Open Design Communities: An Exploration of Key Factors Influencing Intention to Contribute 3D Printable Designs
Description
This study sought to explore key factors that are likely to influence Thingiverse members' intentions to contribute their 3D printable designs. The dependent variable was intention to contribute 3D printable designs to Thingiverse and the independent variables were reputation, financial reward, knowledge self-efficacy, tenure in the field, and commitment. These data were collected via an online survey of Thingiverse members. This dataset contains a total of 78 complete cases consists of the following fields: 1. Q1 - 1 if the participant was over 18 years of age. 2 - No. 2. Q3 - 5 if the participant had contributed a design to Thingiverse. 6 - No. 3. Q4 - Informed consent. 1 is Yes and 2 is No. 4. Q5_Rep1 to Q9_Rep 5 - Reputation items measured using seven-point scales where 1 is Strongly disagree and 7 is Strongly agree. 5. Q10_Fin1 to Q13_Fin4 - Financial reward items measured using seven-point scales where 1 is Strongly disagree and 7 is Strongly agree. 6. Q14_KSE1 to Q18_KSE5 - Knowledge self-efficacy items measured using seven-point scales where 1 is Strongly disagree and 7 is Strongly agree. Q16_KSE3R and Q17_KSE4R were reverse coded. 7. Q19_Ten - Tenure in the field. 1 is less than one year and 14 is more than 12 years. 8. Q20_Com1 to Q23_Com4 - Commitment items measured using seven-point scales where 1 is Strongly disagree and 7 is Strongly agree. 9. Q24_BI1 to Q26_BI3 - Contribution intention items measured using seven-point scales where 1 is Strongly disagree and 7 is Strongly agree. 10. Q27_AC - Attention check. Participants were asked to select 7, i.e., strongly agree. 11. Q28 - Age. Ranging from 1, which is 18 years, to 84, which is over 100 years. 12. Q29 - Gender. 1 is male, 2 is female, and 3 is prefer not to say. 13. filter_$ - Is a filter for the attention check. 14. KSE3 and KSE4 - Reversed KSE3R and KSE4R. 15. Actual_Age - Actual ages. Transformed Q28. 16. Gender_Male and Gender_Other - Binary variables indicating gender male and gender other respectively. The results of our analysis using PLS-SEM indicate that knowledge self-efficacy is a significant positive predictor of individuals' intentions to contribute 3D printable designs.