Deciphering Decision Making: The Interplay of Mood States and Decision Styles

Recent theories of Decision- Making have advocated the role of emotions in the cognitive processes of decisions. One such approach is the EIC model (Lerner et al., 2015) which posits – mood to indirectly affect decision making by impacting current emotions, indicating individual’s overall emotional states to indirectly influence cognitive decision processes. The study aimed to understand the role of emotions on decision making and the nuances that underlie it. The 6 main hypothesis of the paper focused on evaluating whether incidental emotions via current emotions affect decision making significantly or do decision styles have a stronger significant effect on decision making. The paper used a mixed method design where the qualitative data was collected via interviews and thematic analysis and labels were attuned via open coding that helped in triangulation and pattern generation. Tools used were the YDMC, Mood induction videos of OPENLAV Database, Decision Style questionnaire (DSQ), Ryff’s well-being scale and an Interview schedule. A purposive sampling was done to obtain the sample (N=29) for the pre-test post-test design. Results for the quantitative data concluded that sad and angry incidental mood had significant effects on FR1 and FR2 (Resistance to Framing and Sunken costs) of YDMC and, that happy mood had no significant effect on risk perception. The decision styles that have been seen to dominate were Vigilant followed by Dependent and Spontaneous style. For qualitative data, based on the dominant responses for the entire sample an Interactive Hexagonal map was constructed. STB (Self -thoughts & Beliefs), FP (Futuristic perspective), CE (Current experiences), EE (Environmental ‌effects), and PEx (Past experiences) were the dominant pattern connections found for the current sample. Thus, Incidental Mood had a significant effect on Resistance to Framing and Sunken costs whereas had no significant effect on Consistency with Risk Perception. Future work on the nuances of mood on decision-making can help determine the affective, cognitive, and neuropsychological models of decision-making where neural correlates could help delve deeper in these higher cognitive and emotional processes.