Factors influencing cloud AIS adoption: Evidence from Jordan

Olimat, Nofan Al; Jaradat, Zaid; Baker, Mohammad; Shbeil, Seif Al; Alshurafat, Hashem; Shbail, Mohannad Obeid Al

doi:10.1504/ijbex.2021.10042528

Cited by 9 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By analyzing the heterotrait–monotrait (HTMT) ratio, we have introduced another discriminant validity test to reinforce this part. The average of the heterotrait–heteromethod correlations is represented by this indicator in comparison to the average of the monotrait–heteromethod correlations (Al Shbail et al , 2022a, 2022b, 2022c; Henseler et al , 2017). Each correlation’s value is less than 0.85 (see Table 3).…”

Section: Resultsmentioning

confidence: 99%

“…First, our structural model’s multicollinearity was unaffected by the VIF values for the antecedent variables of the endogenous constructs, which ranged from 1.875 to 2.843 (Al Shbail et al , 2022a, 2022b, 2022c; Alshurafat et al , 2022a, 2022b) (see Table 1). The structural model was then checked for any potential nonlinearities.…”

Section: Resultsmentioning

confidence: 99%

“…This test shows that the square root of the AVE of a construct must be bigger than the connections it has with any other construct (the numbers on the diagonal represent the square root of the shared variance between the construct and its measures) (Fornell and Larcker, 1981). A construct's variance must exceed the variance that it shares with other Fraud triangle theory indicators in the model (measured by the squared correlation between the two constructs), as shown by the AVE of that construct , 2022c. Table 2 demonstrates that it satisfies this condition.…”

Section: Measurement Modelmentioning

confidence: 99%

“…By applying the fraud triangle theory, which identifies opportunity, rationalization and pressure as the key factors contributing to fraud, this study can examine how these elements interact and contribute to academic dishonesty among students (Al Shbail et al , 2022c; Becker et al , 2006; Choo and Tan, 2008; Kelly et al , 2022; Smith et al , 2021). This examination can provide valuable insights into the issue and contribute to our understanding of the factors leading to academic dishonesty.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Factors affecting accounting students’ misuse of chatgpt: an application of the fraud triangle theory

Alshurafat,

Al Shbail,

Hamdan

et al. 2023

JFRA

View full text Add to dashboard Cite

Purpose This study aims to explore the factors that contribute to student academic dishonesty through an examination of the misuse of AI language models. Using the fraud triangle theory, which posits that opportunity, rationalization and pressure are key factors for fraudulent behavior, this study investigates how these elements interact and contribute to academic dishonesty among students. Design/methodology/approach In this study, data on how accounting students used ChatGPT to cheat was acquired from 279 accounting students in Jordanian public universities over the course of two months, from January 2023 to March 2023, through previously tested and validated questionnaires. The main tool for gathering data was a questionnaire distributed online using Microsoft Forms. Findings The results show that all of the fraud triangle factors are significant determinants of student academic dishonesty and student misuse of ChatGPT. The findings of this research can be used to guide the development of technology-based preventative measures. Originality/value This study provides valuable insights into the motivations and factors that drive students to engage in academic dishonesty and sheds light on the broader issue of technology-assisted academic dishonesty and its impact on the educational system. This study’s contribution is significant, as it sheds light on a pressing issue in education and provides valuable information for educators and policymakers to address the problem and improve academic standards.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Measurement Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Factors affecting accounting students’ misuse of chatgpt: an application of the fraud triangle theory

Alshurafat,

Al Shbail,

Hamdan

et al. 2023

JFRA

View full text Add to dashboard Cite

show abstract

“…According to TAM, individuals are more likely to use technology if they perceive it to be useful and easy to use (Ali et al, 2020;Alshurafat et al, 2023;Daragmeh et al, 2021;Raza, Qazi, Shah, et al, 2020;Raza, Qazi, Umer, & Khan, 2020;Sani et al, 2020). TAM has been widely used in studies of technology adoption and use, as it provides a framework for understanding how individuals form perceptions of technology and how these perceptions influence their behavior (Al Shbail et al, 2022;Ashrafi et al, 2021;J. Liu et al, 2019).…”

Section: Research Model and Hypotheses Developmentmentioning

confidence: 99%

Factors Affecting Employees Use and Acceptance of Remote Working During the COVID-19 Pandemic: Evidence From the Jordanian Insurance Sector

et al. 2023

Self Cite

View full text Add to dashboard Cite

Remote working during the COVID-19 pandemic comes as an “enforced experiment,” where companies and individuals have turned to work from home to preserve business continuity. Drawing on a theoretical framework that integrates the Theory of Reasoned Action (TRA), Social Capital Theory (SCT), and Technology Acceptance Model (TAM), this research uses a sample of 134 survey responses to assess the factors affecting the acceptance and use of remote work during the COVID-19 pandemic among workers of the insurance industry in Jordan. The results suggest that social trust, perceived usefulness, and perceived ease of use can help elevate employee’s acceptance and use of remote work, whereas social norms have no significant effect. Considering these results, we further discuss implications and recommendations for the insurance sector.

show abstract

Factors Affecting Big Data Analytics in Jordanian Commercial Banks

Alkowmi,

Al-Safadi

2024

Contributions to Environmental Sciences &Amp; Innovative Business Technology

View full text Add to dashboard Cite

Factors influencing cloud AIS adoption: Evidence from Jordan

Cited by 9 publications

References 0 publications

Factors affecting accounting students’ misuse of chatgpt: an application of the fraud triangle theory

Factors affecting accounting students’ misuse of chatgpt: an application of the fraud triangle theory

Factors Affecting Employees Use and Acceptance of Remote Working During the COVID-19 Pandemic: Evidence From the Jordanian Insurance Sector

Factors Affecting Big Data Analytics in Jordanian Commercial Banks

Contact Info

Product

Resources

About