Research-based Article Abstract
Mehta, N. K. (2018). Enhancing engineers’ public speaking efficacy using appreciative inquiry. Industrial and Commercial Training, 50(2), 81-94. http://dx.doi.org.ezaccess.libraries.psu.edu/10.1108/ICT-05-2017-0035
This article on increasing engineers’ public speaking efficacy piqued my interest because I have been challenged throughout my career by this issue. In my work with science and technology organizations in government, engineers are the group that most frequently struggle to explain the importance of their work, which often has national security implications in my sector. Much depends on their ability to articulate the role of their technologies in solving major global issues and so Appreciative Inquiry (AI) could be a transformative tool in this context. This study of engineers at the National Institute of Industrial Engineering in Mumbai, India, explores the issue of whether AI is a better method of improving engineers’ communication in a public setting, which often provokes anxiety and fear, than a deficit-based approach to learning.
Research questions and objectives: Public speaking failures create embarrassment and high social anxiety, and traditional deficit-based approaches to learning public speaking can worsen this anxiety and be detrimental for learners looking to improve this important professional skill. The researchers in this study set out to design a strengths-based pedagogy to improve public speaking among a stereotypically difficult demographic of engineers. The research objective was to determine whether the Appreciative Inquiry methodology improved the efficacy of engineers’ public speaking skills.
Methodology: Researchers launched the study with a psychological contract with the study participants that articulated roles and responsibilities, time, venue, logging expectations, and project involvement. The study consisted of three sessions of group and individual interviews where strengths-based questions were asked of participants. Researchers used the 4D model as a framework for inquiry and data collection. Discovery phase questions asked for stories about participants’ best public speaking experience and what they believed to be their strengths in those settings. In the Dream phase, participants were guided in envisioning future successful public speaking experiences, while in the Design phase, questions probed for ambitions and aspirations in public speaking. Finally, in the Delivery phase, participants explored their positive attributes and created a plan for fulfilling their Dream phase aspirations. Participants crafted stories that included narratives for these four phases.
Approach to sampling: An invitation to all post graduate engineering students at the National Institute of Industrial Engineering was extended by the researchers. Fifteen engineers – 12 men and 3 women – with an average of 1.5 years of work experience elected to participate in the study. The majority of these speakers struggled with English and translations from Hindi to English and vice versa played a role in how engineers viewed their public speaking success.
Data analysis and results: To analyze data, participants were asked to make a list of key points and ideas that they identified in their stories. These key points were then used to develop categories for thematic coding of data by phase. For each phase, open, axial, and selective codes were identified. As part of the contract, participants had agreed to participate in follow-on activities including attending Toastmaster sessions and attaining the “Competent Communicator” Certification, attending drama workshops and emotional intelligence training, and videotaping their public speaking to look for improvements. Participation in these follow-on activities were measured. Results of this study showed that participants’ competence, autonomy, and relatedness – three factors identified as key benchmarks of public speaking – measures were enhanced by AI techniques leading to an improvement in performance of public speaking. Additionally, AI helped engineers recognize the need for them to take ownership for improving their own skills. Participants participated in follow-on activities to a greater degree after the study than before, and core strengths identified during the study helped them alleviate fear and anxiety.
The study’s author did not identify any key limitations in the article, but several key limitations come to mind. First, the sampling size was small and self-selected. This means that the participants already had an openness to improving their public speaking skills and we can assume that AI techniques would be more effective on these types of participants. Second, the study did not compare participants using AI techniques to a second group receiving traditional deficit-based training for skills improvement. Having a measure of the difference between techniques would have been instructive. And third, having a measure that quantified the participant’s improvement might have been illuminating in that we could see exactly what kinds of attributes of public speaking Appreciative Inquiry techniques effectively improved.
The study’s author introduced the fact that participants were bilingual and frequently translating back and forth between English and Hindi. Eliminating that factor may give a more precise estimate to the improvement in public speaking brought about by Appreciative Inquiry. Further investigation may include participants only using their native language to gain a clearer understanding of the effects of the AI framework as a pedagogy. Toastmasters already has evaluative techniques that could be employed in a future study to create a quantative benchmark to measure improvement. Lastly, researching AI pedagogy for public speaking with an older group of professionals would create an interesting data point. Do people get more entrenched in their public speaking habits as they progress in their careers? Will AI help improve public speaking in more experienced engineers?
Public speaking is incredibly important to career advancement. This study explored the application of AI techniques to a commonplace challenge that people face while moving up the ladders of their careers. According to the authors, traditional learning models can actually increase fear and anxiety because the focus is on fixing what is weak rather than building on what is strong. This article is important for students of Appreciative Inquiry in two ways. First, students can apply AI techniques to their own public speaking improvement and know that as a pedagogy, AI will result in a stronger communications skill set. Second, students of AI can use this study as a model for applying AI techniques to other areas of skill development. Whether it is writing, yoga, or fashion, AI has the potential to help create new skills without the associated anxiety that goes along with new skill development.