Popularity or Proximity: Characterizing the Nature of Social Influence in an Online Music Community

被引:101
作者
Dewan, Sanjeev [1 ]
Ho, Yi-Jen [2 ]
Ramaprasad, Jui [3 ]
机构
[1] Univ Calif Irvine, Paul Merage Sch Business, Irvine, CA 92697 USA
[2] Penn State Univ, Smeal Coll Business, University Pk, PA 16802 USA
[3] McGill Univ, Desautels Sch Management, Montreal, PQ H3A 1G5, Canada
关键词
social influence; word of mouth; popularity; proximity; social networks; music industry; online community; WORD-OF-MOUTH; INFORMATIONAL CASCADES; FIELD EXPERIMENT; PEER INFLUENCE; SALES; NETWORK; DIFFUSION; PRODUCT; CONTAGION; BEHAVIOR;
D O I
10.1287/isre.2016.0654
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
We study social influence in an online music community. In this community, users can listen to and "favorite" (or like) songs and follow the favoriting behavior of their social network friends-and the community as a whole. From an individual user's perspective, two types of information on peer consumption are salient for each song: total number of favorites by the community as a whole and favoriting by their social network friends. Correspondingly, we study two types of social influence: popularity influence, driven by the total number of favorites from the community as a whole, and proximity influence, due to the favoriting behavior of immediate social network friends. Our quasi-experimental research design applies a variety of empirical methods to highly granular data from an online music community. Our analysis finds robust evidence of both popularity and proximity influence. Furthermore, popularity influence is more important for narrow-appeal music compared to broad-appeal music. Finally, the two types of influence are substitutes for one another, and proximity influence, when available, dominates the effect of popularity influence. We discuss implications for design and marketing strategies for online communities, such as the one studied in this paper.
引用
收藏
页码:117 / 136
页数:20
相关论文
共 49 条
[1]  
Abel J, 2013, NY TIMES
[2]   Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions [J].
Adomavicius, G ;
Tuzhilin, A .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2005, 17 (06) :734-749
[3]  
[Anonymous], 2008, Working paper
[4]  
[Anonymous], 2010, WORKING PAPER
[5]  
[Anonymous], 1995, Network Models of the Diffusion of Innovations
[6]  
[Anonymous], WORKING PAPER
[7]  
[Anonymous], 2008, Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, DOI [DOI 10.1145/1401890.1401897, 10.1145/1401890.1401897]
[8]  
[Anonymous], P 13 INT C EL COMM
[9]   Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks [J].
Aral, Sinan ;
Walker, Dylan .
MANAGEMENT SCIENCE, 2011, 57 (09) :1623-1639
[10]   Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks [J].
Aral, Sinan ;
Muchnik, Lev ;
Sundararajan, Arun .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2009, 106 (51) :21544-21549