Explaining the Relationship between Female Customers' Online Flow State and Loyalty Through their Smart Phone Addiction and Brand Experience

Journal of International Trade, Logistics and Law, Vol. 11, Num. 2, 2025, 386-402 386 EXPLAINING THE RELATIONSHIP BETWEEN FEMALE CUSTOMERS’ ONLINE FLOW STATE AND LOYALTY THROUGH THEIR SMART PHONE ADDICTION AND BRAND EXPERIENCE Adnan Veysel ERTEMEL Istanbul Technical University, Turkey Mustafa Emre CİVELEK Antalya Bilim University, Turkey Edin GÜÇLÜ SÖZER Istanbul Okan University, Turkey Mustafa ŞEHİRLİ University of Health Sciences, Turkey Received: Sept 01, 2025 Accepted: Nov 23, 2025 Published: Dec 01, 2025 Abstract: This study investigates the relationship between female customers’ online flow state and loyalty and the mediating roles of their smartphone addiction and brand experience in this mechanism. To achieve this purpose, data were collected from 507 female customers in Türkiye through a questionnaire survey and analyzed using the structural equation modeling technique. The findings indicated that brand experience mediated the relationship between online flow experience and customer loyalty. Additionally, smartphone addiction mediated the relationship between brand experience and customer loyalty. The analysis revealed that while online flow state significantly influenced brand experience, its direct effect on customer loyalty was insignificant. Moreover, the study identified that smartphone addiction plays a critical role in shaping customer loyalty, especially among young female users. These results emphasize the importance of designing ethical marketing strategies that enhance brand experience while considering the potential risks of smartphone addiction. This study contributes to the literature by being one of the first to examine these variables in an integrated model within the mobile commerce context. Keywords: Smart Phone Addiction, Flow State, Brand Experience, Customer Loyalty, Strategic Marketing JEL Code: M31 1. Introduction Smartphones have gradually become a necessity in modern life offering a range of functions including email, text messaging, social media, internet search, photography, shopping, and video gaming (Rosen et al., 2013) to serve various purposes including information seeking, entertainment, and social interaction and messaging (Jeong et al., 2016). Prolonged smartphone usage has escalated to the extent that smartphone addiction has become a widespread global issue in recent years. Furthermore, it is argued that technology addiction and smartphone addiction are engineered by digital platforms (Ertemel and Ari, 2020; Ertemel, 2021; Kuss and Griffiths, 2017). This is achieved through various design features, such as notifications, social media updates, and rewards, that encourage constant use and create a feeling of addiction for many users. Digital businesses are accused of employing these practices to maximize user engagement and, consequently, profit within what is termed the 'attention economy (Daven-port and Beck, 2001). In this economy, be it YouTube, Instagram or WhatsApp, the consumers don’t pay for the usage of the Explaining the Relationship Between Female Customers’ Online Flow State and Loyalty Through Their Smart Phone Addiction and Brand Experience 387 digital platforms. However, the platforms have an indirect monetization model owing to the advertising companies seeking to get consumers’ attention. Consequently, e-businesses have a great competition to grab as much attention span of con-sumers as possible in the attention economy. E-commerce websites are no exception to this trend. This study makes use of a related phenomenon called flow theory, which has been increasingly used by various scholars in related contexts. Flow experience, defined as a state of total immersion and involvement in an activity (Csikszentmihalyi, 1975), is the ideal strategy to appeal to the unconscious of the consumers (Ertemel, 2021), which facilitates a pleasant, joyful state while making them forget about how the time passes by (also known as te-lepresence). Flow experience in online context is also known as online flow state. Therefore, the two terms will be used interchangeably in this study. Ertemel et al. (2021) previously investigated the relationship of online flow state, brand experience, customer satisfaction and customer loyalty and found out that online flow state has a key role in building customer loyalty. It reveals that while online flow state plays a crucial role in fostering customer loyalty, it does not have a direct effect. Instead, brand experience mediates this relationship. This study aims to explore the relationship between these phenomena by introducing a new dimension: smartphone addiction, a concept closely related to the online flow state and an emerging yet significant concern in mobile consumer behavior. As evidenced by numerous studies in the literature, online flow state has the potential to exacerbate smartphone addiction (Shin and Lee, 2015; Jovicic, 2020; Wang et al., 2020). This study makes a significant contribution to the literature by proposing smartphone addiction as a determinant of customer loyalty, alongside online flow state and brand experience, within the context of mobile phone usage. The reason why individuals addicted to smartphones develop more customer loyalty has not been clearly explained in a logical manner. However, the reasoning for this can be explained as follows: It's plausible that these a customer loyalty could be shaped by their prolonged exposure to a variety of content, including viral and promotional materials. Furthermore, as problematic smartphone usage does not involve active mind, customer loyalty emerges un-consciously in these individuals (Ertemel, 2021). Supporting this assertion, Kim and Shin (2016) revealed that smartphone addiction positively impacts technostress and customer loyalty. Although not specifically on mobile commerce setting, an additional study carried out by the same authors found out that smartphone addiction has a positive effect on customer loyalty (Kim and Shin, 2016a). Another study carried out in South Korea found out that among other factors, addiction positively affect customer satisfaction and loyalty (Kim and Shin, 2016b). As such, it’s suggested that smartphone addiction plays a role in shaping customer loyalty in the mobile commerce con-text, highlighting the importance of understanding and managing smartphone addiction in mobile commerce setting. The interrelation of these concepts deserves attention. This is because online flow state is a momentary, transitionary state while, in contrast, smartphone addiction and customer loyalty have rather long term, persisting nature. There are previous studies like Mason et al. (2022) that investigates the relationship between smartphone addiction, flow experience and online compulsive buying behavior (Mason, Zamparo et al., 2022). However, this study is significant in that it contributes to the literature by proposing the role of smartphone addiction as a determinant of customer loyalty in conjunction with online flow state and brand experience in the mobile phone usage context. The interrelationship of these concepts, in time, might shed light into important findings. The study focuses, specifically on females. The rationale behind this choice is that females reportedly demonstrate more obsessive smartphone usage behavior than their male counter-parts (Lee, Chang, Lin and Cheng, 2014; Demirci & Akın, 2015; Annoni, Petrocchi, Camerini and Marciano, 2021). Park and Lee (2011) confirm this phenomenon with their study by showing that females are more vulnerable to smartphone addiction (Park and Lee, 2011). Randler et al. (2016) have found a similar result on adolescent females (Randler et al., 2016). Some other studies on the subject have concluded with related results (Beranuy, Chamarro, Graner, and Carbonell, 2009; Mohammadi et al., 2015). Kuss and Griffiths (2017) suggest that females may be more likely to use technology for social and communication purposes, while males may be more likely to use it for entertainment and gaming (Kuss and Griffiths, 2017). Otero-Lopez and Villardefrancos (2014) argue that females are more likely to show compul-sive buying behavior via their smartphones. Similarly, Niu and Chang (2014) have found out in e-commerce context that females spend longer periods online and that flow experience is much stronger on female consumers than males (Niu and Chang, 2014). This finding also confirms the results of the previous study conducted by Crutchfield (1955). Adnan Veysel ERTEMEL & Mustafa Emre CIVELEK & Edin GÜÇLÜ SÖZER & Mustafa ŞEHIRLI 388 The existing studies in the literature on addiction and flow experience didn’t consider today’s de-facto medium, smartphones, for shopping and addiction behavior. When it comes to flow experience in mobile context, it is particularly more important to create a compelling and immersive flow experience on smartphones than any other medium (Ertemel, 2021). This study was conducted mainly by focusing on Generation Z consumer segment which is also known as millennials and relatively younger segments of Generation Y. There is evidence to suggest that both generation Z and generation Y are more prone to smartphone addiction compared to older generations. Kwon et al. (2013a) identified a significant correlation between younger age and increased levels of smartphone addiction (Kwon et al., 2013). This suggests that individuals from generation Z and generation Y, who are generally younger than their elder counterparts, may be at a higher risk for smartphone addiction. Kuss and Griffiths (2017) study confirmed this phenomenon by showing that smartphone addiction was signifi-cantly correlated with younger age and higher levels of anxiety and depression (Kuss and Grif-fiths, 2017). This suggests that individuals from generation Z and generation Y, who may be more likely to experience anxiety and depression due to the unique stressors of their age group (such as the COVID-19 pandemic and social media use), may also be at a higher risk for smartphone addiction. This study focuses mainly on women and aims to clarify the role of addictive behavior in building customer loyalty for women. The main question this research seeks to answer is what the role of online flow experience and addictive smartphone usage behavior in shaping brand experience and customer loyalty. 2. Theoretical Framework The concepts and variables within the scope of the research are discussed under the following sub-headings. 2.1. Online Flow Theory The flow state refers to a psychological condition in which individuals become fully absorbed in an activity, losing their sense of time and external distractions (Csikszentmihalyi, 1990). In digital environments, flow emerges when users perceive a balance between their skills and the task requirements, resulting in deep concentration and intrinsic enjoyment. When applied to online and mobile commerce contexts, flow is characterized by focused attention, a sense of control, and effortless interaction with the platform (Hoffman & Novak, 2009). Prior research emphasizes that online flow can significantly influence user behavior. Webster et al. (1993) demonstrated that individuals who experience flow tend to remain longer on digital platforms and engage more actively with tasks. In e-commerce settings, flow enhances users’ emotional involvement with the website or application, creating a pleasurable experience that may trigger repeated interactions (Bilgihan et al., 2014). Therefore, flow functions not only as a temporary affective state but also as a behavioral driver that supports deeper engagement and future usage intentions. 2.2. Brand Experience Brand experience encompasses the multidimensional responses that consumers form as a result of direct or indirect interaction with a brand. These responses can be sensory, emotional, cognitive, or behavioral (Brakus et al., 2009). In online commerce environments, users evaluate brand experience through elements such as screen design, content clarity, ease of use, and the emotional tone of the communication. Positive brand experiences create meaning beyond functional benefits. They contribute to the formation of psychological attachment, influencing not only how consumers evaluate the brand but also how they act toward it (Zarantonello & Schmitt, 2010). In mobile commerce, where interactions occur through small screens and instant feedback loops, brand experience becomes even more critical. A seamless, aesthetically pleasing, and consistent experience strengthens consumer perceptions and encourages repeat use (Iglesias et al., 2020). 2.3. Smartphone Addiction Smartphone addiction is characterized by the inability to regulate or limit smartphone use, leading to excessive engagement that disrupts daily routines (Kwon et al., 2013). The addictive nature of smartphones is reinforced by constant notifications, instant gratifications, and the convenience of performing multiple tasks through a single device. Explaining the Relationship Between Female Customers’ Online Flow State and Loyalty Through Their Smart Phone Addiction and Brand Experience 389 The literature shows that high levels of smartphone addiction are associated with impulsive decision-making and increased time spent on mobile shopping applications (Lin et al., 2015). Kim et al. (2019) suggest that compulsive smartphone use also heightens emotional dependence on digital environments. As a result, smartphone addiction becomes a relevant psychological variable in explaining how consumers interact with brands in mobile commerce contexts. 2.4. Relationships Among Flow, Brand Experience, and Customer Loyalty Existing research confirms that flow contributes to positive brand experience by increasing users’ emotional involvement and perceived enjoyment (Bilgihan et al., 2014). When consumers experience flow during online shopping, their attention and emotions shift from transactional motives (e.g., “I need to buy something”) toward experiential motives (e.g., “I enjoy interacting with this platform”). Brand experience, in turn, plays a crucial role in loyalty formation. Yoo and Donthu (2001) highlight that intense and positive brand experiences lead consumers to develop favorable attitudes toward the brand, recommend it to others, and choose it over alternatives. Thus, brand experience typically acts as a mediating mechanism between flow and customer loyalty. Despite the importance of smartphone use in digital commerce, very few studies have examined whether smartphone addiction alters or mediates these relationships. Particularly among young female consumers—who often represent the most active segment in mobile shopping—addictive smartphone use may intensify engagement with the platform and amplify loyalty outcomes. 2.5. Research Model and Hypotheses 2.5.1. Online Flow State and Smartphone Addiction Nakamura and Dubin (2015) suggest that engaging in flow-inducing activities can result in addictive tendencies, including smartphone addiction, because the intrinsic rewards associated with the flow state reinforce usage behavior. Smartphone activities often provide clear goals, instant feedback, and opportunities for deep concentration, which are characteristics that facilitate the flow experience and may lead to compulsive use. Supporting this idea, Mulla et al. (2022) demonstrated that flow—particularly the emotional aspects associated with perceptual distortion and enjoyment—strongly influences addictive behaviors (Mulla, Jha, Dawande, & Patil, 2022). They argue that smartphone addiction may arise for several reasons: (1) users may experience flow during smartphone use, leading to prolonged use; (2) the pleasurable nature of flow reinforces continued smartphone engagement; (3) flow increases the gratification derived from smartphone use, prompting users to seek the experience repeatedly; and (4) flow can distort time perception, encouraging users to spend more time on their devices. Similarly, Chou and Ting (2003) found that flow—defined by the presence of enjoyment and perceptual distortion— significantly contributes to addictive tendencies. Flow experiences may overlap with characteristics of behavioral addiction, such as mood regulation or exploratory behavior, making the relationship between flow and addiction complex. Although their research focused on cyber-game addiction, the conceptual link between flow and addictive behaviors applies to other forms of technology use, including smartphones (Meydan & Şen, 2011). Chen et al. (1998) further argue that flow can cause users to become deeply immersed in smartphone activities, resulting in excessive use. Chou and Ting (2003) also show that flow can increase repetitive behaviors, which may ultimately lead to addiction in online contexts. Walker (1998) posits that excessive flow experiences may contribute to addiction, potentially leading to detachment from one’s surroundings and causing negative consequences in daily life (Tse et al., 2016; Wanner et al., 2006; Wu, Scott, & Yang, 2013). Based on the theoretical framework and prior research, the following hypothesis is proposed: H1: Online Flow State has a positive effect on Smartphone Addiction 2.5.2. Online Flow State and Customer Loyalty The literature provides extensive evidence that experiencing flow during mobile commerce activities increases the likelihood of customer loyalty (Bilgihan et al., 2014). Zhou et al. (2010) demonstrated that websites capable of creating memorable flow experiences enhance user loyalty, identifying online flow as the strongest predictor of loyalty in the context of social networking sites. Hausman and Siekpe (2009) also suggest that when consumers enter Adnan Veysel ERTEMEL & Mustafa Emre CIVELEK & Edin GÜÇLÜ SÖZER & Mustafa ŞEHIRLI 390 a flow state—characterized by challenge, concentration, enjoyment, and perceived control—they are more inclined to remain loyal in online shopping environments (Chen, Wigand, & Nilan, 1998). In a similar vein, Koufaris (2002) found that enjoyment derived from a website increases the user’s intention to revisit it. Su et al. (2016) further support this relationship by revealing that flow experiences in mobile gaming applications positively affect emotional responses, which ultimately translate into loyalty behaviors. More recent findings indicate that flow during live-streaming shopping also predicts customer loyalty (Ye & Ching, 2023). Additionally, the authors' earlier work shows that online flow indirectly contributes to customer satisfaction and loyalty by enhancing the overall customer experience on e-commerce websites (Ertemel et al., 2021). Taken together, these studies indicate that experiencing flow not only shapes consumer emotions and behavior during online shopping but also plays a significant role in strengthening customer loyalty. Based on these insights, the following hypothesis is proposed: H2: Online Flow State has a positive effect on Customer Loyalty 2.5.3. Online Flow State and Brand Experience Bridges and Florsheim (2008) argue that e-commerce consumers increasingly seek richer and more engaging experiences when shopping online. According to Webster et al. (1993), when individuals enter a flow state, they become deeply absorbed in the activity to the point that their self-awareness diminishes. Online flow, which reflects both subjective and behavioral responses, is likely to influence all dimensions of brand experience—sensory, affective, intellectual, and behavioral (Hoffman & Novak, 1996; Hoffman & Novak, 2009). For example, when flow occurs on a brand’s website, heightened concentration and involvement can intensify sensory and intellectual brand experiences. In parallel, the enjoyment associated with flow can amplify affective brand experiences, while behavioral brand experience may be enhanced as users engage more with brand-related tasks or explore product-related content. Through flow, online shopping environments can simulate real-life interactions with a brand, reinforcing consumers’ relationship with the brand in both cognitive and emotional terms. Grounded in this conceptual and empirical background, the following hypothesis is proposed: H3: Online Flow State has a positive effect on Brand Experience 2.5.4. Smartphone Addiction and Customer Loyalty The relationship between smartphone addiction and customer loyalty has been increasingly explored in recent research. Prior studies indicate that individuals with higher levels of smartphone addiction tend to demonstrate stronger loyalty tendencies (Park & Lee, 2011; Kim & Shin, 2016; Harun, Soon, Kassim, & Sulong, 2015). Kim and Shin (2016) reported that smartphone addiction is positively associated with customer loyalty in online shopping settings. Likewise, Lee et al. (2014) found that individuals exhibiting symptoms of smartphone addiction display higher levels of loyalty within mobile commerce environments. These findings suggest that consumers who are more attached to or dependent on their smartphones are also more likely to develop loyalty toward mobile commerce brands. Based on these insights, the following hypothesis is proposed: H4: Smartphone Addiction has a positive effect on Customer Loyalty 2.5.5. Brand Experience and Customer Loyalty Consumers generally seek pleasurable experiences and avoid discomfort, and they require stimulation to prevent boredom (Cacioppo & Petty, 1982). From this perspective, brand experiences provide value across multiple dimensions, which can ultimately foster customer loyalty. In line with Brakus et al.’s (2009) brand experience conceptualization, the sensory, affective, and intellectual dimensions reflect consumers’ emotional and cognitive responses to a brand, while the behavioral dimension reflects observable actions, such as using the brand or recommending it to others. Even though brand experiences are often momentary and spontaneous, these short episodes accumulate over time and gradually form a more enduring perception of the brand, which in turn enhances loyalty (Oliver, 1997; Shim, Forsythe, & Kwon, 2015). Thus, brand experience contributes to long-term brand loyalty (Brakus et al., 2009). Additionally, Aaker and Keller (1990) demonstrated that loyal consumers tend to have more favorable attitudes toward the brand and can recall and recognize it more easily—factors that are closely linked to loyalty. More recent Explaining the Relationship Between Female Customers’ Online Flow State and Loyalty Through Their Smart Phone Addiction and Brand Experience 391 evidence from Nilowardono (2022) shows that brand experience positively influences customer loyalty, particularly in digital contexts where website quality shapes user experience. In light of these arguments, the following hypothesis is proposed: H5: Brand Experience has a positive effect on Customer Loyalty 2.5.6. Brand Experience and Smartphone Addiction Only a limited number of studies have examined the relationship between brand experience and smartphone addiction specifically within mobile environments. Zhou et al. (2010) found that when users have a positive brand experience, they are more likely to perceive smartphones as useful and easy to operate. This increased perceived usefulness strengthens engagement with the device, contributing to higher levels of smartphone addiction. However, additional research is still required to better understand how brand experience relates to smartphone addiction in different contexts and user groups. Based on this reasoning, the following hypothesis is proposed: H6: Brand Experience has a positive effect on Smartphone Addiction Table 1 shows the hypotheses proposed in light of the conceptual framework and literature review. The theoretical model and associated hypotheses are presented in Table 1. Table 1. Proposed Hypothesis No Content and Direction 1 Online Flow State has a positive effect on Smart Phone Addiction. 2 Online Flow State has a positive effect on Customer Loyalty. 3 Online Flow State has a positive effect on Brand Experience. 4 Smartphone Addiction has a positive effect on Customer Loyalty. 5 Brand Experience has a positive effect on Customer Loyalty. 6 Brand Experience has a positive effect on Smart Phone Addiction. Theoretical Model and Hypothesis Proposed Figure 1. Research Model Adnan Veysel ERTEMEL & Mustafa Emre CIVELEK & Edin GÜÇLÜ SÖZER & Mustafa ŞEHIRLI 392 3. Method 3.1. Research Design and Participants This research employed a cross-sectional design where the data were collected through a questionnaire survey. The sample was determined through convenience sampling technique. A total of 507 female customers participated in this research. Of participants, 13 (3%) had a doctoral degree, 106 (21%) had a master’s degree, 347 (68 %) had a bachelor’s degree, 41 (8 %) had a highschool degree. 3.2. Data Collection Instruments 3.2.1. Online Flow State Scale A scale developed by Bilgihan et al. (2013), adapted Webster et al., (1993) and adapted to Turkish context by Alperer, S. (2005) has been used. The scale comprised four dimensions and eight items. 3.2.2. Customer Loyalty Scale A scale developed by Yoo and Donthu (2001) has been used. The scale comprised one dimensions and three items. 3.2.3. Brand Experience Scale A scale developed by Brakus et al. (2009) has been used. The scale comprised four dimensions and 12 items. 3.2.4. Smart Phone Addiction Scale A scale developed by Kwon et al., (2013) and adapted to Turkish context by Şata and Karip (2017) has been used. The scale comprised five dimensions and 10 items. Each item on the scales have been scored over five points (1: completely disagree, 5: completely agree). 3.3. Data Collection Procedure The field study was carried out between August 1st to August 31st, 2023. More than 650 were given out, and 507 legitimate surveys from different Turkish female consumers were gathered. The scales were taken directly from extant works. Since the study was not conducted on students of a specific university and since personal information was not asked in the survey form, there is no need for a written ethical consent. Therefore, the verbal consent of the subjects was found sufficient. 3.4. Data Analysis Strategy In order to examine extremely complicated multiple variable models and to identify direct and indirect impacts among variables, the structural equation modeling method was selected. To establish the convergent validity, confirmatory factor analyses were first carried out. To assess the scales' reliability and discriminant validity, respectively, composite reliability and AVE values were computed. In the AMOS statistics program, the structural equation modeling method was used to evaluate the assumptions. The theoretical model's hypotheses have been tested using structural equation modeling, a multivariate sta-tistical technique (Meydan and Şen, 2011). This method was chosen to reduce measurement errors and was applied to comprehend the indirect and direct impacts in the theoretical model (Civelek, 2018). The analyses were carried out using the statistical software SPSS and AMOS. 3.5. Validity and Reliability Tests To prepare the data for confirmatory factor analysis (CFA), first the exploratory factor analysis (EFA) was employed (Anderson and Gerbing, 1988). After principal component anal-ysis, 23 components were still present. CFA was then used to determine convergent validity. The CFA's fit indices values were deemed adequate (i.e., CFI = 0.943, IFI = 0.944, RMSEA = 0.054) (Civelek, 2018). The loads of factors in the CFA Results are shown in Table 2. As can be shown in Table 3, average extracted variance values were close to or greater than the threshold (i.e., 0.5) (Byrne, 2010). These findings demonstrated the constructs' convergent validity. The square roots of AVE values for each variable were calculated to determine dis-criminant validity. The diagonals in Table 3 show the AVE values' square roots. The correla-tion values in the same column are all less than the square roots of the AVE values. This im-plies that the validity of the discriminant is established (Civelek, 2018). Each structure's re-liability was evaluated independently. Composite reliability and Cronbach's scores are close to or higher than the suggested cutoff criterion of 0.7 (Fornell and Larcker, 1981). Explaining the Relationship Between Female Customers’ Online Flow State and Loyalty Through Their Smart Phone Addiction and Brand Experience 393 Table 2. Confirmatory Factor Analysis Results Variables Items Standardized Factor Loadings Unstandardized Factor Loadings Smart Phone Addiction (SPA) SPA39 0.737 1 SPA36 0.774 1.041 SPA38 0.774 1.024 SPA35 0.757 1.041 SPA37 0.719 0.974 SPA34 0.702 0.953 SPA31 0.594 0.872 SPA30 0.565 0.754 SPA33 0.679 0.968 SPA32 0.497 0.700 Brand Experience (BEX) BEX12 0.774 1 BEX09 0.671 0.849 BEX10 0.622 0.781 BEX18 0.738 0.883 BEX14 0.690 0.914 Adnan Veysel ERTEMEL & Mustafa Emre CIVELEK & Edin GÜÇLÜ SÖZER & Mustafa ŞEHIRLI 394 BEX15 0.688 0.875 BEX20 0.664 0.812 Customer Loyalty (CLY) CLY29 0.715 1 CLY28 0.754 0.975 CLY27 0.600 0.816 Online Flow State (FLS) FLS07 0.636 1 FLS06 0.815 1.123 p<0.01 for all items Descriptive statistics of the dimensions, Cronbach α and composite reliabilities, average variance extracted values and Pearson correlations among the dimensions are indicated in Table 3. Table 3. Construct Descriptives, Reliability and Correlation Variables 1 2 3 4 1. Smart Phone Addiction (.686) 2. Brand Experience .285* (.694) 3. Customer Loyalty .273* .512* (.693) 4. Flow State .192* .562* .187* (.731) Composite reliability .897 .866 .733 .693 Average variance ext. .470 .482 .480 .534 Cronbach α .899 .872 .725 .676 *p < 0.05 Note: Values in diagonals are the square root of AVEs 4. Results The correlation values representing the relationships between the variables are presented in Table 3. Structural equation modeling (SEM) was used. Since SEM is a confirmatory method, in this research, it is used to confirm the hypotheses (Civelek, 2018). The goodness of fit indices was used to determine how well the structural model fit. The Explaining the Relationship Between Female Customers’ Online Flow State and Loyalty Through Their Smart Phone Addiction and Brand Experience 401 Niu, H.J, &Chang, C.T.(2014). Addiction in cyberspace: flow experience on e-shopping. International Journal of Web Based Communities.,10(1): 52-68. Novak, T.P., Hoffman, D.L., &Yung, Y.F.(2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing science.,19(1): 22-42. O'Cass, A., &Carlson, J.(2010). Examining the effects of website‐induced flow in professional sporting team websites. Internet Research.,20(2): 115-134. Oliver, R.L.(1997). Satisfaction: A behavioral perspective on the consumer. 1st ed. New York: McGraw-Hill. Orford, J.(1985). Excessive appetites: A psychological view of addictions. New York: Wiley. Choi, D.H., Jung, Y.S. (2022). Temperament, Character and Cognitive Emotional Regulation in the Latent Profile Classification of Smartphone Addiction in University Students. Sustainability., doi:10.3390/su141811643 Otero-López, J.M., &Villardefrancos, E.(2014). Prevalence, sociodemographic factors, psychological distress, and coping strategies related to compulsive buying: a cross-sectional study in Galicia, Spain. BMC Psychiatry.,14(1): 101. Park, B.W, &Lee, K.C.(2011). The effect of users’ characteristics and experiential factors on the compulsive usage of the smartphone. In International Conference on Ubiquitous Computing and Multimedia Applications. 438-446. Springer, Berlin, Heidelberg. Pew Research Center (2019), Defining generations: Where millennials end, and generation Z begins. https://www.pewresearch.org/short-reads/2019/01/17/where-millennials-end-and-generation-z-begins (Access Date: 10/2/2023) Pinker, S.(2005). So how does the mind work? Mind & Language.,20(1): 1-24. Rajesh, S., &Draper, L.(2024). Impact of Adolescent Social Media Use on Body Image, Mental Health and Eating Disorders: A Review. Journal of Student Research.,11(4). doi: 10.47611/jsrhs.v11i4.3388 Randler, C., Wolfgang, L., Matt, K., Demirhan, E., Horzum, M.B. ,&Besoluk, S.(2016). Smartphone addiction Proneness in relation to sleep and morningness eveningness in German adolescents. Journal of Behavioral Addiction. ,5(3): 465-73. Rau, P.L.P., Peng, S.Y., &Yang, C.C.(2006). Time distortion for expert and novice online game players. Cyberpsychology & Behavior.,9(4): 396-403. Rosen, L.D., Whaling, K., Carrier, L.M., Cheever, N.A., & Rokkum, J. (2013). The media and technology usage and attitudes scale: An empirical investigation. Computers in human behavior. 29(6): 2501-2511. Rushan, M. R. I., & Huda, S. S. (2022). Demystifying the Effect of Flow Experience for Mobile App-Based EServices: A Moderated Mediation Study. International Journal of E-Services and Mobile Applications (IJESMA), 14(1), 1-26. Schmitgen, M.M., Wolf, N.D., Sambataro, F., Hirjak, D., Kubera, K.M., Koenig, J.,& Wolf, R.C. (2022). Aberrant intrinsic neural network strength in individuals with “smartphone addiction”: an MRI data fusion study. Brain and Behavior.,12(9): e2739. Shahjehan, A., Shah, S.I., Qureshi, J.A.,& Wajid, A.(2021). A meta-analysis of smartphone addiction and behavioral outcomes. International Journal of Management Studies., 28(2): 103-125. Shim, S.I., Forsythe, S., &Kwon, W.S.(2015). Impact of online flow on brand experience and loyalty. Journal of Electronic Commerce Research.,16(1): 56. Shin, S., &Lee, W.,(2015). The effects of smartphone addiction drivers on work performance. Paper presented at the 2015 Americas Conference on Information Systems, AMCIS. Sinemus, K., Zielke, S., Dobbelstein, T. (2022).Shopping app features: their impact on customer satisfaction and loyalty. The International Review of Retail, Distribution and Consumer Research.,32(4): 423-449. Stavropoulos, V., Alexandraki, K., Motti-Stefanidi, F.(2013). Flow and telepresence contributing to internet abuse: Differences according to gender and age. Computers in Human Behavior.,29(5):1941-1948. Su, Y.S., Chiang, W.L., Lee, C.T.J., &Chang, H.C.(2016). The effect of flow experience on player loyalty in mobile game application. Computers in Human Behavior.,63: 240–248. Şata, M., &Karip, F.(2017). Akıllı telefon bağımlılığı ölçeği-kısa versiyonunun ergenler için Türk kültürüne uyarlanması. Cumhuriyet International Journal of Education.,6(4): 426-440. Tse, D.C.K., Fung, H.H., Nakamura, J., &Csikszentmihalyi, M,.(2016). Teamwork and flow proneness mitigate the negative effect of excess challenge on flow state. The Journal of Positive Psychology.,13(3):284–289. Adnan Veysel ERTEMEL & Mustafa Emre CIVELEK & Edin GÜÇLÜ SÖZER & Mustafa ŞEHIRLI 402 UToday. Gucci opens shop in the metaverse via sandbox, here's what it sells. 2022. Retrieved from https://u.today/gucci-opens-shop-in-the-metaverse-via-sandbox-heres-what-it-sells-details (Access Date: 12/2/2023) Vanketesh, V., Morris, M.G., Davis, G.B., &Davis, F.D.(2003). User acceptance of information technology: Toward a unified view. MIS quarterly.,425-478. Walker, G.J.(1998). On-site optimal experiences and their relationship to off-site benefits. Journal of Leisure Research., 30(4): 453–471. Wang, Z., Yang, X.,&Zhang, X.(2020). Relationships among boredom proneness, sensation seeking and smartphone addiction among Chinese college students: Mediating roles of pastime, flow experience and self-regulation. Technology in Society., 62. https://doi.org/10.1016/j.techsoc.2020.101319 Wanner, B., Ladouceur, R., Auclair, A.V., &Vitaro, F.(2006). Flow and dissociation: Examination of mean levels, cross-links, and links to emotional well-being across sports and recreational and pathological gambling. Journal of Gambling Studies.,22(3): 289–304. Webster, J., Trevino, L.K. and Ryan, L.(1993). The dimensionality and correlates of flow in human-computer interactions. Computers in Human Behavior., 9(4): 411–426. Wu, T.C.E., Scott, D., &Yang, C.C.(2013). Advanced or addicted? exploring the relationship of recreation specialization to flow experiences and online game addiction. Leisure Sciences.,35(3): 203–217. Wu, Y.Y., &Chou, W.H.,(2023). A Bibliometric Analysis to Identify Research Trends in Intervention Programs for Smartphone Addiction. International Journal of Environmental Research and Public Health., doi: 10.3390/ijerph20053840 Ye, M., &Ching, T.C.(2023). Research and application flow-based live-streaming shopping towards compulsive buying. Annals of Operations Research. Yoo, B., &Donthu, N.(2001). Developing and validating a multidimensional consumer-based brand equity scale. Journal of Business Research.,52(1): 1-14. Zhou, T., Li, H., &Liu, Y.(2010). The effect of flow experience on mobile SNS users' loyalty. Industrial Management & Data Systems.,110(6): 930-946. https://doi.org/10.1108/02635571011055126