Predicting creativity in the wild: experience sample and sociometric modeling of teams

ABSTRACT

Relationships between creativity in teamwork, and team members' movement and face-to-face interaction strength were investigated "in the wild" using sociometric badges (wearable sensors), electronic Experience Sampling Methods (ESM), the KEYS team creativity assessment instrument, and qualitative methods, in academic and industry settings. Activities (movement and face-to-face interaction) and creativity of one five-member and two seven-member teams were tracked for twenty-five days, eleven days, and fifteen days respectively. Paired-sample t-test confirmed average daily movement energy during creative days was significantly greater than on non-creative days and that face-to-face interaction tie strength of team members during creative days was significantly greater than for non-creative days. The combined approach of principal component analysis (PCA) and linear discriminant analysis (LDA) conducted on movement and face-to-face interaction data yielded a model that predicted creativity with 87.5% and 91% accuracy, respectively. Computational models that predict team creativity hold particular promise to enhance Creativity Support Tools.

References

1. Amabile, T. (1993). Motivational synergy: toward new conceptualizations of intrinsic and extrinsic motivation in the workplace. Human Resource Management Review, 3(3), 185--201.
2. Amabile, T. (1996). Creativity in context: Westview Press, Boulder, CO.
3. Amabile, T. M. (1983). The social psychology of creativity: a componential conceptualization. Journal of Personality and Social Psychology, 45(2), 357--376.
4. Amabile, T. M., Barsade, S. G., Mueller, J. S., & Staw, B. M. (2005). Affect and creativity at work. Administrative Science Quarterly, 50, 367--403.
5. Amabile, T. M., Conti, R., Coon, H., Lazenby, J., & Herron, M. (1996). Assessing the work environment for creativity. Acad. of Mgt. J., 39(5), 1154--1184.
6. Bain, P., Mann, L., & Pirola-Merlo, A. (2001). The innovation imperative. Small Group Research, 32(1), 55--73.
7. Basu, A. 2002. Conversational scene analysis. Doctoral dissertation. MIT.
8. Besemer, S. P., & O'Quin, K. (1993). Assessing creative products: Progress and potentials. In S. G. Isaksen (Ed.), Nurturing and Developing Creativity: The Emergence of a Discipline (pp. 331--349). Norwood, New Jersey: Ablex Publishing Corp.
9. Burleson, W., Picard, R., Perlin, K., & Lippincott, J. (2004). A platform for affective agent research. Wkshp. on Empathetic Agents, 3rd. Int. Joint Conf. on Autonomous Agents and Multi-Agent Sys.
10. Burt, R. S. (2004). Structural holes and good ideas. American Journal of Sociology, 110(2), 349--399.
11. Choudhary, T. 2004. Sensing and modeling human networks. Doctoral dissertation. MIT.
12. Christensen, R., & Bedrick, E. J. (1997). Testing the independence assumption in linear models. Journal of the American Statistical Assoc., 92, 1006--1016.
13. DiMicco, J. M., & Bender, W. (2007). Group reactions to visual feedback tools. In Y. de Kort, B. J. Fogg, W. I. Jsselsteijn, B. Eggen & C. Midden (Eds.), Persuasive Technology (Vol. 4744, pp. 132--143): Springer Berlin / Heidelberg.
14. Duda, R., Hart, P., & Stork, D. (2000). Pattern Classification (2 ed.). NY: Wiley Interscience.
15. Eagle, N. (2005). Machine Perception and Learning of Complex Social Systems. MIT.
16. Farooq, U., Carrol, J. M., Ganoe, C. H. 2007. Supporting creativity with awareness in distributed collaboration. ACM Group.
17. Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360--1380.
18. Griffin, D., & Gonzalez, R. (1995). Correlational analysis of dyad-level data in the exchangeable case. Psychological Bulletin, 118, 430--439.
19. Guilford, J. P. (1956). The structure of intellect. Psychological Bulletin, 53(4), 267--293.
20. Hoyle, R., Georgesen, J., & Webster, J. (2001). Analyzing data from individuals in groups: The past, the present, and the future. Group Dynamics: Theory, Research, and Practice, 5(1), 41--47.
21. Kahneman, D., Krueger, A., Schkade, D., Schwarz, N., Stone, A. (2004). A survey method for characterizing daily life experience: the day reconstruction method science, 306 (5702), 1776--80.
22. Kapoor, A., Burleson, W., & Picard, R. W. (2007). Automatic prediction of frustration. International Journal of Human-Computer Studies, 65, 724--736.
23. Kenny, D. A., & Judd, C. M. (1996). A general procedure for the estimation of interdependence. Psychological Bulletin, 119, 138--148.
24. Kenny, D. A., Mannetti, L., Pierro, A., Livi, S., & Kashy, D. A. (2002). The statistical analysis of data from small groups. Journal of Personality and Social Psychology, 83(1), 126--137.
25. Kim, T., Chang, A., & Pentland, A. S. (2007). Enhancing organizational communication using sociometric badges. Paper presented at the IEEE 11th Int. Symp. on Wearable Computing.
26. Kraut, R. E., Egido, C., & Galegher, J. (1988). Patterns of contact and communication in scientific research collaboration. CSCW.
27. Lee, L., & Grimson, W. E. (2002). Gait analysis for recognition and classification. 5th IEEE Int. Conf. on Automatic Face and Gesture Recognition (FG'02).
28. Leshed, G. (2009). Automated language-based feedback for teamwork behaviors. Doctoral dissertation. Cornell University.
29. Li, J., Zhao, B., & Zhang, H. (2009). Face recognition based on PCA and LDA combination feature extraction. In 1st. Int. Conf. on Inf. Sci. and Eng.
30. Malone, H. N. (1989). The relationship between traits of creativity and physical activity in the elderly. (Unpublished thesis). Ohio State University.
31. Massetti, B. (1996). An empirical examination of the value of creativity support systems on idea generation. MIS Quarterly, 20, 83--97.
32. Mayer, R. E. (1999). Fifty years of creativity research. In R. J. Sternberg (Ed.), Handbook of Creativity. NY: Cambridge University Press.
33. Moritz, S. E., & Watson, C. B. (1998). Levels of analysis issues in group psychology. Group Dyn.: Theory, Research, and Practice, 2, 285--298.
34. Obstfeld, D. (2005). Social networks, the tertius iungens orientation, and involvement in innovation. Administrative Science Quarterly, 50, 100--130.
35. Ohly, S., Kase, R., & Skerlavaj, M. (2010). Networks for generating and for validating ideas: The social side of creativity. Innovation: Management, Policy & Practice, 12, 41--52.
36. Olguín-Olguín, D., Kam, M., & Pentland, A. S. (2010). Quantifying the effects of centrality and tie strength on performance in face-to-face networks. Workshop on Information Networks, New York, NY.
37. Olguín-Olguín, D., Gloor, P. A., & Pentland, A. S. (2009). Capturing individual and group behavior with wearable sensors. AAAI Spring Symposium.
38. Pentland, A. (2005). Socially aware computation and communication. IEEE Computer (March), 63--70.
39. Pentland, A. (2007). Automatic mapping and modeling of human networks. Physica A: Statistical Mechanics and Its Applications, 378(1), 59--67.
40. Pentland, A. (2008). Honest Signals. Cambridge, Massachusetts: The MIT Press.
41. Pentland, A. (2009). Reality mining of mobile communications: Toward a new deal on data. The Global Information Technology Report, 75--80.
42. Perry-Smith, J. E., & Shalley, C. E. (2003). The social side of creativity: A static and dynamic social network perspective. Academy of Management Review, 28, 89--106.
43. Pirola-Merlo, A., & Mann, L. (2004). The relationship between individual creativity and team creativity: aggregating across people and time. Journal of Organizational Behavior 25, 235--257.
44. Ravi, N., Dandekar, N., Mysore, P., & Littman, M. L. (2005). Activity recognition from accelerometer data. In The Seventeenth Conference on Innovative Applications of Artificial Intelligence (IAAI), Pittsburg, Pennsylvania, July 2005.
45. Saddler, M. S., & Judd, C. M. (2003). Overcoming dependent data: A guide to the analysis of group data. In M. A. Hogg & R. S. Tindale (Eds.), Blackwell Handbook of Social Psychology: Group Processes. Oxford, UK: Blackwell.
46. Scott, S. G., & Bruce, R. A. (1994). Determinants of innovative behavior: a path model of individual innovation in the workplace. Academy of Management Journal, 37(3), 580--607.
47. Shneiderman, B. (2003). Leonardo's Laptop: Human Needs and the New Computing Technologies. Cambridge, MA: The MIT Press.
48. Shneiderman, B., Hewett, T., Fischer, G., Jennings, P., Czerwinski, M., Kules, B., et al. (2006). Creativity support tools, NSF wkshp. Int. J. HCI, 20(2), 61--77.
49. Singh-Manoux, A., Hillsdon, M., Brunner, E., & Marmot, M. (2005). Effects of physical activity on cognitive functioning in middle age: Evidence from the Whitehall II Prospective Cohort Study. American Journal of Public Health, 95(12), 2252.
50. Taggar, S. (2002). Individual creativity and group ability to utilize individual creative resources: a multilevel model. Acad. of Mgt. J., 45(2), 315--330.
51. Torrance, E. P. (1974). The Torrance Tests of Creative Thinking-Norms-Technical Manual Research Edition-Verbal Tests, Forms A and B- Figural Tests, Forms A and B. Princeton, NJ: Personnel Press.
52. Tripathi, P. (2011). Predicting creativity in the wild: Doctoral dissertation. ASU.
53. Turk, M., & Pentland, A. (1991). Eigenfaces for recognition. J. of Cog. Neurosicence, 3(1), 71--86.
54. Weisner, R., Ryan, G., Reese, L., Kroesen, K., Bernheimer, L., & Gallimore, R. (2001). Behavior sampling and ethnogr. Field Methods, 13(1), 20--46.
55. Zhou, J., Shin, S., Brass, D., Choi, J., & Zhang, Z. (2009). Social networks, personal values, and creativity. J. of Applied Psych., 94(6), 1544--1552.

Index Terms

• Human-centered computing

  • Collaborative and social computing