Journal of library and information science in agriculture ›› 2025, Vol. 37 ›› Issue (3): 92-105.doi: 10.13998/j.cnki.issn1002-1248.25-0067
Previous Articles Next Articles
CLC Number:
1 | 李晓, 曲建升, 寇蕾蕾. 众包在证据合成中的实践应用研究: 以Cochrane Crowd公民科学项目中的众包应用为例[J]. 农业图书情报学报, 2023, 35(2): 95-104. |
LI X, QU J S, KOU L L. Applications of crowdsourcing in evidence synthesis: A case study of cochrane crowd[J]. Journal of library and information science in agriculture, 2023, 35(2): 95-104. | |
2 | BROWN A W, ALLISON D B. Using crowdsourcing to evaluate published scientific literature: Methods and example[J]. PLoS One, 2014, 9(7): e100647. |
3 | SUN Y, CHENG P, WANG S, et al. Crowdsourcing information extraction for biomedical systematic reviews[J/OL]. arXiv preprint arXiv:, 2016. |
4 | MORTENSEN M L, ADAM G P, TRIKALINOS T A, et al. An exploration of crowdsourcing citation screening for systematic reviews[J]. Research synthesis methods, 2017, 8(3): 366-386. |
5 | NAMA N, SAMPSON M, BARROWMAN N, et al. Crowdsourcing the citation screening process for systematic reviews: Validation study[J]. Journal of medical Internet research, 2019, 21(4): e12953. |
6 | NAMA N, ILIRIANI K, XIA M Y, et al. A pilot validation study of crowdsourcing systematic reviews: Update of a searchable database of pediatric clinical trials of high-dose vitamin D[J]. Translational pediatrics, 2017, 6(1): 18-26. |
7 | NOEL-STORR A H, REDMOND P, LAMÉ G, et al. Crowdsourcing citation-screening in a mixed-studies systematic review: A feasibility study[J]. BMC medical research methodology, 2021, 21(1): 88. |
8 | NOEL-STORR A, GARTLEHNER G, DOOLEY G, et al. Crowdsourcing the identification of studies for COVID-19-related cochrane rapid reviews[J]. Research synthesis methods, 2022, 13(5): 585-594. |
9 | THOMAS J, NOEL-STORR A, MARSHALL I, et al. Living systematic reviews: Combining human and machine effort[J]. Journal of clinical epidemiology, 2017, 91: 31-37. |
10 | STRANG L, SIMMONS R K. Citizen science: Crowdsourcing for systematic reviews[M]. Cambridge: THIS Institute, 2018. |
11 | FELIZARDO K R, DE SOUZA E F, LOPES R, et al. Crowdsourcing in systematic reviews: A systematic mapping and survey[C]//2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). August 26-28, 2020. Portoroz, Slovenia. IEEE, 2020: 404-412. |
12 | MOREAU D, GAMBLE B. Conducting a meta-analysis in the age of open science: Tools, tips, and practical recommendations[J]. Psychological methods, 2022, 27(3): 426. |
13 | WEISS M, ABUALHAOL I, AMIN M. A leader-driven open collaboration platform for exploring new domains[C]//Proceedings of the 12th International Symposium on Open Collaboration. Berlin Germany. ACM, 2016: 1-4. |
14 | WEISS M. Crowdsourcing literature reviews in new domains[J]. Technology innovation management review, 2016, 6(2): 5-14. |
15 | NAMA N, BARROWMAN N, O’HEARN K, et al. Quality control for crowdsourcing citation screening: The importance of assessment number and qualification set size[J]. Journal of clinical epidemiology, 2020, 122: 160-162. |
16 | KRIVOSHEEV E, CASATI F, CAFORIO V, et al. Crowdsourcing paper screening in systematic literature reviews[J]. Proceedings of the AAAI conference on human computation and crowdsourcing, 2017, 5: 108-117. |
17 | KRIVOSHEEV E, CASATI F, BENATALLAH B. Crowd-based multi-predicate screening of papers in literature reviews[C]//Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18. Lyon, France: ACM, 2018: 55-64. |
18 | SAMPSON M, NAMA N, O'HEARN K, et al. Creating enriched training sets of eligible studies for large systematic reviews: The utility of PubMed's Best Match algorithm[J]. International journal of technology assessment in health care, 2020, 37: e7. |
19 | RAMIREZ J, KRIVOSHEEV E, BAEZ M, et al. Crowdrev: A platform for crowd-based screening of literature reviews[J/OL]. arXiv preprint arXiv:, 2018. |
20 | SANTOS V, IWAZAKI A, SOUZA É, et al. CrowdSLR: A tool to support the use of crowdsourcing in systematic literature reviews[C]//Brazilian Symposium on Software Engineering. Joinville Brazil. ACM, 2021: 341-346. |
21 | NOEL-STORR A, DOOLEY G, ELLIOTT J, et al. An evaluation of Cochrane Crowd found that crowdsourcing produced accurate results in identifying randomized trials[J]. Journal of clinical epidemiology, 2021, 133: 130-139. |
22 | PIANTA M J, MAKRAI E, VERSPOOR K M, et al. Crowdsourcing critical appraisal of research evidence (CrowdCARE) was found to be a valid approach to assessing clinical research quality[J]. Journal of clinical epidemiology, 2018, 104: 8-14. |
23 | ASHKANASE J, NAMA N, SANDARAGE R V, et al. Identification and evaluation of controlled trials in pediatric cardiology: Crowdsourced scoping review and creation of accessible searchable database[J]. Canadian journal of cardiology, 2020, 36(11): 1795-1804. |
24 | SHAH N, GUO Y J, WENDELSDORF K V, et al. A crowdsourcing approach for reusing and meta-analyzing gene expression data[J]. Nature biotechnology, 2016, 34(8): 803-806. |
25 | 荷兰心理统计联盟. 共建管理学元分析数据库[EB/OL]. [2022-03-22]. . |
Netherlands Society for Psychometrics and Statistics. Co-constructing a meta-analysis database for management research[EB/OL]. [2022-03-22]. . | |
26 | BOSCO F A, UGGERSLEV K L, STEEL P. MetaBUS as a vehicle for facilitating meta-analysis[J]. Human resource management review, 2017, 27(1): 237-254. |
27 | LEBEL E P, MCCARTHY R J, EARP B D, et al. A unified framework to quantify the credibility of scientific findings[J]. Advances in methods and practices in psychological science, 2018, 1(3): 389-402. |
28 | SHACKELFORD G E, KEMP L, RHODES C, et al. Accumulating evidence using crowdsourcing and machine learning: A living bibliography about existential risk and global catastrophic risk[J]. Futures, 2020, 116: 102508. |
29 | HONG Q N, BOUIX-PICASSO J, RUCHON C. Creation of an online inventory for choosing critical appraisal tools[J]. Education for information, 2022, 38(2): 205-210. |
30 | AJZEN I. From intentions to actions: A theory of planned behavior[M]//Action Control: From Cognition to Behavior. New York: Springer, 1985: 11-39. |
31 | AJZEN I. The theory of planned behavior[J]. Organizational behavior & human decision processes, 1991, 50(2): 179–211. |
32 | AJZEN I. Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior[J]. Journal of applied social psychology, 2002, 32(4): 665-683. |
33 | DAVIS F D. Perceived usefulness, perceived ease of use, and user acceptance of information technology[J]. MIS quarterly, 1989, 13(3): 319. |
34 | PETRI H L, GOVERN J M. Motivation: Theory, research, and application[M]. Boston: Cengage Learning, 2012. |
35 | MILLER K A, DECI E L, RYAN R M. Intrinsic motivation and self-determination in human behavior[J]. Contemporary sociology, 1988, 17(2): 253. |
36 | ZHENG H C, LI D H, HOU W H. Task design, motivation, and participation in crowdsourcing contests[J]. International journal of electronic commerce, 2011, 15(4): 57-88. |
37 | NOV O, NAAMAN M, YE C. Analysis of participation in an online photo-sharing community: A multidimensional perspective[J]. Journal of the American society for information science and technology, 2010, 61(3): 555-566. |
38 | GOH D H, PE-THAN E P P, LEE C S. Perceptions of virtual reward systems in crowdsourcing games[J]. Computers in human behavior, 2017, 70: 365-374. |
39 | LAKHANI K R, JEPPESEN L B, LOHSE P A, et al. The value of openess in scientific problem solving[M]//Division of Research. Boston, MA: Harvard Business School, 2007. |
40 | KATZ D. The functional approach to the study of attitudes[J]. Public opinion quarterly, 1960, 24(2): 163-204. |
41 | ARMITAGE C J, CONNER M. Efficacy of the theory of planned behaviour: A meta-analytic review[J]. British journal of social psychology, 2001, 40(4): 471-499. |
42 | Collaboration Cochrane. Cochrane crowd[EB/OL]. [2022-06-18]. . |
43 | KE W L, ZHANG P. Motivations in open source software communities: The mediating role of effort intensity and goal commitment[J]. International journal of electronic commerce, 2009, 13(4): 39-66. |
44 | ACAR O A. Motivations and solution appropriateness in crowdsourcing challenges for innovation[J]. Research policy, 2019, 48(8): 103716. |
45 | OREG S, NOV O. Exploring motivations for contributing to open source initiatives: The roles of contribution context and personal values[J]. Computers in human behavior, 2008, 24(5): 2055-2073. |
46 | GARBARINO E C, EDELL J A. Cognitive effort, affect, and choice[J]. Journal of consumer research, 1997, 24(2): 147-158. |
47 | WANG M M. Encouraging solvers to sustain participation intention on crowdsourcing platforms: An investigation of social beliefs[J]. Information technology and management, 2022, 23(1): 39-50. |
48 | AJZEN I, FISHBEIN M. The influence of attitudes on behavior[M]//The Handbook of Attitudes. London, England: Psychology Press, 2014: 187-236. |
49 | TOHIDINIA Z, MOSAKHANI M. Knowledge sharing behaviour and its predictors[J]. Industrial management & data systems, 2010, 110(4): 611-631. |
50 | KANKANHALLI, TAN, WEI. Contributing knowledge to electronic knowledge repositories: An empirical investigation[J]. MIS quarterly, 2005, 29(1): 113. |
51 | BOCK, ZMUD, KIM, et al. Behavioral intention formation in knowledge sharing: Examining the roles of extrinsic motivators, social-psychological forces, and organizational climate[J]. MIS quarterly, 2005, 29(1): 87. |
52 | HAIR JR J F, HULT G T M, RINGLE C M, et al. A primer on partial least squares structural equation modeling(PLS-SEM)[M]. Thousand Oaks: Sage Publications, 2021. |
53 | STRAUB D, GEFEN D. Validation guidelines for IS positivist research[J]. Communications of the association for information systems, 2004, 13(1): 24. |
54 | FORNELL C, LARCKER D F. Evaluating structural equation models with unobservable variables and measurement error[J]. Journal of marketing research, 1981, 18(1): 39-50. |
55 | CASRAI. 14 contributor roles[EB/OL]. [2022-08-22]. . |
[1] | GOU Ruike, LUO Wei. Influencing Factors of Continuous Use Intention of "Generation Z" Users of an AIGC Platform [J]. Journal of library and information science in agriculture, 2025, 37(3): 66-80. |
[2] | SHI Qin, XIE Jing, WU Shang. Influencing Factors and Correlations of User Satisfaction with Mobile Health Applications [J]. Journal of library and information science in agriculture, 2025, 37(1): 33-46. |
[3] | YOU Ge, LI Jielin, ZHANG Fangshun. Generating Mechanism of Online Public Opinion Heat in Public Emergencies from the Perspective of Information Ecology: Fuzzy Set Qualitative Comparative Analysis Based on 50 Cases [J]. Journal of library and information science in agriculture, 2025, 37(1): 86-99. |
[4] | Guowei GAO, Shanshan ZHANG, Jialan YU. A Review of Health Information Behaviors of Older People from the Perspective of Topic Differentiation [J]. Journal of library and information science in agriculture, 2024, 36(7): 34-49. |
[5] | Liqin YAO, Hai ZHANG. Model Construction and Empirical Research on the Influencing Factors of AIGC User Dropout Behavior [J]. Journal of library and information science in agriculture, 2024, 36(5): 79-92. |
[6] | Chunling GAO, Liyuan JIANG. Elderly People's Online Health Information Seeking Behavior Based on Evolutionary Dynamics [J]. Journal of library and information science in agriculture, 2024, 36(5): 65-78. |
[7] | LIU Yang, LYU Shuyue, LI Ruojun. Concept, Task, and Application of Social Robots in Information Behavior Research [J]. Journal of library and information science in agriculture, 2024, 36(3): 4-20. |
[8] | ZHOU Xin. Machine Functionalism and the Digital-Intelligence Divide: Evolutionary Pathways, Generative Logic and Regulatory Strategies [J]. Journal of library and information science in agriculture, 2024, 36(3): 59-71. |
[9] | SHI Yanqing, LI Lu, SHI Qin. Impact of User Heterogeneity on Knowledge Collaboration Effectiveness from a Network Structure Perspective [J]. Journal of library and information science in agriculture, 2024, 36(3): 72-82. |
[10] | WANG Yueying. Exploring the Causes of Low Health Information Literacy Among Rural Middle-Aged and Elderly Adults and its Improvement Strategies [J]. Journal of library and information science in agriculture, 2024, 36(2): 81-93. |
[11] | WANG Weizheng, QIAO Hong, LI Xiaojun, WANG Jingjing. User Willingness to Use Generative Artificial Intelligence Based on AIDUA Framework [J]. Journal of library and information science in agriculture, 2024, 36(2): 36-50. |
[12] | HAN Xi, LIAO Ke. Factors Influencing Misinformation Propagation: A Systemic Review [J]. Journal of library and information science in agriculture, 2024, 36(12): 45-63. |
[13] | Zheng WANG, Miao ZHUANG, Yudi ZHANG, Yaqi ZHANG. Factors Influencing Online Health Information Acquisition Behavior of Rural Elderly Groups in Western China: A Field Study from the Guanzhong Region of Shaanxi Province [J]. Journal of library and information science in agriculture, 2024, 36(10): 23-37. |
[14] | Yijia WAN, Liping GU. Behavioral Motivation and Influencing Factors of Graduate Students Using AIGC Tool: An Empirical Analysis Based on Questionnaire Survey [J]. Journal of library and information science in agriculture, 2024, 36(10): 4-22. |
[15] | SUN Lili, WANG WeiJie, SHENG Jiefei. Influencing Factors of Scientific Data Value Increment Based on System Dynamics [J]. Journal of library and information science in agriculture, 2023, 35(9): 28-42. |
|