INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025  
Determinants of Consumers’ Behavioral Intention to Purchase  
Seafood Online: A UTAUT-Based Analysis in Surigao Del Sur,  
Philippines  
Marinel E. Josol1, Jeonel S. Lumbab2  
1North Eastern Mindanao State University  
2Cebu Technological University  
DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000563  
Received: 02 November 2025; Accepted: 08 November 2025; Published: 18 November 2025  
ABSTRACT  
This study examined the influence of the Unified Theory of Acceptance and Use of Technology (UTAUT)  
constructs—Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions—on  
consumers’ behavioral intention to purchase seafood products through digital platforms in Surigao del Sur,  
Philippines. Employing a descriptive-correlational quantitative design, data were collected from 100 respondents  
selected through stratified random sampling. A validated questionnaire measured perceptions of each UTAUT  
construct and behavioral intention using a five-point Likert scale. Descriptive statistics, Pearson correlation, and  
multiple linear regression were utilized for data analysis. Results revealed that all UTAUT constructs were  
positively perceived by respondents, indicating favorable attitudes toward digital seafood marketing. Correlation  
analysis showed strong interrelationships among constructs, with Performance Expectancy exhibiting the highest  
and most significant association with Behavioral Intention (r = 0.7169, p < 0.01). Regression analysis further  
identified Performance Expectancy (β = 0.4013, p = 0.002) and Facilitating Conditions (β = 0.4559, p = 0.003)  
as significant predictors of Behavioral Intention, whereas Effort Expectancy and Social Influence showed  
moderate yet non-significant effects. These findings suggest that consumers’ online seafood purchasing  
intentions are primarily driven by perceived usefulness and enabling infrastructure rather than ease of use or  
social persuasion. The study affirms the applicability of the UTAUT model in the digital seafood market context  
and underscores the importance of improving technological support, platform performance, and consumer trust  
to enhance adoption in emerging rural economies.  
Keywords: UTAUT, behavioral intention, digital seafood marketing, technology adoption, Surigao del Sur  
INTRODUCTION  
The digitalization of commerce has significantly transformed consumer purchasing patterns, particularly within  
the food and fisheries sectors where efficiency, traceability, and accessibility are increasingly valued (Kaur &  
Bhatt, 2022). In the Philippines, digital platforms have emerged as key enablers for connecting seafood producers  
with consumers, yet their adoption remains uneven across coastal communities such as Surigao del Sur. Despite  
being a resource-rich province with vibrant aquaculture activities, the region exhibits limited engagement in  
digital seafood marketing due to infrastructural, social, and behavioral constraints. Understanding these  
behavioral determinants is essential for supporting inclusive economic participation in local seafood trade.  
Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT), this study explores how  
perceived usefulness, ease of use, social influence, and facilitating conditions shape consumers’ behavioral  
intention to purchase seafood online—a context where empirical insights remain scarce (Abdullah et al., 2023).  
Recent studies applying the UTAUT framework to digital commerce highlight that Performance Expectancy  
consistently emerges as the strongest predictor of consumer technology adoption, followed by Facilitating  
Conditions that ensure resource availability and accessibility (Sharma et al., 2022; Rahim et al., 2022). However,  
the predictive roles of Effort Expectancy and Social Influence vary depending on technological familiarity and  
cultural context (Hong et al., 2021; Nair & Das, 2023). In online food retail, factors such as product freshness,  
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perceived risk, and delivery reliability further complicate adoption behaviors, particularly in perishable goods  
markets like seafood (Pham et al., 2023; Dissanayake et al., 2024). While these studies confirm UTAUT’s  
relevance in e-commerce, there remains limited evidence on its applicability to semi-rural and coastal markets  
where digital readiness and consumer trust are still developing—creating a critical gap in understanding the  
drivers of online seafood purchasing behavior.  
Although UTAUT has been extensively validated in general e-commerce contexts, few studies have tested its  
explanatory power in the digital seafood sector, especially within developing economies. The behavioral  
mechanisms driving online seafood purchasing remain poorly understood, with limited consideration for  
localized challenges such as digital literacy, infrastructural barriers, and perceptions of product authenticity. This  
lack of empirical evidence constrains both theoretical refinement and strategic policy development aimed at  
enhancing digital inclusion in seafood trade. Therefore, a focused analysis of UTAUT constructs in this setting  
is necessary to illuminate the key factors shaping consumer intention and to inform sustainable digital market  
integration in coastal regions like Surigao del Sur.  
This study aims to examine the influence of UTAUT constructs—Performance Expectancy, Effort Expectancy,  
Social Influence, and Facilitating Conditions—on consumers’ behavioral intention to purchase seafood products  
through digital platforms in Surigao del Sur, Philippines. Specifically, it seeks to describe consumers’  
perceptions of each construct, determine the relationships among them, and identify which constructs  
significantly predict behavioral intention. By integrating statistical analysis within a descriptive-correlational  
design, the study endeavors to deepen theoretical understanding of digital consumer behavior and provide  
practical insights for strengthening online seafood marketing systems in emerging market contexts.  
LITERATURE REVIEW  
Performance Expectancy (PE)  
Performance Expectancy (PE), defined as the degree to which individuals perceive that using a system enhances  
their performance (Venkatesh et al., 2003), consistently emerges as the strongest determinant of behavioral  
intention in e-commerce adoption. Numerous studies confirm that consumers’ belief in the usefulness and  
efficiency of digital platforms drives online purchasing intention across industries (Al-Qudah et al., 2021;  
Sharma et al., 2022; Nair & Das, 2023). Within the food and grocery sector, PE significantly predicts online  
buying behavior by reducing perceived transaction risk and enhancing convenience, information accessibility,  
and satisfaction (Lee et al., 2022; Pham et al., 2023). Abdullah et al. (2023) further highlight that technological  
infrastructure moderates this effect in developing markets, suggesting that when digital tools demonstrate  
tangible value—such as faster service or better product traceability—consumers’ adoption likelihood increases.  
Consequently, in the context of online seafood marketing, where freshness and delivery reliability are crucial,  
the perceived performance benefits of digital systems may serve as the primary driver of behavioral intention.  
Effort Expectancy (EE)  
Effort Expectancy (EE), or perceived ease of use, reflects consumers’ assessment of how effortless and intuitive  
a technology is to navigate (Venkatesh et al., 2003). Empirical evidence reveals that EE initially exerts a strong  
influence on behavioral intention, particularly among first-time or low-literacy users, but its importance  
diminishes as consumers gain familiarity with digital interfaces (Hong et al., 2021; Nair & Das, 2023). Studies  
in e-commerce and mobile payment adoption demonstrate that simplicity of design, clear navigation, and  
accessible user interfaces enhance trust and willingness to transact online (Rahim et al., 2022; Al-Qudah et al.,  
2021). However, in mature digital environments, perceived usefulness often overrides ease of use, indicating a  
shift from usability-driven to performance-driven motivation (Sharma et al., 2022). In developing economies or  
rural contexts—such as the Philippine seafood sector—where technological exposure varies, EE remains pivotal  
in shaping users’ confidence and engagement, particularly when consumers transition from traditional to digital  
purchasing channels.  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025  
Social Influence (SI)  
Social Influence (SI), conceptualized as the perceived pressure from significant others to adopt a technology  
(Venkatesh et al., 2003), plays a dynamic role in shaping online consumer behavior. Earlier studies emphasize  
its significance in initial adoption phases, where peer norms and family encouragement strongly affect  
technology acceptance (Hong et al., 2021; Pham et al., 2023). However, recent findings indicate a declining  
influence of SI as consumers internalize digital norms and shift toward self-directed, utilitarian motivations (Nair  
& Das, 2023; Abdullah et al., 2023). In collectivist cultures, including the Philippines, social endorsement and  
community-based influence can still facilitate digital adoption through social media interactions and online  
reviews (Lee et al., 2022; Rahim et al., 2022). Yet, as e-commerce matures, digital trust and perceived platform  
reliability increasingly mediate the relationship between SI and behavioral intention. Hence, in online seafood  
purchasing—where quality assurance and product authenticity are paramount—social persuasion may support  
awareness but not necessarily translate into sustained behavioral intention without functional value  
reinforcement.  
Facilitating Conditions (FC)  
Facilitating Conditions (FC) denote the extent to which individuals perceive the existence of supportive  
infrastructure and technical resources enabling technology use (Venkatesh et al., 2003). Studies across digital  
commerce consistently demonstrate that adequate access to internet connectivity, secure payment systems, and  
responsive customer support significantly enhance adoption and usage intentions (Rahim et al., 2022; Abdullah  
et al., 2023; Pham et al., 2023). Particularly in developing contexts, FC serve as critical enablers bridging the  
gap between intention and actual use (Nair & Das, 2023). Lee et al. (2022) further argue that perceived  
infrastructural stability moderates consumers’ trust and perceived risk, underscoring that even high perceived  
usefulness cannot drive adoption without supportive conditions. For online seafood marketing, where logistical  
reliability and digital payment assurance are key, FC influence consumers’ confidence in the transaction process,  
thereby strengthening behavioral intention and sustaining long-term platform engagement.  
METHODOLOGY  
Research Design  
This study utilized a descriptive-correlational quantitative research design to examine the influence of the  
Unified Theory of Acceptance and Use of Technology (UTAUT) constructs—Performance Expectancy, Effort  
Expectancy, Social Influence, and Facilitating Conditions—on consumers’ behavioral intention to purchase  
seafood products through digital platforms. The design was deemed appropriate because it allowed the  
systematic description of consumer perceptions and the statistical assessment of relationships among the  
constructs without experimental manipulation. This approach is widely recognized in technology adoption  
research for establishing empirical relationships among latent behavioral variables.  
Research Locale  
The research was conducted in Surigao del Sur, a coastal province in the Caraga Region, Mindanao, Philippines.  
The province encompasses both urban and rural communities that actively engage in aquaculture, fish drying,  
and small-scale seafood value-adding. Despite this rich resource base, the penetration of digital marketing in  
seafood trade remains limited, making the province a suitable context for assessing the behavioral drivers and  
constraints of consumers’ online seafood purchasing decisions.  
Participants and Samplin  
A total of 100 respondents participated in the study, representing consumers residing in various municipalities  
of Surigao del Sur. The participants were selected using a stratified random sampling technique to ensure  
proportionate representation of different demographic groups such as age, gender, and location. Inclusion criteria  
required participants to be at least 18 years old, have experience purchasing seafood products, and possess  
awareness or access to digital marketing platforms. Those who did not meet these criteria or who declined to  
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participate were excluded. All respondents were informed about the study’s purpose and confidentiality  
measures, and their participation was voluntary.  
Data Collection Instrument  
Data were collected using a structured questionnaire based on the theoretical framework of the Unified Theory  
of Acceptance and Use of Technology (UTAUT). The instrument consisted of four major components  
corresponding to the key constructs: (1) Performance Expectancy, which assessed the perceived usefulness and  
efficiency of digital seafood marketing tools; (2) Effort Expectancy, which evaluated the perceived ease of use  
of online purchasing platforms; (3) Social Influence and Facilitating Conditions, which measured the impact of  
social pressure, peer recommendations, resource availability, and technological access; and (4) Behavioral  
Intention, which captured the likelihood of consumers to purchase seafood products online. All items were rated  
on a five-point Likert scale ranging from Strongly Disagree (1) to Strongly Agree (5). The questionnaire  
underwent expert validation by specialists in marketing, technology management, and statistics to ensure  
construct validity and content clarity. A pilot test was conducted among a sample of 20 consumers not included  
in the main study, resulting in a Cronbach’s alpha coefficient of 0.94, indicating excellent internal consistency  
and reliability of the instrument.  
Data Gathering Procedure  
Data collection was conducted over a four-week period using both online surveys and face-to-face interviews to  
accommodate respondents with varying levels of internet access. Ethical clearance was obtained from the  
university’s Research Ethics Committee prior to the commencement of data collection. Participants were  
provided with an informed consent form detailing the study’s objectives, confidentiality safeguards, and  
voluntary nature of participation. Online questionnaires were distributed through social media platforms and  
email, while field data were gathered in local markets and community centers. Completed questionnaires were  
reviewed for completeness and accuracy before being encoded for analysis. All responses were anonymized,  
securely stored, and used solely for research purposes.  
Data Analysis  
Data were analyzed using both descriptive and inferential statistics. Descriptive measures such as the mean and  
standard deviation were computed to summarize respondents’ perceptions of each UTAUT construct. Pearson’s  
correlation analysis was employed to determine the strength and direction of relationships among variables,  
while multiple linear regression analysis was conducted to identify which UTAUT constructs significantly  
predicted Behavioral Intention. A 0.05 level of significance was used to determine statistical validity. All  
computations were carried out using SPSS statistical software, ensuring precision, reproducibility, and  
methodological rigor.  
Ethical Considerations  
The study adhered to the ethical principles of informed consent, confidentiality, and voluntary participation.  
Approval was secured from the university’s Research Ethics Committee prior to data collection. Participants  
were fully informed about the study’s nature, objectives, and their rights as respondents, including the freedom  
to withdraw at any time. No personally identifiable information was collected, and all responses were treated as  
confidential. The researcher ensured that all ethical protocols were strictly followed to maintain the integrity and  
ethical soundness of the study.  
RESULTS  
Influence of UTAUT Constructs on Digital Seafood Marketing  
Table 1 presents the descriptive statistics of the UTAUT constructs influencing buyers’ perceptions of  
purchasing seafood online. All constructs yielded mean scores ranging from 3.63 to 3.66, corresponding to the  
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qualitative interpretation of Agree. This indicates that respondents generally hold positive perceptions toward  
digital seafood marketing platforms.  
Among the constructs, Performance Expectancy registered the highest average weighted mean (3.66), suggesting  
that consumers perceive digital platforms as effective tools for improving purchasing convenience, efficiency,  
and product comparison. Effort Expectancy (3.64) and Social Influence (3.65) followed closely, signifying that  
consumers find digital seafood applications user-friendly and that social networks moderately shape their online  
purchasing behavior. Facilitating Conditions (3.63) also showed an affirmative rating, implying that most  
consumers possess the necessary resources (e.g., internet access, devices, and secure payment options) to engage  
in online seafood transactions.  
Table 1The Influence of UTAUT Constructs on Digital Seafood Marketing  
Performance Expectancy  
Mean  
3.74  
SD (σ) Interpretation  
Using digital platforms makes it easier for me to buy seafood products  
online.  
1.0738 Agree  
Digital marketing helps me find better seafood options than traditional  
markets.  
3.59  
0.9185 Agree  
I believe that digital seafood platforms save me time and effort.  
Online seafood platforms improve my overall shopping efficiency.  
3.77  
3.68  
0.8931 Agree  
0.8358 Agree  
Digital seafood marketing enhances my ability to compare and select  
products.  
3.5  
0.9233 Agree  
Agree  
Avarage weighed Mean  
3.66  
Effort Expectancy  
Learning how to buy seafood online is easy for me.  
3.65  
3.65  
0.8881 Agree  
0.8881 Agree  
The process of purchasing seafood on digital platforms is clear and  
understandable.  
I find digital seafood platforms user-friendly.  
I feel comfortable using digital apps or websites for seafood purchases.  
I do not need assistance when shopping for seafood online.  
Avarage weighed Mean  
3.69  
3.48  
3.72  
3.64  
0.9565 Agree  
0.8899 Agree  
0.8616 Agree  
Agree  
Social Influence  
People close to me think I should try buying seafood online.  
I feel influenced by social media content related to seafood products.  
3.54  
3.68  
0.9437 Agree  
1.0191 Agree  
Recommendations from friends or influencers affect my seafood  
purchase decisions.  
3.63  
3.82  
0.9871 Agree  
0.9633 Agree  
I often buy seafood based on online reviews or ratings.  
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I believe that many people around me are already buying seafood online. 3.54  
0.9542 Agree  
Agree  
Avarage weighed Mean  
3.65  
Facilitating Conditions  
I have the necessary internet access to shop for seafood online.  
3.62  
3.89  
3.63  
3.46  
0.9365 Agree  
0.8591 Agree  
0.8213 Agree  
0.8778 Agree  
I own a device (smartphone, computer) suitable for online seafood  
shopping.  
There are enough secure payment options for buying seafood online.  
I feel supported when I encounter issues with seafood e-commerce  
platforms.  
I am confident that local online seafood sellers provide reliable service. 3.56  
0.9101 Agree  
Agree  
Avarage weighed Mean  
3.63  
Level of Behavioral Intention to Purchase Seafood Online  
Table 2 shows the mean scores for behavioral intention indicators. The overall weighted mean of 3.27 indicates  
a general tendency toward agreement, suggesting a moderate inclination among respondents to engage in or  
continue purchasing seafood online. While most respondents expressed neutrality toward future engagement (“I  
intend to continue buying seafood products…” M = 3.19, SD = 0.98), some displayed stronger behavioral  
intentions, particularly in prioritizing online options when available (“I will likely prioritize buying seafood  
online…” M = 3.46, SD = 0.97).  
Table 2 Mean ratings for Behavioral Intention  
Indicators  
Mean  
SD (σ) Interpretation  
I intend to continue buying seafood products through digital platforms in  
the future.  
3.19  
0.9769 Neutral  
I am likely to recommend buying seafood online to others.  
I prefer online seafood shopping over traditional methods.  
3.29  
3.10  
0.9522 Neutral  
0.9540 Neutral  
0.9749 Agree  
I will likely prioritize buying seafood online when the option is available. 3.46  
I plan to increase my use of digital platforms for seafood purchases in the  
near future.  
3.31  
1.0074 Neutral  
Agree  
Overall weighed Mean Interpretation  
3.27  
Correlations among UTAUT Constructs and Behavioral Intention  
Table 3 summarizes the Pearson correlation results between UTAUT constructs and behavioral intention.  
Performance Expectancy exhibited a strong positive and significant correlation with Behavioral Intention (r =  
0.7169, p < 0.01), indicating that perceived usefulness is closely linked to consumers’ intention to purchase  
seafood online. Conversely, Effort Expectancy (r = 0.6901, p = 0.133), Social Influence (r = 0.579, p = 0.226),  
and Facilitating Conditions (r = 0.6852, p = 0.267) showed moderate but statistically non-significant correlations  
with Behavioral Intention.  
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Strong interrelationships were also observed among the constructs themselves—for instance, between Social  
Influence and Facilitating Conditions (r = 0.8398, p < 0.01)—indicating conceptual interdependence within the  
UTAUT framework.  
Table 3 Correlation between each UTAUT construct and Behavioral Intention  
Relationship  
Pearson r  
0.8043  
p-value  
Interpretation  
Performance  
Expectancy vs  
Effort Expectancy  
Social Influence  
0
Significant strong positive correlation  
Significant moderate positive correlation  
Significant moderate positive correlation  
Significant strong positive correlation  
Significant strong positive correlation  
Significant strong positive correlation  
0.6046  
0.002  
0.004  
0
Facilitating Conditions 0.6822  
Behavioral Intention  
Social Influence  
0.7169  
0.7688  
Effort  
Expectancy vs  
0
Facilitating Conditions 0.7434  
Behavioral Intention 0.6901  
0
0.133  
Not significant despite moderate  
correlation  
Social  
Influence vs  
Facilitating Conditions 0.8398  
0
Significant  
correlation  
very strong  
positive  
Behavioral Intention  
Behavioral Intention  
0.579  
0.226  
0.267  
Not significant despite moderate  
correlation  
Facilitating  
Conditions vs  
0.6852  
Not significant despite moderate  
correlation  
Predictive Influence of UTAUT Constructs on Behavioral Intention  
Regression analysis results (Table 4) reveal that only Performance Expectancy (β = 0.4013, p = 0.002) and  
Facilitating Conditions (β = 0.4559, p = 0.003) significantly predict Behavioral Intention. These findings suggest  
that consumers’ perceptions of usefulness and the presence of enabling infrastructure (e.g., reliable internet,  
secure transactions) are primary drivers of intention to buy seafood through digital platforms. Effort Expectancy  
(β = 0.2329, p = 0.132) and Social Influence (β = -0.1304, p = 0.341) were not significant predictors.  
Table 4 Predictive influence of UTAUT constructs on Behavioral Intention)  
Construct  
Coefficient  
-0.2305  
0.4013  
Standard Error t-Value p-Value Interpretation  
Intercept  
0.3007  
0.1280  
0.1535  
0.1363  
0.1512  
-0.767  
3.136  
1.518  
-0.957  
3.016  
0.445  
0.002  
0.132  
0.341  
0.003  
Not significant  
Performance Expectancy  
Effort Expectancy  
Social Influence  
Facilitating Conditions  
Significant positive effect  
Not significant  
0.2329  
-0.1304  
0.4559  
Not significant  
Significant positive effect  
DISCUSSION  
The study findings reveal that Performance Expectancy and Facilitating Conditions play pivotal roles in shaping  
consumers’ behavioral intentions to purchase seafood online. These results align with recent research  
emphasizing that perceived usefulness remains the most influential determinant of technology adoption. For  
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instance, Al-Qudah et al. (2021) and Sharma et al. (2022) found that consumers’ belief in the efficiency and  
performance benefits of online platforms directly impacts purchase intentions across various e-commerce  
sectors.  
Similarly, Facilitating Conditions emerged as a strong determinant, underscoring the need for adequate  
infrastructure, such as reliable internet connectivity, secure payment systems, and technical support. Abdullah  
et al. (2023) and Rahim et al. (2022) confirmed that access to stable technological resources significantly  
enhances user adoption and continued engagement with digital platforms, particularly in emerging markets.  
Conversely, Effort Expectancy and Social Influence did not significantly predict behavioral intention in this  
study, despite showing moderate correlations. This is consistent with findings by Nair and Das (2023) and Hong  
et al. (2021), who observed that as digital literacy improves, the influence of perceived effort diminishes, and  
social factors become less critical once digital adoption becomes normative.  
The moderate mean behavioral intention (3.27) suggests cautious optimism toward digital seafood purchasing,  
potentially constrained by perceived product freshness, trust in online sellers, and delivery logistics. Pham et al.  
(2023) and Lee et al. (2022) similarly highlighted that product tangibility and quality assurance remain barriers  
in online food commerce, despite consumers recognizing the efficiency of digital transactions.  
From a managerial standpoint, enhancing platform performance (e.g., faster transactions, transparent product  
sourcing, freshness guarantees) and strengthening facilitating conditions (e.g., payment security, after-sales  
support) could elevate consumer confidence and conversion rates.  
In summary, the results support the UTAUT framework’s applicability to digital seafood marketing.  
Performance Expectancy and Facilitating Conditions emerge as crucial drivers of behavioral intention, while  
Effort Expectancy and Social Influence show limited influence. The findings emphasize the need for practical  
improvements in digital infrastructure and platform performance to foster stronger consumer engagement and  
behavioral intention toward online seafood purchasing.  
Limitations and Future Research  
This study’s primary limitation lies in its cross-sectional design, which restricts causal inference. Additionally,  
the data were collected from a specific market context, potentially limiting generalizability. Future research may  
employ longitudinal or experimental designs to validate causality and expand the analysis to cross-cultural  
settings. Incorporating additional variables such as trust, perceived risk, or hedonic motivation could also refine  
the UTAUT model’s predictive power in food-related e-commerce.  
CONCLUSION  
The study concludes that the Unified Theory of Acceptance and Use of Technology (UTAUT) model provides  
a robust framework for understanding the behavioral intentions of consumers toward purchasing seafood  
products through digital platforms. Among the UTAUT constructs examined, Performance Expectancy and  
Facilitating Conditions were found to exert statistically significant positive influences on behavioral intention,  
underscoring the importance of perceived usefulness and adequate technological support in shaping consumers’  
online purchasing decisions. While Effort Expectancy and Social Influence demonstrated moderate correlations  
with behavioral intention, their effects were not statistically significant in the regression model, suggesting that  
consumers may prioritize the functional advantages of digital platforms over social persuasion or ease of use in  
this context. Overall, the findings highlight that consumers’ intention to engage in online seafood shopping is  
moderately positive, driven primarily by efficiency, convenience, and access to reliable technological  
infrastructure. These results align with recent empirical evidence emphasizing the evolving digital readiness of  
food consumers and contribute to the theoretical advancement of technology acceptance research within niche  
e-commerce sectors such as online seafood marketing.  
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RECOMMENDATIONS  
Based on these findings, it is recommended that seafood marketers and digital platform developers focus on  
enhancing the performance value of online seafood systems by improving product search efficiency, delivery  
reliability, and user confidence in product quality. Strengthening facilitating conditions—such as providing  
secure payment systems, responsive customer support, and accessible digital literacy programs—may further  
encourage adoption. Policymakers should consider supporting digital infrastructure initiatives that empower  
local seafood producers to integrate into online markets, thereby expanding economic participation. From an  
educational standpoint, awareness campaigns could promote consumer trust and highlight the benefits of digital  
seafood commerce. For future research, it is advisable to employ longitudinal or experimental designs to assess  
causal relationships and explore additional moderating factors such as trust, price sensitivity, and perceived risk.  
Extending the model to cross-cultural or regional comparative contexts may also provide valuable insights. In  
sum, this study contributes to the growing discourse on digital consumer behavior by elucidating the mechanisms  
driving technology adoption in the seafood industry, offering both theoretical enrichment and actionable  
guidance for sustainable digital market development.  
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