On the relationship between number of votes and sentiment in crowdsourcing ideas and comments for innovation: A case study of Canada's digital compass

Recently, PWC facilitated an innovation crowdsourcing effort entitled "Canada's Digital Compass" aimed at generating creative directions for Canada to pursue in its future. As part of this effort, over 70 ideas were submitted and over five hundred votes on those ideas were generated. This paper analyzes data developed as part of that effort, investigating a number of relationships between different variables elicited from the analysis and the number of votes for those ideas. The analysis confirms some previous results, provides a structure for interpreting those results and generates some new results. In particular, this paper generates sentiment measures for both the innovation ideas and for the comments. Using a decision tree approach we find that the number of comments and the extent to which the sentiment in those comments is positive are statistically significantly related to the number of votes. In addition, using regression analysis, this paper finds that the number of votes is statistically significantly related to the interaction between sentiment measures for ideas and comments. Finally, a rationale for these results, based on game theory is proposed and investigated. Statistically significant relationship between number of comments & number of votesTransparency theory based on the game theoretic notion of "tit-for-tat"Information about the idea is related to the number of votes.Comment sentiment is related to number of votes.Interaction between idea and comment sentiment is related to number of votes.

[1]  Desheng Dash Wu,et al.  Using text mining and sentiment analysis for online forums hotspot detection and forecast , 2010, Decis. Support Syst..

[2]  Mokter Hossain,et al.  Ideation through Online Open Innovation Platform: Dell IdeaStorm , 2015 .

[3]  Paul Michael Di Gangi,et al.  Steal my idea! Organizational adoption of user innovations from a user innovation community: A case study of Dell IdeaStorm , 2009, Decis. Support Syst..

[4]  Wonjoon Kim,et al.  From valence to emotions: Exploring the distribution of emotions in online product reviews , 2016, Decis. Support Syst..

[5]  Mohammad Salehan,et al.  Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics , 2014, Decis. Support Syst..

[6]  Katja Hutter,et al.  Evaluation Games - How to Make the Crowd your Jury , 2010, GI Jahrestagung.

[7]  O. Bjelland,et al.  An Inside View of IBM's 'Innovation Jam' , 2008 .

[8]  Yan Liu,et al.  Looking for great ideas: analyzing the innovation jam , 2007, WebKDD/SNA-KDD '07.

[9]  Eric Horvitz,et al.  What's your idea?: a case study of a grassroots innovation pipeline within a large software company , 2010, CHI.

[10]  Daniel E. O'Leary Driving Innovation and Knowledge Management Using Crowdsourcing , 2013, ICIS.

[11]  Daniel E. O'Leary,et al.  Blog mining-review and extensions: "From each according to his opinion" , 2011, Decis. Support Syst..

[12]  Michael Geraci Crowdsourcing: Leveraging Your Social Networks , 2009 .

[13]  Paulo Cortez,et al.  Stock market sentiment lexicon acquisition using microblogging data and statistical measures , 2016, Decis. Support Syst..

[14]  Tina Ding Stock Market Prediction based on Time Series Data and Market Sentiment , 2012 .

[15]  Khairullah Khan,et al.  Sentence based sentiment classification from online customer reviews , 2010, FIT.

[16]  Nir Halevy,et al.  Mind games: the mental representation of conflict. , 2012, Journal of personality and social psychology.

[17]  Michael Vitale,et al.  The Wisdom of Crowds , 2015, Cell.