An Integrated Delphi-AHP Study on the Systematic Improvement of Sea Anchors for Fishing Operations

Abstract

Sea anchors for fishing operations are essential equipment to enhance catch efficiency and ensure operational stability at sea. However, previous studies have mainly focused on theoretical modeling or experiments under restricted conditions, which have not sufficiently reflected the complex operating environments and practical needs of real-world fisheries. To address this gap, this study derived key factors to improve the design and operation of sea anchors and quantitatively analyze the relative importance and rank of these factors. An expert panel was formed from 25 participants, including jigging vessel captains, recreational fishing boat captains, sea anchor manufacturers, and research institute workers. Using a three-round Delphi process followed by Analytic Hierarchy Process (AHP) analysis, we distilled an initial list of 52 improvement suggestions into 15 prioritized items, quantitatively ranked by relative importance based on expert consensus. The highest-ranked factor was ‘Enhancement of fabric drying performance’, followed by ‘Application of low-cost, high-efficiency materials’, ‘Improvement of recovery’, ‘Enhancement of UV resistance’, and ‘Product quality certification’. The highest-weighted metric was ‘Improvement of usability’, followed by ‘Enhanced durability’ and ‘Improvement of functionality’. The consistency ratio (CR) of the pairwise-comparison matrix was 0.0014 (AHP acceptability criterion: CR ≤ 0.1), confirming the reliability and consistency of the analysis. By reflecting real-world priorities through a robust and systematic analytical process, this study offers a foundation for evidence-based improvements in sea anchor design and operation, overcoming the limitations of earlier approaches rooted in subjective judgment or trial-and-error experience.

1. Introduction

Sea anchors are maritime safety devices conceived initially to prevent marine accidents and shipwrecks in poor weather conditions by providing resistance to the vessel being pushed off-course, stabilizing the heel angle of the vessel, preventing yaw, and buffering wave energy [1,2].

In addition to their original purpose, sea anchors are now widely employed within fisheries to increase landing efficiency and ensure operational stability. In particular, sea anchors are especially prevalent in jigging operations [1,3]. The main components of a fishing sea anchor are the canopy, shroud lines, vent hole, main line, sinker, buoy, and hauling line. The canopy generates drag by receiving hydrodynamic resistance, the shroud lines maintain the shape of the canopy during deployment, and the towing line transmits force to the vessel [1,3]. Sea anchors for fishing increase the time fish schools remain in the shadow of the vessel by allowing the vessel to drift naturally with the current and improve catch efficiency by keeping lines vertical and preventing tangles [4,5].

Jigging operations are not only less affected by the marine environment but also are suitable for obtaining high-value-added products because there is almost no bycatch and minimal injury to the fish bodies. Furthermore, it is a well-regarded fishing method in terms of sustainability and resource management [6,7,8]. Global squid jigging fisheries have experienced rapid expansion in recent years. Satellite-based monitoring indicated that jigging vessel effort increased by 68% from 2017 to 2020, largely driven by operations in unregulated high-seas areas, underscoring both the growing importance of this fishing method and the urgent need for effective management [9]. Jigging operations are increasingly adopted for squid fishing, a practice of global importance. Squid is an especially high-profit species that can generate much added value in a short time and a resource for which international competition is intensifying. Consequently, improving the gear for open-sea jigging vessels has become essential to ensure more efficient and safer fishing operations.

Recently, there has been growing interest in leisure fishing using fishing vessels among the South Korean public, and as of 2022, 4383 fishing vessels were reported for fishing operations, used by 5.19 million people per year [10], and sea anchors are being used to improve catch performance and ensure hull stability while drifting. Thus, sea anchors are essential gear for fishing activities.

Given that sea anchors are used repeatedly at sea, performance across multiple factors, including structural stability, usability, durability, and ease of recovery, is an important technical consideration. However, the sea anchors used to date have mostly been manufactured and operated based on the experiences or customs of the manufacturers, and problems with structural inefficiency, inconvenient recovery, poor durability, and repeated repairs and exchanges persist [11,12].

Previous studies on sea anchors for fishing operations, starting with structural form, design, and methods of use in the 1960s, have dealt with diverse topics, including analysis of deployment properties and underwater drag [1,4,13,14]. More recently, the range of research has been expanded to include precise studies to refine the performance of sea anchors, including a proposal on the need for standardization [15], analysis of deployment properties depending on the vent and sinker [16], standardization of canopy material performance [12], and numerical simulation and tank-based experiments on deployment performance [17]. Consequently, sea anchor research has progressed steadily, with ongoing efforts to evaluate the performance of various design elements based on theory and experiment.

Nevertheless, prior studies have still skewed toward theoretical modeling, focusing on structural form and fluid resistance properties, and even experimental studies have often been performed only under restricted conditions. Such approaches have limitations in their ability to richly reflect the complex operating environment and realistic, specific needs of actual fishing operations. Thus, the design and operation of sea anchors remain reliant on manufacturer experience and user habits, and there is a lack of systematic design based on scientific testing [1,11].

Moreover, the limitations of prior research extend beyond the mere lack of study volume. Although valuable theoretical and experimental findings have been accumulated, these outcomes have not been sufficiently translated into practical improvements in fishing operations. Single experimental results or narrowly defined performance indices are inadequate to capture the real discomforts and detailed improvement needs perceived in the field. To effectively address the complex and multifaceted demands of fishing practices, it is necessary to systematically incorporate and quantitatively analyze the experiences and perceptions of diverse stakeholders, including vessel operators, manufacturers, and researchers. Bridging the gap between research and practice therefore requires a transition from the conventional top-down, researcher-oriented approach to a bottom-up framework grounded in end-user experience and field-based requirements.

In particular, sea anchors have long been implemented conventionally, with no significant changes in structure or materials, perpetuating various forms of discomfort and problems during fishing operations [11]. For example, issues such as failure to provide effective resistance at sea due to weak deployment force, the slow draining and drying speed of the canopy, inconvenience of storage or recovery, poor durability with repeated use, and frequent repairs and exchanges have acted as hindering factors in terms of economics and handling efficiency, necessitating real improvements [1,3,12].

These problems go beyond mere physical performance but instead are complex, intertwined problems that extend across all aspects of system quality and operating processes, including the structural design of sea anchors, material properties, convenience of use, operational safety, maintenance and manageability, and cost efficiency. Therefore, this study sought not a piecemeal improvement in technical performance but directions for comprehensive, systematic enhancements that reflect the various demands and realities of actual usage.

To gather real needs and discomforts of users that are difficult to capture with experimental results alone, we used the Delphi method to collect the opinions of experts in the field regarding the design and operation of sea anchors and derived relevant improvement factors. We then applied the Analytic Hierarchy Process (AHP) analysis to these improvement factors to quantify the relative importance of each item and rank improvement strategies by priority [18,19].

Integrating the complementary Delphi and AHP approaches yielded more reliable items and ranks for improvement. Through expert-based multi-criteria analysis, we organically linked the perspectives of real users and manufacturers regarding directions for improvement of the design and operation of sea anchors, and we presented a multi-layered strategy for improvement that is applicable in real fishing operations. This integrated approach supplements previous theory- and experiment-focused studies by offering new insights through a field-validated, evidence-based roadmap for sea anchor improvement. In this way, we aimed to provide practical baseline data to guide future sea anchor design guidelines and technological advances.

2. Materials and Methods

2.1. Study Procedure

To address the shortcomings in the design and operation of sea anchors currently used in fishing, this study applied Delphi and AHP methods, which facilitate the collection and quantitative analysis of expert insights. After convening a panel of sea-anchor experts, improvement factors were derived through three rounds of Delphi surveys. Subsequently, AHP methods were applied to quantify the relative importance and priority ranking of the derived improvement factors (Figure 1). The Delphi and AHP surveys were conducted over a three-month period, from August to October 2024, using in-person, email, and text-based questionnaires to accommodate participants’ varying availability.

Combining Delphi’s consensus-building process with AHP’s structured quantitative ranking allows us to systematically capture expert insight and translate it into a prioritized framework for improvement. This integrated approach ensures both the validity of the items derived and the objectivity of their relative importance.

In Step 1, the relevant literature and industry materials were reviewed to derive design and operation factors requiring improvement, which served as the basis for the Delphi survey. In Step 2, a group of experts with field experience and technical expertise was selected to ensure questionnaire validity and reliability. In Step 3, the first Delphi survey, open-ended questions were used to collect various opinions about sea anchor improvement. In Step 4, the importance of the derived items was rated with closed-ended questions based on a five-point Likert scale. In Step 5, the experts were asked to re-evaluate the results of the second Delphi survey and encouraged to gather their opinions and reach a consensus. This process confirmed the final improvement items. In Step 6, AHP pairwise comparisons of the final items produced a priority ranking for sea anchor improvement.

This systematic approach produced realistic improvement factors for sea anchors, in terms of both design and operation, which reflect usage in the field, and suggested an objective order of priority.

2.2. Delphi Method

To systematically identify improvement factors for the design and operation of sea anchors, we applied the Delphi method, which enables repeated collection and quantitative analysis of the opinions of an expert panel. The Delphi method is a structured survey technique designed to derive consensus opinions based on the intuitive and empirical judgment of experts in an uncertain situation. The advantages of this method are that the validity and reliability of decision-making can be improved based on anonymity, repetition, and feedback [18,20]. When dealing with complex, uncertain problems that lack clear, quantitative data, the Delphi method is an effective means of systematically collecting the experience and intuition of experts and reaching a consensus [21].

• Delphi design and protocol

In this study, we conducted the Delphi survey in three rounds, and each round was composed of the following procedures.

First round: Through an open-ended questionnaire, experts were invited to freely describe their opinions on all aspects of sea anchor design and operation to derive various improvement factors. The responses were refined into item groups through processes of integration of similar items, removal of duplicate items, and classification of concepts.

Second round: Based on the refined improvement items, a closed-ended questionnaire was constructed using a five-point Likert scale (1 = very low, 5 = very high), and the experts rated the importance of each item.

Third round: After providing feedback on the statistical results of the second round (mean and standard deviation per item, group means, etc.), the experts were asked to re-evaluate the same items. This process encouraged experts to converge their opinions and reach a consensus.

2.

Evaluation indices and criteria

To quantitatively test the reliability and validity of the expert opinions, the following indices were used.

① Content validity ratio (CVR)

CVR is a measure of how essential each item was perceived to be by the expert panel and was calculated using Equation (1), as proposed by Lawshe [22]. Here, ne denotes the number of experts who judged the item “essential”, and N represents the total number of experts.

$$\mathrm{C}\mathrm{V}\mathrm{R}={\displaystyle \frac{ne-\frac{N}{2}}{\frac{N}{2}}}$$

(1)

The range of possible CVR values is −1 to +1, with larger values indicating a stronger consensus among experts that the item is essential. A significant result was defined as CVR ≥ 0.37, the reference value for 25 experts in this study (N = 25) [22].

② Mean and standard deviation

The mean importance of each item represents the general tendency of the expert panel’s ratings, and the standard deviation is a measure of the variance in the ratings. We judged each item essential if M ≥ 3.5 in the second Delphi survey and M ≥ 4.0 in the third Delphi survey. SD ≤ 1.0 indicated the convergence of opinions.

③ Convergence

The convergence is a measure of how concentrated the experts’ opinions are around the mean value and is calculated based on the interquartile range (IQR), as shown in Equation (2).

$$\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{e}={\displaystyle \frac{Q_3-Q_1}{2}}$$

(2)

A convergence value closer to 0 indicates stronger convergence of ratings, and we adopted a convergence ≤ 0.5 in this study.

④ Consensus

Consensus is a measure of the level of agreement between the experts’ opinions, and like convergence, it is calculated from the IQR, as shown in Equation (3).

$$\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{u}\mathrm{s}=1-{\displaystyle \frac{Q_3-Q_1}{Mdn}}$$

(3)

Consensus values closer to 1 mean that the experts’ responses were concentrated in a narrower range, and complete agreement is defined as a score of 1. In this study, we used a criterion value of consensus ≥ 0.75.

⑤ Stability—coefficient of variation (CV)

CV is the ratio of the SD to the M of each item, calculated as shown in Equation (4), and is used to evaluate the relative consistency of the experts’ opinions.

$$\mathrm{CV}=\mathrm{Standard} \mathrm{Deviation} ∕ \mathrm{Mean}$$

(4)

A CV value ≤ 0.5 indicates high stability, a value between 0.5 and 0.8 shows relatively stable responses, and a value ≥ 0.8 indicates low stability [23]. In this study, we assessed stability using a criterion of CV ≤ 0.5.

2.3. Analytic Hierarchy Process (AHP)

AHP is a systematic analysis technique for multi-criteria decision-making (MCDM), developed by Saaty [24]. The technique hierarchically structures a decision-making problem and quantifies the relative importance of evaluated factors through pairwise comparison, thereby supporting more logical and quantitative decision-making. In particular, the AHP method is suitable for hierarchically structuring complex decision-making problems and quantifying the relative importance between individual factors to derive a priority ranking of the proposed measures [25].

The principal strength of AHP is that it transforms qualitative ratings from intuitive judgments into quantitative values and allows for numerical assessment of decision consistency. Specifically, expressing relative importance on a ratio scale enables an objective priority rank to be determined [26,27].

3.

AHP protocol

① Step 1: Defining the problem and setting the hierarchical structure

After defining the objective and structuring the criteria and proposed measures (improvement items), the hierarchy was established. The hierarchy in this study was in three levels, where Level 1 was “improvements in the design and operation of sea anchors”, Level 2 was “criteria derived from the Delphi method”, and Level 3 was “specific items requiring improvement.”

② Step 2: Construction of the pairwise comparison matrix and importance evaluation.

The factors in each level were compared to pairwise comparisons from the perspective of higher-order items. Here, the 9-point scale in

Table 1. AHP pairwise comparison scale (1–9) and verbal judgment guidelines.

Importance	Verbal Judgment	Explanation
1	Equal importance	Both elements contribute equally to the objective
2	Between equal and moderate	A compromise between equal and moderate importance.
3	Moderate importance	One element is slightly more important than the other.
4	Between moderate and strong	A compromise between moderate and strong importance.
5	Strong importance	One element is clearly more important than the other.
6	Between strong and very strong	A compromise between strong and very strong importance.
7	Very strong importance	One element is demonstrably more important than the other.
8	Between very strong and extreme	A compromise between very strong and extreme importance.
9	Extreme importance	One element is overwhelmingly more important than the other.

was used [28] to evaluate differences in importance.

For the results, the pairwise comparison matrix A was constructed in the form [a_ij], as shown in Equation (5), and each element a_ij in matrix A was defined according to Equation (6).

$$A=\left[\begin{array}{ccc}\begin{array}{cc}\begin{array}{c}\begin{array}{c}{\displaystyle \frac{w_1}{w_1}}\\ \end{array}\\ \begin{array}{c}{\displaystyle \frac{w_2}{w_1}}\\ \end{array}\end{array}& \begin{array}{cc}\begin{array}{c}\begin{array}{c}{\displaystyle \frac{w_1}{w_2}}\\ \end{array}\\ \begin{array}{c}{\displaystyle \frac{w_2}{w_2}}\\ \end{array}\end{array}& \begin{array}{c}\begin{array}{c}{\displaystyle \frac{w_1}{w_3}}\\ \end{array}\\ \begin{array}{c}{\displaystyle \frac{w_2}{w_3}}\\ \end{array}\end{array}\end{array}\end{array}& \dots & \begin{array}{c}\begin{array}{c}{\displaystyle \frac{w_1}{w_n}}\\ \end{array}\\ \begin{array}{c}{\displaystyle \frac{w_2}{w_n}}\\ \end{array}\end{array}\\ ⋮& \ddots & ⋮\\ \begin{array}{ccc}\begin{array}{c} \\ {\displaystyle \frac{w_n}{w_1}}\end{array}& \begin{array}{c} \\ {\displaystyle \frac{w_n}{w_2}}\end{array}& \begin{array}{c} \\ {\displaystyle \frac{w_n}{w_3}}\end{array}\end{array}& \dots & \begin{array}{c} \\ {\displaystyle \frac{w_n}{w_n}}\end{array}\end{array}\right]$$

(5)

$$a_{ij}={\displaystyle \frac{w_i}{w_j}}$$

(6)

The matrix satisfies a_ij > 0, a_ji = 1/a_ij, and a_ii = 1, and a_ij represents how much more critical item i is than item j.

③ Step 3: Calculating weights and deriving the eigenvector

From the pairwise comparison matrix A, the eigenvector w, which represents relative importance, is calculated using the eigenvalue equation, as shown in Equation (7), in accordance with the eigenvalue method from linear algebra.

A · w = λ · w

(7)

Under perfect consistency, the principal eigenvalue λ_max equals the number of items n. However, in reality, the raters cannot know the exact relative weight w, and it is assumed to be challenging to perform the pairwise comparison perfectly; thus, the eigenvalue equation in Equation (8) is used in the actual analysis to approximate w.

A′ · w′ = λ_max · w′

(8)

Here, λ_max (principal eigenvalue) measures matrix consistency and helps assess the reliability of the weights derived by AHP.

④ Step 4: Consistency review and adjustment

It is essential to review the consistency of judgments from the results of AHP analysis. Consistency is evaluated with Equation (9), representing the consistency index (CI), and Equation (10), representing the consistency ratio (CR). Generally, CR ≤ 0.1 is considered to show good consistency of judgments, and readjustment is required in cases where the CR is higher.

CI = (λ_max − n)/(n − 1)

(9)

CR = (CI/RI) × 100%

(10)

Here, n is the number of rated items, and RI (random index) is the mean CI of a random matrix, using a standard value suggested by Saaty [24] (

Table 2. Random Index (RI) values according to matrix order (Saaty, 1980).

Matrix Order (n)	3	4	5	6	7	8	9	10
RI	0.58	0.90	1.12	1.24	1.32	1.41	1.45	1.51

).

⑤ Step 5: Global weight calculation and derivation of the priority ranking

The weights calculated at each level of the hierarchy are multiplied serially, moving down the hierarchy, to calculate the global weights. In this way, the contribution of each proposed measure in the overall hierarchy to the final goal can be quantified, and the priority ranking is determined according to the magnitude of the global weights.

2.4. Expert Panel Selection

This study applied the Delphi and AHP methods to derive improvement items in the design and operation of sea anchors and to analyze the importance and priority of each item quantitatively. Because both methods quantify expert intuition and experience, systematically consolidate them, and transform them into measurable outcomes, the composition of the expert panel is a key factor in ensuring the reliability and validity of the study [29,30].

To ensure consistency between the Delphi and AHP methods, we used the same expert panel for both, and we selected a panel with practical experience and expertise in the fields of sea anchor operation, design, manufacture, and research. Domain diversity and balance of opinions were considered, and the panel included diverse occupational groups, such as jigging vessel captains, fishing vessel operators, manufacturer employees, and research institute affiliates.

The appropriate size of a Delphi–AHP panel may vary depending on the research subject and the degree of homogeneity or heterogeneity of the panel. In general, panels of 10 to 30 members are considered adequate, and when the panel is highly heterogeneous, 20 or more members are recommended [31,32]. The panel size was 25 experts, based on the criteria proposed in previous studies [31,32]. This panel size is small enough to maintain the quality of experts while also being large enough to ensure sufficient heterogeneity and representation and is based on a practical judgment considering Delphi survey fatigue.

The expert selection criteria emphasized expertise, suitability, representativeness, and diligence [20]. First, we selected experts with at least 5 years of experience in a relevant field, focusing on persons with practical or research experience directly related to the research topic. Considering the importance of field applicability, we ensured a sufficient proportion of the panel was composed of fishery workers, and we regarded them as various age groups to minimize age distribution bias. The general characteristics of the expert panel are shown in

Table 3. General characteristics of the expert panel for Delphi and AHP analysis.

Classification		Number of Respondents	Proportion (%)
Gender	Male	25	(100)
	Female	0	(0)
Affiliation	Squid jigging vessel captains	7	(28)
	Recreational fishing boat captains	6	(24)
	Manufacturers	5	(20)
	Research/education institutions	7	(28)
Age	30s	3	(12)
	40s	5	(20)
	50s	6	(24)
	60s	6	(24)
	70s and older	5	(20)
Industry experience	5–10 years	8	(32)
	10–15 years	5	(20)
	Over 15 years	12	(48)
Sea anchor related experience	5–10 years	10	(40)
	10–15 years	5	(20)
	Over 15 years	10	(40)

.

By following these procedures to construct the expert panel, we aimed to maximize the reliability and validity of this study to present realistic directions for improvement of sea anchors.

3. Results and Discussion

In this study, the same expert panel was used throughout the three Delphi survey rounds and the AHP analysis. The surveys, conducted between August and October 2024, maintained high participation rates at each stage (

Table 4. Composition and participation of the expert panel in the Delphi and AHP study.

Affiliation	No. of Experts	Response Rate (%)
		Delphi			AHP
		1st	2nd	3rd
Squid jigging vessel captains	7	6 (85.7)	7 (100)	7 (100)	6 (85.7)
Recreational fishing boat captains	6	5 (83.3)	6 (100)	6 (100)	5 (83.3)
Manufacturers	5	5 (100)	5 (100)	5 (100)	4 (80)
Research/education institutions	7	7 (100)	7 (100)	7 (100)	7 (100)
Total	25	23 (92.0)	25 (100)	25 (100)	22 (88.0)

).

By maintaining a high participation rate in the surveys at each stage, we were able to collect sufficient expert opinions and improve the reliability of the derived improvement items and priority rankings. We achieved 100% response rates in the second and third Delphi surveys, demonstrating that the quality of expert opinion collection was high. Based on the expert panel composition and response consistency, we aimed to empirically derive key items for improvement in the design and operation of sea anchors for fishing and to systematically present their importance and priority ranking, thereby proposing realistic, field-applicable directions for improvement while simultaneously ensuring objectivity and usability.

3.1. Delphi Analysis Results

We systematically derived items for improvement in the design and operation of sea anchors for fishing through three Delphi survey rounds. The first Delphi survey was an open-ended questionnaire in which experts freely provided their opinions. We received 23 responses. The content of the responses was classified and integrated according to semantic similarity and functional relatedness, and only items with a response frequency of 2 or higher were selected to ensure reliability and representativeness.

Through this process, we derived 52 total items for improvement, and these were organized into six main categories—(A) structural design and shape optimization, (B) improvement of usability, (C) improvement of economic efficiency, (D) improvement of functionality, (E) enhanced durability, and (F) additional technologies and technical applications—and 52 subcategories (

Table 5. Results of the first Delphi survey.

Main Category	Sub-Category
(A) Structural design and shape optimization	(1) Design for improved deployment speed, (2) structural enhancement to increase deployment force, (3) optimization of vent size and design, (4) optimization of canopy shape, (5) optimization of the internal angle of the canopy, (6) minimization of shroud line quantity, (7) rational weight of metal components, (8) optimization of rope materials and diameter
(B) Improvement of usability	(1) Minimization and optimization of storage volume, (2) improvement of recovery, (3) enhancement of quick recovery, (4) improvement of portability, (5) improvement of handling convenience, (6) improvement of maintenance convenience, (7) weight reduction in the product, (8) easier component replacement
(C) Improvement of economic efficiency	(1) Reduction in purchase cost, (2) reduction in maintenance cost, (3) shortening of production lead time, (4) shortening of repair time, (5) application of low-cost, high-efficiency materials, (6) simplification and efficiency improvement of production process, (7) design for minimized maintenance
(D) Improvement of functionality	(1) Improvement of sinking performance, (2) enhancement of fabric drying performance, (3) improvement of fabric air permeability, (4) improvement of fabric absorption, (5) optimization of fabric buoyancy, (6) optimization of fabric specific gravity, (7) enhancement of nighttime visibility, (8) reduction in shroud line entanglement and twisting
(E) Enhanced durability	(1) Enhancement of fabric tensile strength, (2) use of seawater-resistant materials, (3) enhancement of UV resistance, (4) improvement of abrasion resistance, (5) improvement of degradation resistance, (6) improvement of colorfastness, (7) enhancement of corrosion resistance of metal components, (8) Improvement of durability under repeated use, (9) Extension of product life cycle, (10) reduction in shroud line sagging, (11) enhancement of shroud line tensile strength, (12) reinforcement of stitching at fabric joints
(F) Additional technologies and technical applications	(1) Application of eco-friendly materials, (2) enhancement of product use safety, (3) product quality certification, (4) automatic vent hole adjustment system, (5) provision of a user manual, (6) measurement of current and resistance in sea anchor, (7) measurement of sinking depth, (8) measurement of deployment force, (9) deployment and recovery monitoring system

).

The derived items have high utility because they reflect the diverse demands of end users, including not only improvements to physical performance but also usability, economic efficiency, and the application of smart technology. They are especially significant because they extend beyond simply product improvement and also suggest multifaceted directions for improvement, considering field applicability and market competitiveness.

However, one inherent limitation of open-ended surveys is that quantitative comparison of the importance of each item is difficult, and differences in relative perceptions between experts are not reflected. As such, the second Delphi survey was designed for the experts to rate the importance of each of the 52 items, to derive practical implications for improvements in design and operation.

The second Delphi survey was conducted on the 52 items derived from the first Delphi survey, and the entire 25-member expert panel participated. The survey was a closed-ended questionnaire in which the experts rated the importance of each item on a five-point Likert scale. The results were analyzed using the CVR, mean (M), and CV, where item retention was determined according to CVR ≥ 0.37, M ≥ 3.5, and CV ≤ 0.5. Based on these criteria, 18 of the 52 items (34.6%) were retained, and 34 items (65.4%) were excluded. Most of the retained items showed a mean score of approximately 4.0 and high response consistency between experts, with a CV of 0.3 or lower.

The third Delphi survey was conducted on the 18 items retained from the second Delphi survey, and the entire 25-member expert panel participated. In this survey, the experts re-evaluated the importance of each item to improve the level of agreement between experts and finalize the list of improvement items. The respondents were provided with the results of the second survey and their previous responses to encourage more careful re-evaluation.

The criteria for the third Delphi survey were CVR ≥ 0.36, mean score ≥ 4.0 (and equal to or higher than the previous round), SD ≤ 1.0 (and equal to or lower than the last round), CV ≤ 0.5, convergence ≤ 0.5, and consensus ≥ 0.75. Thus, we objectively verified the response consistency and item validity.

Table 6. Results of the 2nd and 3rd Delphi surveys.

Category		CVR		M		SD		Convergence		Consensus		CV		Decision
Main	Sub	2nd	3rd	2nd	3rd	2nd	3rd	2nd	3rd	2nd	3rd	2nd	3rd
A	(1)	0.44	0.52	4.04	4.16	0.98	0.90	1	0.5	0.5	0.75	0.24	0.22	Retained
A	(2)	0.52	0.68	4.16	4.32	0.90	0.85	0.5	0.5	0.75	0.8	0.22	0.20	Retained
A	(3)	0.52	0.68	4.08	4.28	0.86	0.84	0.5	0.5	0.75	0.75	0.21	0.20	Retained
B	(2)	0.44	0.84	4.04	4.32	0.98	0.75	1	0.5	0.5	0.75	0.24	0.17	Retained
B	(4)	0.52	0.68	4.00	4.28	1.19	0.98	0.5	0.5	0.75	0.8	0.30	0.23	Retained
B	(7)	0.52	0.76	4.12	4.28	1.24	0.98	0.5	0.5	0.8	0.8	0.30	0.23	Retained
C	(1)	0.44	0.36	3.92	3.84	1.08	1.21	1	1	0.5	0.5	0.27	0.32	Excluded
C	(5)	0.60	0.76	4.12	4.40	1.01	0.82	0.5	0.5	0.75	0.8	0.25	0.19	Retained
D	(1)	0.44	0.28	3.60	3.56	1.26	1.19	0.5	0.5	0.75	0.75	0.35	0.34	Excluded
D	(2)	0.44	0.84	4.08	4.40	1.12	0.91	1	0.5	0.5	0.8	0.27	0.21	Retained
D	(8)	0.44	0.60	4.20	4.32	0.96	0.90	1	0.5	0.6	0.8	0.23	0.21	Retained
E	(1)	0.44	0.68	4.12	4.16	0.93	0.69	1	0.5	0.5	0.75	0.23	0.17	Retained
E	(3)	0.68	0.92	4.16	4.52	0.94	0.87	0.5	0.5	0.75	0.8	0.23	0.19	Retained
E	(4)	0.44	0.68	4.04	4.32	0.98	0.95	1	0.5	0.5	0.8	0.24	0.22	Retained
E	(12)	0.60	0.68	4.16	4.20	0.94	0.71	0.5	0.5	0.75	0.75	0.23	0.17	Retained
F	(2)	0.44	0.60	4.04	4.12	0.89	0.83	1	0.5	0.5	0.75	0.22	0.20	Retained
F	(3)	0.44	0.76	4.04	4.32	0.89	0.80	1	0.5	0.5	0.75	0.22	0.19	Retained
F	(5)	0.44	0.04	3.64	3.52	1.19	1.05	0.5	0.5	0.75	0.75	0.33	0.30	Excluded

shows the retained and excluded items from the second to the third Delphi survey and the changes in rating indices.

There were 15 items (83.3%) that satisfied the criteria and were retained and three items (16.7%) that were excluded. The retained items all showed increased CVR and mean score and decreased SD and CV compared to the second survey, showing that the experts’ response consistency and reliability were improved.

In particular, ‘Enhancement of UV resistance’ (E-3), ‘Improvement of recovery’ (B-2), and ‘Enhancement of fabric drying performance’ (D-2) showed high priority and response consistency, suggesting that these items reflect actual field demands. Meanwhile, ‘Reduction in purchase cost’ (C-1), ‘Improvement of sinking performance’ (D-1), and ‘Provision of a user manual’ (F-5) were excluded for failing to meet the criteria, and this was interpreted as reflecting insufficient field utility or insufficient consensus due to differences in perception between experts.

Table 7. Final improvement factors from the Delphi survey.

Main Category		Sub-Category
A	Structural design and shape optimization	(1)	Design for improved deployment speed
		(2)	Structural enhancement to increase deployment force
		(3)	Optimization of vent size and design
B	Improvement of usability	(1)	Improvement of recovery
		(2)	Improvement of portability
		(3)	Weight reduction in the product
C	Improvement of economic efficiency	(1)	Application of low-cost, high-efficiency materials
D	Improvement of functionality	(1)	Enhancement of fabric drying performance
		(2)	Reduction in shroud line entanglement and twisting
E	Enhanced durability	(1)	Enhancement of fabric tensile strength
		(2)	Enhancement of UV resistance
		(3)	Improvement of abrasion resistance
		(4)	Reinforcement of stitching at fabric joints
F	Additional technologies and technical applications	(1)	Enhancement of product use safety
		(2)	Product quality certification

shows the finalized list of 15 items and includes diverse aspects of sea anchors, such as improvements in structural design, usability, economic efficiency, functionality, durability, and technical applications. As realistic directions for improvement based on a consensus between experts, these items can provide meaningful implications for future product development and field application.

Having finalized the 15 key improvement items through the Delphi process, we next applied the AHP analysis to quantitatively evaluate the relative importance and priorities of these items. This subsequent step enabled a more structured decision-making framework by assigning weights to each item based on expert pairwise comparisons.

3.2. AHP Analysis Results

For the 15 improvement items for fishing sea anchor design and operation derived from the Delphi study, AHP analysis was employed to quantitatively evaluate the relative importance of each item and derive a priority ranking.

The hierarchical structure for the analysis is shown in Figure 2. The first level focuses on the overarching goal of identifying items for the improvement of the design and operation of sea anchors for fishing. The second level contains the six criteria (A–F) derived through the Delphi survey. The third level consists of the specific improvement items (15 in total) arranged by category. A pairwise comparison survey was administered to the expert panel to quantify the relative importance of each item and to derive category and item weights and ranks.

In pairwise comparison of the criteria (6 categories) in the second level of the AHP structure, the highest weight was assigned to ‘improvement of usability’ (0.2256), followed by ‘enhanced durability’ (0.2171), ‘improvement of functionality’ (0.1765), and ‘structural design and shape optimization’ (0.1656). In contrast, ‘additional technologies and technical applications’ (0.1188) and ‘improvement of economic efficiency’ (0.0964) received relatively low weights (

Table 8. AHP results for main categories: weights, priorities, and CR values.

Main Category	Weight	Priority	CR
Improvement of usability	0.2256	1	0.0014
Enhanced durability	0.2171	2
Improvement of functionality	0.1765	3
Structural design and shape optimization	0.1656	4
Additional technologies and technical applications	0.1188	5
Improvement of economic efficiency	0.0964	6

). The CR was 0.0014, which satisfied the AHP acceptability criterion of CR ≤ 0.1, demonstrating the validity and reliability of the analysis.

Meanwhile, to review the reliability and consistency of the AHP analysis, we reviewed the consistency of the pairwise comparison matrix. After obtaining the product of the weight vector (w) with the original matrix (Aw), the (Aw)i/wi ratio was calculated for each item, and the mean of these ratios was computed. This mean value represents the principal eigenvalue (λ_max) and was around 6.0087 in this analysis. Based on this value, the CI was calculated via Equation (9) as 0.00174. Subsequently, the RI was applied, using a standard value for n = 6 of 1.24 [24], and this served to calculate the CR, according to Equation (10), as 0.0014. This readily satisfied the general acceptability criterion of CR < 0.1. This demonstrates that the results of the AHP analysis were sufficiently reliable and consistent.

After calculating the local weights for the specific items included in each main category, these were multiplied by the main category weights to obtain combined global weights. This enabled more quantitative derivation of the relative importance of each item and can serve as evidence for realistic and systematic decision-making when setting future strategies for improvement.

Table 9. AHP results on main category and sub-criteria weights with CR values.

Main Category (Weight)		Sub-Criteria	Local Weight	Global Weight	CR
(A) Structural design and shape optimization (0.1656)	(1)	Optimization of vent size and design	0.4028	0.06670	0.00187
	(2)	Structural enhancement to increase deployment force	0.3593	0.05950
	(3)	Design for improved deployment speed	0.2379	0.03940
(B) Improvement of usability (0.2256)	(1)	Improvement of recovery	0.4260	0.09611	0.00015
	(2)	Weight reduction in the product	0.3240	0.07309
	(3)	Improvement of portability	0.2500	0.05640
(C) Improvement of economic efficiency (0.0964)	(1)	Application of low-cost, high-efficiency materials	1.0000	0.09640	-
(D) Improvement of functionality (0.1765)	(1)	Enhancement of fabric drying performance	0.6782	0.11970	-
	(2)	Reduction in shroud line entanglement and twisting	0.3218	0.05680
(E) Enhanced durability (0.2171)	(1)	Enhancement of UV resistance	0.3589	0.07792	0.00274
	(2)	Reinforcement of stitching at fabric joints	0.2423	0.05260
	(3)	Enhancement of fabric tensile strength	0.2192	0.04759
	(4)	Improvement of abrasion resistance	0.1796	0.03899
(F) Additional technologies and technical applications (0.1188)	(1)	Product quality certification	0.6219	0.07388	-
	(2)	Enhancement of product use safety	0.3781	0.04492

shows a summary of the local weights, relative priority, and CR of each item in the six main criteria for improvement of the design and operation of sea anchors for fishing. Most criteria showed a CR ≤ 0.01, demonstrating high consistency between responses in the pairwise comparison. This is interpreted as evidence to support the reliability of the expert judgments and the validity of the AHP analysis.

Specifically, in the ‘(A) structural design and shape optimization’ category, ‘optimization of vent size and design’ (0.4028) showed the highest local weight, followed by ‘structural enhancement to increase deployment force’ (0.3593) and ‘design for improved deployment speed’ (0.2379). In the ‘(B) improvement of usability’ category, ‘improvement of recovery’ (0.4260) received the highest priority, followed by ‘weight reduction in the product’ (0.3241) and ‘improvement of portability’ (0.2500). The ‘(E) enhanced durability’ category includes four items, of which ‘enhancement of UV resistance’ (0.3589) showed the highest local weight, followed by ‘reinforcement of stitching at fabric joints’ (0.2423), ‘enhancement of fabric tensile strength’ (0.2192), and ‘improvement of abrasion resistance’ (0.1796). These three criteria all showed very stable consistency, with CR ≤ 0.1.

The ‘(C) improvement of economic efficiency,’ ‘(D) improvement of functionality,’ and ‘(F) additional technologies and technical applications’ categories each contained only one or two items, making CR calculation from pairwise comparison either impossible or meaningless, so the CR for these categories was indicated as ‘–.’ In the ‘(C) improvement of economic efficiency’ category, the local weight of ‘application of low-cost high-efficiency materials’ was 1.000; in the ‘(D) improvement of functionality’ category, the local weights were 0.6782 for ‘enhancement of fabric drying performance’ and 0.3218 for ‘reduction in shroud line entanglement and twisting’; and in the ‘(F) additional technologies and technical applications’ category, the local weights were 0.6219 for ‘product quality certification’ and 0.3781 for ‘enhancement of product use safety.’

These results show that the judgments of the expert panel exhibited a high level of consistency. The local weights for each item were used as the basis for computing global weights. This provided a more quantitative and systematic approach to setting priorities for sea anchor design and operation improvements. Figure 3 indicates the priority rankings based on global weights, which were calculated from the main category weights and local weights.

When the global weights were calculated, ‘enhancement of fabric drying performance’ (0.11970) exhibited the highest importance, and ‘application of low-cost high-efficiency materials’ (0.09640) and ‘improvement of recovery’ (0.09611) had the second and third highest priorities, respectively. This shows that economic efficiency and ease of operation are perceived as the most urgent areas for improvement in fishing operations. ‘Enhancement of UV resistance’ (0.07792), ‘product quality certification’ (0.07388), and ‘weight reduction in the product’ (0.07309) also showed relatively high importance, and this is thought to strongly reflect the demands for improved durability and field applicability of sea anchors.

This result aligns with prior experimental findings regarding the drying performance of sea anchor canopies. In field-based studies conducted by [3], polyester (PES) fabrics, which exhibit low moisture absorption and fast drainage properties, were shown to dry significantly faster than conventional nylon (PA) materials. These studies demonstrated that improved drying performance not only reduces the turnaround time between deployments but also contributes to easier handling and reduced onboard storage burdens in repeated fishing operations. Hence, the high priority assigned to this factor in the AHP analysis may be attributed to its direct impact on the operational efficiency and practicality of sea anchors in real-world conditions.

In addition to drying performance, the second-ranked factor, “application of low-cost, high-efficiency materials”, can be interpreted as a practical challenge in the development of fishing sea anchors, where manufacturers must simultaneously achieve product price competitiveness and maintain quality. This indicates that material efficiency is recognized as an essential consideration, directly related not only to reducing production costs but also to ensuring product reliability and long-term usability. Such findings suggest that an economically grounded approach, which balances affordability with durability, represents a key direction for improvement in terms of both practicality and marketability. This interpretation is also consistent with earlier studies [33], which highlighted the importance of material selection and structural optimization in improving the handling and operational efficiency of sea anchors.

The third-ranked factor, “improvement of recovery”, reflects the reality that repeated deployment and retrieval of sea anchors during operations is directly tied to work efficiency and crew workload. Enhancing recovery performance reduces the physical burden on fishermen and improves safety during intensive fishing activities, underscoring its significance as a field-driven priority.

In contrast, ‘design for improved deployment speed’ (0.03940) and ‘improvement of abrasion resistance’ (0.03899) showed relatively low importance, meaning that these factors were not perceived to affect performance improvements significantly, or the respondents might have judged that existing products already demonstrate a certain level of performance in these areas. In particular, items without clear measurement criteria, such as deployment speed, showed a trend for lower importance in the expert evaluation.

The AHP analysis results are essential because we derived tasks for improving the design and operation of sea anchors for fishing that reflect the overall experience and technical needs of actual users and workers at manufacturing companies and quantified their relative importance. In particular, ‘enhancement of fabric drying performance’ (0.11970), ‘application of low-cost high-efficiency materials’ (0.09640), ‘improvement of recovery’ (0.09611), and ‘enhancement of UV resistance’ (0.07792) were identified as the highest priority tasks to ensure economic efficiency and alleviate realistic discomforts in the field.

Additionally, an item such as ‘product quality certification’ (0.07388) reflects the need for institutional improvements to support the long-term usability and reliability of sea anchors. These results suggest that there is demand for comprehensive improvements, including not only simple improvements to product performance but also work efficiency, safety, and reduction in maintenance and management costs. In summary, the relative importance and priority ranking of the 15 items derived in this study can function as a reference for feasible, strategic decision-making regarding sea anchor development and functional improvement. In this way, the limitations of setting directions for improvement, which previously depended on subjective judgment or qualitative demands, can be overcome, and we anticipate that it will be possible to establish a systematic improvement roadmap that combines field applicability with scientific evidence.

To further ensure the practical applicability and validity of these findings, future research should focus on integrated maritime trials that examine the interrelationships among fabric drying performance, material efficiency, and ease of recovery. For instance, real-time measurements of drying speed, water drainage, and weight variation during retrieval using different fabric materials (e.g., nylon vs. polyester) under varying weather conditions and repeated use scenarios aboard fishing vessels could offer comprehensive evidence to validate the effectiveness of the top-ranked improvement items.

Such experiments would provide a robust basis for evaluating the functional linkages and field applicability of key factors such as “enhancement of fabric drying performance,” “application of low-cost high-efficiency materials,” and “improvement of recovery.” Moreover, this approach would facilitate the development of an integrated improvement strategy that accounts for interactions among these core criteria.

4. Conclusions

This study applied an integrated Delphi–AHP methodology to improve the design and field application of sea anchors for fishing operations by systematically incorporating expert consensus and quantitatively deriving the relative importance of each improvement item. Specifically, three Delphi survey rounds followed by an AHP analysis were conducted, refining an initial set of 52 improvement items into 15 validated key factors. Through this approach, a multi-layered improvement strategy was proposed that balanced technical performance, usability, durability, and economic efficiency. The prioritized items derived in this study can guide manufacturers in developing sea anchors that meet the practical needs of fishing operations better and offer researchers and policymakers a scientific basis for performance enhancement, material selection, and the establishment of standardization systems. Moreover, users are expected to benefit from the improved operational efficiency and safety while also reducing the inconvenience and cost burdens associated with the repeated use of sea anchors.

Based on global weights, the most critical item was ‘enhancement of fabric drying performance’, followed by ‘application of low-cost high-efficiency materials’, ‘improvement of recovery’, ‘enhancement of UV resistance’, ‘product quality certification’, and ‘weight reduction in the product’, which also showed high importance. Among these, the top three priorities clearly highlight the multifaceted nature of field demands. Together, these findings emphasize that the improvement directions identified in this study are not only technically meaningful but also directly applicable to real fishing practices.

Importantly, the quantitative weights and priority rankings derived in this study are not merely nominal values but provide measurable evidence of the relative significance of each factor. By assigning numerical meaning to expert consensus, this study moves beyond qualitative interpretation and establishes an objective foundation that manufacturers and researchers can directly utilize in decision-making. This study therefore provides a reference for realistic and scientific decision-making in future design improvement, material selection, and the establishment of quality standards for sea anchors.

In particular, we were able to confirm that integration of the Delphi and AHP methods is an effective approach to organically linking the multifaceted demands in the field of commercial fishing with directions for technical improvements. This integrated approach supplements previous theory- and experiment-focused studies by offering new insights through a field-validated, evidence-based roadmap for sea anchor improvement.

This study aimed to identify key areas for the improvement of fishing sea anchors, and future research should sequentially conduct empirical validation through sea trials according to the derived priorities. Such validation will provide experimental evidence for the improvement factors identified in this study. In addition, subsequent research should expand the sample size within each occupational group and include diverse regional and international experts to examine whether subgroup-specific perceptions differ from the unified findings of this study and to determine whether refinements or modifications are required for practical application. Building on clearly validated items, test products should then be designed, accompanied by cost-effectiveness analyses and user satisfaction surveys, to develop a more practical and refined improvement model. The outcomes of these efforts can serve as baseline data for establishing standardization and certification systems for sea anchors and are ultimately expected to contribute to improved efficiency and safety in fishing operations that employ sea anchors.

Beyond the scope of sea anchor design and operation, the integrated Delphi–AHP framework presented here also holds promise for wider application in fishery engineering, including gear optimization, aquaculture system improvement, and the development of maritime safety equipment. By outlining both its limitations and broader applicability, this study provides a transparent foundation that future research can build upon to achieve practical advancements in the fishing industry.