Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 4, 74-82 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i4.5939 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 6 February 2025; Revised: 26 March 2025; Accepted: 31 March 2025; Published: 3 April 2025 * Correspondence: hjkim1@joongbu.ac.kr The use of generative AI tools in design work: Motivation and decision- making process of users Jhonghee Kim1, Hyungjoon Kim2* 1Samsung Art & Design Institute; South Korea. 2Joongbu University; South Korea; hjkim1@joongbu.ac.kr (H.K.). Abstract: As generative AI technologies evolve, more designers are integrating these tools into their workflows. While existing research has examined the use of generative AI in design, few studies have conceptualized user engagement within an integrated model of motivational and behavioral factors. This study explores key constructs—attitudes, subjective norms, perceived behavioral control, intention to use, and actual usage—through the Uses and Gratifications Theory (UGT) and the Theory of Planned Behavior (TPB). Results indicate that designers' attitudes and subjective norms significantly affect their intention to adopt generative AI tools, which in turn influences actual usage. Designers generally hold positive attitudes toward these tools, and external social influences are crucial to their adoption. Finally, enhancing perceived control may further promote adoption and integration into design practices. Keywords: Attitudes, Generative AI tools, Intentions, behavior, Perceived behavioral control, Subjective norms. 1. Introduction The design industry has been undergoing rapid transformation due to advancements in digital technology, fundamentally altering creative workflows and expanding design methodologies. One of the most significant developments is the increasing incorporation of generative AI tools, enabling designers to integrate AI as collaborative agents in creative processes [1]. A global survey indicates that 25% of designers currently use generative AI, with more than half expressing their intent to adopt these tools in the future [2]. This trend underscores the need for academic and industry discussions on the shift from conventional design practices to AI-assisted methodologies [3]. Understanding this transition is critical, as it is poised to redefine industry standards, reshape creative workflows, and introduce new paradigms in design innovation. While generative AI adoption has gained momentum, the transition from motivation to actual usage behavior remains insufficiently studied from an empirical perspective [4]. As designers increasingly interact with AI-driven tools, it becomes essential to examine how motivational factors influence engagement with generative AI. The Uses and Gratifications Theory (UGT) provides insights into user motivations, while the Theory of Planned Behavior (TPB) explains the process through which these motivations translate into intentional and actual adoption [5]. By integrating these perspectives, a comprehensive model can be developed to analyze the key determinants of AI adoption in design workflows. Since generative AI applications rely on active user participation, understanding these behavioral patterns is crucial for optimizing human-AI collaboration [6]. Despite the growing body of research on AI-driven design, studies that comprehensively address both motivational and behavioral perspectives remain limited [7]. This study aims to bridge this gap by developing a theoretical framework explains the increasing use of generative AI tools in design. By offering an empirically grounded model, this research contributes to both academic discourse and 75 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 4: 74-82, 2025 DOI: 10.55214/25768484.v9i4.5939 © 2025 by the authors; licensee Learning Gate practical applications, providing valuable insights for design professionals, researchers, and industry stakeholders. 2. Literature Review 2.1. Image-generative AI tools The emergence of user-friendly generative AI technologies, such as DALL·E and Midjourney, has significantly transformed text-to-image synthesis, facilitating applications in visual content creation and digital artistry [8]. Previously, integrating machine learning (ML) into creative fields required specialized knowledge [9]. Now, these platforms allow users to generate refined visuals using natural language commands [10]. Midjourney and Stable Diffusion are leading AI-based image synthesis models known for their accessibility and capabilities. Stable Diffusion is developed by Stability AI, while Midjourney operates independently [11, 12]. Both utilize advanced deep learning techniques, including diffusion models and generative adversarial networks (GANs), to create images based on user prompts. Recently, Vega AI has emerged, enhancing multi-modal generation functionalities and eliminating the need for local deployment. It excels in converting 2D wireframe sketches into fully rendered 3D images. Effective prompt engineering is crucial for optimizing AI-generated outputs. This discipline focuses on structuring user inputs to enhance human-machine interaction and model performance [13]. Prompts typically include contextual parameters that influence the semantic accuracy and aesthetic quality of generated visuals. Midjourney is notable for its ability to integrate large language models (LLMs), facilitating the interpretation of abstract concepts into tangible visual elements [14]. In automotive design, for example, it translates consumer emotional responses into distinct morphological features of car designs. Generative Artificial Intelligence (GAI) is also impacting various fields, including natural language processing, 3D graphics, and video generation [15]. In design-intensive areas such as architecture and product development, GAI enhances ideation, rapid prototyping, and iterative refinement. Its integration into design workflows promotes collaboration between human designers and AI systems, driving innovation and efficiency [16, 17]. 2.2. Motivational Use of Generative AI User motivation is a key factor influencing technology-related decisions and behaviors, driving actions toward specific goals [18]. The Uses and Gratifications (U&G) theory explains that individuals engage with technologies to fulfill psychological needs [19]. This framework reveals motivations behind the adoption and use of various technologies [20, 21]. For instance, Chen, Hsiao, and Li demonstrate that perceived usefulness, enjoyment, and social belonging significantly shape usage habits in location-based mobile applications [22, 23]. In the U&G context, motivation is critical for predicting user behavior [24, 25]. It includes the reasons and goals propelling users toward action. For generative AI tools, motivations are typically categorized as utilitarian and hedonic [26]. Utilitarian motives focus on practical benefits like efficiency, while hedonic motives center on enjoyment and satisfaction derived from technology use [27]. Positive emotions from hedonic gratification enhance attitudes toward AI tools Picot-Coupey, et al. [28] while utilitarian users aim for goal achievement, leading to favorable feelings and future intentions [29]. Studies show that positive attitudes toward generative AI tools often arise from perceived value in product information and interactivity [30, 31]. Experiential value from interacting with generative AI also significantly influences attitudes [32]. This value enhances perceptions of technology, as experiential satisfaction is linked to favorable attitudes [33]. Hsu, Yu, and Chao found that experiential value notably impacts user perceptions [34]. Thus, alongside utilitarian and hedonic motives, experiential intensity is key to user engagement with generative AI tools [35]. Such engagement is derived from immersive experiences that provide deep satisfaction through active participation [36]. 76 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 4: 74-82, 2025 DOI: 10.55214/25768484.v9i4.5939 © 2025 by the authors; licensee Learning Gate Considering the accessible nature of generative AI tools, utilitarian, hedonic, and experiential motives together shape user engagement. Rahman, Khan, and Iqbal found that utilitarian values significantly influence user attitudes more than hedonic values or concerns about trust and privacy [37]. This study integrates these three motivational dimensions—utilitarian, hedonic, and experiential—as antecedents to attitudes toward generative AI tools, exploring their relevance in the m- commerce context. 2.3. Decision-Making Process of Generative AI Tools The adoption of generative AI tools follows a sequential decision-making process from initial exposure to attitude formation and usage intention. The Uses and Gratifications (U&G) theory addresses psychological motivations, while the Theory of Planned Behavior (TPB) offers insights into behavioral intention. TPB identifies three factors influencing intention: attitude toward behavior, subjective norms, and perceived behavioral control (PBC) [38]. Attitudes stem from an individual’s beliefs about the outcomes of behavior, reflecting a predisposition towards usage [39]. These attitudes can be reinforced by psychological satisfaction, boosting adoption likelihood [40]. Subjective norms—social influences from peers and industry trends—also play a critical role in shaping behavioral intention [41]. Research shows that word-of- mouth (WOM) and social persuasion significantly impact technology adoption [42]. Additionally, PBC connects intention to actual behavior, reflecting users' perceptions of control over external constraints [43]. Users may experience a loss of control in complex digital environments, affecting adoption decisions Dabholkar and Sheng [44] however, those with higher technological competence often report greater PBC, facilitating engagement [45]. Behavioral intention serves as a mediator between perceived control and actual behavior; users are more likely to act when they have the intention and confidence to do so [29]. Studies have effectively integrated U&G and TPB to examine technology adoption patterns. For instance, Raza et al. found that social influence, perceived behavioral control, and attitudes significantly affect Facebook usage [46]. Similarly, Chen, Liang, and Cai analyzed motivational and behavioral factors in digital service adoption Chen, et al. [47] while Sun et al. linked continued use of link-sharing tools to intention and subjective norms [48]. This study combines U&G and TPB to provide a comprehensive framework for understanding the decision-making process of generative AI users. 2.4. Hypotheses and Research This study investigates user motivations and components of planned behavior to predict the adoption and use of generative AI tools among South Korean designers. Prior research has integrated Uses and Gratifications Theory (U&G) with the Theory of Planned Behavior (TPB), enhancing explanatory power by incorporating motivational components from U&G, thus increasing theoretical robustness [46]. While many studies have explored user behavior in technology adoption, they typically focus on either motivational drivers or planned behavior frameworks, rather than integrating both into a comprehensive model. Additionally, previous research often emphasizes external environmental influences while neglecting the impact of internal psychological factors on decision- making. To address this gap, this study proposes that user motivation to engage with generative AI tools positively influences attitudes toward adoption. Furthermore, attitudes, subjective norms, and perceived behavioral control are posited as key determinants of behavioral intention, which in turn predicts actual usage behavior. Based on these foundations, the following hypotheses are presented: Hypothesis 1 (H1): Attitudes toward using generative AI tools will positively predict behavioral intention. Hypothesis 2 (H2): Subjective norms regarding generative AI tools will positively predict behavioral intention. Hypothesis 3 (H3): Perceived behavioral control will positively predict behavioral intention. 77 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 4: 74-82, 2025 DOI: 10.55214/25768484.v9i4.5939 © 2025 by the authors; licensee Learning Gate Hypothesis 4 (H4): Perceived behavioral control will positively predict generative AI tool usage behavior. Hypothesis 5 (H5): Behavioral intention will positively predict generative AI tool usage behavior. Figure 1. Research model. 3. Methodology 3.1. Participants This study utilized an online survey to gather insights from 287 designers regarding their perspectives on generative AI adoption. Administered through an online platform, the survey ensured a diverse participant pool. The majority of respondents were women in their 20s and 30s, with professional experience evenly distributed from 1 to over 7 years. Table 1. Demographic variables of respondents. Gender Age Work Experience Group Frequency Percentage (%) Group Frequency Percentage (%) Group Frequency Percentage (%) Male 85 29.7% 20s 119 41.6% 1~2 years 88 30.8% Female 201 70.3% 30s 128 44.8% 3~4 years 18 6.3% 40s 38 13.3% 3~5 years 71 24.8% 50s and above 1 0.3% 5~7 years 51 17.8% More than 7yrs 58 20.2% 3.2. Measurement This study measured four exogenous variables and one endogenous variable based on the U&G theory and the TPB. Questionnaire items were adapted to the context of the study, with all constructs assessed using a five-point Likert scale, ranging from “strongly disagree” (1) to “strongly agree” (5): Attitudes: Five items were adapted from Amaro and Duarte, and Sun, Law, and Schuckert [49]. Subjective Norms: Three items were derived from Tarkiainen and Sundqvist [49]. Perceived Behavioral Control: Four items were adopted from Basole and Major [1]. User Intention: Four items regarding behavioral intention were sourced from Hsiao and Tang [3]. Continuous Use Behavior: Three items on actual usage of generative AI tools were adapted from Hsiao and Tang [3]. 78 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 4: 74-82, 2025 DOI: 10.55214/25768484.v9i4.5939 © 2025 by the authors; licensee Learning Gate 3.3. Procedures A reliability analysis was performed on the questionnaire items, resulting in the exclusion of items that undermined reliability to create the final scale for analysis. Multiple regression and stepwise regression analyses were conducted to test the hypotheses. 4. Results The findings indicate that attitudes, subjective norms, and perceived behavioral control significantly impact behavioral intention, providing empirical support for Hypotheses 1, 2, and 3. Table 2. Results of multiple regression analysis on behavioral intention. Independent Variables β t p R2(ΔR2 F p Attitude 0.623 11.483 0.000 0.520 102.061 0.000 Subjective Norm 0.111 2.032 0.043 Perceived Behavioral Control 0.110 2.632 0.009 The analysis also confirmed that behavioral intention significantly influences continuous usage behavior, supporting Hypothesis 4. Additionally, to assess the direct effect of perceived behavioral control on continuous usage behavior, an analysis was conducted excluding the effects of behavioral intention. Results showed that perceived behavioral control did not have a significant direct effect, leading to the rejection of Hypothesis 5. However, subjective norms were found to have a significant direct effect on continuous usage behavior, underscoring their role in shaping user engagement with generative AI tools. Table 3. Stepwise regression analysis results for continuous use behavior. Analytical Steps Independent Variables β t p R2(ΔR2 F p 1 Behavioral Intention 0.445 8.378 0.000 0.198 70.190 0.000 2 Attitude 0.100 1.221 0.223 0.051 6.372 0.000 Subjective Norm 0.204 2.953 0.003 Perceived Behavioral Control -0.085 -1.592 0.112 5. Discussion The alternative research model based on the Theory of Planned Behavior (TPB) effectively analyzes factors influencing continuous use behavior of generative AI tools. The findings reveal that attitudes, subjective norms, and perceived behavioral control significantly affect behavioral intention, which is crucial in shaping continuous usage. Notably, while subjective norms have both direct and indirect effects on continuous use, perceived behavioral control did not demonstrate a significant direct impact, indicating its influence is mainly mediated through behavioral intention. These results align with existing research highlighting the importance of psychological and social factors in technology adoption [39]. The significance of subjective norms emphasizes the role of social influence in AI adoption, while the connection between behavioral intention and continuous use behavior underscores the need to foster motivational drivers for long-term engagement. 5.1. Influence of Attitude and Subjective Norms on Behavioral Intention The analysis confirms that attitude significantly influences behavioral intention, suggesting users view AI tools as beneficial and enjoyable, which enhances adoption decisions. This finding supports literature indicating that positive perceptions, such as usefulness and ease of use, boost user motivation to integrate AI into workflows. Additionally, subjective norms strongly impact behavioral intention, highlighting the importance of peer influence, organizational expectations, and social acceptance in users' willingness to use AI tools. 79 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 4: 74-82, 2025 DOI: 10.55214/25768484.v9i4.5939 © 2025 by the authors; licensee Learning Gate This indicates that perceived endorsement by colleagues and industry trends contributes to higher adoption rates in design and creative fields. The direct effect of subjective norms on continuous use behavior suggests that external encouragement—through industry trends and workplace integration— reinforces sustained engagement. AI developers and service providers should implement community- driven adoption strategies, including AI education and collaborative environments. In the context of Korean society, subjective norms play a more critical role than perceived behavioral control in shaping behavioral intention and actual behavior, particularly in professional settings. Studies indicate that social expectations significantly influence decision-making beyond individual perceptions of control [50]. 5.2. The Role of Perceived Behavioral Control Contrary to expectations, perceived behavioral control did not significantly affect continuous use behavior, despite influencing behavioral intention. This implies that while users' confidence in using AI tools impacts their intention, it does not independently drive sustained usage. Enhancing user control alone may not ensure long-term engagement; motivational factors like perceived benefits and social validation are crucial for continued usage. 5.3. Behavioral Intention as A Mediator of Continuous Use The study confirms that behavioral intention mediates the relationship between psychological factors and actual usage behavior, reinforcing findings from the TAM and TPB frameworks. A strong intention increases the likelihood of continued engagement with technology. In the growing context of AI tools in creative industries, fostering a positive user experience is essential for sustaining long-term usage. 5.4. Limitations And Suggestions for Future Research This study highlights that subjective norms significantly influence designers' continuous use of generative AI tools within the South Korean cultural context, indicating a crucial cultural distinction. In contrast to individualistic Western societies, where perceived behavioral control is a key driver of AI adoption, collectivist cultures like South Korea place greater emphasis on social expectations in shaping usage behavior. This finding underscores the need for further cross-cultural comparative studies examining the role of subjective norms in user experiences and attitudes toward generative AI tools. Additionally, future research should investigate how subjective norms affect team-based design workflows involving generative AI, particularly in South Korea and in global collaborative settings. Expanding research in these areas may yield deeper insights into culturally adaptive AI integration strategies across design industries worldwide. 6. Conclusion This study affirms that attitude, subjective norms, and behavioral intention are critical determinants of continuous AI adoption, with perceived behavioral control playing an indirect role. The results emphasize the importance of social influence, especially within collectivist cultural contexts, where subjective norms directly impact user behavior. Unlike individualistic societies that prioritize personal control in AI adoption, this research illustrates the significance of external social expectations in fostering sustained engagement with generative AI tools. These insights advocate for community-driven adoption strategies and the reinforcement of positive behavioral intentions to ensure the long-term integration of AI into professional design workflows. Moving forward, this research can offer valuable guidance for AI developers, designers, and industry stakeholders in crafting culturally adaptive strategies that enhance user experience and facilitate seamless AI integration in diverse collaborative environments. 80 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 4: 74-82, 2025 DOI: 10.55214/25768484.v9i4.5939 © 2025 by the authors; licensee Learning Gate Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Copyright: © 2025 by the authors. This open-access article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] R. C. Basole and T. Major, "Generative AI for visualization: Opportunities and challenges," IEEE Computer Graphics and Applications, vol. 44, no. 2, pp. 55-64, 2024. [2] J. Lively, J. Hutson, and E. Melick, "Integrating AI-generative tools in web design education: enhancing student aesthetic and creative copy capabilities using image and text-based AI generators," Journal of Artificial Intelligence and Robotics, vol. 1, no. 1, 2023. [3] H. L. Hsiao and H. H. Tang, "A study on the application of generative ai tools in assisting the user experience design process," presented at the In International Conference on Human-Computer Interaction, (HCII’26). Washington DC, 2024, pp. 175-189, 2024. [4] M. Kalving, A. Colley, and J. Häkkilä, "Where ai and design meet-designers’ perceptions of ai tools," in In Proc. of the 13th Nordic Conference on Human-Computer Interaction, (NordCHI’13). 2024, pp. 1-8, 2024. [5] H. Ma and N. Li, "Exploring user behavioral intentions and their relationship with AI design tools: A future outlook on intelligent design," IEEE Access, 2024. [6] D. Kim, S. Kim, S. Kim, and B. H. Lee, "Generative AI Characteristics, User Motivations, and Usage Intention," Journal of Computer Information Systems, pp. 1-16, 2025. [7] C. Xie, Y. Wang, and Y. Cheng, "Does artificial intelligence satisfy you? A meta-analysis of user gratification and user satisfaction with AI-powered chatbots," International Journal of Human–Computer Interaction, vol. 40, no. 3, pp. 613-623, 2024. [8] H. Vartiainen, P. Liukkonen, and M. Tedre, "Emerging human-technology relationships in a co-design process with generative AI," Thinking Skills and Creativity, vol. 56, p. 101742, 2025. [9] R. Fiebrink, "Machine learning education for artists, musicians, and other creative practitioners," ACM Transactions on Computing Education, vol. 19, no. 4, pp. 1-32, 2019. [10] H. Vartiainen and M. Tedre, "Using artificial intelligence in craft education: crafting with text-to-image generative models," Digital Creativity, vol. 34, no. 1, pp. 1-21, 2023. [11] E. Alışık, "“All compressed and rendered with a pathetic delicacy that astounds the eye”: midjourney renders ambergris as constantinople," CyberOrient, vol. 16, no. 2, pp. 76-88, 2022. [12] A. Stöckl, "Evaluating a synthetic image dataset generated with stable diffusion," presented at the International Congress on Information and Communication Technology, (ICICT’8). London, 2023, pp. 805-818), 2023. [13] C. H. LEE, "Design to improve educational competency using chatgpt," International Journal of Internet, Broadcasting and Communication, vol. 16, no. 1, pp. 182-190, 2024. [14] K. C. Fraser, S. Kiritchenko, and I. Nejadgholi, "A friendly face: Do text-to-image systems rely on stereotypes when the input is under-specified?," arXiv preprint arXiv:2302.07159, 2023. [15] M. Jovanovic and M. Campbell, "Generative artificial intelligence: Trends and prospects," Computer, vol. 55, no. 10, pp. 107-112, 2022. [16] K. Michlewski, "Uncovering design attitude: Inside the culture of designers," Organization studies, vol. 29, no. 3, pp. 373-392, 2008. [17] F. J. Ruiz, C. Raya, A. Samà, and N. Agell, "A transformational creativity tool to support chocolate designers," Pattern Recognition Letters, vol. 67, pp. 75-80, 2015. [18] G. Yingjie, H. Yang, D. Hongyi, M. Chen, and S. Yoo, "Investigating continuous usage intention of xiaohongshu live commerce for health functional products: An integration of ecm and ttf theories," International journal of advanced smart convergence, vol. 13, no. 3, pp. 287-299, 2024. [19] D. McQuail, "Mass communication theory: An introduction." London, UK: SAGE Publications, 2010, pp. 420–430. [20] A. A. Bailey, C. M. Bonifield, and J. D. Elhai, "Modeling consumer engagement on social networking sites: Roles of attitudinal and motivational factors," Journal of retailing and consumer services, vol. 59, p. 102348, 2021. [21] T. Zhang, W. Y. C. Wang, L. Cao, and Y. Wang, "The role of virtual try-on technology in online purchase decision from consumers’ aspect," Internet Research, vol. 29, no. 3, pp. 529-551, 2019. [22] A. Ray, A. Dhir, P. K. Bala, and P. Kaur, "Why do people use food delivery apps (FDA)? A uses and gratification theory perspective," Journal of retailing and consumer services, vol. 51, pp. 221-230, 2019. https://creativecommons.org/licenses/by/4.0/ 81 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 4: 74-82, 2025 DOI: 10.55214/25768484.v9i4.5939 © 2025 by the authors; licensee Learning Gate [23] C.-C. Chen, K.-L. Hsiao, and W.-C. Li, "Exploring the determinants of usage continuance willingness for location- based apps: A case study of bicycle-based exercise apps," Journal of Retailing and Consumer Services, vol. 55, p. 102097, 2020. [24] O. Korhan and M. Ersoy, "Usability and functionality factors of the social network site application users from the perspective of uses and gratification theory," Quality & quantity, vol. 50, pp. 1799-1816, 2016. [25] A. H. Busalim and F. Ghabban, "Customer engagement behaviour on social commerce platforms: An empirical study," Technology in society, vol. 64, p. 101437, 2021. [26] V. Venkatesh and S. A. Brown, "A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges," MIS quarterly, pp. 71-102, 2001. [27] A. Rese, L. Ganster, and D. Baier, "Chatbots in retailers’ customer communication: How to measure their acceptance?," Journal of Retailing and Consumer Services, vol. 56, p. 102176, 2020. [28] K. Picot-Coupey, N. Krey, E. Huré, and C.-L. Ackermann, "Still work and/or fun? Corroboration of the hedonic and utilitarian shopping value scale," Journal of business research, vol. 126, pp. 578-590, 2021. https://doi.org/10.1016/j.jbusres.2019.12.018 [29] H. Qin, D. A. Peak, and V. Prybutok, "A virtual market in your pocket: How does mobile augmented reality (MAR) influence consumer decision making?," Journal of Retailing and Consumer Services, vol. 58, p. 102337, 2021. [30] P. A. Rauschnabel, A. Rossmann, and M. C. tom Dieck, "An adoption framework for mobile augmented reality games: The case of Pokémon Go," Computers in human behavior, vol. 76, pp. 276-286, 2017. https://doi.org/10.1016/j.chb.2017.07.030 [31] A. Dickinger, M. Arami, and D. Meyer, "The role of perceived enjoyment and social norm in the adoption of technology with network externalities," European Journal of Information Systems, vol. 17, no. 1, pp. 4-11, 2008. [32] E.-J. Lee and J. W. Overby, "Creating value for online shoppers: Implications for satisfaction and loyalty," Journal of Consumer Satisfaction, Dissatisfaction and complaining behavior, vol. 17, pp. 54-67, 2004. [33] Y.-S. Chung, "Hedonic and utilitarian shopping values in airport shopping behavior," Journal of Air Transport Management, vol. 49, pp. 28-34, 2015. [34] C.-L. Hsu, L.-C. Yu, and H.-Y. Chao, "How online beauty brand community users' experience contributes to their experiential value, attitudes and continuance intention," Manage Comments, vol. 38, no. 4, pp. 77-88, 2019. [35] N. Q. Nguyen, H. L. Nguyen, and T. G. Trinh, "The impact of online and offline experiences on the repurchase intention and word of mouth of women’s fashion products with the intermediate trust factor," Cogent Business & Management, vol. 11, no. 1, p. 2322780, 2024. [36] H. Vaja, Y. Monpara, M. K. Gafurjiwala, N. Parchani, R. Chauhan, and A. Maseleno, "Exploring Consumer Behavior, Logistics Challenges, and Technological Innovations in Online Grocery Shopping: A Comprehensive Review," Siber Journal of Transportation and Logistics, vol. 2, no. 4, pp. 160-169, 2025. [37] S. u. Rahman, M. A. Khan, and N. Iqbal, "Motivations and barriers to purchasing online: understanding consumer responses," South Asian Journal of Business Studies, vol. 7, no. 1, pp. 111-128, 2018. [38] I. Ajzen, "The theory of planned behavior: Frequently asked questions," Human behavior and emerging technologies, vol. 2, no. 4, pp. 314-324, 2020. [39] I. Ajzen, "The theory of planned behavior," Organizational behavior and human decision processes, vol. 50, no. 2, pp. 179- 211, 1991. [40] C. H. Lien and Y. Cao, "Examining WeChat users’ motivations, trust, attitudes, and positive word-of-mouth: Evidence from China," Computers in human behavior, vol. 41, pp. 104-111, 2014. https://doi.org/10.1016/j.chb.2014.08.013 [41] E. Kim, S. Ham, I. S. Yang, and J. G. Choi, "The roles of attitude, subjective norm, and perceived behavioral control in the formation of consumers’ behavioral intentions to read menu labels in the restaurant industry," International Journal of Hospitality Management, vol. 35, pp. 203-213, 2013. [42] D. L. Hoeksma, M. A. Gerritzen, A. M. Lokhorst, and P. M. Poortvliet, "An extended theory of planned behavior to predict consumers' willingness to buy mobile slaughter unit meat," Meat science, vol. 128, pp. 15-23, 2017. [43] S. Amaro and P. Duarte, "An integrative model of consumers' intentions to purchase travel online," Tourism management, vol. 46, pp. 64-79, 2015. [44] P. A. Dabholkar and X. Sheng, "The role of perceived control and gender in consumer reactions to download delays," Journal of Business Research, vol. 62, no. 7, pp. 756-760, 2009. [45] J. Francis et al., "Constructing questionnaires based on the theory of planned behaviour: A manual for health services researchers." London, UK: University of London, 2004, pp. 1-42. [46] W. Kinnally and H. Bolduc, "Integrating the theory of planned behavior and uses and gratifications to understand music streaming intentions and behavior," Atlantic Journal of Communication, vol. 28, no. 3, pp. 165-179, 2020. [47] Y. Chen, C. Liang, and D. Cai, "Understanding WeChat users’ behavior of sharing social crisis information," International Journal of Human–Computer Interaction, vol. 34, no. 4, pp. 356-366, 2018. [48] J. Sun, D. Sheng, D. Gu, J. T. Du, and C. Min, "Understanding link sharing tools continuance behavior in social media," Online Information Review, vol. 41, no. 1, pp. 119-133, 2017. https://doi.org/10.1016/j.jbusres.2019.12.018 https://doi.org/10.1016/j.chb.2017.07.030 https://doi.org/10.1016/j.chb.2014.08.013 82 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 4: 74-82, 2025 DOI: 10.55214/25768484.v9i4.5939 © 2025 by the authors; licensee Learning Gate [49] A. Tarkiainen and S. Sundqvist, "Subjective norms, attitudes and intentions of Finnish consumers in buying organic food," British food journal, vol. 107, no. 11, pp. 808-822, 2005. [50] J. Choi, H. Kim, and J. Lee, "Factors influencing consumers' behavioral intentions toward eco-friendly products: focusing on the theory of planned behavior," J. Mark. Res, vol. 31, pp. 77-104, 2016.