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研究生: 曾奕翔
Yi-Hsiang Tseng
論文名稱: 員工態度對於人工智慧應用的使用意願和離職傾向
Employees’ attitude toward artificial intelligence on intention to use and turnover intention
指導教授: 朱宇倩
Yu-Qian Zhu
口試委員: 魏小蘭
Hsiao-Lan Wei
方郁惠
Yu-hui Fang
學位類別: 碩士
Master
系所名稱: 管理學院 - 資訊管理系
Department of Information Management
論文出版年: 2020
畢業學年度: 108
語文別: 中文
論文頁數: 71
中文關鍵詞: 抗拒理論科技準備度主觀知識自我工作效能資訊系統品質
外文關鍵詞: Theory of resistance, Technology readiness, Subjective knowledge, Occupational self-efficacy, Information system quality
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  • 人工智慧時代已經來臨, AI 技術正在滲透每一個行業、每一個工作。麥肯錫全球研究院發表了〈未來工作:自動化、就業與生產力〉報告,分析自動化趨勢對各行各業的影響,報告指出,在近六成的職業中,至少有30%的工作內容將被自動化。所以本研究嘗試找出影響員工對於人工智慧的態度的因素及理論,加以分析並探討,透過結合抗拒理論,依人為、系統、互動三個不同的角度出發,嘗試找出任何可能影響員工態度的因素,並接續探討態度又是如何影響員工的使用意願及離職傾向,藉此幫助台灣本土企業在導入人工智慧的時候,可以有參考依據,有效的減少員工的不適與降低公司的離職率。
    本研究發現在人為方面,對新科技抱持樂觀態度的使用者,則對於使用人工智慧應用會抱持著正面的態度;在系統方面,使用者預期的系統品質和態度是呈現顯著的正向影響,說明系統的預期品質若有符合使用者的期望,則對於使用人工智慧應用會抱持著正面的態度;在互動方面,是以使用者對於人工智慧應用所持有的疑慮來探討,發現使用者的疑慮可分為兩種類型,分別是工作上的疑慮及人
    際關係的疑慮,而疑慮和態度的是呈現顯著的負向影響,說明使用者在使用人工智慧應用時,若有此兩類的疑慮,則對於使用人工智慧應用會抱持著負面的態度。
    因此建議欲導入或正在導入人工智慧應用的企業,可以參考本研究結果,在選才時可以透過性格測驗,挑選出對新科技抱持樂觀態度的員工;在系統設計時,可以參考本研究中的六大系統品質作為依據,確保人工智慧應用有符合使用者的預期;在使用疑慮的部分,可以透過一些公司的定期內部教育訓練或是部門活動,確保員工在工作上不會感到不如電腦或擔心被電腦取代,並可以維持人際關係,如此一來,即可增加員工的使用意願及降低及員工的離職傾向。


    The era of AI has come, and AI technology is penetrating into every industry and
    every job. The McKinsey global institute has released a report called “The future of work: automation, employment and productivity”. The report points out that in nearly 60 percent of occupations and at least 30 percent of the work content will be automated. So this research aims to find out the influence factors of employee's
    attitudes towards AI on intention to use and turnover intention, then analyzes and
    discusses it through combining the theory of resistance. In accordance with three
    perspectives which are people-oriented, system-oriented and interaction-oriented, try to find as many factors as possible that may affect employee’s attitude, followed by exploring how attitude affect employee’s intention to use and turnover intention to help Taiwan local companies which are going to deploy AI to have a guide to follow so they can effectively reduce the discomfort from employees and the turnover rate of the employees.
    On the people-oriented, users who were optimistic about new technologies had a positive attitude towards the use of AI applications. In terms of the system-oriented, users' expected system quality and attitude have a significant positive correlation, indicating that if the expected quality of the system meets users' expectations, users will hold a positive attitude towards the AI applications. In terms of interaction-oriented, it discusses the concerns that employee may have at work and can be divided into two types, respectively is work-related and interpersonal-related concerns, and it turns out concerns and attitude have a significant negative correlation, indicating that employees who have the concerns in the use of artificial intelligence application will hold a negative attitude toward AI.
    Therefore, it is suggested that companies who want to deploy the AI applications can refer to the results of this study and select those who are optimistic about new technologies through relevant personality trait tests. In the system design, the six system qualities in this study can be referred to as the basis to ensure that the application of AI meets users' expectations. As for concerns, it is suggested that companies can hold internal training or education regularly to ensure employees will not feel less useful or worry about being replaced by AI and can maintain the relationship with colleagues at the same time.

    目錄 第一章 緒論 第一節 研究背景與動機 第二節 研究問題與目的 第三節 論文結構 第四節 研究流程 第二章 文獻探討 第一節 資訊系統的抗拒使用 第二節 抗拒理論(Resistance theory) 第三節 專家系統與人工智慧 第四節 科技準備度(Technology readiness) 第五節 工作自我效能(Occupational self-efficacy) 第六節 主觀知識(Subjective knowledge) 第七節 資訊系統品質(Information system quality) 第三章 研究架構與假設 第一節 研究架構 第二節 研究假說 第三節 研究設計 第四章 資料分析與結果 第一節 樣本描述性統計 第二節 信效度分析 第三節 冗餘分析 第四節 控制變數 第五節 路徑分析與假說檢定 第六節 中介效果檢定 第五章 結論與建議 第一節 研究發現與結論 第二節 研究貢獻 第三節 研究限制與建議 參考文獻

    Agarwal, R., & Prasad, J. (1998). The antecedents and consequents of user
    perceptions in information technology adoption. Decision support systems,
    22(1), 15-29.
    Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the
    acceptance of new information technologies? Decision sciences, 30(2),
    361-391.
    Al-Gahtani, S. S., Hubona, G. S., & Wang, J. (2007). Information technology (IT) in
    Saudi Arabia: Culture and the acceptance and use of IT. Information &
    Management, 44(8), 681-691.
    Al-Mamary, Y. H., Shamsuddin, A., & Nor Aziati, A. H. (2014). The relationship
    between system quality, information quality, and organizational performance.
    International Journal of Knowledge and Research in Management &
    E-Commerce, 4(3), 07-10.
    ARM, & NORTHSTAR. (2017). AI TODAY, AI TOMORROW. Retrieved from
    https://pages.arm.com/rs/312-SAX-488/images/arm-ai-survey-report.pdf
    Bar-Cohen, Y., & Hanson, D. (2009). The coming robot revolution: Expectations and
    fears about emerging intelligent, humanlike machines: Springer Science &
    Business Media.
    Barbeite, F. G., & Weiss, E. M. (2004). Computer self-efficacy and anxiety scales for
    an Internet sample: testing measurement equivalence of existing measures and
    development of new scales. Computers in Human Behavior, 20(1), 1-15.
    Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in
    social psychological research: Conceptual, strategic, and statistical
    considerations. Journal of personality and social psychology, 51(6), 1173.
    Berger, I. E., Ratchford, B. T., & Haines Jr, G. H. (1994). Subjective product
    knowledge as a moderator of the relationship between attitudes and purchase
    intentions for a durable product. Journal of Economic Psychology, 15(2),
    301-314.
    Bock, G.-W., Zmud, R. W., Kim, Y.-G., & Lee, J.-N. (2005). Behavioral intention
    formation in knowledge sharing: Examining the roles of extrinsic motivators,
    social-psychological factors, and organizational climate. Mis Quarterly, 29(1),
    87-111.
    Brucks, M. (1985). The effects of product class knowledge on information search
    behavior. Journal of consumer research, 1-16.
    Casaló, L. V., Flavián, C., & Guinalíu, M. (2010). Determinants of the intention to
    participate in firm-hosted online travel communities and effects on consumer
    behavioral intentions. Tourism management, 31(6), 898-911.
    Cenfetelli, R. T., & Bassellier, G. (2009). Interpretation of formative measurement in
    information systems research. Mis Quarterly, 689-707.
    Cheah, J.-H., Sarstedt, M., Ringle, C. M., Ramayah, T., & Ting, H. (2018).
    Convergent validity assessment of formatively measured constructs in
    PLS-SEM: on using single-item versus multi-item measures in redundancy
    analyses. International Journal of Contemporary Hospitality Management,
    30(11), 3192-3210.
    Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a
    measure and initial test. Mis Quarterly, 189-211.
    Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests.
    psychometrika, 16(3), 297-334.
    Davenport, T. H. (2018). Artificial Intelligence for the Real World. Retrieved from
    https://hbr.org/2018/01/artificial-intelligence-for-the-real-world
    Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance
    of information technology. Mis Quarterly, 319-340.
    de Jager, P. (1994). Communicating in times of change. Journal of Systems
    Management, 45(6), 28.
    Dodd, T. H., Laverie, D. A., Wilcox, J. F., & Duhan, D. F. (2005). Differential effects
    of experience, subjective knowledge, and objective knowledge on sources of
    information used in consumer wine purchasing. Journal of Hospitality &
    Tourism Research, 29(1), 3-19.
    Eden, D., & Kinnar, J. (1991). Modeling Galatea: Boosting self-efficacy to increase
    volunteering. Journal of Applied psychology, 76(6), 770.
    Flynn, L. R., & Goldsmith, R. E. (1999). A short, reliable measure of subjective
    knowledge. Journal of business research, 46(1), 57-66.
    Gardner, R. M., & Lundsgaarde, H. P. (1994). Evaluation of user acceptance of a
    clinical expert system. Journal of the American Medical Informatics
    Association, 1(6), 428-438.
    Gefen, D., & Straub, D. (2005). A practical guide to factorial validity using
    PLS-Graph: Tutorial and annotated example. Communications of the
    Association for Information systems, 16(1), 5.
    Ginzberg, M. J. (1975). Implementation as a process of change: A framework and
    empirical study.
    Godoe, P., & Johansen, T. (2012). Understanding adoption of new technologies:
    Technology readiness and technology acceptance as an integrated concept.
    Journal of European Psychology Students, 3(1).
    Gorla, N., Somers, T. M., & Wong, B. (2010). Organizational impact of system quality,
    information quality, and service quality. The Journal of Strategic Information
    Systems, 19(3), 207-228.
    Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror,
    mirror on the wall: a comparative evaluation of composite-based structural
    equation modeling methods. Journal of the Academy of Marketing Science,
    45(5), 616-632.
    Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet.
    Journal of Marketing theory and Practice, 19(2), 139-152.
    Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial
    least squares structural equation modeling (PLS-SEM): Sage publications.
    Helm, S., Eggert, A., & Garnefeld, I. (2010). Modeling the impact of corporate
    reputation on customer satisfaction and loyalty using partial least squares
    Handbook of partial least squares (pp. 515-534): Springer.
    Hill, J., Ford, W. R., & Farreras, I. G. (2015). Real conversations with artificial
    intelligence: A comparison between human–human online conversations and
    human–chatbot conversations. Computers in Human Behavior, 49, 245-250.
    Hirschheim, R., & Newman, M. (1988). Information systems and user resistance:
    theory and practice. The Computer Journal, 31(5), 398-408.
    Holden, H., & Rada, R. (2011). Understanding the influence of perceived usability
    and technology self-efficacy on teachers’ technology acceptance. Journal of
    Research on Technology in Education, 43(4), 343-367.
    Holmbeck, G. N. (2002). Post-hoc probing of significant moderational and
    mediational effects in studies of pediatric populations. Journal of pediatric
    psychology, 27(1), 87-96.
    Honeycutt Jr, E. D., Thelen, T., Thelen, S. T., & Hodge, S. K. (2005). Impediments to
    sales force automation. Industrial Marketing Management, 34(4), 313-322.
    Hussain, D., & Hussain, K. M. (1984). Information resource management: Richard d
    Irwin.
    Janson, M. A., Woo, C. C., & Smith, L. D. (1993). Information systems development
    and communicative action theory. Information & Management, 25(2), 59-72.
    Jiang, J. J., Muhanna, W. A., & Klein, G. (2000). User resistance and strategies for
    promoting acceptance across system types. Information & Management, 37(1),
    25-36.
    Judge, T. A., & Bono, J. E. (2001). Relationship of core self-evaluations
    traits—self-esteem, generalized self-efficacy, locus of control, and emotional
    stability—with job satisfaction and job performance: A meta-analysis. Journal
    of applied Psychology, 86(1), 80.
    Keen, P. G. W. (1980). Information systems and organizational change.
    Kim, H.-W., & Kankanhalli, A. (2009). Investigating user resistance to information
    systems implementation: A status quo bias perspective. Mis Quarterly,
    567-582.
    Klaus, T., & Blanton, J. E. (2010). User resistance determinants and the psychological
    contract in enterprise system implementations. European Journal of
    Information Systems, 19(6), 625-636.
    Kunnathur, A. S., Ahmed, M. U., & Charles, R. J. (1996). Expert systems adoption.
    An analytical study of managerial issues and concerns. Information &
    Management, 30(1), 15-25.
    Lacobucci, D., Saldanha, N., & Deng, X. (2007). A meditation on mediation: evidence
    that structural e-quations models perform better than regressions. Journal of
    consumer Psychology, 17(2), 140-154.
    Lam, S. Y., Chiang, J., & Parasuraman, A. (2008). The effects of the dimensions of
    technology readiness on technology acceptance: An empirical analysis.
    Journal of interactive marketing, 22(4), 19-39.
    Lapointe, L., & Rivard, S. (2005). A multilevel model of resistance to information
    technology implementation. Mis Quarterly, 29(3).
    Lee, M.-H., & Tsai, C.-C. (2010). Exploring teachers’ perceived self efficacy and
    technological pedagogical content knowledge with respect to educational use
    of the World Wide Web. Instructional Science, 38(1), 1-21.
    Liker, J. K., & Sindi, A. A. (1997). User acceptance of expert systems: a test of the
    theory of reasoned action. Journal of Engineering and Technology
    management, 14(2), 147-173.
    Lin, C. H., Shih, H. Y., & Sher, P. J. (2007). Integrating technology readiness into
    technology acceptance: The TRAM model. Psychology & Marketing, 24(7),
    641-657.
    Lin, J.-S. C., & Chang, H.-C. (2011). The role of technology readiness in self-service
    technology acceptance. Managing Service Quality: An International Journal,
    21(4), 424-444.
    Lin, J.-S. C., & Hsieh, P.-L. (2006). The role of technology readiness in customers'
    perception and adoption of self-service technologies. International Journal of
    Service Industry Management, 17(5), 497-517.
    Lin, J.-S. C., & Hsieh, P.-L. (2007). The influence of technology readiness on
    satisfaction and behavioral intentions toward self-service technologies.
    Computers in Human Behavior, 23(3), 1597-1615.
    Lowry, P. B., & Gaskin, J. (2014). Partial least squares (PLS) structural equation
    modeling (SEM) for building and testing behavioral causal theory: When to
    choose it and how to use it. IEEE transactions on professional communication,
    57(2), 123-146.
    Madni, A. (1988). The role of human factors in expert systems design and acceptance.
    Human Factors, 30(4), 395-414.
    Markus, M. L. (1983). Power, politics, and MIS implementation. Communications of
    the ACM, 26(6), 430-444.
    Martinho-Truswell, E. (2018). 3 Questions About AI That Nontechnical Employees
    Should Be Able to Answer. Retrieved from
    https://hbr.org/2018/08/3-questions-about-ai-that-nontechnical-employees-sho
    uld-be-able-to-answer
    Mathieson, K. (1991). Predicting user intentions: comparing the technology
    acceptance model with the theory of planned behavior. Information systems
    research, 2(3), 173-191.
    Mckinsey. (2017). A FUTURE THAT WORKS: AUTOMATION, EMPLYMENT,
    AND PRODUCTIVITY. Retrieved from
    https://www.mckinsey.com/~/media/mckinsey/featured%20insights/Digital%2
    0Disruption/Harnessing%20automation%20for%20a%20future%20that%20w
    orks/MGI-A-future-that-works-Executive-summary.ashx
    Megan Beck Thomas H. Davenport, a. B. L. (2019). The AI roles some companies
    forget to fill. Retrieved from
    https://hbr.org/2019/03/the-ai-roles-some-companies-forget-to-fill
    Multon, K. D., Brown, S. D., & Lent, R. W. (1991). Relation of self-efficacy beliefs to
    academic outcomes: A meta-analytic investigation. Journal of counseling
    psychology, 38(1), 30.
    Murphy, C. A., Coover, D., & Owen, S. V. (1989). Development and validation of the
    computer self-efficacy scale. Educational and Psychological measurement,
    49(4), 893-899.
    Nelson, R. R., Todd, P. A., & Wixom, B. H. (2005). Antecedents of information and
    system quality: an empirical examination within the context of data
    warehousing. Journal of management information systems, 21(4), 199-235.
    Oreg, S. (2006). Personality, context, and resistance to organizational change.
    European journal of work and organizational psychology, 15(1), 73-101.
    Parasuraman, A. (2000). Technology Readiness Index (TRI) a multiple-item scale to
    measure readiness to embrace new technologies. Journal of service research,
    2(4), 307-320.
    Park, D.-H., & Lee, J. (2008). eWOM overload and its effect on consumer behavioral
    intention depending on consumer involvement. Electronic Commerce
    Research and Applications, 7(4), 386-398.
    Pieniak, Z., Aertsens, J., & Verbeke, W. (2010). Subjective and objective knowledge
    as determinants of organic vegetables consumption. Food quality and
    preference, 21(6), 581-588.
    Raju, P. S., Lonial, S. C., & Mangold, W. G. (1995). Differential effects of subjective
    knowledge, objective knowledge, and usage experience on decision making:
    An exploratory investigation. Journal of consumer Psychology, 4(2), 153-180.
    Rigotti, T., Schyns, B., & Mohr, G. (2008). A short version of the occupational
    self-efficacy scale: Structural and construct validity across five countries.
    Journal of Career Assessment, 16(2), 238-255.
    Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0 (M3) Beta: Hamburg
    Germany.
    Sadri, G., & Robertson, I. T. (1993). Self‐ efficacy and work‐ related behaviour: a
    review and meta‐ analysis. Applied Psychology, 42(2), 139-152.
    Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair Jr, J. F. (2014). Partial least
    squares structural equation modeling (PLS-SEM): A useful tool for family
    business researchers. Journal of Family Business Strategy, 5(1), 105-115.
    Scholz, U., Doña, B. G., Sud, S., & Schwarzer, R. (2002). Is general self-efficacy a
    universal construct? Psychometric findings from 25 countries. European
    journal of psychological assessment, 18(3), 242.
    Schyns, B., Torka, N., & Gössling, T. (2007). Turnover intention and preparedness for
    change: Exploring leader-member exchange and occupational self-efficacy as
    antecedents of two employability predictors. Career Development
    International, 12(7), 660-679.
    Schyns, B., & Von Collani, G. (2002). A new occupational self-efficacy scale and its
    relation to personality constructs and organizational variables. European
    journal of work and organizational psychology, 11(2), 219-241.
    Selnes, F. (1986). Subjective and objective measures of product knowledge contrasted.
    ACR North American Advances.
    Seong, J. B. a. J. (2018). How competition drives AI’s rapid adoption. Retrieved
    from https://hbr.org/2018/10/how-competition-is-driving-ais-rapid-adoption
    Sexton, T. L., & Tuckman, B. W. (1991). Self-beliefs and behavior: The role of
    self-efficacy and outcome expectation over time. Personality and Individual
    Differences, 12(7), 725-736.
    Shin, B., & Kim, G. (2011). Investigating the reliability of second-order formative
    measurement in information systems research. European Journal of
    Information Systems, 20(5), 608-623.
    Shore, L. M., & Martin, H. J. (1989). Job satisfaction and organizational commitment
    in relation to work performance and turnover intentions. Human relations,
    42(7), 625-638.
    Smith, H. A., & McKeen, J. D. (1992). Computerization and management: A study of
    conflict and change. Information & Management, 22(1), 53-64.
    Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural
    equation models. Sociological methodology, 13, 290-312.
    Soehnlein, K. M. (1998). The relationship of job insecurity, career planning,
    self-efficacy, goal orientation and the self-development of survivors of a
    downsizing corporation.
    Speier, C., & Venkatesh, V. (2002). The hidden minefields in the adoption of sales
    force automation technologies. Journal of Marketing, 66(3), 98-111.
    Srinivasan, V. (2016). Context, Language, and Reasoning in AI: Three Key
    Challenges. Retrieved from
    https://www.technologyreview.com/s/602658/context-language-and-reasoning
    -in-ai-three-key-challenges/
    Stajkovic, A. D., & Luthans, F. (1998). Self-efficacy and work-related performance: A
    meta-analysis. Psychological bulletin, 124(2), 240.
    Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model
    development and test. Computers & Education, 57(4), 2432-2440.
    Tett, R. P., & Meyer, J. P. (1993). Job satisfaction, organizational commitment,
    turnover intention, and turnover: path analyses based on meta‐ analytic
    findings. Personnel psychology, 46(2), 259-293.
    Timmermans, G. H. K. (2018). New supply chain jobs are emerging as AI takes hold.
    Retrieved from
    https://hbr.org/2018/08/new-supply-chain-jobs-are-emerging-as-ai-takes-hold
    Tracey, J. B., Hinkin, T. R., Tannenbaum, S., & Mathieu, J. E. (2001). The influence
    of individual characteristics and the work environment on varying levels of
    training outcomes. Human resource development quarterly, 12(1), 5-23.
    Van der Heijden, H. (2004). User acceptance of hedonic information systems. Mis
    Quarterly, 695-704.
    Vermeir, I., & Verbeke, W. (2006). Sustainable food consumption: Exploring the
    consumer “attitude–behavioral intention” gap. Journal of Agricultural and
    Environmental ethics, 19(2), 169-194.
    Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’
    technology readiness on technology acceptance. Information & Management,
    44(2), 206-215.
    Walter, Z., & Lopez, M. S. (2008). Physician acceptance of information technologies:
    Role of perceived threat to professional autonomy. Decision support systems,
    46(1), 206-215.
    Yang, H.-d., & Yoo, Y. (2004). It's all about attitude: revisiting the technology
    acceptance model. Decision support systems, 38(1), 19-31.
    Ye, L. R., & Johnson, P. E. (1995). The impact of explanation facilities on user
    acceptance of expert systems advice. Mis Quarterly, 157-172.
    Zhang, J., Pantula, S. G., & Boos, D. D. (1991). Robust methods for testing the
    pattern of a single covariance matrix. Biometrika, 78(4), 787-795.

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