English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113656/144643 (79%)
Visitors : 51730017      Online Users : 542
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/137176


    Title: 人力招募網站行動裝置應用程式之使用意願影響因素研究——以104人力銀行為例
    A Study on the Influencing Factors of Use Intention for Mobile Application of Job Search Website — Taking 104 Job Search Service as an Example
    Authors: 邱昱綺
    Chiu, Yu-Chi
    Contributors: 張愛華
    韓志翔

    Chang, Ai-Hwa
    Han, Tzu-Shian

    邱昱綺
    Chiu, Yu-Chi
    Keywords: 整合型科技接受模型
    線上人力招募
    人力招募應用程式
    網站使用經驗
    Theory of Acceptance and Use of Technology
    E-recruitment
    Job Search Application
    Website User Experience
    Date: 2021
    Issue Date: 2021-09-02 18:19:45 (UTC+8)
    Abstract:   行動裝置的移動性與隨時維持登入的特性有利於培養黏著度更高的使用者,隨著行動裝置普及,人力招募平台紛紛推出行動裝置服務,目前網站對於求職者來說仍是不可或缺的求職管道,求職者經常跨載具使用線上人力招募服務,因此本研究希望可以了解影響求職者採用應用程式的因素,並且分析網站的使用經驗對於應用程式行為意圖發展的影響。本研究以整合型科技接受模型(UTAUT)為基礎(Venkatesh, Morris, Davis, G. B. & Davis, F. D., 2003),考量績效預期、努力預期與社群影響,並加入知覺風險與即時性兩項因素,針對有求職需求且使用過104人力銀行網站與應用程式的使用者發放問卷,並針對求職者對於目前「104工作快找」應用程式的使用心得進行調查。
      研究結果顯示績效預期、努力預期、社群影響、知覺風險與即時性皆為影響應用程式行為意圖的重要構念,其中以績效預期與社群影響的影響最大。而越長期使用網站,績效預期以及即時性對於行為意圖的影響會降低,努力預期以及知覺風險的影響力則會提高。網站使用頻率越高,績效預期的影響會降低,社群影響的影響效果會提高。對於網站的整體評價越高,會提高績效預期對使用意圖的影響,即時性影響程度則會降低。此外,介面是否直覺使用是求職者滿意的主要因素,而職缺豐富為採用應用程式的重要原因,然而卻也認為職缺品質參差不齊有待改善。對於大部分求職者來說,應用程式目前仍與網站搭配使用,應用程式提供即時性與移動性,有助於提高使用者的黏著度,然而大部分求職者仍偏好使用網站投遞職缺與編輯履歷,因此網站與應用程式發展不應偏廢,應善用不同載具獨特的優勢,使兩者相輔相承,面對行動裝置趨勢則需要加強應用程式的易用性,使介面更加符合直覺操作。最後,職缺數量與品質是求職者關注的核心,應盡可能在豐富平台上職缺的同時把關職缺品質,如此才能使企業擁有長期競爭力。
    The mobility of mobile devices and the ability to keep logged in any time are conducive to cultivating users with higher stickiness. With the growing popularity of mobile usage, e-recruiting platform companies have launched mobile device services. Job seekers often use online human recruitment services on both PC and mobile phone. The websites are still an indispensable job seeking channel for users. In view of this, this research develops a model based on Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh, Morris, Davis, G.B. & Davis, F.D., 2003) and aims to explore the factors that influence the behavioral intentions of using apps, and to examine the impact of website usage experience. The constructs include Performance Expectancy, Effort Expectancy and Social Influence, Perceived Risks and Mobility. Using an online survey of users with both 104 Job Search website and application experiences.
    The research results show that Performance Expectancy, Effort Expectancy, Social Influence, Perceived Risk, and Immediacy are all important constructs that affect application behavioral intentions. Among them, Performance Expectancy and Social Influence have the greatest impact. The research also finds out the longer you use the website, the lower the impacts of Performance Expectancy and Immediacy are, and the greater the impacts of Effort Expectancy and Perceived Risk are. The higher the frequency of website usage, the lower the impact of Performance Expectancy is, and the greater the impact of Social Influence is. The higher the overall evaluation of the website, the higher the impact of Performance Expectancy is, and the lower the impact of Immediacy is.
    This research also collected job seekers’ experience of using the 104 Job Search app, and found that an intuitive interface is the main factor for job seekers’ satisfaction. The richness of job vacancies is an important reason for adopting the app, but the quality of job openings is still a concern and needs to be improved.
    Most job seekers are both application and website users. While application provides mobility, which helps to increase user stickiness, most job seekers still prefer to use the website to apply for jobs and edit resumes. Therefore, application development should be as important as website development and the advantages of different devices should be utilized to make greatest synergy. With the global mobile trends, it is necessary for e-recruiting companies to enhance the ease of use of the application, make it more compatible and intuitive operational. Finally, because the number and quality of job vacancies are the core concerns of job seekers, the long-term competitiveness of e-recruiting platform companies depends on how great their abilities are to provide opportunities as many as possible but ensure the quality at the same time.
    Reference: 一、 中文文獻
    余鑑、于俊傑、鄭宇珊與張文卿(2012)。有關台灣旅遊業在行動學習的使用意願之研究。中華管理評論,15(3),1-29。
    孫思源、羅月秀、趙珮如與吳章瑤(2008)。人力資源招募網站使用意向影響因素之探討。人力資源管理學報,8(3),1-23。
    陳寬裕(2018)。 結構方程模型分析實務: SPSS 與 SmartPLS 的運用。 五南圖書出版股份有限公司。
    粟四維與莊友豪(2009)。Wiki使用者與使用行為之研究。電子商務學報,11(1),185-212。
    劉仲矩與潘彥蓁(2019)。人力資源網站美學建構衡量重要性分析之研究。科技與人力教育季刊,5(3),53-69。
    郭俊桔(2019)。電子雜誌 Apps 上閱讀介面設計之探討。圖資與檔案學刊,94, 92-127。

    二、 英文文獻
    Adipat, B., Zhang, D., & Zhou, L. (2011). The effects of tree-view based presentation adaptation on mobile web browsing. Mis Quarterly, 99-121.
    Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
    Alsop, R. (2008). The trophy kids grow up: How the millennial generation is shaking up the workplace. San Francisco, CA: Jossey-Bass.
    Barber, L. (2006). E-recruitment Developments. Brighton: Institute for Employment Studies.
    Bart, Y., Shankar, V., Sultan, F., & Urban, G. L. (2005). Are the drivers and role of online trust the same for all web sites and consumers? A large-scale exploratory empirical study. Journal of marketing, 69(4), 133-152.
    Bauer, R.A. (1960). Consumer Behavior as Risk Taking. In: Hancock, R.S., Ed., Dynamic Marketing for a Changing World, Proceedings of the 43rd. Conference of the American Marketing Association, 389-398.
    Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 351-370.
    Böhm, S. (2013). Behavior and expectations of mobile job seekers: an industry study focusing on job boards. Annual conference on Computers and people research, 105-110.
    Cappelli, P. (2001). Making the most of on-line recruiting. Harvard business review, 79(3), 139-148.
    Cheung, C. M., Xiao, B. S., & Liu, I. L. (2014). Do actions speak louder than voices? The signaling role of social information cues in influencing consumer purchase decisions. Decision support systems, 65, 50-58.
    Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers.
    Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
    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.
    Deloitte Touche Tohmatsu Limited (2017). Mobile continues its global reach into all aspects of consumers’ lives. Global mobile consumer trends, 2nd edition.
    Devaraj, S., Fan, M., & Kohli, R. (2002). Antecedents of B2C Channel Satisfaction and Preference: Validating e-Commerce Metrics. Information Systems Research, 13(3), 316-333.
    Devaraj, S., Fan, M., & Kohli, R. (2006). Examination of online channel preference: Using the structure-conduct-outcome framework. Decision Support Systems, 42(2), 1089-1103.
    Flavián, C., Guinalíu, M., & Gurrea, R. (2006). The role played by perceived usability, satisfaction and consumer trust on website loyalty. Information & management, 43(1), 1-14.
    Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics.
    Galletta, D. F., Henry, R. M., McCoy, S. & Polak, P. (2006). When the Wait Isn’t So Bad: The Interacting Effects of Website Delay, Familiarity, and Breadth. Information Systems Research 17 (1) 20-37.
    Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E. & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Prentice-Hall.
    Hee, O. C. (2014). Validity and Reliability of the Customer-Oriented Behaviour Scale in the Health Tourism Hospitals in Malaysia. International Journal of Caring Sciences, 7(3), 771-775.
    Hoehle H. & Venkatesh V. (2015). Mobile application usability: Conceptualization and instrument development. MIS Quarterly, Vol. 39 No.2, 435-472.
    Holbrook, M. B. (2000). The millennial consumer in the texts of our times: Experience and entertainment. Journal of Macromarketing, 20(2), 178-192.
    Holbrook, M. B., & Hirschman, E. C. (1982). The experiential aspects of consumption: Consumer fantasies, feelings, and fun. Journal of consumer research, 9(2), 132-140.
    Jarenpaa, S. L., Lang, K. R., Takeda, Y. & Tuunainen,V. K. (2003). Mobile commerce at crossroads. Communications of the ACM, 46(12), 41-44.
    Kuhn, P. & Mansour, H. (2011). Is internet job search still ineffective? Institute for the study of labor. IZA Discussion Paper, No. 5955.
    Kurniawan, S. (2008). Older people and mobile phones: A multi-method investigation. International Journal of Human-Computer Studies, 66(12), 889-901.
    Kutner, M. H.; Nachtsheim, C. J.; Neter, J. (2004). Applied Linear Regression Models (4th ed.). McGraw-Hill Irwin.
    Lee, M. K., & Turban, E. (2001). A trust model for consumer internet shopping. International Journal of electronic commerce, 6(1), 75-91.
    Ling, K. C., Chai, L. T., & Piew, T. H. (2010). The effects of shopping orientations, online trust and prior online purchase experience toward customers` online purchase intention. International business research, 3(3), 63.
    Li, X., Hess, T. J., McNab, A. L., & Yu, Y. (2009). Culture and acceptance of global web sites: a cross-country study of the effects of national cultural values on acceptance of a personal web portal. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 40(4), 49-74.
    Llorens, J.J. & Kellough, J.E. (2007). A revolution in public personnel administration: the growth of web-based recruitment and selection process in the federal service. Public Personnel Management, Vol. 36 No. 3, 207-221.
    Maurer, S. D. & Liu, Y. (2007). Developing effective e-recruiting websites: Insights for managers from marketers. Business Horizons, 50, 305-314.
    McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). The impact of initial consumer trust on intentions to transact with a web site: a trust building model. The journal of strategic information systems, 11(3-4), 297-323.
    Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research, 2(3), 192-222.
    Parry, E. & Wilson, H. (2009). Factors influencing the adoption of online recruitment. Personnel Review, Vol. 38 No.6, 655-673.
    Pine, B. J., Pine, J., & Gilmore, J. H. (1999). The experience economy: work is theatre & every business a stage. Harvard Business Press.
    Pousttchi, K., & Schurig, M. (2004). Assessment of today`s mobile banking applications from the view of customer requirements. In 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the (pp. 10-pp). IEEE.
    Priyadarshini, C., Sreejesh, S. and Anusree, M.R. (2017). Effect of information quality of employment website on attitude toward the website: A moderated mediation study. International Journal of Manpower, Vol. 38 No. 5, 729-745.
    Sabherwal, R., Jeyaraj, A., & Chowa, C. (2006). Information system success: Individual and organizational determinants. Management science, 52(12), 1849-1864.
    Shi, S., & Chow, W. S. (2015). Trust development and transfer in social commerce: prior experience as moderator. Industrial Management & Data Systems.
    Tan, F. B., Tung, L. L., & Xu, Y. (2009). A study of web-designers` criteria for effective business-to-consumer (B2C) websites using the repertory grid technique. Journal of Electronic Commerce Research, 10(3).
    Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS quarterly, 125-143.
    Venkatesh, V., & Goyal, S. (2010). Expectation disconfirmation and technology adoption: polynomial modeling and response surface analysis. MIS quarterly, 281-303.
    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.
    Westland, J. C. & Clark, T. H. L. (2000). Global Electronic Commerce: Theory and case studies. MIT Press, Cambridge, MA.
    Wold, S., Ruhe, A., Wold, H., & Dunn, Iii, W. J. (1984). The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM Journal on Scientific and Statistical Computing, 5(3), 735-743.
    Zhang, P., & Von Dran, G. M. (2000). Satisfiers and dissatisfiers: A two‐factor model for website design and evaluation. Journal of the American society for information science, 51(14), 1253-1268.

    三、 網站資源
    Alexa (2021, May 29). Competitive analysis, marketing mix and traffic. Retrieved from https://www.alexa.com/siteinfo/104.com.tw#section_traffic
    Blacker, A. & Lenahan, M. (2021). Worldwide & US Download Leaders 2020. Apptopia. Retrieved from https://blog.apptopia.com/worldwide-us-download-leaders-2020
    Deloitte Touche Tohmatsu Limited (2012). So many apps –So little to download. Retrieved from https://www.mondaq.com/uk/it-and-internet/192692/so-many-apps--so-little-to-download
    Forrester Research (2010). How Mature Is Your Mobile Strategy? Retrieved from https://www.forrester.com/report/How+Mature+Is+Your+Mobile+Strategy/-/E-RES57180
    Statista (2020). Number of smartphone users worldwide from 2016 to 2023. Retrieved from https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/
    Youens, R (2011). 7 Habits of Highly Effective Apps. GIGAOM, July 16. Retrieved from: https://gigaom.com/2011/07/16/7-habits-of-highly-effective-apps/
    一零四資訊科技股份有限公司(2021年3月31日)。一零四資訊科技109年度年報。取自https://corp.104.com.tw/archive/files/stock/109Annual%20Report.pdf
    T客邦(2011年9月23日)。誰在用手機找工作?104 公佈手機求職的統計數字,上網日期:2021年7月1日。取自https://www.techbang.com/posts/7077-phone-job-yourself-104-single-handedly-get-work-quickly-to-find
    陳君毅(2021年2月18日)。518人力銀行宣布改名「518熊班」,瞄準哪類人才打造第一徵才品牌。數位時代。取自https://www.bnext.com.tw/article/61394/new-581-bear-works
    Description: 碩士
    國立政治大學
    企業管理研究所(MBA學位學程)
    108363029
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108363029
    Data Type: thesis
    DOI: 10.6814/NCCU202101210
    Appears in Collections:[企業管理研究所(MBA學位學程)] 學位論文

    Files in This Item:

    File Description SizeFormat
    302901.pdf2036KbAdobe PDF20View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback