Factors Affecting the Intention to Use Artificial Intelligence-Based Recruitment System: A Structural Equation Modeling (SEM) Approach

2021 ◽  
pp. 111-124
Author(s):  
Jung Hee Lee ◽  
Ju Hyung Kim ◽  
Yong Hwan Kim ◽  
Yong Min Song ◽  
Gwang Yong Gim
Keyword(s):  
Artificial Intelligence ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Intention To Use ◽  
Factors Affecting ◽  
System A ◽  
Recruitment System

Analyzing Factors Influencing Behavior Intention to Use Snapchat and Instagram Stories

2017 ◽  
Vol 4 (2) ◽  
pp. 75-81 ◽  
Author(s):  
Vincent Valiant Coa ◽  
Johan Setiawan
Keyword(s):  
Structural Equation Modeling ◽  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Social Presence ◽  
Structural Equation ◽  
Equation Modeling ◽  
Intention To Use ◽  
Generation Z ◽  
Acceptance Model ◽  
Behavioral Intention To Use

Snapchat, and Instagram are two social networks which recently gain their users after adopting such a feature called "Story" which allows a certain post to be disappeared after a certain time. This research takes up this technology trends analyzing the factors that probably affect the behavioral intention to use Snapchat and Instagram stories among generation Z. Factors are analyzed using Structural Equation Modeling, with basis model and variables from Technology Acceptance Model. Data collection was targeted to finished within 1 week using online questionnaire with respondent from Jakarta and Tangerang for 100 respondent that are using both Snapchat stories and Instagram Stories. There are two tools researcher usually use to analyze Structural Equation Modeling: SPSS AMOS and LISREL. In this research, researchers choose AMOS. From six hypothesis proposed for Snapchat analysis, four hypothesis is accepted, while the other two are rejected. Meanwhile, on Instagram Stories analysis, five hypothesis is accepted and one hypothesis is rejected. This study finds out the Social Presence is an exogenous variable which has a major role in affecting other variables. While Perceived Enjoyment influenced the behavioral intention to use Snapchat and Instagram Stories the most. Index Terms—Structural Equation Modeling, Technology Acceptance Model, influence, generation Z, Snapchat, Instagram REFERENCES [1] L. Chin and Z. Ahmad, "Perceived Enjoyment and Malaysian Consumers’ Intention to Use a Single Platform EPayment", SHS Web of Conferences, vol. 18, 2015. [2] M. Ariff, T. Shan, N. Zakuan, N. Ishak and M. Wahi, "Examining Users' E-Satisfaction in the Usage of Social Networking Sites; Contribution from Utilitarian and Hedonic Information Systems", IOP Conference Series: Materials Science and Engineering, vol. 58, 2014. [3] K. Hassanein and M. Head, "Manipulating perceived social presence through the web interface and its impact on attitude towards online shopping", International Journal of HumanComputer Studies, vol. 65, no. 8, pp. 689-708, 2007. [4] P. Surendran, "Technology Acceptance Model: A Survey of Literature", 2012. [5] F. Davis, "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology", MIS Quarterly, vol. 13, no. 3, p. 319, 1989

scholarly journals Antecedents Factors Affecting Cyberbullying Risks: A Study In Jordanian Secondary Schools

2020 ◽  
Vol 8 (3) ◽  
pp. 345-349
Author(s):  
Hesham Almomani ◽  
Diya Al-Jabali ◽  
Fayez Bni Mufarrej ◽  
Heba Ahmad
Keyword(s):  
Structural Equation Modeling ◽  
Secondary Schools ◽  
Self Efficacy ◽  
Structural Equation ◽  
Cyber Bullying ◽  
Equation Modeling ◽  
Postgraduate Students ◽  
Factors Affecting ◽  
Research Results

Purpose of the study: In this study, the primary aim is to identify the effects of self-efficacy and cyber bullying knowledge on cyber bullying risks among Jordanian students. Methodology: The population of the study specifically comprised of Jordanian students in Irbid students, with the study sample being 153 students. Accordingly, a questionnaire was developed and disseminated among the students to gather data for the achievement of the study objectives. The study used Structural Equation Modeling (SEM). The study also employed AMOS 23.0 and SPSS 25.0 software in SEM. Main Findings: self-efficacy and cyber bullying knowledge factors do have significant effects on cyber bullying risks. Applications of this study: This research can be used for academic purposes for universities, lecturers of education and management, researchers and undergraduate and postgraduate students. Novelty/Originality of this study: The phenomenon that existed in cyber bullying and referring from various previous research results, the study regarding the cyber bullying was conducted and presented comprehensively and completely. It is necessary examine the effect of self-efficacy and cyber bullying knowledge factors on cyber bullying risks.

scholarly journals FinTech in COVID-19 and Beyond: What Factors Are Affecting Customers’ Choice of FinTech Applications?

2020 ◽  
Vol 6 (4) ◽  
pp. 153 ◽  
Author(s):  
Mohammad K. Al nawayseh
Keyword(s):  
Financial Services ◽  
Structural Equation ◽  
Service Providers ◽  
Equation Modeling ◽  
Intention To Use ◽  
Factors Affecting ◽  
Modeling Techniques ◽  
Customer Trust ◽  
The Relationship

Accessing financial services is considered one of the main challenges facing communities during crises. This research studies the role of using FinTech applications to build resilience during the COVID-19 pandemic. The research empirically examines the factors affecting Jordanian citizens’ intention to use FinTech applications. The sample of the research comprised 500 potential FinTech service users in Jordan. Based on the research conceptual model, five hypotheses were developed and tested using structural equation modeling techniques (SEM-PLS). The research results indicate that perceived benefits and social norms significantly affect the intention to use FinTech applications. However, it has been found that perceived technology risks do not significantly affect the intention to use FinTech applications. Moreover, the results also indicate that customer trust is significantly mediating the relationship between perceived risks and intention to use FinTech applications. FinTech service providers should insure that their products are easy to use, fulfill needs and protect consumers’ data in order to ensure trust, hence positively influencing consumer adoption.

scholarly journals Factors affecting the acceptance of blended learning in medical education: application of UTAUT2 model

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Seyyed Mohsen Azizi ◽  
Nasrin Roozbahani ◽  
Alireza Khatony
Keyword(s):  
Medical Education ◽  
Blended Learning ◽  
Behavioral Intention ◽  
Structural Equation ◽  
Equation Modeling ◽  
Intention To Use ◽  
Validity And Reliability ◽  
Cross Sectional ◽  
Factors Affecting ◽  
Behavioral Intention To Use

Abstract Background Blended learning is a new approach to improving the quality of medical education. Acceptance of blended learning plays an important role in its effective implementation. Therefore, the purpose of this study was to investigate and determine the factors that might affect students’ intention to use blended learning. Methods In this cross-sectional, correlational study, the sample consisted of 225 Iranian medical sciences students. The theoretical framework for designing the conceptual model was the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Venkatesh et al. (2012) proposed UTAUT2 as a framework to explain a person’s behavior while using technology. Data were analyzed using SPSS-18 and AMOS-23 software. Structural equation modeling technique was used to test the hypotheses. Results The validity and reliability of the model constructs were acceptable. Performance Expectance (PE), Effort Expectance (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV) and Habit (HT) had a significant effect on the students’ behavioral intention to use blended learning. Additionally, behavioral intention to use blended learning had a significant effect on the students’ actual use of blended learning (β = 0.645, P ≤ 0.01). Conclusion The study revealed that the proposed framework based on the UTAUT2 had good potential to identify the factors influencing the students’ behavioral intention to use blended learning. Universities can use the results of this study to design and implement successful blended learning courses in medical education.

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