Internal Customer Service Quality - Assignment Example

Introduction Branch performance assessment is a major consideration for most banks so as to take decisions related to improvement in terms of bank’s overall growth. This assessment cannot be based on a simple profit / loss metrics as it may provide undue advantage to an urban located branch but disregard a slow, however potentially good branch. Thus branch profitability cannot be the only standard of measuring the branch performance. This is where a new dimension of measuring the service quality of the internal and external customers of branch holds importance. However, due to the constraint of collecting data from external customers, a measurement of the service quality perceptions of the banks’ internal customers has been the feature of the article ‘An internal customer service quality data envelopment analysis model for bank branches’ by Andreas C. Soteriou and Yiannos Stavrinides. Andreas Soteriou is an eminent author of many other books and journal articles pertaining to service quality in banks such as A Robust Optimization Approach for Improving Service Quality. (July 2000). Manufacturing & Service Operations Management, Volume 2, Issue 3; Benchmarks of the Efficiency of Bank Branches. (March 1999). Interfaces, Volume 29, Issue 3 etc. The article has articulately described the significance as well as the various nuances of discussion on service quality measurement in bank’s branches. A quantitative approach towards the same, by way of using a mathematical model to form the basis of the paper and then graduating to the integration of this mathematical model towards actual service quality assessments of bank branches makes this article highly valid and well researched. This is further corroborated by the primary data collected with clear description of the variable used supported by graphical explanations of the theories undertaken and the findings extracted. This article fundamentally focussed not as a basic performance assessment tool, but an overall analysis of the correlation between outputs vis-a-vis inputs administered and to act as a benchmarking technique as already stated. The suggestions and findings of this research article can very well guide the branches towards improvement of service quality and inspire them to aid this with many other such models so as to develop a more comprehensive efficiency management tool. This paper is an attempt to appropriately capture the essence and dynamism of the above article so as to provide a consolidated view on the understanding as derived from the same. This paper would summarise the contents of the article as put forth by the authors, elaborating their viewpoint and perspective towards the various indicators of branch efficiency. Also an evaluation of the article findings and recommendations would be taken up supported by an assessment of the strengths and weakness or issues pertaining to the finding illustrated in the article. Critical Review The authors have very articulately depicted the validity of the DEA model to be applied in the context of measuring the service quality of the bank branches. DEA model actually aids benchmarking the performance of the bank against various indicators. These indictors in terms of outputs produced relative to the inputs provided forms the basis of DEA model. As illustrated by the authors, DEA model is a quantitative tool and induces a mathematical formulation which gives rise fractioning programming or ultimately a linear programming equation. These equations effectively evaluate the efficiency of the base branch with respect to the branches in the set which is being examined where; efficiency has been defined as the ratio of the sum of weighted outputs to the sum of weighted inputs. This comparison will provide whether the branch being compared is efficient (i.e. the efficiency value should reach 1) or it is inefficient as compared to the base branch in which case the efficiency value would be less than 1. This has been the efficiency reference set as per the model shown by Soteriou & Stavrinides. The illustrations of the DEA model have been aptly accompanied with graphical representation of the efficiency of outputs with respect to inputs and emphasises that in order to increase the efficiency of an inefficient branch, the weights of the outputs and inputs are to be chosen such that the graphical distance between the efficiency barrier and the various units are minimised. The DEA model provides the avenue for assessment of ideal conditions such as minimum inputs and maximum output. Also, the efficiency factor’s highest value is determined by assigning several weights to inputs and outputs. However, this efficiency index can never exceed the value 1 for any branch, under any condition. The article has indeed touched the grassroots issue faced by contemporary managers in the branches and that is to deliver high quality service and to thus monitor the fulfilment of the same. Quality being an intangible aspect of branch performance and efficiency indicator has traditionally been difficult to assess and thus was more often than not, overlooked. Bank’s bottom line, profitability and operational efficiency are however very closely followed. This practice in the modern world fails due to increasing customer awareness, knowledge and technical knowhow. Thus, it is quite possible that even with operational efficiency; a branch may not attract customers owing to neglected service quality. Thus, the strength of the article and the model illustrated therein is its extension beyond traditional measures of performances and the ability to quantify the intangible and unseen. Long term impact of deteriorating service quality can disregard any volumes or profits made in short run. Soteriou & Stavrinides have suitably created a feasible and practical way to quantify and measure service quality which is measures the branch’s utilisation of consumable resources so as to transform them into quality output through serving the customers. The inputs discussed in the article by the author such as surroundings, technology support, account structure, manpower can be further strengthened through personnel training, education etc which can then also be included as an input in the consideration set. The ability to thus weigh multiple outputs with respect to multiple inputs is strength of this mathematical technique. Since this is a quantitative technique, a comparison with the operating efficiency and profit levels would also be possible. Also, two other advantages of the technique are that the equations under DEA can be reduced and treated as normal linear equations and the results of which can then be graphically represented for better interpretations. Another strong point of the model is that it can help indicate those resources which are being underutilised or may be utilised better so as to produce greater levels of service quality. However, one of the constraints is the selection of the set of branches to be included in the evaluation set as this can significantly affect the implications of the result. This means that since homogeneity is one of the major requirements of DEA methodology, only branches which are similar with respect to using similar resources and providing similar services can only be included in the same set for comparison. Another weakness of the model is that since many factors may in different ways contribute to service quality, it is also important to analyse the effect of these in isolation so as to arrive at meaningful managerial implications. Since the results of this quantitative analysis included multiple outputs, the interpretations should be derived very carefully and after incorporating all relevant outputs with existing performance tools. DEA cannot be used in its basic or crude form as it only measures single service quality output and thus might ignore other bank performance measures unless collaborated with other tools for development of richer models as described above. The article demonstrates a valid primary research conducted in a bank so as to determine the applicability of the theory proposed. The duration of sample collection was an approximate 6 months period. The classification of branches was made on the basis of size, location and type of operation performed at each branch. In order to take a set of similar branches (for homogeneity concern) in the first consideration set, only urban branches which are medium sized were first put under assessment. The authors have readily enunciated the various inputs they have used in the data collection and the basis of their choice and their measurement was available in the repository of various day to day activities and resources utilisation. The tool used for measurement of output which is service quality of internal customers was ‘Servqual’ under which a questionnaire was designed and first tested in a pilot study. The results of the same were used to finally decide so as to go ahead with the survey after dropping certain factors as indicated unsuitable by the results of pilot study. The staff and branch personnel were administered the questionnaire and data thus collected with a good response rate of 82 percent. Here, the study included internal customers only as target population of the survey and not the external customers. This is so because it is quite difficult to extract service quality information from external customers due to their non-availability and willingness to participate in a detailed survey. Also, for the same reasons it becomes expensive also. Thus, internal customers were effectively chosen as the target set for the research by the authors. The results of the survey in the form of an overall service quality perception of internal customers count was used as a representative for all the branches which is to be used as a benchmark. Then the individual branch responses’ average was used as the single output indicator of service quality. The results were also analysed using several extreme conditions such as input minimisation (which was useful in assessing that if the same level of service quality is produced as output by the branches, what would be the lowest level of inputs required) as well as output maximisation (which indicates that given the same set of consumable resources to all branches, what would be the highest extent of service quality that can be extracted) for the different branches in the group. The research carried out in the article is valid as well as reliable due to the detailed specification of data collection as well as data analysis which has been provided supported by the various alternative conditions and the changes introduced therein. It has provided an explanation for the collection of the data in a specific manner and also the inputs and outputs which have been kept out of the analysis. The pilot study conducted shows that the research to be conducted is suitable to the targeted population and can thus be administered followed by generalising the results to the entire population of similar nature. The results have been demonstrated by the author in two ways. One is by way of comparison of a particular branch’s actual input and output data with the corresponding targeted data. As explained already, the targeted data is the overall generalised value derived out of the survey as an average result of all the branches. It has been demonstrated in the explanation how the results thus obtained can act as a starting point indicator to the branch manager for factors which require attention to bring about improvement in service quality results. Conversely, if the service quality output is fixed, then the article indicates how the inputs can be minimally utilised so as to achieve the same output and thus add to the bottom line of the branch. It is imperative to point out here that the research conducted have indicated the importance of the type of customer base or account type towards the branch’s efficiency in providing quality service such as those accounts which require frequent but routine transaction indeed increase the efficiency count of the branch as against those where these are excluded. However, it is very evident that only measuring service quality is myopic in nature and thus the DEA model requires to be integrated with other models which would bring about the possibility of assessing more than one output. Other such models like operating model or profitability model would enable the measurement of operating efficiency and profitability count also. In this case, a graphical representation for easier and better understanding of the implications of various factors can be studied on the two axis dimension such as operating efficiency and service quality. The article has therefore coherently provided all possible alternatives through which the study of performance of branches can be equitably determined. The article is thus very valid in the above context. Conclusion This paper has thus aimed at providing a synopsis of the research article as discussed by Soteriou & Stavrinides. Here, the various aspects of the article have been reflected upon. The purpose of the concept of measuring service quality towards indicating the branch performance and measure it with respect to the inputs that are employed have been discussed here. The various viewpoint and ideological framework of the author have been discussed here. Also, the basis of the research conducted was a mathematical model called DEA (Data Envelopment Analysis) has been described with the understanding of its implication from the point of view of the service quality assessment has also been made an attempt of. The research conducted had several nuances to it in terms of the data collected the basis d classifying the target population and also the data set. The considerations of which factors are to be included in the input set and which other are to be eliminated was arrived at through a pilot study which has also been discussed in this paper. Finally, the various interpretations obtained from the data analysis of the research conducted have also been summarised here so as to complete the understanding of the concept and the practical implementation of the same. We have seen that, the model described in the article by Soteriou & Stavrinides is applicable to various set of branches such as rural as well as urban; and small, medium or large sized branches. However, at one time, only a particular type of branch can be assessed owing to the homogeneity consideration of the DEA model. We have also discussed here that the model can be effectively integrated with several other quantitative models so as to provide a more comprehensive comparison between multiple inputs and multiple outputs. The various constraints in using the model and the measurement of the service quality of branches have also been deliberated upon here apart from the strengths of using the same. The summary of the findings and data analysis results of the primary research conducted has been discussed. In conclusion, the research article discussed here has been established to be of contemporary significance in terms of measurement, monitoring and improvement of the performance of branches irrespective of their location or size. Read More