This study aims to examine the influence of AR factors: Interactivity TTT, Escape experience TT, AR quality CL on the perception of AR value: Utilitarian value TD, Satisfaction SHL, Trus
Theoretical
Theory of Planned Behavior-TPB
The Theory of Planned Behavior (TPB), developed by Ajzen in 1991, builds on the earlier Theory of Reasoned Action (TRA) from 1975 and serves as a widely recognized framework for examining consumer purchasing intentions This model is applicable across various fields, including commerce, education, healthcare, information systems, and advertising, as it explores the connections between attitudes, behavioral intentions, and beliefs regarding different products TPB posits that behavioral intention is a crucial determinant of actual behavior, reflecting the effort individuals are willing to invest in executing a specific action.
Figure 2.1: Theoretical model of planned behavior (TPB)
The theory of behavioral intention (BI) posits that three key factors influence an individual's intention to perform a behavior: (1) attitude toward the behavior, which reflects a person's positive or negative feelings about engaging in that behavior; (2) subjective norms, which pertain to the perceived social pressure to conform to certain behaviors; and (3) perceived behavioral control, representing an individual's awareness and ability to execute the behavior According to Ajzen (2005), these factors collectively underscore the significance of attitudes, social norms, and perceived control in shaping behavioral intentions.
Technology acceptance model (TAM) theory and AR technology application
The Technology Acceptance Model (TAM), developed by Fred Davis and Richard Bagozzi, is grounded in intention-based models from cognitive psychology, including the Theory of Planned Behavior (TPB) and the Theory of Reasoned Action (TRA) This model provides a deeper understanding of consumer technology acceptance and usage behaviors, highlighting two key factors that significantly influence consumer attitudes: perceived benefits and perceived ease of use.
The theory of reasoned action (TRA) suggests that attitudes and subjective norms shape individuals' behavioral intentions, but it is limited by its reliance on abstract concepts like beliefs and evaluations To address this, Davis developed the Technology Acceptance Model (TAM), which has become a widely adopted and effective framework for predicting and understanding user behavior and information technology usage.
Figure 2.2: Model of TAM theory
A recent meta-analysis of 51 studies confirmed initial relationships, primarily through Structural Equation Modeling (SEM), and highlighted the significant moderating effect of technology on user behavior The Technology Acceptance Model (TAM) has been instrumental in predicting online shopping behavior, identifying key factors such as usefulness, compatibility, ease of use, and security as critical predictors of user attitudes Furthermore, attitudes, normative beliefs, and self-efficacy significantly influence online shopping intentions Shih (2004) demonstrated that users' attitudes toward e-shopping are strongly correlated with acceptance, with perceived ease of use (PEOU) and perceived usefulness (PU) playing vital roles in shaping individual attitudes towards e-shopping behavior.
Theory of Stimulus - Organism - Response (SOR)
The SOR theory illustrates the relationship between stimuli (inputs), subjects (processes), and responses (outputs), emphasizing that an individual's perception of their environment significantly shapes their experiences and, in turn, affects their behavioral responses.
Figure 2.3: Model of SOR theory
The S-O-R model has been widely utilized in research to analyze how new retail technologies impact consumers' behavioral and emotional responses during online shopping Studies by Prashar, Sai Vijay, & Parsad (2017), Watson, Alexander, & Salavati (2018), and Wu et al (2013) highlight its effectiveness, establishing it as a robust framework for understanding consumer interactions in the digital retail environment.
2018) Previous studies demonstrate that AR creates rich sensory experiences and influences mental imagery, leading to positive behavioral and emotional responses (Park
In this study, we utilize the S-O-R model to test our hypotheses, which are formulated based on key components of the model: stimuli, represented by augmented reality (AR) variables; the process, which involves the perception of AR value; and the response to stimuli, indicated by purchase intention.
Denification
AR
Augmented Reality (AR) uniquely overlays virtual information onto physical objects and environments, seamlessly integrating real-world elements with digital content (Chung, Han, & Joun, 2015; Milgram & Kishino, 1994) This technology enables users to view videos, images, and other virtual objects superimposed on their surroundings, enhancing their interaction with the environment (Javornik, 2016) In the context of shopping, AR significantly reduces the cognitive load on customers by providing clear visual representations of products, eliminating the need for imagination and the necessity to visit physical stores (McLean & Wilson, 2019) This user control over the blend of real and virtual worlds further enhances the shopping experience (McLean & Wilson, 2019; Nikhashemi et al.).
Interactivity is a crucial aspect of augmented reality (AR) shopping applications, serving as a significant driver for assessing consumer benefits As highlighted by Kim et al (2017), interactivity plays an essential role in smart digital media and technology, directly influencing the overall consumer experience (Mollen & Wilson, 2010).
In summary, interaction is understood as social behaviors or human reactions to technology In this study, engagement was created when consumers were willing to associate with AR technology.
Escape experiences involve temporarily distancing oneself from daily life, allowing for complete immersion in activities (Hosany & Witham, 2010) These experiences often arise when individuals seek to break their routines and engage in leisure pursuits (Jung et al., 2021) Research indicates a connection between escapism and various media forms, including virtual reality (Han & tom Dieck, 2019), suggesting that augmented reality (AR) may also facilitate similar vicarious escape experiences.
An escape experience allows individuals to fully immerse themselves in an activity, temporarily disconnecting from everyday life Augmented Reality (AR) can facilitate this immersive experience by creating a captivating escapist environment, enabling users to engage deeply with a virtual world.
This study explores the measurement of augmented reality (AR) quality in e-commerce applications by proposing three key dimensions: information quality, system quality, and service quality The research empirically tests the validity of these dimensions, contributing to the ongoing discussion about AR quality definitions in the field (Wen, 2009).
Product informativeness refers to how effectively online touchpoints on mobile devices deliver valuable product information that aids in decision-making and purchase intentions (Lim & Ting, 2012; Rese et al., 2014) Additionally, websites featuring interactive product presentations facilitate a virtual exploration of goods, enhancing the overall shopping experience (Yoon, Laffey, &).
According to Pushneh (2018) and Pantano et al (2017), augmented reality (AR) effectively addresses the information gap in retail by simulating the shopping experience, enabling customers to interact with virtual products as if they were physically present.
System quality refers to the availability of desired features in mobile devices and web browsing services for users (Chen, 2013) Previous studies have highlighted the significance of system quality (Lee et al., 2009; Wang & Chen, 2011) Research conducted by DeLone & McLean further emphasizes its importance in evaluating system performance.
(2003), system quality has a strong impact on the success of an information system, measured in terms of ease of use, functionality, reliability, flexibility, and quality, data volume, portability, integration.
Service quality, as defined by Yarimoglu (2014), refers to how well companies meet or surpass customer expectations In the context of e-commerce, Zeithaml et al (2016) describe electronic service quality as the effectiveness of a website in facilitating shopping, payment, and product distribution.
The quality of Augmented Reality (AR) is assessed through three key dimensions: Information Quality, System Quality, and Service Quality Evaluating these aspects provides a clearer understanding of AR quality, which is essential for demonstrating its effectiveness in the e-commerce sector This aligns with the fundamental desire to meet basic needs and accomplish functional tasks (Ebrahimi & Tootoonkavan, 2014).
Utilitarian value in social commerce websites highlights the benefits users gain from the functionality and tools available, emphasizing their perception of a product's usefulness.
Trust, as defined by Puranam et al (2009), is an expectation that mitigates the fear of opportunistic behavior from exchange partners, particularly when monitoring is not feasible It encompasses three key elements: (1) expectation, where the truster anticipates specific behaviors, such as the provision of accurate information or effective cooperation; (2) belief, where the trustor is confident that the expected behavior will be fulfilled, supported by the trustee’s demonstrated competence, integrity, and goodwill; and (3) a willingness to take risks, as the trustor accepts potential risks based on this trust (Huang et al., 2010).
In short, trust is an important key to maintaining trading relationships, especially in an unstable environment and a lot of information can be distorted like the e-commerce environment.
Customer satisfaction is defined as an emotional or cognitive experience that evaluates the difference between what is received and what is expected (Rai et al., 2012) Arnould et al (2004) further emphasize this concept by focusing on consumer satisfaction, suggesting that it involves assessing the level of pleasure derived from consumption, as well as the potential for undersatisfaction or oversatisfaction Additionally, Feinberg et al (1990) describe customer satisfaction as the perception that a product or service has fulfilled the customer's expectations.
AR (Quality ■■•■■•■■■•■■■■■••■■■■■•■■■•■■■■■■•■■■■■■•••••■•■■•••■■■■••■■■•■■■■■•■■•■■■•■■■•■■••■•••■■■■••■■■■ 10 2.2.5 Utilitarian Value
This study examines augmented reality (AR) quality in e-commerce applications by proposing and empirically testing three key dimensions: information quality, system quality, and service quality, as outlined by Wen (2009).
Product informativeness refers to how effectively online touchpoints on mobile devices deliver valuable information that aids consumers in their purchasing decisions (Lim & Ting, 2012; Rese et al., 2014) Additionally, websites featuring interactive product presentations enhance the virtual exploration of goods, facilitating a more engaging shopping experience (Yoon, Laffey, &).
Augmented Reality (AR) enhances the shopping experience by simulating real-life interactions, enabling customers to virtually experience products before making a purchase This technology addresses information gaps, providing consumers with a more immersive and informed decision-making process.
System quality refers to the presence of desired features in mobile devices and web browsing services, ensuring a positive user experience (Chen, 2013) Previous studies have highlighted the significance of system quality in enhancing user satisfaction and engagement (Lee et al., 2009; Wang & Chen, 2011) Research by DeLone & McLean further emphasizes its critical role in assessing overall system performance and effectiveness.
(2003), system quality has a strong impact on the success of an information system, measured in terms of ease of use, functionality, reliability, flexibility, and quality, data volume, portability, integration.
Service quality, as defined by Yarimoglu (2014), refers to how well companies meet or surpass customer expectations In the context of e-commerce, Zeithaml et al (2016) describe electronic service quality as the effectiveness of a website in facilitating shopping, processing payments, and distributing products efficiently.
The quality of Augmented Reality (AR) is defined by three key aspects: Information Quality, System Quality, and Service Quality Evaluating these components enhances the understanding of AR quality, which is crucial for measuring its effectiveness in the e-commerce sector This evaluation addresses the fundamental needs and functional tasks of users (Ebrahimi & Tootoonkavan, 2014).
Utilitarian value in social commerce websites pertains to the functional benefits and tools that enhance the user experience, emphasizing how consumers perceive the usefulness of products.
Trust
Trust is an expectation that alleviates the fear of opportunistic behavior from an exchange partner, even in the absence of monitoring (Puranam et al., 2009) It encompasses three key elements: (1) expectation, where the truster anticipates specific behaviors, such as providing accurate information; (2) belief, where the trustor is confident that the expected behavior will occur, supported by the trustee's demonstrated competence, integrity, and goodwill; and (3) risk-taking, where the trustor is prepared to accept risks based on this trust (Huang et al., 2010).
In short, trust is an important key to maintaining trading relationships, especially in an unstable environment and a lot of information can be distorted like the e-commerce environment.
Satisfaction
Satisfaction is defined as an emotional or cognitive experience, evaluated by comparing what is received to what is expected (Rai et al., 2012) Arnould et al (2004) emphasize consumer satisfaction, viewing it as an assessment of the pleasurable feelings associated with consumption, which includes both undersatisfaction and oversatisfaction Additionally, Feinberg et al (1990) describe customer satisfaction as the perception that a product or service has fulfilled their expectations.
Inheriting the above definitions, we can summarize the concept of satisfaction as the customer’s feeling of goodwill when experiencing feelings beyond expectations that products and services bring.
Purchase Intention
Online purchase intention refers to the extent to which consumers are willing to buy products from online stores (Pena-Garcia et al., 2020) It is a decision-making process influenced by the buyer's motivations for choosing a specific brand (Shah et al., 2012) Additionally, it represents a consumer's deliberate plan to purchase a brand's offerings (Spears et al., 2004) As a crucial metric, purchase intention serves as a key predictor of consumer behavior (Wu et al., 2011; Hsu et al., 2017).
Purchase intention refers to the probability that a customer is inclined to buy a specific product or service in the future It serves as a metric to gauge the consumer's readiness to engage in a particular behavior or make a purchasing decision regarding an item.
Overview of related research works
Research by Jung et al (2021)
Jung et al (2021) investigated how user perceptions of augmented reality (AR) affect the purchase intentions of location-based AR navigation systems The study involved collecting data through online surveys sent via email, with participants first experiencing a vicarious driving scenario via a short demo of the AR navigation system on YouTube A total of 353 valid survey responses were analyzed, revealing that three key user perceptions of AR—spatial ability, feeling of presence, and conceptual understanding—significantly influence consumers' intentions to purchase location-based AR systems This relationship is mediated by experiences related to education, entertainment, aesthetics, and escapism.
Research by Chiu et al (2012)
Chiu et al (2012) explored how habit moderates the relationship between trust and repeat purchase intention, using survey data from 454 Yahoo! Kimo customers Their findings revealed that perceived value and satisfaction significantly influence habit development, with habit being a crucial factor driving online repeat purchase intention, even more so than trust in the online seller The study identified satisfaction, value, and familiarity as the three key antecedents of habit, ranked by importance Overall, the research highlights the critical role of online shopping habits and their antecedents in fostering customer retention in e-commerce.
Research by Gan & Wang (2017)
Gan & Wang (2017) explored the impact of perceived benefits on users' purchase intentions in social commerce through an online survey of 277 participants in China The study revealed that user satisfaction significantly influences purchase intention, with hedonic, utilitarian, and social values all positively affecting both satisfaction and purchase intention Notably, utilitarian value emerged as the strongest predictor of purchase intention, while hedonic value primarily influenced satisfaction Additionally, perceived risk was found to have a significant negative effect on satisfaction.
Research by Qin Peak & Prybutok (2021)
Qin, Peak, and Prybutok (2021) investigated the impact of Mobile Augmented Reality (MAR) applications on users' attitudes and shopping behaviors, focusing on customer experience and its effects on consumer perceptions of hedonistic and utilitarian satisfaction, informativeness, and ease of use The research surveyed young individuals in the United States who had utilized MAR applications, yielding significant insights into the influence of these technologies on consumer behavior.
A survey of 162 valid samples reveals that consumers exhibit a positive willingness to reuse Mixed Augmented Reality (MAR) applications Key findings indicate that interactivity with MAR significantly influences consumers' perceptions of both hedonic and utilitarian satisfaction, as well as informativeness and ease of use Furthermore, virtuality is crucial in shaping consumer perceptions regarding utilitarian and hedonic satisfaction and informativeness, although it does not significantly impact perceptions of ease of use.
Figure 2.7: Research model of Qin, Peak & Prybutok (2021)
Research by Wen I (2012)
Wen (2012) explored the factors influencing the intention to purchase online tourism products through a study involving 540 valid survey responses from individuals who have previously bought travel products online The findings indicate that information quality, service quality, and system quality are essential metrics for assessing the effectiveness of tourism-oriented website design Additionally, the quality of website design, along with tourist attitudes and satisfaction, significantly impacts the intention to purchase online tourism products, with tourist attitudes and satisfaction serving as strong mediators in the relationship between website design quality and purchase intention.
Research hypotheses
The Relationship Between Interactivity and Utilitarian Value
AR technology significantly influences consumer decision-making by utilizing the SOR framework, which evaluates how environmental stimuli affect internal emotional assessments and subsequent behavioral responses This framework highlights the relationship between external factors and individual behaviors, showcasing how augmented reality can shape consumer perceptions and actions.
The research paper employs the SOR (Stimulus-Organism-Response) framework to analyze the interrelationships among its three components It investigates environmental stimuli, such as interactivity, escape experiences, and augmented reality (AR) qualities, as experienced by users of digital shopping apps utilizing AR technology These stimuli influence users' cognitive and experiential states, which are evaluated through metrics like utilitarian value, consumer satisfaction, and trust Essentially, the model posits that these stimuli elicit individual cognitive and emotional responses, ultimately leading to specific behavioral outcomes.
(2017) argue that interactivity can have a positive impact on utilitarian value.
TAM theory highlights that the usefulness of AR technology in online shopping significantly enhances consumer value and purchase intention through interactive experiences The interactivity of AR, as noted by Javornik (2016), plays a crucial role in this process Collaborative features that foster a sense of participation positively influence users' perceptions of the technology (Yim et al., 2017) Users engaging with AR technology can experience varying levels of interaction; for instance, those participating in a collaborative virtual tour often achieve higher immersion levels (Zhao, Wang, & Sun, 2020), while those with lower engagement experience diminished immersion (Spielmann & Manlonakis, 2018).
Users viewing high-resolution computer-generated images experience greater enjoyment than those viewing low-resolution images, with 3D consumers reporting higher levels of satisfaction compared to 2D consumers (Yim, Cicchirillo, & Drumwright, 2012) The interactive technologies of MAR applications provide consumers with convenient access to high-quality information, effectively reducing the risks associated with asymmetric information (Zheng et al., 2019).
User interactions with Mobile Augmented Reality (MAR) applications enhance consumer engagement and enjoyment, as highlighted by Chopdar and Balakrishnan (2020) Interactivity plays a crucial role in enriching the user experience and addressing individual needs from a practical standpoint, according to Kourouthanassis and Chasanidou (2015) Furthermore, research by Qin, Peak, and Prybutok (2021) shows that engaging with MAR applications positively influences consumers' utilitarian satisfaction.
Based on the above relationships, the research team proposed the following hypothesis:
Hl: Interactivity has a positive impact on consumers’ utilitarian value
The Relationship betw een Escape Experience and Satisfaction
Isada and colleagues (2017) indicate that the context of an escape experience significantly enhances user satisfaction Examples such as thrilling amusement park rides, gambling, and extreme sports illustrate how these experiences can be measured for satisfaction Research demonstrates a connection between escape experiences and satisfaction across various media, including immersive virtual reality, while augmented reality (AR) also offers opportunities for indirect escape experiences (Han & Tom Dieck, 2019).
Based on the above relationships, the research team proposed the following hypothesis:
H2: Escape experience has a positive impact on consumers’ satisfaction
The Relationship between AR Quality and Trust
Trust is a critical factor in online shopping, as highlighted by Ling (2010), who notes that customers need assurance when sharing personal information with sellers Before completing a transaction, buyers prioritize the safety and reliability of the merchants and websites they encounter Harris & Goode (2010) emphasize that online stores, which operate without direct interaction, must establish and maintain trust to capture and retain customer interest Consequently, delivering quality is essential for building trust with consumers.
Information quality significantly impacts user trust, as it encompasses valuable content, form, and timely characteristics (Marakas & O'Brien, 2013) Consistent management of quality information can enhance public confidence in media integrity Furthermore, studies indicate that higher information quality directly influences consumer trust (Ferdiansyah & Rahayu, 2016).
System quality encompasses aspects like access speed, usability, navigation, and visual appeal (Vance, Christophe, & Straub, 2008) When users encounter a system that is difficult to navigate or has a poor interface, they may perceive that service providers have not invested adequately in enhancing system quality, which can diminish their trust (Zhou, 2012) Furthermore, system quality can affect initial trust via the peripheral route, as users readily notice cues such as visual design and navigation when using the application Vance et al (2008) emphasize that elements of system quality, including visual appeal and navigational structure, play a significant role in shaping customer trust in mobile technology.
Recent studies indicate that service quality significantly enhances trust, particularly when familiarity with transaction security mechanisms is low, as trust mitigates uncertainty Research by Harris & Goode (2010) shows that customers' comprehension of the online environment positively influences trust Additionally, a survey by Alrubaiee & Alkaa'ida (2011) found that perceived service quality directly and positively affects trust, while also having an indirect positive impact through customer satisfaction.
Based on the above relationships, the research team makes the following hypothesis:
H3: AR quality has a positive impact on Trust
The relationship between Utilitarian Value and Satisfaction
Research indicates that utilitarian value significantly influences user satisfaction across various platforms Hsu & Lin (2016) and Xu, Peak, & Prybutok (2015) highlight that higher perceptions of utilitarian value in mobile applications and social commerce websites lead to increased user satisfaction and purchase intentions Additionally, Kesari & Atulkar (2016) demonstrate the strong correlation between utilitarian value and customer satisfaction in shopping malls Furthermore, Gan & Wang (2017) reveal that users who perceive utilitarian value from commercial social networking sites experience enhanced satisfaction.
2.4.5 Relationship between Satisfaction and Trust
Consumer satisfaction with past experiences fosters a belief that they are not being exploited When customers perceive their suppliers as capable, willing to meet their needs, and trustworthy, their satisfaction enhances their tendency to trust these suppliers.
Research indicates that customer satisfaction is a key indicator of trust in banking services Koutsothanassi et al (2017) assert that satisfied consumers are more likely to develop trust in their banks Similarly, studies by Basha et al (2019) and Salem et al (2016) demonstrate that a positive purchasing experience enhances customer trust This relationship underscores the critical role of satisfaction in fostering consumer trust, as confirmed by earlier research (Li et al., 2006).
Based on the above relationships, the research team makes the following hypothesis:
H5: Satisfaction has a positive impact on consumers’ Trust
2.4.6 The relationship between Utilitarian Value and Purchase Intention
Research indicates that a higher perception of utilitarian values leads to increased user satisfaction and a greater intention to purchase from shopping websites (Gan & Wang, 2017) Empirical studies by Hsu & Lin (2016) and Lin & Lu (2015) further confirm that these utilitarian values play a significant role in shaping consumers' purchasing intentions.
When an online store offers greater utilitarian value, it significantly increases customers' intention to make purchases Previous studies have clearly shown the importance of utilitarian value in enhancing purchase intention (Jones, Reynolds, & Arnold, 2006; Ryu, Han, & Jang, 2010).
Based on the above relationships, the research team proposed the following hypothesis:
H6: Utilitarian Value has a positive impact on consumers’ Purchase
Relationship between Satisfaction and Purchase Intention
Studies indicate a significant positive correlation between customer satisfaction and purchase intention, with satisfaction acting as a crucial moderator (Lee et al., 2009; Huang, 2008) Hsu, Chang, and Chen (2012) highlight that online customer satisfaction plays a vital role in influencing purchase intentions Furthermore, users exhibit stronger purchase intentions on social commerce platforms when their satisfaction levels are high (Zeithaml, Berry & Parasuraman, 1996) Additionally, research by Hsu & Lin (2016) confirms that user satisfaction significantly affects purchase intention in mobile applications.
Research indicates that augmented reality (AR) experiences significantly impact user satisfaction, which in turn affects the intention to purchase location-based AR navigation systems (Jung et al., 2021) Additionally, satisfaction with location-based AR games, such as Pokémon Go, plays a crucial role in influencing the purchase intentions of non-paying players (Hsiao, Lytras, & Chen, 2020).
Based on the above relationships, the research team makes the following hypothesis:
H7: Satisfaction has a positive impact on consumers’ Purchase Intention
The relationship between Trust and Purchase Intention
Numerous studies indicate that trust significantly influences purchase intention, with Kim and Park (2013) and Hajli (2015) highlighting its strong impact Furthermore, Ponte, Carvajal-Trujillo, and Escobar-Rodriguez (2015) identified trust as the most powerful predictor of online purchase intention, a finding that is also supported by Ling et al (2011), reinforcing the positive correlation between trust and online purchasing behavior.
Mansour, Kooli, and Utama (2014) utilized an integrative approach to examine the factors influencing trust in online purchases, revealing that trust significantly enhances online purchase intentions This establishes a positive correlation between trust and the likelihood of making online purchases.
H8: Trust has a positive impact on consumers’ Purchase Intention
Proposed research model
Through the above arguments, our team has proposed a research model as follows:
AR features Perception of the value of AR
RESEARCH METHODS
Research process
Qualitative research
The qualitative research aims to investigate the influence of augmented reality (AR) technology on the purchasing intentions of young consumers in Ho Chi Minh City, while also developing a theoretical framework for e-commerce applications Additionally, the study seeks to refine the language used in surveys to ensure clarity and comprehension for young consumers residing and working in the city.
The research team introduced the discussion's purpose and foundational concepts related to the study, focusing on key factors such as Interactivity, Escape Experience, AR Quality, Utilitarian Value, Satisfaction, Trust, and Purchase Intention Following this, the researchers shared previously proposed measurement scales for feedback from the participants.
The research team facilitated a discussion among group members to gather insights on various factors, encouraging everyone to share their opinions on each variable Comments and feedback were meticulously recorded, and in instances of conflicting viewpoints, the team engaged in further discussions to reach a consensus.
The research team consolidated and refined all feedback regarding the model and scale, adjusting the wording based on the discussions and compiling everything into a cohesive document.
Discussion is carried out based on the detailed outline in Appendix 1.
3.2.2 Summary of qualitative research results
- About the observed variables “Interactivity”:
+ Adjust the wording "phone application applying AR" in variables TTT1, TTT2, TTT3, TTT4 to "e-commerce application applying AR".
- About the observed variables “Escape Experience”:
The AR experience allowed me to envision myself as someone else, highlighting the significance of the observed variable "AR Quality."
+ Adjust the words "Quality of e-commerce website design" in the variables CLTT1, CLTT2, CLTT3, CLDV1, CLDV2 to "e-commerce applications using AR".
4- Adjust the wording "Marker-based AR applications" in variables CLHT1, CLHT2, CLHT3, CLHT4 to "e-commerce applications using AR".
- About the observed variables “Utilitarian Value”:
+ Adjust the wording “from this website” in variables TD1, TD2, TD3, TD4 to “e commerce application using AR”
- About the observed variables “Satisfaction”:
+ Adjust the wording “marker-based AR application” to “AR c-commcrcc application” in all variables SHL1, SHL2, SHL3, SHL4.
- About the observed variable “Trust”:
+ Adjusting the wording “Yahoo! Kimo” in the variables NT1, NT2, NT3, NT4 to "e commerce application applying AR”.
- About the observed variable “Purchase Intention”:
+ Adjust the wording “MAR” in variables YD1, YD2, YD3 to “c-commerce application applying AR”.
• About the research model: Includes 7 hypotheses surrounding the relationship between 5 factors: Interactivity, Escape Experience, AR Quality, Utilitarian Value, Satisfaction, Trust and Purchase Intention.
The authors will utilize measurement scales based on the research conducted by Kowalczuk, Siepmann & Adler (2021), Hosany & Witham (2010), Wen (2012), Chiu et al (2012), and Jung, Chung, & Leue (2015), as well as Haile & Kang (2020).
> After conducting the discussion, the results showed:
The research team has decided to uphold the original theoretical model, which consists of five key factors: interactivity, escape experience, augmented reality (AR) quality, price utilitarian value, and their influence on satisfaction, trust, and purchase intention.
The discussion and research groups maintained the majority of the observed variables, while our group received input to refine the language of certain variables for clarity and precision.
TTTl I have an in-depth look at the product through interaction with the virtual product presentation on the AR e-commerce application
TTT2 The interactivity of virtual product presentation on e-commerce applications applying AR has remarkable features
The AR e-commerce application allows for personalized interactions with virtual product presentations, catering to individual needs This level of engagement surpasses that of traditional e-commerce applications, enhancing the overall shopping experience.
The Escape Experience scale is referenced, inherited and adapted from the measurement scale according to the studies of Hosany & Witham (2010); Includes 4 observed variables, as shown in the following table:
TTl I feel like I transformed into a different character
TT2 The AR experience let me imagine that I was someone else TT3 I completely escape from everyday life
TT4 I felt like I was in another time and place
The AR Quality scale is derived and modified from the measurement scale established in Wen, I (2012), incorporating five observed variables from this study alongside four observed variables identified by Jung, Chung, and Leue (2015).
CLl E-commerce applications that apply AR present more customized information.
CL2 E-commerce applications using AR have many detailed descriptions of each product.
CL3 E-commerce applications applying AR provide accurate information about each product.
CL4 The AR e-commerce application quickly answered my questions/concerns.
CL5 E-commerce applications using AR show empathy and concern for my problems when making purchases.
CL6 E-commerce applications using AR are very easy to use
CL7 E-commerce applications using AR are very convenient to view CL8 E-commcrcc applications applying AR have attractive visual images
TDl Product quality on e-commerce applications applying AR is reliable
TD2 Products on e-commerce applications using AR are worth the money TD3 Shopping on e-commerce applications using
AR allows me to quickly find suitable products
TD4 Shopping on e-commerce applications using
The Satisfaction scale refers, inherits and is adjusted from the measurement scale according to research by Jung, Chung, & Leue (2015); Includes 4 observed variables, as shown in the following table:
Bang 3.5: Thang đo Sự hài lòng
SHLl I am satisfied when using e-commerce applications that apply AR
SHL2 I am satisfied with the features of the AR e commerce application SHL3 I am satisfied with the content of the AR c- commerce application
SHL4 I am satisfied with e-commerce applications that apply AR
The Trust scale was referenced, inherited and adjusted from the measurement scale according to Chiu et al.'s (2012) research; Includes 4 observed variables, as shown in the following table:
NTl I find e-commerce applications using AR to be trustworthy
NT2 I find that e-commerce applications that apply
AR are very interested in consumers NT3 I realize that e-commerce applications using
AR have no intention of profiteering NT4 I find e-commerce applications using AR to be very reputable
The Purchase Intention scale is referenced, inherited and calibrated from the measurement scale according to Haile & Kang's (2020) research; includes 3 observed variables, as shown in the table below:
YDl E-commerce applications using AR motivate me to buy products/services
YD2 The e-commerce application applying AR suits my needs YD3 I became interested in products/services because of e-commerce applications that applyAR
Quantitative research
With the rise of AR-based virtual try-on technology, consumers can now test cosmetics on their faces through their cameras, allowing them to select the perfect products without visiting a store Shopee BeautyCam currently offers over 1,000 cosmetic products, marking a significant growth in this innovative shopping experience over the past year.
From July 1 to July 14, 2023, we will conduct an online survey targeting individuals in Ho Chi Minh City who have utilized the BeautyCam feature on Shopee Using a directional sampling method, this survey aims to analyze the impact of AR technology on young consumers' purchase intentions within e-commerce applications Shopee was selected for this case study due to its dominance in the Vietnamese e-commerce market, holding nearly 73% of total sales among the four largest platforms, equivalent to VND 91,000 billion in market share, according to Metric.vn data from 2022 The user-friendly interface of Shopee BeautyCam ensures a representative sample for our research.
The research team conducted online interviews using a survey questionnaire with a 5-level Likert scale.
Sampling method: The study used convenience sampling method This method allowed the research team to select elements that were easily accessible.
Sample unit: Young people (aged 16-30) have or are using e-commerce application X in Ho Chi Minh City.
The research team employs the formula established by Hair et al (2014) to determine the minimum sample size required for evaluating the scale through exploratory factor analysis (EFA).
(n: sample size, p: number of observed variables)
The research model includes 32 observed variables, so at least n0 is needed
Part 1: Questions to filter respondents
Includes 2 questions with "yes" or "no" answers about whether the survey object belongs to the correct research object of the topic:
Question 1: Are you living and working in Ho Chi Minh City?
Question 2: Have you ever experienced the BeautyCam feature on Shopee?
Includes 32 questions, using a 5-level Likert scale to measure the factors
Interaction, Escape Experience, AR Quality, Pragmatic Value, Trust, Satisfaction and Purchase Intent of consumers using children in Ho Chi Minh City.
Record personal information of respondents including gender, age, education level and income.
Following the survey, the research team will organize and classify the collected data, discarding any inappropriate responses The analysis will be conducted using SPSS and Amos software version 20.0, focusing on sample descriptive statistics, reliability testing via Cronbach's Alpha, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), as well as model and hypothesis testing in accordance with the research theory.
The study focused on collecting data samples from young individuals residing in Ho Chi Minh City The research team implemented a comprehensive data collection process, employing methods such as Cronbach's Alpha to test scale reliability, Exploratory Factor Analysis (EFA), and Confirmatory Factor Analysis (CFA) The results were analyzed within a value range of [0,1].
A reliable scale should achieve a Cronbach's Alpha of 0.7 or higher, as noted by Hair et al (2014) and Nunnally (1978) However, for preliminary exploratory studies, a Cronbach's Alpha threshold of 0.6 is considered acceptable.
The correlation coefficient known as Corrected Item - Total Correlation (CoiTected Item - Total Correlation) measures the relationship between each observed variable and the other variables within a scale According to Cristobal et al (2007), a reliable scale is indicated by a Corrected Item - Total Correlation value of 0.3 or higher for the observed variables.
Therefore, in this study, the conditions for accepting observed variables are:
- Corrected Item - Total Correlation value > 0.3
Variables that pass the reliability test will be further analyzed using exploratory factor analysis (EFA).
Do Thi Thanh Thuong (2015) describes a statistical analysis method that simplifies interdependent observed variables into a smaller, more meaningful set, while preserving the majority of the original data's content.
The Bartlett test assesses the correlation among observed variables within a factor scale A significance level below 0.05 (Sig < 0.05) indicates a linear correlation between the observed variables and the representative factor (Hair et al., 2006).
The KMO (Kaiser-Meyer-Olkin) test assesses the suitability of data for exploratory factor analysis (EFA) in market research A KMO coefficient ranging between 0.5 and 1 indicates that the data is appropriate for EFA, as established by Hair et al (2006).
Extracted variance, expressed as cumulative variance, is utilized to assess how well observed variables are explained by a factor For effective explanation, the extracted variance should exceed 50%, and the Eigenvalue must be greater than 1, as outlined by Gerbing and Anderson (1988).
Factor loadings are simple correlation coefficients between variables and factors Refer to the research paper of Do Thi Thanh Thuong (2015), variables with factor loadings smaller than 0.3 will be eliminated.
So, to apply EFA meaningfully, the research team uses the following conditions:
Factor loading is a crucial metric in factor analysis, with a load factor greater than 0.3 indicating a minimum level of significance Load factors above 0.4 are deemed important, while those exceeding 0.5 demonstrate practical significance To ensure convergent validity, any observed variables with factor loadings below 0.5 will be excluded during exploratory factor analysis (EFA) (Hair et al., 1998).
To assess the quality of observed variables and validate factor structures, Confirmatory Factor Analysis (CFA) is employed This analysis determines the appropriate association of observed variables with their respective scales, evaluating whether these variables meet the necessary standards for inclusion within each scale.
Accordingly, the model is considered suitable for real data when it receives the following indicators:
- Chi-Square test (%2): p value = 0.05 (Joserkog & Sorbom, 1989);
- GFI, CFI, TLI > 0.9 (Rentier & Bonett, 1980);
- RMSEA < 0.08 However, if RMSEA < 0.06, the model is considered very good (Steiger, 1990).
For convergent validity and discriminant validity:
Scale reliability is evaluated using the composite reliability coefficient and the average variance extracted (AVE) Specifically, a concept is considered reliable when its extracted variance exceeds 0.5, as outlined by Hair et al (2011).
- Convergent validity: achieved when the standardized weights of the scale are greater than 0.5 and statistically significant (P-value < 0.05) (Anderson & Gebring, 1988).
- Discriminant value: A pair of concepts is considered to achieve discriminant value when the correlation coefficient between the two concepts is less than 1 (1) is retained;
The total variance extracted is 65.895% > 50%, meeting the requirements, indicating that the 7 factors explain 65.895% of the variation in the observed data set (See Appendix 4.2).
The analysis conducted using the Pattern Matrix, detailed in Appendix 4.3, reveals that 32 observed variables have been categorized into 7 distinct groups, with all loading factors exceeding 0.5 This indicates that the criteria for employing exploratory factor analysis are appropriate for the research dataset.
Figure 4.1: Results of confirmatory factor analysis (CFA)
Following the exploratory factor analysis (EFA), our team conducted a confirmatory factor analysis (CFA) to validate the proposed model presented in Chapter 2 This model encompasses seven key concepts for testing: Interactivity (TTT), Escape Experience (TT), AR Quality (CL), Value Pragmatism (TD), Satisfaction (SHL), Trust (NT), and Purchase Intention (YD).
The CFA results indicate that while the GFI index is below the ideal threshold of 0.9, it remains acceptable at 0.8 as suggested by Baumgartner & Homburg (1996) and Doll, Xia, & Torkzadeh (1994) All coefficients are standard, demonstrating the model's compatibility with market data.
The analysis of the discriminant value among variables in the Purchase Intention factor indicates that the estimated correlation coefficients, along with their standard errors, yield p-values below 0.05 This signifies that the correlation coefficients for each pair of concepts differ significantly from one at the 95% confidence level, confirming that these concepts possess discriminant validity.
Convergent validity test results (table 4.10) The weights are all greater than
0.5; The scale has convergent validity.
In addition, we also calculated the composite reliability (CR) and variance extracted (AVE) of the factors according to the formula of Joreskog (1971).
Table 4.11: Composite reliability and extracted variance
CR AVE CL TD NT SHL TT TTT YD
According to Table 4.11, the reliability and variance extracted for all factors exceed 0.5, with CR values surpassing 0.7 and AVE values greater than 0.5 Therefore, the scale demonstrates both reliability and convergence.
4.5.1 Testing the SEM linear structural model
The SEM model developed in this research demonstrates strong compatibility with market data, evidenced by key indicators such as Chi-square/df = 1.823, GFI = 0.859, TLI = 0.912, CFI = 0.919, and RMSEA = 0.052 All critical reliability indicators meet the necessary criteria, confirming that the model is reliable and well-suited for the data.
919 efi 859 gfi 836 agfi 052 rmsea 285 pclose
J I 1 Ĩ3 II IT 1 II II I II TT2~l I U1 II CLS II CL4 II CL9 n cu ^ ^ ^ ^~
I NT2 II NT3 II NT4 II NT1 nn
Figure 4.2: SEM results of the theoretical model (standardized)
Table 4.12: Regression coefficients of the theoretical model
The testing results of the theoretical model, as shown in Table 4.12, confirm that all hypotheses are accepted, demonstrating that augmented reality (AR) characteristics positively influence value perception Specifically, interactivity significantly enhances consumers' Utilitarian Value (H1; pH1 = 0.329; p = 0.000), while the escape experience notably impacts consumer Satisfaction (H2; p = 0.535; p = 0.000), marking it as the most influential AR characteristic in the model Additionally, AR quality positively affects consumers' Trust (H3; pH3 = 0.228; p = 0.000) The findings also reveal that Utilitarian Value significantly influences Satisfaction (H4; pH4 = 0.387; p = 0.000), and Satisfaction, in turn, positively affects Trust (H5; pH5 = 0.564; p = 0.000) In terms of behavioral responses, Utilitarian Value (H6; pH6 = 0.187; p = 0.009), Satisfaction (H7; pH7 = 0.266; p = 0.003), and Trust (H8; pH8 = 0.234; p = 0.006) all positively influence consumers' Purchase Intention, with Satisfaction having the strongest effect.
Relationship SE SE-SE Mean Bias SE-Bias C.R
The results of the Bootstrap test, presented in Table 4.13 with a sample size of n = 800, indicate that the absolute value of C.R is less than 1.96, suggesting that it is not statistically significant at the 95% confidence level Therefore, the estimated model can be considered reliable.
Research results show that with 08 hypotheses, 08 hypotheses were accepted; shows that there is a relationship between the factors Interactivity, Escape Experience,
The study highlights the significant relationship between Augmented Reality (AR) quality, utilitarian value, user satisfaction, trust, and purchase intention among e-commerce application users Consistent with prior research, all identified factors remained influential, affirming their relevance in the context of e-commerce Furthermore, qualitative discussions with research participants confirmed that these findings are applicable and beneficial for enhancing practices on e-commerce platforms.
Research indicates that interactivity significantly enhances consumers' utilitarian value, aligning with findings from Zheng et al (2019), Jeon, Jang & Barrett (2017), and Qin, Peak & Prybutok (2021) Augmented Reality (AR) fosters an interactive environment within e-commerce applications, positively influencing users' perceptions of utilitarian value This interactivity allows for vivid and specific product information display, enabling users to visualize products and their features clearly Consequently, customers can conveniently select products while saving time and effort, ultimately leading to a higher evaluation of the e-commerce application’s usefulness.
Research indicates that the Escape Experience significantly influences Consumer Satisfaction, aligning with findings from Mehmetoglu & Engen (2011) and Thanh & Kirova (2018) E-commerce applications utilizing Augmented Reality (AR) can enhance this escape experience by creating immersive virtual environments To build trust, the information provided by these applications must consistently meet quality standards in content, form, and timeliness Furthermore, system quality plays a crucial role in fostering trust, as evidenced by studies from Vance, Christophe, & Straub (2008) and Zhou (2012) Applications that are user-friendly and easily accessible tend to increase user trust, as they reflect the developers' commitment to quality Additionally, service quality has been shown to positively affect trust, supporting findings from Lien et al (2015).
Service quality is crucial for e-commerce applications, as it determines their ability to deliver efficient shopping, payment, and product delivery experiences High service quality fosters customer confidence in aspects such as reliability, security, and privacy while using the app When consumers perceive the app as trustworthy and feel comfortable shopping through it, it encourages businesses to prioritize the enhancement of their e-commerce services.
Research indicates that the Utilitarian Value factor significantly enhances customer satisfaction, aligning with findings from Hsu & Lin (2016), Xu, Peak, & Prybutok (2015), and Gan & Wang (2017) Utilitarian values greatly influence emotional responses, highlighting their critical role in predicting satisfaction levels Customers who appreciate their shopping experience and recognize practical value during the process are more likely to feel satisfied Specifically, when consumers can quickly and conveniently find products that meet their needs and fit their budget through e-commerce applications, their satisfaction with the application increases, leading to a more positive overall experience.
Research indicates that satisfaction significantly influences trust, aligning with findings from Koutsothanassi et al (2017), Salem et al (2016), and Shamsudin et al (2018) When users are satisfied with their e-commerce applications, their trust in these platforms is likely to grow Meeting customer expectations plays a crucial role in this relationship; satisfied users are more inclined to trust the application, enhancing their overall experience.
Research shows that the Utilitarian Value factor has a positive impact on Purchase Intention, similar to research by Gan & Wang (2017); Hsu & Lin (2016); Lin
According to Lu (2015), users are more likely to intend to purchase from an e-commerce app that offers significant utilitarian value When consumers can easily discover products that meet their preferences and provide good value for their money, they perceive the app as highly practical Additionally, the convenience of shopping through the application enhances consumers' purchasing intentions.
Test models and hypotheses
4.5.1 Testing the SEM linear structural model
The SEM model demonstrates strong compatibility with market data, evidenced by key indicators: Chi-squared/df = 1.823, GFI = 0.859, TLI = 0.912, CFI = 0.919, and RMSEA = 0.052 All critical reliability indicators meet the necessary requirements, confirming that the research model is appropriate for the data.
919 efi 859 gfi 836 agfi 052 rmsea 285 pclose
J I 1 Ĩ3 II IT 1 II II I II TT2~l I U1 II CLS II CL4 II CL9 n cu ^ ^ ^ ^~
I NT2 II NT3 II NT4 II NT1 nn
Figure 4.2: SEM results of the theoretical model (standardized)
Table 4.12: Regression coefficients of the theoretical model
The testing results of the theoretical model, as shown in Table 4.12, reveal that all hypotheses are accepted, confirming that augmented reality (AR) characteristics positively influence value perception Specifically, interactivity significantly enhances consumers' Utilitarian Value (H1; pH1 = 0.329; p = 0.000), while escape experience notably impacts consumer Satisfaction (H2; p = 0.535; p = 0.000), making it the most influential AR characteristic in the model Additionally, AR quality positively affects consumers’ Trust (H3; pH3 = 0.228; p = 0.000) The correlation analysis indicates that Utilitarian Value significantly contributes to Satisfaction (H4; pH4 = 0.387; p = 0.000), and Satisfaction in turn positively influences Trust (H5; pH5 = 0.564; p = 0.000) Furthermore, behavioral responses show that Utilitarian Value (H6; pH6 = 0.187; p = 0.009), Satisfaction (H7; pH7 = 0.266; p = 0.003), and Trust (H8; pH8 = 0.234; p = 0.006) all positively affect consumers' Purchase Intention, with Satisfaction having the strongest impact.
Relationship SE SE-SE Mean Bias SE-Bias C.R
The Bootstrap test results, detailed in Table 4.13 with a sample size of n = 800, indicate that the absolute value of C.R is less than 1.96, suggesting that the findings are not statistically significant at the 95% confidence level; therefore, the estimated model can be considered reliable.
Discuss research results
Research results show that with 08 hypotheses, 08 hypotheses were accepted; shows that there is a relationship between the factors Interactivity, Escape Experience,
The study examined the impact of Augmented Reality (AR) quality, utilitarian value, satisfaction, trust, and purchase intention among e-commerce application users, revealing consistent results with previous research without omitting any factors Furthermore, qualitative discussions with participants confirmed that these findings are applicable to e-commerce platforms, underscoring their relevance in practical settings.
Research indicates that interactivity significantly enhances consumers' utilitarian value, aligning with findings from Zheng et al (2019), Jeon, Jang & Barrett (2017), and Qin, Peak & Prybutok (2021) Augmented Reality (AR) facilitates user interaction within e-commerce applications, positively influencing this utilitarian value By presenting product information in a vivid and specific manner, AR allows users to clearly visualize products and their features This capability not only streamlines the product selection process, saving time and effort, but also fulfills user desires, leading to a perception of high usefulness in e-commerce applications.
Research indicates that the Escape Experience significantly influences Consumer Satisfaction, aligning with findings from Mehmetoglu & Engen (2011) and Thanh & Kirova (2018) E-commerce applications utilizing Augmented Reality (AR) can enhance the escape experience by creating immersive virtual environments For such applications to foster trust, the information provided must consistently meet quality standards in content, format, and timeliness Additionally, system quality plays a crucial role in building trust, as supported by Vance, Christophe, & Straub (2008) and Zhou (2012) User-friendly e-commerce systems encourage trust, as they reflect the application managers' investment in quality Furthermore, both information quality and system quality, along with service quality, have been shown to positively impact trust, corroborating findings from Lien et al (2015).
Service quality is essential for e-commerce applications, as it determines their effectiveness in delivering efficient shopping, payment, and product delivery experiences High service quality fosters customer confidence in reliability, security, and privacy while using the app When consumers feel comfortable and trust the application, it enhances their shopping experience, making it crucial for businesses to prioritize the development of quality in their e-commerce platforms.
Research indicates that the Utilitarian Value factor significantly enhances customer satisfaction, aligning with findings from Hsu & Lin (2016), Xu, Peak, & Prybutok (2015), and Gan & Wang (2017) Utilitarian values greatly influence emotional responses, emphasizing their critical role in predicting satisfaction outcomes Customers who appreciate their shopping experiences and recognize practical value are more likely to feel satisfied Specifically, when consumers can quickly and conveniently find products that meet their needs and budget through e-commerce platforms, they are more likely to be satisfied with the application, fostering a positive overall experience.
Research indicates that satisfaction significantly enhances trust in e-commerce applications, aligning with findings from Koutsothanassi et al (2017), Salem et al (2016), and Shamsudin et al (2018) When users experience satisfaction with an e-commerce platform, their trust in the application tends to increase This correlation suggests that if an e-commerce application fulfills customer expectations, it not only leads to user satisfaction but also fosters a greater sense of trust among its users.
Research shows that the Utilitarian Value factor has a positive impact on Purchase Intention, similar to research by Gan & Wang (2017); Hsu & Lin (2016); Lin
According to Lu (2015), users are more likely to make purchases through an e-commerce app that offers high utilitarian value When consumers can easily find desirable products that justify their spending, they perceive the app as practically valuable Additionally, if the shopping experience through the app is convenient, it significantly enhances users' intentions to buy.
Research indicates that satisfaction significantly influences purchase intention, aligning with findings from Hsu, Chang & Chen (2012), Jung et al (2021), and Hsiao, Lytras, & Chen (2020) Users who are highly satisfied with e-commerce applications utilizing AR technology tend to exhibit stronger purchase intentions, as these applications enhance the shopping experience, making it more engaging and enjoyable This heightened satisfaction positively affects customers' emotions and perceptions, encouraging them to pursue purchases through e-commerce platforms Consequently, businesses should prioritize policies that focus on delivering exceptional customer experiences, as enhancing user satisfaction is crucial for increasing purchase intentions.
Research shows that the Trust factor has a positive influence on Purchase Intention, similar to the study of Sanny et al (2020); Takaya (2017); Kim & Park,
Trust plays a crucial role in influencing purchase intentions, as it helps minimize privacy and security risks By offering safe and useful information along with excellent service, businesses can significantly enhance the likelihood of users making a purchase.
Satisfaction is the most significant factor influencing purchase intention, making it crucial to develop elements that enhance customer satisfaction, such as escape experiences and utilitarian values Engaging escape experiences can spark customer interest in augmented reality (AR) activities offered by e-commerce applications, while high practical value ensures that shopping is perceived as more useful and convenient Additionally, consumers take into account various objective factors when making purchasing decisions, highlighting the complexity of their decision-making process.
Augmented Reality (AR) enhances the online shopping experience by allowing customers to interact with products as if they own them, helping them determine if items meet their needs and preferences With just one click, users can engage with AR features anytime and anywhere, setting it apart from traditional e-commerce shopping experiences This technology combines the convenience of online shopping—eliminating the need for travel—with the ability to virtually try products, making it easier for customers to make informed purchase decisions among a vast array of options available on e-commerce platforms.
Chapter 4 in turn presented the results of testing the scale, the research model and the proposed research hypotheses By testing Cronbach's Alpha, it can be concluded that the scale meets standards and measures well; EFA and CFA analysis showed that the model is appropriate Finally, through the SEM structural model, the conclusion is drawn that the model is satisfactory and suitable for the market The results of the study hold to the 8 initially proposed hypotheses.