The role of organization hierarchy in technology adoption at the workplace

Popular social networking sites have revolutionized the way people interact on the Web, enabling rapid information dissemination and search. In an enterprise, understanding how information flows within and between organizational levels and business units is of great importance. Despite numerous studies in information diffusion in online social networks, little is known about factors that affect the dynamics of technological adoption at the workplace. Here, we address this problem, by examining the impact of organizational hierarchy in adopting new technologies in the enterprise. Our study suggests that middle-level managers are more successful in influencing employees into adopting a new microblogging service. Further, we reveal two distinct patterns of peer pressure, based on which employees are not only more likely to adopt the service, but the rate at which they do so quickens as the popularity of the new technology increases. We integrate our findings into two intuitive, realistic agent-based computational models that capture the dynamics of adoption at both microscopic and macroscopic levels. We evaluate our models in a real-world dataset we collected from a multinational Fortune 500 company. Prediction results show that our models provide great improvements over commonly used diffusion models. Our findings provide significant insights to managers seeking to realize the dynamics of adoption of new technologies in their company, and could assist in designing better strategies for rapid and efficient technology adoption and information dissemination at the workplace.

[1]  Viktor K. Prasanna,et al.  Microblogging in the Enterprise: A Few Comments are in Order , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[2]  Hanghang Tong,et al.  Information spreading in context , 2011, WWW.

[3]  T. Valente Social network thresholds in the diffusion of innovations , 1996 .

[4]  Jure Leskovec,et al.  The dynamics of viral marketing , 2005, EC '06.

[5]  Mark S. Ackerman,et al.  Expertise networks in online communities: structure and algorithms , 2007, WWW '07.

[6]  Christos Faloutsos,et al.  Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining , 2013, ASONAM 2013.

[7]  Jeho Lee,et al.  Role of network structure and network effects in diffusion of innovations , 2010 .

[8]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[9]  Krishna P. Gummadi,et al.  A measurement-driven analysis of information propagation in the flickr social network , 2009, WWW '09.

[10]  Michael W. Macy,et al.  In Search of Excellence: Fads, Success Stories, and Adaptive Emulation1 , 2001, American Journal of Sociology.

[11]  Jure Leskovec,et al.  Inferring networks of diffusion and influence , 2010, KDD.

[12]  Dirk Riehle,et al.  Modeling Microblogging Adoption in the Enterprise , 2009, AMCIS.

[13]  Herbert W. Hethcote,et al.  The Mathematics of Infectious Diseases , 2000, SIAM Rev..

[14]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[15]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[16]  Christel Kamp,et al.  Untangling the Interplay between Epidemic Spread and Transmission Network Dynamics , 2009, PLoS Comput. Biol..

[17]  M. Newman Spread of epidemic disease on networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[19]  Qi He,et al.  TwitterRank: finding topic-sensitive influential twitterers , 2010, WSDM '10.

[20]  Jun Zhang,et al.  A case study of micro-blogging in the enterprise: use, value, and related issues , 2010, CHI.

[21]  Lada A. Adamic,et al.  The role of social networks in information diffusion , 2012, WWW.

[22]  S. Solomon,et al.  Social percolation models , 1999, adap-org/9909001.

[23]  Divyakant Agrawal,et al.  Diffusion of Information in Social Networks: Is It All Local? , 2012, 2012 IEEE 12th International Conference on Data Mining.

[24]  Jure Leskovec,et al.  Modeling Information Diffusion in Implicit Networks , 2010, 2010 IEEE International Conference on Data Mining.

[25]  Lada A. Adamic,et al.  Social influence and the diffusion of user-created content , 2009, EC '09.

[26]  J A Jacquez,et al.  The stochastic SI model with recruitment and deaths. I. Comparison with the closed SIS model. , 1993, Mathematical biosciences.

[27]  Mark S. Granovetter Threshold Models of Collective Behavior , 1978, American Journal of Sociology.

[28]  Ravi Kumar,et al.  Influence and correlation in social networks , 2008, KDD.

[29]  Lori Rosenkopf,et al.  Social Network Effects on the Extent of Innovation Diffusion: A Computer Simulation , 1997 .

[30]  Kristina Lerman,et al.  Predicting Influential Users in Online Social Networks , 2010, ArXiv.