An empirical analysis on the adoption of electronic banking in the financial institutes using structural, behavioral and contextual factors

Abstract

This research examines contextual, structural and organizational factors, which can facilitate or slow down adoption of innovation in Electronic Banking in the financial Institutions. Threedimensional model co-structure, co-behavioral, contextual (3C) is used in this research. This schema is a logical model in the categories of models and many of concepts, events and organizational phenomena can be examined. Structural factors including type of the organization of institution, work distribution, preparing mobilization of resources and equipment and risk of decision-making sophistication influence on adoption of Electronic Banking. There are four contextual factors, which contribute in adoption of Electronic Banking including goals, strategies, culture and common norms. The five Behavioral Factors, which affect on electronic banking are connections and relations, skills and personal characters of employees, education, job satisfaction and banking work process. By studying the mentioned factors, we have realized that contextual factors plays important role on adoption of electronic Banking by employee and the behavioral and structural factors have minor impacts. The mentioned proposals are methods, which facilitate the adoption of electronic banking in the country.

A Short Review of the Electronic Banking System

“…Theories Unit of analysis (Questionnaire) Results Yu and Asgarkhani (2015), Arayesh (2015), Ahmadi and Afrouzi (2012) The results confirmed that all the weights associated with the service channel fit (SCF) were statistically significant. Aderonke A and Charles K (2010), Jalal, Marzooq and Nabi (2011), Abadi and Nematizadeh (2012), Moga, Nor and Mitrica (2012), Raida and Néji (2013), Alikhani and Davarzani (2014), Mansour, Eljelly and Abdullah (2016), Rodrigues, Oliveira and Costa (2016).…”

“…However, there are several theories and models in literature which have been employed to explain the relationships between the factors affecting the acceptance and adoption dominancy and user's attitude and willingness as shown in Appendix (1). Among them, there are three very well-known models which are the following: Technology Acceptance Model (TAM) by (Davis in 1989, Poon (2007), Aderonke A and Charles K. (2010), ), Jalal et al (2011), Johar and Awalluddin (2011, Abadi and Nematizadeh (2012), Ahmadi and Afrouzi (2012), Moga, et al (2012), Raida and Néji. (2013), El-Qirem (2013), Sanayei and Saneian (2013), Alikhani and Davarzani.…”

“…(2013), El-Qirem (2013), Sanayei and Saneian (2013), Alikhani and Davarzani. (2014), Mansour et al (2016), Rodrigues et al (2016), etc.…) which is the adaptation of the Theory of Reason Action (TRA), which was proposed by Fishbein and Ajzen in 1975, Ahmadi and Afrouzi. 2012, Rodrigues et al 2016 and Unified Theory of Acceptance and Use of Technology (UTAUT) developed by Venkatesh et al in 2003. This theory is used very widely for its simplicity, parsimony and robustness (Oshlyansky et al 2007, AbuShanab et al, 2010, Yuen et al, 2010, Foon and Fah, 2011, Venkatesh et al 2012, El-Qirem 2013, Tarhini et al, 2014.…”

“…The result provided evidence of the relationship that exists among the perceptions, the personal characteristics of the respondents and the organizational characteristics of their banks. Abadi and Nematizadeh (2012), Ahmadi and Afrouzi (2012), Azad, Abbaszadeh, Rikhtegar and Asgari (2013), Azad, Seyedaliakbar, Mousavirad and Mousavirad (2013), Esmaeili, Nazarpoori and Najafi (2013), Sanayei & Saneian (2013), Zadeh, Zangeneh and Azad (2014), Asgari, Ahmadi, Shamlou, Farokhi and Farzin (2014), Esmaeili and Horri (2014), Alikhani and Davarzani (2014), Shaemi and Saneian (2014), Yousefi and Nasiripour (2015), Arayesh (2015).…”

Electricity Market Empowered by Artificial Intelligence: A Platform Approach

“…In the area of energy prediction, the artificial neural network technique is well-known for the accurate forecasting of energy usage [29]. However, there is a challenging issue related to accuracy when the scale is reduced (e.g., neighborhood or household level) although precise load forecasting is possible at the aggregated level (e.g., national level), In this vein, the study of Ahmadi [48] suggests that the artificial neural network is developed in as many as 40% of the energy artificial neural networking algorithms [49].…”