Artificial Intelligence In Mobile Devices in Indonesia: The Role of Trust, Quality, and Price in Consumer Satisfaction with AI-Powered Smartphones
Description
Artificial Intelligence in Mobile Devices in Indonesia: The Role of Trust, Quality, and Price in Consumer Satisfaction with AI-Powered Smartphones The research objects focus on the following constructs: 1. User Trust in AI 2. AI Product Quality 3. Pricing 4. Customer Satisfaction This study employs a quantitative, explanatory survey design to investigate the role of User Trust in AI, AI Product Quality, and Pricing Strategies in fostering Customer Satisfaction with AI-powered smartphones. This approach is chosen to quantify relationships between these variables and provide a clear explanatory framework for understanding the impact of these factors on customer satisfaction. The primary instrument used for data collection is a structured questionnaire, utilizing a five-point (1 – 5) Likert scale to measure respondents' perceptions and attitudes. The questionnaire includes various indicators related to User Trust in AI (Benevolence, Integrity, Competence, Honesty, Predictability, Credibility, Reliability, Correctness, Availability), AI Product Quality (Performance, Features, Reliability, Conformance, Durability, Serviceability, Aesthetics, Perceived Quality), Pricing (Price Transparency, Price-Quality Ratio, Relative Price, Price Confidence, Price Reliability, Price Fairness) and Customer Satisfaction (Overall Customer Satisfaction, Expectancy Disconfirmation, Affective Response, Perceived Value, Fulfilling Important Needs, Fulfilling Changing and New Needs). A pilot test was conducted to ensure the validity and reliability of the instrument, involving a small sample of respondents similar to the study population. The unit of analysis for this study comprises approximately 259 respondents who are users of AI-powered smartphones. These participants were selected based on their usage of smartphones that incorporate AI technology, particularly in the context of features such as voice assistants, smart cameras, and automated services. Data collected from the survey were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). This method was chosen due to its suitability for analyzing complex models and its ability to handle small to medium-sized samples effectively. The analysis involved two main stages: the measurement model and the structural model. The measurement model assessed the reliability and validity of the constructs, while the structural model evaluated the hypothesized relationships between User Trust in AI, AI Product Quality, Pricing, and Customer Satisfaction. The PLS-SEM approach provided robust insights into the direct and moderating effects of factors like trust and product quality on customer satisfaction, offering a comprehensive understanding of the drivers behind consumer attitudes toward AI smartphones.
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Institutions
- Bina Nusantara University