| Title | Using a hermeneutic phenomenological approach to Twitter content: a social network's analysis of green accounting as a dimension of sustainability |
|---|---|
| ID_Doc | 31479 |
| Authors | Khan, S; Gupta, S |
| Title | Using a hermeneutic phenomenological approach to Twitter content: a social network's analysis of green accounting as a dimension of sustainability |
| Year | 2023 |
| Published | Qualitative Research In Financial Markets, 15, 4 |
| Abstract | PurposeOwing to the worldwide outbreak of the SARS-CoV-2, social media conversations have increased. Given the increasing pressure from regulatory authorities and society, green accounting - as a dimension of sustainable development - remains the most discussed topic on most social media platforms. This study aims to incorporate a technological approach to green accounting and sustainability to enhance the innovation process inside and outside organizations. Design/methodology/approachThis study uses the hermeneutic phenomenological technique to investigate Twitter content. Tweets were subjected to a manual coding process to analyze their content, including recent advancements, challenges, cross-country initiatives and promotion strategies in green accounting. Public perception of green accounting and the COP26 climate summit was also studied. FindingsTweeters view green accounting favorably; however, they are apprehensive about its implementation. Regarding the challenges in green accounting, "corporate green washing" was the most tweeted content. The UK was the top-rated nation with respect to green accounting development. Furthermore, the most discussed breakthrough was the application of artificial intelligence in the domain of green accounting functions. However, Twitter users were observed to have directed heavy criticism at the COP26 climate summit in Glasgow. Originality/valueThis study's primary innovation is its integration of emerging technologies such as machine learning and data mining with social media platforms such as Twitter. Incorporating manual coding of tweets is a rigorous procedure that amplifies the strength of machine learning software's auto-coding feature. |
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