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Article

The Influence of AR on Purchase Intentions of Cultural Heritage Products: The TAM and Flow-Based Study

by
Siqin Wang
1,†,
Weiqi Sun
2,†,
Jing Liu
3,*,
Ken Nah
1,
Wenjun Yan
1 and
Suqin Tan
1
1
International Design School for Advanced Studies, Hongik University, Seoul 04068, Republic of Korea
2
Department of Design Innovation (Visual Communication Design), Sejong University, Seoul 05006, Republic of Korea
3
Faculty of Innovation and Design, City University of Macau, Macau 999078, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Appl. Sci. 2024, 14(16), 7169; https://doi.org/10.3390/app14167169
Submission received: 17 July 2024 / Revised: 12 August 2024 / Accepted: 13 August 2024 / Published: 15 August 2024
(This article belongs to the Special Issue Intelligent Interaction in Cultural Heritage)
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Abstract

:
AR integrates virtual elements with the real world in real-time to enhance interactivity and vividness, which may influence consumers’ perceptions and payment intentions. This study explores the impact of Augmented Reality (AR) on consumer willingness to pay for cultural heritage products, utilizing the Technology Acceptance Model (TAM) and flow theory. This study analyzes 603 responses (quantitative data) to understand consumer perceptions of AR in the purchasing process of cultural heritage products. The findings reveal that perceived usefulness, ease of use, and flow experience significantly enhance consumer purchase intentions, with ease of use also amplifying the effects of perceived usefulness and flow experience. Additionally, the immersive, interactive, and aesthetic aspects of AR contribute positively to ease of use and flow experiences, with immersion notably impacting perceived usefulness. The results support the research model with robust explanatory power, offering practical insights for employing AR to improve marketability and consumer engagement with cultural heritage products. This paper contributes to the existing literature by bridging the gap in understanding the role of AR in enhancing consumer experiences and financial outcomes in the cultural heritage domain.

1. Introduction

In recent years, technological advancements have enabled the creation of enriched environments that enhance the physical world by integrating real-world objects with virtual ones [1], culminating in the development of Augmented Reality (AR). While various definitions of AR exist, a common element is that it operates interactively, in real-time, vividly, and uniquely within its deployed environment. Azuma [2] describes AR as a live view of the natural world supplemented by virtual computer-generated information such as text, images, video, or other interactive media. Similarly, Faust et al. [3] support this description, defining AR as the overlay of virtual objects—like computer-generated images, text, and sound—onto the user’s actual environment. Compared to traditional media, AR technology offers a high degree of interactivity and vividness, which provides users with a rich, immersive experience [4].
Brands actively explore and evaluate various AR applications across diverse contexts to identify the most effective environments [5]. By far, the most common use of AR in retail, both in private and public settings, is on smart devices and large interactive screens. Cosmetic companies have introduced AR mirrors that allow customers to experience virtual facial makeup [6]. Pokémon Go’s AR digital graphics, which are superimposed onto the real world of gamers through their mobile phone displays, were downloaded over 500 million times in two months [6]. In the cultural heritage sector, integration with AR is more widespread as products are primarily set up for sale in arts and culture-related venue categories. AR on smart devices enables consumers to visualize virtual products within their surroundings or access additional digital content by scanning a product’s logo or associated image. Conversely, large interactive screens facilitate the rendering of most physical environments on screen, with virtual elements incorporated into these displays.
The motivation behind this study stems from the burgeoning application of Augmented Reality (AR) in the retail sector, particularly within the domain of cultural heritage products. Despite its growing prevalence, the specific dynamics of how AR influences consumer behavior and purchase intentions in this unique context remain underexplored. Although researchers in experience design have posited that well-crafted experiences can enhance customers’ willingness to pay [7], the link between design elements of AR and consumer readiness to pay for cultural heritage products warrants additional investigation. In existing research, many studies have focused on tourism-based intangible cultural heritage handicrafts [8,9], ignoring cultural heritage products that span a much more extensive range of audiences and values. Cultural heritage covers all aspects of a country’s history, and its derivatives range from expensive antique artifacts that can be traded to souvenirs from museums and historical sites.
This study attempts to fill this gap in the literature through the technology acceptance model (TAM) [10] and flow theory [11]. Specifically, this study examines how and why AR influences consumer experiences and subsequent payment intentions by influencing consumer user-perceived factors when purchasing a cultural heritage product. The results of this study will provide practitioners with meaningful and concrete insights into how AR can be used to enhance the cultural heritage product purchasing experience and improve their financial outlook.
In order to fulfill the research objectives of this study, the following specific research questions were addressed:
RQ1. 
How do the unique perceptions of AR technology—immersion, interactivity, and aesthetic quality—influence the perceived usefulness, ease of use, and flow experiences consumers experience when purchasing cultural heritage products?
RQ2. 
How do these experiences influence consumer purchase intent for cultural heritage products when mediated by AR technology?
RQ3. 
What role do the TAM and flow theory play in explaining consumer behavior in AR-enhanced retail environments for cultural heritage products?
The remainder of the research is structured as follows. The theoretical background section explains the specifics of TAM and flow theory and their significance in guiding this study. The hypothesis development section presents a literature review that provides critical constructs related to AR-mediated cultural heritage product shopping experiences, including user-perceived factors, experience factors, and willingness to pay, which underpin this study’s hypothesis about the effect of AR on cultural heritage product purchase intention. This section also designs the research model based on the theoretical background and research hypotheses. Next, the structure of the paper is as follows: the methodology section outlines the research design and procedures employed. A description of the data and key findings are presented in the results section, and the paper concludes with a discussion and identification of study limitations.

2. Theoretical Foundations

2.1. The Technology Acceptance Model

The technology acceptance model (TAM) [10] provides this study’s conceptual framework. TAM is based on the theory of rational behavior (TRA) [12] and the theory of planned behavior [13] in social psychology. TAM builds a logical chain to interpret and predict potential users’ acceptance of emerging technologies based on perception–attitude–intention–behavior. As shown in Figure 1, the core of the model is to identify and emphasize perceived usefulness (PU) and perceived ease of use (PEU) as the decisive factors affecting users’ technology adoption intentions [14]. Specifically, from an individual subjective perspective, PU refers to the extent to which the use of a certain technology can improve work performance or achieve goals. On the other hand, PEU focuses on the simplicity of technology, which means the user’s perceived ease of using the technology. TAM further pointed out that PEU significantly affects PU, which can explain the differences in PU levels between users.
In previous research, the effectiveness of TAM has been widely verified when it is used as a framework for understanding technology adoption behavior in diverse contexts [15]. Given the innovative application of AR technology in the retail field of cultural heritage products, it represents an innovation in the retail format and provides consumers with an unprecedented shopping experience. Therefore, TAM has become a compelling theoretical foundation for studying consumers’ acceptance of AR technology to promote the purchase behavior of cultural heritage products. Through the TAM framework, this study can systematically analyze how AR technology affects PU and PEU consumers of cultural heritage products and verify its impact on purchase intention.

2.2. Flow Theory

Csikszentmihalyi [11] took the lead in elaborating the concept of flow theory, defining it as a holistic and deep psychological state experienced by individuals when they are fully immersed in a specific activity. The core of this definition covers four key elements: a sense of control, concentration, curiosity, and intrinsic interest drive. The flow state is the optimal form of experience in a situation with immediate feedback. It is characterized by deep concentration, a strong sense of control over the environment, distortion of time perception, and temporary dissolution of self-consciousness [16,17]. This state widely exists in sports competition, occupational work, shopping behavior, game entertainment, personal hobbies, computer operation, and other activities, reflecting the universal applicability of flow as a unique psychological phenomenon [18].
Csikszentmihalyi et al. [19] further define flow experience as the psychological state of the experiencers in engaging activities that significantly promotes the concentration of attention and the enhancement of pleasure. Given the critical role of flow experience in shaping engaging experiences, a deeper understanding of the drivers that trigger the flow experience for consumers is strategically vital for for-profit organizations to create unique, highly engaging services or products. This process not only helps to optimize the user experience but also significantly increases customer satisfaction and loyalty.
Nevertheless, the factors contributing to the flow experience within the AR context remain largely unexplored [20]. A study by Barhorst et al. [21] examined AR facilitation to enhance the flow experience’s unique features and its positive impact on various consumer outcomes, concluding that vividness, interactivity, and novelty positively impact mindful flow states. This study will further explore the perceptual factors that lead to an AR-enhanced flow experience in the consumer context of cultural heritage purchases.

3. Hypothesis Development and Research Model

AR possesses three distinct media characteristics: it “combines real and virtual environments”, operates “interactively in real-time”, and functions “in a three-dimensional space” [2]. AR technologies that meet this set of media characteristics are included for consideration in this study. AR devices and applications provide a rich content model for cultural heritage products, often providing immersive experiences that are highly interactive and that ensure the aesthetic quality of the product and presentation. To explore the influence of AR on consumer behavior, it is essential to examine how its unique features shape shopping perceptions and the types of responses they provoke from consumers. Accordingly, this study reviews contemporary research on consumer reactions to these media characteristics. It uses these insights to construct a research model integrating AR perception factors with consumer purchase intentions.

3.1. User-Perceived Factors

3.1.1. Immersion

Immersion is often seen as the key to enjoyment through technology or media, and immersion results from a good user experience. The literature on consumer experience has consistently emphasized that two critical dimensions, active/passive “engagement”, and the spectrum from “absorption” to “immersion”, are central to defining the nature of customer experiences [7]. Research, including studies by Raptis et al. [22], has identified that immersion as a subjective experience, along with interactions with other participants and virtual environments, significantly contributes to the user experience. The objective of immersive AR environments is to enable users to perceive the computer-generated world as seamlessly integrated with the real world, thereby fostering a sense of presence, or “being there”, which is deemed beneficial for consumers. The cultural heritage category usually gives consumers a sense of distance and requires extensive background knowledge to help them appreciate it. Immersive AR environments can quickly help consumers understand the value of the product and be useful to them.
Weibel and Wissmath [23] found that while flow experience and immersion are distinct, there is some correlation between them, and Javornik [24] conceptualized the potential of AR in developing immersive flow experience experiences. There is an overlap between flow experience and immersion, with Jennett et al. [25] arguing that immersion is a precursor to flow experience, as the feeling of being fully engaged, where nothing else matters, is the colloquial definition of immersion. Whether or not the immersive experience provided by AR contributes to the flow experience in the consumption of cultural heritage products needs to be explored.
AR adds a dimension to the consumer experience that is not encompassed by ordinary cultural heritage product consumption. Given the utility of AR in the field of commercial marketing, it is important to further isolate the impact of the immersive phenomenological experience of consumer consumption of cultural heritage products. Therefore, the following hypothesis is proposed:
Hypothesis 1a. 
The immersion factor positively affects consumers’ perceived usefulness in cultural heritage product purchases.
Hypothesis 1b. 
The immersion factor positively affects consumers’ perceived ease of use in cultural heritage product purchases.
Hypothesis 1c. 
The immersion factor positively affects consumers’ flow experience in cultural heritage product purchases.

3.1.2. Interactivity

Interactivity, as discussed by Lister et al. [26], has been extensively studied and continues to be a fundamental concept for evaluating the impact of Augmented Reality (AR). Although interactivity is an objective feature, its relevance to consumer behavior is understood through consumers’ perceptions of their control over the medium, their ability to engage in bidirectional communication, and their perceptions of the medium’s responsiveness [27]. Inherently, AR tools are designed to be interactive, facilitating communication between users and the medium [28]. Nevertheless, current commercial AR applications within the cultural heritage sector focus more on machine interaction—such as providing access to various contents and interacting with interfaces—and less on enabling augmented communication among users.
Interactivity centers on the user’s subjective perception and emphasizes the personal characteristics that evoke a sense of interaction [29]. Enhanced interactivity enables consumers to gather information about a product more effectively by visually inspecting virtual products (e.g., shapes, colors, and functions) that realistically display cultural heritage categories [30]. By projecting the viewer’s image into the intended consumption situation, consumers can use the displayed product virtually, increasing their PEU. This consumption experience motivates consumers to engage more actively and efficiently in processing information. This enhancement in information processing elevates the quality of the consumer’s search experience, enhancing the perceived usefulness of the shopping experience and the purchasing decision [31]. Regarding the path between interactivity and online mindfulness, Hoffman and Novak [32] suggest that interactive features in computer-mediated environments may enhance the online mindfulness experience. In environments mediated by AR, the interactivity offered by virtual reality creates an immersive and engaging consumer experience that fosters a flow state [31].
When AR is used in the consumption of cultural heritage products, interactivity is a key factor that improves the user’s acceptance of the technology and optimizes the psychological experience process. Therefore, the following hypothesis is proposed:
Hypothesis 2a. 
The interactivity factor positively affects consumers’ perceived usefulness in cultural heritage product purchases.
Hypothesis 2b. 
The interactivity factor positively affects consumers’ perceived ease of use in cultural heritage product purchases.
Hypothesis 2c. 
The interactivity factor positively affects consumers’ flow experience in cultural heritage product purchases.

3.1.3. Aesthetic Quality

Another critical aspect of AR use is the role of aesthetics [33], which considers perception, interpretation, visualization, and description of reality. Aesthetic quality can be defined as the visual appeal and attractiveness of the marketing environment [34]. Aesthetic quality is a prominent factor in any visualization technology integrated into the environment. Users of AR technology can manipulate aesthetic aspects such as design, vividness, color, and virtual reality elements [35]. These aesthetic features provide consumers of cultural heritage products with a high degree of control and pleasure, contributing to a pleasant shopping experience [36]. These aesthetic elements also enable the best flow experience [37]. In addition, the dynamic visual effects provided by AR technology can lead to a smooth user experience and leave a good impression on consumers [38]. Consumers with high cognitive innovation abilities are likely to pay more attention to the aesthetics brought about by AR technology, which may further affect their sustainable relationship behavior with the technology and related products [6].
Aesthetic perception can be significantly influenced by the flow of consumer experience through technology. According to Mellet et al. [39], individuals develop mental images of products after gaining a conceptual understanding or knowledge of cultural heritage through AR. Carlson and O’Cass [40] further point out that when consumers appreciate the aesthetic quality representations of cultural heritage products, they are more likely to experience the flow state because aesthetic factors play a crucial role in immersing consumers in the AR usage experience. Huang and Liao [35] observed that the aesthetic entertainment and visual appeal provided by AR technology improve customers’ efficiency in completing cultural heritage shopping activities, thus bringing the best shopping experience and generating a flow experience. Building on the existing literature, this study proposes that as AR technology increasingly permeates the design and delivery of cultural heritage products, the aesthetic quality of products demonstrated through AR could significantly impact consumers’ flow experience.
Aesthetic quality is essential in analyzing AR for cultural heritage product purchasing behavior and consumer research. This is because it directly affects the user’s perception and experience, ranging from usability and ease of use to deep emotional engagement. Therefore, the following hypothesis is proposed:
Hypothesis 3a. 
The aesthetic quality factor positively affects consumers’ perceived usefulness in cultural heritage product purchases.
Hypothesis 3b. 
The aesthetic quality factor positively affects consumers’ perceived ease of use in cultural heritage product purchases.
Hypothesis 3c. 
The aesthetic quality factor positively affects consumers’ flow experience in cultural heritage product purchases.

3.2. Technology Acceptance Factors and Psychological Experience Factor for Users

3.2.1. Perceived Ease of Use

The Technology Acceptance Model (TAM) has identified the role of the PU and ease of use constructs in the technology adoption process, that is, intentions towards behavior (willingness to accept) are jointly determined by people’s attitudes and PU [15]. Davis [10] also assumed that PEU is an a priori variable and not just parallel to PU. Therefore, the proposed relationship between PU and PEU is reasonable. TAM effectively expresses the user’s intention to participate in the technology.
Consumers sometimes have a large knowledge background barrier when purchasing cultural heritage products, and AR helps to remove this barrier. Consumers of cultural heritage products tend to derive pleasure from accessing detailed information tailored to their needs, increasing their attentiveness to opportunities for acquiring product information [41]. Dacko [42] highlights that the perception of ‘complete product information’ constitutes the most significant advantage of AR shopping tools, explaining why AR technology is highly effective at capturing customer attention. Owing to AR technology’s capacity to furnish detailed information about product attributes, consumers are more thoroughly engaged in these intriguing and enjoyable experiences within a specific context [43].
AR can supply trustworthy information that assists consumers in making informed and rational purchasing decisions, enhancing their experiential value, and culminating in an optimal shopping experience. Based on this understanding, the following hypothesis is proposed:
Hypothesis 4. 
Consumers perceived ease of use positively affects their perceived usefulness in cultural heritage product purchases.
Hypothesis 5. 
Consumer perceived ease of use positively affects consumers’ flow experience in cultural heritage product purchases.

3.2.2. Perceived Usefulness

Perceived usefulness captures the extent to which consumers perceive themselves as effective and efficient in searching for and accessing the necessary information to evaluate and make purchasing decisions regarding cultural heritage products via AR [44]. AR technology can adeptly educate consumers on how to use a product displayed virtually, offering them a direct or near-direct experience with the product, thus enhancing their understanding of popular products. AR technology affects the perceived benefits of cultural heritage products for consumers, which enhances consumer engagement and psychological incentives and ultimately creates a willingness to consistently use AR applications and pay a premium [45].

3.2.3. Flow Experience

In examining the customer shopping experience, scholars have underscored the significance of the flow experience [46]. When consumers shop for cultural heritage products using AR technology, the flow experience is characterized as a state where the consumer is temporarily absorbed, entirely in control, enjoys, and concentrates on the shopping activity [20]. Flow experience influences cognitive responses in marketing environments, so marketing communication research should use the consumer’s flow experience as a foundational principle for information processing [31]. Enhancing the flow experience is necessary for companies to survive in computer-mediated situations, and flow experience can serve as an effective indicator of a user’s AR experience as it has been shown to influence consumers’ brand attitudes and purchasing behavior [47].

3.2.4. Consumer Purchase Intention

Intention refers to adopting and accepting new technologies that will be used in future purchases. Understanding the factors that drive this decision can go a long way in facilitating the design of technologies with practical applications [48]. TAM has shown that PEU and PU influence behavioral intentions, which influence actual use behavior. Behavioral intention has been identified as a key predictor of actual use behavior [49]. Hoffman and Novak [34] argued that consumers who experienced mindfulness showed more positive subjective intentions than those who did not experience online mindfulness. Wu and Chang’s [50] study further supported the influence of online flow experience, which mediates the idea that interactivity influences behavioral responses. Therefore, based on the previous review, the following hypothesis is proposed:
Hypothesis 6. 
Consumers’ perceived usefulness positively affects consumers’ purchase intentions for cultural heritage products.
Hypothesis 7. 
Consumers’ perceived ease of use positively affects consumers’ purchase intentions for cultural heritage products.
Hypothesis 8. 
Consumers’ flow experience positively affects consumers’ purchase intentions for cultural heritage products.

3.2.5. Research Model

Based on the logic of the above hypothesis, this study summarizes the user-perceived factors (immersion, interactivity, and aesthetic quality), technology acceptance factors (perceived usefulness and perceived ease of use), psychological experience factor (flow experience), and consumer purchase intention and establishes a research model diagram of the effect of AR on the purchase intention of cultural heritage products, which is shown in Figure 2.

4. Methodology

4.1. Questionnaire Design

This study uses a quantitative research method using a questionnaire to test the research model. This study benefits from several advantages associated with employing a survey method. First, the data required for this study are perceptual. Consumer perceptions, which encompass beliefs and attitudes about the consumption experience of cultural heritage products, are best captured through survey methods [51]. Second, using a questionnaire method also strengthens the relevance and impact of the research findings [51]. The researcher collected primary data because the type of data required for this study was consumer perception data of cultural heritage-type products influenced by AR technology, which is not available from any public source.
By reviewing the experience of the related literature, the researchers developed a questionnaire for assessing the impact of AR on the willingness to purchase a cultural heritage product. The questionnaire consisted of three parts. The initial section of the survey included an informed consent form. It verified that respondents had made at least two purchases of cultural heritage products and used AR technology during the purchasing process. Respondents who did not consent to the collection of anonymized data for this study or who failed to meet the experience criteria were excluded from this study. The second section investigated demographic information about the respondents. The third section collects data about the respondent’s user experience. Respondents were asked to rate these items. Each item was assessed using a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree).
In order to measure the impact of consumer use of AR on the cultural heritage product purchasing process, all variables and user experience factors were measured using the items listed in Table 1. These items were derived from research conducted by TAM and flow researchers and other well-established evaluation scales used to assess AR technology’s relevance to the consumer sector.
To ensure a wide range of respondents and the accuracy of the translation, the questionnaire, initially in English, underwent a rigorous back-to-back translation process. First, a researcher, not a native English speaker, translated all the original entries into other languages. Subsequently, a different researcher independently translated these entries back into English. Moreover, both researchers verified the semantic accuracy of the translations by comparing the two English versions. The questionnaire was finally translated into English, Chinese, and Korean. Subsequently, we invited four cultural heritage product marketing professionals and four AR technology professionals to provide suggestions for the tool. Based on the feedback, this study improved the wording of some items to improve clarity and understanding. The two original translators then reviewed the revised version and finalized the questionnaire. To further evaluate the format and wording of the questionnaire, we also conducted a pretest on 30 participants who had purchased cultural heritage products using AR technology. The test results showed that the questionnaire was free of misinterpretation and unclear meaning, and the results were not included in the total sample size.

4.2. Data Collection and General Demographics

This study used survey data to test the hypotheses in line with previous research. Specifically, the researchers invited volunteers from cultural heritage consumer forums and social media online groups to participate. Respondents were limited to those who had made two or more purchases of cultural heritage products and had used AR-related technology in the purchase process.
A total of 700 respondents completed the survey. Incomplete or inappropriate responses (e.g., duplicate entries) were subsequently removed. Following this elimination process, 603 usable responses were included in the sample for structural validation and hypothesis testing. Table 2 details the characteristics of the respondents, showing a slight majority of males (59%) compared to females (41%). The major proportion of respondents’ age (28.0%) was between 30 and 39 years, followed by 18 to 29 years (25.0%) and between 40 and 49 years (23.1%). In total, 85.9% of the respondents had a bachelor’s degree or higher and were well-educated. In addition, 70.0% of users made multiple purchases of cultural heritage products after experiencing AR, and 36.2% of users indicated that their main place of purchase was in museums, which may be related to the atmosphere of the venues and the price of cultural heritage products. Furthermore, non-response bias was evaluated by comparing early and late respondents through extrapolation, as suggested by Armstrong and Overton [54]. The analysis revealed no significant differences in demographic or substantive variables, indicating an absence of substantial non-response bias.

4.3. Research Methodology

Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were performed using AMOS. SEM was chosen over other methods, such as multiple regression, because SEM offers higher statistical rigor [55]. SEM is a validation technique used to examine previous studies’ conceptual models and assess the consistency of these theoretical frameworks with the collected data [56]. This method can estimate measurement errors for independent and dependent variables and simultaneously analyze all relationships in the proposed model. At the same time, this comprehensive approach reduces statistical errors and thus minimizes the risk of drawing incorrect conclusions [57].

5. Results

5.1. Measurement Model

The data analysis employed Anderson and Gerbing’s [58] two-step approach, initially evaluating the convergent and discriminant validity of the measurement model. Subsequently, the research hypotheses and the structural model framework were tested.
Given that data collection was performed via questionnaires, this study implemented Harman’s single-factor test to assess the potential for common method bias. Harman’s single-factor test involves analyzing all of the study’s items that were subjected to principal component analysis or exploratory factor analysis [59]. The results showed that all extracted factors explained 76.351% of the total variance, representing a relatively strong explanatory power of the extracted factors in capturing a large portion of the variance in the responses. The first factor explained 30.597% of the first variance, indicating no common methodological bias.
When evaluating the reliability and validity of test model data, Cronbach’s alpha, Composite Reliability (CR), and Average Variance Extracted (AVE) can be used as statistical indicators [60]. Internal consistency is considered good when Cronbach’s alpha is ≥0.7. The data in Table 3 show that all Cronbach’s alpha values are well above this critical value, indicating that the items for each variable are reliable. Meanwhile, the CR value was also greater than 0.7, strengthening the reliability evaluation provided by Cronbach’s alpha and confirming the variables’ consistency and reliability. AVE measures the average proportion of variance in an observed variable that can be attributed to its underlying factor, thereby assessing the convergent validity of the construct. The AVE values for all variables in Table 3 meet or exceed the criterion of 0.5, and the high loading on the data results indicates that the terms strongly indicate validity.
Moreover, Table 4 was subjected to a validated factor analysis (CFA) using maximum likelihood estimation to estimate the measured customer purchase intention characteristics. The results from the confirmatory factor analysis (CFA) indicated a good model fit: the chi-squared statistic was significant (χ2 (329) = 534, p = 0.000), and the normed chi-squared (χ2/df = 1.524) was below the suggested threshold of 3.0 as recommended by Schumacker and Lomax [61]. Additionally, all other fit indices were within the levels recommended for a good model fit [62], confirming that the proposed model adequately fits the observed data.
Furthermore, in this study, we also tested discriminant validity using the Fornell-Larcker criterion method [63], the specific results of which are shown in Appendix A. Discriminant validity is a form of structural validity, which generally means that items that are not in the same factor will not be constituted in the same factor. The method of evaluating such validity is to conduct correlation analysis, calculate the square root of mean-variance extraction (AVE), and then combine these results for analytical expression. In the matrix, the diagonal entry represents each variable’s square root of the mean-variance extraction. In contrast, the non-diagonal entry for the lower triangle represents the standardized correlation coefficient. The results show that the square root value of each variable AVE is higher than their correlation coefficient with the other variables, which confirms that each variable in this study has discriminant validity.

5.2. Structural Equation Modeling and Hypothesis Verification

Structural models (Figure 3) need to be tested. Structural equation modeling (SEM) is an integrative tool used to test hypothesized relationships between variables [64]. In this study, these are user-perceived factors for the use of AR in the purchase of cultural heritage products, technology acceptance factors for users, psychological experience factors for users, and consumer purchase intention. Figure 3 shows the relationship between the measured variables. The final structural model, which results from applying the refinement criteria mentioned in the modeling section, contains 28 items.
The whole model was then tested. Based on the analysis of the structural model, Table 5 shows values for the structural equation model based on commonly used model fit measures that indicate a good overall model fit [65]. To be specific, the indices of CMIN/DF, IFI, TLI, CFI, and RMSEA demonstrate that the model fits the data well.
Finally, Table 6 presents the path values, critical ratios (C.R.), and significance levels (p-values). Given that the C.R. exceeds 1.96 and the p-value is below 0.05, 10 of the 12 paths are statistically significant.
Specifically, in the relationship between user perceptive factors and technology acceptance factors (Table 7), immersion is correlated with perceived usefulness (β = 0.171, p < 0.05), so that H1a holds. Comparatively, interactivity does not constitute a significant effect on perceived usefulness (β = 0.021, p > 0.05), and aesthetic quality is not a positive influence factor for perceived usefulness (β = 0.023, p > 0.05), so H2a and H3a are not valid. Meanwhile, immersion (β = 0.149, p < 0.05), interactivity (β = 0.206, p < 0.05), and aesthetic quality (β = 0.175, p < 0.05) were each related to perceived ease of use, with interactivity having a more significant effect on perceived ease of use. Thus, H1b, H2b, and H3b were established. Immersion (β = 0.150, p < 0.05), interactivity (β = 0.155, p < 0.05), and aesthetic quality (β = 0.135, p < 0.05) were also correlated with flow experience, respectively, in which interactivity also had a more significant effect on flow experience. Therefore, H1c, H2c, and H3c were established.
From the results of the test, it can be concluded that in the relationship between technology acceptance factors and psychological experience factors, both perceived ease of use have a positive effect on perceived usefulness (β = 0.299, p < 0.05) and flow experience (β = 0.219, p < 0.05). Therefore, H4 and H5 are valid.
Further, in the analysis of the effect of technology acceptance factors, psychological experience factor on consumer purchase intention, perceived usefulness (β = 0.121, p < 0.05), perceived ease of use (β = 0.209, p < 0.05), and flow experience (β = 0.183, p < 0.05) were the significant influences on consumer purchase intention, respectively, with perceived ease of use having a more significant effect on consumer purchase intention. Consumer purchase intention is more significant. Therefore, H6, H7, and H8 are valid.

6. Discussion and Implications

6.1. Discussion and Conclusions

Based on this study, it explores how and why AR influences consumer experience and subsequent payment intention by influencing consumer user-perceived factors during the purchase of a cultural heritage product. A design model was developed to analyze and validate the relationship between, for example, user-perceived factors, technology acceptance factors, psychological experience factors, and consumer purchase intention. This is followed by a discussion based on the results of the data analysis.
Immersion has a positive influence on both the technological acceptance factors and the psychological experience factor of customers influenced by AR to purchase cultural heritage products. This is similar to Daassi and Debbabi’s [66] findings that immersion and consumer behavioral intention encourage acceptance and adoption of AR-related shopping by creating a positive purchase experience, which in turn increases purchase intention. AR technology breaks the limits of reality, enabling consumers to experience new products in depth and simulate real adventures, which stimulates positive emotional evaluations and increases purchase intent and market value [67].
Interactivity plays a driving role in the perceived ease of use and flow experience of customers buying cultural heritage products influenced by AR, which is consistent with the conclusions of existing studies [68]. Under the effect of AR, interactivity will affect the mental image and then affect the consumer’s behavioral intention towards the product. When consumers interact with AR, they can manipulate virtual models of cultural products or explore historical sites in three-dimensional space to achieve personalized learning and exploration and high participation. This process deepens consumers’ emotional connection and appreciation of cultural heritage, thus enhancing consumers’ purchase intentions of related products. However, interactivity is not a key predictor of perceived usefulness, which may be attributed to AR technology development, and its support system still needs to be improved.
Aesthetic quality significantly impacts the perceived ease of use and flow experience of customers buying cultural heritage products under the influence of AR. This is consistent with existing research suggesting the importance of aesthetics as one of the characteristics of AR acceptance and use [8]. Aesthetic issues encompass perception, interpretation, visualization, and reality rather than merely confronting a mechanized environment. These aesthetic features are particularly significant because virtual content should seamlessly blend with the natural environment. AR presents real-world locations where cultural heritage products are sold as visual images and illusions, enhancing the shopping experience aesthetically. Relatively and inconsistent with other studies in the area of AR influencing cultural heritage consumption, aesthetic quality does not significantly affect perceived usefulness.
In a study on technology acceptance factors on customers’ intention to purchase cultural heritage products influenced by AR, consistent with research in the field of cultural consumption, PU had a significant effect on AR attitudes, while perceived ease of use predicted perceived usefulness and AR attitude [69]. In other words, perceived usefulness and ease of AR are determinants of AR acceptance and purchase intention among customers who purchase cultural heritage products. This study confirms that AR is an important tool for guiding consumer behavior. Emphasize the importance of cognitive usefulness and ease of use in using AR. Also, perceived ease of use significantly affected flow experience, which is consistent with the theory proposed by Hsu and Lu [70] that the flow experience is related to the willingness to use the system. It is confirmed that flow experience is related to exploratory behavior and positive subjective experience in customers’ AR-influenced purchase of cultural heritage products.
In examining the impact of psychological experience factors on customers’ intentions to purchase cultural heritage products influenced by AR, this study aligns with previous research in both offline and online environments [16,36]. The findings suggest that the flow experience is a crucial prerequisite for purchase intention. This means consumers of cultural heritage products influenced by the AR state of flow experience will show stronger purchase intentions. Despite the varying attribution of perceived factors of AR technology across different consumer domains [21], the AR features of immersion, interactivity, and aesthetic quality collectively create a flow experience. This flow experience significantly influences consumer acceptance and information utility, ultimately enhancing the consumer’s intention to shop using AR.
In summary, this study reached the following conclusions. In this study of the influence of AR on consumer purchase intention of cultural heritage products, perceived usefulness, perceived ease of use, and flow experience played a positive influence, and perceived ease of use is a significant predictor of the influence of perceived usefulness and flow experience. Meanwhile, the influence of immersion, interactivity, and aesthetic quality on perceived ease of use and flow experience, respectively, was confirmed. However, it should be mentioned that only immersion among user-perceived factors has a driving effect on perceived usefulness. The results validate the proposed model, demonstrating acceptable fit measures and robust explanatory power.
This study demonstrates the strategic utilization of AR to enhance the shopping experience for cultural heritage products. By employing AR, retailers can offer customers immersive and interactive experiences that not only engage them but also facilitate a more intuitive and informative shopping process. Theoretically, this research contributes to the existing literature by applying the Technology Acceptance Model (TAM) and the flow theory within the context of Augmented Reality (AR), an underexplored area in the field of retail. This study extends the understanding of these models by demonstrating how AR-specific features (immersion, interactivity, and aesthetic quality) influence consumer acceptance and behavior. The findings indicate the necessity for modifications to the Technology Acceptance Model (TAM) in the context of emerging technologies such as AR. This is because the inclusion of the flow experience as a connection point between perceived ease of use, usefulness, and consumer purchase intentions is advocated for.

6.2. Implications

This study significantly contributes to consumer behavior and technology acceptance by investigating the interplay between Augmented Reality (AR) and consumer willingness to pay for cultural heritage products. It extends current theoretical models, specifically the Technology Acceptance Model (TAM) and flow theory, by applying them within a novel context—AR-enhanced shopping experiences for cultural heritage goods. By empirically demonstrating that AR’s unique characteristics (immersion, interactivity, and aesthetic quality) positively influence perceived ease of use and flow experience, which in turn affect consumer purchase intentions, the study provides a deeper understanding of how AR can be strategically utilized to enhance user engagement and perceived value. This aligns with and expands upon existing research that underscores the importance of technological interactivity and vividness in influencing consumer behavior. Importantly, this research addresses a gap in the literature concerning the economic implications of AR in cultural and heritage contexts, offering actionable insights for practitioners looking to leverage AR technologies to boost economic outcomes. Further, the research proposes practical applications of marketing and technology deployment in the field of cultural heritage.
The level of consumer immersion in AR is determined not only by their subjective perception but also by the capabilities of AR technology [71]. AR technology can effectively enhance cultural heritage products’ market appeal and economic benefits. By designing AR experiences rich in historical value and product uniqueness, the accessibility and interactivity of the product can be enhanced. This not only deepens consumers’ understanding and appreciation of the product but also justifies its premium price. In addition, AR technology facilitates virtual try-ons, promotes instant purchase decisions, and increases sales conversion rates. At the same time, providing downloadable AR content continues the user experience and extends the brand–consumer interaction cycle, potentially inspiring subsequent purchases or word-of-mouth. In addition, AR, as a featured selling point in the marketing strategy of cultural heritage products, combined with the spreading effect of user-generated content (UGC), can significantly expand market influence. By combining AR augmentation with consumer expectations and modern digital habits, cultural heritage organizations can significantly increase their visibility, customer engagement, and financial returns.

6.3. Limitations

The results of this study should be interpreted with caution. Indeed, there are several limitations inherent in this study that open up new directions for further research. First, the audience is limited, given that the product categories used in this study are cultural heritage products. Due to the limitation of language development, even when the questionnaire was placed on the public platform, some still gave up participating in the research due to language barriers.
Second, the scope of this study is limited to the role of AR in cultural heritage product-selling scenarios. Future research needs to consider the contrasting roles of AR technology in online and offline channels. Moreover, future research could investigate the correlation between product types and different levels of individual engagement. In particular, we call for qualitative research to further explore different aspects of enhanced consumer perceptions. Research could help develop scales that are appropriate for the AR purchasing environment.
Finally, our study was correlational and did not provide evidence of causal conclusions. Future research could use experimental methods to provide further support for the research model proposed in this study by providing insights into the causal relationships between user-perceived factors, technology acceptance factors, psychological experience factors, and consumer purchase intention and their combined effects on the AR-based buying environment, providing further support for the research model proposed in this study. Nevertheless, the proposed model has deepened our understanding of the relationship between these four cognitive factors and their combined role in encouraging the purchase of AR-based cultural heritage products.

Author Contributions

Conceptualization, S.W. and W.S.; data curation, S.W. and W.S.; formal analysis, S.W., S.T. and W.Y.; funding acquisition, J.L.; investigation, S.W. and W.Y.; methodology, S.W. and W.Y.; project administration, S.W., J.L. and K.N.; software, S.W. and W.Y.; validation, S.W. and S.T.; visualization, S.W. and S.T.; writing—original draft, S.W., J.L. and W.S; writing—review and editing, S.W., J.L. and K.N.; resources, S.W., J.L. and K.N.; supervision, J.L. and K.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Results of discriminant validity analysis.
Table A1. Results of discriminant validity analysis.
VariableMeanSD123456789101112
1. Gender1.41 0.49 -
2. Age2.55 1.26 0.008-
3. EDU2.68 1.02 0.082 *0.005-
4. Number2.15 0.97 0.018−0.022−0.052-
5. Channels2.39 1.22 −0.043−0.0060.028−0.002-
6. IM3.60 1.06 0.0370.0760.028−0.014−0.0530.813
7. IN3.60 1.06 0.0520.0760.0040.007−0.0340.451 **0.850
8. AQ3.53 1.01 −0.0540.040−0.026−0.0340.0120.373 **0.439 **0.824
9. PU3.75 0.89 0.0610.0200.030−0.019−0.0030.261 **0.208 **0.191 **0.806
10. PEU3.59 0.98 −0.011−0.0070.060−0.012−0.0160.292 **0.329 **0.303 **0.312 **0.802
11. FE3.71 0.92 −0.0130.0000.082 *−0.028−0.0700.326 **0.349 **0.318 **0.322 **0.321 **0.868
12. CPI3.60 0.99 0.0010.047−0.040−0.085 *−0.0460.252 **0.229 **0.176 **0.224 **0.278 **0.271 **0.800
Note: diagonal numbers are AVE square roots, while others are correlation coefficients. ** indicates p < 0.01; * indicates p < 0.05.

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Applsci 14 07169 g001
Figure 1. The technology acceptance model (TAM).
Figure 1. The technology acceptance model (TAM).
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Figure 2. The research model of the effect of AR on the intention to purchase cultural heritage products.
Figure 2. The research model of the effect of AR on the intention to purchase cultural heritage products.
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Figure 3. Structural equation modeling.
Figure 3. Structural equation modeling.
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Table 1. Modified scale measures.
Table 1. Modified scale measures.
MeasureReference Scale Items
Immersion
(IM)		Jennett et al. [25]IM1I feel like I really empathize with AR’s presentation of the cultural heritage product
IM2I was fully engrossed in the AR technology experience
IM3I didn’t even realize I was using any AR devices and controls
IM4For my shopping experience, it felt like only a short time had passed
Interactivity
(IN)		Yim et al. [6]
Song and Zinkhan [27]		IN1I have some control over what I want to see in the AR
IN2I can control what I want to see in the AR
IN3I can control the pace of interaction with AR
IN4AR was able to respond quickly to my specific needs
Aesthetic Quality
(AQ)		Huang and Liao [35]AQ1The AR presentation of the cultural heritage product is visually appealing
AQ2The way AR displays cultural heritage products is beautiful
AQ3I like the AR visualization of the cultural heritage product
AQ4I thought it was an interesting way to learn about cultural heritage products through AR displays
Perceived Ease of Use
(PEU)		Kim and Forsythe [52]
Chen and Lin [53]		PEU1I have access to the cultural heritage product I need at my fingertips through AR
PEU2AR puts cultural heritage product into the form I want it to take
PEU3I can use AR to browse content features that provide tips, information and fun
PEU4AR gives me the freedom to transition from one product category to the next
Perceived Usefulness
(PU)		Kim and Forsythe [52]PU1AR saves me time buying cultural heritage products
PU2AR has improved the quality of my search for purchasing cultural heritage products
PU3AR allows me to virtually experience cultural heritage products faster!
PU4AR enables me to clearly understand and remember the historical background and design ideas of cultural heritage products
Flow
Experience
(FE)		Hoffman and Novak [32]
Yim et al. [6]		FE1All my attention was drawn to the cultural heritage product on the AR display
FE2AR’s cultural heritage product piqued my curiosity
FE3AR technology has awakened my intrinsic interest in cultural heritage products
FE4I felt like time flew by during the experience
Consumer Purchase
Intention
(CPI)		Chen and Lin [53]CPI1I would like to recommend cultural heritage shopping places or websites that use AR to others
CPI2I found the cultural heritage shopping experience with AR to be enjoyable
CPI3I plan to visit a shopping center or website that uses AR when I need to purchase a cultural heritage product in the future
CPI4I would like to experience AR again to learn more about other cultural heritage products
Table 2. Characteristics of the respondents.
Table 2. Characteristics of the respondents.
ItemVariableFrequencyPercentage
GenderMale35659.0%
Female24741.0%
Age18–2915125.0%
30–3916928.0%
40–4913923.1%
50–599014.9%
60 years and over549.0%
Educational backgroundHigh school diploma6110.1%
Bachelor’s degree23539.0%
Master’s degree16928.0%
Doctor’s degree11418.9%
Other244.0%
Number of cultural heritage products purchased after experiencing ARLess than 3 times18130.0%
3–6 times21736.0%
7–10 times13923.1%
More than 10 times6610.9%
Places where cultural heritage products are most often purchased after an AR experienceMuseums21836.2%
Cultural heritage tourism area9615.9%
Antique stores or auctions12721.1%
Online cultural heritage store16226.9%
Table 3. Result of Harman’s single-factor test.
Table 3. Result of Harman’s single-factor test.
Total Variance Explained
Initial Eigenvalues
% of		Extraction Sums of Squared Loadings
% of
ComponentTotalVarianceCumulative %TotalVarianceCumulative %
18.56730.59730.5978.56730.59730.597
22.90040.95340.9532.90010.35640.953
32.33449.28949.2892.3348.33749.289
42.11456.83956.8392.1147.54956.839
52.20764.07864.0782.0277.24064.078
61.81370.55570.5551.8136.47770.555
71.62376.35176.3511.6235.79676.351
Table 4. Confirmatory factor analysis.
Table 4. Confirmatory factor analysis.
VariableItemFactor LoadingCRAVECronbach’s Alpha
Immersion
(IM)		IM10.8840.8860.6620.886
IM20.840
IM30.744
IM40.778
Interactivity
(IN)		IN10.7860.9120.7220.911
IN20.888
IN30.896
IN40.823
Aesthetic quality
(AQ)		AQ10.7570.8940.6800.893
AQ20.893
AQ30.816
AQ40.826
Perceived ease of use
(PEU)		PEU10.7960.8810.6490.879
PEU20.794
PEU30.781
PEU40.850
Perceived usefulness
(PU)		PU10.8440.8780.6430.876
PU20.791
PU30.778
PU40.792
Flow
experience
(FE)		FE10.8180.9250.7540.917
FE20.880
FE30.909
FE40.864
Consumer purchase
intention
(CPI)		CPI10.8140.8770.6400.875
CPI20.777
CPI30.829
CPI40.779
Table 5. Results of validated factor analysis.
Table 5. Results of validated factor analysis.
Measurement
Indicators		CMINDFCMIN/DFSRMRTLICFIRMSEA
Measured value--<3<0.08>0.9>0.9<0.08
Reference standard501.4023291.5240.0430.9820.9840.03
Table 6. Measurement model fit indices.
Table 6. Measurement model fit indices.
Measurement
Indicators		CMINDFCMIN/DFSRMRTLICFIRMSEA
Measured value--<3<0.08>0.9>0.9<0.08
Reference standard534.8863331.6060.0570.9790.9810.032
Table 7. Results of path tests.
Table 7. Results of path tests.
HypothesisPathSTD. EstimateS.E.C.R.p-ValueResult
H1aIM→PU0.1710.0393.1700.002Supported
H1bIM→PEU0.1490.0432.8330.005Supported
H1cIM→FE0.1500.0422.9980.003Supported
H2aIN→PU0.0210.0450.3800.704Not supported
H2bIN→PEU0.2060.0503.818***Supported
H2cIN→FE0.1550.0503.0100.003Supported
H3aAQ→PU0.0230.0500.4440.657Not supported
H3bAQ→PEU0.1750.0563.413***Supported
H3cAQ→FE0.1350.0552.7520.006Supported
H4PEU→PU0.2990.0445.992***Supported
H5PEU→FE0.2190.0484.785***Supported
H6PU→CPI0.1210.0532.5200.012Supported
H7PEU→CPI0.2090.0514.044***Supported
H8FE→CPI0.1830.0443.897***Supported
Note: *** p < 0.001.
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Wang, S.; Sun, W.; Liu, J.; Nah, K.; Yan, W.; Tan, S. The Influence of AR on Purchase Intentions of Cultural Heritage Products: The TAM and Flow-Based Study. Appl. Sci. 2024, 14, 7169. https://doi.org/10.3390/app14167169

AMA Style

Wang S, Sun W, Liu J, Nah K, Yan W, Tan S. The Influence of AR on Purchase Intentions of Cultural Heritage Products: The TAM and Flow-Based Study. Applied Sciences. 2024; 14(16):7169. https://doi.org/10.3390/app14167169

Chicago/Turabian Style

Wang, Siqin, Weiqi Sun, Jing Liu, Ken Nah, Wenjun Yan, and Suqin Tan. 2024. "The Influence of AR on Purchase Intentions of Cultural Heritage Products: The TAM and Flow-Based Study" Applied Sciences 14, no. 16: 7169. https://doi.org/10.3390/app14167169

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