English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113148/144119 (79%)
Visitors : 50709236      Online Users : 282
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
    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/141560
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/141560


    Title: 探討圖片在語言具體性評論和雙邊評論對於評論有用性之影響
    Exploring image in language concreteness reviews and two-sided reviews on online review helpfulness
    Authors: 王婷
    Wang, Ting
    Contributors: 彭志宏
    Peng, Chih-Hung
    王婷
    Wang, Ting
    Keywords: 評論具體性
    雙邊評論
    圖片數量
    努力精確性框架
    可信度
    評論有用性
    電子商務
    Review concreteness
    Two-sidedness review
    Image amount
    Effort-accuracy framework
    Review helpfulness
    Credibility
    E-commerce
    Date: 2022
    Issue Date: 2022-09-02 14:48:46 (UTC+8)
    Abstract: 隨著電子商務的普及,評論平台風靡一時,評論的有用性被認為是衡量客 戶的重要指標。但是我們如何才能找出哪些評論內容可以使我們的客戶受益 呢?在這項研究中,我們選擇兩個不同的評論屬性作為本次的研究主軸,評論 具體性和雙邊評論。此外,圖像也成為評論平台研究的必要因素,圖像數量在 現今的平台中不斷增加,本次研究中選擇圖像數量作為我們的調節變數,因為 過去文獻很少被研究過。為了發展我們的假設,本研究使用努力準確度框架來 研究評論具體性和評論有用性之間的關係。另外,本研究使用可信度來研究雙 邊評論和評論有用性之間的關聯。本研究使用線上開放評論數據做為研究資料 的目標,時間橫跨 1998 年到 2018 年。實證結果表明,評論具體性對評論有用 性有顯著的負面影響;雙邊對評論有用性有顯著的正向影響。此外,本研究還 發現圖像數量減輕了對評論具體性和評論有用性的負面相關性;更令本研究驚 奇地發現是在不同產品類型上的圖像數量有不同的效果。
    A review system in an E-commerce website is important for customers to make their purchase decisions. Prior studies have examined the antecedents of review helpfulness (e.g., review length). Little is known the impact of review content on review helpfulness. Drawing on effort-accuracy framework and the relevant literature, we propose two critical factors of review content (i.e., language concreteness and two- sidedness) and examine their effects on review helpfulness. We further examine the moderating effect of image amount in a review. We analyze 15,538,094 reviews from Amazon.com across four products. We find that review concreteness is negatively related to review helpfulness, while review two-sidedness is positively related to review helpfulness. Furthermore, image amount mitigates the negative relationship between review concreteness and review helpfulness. The findings provide critical theoretical and practical implications.
    Reference: 李欣欣. (2012). 广告中的模糊语言及其翻译 天津大学].
    Agnihotri, A., & Bhattacharya, S. (2016). Online review helpfulness: Role of
    qualitative factors. Psychology & Marketing, 33(11), 1006-1017. Baek, H., Ahn, J., & Choi, Y. (2012). Helpfulness of online consumer reviews:
    Readers` objectives and review cues. International Journal of Electronic
    Commerce, 17(2), 99-126.
    Baek, H., Lee, S., Oh, S., & Ahn, J. (2015). Normative social influence and online
    review helpfulness: Polynomial modeling and response surface analysis.
    Journal of Electronic Commerce Research, 16(4), 290.
    Batra, R., & Stayman, D. M. (1990). The role of mood in advertising effectiveness.
    Journal of Consumer Research, 17(2), 203-214.
    Bavelas, J., Black, A., Chovil, N., & Mullett, J. (1990). (1990a). Equivocal
    communication. Newbury Park, CA: Sage.
    Berry, D. S., Pennebaker, J. W., Mueller, J. S., & Hiller, W. S. (1997). Linguistic bases
    of social perception. Personality and Social Psychology Bulletin, 23(5), 526-
    537.
    Bracken, C. C. (2006). Perceived source credibility of local television news: The
    impact of television form and presence. Journal of Broadcasting & Electronic
    Media, 50(4), 723-741.
    Bradac, J. J., Bowers, J. W., & Courtright, J. A. (1979). Three language variables in
    communication research: Intensity, immediacy, and diversity. Human
    Communication Research, 5(3), 257-269.
    Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40
    thousand generally known English word lemmas. Behavior research methods,
    46(3), 904-911.
    Cao, Q., Duan, W., & Gan, Q. (2011). Exploring determinants of voting for the
    “helpfulness” of online user reviews: A text mining approach. Decision
    Support Systems, 50(2), 511-521.
    Ceylan, G., Diehl, K., & Proserpio, D. (2021). Words Meet Photos: When and Why
    Visual Content Increases Review Helpfulness. Available at SSRN.
    Channell, J. (1994). ’Vague Language’,(Describing English Language Series) Oxford
    University Press. In: Oxford.
    Chatterjee, P. (2001). Online reviews: do consumers use them?
    Chen, M.-Y. (2016). Can two-sided messages increase the helpfulness of online
    reviews? Online Information Review.
    Chen, M.-Y., Teng, C.-I., & Chiou, K.-W. (2019). The helpfulness of online reviews:
    40
    Images in review content and the facial expressions of reviewers’ avatars.
    Online Information Review.
    Chen, Y., & Xie, J. (2008). Online consumer review: Word-of-mouth as a new
    element of marketing communication mix. Management science, 54(3), 477-
    491.
    Cheung, C. M.-Y., Sia, C.-L., & Kuan, K. K. (2012). Is this review believable? A
    study of factors affecting the credibility of online consumer reviews from an ELM perspective. Journal of the Association for Information Systems, 13(8), 2.
    Chua, A. Y., & Banerjee, S. (2015). Understanding review helpfulness as a function of reviewer reputation, review rating, and review depth. Journal of the Association for Information Science and Technology, 66(2), 354-362.
    Clow, K. E., James, K. E., Kranenburg, K. E., & Berry, C. T. (2008). An examination of the visual element used in generic message advertisements: A comparison of goods and services. Services Marketing Quarterly, 30(1), 69-84.
    Creyer, E. H., Bettman, J. R., & Payne, J. W. (1990). The impact of accuracy and effort feedback and goals on adaptive decision behavior. Journal of Behavioral Decision Making, 3(1), 1-16.
    Crowley, A. E., & Hoyer, W. D. (1994). An integrative framework for understanding two-sided persuasion. Journal of Consumer Research, 20(4), 561-574.
    Ding, M. A., Chen, S. S., Wang, X. S., & Bendle, N. (2021). Show Me You Or the Goods? Effect of Image Content on Review Helpfulness. ACR North American Advances.
    Eisend, M. (2006). Two-sided advertising: A meta-analysis. International Journal of Research in Marketing, 23(2), 187-198.
    Eisend, M. (2007). Understanding two‐sided persuasion: An empirical assessment of theoretical approaches. Psychology & Marketing, 24(7), 615-640.
    Fan, L., & Zhang, X. (2020). The combination signaling effect of text and image on mobile phone review helpfulness-the moderating effect of signaling environment. IEEE Access, 8, 122736-122746.
    Fan, W. (2021). What makes consumer perception of online review helpfulness: synthesizing the past to guide future research. Proceedings of the 54th Hawaii International Conference on System Sciences,
    Fang, B., Ye, Q., Kucukusta, D., & Law, R. (2016). Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics. Tourism Management, 52, 498-506.
    Filieri, R. (2016). What makes an online consumer review trustworthy? Annals of Tourism Research, 58, 46-64.
    41

    Filieri, R., Hofacker, C. F., & Alguezaui, S. (2018). What makes information in online consumer reviews diagnostic over time? The role of review relevancy, factuality, currency, source credibility and ranking score. Computers in Human Behavior, 80, 122-131.
    Filieri, R., McLeay, F., Tsui, B., & Lin, Z. (2018). Consumer perceptions of information helpfulness and determinants of purchase intention in online consumer reviews of services. Information & Management, 55(8), 956-970.
    Filieri, R., Raguseo, E., & Vitari, C. (2018). When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type. Computers in Human Behavior, 88, 134-142.
    Fogg, B. J., & Tseng, H. (1999). The elements of computer credibility. Proceedings of the SIGCHI conference on Human Factors in Computing Systems,
    Fouladfar, F., Dehkordi, M. N., & Basiri, M. E. (2020). Predicting the helpfulness score of product reviews using an evidential score fusion method. IEEE Access, 8, 82662-82687.
    Gao, B., Hu, N., & Bose, I. (2017). Follow the herd or be myself? An analysis of consistency in behavior of reviewers and helpfulness of their reviews. Decision Support Systems, 95, 1-11.
    Gerdt, S.-O., Wagner, E., & Schewe, G. (2019). The relationship between sustainability and customer satisfaction in hospitality: An explorative investigation using eWOM as a data source. Tourism Management, 74, 155- 172.
    Ghose, A., & Ipeirotis, P. G. (2010). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE transactions on knowledge and data engineering, 23(10), 1498-1512.
    Gottschalk, S. A., & Mafael, A. (2017). Cutting through the online review jungle— investigating selective eWOM processing. Journal of Interactive Marketing, 37, 89-104.
    Grether, D. M., & Plott, C. R. (1979). Economic theory of choice and the preference reversal phenomenon. The American Economic Review, 69(4), 623-638.
    Guo, B., & Zhou, S. (2017). What makes population perception of review helpfulness: an information processing perspective. Electronic Commerce Research, 17(4), 585-608.
    Hansen, J., & Wänke, M. (2010). Truth from language and truth from fit: The impact of linguistic concreteness and level of construal on subjective truth. Personality and Social Psychology Bulletin, 36(11), 1576-1588.
    Haugtvedt, C. P., & Petty, R. E. (1992). Personality and persuasion: Need for cognition moderates the persistence and resistance of attitude changes. Journal
    42

    of personality and social psychology, 63(2), 308.
    Hochmeister, M., Gretzel, U., & Werthner, H. (2013). Destination expertise in online
    travel communities. In Information and communication technologies in
    tourism 2013 (pp. 218-229). Springer.
    Hong, H., Xu, D., Wang, G. A., & Fan, W. (2017). Understanding the determinants of
    online review helpfulness: A meta-analytic investigation. Decision Support
    Systems, 102, 1-11.
    Hu, Y.-H., & Chen, K. (2016). Predicting hotel review helpfulness: The impact of
    review visibility, and interaction between hotel stars and review ratings.
    International Journal of Information Management, 36(6), 929-944. Huang, A. H., Chen, K., Yen, D. C., & Tran, T. P. (2015). A study of factors that
    contribute to online review helpfulness. Computers in Human Behavior, 48,
    17-27.
    Hunt, J. M., & Smith, M. F. (1987). The persuasive impact of two-sided selling
    appeals for an unknown brand name. Journal of the Academy of Marketing
    Science, 15(1), 11-18.
    Jiang, J., Gretzel, U., & Law, R. (2014). Influence of star rating and ownership
    structure on brand image of mainland China hotels. Journal of China Tourism
    Research, 10(1), 69-94.
    Jindal, N., & Liu, B. (2006a). Identifying comparative sentences in text documents.
    Proceedings of the 29th annual international ACM SIGIR conference on
    Research and development in information retrieval,
    Jindal, N., & Liu, B. (2006b). Mining comparative sentences and relations. Aaai, Johnson, E. J., & Payne, J. W. (1985). Effort and accuracy in choice. Management
    science, 31(4), 395-414.
    Kahneman, D. (1973). Attention and effort (Vol. 1063). Citeseer.
    Kalakota, R., & Whinston, A. B. (1997). Electronic commerce: a manager`s guide.
    Addison-Wesley Professional.
    Kang, Y., & Zhou, L. (2019). Helpfulness assessment of online reviews: The role of
    semantic hierarchy of product features. ACM Transactions on Management
    Information Systems (TMIS), 10(3), 1-18.
    Karimi, S., & Wang, F. (2017). Online review helpfulness: Impact of reviewer profile
    image. Decision Support Systems, 96, 39-48.
    Kim, M., & Lennon, S. (2008). The effects of visual and verbal information on
    attitudes and purchase intentions in internet shopping. Psychology &
    Marketing, 25(2), 146-178.
    Korfiatis, N., García-Bariocanal, E., & Sánchez-Alonso, S. (2012). Evaluating content
    quality and helpfulness of online product reviews: The interplay of review
    43

    helpfulness vs. review content. Electronic Commerce Research and
    Applications, 11(3), 205-217.
    Korfiatis, N., Rodriguez, D., & Sicilia, M.-A. (2008). The impact of readability on the
    usefulness of online product reviews: a case study on an online bookstore.
    World Summit on Knowledge Society,
    Kwok, L., & Xie, K. L. (2016). Factors contributing to the helpfulness of online hotel
    reviews: does manager response play a role? International Journal of
    Contemporary Hospitality Management.
    Lee, H.-H. (2012). Attributes of online review systems: An environmental design
    perspective. Journal of Global Fashion Marketing, 3(4), 158-171.
    Lee, M., Kwon, W., & Back, K.-J. (2021). Artificial intelligence for hospitality big
    data analytics: developing a prediction model of restaurant review helpfulness for customer decision-making. International Journal of Contemporary Hospitality Management.
    Lee, M., Rodgers, S., & Kim, M. (2009). Effects of valence and extremity of eWOM on attitude toward the brand and website. Journal of Current Issues & Research in Advertising, 31(2), 1-11.
    Lee, S., & Choeh, J. Y. (2018). The interactive impact of online word-of-mouth and review helpfulness on box office revenue. Management Decision.
    Lee, S., Lee, S., & Baek, H. (2021). Does the dispersion of online review ratings affect review helpfulness? Computers in Human Behavior, 117, 106670.
    Li, M., Huang, L., Tan, C.-H., & Wei, K.-K. (2013). Helpfulness of online product reviews as seen by consumers: Source and content features. International Journal of Electronic Commerce, 17(4), 101-136.
    Li, M.-X., Huang, L., Tan, C.-H., & Wei, K.-K. (2011). Assessing the helpfulness of online product review: a progressive experimental approach.
    Li, S., Zhang, Y., Yu, Z., & Liu, F. (2021). Economical user-generated content (UGC) marketing for online stores based on a fine-grained joint model of the consumer purchase decision process. Electronic Commerce Research, 21(4), 1083-1112.
    Li, S.-T., Pham, T.-T., & Chuang, H.-C. (2019). Do reviewers’ words affect predicting their helpfulness ratings? Locating helpful reviewers by linguistics styles. Information & Management, 56(1), 28-38.
    Li, Y., & Xie, Y. (2020). Is a picture worth a thousand words? An empirical study of image content and social media engagement. Journal of Marketing Research, 57(1), 1-19.
    Lin, T. M., Lu, K. Y., & Wu, J. J. (2012). The effects of visual information in eWOM communication. Journal of research in interactive marketing.
    44

    Liu, J., Cao, Y., Lin, C.-Y., Huang, Y., & Zhou, M. (2007). Low-quality product review detection in opinion summarization. Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL),
    Liu, Y., & Hu, H.-f. (2021). Online review helpfulness: the moderating effects of review comprehensiveness. International Journal of Contemporary Hospitality Management.
    Lutz, B., Pröllochs, N., & Neumann, D. (2018). Understanding the role of two-sided argumentation in online consumer reviews: A language-based perspective. arXiv preprint arXiv:1810.10942.
    Ma, Y., Xiang, Z., Du, Q., & Fan, W. (2018). Effects of user-provided photos on hotel review helpfulness: An analytical approach with deep leaning. International Journal of Hospitality Management, 71, 120-131.
    Maity, A. K., & Paul, E. (2022). Jeffreys Prior for Negative Binomial and Zero Inflated Negative Binomial Distributions. Sankhya A, 1-15.
    Mayweg-Paus, E., & Jucks, R. (2018). Conflicting evidence or conflicting opinions? Two-sided expert discussions contribute to experts’ trustworthiness. Journal of Language and Social Psychology, 37(2), 203-223.
    Mayzlin, D., Dover, Y., & Chevalier, J. (2014). Promotional reviews: An empirical investigation of online review manipulation. American Economic Review, 104(8), 2421-2455.
    McFadden, D., Machina, M. J., & Baron, J. (1999). Rationality for economists? In Elicitation of preferences (pp. 73-110). Springer.
    McKenzie, P. J., Burkell, J., Wong, L., Whippey, C., Trosow, S. E., & McNally, M. B. (2012). User-generated online content 1: Overview, current state and context. First Monday.
    Mudambi, S. M., & Schuff, D. (2010). Research note: What makes a helpful online review? A study of customer reviews on Amazon. com. MIS quarterly, 185- 200.
    Nakayama, M., Sutcliffe, N., & Wan, Y. (2010). Has the web transformed experience goods into search goods? Electronic Markets, 20(3), 251-262.
    Nowak, K. L., & McGloin, R. (2014). The influence of peer reviews on source credibility and purchase intention. Societies, 4(4), 689-705.
    Osterbrink, L., Alpar, P., & Seher, A. (2020). Influence of Images in Online Reviews for Search Goods on Helpfulness. Review of Marketing Science, 18(1), 43-73.
    Packard, G., & Berger, J. (2021). How concrete language shapes customer satisfaction. Journal of Consumer Research, 47(5), 787-806.
    Paivio, A. (1969). Mental imagery in associative learning and memory. Psychological 45

    review, 76(3), 241.
    Pan, Y., & Zhang, J. Q. (2011). Born unequal: a study of the helpfulness of user-
    generated product reviews. Journal of retailing, 87(4), 598-612.
    Park, J., Yi, Y., & Kang, D. (2019). The effects of one-sided vs. two-sided review
    valence on electronic word of mouth (e-WOM): the moderating role of
    sponsorship presence. Asia Marketing Journal, 21(2), 1-19.
    Payne, J. W., Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive
    decision maker. Cambridge university press.
    Peng, C.-H., Yin, D., & Zhang, H. (2020). More than words in medical question-and-
    answer sites: A content-context congruence perspective. Information Systems
    Research, 31(3), 913-928.
    Pennebaker, J. W., & King, L. A. (1999). Linguistic styles: language use as an
    individual difference. Journal of personality and social psychology, 77(6),
    1296.
    Punj, G. (2011). Effect of consumer beliefs on online purchase behavior: The
    influence of demographic characteristics and consumption values. Journal of
    Interactive Marketing, 25(3), 134-144.
    Qazi, A., Raj, R. G., Tahir, M., Cambria, E., & Syed, K. B. S. (2014). Enhancing
    business intelligence by means of suggestive reviews. The Scientific World
    Journal, 2014.
    Qazi, A., Syed, K. B. S., Raj, R. G., Cambria, E., Tahir, M., & Alghazzawi, D. (2016).
    A concept-level approach to the analysis of online review helpfulness.
    Computers in Human Behavior, 58, 75-81.
    Ransbotham, S., Lurie, N. H., & Liu, H. (2019). Creation and consumption of mobile
    word of mouth: how are mobile reviews different? Marketing Science, 38(5),
    773-792.
    Ruiz-Mafe, C., Bigné-Alcañiz, E., & Currás-Pérez, R. (2020). The effect of emotions,
    eWOM quality and online review sequence on consumer intention to follow advice obtained from digital services. Journal of Service Management, 31(3), 465-487.
    Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30-40.
    Schlosser, A. E. (2011). Can including pros and cons increase the helpfulness and persuasiveness of online reviews? The interactive effects of ratings and arguments. Journal of Consumer Psychology, 21(3), 226-239.
    Semin, G. R., & Fiedler, K. (1988). The cognitive functions of linguistic categories in describing persons: Social cognition and language. Journal of personality and
    46

    social psychology, 54(4), 558.
    Simon, H. A. (1955). A behavioral model of rational choice. The quarterly journal of
    economics, 69(1), 99-118.
    Snead, K. C., Magal, S. R., Christensen, L. F., & Ndede-Amadi, A. A. (2015).
    Attribution theory: a theoretical framework for understanding information
    systems success. Systemic Practice and Action Research, 28(3), 273-288. Srivastava, V., & Kalro, A. D. (2019). Enhancing the helpfulness of online consumer reviews: the role of latent (content) factors. Journal of Interactive Marketing,
    48, 33-50.
    Thompson, M. M., Zanna, M. P., & Griffin, D. W. (1995). Let’s not be indifferent
    about (attitudinal) ambivalence. Attitude strength: Antecedents and
    consequences, 4, 361-386.
    Timoshenko, A., & Hauser, J. R. (2019). Identifying customer needs from user-
    generated content. Marketing Science, 38(1), 1-20.
    Tyler, S. W., Hertel, P. T., McCallum, M. C., & Ellis, H. C. (1979). Cognitive effort
    and memory. Journal of Experimental Psychology: Human Learning and
    Memory, 5(6), 607.
    Uribe, R., Buzeta, C., & Velásquez, M. (2016). Sidedness, commercial intent and
    expertise in blog advertising. Journal of Business Research, 69(10), 4403-
    4410.
    Wan, Y. (2013). The Matthew effect in online review helpfulness. International
    Conference on Electronic Commerce,
    Wang, G., Liu, X., & Fan, W. (2011). A knowledge adoption model based framework
    for finding helpful user-generated contents in online communities.
    Wang, Y., Zhong, K., & Liu, Q. (2022). Let criticism take precedence: Effect of side
    order on consumer attitudes toward a two-sided online review. Journal of
    Business Research, 140, 403-419.
    Wu, P. F., Van der Heijden, H., & Korfiatis, N. (2011). The influences of negativity
    and review quality on the helpfulness of online reviews. International
    conference on information systems,
    Xiao, Q., Siponen, M., Zhang, X., Lu, F., Chen, S.-h., & Mao, M. (2022). Impacts of
    platform design on consumer commitment and online review intention: does
    use context matter in dual-platform e-commerce? Internet Research.
    Xu, P., Chen, L., & Santhanam, R. (2015). Will video be the next generation of e-
    commerce product reviews? Presentation format and the role of product type.
    Decision Support Systems, 73, 85-96.
    Xu, Q. (2014). Should I trust him? The effects of reviewer profile characteristics on
    eWOM credibility. Computers in Human Behavior, 33, 136-144. 47

    Yang, S., Yao, J., & Qazi, A. (2020). Does the review deserve more helpfulness when its title resembles the content? Locating helpful reviews by text mining. Information Processing & Management, 57(2), 102179.
    Yang, S., Zhou, Y., Yao, J., Chen, Y., & Wei, J. (2019). Understanding online review helpfulness in omnichannel retailing. Industrial Management & Data Systems.
    Zhang, X., & Wang, L. (2019). The Research on Effects of Information Content Quality and Consumer Knowledge on Online Review Helpfulness from Readers’ Perspectives. JOURNAL OF SIMULATION, 7(4), 37.
    Zhou, S., & Guo, B. (2017). The order effect on online review helpfulness: A social influence perspective. Decision Support Systems, 93, 77-87.
    Description: 碩士
    國立政治大學
    資訊管理學系
    109356034
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0109356034
    Data Type: thesis
    DOI: 10.6814/NCCU202201274
    Appears in Collections:[資訊管理學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    603401.pdf1454KbAdobe PDF215View/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