A Derivation of Factors Influencing the Innovation Diffusion of the OpenStreetMap in STEM Education
Abstract
:1. Introduction
2. Literature Review
2.1. GIS and STEM Education
2.2. Openness and STEM Education
2.3. OpenStreetMap
2.4. Innovation Diffusion Theory
2.5. Technology Acceptance Model
2.6. The Integration of TAM and IDT
2.7. Research Hypotheses
3. Research Method
3.1. PLS-SEM
3.2. Sample and Measures
4. Results
5. Discussion
5.1. Implications of STEM Students’ Perceptions on the Diffusion of OSM
5.2. Implications of the Empirical Study Results
5.2.1. Relative Advantage Perspective
5.2.2. Compatibility Perspective
5.2.3. Ease of Use Perspective
5.2.4. Trialability Perspective
5.2.5. Observability Perspective
5.2.6. Perceived Usefulness Perspective
5.2.7. Perceived Attitude Perspective
5.3. Differences in Students’ Perceptions of OSM and Other Similar Technologies
5.4. Limitations and Suggestions for Future Study
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Item Code | Mean | Standard Deviation | Standard Error | Excess Kurtosis | Skewness | Zskewness | Zkurtosis |
---|---|---|---|---|---|---|---|
a1 | 3.979 | 0.834 | 0.069 | 0.488 | −0.537 | 7.046 | −7.753 |
a2 | 3.952 | 0.791 | 0.066 | −0.173 | −0.252 | −2.634 | −3.836 |
a3 | 3.986 | 0.788 | 0.065 | −0.084 | −0.318 | −1.284 | −4.859 |
a4 | 3.510 | 0.84 | 0.070 | −0.230 | 0.214 | −3.297 | 3.068 |
a5 | 3.745 | 0.795 | 0.066 | 0.014 | −0.174 | 0.212 | −2.636 |
a6 | 3.738 | 0.805 | 0.067 | −0.700 | 0.034 | −10.471 | 0.509 |
c1 | 3.634 | 0.768 | 0.064 | 0.150 | −0.093 | 2.352 | −1.458 |
c2 | 3.814 | 0.705 | 0.059 | −0.311 | −0.077 | −5.312 | −1.315 |
c3 | 3.876 | 0.778 | 0.065 | 0.199 | −0.311 | 3.080 | −4.814 |
u1 | 3.759 | 0.873 | 0.072 | 0.057 | −0.324 | 0.786 | −4.469 |
u2 | 3.503 | 0.831 | 0.069 | −0.183 | 0.207 | −2.652 | 3.000 |
u3 | 3.566 | 0.829 | 0.069 | −0.643 | 0.303 | −9.340 | 4.401 |
u4 | 3.655 | 0.834 | 0.069 | 0.651 | −0.287 | 9.399 | −4.144 |
u5 | 3.800 | 0.802 | 0.067 | −0.857 | 0.055 | −12.867 | 0.826 |
u6 | 3.883 | 0.826 | 0.069 | −0.019 | −0.370 | −0.277 | −5.394 |
u7 | 3.690 | 0.867 | 0.072 | −0.376 | −0.119 | −5.222 | −1.653 |
u8 | 3.752 | 0.792 | 0.066 | −0.604 | −0.027 | −9.183 | −0.411 |
t1 | 3.779 | 0.765 | 0.064 | −0.780 | 0.121 | −12.278 | 1.905 |
t2 | 3.297 | 0.983 | 0.082 | −0.331 | −0.099 | −4.055 | −1.213 |
t3 | 3.738 | 0.77 | 0.064 | −1.048 | 0.400 | −16.389 | 6.255 |
t4 | 3.690 | 0.775 | 0.064 | 0.015 | −0.024 | 0.233 | −0.373 |
t5 | 3.738 | 0.743 | 0.062 | −0.933 | 0.366 | −15.121 | 5.932 |
o1 | 2.766 | 1.303 | 0.108 | −0.940 | 0.161 | −8.687 | 1.488 |
o2 | 3.255 | 1.137 | 0.094 | −0.456 | −0.204 | −4.829 | −2.160 |
o3 | 3.641 | 0.802 | 0.067 | 0.002 | −0.147 | 0.030 | −2.207 |
o4 | 2.821 | 1.408 | 0.117 | −1.237 | 0.083 | −10.579 | 0.710 |
o5 | 3.166 | 1.232 | 0.102 | −0.717 | −0.298 | −7.008 | −2.913 |
pu1 | 3.593 | 0.913 | 0.076 | 0.282 | −0.307 | 3.719 | −4.049 |
pu2 | 3.738 | 0.788 | 0.065 | −0.040 | −0.094 | −0.611 | −1.436 |
pu3 | 3.772 | 0.759 | 0.063 | −0.885 | 0.219 | −14.041 | 3.474 |
pu4 | 3.869 | 0.763 | 0.063 | −0.886 | 0.040 | −13.983 | 0.631 |
i1 | 3.855 | 0.761 | 0.063 | −0.878 | 0.062 | −13.893 | 0.981 |
i2 | 3.793 | 0.778 | 0.065 | −0.850 | 0.115 | −13.156 | 1.780 |
i3 | 3.745 | 0.82 | 0.068 | −0.314 | −0.024 | −4.611 | −0.352 |
i4 | 3.517 | 0.94 | 0.078 | −0.448 | 0.050 | −5.739 | 0.641 |
i5 | 3.634 | 0.854 | 0.071 | −0.336 | −0.023 | −4.738 | −0.324 |
i6 | 3.559 | 0.893 | 0.074 | −0.510 | 0.115 | −6.877 | 1.551 |
i7 | 3.634 | 0.812 | 0.067 | −0.244 | 0.142 | −3.618 | 2.106 |
pa1 | 3.924 | 0.831 | 0.069 | −0.277 | −0.293 | −4.014 | −4.246 |
pa2 | 3.952 | 0.799 | 0.066 | −1.059 | −0.076 | −15.960 | −1.145 |
pa3 | 3.931 | 0.785 | 0.065 | −0.789 | −0.137 | −12.103 | −2.102 |
pa4 | 3.772 | 0.777 | 0.065 | −0.849 | 0.155 | −13.157 | 2.402 |
pa5 | 3.910 | 0.838 | 0.070 | −0.508 | −0.184 | −7.300 | −2.644 |
Latent Variables | Cronbach’s Alpha | R2 | Composite Reliability | Average Variance Extracted | Redundancy |
---|---|---|---|---|---|
Compatibility | 0.877 | N.A. | 0.925 | 0.804 | N.A. |
Intention of Continued Usage | 0.935 | 0.812 | 0.947 | 0.719 | 0.576 |
Ease of Use | 0.921 | N.A. | 0.936 | 0.645 | N.A. |
Observability | 0.843 | N.A. | 0.885 | 0.607 | N.A. |
Perceived Attitude | 0.957 | 0.717 | 0.967 | 0.853 | 0.604 |
Perceived Usefulness | 0.930 | 0.641 | 0.950 | 0.827 | 0.522 |
Relative Advantage | 0.900 | N.A. | 0.924 | 0.670 | N.A. |
Trialability | 0.852 | N.A. | 0.894 | 0.629 | N.A. |
Latent Variables | Compatibility | Intention of Continued Usage | Ease of Use | Observability | Perceived Attitude | Perceived Usefulness | Relative Advantage | Trialability |
---|---|---|---|---|---|---|---|---|
Compatibility | 0.897 | |||||||
Intention of Continued Usage | 0.753 | 0.848 | ||||||
Ease of Use | 0.875 | 0.793 | 0.803 | |||||
Observability | 0.560 | 0.667 | 0.614 | 0.779 | ||||
Perceived Attitude | 0.734 | 0.814 | 0.786 | 0.508 | 0.924 | |||
Perceived Usefulness | 0.709 | 0.867 | 0.730 | 0.688 | 0.751 | 0.910 | ||
Relative Advantage | 0.803 | 0.742 | 0.790 | 0.534 | 0.759 | 0.683 | 0.818 | |
Trialability | 0.764 | 0.730 | 0.789 | 0.567 | 0.718 | 0.690 | 0.720 | 0.793 |
Item code | Compatibility | Intention of Continued Usage | Ease of Use | Observability | Perceived Attitude | Perceived Usefulness | Relative Advantage | Trialability |
---|---|---|---|---|---|---|---|---|
a1 | 0.700 | 0.596 | 0.696 | 0.463 | 0.691 | 0.588 | 0.833 | 0.652 |
a2 | 0.686 | 0.603 | 0.650 | 0.409 | 0.653 | 0.569 | 0.878 | 0.645 |
a3 | 0.574 | 0.514 | 0.535 | 0.322 | 0.592 | 0.477 | 0.794 | 0.588 |
a4 | 0.557 | 0.598 | 0.547 | 0.411 | 0.487 | 0.500 | 0.698 | 0.415 |
a5 | 0.721 | 0.638 | 0.707 | 0.467 | 0.649 | 0.561 | 0.866 | 0.614 |
a6 | 0.691 | 0.702 | 0.726 | 0.570 | 0.631 | 0.653 | 0.827 | 0.584 |
c1 | 0.846 | 0.683 | 0.760 | 0.508 | 0.634 | 0.594 | 0.668 | 0.644 |
c2 | 0.914 | 0.668 | 0.795 | 0.512 | 0.679 | 0.675 | 0.732 | 0.707 |
c3 | 0.929 | 0.675 | 0.801 | 0.501 | 0.662 | 0.637 | 0.765 | 0.703 |
i1 | 0.648 | 0.839 | 0.626 | 0.557 | 0.672 | 0.816 | 0.677 | 0.609 |
i2 | 0.657 | 0.845 | 0.619 | 0.614 | 0.654 | 0.802 | 0.659 | 0.618 |
i3 | 0.662 | 0.859 | 0.679 | 0.585 | 0.685 | 0.729 | 0.685 | 0.612 |
i4 | 0.646 | 0.863 | 0.720 | 0.602 | 0.658 | 0.722 | 0.617 | 0.626 |
i5 | 0.623 | 0.876 | 0.711 | 0.562 | 0.761 | 0.710 | 0.607 | 0.634 |
i6 | 0.625 | 0.854 | 0.697 | 0.584 | 0.667 | 0.689 | 0.573 | 0.611 |
i7 | 0.602 | 0.795 | 0.658 | 0.479 | 0.733 | 0.662 | 0.613 | 0.624 |
o1 | 0.327 | 0.372 | 0.337 | 0.767 | 0.253 | 0.395 | 0.279 | 0.313 |
o2 | 0.466 | 0.529 | 0.530 | 0.793 | 0.399 | 0.520 | 0.476 | 0.455 |
o3 | 0.627 | 0.695 | 0.663 | 0.742 | 0.644 | 0.698 | 0.583 | 0.662 |
o4 | 0.310 | 0.405 | 0.354 | 0.803 | 0.204 | 0.429 | 0.276 | 0.296 |
o5 | 0.321 | 0.468 | 0.369 | 0.781 | 0.315 | 0.506 | 0.358 | 0.341 |
pa1 | 0.666 | 0.699 | 0.704 | 0.443 | 0.925 | 0.650 | 0.665 | 0.658 |
pa2 | 0.653 | 0.724 | 0.698 | 0.431 | 0.929 | 0.694 | 0.689 | 0.663 |
pa3 | 0.712 | 0.766 | 0.728 | 0.481 | 0.956 | 0.710 | 0.730 | 0.657 |
pa4 | 0.683 | 0.806 | 0.748 | 0.537 | 0.903 | 0.730 | 0.719 | 0.692 |
pa5 | 0.673 | 0.754 | 0.746 | 0.475 | 0.905 | 0.682 | 0.692 | 0.633 |
pu1 | 0.593 | 0.744 | 0.610 | 0.732 | 0.651 | 0.866 | 0.564 | 0.560 |
pu2 | 0.666 | 0.774 | 0.693 | 0.658 | 0.642 | 0.919 | 0.601 | 0.644 |
pu3 | 0.666 | 0.820 | 0.705 | 0.615 | 0.715 | 0.938 | 0.666 | 0.668 |
pu4 | 0.651 | 0.811 | 0.644 | 0.517 | 0.724 | 0.914 | 0.662 | 0.637 |
t1 | 0.684 | 0.641 | 0.723 | 0.456 | 0.663 | 0.547 | 0.658 | 0.819 |
t2 | 0.508 | 0.489 | 0.541 | 0.507 | 0.404 | 0.456 | 0.425 | 0.725 |
t3 | 0.410 | 0.390 | 0.384 | 0.287 | 0.414 | 0.397 | 0.413 | 0.693 |
t4 | 0.675 | 0.639 | 0.728 | 0.530 | 0.670 | 0.627 | 0.627 | 0.856 |
t5 | 0.681 | 0.670 | 0.667 | 0.475 | 0.609 | 0.658 | 0.644 | 0.858 |
u1 | 0.714 | 0.708 | 0.827 | 0.528 | 0.730 | 0.619 | 0.701 | 0.634 |
u2 | 0.701 | 0.618 | 0.781 | 0.490 | 0.556 | 0.557 | 0.561 | 0.610 |
u3 | 0.706 | 0.658 | 0.820 | 0.521 | 0.573 | 0.628 | 0.612 | 0.590 |
u4 | 0.685 | 0.547 | 0.742 | 0.376 | 0.566 | 0.505 | 0.579 | 0.511 |
u5 | 0.739 | 0.621 | 0.808 | 0.448 | 0.616 | 0.557 | 0.701 | 0.650 |
u6 | 0.726 | 0.664 | 0.821 | 0.519 | 0.706 | 0.578 | 0.679 | 0.655 |
u7 | 0.643 | 0.619 | 0.800 | 0.521 | 0.637 | 0.586 | 0.619 | 0.668 |
u8 | 0.717 | 0.649 | 0.824 | 0.559 | 0.642 | 0.649 | 0.631 | 0.734 |
Hypothesis | Original Sample (O) | Sample Mean (M) | Std. Deviation (STDEV) | t Statistics (|O/STDEV|) | p Values |
---|---|---|---|---|---|
Compatibility → Perceived Attitude | −0.056 | −0.064 | 0.153 | 0.365 | 0.715 |
Compatibility → Perceived Usefulness | 0.265 | 0.262 | 0.118 | 2.253 | 0.024 |
Ease of Use → Perceived Attitude | 0.314 | 0.324 | 0.157 | 2.007 | 0.045 |
Ease of Use → Perceived Usefulness | 0.267 | 0.274 | 0.116 | 2.305 | 0.021 |
Observability → Perceived Usefulness | 0.376 | 0.376 | 0.074 | 5.112 | 0.000 |
Perceived Attitude → Intention of Continued Usage | 0.374 | 0.374 | 0.071 | 5.245 | 0.000 |
Perceived Usefulness → Intention of Continued Usage | 0.586 | 0.586 | 0.069 | 8.455 | 0.000 |
Perceived Usefulness → Perceived Attitude | 0.301 | 0.300 | 0.091 | 3.308 | 0.001 |
Relative Advantage → Perceived Attitude | 0.271 | 0.276 | 0.096 | 2.820 | 0.005 |
Trialability → Perceived Attitude | 0.111 | 0.105 | 0.105 | 1.053 | 0.293 |
References
- Franklin, C. An introduction to geographic information systems: Linking maps to databases. Database 1992, 15, 12–21. [Google Scholar]
- Brown, G.; Kelly, M.; Whitall, D. Which ‘public’? Sampling effects in public participation GIS (PPGIS) and volunteered geographic information (VGI) systems for public lands management. J. Environ. Plan. Manag. 2014, 57, 190–214. [Google Scholar] [CrossRef]
- Senaratne, H.; Mobasheri, A.; Ali, A.L.; Capineri, C.; Haklay, M. A review of volunteered geographic information quality assessment methods. Int. J. Geogr. Inf. Sci. 2017, 31, 139–167. [Google Scholar] [CrossRef]
- Flanagin, A.J.; Metzger, M.J. The credibility of volunteered geographic information. GeoJournal 2008, 72, 137–148. [Google Scholar] [CrossRef]
- Bartoschek, T.; Keßler, C. VGI in education: From K-12 to graduate studies. In Crowdsourcing Geographic Knowledge; Springer: Berlin, Germany, 2013; pp. 341–360. [Google Scholar]
- Fritz, S.; See, L.; Brovelli, M. Motivating and sustaining participation in VGI. In Mapping and the Citizen Sensor; Foody, G., See, L., Fritz, S., Mooney, P., Olteanu-Raimond, A.-M., Fonte, C.C., Antoniou, V., Eds.; Ubiquity Press Ltd.: London, UK, 2017. [Google Scholar]
- Fonseca Filho, H.; Leite, B.P.; Pompermayer, G.A.; Werneck, M.G.; Leyh, W. Teaching VGI as a strategy to promote the production of urban digital cartographic databases. In Proceedings of the Urban Remote Sensing Event (JURSE), 2013 Joint, Sao Paulo, Brazil, 21–23 April 2013; pp. 222–225. [Google Scholar]
- Aitken, S.; Valentine, G. Approaches to Human Geography; SAGE Publications Ltd.: London, UK, 2006. [Google Scholar] [CrossRef]
- Bonham-Carter, G.F. Geographic Information Systems for Geoscientists: Modelling with GIS; Elsevier: New York, NY, USA, 2014; Volume 13. [Google Scholar]
- Demirci, A. Evaluating the implementation and effectiveness of GIS-based application in secondary school geography lessons. Am. J. Appl. Sci. 2008, 5, 169–178. [Google Scholar] [CrossRef]
- Kawabata, M.; Thapa, R.B.; Oguchi, T.; Tsou, M.-H. Multidisciplinary cooperation in GIS education: A case study of US colleges and universities. J. Geogr. High. Educ. 2010, 34, 493–509. [Google Scholar] [CrossRef]
- Asghar, A.; Ellington, R.; Rice, E.; Johnson, F.; Prime, G.M. Supporting STEM education in secondary science contexts. Interdiscip. J. Probl. Based Learn. 2012, 6, 4. [Google Scholar] [CrossRef]
- Uttal, D.H.; Cohen, C.A. Spatial thinking and STEM education: When, why, and how? In Psychology of Learning and Motivation; Elsevier: New York, NY, USA, 2012; Volume 57, pp. 147–181. [Google Scholar]
- Stieff, M.; Uttal, D. How much can spatial training improve STEM achievement? Educ. Psychol. Rev. 2015, 27, 607–615. [Google Scholar] [CrossRef]
- Baker, T. Advancing STEM Education with GIS; ESRI: Redlands, CA, USA, 2012. [Google Scholar]
- Ercan, S.; Altan, E.B.; Tastan, B.; Ibrahim, D. Integrating GIS into Science Classes to Handle STEM Education. J. Turk. Sci. Educ. 2016, 13, 30–34. [Google Scholar]
- Goodchild, M. NeoGeography and the nature of geographic expertise. J. Locat. Based Serv. 2009, 3, 82–96. [Google Scholar] [CrossRef]
- Anthony, D.; Smith, S.W.; Williamson, T. Reputation and reliability in collective goods the case of the online encyclopedia wikipedia. Ration. Soc. 2009, 21, 283–306. [Google Scholar] [CrossRef]
- OpenStreetMap Project. Available online: https://www.openstreetmap.org (accessed on 31 August 2018).
- Koletsis, E.; van Elzakker, C.P.; Kraak, M.-J.; Cartwright, W.; Arrowsmith, C.; Field, K. An investigation into challenges experienced when route planning, navigating and wayfinding. Int. J. Cartogr. 2017, 3, 4–18. [Google Scholar] [CrossRef]
- Maps.me Application. Available online: https://maps.me (accessed on 25 August 2018).
- Haklay, M. How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets. Environ. Plan. B Plan. Des. 2010, 37, 682–703. [Google Scholar] [CrossRef]
- Goodchild, M.F.; Li, L. Assuring the quality of volunteered geographic information. Spat. Stat. 2012, 1, 110–120. [Google Scholar] [CrossRef]
- Fan, H.; Zipf, A.; Fu, Q.; Neis, P. Quality assessment for building footprints data on OpenStreetMap. Int. J. Geogr. Inf. Sci. 2014, 28, 700–719. [Google Scholar] [CrossRef]
- Neis, P.; Zielstra, D.; Zipf, A. Comparison of volunteered geographic information data contributions and community development for selected world regions. Future Internet 2013, 5, 282–300. [Google Scholar] [CrossRef]
- Barron, C.; Neis, P.; Zipf, A. A comprehensive framework for intrinsic OpenStreetMap quality analysis. Trans. GIS 2014, 18, 877–895. [Google Scholar] [CrossRef]
- Touya, G.; Reimer, A. Inferring the scale of OpenStreetMap features. In OpenStreetMap in GIScience; Springer: Berlin, Germany, 2015; pp. 81–99. [Google Scholar]
- Girres, J.F.; Touya, G. Quality assessment of the French OpenStreetMap dataset. Trans. GIS 2010, 14, 435–459. [Google Scholar] [CrossRef]
- Council, N.R.; Committee, G.S. Learning to Think Spatially; National Academies Press: Washington, DC, USA, 2005. [Google Scholar]
- Rogers, E.M. Diffusion of Innovations; Free Press: New York, NY, USA, 1962. [Google Scholar]
- Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 319–340. [Google Scholar] [CrossRef]
- Giovanis, A.N.; Binioris, S.; Polychronopoulos, G. An extension of TAM model with IDT and security/privacy risk in the adoption of internet banking services in Greece. EuroMed J. Bus. 2012, 7, 24–53. [Google Scholar] [CrossRef]
- Shiau, W.-L.; Chau, P.Y. Understanding behavioral intention to use a cloud computing classroom: A multiple model comparison approach. Inf. Manag. 2015, 53, 355–365. [Google Scholar] [CrossRef]
- Wu, J.-H.; Wang, S.-C. What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Inf. Manag. 2005, 42, 719–729. [Google Scholar] [CrossRef]
- Zhang, N.; Guo, X.; Chen, G. IDT-TAM integrated model for IT adoption. Tsinghua Sci. Technol. 2008, 13, 306–311. [Google Scholar] [CrossRef]
- Baker, T. Advancing STEM Education with GIS. Available online: http://www.esri.com/library/ebooks/advancing-stem-education-with-gis.pdf (accessed on 7 June 2018).
- Wold, S.; Ruhe, A.; Wold, H.; Dunn, W.J., III. The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM J. Sci. Stat. Comput. 1984, 5, 735–743. [Google Scholar] [CrossRef]
- Abdi, H. Partial least square regression (PLS regression). Encycl. Res. Methods Soc. Sci. 2003, 6, 792–795. [Google Scholar]
- Hair, J.F.; Ringle, C.M.; Sarstedt, M. PLS-SEM: Indeed a silver bullet. J. Mark. Theory Pract. 2011, 19, 139–152. [Google Scholar] [CrossRef]
- Almendros-Jiménez, J.M.; Becerra-Terón, A.; Torres, M. Integrating and Querying OpenStreetMap and Linked Geo Open Data. Comput. J. 2017, 1–25. [Google Scholar] [CrossRef]
- Nugent, G.; Barker, B.; Grandgenett, N.; Adamchuk, V.I. Impact of robotics and geospatial technology interventions on youth STEM learning and attitudes. J. Res. Technol. Educ. 2010, 42, 391–408. [Google Scholar] [CrossRef]
- Demirci, A.; Karaburun, A.; Ünlü, M.; Özey, R. Using GIS-based projects in learning: Students help disabled pedestrians in their school district. Eur. J. Geogr. 2011, 2, 48–61. [Google Scholar]
- Ratinen, I.; Keinonen, T. Student-teachers’ use of Google Earth in problem-based geology learning. Int. Res. Geogr. Environ. Educ. 2011, 20, 345–358. [Google Scholar] [CrossRef]
- Kerski, J.J. The implementation and effectiveness of geographic information systems technology and methods in secondary education. J. Geogr. 2003, 102, 128–137. [Google Scholar] [CrossRef]
- McWilliams, H.; Rooney, P. Mapping Our City: Learning to Use Spatial Data in the Middle School Science Classroom; National Science Foundation: Arlington, VA, USA, 1997.
- Whitaker, D. Using geographic information systems in science classrooms. Educ. Rev. 2011, 51–68. [Google Scholar] [CrossRef]
- Peters, M.A.; Britez, R.G. Open Education and Education for Openness; Sense Publishers Rotterdam: Dordrecht, The Netherlands, 2008. [Google Scholar]
- Meiszner, A.; Squire, L.; Husmann, E. Openness and Education; Emerald Group Publishing: Bingley, UK, 2013. [Google Scholar]
- Peter, S.; Deimann, M. On the role of openness in education: A historical reconstruction. Open Prax. 2013, 5, 7–14. [Google Scholar] [CrossRef]
- Porcello, D.; Hsi, S. Crowdsourcing and curating online education resources. Science 2013, 341, 240–241. [Google Scholar] [CrossRef] [PubMed]
- Johnson, L.; Adams Becker, S.; Estrada, V.; Martín, S. Technology Outlook for STEM+ Education 2013–2018: An NMC Horizon Project Sector Analysis; New Media Consortium: Austin, TX, USA, 2013. [Google Scholar]
- Peters, M.A.; Roberts, P. Virtues of Openness: Education, Science, and Scholarship in the Digital Age; Routledge: Abingdon, UK, 2015. [Google Scholar]
- Kafai, Y.B. From computational thinking to computational participation in K—12 education. Commun. ACM 2016, 59, 26–27. [Google Scholar] [CrossRef]
- Hicks, S.; Aufdenkampe, A.; Horsburgh, J.; Arscott, D.; Muenz, T.; Bressler, D. Open source hardware solutions for low-cost, do-it-yourself environmental monitoring, citizen science, and STEM education. In AGU Fall Meeting Abstracts; American Geophysical Union: Washington, DC, USA, 2016. [Google Scholar]
- Paulos, E.; Kim, S.; Kuznetsov, S. The Rise of the Expert Amateur: Citizen Science and Microvolunteerism. In From Social Butterfly to Engaged Citizen: Urban Informatics, Social Media, Ubiquitous Computing, and Mobile Technology to Support Citizen Engagement; MIT Press: Cambridge, MA, USA, 2011; p. 167. [Google Scholar]
- Meyer, M. Hacking Life? The Politics and Poetics of DIY Biology; MIT Press: Cambridge, MA, USA, 2014. [Google Scholar]
- Ostuzzi, F.; Conradie, P.; De Couvreur, L.; Detand, J.; Saldien, J. The Role of Re-Appropriation in Open Design: A Case Study on How Openness in Higher Education for Industrial Design Engineering Can Trigger Global Discussions on the Theme of Urban Gardening. Int. Rev. Res. Open Distrib. Learn. 2016, 17. [Google Scholar] [CrossRef]
- O’reilly, T. What Is Web 2.0: Design Patterns and Business Models for the Next Generation of Software. 2007, pp. 17–37. Available online: https://mpra.ub.uni-muenchen.de/4580/ (accessed on 27 September 2018).
- Vickery, G.; Wunsch-Vincent, S. Participative Web and User-Created Content: Web 2.0 Wikis and Social Networking; Organization for Economic Cooperation and Development (OECD): Paris, France, 2007. [Google Scholar]
- Krumm, J.; Davies, N.; Narayanaswami, C. User-generated content. IEEE Pervasive Comput. 2008, 7, 10–11. [Google Scholar] [CrossRef]
- Drummond, W.J.; French, S.P. The future of GIS in planning: Converging technologies and diverging interests. J. Am. Plan. Assoc. 2008, 74, 161–174. [Google Scholar] [CrossRef]
- Fritz, S.; McCallum, I.; Schill, C.; Perger, C.; See, L.; Schepaschenko, D.; Van der Velde, M.; Kraxner, F.; Obersteiner, M. Geo-Wiki: An online platform for improving global land cover. Environ. Model. Softw. 2012, 31, 110–123. [Google Scholar] [CrossRef]
- Aly, H.; Basalamah, A.; Youssef, M. Map++: A crowd-sensing system for automatic map semantics identification. In Proceedings of the Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Singapore, 30 June–3 July 2014; pp. 546–554. [Google Scholar]
- Boulton, A. Just maps: Google’s democratic map-making community? Cartogr. Int. J. Geogr. Inf. Geovisual. 2010, 45, 1–4. [Google Scholar] [CrossRef]
- Coleman, D.J. Volunteered geographic information in spatial data infrastructure: An early look at opportunities and constraints. In Proceedings of the GSDI 12 World Conference, Singapore, 19 October 2010. [Google Scholar]
- Mummidi, L.N.; Krumm, J. Discovering points of interest from users’ map annotations. GeoJournal 2008, 72, 215–227. [Google Scholar] [CrossRef] [Green Version]
- Hind, S.; Gekker, A. ‘Outsmarting Traffic, Together’: Driving as Social Navigation. Exch. Warwick Res. J. 2014, 1, 165–180. [Google Scholar] [CrossRef]
- Haklay, M.; Basiouka, S.; Antoniou, V.; Ather, A. How many volunteers does it take to map an area well? The validity of Linus’ law to volunteered geographic information. Cartogr. J. 2010, 47, 315–322. [Google Scholar] [CrossRef]
- Haklay, M.; Weber, P. Openstreetmap: User-generated street maps. Pervasive Comput. IEEE 2008, 7, 12–18. [Google Scholar] [CrossRef]
- Neis, P.; Zielstra, D. Recent developments and future trends in volunteered geographic information research: The case of OpenStreetMap. Future Internet 2014, 6, 76–106. [Google Scholar] [CrossRef] [Green Version]
- Agarwal, R. Individual acceptance of information technologies. In Framing the Domains of IT Management: Projecting the Future through the Past; Pinnaflex: Cincinnati, OH, USA, 2000; pp. 85–104. [Google Scholar]
- Lee, Y.H.; Hsieh, Y.C.; Hsu, C.N. Adding Innovation Diffusion Theory to the Technology Acceptance Model: Supporting Employees’ Intentions to use E-Learning Systems. Educ. Technol. Soc. 2011, 14, 124–137. [Google Scholar]
- Alajmi, M.A. Predicting the Use of a Digital Library System: Public Authority for Applied Education and Training (PAAET). Int. Inf. Libr. Rev. 2014, 46, 63–73. [Google Scholar] [CrossRef]
- Putzer, G.J. Are physicians likely to adopt emerging mobile technologies? Attitudes and innovation factors affecting smartphone use in the Southeastern United States. In Perspectives in Health Information Management; American Health Information Management Association: Chicago, IL, USA, 2012; p. 1. [Google Scholar]
- Gillenson, M.L.; Sherrell, D.L. Enticing online consumers: An extended technology acceptance perspective. Inf. Manag. 2002, 39, 705–719. [Google Scholar]
- Agag, G.; El-Masry, A.A. Understanding consumer intention to participate in online travel community and effects on consumer intention to purchase travel online and WOM: An integration of innovation diffusion theory and TAM with trust. Comput. Hum. Behav. 2016, 60, 97–111. [Google Scholar] [CrossRef] [Green Version]
- Wang, Y.-S.; Wu, S.-C.; Lin, H.-H.; Wang, Y.-M.; He, T.-R. Determinants of user adoption of web ATM: An integrated model of TCT and IDT. Serv. Ind. J. 2012, 32, 1505–1525. [Google Scholar] [CrossRef]
- Yang, Z.; Sun, J.; Zhang, Y.; Wang, Y. Understanding SaaS adoption from the perspective of organizational users: A tripod readiness model. Comput. Hum. Behav. 2015, 45, 254–264. [Google Scholar] [CrossRef]
- Ferro, E.; Loukis, E.N.; Charalabidis, Y.; Osella, M. Policy making 2.0: From theory to practice. Gov. Inf. Q. 2013, 30, 359–368. [Google Scholar] [CrossRef]
- Estermann, B. Diffusion of open data and crowdsourcing among heritage institutions: Results of a pilot survey in Switzerland. J. Theor. Appl. Electron. Commer. Res. 2014, 9, 15–31. [Google Scholar] [CrossRef]
- Dedrick, J.; West, J. Why firms adopt open source platforms: A grounded theory of innovation and standards adoption. In Proceedings of the Workshop on Standard Making: A Critical Research Frontier for Information Systems, Seattle, DC, USA, 12–14 December 2006; pp. 236–257. [Google Scholar]
- Macredie, R.D.; Mijinyawa, K. A theory-grounded framework of Open Source Software adoption in SMEs. Eur. J. Inf. Syst. 2011, 20, 237–250. [Google Scholar] [CrossRef] [Green Version]
- Nelson, M.; Sen, R.; Subramaniam, C. Understanding open source software: A research classification framework. Commun. Assoc. Inf. Syst. 2006, 17, 12. [Google Scholar]
- Venkatesh, V.; Morris, M.G. Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Q. 2000, 115–139. [Google Scholar] [CrossRef]
- Lu, J.; Yu, C.-S.; Liu, C.; Yao, J.E. Technology acceptance model for wireless Internet. Internet Res. 2003, 13, 206–222. [Google Scholar] [CrossRef]
- Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
- Kanchanatanee, K.; Suwanno, N.; Jarernvongrayab, A. Effects of attitude toward using, perceived usefulness, perceived ease of use and perceived compatibility on intention to use E-marketing. J. Manag. Res. 2014, 6, 1–3. [Google Scholar] [CrossRef]
- Oh, J.; Yoon, S.-J. Validation of haptic enabling technology acceptance model (HE-TAM): Integration of IDT and TAM. Telemat. Inform. 2014, 31, 585–596. [Google Scholar] [CrossRef]
- Mutahar, A.; Daud, N.M.; Ramayah, T.; Isaac, O.; Alrajawy, I. Integration of Innovation Diffusion Theory (IDT) and Technology Acceptance Model (TAM) to Understand Mobile Banking Acceptance in Yemen: The Moderating Effect of Income. Int. J. Soft Comput. 2017, 12, 164–177. [Google Scholar]
- Hwang, B.-N.; Huang, C.-Y.; Yang, C.-L. Determinants and their causal relationships affecting the adoption of cloud computing in science and technology institutions. Innovation 2016, 18, 1–27. [Google Scholar] [CrossRef]
- Venkatesh, V.; Davis, F.D. A model of the antecedents of perceived ease of use: Development and test. Decis. Sci. 1996, 27, 451–481. [Google Scholar] [CrossRef]
- El-Gohary, H. Factors affecting E-Marketing adoption and implementation in tourism firms: An empirical investigation of Egyptian small tourism organisations. Tour. Manag. 2012, 33, 1256–1269. [Google Scholar] [CrossRef]
- Tung, F.-C.; Chang, S.-C.; Chou, C.-M. An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry. Int. J. Med. Inform. 2008, 77, 324–335. [Google Scholar] [CrossRef] [PubMed]
- Yu, C.-S. Consumer switching behavior from online banking to mobile banking. Int. J. Cyber Soc. Educ. 2014, 7, 1–28. [Google Scholar] [CrossRef]
- Ayo, C.K.; Oni, A.A.; Adewoye, O.J.; Eweoya, I.O. E-banking users’ behaviour: E-service quality, attitude, and customer satisfaction. Int. J. Bank Mark. 2016, 34, 347–367. [Google Scholar] [CrossRef]
- Schierz, P.G.; Schilke, O.; Wirtz, B.W. Understanding consumer acceptance of mobile payment services: An empirical analysis. Electron. Commer. Res. Appl. 2010, 9, 209–216. [Google Scholar] [CrossRef]
- Plewa, C.; Troshani, I.; Francis, A.; Rampersad, G. Technology adoption and performance impact in innovation domains. Ind. Manag. Data Syst. 2012, 112, 748–765. [Google Scholar] [CrossRef]
- Putzer, G.J.; Park, Y. The effects of innovation factors on smartphone adoption among nurses in community hospitals. In Perspectives in Health Information Management/AHIMA; American Health Information Management Association: Chicago, IL, USA, 2010; p. 7. [Google Scholar]
- Amin, M.; Rezaei, S.; Abolghasemi, M. User satisfaction with mobile websites: The impact of perceived usefulness (PU), perceived ease of use (PEOU) and trust. Nankai Bus. Rev. Int. 2014, 5, 258–274. [Google Scholar] [CrossRef]
- Alalwan, A.A.; Dwivedi, Y.K.; Rana, N.P.; Williams, M.D. Consumer adoption of mobile banking in Jordan: Examining the role of usefulness, ease of use, perceived risk and self-efficacy. J. Enterp. Inf. Manag. 2016, 29, 118–139. [Google Scholar] [CrossRef]
- Mehta, V. Model for technology acceptance: A study of student’s attitude towards usage of wi-fi technology. Int. J. Innov. Res. Dev. 2013, 2, 122–137. [Google Scholar]
- Gyamfi, S.A. Informal tools in formal context: Adoption of web 2.0 technologies among geography student teachers in Ghana. Int. J. Educ. Dev. Using Inf. Commun. Technol. 2017, 13, 24–40. [Google Scholar]
- Md Nor, K.; Pearson, J.M.; Ahmad, A. Adoption of internet banking theory of the diffusion of innovation. Int. J. Manag. Stud. 2010, 17, 69–85. [Google Scholar]
- Wang, X.; Yuen, K.F.; Wong, Y.D.; Teo, C.C. An innovation diffusion perspective of e-consumers’ initial adoption of self-collection service via Automated Parcel Station. Int. J. Logist. Manag. 2018, 29, 237–260. [Google Scholar] [CrossRef]
- Ajjan, H.; Hartshorne, R.; Cao, Y.; Rodriguez, M. Continuance use intention of enterprise instant messaging: A knowledge management perspective. Behav. Inf. Technol. 2014, 33, 678–692. [Google Scholar] [CrossRef]
- Islam, Z.M.; Low, K.C.P.; Hasan, I. Intention to use advanced mobile phone services (AMPS). Manag. Decis. 2013, 51, 824–838. [Google Scholar] [CrossRef]
- Sheng, M.; Wang, L.; Yu, Y. An empirical model of individual mobile banking acceptance in China. In Proceedings of the International Conference on Computational and Information Sciences (ICCIS), Chengdu, China, 21–23 October 2011; pp. 434–437. [Google Scholar]
- Ho, C.-H. Continuance intention of e-learning platform: Toward an integrated model. Int. J. Electron. Bus. Manag. 2010, 8, 206. [Google Scholar]
- Jöreskog, K.G.; Sörbom, D. LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language; Scientific Software International: Skokie, IL, USA, 1993. [Google Scholar]
- Byrne, B.M. Structural Equation Modeling with EQS: Basic Concepts, Applications, and Programming; Routledge: Abingdon, UK, 2013. [Google Scholar]
- Chin, W.W. The partial least squares approach to structural equation modeling. Mod. Methods Bus. Res. 1998, 295, 295–336. [Google Scholar]
- Walczuch, R.; Lemmink, J.; Streukens, S. The effect of service employees’ technology readiness on technology acceptance. Inf. Manag. 2007, 44, 206–215. [Google Scholar] [CrossRef]
- Johnson, R.A.; Wichern, D.W. Applied Multivariate Statistical Analysis; Prentice Hall: Upper Saddle River, NJ, USA, 2002; Volume 5. [Google Scholar]
- Premkumar, G.; Roberts, M. Adoption of new information technologies in rural small businesses. Omega 1999, 27, 467–484. [Google Scholar] [CrossRef]
- Moon, J.-W.; Kim, Y.-G. Extending the TAM for a World-Wide-Web context. Inf. Manag. 2001, 38, 217–230. [Google Scholar] [CrossRef]
- Lee, C.-F. The Acceptance Investigation of Production Management Application with RFID; National Sun Yat-Sen University: Kaohsiung, Taiwan, 2010. [Google Scholar]
- Park, Y.; Chen, J.V. Acceptance and adoption of the innovative use of smartphone. Ind. Manag. Data Syst. 2007, 107, 1349–1365. [Google Scholar] [CrossRef]
- Malek, A. Modeling the antecedents of internet banking service adoption (IBSA) in Jordan: A Structural Equation Modeling (SEM) approach. J. Internet Bank. Commer. 2011, 16, 1. [Google Scholar]
- Liu, Y.; Li, H. Mobile internet diffusion in China: An empirical study. Ind. Manag. Data Syst. 2010, 110, 309–324. [Google Scholar] [CrossRef]
- Shih, T.-Y. Key factors of marketing strategies of mobile service innovations. Int. J. Innov. Learn. 2014, 16, 448–466. [Google Scholar] [CrossRef]
- Hair Jr, J.F.; Hult, G.T.M.; Ringle, C.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); Sage Publications: Thousand Oaks, CA, USA, 2016. [Google Scholar]
- Savery, J.R. Overview of problem-based learning: Definitions and distinctions. Essent. Read. Probl. Based Learn. 2015, 9, 5–15. [Google Scholar] [CrossRef]
- Rickles, P.; Ellul, C.; Haklay, M. A suggested framework and guidelines for learning GIS in interdisciplinary research. Geo Geogr. Environ. 2017, 4, e00046. [Google Scholar] [CrossRef] [Green Version]
- Mocnik, F.-B.; Zipf, A.; Raifer, M. The OpenStreetMap folksonomy and its evolution. Geo-Spat. Inf. Sci. 2017, 20, 219–230. [Google Scholar] [CrossRef] [Green Version]
- Pylarinos, D.; Pellas, I. Incorporating Open/Free GIS and GPS Software in Power Transmission Line Routine Work: The Case of Crete and Rhodes. Eng. Technol. Appl. Sci. Res. 2017, 7, 1316–1322. [Google Scholar]
- Dornhofer, M.; Bischof, W.; Krajnc, E. Comparison of Open Source routing services with OpenStreetMap Data for blind pedestrians. Online Proc. FOSS4G-Eur. 2014, 2014, 1–6. [Google Scholar]
- Jacob, R.; Smithers, S.; Winstanley, A.C. Performance evaluation of storing and querying spatial data on mobile devices for offline location based services. In Proceedings of the IET Irish Signals and Systems Conference (ISSC 2012), Maynooth, Republic of Ireland, 28–29 June 2012. [Google Scholar]
- Hristova, D.; Quattrone, G.; Mashhadi, A.J.; Capra, L. The Life of the Party: Impact of Social Mapping in OpenStreetMap. In Proceedings of the ICWSM, Boston, MA, USA, 8–11 July 2013. [Google Scholar]
- Ciepłuch, B.; Jacob, R.; Mooney, P.; Winstanley, A.C. Comparison of the accuracy of OpenStreetMap for Ireland with Google Maps and Bing Maps. In Proceedings of the Ninth International Symposium on Spatial Accuracy Assessment in Natural Resuorces and Enviromental Sciences, Fort Collins, CO, USA, 20–23 July 2010; p. 337. [Google Scholar]
- Steiniger, S.; Hunter, A.J. Free and open source GIS software for building a spatial data infrastructure. In Geospatial Free and Open Source Software in the 21st Century; Springer: Berlin, Germany, 2012; pp. 247–261. [Google Scholar]
- Vatsavai, R.R.; Burk, T.E.; Lime, S.; Hugentobler, M.; Neumann, A.; Strobl, C. Open-source GIS. In Springer Handbook of Geographic Information; Springer: Berlin, Germany, 2011; pp. 579–595. [Google Scholar]
- Khan, S.; Aaqib, S.M. Empirical Evaluation of ArcGIS with Contemporary Open Source Solutions–A Study. In Proceedings of the International Conference on Recent Innovations in Science, Engineering and Technology (ICRISET-2017), Goa, India, 23 December 2017; pp. 1085–1097. [Google Scholar]
- Md Nor, K.; Pearson, J.M. The Influence of Trust on Internet Banking Acceptance. J. Internet Bank. Commer. 2007, 12, 1–10. [Google Scholar]
- Montazemi, A.R.; Qahri-Saremi, H. Factors affecting adoption of online banking: A meta-analytic structural equation modeling study. Inf. Manag. 2015, 52, 210–226. [Google Scholar] [CrossRef]
- Chau, P.Y.; Hu, P.J.H. Information technology acceptance by individual professionals: A model comparison approach. Decis. Sci. 2001, 32, 699–719. [Google Scholar] [CrossRef]
- Oh, S.; Ahn, J.; Kim, B. Adoption of broadband Internet in Korea: The role of experience in building attitudes. J. Inf. Technol. 2003, 18, 267–280. [Google Scholar] [CrossRef]
- Chen, J.V.; Yen, D.C.; Chen, K. The acceptance and diffusion of the innovative smart phone use: A case study of a delivery service company in logistics. Inf. Manag. 2009, 46, 241–248. [Google Scholar] [CrossRef]
- Mooney, P.; Corcoran, P.; Winstanley, A.C. Towards quality metrics for OpenStreetMap. In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, USA, 2–5 November 2010; pp. 514–517. [Google Scholar]
- Ho Cheong, J.; Park, M.-C. Mobile internet acceptance in Korea. Internet Res. 2005, 15, 125–140. [Google Scholar] [CrossRef]
- Phuangthong, D.; Malisuwan, S. User acceptance of multimedia mobile internet in Thailand. Int. J. Comput. Internet Manag. 2008, 16, 22–33. [Google Scholar]
- Huang, L.-Y. A Study about the Key Factors Affecting Users to Accept Chunghwa Telecom’s Multimedia on Demand. Master’s Thesis, National Sun Yat-Sen University, Kaohsiung, Taiwan, 2004. [Google Scholar]
- Agarwal, R.; Prasad, J. The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decis. Sci. 1997, 28, 557–582. [Google Scholar] [CrossRef]
- Why Would You Use OpenStreetMap if There Is Google Maps? Available online: http://geoawesomeness.com/why-would-you-use-openstreetmap-if-there-is-google-maps/ (accessed on 31 August 2018).
- Adelakun, O.; Garcia, R. Evaluation of Augmented Reality Systems for the Enhancement of Voluntary Geographic Information. In Proceedings of the Twenty-Third Americas Conference on Information Systems, Boston, MA, USA, 10–12 August 2017. [Google Scholar]
- Chilton, S. Crowdsourcing is radically changing the geodata landscape: Case study of OpenStreetMap. In Proceedings of the UK 24th International Cartography Conference, London, UK, 2–7 September 2018. [Google Scholar]
- Haklay, M.; Singleton, A.; Parker, C. Web mapping 2.0: The neogeography of the GeoWeb. Geogr. Compass 2008, 2, 2011–2039. [Google Scholar] [CrossRef]
- Markieta, M. Using OpenStreetMap Data with Open-Source GIS. Cartogr. Perspect. 2012, 71, 91–104. [Google Scholar] [CrossRef]
- Kibelloh, M.; Bao, Y. Can online MBA programmes allow professional working mothers to balance work, family, and career progression? A case study in China. Asia Pac. Educ. Res. 2014, 23, 249–259. [Google Scholar] [CrossRef]
- Kibelloh, M.; Bao, Y. Perceptions of international female students toward E-learning in resolving high education and family role strain. J. Educ. Comput. Res. 2014, 50, 467–487. [Google Scholar] [CrossRef]
- Kay, R.H.; Lauricella, S. Gender differences in the use of laptops in higher education: A formative analysis. J. Educ. Comput. Res. 2011, 44, 361–380. [Google Scholar] [CrossRef]
- Bao, Y.; Xiong, T.; Hu, Z.; Kibelloh, M. Exploring gender differences on general and specific computer self-efficacy in mobile learning adoption. J. Educ. Comput. Res. 2013, 49, 111–132. [Google Scholar] [CrossRef]
- Juhász, L.; Hochmair, H.H. OSM Data Import as an Outreach Tool to Trigger Community Growth? A Case Study in Miami. ISPRS Int. J. Geo-Inf. 2018, 7, 113. [Google Scholar] [CrossRef]
- Steinmann, R.; Häusler, E.; Klettner, S.; Schmidt, M.; Lin, Y. Gender Dimensions in UGC and VGI: A Desk-Based Study; Wichmann: Berlin, Germany, 2013. [Google Scholar]
- Kim, H.-Y. Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. Restor. Dent. Endod. 2013, 38, 52–54. [Google Scholar] [CrossRef] [PubMed]
- Barclay, D.; Higgins, C.; Thompson, R. The partial least squares (PLS) approach to causal modeling: Personal computer adoption and use as an illustration. Technol. Stud. 1995, 2, 285–309. [Google Scholar]
- Fornell, C.; Larcker, D.F. Structural equation models with unobservable variables and measurement error: Algebra and statistics. J. Mark. Res. 1981, 18, 382–388. [Google Scholar] [CrossRef]
- Bagozzi, R.P.; Yi, Y. On the evaluation of structural equation models. J. Acad. Mark. Sci. 1988, 16, 74–94. [Google Scholar] [CrossRef]
- Gefen, D.; Straub, D.; Boudreau, M.-C. Structural equation modeling and regression: Guidelines for research practice. Commun. Assoc. Inf. Syst. 2000, 4, 7. [Google Scholar]
- Bontis, N. Intellectual capital: An exploratory study that develops measures and models. Manag. Decis. 1998, 36, 63–76. [Google Scholar] [CrossRef]
- Wechsung, I. An evaluation framework for multimodal interaction. T-Labs Series in Telecommunication Services; Springer International: Cham, Switzerland, 2014; Volume 10, pp. 978–973. [Google Scholar]
- Backhaus, K.; Erichson, B.; Plinke, W.; Weiber, R. Multivariate Analysemethoden: Eine Anwendungsorientierte Einführung; Springer: Berlin, Germany, 2006. [Google Scholar]
- Chin, W.W. How to write up and report PLS analyses. In Handbook of Partial Least Squares; Springer: Berlin, Germany, 2010; pp. 655–690. [Google Scholar]
- Cassel, C.M.; Hackl, P.; Westlund, A.H. On measurement of intangible assets: A study of robustness of partial least squares. Total Qual. Manag. 2000, 11, 897–907. [Google Scholar] [CrossRef]
- Diamantopoulos, A.; Winklhofer, H.M. Index construction with formative indicators: An alternative to scale development. J. Mark. Res. 2001, 38, 269–277. [Google Scholar] [CrossRef]
- Grewal, R.; Cote, J.A.; Baumgartner, H. Multicollinearity and measurement error in structural equation models: Implications for theory testing. Mark. Sci. 2004, 23, 519–529. [Google Scholar] [CrossRef]
- Montgomery, D.C.; Peck, E.A.; Vining, G.G. Introduction to Linear Regression Analysis, 3rd ed.; Wiley: New York, NY, USA, 2001. [Google Scholar]
- Montgomery, D.C.; Peck, E.A.; Vining, G.G. Introduction to Linear Regression Analysis, 4th ed.; John Wiley & Sons: Hoboken, NJ, USA, 2012; Volume 821. [Google Scholar]
- Falk, R.F.; Miller, N.B. A Primer for Soft Modeling; University of Akron Press: Akron, OH, USA, 1992. [Google Scholar]
- Cohen, J. Statistical Power Analysis for the Behavioral Sciences; Lawrence Earlbaum Associates: Hillsdale, NJ, USA, 1988. [Google Scholar]
Latent Variables (LV) | Item Code | Descriptions | Source |
---|---|---|---|
Relative Advantage (A) | a1 | OSM is free of charge and lets me use GIS in work or life at a low cost | Revised from Premkumar and Roberts [114], Moon and Kim [115], Lee [116], Liu and Li [119] |
a2 | The scale/granularity of OSM provides diverse services to meet different kinds of needs | ||
a3 | OSM is good and suitable for extensive use of GIS | ||
a4 | The visualization techniques and symbolization of OSM lets me communicate with others easily | ||
a5 | OSM lets me finish a job more quickly | ||
a6 | OSM can increase my efficiency in work | ||
Compatibility (C) | c1 | OSM is compatible with other systems/services I am using and consistent with my habits | Revised from Park and Chen [117], Lee [116], Liu and Li [119], Giovanis et al. [32] |
c2 | OSM is interoperable with other formats of GIS, so it’s spatial reference system can be integrated into geographic information applications | ||
c3 | The dynamic data can be combined with OSM map layer to show a real-time map, and it can be published on a website | ||
Ease of Use (U) | u1 | OSM meets my own values | Revised from Moon and Kim [115], Park and Chen [117], Lee [116], Liu and Li [119] |
u2 | OSM is very consistent with my working model | ||
u3 | OSM is very consistent with needs in work | ||
u4 | I believe OSM data are guaranteed | ||
u5 | I can use OSM system service anytime, anywhere | ||
u6 | I believe OSM is easy to use | ||
u7 | I can understand the functions of OSM and think it is not complex when using it, such as the procedures to contribute the data | ||
u8 | It is easy for me to find the usage info or material of OSM | ||
Trialability (T) | t1 | I can try any kind of function before using OSM officially | Revised from Park and Chen [117], Malek [118], Shih [120] |
t2 | I know how to try it out before using OSM officially | ||
t3 | I can quit it if I am not satisfied after trying OSM | ||
t4 | I can try the technology provided by the OSM vendor to evaluate if it meets my needs in work or research | ||
t5 | The OSM technology I am using has accumulated some good testing results | ||
Observability (O) | o1 | I have seen people around me using OSM | Revised from Park and Chen [117], Liu and Li [119], Shih [120] |
o2 | It’s easy for me to find others sharing and discussing the usage of OSM | ||
o3 | I can easily feel that OSM could bring me some benefits | ||
o4 | I have seen my coworkers or friends using OSM | ||
o5 | I have seen the demonstrations and applications of OSM | ||
Perceived Attitude (PA) | pa1 | Overall, I believe it’s a good idea to adopt OSM | Revised from Moon and Kim [115], Lee [116], Park and Chen [117] |
pa2 | Overall, I am positive about adopting OSM | ||
pa3 | Overall, I support adopting OSM | ||
pa4 | I believe it’s very good to use OSM in work | ||
pa5 | I like the OSM technology | ||
Perceived Usefulness (PU) | pu1 | I can describe the possible benefits of using OSM in work or life | Revised from Premkumar and Roberts [114], Moon and Kim [115], Park and Chen [117], Tung et al. [93] |
pu2 | I believe OSM makes work or life more efficient | ||
pu3 | I believe OSM can cut costs in work or life | ||
pu4 | I believe OSM is helpful in work or life | ||
Intention of Continued Usage (I) | i1 | I believe OSM can make people use VGI more frequently | Revised from Moon and Kim [115], Lee [116], Liu and Li [119] |
i2 | I believe OSM makes me more willing to use VGI | ||
i3 | I will adopt OSM as the tool for constructing VGI | ||
i4 | I will increase the frequency of OSM use | ||
i5 | I will do more to understand the functions and user interface of OSM | ||
i6 | I will highly recommended OSM to others | ||
i7 | I look forward to using OSM to meet the needs of work or life in the future |
Profile Category | Frequency | Percentage (%) |
---|---|---|
Gender | ||
Female (left 0.3 cm) | 82 | 56.6 |
Male | 63 | 43.4 |
Age (years) | ||
<17 | 1 | 0.7 |
18–22 | 141 | 97.2 |
23–26 | 3 | 2.1 |
Education | ||
Graduate | 2 | 1.4 |
Undergraduate. | 143 | 98.6 |
Others | 0 | 0 |
Major | ||
Civil engineering | 43 | 29.7 |
Visual communication design | 102 | 70.3 |
Use OSM time (years) | ||
1–3 | 1 | 0.7 |
<1 | 144 | 99.3 |
OSM use frequency | ||
A few times in a week | 31 | 21.4 |
Seldom use | 114 | 78.6 |
Item Code | Hypotheses | Results |
---|---|---|
H1 | Relative Advantage → Perceived Attitude | Supported |
H2 | Compatibility → Perceived Usefulness | Supported |
H3 | Compatibility → Perceived Attitude | Not supported |
H4 | Ease of Use → Perceived Usefulness | Supported |
H5 | Ease of Use → Perceived Attitude | Supported |
H6 | Trialability → Perceived Attitude | Not supported |
H7 | Observability → Perceived Usefulness | Supported |
H8 | Perceived Usefulness → Perceived Attitude | Supported |
H9 | Perceived Usefulness → Intention of Continued Usage | Supported |
H10 | Perceived Attitude → Intention of Continued Usage | Supported |
Relationships | Direct | Indirect | Total |
---|---|---|---|
Relative Advantage → Perceived Attitude | 0.271 | - | 0.271 |
Trialability → Perceived Attitude | - | - | - |
Ease of Use → Perceived Attitude | 0.314 | 0.080 | 0.394 |
Ease of Use → Perceived Usefulness | 0.267 | - | 0.267 |
Compatibility → Perceived Attitude | - | 0.080 | 0.080 |
Compatibility → Perceived Usefulness | 0.265 | - | 0.265 |
Observability → Perceived Usefulness | 0.376 | - | 0.376 |
Perceived Attitude → Intention of Continued Usage | 0.374 | - | 0.374 |
Perceived Usefulness → Perceived Attitude | 0.301 | - | 0.301 |
Perceived Usefulness → Intention of Continued Usage | 0.586 | 0.113 | 0.699 |
Relative Advantage → Intention of Continued Usage | - | 0.101 | 0.101 |
Trialability → Intention of Continued Usage | - | - | - |
Ease of Use → Intention of Continued Usage | - | 0.304 | 0.304 |
Compatibility → Intention of Continued Usage | - | 0.185 | 0.185 |
Observability → Intention of Continued Usage | - | 0.263 | 0.263 |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Shiau, S.J.H.; Huang, C.-Y.; Yang, C.-L.; Juang, J.-N. A Derivation of Factors Influencing the Innovation Diffusion of the OpenStreetMap in STEM Education. Sustainability 2018, 10, 3447. https://doi.org/10.3390/su10103447
Shiau SJH, Huang C-Y, Yang C-L, Juang J-N. A Derivation of Factors Influencing the Innovation Diffusion of the OpenStreetMap in STEM Education. Sustainability. 2018; 10(10):3447. https://doi.org/10.3390/su10103447
Chicago/Turabian StyleShiau, Steven J. H., Chi-Yo Huang, Chia-Lee Yang, and Jer-Nan Juang. 2018. "A Derivation of Factors Influencing the Innovation Diffusion of the OpenStreetMap in STEM Education" Sustainability 10, no. 10: 3447. https://doi.org/10.3390/su10103447
APA StyleShiau, S. J. H., Huang, C.-Y., Yang, C.-L., & Juang, J.-N. (2018). A Derivation of Factors Influencing the Innovation Diffusion of the OpenStreetMap in STEM Education. Sustainability, 10(10), 3447. https://doi.org/10.3390/su10103447