A Study on Acceptance Intention of Extruded Pellet for Olive Flounder (Paralichthys olivaceus) Based on the UTAUT2 Model

Abstract

This study aims to examine the factors influencing the acceptance of extruded pellet (EP) usage among Korea’s olive flounder farming households by analyzing their acceptance factors to provide recommendations for its wider adoption. A survey was conducted among olive flounder farming households, and 188 valid questionnaires were collected. To examine the factors influencing EP acceptance intention, the UTAUT2 (extended unified theory of acceptance and use of technology) model was used. The independent variables were categorized into performance expectancy, effort expectancy, facilitating conditions, social influence, price value, and reliability as independent variables, while acceptance intention was considered as the dependent variable, to derive measurement items. In addition, the differences between the two groups were analyzed by using the aquaculture region and the manager’s experience as moderating variables. The hypothesis testing showed that performance expectancy, effort expectancy, social influence, price value, and reliability factors had a positive effect on acceptance intention, while facilitating conditions did not show a significant effect. The analysis of the moderating effect of the aquaculture region indicated a significant difference between the Jeju-do and Jeollanam-do groups. Conversely, the moderating effect of experience showed no significant difference between those with more experience (≥10 years) and those with less (<10 years).

1. Introduction

Despite the numerous potential benefits of the aquaculture industry, concerns over its environmental impact have always been present [1]. Recently, increasing attention has been paid to feed quality to alleviate the harmful environmental impacts of intensive aquaculture [2]. It is recognized that the quality and efficient utilization of feed are important factors in both economic and sustainability terms [3]. In Norway, for example, the replacement of fishmeal and fish oil in salmon feed with plant-based proteins has reduced the reliance on fishery resources for salmon feed from 90% in 1990 to 30% in 2013 [4]. Furthermore, there are ongoing efforts to explore new protein substitutes such as plant and insect feeds for sustainable aquaculture, aiming to prevent the decline in fish stocks and reduce feed costs [5,6,7]. In the Korean aquaculture industry, raw fish-based moist pellets, which are ground fish used as feed for farmed fish production, currently account for 84% of the total feed [8]. However, due to their high moisture content, moist pellets easily dissolve in water, resulting in a high proportion of uneaten and wasted feed [9]. Feed scraps that decompose in the water can eutrophicate nearby waters, causing environmental pollution. In addition, criticism has been steadily raised over the depletion of fisheries resources due to the use of edible fish as aquaculture feed. There is a growing awareness among consumers regarding the food safety of moist pellets, which are distributed without proper safety inspections, and demanding improvements. Against this backdrop, Korea has developed extruded pellets to replace moist pellets to prevent the deterioration of the aquatic environment and improve the competitiveness of the aquaculture industry. Raw fish-based moist pellets used in Korean aquaculture are typically produced using approximately 70% whole raw fish and 30% fishmeal, meaning that their formulation relies entirely on fish-derived ingredients. In contrast, extruded pellets (EPs) are manufactured with 60% fishmeal supplemented by plant-based proteins and oils [10,11]. Consequently, EP production requires substantially less raw fish than MP production, offering significant advantages in terms of sustainability and resource efficiency. Extruded pellets (EPs) are manufactured at high temperatures above 130 °C, then dried to reduce the moisture content to less than 14% before being finally cooled [12]. As a result of water quality evaluation and growth experiments with EP supply, it is expected that this feed will be more environmentally friendly, reducing pollution in the marine environment, and preserving fishery resources compared to conventional moist pellets. Additionally, the use of EP is anticipated to be cost-effective by enhancing management convenience and reducing labor and electricity costs [13,14,15]. However, fish farming households face challenges in immediately adopting EP due to its higher purchase cost than moist pellets, as well as the presence of risk factors such as the uncertainty of productivity. Without verifiable productivity and profitability gains from the use of EP, fish farmers are likely to reject or delay its adoption. Despite being an integral part of aquaculture and an area for efficiency improvement, there is a lack of research focusing on the acceptance intention of developed feeds in aquaculture [16]. Therefore, this study aimed to analyze the determinants of EP acceptance from the perspective of fish farmers and design policies based on the results to promote the use of this more efficient feed. Olive flounder is a major aquaculture species in South Korea, accounting for more than 40% of the country’s total aquaculture production, with an annual output exceeding 40,000 tons. Currently, over 90% of the feed supplied for olive flounder farming consists of moist pellets made from ground mackerel, herring, and other fish. The current analysis focuses on the acceptability and acceptance factors of EP among olive flounder farming households, where the use of EP is currently minimal, aiming to provide implications for the increased adoption of EP.

Building upon this foundation, previous research has provided essential insights into the factors affecting technology adoption in aquaculture and related agricultural contexts. These studies underscore the importance of performance expectancy, effort expectancy, social influence, facilitating conditions, and price value in shaping behavioral intention. They also suggest that fishermen’s habits and hedonic motivations—including their comfort with traditional moist pellets and perceived satisfaction with feeding practices—may serve as powerful intrinsic motivators influencing adoption behavior. Integrating these perspectives, this study situates the adoption of extruded pellets within a broader behavioral and technological framework, aiming to understand how external and internal factors jointly determine sustainable feed transition decisions.

To foster the competitive development of aquaculture, studies are being actively conducted on the development of fish feed, aquaculture technology, new breeds, and eco-friendly aquaculture. Recently, there has been a growing body of research on fishermen’s acceptance of various developed technologies and on analyzing the factors that influence their acceptance intention [16,17]. In a study investigating the acceptance intentions toward developed feed, Brugere et al. [16] analyzed the acceptance intention of aquaculture feeds containing novel ingredients (plants, seaweed, microalgae, etc.) as fishmeal substitutes. The findings highlighted the importance of involving fishermen from the early stages of feed development to mitigate adoption barriers. Ouko et al. [18] analyzed the acceptance intention of insect meal developed as an alternative protein source for tilapia feed. Their findings suggest that policymakers need to provide education to highlight the ease of use and benefits of alternative feeds, along with practical guidelines that demonstrate feeding methods and production results to enhance knowledge and information utilization of alternative feeds. Le [19] argued that training in decision support systems (DSSs) is crucial to increase the acceptance of risk management frameworks developed to manage risks (price, institutional, financial, etc.) that affect the profitability of catfish farming. Florestiyanto et al. [20] analyzed the acceptance factors for adopting IoT-based aqua-culture management systems and found that domain-specific knowledge of fishermen is the most important factor. A study by Hernandez & Hernandez [21], which examined the acceptance intention of a mobile application to identify and classify tilapia, suggested that it is necessary to develop training and support programs to enhance the acceptance intention of mobile applications in aquaculture. Similarly, Beza et al. [22] analyzed the acceptance factors for mobile SMS in agricultural data collection and information provision. Their findings suggest the importance of building farmers’ trust in mobile SMS for agricultural information, as well as reducing the cost burden of sending SMS to increase farmers’ behavioral intention. Nugroho et al. [23] recommended that the government should provide farmers with farming machinery practice and increase the social impact of farmers to improve their acceptance intention toward farming machinery innovation. Chung & Kang [24], who examined the acceptance factors of smart farm adoption, proposed the need to secure a competitive advantage through differentiation of performance expectancy and price utility, and emphasized the importance of developing marketing strategies that consider these factors. Kang et al. [25] found that higher levels of trust in smart farms and a country’s information technology (IT) proficiency are associated with a greater acceptance intention of smart farms. To sum up, acceptance intention is influenced by various factors depending on the specific aspects of the development factors. It is crucial to design policies and marketing strategies that incorporate the findings of the analysis in order to enhance the acceptance intention of EP. However, previous studies have not fully considered the characteristics of the research subjects. In this study, by considering the unique characteristics of aquaculture and fishermen, we establish a research model that evaluates the effects of career, environment, and region on the acceptance of EP.

By synthesizing insights from prior studies and the theoretical constructs of UTAUT2, the present research advances an integrated understanding of feed technology adoption behavior. This unified approach bridges the gap between technological innovation and behavioral adaptation, providing implications for both aquaculture policy and sustainable feed management strategies.

2. Theoretical Background

Extended Unified Theory of Acceptance and Use of Technology (UTAUT2)

We utilized the UTAUT2 model, which is an extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) proposed by Venkatesh et al. [26]. The UTAUT model integrates eight previous models related to the intention to accept and use innovative technologies. It considers several variables that influence users’ behavioral intention, including performance expectancy, effort expectancy, social influence, and facilitating conditions. In addition, the four variables of gender, age, experience, and voluntariness are proposed to play a moderating role in behavioral intention.

Performance expectancy represents the belief in how much a particular technology or system can enhance work performance. Effort expectancy refers to the perceived convenience and usability of an information system. Social influence is the degree to which individuals perceive that influential people around them should accept the new system. Finally, facilitating conditions refer to the belief that the organizational and technical foundations are in place to facilitate the use of the system [26].

However, since the UTAUT model is proposed in an organizational context, it has limitations in explaining the technology adoption intention of general consumers [27]. Therefore, Venkatesh et al. [27] proposed the UTAUT2 model, an extension of UTAUT specifically designed for a consumer context. The UTAUT2 model incorporates additional variables, including hedonic motivation, price value, and habit, to enhance its explanatory power. Hedonic motivation is derived from the pleasure or fun gained from using a new technology and influences technology acceptance and use. Price value is defined as the cognitive transaction consumers make between the perceived benefits of a technology and its monetary cost. If the benefits of using a technology outweigh the monetary costs, the technology has a positive price value, which influences the behavioral intention of users. Habit, defined as the natural tendency of people to perform a behavior based on past learning, significantly impacts technology adoption [27]. Lastly, acceptance intention (behavioral intention) refers to the degree of intention to perform a specific behavior [28].

In this study, the UTAUT2 model was selected as the most appropriate theoretical framework for analyzing fish farmers’ acceptance intention toward extruded pellets (EPs). The adoption of EP is not mandated but rather voluntary, influenced by perceptions of cost, convenience, and environmental benefit. UTAUT2 enables a more comprehensive examination of behavioral intention by capturing not only the cognitive (e.g., performance expectancy, effort expectancy) but also the affective and contextual (e.g., habit, price value, social influence) dimensions that characterize real-world decision-making among aquaculture managers.

The model’s comprehensiveness and adaptability allow it to explain a larger portion of variance in behavioral intention compared with earlier models such as TAM or TPB. It effectively integrates both utilitarian and emotional factors, offering nuanced insights into technology acceptance in diverse contexts [27]. UTAUT2 has been widely validated in studies of agricultural technologies, mobile applications, and sustainability-oriented innovations, confirming its flexibility across domains.

Despite its explanatory strength, UTAUT2 also presents several limitations. The model includes many variables, which can complicate empirical testing and interpretation when sample sizes are limited. It may also overlook external, domain-specific factors such as environmental awareness, regulatory influence, or perceived sustainability value—dimensions that can be especially relevant in aquaculture technology adoption. Moreover, its original constructs were derived from consumer behavior studies, so contextual adaptation is required to ensure validity in professional or production environments.

Overall, UTAUT2 provides a theoretically sound and empirically robust foundation for examining the behavioral determinants of EP adoption among olive flounder farmers. Its structure enables a multidimensional understanding of how perceived performance, cost efficiency, and habitual behavior influence acceptance intention, thereby contributing to the promotion of sustainable feed technologies in aquaculture. As illustrated in Figure 1, the UTAUT2 model consists of seven core determinants that influence individuals’ behavioral intention to use a technology and their actual use behavior. These determinants include performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit.

At the center of the UTAUT2 model lies behavioral intention, which functions as a key mediating construct that links motivational, cognitive, and contextual factors to use behavior, the ultimate dependent variable. Behavioral intention represents the user’s conscious decision to engage with a technology, while use behavior refers to the actual implementation or continued utilization of that technology in practice. The model posits that behavioral intention mediates the effects of the independent variables on actual behavior, providing a structured pathway through which psychological and contextual influences translate into observable adoption outcomes.

Through the inclusion of these constructs, the UTAUT2 model provides an integrative and empirically validated framework for analyzing technology acceptance. It captures both utilitarian motivations (e.g., performance and effort expectancies) and hedonic or experiential motivations (e.g., enjoyment, habit), while also acknowledging the role of demographic differences and contextual influences.

3. Research Model and Hypotheses

3.1. Research Model

Given the nature of aquaculture, we excluded hedonic motivation and habits from the variables in the UTAUT2 model. Hedonic motivation was excluded because fishermen are not seeking pleasure but rather the practical benefits of using EP. Habit was excluded as an independent variable because fishermen’s purchase of EP is a conscious and planned behavior, not a natural and unintentional one.

Meanwhile, we introduced a new variable of trust to the model. The addition aimed to investigate the impact of EP quality and safety on fishermen’s EP acceptance intention. While the UTAUT2 model of Venkatesh et al. [27] verifies that acceptance intention influences actual use behavior, this study focuses solely on acceptance intention as the dependent variable, excluding use behavior. The construct behavioral intention in this study refers to the intention to continue or expand the use of EP in future aquaculture operations. Thus, the measurement items were framed to capture ongoing behavioral commitment rather than initial adoption. Acceptance intention, in this context, is defined as the extent to which fishermen intend to use EP in the future. The specific model developed for this study is shown in Figure 2. The hypothesized relationships, denoted as H1 through H6, represent the direct effects of the six independent constructs—performance expectancy, effort expectancy, facilitating conditions, social influence, price value, and trust—on behavioral intention. Specifically, it is hypothesized that each of these variables exerts a positive and significant influence on farmers’ intention to adopt extruded pellets. The moderating effects of experience and region are expected to further clarify how contextual and personal factors shape the decision-making process in aquaculture technology adoption.

In summary, Figure 2 depicts the conceptual research framework that integrates key determinants of behavioral intention within the UTAUT2 theoretical structure, while incorporating aquaculture-specific extensions to enhance its contextual relevance. The inclusion of trust, as well as the moderating roles of experience and region, allows for a more nuanced understanding of how technological, economic, and social factors interact to influence sustainable feed adoption in olive flounder aquaculture. This model provides the basis for the empirical analysis conducted in subsequent chapters, through which the proposed hypotheses (H1–H6) are statistically tested and validated.

3.2. Hypotheses Development

3.2.1. Performance Expectancy (PE)

Drawing from the variables presented in Venkatesh et al. [26] and Venkatesh et al. [27], this study adapted and redefined them in the context of aquaculture. Performance expectancy is redefined as the degree to which a farmer believes that the use of EP will enhance their performance in terms of productivity, farm income, disease prevention, and efficiency. This perception of improved performance is expected to have a significant influence on fishermen’s acceptance intention. Previous studies by Le [19] and Chung & Kang [24] have suggested a positive correlation between performance expectancy and acceptance intention. Therefore, the following hypothesis is proposed:

H1. 

Performance expectancy has a positive effect on fishermen’s EP acceptance intention.

3.2.2. Effort Expectancy (EE)

Effort expectancy was defined as the perceived ease of use associated with EP, specifically in terms of fishermen’s ability to use it without any prior knowledge of EP and their ease of accessing relevant information about EP. Ouko et al. [18] found that the perceived ease of use is positively associated with acceptance intention, while Le [19] found that effort expectancy plays an important role in forming behavior intentions. Therefore, we propose the following hypothesis:

H2. 

Effort expectancy has a positive effect on fishermen’s EP acceptance intention.

3.2.3. Facilitating Conditions (FCs)

Facilitating conditions refer to the perception of the infrastructure required to use EP in aquaculture. This infrastructure includes the availability of expert assistance in case of problems such as disease outbreaks or deaths related to EP use, as well as the provision of guidance and training on feeding methods and husbandry management. When users perceive that the technical and organizational infrastructure for a new technology is in place, their intention to accept and use that technology tends to increase [26]. Previous studies such as Kang et al. [25] and Nugroho et al. [23] have found a significant positive effect of facilitating conditions on acceptance intention. However, no significant difference was found by Chung & Kang [24]. Accordingly, the following hypothesis is proposed to test the relationship between facilitating conditions and acceptance intention:

H3. 

Facilitating conditions have a positive effect on fishermen’s EP acceptance intention.

3.2.4. Social Influence (SI)

Social influence is defined as the extent to which the fishermen are influenced by their fellow fishermen to use EP. This influence can come in the form of recommendations, suggestions, or a social atmosphere that encourages the adoption of EP. Venkatesh et al. [26] found that greater social influence leads to higher intention to use a technology. Similarly, Le [19], Kang et al. [25], and Chung & Kang [24] have shown that social influence has a positive impact on acceptance intention. Therefore, the following hypothesis is proposed:

H4. 

Social influence expectancy has a positive effect on fishermen’s EP acceptance intention.

3.2.5. Price Value (PV)

Price value is defined as the degree to which the purchase price of EP is considered reasonable and the monetary value of using EP is deemed appropriate. While price value has been shown to have a positive effect on the acceptance intention of farmers for technologies such as SMS and smart farms [22,24], it was not estimated to be significant for the acceptance intention for farm equipment [23]. This study assumes that the perceived benefits (convenience, continuity, etc.) and value of using EP outweigh the costs associated with it. Therefore, the following hypothesis is proposed:

H5. 

Price value has a positive effect on fishermen’s EP acceptance intention.

3.2.6. Trust (TR)

Trust is essentially taking risks and having confidence in what one believes in, and it plays a key role in building long-term business relationships [29]. In this study, trust is defined as the general level of confidence that fishermen have in the quality of EP and the safety of the fish produced with EP. The formation of trust has been confirmed in many studies as an important factor in acceptance intention [22,25]. Therefore, the following hypothesis is formulated:

H6. 

Trust has a positive effect on fishermen’s EP acceptance intention.

In the UTAUT2 model proposed by Venkatesh et al. [27], demographic variables such as gender, age, and experience were used as moderating variables that influence the relationship between the independent and dependent variables. However, in this study, instead of using general demographic variables, we have selected variables that consider the characteristics of aquaculture. Specifically, we analyzed the moderating effect of managers’ experience, considering that management performance may depend on their expertise and experience in aquaculture. In addition, the moderating effect of the aquaculture region was examined, recognizing that aquaculture methods differ depending on the regional environment.

4. Methodology

4.1. Data Collection and Questionnaire

We selected households engaged in olive flounder farming as the appropriate target group for analyzing EP acceptance intention, as olive flounder is a representative aquaculture fish species in Korea with low EP usage. The survey area was centered on Jeju-do and Jeollanam-do, the two main production areas for cultured olive flounder. Jeju-do alone accounts for approximately 51% of the nation’s total production, while Jeollanam-do accounts for 44% [8]. The survey was conducted over a period of approximately two months, from July to August 2022, and a total of 198 survey responses were collected. Of these, 188 complete responses were used for the final analysis, excluding 10 incomplete responses that were deemed unusable for analysis.

The questionnaire was designed based on previous studies and is divided into two parts. Part 1 focused on the demographic characteristics, which include items related to the respondents’ experience of using EP, the type of feed currently used, gender, age, experience, aquaculture region, aquaculture farm water surface area, and annual revenue. Part 2 of the questionnaire consisted of items related to the respondents’ opinions on EP acceptance intention. These items were divided into the independent variables of performance expectancy, effort expectancy, facilitating conditions, social influence, price value, and trust. The questionnaire consists of a total of 20 items. All questions were scored on a 5-point Likert scale (1 being “Strongly disagree,” 2 being “Disagree,” 3 being “Somewhat agree,” 4 being “Agree,” and 5 being “Strongly agree”). The specific questionnaire organization is shown in

Table 1. Measurement items.

Construct	Item	Scale	Sources
Performance Expectancy (PE)	PE1	The use of EP will maintain or improve productivity.	Venkatesh et al. [27], Beza et al. [22] Nugroho et al. [23], Kang et al. [25]
	PE2	The use of EP will maintain or improve my farm revenue.
	PE3	The use of EP will help prevent disease.
	PE4	The use of EP will be more efficient than conventional feeding methods.
Effort Expectancy (EE)	EE1	It will be easy to obtain information on the use of EP.	Venkatesh et al. [27], Ouko et al. [18], Le [19]
	EE2	Even if you have never used EP before, you should have no problem using the manual as a reference.
Facilitating Conditions (FCs)	FC1	If you encounter any problems with your EP, such as disease, you will be able to get help from the experts around you.	Venkatesh et al. [27], Le [19], Kang et al. [25], Chung et al. [24]
	FC2	If you use EP, you will receive guidance on proper husbandry practices.
	FC3	If you use EP, you will be able to receive training, including the relevant supply manuals.
Social Influence (SI)	SI1	I will use EP if someone who has used it recommends it.	Venkatesh et al. [27], Beza et al. [22], Chung et al. [24]
	SI2	I will use EP if I see other fish farms around me using it.
	SI3	I feel like I should use EP because of media coverage, publicity, or social trends about EP.
Price Value (PV)	PV1	It is worth using EP if the cost of EP is justified by productivity, economics, etc.	Venkatesh et al. [27], Beza et al. [22], Chung et al. [24]
	PV2	It is worth using EP if it improves breeding management convenience and reduces labor compared to the cost of EP.
	PV3	I believe the current price of EP is reasonable.
Trust (TR)	TR1	I can trust the quality and safety of EP.	Beza et al. [22], Kang et al. [25]
	TR2	I have trust in the quality of EP-fed aquaculture products.
Behavioral Intention (BI)	BI1	I intend (plan) to use EP in the future.	Venkatesh et al. [27], Beza et al. [22], Nugroho et al. [23], Kang et al. [25], Ouko et al. [18]
	BI2	I intend (plan) to use EP in the future, even if there is no government subsidy for EP.
	BI3	I believe that the use of EP is a necessary part of aquaculture in the future.

.

4.2. Data Analysis

Descriptive statistical analysis was conducted to identify the demographic characteristics of the sample. To ensure the validity and reliability of the research model, confirmatory factor analysis was conducted. First, convergent validity analysis was performed, measuring standardized factor loadings, average variance extracted (AVE), conceptual reliability (CR), and Cronbach’s Alpha values.

Here, (i) standardized factor loading is considered appropriate if the correlation between the observed variables to measure the latent variable is greater than or equal to 0.5 [30,31]; (ii) the AVE value indicates the amount of variance explained by the metric for the construct, with a value of 0.5 or greater being desirable [31]; (iii) the CR value must be at least 0.7; (iv) a Cronbach’s Alpha value of 0.7 or higher is considered adequate to assess internal consistency [31,32].

Second, discriminant validity was evaluated by reviewing the relationship between the latent variables, which is judged to be valid if the AVE value exceeds the squared correlation coefficient between the variables [33].

After confirming the measurement model, the structural model was tested to validate the adoption of the hypotheses (AMOS version 29.0). The goodness-of-fit of the structural model was evaluated based on various model fit indices. A Root Mean Square Error of Approximation (RMSEA) of ≤0.08 is considered a good fit, while 0.08 < RMSEA < 0.1 represents an adequate fit, and RMSEA ≥ 0.1 indicates a poor fit [34]. A Minimum Discrepancy Function divided by Degrees of Freedom (CMIN/DF) of <3 is considered a good fit. Meanwhile, the normed fit index (NFI), incremental fit index (IFI), comparative fit index (CFI), and Turkey–Lewis index (TLI), all ranging from 0 to 1, are expected to have values of 0.9 or higher for a good fit [35,36]. Lastly, a moderation analysis was conducted to examine the impact of the manager’s aquaculture experience and aquaculture region on each group.

5. Results

5.1. Descriptive Analysis

The demographic characteristics of the respondents are presented in

Table 2. Demographic characteristics of the sample (N = 188).

Category	Frequency	%
Have you used EP before?
Yes	176	93.6
No	12	6.4
Type of feed currently used
Only moist pellets	61	32.4
Combination of EP and moist pellets	108	57.4
Only EP	19	10.1
Gender
Male	164	87.2
Female	24	12.8
Age
20–29	3	1.6
30–39	6	3.2
40–49	63	33.5
50–59	80	42.6
60 or older	36	19.1
Experience
<5 years	10	5.3
5 to 10 years	24	12.8
11 to 20 years	69	36.7
21 to 30 years	79	42.0
>30 years	6	3.2
Aquaculture region
Jeju-do	92	48.9
Jeollanam-do	96	51.1
Aquaculture farm area
<1000 m2	4	2.1
1000–3000 m2	27	14.4
3001–5000 m2	103	54.8
5001–7000 m2	42	22.3
>7000 m2	12	6.4
Annual income
<USD 77,000	33	17.6
USD 77,000–USD 230,000	85	45.2
USD 230,001–USD 390,000	33	17.6
USD 390,001–USD 540,000	29	15.4
>USD 540,000	8	4.3

. Of the 188 respondents, 93.6% reported using EP, while 6.4% had never used EP, indicating a high prevalence of EP usage among olive flounder farming households. Regarding the type of feed currently used, 32.4% of respondents used only moist pellets, 57.4% used a combination of EP and moist pellets, and 10.1% used only EP.

Regarding the gender distribution of managers, 87.2% were male and 12.8% were female, with male managers accounting for the majority. In terms of age, 1.6% of respondents were in their 20s, 3.2% in their 30s, 33.5% in their 40s, 42.6% in their 50s, and 19.1% were in their 60s or older. Managers in their 40s and 50s accounted for 76.1% of the sample (N = 188). Regarding experience in olive flounder farming, 5.3% had less than five years of experience, 12.8% had 5–10 years, 36.7% had 11–20 years, 42.0% had 21–30 years, and 3.2% had more than 30 years of experience, with the majority having 21–30 years of experience in olive flounder farming. The olive flounder aquaculture regions were evenly distributed, with Jeju-do accounting for 48.9% and Jeollanam-do for 51.1%.

The water surface area of the farms was categorized as follows: 2.1% for less than 1000 square meters, 14.4% for 1000 to 3000 square meters, 54.8% for 3001 to 5000 square meters, 22.3% for 5001 to 7000 square meters, and 6.4% for more than 7000 square meters, indicating that olive flounder farming is most prevalent in the 3001 to 5000 square meter water surface area. Regarding annual revenue from olive flounder farming, approximately half of the farmers (45.2%) earned between USD 77,000 and USD 230,000 annually. The income distribution was as follows: less than USD 77,000 (17.6%), between USD 77,000 and USD 230,000 (45.2%), between USD 230,001 and USD 390,000 (17.6%), between USD 390,001 and USD 540,000 (15.4%), and more than USD 540,000 (4.3%).

5.2. Measurement Model Results

First, we assessed the convergent validity of the factors and excluded items PE3, PE4, SI3, and PV3 as they did not meet the criterion of 0.7. However, the other items demonstrated satisfactory convergent validity with factor loadings ranging from 0.732 to 0.984. Reliability testing of each factor indicated Cronbach’s Alpha values of 0.862–0.946, indicating high reliability. In addition, all CR values were above 0.7, confirming the internal consistency and reliability of the measurement model (

Table 3. Reliability and validity of the measurement model.

Construct	Item	M	SD	Factor Loading	Reliability
					Cronbach’s Alpha	C.R.	AVE
PE	PE1	1.71	1.004	0.975	0.939	0.939	0.885
	PE2	1.71	1.015	0.907
EE	EE1	2.80	1.059	0.798	0.873	0.869	0.771
	EE2	2.60	1.063	0.971
FC	FC1	3.01	0.821	0.868	0.894	0.934	0.825
	FC2	3.02	0.846	0.912
	FC3	3.22	0.731	0.805
SI	SI1	2.47	1.163	0.899	0.901	0.869	0.768
	SI2	2.51	1.195	0.912
PV	PV1	2.69	1.364	0.816	0.862	0.784	0.646
	PV2	2.98	1.275	0.930
TR	TR1	2.50	1.077	0.964	0.946	0.936	0.880
	TR2	2.48	1.106	0.931
BI	BI1	2.43	1.296	0.984	0.916	0.923	0.802
	BI2	1.89	0.961	0.732
	BI3	2.47	1.310	0.963

Note: PE = Performance Expectancy, EE = Effort Expectancy, FC = Facilitating Conditions, SI = Social Influence, PV = Price Value, TR = Trust, BI = Behavioral Intention.

). The AVE value exceeded the squared value of the correlation coefficient, indicating discriminant validity (

Table 4. Inter-construct correlation matrix.

	PE	EE	FC	SI	PV	TR	AVE
EE	0.220						0.771
FC	0.147	0.192					0.825
SI	0.456	0.181	0.282				0.768
PV	0.188	0.202	0.124	0.353			0.646
TR	0.441	0.375	0.278	0.320	0.506		0.880
BI	0.477	0.377	0.301	0.462	0.457	0.654	0.802

Note: Diagonal line represents the square roots of AVEs.

).

5.3. Structural Equation Model Results

The results of the goodness-of-fit test for the structural equation model are shown in

Table 5. Summary of fit indices for the measurement and structural models.

Model Fit Indices	Model Results	Recommended Value	References
CMIN/DF	2.613	<3	Hu et al. [36]
NFI	0.927	≥0.9	Bollen et al. [35], Hu et al. [36]
IFI	0.954	≥0.9	Bollen et al. [35], Hu et al. [36]
CFI	0.953	≥0.9	Bollen et al. [35], Hu et al. [36]
TLI	0.932	≥0.9	Bollen et al. [35], Hu et al. [36]
RMSEA	0.093	<0.1	Kenny et al. [34]

Note: CMIN/DF = Ratio between chi-square and the degrees of freedom, NFI = Normed fit index, IFI = Incremental fit index, CFI = Comparative fit index, TLI = Tucker–Lewis index, RMSEA = Root mean square error of approximation.

. The model exhibited a CMIN/DF value of 2.613, an NFI value of 0.927, an IFI value of 0.954, a CFI value of 0.953, a TLI value of 0.932, and an RMSEA value of 0.093. Although the RMSEA value is slightly higher than desired, Kenny et al. [34] consider a range of 0.08 ≤ RMSEA ≤ 0.1 to be a good fit. Therefore, the structural model can be considered a good fit overall.

The validity of the measurement model was assessed through confirmatory factor analysis, and the fit of the structural model was verified. Then, a path analysis was conducted for hypothesis testing to determine whether the hypotheses were accepted or rejected. The results are shown in

Table 6. Summary of results of path analysis of the structural model.

Hypothesis	Structural Path	Estimates	p-Value	Result
H1	PE → BI	0.231	0.017 **	Supported
H2	EE → BI	0.168	0.012 **	Supported
H3	FC → BI	0.130	0.264	Not supported
H4	SI → BI	0.208	0.015 **	Supported
H5	PV → BI	0.136	0.083 *	Supported
H6	TR → BI	0.477	0.000 ***	Supported

*** p < 0.01, ** p < 0.05, * p < 0.1.

. The factors that significantly influenced fishermen’s EP acceptance intention were performance expectancy (β = 0.231, p = 0.017), effort expectancy (β = 0.168, p = 0.012), social influence (β = 0.208, p = 0.015), price value (β = 0.136, p = 0.083), and trust (β = 0.477, p = 0.000), all showing significant positive effects. Thus, hypotheses H1, H2, H4, H5, and H6 were accepted. However, facilitating conditions were not found to demonstrate significant effects, and therefore, H3 was rejected (Table 6, Figure 3).

The analysis revealed that trust had the highest influence (β = 0.477 ***) on the relationship between the independent and dependent variables, followed by performance expectancy (β = 0.231 **), social influence (β = 0.208 **), effort expectancy (β = 0.168 **), and price value (β = 0.136 *). This suggests that the perceived quality and safety of the feed have a greater impact on fishermen’s acceptance intention than the purchase price and cost of the feed.

5.4. Moderating Variable

A moderator variable is a third variable that influences the magnitude or nature of the relationship between an independent variable and a dependent variable [37]. In this study, the aquaculture region and experience were used as the moderator variables. The aquaculture region consisted of two regions, Jeju and Jeollanam-do, and the survey aimed to analyze whether there were differences in EP acceptance intention between these regions. Experience was categorized into two groups: “less experienced” (less than 10 years of experience) and “more experienced” (10 years or more of experience). For each of the two groups, the relationship between factors was analyzed in an unconstrained model and a constrained model, where each path was constrained to be the same for both groups to verify the differences between the models.

First, a comparative analysis was conducted between the two groups, considering the aquaculture region as a control variable. The difference between the unconstrained and constrained models was found to be statistically significant at the p < 0.01 level, indicating a distinction between the groups (

Table 7. Measurement invariance testing of models (region).

Model	CMIN	DF	p-Value	NFI Delta-1	RFI Delta-2	IFI rho-1	TLI rho-2
Constrained	19.704	6	0.003	0.007	0.007	0.002	0.002

). In Jeju-do, price value and trust were found to have significant effects on EP acceptance intention. In Jeollanam-do, effort expectancy and social influence displayed significant effects on EP acceptance intention, confirming differences in acceptance intention by region (

Table 8. Path coefficient gap result by region.

Structural Path	Jeju-do		Jeollanam-do
	Estimates	p-Value	Estimates	p-Value
PE→BI	0.01	0.945	0.082	0.572
EE→BI	0.068	0.311	0.116	0.041 **
FC→BI	−0.185	0.264	0.049	0.748
SI→BI	0.256	0.181	0.248	0.002 ***
PV→BI	0.705	0.088 *	0.066	0.19
TR→BI	0.499	0.01 **	0.152	0.075

*** p < 0.01, ** p < 0.05, * p < 0.1.

).

Next, we conducted a comparative analysis between the two groups using experience as a control variable. The results revealed that the difference between the unconstrained and constrained models was not statistically significant at p = 0.802, indicating no significant difference between the two groups (

Table 9. Measurement invariance testing of models (experience).

Model	CMIN	DF	p-Value	NFI Delta-1	RFI Delta-2	IFI rho-1	TLI rho-2
Constrained	3.057	6	0.802	0.001	0.001	−0.004	−0.004

). When comparing the influence between the groups, all paths were found to be insignificant in the group with less experience (<10 years), while in the group with more experience (≥10 years), performance expectancy, effort expectancy, social influence, and trust were found to have significant effects on EP acceptance intention (

Table 10. Path coefficient gap results by experience.

Structural Path	<10 Years		≥10 Years
	Estimates	p-Value	Estimates	p-Value
PE→BI	0.18	0.29	0.225	0.085 *
EE→BI	0.212	0.39	0.134	0.038 **
FC→BI	0.337	0.252	0.071	0.588
SI→BI	0.179	0.28	0.247	0.038 **
PV→BI	0.123	0.406	0.133	0.204
TR→BI	0.338	0.176	0.554	0.000 ***

*** p < 0.01, ** p < 0.05, * p < 0.1.

).

6. Discussion

The present study employed the UTAUT2 model to examine various factors influencing fishermen’s acceptance of EP in Korean olive flounder aquaculture and analyzed their impact on acceptance intention. The findings revealed that all paths, except for facilitating conditions, exhibited a significant positive effect on acceptance intention. The significant relationship between performance expectancy and acceptance intention is consistent with the findings of Beza et al. [22], Le [19], Chung & Kang [24], and Nugroho et al. [23]. The use of EP has been associated with increased productivity and profitability, underscoring the importance of further research on improving growth outcomes through EP implementation. In addition, it is necessary to actively disseminate information to fishermen regarding efficient management practices while using EP, including improved feeding methods and reduced production costs.

Similar to the findings of Ouko et al. [18], Beza et al. [22], and Le [19], effort expectancy was found to have a positive impact on acceptance intention. This can be attributed to the inclination of consumers or users to seek easy-to-use and convenient approaches when adopting new technologies. Conversely, Kang et al. [25] found that effort expectancy had a negative effect on smart farm acceptance intention, presumably due to the difficulty of respondents in accurately assessing the efforts they would expend as potential users. In the current study, considering that 93.6% of the respondents had previous experience using EP, it can be concluded that there was a fairly accurate assessment of effort expectancy. Therefore, ongoing training initiatives are warranted to improve fishermen’s understanding of EP and assist them in addressing the technical issues associated with using EP.

By its very nature, aquaculture is a collective rather than an individual endeavor that is influenced by factors such as the region and species of fish being farmed, as confirmed in this study. Consequently, it can be inferred that the acceptance intention of fishermen to use EP is influenced more by the perception of opinions and social pressures from others, rather than individual judgment. These results are in line with the findings of Le [19], Kang et al. [25], and Chung & Kang [24].

In addition, price value was found to have a significant impact on acceptance intention, with the purchase price of EP, at USD 2.06/kg, being higher than that of moist pellets, which are currently used by fishermen at USD 0.53/kg. As such, it is evident that fishermen are sensitive to the cost-effectiveness of adopting EP. Therefore, it is necessary to identify factors that can increase the competitiveness of EP compared to moist pellets, in order to help fishermen understand why they should adopt EP. Further, efforts should be made to improve the value for money associated with EP in order to change the perceptions of fishermen.

The formation of trust has been identified as an important factor in acceptance intention, as highlighted by Beza et al. [22], Kang et al. [25], and others. In this study, trust emerged as the most influential factor. The quality and safety of EP can not only affect productivity but also influence the purchase of seafood by consumers. Accordingly, fishermen appear to be particularly sensitive to trust, including quality and safety, in relation to their EP acceptance intention. Above all, it is essential to further enhance the quality and safety of EP and foster trust among fishermen by raising their awareness of the superior quality of EP, thereby promoting widespread adoption.

On the contrary, it was found that facilitating conditions had no significant effect on EP acceptance intention, which is contradictory to the results of Kang et al. [25] and Nugroho et al. [23]. This could be interpreted as respondents in this study having a strong understanding of the feeding method due to their experience in aquaculture. Therefore, factors such as assistance from experts or the presence of infrastructure may not be as important as they generally play a more significant role during the initial stages of the introduction of new technology or machinery. However, to encourage the sustained use of EP in fish farming households in the future, it will be important to mitigate production risks through professional follow-up management, including disease control and husbandry management.

Furthermore, we conducted various tests to identify significant differences between groups while controlling for aquaculture region and manager’s experience, finding only significant differences in relation to aquaculture region. The aquaculture industry has different breeding environments and farming methods, including water temperature, depending on the region. As confirmed in this study, differences in EP acceptance intention were observed according to the aquaculture region. Accordingly, it is necessary to approach EP usage by considering regional and environmental differences and developing feeds appropriate for specific environments and fish species.

7. Conclusions

As the importance of eco-friendliness and safety in aquaculture continues to grow, effectively communicating the true value of using EP to fishermen and establishing trust becomes a top priority, based on the findings of this study. In conclusion, this study affirms that the acceptance intention of extruded pellet feeds represents a pivotal determinant in the sustainable transformation of olive flounder aquaculture. While earlier feed technologies—such as trash fish, semi-moist, and conventional pellets—contributed to the growth of the aquaculture sector, their environmental and operational limitations necessitate a shift toward more efficient and standardized feeding systems [38]. The transition toward EP feeds, supported by higher behavioral acceptance, reliable supply chains, and institutional facilitation, can promote a cleaner, safer, and more resource-efficient aquaculture environment.

Lastly, there are several limitations that need to be addressed in future research. In addition to the independent variables used in this study, other variables such as resistance to change and risk could be incorporated to derive further significant insights. Although the study excluded habit, future research should incorporate this construct to better capture the habitual and routinized aspects of EP use, particularly as user familiarity increases. In addition, considering that more than 90% of the fishermen in this study already had experience using EP, it would be beneficial to conduct research that proposes a model for the intention to continue using EP. Such efforts will help validate the long-term sustainability benefits of EP feeds and guide policy and industry strategies for enhancing feed innovation diffusion in marine aquaculture systems.