ENTERPRISE AI ANALYSIS
Satisficing vs. Maximizing in Prompt Writing: Trait and Task Effects in Human-AI Interaction
Authors: Marc Wyszynski (University of Bremen), Sebastian Weber (University of Bremen), Robin Fritzsche (University Bremen), Marcel Hofgesang (Universität Bremen), Björn Niehaves (University of Bremen)
Abstract: Generative AI systems are increasingly used for cognitively demanding tasks, yet little is known about how psychological factors shape user prompting behavior. This study investigates the role of individual satisficing tendencies in maximizing behavior when selecting prompt strategies across different task domains. In an online vignette experiment with 132 participants, individuals selected between satisficing and maximizing prompt options in five problem-solving scenarios. Satisficing tendencies were assessed using the Short Maximization Inventory, with algorithm aversion and prompt-writing competence included as controls. Linear mixed models showed that stronger satisficing tendencies were associated with reduced maximizing behavior, while higher self-reported competence predicted more maximizing. Participants maximized more in job-related and creative tasks, but satisficed more in writing and technical support tasks, suggesting that task characteristics shape prompting strategies. The results demonstrate that individual differences systematically affect interactions with generative AI. This highlights the importance of considering psychological dispositions in future research on human-AI collaboration.
CCS Concepts: Human-centered computing → Empirical studies in HCI; User studies; Scenario-based design.
Keywords: Artificial Intelligence, Decision Style, Agentic Information Systems, Human-AI Interaction, Delegation, Satisficing
Executive Impact & Key Findings
This research provides critical insights into how individual psychological traits influence the effectiveness of AI interaction, offering a roadmap for optimizing human-AI collaboration in enterprise settings.
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Participants
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Problem Scenarios
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Satisficing Effect (β)
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Competence Effect (β)
Deep Analysis & Enterprise Applications
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Introduction & Background
Methodology
Results & Discussion
Implications & Limitations
Introduction & Background
This section sets the stage by highlighting the increasing use of generative AI (GenAI) systems for complex tasks and the limited understanding of how psychological factors influence user prompting behavior. It introduces the core research question: how human agents' satisficing tendencies influence maximization behavior when formulating prompts for problem-solving with GenAI chatbots. The study aims to bridge gaps in HCI research by applying psychological concepts to explain prompting differences and inform adaptive GenAI system design.
Methodology
The study employed an online vignette experiment with 132 participants. Five problem-solving scenarios were presented, each offering satisficing, moderate, and maximizing prompt options. Participants selected their most and least likely behaviors. Individual satisficing tendencies were measured using the Short Maximization Inventory (SMI), with Algorithm Aversion Scale (AAS) and self-reported prompt-writing competence as controls. Linear mixed models and ANCOVA were used for analysis, with a scoring system for maximizing behavior ranging from +2 to -2.
Results & Discussion
The analysis revealed that stronger satisficing tendencies significantly correlated with reduced maximizing behavior in prompt formulation. Conversely, higher self-reported prompt-writing competence predicted more maximizing. Task characteristics also played a role: participants maximized more in 'Job Offer Choice' and 'Creativity Support' scenarios, but satisficed more in 'Writing Feedback' and 'Technical Support' tasks. This indicates that both individual traits and situational factors influence human-AI interaction strategies.
Implications & Limitations
The findings position satisficing as a stable cognitive style impacting human-GenAI interaction, but also highlight task-contingent differences. Theoretically, this extends bounded rationality to GenAI. Practically, it suggests that GenAI systems should adapt their support based on user tendencies and task context, offering lightweight assistance for routine tasks and structured guidance for high-stakes/creative ones. Limitations include predefined prompt options (vignette study) and a general participant pool, suggesting avenues for future research with real-world interaction logs and diverse contexts.
Core Finding
-0.332 β (Satisficing Effect)A one-point increase in satisficing tendency is associated with a 0.332-point decrease in maximizing behavior. This confirms Hypothesis 1: higher satisficing tendencies lead to lower maximizing behavior when writing prompts for GenAI.
Prompt Formulation Process (Conceptual)
Identify Task
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Consider Goal (Satisfice/Maximize)
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Formulate Initial Prompt
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Evaluate AI Response
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Refine Prompt (Iterate/Stop)
This conceptual flow illustrates the decision points in prompt formulation, showing where individual tendencies (satisficing vs. maximizing) and task characteristics can influence the user's interaction strategy with generative AI.
| Task Type | Maximizing Tendency (Mean Score) | Strategic Implications |
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| Job Offer Choice | 0.376 (Maximizing) |
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| Creativity Support | 0.280 (Maximizing) |
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| Research Support | 0.096 (Neutral) |
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| Writing Feedback | -0.256 (Satisficing) |
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| Technical Support | -0.384 (Satisficing) |
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The study found significant differences in maximizing behavior across task types, indicating that task characteristics (e.g., stakes, need for originality) moderate the impact of individual satisficing tendencies. This informs adaptive GenAI guidance.
Adaptive GenAI for Satisficers
Problem: A user with high satisficing tendencies consistently uses minimal prompts for critical tasks, leading to suboptimal AI outputs and potential errors, especially in areas like academic writing or legal document drafting.
Solution: An adaptive GenAI assistant detects patterns of short, unrefined prompts in high-stakes contexts. Instead of forcing complex prompts, it offers a subtle, user-controlled nudge: 'Would you like to refine this section for clarity or completeness?'. It also provides a collapsible 'refinement panel' with optional suggestions, balancing support and user autonomy.
Outcome: By offering contextual, optional guidance, the system gently encourages deeper engagement, improving output quality without alienating satisficers. This intervention leverages the insight that while satisficing is a trait, its negative effects can be mitigated by intelligent system design that respects user agency.
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