Assessing the attention-interest-search-action-share (AISAS) model on the traditional textile exhibition visitors

Indonesia, rich in diverse ethnicities, celebrates numerous traditional textile traditions. Traditional textile marketing often finds expression through exhibitions. This study explores the applicability of the AISAS model in the exhibitions of traditional textiles in Jakarta. The research engages 235 participants aged 17 and above, all social media users with prior attendance at such exhibitions. Data analysis uses exploratory and confirmatory factor analyses and structural equation modelling. Hypothesis testing for the linear AISAS model affirms the impact of attention on interest, interest on search, search on action, and action on share, demonstrating positive outcomes. The non-linear AISAS model also confirms the impact of attention on interest and interest on search. However, the impact of interest on action reveals a nuanced result and the effects of search on action face rejection. This study holds significance for MICE (meetings, incentives, conferences, and exhibitions) marketing and textile exhibition strategies, providing valuable insights into consumer behaviour during traditional textile exhibitions.


Introduction
Research on MICE in the context of traditional textiles, especially exhibitions, has received little attention from researchers.In the study by Chaobanpho et al. [1] , Gibson and O'Rawe [2] , Teerakunpisut [3] , and Zheng [4] , MICE (meetings, incentive travel, conventions, exhibitions) refers to the tourism industry focusing on organising meetings, incentive travels, international conventions, and exhibitions under perfect and professional management.The term "exhibition" pertains explicitly to the exhibition segment of the MICE industry, involving the organisation of exhibitions to showcase specific products, services, or information to event participants.
In this research, we apply the AISAS model introduced by Sugiyama and Andree [5] to predict consumer behaviour at traditional textile exhibitions.Researching the AISAS model developed by Sugiyama and Andree [5] offers several significant benefits.Firstly, exploring this model contributes to a deeper understanding of online consumer behaviour, providing valuable insights into the sequential stages of attention, interest, search, action, and share.By delving into the intricacies of each phase, researchers gain comprehensive knowledge about how individuals navigate and make decisions in the digital realm.Studying the AISAS model also enables researchers to evaluate its applicability across diverse contexts and industries [6][7][8][9] .The model's versatility makes it a valuable framework for investigating consumer behaviour in e-commerce and tourism, finance, and social issues.This versatility fosters a broader understanding of the model's effectiveness in capturing the nuances of various online decision-making processes.
Several studies have explored the AISAS model, focusing on diverse objectives and research outcomes.In the study of Ding and Wang's [10] , the aim was to develop and test a theoretical model predicting factors influencing users' use of healthcare applications.The research utilised the AISAS model, combined with personal intention and social identity indicators, contributing to the theoretical development of AISAS, weintention, and healthcare applications.Yuliati and Simanjuntak [11] also investigated consumer behaviour in the internet era using the AISAS model, explicitly analysing its effects on knowledge sharing regarding food waste.The study reinforced the AISAS model theory, revealing the direct influence of attention, interest, information search, and action on knowledge sharing.In addition, Li and Zhu [12] delved into the communication influence mechanism of brand information in WeChat red envelope covers using the AISAS model.The study identified factors impacting consumers' willingness to use WeChat red envelope covers and emphasised the significant positive influence of various factors on information experience.Xue et al. [13] examined the online behaviour of Taiwan's Net Generation based on Dentsu's AISAS model.The study filled a gap in the literature by exploring relationships among stages in the online purchase and consumption of travel products and services.Furthermore, Alhudha et al. [14] analysed the use of the AISAS method in telemedicine advertisement, finding that social media significantly influenced attention, interest, and search but did not affect purchase intention significantly.Suhud et al. [7] also focused on the online behaviour of micro and small business owners, using both linear and non-linear AISAS models.While the linear model successfully predicted online behaviour, the non-linear model faced challenges in predicting the behaviour of small business owners.Moreover, Qinghao's [15] study addressed the post-epidemic era's impact on online tourism consumption.Combining the AISAS model, the research identified predictor variables for each closed-loop segment and highlighted factors influencing consumers' purchase intention in the tourism e-commerce platform.
Despite numerous studies incorporating the AISAS model across diverse object backgrounds, we recognise a gap in research regarding the AISAS model in the context of traditional textile exhibitions.Consequently, our study aims to apply and test the AISAS model to predict consumer behaviour related to traditional textile exhibitions.This research represents a novel exploration, as previous scholars have yet to delve extensively into this specific domain.Focusing on the exhibitions of traditional textiles, we intend to contribute valuable insights into consumer behaviour within this unique cultural context, enriching the broader understanding of the AISAS model's applicability across various settings.
The AISAS model fundamentally assesses the determinants influencing consumer sharing behaviour following product acquisition.This framework delves into the multifaceted aspects that shape post-purchase sharing conduct.By scrutinising various factors, AISAS aims to comprehensively understand the dynamics governing consumer actions in the aftermath of product procurement.The model's analytical focus encompasses the intricate interplay of elements that contribute to the complex phenomenon of consumer sharing behaviour post-purchase.
The research background centres on traditional textile exhibitions, frequently organised on various scales to showcase diverse textiles from different Indonesian regions.Notably, Indonesia's distinctive textile, batik, has gained recognition from UNESCO [16] .Designated as an Intangible Cultural Heritage, UNESCO

Attention and search
The research conducted by Mulyana et al. [20] focuses on developing Cash Waqf Linked Sukuk (CWLS) as a new financial instrument in Indonesia.The study aims to assess the AISAS model's applicability to understand the purchasing behaviour of the millennial generation concerning Sovereign Sukuk based on CWLS.The study involves millennial respondents.The results reveal seven accepted hypotheses, demonstrating positive and significant effects of attention, interest, and search on various stages of retail CWLS purchases.
The research findings presented by Mulyana et al. [20] , asserting the significant effects of attention on search, are substantiated by corroborating evidence from studies conducted by Suhud and Allan [6] , Yuliati and Simanjuntak [11] , and Sumerta et al. [21] .These independent studies collectively support and reinforce the notion that the attention stage plays a pivotal role in influencing subsequent search behaviour.The consistent alignment of results across multiple investigations lends credibility to the argument that attention is a crucial driver in shaping the search patterns within the context of the examined models or frameworks.This convergence of findings underscores the robustness of the relationship between attention and search, contributing to a more comprehensive understanding of consumer behaviour in various research contexts.
Based on the discussion of previous findings, the hypothesis to be tested is: H1-Attention will have a significant influence on interest.

Interest
In the AISAS model proposed by Sugiyama and Andree [5] , customer interest in a pivotal position is a critical factor influencing and guiding online consumer behaviour.This model, consisting of attention, Interest, Search, Action, and Share, outlines the sequential stages.Otherwise, through the stages, consumers navigate in their online interactions.
Customer interest, as conceptualised in the AISAS model, signifies the curiosity or engagement degree that a consumer demonstrates towards a particular product, service, or content [22] .This stage follows attention, emphasising the transition from merely capturing the consumer's focus to eliciting a deeper level of engagement.During the interest stage, consumers move beyond initial awareness and demonstrate a genuine curiosity that may drive them to explore further [23] .In the context of online consumer behaviour, the role of customer interest is multifaceted.Firstly, it acts as a precursor to more active involvement in the consumer journey.It is more likely that a consumer who expresses interest is to delve into detailed searches, exploring specific product or service aspects [24] .This increased engagement often indicates a higher likelihood of progressing towards subsequent stages, such as acting and sharing experiences.Customer interest also influences decision-making processes [25] .As consumers express interest, they signal a potential alignment between their preferences or needs and the product or service attributes [26] .This alignment can significantly impact the likelihood of a consumer moving towards actions, such as establishing a purchase or participating in online discussions.

Interest and search
Suhud and Allan's [6] research focuses on evaluating the AISAS model within the realm of tourism.Their study engages participants who utilise mobile instant messaging applications for information exchange during tourism experiences.With a robust participant cohort of 408 individuals, the investigation highlights the AISAS model's efficacy in predicting the behavioural patterns of tourists as they engage with tourism activities through mobile instant messengers, portraying tourists as intricately connected 'cyborgs' within the Internet of things (IoT).Particularly noteworthy is the finding that interest significantly influences search behaviour.This discovery aligns with corroborating evidence from studies such as those conducted by Mulyana et al. [20] , Yuliati and Simanjuntak [11] , Javed et al. [27] , Pelawi and Aprilia [28] , Xue et al. [13] , and Sumerta et al. [21] , all of which also report a significant impact of interest on search behaviour.These consistent findings underscore the robust relationship between interest and search across diverse contexts and contribute to a more comprehensive understanding of consumer behaviour.

Interest and action
Several studies have examined the impact of action interest, exemplified by Yuliati and Simanjuntak's [11] investigation.Their research delves into the influence of the AISAS model on consumer behaviour, specifically addressing the issue of food waste.Focusing on attention, interest, information search, and action, the study involving students underscores the linear operational dynamics of the AISAS model.The research findings reveal direct effects, highlighting the significance of government interventions and community awareness to tackle the challenge of food waste effectively.
These findings align and corroborate with previously published research, as evidenced by studies conducted by Mulyana et al. [20] , Suhud and Allan [6] , and Sumerta et al. [21] .The consistent pattern of results across these studies further strengthens the understanding of the relationship between interest and action, emphasising its importance in shaping consumer behaviour in various contexts.
After formulating and considering the relevant studies discussed above, the hypotheses examined in the present study provide a foundation for further exploration.
H2-Interest will have a significant influence on search.
H2a-Interest will have a substantial effect on action.

Search
In the AISAS model proposed by Sugiyama and Andree [5] , customer search is the third stage in the sequential process that outlines online consumer behaviour.This model, comprised of attention, interest, search, action, and share, delineates the essential steps through which consumers progress in their online interactions.Customer search involves consumers' active exploration and gathering of information after expressing interest in a particular product or service [29] .In this stage, consumers move beyond initial curiosity, seeking detailed and relevant information to make informed decisions.Consumers characterise the search phase, utilising various online channels and resources to gather comprehensive insights about the product, service, or topic of interest [30] .
In the context of online consumer behaviour, customer search is instrumental [31] .Firstly, it bridges between expressing interest and taking concrete actions.A consumer engaged in search activities will likely be more committed to the decision-making process, actively seeking to fulfil their information needs before proceeding.
Customer search also influences the decision-making dynamics of online consumers [7] .As individuals gather information, they are better equipped to evaluate the suitability of a product or service based on their preferences, needs, and criteria.This informed decision-making process enhances the likelihood of consumers progressing towards the action stage, which may involve purchasing or engaging in other desired behaviours.
Businesses strategically address the customer search phase by optimising online content, ensuring accessibility of relevant information, and employing search engine optimisation (SEO) techniques.Providing clear and concise information during this stage is crucial for influencing consumer perceptions and facilitating a seamless transition towards subsequent actions.

Search and action
Several studies unveil the role of search in eliciting action, exemplified by Hoang's [8] investigation.Hoang's study evaluates the influence of digital marketing on consumer behaviour at Pilgrimage Village Boutique Resort & Spa, employing the AISAS model.The research identifies non-sequential relationships within the AISAS stages, emphasising the substantial impact of information-searching behaviour on subsequent actions and the consequential influence of actions on information sharing.The study proposes implications to enhance digital marketing activities based on these non-sequential relationships, providing valuable insights for marketing strategies.
These research findings align and support similar observations from other studies, including Sumerta et al. [21] , Mulyana et al. [20] , Suhud and Allan [6] , Yuliati and Simanjuntak [11] , Javed et al. [27] , Pelawi and Aprilia [28] , and Xue et al. [13] .The consistency across these studies reinforces the understanding of the pivotal role of search in shaping consumer actions, contributing to a comprehensive view of consumer behaviour in different contexts.

Search and share
The following studies evaluate the impact of search on the share, with significant results observed in various contexts: Mulyana et al. [20] , Suhud and Allan [6] , Yuliati and Simanjuntak [11] , Hoang [8] , and Sumerta et al. [21] .For instance, Sumerta et al.'s [21] investigation into online consumer behaviour in online marketplaces utilises the AISAS model.The study provides valuable insights into the intricate relationship between attention, interest, search, actions, and shares.It underscores the unique characteristics of consumers in the online media landscape, enriching our comprehension of consumer behaviour in the realm of online marketing strategies.
Drawing from the findings elucidated in the preceding discussions of relevant studies, the study formulates and intends to test the following hypotheses.
H3-Search will have a significant influence on action.
H3a-Search will have a significant influence on share.

Action
In the AISAS model proposed by Sugiyama and Andree [5] , customer action represents a pivotal stage in understanding and predicting online consumer behaviour.Customer action, as the fourth stage in the AISAS model, signifies the culmination of the consumer's journey from initial attention to a point where they actively engage in a desired behaviour [32] .This behaviour can encompass various actions, such as purchasing, signing up for a service, or participating in online activities associated with the product or service of interest [33,34] .
In online consumer behaviour, the role of customer action is multifaceted.Firstly, it represents the tangible manifestation of a consumer's decision-making process [35] .After progressing through the earlier stages of attention, interest, and search, consumers reach a point where they are ready to take concrete steps based on their assessments and evaluations.
Customer action is a critical indicator of the effectiveness of an online marketing strategy or campaign [36] .The successful conversion of consumer interest into action reflects the resonance of the product or service with the target audience.It also highlights the efficiency of the digital marketing efforts employed to guide consumers through the sequential stages of the AISAS model.Furthermore, customer action contributes significantly to establishing brand loyalty and customer retention [37] .Positive experiences during this stage enhance the likelihood of repeat engagements, fostering a long-term relationship between the consumer and the brand.

Action and share
Several studies analyse the impact of action on sharing, revealing significant insights.For instance, Javed et al. [27] contribute to understanding influencer marketing on Instagram by employing the dual AISAS model.This upgraded model explores the multifaceted influence of fashion influencers on consumers' decisionmaking processes and content outreach.The study highlights the profound impact of fashion influencers in capturing attention, engaging consumers, fostering content outreach, and influencing decision-making processes within private and extended networks.In addition, Pelawi and Aprilia [28] investigate implementing the AISAS model through vloggers, focusing specifically on YouTube as a powerful platform for full video content sharing.The study illuminates how vloggers contribute to the stages of the AISAS model, influencing consumers' purchasing decisions and information sharing.These findings align with the studies above, collectively demonstrating the significant influence of action on sharing in various contexts.Studies by Mulyana et al. [20] , Suhud and Allan [6] , Yuliati and Simanjuntak [11] , Javed et al. [27] , Pelawi and Aprilia [28] , Xue et al. [13] , Hoang [8] , and Sumerta et al. [21] consistently emphasise the noteworthy impact of consumer actions on their sharing behaviours.This study will investigate the hypothesis formulated based on the findings and discussions presented in the relevant research.
H4-Action will have a significant influence on share.

Measures
The AISAS model comprises variables such as attention, interest, search, action, and share, each measured using indicators adapted from previous studies, specifically Suhud and Allan [6] and Wei and Lu [38] and tailored to align with the focus of the current study.These indicators were employed to gauge participants' responses on a Likert-type scale with six points, ranging from 1 for strongly disagree, 2 for disagree, 3 for somewhat disagree, 4 for somewhat agree, 5 for agree, and 6 for strongly agree.Adapting indicators from established studies ensures the reliability and validity of the measurement instrument, aligning it with the nuances of the present research topic.Utilising a standardised Likert-type scale facilitates consistent and comparable data collection, allowing for a robust analysis of the relationships between the variables in the AISAS model within the context of the current study.

Data analysis methods
The data in this research underwent several testing stages.Firstly, the validity was examined through exploratory factor analysis conducted using SPSS version 29, with a minimum acceptable score of 0.4 [39] .Secondly, the reliability was assessed using Cronbach's alpha, implemented in SPSS version 29, with a minimum threshold of 0.6 to ensure the robustness of the measurement instrument.The third stage involved a second validity test through confirmatory factor analysis, executed using AMOS version 29.Notably, the results of this analysis were not included in the manuscript.The fourth stage comprised hypothesis testing using structural equation modelling, conducted with AMOS version 29.The critical ratio (CR) was set at a minimum of 1.96 [39] , and the fitted model's adequacy was evaluated according to the criteria presented in Table 1.
Table 1 outlines the criteria for the fitted model, considering various fit measures.These measures include the probability (p), where a range of 0.05 < p ≤ 1.00 was deemed acceptable, following Schermelleh-Engel et al. [40] .The ratio of X 2 /DF was evaluated with the criterion 0 ≤ X 2 /DF ≤ 2.00, as Tabachnick et al. [41] suggested.The comparative fit index (CFI) was required to fall within the range of 0.95 ≤ CFI ≤ 1.00, following the guidelines by Hu and Bentler [42] .Lastly, the root mean square error of approximation (RMSEA) was expected to be within the range of 0 ≤ RMSEA ≤ 0.05, as per Browne and Cudeck [43] .These criteria ensured the adequacy and reliability of the structural equation model used for hypothesis testing.

Participants
The research in Jakarta involved visitors who had attended traditional textile exhibitions, possessed social media accounts, and were at least 17 years old.Participants were selected using the convenient sampling method, with those willing to participate provided with an online questionnaire link.
Table 2 presents the profile of participants.The distribution of participants by sex reveals that 10.6% were male, while the majority, accounting for 89.4%, were female.Regarding age demographics, the most significant proportion, 59.5%, fell into the category of 20 years old or younger.Participants aged 21-24 constituted 25.5%, with only 3.8% falling into the 25-29 age group.Those aged 30 and older comprised 11.1% of the sample.
Regarding educational attainment, most participants, accounting for 59.1%, had completed high school, while 12.8% held diplomas.Undergraduate holders comprised 26.8%, and only 0.9% had post-graduate qualifications.Analysing the occupational status of participants, the majority, accounting for 68.5%, were employed.Students comprised 20.9% of the sample, while the unemployed represented a smaller proportion at 2.6%.

Data validity and reliability tests
Table 3 presents the results of the validity and reliability tests.The attitude construct comprises six indicators with an alpha value of 0.672.Meanwhile, the interest construct maintains nine indicators with an alpha value of 0.939, and the search construct consists of five indicators with an alpha value of 0.930.Additionally, the action construct comprises five indicators, yielding an alpha value of 0.917, while the share construct preserves six indicators with an alpha value of 0.910.Researchers conclude that all constructs are reliable, exhibiting alpha values above 0.6, indicating satisfactory internal consistency.

Linear model of AISAS
The structural model depicted in Figure 2 represents the linear AISAS model, and the researchers evaluated its overall fit through several vital indicators.The model exhibits a probability value of 0.070, suggesting a reasonable likelihood that the observed data aligns with the proposed structure.The CFI attains a high value of 0.992, indicating a solid fit between the hypothesised model and the empirical data.In addition, the CMIN/DF ratio is 1.397, a relatively low value that signifies a good fit, considering lower values indicate a better fit in structural equation modelling.The RMSEA stands at 0.041, a small value that further supports the adequacy of the model fit.These combined indicators suggest that the linear AISAS model demonstrates a satisfactory fit to the observed data, providing a reliable framework for understanding the relationships between attention, interest, search, action, and share in the context under investigation.Table 4 presents the outcomes of hypotheses testing for the linear AISAS model, exploring the relationships between different stages of user engagement.Hypothesis 1, investigating the impact of attention on interest, yielded a CR score of 9.971, leading to the acceptance of the hypothesis.Similarly, hypothesis 2, assessing the influence of interest on search, obtained a CR score of 11.650, supporting this hypothesis's acceptance.Moving forward, hypothesis 3 focused on the relationship between search and action, producing a CR score of 11.987, resulting in the acceptance of the hypothesis.Lastly, hypothesis 4, examining the impact of action on share, obtained a CR score of 12.404, leading to this hypothesis's acceptance.

Nonlinear model of AISAS
The structural model represented in Figure 3 illustrates the non-linear AISAS model, which has been successfully fitted to the following performance metrics: a probability score (p) of 0.155, a CMIN/DF score of 1.235, a CFI score of 0.995, and an RMSEA score of 0.032.These scores collectively show the model's robustness and effectiveness in capturing the intricacies of the non-linear relationships within the attention, interest, search, action, and share stages.The low probability score suggests a high confidence level in the model's predictions, while the CMIN/DF score of 1.235 indicates a good fit of the model to the data.The high CFI score of 0.995 signifies a close fit between the estimated model and the observed data, further affirming the model's validity.The low RMSEA score of 0.032 also reflects a small error in the model's representation of the underlying relationships.Table 5 presents the results of hypotheses testing for the non-linear AISAS model, examining the relationships between different stages of user engagement.The first hypothesis, assessing the impact of attention on interest, yielded a CR score of 9.760, leading to the hypothesis's acceptance.Similarly, hypothesis 2, investigating the influence of interest on search, obtained a CR score of 11.043, supporting the hypothesis's acceptance.
Moving on to hypothesis 2a, which explores the impact of interest on action, it achieved a CR score of 3.617, resulting in the acceptance of this hypothesis.However, hypothesis 3, focusing on the relationship between search and action, produced a CR score of 0.299, leading to the rejection of the hypothesis.Shifting to Hypothesis 3a, examining the influence of search on share, it obtained a CR score of 3.216, supporting the acceptance of the hypothesis.Finally, hypothesis 4, investigating the impact of action on share, resulted in a CR score of 3.434, leading to this hypothesis's acceptance.

Discussion
The research results in this study provide important insights into the relationship between several relevant variables.The following is a discussion of the research findings.The research results indicate that attention significantly affects interest (CR score = 9.760).This finding suggests that the attention given to visitors at traditional textile exhibitions positively impacts their interest level.When visitors receive focused attention and engagement from exhibition organisers, it captures their interest and increases their engagement with the exhibition.This finding aligns with previous research conducted by Lin and Chen [44] and Ruswandi et al. [45] , who highlight the importance of attention in creating consumer interest.These studies emphasise that capturing and maintaining consumers' attention is crucial in generating interest and engaging them effectively.Attention is a gateway that piques consumers' curiosity and encourages them to explore further.
The study results indicate that interest significantly affects search (CR score = 11.043).This finding suggests that visitors with a higher interest level in the context of traditional textile exhibitions are more likely to conduct further information searches.As genuinely interested in the exhibition, visitors seek additional information about the products, brands, or services offered.This finding aligns with the research conducted by Sumerta et al. [21] and Xue et al. [13] , claiming that interest strongly influences search behaviour.These studies emphasise that when individuals are highly interested in a particular topic or event, they are motivated to search for more information to satisfy their curiosity and gain a deeper understanding actively.
The study's research findings indicate that interest significantly affects action (CR score = 3.617).It implies that as visitors develop a more substantial interest in traditional textile exhibitions, they are more likely to engage in concrete actions such as making purchases or participating in activities within the exhibition.This finding aligns with the research conducted by Suhud et al. [7] and Zhang and Tan [46] , arguing that interest significantly influences action.These studies emphasise that when individuals are genuinely interested in a particular exhibition, their motivation and willingness to act increase.It aligns with the common understanding that interest drives consumer behaviour and decision-making.
Indeed, the research results indicate that search does not significantly affect action (CR score = 0.299).This finding suggests that although visitors search for information, it does not directly translate into tangible actions.It implies that other factors, such as interest, satisfaction, or external influences, may substantially impact visitors' actions within the context of traditional textile exhibitions.However, the research findings can vary due to differences in methodologies, sample sizes, and contextual factors.The studies conducted by Javed et al. [27] and Ramadhani et al. [47] may have yielded different results, emphasising the significant contribution of search to action within their specific research contexts.
The study results indicate a significant relationship between search and share (CR score = 3.216).The findings suggest that when visitors are more likely to commit to sharing information or experiences with others, they actively seek information about traditional textile exhibitions.This finding aligns with prior studies conducted by Sumerta et al. [21] and Suhud et al. [7] , highlighting the significant contribution of search behaviour to sharing.These studies emphasise that when consumers actively search for information, they are more likely to gather valuable knowledge and insights about the exhibition.This accumulated information can then influence their perceptions and decisions.So, it leads to a greater likelihood of sharing their experiences with others.
The study results reveal an essential finding regarding the relationship between Action and Share.The finding indicates that the actions taken by visitors during the traditional textile exhibitions significantly influence their inclination to share positive information or experiences with others (CR score = 3.434).The significance of this relationship gets support from previous research conducted by Laan [48] and Zhang and Tan [46] , who also emphasised the role of action as a critical driver for increasing consumer sharing behaviour.These studies highlight that when consumers engage in specific actions, such as purchasing or actively participating in activities, they are more likely to share their positive experiences with others.This sharing behaviour can take the form of recommendations, word-of-mouth communication, or online reviews, which can significantly impact the reputation and visibility of traditional textile exhibitions.

Conclusion
This research aims to assess the AISAS model within the context of traditional textile exhibitions.The results of hypotheses testing for both the linear and non-linear AISAS models provide valuable insights into the dynamics of user engagement in this specific setting.In the linear AISAS model, all four hypotheses received strong support, with CR scores ranging from 9.971 to 12.404.These findings suggest a sequential and positive impact of attention on interest, interest on search, search on action, and action on share.The high CR scores indicate a significant association between these stages, emphasising the interconnected nature of user engagement within the linear model.
On the other hand, the non-linear AISAS model exhibited varied results.While the first five hypotheses received acceptance, indicating positive impacts, hypothesis 4, examining the influence of search on action, faced rejection with a CR score of 0.299.It suggests a non-linear relationship between these two stages, highlighting a potential deviation from a straightforward progression.
The theoretical contribution of this study lies in validating and refining the AISAS model within the distinctive context of traditional textile exhibitions.The results from the hypotheses testing in the linear and non-linear AISAS models offer valuable insights into the underlying user engagement mechanisms in this concrete setting.
In the linear AISAS model, the acceptance of all four hypotheses indicates a sequential and positive relationship between attention, interest, search, action, and share.The robust CR scores, ranging from 9.971 to 12.404, underscore the strength of these associations.It contributes to the existing literature by confirming the applicability of the linear model in understanding and predicting user engagement stages within traditional textile exhibitions.
The non-linear AISAS model, with its mixed results, adds a nuanced dimension to the theoretical framework.While the acceptance of five hypotheses aligns with a positive impact of certain stages on user engagement, the rejection of hypothesis 4 introduces the concept of non-linearity in the search-to-action relationship.It challenges the traditional linear progression assumed in the model, emphasising the need for a more nuanced understanding of how users navigate and engage in the exhibition space.
The findings of this research offer significant managerial contributions, particularly for stakeholders involved in traditional textile exhibitions in Indonesia, including traditional textile businesses, exhibition organisers, government institutions providing support to traditional textile entrepreneurs, private companies implementing CSR initiatives for traditional textile businesses, and the MICE industry.
For traditional textile businesses and exhibition organisers, understanding the linear and non-linear relationships within the AISAS model can enhance their marketing strategies and improve their effectiveness in engaging with visitors.The acceptance of hypotheses regarding the influence of attention on interest, interest on search, search on action, and action on share in the linear model suggests that a sequential approach to engaging visitors may yield positive outcomes.Therefore, businesses and organisers can focus on attracting attention initially, fostering interest, facilitating the search for information, encouraging actions such as purchases or inquiries, and promoting sharing experiences to enhance engagement and expand reach.
However, accepting certain non-linear relationships, such as interest directly influencing action and search directly influencing share, highlights alternative pathways for visitor engagement.Traditional textile businesses and exhibition organisers can leverage these findings to tailor their strategies based on visitors' behavioural patterns.For example, they may explore methods to directly convert visitor interest into actions or facilitate sharing without relying solely on search behaviour.Moreover, government institutions and private companies supporting traditional textile businesses can use these insights to refine their assistance programs and CSR initiatives.By understanding the dynamics of visitor engagement, they can provide targeted support in areas such as marketing training, digital presence enhancement, or networking opportunities to empower traditional textile entrepreneurs and maximise the impact of their interventions.
Lastly, the MICE industry can benefit from these findings by incorporating strategies informed by the AISAS model into its event planning and management processes.By understanding how different stages of visitor engagement influence each other, event organisers can design more engaging and interactive traditional textile exhibitions that resonate with attendees and create memorable experiences, ultimately contributing to the success of the MICE sector in promoting cultural heritage and economic development.
Despite the insightful findings derived from testing the AISAS model in traditional textile exhibitions, this study has notable limitations which we should acknowledge.Firstly, the research focused exclusively on a specific setting, namely traditional textile exhibitions, which may limit the generalisability of the results to other contexts.Future research could explore the applicability of the AISAS model in diverse exhibition environments to enhance its external validity.
Secondly, the study predominantly relied on quantitative methods, emphasising numerical data and statistical analyses.While this approach provided valuable insights into the relationships between attention, interest, search, action, and share, incorporating qualitative methodologies could present a more comprehensive understanding.Qualitative data, such as user perceptions, preferences, and experiences, could enrich the interpretation of the findings and offer a more holistic view of user engagement in traditional textile exhibitions.
Thirdly, the linear and non-linear AISAS models primarily focused on user behaviours within the exhibition setting.Future research could explore the impact of external factors, such as cultural influences or technological advancements, on user engagement.Exploring these broader contextual elements could provide a more nuanced understanding of the dynamics shaping user interactions in traditional textile exhibitions.
Regarding future research directions, scholars are encouraged to investigate the effectiveness of interventions or strategies to optimise each stage of the AISAS model.It could involve experimenting with different exhibition layouts, interactive technologies, or informational approaches to enhance user engagement.Moreover, considering the dynamic nature of technology and evolving user preferences, continuous investigation and adaptation of the AISAS model are crucial to maintaining its relevance in the ever-changing exhibition landscape.

Figure 1
Figure 1 presents the theoretical framework of the AISAS non-linear model.This study aims to assess both the linear AISAS model and the non-linear AISAS model.In the linear model, researchers link attention to interest, connect interest to search, associate search with action, and correlate action with share.On the other hand, in the non-linear model, we relate attention to interest, link interest to search and action, associate search with action and share, and tie action to share.Examining these models seeks to provide insights into the intricate relationships within the AISAS framework, exploring both linear and non-linear pathways of attention, interest, search, action, and sharing.

Figure 1 .
Figure 1.Theoretical framework of the AISAS non-linear model.

0. 904 SE1
After reading my friend's review of traditional clothing exhibition on social media, I will look for additional information about the place of tourism from other sources.0.898 SE4 After seeing photos of my friend's selfie about traditional clothing exhibition on social media, I hope to visit those places of tourism.0.881 SE2 After reading my friend's review about traditional clothing exhibition on social media, I hope to visit that tourist spot.0.873 SE5 I asked my friend about the traditional clothing exhibition he had visited.my friend's review about the traditional clothing exhibition, I think the exhibition will benefit me.0.882 AC1 After reading my friend's review about the traditional clothing exhibition, I think the tourism place is worth a visit.0.869 AC5 I visited a traditional clothing exhibition, which my friend had visited after I saw their selfies there.0.863 AC4 I visited the traditional clothing exhibition, which my friend had attended after reading my friend's review of the exhibition.0.861 AC2 After reading my friend's review about traditional clothing exhibition, I think I am willing to visit this tourist place.the same traditional clothing exhibition as my friend had done, I updated the status on my wall/timeline media account.0.897 SH6 When posting a selfie photo/photo on my social media account about the experience of visiting a traditional clothing exhibition like the friend I attended, I tagged my friend.0.897 SH4 After visiting the same traditional clothing exhibition as my friend's visit, I share my experience by posting reviews about the place on my social media account.0.882 SH5 After visiting the same traditional clothing exhibition as my friend had done, I shared my experience by posting selfie photos of the place on my social media account.0.872 SH2 I recommend the traditional clothing exhibition.0.865 SH1 After visiting the traditional clothing exhibition just like my friend's visit, I think I will share my experience on social media.0.846

Figure 2 .
Figure 2. Structural model of the linear AISAS model.

Figure 3 .
Figure 3. Structural model of the non-linear AISAS model.

Table 1 .
Criteria of the fitted model.

Table 2 .
Profile of participants.

Table 3 .
Results of data validity and reliability tests.
AT4 I read my friend's post about a traditional clothing exhibition on my social media timeline.
SE3 After viewing my friend's selfie photos in a tourist place on social media, I will look for information about the traditional clothing exhibition from my friend and/or other sources.

Table 4 .
Results of the hypotheses testing of the linear AISAS model.

Table 5 .
Results of the hypotheses testing of the non-linear AISAS model.