Comparison of the inter-item correlations of the Big Five Inventory-10 (BFI-10) between Western and non-Western contexts

Park, Joonha; Broek, Karlijn L. van den; Bhullar, Navjot; Ogunbode, Charles Adedayo; Schermer, Julie Aitken; Doran, Rouven; Ardi, Rahkman; Hanss, Daniel; Maran, Daniela Acquadro; Albzour, Mai; Aquino, Sibele; Ayanian, Arin H.; Chegeni, Razieh; Chukwuorji, JohnBosco Chika; Enea, Violeta; Ghanbarian, Elahe; Ghorayeb, Jihane; Jiang, Feng; Kehinde, Ojewumi Aderemi; Lins, Samuel Lincoln Bezerra; Lomas, Michael J.; Lu, Su; Marot, Tiago Azevedo; Mbungu, Winfred; Navarro‐Carrillo, Ginés; Onyutha, Charles; Reyes, Marc Eric S.; Salmela‐Aro, Katariina; Sollár, Tomáš; Tahir, Hajra; Tan, Chee‐Seng; Torres‐Marín, Jorge; Tsubakita, Takashi; Volkodav, Tatiana; Włodarczyk, Anna; Yadav, Radha

doi:10.1016/j.paid.2022.111751

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…If one of the questions were not valid or reliable, it could not be deleted because SEM calculates validity and reliability based on several questions 49 . Although the validity and reliability of the BFI-10 are affected by factors such as age, culture, and language 39 , 50 , this study used this scale to capture the overall effect of personality traits on privacy fatigue and not to compare individuals’ differences. However, the long version of personality traits and the SEM method are suggested for use in future research to ensure higher validity and reliability and to capture the full complexity of an individual's personality.…”

Section: Discussionmentioning

confidence: 99%

“…However, BFI-10 is the most recent and was clearer when translated into Arabic. Additionally, a study mentioned that the BFI-10 could be an option if the author is not interested in inferring individual differences 39 . This study does not aim to investigate individual differences but to capture the overall effect of personality traits on privacy fatigue.…”

Section: Methodsmentioning

confidence: 99%

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A machine learning model to predict privacy fatigued users from social media personalized advertisements

Alwafi,

Fakieh

2024

Sci Rep

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The increasing use of social media platforms as personalized advertising channels is a double-edged sword. A high level of personalization on these platforms increases users’ sense of losing control over personal data: This could trigger the privacy fatigue phenomenon manifested in emotional exhaustion and cynicism toward privacy, which leads to a lack of privacy-protective behavior. Machine learning has shown its effectiveness in the early prediction of people’s psychological state to avoid such consequences. Therefore, this study aims to classify users with low and medium-to-high levels of privacy fatigue, based on their information privacy awareness and big-five personality traits. A dataset was collected from 538 participants via an online questionnaire. The prediction models were built using the Support Vector Machine, Naïve Bayes, K-Nearest Neighbors, Decision Tree, and Random Forest classifiers, based on the literature. The results showed that awareness and conscientiousness trait have a significant relationship with privacy fatigue. Support Vector Machine and Naïve Bayes classifiers outperformed the other classifiers by attaining a classification accuracy of 78%, F1 of 87%, recall of 100% and 98%, and precision of 78% and 79% respectively, using five-fold cross-validation.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A machine learning model to predict privacy fatigued users from social media personalized advertisements

Alwafi,

Fakieh

2024

Sci Rep

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“…This is done to ensure the quality of the data obtained, because the more questions in a questionnaire, the lower the quality of the data obtained from the respondents. Studies using BFI-10 include Hussain et al [26], Park et al [27], and Rammstedt and John [28]. Research related to behavior is also related to the perspective of time, including Fekih-Romdhane et al [29], Kocayoruk and Simsek [30], Košťál et al [31], Linkov et al [32], Orosz et al [33], Peng et al [34], Sircova et al [35], Temple et al [36], and Lemarié et al [37]; which measures a person's time perspective, including using the Adolescent Time Attitude Scale (ATAS) and the Zimbardo Time Perspective Inventory (ZTPI).…”

Section: Introductionmentioning

confidence: 99%

The Impact of Social Norms, Personality, and Time Perspective Factors On Indonesian Highways Safety Driving Behavior

Azzikrullah,

Widyanti,

Sari

2024

SHS Web Conf.

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The number of traffic accidents per year increases in proportion to the number of drivers. The higher traffic accident in particular on highways is due to speeding behavior. Traffic accidents are caused by the environment, the vehicle, and the driver factors. The purpose of this study is to incorporate driver personality, time perspective, and applicable norms, on the speeding behavior model and design proposals for intervention policies, and improve current policies on driving behavior to maintain and increase safety driving the highways speed limit. Personality (BFI-10) was incorporated into the model to investigate which personality types that often speed, time perspective (ZTPI-18) was use to understand the current driver behavior from past experience and the driver will likely take in future, while the theory of normative social behavior (TNSB) was incorporated to enhance the theory of planned behavior (TPB) model used in previous studies. The method used in this paper is the PRISMA framework. Result of this study is a conceptual model that incorporates variables of TPB, TNSB, BFI-10 personality inventory, and ZTPI-18 and consists of 23 variables. Further study will be conducted to prove the conceptual model by using the method of self-report questionnaires, field observations, and driving simulations.

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RETRACTED: Personality traits that associated with generalized anxiety disorder among PhD students

Zhao

2023

Acta Psychologica

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Comparison of the inter-item correlations of the Big Five Inventory-10 (BFI-10) between Western and non-Western contexts

Cited by 4 publications

References 19 publications

A machine learning model to predict privacy fatigued users from social media personalized advertisements

A machine learning model to predict privacy fatigued users from social media personalized advertisements

The Impact of Social Norms, Personality, and Time Perspective Factors On Indonesian Highways Safety Driving Behavior

RETRACTED: Personality traits that associated with generalized anxiety disorder among PhD students

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