PurposeThe current study employs a modified framework of the unified theory of acceptance and use of technology (UTAUT) to predict students' intention to adopt online learning in India. The moderating role of openness to change in influencing the proposed relationships is also assessed.Design/methodology/approachA structured questionnaire was emailed to 650 students enrolled in various courses in public and private universities in India. In total 424 responses were considered for analysis using structural equation modeling (SEM). Moderation analysis was carried out with multi-group SEM and chi-square difference tests.FindingsThe results reveal that there is a significant impact of performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FS) and perceived security (PS) on students' intention to adopt online learning. Further, openness to change moderates the impact of PE, FS and PS on intention of students to opt for online learning.Originality/valueThis study is one of the initial efforts to examine the factors affecting students' intention to adopt online learning at the onset of third wave of coronavirus disease 2019 (COVID-19) pandemic in India. Besides the factors of the UTAUT model, this study highlights the importance of PS and openness to change in influencing students' intention to opt for online learning.

A modified UTAUT framework to predict students' intention to adopt online learning: moderating role of openness to change

This study seeks to find the moderating role of AI in the association between a bank’s innovative financial process and the bank’s market share. The data were analyzed using SPSS and SmartPLS software. The estimations were performed using structural equation modeling estimation techniques such as the measurement model, outer loading, convergent validity, discriminant validity, and SEM estimations. The initial estimations indicated factor as well as construct reliability and validity. The study concluded that an innovative financial process plays a vital role in enhancing the bank’s market share. However, artificial intelligence could not significantly moderate the relationship. The policymakers in the banking industry of Pakistan need to consider the up-gradation in the system of their financial process by innovation and artificial intelligence usage awareness in their existing staff as well their banking customers. Future research may include a similar model for Islamic as well as commercial banks in a comparative model. Additionally, future research may also include more banks as innovative financial institutions to get a greater sample size for a possible influence of artificial intelligence.

/pdf/role-of-artificial-intelligence-in-moderating-the-innovative-3jlmrnep.pdf

Role of artificial intelligence in moderating the innovative financial process of the banking sector: a research based on structural equation modeling

The study of digital financial services is not recent; however, since COVID-19, new attention has been given to solutions that avoid face-to-face interactions. Accordingly, the number of studies on the topic of digital financial services has increased, covering new topics, such as digital wallets. Thus, this study presents a weight and meta-analysis that synthesises previous literature on digital financial services use. Using 121 articles, four analyses were performed for all financial solutions, but also per main service, such as digital banking, digital management and payment services, and digital wallets. Results show the relevance of technology adoption factors such as perceived usefulness, ease of use, but also security and trust dimensions for individuals to use these solutions. Additionally, results from the moderation analysis show that more money-oriented cultures tend to use these services more when perceiving them as easy to use. The findings of this study support practitioners and future research. 

Adoption and use of digital financial services: A meta analysis of barriers and facilitators

Abstract This study examined the technological factor's utilization to promote the e-commerce in small and medium sized enterprises in Bangladesh. The technological factors are, Information and communication adoption, Internet connectivity, and Business data management. The e-commerce adoption factors involve the strategic innovation, research and development activity, and Productivity growth. The electronic commerce is being widely adopted in Bangladesh in the time of covid-19 pandemic. In the adoption of e-commerce, prominent factors are the internet connectivity, which plays a major role and Business data management which has equal importance for SMEs. SMEs have limited access to data as compared to multinational industries. However, the data has equal importance for multinational and SMEs to improve the e-commerce business. The research collects data from 115 SMEs from Bangladesh relating to different fields. The random selection method has been advised to collect data from SMEs aged not less than five years. The data collected for this research is through the interested parties of small and medium sized enterprises in Bangladesh. The data of the research is interpreted and analyzed through statistical software SPSS. 

/pdf/evaluating-the-utilization-of-technological-factors-to-45mruo0t.pdf

Evaluating the utilization of technological factors to promote e-commerce adoption in small and medium enterprises

Abstract A sound credit assessment mechanism has been explored for many years and is the key to internet finance development, and scholars divide credit assessment mechanisms into linear assessment and nonlinear assessment. The purpose is to explore the role of two important data analytics models including machine learning and deep learning in internet credit risk assessment and improve the accuracy of financial prediction. First, the problems in the current internet financial risk assessment are understood, and data of MSE (Micro small Enterprises) are chosen for analysis. Then, a feature extraction method based on machine learning is proposed to solve data redundancy and interference in enterprise credit risk assessment. Finally, to solve the data imbalance problem in the credit risk assessment system, a credit risk assessment system based on the deep learning DL algorithm is introduced, and the proposed credit risk assessment system is verified through a fusion algorithm in different models with specific enterprise data. The results show that the credit risk assessment model based on the machine learning algorithm optimizes the standard algorithm through the global optimal solution. The credit risk assessment model based on deep learning can effectively solve imbalanced data. The algorithm generalization is improved through layer-by-layer learning. Comparison analysis shows that the accuracy of the proposed fusion algorithm is 25% higher than that of the latest CNN (Convolutional Neural Network) algorithm. The results can provide a new research idea for the assessment of internet financial risk, which has important reference value for preventing financial systemic risk. 

https://link.springer.com/content/pdf/10.1007/s12063-022-00293-5.pdf

Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models

E-commerce and e-business are necessary components of today’s internet banking due to the developing global economy. Alternatively, in this technological era, the banking sector’s success is associated with creating bank users’ intention to adopt internet banking services. Therefore, the aim of this study is to determine the influencing factors of intention to adopt internet banking services of commercial bank users’ in the Bangladeshi context. A survey questionnaire was formulated based on past works of literature to find out the research objective. The convenience sampling method has been used in this study. For the data collection purpose, 250 bank users were asked request to participate in the research. As a fully completed survey, 180 responses were received where the response rate was 72% and the sample size was n = 180. For correlation analysis and hypotheses testing, SPSS version 26.0 was used. The results of the study show that Perceived Security Risk (PSR), Perceived Usefulness (PU), Perceived Ease of Use (PEU), Social Influence (SI), and Consumer Innovativeness (CI) have a statistical and significant impact on the intention to adopt internet banking services. It is concluded that the bank management committee should utilize PU, PEU, SI, and CI to amplify the level of willingness to adopt and embrace general banking services through internet platforms among bank users in their online banking transactions. For the future research study, this paper outlines several significant implications and offers some directions for the bank management committee of a commercial bank.

/pdf/determining-factors-of-intention-to-adopt-internet-banking-97ctwfjb.pdf

Determining factors of intention to adopt internet banking services: A study on commercial bank users in Bangladesh

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Determining factors of intention to adopt internet banking services: A study on commercial bank users in Bangladesh