Personalization versus privacy a research on disclosing personal information behavior on social media among ueh students

Practically, research results help businesses understand the general situation and have a basis to determine the causes affecting the intention to share personal information on social ne

INTRODUCTION

Problem statement

Over the past ten years, there has been a significant increase in the number of users on social networking sites Notably, as of June 2016, Facebook ranked as the third most popular website in the world, following Google and YouTube (Alexa, 2016).

In today's digital age, the Internet has transformed information sharing, with social networks becoming a primary platform for communication Initially, each social network offered unique features, such as chatting, connecting, and video sharing (Antony Mayfield, 2008) However, modern social networks have evolved to integrate these functionalities, providing users with a comprehensive experience.

Sharing personal information on social media can offer significant benefits, such as enhancing personal branding and providing tailored shopping experiences that meet individual needs However, this practice also heightens the risk of personal privacy being compromised Interestingly, despite being aware of privacy concerns, many individuals continue to disclose personal information online, highlighting a paradox in user behavior.

Effective management of personal information on social media enhances user safety and experience Understanding the factors that influence users' willingness to share information is crucial This research will start with a thorough literature review to identify key theoretical frameworks and factors that affect personal information disclosure intentions among students Ultimately, this study aims to deepen the understanding of student behavior on social media, offering valuable insights for policymakers and marketers.

For the reasons above, our group decided to conduct research on the topic:

"Personalization versus Privacy: A research on disclosing personal information behavior on social media among UEH students".

Aim of research

This article explores the application of the Theory of Planned Behavior alongside the foundational elements of Privacy Calculus Theory (Hann et al., 2007) It utilizes academic information sources to clarify the relationships among the factors outlined in these theories and examines the interactions between the relevant variables.

Research findings enable businesses to comprehend the factors influencing users' willingness to share personal information on social networks This understanding allows companies to conduct further studies and develop effective personalization strategies in their commercial activities while prioritizing user information security By identifying key influencing factors and implementing policies that enhance security and foster customer trust in personalized content, businesses can improve user experience and drive growth.

LITERATURE REVIEW AND RESEARCH MODEL

Theoretical Basis

The authors utilize the Privacy Calculus Theory and the Theory of Planned Behavior to explore behavioral intentions among social media users, emphasizing the importance of weighing pros and cons in decision-making Their research, as detailed in "Research on Influencing Factors of Personal Information Disclosure Intention of Social Media in China" (A Fan et al., 2020), highlights that Perceived Risk and Perceived Benefit are the primary factors influencing users' disclosure of personal information They found that users are likely to share personal information only when the perceived benefits outweigh the perceived risks This theoretical framework is essential for understanding the users' decision-making process regarding personalization versus privacy, as many users are willing to sacrifice personal privacy for personalized online services.

Related research

Numerous research studies have explored customer behavior regarding the disclosure of personal information for personalized services, highlighting the balance between personalization benefits and privacy concerns.

2.2.1 The willingness to disclose personal information: Trade-off between privacy concerns and benefits - Al-Jabri, I.M., Eid, M.I., and Abed, A (2020)

This study aims to explore the relationship between privacy concerns and the willingness to share personal data The author identifies three key characteristics that influence consumers' readiness to provide personal information to online businesses The first critical factor is privacy anxiety, which encompasses three dimensions: collection (COL), errors (ERR), and improper access and use (IAU).

The study identified three key factors influencing the willingness to disclose personal information: perceived control over personal data (PCN), government and technology-based control (GTC), and perceived benefits Among these, perceived benefits included financial rewards (FNR), personalization (PRS), and convenience (CNV) Notably, only three dimensions—emotional regulation (ERR), financial rewards (FNR), and convenience (CNV)—demonstrated significant relationships with the willingness to share personal information.

Research indicates that the collection of personal data can facilitate illegal access and misuse, acting as a mediator between errors and improper usage Additionally, customers often lack control over the access and validation of their data, as technological and governmental measures fail to ensure privacy effectively Moreover, privacy concerns significantly influence the willingness to share personal information, and these concerns are not alleviated by government or technological controls Ultimately, consumers are driven to share their personal information primarily by convenience and the desire for customization.

The research article has notable limitations, including an unequal representation of male and female respondents in the study sample Additionally, the dependent variable focused on the desire to disclose personal information rather than actual exposure Interestingly, individuals with a lower readiness to share are often more inclined to reveal private information, highlighting the "privacy paradox," which describes the inconsistency between the willingness to provide personal data and the more cautious behavior exhibited in practice Furthermore, the study overlooked the sensitivity of personal data as a critical factor in the information-sharing process.

Figure 2.1 Research model by Al-Jabri, I.M., Eid, M.I., and Abed, A (2020)

Source: Al-Jabri, Ỉ.M., Eid, M.Ỉ., and Abed, A (2020)

2.2.2 Personalization versus Privacy: An Empirical Examination of the Online Consumer’s Dilemma - Ramnath K Chellappa & Raymond G Sin (2005)

This research introduces a model that predicts consumer usage of online personalization by analyzing the tradeoff between their appreciation for personalization and their privacy concerns A key finding reveals that while consumers value personalization, their privacy worries negatively affect their usage decisions Thus, the overall utilization of personalized services hinges on the balance between these two factors, despite them being distinct concepts Although the absolute values of parameter estimations are not critical for analysis, their relative weights provide valuable insights for management This suggests that while privacy concerns should not be overlooked, enhancing the quality of personalized services can lead to increased consumer engagement.

This paper aimed to create a simplified model of the tradeoff between consumer personalization and privacy It did not consider individual-specific factors like gender, ethnicity, education, and expertise within the broader nomological network Similar to other studies, it faces limitations regarding generalizability, and findings should be regarded as scientific only if replicable in future research Additionally, the author assessed intentions rather than actual behaviors due to a lack of access to real usage statistics for personalization services.

Figure 2.2 Research model by Ramnath K Chellappa & Raymond G Sin (2005)

Source: Ramnath K CheUappa & Raymond G Sin (2005)

Previous research has primarily focused on a single aspect influencing customers' willingness to disclose information for personalized services In contrast, our team has identified new avenues for exploration, aiming to broaden the understanding of how personalized services and privacy concerns collectively impact the intention to share personal information This study offers a comprehensive perspective on the various factors that influence online information disclosure, providing readers with deeper insights into this critical issue.

Hypothesis Development and Proposed Research Model

2.3.1.2 The relationship between Perceived Benefit and Personal Information Disclosure Intention

Perceived Benefit refers to the positive outcomes users anticipate from utilizing a product or service In social media, users share personal information with the goal of gaining advantages like fostering interpersonal relationships, feeling a sense of community, and accessing extra services These expected rewards motivate users to disclose information on social media platforms (Lu, Tan, & Hui, 2004; Culnan & Bies, 2003).

The relationship between perceived benefits and the intention to disclose personal information is generally positive, as individuals are more inclined to share their data on social media when they believe it will enhance their relationships and provide valuable information However, this tendency is moderated by privacy concerns, with some individuals prioritizing perceived benefits over the need for privacy protection It's crucial to recognize that this dynamic can differ based on individual preferences and cultural influences.

Hl Perceived Benefit will positively affect Personal Information Disclosure Intention

2.3.1.3 The relationship between Perceived Risk and Personal Information

Perceived Risk refers to the potential negative consequences that users face when disclosing personal information on social media, stemming from fears of improper or illegal use of their data This apprehension is fueled by users' predictions of worst-case scenarios, which significantly shape their perception of risk The primary concerns revolve around unauthorized access and misuse of personal information, ultimately threatening users' privacy and security (Xu et al., 2008; Dinev et al., 2013).

Research on the relationship between Perceived Risk and the intention to share personal information reveals inconsistent findings While some studies indicate that higher Perceived Risk correlates with a decreased willingness to disclose personal details, others suggest that this impact is negligible This disparity underscores the necessity for additional research to clarify how Perceived Risk affects individuals' intentions to share their information.

H2 Perceived Risk will negatively affect Personal Information Disclosure Intention

2.3.1.4 The relationship between Privacy View and Personal Information Disclosure Intention

Privacy View highlights the importance of how individuals manage their personal privacy, shaped by their unique traits, cultural backgrounds, and life experiences Information sensitivity, which refers to the level of privacy concerns social media users have regarding specific types of information, influences their intention to disclose personal information (Li & Wang, 2015; Li, Hong & Zhu, 2016).

The relationship between Privacy View and the intention to disclose personal information is predominantly negative Research indicates that individuals with a stronger privacy perspective tend to be more cautious when asked to share personal details, as they are more aware of cybersecurity threats This leads to the hypothesis that heightened privacy concerns reduce the likelihood of personal information disclosure.

H3 Privacy View will negatively affect Personal Information Disclosure Intention

2.3.1.5 The relationship between Subjective Norm and Personal Information Disclosure Intention

Subjective Norm refers to the social pressure individuals perceive from family, friends, and influential figures when deciding whether to take a specific action This concept highlights how the opinions and expectations of those close to us can significantly impact our choices (Ajzen, 1991).

Numerous studies have examined the role of subjective norms in the disclosure of personal information, revealing that the relationship between these norms and personal information exposure is influenced by general behavioral tendencies Research indicates a positive correlation between subjective norms and the intention to disclose personal information, as individuals are often motivated by the willingness of others to share (Heirman, Walrave, & Ponnet, 2013; Varnali & Toker, 2015; Jiao, 2019) Conversely, some scholars argue that perceived risk may lead subjective norms to negatively impact the intention to disclose personal information Thus, we propose hypothesis 4.

H4 Subjective Norm will negatively affect Personal Information Disclosure Intention

In our original research, we proposed a hypothesis linking 'Privacy View' to 'Perceived Risk' However, after thorough discussions with our group and instructor, we chose to exclude this relationship to align with research parameters and enhance our focus on achievable outcomes Consequently, we present our refined research model below.

Summary

Chapter 2 defined key concepts relevant to the research topic and introduced a model grounded in existing studies It also presented five hypotheses, with the first four focusing on users' individual perceptions regarding online personal information disclosure: (1) Perceived Risk, (2) Perceived Benefit, and (3) Privacy View, all of which influence the intention to disclose personal information The methodology for the current thesis will be discussed in the subsequent chapter.

RESEARCH METHODS

Research methods of the topic

The research team categorized various variables into distinct factors to effectively assess the constructs within the research model These factors include the Perceived Benefits of sharing information online, the Perceived Risks associated with cybersecurity, the Privacy Perspectives of social media users, and the Subjective Norms influencing online information disclosure.

To carry out this study, the authors used two main research methods: Preliminary research through qualitative methods and Formal research through quantitative methods

Preliminary research involves utilizing theoretical frameworks and prior studies alongside surveys to gather and analyze data This approach aims to identify key factors influencing the impacts of sharing personal information online.

Formal research: Using quantitative approaches, statistical methodologies, and SPSS software to analyze data and discover key variables affecting online information disclosure (Borry, 2012)

The research team utilized questionnaires to gather essential data for their study, creating a comprehensive set of 20 Likert scale questions Respondents rated their agreement on a 5-point scale ranging from "Strongly disagree" to "Strongly agree." The data was analyzed using SPSS software, employing Cronbach's Alpha to assess the reliability of the scale and eliminate unsuitable variables Following this, exploratory factor analysis (EFA) and multivariate regression analysis were conducted to evaluate the impact of each factor Finally, the team assessed the proposed model and tested their hypotheses.

The authors have used this method of data collection by creating a questionnaire and collected directly the opinions and evaluation levels of UEH students.

Measurement Scale

In a study building on the findings of Anrong Fan et al (2020), we assessed key factors including Perceived Risk, Perceived Benefit, Privacy View, Subjective Norm, and Personal Information Disclosure Intention.

4 items, using a 5-point scale, ranging from 1 (strongly disagree) to 5 (strongly agree), specifically as below:

Original Items Adjusted Items Source

PRl Overall, I see no real threat to my privacy due to my presence on the OSN

I believe that posting personal information on social networks contains risks of unauthorized exploitation

PR2 I fear that something unpleasant can happen to me due to my presence on the OSN

I think posting personal information on social media is the reason for the increase in spam calls and messages

PR3 I worry that if I use my credit card to buy something on the internet my credit card number will be obtained intercepted by someone else

I believe that providing information on social networks leads to risks of personal property damage

PR4 I feel safe publishing my personal

I believe that sharing personal information on social networks

Spiekermann et al (2010) information on the OSN contains risks of invasion of privacy

PB1 Social media can help me build identity and a sense of belonging in a virtual community.

I can build a personal brand by regularly sharing personal information on social media

Site, i like that i can reach a lot of people with my posts

I disclose personal information on social media because I feel more connected to the online community

PB3 I trust this kind of retailer which recommends shopping options for me keeps my best interests in mind.

I share personal information on social media because I want personalized recommendations while shopping online or using services

PB4 I use social media to communicate ideas with others.

I share personal information on social media because I want to have a say on the issues I care about

PVl I think MCC apps providers have sufficient technical capacity to ensure that the data 1 send cannot

I care about setting up a 2-Factor Authentication (2FA) on social media platforms

Nikkhah and Sabherwal (2021) be modified by a third party

PV2 Facebook could cause serious privacy problems.

I care about the privacy rights on the social media platforms that I often use

PV3 To me, it is an important thing to keep my privacy intact from others

I think it is important to keep personal information private from being compromised by others

PV4 Compared to others, 1 am more sensitive about the way others handle my personal information.

I am concerned about how others may hold and use my personal information

SNl Most people who are important to me, they implement PPBs online

I see most people around me sharing their information on social media

SN2 Most people who are important to me, they think 1 should implement PPBs online

Most people around me advise me to pay close attention to the security of my personal information

SN3 I see there are many education outreach programs about cyberinformation security in the media

SN4 I believe other people are too concerned about online privacy issues.

I see many people around me concerned about information privacy

Personal Information Disclosure Intention (PIDI)

P1DI1 1 will avoid giving real and accurate personal information online (Reversed)

I used to share real information about myself on social media

P1DI2 I expect my use of mobile data services to continue in the future

I will continue to share personal information on social media in the future

PIDI3 I frequently update my knowledge about cyberinformation

PIDI4 I have a comprehensive profile on FB

I will always allow the social media platforms I use to access my personal information

Target: Identifying the factors that influence students1 intentions to disclose their personal information on social media.

Sharing personal information on social media can strengthen connections with friends and enhance interactions with like-minded individuals However, it also poses risks, including the potential for information to be leaked and misused.

In the future, I will persist in sharing personal information on social media Encouraging users to reflect on their willingness to share personal details on social networks can lead to more informed and thoughtful decisions regarding their online interactions.

PIDI3: I frequently update my knowledge about cyberinformation security:

Enhancing your cybersecurity knowledge is essential in today's digital landscape, where cyber threats are increasingly complex and dangerous Understanding how to protect personal information and ensure network security is crucial for safeguarding against these evolving risks.

When granting social media platforms access to your personal information, it's crucial to consider the implications for your privacy Understand the type of information these platforms collect and use, and make sure you engage with the network in a safe and responsible manner.

Sharing personal information on social media poses significant risks of unauthorized exploitation When individuals disclose their personal details online, this information can be improperly collected, used, and shared without their consent, leading to serious privacy concerns.

Posting personal information on social media can significantly contribute to the rise in spam calls and messages When individuals share personal details online, it becomes easier for others to access this information, leading to unwanted contact and unsolicited communications.

Sharing personal information on social media can pose significant risks to your property By revealing sensitive details like bank account numbers or credit card information, you inadvertently provide attackers with the opportunity to exploit this data, potentially leading to financial loss or asset theft It's crucial to be mindful of the information you disclose online to safeguard your personal property.

Sharing personal information on social media poses significant privacy risks, as it allows others to access and potentially misuse your data This exposure can lead to invasions of privacy and unwanted attention Moreover, advertising companies may collect this personal information to generate targeted advertisements tailored to your interests and behaviors, further compromising your privacy.

Building a personal brand on social media involves consistently sharing personal insights and experiences By doing so, you can strengthen your brand, impress your audience, and attract more followers To establish yourself as an authority in your field, leverage social media to showcase your interests, skills, experiences, and achievements.

Sharing personal information on social media fosters a sense of connection with the online community, enabling us to build valuable relationships with others who share similar interests and goals This engagement not only enhances our social networks but also opens up numerous opportunities for personal and professional growth in the future.

Sharing personal information on social media allows companies to create personalized recommendations and advertisements that align with your unique needs and interests This tailored approach can enhance your online shopping experience by helping you discover ideal products and services more efficiently, ultimately saving you valuable time.

Sharing personal information on social media empowers individuals to exercise their right to free expression and actively participate in discussions on important issues By engaging in these conversations, users can voice their opinions and contribute to topics that matter to them.

PVL I care about setting up a 2-Factor Authentication (2FA) on social media platforms: Installing a 2-Factor Authentication social media platforms is an important measure to enhance the security of your account This helps ensure that users can't access your account just by guessing the correct password.

Privacy rights on social media platforms are crucial and deserve significant attention to reduce the risk of personal information loss Safeguarding privacy on these networks is essential for users who frequently engage with them.

Quantitative research

All UEH students who share personal information via social media.

To determine the appropriate sample size for conducting exploratory factor analysis (EFA) and multivariate regression analysis, two essential conditions must be met, represented by the formulas n ≥ 5m and n ≥ 50 + 8p Here, 'n' denotes the sample size, 'm' refers to the number of variables in the analysis, and 'p' indicates the number of predictors in the regression model.

Formula (1): For EFA exploratory factor analysis: Based on the study of Hair et al

According to Comrey (1973) and Roger (2006), the minimum sample size for research utilizing factor analysis should be at least five times the total number of observed variables, ensuring a robust and reliable analysis.

Formula (2): For multivariate regression analysis: the minimum sample size to be achieved is equal to 50 plus eight times the number of independent variables (Tabachnick and Fidell, 1996).

When determining the sample size for a study, it is essential to meet specific criteria, prioritizing having more samples rather than fewer In this research, a sample size of n = 136 was utilized, fulfilling both required formulas The participants for the survey were chosen using a random sampling method.

In this study, two methods were used:

- Convenience sampling method: The authors’ team created a questionnaire about

A recent survey explores the intentions of individuals to share personal information on social networks, focusing on platforms like Facebook The study emphasizes the significance of sharing within large groups, classes, and fan pages populated by UEFI students, such as the UEH Study Group This research aims to understand the factors influencing privacy concerns and information sharing behavior among students in digital communities.

- Snowball method: The authors shared the questionnaire for the friends at ƯEH and asked them to survey and share with their friends at UEH

Data analysis method

Since we have the result from the Google Form, we recheck the data and upload it to the SPSS.25 for further processing and data analysis Specifically, as follow:

Cronbach’s Alpha: The coefficient used to check the reliability of the scale and remove the observed variables that do not ensure reliability based on the following criteria:

- Using Cronbach's alpha to test the variability of each measurement scale.

- Cronbach’s Alpha coefficient of the scale greater than 0.6 is accepted.

If Cronbach's Alpha falls below 0.6, it is essential to eliminate certain variables to improve the reliability of the scale By focusing on the Cronbach's Alpha and the "Cronbach's Alpha If Item Deleted" values, we can identify which variables should be removed This process should continue until the Cronbach's Alpha coefficient meets the acceptable threshold of 0.6 or higher.

- Remove the variables with the total correlation coefficient or Corrected Item - Total Correlation less than 0.2.

Following the assessment of reliability and the elimination of unqualified variables, the authors proceed with Exploratory Factor Analysis (EFA) to evaluate the variability of measurement scales EFA aims to condense the number of observed variables and categorize them into distinct factors based on established criteria.

- KMO coefficient (Kaiser-Meyer-Olkin) is used to consider the suitability of the factor The KMO coefficient must have a value of 0.5 or more.

- Bartlett's test of sphericity is used to examine the correlation between the observed variables in a factor and has Bartlett's Test sig coefficient < 0.05 (statistically significant).

- Eigenvalue is used to determine the number of factors in EFA analysis With this criterion, only factors with Eigenvalue > 1 are kept in the model.

A Total Variance Explained value greater than 50% indicates that the Exploratory Factor Analysis (EFA) model is appropriate Given that the total variation is 100%, this percentage reflects the extent to which the extracted factors account for the variance, highlighting the proportion of observed variables that may be overlooked.

Factor Loading, or factor weight, indicates the correlation between an observed variable and a factor A higher factor loading coefficient signifies a stronger correlation, while a lower coefficient suggests a weaker relationship Authors typically consider a factor loading of 0.4 as the standard threshold for statistical significance, particularly in studies with a sample size of N = 180.

In conducting the EFA test, the authors develop representative factors for each group of observed variables They then utilize these factors to perform Pearson Correlation analysis, examining the relationships between independent and dependent variables This analysis also helps identify instances of potential dynamic collinearity based on specific criteria.

A Sig value below 0.05, combined with an absolute Pearson correlation coefficient greater than 0, indicates a significant correlation between the independent and dependent variables Conversely, if these conditions are not met, the authors will conclude that no correlation exists.

- In addition, question the phenomenon of multicollinearity between independent variables if the Sig value is less than 0.05 and high Pearson correlation coefficient.

Following the examination of the relationship between independent variables and the dependent variable, the authors proceed with a multivariable regression analysis to further elucidate this correlation This involves testing the proposed model's hypothesis and addressing the issue of multicollinearity through a systematic approach.

(taking 0.5 as a landmark to distinguish between the good model and the bad model)

Al the same time, the sig value in the ANOVA table is less than 0.05 (with statistical significance).

- Residual normal distribution test based on Histogram, Normal P-P Plot.

- The conclusion of the multicollinearity question is based on the VIF coefficient (less than 10).

- Provide the regression equations (standardized and unstandardized) based on the obtained results to evaluate the influence of the factors on the dependent variable.

In this chapter, the author outlines the comprehensive research process, detailing the research objectives and theoretical foundations that inform the proposed research model The author develops an official scale through qualitative research, followed by quantitative research that involves survey selection and data collection Data analysis is conducted using SPSS software, employing techniques such as Cronbach’s Alpha coefficient, exploratory factor analysis (EFA), and regression analysis to derive results and suggest implications.

The scale was developed using the references from earlier studies and a combination of qualitative and quantitative methods to clarify the questions to support the research process.

The author used SPSS software to describe statistical samples for the data processing section and evaluated the measurement model.

RESEARCH RESULT

Descriptive statistics of the survey sample

The author team successfully gathered 136 complete answer samples from UEH students using Google Forms, after reviewing and eliminating any unacceptable or redundant responses The accompanying charts illustrate the characteristics of this sample group.

According to Figure 4.1, the survey conducted among UEH students revealed that females comprised the majority, accounting for 69.1% (94 students), while males represented 30.1% (41 students) Additionally, only 0.7% of respondents identified as other, which corresponds to one student.

According to Figure 4.2, the survey results from UEH students reveal that freshmen represent the largest group, accounting for 71% (97 students) of the total 136 responses Sophomores and juniors make up 15% (20 students) and 8% (11 students), respectively, while no seniors participated in the survey Additionally, students from other batches constitute 6% (8 students) of the responses.

4.1.3 The social networking platforms which are frequently used

Figure 4.3 Social networking platforms which are frequently used

According to Figure 4.3, Facebook leads as the most popular social networking platform with a usage rate of 94.1%, solidifying its position as the market leader Instagram follows with a usage rate of 72.1%, while YouTube captures 67.6% of users The messaging app Zalo and the short-form video platform TikTok also show significant popularity, with usage rates of 54.4% and 58.1%, respectively In contrast, Twitter's usage rate stands at a mere 13.2%, indicating its lower presence in this region.

The term "Others" accounts for 2.9% of social networking platform usage, suggesting the existence of lesser-known platforms that are not as widely embraced by users These statistics underscore the diverse popularity of various social networking sites among consumers.

4.1.4 Experience when using social networks

Figure 4.4 Experience when using social networks

According to Figure 4.4, a survey of UEII students reveals that approximately 42% report a positive experience with social media, while only 2.2% indicate a negative impact Furthermore, 55.9% of students at UEH perceive their social media experience as neutral.

4.1.5 Average time spent on social media per day

Figure 4.5 Average time spent on social media per day

A comparison of the average daily time spent on social networks by UEH students reveals that 45% of students, or 61 individuals, spend between 2 to 4 hours online, while another 45% exceed 4 hours of usage Additionally, 10% of respondents report spending less than 2 hours on social networks each day.

Analytical data from key questions

Cronbach's alpha coefficient is a key metric for assessing the reliability of variables in research models A scale is deemed reliable when its Cronbach's alpha value exceeds 0.6, while a variable is considered appropriate if its coefficient is greater than 0.2.

Table 4.1 Table of results to evaluate the Reliability

Cronbach’s Alpha if Item Deleted

Perceived Risk Scale: Cronbach's Alpha = 0.799

PR1 I believe that posting personal information on social networks contains risks of unauthorized exploitation

PR2 I think posting personal information on social media is the reason for the increase in spam calls and messages

PR3 I believe that providing information on social networks leads to risks of personal property damage

PR4 I believe that sharing personal information on social networks contains risks of invasion of privacy

Perceived Benefit Scale: Cronbach's Alpha = 0.812

PB1 I can build a personal brand by regularly sharing personal information on social media

PB2 I disclose personal information on social media because I feel more connected to the online community

PB3 I share personal information on social media because I want personalized recommendations while shopping online or using services

PB4 I share personal information on social media because I want to have a say in the issues 1 care about

Privacy View Scale: Cronbach’s Alpha = 0.834

PV1 I care about setting up a 2-Factor

Authentication (2FA) on social media platforms

0.659 0.799 / social media platforms that I often use 0.736 0.756 /

PV3 I think it is important to keep personal information private from being compromised by others

PV4 I am concerned about how others may hold and use my personal information 0.668 0.790 ✓

Subjective Norm Scale: Cronbach's Alpha = 0.693

SN1 I see most people around me sharing their information on social media 0.406 0.670 ✓

SN2 Most people around me advise me to pay close attention to the security of my personal information

SN3 I see there are many education outreach programs about cyberinformalion security in the media

SN4 I see many people around me concern about information privacy 0.563 0.571 /

Personal Information Disclosure Intention Scale: Cronbach’s Alpha = 0.683

PIDI1 I used to share real information about myself on social media 0.501 0.594 ✓

PIDI2 I will continue to share personal information on social media in the future 0.572 0.547 ✓ PIDI3 I frequently update my knowledge about cyber information security 0.295 0.719 /

(Source: The results of the data analysis of the research group)

PIDI4 I will always allow the social media platforms I use to access my personal information

All scales exhibit Cronbach's Alpha values exceeding 0.6, indicating acceptable internal consistency Furthermore, the corrected item-total correlation coefficients for all observable variables within each scale are above 0.2, confirming their relevance Consequently, all observed variables meet the necessary criteria and will be utilized in future exploratory factor analysis.

4.2.2 Validity test: EFA - exploratory factor analysis

An analysis of the Cronbach’s Alpha reliability coefficient revealed that 20 observable variables across five components effectively evaluate UEH students' intentions to disclose personal information on social networks, meeting reliability standards Consequently, exploratory factor analysis (EFA) was conducted to further investigate these 20 observable variables.

When assessing factors for 20 observable variables, the factor extraction method Principal Component Analysis with Vari max rotation is used.

For factor analysis to be deemed suitable, the KMO index should exceed 0.5 (Garson, 2003), and Bartlett's test must yield a significance level of less than 0.05, indicating that the data is appropriate and exhibits correlation among the variables.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.788

Bartlett's Test of Sphericity Approx Chi-Square 1195.896 df 190

The Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) value is 0.788, indicating that the data is suitable for factor analysis, as it exceeds the threshold of 0.5 Additionally, Bartlett’s test yielded a result of 1195.896 with a significance level of 0.000, which is less than 0.05 This confirms that the variables are interrelated and fulfill the necessary criteria for effective factor analysis.

PB3 1 share personal information on social media because I want personalized recommendations while shopping online or using services

PB4 I share personal information on social media because I want to have a say on the issues I care about

PV2 I care about the privacy rights on the social media platforms that I often use

PV3 I think it's important to keep personal information private from being compromised by others

PV4 I am concerned about how others may hold and use my personal information

SN1 I see most people around me sharing their information on social media

SN2 Most people around me advise me to pay close attention to the security of my personal information

SN4 I see many people around me 0.624

(Source: The results of the data analysis of the research group) concerned about the privacy of personal information

PIDI1 I used to share real information about myself on social media

P1DI2 I will continue to share personal information on social media in the future

PIDI3 I frequently update my knowledge about cyberinformation security

In conducting factor analysis using Principal Components with Varimax rotation, two observable variables, SN2 and PIDI3, were identified as inappropriate due to their high discriminant nature Specifically, SN2 exhibited a coefficient value below 0.5, while PIDI3 appeared in a different column than the other PIDI variables, indicating a misalignment Consequently, the authors opted to eliminate both SN2 and PIDI3 from the analysis and proceeded with a revised Exploratory Factor Analysis (EFA) using 18 observable variables The subsequent tests for KMO, Bartlett’s test, and factor loadings were then conducted to assess the validity of the new model.

18 observable variables are presented in the following tables:

Table 4.4 Results of KMO and Bartlett tests of independent variables (2)

Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.772

Bartlett's Test of Sphericity Approx Chi-Square 1018.387

(Source: The results of the data analysis of the research group) df 153

PR1 1 believe that posting personal information on social networks contains risks of unauthorized exploitation

PB3 I share personal information on 0.784 social media because I want personalized recommendations while shopping online or using services

SN4 I see many people around me concerned about the privacy of personal information

PID1I 1 used to share real information about myself on social media

PIDI2 I will continue to share personal information on social media in the future

Table 4.6 Table of Total Variance Explained

Components Rotation Sums of Squared Loadings

The second exploratory factor analysis (EFA) yielded a KMO value of 0.772, indicating that the data is suitable for factor analysis Additionally, Bartlett’s test result was 1018.387 with a significance level of 0.000, confirming that the variables are significantly correlated and meet the necessary conditions for effective factor analysis.

The 18 observable variables are grouped into 5 groups Value of total variance explained = 66.566% > 50%: satisfactory; then it can be said that these four factors explain 66.566% of the variation in the data The Eigenvalues of the factors arc all high (>1), the fifth factor has the lowest eigenvalue of 1.655 > 1 The factor loading coefficients of the observable variables are all greater than 0.5.

Pearson Correlation

The analysis of Pearson correlation reveals that all variables—Perceived Risk, Perceived Benefit, Privacy View, and Subjective Norm—exhibit positive correlations with Personal Information Disclosure Intention Notably, the Perceived Benefit and Subjective Norm show significant statistical significance with coefficients of 0.000 and 0.001, respectively In contrast, Perceived Risk and Privacy View present lower coefficients of 0.648 and 0.116, indicating lesser statistical significance.

Testing research model with Regression Analysis

The authors conducted a regression analysis to assess the impact of four independent variables on the dependent variable, Personal Information Disclosure Intention, following their examination of the linear correlation between these variables.

We conducted a multivariate regression analysis using SPSS to evaluate the effectiveness of our regression model The analysis focused on the relationships between several independent variables, including Perceived Risk (PR), Perceived Benefit (PB), Privacy View (PV), and Subjective Norm (SN), and their impact on the dependent variable, Personal Information Disclosure Intention (PID1).

Std Error of the Estimate

1 0.440a 0.193 0.169 0.72385 2.051 a Predictors: (Constant), Subjective Norm, Perceived Benefit, Perceived Risk, Privacy View b Dependent Variable: Personal Information Disclosure Intention

The model summary table reveals key data processing outcomes, indicating an R square of 0.193 and an adjusted R square of 0.169 This adjusted R square value suggests that the independent variables in the regression analysis explain 16.9% of the variation in the dependent variable, while 83.1% is attributed to out-of-model factors and random errors The adjusted R square is crucial for assessing the model's fit more accurately than the coefficient R square Additionally, the Durbin-Watson value of 2.051, which falls between 1.5 and 2.5, confirms that the assumption of first-order series autocorrelation is not violated (Yahua Ọiao, 2011).

Table 4.9 Analysis of variance (ANOVA) table

Squares df Mean Square F Sig.

Total 85.086 135 a Dependent Variable: Personal Information Disclosure Intention b Predictors: (Constant), Subjective Norm, Perceived Benefit, Perceived Risk, Privacy View

From the ANOVA table, we get the result that the Sig value of the F-test is 0.000 <

0.05, so the regression model is built with statistical significance.

Figure 4.6 Normalized Residual Frequency chart Histogram

From the histogram, we can see that the Mean is 3.40E-16 The standard deviation is 0.985 - which is close to 1 From a general perspective, the columns of residuals have a multimodal distribution.

Normal P-P Plot of Regression standardized Residual Dependent Variable: Personal Information Disclosure Intention

Figure 4.7 Normalized Residual Frequency chart Normal P-P Plot

The majority of residual data points are clustered near the diagonal, supporting the hypothesis of an approximately normal distribution of residuals This observation confirms that there is no violation of the normal approximation for the collected residuals.

Figure 4.8 Normalized Residual Frequency chart Scatterplot

Dependent Variable: Personal Information Disclosure Intention

(Source: The results of the date/ analysis of the research group)

The scatter plot indicates that the normalized residuals are centered around the zero line, exhibiting a pattern of parallel lines This observation confirms that the assumption of a linear relationship remains intact.

0.222 0.100 0.216 2.209 0.029 0.641 1.559 a Dependent Variable: Personal Information Disclosure Intention

The study examined the variables Perceived Risk (PR) and Privacy View (PV), revealing test values of 0.164 and 0.762, respectively, which exceed the significance level of 0.05, indicating a lack of statistical significance in relation to Personal Information Disclosure Intention (PIDI) Consequently, there is insufficient evidence to suggest that PR and PV influence PIDI In contrast, the variables Perceived Benefit (PB) and Subjective Norm (SN) demonstrated significant test values below 0.05, confirming their influence on PIDI Notably, Perceived Benefit (PB) emerged as the most impactful factor affecting Personal Information Disclosure Intention (PIDI) among the independent variables.

We assessed the variance inflation factor (VIF) to evaluate collinearity in our regression model, finding that a VIF value below 10 indicates minimal risk of multicollinearity, which can skew regression estimates Our analysis revealed that Perceived Benefit (PB) and Subjective Norm (SN) are significant independent variables influencing Personal Information Disclosure Intention (PIDI), confirming that multicollinearity is not a concern in this model.

CONCLUSION AND DISCUSSION

Conclusion

In a study examining the factors influencing the intention of UEH students to disclose information on social networking platforms, the authors identified four key factors ranked by their impact: Perceived Benefit, Subjective Norm, Perceived Risk, and Privacy View.

The research also found that “Perceived Benefit” is the element that has the greatest influence among the four variables that affect UEH students’ disclosure intention

The "Subjective Norm" is the second most influential factor affecting UEH students' online behavior, while "Perceived Risk" and "Privacy View" have minimal impact This suggests that students prioritize their connection to the online community and its influence on social networks Furthermore, the desire for personalized services motivates them to share information online.

In light of the findings, our group put out several suggestions.

Recommendation

From the study’s results, the author team offers several recommendations that will support social media users and businesses in their sharing of information online for personalization.

To enhance the cybersecurity education of UEH students, we recommend two effective strategies: inviting guest speakers from the cybersecurity industry to share their valuable experiences and insights, which will offer students a real-world understanding of cybersecurity's significance; and conducting simulated cybersecurity incidents or tabletop exercises to help students cultivate incident response skills and appreciate the necessity of a swift and coordinated reaction during security events.

To effectively manage user privacy data, businesses must create a clear and concise privacy policy detailing the collection, processing, and usage of customer data Providing customers with granular privacy settings empowers them to control the information they share, while user-friendly interfaces facilitate easy adjustments to privacy preferences Adopting an opt-in approach for data collection encourages user participation and emphasizes the benefits of sharing information Additionally, implementing user-friendly data access portals allows customers to request, download, and manage their personal data, including options to correct or delete their information Finally, obtaining and displaying relevant privacy certifications from trusted third-party organizations helps build customer trust and demonstrates adherence to industry best practices.

Limitation and Further research

The survey process faces limitations due to restricted access to diverse audience segments, primarily caused by geographical constraints This results in participants often being from similar backgrounds, such as sharing the same class or instructor, which increases the likelihood of similar experiences and potential duplication in responses Additionally, the sample primarily consists of K48 students, which is insufficient to accurately represent the entire student population of UEH, leading to potential "sampling bias" and affecting the overall accuracy of the study's findings.

It is important to note that the respondents of the survey may not constitute a truly random sample in this scenario.

Instances have been noted where students participating in the survey may have provided random answers rather than thoughtful responses This behavior can lead to inaccuracies, resulting in data that do not accurately represent their true thoughts, opinions, and experiences Consequently, such actions can undermine the objectivity and reliability of the research findings.

The current research has limitations, and to enhance future studies, our group recommends several strategies Firstly, conducting in-person surveys with larger participant numbers is essential for improving data reliability, as online surveys may present challenges It is also important to include a diverse range of courses to achieve comprehensive and unbiased results, rather than focusing on a specific student subset Additionally, to encourage accurate responses, face-to-face interviews, clear questionnaires, and incentives such as study materials should be utilized.

To improve the study on awareness levels of information sharing on social networks, it is essential to broaden the survey's scope nationwide and include diverse age groups This will help identify variations in awareness across different demographics Additionally, employing proven data collection techniques will enhance the reliability of the datasets, while focusing on in-depth data analysis will strengthen the credibility of the results and conclusions Lastly, the research should reference a wider range of related studies rather than relying solely on a single research model.

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The content of the survey

Chúng tôi là nhóm sinh viên đang nghiên cứu về "Ý định chia sẻ thông tin cá nhân trên mạng xã hội" Rất mong các bạn dành thời gian tham gia khảo sát của chúng tôi!

Nhóm cam kết rằng tất cả thông tin thu thập được chỉ nhằm mục đích nghiên cứu và sẽ không gây ảnh hưởng xấu đến bất kỳ cá nhân hay tổ chức nào.

Cám on mọi người rất nhiều.

SECTION 1: THONG TIN CÁ NHÂN

2 Bạn là sinh viên năm mấy

3 Nen tăng mạng xã hội mà tôi thường xuyên sử dụng

4 Trải nghiệm khi dùng mạng xã hội

5 Thỏi gian trung binh tôi dùng mạng xã hội trong 1 ngày

SECTION 2: CÁC YỂU TÓ ẢNH HƯỞNG

Dưới đây là một số yếu tố ảnh hưởng đến ý định chia sẻ thông tin trên mạng xã hội Bạn hãy cho biết mức độ đồng ý hoặc không đồng ý theo quy ước đã đề ra.

I Nhận thức vê lọi ích

Tôi có thê xây dựng thương hiệu cá nhân bằng cách thường xuyên chia sê thông tin cá nhân lên MXH

Tôi cảm thây kêt nôi với cộng đông mạng khi chia sé thông tin cá nhân lên MXH

Tôi chia sẻ thông tin cá nhân trên mạng xã hội với hy vọng nhận được những gợi ý cá nhân hóa khi mua sắm trực tuyến hoặc sử dụng dịch vụ.

Tôi chia sẻ thông tin cá nhân lên MXH vì muốn có tiếng nói trong những vấn đề mà

Nhận thức vê rủi ro

Tôi cho răng việc đăng thông tin cá nhân lên

MXH chứa đựng những rủi ro về việc bị khai thác một cách trái phép

Tôi cho rằng việc đăng thông tin cá nhân lên

MXH là nguyôn nhân cho nhCmg cuộc gọi và tin nhăn rác gia tăng

Tôi cho rằng việc cung cấp thông tin trên

MXH dần đến những rủi ro về tốn hại tài sán cá nhân

Tôi cho rằng việc chia sẻ thông tin cá nhân lẻn MXH chứa đựng những rủi ro về việc bị xâm phạm quyền riêng tư

Góc nhìn về bảo mật

Tôi quan tâm đến việc cài đặt mật khẩu 2 lớp trên các nên tàng MXH

Tôi quan tâm đến các quyên riêng tư trên các nen láng MXH mà lôi thường dùng

Tôi cho rằng việc bảo mật thông tin cá nhân khói bị xâm phạm bời người khác là quan trọng

Tôi quan tâm về cách thức người khác có thế nắm giừ và sử dụng thông tin cá nhân của tôi

Tôi thấy hầu hết mọi người xung quanh tôi chia sé thông tin thực của bản thân lên MXH

Tôi thấy hầu hết mọi người xung quanh tôi khuyên tôi nên chú ý đến việc bảo vệ thông tin cá nhân của mình

Tôi thấy có rất nhiều chương trình phố cập về an toàn thông tin mạng trên các phương tiện truyền thông

Tôi thấy nhiều người xung quanh lôi quan lâm đến vấn đề bào mật thông tin cá nhân

Y định chia sẻ thông tin

Tôi đã từng chia SC thông tin thật và cụ thế về bán thân trên MXH

Tôi sẽ tiếp tục chia sé thông tin cá nhân trên

Tôi thường xuyên dành thời gian cho việc cập nhật kiến thức về an toàn thông tin mạng

Tôi sẽ luôn cho phép các nền táng MXH mà tôi dùng truy cập vào các thông tin cá nhân của tôi

Result of Cronbach’s Alpha

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Cronbach's Alpha if Item Deleted

PV1 I care about setting up a 2-Factor Authentication

(2FA) on social media platforms

Scale Variance Total if Item Deleted Correlation

Cronbach’s Alpha if Item Deleted

SN2 Most people around me are telling me to pay close attention to the security of my personal information

SN3 I see there are many education outreach programs about cybehnformation security in the media

SN4 I see many people around me concerned about the privacy of personal information

Scale: Personal Information Disclosure Intention

PIDI1 I used to share real 8.49 5.763 501 594 information about myself on social media

Corrected Item- Cronbach's Scale Mean if Scale Variance Total Alpha if Item Item Deleted if Item Deleted Correlation Deleted

PIDI3 I frequently update my knowledge about

PIDI41 will always allow the 9.25 6.248 511 589 social media platforms I use to access my personal information

Result of EFA

Results of KMO and Bartlett tests of independent variables (1)

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .794

Bartlett's Test of sphericity Approx Chi-Square 907.078 df 120

Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative %

Extraction Method: Principal Component Analysts

821 PV1 I care about setting up a 2-Factor Authentication (2FA) on social media platforms 685

PV2 I care about the privacy rights on the social media platforms that I often use 842

PV3 I think it’s important to keep personal information private from being compromised by others

PV4 I am concerned about how others may hold and use my personal information _.788

SN1 I see most people around me sharing their information on social media 839

SN4 I see many people around me concerned about the privacy of personal information 469 652

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 6 iterations.

Results of KMO and Bartlett tests of independent variables (2)

Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative %

Extraction Method: Principal Component Analysts.

PR4 I believe that sharing personal information on social networks contains nsks of invasion of privacy

PB1 1 can build a personal brand by regularly sharing personal information on social media 680

PB2 1 disclose personal information on social media because 1 feel more connected to the online community

PB3 1 share personal information on social media because 1 want personalized recommendations while shopping online or using services

PB4 1 share personal information on social media because 1 want to have a say on the issues 1 care about

PV1 1 care about setting up a 2-Factor Authentication (2FA) on social media platforms 641

PV2 1 care about the privacy rights on the social media platforms that 1 often use 806

PV3 1 think it's important to keep personal information private from being compromised by others 758

PV4 1 am concerned about how others may hold and use my personal information 776

SN3 1 see there are many education outreach programs about cyberinformation security in the media

SN4 1 see many people around me concerned about the privacy of personal information 710 Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 6 iterations.

Initial Eigenvalues Extraction Sums of Squared Loadings Component Total % of Variance Cumulative % Total % of Variance Cumulative %

Extraction Method: Principal Component Analysis.

PIDI1 I used to share real information about myself on social media

PIDI3 I frequently update my knowledge about cyberinformation security

Result of Pearson Correlation and Regression Analysis

Intention Perceived Risk Benefit Privacy View Norm

Correlation is significant at the 0.01 level (2-tailed).

•- Correlation is significant at the 0.05 level (2-tailed).

Adjusted R Std Error of the Model R R Square Square Estimate Durbin-Watson

1 440a 193 169 72385 2.051 a Predictors: (Constant), Subjective Norm, Perceived Benefit, Perceived Risk,

Privacy View b Dependent Variable: Personal Information Disclosure Intention

ANOVA a a Dependent Variable: Personal Information Disclosure Intention

Squares df Mean Square F Sig.

Total 85.086 135 b Predictors: (Constant), Subjective Norm, Perceived Benefit, Perceived Risk, Privacy

Coefficients3 a Dependent Variable: Personal Information Disclosure Intention

Histogram Dependent Variable: Personal Information Disclosure Intention

Normal P-P Plot of Regression standardized Residual Dependent Variable: Personal Information Disclosure Intention

Tiêu đề	Personalization versus privacy: A research on disclosing personal information behavior on social media among ueh students
Trường học	University of Economics Ho Chi Minh City
Chuyên ngành	Economics
Thể loại	Báo cáo
Năm xuất bản	2024
Thành phố	Ho Chi Minh City

Định dạng
Số trang	73
Dung lượng	1,7 MB