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Abstract

Researchers have employed Appreciative Inquiry (AI) in a variety of methodological contexts, in a variety of settings, and toward a variety of outcomes. For practitioners seeking to both identity and amplify the best of what is, AI has been a sort of multi-functional toolset, improving outcomes both small and grand. Amidst this successful history of the application of Appreciative Inquiry (AI), little attention has been given to some of the limitations or even risks of applying its practices to whatever extent and toward whichever outcomes. The models supplied by AI may prove problematic in several ways, among them: ontological realism, epistemological objectivism, the potential for axiological denial and ethical deception, the potential for methodological discord, a posture rooted in problems, blind spotting, and a neglect of the integral nature of things. This paper brings together the theoretical premises of Appreciative Inquiry methodologies, emerging considerations from transdisciplinarity and consciousness studies, and practical applications from a recent AI project, so as to construct considerations and recommendations for AI practitioners for resolving some of the methodological and paradigmatic conflicts that may arise.

Keywords

appreciative inquiry, transdisciplinarity, integral theory, four quadrant model

Author Bio(s)

Bryan D. Jennewein holds a doctorate in Transformative Studies with an emphasis in Conscious Studies from the California Institute of Integral Studies and currently focuses their research in areas including Appreciative Inquiry, Organizational Theory, and Mindfulness-based interventions in organizational settings. Having worked in the American High-Tech sector for several years, he currently works professionally in marketing while also contributing to emerging areas of research rooted in the areas noted above. Please direct correspondence to bryanjennewein@gmail.com.

Publication Date

12-3-2021

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 International License.

DOI

10.46743/2160-3715/2021.5040

ORCID ID

https://orcid.org/0000-0002-5678-3329

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