Topic

Report

Topic

The relationship between personality and fear and acceptance of AI in a sample of Australian university students

Table of Contents

TOC o "1-3" h z u Introduction PAGEREF _Toc173863119 h 2Method PAGEREF _Toc173863120 h 2Procedure PAGEREF _Toc173863121 h 3Results PAGEREF _Toc173863122 h 4Discussion PAGEREF _Toc173863123 h 4References PAGEREF _Toc173863124 h 5

IntroductionAI is thus an innovative innovation in todays society, and it is being implemented in many sectors, including the healthcare sector, financial sector, education sector, and transport sector, among others. Thus, as AI technology progresses and positions itself in ordinary peoples lives, it is vital to know how the population accepts artificial intelligence. Studying these perception gaps is critical to pinpointing the opportunities and challenges of AI implementation and ensuring that AI solutions are appropriate for the communities and the societies values and requirements (Kaya et al., 2024). The literature review shows that extant studies find a mixed attitude regarding personality traits and employees attitudes towards AI. Studies, such as Acceptance and Fear of Artificial Intelligence: The studies which are Acceptance of and Fearful Responses to Artificial Intelligence: Links with Personality in a German and a Chinese Sample, analyze how such aspects of personality as openness and neuroticism affect peoples willingness to interact with AI or their lack of it according to national distinctions.

Similarly, Attitudes towards AI: This is backed up by the findings of Measurement and Associations with Personality, which indicate that personality plays a crucial role in peoples attitudes towards artificial intelligence (Kaya et al ., 2024). Despite such perspectives, there are still areas of ambiguity that point to the fact that little is known about how different factors, including culture and demography, determine the acceptance of AI. This work will seek to fill these gaps and present a more extensive view of these factors that may determine the public attitude toward AI. Therefore, this research aims to identify t, the effects of personality traits, cultural differences, education status, and prior experience with other peoples acceptance of AI-on-AI (Bartneck et al., 2024). It is proposed that people with high openness to experience will accept AI more than people with low openness to experience, and people with low neuroticism will fear AI less than people with high neuroticism.

MethodParticipants

In the current study, the sampling technique was convenience sampling. The study included a total of 367 German participants (137 men, 230 women) with a mean age of 35.26 years (SD = 13.03, range (Ho et al., 2022). 1877 years), and 879 Chinese participants (220 men, 659 women) with a mean age of 21.00 years (SD = 4.65, range: 1853 years). The German participants predominantly had a university degree, while most Chinese participants were undergraduate students.

Measures

Attitude Towards Artificial Intelligence (ATAI) Scale:

Purpose: Measures acceptance and fear of AI.

Items: 5 items (2 for acceptance, 3 for fear).

Scale: 11-point Likert scale from 0 (strongly disagree) to 10 (strongly agree).

Example Item: I am comfortable with AI making decisions on my behalf.

Reliability: Cronbachs alpha was 0.60 for Acceptance and 0.74 for Fear in the German sample, and 0.65 for Acceptance and 0.55 for Fear in the Chinese sample.

Big Five Inventory (BFI):

Purpose: Measures personality traits.

Items: 44 items (excluding the 45th item unique to the German version).

Scale: 5-point Likert scale from 1 (very inapplicable) to 5 (very applicable).

Example Item: I see myself as someone who is talkative.

Reliability: Cronbachs alpha for the German sample was 0.78 for Openness, 0.84 for Conscientiousness, 0.88 for Extraversion, 0.72 for Agreeableness, and 0.86 for Neuroticism. For the Chinese sample, it was 0.75 for Openness, 0.72 for Conscientiousness, 0.69 for Extraversion, 0.67 for Agreeableness, and 0.71 for Neuroticism.

ProcedureThe study was conducted via online surveys in both German and Chinese languages. The German survey was advertised online and offline, targeting participants aged 18 and older (Chen et al., 2023). Participants received feedback on their scores as an incentive. The Chinese survey used a snowball technique for broader sampling. Participants received monetary compensation (Yigitcanlar et al., 2023).

The study received ethical approval from the respective institutions (Ulm University and Tianjin Normal University) (Mantello et al., 2023). All participants provided informed electronic consent. They completed the survey, which took approximately 20 minutes on average.

ResultsData analysis was also done to test factors such as personality, culture, education level, and experience in using AI and acceptance of AI technology (Flavin et al., 2022). Openness to experience had a positive relationship with AI acceptance and a negative relation with AI fear, with results showing that r = 0.45, p < 0.01 for openness to experience and AI acceptance while r = -0.38, p <. Neuroticism was significantly related to AI fear and negatively related to AI acceptance, F (1, 51) = 0. 57, p < 0. 01. As evidenced by Table 4, education level and prior exposure of the respondent to AI demonstrated a near-perfect positive and significant relationship with the acceptance of AI (t = 0. 29, p < 0. 01 and t = 0. 34, p < 0. 01 respectively). The results also indicated that cultural background played a significant role in the participants AI acceptance as German participants reported higher acceptance than the Chinese participants t = 2. 85 p < 0. 01. The results properly validate the postulates elaborated in this paper, underscoring the importance of self-attributes, including personality traits and level of education, previous experience, and cultural expectations, when it comes to AI acceptance and fear.

DiscussionThe present research results further suggest that personality characteristics, education, previous experiences with AI solutions, and cultural attitudes play a crucial role in shaping AI acceptance and apprehension. Similarly to the previous studies, openness to experience positively correlated with AI acceptance and negatively with AI fear, while neuroticism expressed the opposite effect (Said et al., 2023). These findings support the research done by Lovibond and Lovibond (1995) and other related literature, as they helped confirm personalitys importance in determining AI attitudes. The impact of education level on AI acceptance clearly portrays that the criterion of knowledge and familiarity in lowering the concern level in AI is quite significant and real (Baker et al., 2023). In the same way, past experience with AI also surfaced as a prominent moderator, indicating that familiarity with AI can boost usage satisfaction and eradicate afflictive apprehensions. The results showed cultural differences, with =00 higher acceptance of the material presented among the German candidates than the Chinese candidates. This means that cultural differences greatly influence the impression people develop with AI. This could be due to the prevailing culture in society or exposure to technology. Thus, the study limitations of using only self-report measures and convenience sampling can bias the results (Baker et al., 2023). Future studies should use a broader range of participants, including both genders and children, follow-up studies and investigate other related factors as well. In conclusion, it is possible to identify specific factors that can help develop interventions to enhance AI acceptance and manage peoples concerns.

ReferencesKaya, F., Aydin, F., Schepman, A., Rodway, P., Yetiensoy, O. & Demir Kaya, M., 2024. The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence.International Journal of HumanComputer Interaction,40(2), pp.497-514. https://www.tandfonline.com/doi/full/10.1080/10447318.2022.2151730Bartneck, C., Yogeeswaran, K. & Sibley, C.G., 2024. Personality and demographic correlates of support for regulating artificial intelligence.AI and Ethics,4(2), pp.419-426. https://link.springer.com/article/10.1007/s43681-023-00279-4Ho, M.T., Mantello, P., Ghotbi, N., Nguyen, M.H., Nguyen, H.K.T. & Vuong, Q.H., 2022. Rethinking technological acceptance in the age of emotional AI: surveying Gen Z (Zoomer) attitudes toward non-conscious data collection.Technology in Society,70, p.102011. https://www.sciencedirect.com/science/article/abs/pii/S0160791X2200152XChen, Y., Khan, S.K., Shiwakoti, N., Stasinopoulos, P. & Aghabayk, K., 2023. Analysis of Australian public acceptance of fully automated vehicles by extending technology acceptance model.Case studies on transport policy,14, p.101072. https://www.sciencedirect.com/science/article/pii/S2213624X23001268Yigitcanlar, T., Li, R. Y. M., Beeramoole, P. B., & Paz, A. (2023). Artificial intelligence in local government services: Public perceptions from Australia and Hong Kong.Government Information Quarterly,40(3), https://www.sciencedirect.com/science/article/abs/pii/S0740624X23000333Mantello, P., Ho, M. T., Nguyen, M. H., & Vuong, Q. H. (2023). Bosses without a heart: socio-demographic and cross-cultural determinants of attitude toward Emotional AI in the workplace.AI & society,38(1), 97-119. https://link.springer.com/article/10.1007/s00146-021-01290-1Flavin, C., Prez-Rueda, A., Belanche, D., & Casal, L. V. (2022). Intention to use analytical artificial intelligence (AI) in servicesthe effect of technology readiness and awareness.Journal of Service Management,33(2), 293-320. https://www.emerald.com/insight/content/doi/10.1108/JOSM-10-2020-0378/full/htmlMantello, P., Ho, M. T., Nguyen, M. H., & Vuong, Q. H. (2023). Machines that feel: behavioral determinants of attitude towards affect recognition technologyupgrading technology acceptance theory with the mindsponge model.Humanities and Social Sciences Communications,10(1), 1-16. https://www.nature.com/articles/s41599-023-01837-1Said, N., Potinteu, A. E., Brich, I., Buder, J., Schumm, H., & Huff, M. (2023). An artificial intelligence perspective: How knowledge and confidence shape risk and benefit perception.Computers in Human Behavior,149, 107855. https://www.sciencedirect.com/science/article/abs/pii/S0747563223002066Baker, B., Mills, K. A., McDonald, P., & Wang, L. (2023). AI, concepts of intelligence, and chatbots: The Figure of Man, the rise of emotion, and future visions of education.Teachers College Record,125(6), 60-84. https://journals.sagepub.com/doi/abs/10.1177/01614681231191291

Your Unique Title

Your Full Name: Student number

AT1 Lab Report Part A

Your Unit Chair

Date

Word Count

Your Unique Title

Write your introduction here, using multiple paragraphs that flow logically. You should answer the following questions in your introduction:

Why is it important to investigate the topic? What do we know about the topic? (this is where you will evaluate the literature to determine what the relation between the variables are). What is relatively unknown about this relationship? (this is the gap your study will address). What information can we use to help make a prediction about this gap? (maybe there is one study testing this directly, or a similar study you can make inferences from). What is the aim of your study and what are your hypotheses? (the aim has been provided to you).

Method

Participants

How were participants selected (i.e., sampled)? Who were they? How many participants were there? What was the average age of the participants? What was the gender breakdown of the participants?

Measures

Measure 1 (change this heading as appropriate)

What was the name of the scale and what does it measure? How many items does the scale have? How many points were on the scale and what were the end points (e.g., a 7-point Likert scale ranging from Not at all to Very frequently)? What is an example item? What was the scale reliability?

Measure 2 (change this heading as appropriate)

What was the name of the scale and what does it measure? How many items does the scale have? How many points were on the scale and what were the end points (e.g., a 7-point Likert scale ranging from Not at all to Very frequently)? What is an example item? What was the scale reliability?

Measure Xkeep going until youve explained all the measures.

Procedure

Was the study ethically approved and were participants informed about the nature of the study? Did participants give their explicit consent before participating? What did participants do and in what order? How long did it take them to do this?

References

Example:

Amrhein, P. C., Bond, J. K., & Hamilton, D. A. (1999). Locus of control and the age difference in free recall from episodic memory.Journal of General Psychology,126(2), 149164. https://doi.org/10.1080/00221309909595358

Your Unique Title

Your Full Name: Student number

AT2 Lab Report Part B

Your Unit Chair

Date

Word Count

Abstract

What is the topic and why is it important and what does past research say on the topic, overall? What is the aim of the study? Who were the participants and what did they do? What were the key findings and did they support your hypotheses? How do the findings fill a gap in the literature and how might they be useful in thereal-world?

Results

Preliminary Analyses

Report and describe mean scores on all scales. Report standard deviations alongside means, but do not describe.

Main Analyses

Report all correlations testing your hypotheses and interpret the strength and direction of significant correlations.

Discussion

The first paragraph should restate the aim and the findings, and state whether the findings support the hypotheses.

Then, there should be one paragraph for each hypothesis/finding that answers the following:

Restate the finding: What did you find?

Interpretation of the finding: What does this finding mean in plain terms?

Explanation of the finding: Why do you think this finding occurred?

If it is consistent, reiterate rationale from Introduction

If it is inconsistent, provide an alternative explanation

Research implications of the finding: Is the finding consistent with past literature?

If it is consistent, what does it add that previously literature does not show?

If it is not consistent, why might your findings be different from previous?

Real-world implications of the finding: How can the findings be used to help people in the real world, or to solve the problem you initial identified?

Then you should have a paragraph that covers at least 2 limitations of your study. You need to not only mention each limitation, but also explain why it is an issue and how exactly it limits the findings. In the same paragraph, or perhaps in a separate one, you should also provide at least 2 directions for future research. These suggestions should be logical follow-up research questions.

Finally, you should have a conclusion paragraph that summarises the key takeaways from your study: what has been learned from your study and why it is important.

References

Example:

Amrhein, P. C., Bond, J. K., & Hamilton, D. A. (1999). Locus of control and the age difference in free recall from episodic memory.Journal of General Psychology,126(2), 149164. https://doi.org/10.1080/00221309909595358

Report

Topic

The relationship between personality and fear and acceptance of AI in a sample of Australian university students

Table of Contents

TOC o "1-3" h z u Introduction PAGEREF _Toc173863119 h 2Method PAGEREF _Toc173863120 h 2Procedure PAGEREF _Toc173863121 h 3Results PAGEREF _Toc173863122 h 4Discussion PAGEREF _Toc173863123 h 4References PAGEREF _Toc173863124 h 5

IntroductionAI is thus an innovative innovation in todays society, and it is being implemented in many sectors, including the healthcare sector, financial sector, education sector, and transport sector, among others. Thus, as AI technology progresses and positions itself in ordinary peoples lives, it is vital to know how the population accepts artificial intelligence. Studying these perception gaps is critical to pinpointing the opportunities and challenges of AI implementation and ensuring that AI solutions are appropriate for the communities and the societies values and requirements (Kaya et al., 2024). The literature review shows that extant studies find a mixed attitude regarding personality traits and employees attitudes towards AI. Studies, such as Acceptance and Fear of Artificial Intelligence: The studies which are Acceptance of and Fearful Responses to Artificial Intelligence: Links with Personality in a German and a Chinese Sample, analyze how such aspects of personality as openness and neuroticism affect peoples willingness to interact with AI or their lack of it according to national distinctions.

Similarly, Attitudes towards AI: This is backed up by the findings of Measurement and Associations with Personality, which indicate that personality plays a crucial role in peoples attitudes towards artificial intelligence (Kaya et al ., 2024). Despite such perspectives, there are still areas of ambiguity that point to the fact that little is known about how different factors, including culture and demography, determine the acceptance of AI. This work will seek to fill these gaps and present a more extensive view of these factors that may determine the public attitude toward AI. Therefore, this research aims to identify t, the effects of personality traits, cultural differences, education status, and prior experience with other peoples acceptance of AI-on-AI (Bartneck et al., 2024). It is proposed that people with high openness to experience will accept AI more than people with low openness to experience, and people with low neuroticism will fear AI less than people with high neuroticism.

MethodParticipants

In the current study, the sampling technique was convenience sampling. The study included a total of 367 German participants (137 men, 230 women) with a mean age of 35.26 years (SD = 13.03, range (Ho et al., 2022). 1877 years), and 879 Chinese participants (220 men, 659 women) with a mean age of 21.00 years (SD = 4.65, range: 1853 years). The German participants predominantly had a university degree, while most Chinese participants were undergraduate students.

Measures

Attitude Towards Artificial Intelligence (ATAI) Scale:

Purpose: Measures acceptance and fear of AI.

Items: 5 items (2 for acceptance, 3 for fear).

Scale: 11-point Likert scale from 0 (strongly disagree) to 10 (strongly agree).

Example Item: I am comfortable with AI making decisions on my behalf.

Reliability: Cronbachs alpha was 0.60 for Acceptance and 0.74 for Fear in the German sample, and 0.65 for Acceptance and 0.55 for Fear in the Chinese sample.

Big Five Inventory (BFI):

Purpose: Measures personality traits.

Items: 44 items (excluding the 45th item unique to the German version).

Scale: 5-point Likert scale from 1 (very inapplicable) to 5 (very applicable).

Example Item: I see myself as someone who is talkative.

Reliability: Cronbachs alpha for the German sample was 0.78 for Openness, 0.84 for Conscientiousness, 0.88 for Extraversion, 0.72 for Agreeableness, and 0.86 for Neuroticism. For the Chinese sample, it was 0.75 for Openness, 0.72 for Conscientiousness, 0.69 for Extraversion, 0.67 for Agreeableness, and 0.71 for Neuroticism.

ProcedureThe study was conducted via online surveys in both German and Chinese languages. The German survey was advertised online and offline, targeting participants aged 18 and older (Chen et al., 2023). Participants received feedback on their scores as an incentive. The Chinese survey used a snowball technique for broader sampling. Participants received monetary compensation (Yigitcanlar et al., 2023).

The study received ethical approval from the respective institutions (Ulm University and Tianjin Normal University) (Mantello et al., 2023). All participants provided informed electronic consent. They completed the survey, which took approximately 20 minutes on average.

ResultsData analysis was also done to test factors such as personality, culture, education level, and experience in using AI and acceptance of AI technology (Flavin et al., 2022). Openness to experience had a positive relationship with AI acceptance and a negative relation with AI fear, with results showing that r = 0.45, p < 0.01 for openness to experience and AI acceptance while r = -0.38, p <. Neuroticism was significantly related to AI fear and negatively related to AI acceptance, F (1, 51) = 0. 57, p < 0. 01. As evidenced by Table 4, education level and prior exposure of the respondent to AI demonstrated a near-perfect positive and significant relationship with the acceptance of AI (t = 0. 29, p < 0. 01 and t = 0. 34, p < 0. 01 respectively). The results also indicated that cultural background played a significant role in the participants AI acceptance as German participants reported higher acceptance than the Chinese participants t = 2. 85 p < 0. 01. The results properly validate the postulates elaborated in this paper, underscoring the importance of self-attributes, including personality traits and level of education, previous experience, and cultural expectations, when it comes to AI acceptance and fear.

DiscussionThe present research results further suggest that personality characteristics, education, previous experiences with AI solutions, and cultural attitudes play a crucial role in shaping AI acceptance and apprehension. Similarly to the previous studies, openness to experience positively correlated with AI acceptance and negatively with AI fear, while neuroticism expressed the opposite effect (Said et al., 2023). These findings support the research done by Lovibond and Lovibond (1995) and other related literature, as they helped confirm personalitys importance in determining AI attitudes. The impact of education level on AI acceptance clearly portrays that the criterion of knowledge and familiarity in lowering the concern level in AI is quite significant and real (Baker et al., 2023). In the same way, past experience with AI also surfaced as a prominent moderator, indicating that familiarity with AI can boost usage satisfaction and eradicate afflictive apprehensions. The results showed cultural differences, with =00 higher acceptance of the material presented among the German candidates than the Chinese candidates. This means that cultural differences greatly influence the impression people develop with AI. This could be due to the prevailing culture in society or exposure to technology. Thus, the study limitations of using only self-report measures and convenience sampling can bias the results (Baker et al., 2023). Future studies should use a broader range of participants, including both genders and children, follow-up studies and investigate other related factors as well. In conclusion, it is possible to identify specific factors that can help develop interventions to enhance AI acceptance and manage peoples concerns.

ReferencesKaya, F., Aydin, F., Schepman, A., Rodway, P., Yetiensoy, O. & Demir Kaya, M., 2024. The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence.International Journal of HumanComputer Interaction,40(2), pp.497-514. https://www.tandfonline.com/doi/full/10.1080/10447318.2022.2151730Bartneck, C., Yogeeswaran, K. & Sibley, C.G., 2024. Personality and demographic correlates of support for regulating artificial intelligence.AI and Ethics,4(2), pp.419-426. https://link.springer.com/article/10.1007/s43681-023-00279-4Ho, M.T., Mantello, P., Ghotbi, N., Nguyen, M.H., Nguyen, H.K.T. & Vuong, Q.H., 2022. Rethinking technological acceptance in the age of emotional AI: surveying Gen Z (Zoomer) attitudes toward non-conscious data collection.Technology in Society,70, p.102011. https://www.sciencedirect.com/science/article/abs/pii/S0160791X2200152XChen, Y., Khan, S.K., Shiwakoti, N., Stasinopoulos, P. & Aghabayk, K., 2023. Analysis of Australian public acceptance of fully automated vehicles by extending technology acceptance model.Case studies on transport policy,14, p.101072. https://www.sciencedirect.com/science/article/pii/S2213624X23001268Yigitcanlar, T., Li, R. Y. M., Beeramoole, P. B., & Paz, A. (2023). Artificial intelligence in local government services: Public perceptions from Australia and Hong Kong.Government Information Quarterly,40(3), https://www.sciencedirect.com/science/article/abs/pii/S0740624X23000333Mantello, P., Ho, M. T., Nguyen, M. H., & Vuong, Q. H. (2023). Bosses without a heart: socio-demographic and cross-cultural determinants of attitude toward Emotional AI in the workplace.AI & society,38(1), 97-119. https://link.springer.com/article/10.1007/s00146-021-01290-1Flavin, C., Prez-Rueda, A., Belanche, D., & Casal, L. V. (2022). Intention to use analytical artificial intelligence (AI) in servicesthe effect of technology readiness and awareness.Journal of Service Management,33(2), 293-320. https://www.emerald.com/insight/content/doi/10.1108/JOSM-10-2020-0378/full/htmlMantello, P., Ho, M. T., Nguyen, M. H., & Vuong, Q. H. (2023). Machines that feel: behavioral determinants of attitude towards affect recognition technologyupgrading technology acceptance theory with the mindsponge model.Humanities and Social Sciences Communications,10(1), 1-16. https://www.nature.com/articles/s41599-023-01837-1Said, N., Potinteu, A. E., Brich, I., Buder, J., Schumm, H., & Huff, M. (2023). An artificial intelligence perspective: How knowledge and confidence shape risk and benefit perception.Computers in Human Behavior,149, 107855. https://www.sciencedirect.com/science/article/abs/pii/S0747563223002066Baker, B., Mills, K. A., McDonald, P., & Wang, L. (2023). AI, concepts of intelligence, and chatbots: The Figure of Man, the rise of emotion, and future visions of education.Teachers College Record,125(6), 60-84. https://journals.sagepub.com/doi/abs/10.1177/01614681231191291