PurposeCOVID-19 has become a global challenge with a significant rate of prevalence, and it has exerted devastating consequences in epidemic, economic and social terms. Therefore, a number of studies have already been, or are now being, conducted on the detrimental effects of the virus. In this respect, a question may arise: Is there any possibility to turn the threat of the virus outbreak into an opportunity in some sectors such as the banking industry? In this research, the effects of COVID-19 outbreak as an intervening element on the acceptance of branchless banking were studied.Design/methodology/approachIn this research, the factors affecting the acceptance and development of branchless banking in Iran at the time of COVID-19 outbreak were identified by systematically studying the theoretical framework, conducting further research and interviewing the experts; then, a causal loop diagram of the problem in the proposed case study and the flow rate model were presented.FindingsThe simulation results showed that banking transactions and a bank's financial resources would increase by implementing the package policy of reducing the number of branches, promoting incentive policies and increasing the budget rate of the bank in Information Technology (IT). Further, by promoting customers' acceptance of new technologies, the spread of COVID-19 can be viewed as a positive factor, or even a catalyst, in the acceptance and development of branchless banking in Iran.Originality/valueBased on the proposed model, the difficulties faced by individuals during the spread of COVID-19 could act as justifiable incentives to boost appropriate organizational preparations for making changes to the classic working processes. Processes such as telecommuting, job rotation and so on are among these changes.

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Purpose The purpose of this paper is twofold: the selection of project portfolios through hybrid artificial neural network algorithms, feature selection based on grey relational analysis, decision tree and regression; and the identification of the features affecting project portfolio selection using the artificial neural network algorithm, decision tree and regression. The authors also aim to classify the available options using the decision tree algorithm. Design/methodology/approach In order to achieve the research goals, a project-oriented organization was selected and studied. In all, 49 project management indicators were chosen from A Guide to the Project Management Body of Knowledge (PMBOK Guide), and the most important indicators were identified using a feature selection algorithm and decision tree. After the extraction of rules, decision rule-based multi-criteria decision making matrices were produced. Each matrix was ranked through grey relational analysis, similarity to ideal solution method and multi-criteria optimization. Finally, a model for choosing the best ranking method was designed and implemented using the genetic algorithm. To analyze the responses, stability of the classes was investigated. Findings The results showed that projects ranked based on neural network weights by the grey relational analysis method prove to be better options for the selection of a project portfolio. The process of identification of the features affecting project portfolio selection resulted in the following factors: scope management, project charter, project management plan, stakeholders and risk. Originality/value This study presents the most effective features affecting project portfolio selection which is highly impressive in organizational decision making and must be considered seriously. Deploying sensitivity analysis, which is an innovation in such studies, played a constructive role in examining the accuracy and reliability of the proposed models, and it can be firmly argued that the results have had an important role in validating the findings of this study.

Purpose The purpose of this study is to investigate the effects of electronic banking (e-banking) on the profitability of banks is an important subject. Although there are many studies in this area, the effect of using different e-banking instruments, such as internet banking, telephone banking, ATM and POS, was not investigated comprehensively, using a system dynamics approach. To fill this gap, the present study tried to develop an analytical model with a systematic approach through identifying the effects of different areas of e-banking services in a financial institution. Design/methodology/approach The income and cost of each transaction via different e-banking services were identified and the incomes and costs, number of transactions and other model variables were predicted for the next period using a single-layer neural network (perceptron). The proposed model was designed based on the system dynamics approach. Then, rates and auxiliary variables were introduced to the model based on the prediction data. Finally, the model was validated and different scenarios were examined. Findings Results showed that increased investment on e-banking can increase online customers, thereby boosting the bank’s incomes through raising transaction fees and acquiring additional resources. On the other hand, the need for physical branches and associated costs reduces with increasing the tendency of traditional customers toward using e-banking services. Simulation results showed that although the expansion of e-banking carries a huge financial burden on the bank, the costs reduce and profitability significantly increases with time. Originality/value This study can be used by senior managers of the bank throughout e-banking planning. This is because it systematically addressed the effects of e-banking expansion on different (banking) areas. The present study may also encourage researchers to conduct more extensive studies.

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