這項研究測試了人工智慧（AI）的收斂效度和預測效度，特別是一種透過視訊面試解釋大五人格特質的AI招募工具，以及在面試環境下影響AI使用者決策過程的因素。透過比較AI解釋的大五人格特質與面試者自我評分之間的差異，來檢驗AI招募工具的收斂效度，接著進一步與不同績效測量進行測試，包括（1）實際工作表現和（2）創意任務表現。隨後，本研究測試了人類評估者的三個個體特徵對參考AI時決策過程的影響，包括（1）信任、（2）科技接受度和（3）獨特感。為了檢驗上述關係，透過準實驗法進行研究。實驗一設置在一個模擬實習計畫面試中，有43名學生面試者和45名外部人類評估者。與此同時，實驗二是在一家資訊科技公司進行，有67名面試者和5名內部人類評估者。研究結果顯示了三個有趣的結果。首先，AI在解釋外在人格特質（如盡責性、外向性和親和性）時具有顯著的收斂效度，但只有部份的人格特質對工作與創意績效具有顯著的預測作用。其次，在AI招募工具的協助下，人類評估者對創意績效的預測能力較低。第三，具有較高技術能力的人類評估者在參考人工智慧時，對學生的創意任務表現會有過高的評估。整體而言，這次研究的結果為改進AI招募工具以及在人才招募環境中最大化人工智慧與人類協作效果的關鍵措施提供了見解。

This study tests the convergent and prediction validity of artificial intelligence (AI), specifically an AI interview tool that interprets big five personalities through videos, and factors affecting the decision making process of AI users under an interview setting. The convergent validity of the AI interview tool is examined by comparing the interpreted big five personality of AI to the self-rated scores of interviewees, then further tested against different performance measurements, including (1) supervisor-rated job performance and (2) an objective creative task performance rated by independent raters. Afterwards, three individual characteristics of human raters, including (1) trust, (2) technology acceptance and (3) sense of uniqueness, are tested on its influence on the decision making process when referencing an AI. A quasi-experimental research is applied in two experiments to examine the above relations. The first experiment is set in a mock internship programme interview with 43 student interviewees and 45 external human raters. Meanwhile, the second experiment is conducted in a local information technology firm with 67 interviewees and 5 internal human raters. Research results show three noteworthy findings. First, AI has significant convergent validity in interpreting outer personality traits such as extraversion, agreeableness and conscientiousness in a self-rated personality context, yet only selected traits such as extraversion and agreeableness have a significant prediction on performance. Second, human raters have low performance predictability with the assistance of AI interview tools. Third, human raters with greater technical competency will overestimate the creative task performance of students when referencing an AI. Collectively, these results offer insights in improving AI interview tools and essential measures to maximise human-AI collaboration effectiveness in an employee selection context.

論 文 審 定 書 i
ACKNOWLEDGEMENT ii
摘 要 iii
ABSTRACT iv
1 Introduction 1
2 Literature Review 5
2.1 AI Research Development 5
2.1.1 AI Predictability 5
2.1.2 AI Decision Making 7
2.2 Performance Research Development 8
2.2.1 Job Performance 9
2.2.1.1 Personality and Job Performance 10
2.2.2 Creative Performance 14
2.2.2.1 Personality and Creative Performance 15
2.3 Research Hypothesis Development 18
2.3.1 Convergent Validity of AI Personality Interpretation 18
2.3.2 Prediction Validity of Job Performance and Creative Performance 19
2.3.3 AI interview Tool as Decision Making Aid 20
2.3.4 Trust as Influencer of Adopting AI Decision Making 21
2.3.5 Technology Acceptance as Influencer of Adopting AI Decision Making 21
2.3.6 Sense of Uniqueness as Influencer of Adopting AI Decision Making 22
3 Study One 23
4 Study One - Research Method 24
4.1 Participants 24
4.1.1 Interviewee 24
4.1.2 Human Rater 24
4.2 Procedure 25
4.2.1 Experiment Design 25
4.2.2 AI Apparatus 26
4.2.3 Video Interview 27
4.2.4 Creative Task 29
4.3 Measures 29
4.3.1 Personality Validation 29
4.3.2 Creative Task 33
4.3.3 Creative Performance 34
4.3.4 Trust 36
4.3.5 Technology Acceptance 38
4.3.6 Sense of Uniqueness 39
4.4 Control Variables 40
4.5 Common Method Variance 41
4.6 Data Analysis 41
5 Study One - Results 42
5.1 Confirmatory Factor Analysis 42
5.2 Descriptive And Correlation Analysis 43
5.3 Hypothesis Testing 47
5.3.1 Testing of Hypothesis 1a to 1e 47
5.3.2 Testing of Hypothesis 2a to 2b 47
5.3.3 Testing of Hypothesis 3 48
5.3.4 Testing of Hypothesis 4a to 4c 49
6 Study Two 52
7 Study Two - Research Method 53
7.1. Participants 53
7.1.1 Interviewee 53
7.1.2 Human Rater 53
7.2 Procedure 54
7.2.1 Experiment Design 54
7.2.2 AI Apparatus 55
7.2.3 Video Interview 55
7.3 Measures 57
7.3.1 Job Performance Prediction 57
7.3.2 Actual Job Performance 58
7.3.3 Possibility of Hiring 58
7.3.4 Familiarity 59
7.4 Control Variables 59
7.5 Data Analysis 59
8 Study Two - Results 59
8.1 Descriptive And Correlation Analysis 59
8.2 Hypothesis Testing 62
8.3 Supplementary Analysis 62
9 Discussion 65
9.1 Theoretical Implications 65
9.2 Practical Implications 68
9.3 Study Limitations 70
9.4 Future Research Directions 71
10 Conclusions 72
Reference 73
Appendix 90
Appendix 1 Job Performance Prediction Questionnaire for Human Raters 90
Appendix 2 Actual Job Performance Rating Questionnaire for Direct Managers 92
Appendix 3 Expert Rating Questionnaire on Creative Performance Measurement 93
Appendix 4 Individual Characteristics and Pairing Questionnaire for Human Raters 97
Appendix 5 Creative Task Performance Rating Questionnaire for Human Raters 102
Appendix 6 Creative Task Test Platform for Students 104
Appendix 7 IPIP-50 Scale Embedded in Creative Task Test Platform for Students 105
Appendix 8 Chinese Version of Interview Questions used by Collaborated Firm 107
Appendix 9 Chinese Version of Interview Questions used in Mock Interview 108

Arkes, H. R., Shaffer, V. A., & Medow, M. A. (2007). Patients Derogate Physicians Who Use a Computer-Assisted Diagnostic Aid. Medical Decision Making, 27(2), 189–202. https://doi.org/10.1177/0272989x06297391
Aurora Cloud. (2022). AI面試系統. 產品服務. https://www.aurora.com.tw/cloud/product/0j325651801824998230
Bansal, A., Panchal, T., Jabeen, F., Mangla, S. K., & Singh, G. (2023). A study of human resource digital transformation (HRDT): A phenomenon of innovation capability led by digital and individual factors. Journal of Business Research, 157, 113611. https://doi.org/10.1016/j.jbusres.2022.113611
Barrick, M., & Mount, M. (1991). The Big Five Personality Dimensions and Job Performance: A Meta-Analysis. Personnel Psychology, 44(1), 1–26. https://doi.org/10.1111/j.1744-6570.1991.tb00688.x
Barron, F. (1957). Originality in Relation to Personality and Intellect1. Journal of Personality, 25(6), 730–742. https://doi.org/10.1111/j.1467-6494.1957.tb01561.x
Bartram, D. (2005). The Great Eight Competencies: A Criterion-Centric Approach to Validation. Journal of Applied Psychology, 90(6), 1185–1203. https://doi.org/10.1037/0021-9010.90.6.1185
Batey, M., Chamorro-Premuzic, T., & Furnham, A. (2010). Individual Differences in Ideational Behavior: Can the Big Five and Psychometric Intelligence Predict Creativity Scores? Creativity Research Journal, 22(1), 90–97. https://doi.org/10.1080/10400410903579627
Batey, M., Furnham, A., & Safiullina, X. (2010). Intelligence, general knowledge and personality as predictors of creativity. Learning and Individual Differences, 20(5), 532–535. https://doi.org/10.1016/j.lindif.2010.04.008
Beghetto, R. (2006). Creative Self-Efficacy: Correlates in Middle and Secondary Students. Creativity Research Journal, 18(4), 447–457. https://doi.org/10.1207/s15326934crj1804_4
Berry, C. M., Ones, D. S., & Sackett, P. R. (2007). Interpersonal deviance, organisational deviance, and their common correlates: A review and meta-analysis. Journal of Applied Psychology, 92(2), 410–424. https://doi.org/10.1037/0021-9010.92.2.410
Black, J. S., & van Esch, P. (2020). AI-enabled recruiting: What is it and how should a manager use it? Business Horizons, 63(2), 215–226. https://doi.org/10.1016/j.bushor.2019.12.001
Booth, R. (2019). Unilever saves on recruiters by using AI to assess job interviews. The Guardian; The Guardian. https://www.theguardian.com/technology/2019/oct/25/unilever-saves-on-recruiters-by-using-ai-to-assess-job-interviews
Borman, W. C., & Motowidlo, S. J. (1997). Task Performance and Contextual Performance: The Meaning for Personnel Selection Research. Human Performance, 10(2), 99–109. https://doi.org/10.1207/s15327043hup1002_3
Borman, W. C., & Motowidlo, S. M. (1993). Expanding the Criterion Domain to Include Elements of Contextual Performance. Personnel Selection in Organizations, 71–98. https://digitalcommons.usf.edu/psy_facpub/1111
Bresciani, S., Huarng, K.-H., Malhotra, A., & Ferraris, A. (2021). Digital transformation as a springboard for product, process and business model innovation. Journal of Business Research, 128, 204–210. https://doi.org/10.1016/j.jbusres.2021.02.003
Brewer, M. B. (1991). The Social Self: On Being the Same and Different at the Same Time. Personality and Social Psychology Bulletin, 17(5), 475–482. https://doi.org/10.1177/0146167291175001
Campbell, J. P. (1990). Modelling the performance prediction problem in industrial and organisational psychology. In M. D. Dunnette & L. M. Hough (Eds.), Handbook of industrial and organisational psychology (pp. 687–732). Consulting Psychologists Press.
Carson, S. H., Peterson, J. B., & Higgins, D. M. (2005). Reliability, Validity, and Factor Structure of the Creative Achievement Questionnaire. Creativity Research Journal, 17(1), 37–50. https://doi.org/10.1207/s15326934crj1701_4
Chiaburu, D. S., Oh, I.-S., Berry, C. M., Li, N., & Gardner, R. G. (2011). The five-factor model of personality traits and organisational citizenship behaviours: A meta-analysis. Journal of Applied Psychology, 96(6), 1140–1166. https://doi.org/10.1037/a0024004
Chien, C.-F., & Chen, L.-F. (2008). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications, 34(1), 280–290. https://doi.org/10.1016/j.eswa.2006.09.003
Costa, P. T., & McCrae, R. R. (1995). Solid ground in the wetlands of personality: A reply to Block. Psychological Bulletin, 117(2), 216–220. https://doi.org/10.1037/0033-2909.117.2.216
Crant, J. M. (1995). The Proactive Personality Scale and objective job performance among real estate agents. Journal of Applied Psychology, 80(4), 532–537. https://doi.org/10.1037/0021-9010.80.4.532
Davis, C. D., Kaufman, J. C., & McClure, F. H. (2011). Non-Cognitive Constructs and Self-Reported Creativity by Domain. The Journal of Creative Behavior, 45(3), 188–202. https://doi.org/10.1002/j.2162-6057.2011.tb01426.x
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
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: a Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
de Visser, E. J., Monfort, S. S., Goodyear, K., Lu, L., O’Hara, M., Lee, M. R., Parasuraman, R., & Krueger, F. (2017). A Little Anthropomorphism Goes a Long Way. Human Factors: The Journal of the Human Factors and Ergonomics Society, 59(1), 116–133. https://doi.org/10.1177/0018720816687205
DeYoung, C. G., Quilty, L. C., & Peterson, J. B. (2007). Between facets and domains: 10 aspects of the Big Five. Journal of Personality and Social Psychology, 93(5), 880–896. https://doi.org/10.1037/0022-3514.93.5.880
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126. https://doi.org/10.1037/xge0000033
Duckworth, A. L., Taxer, J. L., Eskreis-Winkler, L., Galla, B. M., & Gross, J. J. (2019). Self-Control and Academic Achievement. Annual Review of Psychology, 70(1), 373–399. https://doi.org/10.1146/annurev-psych-010418-103230
Dudley, N. M., Orvis, K. A., Lebiecki, J. E., & Cortina, J. M. (2006). A meta-analytic investigation of conscientiousness in the prediction of job performance: Examining the intercorrelations and the incremental validity of narrow traits. Journal of Applied Psychology, 91(1), 40–57. https://doi.org/10.1037/0021-9010.91.1.40
Feist, G. J. (1998). A Meta-Analysis of Personality in Scientific and Artistic Creativity. Personality and Social Psychology Review, 2(4), 290–309. https://doi.org/10.1207/s15327957pspr0204_5
Felfe, J., & Schyns, B. (2004). Is Similarity in Leadership Related to Organisational Outcomes? The Case of Transformational Leadership. Journal of Leadership & Organisational Studies, 10(4), 92–102. https://doi.org/10.1177/107179190401000407
Fenner, G. H., & Renn, R. W. (2009). Technology-assisted supplemental work and work-to-family conflict: The role of instrumentality beliefs, organisational expectations and time management. Human Relations, 63(1), 63–82. https://doi.org/10.1177/0018726709351064
Fromkin, H. L., & Snyder, C. R. (1980). The Search for Uniqueness and Valuation of Scarcity. Social Exchange, 57–75. https://doi.org/10.1007/978-1-4613-3087-5_3
Furnham, A., Batey, M., Booth, T. W., Patel, V., & Lozinskaya, D. (2011). Individual difference predictors of creativity in Art and Science students. Thinking Skills and Creativity, 6(2), 114–121. https://doi.org/10.1016/j.tsc.2011.01.006
Gelade, G. A. (1997). Creativity in Conflict: The Personality of the Commercial Creative. The Journal of Genetic Psychology, 158(1), 67–78. https://doi.org/10.1080/00221329709596653
Gregory, R. J. (2007). Psychological testing : history, principles, and applications. Pearson/Allyn And Bacon.
Gigauri, I. (2020). Challenges HR Managers Facing Due to COVID-19 and Overcoming Strategies: Perspectives from Georgia. Archives of Business Research, 8(11), 1–18. https://doi.org/10.14738/abr.811.9313
Gnambs, T. (2015). What makes a computer wiz? Linking personality traits and programming aptitude. Journal of Research in Personality, 58, 31–34. https://doi.org/10.1016/j.jrp.2015.07.004
Goldberg, L. R. (1999). A broad-bandwidth, public domain, personality inventory measuring the lower-level facets of several five-factor models. Personality psychology in Europe, 7(1), 7-28.
Hall, J. A., Andrzejewski, S. A., & Yopchick, J. E. (2009). Psychosocial Correlates of Interpersonal Sensitivity: A Meta-Analysis. Journal of Nonverbal Behavior, 33(3), 149–180. https://doi.org/10.1007/s10919-009-0070-5
He, Y., Donnellan, M. B., & Mendoza, A. M. (2019). Five-factor personality domains and job performance: A second order meta-analysis. Journal of Research in Personality, 82, 103848. https://doi.org/10.1016/j.jrp.2019.103848
Hickman, L., Bosch, N., Ng, V., Saef, R., Tay, L., & Woo, S. E. (2021). Automated video interview personality assessments: Reliability, validity, and generalizability investigations. Journal of Applied Psychology. https://doi.org/10.1037/apl0000695
Howard, J. J., Rabbitt, L. R., & Sirotin, Y. B. (2020). Human-algorithm teaming in face recognition: How algorithm outcomes cognitively bias human decision-making. PLOS ONE, 15(8), e0237855. https://doi.org/10.1371/journal.pone.0237855
Janusz, A., Stawicki, S., Drewniak, M., Ciebiera, K., Ślęzak, D., & Stencel, K. (2018). How to Match Jobs and Candidates - A Recruitment Support System Based on Feature Engineering and Advanced Analytics. Communications in Computer and Information Science, 503–514. https://doi.org/10.1007/978-3-319-91476-3_42
Jaser, Z., Petrakaki, D., Starr, R., & Oyarbide-Magaña, E. (2022). Where Automated Job Interviews Fall Short. Harvard Business Review. https://hbr.org/2022/01/where-automated-job-interviews-fall-short
Jaussi, K. S., Randel, A. E., & Dionne, S. D. (2007). I Am, I Think I Can, and I Do: The Role of Personal Identity, Self-Efficacy, and Cross-Application of Experiences in Creativity at Work. Creativity Research Journal, 19(2-3), 247–258. https://doi.org/10.1080/10400410701397339
Jonmarker, O., Strand, F., Brandberg, Y., & Lindholm, P. (2019). The future of breast cancer screening: what do participants in a breast cancer screening program think about automation using artificial intelligence? Acta Radiologica Open, 8(12), 205846011988031. https://doi.org/10.1177/2058460119880315
Judge, T. A., Bono, J. E., Ilies, R., & Gerhardt, M. W. (2002). Personality and leadership: A qualitative and quantitative review. Journal of Applied Psychology, 87(4), 765–780. https://doi.org/10.1037/0021-9010.87.4.765
Judge, T. A., Rodell, J. B., Klinger, R. L., Simon, L. S., & Crawford, E. R. (2013). Hierarchical representations of the five-factor model of personality in predicting job performance: Integrating three organising frameworks with two theoretical perspectives. Journal of Applied Psychology, 98(6), 875–925. https://doi.org/10.1037/a0033901
Jutzi, T. B., Krieghoff-Henning, E. I., Holland-Letz, T., Utikal, J. S., Hauschild, A., Schadendorf, D., Sondermann, W., Fröhling, S., Hekler, A., Schmitt, M., Maron, R. C., & Brinker, T. J. (2020). Artificial Intelligence in Skin Cancer Diagnostics: The Patients’ Perspective. Frontiers in Medicine, 7. https://doi.org/10.3389/fmed.2020.00233
Kaplan, S., Bradley, J. C., Luchman, J. N., & Haynes, D. (2009). On the role of positive and negative affectivity in job performance: A meta-analytic investigation. Journal of Applied Psychology, 94(1), 162–176. https://doi.org/10.1037/a0013115
Kappe, R., & van der Flier, H. (2012). Predicting academic success in higher education: what’s more important than being smart? European Journal of Psychology of Education, 27(4), 605–619. https://doi.org/10.1007/s10212-011-0099-9
Karwowski, M. (2011). It doesn’t hurt to ask…But sometimes it hurts to believe: Polish students’ creative self-efficacy and its predictors. Psychology of Aesthetics, Creativity, and the Arts, 5(2), 154–164. https://doi.org/10.1037/a0021427
Karwowski, M. (2012). Did Curiosity Kill the Cat? Relationship Between Trait Curiosity, Creative Self-Efficacy and Creative Personal Identity. Europe’s Journal of Psychology, 8(4). https://doi.org/10.5964/ejop.v8i4.513
Karwowski, M., & Barbot, B. (2020). Creative Self-Beliefs: Their Nature, Development, and Correlates. Creativity and Reason in Cognitive Development, 302–326. https://doi.org/10.1017/cbo9781139941969.016
Karwowski, M., & Lebuda, I. (2016). The big five, the huge two, and creative self-beliefs: A meta-analysis. Psychology of Aesthetics, Creativity, and the Arts, 10(2), 214–232. https://doi.org/10.1037/aca0000035
Kaufman, S. B., Quilty, L. C., Grazioplene, R. G., Hirsh, J. B., Gray, J. R., Peterson, J. B., & DeYoung, C. G. (2015). Openness to Experience and Intellect Differentially Predict Creative Achievement in the Arts and Sciences. Journal of Personality, 84(2), 248–258. https://doi.org/10.1111/jopy.12156
Kepes, S., & McDaniel, M. A. (2015). The Validity of Conscientiousness Is Overestimated in the Prediction of Job Performance. Plos One, 10(10), e0141468. https://doi.org/10.1371/journal.pone.0141468
Kirimi, J. M., & Moturi, C. A. (2016). Application of Data Mining Classification in Employee Performance Prediction. International Journal of Computer Applications, 146(7), 28–35. https://doi.org/10.5120/ijca2016910883
Kleysen, R. F., & Street, C. T. (2001). Toward a multi‐dimensional measure of individual innovative behaviour. Journal of Intellectual Capital, 2(3), 284–296. https://doi.org/10.1108/eum0000000005660
Köchling, A., Riazy, S., Wehner, M. C., & Simbeck, K. (2020). Highly Accurate, But Still Discriminatory. Business & Information Systems Engineering, 63(1), 39–54. https://doi.org/10.1007/s12599-020-00673-w
Langer, M., König, C. J., & Papathanasiou, M. (2019). Highly automated job interviews: Acceptance under the influence of stakes. International Journal of Selection and Assessment, 27(3), 217–234. https://doi.org/10.1111/ijsa.12246
Langer, M., & Landers, R. N. (2021). The future of artificial intelligence at work: A review on effects of decision automation and augmentation on workers targeted by algorithms and third-party observers. Computers in Human Behavior, 123, 106878. https://doi.org/10.1016/j.chb.2021.106878
Laumer, S., Eckhardt, A., & Weitzel, T. (2012). Online Gaming to Find a New Job - Examining Job Seekers’ Intention to Use Serious Games as a Self-Assessment Tool. German Journal of Human Resource Management: Zeitschrift Für Personalforschung, 26(3), 218–240. https://doi.org/10.1177/239700221202600302
Laumer, S., Maier, C., Eckhardt, A., & Weitzel, T. (2016). Work routines as an object of resistance during information systems implementations: theoretical foundation and empirical evidence. European Journal of Information Systems, 25(4), 317–343. https://doi.org/10.1057/ejis.2016.1
Lee, K., & Allen, N. J. (2002). Organisational citizenship behaviour and workplace deviance: The role of affect and cognitions. Journal of Applied Psychology, 87(1), 131–142. https://doi.org/10.1037/0021-9010.87.1.131
Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2019). The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity. Journal of Business Ethics, 160(2), 377–392. https://doi.org/10.1007/s10551-019-04204-w
Leutner F., Liff J., Zuloaga L., Mondragon N. (2021). HireVue’s assessment science. HireVue. https://webapi.hirevue.com/wp-content/uploads/2021/03/HireVue-Assessment-Science-whitepaper-2021.pdf?_ga=2.239366848.21559469.1628169017-383464143.1628169017
Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to Medical Artificial Intelligence. Journal of Consumer Research, 46(4), 629–650. https://doi.org/10.1093/jcr/ucz013
Lowe, D. J., Reckers, P. M. J., & Whitecotton, S. M. (2002). The Effects of Decision-Aid Use and Reliability on Jurors’ Evaluations of Auditor Liability. The Accounting Review, 77(1), 185–202. https://doi.org/10.2308/accr.2002.77.1.185
Madsen, M., & Gregor, S. (2000). Measuring human-computer trust. In 11th australasian conference on information systems (Vol. 53, pp. 6-8). Australasian Association for Information System.
Mahmoud, A. A., Shawabkeh, T. A., Salameh, W. A., & Amro, I. A. (2019). Performance Predicting in Hiring Process and Performance Appraisals Using Machine Learning. IEEE Xplore. https://doi.org/10.1109/IACS.2019.8809154
Manzey, D., Reichenbach, J., & Onnasch, L. (2012). Human Performance Consequences of Automated Decision Aids. Journal of Cognitive Engineering and Decision Making, 6(1), 57–87. https://doi.org/10.1177/1555343411433844
Marsh, H. W., Trautwein, U., Ludtke, O., Koller, O., & Baumert, J. (2006). Integration of Multidimensional Self-Concept and Core Personality Constructs: Construct Validation and Relations to Well-Being and Achievement. Journal of Personality, 74(2), 403–456. https://doi.org/10.1111/j.1467-6494.2005.00380.x
Maskey, S. (2022). Council Post: How AI Is Primed To Disrupt HR And Recruiting. Forbes. https://www.forbes.com/sites/forbestechcouncil/2022/03/23/how-ai-is-primed-to-disrupt-hr-and-recruiting/?sh=2a91f52a1078
McAllister, D. J. (1995). Affect- and Cognition-Based Trust as Foundations for Interpersonal Cooperation in Organizations. Academy of Management Journal, 38(1), 24–59. https://doi.org/10.5465/256727
McCarthy, J. M., Bauer, T. N., Truxillo, D. M., Anderson, N. R., Costa, A. C., & Ahmed, S. M. (2017). Applicant Perspectives During Selection: A Review Addressing “So What?,” “What’s New?,” and “Where to Next?” Journal of Management, 43(6), 1693–1725. https://doi.org/10.1177/0149206316681846
Michinov, N., Jamet, E., Métayer, N., & Le Hénaff, B. (2015). The eyes of creativity: Impact of social comparison and individual creativity on performance and attention to others’ ideas during electronic brainstorming. Computers in Human Behavior, 42, 57–67. https://doi.org/10.1016/j.chb.2014.04.037
Mount, M. K., Barrick, M. R., & Stewart, G. L. (1998). Five-Factor Model of personality and Performance in Jobs Involving Interpersonal Interactions. Human Performance, 11(2-3), 145–165. https://doi.org/10.1080/08959285.1998.9668029
Murphy, K. R. (2005). Why Don’t Measures of Broad Dimensions of Personality Perform Better As Predictors of Job Performance? Human Performance, 18(4), 343–357. https://doi.org/10.1207/s15327043hup1804_2
National Center for O*NET Development. (2022). O*NET OnLine. Onetonline.org. https://www.onetonline.org/
Nguyen, T.-M., & Malik, A. (2021). Employee acceptance of online platforms for knowledge sharing: exploring differences in usage behaviour. Journal of Knowledge Management, ahead-of-print(ahead-of-print). https://doi.org/10.1108/jkm-06-2021-0420
Nolan, K., Carter, N., & Dalal, D. (2016). Threat of Technological Unemployment: Are Hiring Managers Discounted for Using Standardised Employee Selection Practices? Personnel Assessment and Decisions, 2(1). https://doi.org/10.25035/pad.2016.004
Parasuraman, R., & Riley, V. (1997). Humans and Automation: Use, Misuse, Disuse, Abuse. Human Factors: The Journal of the Human Factors and Ergonomics Society, 39(2), 230–253. https://doi.org/10.1518/001872097778543886
Pessach, D., Singer, G., Avrahami, D., Chalutz Ben-Gal, H., Shmueli, E., & Ben-Gal, I. (2020). Employee recruitment: A prescriptive analytics approach via machine learning and mathematical programming. Decision Support Systems, 134, 113290. https://doi.org/10.1016/j.dss.2020.113290
Plucker, J. A., Beghetto, R. A., & Dow, G. T. (2004). Why Isn’t Creativity More Important to Educational Psychologists? Potentials, Pitfalls, and Future Directions in Creativity Research. Educational Psychologist, 39(2), 83–96. https://doi.org/10.1207/s15326985ep3902_1
Poropat, A. E. (2009). A meta-analysis of the five-factor model of personality and academic performance. Psychological Bulletin, 135(2), 322–338. https://doi.org/10.1037/a0014996
Pretz, J. E., & McCollum, V. A. (2014). Self-perceptions of creativity do not always reflect actual creative performance. Psychology of Aesthetics, Creativity, and the Arts, 8(2), 227–236. https://doi.org/10.1037/a0035597
Puryear, J. S., Kettler, T., & Rinn, A. N. (2017). Relationships of personality to differential conceptions of creativity: A systematic review. Psychology of Aesthetics, Creativity, and the Arts, 11(1), 59–68. https://doi.org/10.1037/aca0000079
Raisch, S., & Krakowski, S. (2020). Artificial Intelligence and Management: The Automation-Augmentation Paradox. Academy of Management Review. https://doi.org/10.5465/2018.0072
Reiter-Palmon, R., Illies, J., & Kobe-Cross, L. (2009). Conscientiousness Is Not Always a Good Predictor of Performance: The Case of Creativity. The International Journal of Creativity & Problem Solving, 19(2), 27–45. https://digitalcommons.unomaha.edu/psychfacpub/20
Runco, M. A., Plucker, J. A., & Lim, W. (2001). Development and Psychometric Integrity of a Measure of Ideational Behavior. Creativity Research Journal, 13(3-4), 393–400. https://doi.org/10.1207/s15326934crj1334_16
Russell, S., & Norvig, P. (2010). Artificial intelligence: a modern approach (3rd ed.). Pearson.
Salgado, J. F. (2002). The Big Five Personality Dimensions and Counterproductive Behaviours. International Journal of Selection and Assessment, 10(1&2), 117–125. https://doi.org/10.1111/1468-2389.00198
Salgado, J. F. (2017). Moderator effects of job complexity on the validity of forced-choice personality inventories for predicting job performance. Revista de Psicología Del Trabajo Y de Las Organizaciones, 33(3), 229–239. https://doi.org/10.1016/j.rpto.2017.07.001
Shehu, M. A., & Saeed, F. (2016). An adaptive personnel selection model for recruitment using domain-driven data mining. Journal of Theoretical and Applied Information Technology, 91(1), 117.
Shin, Y., Kim, M., & Lee, S.-H. (2016). Reflection Toward Creativity: Team Reflexivity as a Linking Mechanism Between Team Goal Orientation and Team Creative Performance. Journal of Business and Psychology, 32(6), 655–671. https://doi.org/10.1007/s10869-016-9462-9
Silvia, P. J., Nusbaum, E. C., Berg, C., Martin, C., & O’Connor, A. (2009). Openness to experience, plasticity, and creativity: Exploring lower-order, high-order, and interactive effects. Journal of Research in Personality, 43(6), 1087–1090. https://doi.org/10.1016/j.jrp.2009.04.015
Simmons, A. L., Payne, S. C., & Pariyothorn, M. M. (2014). The Role of Means Efficacy When Predicting Creative Performance. Creativity Research Journal, 26(1), 53–61. https://doi.org/10.1080/10400419.2014.873667
Şimşek, Ö. F., & Yalınçetin, B. (2010). I feel unique, therefore I am: The development and preliminary validation of the personal sense of uniqueness (PSU) scale. Personality and Individual Differences, 49(6), 576–581. https://doi.org/10.1016/j.paid.2010.05.006
Srivastava, M., Heidari, H., & Krause, A. (2019). Mathematical Notions vs. Human Perception of Fairness. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3292500.3330664
Stewart, G. L. (1999). Trait bandwidth and stages of job performance: Assessing differential effects for conscientiousness and its subtraits. Journal of Applied Psychology, 84(6), 959–968. https://doi.org/10.1037/0021-9010.84.6.959
Suen, H.-Y., Chen, M. Y.-C., & Lu, S.-H. (2019). Does the use of synchrony and artificial intelligence in video interviews affect interview ratings and applicant attitudes? Computers in Human Behavior, 98(98), 93–101. https://doi.org/10.1016/j.chb.2019.04.012
Suen, H.-Y., Hung, K.-E., & Lin, C.-L. (2019). TensorFlow-based Automatic Personality Recognition Used in Asynchronous Video Interviews. IEEE Access, 1–1. https://doi.org/10.1109/access.2019.2902863
Suen, H.-Y., Hung, K.-E., & Lin, C.-L. (2020). Intelligent video interview agent used to predict communication skill and perceived personality traits. Human-Centric Computing and Information Sciences, 10(1). https://doi.org/10.1186/s13673-020-0208-3
Tobia, K., Nielsen, A., & Stremitzer, A. (2020). When Does Physician Use of AI Increase Liability? Journal of Nuclear Medicine, 62(1), 17–21. https://doi.org/10.2967/jnumed.120.256032
Tong, S., Jia, N., Luo, X., & Fang, Z. (2021). The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance. Strategic Management Journal, 42(9), 1600–1631. https://doi.org/10.1002/smj.3322
Uppal, N. (2014). Moderation Effects of Job Characteristics on the Relationship Between Neuroticism and Job Performance. International Journal of Selection and Assessment, 22(4), 411–421. https://doi.org/10.1111/ijsa.12087
van Esch, P., & Black, J. S. (2019). Factors that influence new generation candidates to engage with and complete digital, AI-enabled recruiting. Business Horizons, 62(6), 729–739. https://doi.org/10.1016/j.bushor.2019.07.004
Van Scotter, J. R., & Motowidlo, S. J. (1996). Interpersonal facilitation and job dedication as separate facets of contextual performance. Journal of Applied Psychology, 81(5), 525–531. https://doi.org/10.1037/0021-9010.81.5.525
Vinciarelli, A., & Mohammadi, G. (2011). Towards a Technology of Nonverbal Communication. Affective Computing and Interaction, 133–156. https://doi.org/10.4018/978-1-61692-892-6.ch007
Vinciarelli, A., & Mohammadi, G. (2014). A Survey of Personality Computing. IEEE Transactions on Affective Computing, 5(3), 273–291. https://doi.org/10.1109/taffc.2014.2330816
Visser, V. A., van Knippenberg, D., van Kleef, G. A., & Wisse, B. (2013). How leader displays of happiness and sadness influence follower performance: Emotional contagion and creative versus analytical performance. The Leadership Quarterly, 24(1), 172–188. https://doi.org/10.1016/j.leaqua.2012.09.003
Wall, S., & Schellmann, H. (2021). We Tested AI Interview tools. Here’s What We found. MIT Technology Review. https://www.technologyreview.com/2021/07/07/1027916/we-tested-ai-interview-tools
Werner, C. H., Tang, M., Kruse, J., Kaufman, J. C., & Spörrle, M. (2014). The Chinese Version of the Revised Creativity Domain Questionnaire (CDQ-R): First Evidence for its Factorial Validity and Systematic Association with the Big Five. The Journal of Creative Behavior, 48(4), 254–275. https://doi.org/10.1002/jocb.51
Williams, L. J., & Anderson, S. E. (1991). Job Satisfaction and Organisational Commitment as Predictors of Organisational Citizenship and In-Role Behaviours. Journal of Management, 17(3), 601–617. https://doi.org/10.1177/014920639101700305
Wolf, J. R. (2014). Do IT students prefer doctors who use IT? Computers in Human Behavior, 35, 287–294. https://doi.org/10.1016/j.chb.2014.03.020
Yang, J., Im, M., Choi, S., Kim, J., & Ko, D. H. (2021). Artificial Intelligence-Based Hiring: An Exploratory Study of Hiring Market Reactions. Japan Labor Issues, 5(32), 41-55.
Yuan, Y.-H., Wu, M.-H., Hu, M.-L. M., & Lin, I-Chien. (2017). Teacher’s encouragement on creativity, intrinsic motivation, and creativity: The mediating role of creative process engagement. The Journal of Creative Behavior, 53(3). https://doi.org/10.1002/jocb.181