Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Applied Business and International Management Journal of the Community Development in Asia
Tek Yew Lew
Universiti Sains Malaysia
Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Social Sciences

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Consumer Attitude and Intention Toward AI-Based Smart Ordering and Delivery Systems in the Fast-Food Industry Azura Abdullah Effendi; Tek Yew Lew; Wei Ning Ooi; Soak Suan P’ng; Mei Shan Poh; Puteri Balqis Binti Hamizan; Arun Singh Tomar; Daisy Mui Hung Kee
International Journal of Applied Business and International Management Vol 10, No 3 (2025): December 2025
Publisher : AIBPM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32535/ijabim.v10i3.4325

Abstract

The increasing use of artificial intelligence (AI) in fast-food ordering and delivery systems has transformed service processes, yet consumer adoption remains uneven. This study examines the factors influencing consumer attitude and behavioral intention toward AI-based smart ordering and delivery systems in the fast-food industry, using McDonald’s AI-based Smart Ordering and Delivery System in Malaysia as the empirical context. Using a quantitative approach, data were collected from 150 consumers in Malaysia and analyzed using multiple regression analysis. The results show that social influence has a significant positive effect on consumer attitude (? = 0.313, p 0.001), while ease of use, enjoyment, perceived convenience, and trust do not significantly affect attitude. For behavioral intention, perceived convenience (? = 0.218, p 0.01), social influence (? = 0.291, p 0.001), and trust (? = 0.248, p 0.01) are significant predictors, whereas consumer attitude is not (? = 0.066, p 0.05). The models explain 29.8% of the variance in consumer attitude and 44.5% of the variance in behavioral intention. These findings suggest that adoption of AI-based ordering systems in fast-food contexts is driven more by social endorsement and functional considerations than by attitudinal evaluation.
From Order to Satisfaction: Examining Food, Service, and Value Drivers of Customer Satisfaction in the Fast-Food Industry Lay Hong Tan; Tek Yew Lew; Yee Ros Tan; Wan Ling Tan; Wei Chi Tan; Wei Len Tan; Yee Ling Tan; Anees Jane Ali; Daisy Mui Hung Kee
Journal of The Community Development in Asia Vol 8, No 3 (2025): September 2025
Publisher : AIBPM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32535/jcda.v8i3.4175

Abstract

Customer satisfaction is a critical determinant of competitiveness in the fast-food industry, even for global leaders like McDonald’s, which is widely recognized for efficiency, consistency, and strong branding. Despite its success, adapting to evolving customer expectations remains essential. This study aims to evaluate the factors influencing customer satisfaction at McDonald’s in Malaysia, focusing on food quality, service quality, pricing and value, product variety, and service method. A quantitative survey was conducted with 100 respondents using structured questionnaires. The findings indicate that customer satisfaction is most strongly influenced by service method (? = 0.449) and product variety (? = 0.279), while food quality, service quality, and pricing and value showed weaker effects and did not significantly predict satisfaction. The overall model explained 49% of the variance in customer satisfaction (R² = 0.492). These results highlight that customers place the greatest emphasis on convenience, efficiency, and diverse menu options. McDonald’s should therefore prioritize enhancing service delivery across dine-in, drive-thru, and self-service kiosks, while maintaining food quality and competitive pricing as baseline expectations. Overall, this study provides practical insights for improving customer satisfaction and offers strategic implications for fast-food chains in highly competitive markets
Co-Authors Anees Jane Ali Arun Singh Tomar Azura Abdullah Effendi Daisy Mui Hung Kee Lay Hong Tan Mei Shan Poh Puteri Balqis Binti Hamizan Soak Suan P’ng Wan Ling Tan Wei Chi Tan Wei Len Tan Wei Ning Ooi Yee Ling Tan Yee Ros Tan