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ANALYSING THE EFFECT OF SELF-EFFICACY AND INFLUENCERS ON DESIGN TEAM PERFORMANCE

Part of: Engineering Design Practice

Published online by Cambridge University Press:  11 June 2020

H. Singh ,
G. Cascini  and
C. McComb
H. Singh*
Affiliation:
Politecnico di Milano, Italy
G. Cascini
Affiliation:
Politecnico di Milano, Italy
C. McComb
Affiliation:
The Pennsylvania State University, United States of America
*
*[email protected]

Abstract

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Social media influencers (SMI) are gaining interest and many are studying their influence on the online audience, little is known about the role played by them in offline teams. One such attempt to study the effect of influencers in co-design team is presented in this paper, where individuals who are confident in their abilities drive the team process. Thus, self-efficacy is considered for determining influencer behaviour. Results expose the relationship between self-efficacy and influencer status on the design process, besides briefly highlighting the effects on above-average teams.

Keywords

co-designdesign teamsinfluencersself-efficacydesign education
Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 2571 - 2580
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2020. Published by Cambridge University Press

References

A.S.P. (2004), Alta Scuola Politecnica (ASP). [Online] Available at: http://www.asp-poli.it/Google Scholar
Aries, E.J., Gold, C. and Weigel, R.H. (1983), “Dispositional and situational influences on dominance behavior in small groups”, Journal of Personality and Social Psychology, Vol. 44 No. 4, pp. 779–786.10.1037/0022-3514.44.4.779CrossRefGoogle Scholar
Baker, S.J. (2015), “Exploration of Equality and Processes of Non-Hierarchical Groups”, Journal of Organisational Transformation & Social Change, Vol. 12 No. 2, pp. 138–158. https://doi.org/10.1179/1477963315Z.00000000039CrossRefGoogle Scholar
Bandura, A. (1977), “Self-efficacy: Toward a Unifying Theory of Behavioral Change”, Psychological Review, Vol. 84 No. 2, pp. 191–215.CrossRefGoogle Scholar
Bandura, A. (2010), “Self-Efficacy”, In: The Corsini Encyclopedia of Psychology, Wiley Online Library, pp. 1–65. https://doi.org/10.1002/9780470479216.corpsy0836Google Scholar
Bass, B.M. (1985), Leadership and performance beyond expectations, Free Press, New York.Google Scholar
Brown, R. and Pehrson, S. (2019), “Innovation and changes in groups: Minority Influence”, In Wiley-Blackwell, (Ed.), Group Processes: Dynamics within and Between Groups, pp. 85–100.10.1002/9781118719244.ch4CrossRefGoogle Scholar
Capuano, N. et al. (2019), “Fuzzy Group Decision Making for influence-aware recommendations”, Computers in Human Behavior, Vol. 101, pp. 371–379. https://doi.org/10.1016/j.chb.2018.11.001CrossRefGoogle Scholar
Carberry, A.R., Lee, H.-S. and Ohland, M.W. (2010), “Measuring Engineering Design Self-Efficacy”, Journal of Engineering Education, Vol. 99 No. 1, pp. 71–79. https://doi.org/10.1002/j.2168-9830.2010.tb01043.xCrossRefGoogle Scholar
Cascini, G. et al. (2017), “On the Factors Affecting Design Education Within a Multi-Disciplinary Class”, Journal of Integrated Design and Process Science, Vol. 21 No. 2, pp. 21–44. https://doi.org/10.3233/jid-2017-0001CrossRefGoogle Scholar
Deutsch, M. and Gerard, H.B. (1955), “A study of normative and informational social influences upon individual judgment”, Journal of Abnormal and Social Psychology, Vol. 51 No. 3, pp. 629–636.10.1037/h0046408CrossRefGoogle Scholar
Erz, A., Marder, B. and Osadchaya, E. (2018), “Hashtags: Motivational drivers, their use, and differences between influencers and followers”, Computers in Human Behavior, Vol. 89, pp. 48–60. https://doi.org/10.1016/j.chb.2018.07.030CrossRefGoogle Scholar
Freberg, K. et al. (2011), “Who are the social media influencers? A study of public perceptions of personality”, Public Relations Review, Vol. 37 No. 1, pp. 90–92. https://doi.org/10.1016/j.pubrev.2010.11.001CrossRefGoogle Scholar
Gist, M.E. (1989), “The influence of training method on self-efficacy and idea generation among managers”, Personnel psychology, Vol. 42, pp. 787–805. https://doi.org/10.1111/j.1744-6570.1989.tb00675.xCrossRefGoogle Scholar
Granovetter, M.S. (1973), “The Strength of Weak Ties”, American Journal of Sociology, Vol. 78 No. 6, pp. 1360–1380.10.1086/225469CrossRefGoogle Scholar
Hosseinpour, M., Malek, M.R. and Claramunt, C. (2019), “Socio-spatial influence maximization in location-based social networks”, Future Generation Computer Systems, Vol. 101, pp. 304–314. https://doi.org/10.1016/j.future.2019.06.024CrossRefGoogle Scholar
Jun-Lan, X. et al. (2019), “Identification of influencers in networks with dynamic behaviors”, Physica A Statistical Mechanics and its Applications, Vol. 527, https://doi.org/10.1016/j.physa.2019.121318CrossRefGoogle Scholar
Liu, S. et al. (2015), “Identifying effective influencers based on trust for electronic word-of-mouth marketing: A domain-aware approach”, Information Sciences, Vol. 306, pp. 34–52. https://doi.org/10.1016/j.ins.2015.01.034CrossRefGoogle Scholar
More, J.S. and Lingam, C. (2019), “A SI model for social media influencer maximization”, Applied Computing and Informatics, Vol. 15 No. 2, pp. 102–108. https://doi.org/10.1016/j.aci.2017.11.001CrossRefGoogle Scholar
Oyibo, K. and Vassileva, J. (2019), “The relationship between personality traits and susceptibility to social influence”, Computers in Human Behavior, Vol. 98, pp. 174–188. https://doi.org/10.1016/j.chb.2019.01.032CrossRefGoogle Scholar
Pei, S., Morone, F. and Makse, H.A. (2018), “Theories for influencer identification in complex networks”, In: Lehmann, S. and Ahn, Y.Y. (Eds.), Complex Spreading Phenomena in Social Systems, Springer, pp. 125–148.CrossRefGoogle Scholar
Pillai, R. and Williams, E.A. (2004), “Transformational leadership, self- efficacy, group cohesiveness, commitment, and performance”, Journal of Organizational Change Management, Vol. 17 No. 2, pp. 144–159. https://doi.org/10.1108/09534810410530584CrossRefGoogle Scholar
Solis, B. (2009), Social Media Influencers Are Not Traditional Influencers. [Online] Available at: https://www.briansolis.com/2009/11/social-media-influencers-are-not-traditional-influencers/ [Accessed 5 November 2009].Google Scholar
Taninmis, K., Aras, N. and Altınel, I.K. (2019), “Influence maximization with deactivation in social networks”, European Journal of Operational Research, Vol. 278, pp. 105–119. https://doi.org/10.1016/j.ejor.2019.04.010CrossRefGoogle Scholar
Tsugawa, S. and Kimura, K. (2018), “Identifying influencers from sampled social networks”, Physica A: Statistical Mechanics and its Applications, Vol. 507, pp. 294–303. https://doi.org/10.1016/j.physa.2018.05.105CrossRefGoogle Scholar
Woodman, R.W., Sawyer, J.E. and Griffin, R.W. (1993), “Toward a Theory of Organizational Creativity”, The Academy of Management Review, Vol. 18 No. 2, pp. 293–321. https://doi.org/10.5465/amr.1993.3997517CrossRefGoogle Scholar
Woodman, R.W. and Schoenfeldt, L.F. (1989), “Individual Differences in Creativity”, In: Glover, J.A., Ronning, R.R. and Reynolds, C.R. (Eds.), Handbook of Creativity. Perspectives on Individual Differences, Springer, Boston, MA, pp. 77–91.Google Scholar
Yerasani, S. et al. (2019), “Estimation and maximization of user influence in social networks”, International Journal of Information Management, Vol. 47, pp. 44–51. https://doi.org/10.1016/j.ijinfomgt.2018.12.016CrossRefGoogle Scholar
Zhou, F., Lü, L. and Mariani, M.S. (2019), “Fast influencers in complex networks”, Communications in Nonlinear Science and Numerical Simulation, Vol. 74, pp. 69–83. https://doi.org/10.1016/j.cnsns.2019.01.032CrossRefGoogle Scholar