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Assessing Learner’s Privacy Behaviors Through the Lens of Gender Egalitarianism Cultural Factor in a Blended-Learning Context: Student’s Perspective at Saudi Electronic University During COVID-19 Pandemic

Author

May Bantan, Nova Southeastern UniversityFollow

Date of Award

2021

Document Type

Dissertation - NSU Access Only

Degree Name

Doctor of Philosophy (PhD)

Department

College of Computing and Engineering

Advisor

Ling Wang

Committee Member

Inkyoung Hur

Committee Member

Sun Junping

Keywords

blended learning, culture, data privacy, gender, information systems researchers, online learning, online security, Saudi Arabia, Saudi Electronic University (SEU), user privacy

Abstract

Securing the privacy of users’ personal information has long been a fundamental concern for information systems researchers. The focus on learners’ data privacy in academic institutions is no less important. The evolution of blended learning to enrich the learning experience and outcomes presents information privacy challenges for many students. Previous research indicates that changes and ambiguity in self-disclosure behaviors due to cultural and gender differences intensify these challenges. However, research that focuses on the role of culture and gender disparities on learners’ privacy related behaviors, at organizational and individual levels, is not mature.

This study empirically examined the moderating effects of the organization’s gender egalitarianism level on the relationships between the online privacy policy and privacy-related antecedents to form an engaging learning environment that fits all students while minimizing privacy risk of their personal identifiable information (PII). Prior work by Wu et al. (2012) was extended, incorporating gender egalitarianism (GE), which is one of the Global Leadership and Organizational Behavior Effectiveness (GLOBE) study’s cultural dimension, as a moderating variable. Due to growing privacy concerns about online learning in Saudi Arabia, the study focuses on students at Saudi Electronic University (SEU) while been transitioned to online learning due to the COVID-19 pandemic.

A mixed-methods design was implemented to evaluate privacy policy in an online-learning environment, as well as to assess learners’ self-reported perceptions and behaviors related to their information privacy. The data collected from 364 students were analyzed using IBM SPSS to check for multivariate outliers. Additionally, Smart PLS 3.0 software was utilized to perform PLS-SEM analysis to examine the significance and effects of path coefficients in the model. Our empirical findings showed that Enforcement and Security dimensions of Online Privacy Policy have increased learners’ Trust, and enhanced their decisions to self-disclose their PII online. On the contrary, Notice and Security dimensions of Online Privacy Policy have decreased learners’ Privacy Concerns, while Choice and Enforcement dimensions have increased learners’ Privacy Concerns, potentially causing a privacy paradox, influencing learners’ decisions to self-disclose their PII online via LMSs.

This study also demonstrated that the organization’s level of GE plays a significant role in moderating the impacts of Choice and Notice on learners’ privacy Concerns, eventually moderating the relationship between their Privacy Concerns and Trust toward self-disclosing their PII. In addition, privacy perceptions were different among male and female learners at different campus locations. The study’s implications include that Chief Privacy Officers must carefully institute online privacy policies with regards to their GE level along with the gender and cultural disparities among their users to enhance their information privacy practices.

NSUWorks Citation

May Bantan. 2021. Assessing Learner’s Privacy Behaviors Through the Lens of Gender Egalitarianism Cultural Factor in a Blended-Learning Context: Student’s Perspective at Saudi Electronic University During COVID-19 Pandemic. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, College of Computing and Engineering. (1158)
https://nsuworks.nova.edu/gscis_etd/1158.