Factors Influencing Digital Technology Adoption and Usage Among Workers in Fisheries and Aquaculture in South Korea

Abstract

Digital technologies can improve efficiency and sustainability in fisheries and aquaculture, yet adoption in Korea remains limited. This study analyzes structural and perceptual factors affecting digital uptake among 201 respondents (100 capture fishers, 101 aquaculture farmers). Results show that high initial costs, limited information access, and regional infrastructure gaps are the main barriers. Perceived urgency and expected benefits promote positive attitudes but do not consistently lead to actual device use, revealing a perception–behavior gap. Traditional work routines and the limited impact of government programs further constrain adoption. This study provides one of the first integrated assessments of structural, perceptual, and demographic influences across both fisheries and aquaculture in Korea. The findings highlight the need for combined strategies—improving infrastructure, reducing financial burdens, and delivering practical, localized training—to support a more effective digital transition in the sector.

1. Introduction

Digital technology and data have fundamentally changed the way individuals, businesses and societies operate. These innovations have facilitated unprecedented levels of human interaction, enabling novel ways of living and working that were previously unimaginable. The accelerating pace of these developments is a salient feature of our current era [1,2,3,4]. In this rapidly evolving landscape, there is a growing anticipation about how the vast potential of digital technology and data can revolutionize the future of the fisheries and aquaculture sector [3,4]. In response, policies and technological developments are being introduced to support this transformation, with digitalization being positioned as a key pathway for improving the efficiency, sustainability, and resilience of fisheries and aquaculture [5,6]. The digital transformation of the fisheries sector is already underway in developed countries. For instance, Scandinavian countries such as Norway are at the forefront of the smart aquaculture industry. Concurrently, the United States and the European Union have proactively embraced information and communication technology (ICT) within fishery systems, with the objective of optimizing catch volumes and enhancing management efficiency [1,7]. However, a closer examination of the fisheries and aquaculture sectors reveals a stark contrast to this. The tangible impact of digital transformation remains limited, and the adoption of digital technologies has met with significant resistance from fishers and farmers. Key challenges include rising operational costs and a declining labor force, compounded by environmental challenges such as climate change and pollution. This combination of factors has resulted in a decline in productivity, escalating management costs, and deteriorating working conditions [1,8]. Notably, labor shortages have led to an increased reliance on manual processes [9], as evidenced by surveys indicating a labor-to-machinery utilization ratio of approximately 6:4. This reliance has been identified as a significant contributing factor to the growing dissatisfaction among fishers and farmers, who frequently cite harsh working conditions, including exposure to extreme weather and physically demanding tasks, as a major concern.

While there is a consensus within the fishing and farming communities, as well as among experts, on the necessity of digital transformation to address these challenges, significant barriers exist to its implementation. These barriers include cost burdens, insufficient access to information, and a lack of awareness about the urgency of adopting new technologies [2]. Furthermore, the utilization of digital instruments exhibits disparities across various fisheries. In the context of capture fisheries, the primary applications of these devices are for navigational purposes and facilitating port entry. Conversely, in the domain of aquaculture, technologies such as underwater sensors and automated feeding systems are employed. However, these tools are underutilized, and the integration of digital data into operational processes remains minimal [1,8].

These findings underscore the urgent need for a comprehensive shift towards digital transformation. The current “semi-automated” state of the fisheries and aquaculture sector, characterized by heavy reliance on manual labor and limited adoption of advanced technologies, highlights the gap between the potential of digital tools and their practical application. Achieving this transformation is essential to address the sector’s challenges, including addressing labor shortages, improving productivity, and enhancing sustainability. This study aims to examine the factors influencing the adoption and use of digital technologies by fishers and farmers and to explore both the challenges and opportunities they face. By identifying the barriers and drivers of digital adoption, this research seeks to propose actionable strategies for facilitating the digital transformation of Korea’s fisheries and aquaculture and unlocking its potential for sustainable growth.

Despite the growing global interest in digitalization, empirical research on digital technology adoption among fishers and aquaculture farmers remains limited, particularly in studies that simultaneously consider structural conditions, individual perceptions, demographic characteristics, and program participation. Previous studies often focus on specific technologies, single regions, or individual subsectors, leaving a gap in understanding how multiple factors jointly shape adoption patterns in the fisheries context. This study addresses this gap by providing one of the first integrated analyses of digital adoption in Korea’s fisheries and aquaculture sector using a nationwide sample of 201 workers. By examining both perceptual and structural determinants, this research offers novel empirical evidence and policy-relevant insights that advance understanding of digital transformation in resource-dependent industries.

2. Materials and Methods

2.1. Study Design and Sample Characteristics

This study examined the factors influencing the digital technology adoption by fishers and farmers. The investigation included an analysis of their experiences, current use, future adoption plans, and perceived barriers [10]. A structured survey was conducted from 1 May to 30 July 2024, with 100 fishers and 101 farmers from diverse regions, fisheries and aquaculture practices. The sample was stratified into two main groups: capture fisheries (100 respondents) and aquaculture (101 respondents), to ensure representation of different types of fishing and aquaculture. This stratification approach was adopted to ensure the capture of the diversity of practices and challenges within the fisheries and aquaculture, thereby providing a robust dataset for analysis. The demographic and operational characteristics of the survey participants are summarized in

Table 1. Characteristics of survey respondents.

Basic Questionnaire Components		Number of Respondents	Ratio (%)
Fisheries Type	Capture Fisheries	100	49.8
	Aquaculture	101	50.2
Years of experience	Less than 10 years	22	10.9
	10 to less than 20 years	38	18.9
	20 to less than 30 years	48	23.9
	30 to less than 40 years	56	27.9
	40 years or more	38	18.4
Participation in Government Projects	Participated	76	37.8
	Not Participated	125	62.2
Age Group	Under 40s	21	10.4
	50s	55	27.4
	60s	85	42.3
	70s and above	40	19.9
Level of Education	Middle school or below	46	22.9
	High School Graduate	123	61.2
	College enrollment/Graduate or Higher	32	15.9

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The survey methodology was designed to consider the novelty of digital transformation for many fishers and farmers. Simplified language and relatable terms were used to improve understanding, drawing on strategies described by ([11]), who emphasized the importance of accessibility in survey design [12]. The digital transformation concepts were contextualized using examples such as digital navigation tools and automated feeding systems, paired with visual aids to bridge the gap between technical knowledge and practical application [13,14,15].

The implementation of structured interviews facilitated meaningful engagement and enabled participants to seek clarification and provide detailed responses. In order to ensure the reliability of the findings, the sample design followed principles highlighted in previous research. This included the tailored design method by Dillman et al. [16], which focuses on logical sequencing and visual clarity to reduce response bias. The inclusion of diverse fisheries and aquaculture types and operational contexts was essential to ensure the dataset reflected the multifaceted nature of the industry [14]. The survey context also parallels broader public attitudes towards digital data, as observed in Taylor et al.’s research on data awareness and trust [15]. The survey enhanced relevance and response accuracy by aligning questions with fishers’ day-to-day operations [12,17]. The collected data yielded a margin of error of ±6.91 percentage points at a 95% confidence level, ensuring reliable and generalizable results.

To clarify the sampling approach, respondents were recruited through regional fisheries cooperatives, aquaculture associations, and local extension offices. This ensured participation from individuals directly engaged in daily operations. Most respondents were independent small-scale operators, reflecting the dominant structure of Korea’s fisheries and aquaculture sector, while a smaller proportion were employees or family workers in medium-scale or larger operations. This composition allowed the survey to capture variation in economic contexts, including differences in operational scale, capital intensity, labor use, and access to digital tools. To minimize bias, participation was entirely voluntary, and no incentives were provided. Enumerators used a standardized script and neutral explanations to avoid leading respondents. All participants were informed of the study purpose, confidentiality measures, and their right to withdraw. Ethical approval was obtained under Approval Code No. 3204 (7 May 2024), and written informed consent was collected before data collection. To enhance response accuracy, the survey was conducted through interview-assisted sessions. This format allowed respondents to ask for clarification without influencing their answers, reduced misunderstanding, and ensured that questions were interpreted consistently. Emphasizing anonymity and the absence of “correct” responses helped reduce social desirability bias.

Based on these, we formulated three research questions:

• RQ1: How do training experience and participation in government programs influence digital device use and adoption intentions among workers in fisheries and aquaculture?
• RQ2: How do differences between capture fisheries and aquaculture shape digital technology experience and usage?
• RQ3: How do perceptions of digital transformation and demographic factors relate to digital adoption behaviors?

These questions were operationalized through hypotheses and analyzed by statistical methods.

2.2. Statistical Analysis

The statistical analysis examined the factors influencing fishers’ and farmers’ acceptance of digital transformation, focusing on digital experience, current usage patterns, and future adoption intentions. The dataset consisted largely of binary and ordinal variables; therefore, non-parametric methods were used where appropriate. Descriptive statistics were first generated for all variables. Spearman correlation analyses were employed to identify associations among key predictors, and group differences were assessed using t-tests or Kruskal–Wallis tests depending on variable characteristics and distributional properties [10,17,18]. All analyses were conducted at a significance level of α = 0.05 to ensure analytical rigor. The Summary of Variables and Their Descriptions in the Study on Digital Technology Adoption is presented in

Table 2. Summary of Variables and Their Descriptions in the Study on Digital Technology Adoption.

Variables	Type	Description
Device Use	Binary (Yes/No)	Whether fishers and farmers have used digital devices for work purposes
Plans to Adopt Technologies	Ordinal	Intention to actively integrate digital technologies into future fishing practices.
Training Experience	Binary (Yes/No)	Whether fishers and farmers have received formal training in the use of digital technology.
Government Program	Binary (Yes/No)	Participation in government support or dissemination programs.
Fisheries Type	Categorical	Operational classification: capture fisheries or aquaculture.
Perception of Necessity	Ordinal	Views on whether digital transformation is essential for the industry.
Perception of Urgency	Ordinal	Opinions on the immediacy of implementing digital transformation.
Perception of Benefits	Ordinal	Beliefs about the benefits of adopting digital technologies.
Education Level	Ordinal	The highest level of education among fishers and farmers.
Age Group	Ordinal	Age of those surveyed
Region	Categorical	Geographical location of the fisheries and aquaculture

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The analysis was structured to validate the following hypotheses:

H1. 

Training Experience.

H1-1. 

Training experience is associated with higher digital device use.

H1-2. 

Training experience positively influences attitudes toward adopting digital technologies.

H2. 

Government Program Participation.

H2-1. 

Participation in government programs increases the likelihood of device use.

H2-2. 

Program participation positively affects future adoption intentions.

H3. 

Fisheries Type.

H3-1. 

Fisheries type (capture vs. aquaculture) significantly affects device use.

H3-2. 

Fisheries type influences future adoption plans.

H4. 

Perceptions of Digital Transformation.

H4-1. 

Perceived necessity is positively associated with device use.

H4-2. 

Perceived urgency is positively related to future adoption plans.

H4-3. 

Perceived benefits are associated with greater device use and stronger adoption intentions.

H5. 

Demographic Factors.

H5-1. 

Higher education is associated with greater device use.

H5-2. 

Younger respondents show stronger future adoption intentions.

H5-3. 

Regional differences affect device use and adoption intentions.

2.3. Terminology Definitions

To ensure conceptual clarity and consistency throughout this study, three key terms related to digitalization are defined as follows. Digital transformation refers to the sector-wide transition toward integrating digital systems, data-driven practices, and technological innovations into fisheries and aquaculture operations. This concept encompasses institutional, infrastructural, and organizational changes that enable more efficient, sustainable, and resilient production systems. In contrast, digital adoption is used to describe individual fishers’ and farmers’ willingness and intention to incorporate digital tools into their work practices, as well as their behavioral commitment to using such technologies. Finally, digital technology experience refers specifically to the actual, measurable use of digital devices—such as navigation systems, sensors, automated feeding systems, or mobile applications—in daily operations. This variable captures hands-on familiarity rather than attitudes or intentions. Throughout this manuscript, these terms are used distinctively to differentiate structural transformation from individual-level behavior and practical device use.

3. Results

3.1. Descriptive Statistics

A total of 201 respondents participated in the survey (100 capture fishers and 101 aquaculture farmers). Overall digital experience was moderate (M = 2.56), and 59% reported using digital devices for work. Perceptions of digital transformation were relatively positive, with higher mean scores for necessity (M = 3.72) and expected benefits (M = 3.76), while urgency was slightly lower (M = 3.40). The descriptive statistics for these key variables are presented in

Table 3. Descriptive Statistics of Key Variables.

Variables	Mean/%	Description
Digital Experience	2.56	Experience with digital tools and equipment
Device Use (Yes)	59%	Use of digital devices for work
Perceived Necessity	3.72	Importance of digital transformation
Perceived Urgency	3.40	Immediate need for adoption
Expected Benefits	3.76	Anticipated advantages of digitalization

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3.2. Correlation Analysis

Spearman correlation analysis was conducted to explore relationships among variables (

Table 4. Summary of Correlation Results.

Variable Pair	Strength	Notes	p-Value	Sig.
Fishery Type→Device Use	Strong	Aquaculture > Capture	<0.001	***
Training→Device Use	Moderate	Training linked with use	0.012	*
Digital Experience→Device Use	Moderate	Experience supports usage	<0.001	***
Digital Experience→Adoption Plans	Weak–Moderate	More experience→stronger plans	0.034	*
Perception Variables (Necessity–Urgency–Benefits)	Strong	Perceptions cluster tightly	<0.001	***
Perceptions→Device Use/Plans	-	No behavioral association	>0.10	n.s.

Note: * p < 0.05; *** p < 0.001; n.s. = not significant (p > 0.10).

). The strongest link was between fishery type and device use, showing that aquaculture farmers are more likely to use digital tools than capture fishers. Digital experience was positively related to both device use and future adoption plans. The three perception variables—necessity, urgency, and expected benefits—were highly interconnected, indicating they form a consistent attitudinal group. However, none of these perceptual variables showed a significant association with actual device use or future intentions to adopt digital technologies.

3.3. Group Comparison

Group comparisons showed significant differences between capture and aquaculture respondents. Aquaculture farmers demonstrated higher levels of device use and digital experience, indicating greater engagement with digital technologies compared to capture fishers. However, no statistically significant differences were observed in future adoption intentions or the three perception variables—necessity, urgency, and expected benefits—suggesting that both groups exhibit similar attitudinal readiness toward digital transformation, even if their actual usage behaviors differ. These group differences are summarized in

Table 5. Summary of Group Comparison.

Variable	Capture Fisheries	Aquaculture Farmers	Difference	p-Value	Sig.
Digital Experience	Lower	Higher	Significant	<0.001	***
Device Use	Lower	Higher	Significant	<0.001	***
Future Adoption Plans	Similar	Similar	Not Significant	0.218	n.s.
Perceived Necessity	Similar	Similar	Not Significant	0.463	n.s.
Perceived Urgency	Similar	Similar	Not Significant	0.512	n.s.
Perceived Benefits	Similar	Similar	Not Significant	0.389	n.s.

Note: *** p < 0.001; n.s. = not significant (p > 0.10).

.

3.4. Regression Analysis

Two regression models were estimated: a logistic regression predicting actual device use and a linear regression predicting future adoption intentions. No predictors reached statistical significance at α = 0.05. However, the direction of the coefficients aligned with the descriptive and correlation results. In the model predicting device use, fishery type, training experience, and digital experience showed positive effects. In the model predicting future adoption intentions, digital experience and perceived necessity were positive, whereas training experience had a negative coefficient.

4. Discussion

4.1. Perceptions and the Emergence of a Perception–Behavior Gap

Respondents expressed generally strong perceptions regarding the necessity and expected benefits of digital transformation. These findings echo previous studies in fisheries and agriculture that highlight the importance of perceived usefulness and relevance when considering new technologies. Research by Rowan (2023) and Tilley et al. (2024) [3,4], for instance, notes that positive attitudes help create a supportive environment for technology uptake. However, this study found no statistical evidence that perceptions—whether necessity, urgency, or expected benefits—translate into actual device use or future adoption intentions. This disconnect suggests the presence of a perception–behavior gap, a phenomenon noted in other resource-dependent sectors where positive attitudes do not always lead to behavioral change. Such gaps may arise when fishers acknowledge the value of digital technology in theory but lack the conditions, capacity, or incentives to apply it in practice. This finding highlights the need for digital transformation strategies that go beyond awareness raising and focus on enabling practical, everyday use of technologies.

4.2. Structural and Systemic Barriers

The results show that structural constraints remain substantial. Device use was uneven across regions and production systems, and participation in government dissemination programs was relatively low. These patterns suggest that, although statistically non-significant, the directional tendencies are consistent with the broader data patterns. The absence of significant predictors may reflect limited statistical power due to the sample size or low variance in device-use behaviors. Neither training nor program participation produced statistically significant effects in regression models, descriptive patterns indicated that those with training or greater digital experience were more likely to use devices—aligning with international evidence that early exposure and hands-on familiarity support digital engagement. These structural limitations correspond to findings from Norway, Southeast Asia, Timor-Leste, and other small-scale fisheries, where insufficient connectivity, high upfront costs, and minimal technical support impede the long-term adoption of digital tools. Probst (2020) [8] and Kruk et al. (2023) [9] similarly argue that dissemination alone is not enough; sustainable digital transformation requires stable technical ecosystems, maintenance support, and targeted financial mechanisms. Korea’s structural challenges, therefore, reflect global patterns in digitalizing traditional production sectors.

4.3. Operational Differences Between Capture Fisheries and Aquaculture

A major finding of this study is the statistically significant difference between capture fishers and aquaculture farmers. Aquaculture respondents reported higher digital experience and greater device use. This aligns with international research, which shows that aquaculture—being a more controlled and predictable production environment—is inherently more conducive to adopting monitoring systems, sensors, and automated equipment. Capture fisheries, by contrast, operate under conditions of environmental variability, mobility, and uncertainty. These factors can limit both the applicability and perceived reliability of digital tools at sea. However, future adoption intentions did not significantly differ between the two groups, indicating that although aquaculture is currently more digitally advanced, capture fishers may be equally open to future adoption if structural barriers are addressed. This finding underscores the importance of designing subsector-specific strategies rather than uniform digital transformation policies.

5. Conclusions

This study demonstrates that digital technology adoption in Korea’s fisheries and aquaculture sector is shaped by a combination of structural constraints and behavioral perceptions. Although respondents generally viewed digital transformation as necessary and beneficial, these positive perceptions did not directly translate into higher device use or stronger adoption intentions. Instead, practical factors—such as regional infrastructure gaps, cost burdens, limited training opportunities, and differences between capture and aquaculture working environments—played a more decisive role in actual adoption. Aquaculture farmers showed higher levels of digital experience and device use, yet both groups expressed similar willingness to adopt digital tools in the future, suggesting that readiness exists, but structural barriers continue to hinder progress. To advance a more inclusive and effective digital transition, policy efforts must strengthen regional infrastructure, lower financial barriers, and expand hands-on, sector-specific training and demonstration programs. Enhancing fishers’ practical experience with digital tools will be essential for turning positive attitudes into real behavioral change. At the same time, this study has several limitations. The analysis is based on cross-sectional, self-reported survey data, which may not fully capture behavioral dynamics or long-term technological change. Regional and subsector representation is uneven, and unobserved contextual variables may influence results. Future research would benefit from longitudinal designs, qualitative fieldwork, and mixed-method approaches to gain a deeper understanding of how digital adoption evolves over time and how policy interventions affect real-world practices.