How technology readiness influences behavioral and purchasing intention: serial multiple mediating role of attitude toward AI and AI-driven consumer chatbot experience

Purpose This study investigates the impact of technology readiness on consumer behavioral and purchasing intentions in the context of AI-driven chatbots. It further explored the serial multiple mediating roles of consumers’ attitudes toward artificial intelligence (AI) and their experiences with AI-driven chatbots. This research responds to a gap in the literature by integrating technology readiness with AI-related consumer experience in the context of an emerging market. Design/methodology/approach A hypothesis-driven, deductive research design was employed using a cross-sectional survey method. Data were collected from 423 Turkish consumers who had previously interacted with AI-driven chatbots during online shopping. Structural equation modeling (SEM) and mediation analysis were conducted to test the proposed serial mediation model, which examines the pathways from technology readiness to behavioral and purchasing intentions through attitudes toward AI and chatbot experience. Findings The results reveal that technology readiness significantly influences behavioral and purchasing intentions. This relationship is serially mediated by consumers’ attitudes toward AI and their chatbot experiences. The study demonstrates that higher technology readiness levels lead to more favorable attitudes toward AI, enhance chatbot experiences, and positively affect intention outcomes. Research limitations/implications This study focused on consumers in Turkey, and cultural factors may have influenced the results. Therefore, the results cannot be generalized to different ethnic groups and cultures, as sociocultural factors may lead to variations in technology readiness, attitudes toward artificial intelligence technology, and chatbot experiences. In addition, the sample size was limited because some businesses in Turkey have only recently started interacting with consumers through AI-driven chatbots in their marketing activities. This study used a cross-sectional design. Changes in consumer perceptions and technology acceptance over time can be better analyzed through a longitudinal study. The evolution of consumer attitudes toward AI chatbots over time can be examined through longitudinal research. Practical implications The findings indicate that AI-driven chatbots positively affect customer behavioral and purchase intentions. On this basis, businesses can utilize chatbots as customer service and sales tools, offering personalized recommendations and shopping guidance. Additionally, it can be easily stated that chatbot experiences shape a positive attitude toward AI. Therefore, brands should clearly explain how AI works and ensure transparency to strengthen the perception that chatbots are user-friendly and reliable. Another critical point is that consumers’ motivations for technology readiness positively influence chatbot experience and purchase intention. Brands should develop different strategies for consumers with high and low technology readiness levels and personalize the chatbot interface according to these segments. Companies can create a more efficient user experience in the digital shopping process by integrating chatbots with customer relationship management (CRM) and data analytics systems. Companies that invest in AI-based systems can improve the process by continuously collecting user feedback to understand how consumers are affected by their chatbot experience. Originality/value This study is among the first to apply a serial multiple mediation model to analyze the impact of technology readiness on consumer behavior in AI-based interactions. By integrating elements of the Technology Acceptance Model (TAM), stimulus–organism–response (SOR) framework, and Customer Experience Theory, this study contributes to the theoretical development of AI adoption in emerging markets. It also offers practical guidance for businesses seeking to optimize AI-powered customer engagement strategies.