Purpose Recognizing the fundamental role of quality as a means to differentiate service organizations, the purpose of this paper is to propose a strategic decision making framework for service organizations, which prioritizes performance improvement strategies that are rooted to customer requirements, organizational goals and constrained by organizational resources. Design/methodology/approach The proposed framework is realized through the implementation of two stages and four distinct phases mirroring the combination of enhanced quality function deployment (first stage), and zero-one goal programming (second stage). It proposes the utilization of a mix of qualitative and quantitative methods, and the collection of data from multiple sources including customers, middle, and top management. Findings The application and validation of the proposed framework utilizes information from both customers and employees in the bank services sector. Overall, results from the specific study revealed that a combination of “reengineering” and “expansion” strategies was more appropriate corresponding to customer priorities, organizational goals and effective utilization of available resources. Originality/value The paper presents a novel two stage strategic framework for service organizations. It utilizes a balanced mixture of qualitative and quantitative methods in an effort to capture and delineate elusive customer requirements and design characteristics of services, allowing the assessment of different combinations of quality improvement strategies in response to management objectives.

Purpose -The purpose of this paper is to propose a quality function deployment analytic hierarchy process (QFD-AHP) framework to improve quality and competitive positioning within the financial services context. The applicability of the model is demonstrated through a case study. Design/methodology/approach -Results of two empirical surveys were utilized to detect the "Voice of the Customer". The first identified main bank selection criteria and questionnaires and exploratory factor analysis were employed. The second utilized structured interviews for the development of the first house of quality (HOQ), relating customer requirements to key market segments. The AHP was used to determine the intensity of the relationships within the HOQ. Findings -In total, six key selection criteria were designated. Analysis incorporated customer needs and evaluations for the bank's current position, future goals related to specific market segments, and competition allocations. "Pricing" was the most important criterion, followed by "effective services" and "location". Research limitations/implications -Data analysis was carried out with data from one relatively large organization. Future research might comprise additional financial institutions and possible comparative analysis regarding criteria and modeling alternatives. In addition, integration of tools such as fuzzy AHP to account for the volatility of bank selection criteria could be of great interest. Practical implications -Prioritizing selection criteria is a valuable tool to help managers focus future improvement efforts and goals in a sector which faces important challenges due to the global financial crisis. As selection criteria and relative importance change due to turbulent financial situation, models such as the proposed allow adaptation, echoing the evolving voice of the customer in the key attributes and performance improvement priorities. Originality/value -The paper develops a model that integrates the AHP and the QFD through which banks will identify key customer segments' needs, as a first step in their effort to improve quality and performance. The authors emphasized the research design to foster face validity.

PurposeThis paper presents a decision-making framework for e-banking operations, based on the Quality Function Deployment and the Analytic Hierarchy Process. The main research question is the development of a framework and its potential contributions in supporting decision makers in implementing quality strategies that will encourage the usage of e-banking services (EBS).Design/methodology/approachThe introduced framework comprises four distinct stages which include criteria determination, field surveys and EFA, QFD application including AHP and finally sensitivity analysis investigating the dynamic nature of the environment. AHP determines the intensity of the relationship between e-banking quality criteria and customer banking activities. A novel House of Quality (HoQ) is proposed, based on a market mix founded on key e-banking activities, and sensitivity analysis is used to investigate alternative scenarios. To illustrate the steps of applying this framework, the authors use a convenience sample from the Greek e-banking sector.FindingsThrough the illustrated example is supported that the proposed approach can reveal valuable information when contemplating strategies to improve e-banking usage and expand its acceptance. In addition, sensitivity analysis leads to purposeful insights regarding the effects of market segmentation and/or target settings on the ranking of e-banking quality/selection criteria provided by the HoQ. In the specific numerical example, the most critical quality factors were “Security and Reliability”, “Convenience”, “Design”, “Pricing” and “Skills,” although different rankings night well appear in different contexts or geographical regions. Moreover, sensitivity analysis showed that these results depend on the specific market mix and targets. As mentioned above, the implementation of the framework in different geographical regions or e-service sectors might certainly reveal different critical factors.Research limitations/implicationsDiscussed in the paper body.Practical implicationsDiscussed in the paper body.Originality/valueThe paper presents a well-defined four stage framework for improving EBS penetration. It utilizes a structured qualitative and quantitative approach and outlines and ranks e-banking quality factors stemming from the market mix and allows assessment of alternative scenarios through sensitivity analysis.