Dimensions of User Behavior in Enterprise Social Networks

2017 ◽  
pp. 125-146 ◽  
Author(s):  
Janine Hacker ◽  
Rebecca Bernsmann ◽  
Kai Riemer
Keyword(s):  
Social Networks ◽  
User Behavior ◽  
Enterprise Social Networks

scholarly journals Investigating the Impact of Critical Factors on Continuous Usage Intention towards Enterprise Social Networks: An Integrated Model of IS Success and TTF

2021 ◽  
Vol 13 (14) ◽  
pp. 7619
Author(s):  
Run-Ze Wu ◽  
Xiu-Fu Tian
Keyword(s):  
Social Networks ◽  
Structural Equation ◽  
Equation Modeling ◽  
Critical Factors ◽  
Information Systems Success ◽  
Is Success ◽  
Enterprise Social Networks ◽  
Task Technology Fit ◽  
Continuous Usage Intention ◽  
The Impact

Due to the outbreak of COVID-19, many people have to accept remote working. However, as COVID-19 has been effectively controlled in China, remote office services provided by enterprise social networks (ESNs) is no longer a necessary choice of users. There has not yet been any referential research for ESN enterprises concerning how to encourage users willing to use ESNs continuously. Therefore, the purpose of this research is to identify the critical factors of ESN continuous usage intention to make up the research gap of ESN continuous usage intention and to help enterprises address the issue of sustained growth. This research combines elements of the task technology fit (TTF) model and D&M information systems success (ISS) model, explaining the continuous usage intention of ESN users. The empirical analysis results are based on the sample data of 668 Chinese respondents with experience in ESNs use and analyzed using structural equation modeling (SEM). Results show that task technology fit, performance expectancy and the satisfaction degree have a significant influence on continuous usage intention of ESNs. The research findings can provide the theoretical basis for sustained development and follow-up research of the ESN industry.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

Using enterprise social networks as a knowledge management tool in higher education

2017 ◽  
Vol 47 (4) ◽  
pp. 555-570 ◽  
Author(s):  
Niall Corcoran ◽  
Aidan Duane
Keyword(s):  
Higher Education ◽  
Social Networks ◽  
Social Media ◽  
Knowledge Sharing ◽  
Conceptual Model ◽  
Higher Education Institutions ◽  
Management Tool ◽  
Content Type ◽  
Enterprise Social Networks ◽  
Practical Implications

Purpose The management of organisational knowledge and the promotion of staff knowledge sharing are largely neglected in higher education institutions. The purpose of this study is to examine how enterprise social networks can enable staff knowledge sharing in communities of practice in that context. Design/methodology/approach The study is framed as an Action Research project, covering three cycles over a 12-month period. During the Diagnosing phase, a conceptual model was developed for empirical testing. Data were collected through 30 semi-structured interviews and a number of focus groups. This was supplemented by content analysis and reflective journaling. Findings The findings support the conceptual model and provide insight into the antecedents necessary for the creation of an enterprise social network-enabled knowledge-sharing environment, the motivators for and barriers to participation, and the perceived organisational and individual benefits of increased staff knowledge-sharing activity. Research limitations/implications As the study has a higher education focus, all of the findings may not be generalizable to other types of organisation. Further development of the conceptual model and testing in other contextual settings will yield greater generalizability. Practical implications A number of findings have practical implications for the management of higher education institutions, such as the evidence of a divide between faculty and other staff. In general, the study findings provide an opportunity for educationalists to better understand the scope and impact of employing social media platforms for knowledge sharing. Originality/value This paper adds to the growing body of work on organisational implementations of social media, and should be of interest to practitioners and researchers undertaking similar projects.

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