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| Research on the Influence of Social Interaction on User Perception and Information Adoption of the Recommendation System |
| Li Zhi1,2, Sun Rui1,2 |
1.School of Business Administration, Huaqiao University, Quanzhou 362021
2.Oriental Enterprise Management Research Center, Huaqiao University, Quanzhou 362021 |
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Abstract The personalized recommendation system (PRS) generates recommendation information by considering the preferences of target users and similar users. In this study, based on the situational experiment analysis, participantsevaluation of PRS is obtained based on social interaction by changing the level of social interaction (social reference and self-reference) and using six PRSs for the application to establish the experimental operation under the Web. Moreover, the experimental data are analyzed and processed by SPSS23.0 and Smart PLS2.0 software. The results indicate that the social interaction environment significantly improved the perceived accuracy and novelty of PRS. The results also confirm the positive impact of perceived accuracy and novelty on user satisfaction and that of satisfaction and perceived novelty on information adoption. In addition, the research verifies the mediating effect of perception accuracy, novelty perception, and satisfaction. This study aims to explore the influence of social interaction on perceived accuracy and novelty, which, in turn, affect satisfaction and information adoption. By integrating the functions of PRS and social interaction, we can improve our understanding of social cognitive processes related to PRS user perceptions.
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Received: 26 June 2018
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1 王军, 周淑玲. 一致性与矛盾性在线评论对消费者信息采纳的影响研究——基于感知有用性的中介作用和自我效能的调节作用[J]. 图书情报工作, 2016, 60(22): 94-101.
2 HillS R, TroshaniI. Factors influencing the adoption of personalisation mobile services: Empirical evidence from young Australians[J]. International Journal of Mobile Communications, 2010, 8(2): 150-168.
3 FoshayN, KuziemskyC. Towards an implementation framework for business intelligence in healthcare[J]. International Journal of Information Management, 2014, 34(1): 20-27.
4 AlhamidM F, RawashdehM, DongH W, et al. Exploring latent preferences for context-aware personalized recommendation systems[J]. IEEE Transactions on Human-Machine Systems, 2016, 46(4): 615-623.
5 陈文斌, 杨瑞瑞, 于俊清. 基于GPU/CPU混合架构的流程序多粒度划分与调度方法研究[J]. 计算机工程与科学, 2017, 39(1): 15-26.
6 ChoiJ, LeeH J, SajjadF, et al. The influence of national culture on the attitude towards mobile recommender systems[J]. Technological Forecasting and Social Change, 2014, 86: 65-79.
7 ChoiJ, LeeH J, KimY C. The influence of social presence on customer intention to reuse online recommender systems: The roles of personalization and product type[J]. International Journal of Electronic Commerce, 2011, 16(1): 129-154.
8 KimH W, KankanhalliA, LeeH L. Investigating decision factors in mobile application purchase: A mixed-methods approach[J]. Information & Management, 2016, 53(6): 727-739.
9 GaiL L. The selling power of customer-generated product reviews: The matching effect between consumers’ cognitive needs and persuasive message types[M]// Thriving in a New World Economy. Cham: Springer International Publishing, 2015.
10 WangW, BenbasatI. Interactive decision aids for consumer decision making in e-commerce: The influence of perceived strategy restrictiveness[J]. MIS Quarterly, 2009, 33(2): 293-320.
11 XiaoB, BenbasatI. Product-related deception in e-commerce: A theoretical perspective[J]. MIS Quarterly, 2011, 35(1): 169-195.
12 邹凌君, 陈崚, 李娟. 时间加权的混合推荐算法[J]. 计算机科学, 2016, 43(S2): 451-454.
13 潘威, 汪寅, 陈巍. 心智化社会认知观的演变及发展——来自潜心智化的思考[J]. 心理科学, 2017, 40(5): 1274-1279.
14 殷杰, 张海燕. 论逻辑经验主义的“社会参与”思想[J]. 大连理工大学学报(社会科学版), 2017, 38(2): 98-104.
15 BurnkrantR E, UnnavaH R. Effects of self-referencing on persuasion[J]. Journal of Consumer Research, 1995, 22(1): 17-26.
16 姜信景, 齐小刚, 刘立芳. 个性化信息推荐方法研究[J]. 智能系统学报, 2018, 13(2): 189-195.
17 ZhuL, BenbasatI, JiangZ H. Let’s shop online together: An empirical investigation of collaborative online shopping support[J]. Information Systems Research, 2010, 21(4): 872-891.
18 徐玲玲, 朱婧. 移动电商个性化推荐对消费者购买意愿影响分析[J]. 商业经济研究, 2018(6): 54-57.
19 CyrD, HassaneinK, HeadM, et al. The role of social presence in establishing loyalty in e-Service environments[J]. Interacting with Computers, 2007, 19(1): 43-56.
20 LiG, YangX, HuangS. Effects of social capitaland community support on online community membersintention to create user-generated content[J]. Journal of Electronic Commerce Research, 2014, 15(3): 190-199.
21 Al-NatourS, BenbasatI, CenfetelliR T. The adoption of online shopping assistants: Perceived similarity as an antecedent to evaluative beliefs[J]. Journal of the Association for Information Systems, 2011, 12(5): 347-374.
22 单晓菲, 米传民, 马静. 基于选择性随机游走的协同过滤推荐算法研究[J]. 中国管理科学, 2014, 22(S1): 73-78.
23 张琳, 闫强. 基于管理和消费者行为视角的个性化推荐研究与展望[J]. 北京邮电大学学报(社会科学版), 2016, 18(6): 24-30.
24 常亚平, 肖万福, 覃伍, 等. 网络环境下第三方评论对冲动购买意愿的影响机制: 以产品类别和评论员级别为调节变量[J]. 心理学报, 2012, 44(9): 1244-1264.
25 RezaeenourJ, Sadat LesaniF. A context aware recommender system for mobile phone selection using combination of elimination method and analytic hierarchy processing[J]. Iranian Journal of Information Processing & Management, 2017, 32(4): 1203-1228.
26 常亚平, 朱东红, 李荣华. 感知产品创新对冲动购买的作用机制研究[J]. 科研管理, 2012, 33(3): 18-26, 35.
27 FoxJ, MorelandJ J. The dark side of social networking sites: An exploration of the relational and psychological stressors associated with Facebook use and affordances[J]. Computers in Human Behavior, 2015, 45: 168-176.
28 WongK K. Partial least square structural equation modeling (PLS-SEM) techniques using SmartPLS[J]. Marketing Bulletin, 2013, 24(1): 1-32.
29 FornellC, LarckerD F. Evaluating structural equation models with unobservable variables and measurement error[J]. Journal of Marketing Research, 1981, 18(1): 39-50.
30 Al-GahtaniS S. Empirical investigation of e-learning acceptance and assimilation: A structural equation model[J]. Applied Computing and Informatics, 2016, 12(1): 27-50.
31 PodsakoffP M, MacKenzieS B, LeeJ Y, et al. Common method biases in behavioral research: A critical review of the literature and recommended remedies[J]. Journal of Applied Psychology, 2003, 88(5): 879-903.
32 SharmaR, YettonP, CrawfordJ. Estimating the effect of common method variance: The method—method pair technique with an illustration from TAM research[J]. MIS Quarterly, 2009, 33(3): 473-490.
33 MalhotraN K, KimS S, PatilA. Common method variance in is research: A comparison of alternative approaches and a reanalysis of past research[J]. Management Science, 2006, 52(12): 1865-1883.
34 BataH, PentinaI, TarafdarM, et al. Mobile social networking and salesperson maladaptive dependence behaviors[J]. Computers in Human Behavior, 2018, 81: 235-249.
35 Ben-ZeevD, BrianR, WangR, et al. CrossCheck: Integrating self-report, behavioral sensing, and smartphone use to identify digital indicators of psychotic relapse[J]. Psychiatric Rehabilitation Journal, 2017, 40(3): 266-275.
36 余以胜, 徐剑彬, 刘鑫艳. 基于社群挖掘的用户个性化信息推荐方法研究[J]. 情报学报, 2017, 36(10): 1093-1098.
37 HerlockerJ L, KonstanJ A, TerveenL G, et al. Evaluating collaborative filtering recommender systems[J]. ACM Transactions on Information Systems, 2004, 22(1): 5-53.
38 DeLoneW H, McLeanE 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.
39 DavisF D. Perceived usefulness, perceived ease of use, and user acceptance of information technology[J]. MIS Quarterly, 1989, 13(3): 319-340.
40 马庆国, 王凯, 舒良超. 积极情绪对用户信息技术采纳意向影响的实验研究——以电子商务推荐系统为例[J]. 科学学研究, 2009, 27(10): 1557-1563. |
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