Interpretation of Corporate Digital Responsibility Risks and Concerns by Automated Service Technologies: An AI Co-Created Article

Abstract

Corporate Digital Responsibility (CDR) is an emergent research area in the service domain. We used this relatively new literature domain as context to explore the capability of LLM (Large Language Model)-enabled Artificial Intelligence (AI) service offerings in identifying CDR concepts and in expanding the current scope of academic knowledge in this domain. A hybrid Chain and Critical Agent approach was employed in producing AI prompts to guide the LLM in producing CDR insights. A total of 18 chained prompts were entered into BingChat. Our findings confirm that the hybrid Chain and Critical Agent AI prompt construction method is a viable approach for guiding LLMs in retrieval of top-level information on academic domains such as CDR, the CDR Data and Technology Life Cycle, and the CDR Calculus. Furthermore, we found the hybrid AI prompting approach to be effective in establishing the correct context of the information and subject domain, alongside the required writing conventions, and directing the LLM to prioritise usage of academic CDR sources. However, we found that this method cannot adequately produce academic-level or original insights that could be published in academic peer-reviewed journals.