \left\{ \begin{array}{l} \displaystyle S = \frac{1}{100}\sum_{m = 1}^{\alpha} p^{m}y^{*m} \\\noalign{\vspace*{2pt}} \displaystyle S_{i} = \frac{1}{100}\sum_{k = 1}^{\alpha _{i}} p_{i}^{k}x_{i}^{*k} \quad \mbox{for }i = 1,2, \ldots,n \end{array} \right. $$
(A.5)

where S and S i are the average global and partial satisfaction indices, and p m and \(p_{i}^{k}\) are the frequencies of customers belonging to the y m and \(x_{i}^{k}\) satisfaction levels, respectively.

Appendix B: Bank branches data

Table 4 Operational data
Table 5 Service quality data

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Grigoroudis, E., Tsitsiridi, E. & Zopounidis, C. Linking customer satisfaction, employee appraisal, and business performance: an evaluation methodology in the banking sector. Ann Oper Res 205, 5–27 (2013). https://doi.org/10.1007/s10479-012-1206-2

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