AI IMPACT ANALYSIS
Metrics of Success: Evaluating User Satisfaction in AI Chatbots
This paper presents a new instrument for measuring user satisfaction with AI chatbots in customer support roles. It highlights the rapid advancement of AI-driven chatbots due to LLMs, their widespread adoption, and the critical need to evaluate their effectiveness beyond traditional service quality assessment tools like SERVQUAL and E-SERVQUAL. The research identifies key factors affecting user satisfaction and continued use of AI chatbots, addressing gaps in existing scholarship.
Key Enterprise Impact Metrics
Quantifying the tangible benefits of AI integration based on this cutting-edge research, tailored for immediate enterprise relevance.
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Chatbot User Responses Analyzed
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Quality Dimensions Identified
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Item Scale for Satisfaction
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Continuance Intention Reliability (Alpha)
Deep Analysis & Enterprise Applications
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Methodology
Key Findings
Limitations & Future Work
Enterprise Process Flow
Empathise & Define (Literature Review)
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Ideation (Cheatstorming & SCAMPER)
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Prototype (Instrument Implementation & Testing)
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Scale Development (Prentice & Nguyen's 3-stage Process)
Scale Development Process
3 Stages of Prentice and Nguyen's Process Adapted| Feature | Positive Predictors | Influencing Factors |
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| Dialogic Communication |
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| Information Quality |
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| Privacy & Trust |
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| Hedonic Qualities |
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Key Reliability Scores
0.93 Cronbach's Alpha for Continuance Intention (High Reliability)Case Study: AI Chatbot in a Nordic Automotive Company
Summary: The instrument was tested in a leading Nordic automotive company that uses an internal AI service chatbot as a knowledge management tool for employees.
Challenge: Assessing the effectiveness and user satisfaction of their proprietary AI chatbot beyond basic functionalities.
Solution: Implementation of the proposed 40-item scale to evaluate user satisfaction across 8 constructs including Humanness, Dialogic Communication, Information Quality, Perceived Privacy Risk, Perceived Usefulness, Human-AI Collaboration, Satisfaction, and Continuance Intention.
Result: Identified low internal consistency for 'Humanness' and 'Dialogic Communication' (alpha < 0.70), indicating areas for revision. 'Information Quality', 'Perceived Privacy Risk', 'Perceived Usefulness', 'Human-AI Collaboration', and 'Continuance Intention' showed high reliability (alpha > 0.8).
Sample Size Recommendation
150 Minimum Observations for Exploratory Factor Analysis (EFA)Advanced ROI Calculator
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Phase 4: Optimization & Scaling
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