Knowledge sharing behaviors in social media

Abstract Purpose Users on Social Media (SM) platforms make many decisions related to content sharing, such as whether to create or reuse content, whether to label for easy access by an interest group or not, and whether to disseminate to targeted individuals or broadcast to general audiences. In this study, we investigated if these content-related strategies on SM, called social media knowledge sharing behaviors, are determined by user characteristics. Method ology: Using concepts from Self-Motivation Theory and the Affordance Theory, we examined if the knowledge sharing behaviors are influenced or correlated with user characteristics, such as the intensity of engagement on SM, a strong preference attitude for a SM platform, and multiple functional intentions for using SM. Based on this survey study of one hundred and twenty-three subjects, we developed hierarchical regression analyses to test if the SM user's knowledge decisions (Creation, Framing and Targeting) are corelated with the user's online usage intensity, their SM online platform preferences, and their functional intentions (Intensity, Preferences and Functionality). We complemented the regression models with a more comprehensive path analysis for an integrative hypothesis testing. Findings The main findings show that knowledge creation and knowledge targeting behaviors were correlated with multiple functional intentions (or needs) of users, meaning that users who utilized SM in order to fulfill many needs create and broadcast knowledge more than users that utilized SM in order to fulfill fewer needs. Originality The study investigates the relationship between detailed knowledge sharing behaviors afforded by the social media tools and different user self-determination factors, such as intensity, preference and needs. This study further describes the attributes of social media sharing as a bundle of content sharing strategies of creation, sharing and targeting, which are used differently based on different user characteristics and motivations.

[1]  Xiaohua Zeng,et al.  Social Ties and User Content Generation: Evidence from Flickr , 2013, Inf. Syst. Res..

[2]  Eyun‐Jung Ki,et al.  Factors affecting social presence and word-of-mouth in corporate social responsibility communication: Tone of voice, message framing, and online medium type , 2019, Public Relations Review.

[3]  Puneet Kaur,et al.  Why do people share fake news? Associations between the dark side of social media use and fake news sharing behavior , 2019, Journal of Retailing and Consumer Services.

[4]  Heather Skinnner,et al.  Who really creates the place brand?: Considering the role of user generated content in creating and communicating a place identity , 2018 .

[5]  Gregory J. L. Tourte,et al.  Twitter, information sharing and the London riots? , 2012 .

[6]  Filippo Menczer,et al.  Virality Prediction and Community Structure in Social Networks , 2013, Scientific Reports.

[7]  Petter Bae Brandtzæg,et al.  Social Networking Sites: Their Users and Social Implications - A Longitudinal Study , 2012, J. Comput. Mediat. Commun..

[8]  Sanghee Oh,et al.  Why do social network site users share information on Facebook and Twitter? , 2015, J. Inf. Sci..

[9]  Ann Majchrzak,et al.  Research Commentary-Vigilant Interaction in Knowledge Collaboration: Challenges of Online User Participation Under Ambivalence , 2010, Inf. Syst. Res..

[10]  Ann Majchrzak,et al.  Interactive Self-Regulatory Theory for Sharing and Protecting in Interorganizational Collaborations , 2015 .

[11]  Axel Bruns,et al.  TWITTER AS A TECHNOLOGY FOR AUDIENCING AND FANDOM , 2013 .

[12]  Christian Christensen,et al.  WAVE-RIDING AND HASHTAG-JUMPING , 2013 .

[13]  Hala Khayr Yaacoub,et al.  Effect of Facebook Friends on Each Other's Consumption Patterns , 2016 .

[14]  Dave Yates,et al.  The impact of focus, function, and features of shared knowledge on re-use in emergency management social media , 2016, J. Knowl. Manag..

[15]  Mahesh S. Raisinghani,et al.  The contributing factors of continuance usage of social media: An empirical analysis , 2018, Inf. Syst. Frontiers.

[16]  Maurizio Cavallari,et al.  Vulnerabilities of Smartphones Payment Apps: The Relevance in Developing Countries , 2017 .

[17]  Danah Boyd,et al.  I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience , 2011, New Media Soc..

[18]  Kazuyuki Aihara,et al.  Quantifying Collective Attention from Tweet Stream , 2013, PloS one.

[19]  H. Park,et al.  Predicting Opinion Leaders in Twitter Activism Networks , 2014 .

[20]  Giuseppe Carenini,et al.  Interactive topic hierarchy revision for exploring a collection of online conversations , 2019, Inf. Vis..

[21]  Ruth Page,et al.  The linguistics of self-branding and micro-celebrity in Twitter: The role of hashtags , 2012 .

[22]  Domenico Rosaci,et al.  Trust and Compactness in Social Network Groups , 2015, IEEE Transactions on Cybernetics.

[23]  M. S. Balaji,et al.  Measuring brand-related content in social media: a socialization theory perspective , 2019, Inf. Technol. People.

[24]  Seungyoon Lee,et al.  College Students' Motivations for Facebook Use and Psychological Outcomes , 2014 .

[25]  Hsia-Ching Chang,et al.  Trends in Twitter Hashtag Applications: Design Features for Value-Added Dimensions to Future Library Catalogues , 2012, Libr. Trends.

[26]  DongHee Kim,et al.  Online sharing behavior on social networking sites: Examining narcissism and gender effects , 2018 .

[27]  Stefan Stieglitz,et al.  Towards more systematic Twitter analysis: metrics for tweeting activities , 2013 .

[28]  Ann Majchrzak,et al.  The Impact of Shaping on Knowledge Reuse for Organizational Improvement with Wikis , 2013, MIS Q..

[29]  Anders Olof Larsson,et al.  Studying political microblogging: Twitter users in the 2010 Swedish election campaign , 2012, New Media Soc..

[30]  Jamie Carlson,et al.  Examining the drivers and brand performance implications of customer engagement with brands in the social media environment , 2014 .

[31]  Wondwesen Tafesse,et al.  Implementing social media marketing strategically: an empirical assessment , 2018, Journal of Marketing Management.

[32]  Tingting Zhang,et al.  Understanding user motivation for evaluating online content: a self-determination theory perspective , 2015, Behav. Inf. Technol..

[33]  J. Gibson The Ecological Approach to Visual Perception , 1979 .

[34]  William W. Gaver Technology affordances , 1991, CHI.

[35]  A. Bruns,et al.  RESEARCHING NEWS DISCUSSION ON TWITTER , 2012 .

[36]  Ainin Sulaiman,et al.  Facebook usage, socialization and academic performance , 2015, Comput. Educ..

[37]  Wei-Tsong Wang,et al.  Motivations of employees' knowledge sharing behaviors: A self-determination perspective , 2015, Inf. Organ..

[38]  E. Litt Knock, Knock. Who's There? The Imagined Audience , 2012 .

[39]  Edward A. Fox,et al.  Social media use by government: From the routine to the critical , 2012, Gov. Inf. Q..

[40]  Karim R. Lakhani,et al.  Special Section Introduction - Online Community as Space for Knowledge Flows , 2016, Inf. Syst. Res..

[41]  J. J. Gibson The theory of affordances , 1977 .

[42]  Paul M. Leonardi,et al.  Recognizing Expertise , 2017, Commun. Res..

[43]  Shintaro Okazaki,et al.  Exploring digital corporate social responsibility communications on Twitter , 2020, Journal of Business Research.

[44]  Olga Levina,et al.  Motivating social sharing of e-business content: Intrinsic motivation, extrinsic motivation, or crowding-out effect? , 2018, Comput. Hum. Behav..

[45]  Stefan Stieglitz,et al.  Quantitative Approaches to Comparing Communication Patterns on Twitter , 2012 .

[46]  A. Kaplan,et al.  Users of the world, unite! The challenges and opportunities of Social Media , 2010 .

[47]  Victor R. Prybutok,et al.  The Social Network Application Post-Adoptive Use Model (SNAPUM): A Model Examining Social Capital and Other Critical Factors Affecting the Post-Adoptive Use of Facebook , 2013, Informing Sci. Int. J. an Emerg. Transdiscipl..

[48]  Henning Rode,et al.  To share or not to share: the effects of extrinsic and intrinsic motivations on knowledge-sharing in enterprise social media platforms , 2016, J. Inf. Technol..

[49]  Bernd W. Wirtz,et al.  SOCIAL NETWORKS : USAGE INTENSITY AND EFFECTS ON PERSONALIZED ADVERTISING , 2017 .

[50]  Andrew Gelman,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models , 2006 .

[51]  P. Leonardi,et al.  Social Media Use in Organizations: Exploring the Affordances of Visibility, Editability, Persistence, and Association , 2013 .

[52]  Rafael L. G. Raimundo,et al.  Gatekeeping Twitter: message diffusion in political hashtags , 2013 .

[53]  Robin L. Wakefield,et al.  Social media network behavior: A study of user passion and affect , 2016, J. Strateg. Inf. Syst..

[54]  Suleyman Serdar Kozat,et al.  An Online Causal Inference Framework for Modeling and Designing Systems Involving User Preferences: A State-Space Approach , 2017, J. Electr. Comput. Eng..

[55]  Isabell M. Welpe,et al.  Tweets and Trades: The Information Content of Stock Microblogs , 2010 .

[56]  Aysegul Toker,et al.  Location sharing on social networks: implications for marketing , 2014 .

[57]  Joonghwa Lee,et al.  Exploring sharing behaviors across social media platforms , 2018, International Journal of Market Research.

[58]  Kathrin Kirchner,et al.  What factors influence knowledge sharing in organizations? A social dilemma perspective of social media communication , 2016, J. Knowl. Manag..

[59]  Enrique Bonsón,et al.  Citizens' engagement on local governments' Facebook sites. An empirical analysis: The impact of different media and content types in Western Europe , 2015, Gov. Inf. Q..

[60]  E. Deci,et al.  Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. , 2000, The American psychologist.

[61]  Colin Potts,et al.  Design of Everyday Things , 1988 .