跳到主要內容

臺灣博碩士論文加值系統

(44.200.140.218) 您好!臺灣時間:2024/07/18 05:08
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:陳書安
研究生(外文):CHEN, SHU-AN
論文名稱:個人適應人工智慧意圖研究
論文名稱(外文):To worry or not to worry AI: that is the question about the investigation on individual intention to adapt AI
指導教授:黃正魁黃正魁引用關係
指導教授(外文):HUANG, CHENG-KUEI
口試委員:陳純德楊溥泰陳信宏
口試委員(外文):CHEN, CHUN-DERYANG, PU-TAICHEN, HSIN-HUNG
口試日期:2022-04-21
學位類別:碩士
校院名稱:國立中正大學
系所名稱:企業管理系研究所
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:英文
論文頁數:53
中文關鍵詞:人工智慧人工智慧焦慮健康信念模型行為意圖人工智慧調適
外文關鍵詞:Artificial IntelligenceAI AnxietyHealth Belief ModelBehavioral IntentionAI Adaptation
相關次數:
  • 被引用被引用:1
  • 點閱點閱:235
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
人工智慧(Artificial Intelligence, AI)已經帶來了各式各樣的效益,來幫助人們去有效地解決實際生活上的問題。人工智慧呈現出來令人喜愛的印象,讓我們毫無疑問地應當去接受且採用它才是。然而,人工智慧卻依然存在很多受爭議的論戰,使得我們必須去評估且調適它,並且讓人充滿了對人工智慧的焦慮(AI anxiety),因為它使人懼怕與惶惶不安。在本次的研究當中,我們提出了一個新的實徵模型,主要是用來說明人類在人工智慧的焦慮情境下,要如何去適應人工智慧。我們利用了在公衛領域裡一個有名的模型−健康信念模型(Health Belief Model, HBM),來仔細探究人工智慧適應的關鍵因子為何。
結果顯示,感知人工智慧的效益,是正向地去影響到採用人工智慧的意圖,但卻負向地去影響到逃避人工智慧的意圖;感知到人工智慧的障礙,會去正向地影響到人們想要逃避人工智慧的意圖;感知電腦自我效能(也就是電腦能力的自我評估),會去正向地影響到感知人工智慧的效益,但會負向地去影響到感知人工智慧的障礙。最後,行動的線索可以顯著地且正面地影響到感知人工智慧的威脅,但卻沒有直接地影響到採用人工智慧的意圖以及逃避人工智慧的意圖。本研究的發現,提供了建議讓我們更瞭解人工智慧的焦慮,以處理實務的議題,另外也開啟了於學術領域的新視野。

Artificial Intelligence (AI) has brought manifold benefits to help human beings for efficiently solving real-life problems. AI presents a favorable impression; thus, there is no doubt that we ought to accept and adopt it. However, AI still exists contentious debates to date so that we are necessary to evaluate and adapt AI further and fill with AI anxiety because of phobias and trepidation. In this study, we propose a new empirical model to unravel how people to adapt AI under the circumstance of AI anxiety. We capitalize a famous model in the public health field, Health Belief Model (HBM), to dissect the determinants of AI adaption. The results reveal that perceived benefits of AI are positively influencing on intention to exploit AI, yet are negatively on intention to avoid AI. Perceived barriers of AI are positively influencing on intention to avoid AI. Perceived computer self-efficacy is positively on perceived benefits of AI but negatively on perceived barriers of AI. Ultimately, cues to action can significantly and positively influence on perceived threat of AI; however, the variable is no direct impact on intention to exploit AI and intention to avoid AI. The findings are able to proffer suggestions to address AI anxiety for tackling practical issues and opening up a new viewpoint in the academic realm.
Abstract
摘要
Table of Contents
List of Figures
List of Tables
1. Introduction
2. Literature review
2.1. AI anxiety
2.2. Health belief model (HBM)
2.3. Behavioral intention
3. Research model and hypotheses
3.1. Impacts of AI benefits and barriers on two dependent variables
3.2. Impact of computer self-efficacy on AI benefits and barriers
3.3. Impact of AI threat on two dependent variables
3.4. Impact of cues to action on AI threat and two dependent variables
4. Research methodology
4.1. Questionnaire development
4.1.1. Item development
4.1.2. Basic information
4.2. Design and procedure
4.3. PLS-SEM
5. Data analysis and results
5.1. Demographic characteristics
5.2. Common method bias
5.3. Measurement model examining
5.3.1. Reliability analysis
5.3.2. Validity analysis
5.4. Structural model examining
5.5. Results
6. Conclusion and implications
6.1. Theoretical implications
6.2. Practical implications
6.3. Limitations and future research
6.3.1. Research methods
6.3.2. Sample restrictions
6.3.3. Location restrictions
6.3.4. Future research
6.4. Conclusion
Appendix
Appendix A. Questionnaire items
Appendix B. 問卷問項
References

Abraham, C., & Sheeran, P. (2015). The Health Belief Model. In M. Conner & P. Norman (Eds.), Predicting and Changing Health Behavior (3 ed.). McGraw-Hill.
Aguila, E. J. T., & Cua, I. H. Y. (2021). Adapting digital technology to the gastroenterology and endoscopy practice in the pandemic era. Advances in Digestive Medicine. https://doi.org/10.1002/aid2.13262
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-t
Ajzen, I., & Driver, B. L. (1991). Prediction of leisure participation from behavioral, normative, and control beliefs: An application of the theory of planned behavior. Leisure Sciences, 13(3), 185-204. https://doi.org/10.1080/01490409109513137
Ajzen, I., & Madden, T. J. (1986). Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. Journal of Experimental Social Psychology, 22(5), 453-474. https://doi.org/10.1016/0022-1031(86)90045-4
Alpert, R., & Haber, R. N. (1960, Sep). Anxiety in academic achievement situations. Abnormal and Social Psychology, 61, 207-215. https://doi.org/10.1037/h0045464
Anderson, J., & Gerbing, D. W. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin, 103, 411-423.
Anderson, J., & Rainie, L. (2020). Many Tech Experts Say Digital Disruption Will Hurt Democracy. Pew Research Center. https://www.pewresearch.org/internet/2020/02/21/many-tech-experts-say-digital-disruption-will-hurt-democracy/
Anderson, M., & Anderson, S. L. (2007). Machine ethics: creating an ethical intelligent agent. AI Magazine, 28(4), 15-26.
Bala, H., & Venkatesh, V. (2016). Adaptation to Information Technology: A Holistic Nomological Network from Implementation to Job Outcomes. Management Science, 62(1), 156-179. https://doi.org/10.1287/mnsc.2014.2111
Barlow, D. H. (2000). Unraveling the mysteries of anxiety and its disorders from the perspective of emotion theory. American Psychologist, 55(11), 1247-1263. https://doi.org/10.1037/0003-066x.55.11.1247
Becker, M. H., & Maiman, L. A. (1975). Sociobehavioral determinants of compliance with health and medical care recommendations. Med Care, 13(1), 10-24. https://doi.org/10.1097/00005650-197501000-00002
Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021). Special Issue Editor’s Comments: Managing Artificial Intelligence. MIS Quarterly, 45(3), 1433–1450. https://doi.org/10.25300/MISQ/2021/16274
Bostrom, N. (2016). Existential risks: analyzing human extinction scenarios and related hazards. Journal of Evolution and Technology, 9.
Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard business review, 2017.
Buttazzo, G. (2008, Oct). Artificial consciousness: hazardous questions (and answers). Artif Intell Med, 44(2), 139-146. https://doi.org/10.1016/j.artmed.2008.07.004
Champion, V. L., & Skinner, C. S. (2008). The health belief model. In K. GLANZ, B. K. RIMER, & K. VISWANATH (Eds.), Health behavior and health education: Theory, research, and practice, 4th ed (pp. 45-65). US: Jossey-Bass.
Chen, J., Liao, Y., Li, Z., Tian, Y., Yang, S., He, C., Tu, D., & Sun, X. (2013). Determinants of salt-restriction-spoon using behavior in China: application of the health belief model. PLoS One, 8(12), e83262. https://doi.org/10.1371/journal.pone.0083262
Chou, Y. J., & Shih, C. M. (2018, Dec). Using the health belief model to predict those seeking treatment for Hypoactive Sexual Desire Disorder among premenopausal women. Taiwanese journal of obstetrics & gynecology, 57(6), 791-795. https://doi.org/10.1016/j.tjog.2018.10.003
Clarke, R. (2019). Why the world wants controls over Artificial Intelligence. Computer Law & Security Review, 35(4), 423-433. https://doi.org/10.1016/j.clsr.2019.04.006
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211.
Cummings, K. M., Becker, M. H., Kirscht, J. P., & Levin, N. W. (1982). Psychosocial factors affecting adherence to medical regiments in a group of hemodialysis patients. Medical care, 20(6), 567–580. https://doi.org/https://doi.org/10.1097/00005650-198206000-00003
Das, E. J., & Walden, E. A. (2021). Why Do People Fear AI? Let’s Talk Morality. SIGHCI 2021 Proceedings, 10. https://aisel.aisnet.org/sighci2021/10
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
Deloitte. (2014). Automated document review_ Case study. https://www2.deloitte.com/us/en/pages/about-deloitte/articles/business-innovation-automated-document-review.html
Denscombe, M. (2016). Web-Based Questionnaires and the Mode Effect. Social Science Computer Review, 24(2), 246-254. https://doi.org/10.1177/0894439305284522
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71.
Epstein, S. (1972). The nature of anxiety with emphasis upon its relationship to expectancy (C. D. Spielberger, Ed. Vol. 2). New York: Academic Press. (Anxiety: Current trends in theory and research)
Fall, E., Izaute, M., & Chakroun-Baggioni, N. (2018, Jun). How can the health belief model and self-determination theory predict both influenza vaccination and vaccination intention ? A longitudinal study among university students. Psychol Health, 33(6), 746-764. https://doi.org/10.1080/08870446.2017.1401623
Folkes, V. S. (1988). Recent Attribution Research in Consumer Behavior: A Review and New Directions. Journal of Consumer Research, 14(4), 548-565. https://doi.org/10.1086/209135
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Gonzalez-Jimenez, H. (2018). Taking the fiction out of science fiction: (Self-aware) robots and what they mean for society, retailers and marketers. Futures, 98, 49-56. https://doi.org/10.1016/j.futures.2018.01.004
Goodhue, D. L., Lewis, W., & Thompson, R. (2012). Does PLS have advantages for small sample size or non-normal data? MIS Quarterly, 36(3), 981-1001.
Granter, S. R., Beck, A. H., & Papke, D. J., Jr. (2017, May). AlphaGo, Deep Learning, and the Future of the Human Microscopist. Archives of pathology & laboratory medicine, 141(5), 619-621. https://doi.org/10.5858/arpa.2016-0471-ED
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2014). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19(2), 139-152. https://doi.org/10.2753/mtp1069-6679190202
Hair Jr, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review, 26(2), 106-121. https://doi.org/10.1108/ebr-10-2013-0128
Herold, E. S. (1983, Jan). The health belief model: can it help us to understand contraceptive use among adolescents? The Journal of school health, 53(1), 19-21. https://doi.org/10.1111/j.1746-1561.1983.tb04047.x
Hochbaum, G. M. (1958). Public participation in medical screening programs: A socio-psychological study. US Department of Health, Education, and Welfare, Public Health Service.
Hong, J.-W. (2022). I was born to love AI: The influence of social status on AI self-efficacy and intentions to use AI. International Journal of Communication, 16, 172-191.
Huang, H. T., Kuo, Y. M., Wang, S. R., Wang, C. F., & Tsai, C. H. (2016, Apr 1). Structural Factors Affecting Health Examination Behavioral Intention. International Journal of Environmental Research and Public Health, 13(4), 395. https://doi.org/10.3390/ijerph13040395
Huang, T. C.-K., Liu, C.-C., & Chang, D.-C. (2012). An empirical investigation of factors influencing the adoption of data mining tools. International Journal of Information Management, 32(3), 257-270. https://doi.org/10.1016/j.ijinfomgt.2011.11.006
Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strategic Management Journal, 20(2), 195-204.
Institute, M. G. (2017). Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation, 2017. https://reurl.cc/vepjmk
Johnson, D. G., & Verdicchio, M. (2017). AI Anxiety. Journal of the Association for Information Science and Technology, 68(9), 2267-2270. https://doi.org/10.1002/asi.23867
Joreskog, K. G., & Wold, H. O. A. (1982). Systems under indirect observation: Causality, structure, prediction (Vol. 139).
Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544-564. https://doi.org/10.1016/j.dss.2007.07.001
Kim, Y. J., & Yoon, H. J. (2017). Predicting green advertising attitude and behavioral intention in South Korea. Social Behavior and Personality An International Journal, 45(8), 1345-1364. https://doi.org/10.2224/sbp.5675
Leavy, S. (2018). Gender Bias in Artificial Intelligence: The need for diversity and gender theory in machine learning. 1st International Workshop on Gender Equality in Software Engineering (GE), 14-16.
Li, J., & Huang, J.-S. (2020). Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory. Technology in Society, 63(9), 101410-101419. https://doi.org/10.1016/j.techsoc.2020.101410
Lloyd, K. (2018). Bias Amplification in Artificial Intelligence Systems. arXiv preprint arXiv:1809.07842. https://arxiv.org/abs/1809.07842
Marr, B. (2020). Is Artificial Intelligence (AI) A Threat To Humans? Forbes. https://www.forbes.com/sites/bernardmarr/2020/03/02/is-artificial-intelligence-ai-a-threat-to-humans/?sh=6ff84095205d
McCarthy, J. (2007). What is AI? / Basic Questions. http://jmc.stanford.edu/artificial-intelligence/what-is-ai/index.html
McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955. AI Magazine, 27(4), 12. https://doi.org/https://doi.org/10.1609/aimag.v27i4.1904
McClenahan, C., Shevlin, M., Adamson, G., Bennett, C., & O'Neill, B. (2007, Apr). Testicular self-examination: a test of the health belief model and the theory of planned behaviour. Health Education Research, 22(2), 272-284. https://doi.org/10.1093/her/cyl076
Montaño, D. E., Kasprzyk, D., Rimer, B., & Glanz, K. (2008). Theory of reasoned action, theory of planned behavior, and the integrated behavior model. In Health behavior and health education: Theory, Research, and Practice, 4th ed (pp. 68-96). US: Jossey-Bass.
Nunnally, J. C. (1978). Psychometric Theory (2nd ed.).
Nyholm, S., & Smids, J. (2016). The ethics of Accident-algorithms for self-driving cars: an applied trolley problem? Ethical Theory and Moral Practice, 19(5), 1275–1289. https://doi.org/10.1007/s10677-016-9745-2
Orji, R., Vassileva, J., & Mandryk, R. (2012). Towards an effective health interventions design: an extension of the health belief model. Online journal of public health informatics, 4(3). https://doi.org/10.5210/ojphi.v4i3.4321
Piniel, K., & Csizér, K. (2013). L2 motivation, anxiety and self-efficacy: The interrelationship of individual variables in the secondary school context. Studies in Second Language Learning and Teaching, 3(4), 523–550.
Skinner, B. F. (1938). The Behavior of Organisms: An Experimental Analysis. Appleton-Century-Crofts.
Strecher, V. J., & Rosenstock, I. M. (1997). The Health Belief Model. In Glanz, K., Lewis, F.M. and Rimer, B.K., (Eds.). Jossey-Bass.
Sun, X., Guo, Y., Wang, S., & Sun, J. (2006, Sep-Oct). Predicting iron-fortified soy sauce consumption intention: application of the theory of planned behavior and health belief model. Journal of Nutrition Education and Behavior 38(5), 276-285. https://doi.org/10.1016/j.jneb.2006.04.144
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2014). Intriguing properties of neural networks. arXiv:1312.6199, 1312, 6199. https://doi.org/https://doi.org/10.48550/arXiv.1312.6199
Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia Manufacturing, 22, 960-967.
Umeh, K., & Rogan-Gibson, J. (2001, Nov). Perceptions of threat, benefits, and barriers in breast self-examination amongst young asymptomatic women. Br J Health Psychol, 6(Part 4), 361-372. https://doi.org/10.1348/135910701169269
V-Dem. (2021). DEMOCRACY REPORT 2021.
Venkatesh, V. (2000). Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., Oh, J., Horgan, D., Kroiss, M., Danihelka, I., Huang, A., Sifre, L., Cai, T., Agapiou, J. P., Jaderberg, M., Vezhnevets, A. S., Leblond, R., Pohlen, T., Dalibard, V., Budden, D., Sulsky, Y., Molloy, J., Paine, T. L., Gulcehre, C., Wang, Z., Pfaff, T., Wu, Y., Ring, R., Yogatama, D., Wunsch, D., McKinney, K., Smith, O., Schaul, T., Lillicrap, T., Kavukcuoglu, K., Hassabis, D., Apps, C., & Silver, D. (2019, Nov). Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature, 575(7782), 350-354. https://doi.org/10.1038/s41586-019-1724-z
Vinzi, V. E., Trinchera, L., & Amato, S. (2010). PLS Path Modeling: From Foundations to Recent Developments and Open Issues for Model Assessment and Improvement. In Handbook of Partial Least Squares (pp. 47-82). https://doi.org/10.1007/978-3-540-32827-8_3
Walsh, T. (2015). Autonomous weapons: An open letter from AI & robotics researchers. http://futureoflife.org/open-letter-autonomous-weapons/
Wang, Y.-Y., & Wang, Y.-S. (2019). Development and validation of an artificial intelligence anxiety scale: an initial application in predicting motivated learning behavior. Interactive Learning Environments, 1-16. https://doi.org/10.1080/10494820.2019.1674887
Wdowik, M. J., Kendall, P. A., Harris, M. A., & Auld, G. (2001). Expanded Health Belief Model Predicts Diabetes Self-Management in College Students. Journal of Nutrition Education, 33(1), 17-23. https://doi.org/10.1016/s1499-4046(06)60005-5
Wold, H. (1982). Soft modeling: the basic design and some extensions. Systems under indirect observation. Systems Under Indirect Observation, 36-37.
電子全文 電子全文(網際網路公開日期:20270715)
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top