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.

Probability resembles the ancient Roman God Janus since, like Janus, probability also has a face with two different sides, which correspond to the metaphorical gateways and transitions between the past and the future

A mathematical model in a multi-level manpower planning setting is developed and analyzed incorporating the divisions of an organization’s personnel into several homogeneous groups. The proposed framework builds upon recent research to develop, via the continuous time scale, a departmental model encompassing employees flows within departments (intra-departmental transitions), as well as transfers among departments (inter-departmental transitions). Management-wise, this is a common practice under certain conditions as in restructuring and rightsizing ventures both in private industries and in the public sector. After establishing the baseline differential equations of the system, an investigation follows regarding the system’s limiting behavior integrating the concept of department contraction which can be seen as an indirect and mild control that can be exercised on the manpower system. It is proved that under a set of conditions, the system’s limiting structure exists and is specified. The aforementioned asymptotic analysis can be utilized to predict, in the long run, future stock structures of departments and thus, in the case where these structures diverge from the desired ones, identify departments that could be considered as candidates for probable rightsizing or mergers. The paper concludes with a numerical illustration and some concluding remarks.

One method that has been proposed for the measurement of sustainability is Data Envelopment Analysis (DEA). Despite its advantages, the method has limitations: First, the efficiency of Decision-Making Units is calculated with weights that are favorable to themselves, which might be unrealistic, and second, it cannot account for different perceptions of sustainability; since there is not an established and unified definition, each analyst can use different data and variations that produce different results. The purpose of the current paper is twofold: (a) to propose an alternative, multi-dimensional DEA model that handles weight flexibility using a different metric (an alternative optimization criterion) and (b) the inclusion of a computational stage that attempts to incorporate different perceptions in the measurement of sustainability and integrates machine learning to explore country sustainability composite indices under different perceptions and assumptions. This approach offers insights in areas such as feature selection and increases the trust in the results by exploiting an inclusive approach to the calculations. The method is used to calculate the sustainability of the 28 EU countries.