How Technology Readiness Influences Behavioral and Purchasing Intention: Serial Multiple Mediating Role of Attitude toward AI and AI-Driven Consumer Chatbot Experience

How Technology Readiness Influences Behavioral and Purchasing Intention: Serial Multiple Mediating Role of Attitude toward AI and AI-Driven Consumer Chatbot Experience | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article How Technology Readiness Influences Behavioral and Purchasing Intention: Serial Multiple Mediating Role of Attitude toward AI and AI-Driven Consumer Chatbot Experience Cihan Becan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6135960/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Sep, 2025 Read the published version in Digital Transformation and Society → Version 1 posted You are reading this latest preprint version Abstract With the rise of AI in digital consumer experiences, one of the key challenges for businesses is predicting and influencing consumers’ behavioral and purchase intentions in AI-driven interactions. In this regard, there appears to be a knowledge gap in the literature that cannot be determined regarding the effect of technology readiness on behavioral and purchasing intentions and, accordingly, the mediating role of attitude toward AI technology and the AI-driven consumer chatbot experience. Therefore, this research aims to determine the relationships among technology readiness, attitude toward AI technology, AI-driven consumer chatbot experience, behavioral intentions, and purchase intentions. The primary focus of the study is to evaluate the impact of Turkish consumers' technology readiness on behavioral and purchase intentions through the serial multiple mediating roles of attitude toward AI technology and the AI-driven customer chatbot experience. A questionnaire designed in line with the purpose of the research was applied to 423 respondents using AI-driven chatbots during the digital shopping experience. Although technology readiness does not directly affect behavioral and purchase intentions, attitude toward AI technology and the AI-driven consumer chatbot experience play a serial multiple mediating role in the relationship between motivations for technology readiness and purchase intention. Marketing Technology readiness AI-driven chatbot Digital shopping Consumer experience Behavioral intention Purchasing intention Figures Figure 1 Figure 2 Figure 3 Introduction In the past decade, innovative developments such as mobile commerce, social media, and smartphone technology have transformed the lifestyles of nearly every consumer globally (Claudy et al., 2015 ). Recently, scholars have increasingly explored the role of consumer characteristics in explaining technology usage (Westjohn et al., 2009 ; Zhu et al., 2007 ; Sääksjärvi & Samiee, 2011 ). These studies provide valuable insights for marketers in identifying consumer groups that are more likely to adopt specific technologies (Blut & Wang, 2019 ). Given the expanding role of technologies in service delivery, understanding customers' readiness to adopt technology-based systems, particularly e-services, is highly important (Lin et al., 2007 ; Burke, 2002 ). As a reflection of these technological advancements, digital marketing, and AI-driven interactive messaging services are rapidly evolving, with such services commonly referred to as ‘chatbots’ (Desaulniers, 2016 ). AI-driven chatbots enhance the customer experience by allowing consumers to interact with virtual marketing representatives anytime and anywhere (Cheng & Jiang, 2020 ). Today, chatbot usage is increasing across both mobile and web interfaces, and the chatbot market is expected to reach $ 1.25 billion by 2025 (Forbes, 2018 ). Additionally, a study by Nielsen reported that 53% of consumers are more likely to purchase products from businesses with which they can communicate via real-time messaging (Nielsen, 2018). Research by Manchester Business School has shown that participants perceive this technology as highly intriguing, exciting, and futuristic (Sidaoui et al., 2020 ). Moreover, these AI-driven assistants help companies reduce customer support costs by 30% while saving time (Popescu, 2020 ; Juniper Research, 2021 ). A study by Arsenijevic & Jovic ( 2019 ) revealed that the most significant advantage of chatbot usage in marketing is the ability to provide quick and straightforward information. However, the research also highlighted concerns about the risk of misinformation, identifying this as a crucial issue that needs to be addressed in the future. Research on the use of artificial intelligence tools has highlighted the importance of consumers' experiences with AI technology; however, studies specifically examining AI-driven consumer chatbot experiences during the digital shopping process are quite limited. Additionally, there is a scarcity of research addressing the effects of technology readiness on behavioral and purchase intentions. Furthermore, while the significance of attitudes toward AI technology and AI-driven chatbot experiences as mediating variables has been documented in the consumer behavior and digital marketing literature, no study has demonstrated the series of multiple mediating effects of these variables on the relationship between technology readiness and its motivating factors on behavioral and purchase intentions. This study aims to explore the potential relationships among technology readiness, attitudes toward AI technology, AI-driven consumer chatbot experience, and behavioral and purchase intentions. Furthermore, within the proposed conceptual model, this study aims to understand the decision-making behavior of Turkish consumers using AI-driven chatbots in digital shopping processes by identifying the mediating roles of attitudes toward AI technology and the AI-driven consumer chatbot experience in the relationship between technology readiness and behavioral and purchasing intentions. Conceptual Background and Literature Review Technology Readiness and Technology Acceptance Model Considering the expanding roles of technologies in service delivery, understanding customers' readiness to use technology-based systems, particularly e-services, is of great importance (Lin et al., 2007 ; Burke, 2002 ). Technology readiness refers to the degree to which individuals or organizations are prepared to adopt and utilize new technologies (Shwedeh et al., 2022 ; Marthasari et al., 2020 ; Nasution et al., 2021 ). It is a multifaceted concept encompassing various factors, such as technological infrastructure, technical skills, management support, and user attitudes (Astuti & Nasution, 2014 ; Rahayu & Day, 2015 ). The technology readiness index, initially conceptualized by Parasuraman, is a foundational framework for assessing individuals' propensity to embrace technology. Optimism, innovativeness, discomfort, and insecurity collectively influence technology adoption behaviors (Parasuraman, 2000 ; Parasuraman & Colby, 2014 ). These dimensions collectively shape an individual's propensity to engage with technology, affecting both personal and professional contexts (Kadiyono & Pardosi, 2023 ; Blut & Wang, 2019 ). This concept encompasses various psychological and contextual factors influencing the willingness to embrace technological advancements. Marketers adopt technology readiness as a tool to assess the extent to which new technologies can be integrated into customer-company interactions, determine which types of technologies should be introduced, decide on the implementation pace, and identify the necessary customer support (Parasuraman, 2000 ). Understanding and enhancing technology readiness will be essential for fostering the successful adoption and integration of innovative solutions as technology evolves. Research has shown that technology readiness is crucial in accepting and adopting new technologies (Nasution et al., 2021 ). Individuals or organizations with greater technology readiness are more likely to embrace and utilize new technologies, improving performance and productivity (Shwedeh et al., 2022 ; Astuti & Nasution, 2014 ; Rahayu & Day, 2015 ). The technology acceptance model (TAM) is a theoretical framework developed to understand the factors influencing users' technology acceptance and usage. The model posits that perceived usefulness (PU) and perceived ease of use (PEOU) are the primary determinants influencing users' intentions to adopt new technologies (Venkatesh & Davis, 2000 ; Ma & Liu, 2004 ; Chan & Teo, 2007 ). Over the years, numerous studies have validated the TAM's predictive power across various contexts, demonstrating its robustness in explaining user behavior and technology adoption (Venkatesh & Davis, 2000 ; Chan & Teo, 2007 ). The Technology Acceptance Model has evolved significantly since its inception, with various extensions and adaptations enhancing its explanatory power. The model continues to serve as a critical framework for understanding technology acceptance, with ongoing research exploring the integration of additional constructs and contextual factors to refine its applicability across diverse technological landscapes further. Research indicates that integrating TR with TAM enhances the explanatory power of technology adoption models. Lin et al. ( 2005 ) demonstrated that technology readiness significantly influences users' intentions to adopt e-services, with its effects being mediated by perceived usefulness and ease of use (Lin et al., 2005 ). This suggests that an individual's readiness to engage with technology can shape their perceptions of its utility and usability, thereby impacting their acceptance decisions. Similarly, the Technology Readiness and Acceptance Model (TRAM) proposed by Lin et al. ( 2007 ) empirically supports the notion that incorporating TR into TAM broadens its applicability and enhances its predictive capabilities regarding consumer adoption of innovations (Lin et al., 2007 ). The interplay between TR and TAM is further evidenced in various contexts, including e-commerce and industrial automation. Pires et al. ( 2011 ) explored how TRI factors differentiate between users and non-users of internet banking, highlighting that technology readiness can serve as an antecedent to TAM constructs. Godoe & Johansen ( 2012 ) also emphasized that integrating TR and TAM provides a more comprehensive understanding of technology adoption by considering individual readiness and system-specific factors. This integrated approach is crucial for identifying the antecedents of technology use, as it acknowledges the role of personal traits in shaping technology acceptance behaviors. Use of Artificial Intelligence (AI) for Virtual Shopping and SOR (Stimulus-Response-Organism) Model AI technologies such as chatbots, virtual assistants, and personalized recommendation systems have revolutionized how consumers engage with online retail platforms. One of the primary applications of AI in virtual shopping is through chatbots and virtual assistants. These AI-driven tools facilitate customer interactions by providing real-time support and personalized recommendations on the basis of user behavior and preferences. The ability of these virtual anchors to simulate human-like interactions allows retailers to replace traditional sales methods with a more engaging digital experience (Zhong, 2024 ). Moreover, AI's predictive capabilities are crucial in personalizing the shopping journey. By analyzing past purchases and browsing behaviors, AI systems can anticipate consumer needs and suggest products that align with individual preferences. This level of personalization not only enhances user satisfaction but also increases the likelihood of purchase, as consumers are presented with options that resonate with their interests (Ntumba et al., 2023 ; Srivastava & Pal, 2024 ; Wang et al., 2023 ). The integration of AI with technologies such as augmented reality (AR) and virtual reality (VR) further enriches the shopping experience by providing immersive environments where consumers can visualize products in real-time (Sun et al., 2021 ; Cha, 2020 ). Integrating artificial intelligence (AI) into virtual shopping environments has garnered significant attention in recent years, particularly through the lens of the Stimulus-Organism-Response (SOR) model. This model provides a framework for understanding how external stimuli (such as AI-driven technologies) influence consumer behavior and emotional responses, ultimately affecting purchasing decisions. This model posits that external stimuli (in this case, AI technologies) influence internal processes (the organism's perceptions and emotional responses), which ultimately lead to behavioral responses (such as purchase intentions or continued use of the chatbot). The response aspect of the SOR model encapsulates the behavioral intentions that arise from the interaction with the chatbot. The organism component of the SOR model refers to the internal processes that consumers undergo in response to stimuli. This includes emotional responses, cognitive evaluations, and behavioral intentions. Various studies have explored the role of AI in enhancing the e-service scape, an online shopping environment characterized by its design and functionality. Yadav and Mahara ( 2020 ) argue that e-servicescape features serve as stimuli that influence customer trust and purchase intentions, further illustrating the SOR model's applicability in understanding consumer behavior in AI-enhanced shopping contexts. Research by Kim and Lennon demonstrates that website quality and reputation—both influenced by AI technologies—affect consumers' emotions and perceived risks, ultimately shaping their purchasing decisions (Kim & Lennon, 2013 ). Additionally, the findings of Erwei et al. ( 2023 ) emphasize how social presence in live streaming can enhance impulse buying, showcasing the direct impact of AI-driven environments on consumer behavior. Research indicates that AI technologies' perceived enjoyment and trust can significantly influence these internal processes. Zhu et al. found that environmental cues in online shopping affect Generation Y consumers' repurchase intentions, suggesting that positive emotional responses can enhance loyalty (Zhu et al., 2019 ). AI-Driven Consumer Chatbot Experience and Consumer Experience Theory The integration of AI-driven chatbots into consumer interactions has garnered significant attention in recent years, particularly in the context of enhancing consumer experience. The literature on consumer experiences with chatbots reveals a complex interplay of factors influencing user interactions, satisfaction, and continued usage intentions. As automated customer service agents, chatbots have become increasingly prevalent in various sectors, particularly e-commerce and retail. AI-driven chatbots have emerged as transformative forces in the e-commerce landscape, revolutionizing the customer experience through personalized and efficient interactions (Ntumba et al., 2023 ; Zhang et al., 2023 ). One significant aspect of consumer chatbot interaction is the quality of communication. Lee ( 2022 ) emphasized the importance of parasocial relationships in enhancing communication quality, suggesting that fostering a sense of intimacy can improve consumers' perceptions of chatbots' accuracy and credibility. Cheng et al. ( 2021 ) further contributed to this discourse by highlighting that chatbots can effectively manage customer inquiries without the emotional fatigue that human agents might experience, thereby maintaining consistent service quality. The existing research suggests that AI-driven chatbots can significantly enhance the virtual shopping experience by providing personalized, efficient, and engaging interactions. However, the effective design and deployment of chatbots require careful consideration of various factors, including communication quality, anthropomorphism, task complexity, and consumer trust, to optimize the customer experience and drive positive business outcomes (Cheng et al., 2021 ; Klein & Martinez, 2022 ; Li et al., 2023 ). As chatbots evolve, understanding these dynamics will be crucial for businesses aiming to optimize customer service strategies and enhance user satisfaction. Consumer Experience Theory (CET) examines the impact of consumers' experiences with a brand, product, or service on their perceptions, emotions, and future behaviors. This theory encompasses sensory, emotional, cognitive, behavioral, and social experience components (Schmitt, 1999 ). The emotional and psychological dimensions of consumer interactions with chatbots are crucial for understanding their impact on consumer experience. The theoretical framework surrounding consumer experience theory also finds relevance in the context of AI chatbots. Cheng and Jiang ( 2022 ) explore how chatbot marketing efforts can strengthen customer-brand relationships, thereby enhancing consumer behavioral intentions. This aligns with the principles of relationship marketing, which posits those strong emotional connections between consumers and brands lead to increased loyalty and satisfaction. Furthermore, Khoa ( 2021 ) illustrates that chatbots can influence consumer emotions, leading to impulse purchasing behavior, which underscores the emotional engagement aspect of consumer experience theory. Lee and Park (2022) discuss how parasocial relationships—perceived emotional connections with chatbots—can enhance communication quality and influence continued usage intentions among consumers. This notion is supported by the work of Xu et al. ( 2022 ), which highlights that communication styles and consumer attachment anxiety significantly affect satisfaction with chatbot interactions. The ability of chatbots to simulate human-like interactions can foster a sense of intimacy and trust, which are essential components of positive consumer experiences. Attitude toward AI Technology Recent research on public attitudes toward artificial intelligence reveals a complex landscape characterized by optimism and apprehension. Evolving perceptions of AI are shaped by various factors, including personal relevance, media influence, and the specific context in which AI is applied. A foundational aspect of consumer attitudes toward AI in virtual shopping is the perceived utility and personalization that AI technologies offer. Misra et al. ( 2024 ) emphasized that AI-driven personalization algorithms significantly enhance consumer engagement and satisfaction, fostering a more favorable shopping experience. This aligns with findings from Liang et al. ( 2019 ), who assert that consumers' attitudes toward AI devices directly impact their purchase intentions, reinforcing the importance of positive consumer perceptions in technology adoption. Moreover, the role of AI in facilitating the shopping process is critical, especially through voice assistants and chatbots. Calahorra-Candao and Martin-de Hoyos (2024) noted a research gap regarding the comprehensive impact of AI voice assistants on the shopping journey, particularly in later stages such as final purchase decisions. This is echoed by Rana et al. ( 2024 ), who discuss how AI-driven chatbots shape consumer attitudes by enhancing perceived usefulness and usability during online shopping. The effectiveness of these AI tools in improving the shopping experience is further supported by Zhang ( 2021 ), who discusses the importance of public opinion and consumer attitudes in shaping the future of AI technologies in retail. Relationships among the Variables, Hypothesis Development and Research Model Research indicates that individuals with high optimism and innovativeness are more likely to engage positively with new technologies, leading to increased behavioral and purchasing intentions. Studies have shown that consumers with high technology readiness tend to have a more favorable perception of digital technologies, which is directly correlated with their willingness to purchase products or services that utilize such technologies (Song, 2021). This is particularly evident in contexts such as mobile commerce, where consumers with a positive outlook toward technology are more inclined to purchase (Ismail & Wahid, 2020). Research has demonstrated that technology readiness influences perceived usefulness and ease of use, which are critical determinants of behavioral intention (Chen et al., 2019; Lai & Lee, 2020). This suggests that a consumer's readiness to adopt technology can enhance their perception of its utility, increasing their intention to use it. Conversely, consumers with low technology readiness often distrust new technologies, which can diminish their purchasing intentions. For example, individuals who feel discomfort or insecurity regarding technology are less likely to engage with it, leading to reduced behavioral intentions (Munthe et al., 2020). Research indicates that negative inhibitors can diminish an individual's technology readiness index, decreasing behavioral intentions toward technology products (Yang et al., 2023; Yang & Kim, 2024). Inhibitors of technology readiness, such as discomfort and insecurity, profoundly negatively impact consumers' behavioral and purchasing intentions. Therefore, the following hypotheses are formulated: H1a: Consumers’ inhibitors of technology readiness have a negative effect on their behavioral intentions. H1b: Consumers’ inhibitors of technology readiness have a negative effect on purchasing intention. H2a: Consumers’ motivations for technology readiness positively affect their behavioral intentions. H2b: Consumer motivations for technology readiness positively affect purchasing intention. The relationship between technology readiness and attitudes toward artificial intelligence (AI) technology is multifaceted, encompassing motivators and inhibitors that significantly influence consumer behavior. Motivators such as optimism and innovativeness are pivotal in shaping positive attitudes toward AI technology. Studies have shown that consumers with high technology readiness are more likely to perceive AI as valuable and easy to use. This directly correlates with their intention to adopt such technologies in shopping scenarios (Anh et al., 2024). Furthermore, positive prior experiences with AI technologies significantly increase consumers' readiness and acceptance, indicating that familiarity can mitigate initial apprehensions (Kolar et al., 2024). Research suggests that inhibiting factors can create significant barriers to the adoption of AI, particularly among those who may already feel vulnerable, such as women, who report higher levels of anxiety related to AI technologies (Kolar et al., 2024). Additionally, the anxiety surrounding AI can be exacerbated by discussions about its capabilities and implications, which may inadvertently increase fears rather than alleviate them (Lemay et al., 2020). Therefore, the following hypothesis is proposed: H3a: Consumers’ inhibitors of technology readiness do not significantly affect their attitudes toward AI technology. H3b: Consumer motivation for technology readiness significantly affects attitudes toward AI technology. Research also indicates that consumers who exhibit greater optimism and innovativeness are more likely to engage positively with AI technologies, including chatbots. Flavián et al. (2021) reported that technology readiness significantly influences consumers' intentions to use AI in service contexts, highlighting that optimistic consumers are more inclined to embrace AI solutions. Similarly, Fu's study emphasized that characteristics such as innovativeness positively affect expectations regarding chatbot performance, thereby enhancing trust and willingness to use these technologies in online shopping environments (Fu, 2024). Moreover, the interplay between technology readiness and consumer experience is further illustrated by Yoon and Yu, who explored how consumer attitudes toward chatbot services influence their acceptance intentions (Yoon & Yu, 2022). Their findings suggest that positive experiences with chatbots lead to greater acceptance and utilization, reinforcing that technology readiness plays a critical role in shaping consumer perceptions and interactions with AI technologies. Therefore, the following hypothesis can be derived from the preceding discussion: H4: Consumer motivation for technology readiness significantly influences the AI-driven consumer chatbot experience. Research has shown that positive experiences with chatbots can lead to increased purchase intentions and loyalty (Mehta et al., 2022; Rana et al., 2024). Research has indicated that AI chatbots can effectively influence impulse purchasing behavior. This is supported by findings from Ameen et al., who emphasized that AI integration in shopping experiences can significantly improve customer satisfaction and loyalty, mainly through personalized and proactive interactions (Ameen et al., 2021). Such interactions enhance the shopping experience and create a sense of engagement that can drive purchasing decisions. Moreover, the quality of interaction with AI chatbots plays a crucial role in shaping consumer attitudes and behaviors. Lee's (2022) study revealed that effective communication with AI shopping chatbots can increase consumer satisfaction and decision-making, ultimately supporting purchase intentions. On the basis of the literature, we propose the following hypothesis: H5a: AI-driven consumer chatbot experience has a positive direct effect on behavioral intention. H5b: AI-driven consumer chatbot experience has a positive direct effect on purchase intentions. The relationship between purchase intention and behavioral intention during AI-driven consumer chatbot experiences is a multifaceted area of study that integrates various psychological and technological factors. Jiang et al. (2022) propose a theoretical model that connects social presence, experiential innovativeness, and self-determined satisfaction to consumer behavior intention. This finding suggests that chatbots exhibiting human-like features can increase purchase intentions by fostering a sense of social interaction. According to Kaplan, attitudes and perceived behavioral controls positively influence purchasing behaviors, indicating that a consumer's intention to purchase is closely linked to their behavioral intentions (Kaplan, 2018). This is echoed by Eissa, who noted that purchasing intentions predict actual buying behavior, reinforcing the notion that intention serves as a precursor to action (Eissa, 2024). Thus, the following hypothesis is formulated: H6: Purchasing intention during AI-driven consumer chatbot experience significantly influences behavioral intention. The relationship between attitudes toward artificial intelligence (AI) technology and consumer behavior, particularly in the context of virtual shopping, is increasingly significant in today's digital marketplace. Guerra-Tamez et al. (2024) highlighted that AI exposure and the perception of AI accuracy significantly enhance brand trust among consumers, particularly those in Generation Z, which subsequently positively influences their purchasing decisions. This finding is supported by Yazdani (2023), who emphasized that consumer trust and acceptance of AI recommendations are critical for effective interactive marketing, which impacts consumer decision-making processes. Furthermore, Nagy and Hajdú's (2021) research underscores that perceived usefulness is a vital factor influencing consumer attitudes toward AI, affecting behavioral intentions in online shopping contexts. This aligns with findings from Cheng and Jiang (2022), who explore how AI-driven marketing efforts, such as chatbots, can enhance customer–brand relationships and influence online consumer behavioral intentions. Thus, the following hypothesis is developed: H7a: Attitudes toward AI technology significantly influence behavioral intentions. H7b: Attitudes toward AI technology significantly influence purchasing intentions. The demand for AI facilities, particularly chatbots, in retail settings highlights consumers' desire for personalized and memorable shopping experiences. Srivastava and Pal (2024) emphasized that consumers prefer chatbots to assist them at various purchase points, indicating a strong inclination toward technology that enhances their shopping journey. This preference suggests that when consumers experience effective AI interactions, their attitudes toward AI technology become more favorable, as they associate it with improved service delivery and satisfaction. Moreover, the anthropomorphic design of chatbots plays a crucial role in shaping consumer perceptions. Youn and Cho (2023) discuss how the effectiveness of anthropomorphic cues in AI chatbots varies across different business types, suggesting that consumers respond more positively to chatbots that exhibit human-like characteristics. The quality of interaction with AI chatbots also significantly affects consumer attitudes. Lee's research indicates that effective communication with AI shopping chatbots leads to higher satisfaction levels and a greater likelihood of continued usage (Lee, 2022). This aligns with the findings of Tsai et al., who assert that the social presence of chatbots enhances consumer engagement through parasocial interactions, further solidifying positive attitudes toward AI (Tsai et al., 2021). When consumers feel a connection with AI, their trust in and acceptance of the technology increase, fostering a more positive overall perception. On this basis, the following is proposed: H8: AI-driven consumer chatbot experience positively influences attitudes toward AI technology. On the basis of the direct and indirect effects of all of the variable constructs discussed above, this study suggests that AI-driven consumer chatbot experience and attitudes toward AI technology might indirectly affect behavioral and purchasing intentions through technology readiness. We therefore propose the following hypothesis: H9a: There is a serial multiple mediation effect of AI-driven consumer chatbot experience and attitudes toward AI technology on the impact of consumer motivation for technology readiness on behavioral intention. H9b: There is a serial multiple mediation effect of AI-driven consumer chatbot experience and attitudes toward AI technology on the impact of consumer motivation for technology readiness on purchase intentions. Figure 1 presents the conceptual framework model of the current study to summarize the research hypotheses mentioned above. Our conceptual model includes technology readiness, AI-driven customer chatbot experience, attitudes toward AI, and behavioral and purchase intentions for virtual shopping. It encompasses the hypothetical relationships (H1–H8) among these constructs. Data and Methodology Sampling and Data Collection A quantitative survey approach was employed in line with the research objectives, and data were collected through cost-effective and easily implementable questionnaires. The study data were collected through an online survey designed and hosted by Google Online Forms. The survey targeted consumers residing in Turkey who were confirmed to have received chatbot support during their shopping experiences. The researchers recruited participants from October to November 2024 via social media and personal e-mails with an anonymous invitation link through a purposive sampling method. At the beginning of the survey, instructions were provided about the definition of chatbots and some examples to help participants understand applications or platforms that could be considered AI-driven chatbot activities in their virtual shopping experiences. In total, 550 participants clicked on the survey link and participated in this research study. Among these participants, 127 (23%) stated that they had not used chatbots and were therefore removed from the study. Consequently, we received 423 usable responses. Before the survey was finalized and launched, the pilot test was conducted with 30 respondents using chatbots while shopping in the past year. We also asked the participants if there were any confusing points in the survey questions. The survey instrument was revised and finalized on the basis of feedback from the pilot test results to ensure its content validity. We conducted a series of reliability tests via SPSS 27.0 to ensure that Cronbach’s alpha value for each variable exceeded 0.65. Measurement Instrument and Analysis Procedures The survey used in this study is divided into two sections: background data and the main research questions. Gender, age, level of education, and household income constitute the demographic data. For the main research questions, the researchers adopted criteria and structures from existing scales to ensure content validity and modified the wording to fit the research context. All the constructs were measured on a 5-point Likert scale (e.g., 1 = definitely disagree, 2 = disagree, 3 = neutral, 4 = somewhat agree, 5 = definitely agree). Sixteen questions concerning technology readiness, four concerning behavioral intention, four concerning purchasing intention, seven concerning attitudes toward AI technology, and twelve on concerning AI-driven consumer chatbot experience are adapted. The scale for technology readiness, which comprises four dimensions adopted by Blut and Wang (2020) and Flavian et al. (2022), was used: the technological motivator dimension, which includes four items of optimism and four items of innovativeness; the technological inhibitor dimension, which includes four items of discomfort and four items of insecurity. The attitude toward the AI technology dimension was measured via a 7-item scale adopted by Bhagat et al. ( 2023 ) and Kim et al. ( 2021 ). The AI-driven consumer chatbot experience dimension was measured via 3-item sub dimensions, such as interaction, accessibility, entertainment, and customization, adopted by Cheng and Jiang ( 2022 ). The behavioral intention was measured via a 4-item scale adopted by Pan et al. ( 2024 ) and Nagy and Hadju (2021). Purchasing intention was measured via a 4-item scale adopted from Jangra and Jangra ( 2022 ) and Beyari and Garamoun ( 2022 ). This study conducted a two-step structural equation modeling (SEM) analysis via the AMOS 24.0 program to test the proposed hypotheses. The first step involved confirmatory factor analysis (CFA) for the measurement model. The second step examined the structural relationships among the variables in our model. To determine the data-model fit in the analyses, the criteria proposed by Hu and Bentler ( 1999 ) were as follows: a comparative fit index (CFI) of 0.96, a standardized root mean square residual (SRMR) of 0.10, or a root mean square error of approximation (RMSEA) of 0.06 and SRMR of 0.10. Table 1. Participants’ demographic characteristics Measures Values Frequency Percentage (%) Gender Female 271 64,1% Male 152 35,9% Age 18-25 225 53,2% 26-35 89 21,0% 36-45 66 15,6% 46-55 29 6,9% 56 and above 14 3,3% Level of education High school and below 107 25,2% Professional degree/ vocational school 48 11,3% Undergraduate 195 46,1% Master’s and above 73 17,3% Average monthly income <20.000 TL 48 11,3% 20.000-39.999 118 27,9% 40.000-59.999 97 22,9% 60.000-79.999 73 17,3% 80.000 TL and above 87 20,6% The average daily time you spend shopping online (digital shopping platforms, web stores, etc.) <30 minutes 203 48,0% 30-59 minutes 119 28,1% 1-2 hour 72 17,0% 3-4 hour 18 4,3% More than 4 hours 11 2,6% The average amount you spend on shopping online (digital shopping platforms, web stores, etc.). <1.000 TL (Turkish Liras) 93 22,0% 1.000-2.999 TL 153 36,2% 3.000-4.999 TL 93 22,0% 5.000 TL and above 84 19,9% Empirical Results Preliminary Results The demographic characteristics of the respondents to illustrate the sample structure (See Table 1 ). In this survey, 64.1% of the participants were female, and 35.9% were male. When examining the participants' ages, 53.2% were aged 18–25, 21% were aged 26–35, 15.6% were aged 36–45, and 10.2% were aged 45 years above. In terms of education level, 25.2% had a high school education or below, 11.3% had specialized field education, 46.1% held a bachelor's degree, and 17.3% held a master's degree or higher. A total of 215 participants (50.8%) reported their annual household income range as 20,000–59,999 TL, followed by 60,000 TL and above (n = 160; 37.9%). The study also revealed that 76.1% (n = 322) of the participants spent no more than 1 hour per day shopping online (digital shopping platforms, web stores, etc.), whereas 17% (n = 72) spent between 1 and 2 hours shopping online. Additionally, 6.9% (n = 29) of the participants dedicated more than 3 hours to online shopping. With respect to the average amount that participants spend on online shopping, 36.2% spend between 1,000 and 2,999 TLs, 22% spend less than 1,000 TLs, 22% spend between 3,000 and 4,999 TLs, and 19.9% spend 5,000 TLs or more. Test of the Measurement Model Before applying SEM, an exploratory factor analysis (EFA) was conducted first to observe whether the validity and reliability values for the variables in the study were met. These variables include technology readiness, behavioral intentions, purchase intentions, attitudes toward AI technology, and AI-driven consumer chatbot experience. Since there was no prior information about whether EFA was suitable for measuring the latent variable, the researcher explored how and to what extent the observed variables were related to their underlying structures (factors) (Byrne, 2010 ; Orcan, 2018 ). Exploratory factor analysis (EFA) was performed for each primary variable in the survey to redefine the factor structures. In this context, statements with factor loadings of 0.45 and above were included in the analysis. The EFA results for the variables in the study are summarized (see Table 2 ). Table 2 Results of the CFA Variable Items Outer loading KMO Cronbach's alpha Variable Items Outer loading KMO Cronbach's alpha Technological motivators OP1 0,796 0,859 0,757 Technological inhibitors DISC2 0,645 0,859 0,658 OP2 0,770 DISC3 0,632 OP4 0,676 DISC1 0,605 OP3 0,544 INS3 0,857 0,859 0,813 IN2 0,762 0,859 0,726 INS1 0,836 IN4 0,739 INS4 0,69 IN1 0,680 INS2 0,661 IN3 0,648 INS5 0,495 Behavioral intention BI2 0,930 0,864 0,939 Purchasing intention PI2 0,941 0,812 0,917 BI1 0,920 PI3 0,930 BI4 0,919 PI1 0,915 BI3 0,906 PI4 0,813 AI-driven consumer chatbot experience INT1 0,786 0,925 0,864 Attitude to AI technology ATT5 0,878 0,898 0,92 INT2 0,773 ATT6 0,870 INT3 0,718 ATT7 0,855 ACC3 0,816 0,925 0,887 ATT2 0,810 ACC1 0,752 ATT4 0,808 ACC2 0,659 ATT3 0,806 ENT2 0,862 0,925 0,840 ATT1 0,727 ENT1 0,740 ENT3 0,670 CUST3 0,822 0,925 0,824 CUST2 0,674 CUST1 0,641 The normality assumption must be met for hypothesis testing and confirmatory factor analysis in the subsequent parts of the research. Within this scope, the skewness and kurtosis values of the variables included in the study are considered sufficient. For the variables in the study, the normality assumptions are as follows: BehaInte (skewness: -0.532; kurtosis: -0.272), PurchaInte (skewness: -0.392; kurtosis: -0.193), AttituAITE (skewness: -0.608; kurtosis: 0.125), ALENCOEX (skewness: -0.357; kurtosis: 0.217), TEIN (skewness: -0.585; kurtosis: 0.503), and TEMO (skewness: -0.644; kurtosis: 1.122). In the social sciences, skewness and kurtosis values for variables within the range of + 1.5–1.5 indicate that the normality assumption is satisfied (Tabachnick & Fidell, 2015 ). The results of the obtained data demonstrate that the normal condition is met. In the measurement model, establishing covariance connections between the error terms of the observed variables, starting from the highest, is recommended to improve and ensure model fit (Jöreskog and Sorbom, 1993). Within this scope, modifications are made between e21-e22, e21-e23, e22-e23, e17-e18, e28-e30, e28-e31, e38-e39, and e41-e42 to bring the goodness-of-fit of the CFA model within the limits accepted in the literature. The resulting CFA measurement model, following these modifications and removing certain items, is shown in Fig. 2 . When examining Fig. 2 , the collected data confirm a first-order CFA model comprising 43 observed and 11 latent variables. Although there is no universally accepted criterion for goodness-of-fit indices, the goodness-of-fit values for the measurement model fall within the acceptable thresholds in the literature (Hu and Bentler, 1999 ; Byrne, 2010 ). In this regard, the goodness-of-fit indices are satisfied. All observed variables corresponding to each latent variable are statistically significant and well above the acceptable threshold of 0.50. Although there are different acceptance criteria for standardized factor loadings (SFLs) in the literature, it is emphasized that they should be at least 0.32 and preferably 0.50 or higher for good validity (Hair et al., 2009 ; Kline, 2010 ). Accordingly, all observed variables in the model are retained without modification. After the measurement model, ensuring the convergent and discriminant validity of the scales allows the study to have more reliable and valid variables. In this context, the AVE, CR, ASV, and MSV values are calculated for the variables included in the research to test their convergent and discriminant validity. The relevant results are presented in Table 3 . Table 3 Discriminant validity and correlation matrix. CR AVE MSV ASV CUST INS OP IN DISC ATT BI PI INT ACC ENT CUST 0,849 0,653 0,808 0,336 0,808 INS 0,821 0,481 0,517 0,083 0,207 0,694 OP 0,768 0,454 0,361 0,192 0,344 0,233 0,674 IN 0,728 0,401 0,340 0,157 0,337 0,297 0,583 0,633 DISC 0,664 0,398 0,517 0,133 0,171 0,719 0,585 0,432 0,630 ATT 0,913 0,603 0,426 0,256 0,535 0,028 0,601 0,478 0,236 0,777 BI 0,939 0,793 0,704 0,297 0,660 0,078 0,368 0,378 0,142 0,611 0,890 PI 0,936 0,787 0,704 0,283 0,615 0,099 0,414 0,411 0,130 0,653 0,839 0,887 INT 0,865 0,682 0,759 0,303 0,780 0,176 0,376 0,216 0,217 0,482 0,577 0,519 0,826 ACC 0,886 0,722 0,759 0,305 0,735 0,120 0,399 0,267 0,277 0,502 0,603 0,528 0,871 0,850 ENT 0,801 0,574 0,808 0,350 0,899 0,255 0,317 0,431 0,205 0,583 0,674 0,640 0,751 0,729 0,757 AVE = average variance extracted, MSV = maximum shared variance, ASV = average shared variance, CR = composite reliability. When considering Table 3 , the composite reliability (CR) values are significantly above the accepted threshold of 0.70 in the literature, indicating high reliability. Furthermore, having an average variance extracted (AVE) value above 0.50 for each variable and CR values larger than the AVE values implies that convergent validity of the scale is achieved (Hair et al., 2009 ). For discriminant validity, the maximum shared variance (MSV) must be greater than the average shared variance (ASV), and the square root of the AVE must be greater than the interfactor correlations. Additionally, the AVE should be greater than the MSV. In terms of these criteria, the MSV > ASV criterion is met for all the variables. However, for the AVE > MSV criterion, the OP, IN, ATT, BI, and PI variables meet the requirements, whereas the others do not. In this context, although some variables have issues with discriminant validity, the reliability, and convergent validity are considered sufficient overall, as the AVE and CR values are adequate, and the ASV and MSV values exceed the accepted thresholds. Ultimately, both the convergence and discriminant validity necessary for construct validity are achieved. Structural Equation Model and Hypothesis Testing The use of a structural equation model (SEM) is recommended to validate the dimensions obtained through exploratory factor analysis and test the hypotheses proposed in the research model. SEM is a statistical method that facilitates the understanding and predicting relationships between variables (Raykov & Marcoulides, 2006 ; Schumacker & Lomax, 2010 ). Specifically, SEM enables the analysis of relationships involving multiple dependent and independent variables within a single model (Schreiber et al., 2006 ; Tabachnick & Fidell, 2015 ). After confirming the convergent and discriminant validity and approving the measurement model, structural equation modeling (SEM) was conducted to test the research hypotheses. The aim is to evaluate the direct and indirect hypotheses formulated via the structural equation model for the observed variables. In this context, the results of the study's direct and indirect hypothesis tests are presented in Fig. 3 . It is recommended that the model be reanalyzed by sequentially removing insignificant regression coefficients to improve the model fit and refine the adjusted model (Gürbüz, 2024 ). The model is constructed to estimate both direct and indirect effects. The obtained data support the goodness-of-fit indices of the constructed model. The results of the direct and total effect hypotheses in the study are presented in Table 4 . Table 4. Summary of hypothesis test results Note 1: TEMO: Motivations for technology readiness; TEIN: Inhibitors for technology readiness; ALENCOEX: AI-driven consumer chatbot experience; AttituAITE: Attitude toward AI technology; BehaInte: Behavioral intention; PurchaInte: Purchasing intention Note 2: TE: Total effects; β: Standardized coefficients; SE: Standard error; CR= Critic ratio; P= Path Note 3: p indicates significance, *** means p < 0.001; typically, a two-tailed test p < 0.05 indicates that the path coefficient estimate is significantly nonzero, meaning that the unstandardized path coefficients are significant. The findings in Table 4 show the direct and total effects of the independent variables on the dependent variables. Total effects explain the loss of significance in direct effects caused by mediator variables. Additionally, the R² values reveal the extent to which the dependent variables are described. The effect of TEMO on ALENCOEX was found to be significant both in terms of direct effects (β = 0.444, C. R = 7.947; R²= 0,130; p < 0.001) and total effects (TE = 0.444). R²=0.130 indicates that TEMO explains 13% of the ALENCOEX variable. The impact of TEMO on ALENCOEX is strong. Therefore, this result demonstrates that H4 is accepted. The effect of TEMO on AttituAITE is significant both in terms of direct effects (β = 0.452; C. R = 10.997; R²= 0,450; p < 0.001) and total effects (TE = 0.787). Additionally, the direct impact of ALENCOEX on AttituAITE (β = 0.397; C. R = 10.264; R²= 0,450; p < 0.001) and its total effect (TE = 0.426) are also significant. Finally, the direct impact of TEIN on the AttituAITE variable (β=-0.148; C. R=-3.824, R²= 0,450, p < 0.001) and its total effect (TE=-0.180) are significant. TEMO, ALENCOEX, and TEIN explain 45% of this variable in this context. These findings highlight the determinant roles of TEMO, TEIN, and ALENCOEX on AttituAITE and support hypotheses H3a , H3b , and H8 . The effect of ALENCOEX on PurchaInte is significant both in terms of direct effects (β = 0.387; C. R = 9.366; R²= 0,496; p < 0.001) and total effects (TE = 0.625). Additionally, the direct impact of AttituAITE on PurchaInte (β = 0.368; C. R = 8.029; R²= 0,450; p < 0.001) and its total effect (TE = 0.402) are also significant. ALENCOEX, TEMO, and AttituAITE explain 49.6% of this variation. These findings support hypotheses H5b and H7b . In contrast, the direct effect of TEMO on PurchaInte was not significant (C. R = 1.811, p = 0.07); however, the total effects (TE = 0.626; p < 0.05) were substantial. This finding indicates that the impact of TEMO on PurchaInte is mediated by other variables (e.g., ALENCOEX or AttituAITE). On the other hand, the direct and total effects of TEIN on PurchaInte are not significant. Consequently, hypotheses H1b and H2b are not supported. The effects on BehaInte are significant for both the ALENCOEX and PurchaInte variables. The direct impact of ALENCOEX on BehaInte (β = 0.24; C. R = 6.78; R²= 0,695; p < 0.001) and the direct effect of PurchaInte (β = 0.643; C. R = 17.032; R²= 0,695; p < 0.001) are strong. Therefore, hypotheses H5a and H6 are accepted. On the other hand, while the direct effect of TEIN on BehaInte is not significant (β=-0.032; C. R=-1.181; R²= 0,695; p = 0.237), the total effect is substantial and negative (TE=-0.100; p < 0.05). Additionally, the total effect is substantial and negative (TE = 0.311; p < 0.05), whereas the direct effect of AttituAITE on BehaInte is not significant (β = 0.036; C. R = 1.025; R²= 0,695; p = 0.305). Finally, although the direct impact of TEMO on BehaInte is not significant (β = 0.009; C. R = 0.257; R²= 0,695; p = 0.797), the total effect is substantial (TE = 0.599; p < 0.01). Accordingly, hypotheses H1a , H2a , and H7a are rejected. Additionally, 69.5% of the variations in BehaInte are explained by the variables ALENCOEX, PurchaInte, TEIN, and TEMO. This high explanatory rate indicates that the model's strong structure explains BehaInte. The effects of TEIN on AttituAITE and BehaInte should be carefully examined in terms of both direct and total effects. The direct impact of TEIN on AttituAITE is significant and negative (β0 = − 0.148, C. R = − 3.824, p < 0.001), indicating an inverse relationship between TEIN and AttituAITE. Although the direct effect of TEIN on BehaInte is not significant (C. R = − 1.181, p = 0.237), the total effects are substantial (TE = − 0.100, p = 0.016). This finding suggests that the impact of TEIN on BehaInte occurs indirectly, with mediating variables playing a significant role. In this context, observing indirect effects and calculating specific indirect effects is crucial, making it necessary to provide definitions for these variables. Serial Multiple Mediation Analysis To view specific indirect effects and the results of serial multiple mediation, mediation effect models are constructed by coding within the model, considering paths for each indirect effect. In this context, the equations representing specific indirect effects are as follows: Ind1 = a*m*k (serial multiple mediation), Ind2 = d*g*m (serial multiple mediation), Ind3 = d*g*b (serial multiple mediation), Ind4 = d*g*m*k (serial multiple mediation), Ind5 = d*n*k (serial multiple mediation), Ind6 = l*m (standard mediation), Ind7 = d*n (standard mediation), Ind8 = a*b (standard mediation), Ind9 = l*b (standard mediation), Ind10 = d*h (standard mediation), Ind11 = f*k (standard mediation), Ind12 = g*b (standard mediation), and Ind13 = l*m*k (serial multiple mediation). Table 5 presents the indirect effects between variables through the specified indirect paths (Ind1-Ind13). Each indirect effect has been re-evaluated, considering the respective mediation paths. In this context, the type of mediation (standard or serial multiple), coefficients, and significance levels are analyzed in detail. Table 5. Mediating effect analysis The effect of TEMO on PurchaInte occurs through ALENCOEX (g) and AttituAITE (m) (Ind2). The coefficient of this serial multiple mediation effect is 0.076 and is statistically significant (p < 0.001). This result indicates that TEMO has a positive and significant indirect effect on attitudes. Therefore, this result clearly supports H9b . The impact of TEMO on BehaInte occurs through ALENCOEX (g) and AttituAITE (b) (Ind3). The coefficient of the indirect effect is 0.008 and is not significant (p = 0.418). This suggests that TEMO does not have a notable effect on BehaInte through this indirect pathway. Accordingly, H9a is not supported. The effect of TEIN on BehaInte occurs through AttituAITE (m) and PurchaInte (k) (Ind1). The total indirect effect obtained via serial multiple mediation is -0.048, and this effect is statistically significant (p < 0.001). This result indicates that TEIN has an indirect but negative effect on BehaInte through the sequential mediation of AttituAITE and PurchaInte. The effect of TEMO on BehaInte occurs through ALENCOEX (g), AttituAITE (m), and PurchaInte (k) (Ind4). This serial multiple mediation effect is 0.051 and is statistically significant (p < 0.001). This result indicates that TEMO has a positive indirect effect on behavioral intentions. The effect of TEMO on BehaInte occurs through ALENCOEX (n) and PurchaInte (k) (Ind5). The coefficient of the indirect effect is 0.135 and is statistically significant (p < 0.001). This finding indicates that TEMO has a strong positive indirect effect on behavioral intentions. The effect of TEMO on PurchaInte occurs through AttituAITE (m) (Ind6). The coefficient of the indirect effect obtained through simple mediation is 0.24 and is statistically significant (p < 0.001). Thus, TEMO positively influences purchase intentions through attitudes. The effect of TEMO on PurchaInte occurs through ALENCOEX (n) (Ind7). The coefficient of the indirect effect is 0.202 and is statistically significant (p < 0.001). The positive indirect effect of TEMO on purchase intentions is evident. The effect of TEIN on BehaInte occurs through AttituAITE (b) (Ind8). This indirect effect is -0.007 and is not statistically significant (p = 0.418). This result indicates that TEIN does not indirectly affect behavioral intentions through attitudes. The effect of TEMO on BehaInte occurs through AttituAITE (b) (Ind9). The coefficient of the indirect impact is 0.025 and is not statistically significant (p = 0.418). This finding indicates that TEMO does not significantly affect behavioral intentions through attitudes. The effect of TEMO on BehaInte occurs through ALENCOEX (h) (Ind10). The coefficient of the indirect impact is 0.13 and is significant (p < 0.001). TEMO has a positive indirect effect on behavioral intentions. The effect of TEMO on BehaInte occurs through PurchaInte (k) (Ind11). The coefficient of the indirect effect is 0.072 and is marginally significant (p = 0.074). This suggests that TEMO may have an effect on behavioral intentions through purchase intentions, but this effect is not definitive. The effect of ALENCOEX on BehaInte occurs through AttituAITE (b) (Ind12). This indirect effect is 0.018 and is not statistically significant (p = 0.418). This finding indicates that ALENCOEX does not have an indirect effect on behavioral intentions through attitudes. The effect of TEMO on BehaInte occurs through AttituAITE (m) and PurchaInte (k) (Ind13). The indirect effect obtained through serial mediation is 0.161, and this effect is significant (p < 0.001). This result indicates that TEMO has a strong positive effect on behavioral intentions through attitudes and purchase intentions. Conclusion Summary and Discussion This study is one of the few that focuses on the AI-driven chatbot experience of consumers in Turkey and seeks to clarify the relationships between technology readiness and behavioral and purchase intentions. While the study highlights the mediating role of attitudes toward AI technology and AI-driven chatbot consumer experience, it also defines the two main factors of technology readiness: optimism and innovativeness as motivators and discomfort and insecurity as inhibitors, as identified by Parasuraman and Colby ( 2014 ) and Blut and Wang ( 2019 ). According to the literature review, until now, the relationships among technology readiness, attitudes toward AI technology, AI-driven chatbot consumer experience, and behavioral and purchase intentions have not been evaluated with multiple common mediators. Furthermore, this study is innovative in applying multiple sequential mediation models to explore the role of attitudes toward AI technology and the AI-driven chatbot consumer experience in the effect of technology readiness on behavioral and purchase intentions. Theoretically and practically, this study makes significant contributions by linking consumers' technology readiness levels with their AI-driven chatbot experiences. Especially with the rise of AI and chatbot technologies, deepening the understanding in this area will allow businesses to reshape their consumer relationships and marketing strategies. Consumers' adoption of technology and attitudes toward AI enable companies to determine how they will use and adopt these technologies. Additionally, an in-depth understanding of consumer behaviors through multiple mediation analyses enables a more targeted design of marketing strategies and customer interactions. This study offers practical findings in terms of marketing, chatbot consumer experiences, and technology, making it of outstanding academic and practical importance. The results of this study revealed the connections between the concepts within the scope of the subject. The supported H3a and H4 hypotheses clearly demonstrate that consumers' motivations regarding technology readiness significantly affect their attitudes toward artificial intelligence, AI-driven consumer chatbot experiences, and purchase intentions. These results are consistent with the literature. Leong et al. ( 2023 ) revealed that individuals' motivations toward technology shape their interaction experiences with technology and can enhance the adoption of new digital services such as chatbots. Deng et al. ( 2010 ) explored the mediating role of technology readiness in its effect on purchase intentions through technological experience and attitudes. Moreover, H3b, which suggests that inhibitors of technology readiness do not significantly affect attitudes toward AI technology, is supported in the literature. Inhibitors of technology readiness may negatively influence individuals' attitudes toward technology. However, it is debated whether these inhibitors consistently demonstrate this effect. Venkatesh et al. ( 2003 ) reported that inhibitory factors sometimes exert influence only in specific contexts without being prominent. Furthermore, the AI-driven consumer chatbot experience significantly impacts behavioral and purchase intentions, as H5a , H5b , and H6 were meaningfully supported. Studies in the literature have demonstrated that AI-driven chatbots significantly affect user behavior. Myin and Watchravesringkan ( 2024 ) stated that chatbots are potent tools that influence users' behavioral intentions. Similarly, Bakkouri et al. ( 2022 ) revealed that AI-driven services influence users' behavioral intentions and purchase decisions. The results revealed a significant pathway between attitudes toward AI technology, AI-driven consumer chatbot experience, and purchase intention (H7b, H8). Attitudes toward AI technology are considered to have a strong relationship with purchase intentions. Wang et al. ( 2023 ) demonstrated that positive attitudes toward AI-driven products significantly increase purchase intentions. The literature suggests that AI experiences can shape users' attitudes toward technology. Garrett ( 2011 ) reported that AI-driven chatbots can influence users' attitudes toward technologies. No direct connection was found between the motivating and inhibiting factors of technology readiness and the variables of behavioral and purchase intentions (H1a, H1b, H2a, and H2b). In other words, the motivating and inhibiting factors of technology readiness do not affect behavioral or purchase intentions. However, there is evidence for the indirect effect of the motivating factors of technology readiness on purchase intentions through attitudes toward AI technology and the AI-driven consumer chatbot experience; H9b is meaningfully supported. Although we cannot directly observe the effect of motivational factors of technology readiness (TEMO) on behavioral and purchase intentions, meaningful results emerge regarding indirect effects. It has been determined that TEMO indirectly and positively influences behavioral intention through attitudes toward AI technology, AI-supported consumer chatbot experience, and purchase intention separately. The total indirect effect obtained through serial multiple mediation via attitudes toward AI technology and purchase intentions is statistically significant. It has also been determined that TEMO indirectly and positively influences purchase intentions through attitudes toward AI technology and AI-driven consumer chatbot experience separately. Overall, the findings of this study indicate that the direct effect of TEMO on behavioral and purchase intentions is insignificant. However, its indirect effects through attitudes toward AI technology and AI-driven consumer chatbot experience are significant, indicating complete mediation. By integrating all the results, it is possible to construct a potential relationship model demonstrating how attitudes toward AI technology and AI-driven consumer chatbot experience together mediate the relationship between TEMO and purchase intention. Previous studies have not examined the sequential mediating effects of these variables. Additionally, the direct impact of TEMO on behavioral and purchase intentions is insignificant, indicating that the two aforementioned joint mediators play a role in recognizing the relationships between these external and internal variables. Theoretical Contributions The findings of this study hold significant theoretical implications, as the proposed extended causal chain relationship model—technology readiness—attitudes toward AI technology—AI-driven consumer chatbot experience—behavioral and purchase intentions—demonstrates an acceptable model fit. These findings support Foxall and Goldsmith’s ( 1994 ) C-A-B (cognitive-affective-behavior) model in the decision-making literature. The cognitive component represents consumers' knowledge, awareness, and perceptions. Hypotheses H3a and H4 indicate that consumers' technology readiness motivations significantly impact their attitudes toward AI technology and AI-driven chatbot experience. This highlights how cognitive processes shape consumers' perceptions of technology. The affective component encompasses individuals' positive or negative emotional responses to a product or experience. Hypothesis H8 demonstrates that AI-supported chatbot experience positively affects attitudes toward AI, serving as a direct example of the affective component of the C-A-B model. The behavioral component reflects consumers' tendencies to take action. In hypotheses, H5a, H5b, and H6, AI-driven chatbot experiences are found to have a positive effect on both behavioral intention and purchase intention. This explains how individuals' positive cognitive and emotional responses translate into behavioral outcomes. In Hypothesis H9b, the impact of technology readiness motivations on purchase intentions exhibits serial multiple mediations through AI experience and attitudes toward AI, aligning perfectly with the sequential structure of the C-A-B model. Moreover, these findings align well with the TAM (technology acceptance model) and SOR (stimulus‒organism‒response) models. The results indicate that technology readiness motivations influence attitudes toward AI and chatbot experience, which supports the TAM’s framework, which links perceived usefulness and ease of use to consumer intentions. The AI-driven chatbot experience enhances purchase intentions by creating a positive customer experience associated with technology adoption within the technology acceptance framework (Davis, 1989 ; Venkatesh & Davis, 2000 ). Furthermore, the role of the AI-driven chatbot experience as a stimulus that shapes consumer attitudes and ultimately leads to purchase intentions is consistent with the mechanism proposed by the SOR model (Chen et al., 2021 ). This study's observed serial multiple mediation effect (H9b) demonstrates that consumer decision-making unfolds stepwise, with AI experience playing a crucial role in this process. The personalized interactions of AI chatbots have been shown to positively impact consumer experience, enhancing both behavioral and purchase intentions. This finding aligns with Schmitt’s ( 1999 ) sensory, cognitive, behavioral, and emotional experience components, demonstrating that chatbots personalize customer experiences to foster stronger engagement. Additionally, it highlights that chatbot interactions are not merely mechanical but also provide emotional and cognitive experiences. AI-driven chatbots enhance individual and contextualized experiences by offering personalized recommendations to customers. Moreover, motivation for technology readiness positively influences attitudes toward AI technology, whereas less technologically prepared individuals may have a negative experience with AI-driven chatbots. In this regard, the trust factor emerges as a more prominent component in AI-driven interactions within Consumer Experience Theory. Chatbots are key in building customer trust by ensuring fast response times, providing accurate information, and offering a user-friendly interface. Consequently, this study expands Consumer Experience Theory within the context of AI and chatbot-based interactions, contributing to understanding customer experience in the digital age. In summary, this study integrates theoretical frameworks to develop a model that explains the relationships among technology readiness, attitudes toward AI technology, AI-driven consumer chatbot experience, behavioral intentions, and purchase intentions, providing insights into customers' experiential and decision-making processes in Turkey. The findings offer a more holistic perspective on the decision-making process of Turkish consumers who use chatbots on digital shopping platforms. A causal chain can be established between technology readiness, attitudes toward AI technology, AI-driven consumer chatbot experience, behavioral intentions, and purchase intentions, demonstrating the role of AI attitudes and chatbot experience in the decision-making process and their influence on behavioral and purchase intentions. Previous studies have not examined this serial multiple mediation relationship in digital shopping. Serial multiple mediation can help us understand the connection between technology readiness and behavioral/purchase intentions and identify the key mediators within this chain. Furthermore, this study highlights that, in the context of chatbot use in digital shopping, the relationship between technology readiness and behavioral/purchase intentions is mediated by attitudes toward AI technology and the AI-driven consumer chatbot experience. Managerial Implications The findings indicate that AI-driven chatbots positively affect customer behavioral intention and purchase intention. On this basis, businesses can utilize chatbots for customer service and sales tools offering personalized recommendations and shopping guidance. Additionally, it can be easily stated that the chatbot experience shapes 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' technology readiness motivations positively influence the 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 experiences. On the other hand, it becomes inevitable for companies to act within a strategic plan, considering future challenges and implementation barriers. It has been found that obstacles to technology readiness do not significantly impact attitudes toward AI. This implies businesses should address consumers' concerns about AI usage by providing more information and offering control mechanisms. In the context of the TAM model, some consumers may still resist using AI-driven chatbots, which could hinder adoption (Venkatesh & Bala, 2008 ). AI-driven chatbots process vast amounts of customer data. To ensure data security and privacy, companies should develop transparent policies and comply with data protection regulations. This research highlights the impact of AI-driven consumer chatbots on customer intentions, helping businesses guide their AI investments. Consumers' technology readiness motivations, chatbot experiences, and attitudes toward AI directly influence their purchasing behavior. Therefore, companies should design chatbots in line with customer expectations, develop personalization strategies, and ensure transparency in data security. Limitations and Future Research This study has several limitations and various aspects that should be considered in future research. This study focuses on consumers in Turkey, and cultural factors may influence the results. Therefore, the results cannot be generalized to different ethnic groups and cultures, as socio-cultural factors may lead to variations in technology readiness, attitudes toward artificial intelligence technology, and chatbot experiences. Replicating the study in different countries could help understand the impact of cultural factors. The sample may consist of younger and more tech-savvy consumers. The reactions of individuals with lower technology readiness have not been evaluated. Similar studies can be conducted on consumers from different age groups, income levels, and digital literacy levels. Also, the sample size has been limited due to the fact that some businesses in Turkey have only recently started interacting with consumers through AI-driven chatbots in their marketing activities. A cross-sectional design was used in this research. Changes in consumer perceptions and technology acceptance over time could be better analyzed through a longitudinal study. The evolution of consumer attitudes toward AI chatbots over time can be examined by conducting longitudinal research. The impact of chatbot usage on consumer behavior can be tested via data from real experiences. Additionally, future research could utilize detailed consumer motivation data through in-depth interviews alongside surveys to understand real-time experiences and examine how emotional and psychological factors (e.g., consumer trust and privacy concerns) influence the chatbot experience. The effects of AI chatbots on customer loyalty, brand trust, and long-term customer relationships can also be explored. Declarations This study was reviewed and approved by Uskudar University Non- Interventional Studies Ethics Committee with the approval number: 61351342/020-411, dated September 30, 2024. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6135960","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":422732066,"identity":"5ffa80d3-92f0-4cc0-b33b-091a37861558","order_by":0,"name":"Cihan Becan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYDACZh4QeQBEMD5gYEiAstmI08JsgKKFB6cehBY2CaK08LfzHvxcwHBHjr/97LFqnj9piX3Hzz5g+FB2mMFe+gBWLRKH+ZKlZzA8M5Y4k5d2m7ctJ3HmmXQDxhnnDjPw8CVg1WLAzGMgzcNwOHEDQ47Zbd6GisQNB9IYmHnbgFpwuAyoxfg3UEv9Bv43ZsU8f4Bazj9jYP6LX4sZyJYEA4kcM2YetpzEDTeAtjDi0SJxmMfMmsfgmeGMG2+MJee2pRnPvPGM4WDPuXQenjM4Qqz/jPFtnoo78vz9OYYf3vxJlu07n8b44EeZtRx7D3YtUOeh8Q8w4IvJUTAKRsEoGAUEAQBeMFfD634lxwAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Cihan","middleName":"","lastName":"Becan","suffix":""}],"badges":[],"createdAt":"2025-03-01 17:25:39","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6135960/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6135960/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1108/DTS-04-2025-0082","type":"published","date":"2025-09-15T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":77893459,"identity":"72f806d5-26ea-4b61-bf79-42d3fe53dba0","added_by":"auto","created_at":"2025-03-06 14:08:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":273522,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual model\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6135960/v1/6f10a7ca96036bc7e897010e.png"},{"id":77893440,"identity":"89cbbbf5-1944-4d74-b09f-496c6af314ce","added_by":"auto","created_at":"2025-03-06 14:08:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1150846,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the measurement model\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6135960/v1/f1d802e8120af0a155b05467.png"},{"id":77893456,"identity":"a0bab1d0-4f8a-4e5c-84d0-8f6d9466d910","added_by":"auto","created_at":"2025-03-06 14:08:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":377196,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the structural model test\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6135960/v1/3a783ef24c790368fb523ace.png"},{"id":91743879,"identity":"ea67e015-0235-4145-a61c-4e07a3ef9383","added_by":"auto","created_at":"2025-09-19 20:46:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3629052,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6135960/v1/1ea228de-a463-4728-bc92-a479e08fdfd6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eHow Technology Readiness Influences Behavioral and Purchasing Intention: Serial Multiple Mediating Role of Attitude toward AI and AI-Driven Consumer Chatbot Experience\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the past decade, innovative developments such as mobile commerce, social media, and smartphone technology have transformed the lifestyles of nearly every consumer globally (Claudy et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Recently, scholars have increasingly explored the role of consumer characteristics in explaining technology usage (Westjohn et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Sääksjärvi \u0026amp; Samiee, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These studies provide valuable insights for marketers in identifying consumer groups that are more likely to adopt specific technologies (Blut \u0026amp; Wang, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Given the expanding role of technologies in service delivery, understanding customers' readiness to adopt technology-based systems, particularly e-services, is highly important (Lin et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Burke, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs a reflection of these technological advancements, digital marketing, and AI-driven interactive messaging services are rapidly evolving, with such services commonly referred to as ‘chatbots’ (Desaulniers, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). AI-driven chatbots enhance the customer experience by allowing consumers to interact with virtual marketing representatives anytime and anywhere (Cheng \u0026amp; Jiang, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Today, chatbot usage is increasing across both mobile and web interfaces, and the chatbot market is expected to reach \u003cspan\u003e$\u003c/span\u003e1.25\u0026nbsp;billion by 2025 (Forbes, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, a study by Nielsen reported that 53% of consumers are more likely to purchase products from businesses with which they can communicate via real-time messaging (Nielsen, 2018). Research by Manchester Business School has shown that participants perceive this technology as highly intriguing, exciting, and futuristic (Sidaoui et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, these AI-driven assistants help companies reduce customer support costs by 30% while saving time (Popescu, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Juniper Research, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A study by Arsenijevic \u0026amp; Jovic (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) revealed that the most significant advantage of chatbot usage in marketing is the ability to provide quick and straightforward information. However, the research also highlighted concerns about the risk of misinformation, identifying this as a crucial issue that needs to be addressed in the future.\u003c/p\u003e \u003cp\u003eResearch on the use of artificial intelligence tools has highlighted the importance of consumers' experiences with AI technology; however, studies specifically examining AI-driven consumer chatbot experiences during the digital shopping process are quite limited. Additionally, there is a scarcity of research addressing the effects of technology readiness on behavioral and purchase intentions. Furthermore, while the significance of attitudes toward AI technology and AI-driven chatbot experiences as mediating variables has been documented in the consumer behavior and digital marketing literature, no study has demonstrated the series of multiple mediating effects of these variables on the relationship between technology readiness and its motivating factors on behavioral and purchase intentions. This study aims to explore the potential relationships among technology readiness, attitudes toward AI technology, AI-driven consumer chatbot experience, and behavioral and purchase intentions. Furthermore, within the proposed conceptual model, this study aims to understand the decision-making behavior of Turkish consumers using AI-driven chatbots in digital shopping processes by identifying the mediating roles of attitudes toward AI technology and the AI-driven consumer chatbot experience in the relationship between technology readiness and behavioral and purchasing intentions.\u003c/p\u003e\n\n\n\n \n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"},{"header":"Conceptual Background and Literature Review","content":"\u003ch2\u003eTechnology Readiness and Technology Acceptance Model\u003c/h2\u003e\u003cp\u003eConsidering the expanding roles of technologies in service delivery, understanding customers' readiness to use technology-based systems, particularly e-services, is of great importance (Lin et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Burke, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Technology readiness refers to the degree to which individuals or organizations are prepared to adopt and utilize new technologies (Shwedeh et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Marthasari et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nasution et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is a multifaceted concept encompassing various factors, such as technological infrastructure, technical skills, management support, and user attitudes (Astuti \u0026amp; Nasution, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rahayu \u0026amp; Day, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The technology readiness index, initially conceptualized by Parasuraman, is a foundational framework for assessing individuals' propensity to embrace technology. Optimism, innovativeness, discomfort, and insecurity collectively influence technology adoption behaviors (Parasuraman, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Parasuraman \u0026amp; Colby, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These dimensions collectively shape an individual's propensity to engage with technology, affecting both personal and professional contexts (Kadiyono \u0026amp; Pardosi, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Blut \u0026amp; Wang, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This concept encompasses various psychological and contextual factors influencing the willingness to embrace technological advancements. Marketers adopt technology readiness as a tool to assess the extent to which new technologies can be integrated into customer-company interactions, determine which types of technologies should be introduced, decide on the implementation pace, and identify the necessary customer support (Parasuraman, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnderstanding and enhancing technology readiness will be essential for fostering the successful adoption and integration of innovative solutions as technology evolves. Research has shown that technology readiness is crucial in accepting and adopting new technologies (Nasution et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Individuals or organizations with greater technology readiness are more likely to embrace and utilize new technologies, improving performance and productivity (Shwedeh et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Astuti \u0026amp; Nasution, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rahayu \u0026amp; Day, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe technology acceptance model (TAM) is a theoretical framework developed to understand the factors influencing users' technology acceptance and usage. The model posits that perceived usefulness (PU) and perceived ease of use (PEOU) are the primary determinants influencing users' intentions to adopt new technologies (Venkatesh \u0026amp; Davis, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Ma \u0026amp; Liu, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Chan \u0026amp; Teo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Over the years, numerous studies have validated the TAM's predictive power across various contexts, demonstrating its robustness in explaining user behavior and technology adoption (Venkatesh \u0026amp; Davis, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Chan \u0026amp; Teo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The Technology Acceptance Model has evolved significantly since its inception, with various extensions and adaptations enhancing its explanatory power. The model continues to serve as a critical framework for understanding technology acceptance, with ongoing research exploring the integration of additional constructs and contextual factors to refine its applicability across diverse technological landscapes further.\u003c/p\u003e\u003cp\u003eResearch indicates that integrating TR with TAM enhances the explanatory power of technology adoption models. Lin et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) demonstrated that technology readiness significantly influences users' intentions to adopt e-services, with its effects being mediated by perceived usefulness and ease of use (Lin et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This suggests that an individual's readiness to engage with technology can shape their perceptions of its utility and usability, thereby impacting their acceptance decisions. Similarly, the Technology Readiness and Acceptance Model (TRAM) proposed by Lin et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) empirically supports the notion that incorporating TR into TAM broadens its applicability and enhances its predictive capabilities regarding consumer adoption of innovations (Lin et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The interplay between TR and TAM is further evidenced in various contexts, including e-commerce and industrial automation. Pires et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) explored how TRI factors differentiate between users and non-users of internet banking, highlighting that technology readiness can serve as an antecedent to TAM constructs. Godoe \u0026amp; Johansen (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) also emphasized that integrating TR and TAM provides a more comprehensive understanding of technology adoption by considering individual readiness and system-specific factors. This integrated approach is crucial for identifying the antecedents of technology use, as it acknowledges the role of personal traits in shaping technology acceptance behaviors.\u003c/p\u003e\u003ch3\u003eUse of Artificial Intelligence (AI) for Virtual Shopping and SOR (Stimulus-Response-Organism) Model\u003c/h3\u003e\u003cp\u003eAI technologies such as chatbots, virtual assistants, and personalized recommendation systems have revolutionized how consumers engage with online retail platforms. One of the primary applications of AI in virtual shopping is through chatbots and virtual assistants. These AI-driven tools facilitate customer interactions by providing real-time support and personalized recommendations on the basis of user behavior and preferences. The ability of these virtual anchors to simulate human-like interactions allows retailers to replace traditional sales methods with a more engaging digital experience (Zhong, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, AI's predictive capabilities are crucial in personalizing the shopping journey. By analyzing past purchases and browsing behaviors, AI systems can anticipate consumer needs and suggest products that align with individual preferences. This level of personalization not only enhances user satisfaction but also increases the likelihood of purchase, as consumers are presented with options that resonate with their interests (Ntumba et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Srivastava \u0026amp; Pal, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The integration of AI with technologies such as augmented reality (AR) and virtual reality (VR) further enriches the shopping experience by providing immersive environments where consumers can visualize products in real-time (Sun et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cha, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIntegrating artificial intelligence (AI) into virtual shopping environments has garnered significant attention in recent years, particularly through the lens of the Stimulus-Organism-Response (SOR) model. This model provides a framework for understanding how external stimuli (such as AI-driven technologies) influence consumer behavior and emotional responses, ultimately affecting purchasing decisions. This model posits that external stimuli (in this case, AI technologies) influence internal processes (the organism's perceptions and emotional responses), which ultimately lead to behavioral responses (such as purchase intentions or continued use of the chatbot). The response aspect of the SOR model encapsulates the behavioral intentions that arise from the interaction with the chatbot. The organism component of the SOR model refers to the internal processes that consumers undergo in response to stimuli. This includes emotional responses, cognitive evaluations, and behavioral intentions.\u003c/p\u003e\u003cp\u003eVarious studies have explored the role of AI in enhancing the e-service scape, an online shopping environment characterized by its design and functionality. Yadav and Mahara (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) argue that e-servicescape features serve as stimuli that influence customer trust and purchase intentions, further illustrating the SOR model's applicability in understanding consumer behavior in AI-enhanced shopping contexts. Research by Kim and Lennon demonstrates that website quality and reputation—both influenced by AI technologies—affect consumers' emotions and perceived risks, ultimately shaping their purchasing decisions (Kim \u0026amp; Lennon, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Additionally, the findings of Erwei et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) emphasize how social presence in live streaming can enhance impulse buying, showcasing the direct impact of AI-driven environments on consumer behavior. Research indicates that AI technologies' perceived enjoyment and trust can significantly influence these internal processes. Zhu et al. found that environmental cues in online shopping affect Generation Y consumers' repurchase intentions, suggesting that positive emotional responses can enhance loyalty (Zhu et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003ch3\u003eAI-Driven Consumer Chatbot Experience and Consumer Experience Theory\u003c/h3\u003e\u003cp\u003eThe integration of AI-driven chatbots into consumer interactions has garnered significant attention in recent years, particularly in the context of enhancing consumer experience. The literature on consumer experiences with chatbots reveals a complex interplay of factors influencing user interactions, satisfaction, and continued usage intentions. As automated customer service agents, chatbots have become increasingly prevalent in various sectors, particularly e-commerce and retail. AI-driven chatbots have emerged as transformative forces in the e-commerce landscape, revolutionizing the customer experience through personalized and efficient interactions (Ntumba et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). One significant aspect of consumer chatbot interaction is the quality of communication. Lee (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) emphasized the importance of parasocial relationships in enhancing communication quality, suggesting that fostering a sense of intimacy can improve consumers' perceptions of chatbots' accuracy and credibility. Cheng et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) further contributed to this discourse by highlighting that chatbots can effectively manage customer inquiries without the emotional fatigue that human agents might experience, thereby maintaining consistent service quality. The existing research suggests that AI-driven chatbots can significantly enhance the virtual shopping experience by providing personalized, efficient, and engaging interactions. However, the effective design and deployment of chatbots require careful consideration of various factors, including communication quality, anthropomorphism, task complexity, and consumer trust, to optimize the customer experience and drive positive business outcomes (Cheng et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Klein \u0026amp; Martinez, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As chatbots evolve, understanding these dynamics will be crucial for businesses aiming to optimize customer service strategies and enhance user satisfaction.\u003c/p\u003e\u003cp\u003eConsumer Experience Theory (CET) examines the impact of consumers' experiences with a brand, product, or service on their perceptions, emotions, and future behaviors. This theory encompasses sensory, emotional, cognitive, behavioral, and social experience components (Schmitt, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The emotional and psychological dimensions of consumer interactions with chatbots are crucial for understanding their impact on consumer experience. The theoretical framework surrounding consumer experience theory also finds relevance in the context of AI chatbots. Cheng and Jiang (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) explore how chatbot marketing efforts can strengthen customer-brand relationships, thereby enhancing consumer behavioral intentions. This aligns with the principles of relationship marketing, which posits those strong emotional connections between consumers and brands lead to increased loyalty and satisfaction. Furthermore, Khoa (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) illustrates that chatbots can influence consumer emotions, leading to impulse purchasing behavior, which underscores the emotional engagement aspect of consumer experience theory. Lee and Park (2022) discuss how parasocial relationships—perceived emotional connections with chatbots—can enhance communication quality and influence continued usage intentions among consumers. This notion is supported by the work of Xu et al. (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which highlights that communication styles and consumer attachment anxiety significantly affect satisfaction with chatbot interactions. The ability of chatbots to simulate human-like interactions can foster a sense of intimacy and trust, which are essential components of positive consumer experiences.\u003c/p\u003e\u003ch3\u003eAttitude toward AI Technology\u003c/h3\u003e\u003cp\u003eRecent research on public attitudes toward artificial intelligence reveals a complex landscape characterized by optimism and apprehension. Evolving perceptions of AI are shaped by various factors, including personal relevance, media influence, and the specific context in which AI is applied. A foundational aspect of consumer attitudes toward AI in virtual shopping is the perceived utility and personalization that AI technologies offer. Misra et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) emphasized that AI-driven personalization algorithms significantly enhance consumer engagement and satisfaction, fostering a more favorable shopping experience. This aligns with findings from Liang et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who assert that consumers' attitudes toward AI devices directly impact their purchase intentions, reinforcing the importance of positive consumer perceptions in technology adoption. Moreover, the role of AI in facilitating the shopping process is critical, especially through voice assistants and chatbots. Calahorra-Candao and Martin-de Hoyos (2024) noted a research gap regarding the comprehensive impact of AI voice assistants on the shopping journey, particularly in later stages such as final purchase decisions. This is echoed by Rana et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who discuss how AI-driven chatbots shape consumer attitudes by enhancing perceived usefulness and usability during online shopping. The effectiveness of these AI tools in improving the shopping experience is further supported by Zhang (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who discusses the importance of public opinion and consumer attitudes in shaping the future of AI technologies in retail.\u003c/p\u003e\u003ch3\u003eRelationships among the Variables, Hypothesis Development and Research Model\u003c/h3\u003e\u003cp\u003eResearch indicates that individuals with high optimism and innovativeness are more likely to engage positively with new technologies, leading to increased behavioral and purchasing intentions. Studies have shown that consumers with high technology readiness tend to have a more favorable perception of digital technologies, which is directly correlated with their willingness to purchase products or services that utilize such technologies (Song, 2021). This is particularly evident in contexts such as mobile commerce, where consumers with a positive outlook toward technology are more inclined to purchase (Ismail \u0026amp; Wahid, 2020). Research has demonstrated that technology readiness influences perceived usefulness and ease of use, which are critical determinants of behavioral intention (Chen et al., 2019; Lai \u0026amp; Lee, 2020). This suggests that a consumer's readiness to adopt technology can enhance their perception of its utility, increasing their intention to use it.\u003c/p\u003e\u003cp\u003eConversely, consumers with low technology readiness often distrust new technologies, which can diminish their purchasing intentions. For example, individuals who feel discomfort or insecurity regarding technology are less likely to engage with it, leading to reduced behavioral intentions (Munthe et al., 2020). Research indicates that negative inhibitors can diminish an individual's technology readiness index, decreasing behavioral intentions toward technology products (Yang et al., 2023; Yang \u0026amp; Kim, 2024). Inhibitors of technology readiness, such as discomfort and insecurity, profoundly negatively impact consumers' behavioral and purchasing intentions. Therefore, the following hypotheses are formulated:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH1a:\u003c/strong\u003e Consumers’ inhibitors of technology readiness have a negative effect on their behavioral intentions.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH1b:\u003c/strong\u003e Consumers’ inhibitors of technology readiness have a negative effect on purchasing intention.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2a:\u003c/strong\u003e Consumers’ motivations for technology readiness positively affect their behavioral intentions.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2b:\u003c/strong\u003e Consumer motivations for technology readiness positively affect purchasing intention.\u003c/p\u003e\u003cp\u003eThe relationship between technology readiness and attitudes toward artificial intelligence (AI) technology is multifaceted, encompassing motivators and inhibitors that significantly influence consumer behavior. Motivators such as optimism and innovativeness are pivotal in shaping positive attitudes toward AI technology. Studies have shown that consumers with high technology readiness are more likely to perceive AI as valuable and easy to use. This directly correlates with their intention to adopt such technologies in shopping scenarios (Anh et al., 2024). Furthermore, positive prior experiences with AI technologies significantly increase consumers' readiness and acceptance, indicating that familiarity can mitigate initial apprehensions (Kolar et al., 2024). Research suggests that inhibiting factors can create significant barriers to the adoption of AI, particularly among those who may already feel vulnerable, such as women, who report higher levels of anxiety related to AI technologies (Kolar et al., 2024). Additionally, the anxiety surrounding AI can be exacerbated by discussions about its capabilities and implications, which may inadvertently increase fears rather than alleviate them (Lemay et al., 2020). Therefore, the following hypothesis is proposed:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH3a:\u003c/strong\u003e Consumers’ inhibitors of technology readiness do not significantly affect their attitudes toward AI technology.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH3b:\u003c/strong\u003e Consumer motivation for technology readiness significantly affects attitudes toward AI technology.\u003c/p\u003e\u003cp\u003eResearch also indicates that consumers who exhibit greater optimism and innovativeness are more likely to engage positively with AI technologies, including chatbots. Flavián et al. (2021) reported that technology readiness significantly influences consumers' intentions to use AI in service contexts, highlighting that optimistic consumers are more inclined to embrace AI solutions. Similarly, Fu's study emphasized that characteristics such as innovativeness positively affect expectations regarding chatbot performance, thereby enhancing trust and willingness to use these technologies in online shopping environments (Fu, 2024). Moreover, the interplay between technology readiness and consumer experience is further illustrated by Yoon and Yu, who explored how consumer attitudes toward chatbot services influence their acceptance intentions (Yoon \u0026amp; Yu, 2022). Their findings suggest that positive experiences with chatbots lead to greater acceptance and utilization, reinforcing that technology readiness plays a critical role in shaping consumer perceptions and interactions with AI technologies. Therefore, the following hypothesis can be derived from the preceding discussion:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH4:\u003c/strong\u003e Consumer motivation for technology readiness significantly influences the AI-driven consumer chatbot experience.\u003c/p\u003e\u003cp\u003eResearch has shown that positive experiences with chatbots can lead to increased purchase intentions and loyalty (Mehta et al., 2022; Rana et al., 2024). Research has indicated that AI chatbots can effectively influence impulse purchasing behavior. This is supported by findings from Ameen et al., who emphasized that AI integration in shopping experiences can significantly improve customer satisfaction and loyalty, mainly through personalized and proactive interactions (Ameen et al., 2021). Such interactions enhance the shopping experience and create a sense of engagement that can drive purchasing decisions. Moreover, the quality of interaction with AI chatbots plays a crucial role in shaping consumer attitudes and behaviors. Lee's (2022) study revealed that effective communication with AI shopping chatbots can increase consumer satisfaction and decision-making, ultimately supporting purchase intentions. On the basis of the literature, we propose the following hypothesis:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH5a:\u003c/strong\u003e AI-driven consumer chatbot experience has a positive direct effect on behavioral intention.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH5b:\u003c/strong\u003e AI-driven consumer chatbot experience has a positive direct effect on purchase intentions.\u003c/p\u003e\u003cp\u003eThe relationship between purchase intention and behavioral intention during AI-driven consumer chatbot experiences is a multifaceted area of study that integrates various psychological and technological factors. Jiang et al. (2022) propose a theoretical model that connects social presence, experiential innovativeness, and self-determined satisfaction to consumer behavior intention. This finding suggests that chatbots exhibiting human-like features can increase purchase intentions by fostering a sense of social interaction. According to Kaplan, attitudes and perceived behavioral controls positively influence purchasing behaviors, indicating that a consumer's intention to purchase is closely linked to their behavioral intentions (Kaplan, 2018). This is echoed by Eissa, who noted that purchasing intentions predict actual buying behavior, reinforcing the notion that intention serves as a precursor to action (Eissa, 2024). Thus, the following hypothesis is formulated:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH6:\u003c/strong\u003e Purchasing intention during AI-driven consumer chatbot experience significantly influences behavioral intention.\u003c/p\u003e\u003cp\u003eThe relationship between attitudes toward artificial intelligence (AI) technology and consumer behavior, particularly in the context of virtual shopping, is increasingly significant in today's digital marketplace. Guerra-Tamez et al. (2024) highlighted that AI exposure and the perception of AI accuracy significantly enhance brand trust among consumers, particularly those in Generation Z, which subsequently positively influences their purchasing decisions. This finding is supported by Yazdani (2023), who emphasized that consumer trust and acceptance of AI recommendations are critical for effective interactive marketing, which impacts consumer decision-making processes. Furthermore, Nagy and Hajdú's (2021) research underscores that perceived usefulness is a vital factor influencing consumer attitudes toward AI, affecting behavioral intentions in online shopping contexts. This aligns with findings from Cheng and Jiang (2022), who explore how AI-driven marketing efforts, such as chatbots, can enhance customer–brand relationships and influence online consumer behavioral intentions. Thus, the following hypothesis is developed:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH7a:\u003c/strong\u003e Attitudes toward AI technology significantly influence behavioral intentions.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH7b:\u003c/strong\u003e Attitudes toward AI technology significantly influence purchasing intentions.\u003c/p\u003e\u003cp\u003eThe demand for AI facilities, particularly chatbots, in retail settings highlights consumers' desire for personalized and memorable shopping experiences. Srivastava and Pal (2024) emphasized that consumers prefer chatbots to assist them at various purchase points, indicating a strong inclination toward technology that enhances their shopping journey. This preference suggests that when consumers experience effective AI interactions, their attitudes toward AI technology become more favorable, as they associate it with improved service delivery and satisfaction. Moreover, the anthropomorphic design of chatbots plays a crucial role in shaping consumer perceptions. Youn and Cho (2023) discuss how the effectiveness of anthropomorphic cues in AI chatbots varies across different business types, suggesting that consumers respond more positively to chatbots that exhibit human-like characteristics. The quality of interaction with AI chatbots also significantly affects consumer attitudes. Lee's research indicates that effective communication with AI shopping chatbots leads to higher satisfaction levels and a greater likelihood of continued usage (Lee, 2022). This aligns with the findings of Tsai et al., who assert that the social presence of chatbots enhances consumer engagement through parasocial interactions, further solidifying positive attitudes toward AI (Tsai et al., 2021). When consumers feel a connection with AI, their trust in and acceptance of the technology increase, fostering a more positive overall perception. On this basis, the following is proposed:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH8:\u003c/strong\u003e AI-driven consumer chatbot experience positively influences attitudes toward AI technology.\u003c/p\u003e\u003cp\u003eOn the basis of the direct and indirect effects of all of the variable constructs discussed above, this study suggests that AI-driven consumer chatbot experience and attitudes toward AI technology might indirectly affect behavioral and purchasing intentions through technology readiness. We therefore propose the following hypothesis:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH9a:\u003c/strong\u003e There is a serial multiple mediation effect of AI-driven consumer chatbot experience and attitudes toward AI technology on the impact of consumer motivation for technology readiness on behavioral intention.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH9b:\u003c/strong\u003e There is a serial multiple mediation effect of AI-driven consumer chatbot experience and attitudes toward AI technology on the impact of consumer motivation for technology readiness on purchase intentions.\u003c/p\u003e\u003cp\u003eFigure 1 presents the conceptual framework model of the current study to summarize the research hypotheses mentioned above. Our conceptual model includes technology readiness, AI-driven customer chatbot experience, attitudes toward AI, and behavioral and purchase intentions for virtual shopping. It encompasses the hypothetical relationships (H1–H8) among these constructs.\u003c/p\u003e"},{"header":"Data and Methodology","content":"\u003ch2\u003eSampling and Data Collection\u003c/h2\u003e\n\u003cp\u003eA quantitative survey approach was employed in line with the research objectives, and data were collected through cost-effective and easily implementable questionnaires. The study data were collected through an online survey designed and hosted by Google Online Forms. The survey targeted consumers residing in Turkey who were confirmed to have received chatbot support during their shopping experiences. The researchers recruited participants from October to November 2024 via social media and personal e-mails with an anonymous invitation link through a purposive sampling method. At the beginning of the survey, instructions were provided about the definition of chatbots and some examples to help participants understand applications or platforms that could be considered AI-driven chatbot activities in their virtual shopping experiences. In total, 550 participants clicked on the survey link and participated in this research study. Among these participants, 127 (23%) stated that they had not used chatbots and were therefore removed from the study. Consequently, we received 423 usable responses. Before the survey was finalized and launched, the pilot test was conducted with 30 respondents using chatbots while shopping in the past year. We also asked the participants if there were any confusing points in the survey questions. The survey instrument was revised and finalized on the basis of feedback from the pilot test results to ensure its content validity. We conducted a series of reliability tests via SPSS 27.0 to ensure that Cronbach\u0026rsquo;s alpha value for each variable exceeded 0.65.\u003c/p\u003e\n\u003ch3\u003eMeasurement Instrument and Analysis Procedures\u003c/h3\u003e\n\u003cp\u003eThe survey used in this study is divided into two sections: background data and the main research questions. Gender, age, level of education, and household income constitute the demographic data. For the main research questions, the researchers adopted criteria and structures from existing scales to ensure content validity and modified the wording to fit the research context. All the constructs were measured on a 5-point Likert scale (e.g., 1\u0026thinsp;=\u0026thinsp;definitely disagree, 2\u0026thinsp;=\u0026thinsp;disagree, 3\u0026thinsp;=\u0026thinsp;neutral, 4\u0026thinsp;=\u0026thinsp;somewhat agree, 5\u0026thinsp;=\u0026thinsp;definitely agree). Sixteen questions concerning technology readiness, four concerning behavioral intention, four concerning purchasing intention, seven concerning attitudes toward AI technology, and twelve on concerning AI-driven consumer chatbot experience are adapted.\u003c/p\u003e\n\u003cp\u003eThe scale for technology readiness, which comprises four dimensions adopted by Blut and Wang (2020) and Flavian et al. (2022), was used: the technological motivator dimension, which includes four items of optimism and four items of innovativeness; the technological inhibitor dimension, which includes four items of discomfort and four items of insecurity. The attitude toward the AI technology dimension was measured via a 7-item scale adopted by Bhagat et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Kim et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The AI-driven consumer chatbot experience dimension was measured via 3-item sub dimensions, such as interaction, accessibility, entertainment, and customization, adopted by Cheng and Jiang (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The behavioral intention was measured via a 4-item scale adopted by Pan et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Nagy and Hadju (2021). Purchasing intention was measured via a 4-item scale adopted from Jangra and Jangra (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Beyari and Garamoun (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). This study conducted a two-step structural equation modeling (SEM) analysis via the AMOS 24.0 program to test the proposed hypotheses. The first step involved confirmatory factor analysis (CFA) for the measurement model. The second step examined the structural relationships among the variables in our model. To determine the data-model fit in the analyses, the criteria proposed by Hu and Bentler (\u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e) were as follows: a comparative fit index (CFI) of 0.96, a standardized root mean square residual (SRMR) of 0.10, or a root mean square error of approximation (RMSEA) of 0.06 and SRMR of 0.10.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Participants\u0026rsquo; demographic characteristics\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"85%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasures \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u003cstrong\u003ePercentage (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e64,1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e35,9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e18-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e53,2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e26-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e21,0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e36-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e15,6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e46-55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e6,9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e56 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e3,3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eLevel of education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eHigh school and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e25,2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eProfessional degree/\u003c/p\u003e\n \u003cp\u003evocational school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e11,3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eUndergraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e46,1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eMaster\u0026rsquo;s and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e17,3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAverage monthly income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026lt;20.000 TL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e11,3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e20.000-39.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e27,9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e40.000-59.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e22,9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e60.000-79.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e17,3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e80.000 TL and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e20,6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eThe average daily time you spend shopping online (digital shopping platforms, web stores, etc.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026lt;30 minutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e48,0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e30-59 minutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e28,1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e1-2 hour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e17,0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e3-4 hour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e4,3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eMore than 4 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e2,6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eThe average amount you spend on shopping online (digital shopping platforms, web stores, etc.).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026lt;1.000 TL (Turkish Liras)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e22,0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e1.000-2.999 TL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e36,2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e3.000-4.999 TL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e22,0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e5.000 TL and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e19,9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Empirical Results","content":"\u003ch2\u003ePreliminary Results\u003c/h2\u003e\u003cp\u003eThe demographic characteristics of the respondents to illustrate the sample structure (See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In this survey, 64.1% of the participants were female, and 35.9% were male. When examining the participants' ages, 53.2% were aged 18–25, 21% were aged 26–35, 15.6% were aged 36–45, and 10.2% were aged 45 years above. In terms of education level, 25.2% had a high school education or below, 11.3% had specialized field education, 46.1% held a bachelor's degree, and 17.3% held a master's degree or higher. A total of 215 participants (50.8%) reported their annual household income range as 20,000–59,999 TL, followed by 60,000 TL and above (n = 160; 37.9%). The study also revealed that 76.1% (n = 322) of the participants spent no more than 1 hour per day shopping online (digital shopping platforms, web stores, etc.), whereas 17% (n = 72) spent between 1 and 2 hours shopping online. Additionally, 6.9% (n = 29) of the participants dedicated more than 3 hours to online shopping. With respect to the average amount that participants spend on online shopping, 36.2% spend between 1,000 and 2,999 TLs, 22% spend less than 1,000 TLs, 22% spend between 3,000 and 4,999 TLs, and 19.9% spend 5,000 TLs or more.\u003c/p\u003e\u003ch2\u003eTest of the Measurement Model\u003c/h2\u003e\u003cp\u003eBefore applying SEM, an exploratory factor analysis (EFA) was conducted first to observe whether the validity and reliability values for the variables in the study were met. These variables include technology readiness, behavioral intentions, purchase intentions, attitudes toward AI technology, and AI-driven consumer chatbot experience. Since there was no prior information about whether EFA was suitable for measuring the latent variable, the researcher explored how and to what extent the observed variables were related to their underlying structures (factors) (Byrne, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Orcan, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Exploratory factor analysis (EFA) was performed for each primary variable in the survey to redefine the factor structures. In this context, statements with factor loadings of 0.45 and above were included in the analysis. The EFA results for the variables in the study are summarized (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the CFA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOuter \u003c/p\u003e \u003cp\u003eloading\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKMO\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCronbach's \u003c/p\u003e \u003cp\u003ealpha\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOuter \u003c/p\u003e \u003cp\u003eloading\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eKMO\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCronbach's \u003c/p\u003e \u003cp\u003ealpha\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eTechnological \u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003emotivators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOP1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,796\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0,859\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0,757\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eTechnological\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003einhibitors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDISC2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,645\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0,859\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0,658\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOP2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,770\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDISC3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,632\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOP4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,676\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDISC1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,605\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOP3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,544\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eINS3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,857\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0,859\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0,813\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIN2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,762\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0,859\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0,726\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eINS1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,836\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIN4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,739\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eINS4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,69\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIN1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,680\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eINS2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,661\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIN3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,648\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eINS5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,495\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eBehavioral \u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eintention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,930\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0,864\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0,939\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003ePurchasing \u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eintention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePI2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,941\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0,812\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0,917\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,920\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePI3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,930\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,919\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePI1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,915\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,906\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePI4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,813\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003e\u003cb\u003eAI-driven consumer\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003echatbot experience\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINT1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,786\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0,925\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0,864\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eAttitude to\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAI technology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eATT5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,878\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e0,898\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e0,92\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINT2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,773\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eATT6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,870\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINT3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,718\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eATT7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,855\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACC3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,816\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0,925\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0,887\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eATT2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,810\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACC1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,752\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eATT4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,808\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACC2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,659\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eATT3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,806\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENT2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,862\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0,925\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0,840\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eATT1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,727\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENT1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,740\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" morerows=\"4\" nameend=\"c10\" namest=\"c6\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENT3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,670\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCUST3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,822\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0,925\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0,824\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCUST2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,674\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCUST1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,641\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe normality assumption must be met for hypothesis testing and confirmatory factor analysis in the subsequent parts of the research. Within this scope, the skewness and kurtosis values of the variables included in the study are considered sufficient. For the variables in the study, the normality assumptions are as follows: BehaInte (skewness: -0.532; kurtosis: -0.272), PurchaInte (skewness: -0.392; kurtosis: -0.193), AttituAITE (skewness: -0.608; kurtosis: 0.125), ALENCOEX (skewness: -0.357; kurtosis: 0.217), TEIN (skewness: -0.585; kurtosis: 0.503), and TEMO (skewness: -0.644; kurtosis: 1.122). In the social sciences, skewness and kurtosis values for variables within the range of + 1.5–1.5 indicate that the normality assumption is satisfied (Tabachnick \u0026amp; Fidell, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The results of the obtained data demonstrate that the normal condition is met.\u003c/p\u003e\u003cp\u003eIn the measurement model, establishing covariance connections between the error terms of the observed variables, starting from the highest, is recommended to improve and ensure model fit (Jöreskog and Sorbom, 1993). Within this scope, modifications are made between e21-e22, e21-e23, e22-e23, e17-e18, e28-e30, e28-e31, e38-e39, and e41-e42 to bring the goodness-of-fit of the CFA model within the limits accepted in the literature. The resulting CFA measurement model, following these modifications and removing certain items, is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. When examining Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the collected data confirm a first-order CFA model comprising 43 observed and 11 latent variables. Although there is no universally accepted criterion for goodness-of-fit indices, the goodness-of-fit values for the measurement model fall within the acceptable thresholds in the literature (Hu and Bentler, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Byrne, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In this regard, the goodness-of-fit indices are satisfied. All observed variables corresponding to each latent variable are statistically significant and well above the acceptable threshold of 0.50. Although there are different acceptance criteria for standardized factor loadings (SFLs) in the literature, it is emphasized that they should be at least 0.32 and preferably 0.50 or higher for good validity (Hair et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Kline, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Accordingly, all observed variables in the model are retained without modification.\u003c/p\u003e\u003cp\u003eAfter the measurement model, ensuring the convergent and discriminant validity of the scales allows the study to have more reliable and valid variables. In this context, the AVE, CR, ASV, and MSV values are calculated for the variables included in the research to test their convergent and discriminant validity. The relevant results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant validity and correlation matrix.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"16\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMSV\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCUST\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eINS\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOP\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIN\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDISC\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eATT\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eBI\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003ePI\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eINT\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eENT\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCUST\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,849\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,653\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,808\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,336\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,808\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eINS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,821\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,481\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,517\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,083\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,207\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,694\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,768\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,454\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,361\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,192\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,344\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e 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colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDISC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,664\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,398\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,517\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,133\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,171\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,719\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,585\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,432\u003c/p\u003e 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colname=\"c5\"\u003e \u003cp\u003e0,256\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,535\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,028\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,601\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,478\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,236\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0,777\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,939\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,793\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,704\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,297\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,660\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,078\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,368\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,378\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e 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colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eINT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,865\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,682\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,759\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,303\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,780\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,176\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,376\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,216\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e 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align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,305\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,735\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,120\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,399\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,267\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,277\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0,502\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,603\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0,528\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0,871\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0,850\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eENT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,801\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,574\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,808\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,350\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,899\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,255\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,317\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,431\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,205\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0,583\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,674\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0,640\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0,751\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0,729\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0,757\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eAVE = average variance extracted, MSV = maximum shared variance, ASV = average shared variance, CR = composite reliability.\u003c/p\u003e\u003cp\u003eWhen considering Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the composite reliability (CR) values are significantly above the accepted threshold of 0.70 in the literature, indicating high reliability. Furthermore, having an average variance extracted (AVE) value above 0.50 for each variable and CR values larger than the AVE values implies that convergent validity of the scale is achieved (Hair et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For discriminant validity, the maximum shared variance (MSV) must be greater than the average shared variance (ASV), and the square root of the AVE must be greater than the interfactor correlations. Additionally, the AVE should be greater than the MSV. In terms of these criteria, the MSV \u0026gt; ASV criterion is met for all the variables. However, for the AVE \u0026gt; MSV criterion, the OP, IN, ATT, BI, and PI variables meet the requirements, whereas the others do not. In this context, although some variables have issues with discriminant validity, the reliability, and convergent validity are considered sufficient overall, as the AVE and CR values are adequate, and the ASV and MSV values exceed the accepted thresholds. Ultimately, both the convergence and discriminant validity necessary for construct validity are achieved.\u003c/p\u003e\u003ch2\u003eStructural Equation Model and Hypothesis Testing\u003c/h2\u003e\u003cp\u003eThe use of a structural equation model (SEM) is recommended to validate the dimensions obtained through exploratory factor analysis and test the hypotheses proposed in the research model. SEM is a statistical method that facilitates the understanding and predicting relationships between variables (Raykov \u0026amp; Marcoulides, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Schumacker \u0026amp; Lomax, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Specifically, SEM enables the analysis of relationships involving multiple dependent and independent variables within a single model (Schreiber et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Tabachnick \u0026amp; Fidell, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). After confirming the convergent and discriminant validity and approving the measurement model, structural equation modeling (SEM) was conducted to test the research hypotheses. The aim is to evaluate the direct and indirect hypotheses formulated via the structural equation model for the observed variables. In this context, the results of the study's direct and indirect hypothesis tests are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eIt is recommended that the model be reanalyzed by sequentially removing insignificant regression coefficients to improve the model fit and refine the adjusted model (Gürbüz, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The model is constructed to estimate both direct and indirect effects. The obtained data support the goodness-of-fit indices of the constructed model. The results of the direct and total effect hypotheses in the study are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003eSummary of hypothesis test results\u003c/p\u003e\n\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n\u003cp\u003eNote 1: TEMO: Motivations for technology readiness; TEIN: Inhibitors for technology readiness; ALENCOEX: AI-driven consumer chatbot experience; AttituAITE: Attitude toward AI technology; BehaInte: Behavioral intention; PurchaInte: Purchasing intention\u003c/p\u003e\n\u003cp\u003eNote 2: TE: Total effects; \u0026beta;: Standardized coefficients; SE: Standard error; CR= Critic ratio; P= Path\u003c/p\u003e\n\u003cp\u003eNote 3: \u003cem\u003ep\u003c/em\u003e indicates significance, *** means p \u0026lt; 0.001; typically, a two-tailed test p \u0026lt; 0.05 indicates that the path coefficient estimate is significantly nonzero, meaning that the unstandardized path coefficients are significant.\u003c/p\u003e\n\u003cdiv align=\"left\" class=\"colspec\"\u003eThe findings in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e show the direct and total effects of the independent variables on the dependent variables. Total effects explain the loss of significance in direct effects caused by mediator variables. Additionally, the R\u0026sup2; values reveal the extent to which the dependent variables are described.\u003c/div\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe effect of TEMO on ALENCOEX was found to be significant both in terms of direct effects (\u0026beta;\u0026thinsp;=\u0026thinsp;0.444, C. R\u0026thinsp;=\u0026thinsp;7.947; R\u0026sup2;= 0,130; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and total effects (TE\u0026thinsp;=\u0026thinsp;0.444). R\u0026sup2;=0.130 indicates that TEMO explains 13% of the ALENCOEX variable. The impact of TEMO on ALENCOEX is strong. Therefore, this result demonstrates that \u003cem\u003eH4\u003c/em\u003e is accepted. The effect of TEMO on AttituAITE is significant both in terms of direct effects (\u0026beta;\u0026thinsp;=\u0026thinsp;0.452; C. R\u0026thinsp;=\u0026thinsp;10.997; R\u0026sup2;= 0,450; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and total effects (TE\u0026thinsp;=\u0026thinsp;0.787). Additionally, the direct impact of ALENCOEX on AttituAITE (\u0026beta;\u0026thinsp;=\u0026thinsp;0.397; C. R\u0026thinsp;=\u0026thinsp;10.264; R\u0026sup2;= 0,450; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and its total effect (TE\u0026thinsp;=\u0026thinsp;0.426) are also significant. Finally, the direct impact of TEIN on the AttituAITE variable (\u0026beta;=-0.148; C. R=-3.824, R\u0026sup2;= 0,450, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and its total effect (TE=-0.180) are significant. TEMO, ALENCOEX, and TEIN explain 45% of this variable in this context. These findings highlight the determinant roles of TEMO, TEIN, and ALENCOEX on AttituAITE and support hypotheses \u003cem\u003eH3a\u003c/em\u003e, \u003cem\u003eH3b\u003c/em\u003e, and \u003cem\u003eH8\u003c/em\u003e. The effect of ALENCOEX on PurchaInte is significant both in terms of direct effects (\u0026beta;\u0026thinsp;=\u0026thinsp;0.387; C. R\u0026thinsp;=\u0026thinsp;9.366; R\u0026sup2;= 0,496; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and total effects (TE\u0026thinsp;=\u0026thinsp;0.625). Additionally, the direct impact of AttituAITE on PurchaInte (\u0026beta;\u0026thinsp;=\u0026thinsp;0.368; C. R\u0026thinsp;=\u0026thinsp;8.029; R\u0026sup2;= 0,450; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and its total effect (TE\u0026thinsp;=\u0026thinsp;0.402) are also significant. ALENCOEX, TEMO, and AttituAITE explain 49.6% of this variation. These findings support hypotheses \u003cem\u003eH5b\u003c/em\u003e and \u003cem\u003eH7b\u003c/em\u003e. In contrast, the direct effect of TEMO on PurchaInte was not significant (C. R\u0026thinsp;=\u0026thinsp;1.811, p\u0026thinsp;=\u0026thinsp;0.07); however, the total effects (TE\u0026thinsp;=\u0026thinsp;0.626; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were substantial. This finding indicates that the impact of TEMO on PurchaInte is mediated by other variables (e.g., ALENCOEX or AttituAITE). On the other hand, the direct and total effects of TEIN on PurchaInte are not significant. Consequently, hypotheses \u003cem\u003eH1b\u003c/em\u003e and \u003cem\u003eH2b\u003c/em\u003e are not supported.\u003c/p\u003e\n\u003cp\u003eThe effects on BehaInte are significant for both the ALENCOEX and PurchaInte variables. The direct impact of ALENCOEX on BehaInte (\u0026beta;\u0026thinsp;=\u0026thinsp;0.24; C. R\u0026thinsp;=\u0026thinsp;6.78; R\u0026sup2;= 0,695; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the direct effect of PurchaInte (\u0026beta;\u0026thinsp;=\u0026thinsp;0.643; C. R\u0026thinsp;=\u0026thinsp;17.032; R\u0026sup2;= 0,695; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) are strong. Therefore, hypotheses \u003cem\u003eH5a\u003c/em\u003e and \u003cem\u003eH6\u003c/em\u003e are accepted. On the other hand, while the direct effect of TEIN on BehaInte is not significant (\u0026beta;=-0.032; C. R=-1.181; R\u0026sup2;= 0,695; p\u0026thinsp;=\u0026thinsp;0.237), the total effect is substantial and negative (TE=-0.100; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, the total effect is substantial and negative (TE\u0026thinsp;=\u0026thinsp;0.311; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas the direct effect of AttituAITE on BehaInte is not significant (\u0026beta;\u0026thinsp;=\u0026thinsp;0.036; C. R\u0026thinsp;=\u0026thinsp;1.025; R\u0026sup2;= 0,695; p\u0026thinsp;=\u0026thinsp;0.305). Finally, although the direct impact of TEMO on BehaInte is not significant (\u0026beta;\u0026thinsp;=\u0026thinsp;0.009; C. R\u0026thinsp;=\u0026thinsp;0.257; R\u0026sup2;= 0,695; p\u0026thinsp;=\u0026thinsp;0.797), the total effect is substantial (TE\u0026thinsp;=\u0026thinsp;0.599; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Accordingly, hypotheses \u003cem\u003eH1a\u003c/em\u003e, \u003cem\u003eH2a\u003c/em\u003e, and \u003cem\u003eH7a\u003c/em\u003e are rejected. Additionally, 69.5% of the variations in BehaInte are explained by the variables ALENCOEX, PurchaInte, TEIN, and TEMO. This high explanatory rate indicates that the model\u0026apos;s strong structure explains BehaInte. The effects of TEIN on AttituAITE and BehaInte should be carefully examined in terms of both direct and total effects. The direct impact of TEIN on AttituAITE is significant and negative (\u0026beta;0\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.148, C. R\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.824, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating an inverse relationship between TEIN and AttituAITE. Although the direct effect of TEIN on BehaInte is not significant (C. R\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.181, p\u0026thinsp;=\u0026thinsp;0.237), the total effects are substantial (TE\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.100, p\u0026thinsp;=\u0026thinsp;0.016). This finding suggests that the impact of TEIN on BehaInte occurs indirectly, with mediating variables playing a significant role. In this context, observing indirect effects and calculating specific indirect effects is crucial, making it necessary to provide definitions for these variables.\u003c/p\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eSerial Multiple Mediation Analysis\u003c/h2\u003e\n \u003cp\u003eTo view specific indirect effects and the results of serial multiple mediation, mediation effect models are constructed by coding within the model, considering paths for each indirect effect. In this context, the equations representing specific indirect effects are as follows: Ind1\u0026thinsp;=\u0026thinsp;a*m*k (serial multiple mediation), Ind2\u0026thinsp;=\u0026thinsp;d*g*m (serial multiple mediation), Ind3\u0026thinsp;=\u0026thinsp;d*g*b (serial multiple mediation), Ind4\u0026thinsp;=\u0026thinsp;d*g*m*k (serial multiple mediation), Ind5\u0026thinsp;=\u0026thinsp;d*n*k (serial multiple mediation), Ind6\u0026thinsp;=\u0026thinsp;l*m (standard mediation), Ind7\u0026thinsp;=\u0026thinsp;d*n (standard mediation), Ind8\u0026thinsp;=\u0026thinsp;a*b (standard mediation), Ind9\u0026thinsp;=\u0026thinsp;l*b (standard mediation), Ind10\u0026thinsp;=\u0026thinsp;d*h (standard mediation), Ind11\u0026thinsp;=\u0026thinsp;f*k (standard mediation), Ind12\u0026thinsp;=\u0026thinsp;g*b (standard mediation), and Ind13\u0026thinsp;=\u0026thinsp;l*m*k (serial multiple mediation). Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e presents the indirect effects between variables through the specified indirect paths (Ind1-Ind13). Each indirect effect has been re-evaluated, considering the respective mediation paths. In this context, the type of mediation (standard or serial multiple), coefficients, and significance levels are analyzed in detail.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5.\u003c/strong\u003e Mediating effect analysis\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eThe effect of TEMO on PurchaInte occurs through ALENCOEX (g) and AttituAITE (m) (Ind2). The coefficient of this serial multiple mediation effect is 0.076 and is statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This result indicates that TEMO has a positive and significant indirect effect on attitudes. Therefore, this result clearly supports \u003cem\u003eH9b\u003c/em\u003e. The impact of TEMO on BehaInte occurs through ALENCOEX (g) and AttituAITE (b) (Ind3). The coefficient of the indirect effect is 0.008 and is not significant (p\u0026thinsp;=\u0026thinsp;0.418). This suggests that TEMO does not have a notable effect on BehaInte through this indirect pathway. Accordingly, \u003cem\u003eH9a\u003c/em\u003e is not supported.\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe effect of TEIN on BehaInte occurs through AttituAITE (m) and PurchaInte (k) (Ind1). The total indirect effect obtained via serial multiple mediation is -0.048, and this effect is statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This result indicates that TEIN has an indirect but negative effect on BehaInte through the sequential mediation of AttituAITE and PurchaInte. The effect of TEMO on BehaInte occurs through ALENCOEX (g), AttituAITE (m), and PurchaInte (k) (Ind4). This serial multiple mediation effect is 0.051 and is statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This result indicates that TEMO has a positive indirect effect on behavioral intentions. The effect of TEMO on BehaInte occurs through ALENCOEX (n) and PurchaInte (k) (Ind5). The coefficient of the indirect effect is 0.135 and is statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding indicates that TEMO has a strong positive indirect effect on behavioral intentions. The effect of TEMO on PurchaInte occurs through AttituAITE (m) (Ind6). The coefficient of the indirect effect obtained through simple mediation is 0.24 and is statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Thus, TEMO positively influences purchase intentions through attitudes. The effect of TEMO on PurchaInte occurs through ALENCOEX (n) (Ind7). The coefficient of the indirect effect is 0.202 and is statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The positive indirect effect of TEMO on purchase intentions is evident. The effect of TEIN on BehaInte occurs through AttituAITE (b) (Ind8). This indirect effect is -0.007 and is not statistically significant (p\u0026thinsp;=\u0026thinsp;0.418). This result indicates that TEIN does not indirectly affect behavioral intentions through attitudes.\u003c/p\u003e\n \u003cp\u003eThe effect of TEMO on BehaInte occurs through AttituAITE (b) (Ind9). The coefficient of the indirect impact is 0.025 and is not statistically significant (p\u0026thinsp;=\u0026thinsp;0.418). This finding indicates that TEMO does not significantly affect behavioral intentions through attitudes. The effect of TEMO on BehaInte occurs through ALENCOEX (h) (Ind10). The coefficient of the indirect impact is 0.13 and is significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). TEMO has a positive indirect effect on behavioral intentions. The effect of TEMO on BehaInte occurs through PurchaInte (k) (Ind11). The coefficient of the indirect effect is 0.072 and is marginally significant (p\u0026thinsp;=\u0026thinsp;0.074). This suggests that TEMO may have an effect on behavioral intentions through purchase intentions, but this effect is not definitive. The effect of ALENCOEX on BehaInte occurs through AttituAITE (b) (Ind12). This indirect effect is 0.018 and is not statistically significant (p\u0026thinsp;=\u0026thinsp;0.418). This finding indicates that ALENCOEX does not have an indirect effect on behavioral intentions through attitudes. The effect of TEMO on BehaInte occurs through AttituAITE (m) and PurchaInte (k) (Ind13). The indirect effect obtained through serial mediation is 0.161, and this effect is significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This result indicates that TEMO has a strong positive effect on behavioral intentions through attitudes and purchase intentions.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSummary and Discussion\u003c/h2\u003e \u003cp\u003eThis study is one of the few that focuses on the AI-driven chatbot experience of consumers in Turkey and seeks to clarify the relationships between technology readiness and behavioral and purchase intentions. While the study highlights the mediating role of attitudes toward AI technology and AI-driven chatbot consumer experience, it also defines the two main factors of technology readiness: optimism and innovativeness as motivators and discomfort and insecurity as inhibitors, as identified by Parasuraman and Colby (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Blut and Wang (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). According to the literature review, until now, the relationships among technology readiness, attitudes toward AI technology, AI-driven chatbot consumer experience, and behavioral and purchase intentions have not been evaluated with multiple common mediators. Furthermore, this study is innovative in applying multiple sequential mediation models to explore the role of attitudes toward AI technology and the AI-driven chatbot consumer experience in the effect of technology readiness on behavioral and purchase intentions.\u003c/p\u003e \u003cp\u003eTheoretically and practically, this study makes significant contributions by linking consumers' technology readiness levels with their AI-driven chatbot experiences. Especially with the rise of AI and chatbot technologies, deepening the understanding in this area will allow businesses to reshape their consumer relationships and marketing strategies. Consumers' adoption of technology and attitudes toward AI enable companies to determine how they will use and adopt these technologies. Additionally, an in-depth understanding of consumer behaviors through multiple mediation analyses enables a more targeted design of marketing strategies and customer interactions. This study offers practical findings in terms of marketing, chatbot consumer experiences, and technology, making it of outstanding academic and practical importance.\u003c/p\u003e \u003cp\u003eThe results of this study revealed the connections between the concepts within the scope of the subject. The supported \u003cem\u003eH3a\u003c/em\u003e and \u003cem\u003eH4\u003c/em\u003e hypotheses clearly demonstrate that consumers' motivations regarding technology readiness significantly affect their attitudes toward artificial intelligence, AI-driven consumer chatbot experiences, and purchase intentions. These results are consistent with the literature. Leong et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) revealed that individuals' motivations toward technology shape their interaction experiences with technology and can enhance the adoption of new digital services such as chatbots. Deng et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) explored the mediating role of technology readiness in its effect on purchase intentions through technological experience and attitudes. Moreover, H3b, which suggests that inhibitors of technology readiness do not significantly affect attitudes toward AI technology, is supported in the literature. Inhibitors of technology readiness may negatively influence individuals' attitudes toward technology. However, it is debated whether these inhibitors consistently demonstrate this effect. Venkatesh et al. (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) reported that inhibitory factors sometimes exert influence only in specific contexts without being prominent.\u003c/p\u003e \u003cp\u003eFurthermore, the AI-driven consumer chatbot experience significantly impacts behavioral and purchase intentions, as \u003cem\u003eH5a\u003c/em\u003e, \u003cem\u003eH5b\u003c/em\u003e, and \u003cem\u003eH6\u003c/em\u003e were meaningfully supported. Studies in the literature have demonstrated that AI-driven chatbots significantly affect user behavior. Myin and Watchravesringkan (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) stated that chatbots are potent tools that influence users' behavioral intentions. Similarly, Bakkouri et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) revealed that AI-driven services influence users' behavioral intentions and purchase decisions. The results revealed a significant pathway between attitudes toward AI technology, AI-driven consumer chatbot experience, and purchase intention (H7b, H8). Attitudes toward AI technology are considered to have a strong relationship with purchase intentions. Wang et al. (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) demonstrated that positive attitudes toward AI-driven products significantly increase purchase intentions. The literature suggests that AI experiences can shape users' attitudes toward technology. Garrett (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) reported that AI-driven chatbots can influence users' attitudes toward technologies.\u003c/p\u003e \u003cp\u003eNo direct connection was found between the motivating and inhibiting factors of technology readiness and the variables of behavioral and purchase intentions (H1a, H1b, H2a, and H2b). In other words, the motivating and inhibiting factors of technology readiness do not affect behavioral or purchase intentions. However, there is evidence for the indirect effect of the motivating factors of technology readiness on purchase intentions through attitudes toward AI technology and the AI-driven consumer chatbot experience; H9b is meaningfully supported. Although we cannot directly observe the effect of motivational factors of technology readiness (TEMO) on behavioral and purchase intentions, meaningful results emerge regarding indirect effects. It has been determined that TEMO indirectly and positively influences behavioral intention through attitudes toward AI technology, AI-supported consumer chatbot experience, and purchase intention separately. The total indirect effect obtained through serial multiple mediation via attitudes toward AI technology and purchase intentions is statistically significant. It has also been determined that TEMO indirectly and positively influences purchase intentions through attitudes toward AI technology and AI-driven consumer chatbot experience separately.\u003c/p\u003e \u003cp\u003eOverall, the findings of this study indicate that the direct effect of TEMO on behavioral and purchase intentions is insignificant. However, its indirect effects through attitudes toward AI technology and AI-driven consumer chatbot experience are significant, indicating complete mediation. By integrating all the results, it is possible to construct a potential relationship model demonstrating how attitudes toward AI technology and AI-driven consumer chatbot experience together mediate the relationship between TEMO and purchase intention. Previous studies have not examined the sequential mediating effects of these variables. Additionally, the direct impact of TEMO on behavioral and purchase intentions is insignificant, indicating that the two aforementioned joint mediators play a role in recognizing the relationships between these external and internal variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eTheoretical Contributions\u003c/h2\u003e \u003cp\u003eThe findings of this study hold significant theoretical implications, as the proposed extended causal chain relationship model\u0026mdash;technology readiness\u0026mdash;attitudes toward AI technology\u0026mdash;AI-driven consumer chatbot experience\u0026mdash;behavioral and purchase intentions\u0026mdash;demonstrates an acceptable model fit. These findings support Foxall and Goldsmith\u0026rsquo;s (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) C-A-B (cognitive-affective-behavior) model in the decision-making literature. The cognitive component represents consumers' knowledge, awareness, and perceptions. Hypotheses H3a and H4 indicate that consumers' technology readiness motivations significantly impact their attitudes toward AI technology and AI-driven chatbot experience. This highlights how cognitive processes shape consumers' perceptions of technology. The affective component encompasses individuals' positive or negative emotional responses to a product or experience. Hypothesis H8 demonstrates that AI-supported chatbot experience positively affects attitudes toward AI, serving as a direct example of the affective component of the C-A-B model. The behavioral component reflects consumers' tendencies to take action. In hypotheses, H5a, H5b, and H6, AI-driven chatbot experiences are found to have a positive effect on both behavioral intention and purchase intention. This explains how individuals' positive cognitive and emotional responses translate into behavioral outcomes. In Hypothesis H9b, the impact of technology readiness motivations on purchase intentions exhibits serial multiple mediations through AI experience and attitudes toward AI, aligning perfectly with the sequential structure of the C-A-B model.\u003c/p\u003e \u003cp\u003eMoreover, these findings align well with the TAM (technology acceptance model) and SOR (stimulus‒organism‒response) models. The results indicate that technology readiness motivations influence attitudes toward AI and chatbot experience, which supports the TAM\u0026rsquo;s framework, which links perceived usefulness and ease of use to consumer intentions. The AI-driven chatbot experience enhances purchase intentions by creating a positive customer experience associated with technology adoption within the technology acceptance framework (Davis, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Venkatesh \u0026amp; Davis, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Furthermore, the role of the AI-driven chatbot experience as a stimulus that shapes consumer attitudes and ultimately leads to purchase intentions is consistent with the mechanism proposed by the SOR model (Chen et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study's observed serial multiple mediation effect (H9b) demonstrates that consumer decision-making unfolds stepwise, with AI experience playing a crucial role in this process.\u003c/p\u003e \u003cp\u003eThe personalized interactions of AI chatbots have been shown to positively impact consumer experience, enhancing both behavioral and purchase intentions. This finding aligns with Schmitt\u0026rsquo;s (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) sensory, cognitive, behavioral, and emotional experience components, demonstrating that chatbots personalize customer experiences to foster stronger engagement. Additionally, it highlights that chatbot interactions are not merely mechanical but also provide emotional and cognitive experiences. AI-driven chatbots enhance individual and contextualized experiences by offering personalized recommendations to customers. Moreover, motivation for technology readiness positively influences attitudes toward AI technology, whereas less technologically prepared individuals may have a negative experience with AI-driven chatbots. In this regard, the trust factor emerges as a more prominent component in AI-driven interactions within Consumer Experience Theory. Chatbots are key in building customer trust by ensuring fast response times, providing accurate information, and offering a user-friendly interface. Consequently, this study expands Consumer Experience Theory within the context of AI and chatbot-based interactions, contributing to understanding customer experience in the digital age.\u003c/p\u003e \u003cp\u003eIn summary, this study integrates theoretical frameworks to develop a model that explains the relationships among technology readiness, attitudes toward AI technology, AI-driven consumer chatbot experience, behavioral intentions, and purchase intentions, providing insights into customers' experiential and decision-making processes in Turkey. The findings offer a more holistic perspective on the decision-making process of Turkish consumers who use chatbots on digital shopping platforms. A causal chain can be established between technology readiness, attitudes toward AI technology, AI-driven consumer chatbot experience, behavioral intentions, and purchase intentions, demonstrating the role of AI attitudes and chatbot experience in the decision-making process and their influence on behavioral and purchase intentions. Previous studies have not examined this serial multiple mediation relationship in digital shopping. Serial multiple mediation can help us understand the connection between technology readiness and behavioral/purchase intentions and identify the key mediators within this chain. Furthermore, this study highlights that, in the context of chatbot use in digital shopping, the relationship between technology readiness and behavioral/purchase intentions is mediated by attitudes toward AI technology and the AI-driven consumer chatbot experience.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eManagerial Implications\u003c/h2\u003e \u003cp\u003eThe findings indicate that AI-driven chatbots positively affect customer behavioral intention and purchase intention. On this basis, businesses can utilize chatbots for customer service and sales tools offering personalized recommendations and shopping guidance. Additionally, it can be easily stated that the chatbot experience shapes 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' technology readiness motivations positively influence the 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 experiences.\u003c/p\u003e \u003cp\u003eOn the other hand, it becomes inevitable for companies to act within a strategic plan, considering future challenges and implementation barriers. It has been found that obstacles to technology readiness do not significantly impact attitudes toward AI. This implies businesses should address consumers' concerns about AI usage by providing more information and offering control mechanisms. In the context of the TAM model, some consumers may still resist using AI-driven chatbots, which could hinder adoption (Venkatesh \u0026amp; Bala, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). AI-driven chatbots process vast amounts of customer data. To ensure data security and privacy, companies should develop transparent policies and comply with data protection regulations. This research highlights the impact of AI-driven consumer chatbots on customer intentions, helping businesses guide their AI investments. Consumers' technology readiness motivations, chatbot experiences, and attitudes toward AI directly influence their purchasing behavior. Therefore, companies should design chatbots in line with customer expectations, develop personalization strategies, and ensure transparency in data security.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Research\u003c/h2\u003e \u003cp\u003eThis study has several limitations and various aspects that should be considered in future research. This study focuses on consumers in Turkey, and cultural factors may influence the results. Therefore, the results cannot be generalized to different ethnic groups and cultures, as socio-cultural factors may lead to variations in technology readiness, attitudes toward artificial intelligence technology, and chatbot experiences. Replicating the study in different countries could help understand the impact of cultural factors. The sample may consist of younger and more tech-savvy consumers. The reactions of individuals with lower technology readiness have not been evaluated. Similar studies can be conducted on consumers from different age groups, income levels, and digital literacy levels. Also, the sample size has been limited due to the fact that some businesses in Turkey have only recently started interacting with consumers through AI-driven chatbots in their marketing activities. A cross-sectional design was used in this research. Changes in consumer perceptions and technology acceptance over time could be better analyzed through a longitudinal study. The evolution of consumer attitudes toward AI chatbots over time can be examined by conducting longitudinal research. The impact of chatbot usage on consumer behavior can be tested via data from real experiences. Additionally, future research could utilize detailed consumer motivation data through in-depth interviews alongside surveys to understand real-time experiences and examine how emotional and psychological factors (e.g., consumer trust and privacy concerns) influence the chatbot experience. The effects of AI chatbots on customer loyalty, brand trust, and long-term customer relationships can also be explored.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003eThis study was reviewed and approved by Uskudar University Non- Interventional Studies Ethics Committee with the approval number: 61351342/020-411, dated September 30, 2024.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDeclaration of Conflicting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAmeen, N., Tarhini, A., Reppel, A., and Anand, A. 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(2019), “Generation Y Consumers Have Online Repurchase Intention in Bangkok”. \u003cem\u003eInternational Journal of Retail \u0026amp;Amp; Distribution Management\u003c/em\u003e, 48(1), 53-69. https://doi.org/10.1108/ijrdm-04-2018-0071 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Technology readiness, AI-driven chatbot, Digital shopping, Consumer experience, Behavioral intention, Purchasing intention","lastPublishedDoi":"10.21203/rs.3.rs-6135960/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6135960/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the rise of AI in digital consumer experiences, one of the key challenges for businesses is predicting and influencing consumers\u0026rsquo; behavioral and purchase intentions in AI-driven interactions. In this regard, there appears to be a knowledge gap in the literature that cannot be determined regarding the effect of technology readiness on behavioral and purchasing intentions and, accordingly, the mediating role of attitude toward AI technology and the AI-driven consumer chatbot experience. Therefore, this research aims to determine the relationships among technology readiness, attitude toward AI technology, AI-driven consumer chatbot experience, behavioral intentions, and purchase intentions. The primary focus of the study is to evaluate the impact of Turkish consumers' technology readiness on behavioral and purchase intentions through the serial multiple mediating roles of attitude toward AI technology and the AI-driven customer chatbot experience. A questionnaire designed in line with the purpose of the research was applied to 423 respondents using AI-driven chatbots during the digital shopping experience. Although technology readiness does not directly affect behavioral and purchase intentions, attitude toward AI technology and the AI-driven consumer chatbot experience play a serial multiple mediating role in the relationship between motivations for technology readiness and purchase intention.\u003c/p\u003e","manuscriptTitle":"How Technology Readiness Influences Behavioral and Purchasing Intention: Serial Multiple Mediating Role of Attitude toward AI and AI-Driven Consumer Chatbot Experience","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-06 14:08:35","doi":"10.21203/rs.3.rs-6135960/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"957818fb-c42a-48f7-ad79-7dd8c1e25494","owner":[],"postedDate":"March 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45280702,"name":"Marketing"}],"tags":[],"updatedAt":"2025-09-19T20:46:44+00:00","versionOfRecord":{"articleIdentity":"rs-6135960","link":"https://doi.org/10.1108/DTS-04-2025-0082","journal":{"identity":"digital-transformation-and-society","isVorOnly":true,"title":"Digital Transformation and Society"},"publishedOn":"2025-09-15 00:00:00","publishedOnDateReadable":"September 15th, 2025"},"versionCreatedAt":"2025-03-06 14:08:35","video":"","vorDoi":"10.1108/DTS-04-2025-0082","vorDoiUrl":"https://doi.org/10.1108/DTS-04-2025-0082","workflowStages":[]},"version":"v1","identity":"rs-6135960","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6135960","identity":"rs-6135960","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}