Regression analysis: Simple Linear Regression Multiple Linear Regression
Social Media And Project Management
1. Investigation in to the factors influencing the intention to use social media tools within virtual project teamsPresented at PMI Global Congress – EMEA - 2011 Jerry Giltenane
2.
3. This research is based on my 2011 MBA dissertation for Henley Business School.
4.
5.
6. But project success rates static (Standish group – 44% of projects challenged).
7. Communication key to all projects but particularly for virtual teams and a key project success factor.
8. Social media the communication medium of choice for next generation of project managers and teams.
9.
10. The Social Network There is an argument supported by a McKinsey Quarterly 2007 report that more useful business information flows through informal social networks then official channels within organizations. Social media tools are suited to employee to employee communication improvement, lessens the isolation issues for remote employees and tools such as micro-blogging can improve real time communication leading to competitive advantage. Elizabeth Harrin (Member of PMI New Media Council) 2010 study : * 62% of respondents believe that use of social media and enterprise collaboration give communication efficiency benefits. * 73% of respondents noted that social media will be a key issue for project management going forward
11. Technology Adoption models Technology adoption models try to explain the factors that influence / predict intention or usage of technology . Such models are based on psychology theory. Intention seen as a good predictor of Intent. Many model exist such as TRA, TPB, TAM and diffusion of innovation to mention a few. UTAUT Model developed in 2003 by Venkatesh to combine all previous models in to one combined model. The UTAUT model has shown to be able to explain 70% . ( Adjusted R2) of the variance in intention to adopt a technology.
23. Survey only looked for respondents that worked in virtual project teams.
24.
25. Survey Demographics Sample Size : 84 Male 70.5% , Female – 29.5% =< 40 Years 58%, > 40 Years 42% Project Managers 73.9 % ,Other 23.1 % =< 3 years of virtual experience 34.1% > 3 years virtual experience 65.9% IT Industry 33% , Other 67%
26. Results Descriptive analysis of the key constructs. Inferential analysis of the proposed model predicitive ability using multiple regression analysis in SPSS. Correlation of the constructs to behavioural intent. Group comparison in terms of intention to use social media over the next 6 months.
33. Model Regression Result An adjusted R Square of .527 means that taken together, social influence, performance expectancy, effort expectancy and team trust can be seen to account for 52.7 % of the variance in the intention rate to adopt social media technology. This result is a relatively strong outcome and shows the variables taken together as a good predictor on behavioural intention.
34. Construct Correlations Beta values here indicate the strength of the contribution of a variable in explaining the dependant variable behavioural intent, when the variance explained by all other variables is controlled. Performance Expectancy the strongest contributor at .421 or 42%. Other three variables still significant contributors. Team Trust has negative correlation indicating the less trust in a team the greater intent to use social media
35. Behavioural Intent By Group Independent Samples t-test method in SPSS was used to understand whether intention to use social media varies by a number of modifying factors. Gender Age group IT Industry V others Experience In Each case the Sig. 2-tailed value for each test was greater then .05. This indicates that there was no significant difference between the groups in terms of intention to adopt social media.
36. Conclusions Performance expectancy is the strongest influencing factor in adoption intention. Social influence, effort expectancy and team trust are also significant factors. Team trust is negatively correlated – perhaps respondents see social media as a way to overcome trust issues. The UTAUT model is shown to have a reasonably strong predictive ability. Age, experience, gender and industry does not moderate intention to adopt.
37. Recommendations Be cognitive of the importance of ensuring that social media tools are designed to clearly aid staff productivity - Vroom’s expectancy theory of motivation where outcomes and not needs are what motivate people to do certain acts Take into account simultaneously both the instrumental and effortless side of technology – make internal tools work and look like public social media tools Hire a Internal Social Media Tools manager: help enforce social influence Negative trust correlation: further research needed – may indicate that social media technology can help overcome trust issue. Might be important in team formation Internal social media adoption does not vary on team composition – reflects popularity of external social networks
38. Finally Andrew S. Grove of Intel Corporation quotation below holds as true today as it did in 1988. “You have no choice but to operate in a world shaped by globalization and the information revolution. There are two options: Adapt or die.”
![Introduction ,[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)