  ChatGPT can improve Software Engineering (SE) research practices by offering
efficient, accessible information analysis and synthesis based on natural
language interactions. However, ChatGPT could bring ethical challenges,
encompassing plagiarism, privacy, data security, and the risk of generating
biased or potentially detrimental data. This research aims to fill the given
gap by elaborating on the key elements: motivators, demotivators, and ethical
principles of using ChatGPT in SE research. To achieve this objective, we
conducted a literature survey, identified the mentioned elements, and presented
their relationships by developing a taxonomy. Further, the identified
literature-based elements (motivators, demotivators, and ethical principles)
were empirically evaluated by conducting a comprehensive questionnaire-based
survey involving SE researchers. Additionally, we employed Interpretive
Structure Modeling (ISM) approach to analyze the relationships between the
ethical principles of using ChatGPT in SE research and develop a level based
decision model. We further conducted a Cross-Impact Matrix Multiplication
Applied to Classification (MICMAC) analysis to create a cluster-based decision
model. These models aim to help SE researchers devise effective strategies for
ethically integrating ChatGPT into SE research by following the identified
principles through adopting the motivators and addressing the demotivators. The
findings of this study will establish a benchmark for incorporating ChatGPT
services in SE research with an emphasis on ethical considerations.
