人工智慧面試官與人類專業面試官對於面試者在錄影面試中進行印象管理辨別程度之比較性研究

The Comparative Study on Accuracy of Identifying Interviewees' Impression Management Used in Recorded Interview by Embedded Artificial Intelligence and Human Interviewers

范瀚之(Fan Han-Chih)

指導教授 ： 孫弘岳

國立臺灣師範大學/科技與工程學院/科技應用與人力資源發展學系人力資源發展碩士在職專班/碩士(2021年)

https://doi.org/10.6345/NTNU202101482

摘要

面試是最常見的招募甄選工具，幾乎每個組織都會採用面試。在面試過程當中，面試者會努力運用誠實性或是欺騙性的印象管理(Impression management, IM)技巧來影響面試官的評分，以期提高錄取的機會。隨著科技的發展，不同型態的面試方式如視訊會議或錄影面試，以及面試決策輔助工具像是人工智慧面試官(Embedded artificial intelligence, embedded AI)等也越來越常見。與人工智慧面試官相比，人類專業面試官透過口語與非口語線索如臉部表情(Facial expressions)，細微臉部表情(Subtle facial expressions)或微表情 (Micro expressions）來判斷面試者表現的敏感度較低，且無法準確判斷面試者是否有使用印象管理技巧。
本研究邀請30位曾任或現職為管理職或人力資源之從業者，作為人類專業面試官。再邀請32位有工作經驗之社會人士或即將畢業的學生作為面試者進行錄影面試（又稱非同步視訊面試）並填寫自我印象管理問卷。每位面試者由3位面試官看完錄影面試後進行印象管理評分；再將錄影面試資料由先前研究所開發出能夠自動辨認印象管理技巧之人工智慧面試官來產出印象管理評分。資料收集完全後，將面試者印象管理自評之分數與人工智慧面試官評分與人類專業面試官評分與進行相關分析。
結論顯示人工智慧面試官評分與面試者自評在自我推銷、自我辯護、誇大不實與避重就輕等四種印象管理構面上都有顯著正相關，而人類專業面試官評分與面試自評在此四個構面上皆無顯著相關。故可得知人工智慧面試官較人類專業面試官更能辨別面試者在印象管理技巧之使用。

關鍵字

印象管理 ； 臉部表情 ； 卷積神經網路 ； 人工智慧面試官

並列摘要

Interview is the most common tool for recruitment and selection which adopted by almost every organization. Interviewees will try their best to use either honest or deceptive impression management (IM) to increase the possibility of getting job offer by influencing interview score of interviewers. With the development of technology there’re more and more methods for conducting interview (e.g. teleconference or asynchronous video interview, AVI) and for interview decision assisting tool (e.g. embedded artificial intelligence, embedded AI). Human expert interviewers are lesser sensitive to identify performance of interviewees according to their verbal and non-verbal clues (e.g. facial expressions, subtle facial expressions or micro-expressions) as well as weather applying impression management or not compared to embedded AI.
This study invited 30 former or incumbent managers or human resource practitioners as human expert interviewers to evaluate IM score of interviewees together with embedded AI. And invited 32 experienced office workers or fresh graduate students as interviewees to attend AVI and complete self-report IM. Each interviewee was evaluated by 3 human expert interviewers after reviewing his/her recorded video interview and average their scores of IM as final score. On the other hand, each recorded video interview was evaluated by embedded AI developed to automatically identify and recognize IM in previous study as well. Correlation analysis was adopted for understanding the correlation between self-report IM score and IM score evaluated by embedded AI and between self-report IM score and IM score evaluated by human expert interviewer respectively.
The result shows that IM scores evaluated by embedded AI was highly correlated to self-report IM score but there’s no significant correlation between IM scores evaluated by human expert interviewers and self-report IM score. In conclusion embedded AI can accurately identify IM applied by interviewees compared to human expert interviewers.

並列關鍵字

Impression management ； Facial expression ； Embedded artificial intelligence ； Convolutional neural networks

參考文獻

一、外文部份

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