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1 INTRODUCTION

Maritime autonomy is changing where and how

maritime work is performed. Higher levels of

automation can transfer routine control, monitoring,

and diagnostic functions from shipboard crews to

automated systems and shore-based personnel, but

they do not eliminate the need for human judgment.

Operators still have to interpret imperfect information,

identify abnormal conditions, coordinate across

organizational boundaries, and intervene when

technology reaches its limits [6, 11, 16, 17].

This transition creates a practical education

problem. Conventional maritime competence has been

organized mainly around shipboard watchkeeping,

navigation, seamanship, and vessel management.

MASS-related operations introduce additional

demands in digital-system understanding, remote

supervision, interface-based situational awareness,

cyber-related risk, distributed communication, and

professional accountability [1, 2, 10, 14]. These

demands affect both shipboard navigation officers and

Vessel Traffic Services (VTS) operators because mixed-

autonomy traffic will require cooperation between

conventional vessels, remotely supervised vessels, and

shore-based traffic services.

Human-Centered Competencies for MASS Operations:

Practitioner Priorities and Exploratory Comparisons

Between VTS Operators and Shipboard Navigation

Officers

G.Y. Im

1,2

John B. Lacson Foundation Maritime University, Iloilo City, Philippines

Masan Port Vessel Traffic Service Center, Korea Coast Guard, Changwon-si, Gyeongsangnam-do, Republic of Korea

ABSTRACT: Maritime Autonomous Surface Ships (MASS) are expected to redistribute rather than eliminate

human work, creating new competency requirements for Vessel Traffic Services (VTS) operators and shipboard

navigation officers. This exploratory study examined practitioner priorities within a literature-informed six-

domain framework and explored occupational-group differences. Three substantively aligned online survey

streams generated 198 submissions, of which 146 cases were retained after screening (76 VTS operators and 70

shipboard navigation officers). Group comparisons, covariate-adjusted models, response-pattern sensitivity

analyses, and exploratory dimensional analyses were conducted. Safety, risk, and emergency response

competence was the most frequently selected future priority (61.0%) and the most frequently identified area

requiring current strengthening (54.8%). Technical and digital competence ranked second as a future priority,

whereas communication and coordination ranked second among current strengthening needs. Shipboard

navigation officers reported higher unadjusted appraisal scores across the domains, but the adjusted occupational

pattern was sensitive to covariate coding and recruitment-context differences. The exploratory dimensional

analyses suggested a dominant general appraisal dimension rather than six clearly distinct dimensions. The

framework should be regarded as a conceptually organized practitioner-appraisal model, not a validated six-

factor instrument. The findings support continued development of integrated MASS education, including role-

specific assessment using behavioral anchors and operational scenarios.

http://www.transnav.eu

the International Journal

on Marine Navigation

and Safety of Sea Transportation

Volume 20

Number 3

September 2026

DOI: 10.12716/1001.20.03.21

748

Existing literature has identified important

technical and human-factors requirements, but these

domains are often examined separately. Research on

autonomous-ship operators emphasizes system

knowledge and remote-operations tasks [1, 17],

whereas VTS research highlights traffic interpretation,

coordination, and the cognitive demands of shore-

based monitoring [2, 10]. Maritime education studies

additionally stress simulation, digital readiness, and

the continuing importance of conventional seamanship

[4, 13]. A combined framework is therefore useful for

examining how operational practitioners understand

MASS readiness across shipboard and shore-based

settings. However, limited empirical evidence has

directly compared how VTS operators and shipboard

navigation officers appraise a common set of human-

centered competency requirements for MASS-related

operations.

The present analysis incorporates responses from a

supplementary shipboard survey and includes 76 VTS

operators and 70 shipboard navigation officers to

enable a more balanced exploratory appraisal across

the two occupational groups. This study does not claim

final validation of a competency instrument. Instead, it

examines whether the proposed domains are

considered operationally relevant, whether priorities

differ between the two occupational groups, and what

the results imply for Maritime Education and Training

(MET). The study is positioned as an initial empirical

stage in a broader program of MASS competency

research, intended to establish practitioner-informed

priority areas and identify methodological

requirements for subsequent instrument development,

validation, and educational application.

The study addresses three questions:

− RQ1. How strongly do VTS operators and

shipboard navigation officers appraise the six

proposed MASS competency domains?

− RQ2. Do the two occupational groups differ in their

competency-domain appraisals?

− RQ3. Which competency domains are viewed as the

highest priorities and the greatest current

strengthening needs?

Secondary exploratory analyses examined

respondents' appraisals of the framework and current

education, as well as the internal consistency,

dimensionality, and response-pattern sensitivity of the

competency items.

The study contributes to the early empirical

discussion of MASS competency requirements by

applying a common framework across VTS and

shipboard practice, identifying practitioner-informed

priority areas, and clarifying methodological

requirements for the next stage of competency-

assessment development.

2 CONCEPTUAL FRAMEWORK

2.1 Human Work in MASS Operations

Automation changes the allocation of work and

responsibility. In a MASS environment, routine

functions may become automated, whereas human

tasks shift toward supervision, exception handling,

diagnosis, cross-boundary coordination, and

intervention under uncertainty. Studies of autonomous

ships commonly identify risks related to mode

awareness, delayed information, trust, degraded-

system behavior, and overreliance on automation [6,

11, 14-17]. These risks are not confined to a remote

operations center. They also affect bridge teams, pilots,

VTS operators, rescue organizations, and maritime

authorities that must interpret the behavior of different

vessel types in the same traffic environment.

2.2 Six Competency Domains

A targeted, non-systematic review of recent peer-

reviewed literature on MASS operations, VTS, human-

automation interaction, remote supervision, and

maritime education informed conceptual item

development. Recent MASS competency and training-

framework studies have similarly highlighted the need

to identify future operator skills and adapt MET for

autonomous shipping readiness [3, 5]. The review was

organized around five recurring topic clusters:

automation functions and limits [1, 6, 11, 16, 17];

situational awareness, anomaly detection, and

judgment [6, 11, 14, 16, 17]; ship-shore and multi-actor

coordination [2, 10]; safety, degraded-mode

operations, and emergency response [6, 15, 17]; and

education, adaptation, and recurrent professional

development [4, 13]. Regulatory accountability and

responsible automation use were additionally

informed by the MASS governance and cybersecurity

literature [11, 14]. Candidate statements were

developed from these recurring themes, compared for

conceptual overlap, and consolidated iteratively into

six operational domains. The draft domains and item

wording were also reviewed by university faculty with

maritime education expertise for terminology,

technical accuracy, duplication, operational relevance,

and applicability to both VTS and shipboard settings.

Revisions were made where wording was ambiguous,

overly technical, or role-specific. No systematic-review

protocol, formal content-validity index, or

psychometric validation procedure was used at this

stage. The process was therefore intended for

transparent conceptual organization and preliminary

item development rather than exhaustive evidence

synthesis or validation of six independent latent

constructs.

Table 1. Conceptual Domains of the Proposed MASS

Competency Framework

Domain

Core focus

Operational relevance

Technical and

Digital Competence

Automation functions

and limits; digital data

interpretation; system

monitoring

Supports safe use of

automated and

remotely supervised

systems

Cognitive and

Situational

Competence

Situational awareness;

anomaly detection;

judgment; diagnostic

reasoning

Supports decisions

when information is

incomplete, delayed,

or interface-mediated

Communication and

Coordination

Competence

Shared understanding;

escalation; ship-shore

and multi-actor

teamwork

Supports distributed

operations and mixed-

autonomy traffic

coordination

Safety, Risk, and

Emergency

Response

Competence

Risk recognition;

contingency response;

degraded-mode

operations

Supports transition

from routine

monitoring to

abnormal or

emergency action

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Regulatory and

Ethical Competence

Accountability; legal

duties; responsible

automation use

Supports explainable

and lawful decisions

across autonomy

levels

Adaptive Learning

and Resilience

Competence

Continuous learning;

role adaptation; coping

with change

Supports recurrent

upskilling as systems,

procedures, and roles

evolve

3 METHOD

3.1 Survey Design

The questionnaire contained respondent-profile items,

28 five-point Likert items, two multiple-response

priority questions, and four open-ended questions.

Eighteen Likert items represented the six competency

domains, with three items per domain. These items

addressed complementary aspects of perceived

importance, operational relevance, and future need

rather than interchangeable indicators of narrowly

defined latent traits. Six further items assessed the

clarity, relevance, and applicability of the framework,

and four items addressed related aspects of the

adequacy and future strengthening of MET. Response

options ranged from 1 (strongly disagree) to 5 (strongly

agree). Respondents were instructed to select up to two

domains for each priority question; however, the

online forms did not technically enforce that limit. The

open-ended responses were collected for subsequent

qualitative refinement of the framework and are

outside the scope of the present quantitative article.

The complete wording and domain allocation of the 28

Likert items are provided in Appendix A.

3.2 Recruitment and Data Integration

Data collection was conducted in two stages using

three related survey streams: a general bilingual

maritime-practitioner survey, a Philippine Coast

Guard (PCG)-specific English survey, and a

supplementary English survey for shipboard

navigation personnel. The general bilingual survey

was administered from April 13 to May 19, 2026; the

PCG-specific survey, from May 6 to 19, 2026; and the

supplementary shipboard survey, from May 23 to June

16, 2026.

The general maritime-practitioner survey was

launched first and was open to maritime professionals

with relevant knowledge or experience. It primarily

targeted VTS operators and shipboard navigation

officers but also permitted participation by other

maritime professionals. The survey described the

study as an exploratory practitioner assessment of a

proposed MASS competency framework rather than a

full psychometric validation exercise. In the present

article, the resulting responses are interpreted as

practitioner appraisals. It explained the study purpose,

the intended academic use of the responses, the

operational meaning of MASS as used in the study, the

researcher's identity and affiliation, and the expected

completion time. The questionnaire was presented in

English and Korean within the same survey form.

After the general survey had begun, the survey was

proposed for distribution to PCG personnel. During

that process, the PCG advised that data collection

involving its personnel had to meet separate

institutional requirements concerning permission,

confidentiality, data privacy, and information

handling. A PCG-specific form was therefore prepared.

The substantive competency, framework, education,

and priority questions were kept aligned with the

general survey, while selected introductory and

administrative information was adapted to the PCG

context. The PCG-specific survey began after the

general survey, but both first-stage surveys were

closed on the same date.

The questionnaire was originally prepared in

English. Before data collection, the complete English

survey instrument was reviewed by the Graduate

School for academic data-gathering purposes.

University academic personnel with maritime

education expertise also reviewed the draft domains

and items for terminology, technical accuracy,

duplication, operational relevance, and applicability to

both VTS and shipboard settings. This review was

qualitative; no formal quantitative content-validity

index was calculated. The researcher subsequently

translated the reviewed English questionnaire into

Korean. A bilingual Korean-English reviewer

compared the Korean wording with the original

English version and refined it where necessary to

improve linguistic clarity and conceptual equivalence.

The PCG-specific survey was administered in English.

After the general and PCG-specific surveys had

both closed, incomplete Q9-Q36 response capture was

identified in 25 general-survey records. Two records

were from VTS operators, whereas the remaining 23

were associated with shipboard-related roles. The

incomplete records disproportionately affected the

shipboard-related portion of the initial sample and

further reduced the number of eligible shipboard

navigation officers available for comparison.

Therefore, a second-stage supplementary survey was

administered specifically to maritime professionals

with current or previous shipboard navigation and

watchkeeping experience. It retained the same

substantive competency-appraisal, framework-

appraisal, education-related, and priority items while

adapting profile questions to shipboard roles and

using an equivalent introductory statement for the

supplementary stream.

Recruitment procedures differed across the three

survey streams. For Korean VTS personnel recruited

through the general bilingual survey, permission to

circulate the survey was first obtained from the head of

each participating VTS center, and the survey link was

then distributed to VTS operators through center-

based KakaoTalk communication channels.

Recruitment of PCG personnel followed a formal

institutional process. After the data-gathering request

was submitted to the Office of CG-12, the researcher

submitted the required credential-verification

documents. The PCG-specific survey was circulated

only after permission to proceed had been granted

through the relevant institutional channel. The

supplementary shipboard survey was distributed

through the researcher's maritime professional and

personal networks and through referrals from eligible

contacts with shipboard or navigation-related

experience. The supplementary referral networks

included maritime professionals connected to Korea,

the Philippines, Indonesia, Australia, Vietnam,

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Malaysia, and Nigeria. Because nationality was not

collected consistently across all streams, these

countries indicate recruitment-network reach rather

than verified country-specific subsamples.

The three datasets were aligned by substantive item

wording, and only equivalent items were combined.

Across all three streams, 198 submissions were

received. Although the general survey permitted

participation by other maritime professionals with

relevant knowledge or experience, the present

comparative analysis was restricted to respondents

who met the final eligibility criteria for VTS operation

or an eligible shipboard navigation role. Fifty-two

records were excluded: 25 because the Q9–Q36 Likert

section was incomplete and 27 because the respondent

did not meet the final VTS or shipboard-navigation

eligibility criteria. The latter group included cadets or

trainees, ratings and able seafarers, engineers, catering

personnel, and other shore-based or non-VTS roles

outside the two comparison groups.

The final comparison dataset comprised 146

respondents who completed all 28 Likert items and

could be classified into either the VTS-operator group

or the eligible shipboard navigation group. The final

sample consisted of 27 VTS operators and 17 shipboard

navigation officers from the general bilingual survey,

49 VTS operators from the PCG-specific English

survey, and 53 shipboard navigation officers from the

supplementary English survey, producing final

occupational groups of 76 VTS operators and 70

shipboard navigation officers. The shipboard group

included masters, chief officers, second officers, third

officers, deck officers, and former deck officers with

navigation-watchkeeping experience. Former deck

officers were retained only when they reported

previous navigation-watchkeeping experience

relevant to the competency-appraisal focus of the

study.

The supplementary shipboard survey did not

collect email addresses. Because the survey streams

were administered separately and the analytical

datasets were de-identified, complete cross-stream

verification of duplicate participation was not possible.

Any undetected duplicate participation would reduce

the effective independence and effective sample size of

the shipboard group; consequently, the nominal

sample size should not be interpreted as proof that

every record represents a unique individual across all

streams.

Table 2. Final Sample Profile

Profile category

VTS n

(%)

Shipboard n

(%)

Total n

(%)

Group size

(100)

70 (100)

146

(100)

Professional experience: <5 years

(48.7)

5 (7.1)

(28.8)

Professional experience: 5-10 years

(19.7)

25 (35.7)

(27.4)

Professional experience: 11-15 years

9 (11.8)

17 (24.3)

(17.8)

Professional experience: 16-20 years

8 (10.5)

3 (4.3)

11 (7.5)

Professional experience: >20 years

7 (9.2)

20 (28.6)

(18.5)

MASS familiarity: moderate or higher

(55.3)

46 (65.7)

(60.3)

Experience with advanced

automation/digital decision support:

yes

(35.5)

27 (38.6)

(37.0)

3.3 Statistical Analysis

Domain appraisal scores were calculated as the mean

of the three items assigned to each conceptual domain

and were treated as approximately continuous for the

exploratory OLS-based analyses. Descriptive statistics

were reported for the full sample and by occupational

group. Internal consistency was examined using

Cronbach's alpha. Group differences were tested using

Welch independent-samples t-tests because the two

groups showed different variances on several domains.

Six domain-level p-values were adjusted using the

Holm procedure [7]. Cohen's d was calculated using

the pooled standard deviation and interpreted as an

estimate of practical group separation. The ten

framework and education-related item comparisons

were also examined using Welch tests, with Holm

adjustment applied across the ten comparisons.

Multiple-response items were summarized as the

number and percentage of respondents selecting each

domain. Because some respondents selected more than

the instructed maximum of two domains, a sensitivity

analysis repeated each priority summary after

excluding records with more than two selections for

that question. Six OLS regression models were fitted,

one for each domain appraisal score, to adjust for

differences in respondent profiles. Occupational

group, professional experience, MASS familiarity, and

prior experience with advanced automation, remote

monitoring, or digitally integrated decision-support

systems were entered simultaneously. Professional

experience and MASS familiarity were entered as

ordered numeric covariates to adjust parsimoniously

for ordered category differences. This coding

imposed a single linear trend across categories, but the

resulting coefficients were not interpreted as exact

interval-scale effects. The automation-experience

variable was coded as yes versus no or unsure. Twelve

respondents (8.2%) selected “not sure”; these

responses were grouped with “no” because they did

not provide affirmative evidence of prior experience.

HC3 heteroskedasticity-consistent standard errors

were used [12], and the six group-effect p-values were

adjusted using the Holm procedure [7]. Alternative

categorical-coding models represented professional

experience and MASS familiarity with category

indicators and retained “not sure” as a separate

automation-experience category. Analyses were

conducted in Python using NumPy, SciPy,

statsmodels, and scikit-learn.

The dimensional analysis was deliberately

exploratory. Principal Component Analysis (PCA) was

used as a dimensionality diagnostic rather than as a

formal factor-validation procedure. The Kaiser-Meyer-

Olkin measure, Bartlett's test, and principal-

component results were used to examine whether the

18 domain items behaved as six separable dimensions

or as a broad general appraisal dimension. The

eigenvalue-greater-than-one rule was treated only as a

conventional descriptive criterion. Horn's parallel

analysis was additionally conducted using 5,000

random normal datasets with the same sample size and

number of variables; observed eigenvalues were

compared with the 95th-percentile random

eigenvalues [8]. Identical responses across many

consecutive items may reflect a strong global

judgment, uniformly affirmative appraisal, or response

simplification. For this reason, a response-pattern

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sensitivity analysis was also conducted after excluding

respondents who selected one identical score across all

18 competency-appraisal items.

3.4 Ethical Considerations and Data Protection

All respondents received introductory information

explaining the academic purpose of the study, its

preliminary appraisal focus, the intended academic use

of the responses, the researcher's identity and

affiliation, and the expected completion time. The

survey introductions explained that direct experience

with fully autonomous ships was not required and that

responses could be based on professional judgment,

operational experience, training background, or

informed understanding of MASS-related

developments.

The general bilingual survey provided information

on the study purpose, the proposed competency

framework, the operational meaning of MASS, and the

intended academic use of the responses. The PCG-

specific and supplementary shipboard surveys also

explicitly stated that participation was voluntary, that

no confidential operational information was requested,

and that responses would be handled confidentially in

accordance with the stated procedures for research use,

confidentiality, and data privacy.

No separate written or electronically recorded

consent form was used. Questionnaire submission was

treated as consent to participate after respondents had

reviewed the introductory information for their survey

stream. The PCG-specific and supplementary

shipboard forms explicitly stated that participation

was voluntary, and no sensitive personal data or

confidential operational information was requested.

Survey records used in the analysis were de-

identified after export from the survey platforms.

Personally identifiable information, including email

addresses and optional contact or raffle information

where collected, was removed from the substantive

analytical dataset. The supplementary shipboard

survey did not collect email addresses. A separate de-

identified dataset for potential reviewer access and

reproducibility was prepared; it excludes direct

identifiers, exact timestamps, institutional affiliations,

original row-linkage fields, detailed quasi-identifying

role information, and free-text responses that could

contain identifying information.

Before data collection, the complete survey

instrument was submitted to the Dean of the Graduate

School in PDF format and was reviewed and endorsed

for academic data-gathering purposes. Before the PCG-

specific data collection, the formal data-gathering

request and its procedures concerning voluntary

participation, confidentiality, data privacy, and

academic use were also reviewed and signed by the

Dean of the Graduate School of John B. Lacson

Foundation Maritime University. No separate

institutional ethics approval number was issued. The

Dean's endorsement therefore constituted prior

academic permission for the survey instrument and

data-gathering procedure, not approval by a separate

institutional ethics committee. The PCG-specific

survey was subsequently conducted in accordance

with the institutional permission and credential-

verification requirements communicated by the PCG.

Participation was voluntary, no sensitive personal data

or confidential operational information was requested,

and direct identifiers were removed from the analytical

dataset after export.

4 RESULTS

4.1 Appraisal of the Six Competency Domains

All six competency domains received high mean

appraisal scores, with means ranging from 3.98 to 4.19.

Safety, risk, and emergency response competence

received the highest overall mean. In the unadjusted

comparisons, shipboard navigation officers rated every

domain higher than VTS operators. All six unadjusted

differences remained statistically significant after

Holm adjustment; however, the adjusted occupational

pattern was less stable, as reported in Section 4.5. The

smallest effect was found for technical and digital

competence (d=0.37), whereas the largest was found

for regulatory and ethical competence (d=0.61).

Covariate-adjusted results are presented in Section 4.5.

Table 3. Comparison of Competency-Domain Appraisal

Scores

Competency Domain

Overall

M (SD)

VTS

(SD)

Shipboard

M (SD)

Welch

Holm

Cohen

Technical and Digital

Competence

4.02

(1.03)

3.84

(1.09)

4.21 (0.91)

2.26

.030

0.37

Cognitive and

Situational

Competence

3.98

(0.95)

3.75

(1.01)

4.23 (0.80)

3.18

.007

0.52

Communication and

Coordination

Competence

4.14

(0.96)

3.96

(1.05)

4.34 (0.82)

2.46

.030

0.40

Safety, Risk, and

Emergency Response

Competence

4.19

(0.99)

3.95

(1.07)

4.45 (0.84)

3.20

.007

0.52

Regulatory and

Ethical Competence

4.12

(1.01)

3.84

(1.07)

4.43 (0.84)

3.70

.002

0.61

Adaptive Learning

and Resilience

Competence

4.12

(1.00)

3.87

(1.03)

4.40 (0.89)

3.29

.006

0.54

Note: Positive d values indicate higher appraisal scores among

shipboard navigation officers. Holm p values adjust the six domain

comparisons.

4.2 Priority and Strengthening Needs

Responses to the multiple-response questions showed

a consistent pattern. Safety, risk, and emergency

response competence was selected most often as both

the highest priority for future preparation and the area

requiring the greatest current strengthening. Technical

and digital competence ranked second as a future

priority, whereas communication and coordination

competence ranked second as a current strengthening

need. Regulatory and ethical competence received a

relatively high mean appraisal score but was selected

less often by respondents. Twenty-four respondents

exceeded the two-selection limit for the future-priority

question, and 24 exceeded it for the current-

strengthening question. After records exceeding the

selection limit were excluded separately for each

question, 122 cases remained in each sensitivity

analysis. The leading-domain rankings did not change:

safety remained first for both questions, technical and

digital competence remained second for future

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priority, and communication and coordination

competence remained second for current

strengthening needs.

Table 4. Competency Priorities and Perceived Strengthening

Needs (N=146)

Competency Domain

Highest

priority n (%)

Greatest

strengthening need n

(%)

Safety, Risk, and Emergency

Response Competence

89 (61.0)

80 (54.8)

Technical and Digital

Competence

73 (50.0)

64 (43.8)

Communication and

Coordination Competence

63 (43.2)

71 (48.6)

Cognitive and Situational

Competence

44 (30.1)

42 (28.8)

Adaptive Learning and

Resilience Competence

23 (15.8)

25 (17.1)

Regulatory and Ethical

Competence

16 (11.0)

22 (15.1)

Note: This was a multiple-response question, so percentages do not

sum to 100%. Respondents were instructed to select up to two

domains for each question; excluding records with more than two

selections retained the same leading-domain rankings.

4.3 Framework and Education-Related Appraisals

The framework was generally viewed as clear,

relevant, and useful, with overall item means ranging

from 3.91 to 4.09. Respondents also supported further

strengthening of MASS-related education and training

(M=4.15). The lowest education-related mean

concerned whether current preparation already

provides an adequate foundation for MASS adaptation

(M=3.54), indicating qualified rather than complete

confidence in existing preparation.

Table 5. Overall Appraisal and Education-Related Items

With Multiplicity-Adjusted Comparisons

Item

Overall

VTS

Shipboard

Welch p

(raw /

Holm)

Framework is clear and

understandable

3.91

3.71

4.13

.008 / .082

Relevant to real-world

maritime operations

4.00

3.86

4.16

.066 / .265

Useful for maritime education

and training

4.08

3.91

4.27

.024 / .121

Reflects VTS and shipboard

settings

4.05

3.84

4.27

.010 / .088

Useful for future shore

supervision/mixed autonomy

4.09

3.96

4.23

.087 / .265

Captures major competency

needs

4.02

3.84

4.21

.018 / .109

Current education may not

adequately address MASS

needs

3.94

3.75

4.14

.015 / .105

Conventional and MASS

competency requirements

differ

3.97

3.88

4.06

.254 / .507

Current preparation provides

an adequate foundation

3.54

3.49

3.60

.548 / .548

Education and training should

be strengthened

4.15

3.96

4.36

.013 / .101

Note: Each cell reports the unadjusted p value followed by the

Holm-adjusted p value. None of the ten item-level comparisons

remained statistically significant after correction. Standard

deviations are omitted for table compactness.

4.4 Reliability, Dimensionality, and Sensitivity Analysis

Internal consistency was high for the 18 competency-

appraisal items (alpha=.983). Domain-level alpha

coefficients ranged from .852 to .950, and the six

framework-appraisal items produced alpha=.974. The

four education-related items addressed distinct and

partly contrasting judgments concerning current

educational adequacy, differences between

conventional and MASS-related requirements, and the

need for future strengthening. For this reason, they

were analyzed individually and were not combined

into a composite scale. Overall, the high coefficients for

the competency and framework-appraisal items

demonstrate response consistency but also raise the

possibility of overlapping item content or a strong

general evaluative tendency.

The dimensional analysis supported this caution.

The KMO value was .963, and Bartlett's test was

significant, chi-square(153)=3670.82, p<.001, indicating

that the correlation matrix was suitable for dimension

reduction. The first principal component had an

eigenvalue of 13.98 and explained 77.6% of total

variance; all remaining observed eigenvalues were

below 1. Parallel analysis retained only the first

component: its observed eigenvalue exceeded the 95th-

percentile random reference value (1.80), whereas the

second observed eigenvalue (0.74) was below its

corresponding reference value (1.62). Correlations

among the six domain appraisal scores ranged from

.838 to .929. Because the exploratory PCA used

conventional correlations for five-point ordinal

responses, the exact dimensional results may differ

under polychoric-correlation methods. Nevertheless,

the data did not support six clearly separated empirical

dimensions. The domains remain useful as

conceptually distinct operational categories, but the

responses were dominated by a broad general

appraisal dimension concerning MASS-related

competencies. The domain-level comparisons should

therefore be read as comparisons of conceptually

grouped appraisal summaries, not as comparisons of

validated independent latent constructs.

Thirty-seven respondents (25.3%) selected an

identical response across all 18 competency-appraisal

items; 23 were VTS operators, and 14 were shipboard

navigation officers. After excluding these cases, the

sample for the response-pattern sensitivity analysis

comprised 109 respondents. The direction of all six

occupational-group differences was unchanged, and

shipboard navigation officers continued to report

higher mean appraisal scores across every domain. The

identical-response pattern may reflect not only

response simplification but also the uniformly

affirmative wording of the items, which allowed

respondents who regarded all MASS-related

competencies as important to assign the same rating

across domains. The reduced-sample result therefore

suggests that the observed direction was not produced

solely by identical-response cases; it should not be

interpreted as a separate confirmatory significance test.

4.5 Covariate-Adjusted Occupational Comparisons

Professional experience differed markedly between the

groups, whereas MASS familiarity and exposure to

advanced automation or digital decision-support

753

environments differed more modestly. The ordinal-

coding models examined whether this occupational

pattern persisted after simultaneous adjustment for

these three respondent characteristics. The adjusted

shipboard-officer coefficient remained positive for

every domain. After Holm adjustment, significant

group differences remained for cognitive and

situational competence, regulatory and ethical

competence, and adaptive learning and resilience

competence. The safety, risk, and emergency response

difference remained positive but did not remain

statistically significant after Holm adjustment (p=.053),

whereas the technical and digital competence and

communication and coordination competence

differences were also no longer statistically significant.

In the alternative categorical-coding models, after

harmonizing equivalent English-only and bilingual

response labels before dummy-variable construction,

all six occupational-group coefficients remained

positive (B=0.21-0.46), but none remained statistically

significant after Holm adjustment (adjusted p=.258-

.685). The statistical significance of the occupational-

group coefficients was therefore sensitive to covariate

coding. Detailed categorical-coding results are

reported in Table 7.

Table 6. Occupational-Group Effects in the Ordinal-Coding

Covariate-Adjusted Models

Competency Domain

Adjusted

95% CI

Group-

effect p

Holm

Technical and Digital

Competence

0.34

-0.03 to

0.71

.074

.126

Cognitive and Situational

competence

0.47

0.13 to

0.81

.007

.034

Communication and

Coordination Competence

0.34

-0.02 to

0.70

.063

.126

Safety, Risk, and Emergency

Response Competence

0.45

0.08 to

0.82

.018

.053

Regulatory and Ethical

Competence

0.57

0.20 to

0.94

.003

.017

Adaptive Learning and

Resilience Competence

0.48

0.12 to

0.85

.009

.038

Note: N=146 for all six ordinal-coding models. B represents the

adjusted mean difference for shipboard navigation officers relative

to VTS operators after controlling for professional experience,

MASS familiarity, and prior advanced-automation or digital

decision-support experience. HC3 robust standard errors were

used. Positive coefficients indicate higher adjusted appraisal scores

among shipboard navigation officers. Group-effect p values are the

occupational-group coefficient p values before Holm adjustment;

Holm p values adjust the six occupational-group comparisons.

Model R-squared values ranged from .043 to .089, and adjusted R-

squared values ranged from .015 to .063. Alternative categorical-

coding results are reported in Table 7.

Table 7. Occupational-Group Effects in the Categorical-

Coding Covariate-Adjusted Models

Competency Domain

Adjusted

95% CI

Group-

effect p

Holm

Technical and Digital

Competence

0.23

-0.25 to

0.70

.346

.685

Cognitive and Situational

Competence

0.41

0.01 to

0.81

.043

.258

Communication and

Coordination Competence

0.21

-0.22 to

0.63

.343

.685

Safety, Risk, and Emergency

Response Competence

0.37

-0.07 to

0.81

.102

.343

Regulatory and Ethical

Competence

0.46

0.01 to

0.91

.045

.258

Adaptive Learning and

Resilience Competence

0.38

-0.05 to

0.82

.086

.343

Note: N=146 for all six categorical-coding models. Equivalent

English-only and bilingual response labels were harmonized before

professional experience and MASS familiarity were represented by

category indicators; “not sure” was retained as a separate

automation-experience category. HC3 robust standard errors were

used. Positive coefficients indicate higher adjusted appraisal scores

among shipboard navigation officers. Group-effect p values are the

occupational-group coefficient p values before Holm adjustment;

Holm p values adjust the six occupational-group comparisons.

Model R-squared values ranged from .068 to .126; adjusted R-

squared values ranged from -.009 to .054.

5 DISCUSSION

5.1 Shared Priorities for MASS Readiness

The clearest common result is that practitioners did not

treat MASS competence as a narrowly technical issue.

Technical and digital competence was highly rated, but

safety, risk, and emergency response competence

received the highest mean and the highest priority

frequency. Communication and coordination

competence was also prominent, especially as a current

strengthening need. This pattern is consistent with the

wider literature suggesting that autonomous and

remotely supervised operations continue to depend on

anomaly recognition, communication, trust

calibration, and coordinated intervention [2, 6, 10, 11,

14-17]. It is also consistent with recent MASS

competency and training-framework research

emphasizing operational readiness, digital capability,

and future operator training requirements [3, 5]. The

comparatively lower frequency with which

respondents selected regulatory and ethical

competence as a priority, despite its high mean

appraisal score, suggests that they regarded it as

important but less immediately urgent than safety,

technical capability, and coordination.

For maritime education, this suggests that

automation should not be taught as a stand-alone topic

disconnected from operational judgment. Automation

functions and data interpretation should be taught

together with degraded-mode response, bridge–shore

team coordination, emergency escalation, and decision

accountability. Simulation and scenario-based learning

are particularly relevant because they allow technical

failures, ambiguous information, and distributed

communication to be examined together [4, 13].

5.2 Interpreting the Occupational Differences

Shipboard navigation officers gave consistently higher

unadjusted appraisal scores than VTS operators, with

small-to-moderate effects. The largest unadjusted

difference concerned regulatory and ethical

competence. In the parsimonious ordinal-coding

models, adjustment for professional experience, MASS

familiarity, and advanced-automation exposure

narrowed the pattern: cognitive and situational

competence, regulatory and ethical competence, and

adaptive learning and resilience competence remained

significant after Holm adjustment, whereas the other

three domains did not. One plausible operational

interpretation is that shipboard navigation officers

may experience more immediate responsibility for

vessel watchkeeping, onboard safety, and emergency

action, making legal accountability, adaptive response,

and intervention especially salient. Conversely, the

comparatively lower appraisal scores observed among

754

VTS respondents in this sample may reflect differences

in perceived role relevance, organizational exposure,

recruitment context, or training context rather than

lower awareness or competence among VTS operators.

These differences require caution. When the

covariates were represented categorically, the group

coefficients remained positive, but none remained

statistically significant after Holm adjustment. The

occupational group was also closely linked to

recruitment stream, organizational context, survey

language, likely nationality, and survey period: the

PCG-specific form contributed a substantial

proportion of the VTS sample, whereas the

supplementary form contributed a substantial

proportion of the shipboard sample. In particular, the

supplementary shipboard survey was conducted after

the non-mandatory IMO MASS Code was adopted on

May 22, 2026, whereas the first-stage surveys had

already closed. This created a potential temporal-

context difference that cannot be separated from

occupation in the present data. These influences cannot

be separated statistically because several stream-by-

occupation combinations are absent or sparse. The

results are therefore best read as descriptive group

associations that depend partly on modeling choices,

rather than as evidence of a causal or generalizable role

effect. The adjusted models explained only a modest

proportion of score variance, indicating that

unmeasured respondent, organizational, and survey-

context characteristics likely contributed to the

observed differences. Future studies should recruit

both groups within the same countries, organizations,

languages, time periods, and sampling frames and

should prespecify models that separate occupation

from recruitment context.

5.3 Meaning of the Dominant General Dimension

The very high reliability coefficients and dominant first

component are not proof that the six-domain

framework has been validated as six separate scales.

The three items assigned to each domain represented

complementary aspects of perceived importance,

operational relevance, and future need rather than

interchangeable indicators of a narrowly defined latent

trait. All competency items were positively framed,

and respondents could reasonably apply a broad

judgment that all MASS-related competencies are

important. Thus, the high means, strong inter-item

correlations, and identical-response patterns are

compatible with acquiescence, common-method

influence, and a broad general appraisal dimension.

Accordingly, the framework is more appropriately

described as a conceptually structured appraisal

model. The six domains help organize curriculum and

operational discussion, whereas the present

instrument functions more convincingly as an overall

MASS-readiness appraisal than as six empirically

independent psychometric scales. Future domain-level

comparisons should be supported by more

discriminating and behaviorally anchored items before

being interpreted as comparisons of distinct latent

competencies.

This distinction matters for subsequent

development. Future instrument work should reduce

overlapping wording, include behaviorally anchored,

discriminating, and contrastive items where

appropriate, and use a larger multicountry sample for

exploratory and confirmatory factor analysis. Domain-

specific scenarios may also separate constructs more

effectively than general agreement statements. For

example, technical competence could be assessed

through automation-mode interpretation, whereas

communication competence could be assessed through

a ship-shore escalation scenario. Future validation

should also use polychoric correlations and factor-

analytic methods designed for ordinal item responses.

Accordingly, the present findings should not be treated

as the endpoint of competency-model development.

The main methodological contribution of this study is

to show which domains merit further development

and why future assessments should move beyond

general agreement statements toward role-specific,

behaviorally anchored, and scenario-based indicators.

6 IMPLICATIONS FOR MARITIME EDUCATION

AND OPERATIONS

First, maritime institutions may consider integrating

MASS content across existing navigation, human-

factors, safety, and regulatory courses rather than

treating autonomy as a completely separate technical

subject. Second, recurrent training could extend

beyond cadets to active shipboard navigation officers,

VTS personnel, instructors, and future shore-control

operators. Third, assessment may need to emphasize

observable performance: recognizing automation

limits, interpreting uncertain outputs, maintaining

situational awareness, escalating hazards, and

coordinating during degraded operations.

The results also provide a preliminary rationale for

developing and evaluating shared exercises between

shipboard and shore-based personnel. Mixed-

autonomy operations are likely to require a common

understanding of vessel intent, information quality,

intervention authority, and emergency responsibility.

Joint scenarios involving VTS operators and shipboard

navigation officers could reveal differences in mental

models and communication expectations before those

differences contribute to operational failures. Such

training would complement, rather than replace, role-

specific competence requirements.

Finally, curriculum reform should remain gradual

and operationally realistic. Conventional seamanship,

collision avoidance, watchkeeping, and emergency

response remain essential. MASS preparation should

build on these foundations while adding digital

literacy, remote-monitoring awareness, automation-

failure management, and a clearer understanding of

redistributed accountability [9, 11]. Although most

first-stage responses were collected before this

regulatory development, the IMO announcement of

the non-mandatory MASS Code in May 2026 further

underscores the policy relevance of competencies

related to accountability, human oversight, and lawful

decision-making across autonomy levels [9].

7 LIMITATIONS

The study used purposive, convenience, and referral-

based recruitment and cannot be treated as

755

representative of all VTS operators or shipboard

navigation officers. Respondents with greater interest

in MASS, automation, or maritime education may have

been more likely to participate. The general and PCG-

specific surveys closed on the same date. Afterward, 25

general-survey records were found to have incomplete

Q9–Q36 capture, including two VTS-related records

and 23 records associated with shipboard-related roles.

A later supplementary shipboard survey partially

addressed the disproportionate loss of eligible

shipboard records and increased the number available

for exploratory comparison, but it did not create a

common sampling frame. Differences in recruitment

route, organizational context, survey timing,

introductory wording, and profile questions may

therefore have affected response patterns. An

additional temporal consideration is that the non-

mandatory IMO MASS Code was adopted on May 22,

2026, after the general and PCG-specific surveys had

closed and one day before the supplementary

shipboard survey began. Consequently, most VTS

responses were collected before the formal adoption,

whereas most shipboard responses were collected

afterward. The study did not assess whether

respondents were aware of this regulatory

development, and its influence on competency

appraisals cannot be determined. The occupational

comparisons should therefore not be interpreted as

evidence of responses specifically attributable to the

adopted IMO MASS Code.

The occupational group was not separable from

survey source, organizational context, language, and

likely nationality. The PCG-specific form contributed

substantially to the VTS group, whereas the

supplementary form contributed substantially to the

shipboard group, and nationality was not collected

consistently. Adjustment for professional experience,

MASS familiarity, and advanced-automation exposure

did not remove these sources of confounding. Three

occupational differences remained significant under

ordinal covariate coding, but none remained

significant after Holm adjustment under categorical

coding. Complete verification of duplicate

participation across survey streams was also

impossible because the supplementary form collected

no email addresses and the analytical datasets were de-

identified. This approach reduced direct identifiability

but limited complete duplicate verification across

survey streams. Any undetected duplicates could

reduce effective independence and sample size. The

bilingual general survey underwent comparative

review but not formal back-translation or cross-

language psychometric validation.

Most respondents reported low-to-moderate MASS

familiarity, and only 37.0% reported experience with

advanced automation or integrated decision-support

environments. The findings therefore represent

informed practitioner appraisals rather than

performance evidence from operational MASS

systems. The uniformly affirmative item format may

have contributed to high scores, strong inter-item

correlations, and identical-response patterns through

acquiescence, common-method influence, or broad

global evaluation. In addition, the online forms did not

technically enforce the instruction to select no more

than two priority domains. Although exclusion of

over-limit responses did not alter the leading-domain

rankings, the reported full-sample percentages should

be interpreted as descriptive selection frequencies. The

exploratory PCA used conventional correlations for

five-point ordinal responses, and dimensional

estimates may differ under polychoric-correlation

methods. The dimensional analyses do not establish six

independent scales, and domain means should be

interpreted as conceptually organized appraisal

summaries rather than validated latent-trait scores or

direct measures of competence. Future studies should

use matched multicountry samples, technically

enforced response limits, more discriminating and

behaviorally anchored items, external performance

measures, and prespecified ordinal factor-analytic

methods.

8 CONCLUSIONS

This study identifies practitioner-informed priorities

across six human-centered competency domains and

provides an exploratory comparison between 76 VTS

operators and 70 shipboard navigation officers. Safety,

risk, and emergency response competence emerged as

the leading priority, with technical and digital

competence and communication and coordination

competence also central. Shipboard navigation officers

reported higher unadjusted appraisals across all

domains; however, adjusted occupational associations

varied with covariate coding and recruitment context.

These differences should accordingly be interpreted as

exploratory descriptive associations rather than stable,

causal, or generalizable role effects.

The findings support further development of

integrated maritime education linking automation

literacy with situational judgment, ship-shore

coordination, emergency response, accountability, and

continuous learning. The framework is best treated as

a conceptually organized practitioner-appraisal model

rather than six validated independent scales or a direct

measure of individual competence. It may provide a

useful basis for curriculum discussion, instrument

refinement, matched cross-role validation, and the

development of behaviorally anchored, scenario-based

assessments.

ACKNOWLEDGEMENTS

The author thanks the maritime practitioners who

contributed to the three survey streams and the academic and

professional contacts who assisted with survey distribution.

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APPENDIX A. COMPLETE LIKERT ITEM

WORDING AND DOMAIN ALLOCATION

Items were rated from 1 (strongly disagree) to 5

(strongly agree). Item numbers correspond to the

aligned Q9–Q36 survey sequence used in the analysis.

Scale/Domain

Item

Complete Wording

Technical and Digital

Competence

Technical and digital competence

should be treated as a priority area in

MASS-related education and training.

Technical and Digital

Competence

Q10

Maritime professionals need the ability

to understand the functions and limits

of automation systems.

Technical and Digital

Competence

Q11

Maritime professionals should be able

to interpret digital data and system

outputs critically rather than rely on

them automatically.

Cognitive and

Situational

Competence

Q12

Cognitive and situational competence

becomes more difficult to maintain in

higher-automation maritime

environments.

Cognitive and

Situational

Competence

Q13

Maritime professionals need strong

anomaly-detection and judgment skills

even when automated support systems

are available.

Cognitive and

Situational

Competence

Q14

Indirect or interface-based monitoring

in automated environments requires a

high level of situational awareness.

Communication and

Coordination

Competence

Q15

Communication and coordination

failures are likely to become more

safety-critical in mixed-autonomy

maritime environments.

Communication and

Coordination

Competence

Q16

Coordination between VTS personnel,

shipboard navigation officers, and

shore-based operators becomes more

important as automation increases.

Communication and

Coordination

Competence

Q17

Shared understanding and timely

escalation are critical in mixed-

autonomy maritime environments.

Safety, Risk, and

Emergency Response

Competence

Q18

Safety, risk, and emergency-response

competence should remain a core

priority despite increasing automation.

Safety, Risk, and

Emergency Response

Competence

Q19

Maritime professionals should be

prepared to respond effectively when

automated systems fail, degrade, or

generate uncertain outputs.

Safety, Risk, and

Emergency Response

Competence

Q20

Risk recognition in MASS-related

operations includes both conventional

navigational risks and technology-

related risks.

Regulatory and

Ethical Competence

Q21

Regulatory and ethical competence is

important for responsible MASS-

related operations.

Regulatory and

Ethical Competence

Q22

Maritime professionals need a practical

understanding of legal accountability

and decision responsibility in

automated environments.

Regulatory and

Ethical Competence

Q23

Responsible use of automation requires

awareness of ethical and regulatory

implications, not only technical

knowledge.

Adaptive Learning

and Resilience

Competence

Q24

Adaptive learning and resilience

should be treated as priority

competencies for future maritime

professionals in MASS contexts.

Adaptive Learning

and Resilience

Competence

Q25

Maritime professionals will require

continuous upskilling as automation

and digitalization develop further.

Adaptive Learning

and Resilience

Competence

Q26

The ability to adjust to changing roles,

interfaces, and procedures is a key

competency in MASS-related

environments.

Framework Appraisal

Q27

The proposed six-domain competency

framework is clear and understandable.

757

Framework Appraisal

Q28

The framework is relevant to real-world

maritime operations.

Framework Appraisal

Q29

The framework is useful for maritime

education and training.

Framework Appraisal

Q30

The framework appropriately reflects

competency needs across both VTS-

related and shipboard settings.

Framework Appraisal

Q31

The framework is potentially useful for

future shore-based supervision and

mixed-autonomy traffic environments.

Framework Appraisal

Q32

The framework adequately captures the

major competency needs of VTS

operators and shipboard navigation

officers in MASS-related operations.

Education and

Training Appraisal

Q33

Current maritime education and

training may not yet adequately

address MASS-related competency

needs.

Education and

Training Appraisal

Q34

Competency requirements are

perceived to differ meaningfully

between conventional maritime

operations and MASS-related

operations.

Education and

Training Appraisal

Q35

In your opinion, the current

competency preparation of maritime

professionals provides an adequate

foundation for adapting to MASS-

related operations.

Education and

Training Appraisal

Q36

Current education and training for

maritime professionals should be

further strengthened to support MASS-

related operations.