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2023 , Vol. 67 >Issue 11: 88 - 100

DOI: https://doi.org/10.13266/j.issn.0252-3116.2023.11.009

INFORMATION RESEARCH

The Impact of Personalized Content Recommendation Close on Continuous Use Intention of Mobile Social Media

  • Wang Wentao ,
  • Qian Pengbo ,
  • Ding Yuchen ,
  • Tang Sijie ,
  • Song Tianxiao ,
  • Ni Yue
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  • 1 School of Management, Anhui University, Hefei 230601;
    2 Fudan University Library, Shanghai 200433;
    3 Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190

Received date: 2022-11-09

  Revised date: 2023-02-20

  Online published: 2023-06-19

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Abstract

[Purpose/Significance] To explore the changes in user experience and the impact on continued usage intention after personalized content recommendation is turned off, which helps relevant regulatory authorities to properly and appropriately regulate the unreasonable application of algorithms and promote the healthy and orderly development of algorithm-related industries. [Method/Process] Based on the traditional SOR theoretical framework, this paper introduced the antecedent dimensions to construct a model of the influence of users’ intention to continue using, and adopted the mobile experience sampling method to track the usage experience and emotional change data of 53 users with long-term experience of using mobile social media after personalized content recommendations were turned off within 3 days, and combined with real-time questionnaires and semi-structured interviews to conduct a mixture of quantitative and qualitative analysis. [Result/Conclusion] In the case of personalized content recommendation closing, the decline in information quality and the increase in information redundancy will cause users to have negative emotions of anxiety and burnout, weaken the user’s immersion experience, and promote users’ willingness to use it unsustainably. According to the research results, the following enlightenments are drawn: at the algorithm level, gradient control mechanism should be constructed to provide users with a variety of personalized recommendation intensity options to avoid triggering abstinence reactions; at the enterprise level, information distribution system should be optimized and users’ right to know information should be respected; at the social level, leading enterprises should be urged to strengthen their sense of social responsibility, and joint enterprises should adopt active intervention measures to help users relieve anxiety and burnout; at the discipline level, the discipline of information resources management should always focus on users, guard against user behavior imbalances under sudden changes, and make greater contributions to the healthy and sustainable development of the information environment.

Key words： personalized content recommendation closing; continuous use intention; SOR theory; mobile experience sampling method

Cite this article

Wang Wentao , Qian Pengbo , Ding Yuchen , Tang Sijie , Song Tianxiao , Ni Yue . The Impact of Personalized Content Recommendation Close on Continuous Use Intention of Mobile Social Media[J]. Library and Information Service, 2023 , 67(11) : 88 -100 . DOI: 10.13266/j.issn.0252-3116.2023.11.009

References

[1] 魏娟,李敏.信息过载影响消费者决策研究的知识图谱分析[J].管理现代化, 2022, 42(1):156-161.
[2] 丁晓东.基于信任的自动化决策:算法解释权的原理反思与制度重构[J].中国法学, 2022(1):99-118.
[3] 喻国明学术工作室,杨雅,陈雪娇,等.类脑、具身与共情:如何研究人工智能对于传播学与后人类的影响——基于国际三大刊Science、Nature和PNAS人工智能相关议题的分析[J].学术界, 2021(8):108-117.
[4] 彭燕林.个性化推荐中的"过滤气泡"现象相关研究综述[J].科技创业月刊, 2019, 32(4):135-139.
[5] 国家互联网信息办公室.解读《互联网信息服务算法推荐管理规定》[EB/OL].[2023-01-26]. http://www.gov.cn/zhengce/2022-01/04/content_5666428.htm.
[6] GROSHEK J, KOC-MICHALSKA K. Helping populism win?social media use, filter bubbles, and support for populist presidential candidates in the 2016 US election campaign[J]. Information, communication & society, 2017, 20(9):1389-1407.
[7] 蔡立媛,张金海."媒介涵化受众"与"受众涵化媒介":大数据环境下网络涵化模式的重构[J].出版广角, 2015(6):88-91.
[8] 吕巍,杨颖,张雁冰. AI个性化推荐下消费者感知个性化对其点击意愿的影响[J].管理科学, 2020, 33(5):44-57.
[9] 耿立校,晋高杰,李亚函,等.基于改进内容过滤算法的高校图书馆文献资源个性化推荐研究[J].图书情报工作, 2018, 62(21):112-117.
[10] 汤文兵.基于深度学习的Top-N个性化推荐技术研究与应用[D].上海:东华大学, 2021.
[11] HE X, DENG K, WANG X, et al. LightGCN:simplifying and powering graph convolution network for recommendation[C]//Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. New York:Association for Computing Machinery, 2020:639-648.
[12] 武慧娟,孙鸿飞,金永昌.社会化标注系统中个性化信息推荐多维度融合与优化模型研究[J].现代情报, 2019, 39(1):37-42, 85.
[13] 段尧清,刘宇明,蔡诗茜,等.数字图书馆个性化推荐用户信息采纳行为影响研究——信息采纳意向的中介效应[J].现代情报, 2019, 39(2):85-93.
[14] 席运江,郭黛翎,廖晓,等.基于改进RFM模型的直播平台用户细分及个性化推荐方法研究[J].竞争情报, 2022, 18(3):36-47.
[15] 杨雨娇,袁勤俭.个性化推荐的隐忧:基于扎根理论的信息茧房及其前因后果探析[J].情报理论与实践, 2023, 46(3):117-126.
[16] STRAUSS S. Datafication and the seductive power of uncertainty-a critical exploration of big data enthusiasm[J]. Information, 2015, 6(4):836-847.
[17] 李伶俐.繁荣表象下的隐忧:短视频对青少年的负面影响及应对策略[J].中共云南省委党校学报, 2020, 21(3):133-138.
[18] 五成受访者会选择关闭算法推荐何去何从?[EB/OL].[2023-01-26]. https://baijiahao.baidu.com/s?id=1728309291819627518&wfr=spider&for=pc.
[19] 万立良,蒲坤.微信视频号用户持续使用意愿的影响因素探究[J].情报探索, 2022(3):10-18.
[20] ANDERSON A, HUTTENLOCHER D, KLEINBERG J, et al. Engaging with massive online courses[C]//Proceedings of the 23rd international conference on World Wide Web. New York:Association for Computing Machinery, 2014:687-698.
[21] 赵保国,姚瑶.用户持续使用知识付费APP意愿的影响因素研究[J].图书馆学研究, 2017(17):96-101.
[22] DAVIS F. Perceived usefulness, perceived ease of use, and user acceptance of information technology[J]. MIS quarterly, 1989, 13(3):319-340.
[23] 熊强,李文元,陈晓燕,等.在线教学平台交互性、体验价值和持续使用意愿的关系研究——一个有调节的中介效应[J].管理评论, 2022, 34(6):153-161.
[24] WOODWORTH R S. Dynamic psychology[J]. The pedagogical seminary and journal of genetic psychology, 1926, 33(1):103-118.
[25] 周涛,刘佳怡,邓胜利.基于SOR模型的在线知识社区用户潜水行为研究[J].情报杂志, 2022, 41(7):160-165, 83.
[26] 王文韬,张震,张坤,等.融合SOR理论的智能健康手环用户不持续使用行为研究[J].图书馆论坛, 2020, 40(5):92-102.
[27] LEE S K, MIN S R. Effects of information quality of online travel agencies on trust and continuous usage intention:an application of the SOR model[J]. The journal of Asian finance, economics and business, 2021, 8(4):971-982.
[28] 陈明红,潘子璇,曾庆彬.政务微信用户持续使用行为及用户契合的调节作用研究[J].现代情报, 2020, 40(11):85-98.
[29] JOHNSON J D, MEISCHKE H. A comprehensive model of cancer-related information seeking applied to magazines[J]. Human communication research, 1993, 19(3):343-367.
[30] MARKUS H, ZAJONC R. The cognitive perspective in social psychology[J]. Handbook of social psychology, 1985, 1(1):137-230.
[31] CHO J, SHAH D, MCLEOD J, et al. Campaigns, reflection, and deliberation:advancing an OSROR model of communication effects[J]. Communication theory, 2009, 19(1):66-88.
[32] DELONE W, MCLEAN E. The DeLone and McLean model of information systems success:a ten-year update[J]. Journal of management information systems, 2003, 19(4):9-30.
[33] 范波,李金曈,白天,等.基于混合机器学习优化的协同过滤算法[J].湖南理工学院学报(自然科学版), 2021, 34(3):9-12.
[34] 李玉.基于深度确定性策略梯度算法的信任推荐研究[D].烟台:烟台大学, 2022.
[35] 王志远,王兴芬.基于用户兴趣差异改进矩阵填充的个性化推荐算法[J].计算机应用与软件, 2020, 37(12):224-230, 237.
[36] CAHILL S, BANDURA A. Social foundations of thought and action:a social cognitive theory[J]. Contemporary sociology a journal of reviews, 1987, 16(1):12.
[37] BRIDGES E, FLORSHEIM R. Hedonic and utilitarian shopping goals:the online experience[J]. Journal of business research, 2007, 61(4):309-314.
[38] LEE Y, CHEN A, HESS T. The online waiting experience:using temporal information and distractors to make online waits feel shorter[J]. Journal of the Association for Information Systems, 2017, 18(3):231-263.
[39] PELET J, ETTIS S, COWART K. Optimal experience of flow enhanced by telepresence:evidence from social media use[J]. Information & management, 2017, 54(1):115-128.
[40] 张玥,李青宇.基于PPM理论的网络用户信息茧房滞留意愿影响因素研究[J].现代情报, 2022, 42(4):52-61
[41] 薛杨,许正良.微信营销环境下用户信息行为影响因素分析与模型构建——基于沉浸理论的视角[J].情报理论与实践, 2016, 39(6):104-109.
[42] BANDURA A. Social foundations of thought and action[M].Englewood Cliffs:Prentice Halll, 1986:23-28.
[43] 王畅.信息焦虑量表的编制研究[D].长春:吉林大学, 2010.
[44] 刘国亮,张汇川,刘子嘉.移动社交媒体用户不持续使用意愿研究——整合错失焦虑与社交媒体倦怠双重视角[J].情报科学, 2020, 38(12):128-133.
[45] 袁顺波.社会化阅读用户流失意愿实证研究[J].浙江学刊, 2022(2):99-110.
[46] 包家帅.基于S-O-R模型的新浪微博用户倦怠研究[D].大连:大连理工大学, 2020.
[47] OZKARA B, OZMEN M, KIM J. Examining the effect of flow experience on online purchase:a novel approach to the flow theory based on hedonic and utilitarian value[J]. Journal of retailing and consumer services, 2017, 37:119-131.
[48] 李慧.不良推荐对用户持续使用电商平台影响研究[D].青岛:山东科技大学, 2020.
[49] 林渊渊.互联网信息冗余现象[J].当代传播, 2004(5):58-60.
[50] 陈琼,宋士杰,赵宇翔.突发公共卫生事件中信息过载对用户信息规避行为的影响:基于COVID-19信息疫情的实证研究[J].情报资料工作, 2020, 41(3):76-88.
[51] CURRAN S, SAGUY A. Migration and cultural change:a role for gender and social networks?[J]. Journal of international women's studies, 2001, 2(3):54-77.
[52] LIN H. Determinants of successful virtual communities:contributions from system characteristics and social factors[J]. Information & management, 2008, 45(8):522-527.
[53] SÁNCHEZ-FRANCO M, ROLDÁN J. Web acceptance and usage model:a comparison between goal-directed and experiential web users[J]. Internet research, 2005, 15(1):21-48.
[54] 李曼静.学术虚拟社区用户持续使用意愿研究[D].武汉:华中师范大学, 2015.
[55] 丁晓燕.社会化商务情境下品牌转换意愿的影响机理研究[D].济南:山东财经大学, 2018.
[56] KUMMER T F, RECKER J, BICK M. Technology-induced anxiety:manifestations, cultural influences, and its effect on the adoption of sensor-based technology in German and Australian hospitals[J]. Information & management, 2017, 54(1):73-89.
[57] RAVINDRAN T, YEOW KUAN A C, HOE LIAN D G. Antecedents and effects of social network fatigue[J]. Journal of the Association for Information Science and Technology, 2014, 65(11):2306-2320.
[58] BRIGHT L, KLEISER S, GRAU S. Too much Facebook?an exploratory examination of social media fatigue[J]. Computers in human behavior, 2015, 44:148-155.
[59] 张肖,王文韬,谢阳群,等.量化自我场域下个人健康信息组织实证与优化——以智能手环为例[J].现代情报, 2021, 41(10):21-29, 39.
[60] JEONG J K. A systematic comparison of time use instruments:time diary and experience sampling method[J]. Survey research, 2008, 9(1):43-68.
[61] 查道林,蒋智慧,曹高辉.信息系统用户感知算法焦虑的内涵及其结构维度研究[J].情报科学, 2022, 40(6):66-73.
[62] 查先进,张晋朝,严亚兰.微博环境下用户学术信息搜寻行为影响因素研究——信息质量和信源可信度双路径视角[J].中国图书馆学报, 2015, 41(3):71-86.
[63] KARR-WISNIEWSKI P, LU Y. When more is too much:operationalizing technology overload and exploring its impact on knowledge worker productivity[J]. Computers in human behavior, 2010, 26(5):1061-1072.
[64] 汪雅倩.焦虑视角下强关系社交媒体不持续使用研究——以微信朋友圈为例[J].新闻界, 2019(10):81-91.
[65] SKADBERG Y X, KIMMEL J R. Visitors'flow experience while browsing a website:its measurement, contributing factors and consequences[J]. Computers in human behavior, 2004, 20(3):403-422.
[66] 赵启南.关系性压力下青年使用者社交媒体倦怠影响及其行为结果[J].新闻与传播研究, 2019, 26(6):59-75, 127.
[67] 王哲.社会化问答社区知乎的用户持续使用行为影响因素研究[J].情报科学, 2017, 35(1):78-83, 143.
[68] 朱庆华,徐孝婷,赵宇翔,等.基于移动经验取样法的量化自我参与流程及内在机理研究[J].情报学报, 2022, 41(3):217-228.
[69] 苏斌原,李江雪,叶婷婷,等.青少年网络成瘾治疗研究的新进展[J].广州大学学报(社会科学版), 2014, 13(12):23-29.
[70] KUSS D, GRIFFITHS M. Internet gaming addiction:a systematic review of empirical research[J]. International journal of mental health and addiction, 2012, 10(2):278-296.
[71] KO C, YEN J, CHEN C, et.al. Gender differences and related factors affecting online gaming addiction among Taiwanese adolescents[J]. The journal of nervous and mental disease, 2014, 193(4):273-277.
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