Purpose: This 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/approach: The 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.Findings: Through 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/implications: Discussed in the paper body.Practical implications: Discussed in the paper body.Originality/value: The 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.

The optimal allocation of limited resources along with output target setting are critical in pursuing the sustainability and competitiveness of organizations. The process of resource distribution is usually implemented through a central unit that routes resources to the subordinate decision-making units (DMUs) along with DMUs lower bounds of desired efficiency. Moreover, the central unit has the authority to set the overall expected output targets so as to maximize organizational effectiveness. In this paper, we tackle evaluation efficiency questions using a type of bilevel network data envelopment analysis (DEA) approach within a stochastic framework. The proposed bilevel DEA model takes into account stochastic conditions and optimizes centralized resource allocation and target setting, imposing lower bounds on the efficiencies of all DMUs affiliated to the organization. Consequently, the total input consumption is minimized, and the total output production is maximized while considering additional bounds and availability constraints for inputs. In the proposed bilevel model, uncertainty is introduced through the upper level (leader) problem that attempts to maximize organizational effectiveness, while in the lower level (follower) problem, the efficiency of the subordinate DMUs is evaluated. A solution methodology for the bilevel network DEA-based model is presented and numerical results are obtained using data from the literature. The obtained results are compared with those published in other case studies for centralized resource allocation DEA models.

The effective allocation of limited resources and the establishment of targeted goals play a pivotal role in enhancing the overall efficiency of large enterprises and organizations. To achieve optimal organizational efficiency, managers seek dynamic strategies that adapt to the constraints of limited and uncertain historical data. This paper introduces an evaluation of organizational efficiency through a stochastic framework, employing a bilevel data envelopment analysis (DEA) approach. This decision-making process is centralized within a decision-making unit (DMU) overseeing subordinate decision-making units (subDMUs). Discrete scenarios, each associated with a realization probability, define the uncertain parameters in the bilevel DEA-based model. This stochastic approach allows for recourse actions upon scenario realization leading to an enhanced overall organizational strategy. Decision-makers acting within uncertain and dynamic environments can benefit from this research since it allows the investigation of efficiency assessment under alternative scenarios in the presence of volatility and risk. The potential impact of applying this methodology varies depending on the specific domain. Although, the context of this paper focuses on banking, in general, enhancing resource allocation and target setting under stochasticity, contributes to advancing sustainability across all its three dimensions (economic, environmental, social). As mentioned earlier, the practical application of our approach is demonstrated via a case study in the banking sector.