This paper aims to explore how artificial intelligence (AI) can transform marketing measurement by enabling dynamic scales that adapt to different service contexts and cultures. It challenges the assumption that measurement tools must remain fixed to be valid, demonstrating how AI-driven scales enhance accuracy while maintaining theoretical integrity.
This study uses AI models to adapt the SERVQUAL scale for application in both traditional and digital service contexts. Recognizing that SERVQUAL has been extensively critiqued, this paper uses the scale illustratively to demonstrate the dynamic adaptation of traditional frameworks through AI. A sample of 1,655 respondents is analyzed to assess the reliability and validity of AI-generated scales using context-sensitive reliability metrics and dynamic equivalence testing.
AI-adapted scales maintain psychometric robustness while offering greater relevance by adjusting to service-specific contexts. Tailoring measurement items to cultural and situational factors improves construct validity and provides a more precise understanding of customer perceptions. Adaptive measurement proves more effective than static models in capturing evolving service experiences.
This study advances service marketing research by demonstrating that AI-driven dynamic scales maintain construct validity while adapting to contextual variations, challenging traditional notions of measurement invariance. By introducing a structured framework for AI-assisted scale adaptation, this research bridges the gap between academic measurement tools and real-world applicability. The practical guide provides a step-by-step approach for researchers and practitioners to refine measurement scales using large language models, ensuring contextual relevance while maintaining reliability.
AI-driven measurement tools provide a scalable, cost-effective solution for businesses operating in diverse markets. By enabling real-time, context-sensitive insights, these tools help managers make better strategic decisions and enhance customer experiences.
AI-driven dynamic scales reduce biases inherent in traditional marketing research, ensuring measurement tools reflect diverse cultural, economic and social realities. This inclusivity supports fairer service design and better-informed public policy.
This study introduces AI-enabled dynamic measurement as a paradigm shift in marketing, bridging academic research and real-world application. It advances marketing theory and practice by offering a flexible, culturally responsive approach to service quality measurement.
