数字图书馆智能信息推荐服务满意度影响机理的扎根研究

doi:10.3772/j.issn.1000-0135.2022.01.009

情报学报

2022, Vol. 41

Issue (1): 83-95 DOI: 10.3772/j.issn.1000-0135.2022.01.009

情报用户与行为研究

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数字图书馆智能信息推荐服务满意度影响机理的扎根研究

查先进^1,2, 张坤^2,3, 严亚兰⁴

1.武汉大学信息资源研究中心,武汉 430072
2.武汉大学信息管理学院,武汉 430072
3.武汉大学图书情报国家级实验教学示范中心,武汉 430072
4.武汉科技大学恒大管理学院,武汉 430065

Grounded Research on the Impacting Mechanism of Satisfaction with Intelligent Information Recommendation Services by Digital Libraries

Zha Xianjin^1,2, Zhang Kun^2,3, Yan Yalan⁴

1.Center for Studies of Information Resources, Wuhan University, Wuhan 430072
2.School of Information Management, Wuhan University, Wuhan 430072
3.National Demonstration Center for Experimental Library and Information Science Education, Wuhan University, Wuhan 430072
4.Evergrande School of Management, Wuhan University of Science and Technology, Wuhan 430065

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摘要当前，数字图书馆的信息环境已由信息不足转变为信息过载，其对应的服务模式也应由“人找信息”转变为“信息找人”。智能信息推荐服务的应用有助于应对新信息环境转变给数字图书馆带来的新挑战，但目前国内外对数字图书馆智能信息推荐服务满意度的影响机理尚缺乏深入研究。本文利用扎根理论方法，通过对访谈获取的原始资料进行编码分析，提炼出78个初始概念、24个基本范畴和6个主范畴。在此基础上，梳理范畴间的关系路径与作用机制，并构建了数字图书馆智能信息推荐服务满意度影响机理的理论模型。研究结果发现，在用户偏好的调节作用下，推荐系统质量、推荐信息质量、推荐服务质量和推荐形式共同对数字图书馆智能信息推荐服务满意度产生影响。本研究结果可为数字图书馆服务优化与良性发展提供有益参考，具有理论和实践意义。

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	查先进
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关键词 ：数字图书馆, 智能信息推荐服务, 满意度, 影响机理, 扎根理论

收稿日期: 2020-09-28

基金资助:国家自然科学基金项目“社会连接和认知负荷视角下网络用户从众信息行为研究”（71874124），“交流可见度和结构洞视角下交互记忆系统及知识行为研究”（71774126），“社会学习和心理授权视角下智能推荐用户信息行为影响规律研究”（72174148）。

作者简介: 查先进，男，1967年生，博士，教授，珞珈特聘教授，博士生导师，主要研究方向为信息资源配置与管理、信息行为、信息分析与竞争情报、信息系统，E-mail：xianjinzha@163.com；张坤，男，1992年生，博士研究生，主要研究方向为信息行为、信息系统；严亚兰，女，1967年生，博士，教授，博士生导师，主要研究方向为知识共享与知识管理、信息资源管理、信息系；

引用本文:

查先进, 张坤, 严亚兰. 数字图书馆智能信息推荐服务满意度影响机理的扎根研究[J]. 情报学报, 2022, 41(1): 83-95.
Zha Xianjin, Zhang Kun, Yan Yalan. Grounded Research on the Impacting Mechanism of Satisfaction with Intelligent Information Recommendation Services by Digital Libraries. 情报学报, 2022, 41(1): 83-95.

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https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2022.01.009 或 https://qbxb.istic.ac.cn/CN/Y2022/V41/I1/83

1 Li S Q, Jiao F S, Zhang Y, et al. Problems and changes in digital libraries in the age of big data from the perspective of user services[J]. The Journal of Academic Librarianship, 2019, 45(1): 22-30.
2 Li X D, Wang J J. Trends in CASHL's document delivery service in China[J]. The Journal of Academic Librarianship, 2019, 45(4): 321-325.
3 马晓亭, 陈臣. 基于可信小数据的图书馆个性化服务研究[J]. 图书情报工作, 2015, 59(4): 70-75.
4 苏新宁. 大数据时代数字图书馆面临的机遇和挑战[J]. 中国图书馆学报, 2015, 41(6): 4-12.
5 何胜, 冯新翎, 武群辉, 等. 基于用户行为建模和大数据挖掘的图书馆个性化服务研究[J]. 图书情报工作, 2017, 61(1): 40-46.
6 田野, 祝忠明. 关联数据驱动的数字图书推荐模型[J]. 图书情报工作, 2013, 57(17): 34-38, 52.
7 李树青, 丁浩, 徐侠. 大数据时代数字图书馆用户服务思考[J]. 情报学报, 2018, 37(6): 569-579.
8 Tsakonas G, Mitrelis A, Papachristopoulos L, et al. An exploration of the digital library evaluation literature based on an ontological representation[J]. Journal of the American Society for Information Science and Technology, 2013, 64(9): 1914-1926.
9 Oliver R L. A cognitive model of the antecedents and consequences of satisfaction decisions[J]. Journal of Marketing Research, 1980, 17(4): 460-469.
10 Azzopardi J, Ivanovic D, Kapitsaki G. Comparison of collaborative and content-based automatic recommendation approaches in a digital library of Serbian PhD dissertations[C]// Proceedings of the International KEYSTONE Conference on Semantic Keyword-Based Search on Structured Data Sources. Cham: Springer, 2017: 100-111.
11 Guo S S, Zhang W Y, Zhang S. A PageRank-based collaborative filtering recommendation approach in digital libraries[J]. Tehni?ki Vjesnik, 2017, 24(4): 1051-1058.
12 Salehi M. Application of implicit and explicit attribute based collaborative filtering and BIDE for learning resource recommendation[J]. Data & Knowledge Engineering, 2013, 87: 130-145.
13 Ghauth K I, Abdullah N A. Learning materials recommendation using good learners' ratings and content-based filtering[J]. Educational Technology Research and Development, 2010, 58(6): 711-727.
14 Porcel C, Herrera-Viedma E. Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries[J]. Knowledge-Based Systems, 2010, 23(1): 32-39.
15 孙洁丽, 张荣梅. 基于案例推理的数字图书馆个性化推荐系统研究[J]. 情报科学, 2008, 26(9): 1380-1384.
16 Liang T P. Recommendation systems for decision support: an editorial introduction[J]. Decision Support Systems, 2008, 45(3): 385-386.
17 Goldberg D, Nichols D, Oki B M, et al. Using collaborative filtering to weave an information tapestry[J]. Communications of the ACM, 1992, 35(12): 61-70.
18 Balabanovi? M, Shoham Y. Learning information retrieval agents: experiments with automated web browsing[C]// Proceedings of the AAAI Spring Symposium on Information Gathering from Heterogenous, Distributed Resources. Palo Alto: AAAI Press, 1995: 13-18.
19 Balabanovi? M, Shoham Y. Fab: content-based, collaborative recommendation[J]. Communications of the ACM, 1997, 40(3): 66-72.
20 Bollacker K D, Lawrence S, Lee Giles C. CiteSeer: an autonomous Web agent for automatic retrieval and identification of interesting publications[C]// Proceedings of the Second International Conference on Autonomous Agents. New York: ACM Press, 1998: 116-123.
21 曹树金, 罗春荣, 马利霞. 论图书馆个性化服务的几个基本问题[J]. 大学图书馆学报, 2005, 23(6): 33-39.
22 Sch?pfel J, Leduc C. Big deal and long tail: e-journal usage and subscriptions[J]. Library Review, 2012, 61(7): 497-510.
23 Tian Y H, Zheng B, Wang Y F, et al. College library personalized recommendation system based on hybrid recommendation algorithm[J]. Procedia CIRP, 2019, 83: 490-494.
24 李贺, 侯力铁, 祝琳琳. 移动图书馆情景感知信息推荐服务用户接受行为研究[J]. 图书情报工作, 2019, 63(12): 94-104.
25 Nguyen T T S. Model-based book recommender systems using Na?ve Bayes enhanced with optimal feature selection[C]// Proceedings of the 2019 8th International Conference on Software and Computer Applications. New York: ACM Press, 2019: 217-222.
26 Nishioka C, Hauke J, Scherp A. Influence of tweets and diversification on serendipitous research paper recommender systems[J]. PeerJ Computer Science, 2020, 6: e273.
27 Feng X Y, Zhang H, Ren Y J, et al. The deep learning-based recommender system “Pubmender” for choosing a biomedical publication venue: development and validation study[J]. Journal of Medical Internet Research, 2019, 21(5): e12957.
28 Zhang D, Zhao S, Duan Z, et al. A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation[J]. ACM Transactions on Information Systems, 2020, 38(1): 1-20.
29 Osborne F, Thanapalasingam T, Salatino A A, et al. Smart book recommender: a semantic recommendation engine for editorial products[C]// Proceedings of the International Semantic Web Conference, Vienna, Austria, 2017: 21-25.
30 Xu F, Du J T. Examining differences and similarities between graduate and undergraduate students' user satisfaction with digital libraries[J]. The Journal of Academic Librarianship, 2019, 45(6): 102072.
31 胡昌平, 胡媛, 严炜炜. 高校数字图书馆服务的用户满意度实证研究[J]. 国家图书馆学刊, 2013, 22(6): 23-32.
32 Liu Z M, Luo L L. A comparative study of digital library use: factors, perceived influences, and satisfaction[J]. The Journal of Academic Librarianship, 2011, 37(3): 230-236.
33 李贺, 关欣, 邵长安. 高校数字图书馆用户满意度的测评研究[J]. 情报理论与实践, 2010, 33(4): 80-83.
34 夏立新, 孙丹霞, 王忠义. 网络环境下数字图书馆知识服务用户满意度评价指标体系构建[J]. 图书馆杂志, 2015, 34(3): 27-34.
35 Xu F, Du J T. Factors influencing users’ satisfaction and loyalty to digital libraries in Chinese universities[J]. Computers in Human Behavior, 2018, 83: 64-72.
36 Peng K P, Nisbett R E. Culture, dialectics, and reasoning about contradiction[J]. American Psychologist, 1999, 54(9): 741-754.
37 孙晓娥. 扎根理论在深度访谈研究中的实例探析[J]. 西安交通大学学报(社会科学版), 2011, 31(6): 87-92.
38 Strauss A L. Qualitative analysis for social scientists[M]. Cambridge: Cambridge University Press, 1987: 5.
39 陈向明. 扎根理论的思路和方法[J]. 教育研究与实验, 1999(4): 58-63, 73.
40 Barahmand N, Nakhoda M, Fahimnia F, et al. Understanding everyday life information seeking behavior in the context of coping with daily hassles: a grounded theory study of female students[J]. Library & Information Science Research, 2019, 41(4): 100980.
41 Wang C Y, Huang R H, Li J Y, et al. Towards better information services: a framework for immigrant information needs and library services[J]. Library & Information Science Research, 2020, 42(1): 101000.
42 Guo J X. Measuring information system project success through a software-assisted qualitative content analysis[J]. Information Technology and Libraries, 2019, 38(1): 53-70.
43 Brand?o C, Miguez J. Using NVivo to assess a program of goal-corrected empathic attunement skills: a case study in the context of higher education[J]. Universal Access in the Information Society, 2017, 16(4): 863-876.
44 Min M N, Anderson J A, Chen M G. What do we know about full-service community schools? Integrative research review with NVivo[J]. School Community Journal, 2017, 27(1): 29-54.
45 王晰巍, 贾若男, 王雷, 等. 社交媒体用户转移行为影响因素模型及实证研究[J]. 图书情报工作, 2018, 62(18): 45-54.
46 Davis F D. Perceived usefulness, perceived ease of use, and user acceptance of information technology[J]. MIS Quarterly, 1989, 13(3): 319-340.
47 查先进, 陈明红. 信息资源质量评估研究[J]. 中国图书馆学报, 2010, 36(2): 46-55.
48 Zhang Z J. Feeling the sense of community in social networking usage[J]. IEEE Transactions on Engineering Management, 2010, 57(2): 225-239.
49 杭孝平, 李彦冰. 政务微博的内容特征与发布标准——以“@北京发布”的微博内容为个案[J]. 当代传播, 2014(6): 73-75.
50 Zheng Y M, Zhao K X, Stylianou A. The impacts of information quality and system quality on users' continuance intention in information-exchange virtual communities: an empirical investigation[J]. Decision Support Systems, 2013, 56: 513-524.
51 Witt P. Entrepreneurs' networks and the success of start-ups[J]. Entrepreneurship & Regional Development, 2004, 16(5): 391-412.
52 Resnick P, Varian H R. Recommender systems[J]. Communications of the ACM, 1997, 40(3): 56-58.
53 伍婵提. 旅游网站用户满意度影响因素研究——以同程网为例[J]. 企业经济, 2017, 36(11): 136-141.
54 Ozer B, Baris M E. Landscape design and park users' preferences[J]. Procedia-Social and Behavioral Sciences, 2013, 82: 604-607.
55 DeLone W H, McLean E R. Information systems success: the quest for the dependent variable[J]. Information Systems Research, 1992, 3(1): 60-95.
56 DeLone W H, McLean E R. The DeLone and McLean model of information systems success: a ten-year update[J]. Journal of Management Information Systems, 2003, 19(4): 9-30.
57 Francis J J, Johnston M, Robertson C, et al. What is an adequate sample size? Operationalising data saturation for theory-based interview studies[J]. Psychology & Health, 2010, 25(10): 1229-1245.
58 Kim M S, Kim S. Factors influencing willingness to provide personal information for personalized recommendations[J]. Computers in Human Behavior, 2018, 88: 143-152.
59 Nilashi M, Ibrahim O, Yadegaridehkordi E, et al. Travelers decision making using online review in social network sites: a case on TripAdvisor[J]. Journal of Computational Science, 2018, 28: 168-179.
60 Syed T A, Nair S S K. Personalized recommendation system for advanced learning management systems[C]// Proceedings of the 8th International Conference on Information Communication and Management. New York: ACM Press, 2018: 90-95.
61 喻国明, 刘钰菡, 王畅颖, 等. 推荐算法: 信息推送的王者品性与进阶重点[J]. 山东社会科学, 2018(3): 142-147, 192.