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1 INTRODUCTION
Since the end of the last decade, numerous near-coastal
projects involving autonomous and automated
shipping have moved beyond the theoretical stage.
However, the experience gained from near-coastal
shipping can only be applied to a limited extent to the
future operation of MASS in worldwide trade. The
roles and responsibilities of the onshore Remote
Operators and onboard navigators differ depending on
the Operational Envelope. There is a common
understanding among researchers that seafarers will
need to undergo new forms of training to acquire
MASS-specific technical and non-technical skills. Li
and Yuen [25] even anticipate that the most profound
impact on MASS would be related to the necessary
modification to human skillsets. Reflecting this, the
number of papers discussing skills and competencies
associated with MASS grew from a single publication
in 2017 to 15 in 2022 [2]. However, despite the interest
in future skills, detailed competencies have rarely been
the subject of investigation. This shortcoming can be
attributed to the disregard of certain conceptual steps,
from defining tasks and processes to identifying the
associated skills and ultimately determining
competencies. There is an evident gap in the research
on work tasks, alongside a lack of detailed examination
of the roles and responsibilities of MASS remote and
onboard operators. To address this gap, the first part of
the study employs a systematic literature review (SLR)
to answer the following research question (RQ1): What
conceptual and methodological factors explain the lack
of specificity in existing competency frameworks for
MASS operators?
The second part of this paper develops a
methodological approach that overcomes the
shortcomings identified and lays the foundations for
Addressing Gaps in MASS Operator Competency
Research: Developing the STISA Methodology for
Future Work Process Analysis
D. Rostek
1
& M. Baldauf
2
1
University of Vechta, Vechta, Germany
2
Hochschule Wismar, University of Applied Sciences – Technology, Business and Design, Rostock-Warnemünde, Germany
ABSTRACT: The introduction of Maritime Autonomous Surface Ships will require new competencies for Remote
Operators and onboard navigators. However, existing competency frameworks are too general to be used for
developing curricula in Maritime Education and Training. This paper examines why current frameworks lack
specificity and develops the Sensor Technology Impact on MASS Situational Awareness (STISA) methodology as
a structured response. A systematic literature review was conducted, in which 25 articles were analysed using
qualitative content analysis. The review identified research gaps concerning missing task and process
foundations, unsuitable methods, insufficient consideration of sensor technologies, and weak integration of
Modes of Operation. These gaps were translated into methodological requirements and used to develop the
STISA methodology abductively. The methodology links sensor information, sensor limitations, operational
challenges, situational awareness, and Modes of Operation as a basis for deriving future MASS operator work
processes. The resulting STISA model remains conceptual and requires empirical validation in future studies.
http://www.transnav.eu
the International Journal
on Marine Navigation
and Safety of Sea Transportation
Volume 20
Number 3
September 2026
DOI: 10.12716/1001.20.03.22
760
detailed work processes, which are essential for
successful competency development. This approach
forms part of a comprehensive study investigating the
future competencies of MASS onboard navigators in
worldwide trade, based on an understanding of the
changing navigational work processes. Since these are
future processes, the research builds on an extensive
hypothetical model, which posits correlations of
System Variables influencing the work processes of
MASS operators. The present study focuses on a critical
section of the hypothetical model, investigating the
hypothetical correlation between MASS sensor
technologies and their impact on the situational
awareness of Remote Operators and onboard
navigators. The aim is to develop a holistic analytical
methodology that allows for a systematic analysis of all
system components and their causal relationships. In
view of the future implementation of the consolidated
MASS Code, this methodological approach must be
flexible enough to adapt to the Code's goal-oriented,
technology-open approach [17]. The second research
question (RQ2), which tests the hypothesis and
addresses the shortcomings identified in the SLR, is
therefore as follows: How can the identified gaps be
addressed through a structured methodology that
provides the basis for deriving MASS operator work
processes from sensor information?
This methodological approach is called Sensor
Technology Impact on MASS Situational Awareness
(STISA) methodology. In a final step, an early draft of
a STISA model is designed, which simplifies the
methodology to focus on the information most relevant
for practical application.
2 RESEARCH METHOD
Based on the research problem, this study applies a
qualitative theory-building research design to develop
the STISA methodology for MASS. The first step of the
study is an SLR using the Web of Science (WoS)
database to identify and structure the underlying gaps
in contemporary competency research. These gaps
serve as the basis for the development of the STISA
methodology. This second step involves an abductive
research approach, which combines a systematic
analysis of the research findings with iterative
conceptual development. The approach is appropriate
given that the future work processes of MASS
operators are not yet fully observable, and the
theoretical understanding of this field remains
fragmented.
In order to be reproducible and transparent, the
PRISMA guideline was adopted for the SLR [27, 39].
Guided by RQ1, a combination of keywords related to
autonomous shipping and competencies was used. The
search string was as follows: ("Maritime Autonomous
Surface Ship*" OR "autonom* ship*" OR "autonom*
vessel*" OR "unmanned vessel*" OR "unmanned
ship*") AND ("skill*" OR "competenc*" OR
"capabilit*").
The search included peer-reviewed journal articles
published between 2014 and 2025. To meet the
PRISMA requirements, the review followed the multi-
stage screening and selection process shown in Table 1.
Publications were included if they addressed
competencies, skills, or operator capabilities in the
context of MASS, were written in English, and made
their methodological approach explicit. Articles
outside the scope of the study were excluded.
Table 1. Review process and final sample selection.
Step
Criteria
Identification,
screening
2014–2025; peer-
reviewed journal;
English; title within
scope
Abstract
analysis
Potential contribution to
RQ1
Full-text
assessment
Contribution to RQ1;
method explained
Citation
searching
Same criteria as full-text
assessment
Final sample
and coding
Included in qualitative
content analysis
From an initial set of 215 records, a final sample of
25 articles was analysed through qualitative content
analysis supported by MAXQDA data analysis
software (release 24.07.0). The articles included in the
final sample are listed in Appendix A. The coding
process focused on identifying conceptual limitations
and methodological weaknesses that explain why
current competency frameworks remain too general.
To complement the database search, backward
snowballing was applied by screening the reference
lists of included studies for additional relevant sources.
Through iterative coding and comparison, specific
research gaps were identified and grouped into
broader gap categories.
In a subsequent second step, the identified gaps
were translated into methodological requirements that
a new approach would need to fulfil. This gap-to-
requirement translation provided a structured basis for
the abductive development of the STISA methodology.
The resulting methodology was iteratively refined
through continuous comparison between identified
gaps, derived requirements, and developed STISA
elements. A structured mapping of these elements is
used to ensure transparency and traceability of the
development process.
As a conceptual study, the proposed methodology
is not empirically validated. Its contribution lies in
providing a theoretically grounded and systematic
framework for deriving work processes as a basis for
future competency development and empirical
research.
3 RESULTS
3.1 Research gaps
This subsection presents the identified research gaps
according to four gap categories, which provide the
basis for deriving the methodological requirements of
the STISA methodology. The first gap category is as
follows:
Work-process foundation gaps: insufficient
role/task clarity, skipped conceptual steps (process →
task → skill → competency)
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The literature indicates that current competency
frameworks remain too general to support detailed
curriculum development and performance-based
assessment. A central reason for this limitation is the
insufficient clarity regarding the future roles,
responsibilities, work processes, and tasks of Remote
Operators and onboard navigators. The United
Nations Industrial Development Organisation [38]
defines competency as a set of skills, related
knowledge, and attributes that allow an individual to
successfully perform a task or an activity within a
specific function. A set of key skills, as well as specific
skills, needs to be observable and measurable [19].
Generally, a skill can be defined as the ability to do a
task well. Consequently, each task – defined as a
discrete unit of work – has an associated skill. This
relationship is strictly one-to-one. The content of a task,
which is what people do at work, is based on the
underlying work processes. Only on the basis of work
processes can competencies be developed with
sufficient detail to be linked to performance
statements. Competencies without performance
statements are not observable and measurable. This
makes them unsuitable for effective usage in Maritime
Education and Training (MET). This imprecision is
attributed to inadequate exploration into the roles and
responsibilities of MASS operators. Belabyad, et al. [2]
propose detailed job analyses and task inventories to
map out precise activities and functions performed by
the operators. In doing so, they follow Ghosh and
Emad [13], who identified inadequate knowledge
about the future workplace and associated tasks as a
key challenge in defining competencies. In a related
study, Ghosh and Emad [14] further point to the
absence of a proper competency definition. This study
addresses this gap by proposing a working definition
of MASS operator competencies as observable and
measurable combinations of skills, knowledge, and
behavioural attributes that enable Remote Operators
and onboard navigators to perform defined tasks
derived from MASS-specific work processes.
The second gap category concerns the
inappropriate methodological approaches used in
existing competency research.
Methodological gaps: methodological mismatch,
inappropriate reliance on quantitative methods
Eight of the 25 reviewed studies rely on empirical
input from experts, practitioners, or students, using
either quantitative or qualitative methods.
Quantitative studies are of limited suitability for MASS
competency research at this stage, as they require well-
founded theories and in-depth researcher knowledge
of the field. The qualitative interview studies utilise a
standardised or semi-standardised approach. This
approach, in turn, presupposes a known level of
expertise on the part of the respondents [16]. As all
studies have similar non-specific competency results, a
fundamental question remains: Did the participants
possess sufficient understanding of future processes on
board MASS – something that will be influenced by
technological advancements? The literature reveals
that both approaches are problematic in a field where
work processes are not yet established and where
future MASS workplaces remain largely hypothetical.
This methodological weakness is closely connected
to the third gap category: the insufficient integration of
future MASS technologies into competency
development.
Sensor-technology integration gaps:
underrepresentation of sensor information, neglected
sensor-technology repercussions, missing focus on
sensor limitations, weak grounding in future sensor
technologies, technology-specific competency risk,
missing link: sensor limitations → operator workflow
impacts
Sensor technologies are an essential component of
future MASS operations, as they determine the
available information and how operators can establish
situational awareness. Nevertheless, competency
research has so far avoided making its results
dependent on specific new sensor technologies. In
contrast to contemporary sensor technologies, such as
the automatic identification system (AIS) or radar, new
sensor technologies are not subject to current
equipment requirements for seagoing vessels. An
unforeseen technological development would render
the researched competencies worthless. According to
Rostek and Baldauf [30], competencies and associated
performance statements, however, need to reflect the
development of new sensor technologies and their
technical limitations. Modelling work processes is
therefore a prerequisite for determining the influence
of sensor technologies on MASS operation. This
influence depends on the access to inputs that both the
MASS remote crew and the onboard crew should have
to gain sufficient situational awareness [17].
A related gap is that existing research does not
sufficiently explain how technical limitations of sensor
technologies affect the practical work of MASS
operators, something that is defined by the Operational
Envelope. Existing research identifies both the
importance of sensor technologies for MASS
situational awareness [7, 30, 37] and the need for more
specific competency frameworks [2, 13, 14]. However,
the literature does not provide a systematic
explanation of how constrained, delayed, or unreliable
sensor information translates into concrete operator
perception, comprehension, and decision-making. This
missing conceptual link prevents the derivation of
detailed work processes and, consequently,
measurable competency requirements.
The SLR revealed two further gaps that can be
summarised under a fourth gap category: mode- and
situational-awareness-related gaps.
3.1.1 Gap: missing consideration of Modes of Operation
Ghosh and Emad [13] mention an aspect that has
received inadequate attention in the development of
competency frameworks: the need to consider the
different degrees of MASS autonomy. The distinction
between the degrees of MASS autonomy is reflected in
the consolidated MASS Code by the Modes of
Operation. Modes of Operation means the condition(s)
under which the functions of a MASS are controlled,
i.e. remote-control or autonomous with or without
persons on board [17]. Different Modes of Operation
require a classification of situational awareness
capabilities, as the details and preconditions not only
vary with the respective Mode of Operation but also
with the operator.
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3.1.2 Gap: case-specific situational awareness approaches
A final gap emerges from the discrepancy between
prevailing case-specific methodological approaches
and the technologically open, mode-spanning logic of
the emerging MASS regulatory framework. Traditional
situational awareness measurement techniques like
performance measurements, eye tracker, rating
techniques, or freeze probe techniques require the
simulation and analysis of existing systems or the
subsequent evaluation of trials [32]. As such
prerequisites have yet to be created in autonomous
shipping, current research is moving away from the
utilisation of traditional techniques. Instead, the focus
is on case-specific analysis of the situational awareness
of MASS operators. Adopting Endsley's Situation
Awareness Global Assessment Technique [12],
Yoshida, et al. [42] use a gap analysis to assess the lack
of ship sense and information required for the Remote
Operator. Porathe, et al. [29] list 165 pieces of
information for a Remote Operator to acquire
situational awareness, and the HUMANE project
addresses the challenges faced by the Remote Operator
to derive recommendations that support situational
awareness [26]. The case specification comes with the
current research techniques applied, which require
researchers to focus on a particular MASS operator and
Mode of Operation. While such studies are valuable,
they are insufficient for developing a general
methodology applicable to different Modes of
Operation and evolving sensor configurations.
3.2 Methodological requirements
Table 2 maps the identified research gaps to derived
methodological requirements and corresponding
STISA elements. This gap-to-requirement translation
guides the abductive construction of the STISA
methodology. It ensures that its elements address the
shortcomings identified in the literature. The STISA
elements are reflected in the visualisation of the STISA
methodology shown in Figures 2 and B1. To avoid
redundancy and maintain analytical focus, the
following discussion does not describe each research
gap and requirement in detail. Instead, three
representative gaps are chosen to illustrate the
underlying translation logic from research gaps to
methodological requirements and STISA elements.
One central requirement concerns the need to
account for different MASS Modes of Operation. This
requirement corresponds to the gap “missing
consideration of Modes of Operation” and is addressed
through Steps 10 and 12 of the STISA methodology.
The problem of situational awareness was considered
to be largely solved when the concept of remote
operations fuelled the discussion on ensuring the
situational awareness of MASS and the Remote
Operator [5]. The main challenge seems to remain in
the sensor information that is required to provide the
Remote Operator with the same situational awareness
as the onboard navigator [23]. As the information
needs, operator responsibilities, and consequently the
situational awareness requirements differ depending
on the Mode of Operation, a remotely controlled MASS
with navigators on board requires a distinction
between two types of situation awareness: Remote
Operator Situational Awareness (ROSA) and Onboard
Navigational Officer Situational Awareness (NOSA). It
is to be noted here that this distinction does not
contradict the requirement of Yoshida, et al. [42] to
keep the same quality of situational awareness for
Remote Operators as onboard navigators. To enable
the STISA methodology to distinguish between
different operators, roles, and Modes of Operation, the
methodology introduces the element of setups of
sensor information. These setups represent the sensor
information required by each operator and
simultaneously account for the respective Mode of
Operation.
Table 2. Gap-to-requirement mapping.
Code
Research gap
Derived requirement
STISA element
A
Underrepresentation of
sensor information
Method must position
sensor capabilities
and sensor
information as drivers
of processes and
situational awareness
Steps 1, 2 and
15
B
Neglected sensor-
technology
repercussions
Method must
translate sensor
limitations into
operationally relevant
implications
Step 11;
operational
challenges
approach
C
Missing focus on sensor
limitations
Method must
incorporate sensor
limitations relevant to
the Operational
Envelope
Steps 4 and 5
D
Insufficient role/task
clarity
Method must
differentiate operator
roles and associated
tasks
Steps 9 and 10;
setups of
sensor
information
approach
E
Weak grounding in
future sensor
technologies
Method must
integrate relevant
evolving sensor
technology
characteristics
Step 3
F
Technology-specific
competency risk
Method must remain
valid across changing
sensor technologies
Steps 5–8;
Type-based,
technology-
open
abstraction
G
Missing consideration of
Modes of Operation
Method must
incorporate different
MASS Modes of
Operation
Steps 10 and
12; setups of
sensor
information
approach
H
Missing link: sensor
limitations → operator
workflow impacts
Method must link
sensor limitations to
operator workflow
impacts
Step 11;
operational
challenges
approach
I
Skipped conceptual
steps (process → task
→ skill →
competency)
Method must enable a
structured derivation
from processes to
competencies
Process-to-
competency
logic
J
Methodological
mismatch
Method must not rely
on prior expert
knowledge of future
MASS operations
Steps 3 and 11
K
Inappropriate reliance
on quantitative methods
Method must be
applicable in
emerging fields with
limited theoretical
foundations
Step 2;
abductive
qualitative
modelling
L
Case-specific situational
awareness approaches
Method must provide
a generalisable
situational awareness
framework that
accounts for all
system components
Steps 5–8;
adaptive
approach
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A second key requirement results from the missing
link between sensor limitations and operator workflow
impacts. This requirement corresponds to the gaps “
missing focus on sensor limitations” and “missing
link: sensor limitations → operator workflow
impacts” and is addressed through Steps 4, 5 and 11
and the operational challenges approach. Situational
awareness is formally defined as the “perception of
environmental elements and events with respect to
time or space, the comprehension of their meaning,
and the projection of their future status” [12]. When
applied to MASS, situational awareness refers to the
ability of the autonomous system to understand and
interpret its environment in order to make or support
navigational decisions. In this context, MASS relies on
the input of new sensor technologies. Whether this
input is sufficient depends on the capabilities and
technical limitations of the sensor technologies
implemented. Rostek and Baldauf [30] introduce the
term operational challenges for this purpose, but do
not provide an explicit definition. This study uses the
term operational challenge to describe a specific
restriction of the situational awareness of MASS
operators resulting from technical sensor limitations.
The methodological requirement is therefore that the
STISA methodology must not only list sensor
limitations but translate them into operationally
meaningful effects on operator work processes.
A third requirement concerns the need for an
adaptable situational awareness framework that
accounts for all system components. This requirement
corresponds to the gap “case-specific situational
awareness approaches” and is addressed through
Steps 5–8 and the adaptive approach of the STISA
methodology. The consolidated MASS Code’s goal-
oriented and technology-open approach is
incompatible with purely case-specific situational
awareness approaches that are used in contemporary
research. A principle of the MASS Code is to allow the
safe adoption and integration of new technologies,
regardless of a Mode of Operation. For the practical
MASS risk assessment, this results in a multitude of
case-specific scenarios for which it must be possible to
determine the impact of possible operational
challenges on situational awareness. In other words,
while the MASS Code contains no technology
limitations, the actual MASS implementation requires
case-specific and complex insights. Chae [6] predicts
that solving this issue will be challenging, as MASS
technology is still being developed, and different
options are on the table.
Taken together, the three representative
requirements demonstrate the necessity of integrating
four system components identified through the gap-to-
requirement analysis into the STISA methodology:
situational awareness, sensor technologies, operational
challenges and Modes of Operation. These components
are necessary because the identified gaps link sensor
information, technical limitations, operator-specific
situational awareness, and mode-dependent
operational contexts. Accordingly, the STISA
methodology must be sensor-centred, role-sensitive,
mode-sensitive, technology-open, and applicable in an
emerging field with limited theoretical foundations.
The flexible adaptation of these system components
supports alignment with the goal-based and
technology-open logic of the future MASS Code.
3.3 Hypothetical model extract
This study is based on a comprehensive, hypothetical
model that was developed prior to the study based on
theoretical considerations. It is subject to constant
adjustments as the field of research evolves. Within the
model, several System Variables are assumed to
influence the future work processes of MASS
operators. Since an analysis of all variables would
exceed the scope of a single study, this paper
investigates a section of the hypothetical model that
posits a correlation between MASS sensor technologies
and the situational awareness of both the Remote
Operator (ROSA) and the MASS onboard navigator
(NOSA). This hypothetical correlation is supported by
the gap-to-requirement analysis. Within this sensor-
related section, MASS sensor technologies are at the
centre of attention. There are two reasons why this
focus is justified. Firstly, the research gap analysis
revealed that future MASS sensor technologies are not
sufficiently integrated into competency development.
Secondly, sensor technologies provide the primary
information basis for establishing situational
awareness.
Being at the centre of this study, the variable MASS
sensor technologies is referred to as the Core System
Variable. However, the theoretical considerations
within the hypothetical model incorporate the sensor
technologies via their inherent capabilities and
limitations. Therefore, the Core System Variable is
called MASS sensor capabilities. The assumed
interdependencies between the Core System Variable
and the other System Variables are indicated by arrows
in the hypothetical model extract (Figure 1). The extract
shows the System Variables that are relevant to the
present study.
Figure 1. Hypothetical model extract.
The Core System Variable is, in turn, defined by
dimensions. These dimensions are derived either from
the hypothetical model's theoretical pre-considerations
or from its ongoing refinement during the research
process. Dimensions define what aspect of a variable is
analysed. Thus, MASS sensor technology remains a
dimension of the Core System Variable and requires
analysis with regard to an Operational Envelope (see
Step 2 of Figure 2). Attribute values in turn define how
this aspect appears in the data. They are described in
the following subsection.
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3.4 STISA methodology
This section develops the STISA methodology to fulfil
the requirements derived in the above subsections.
Adopting a structured abductive approach, the
methodology must implement the developed
methodological requirements, integrate the four
identified system components, and account for the
assumed correlations between the Core System
Variable and other System Variables. An overview of
the STISA methodology is illustrated in Figure 2. A
more detailed version, including additional
explanatory labels, is provided in Appendix B.
Although the steps are numbered continuously from
Step 1 to Step 15, they overlap in practice and cannot
be considered consecutive. The elements of the STISA
methodology are highlighted in italics in the text.
Figure 2. Overview of the STISA methodology.
To identify the relevant MASS sensor technologies,
an SLR is proposed, conducted as described by Rostek
and Baldauf [30], but with a modified focus on sensor
limitations and sensor information. Combining these
results with those of a narrative literature review
(NLR) of grey literature and company websites
provides a comprehensive overview of potential MASS
sensor technologies (Step 1, gap A). However, a
literature review based on quantitative secondary data
would not provide the MASS sensor technologies
relevant to a specific Operational Envelope. For this
reason, a mixed-methods approach has been chosen.
Qualitative primary data from a non-standardised,
guideline-based expert interview study serves as an
additional input source. The necessary information is
extracted from this data using qualitative content
analysis (Step 2, gap A). The resulting MASS sensor
technologies from both input sources are then
compared to validate the case-specific MASS sensor
technologies applicable to a defined Operational
Envelope (Step 3, gap E). The open-dialogue approach
associated with the chosen interview method differs
from previous research in that it considers unknown
expert knowledge in an emerging field with limited
theoretical foundations (gap K). Furthermore,
systematically coding the expert statements and
validating them against the SLR results (Step 3)
prevents over-reliance on the experts' prior knowledge
(gap J). A separate validation step is carried out in Step
11, when the operational challenges are defined.
In Step 4, the research studies are analysed
according to sensor limitations applicable to the case-
specific MASS sensor technologies that could
potentially impair the MASS operator's situational
awareness (gap C). Rostek and Baldauf [30] argue that
specific sensor limitations of different MASS sensor
technologies are comparable. They hypothesise that
specific sensor limitations can be categorised into types
of sensor limitations (Step 5). These types of limitations
are no longer sensor-specific but rather can be allocated
to certain types of sensor technology (Step 7). For the
grouping into types of sensor technology, the different
research focuses of the research papers and the fields
of application of MASS sensor technologies are
investigated in a parallel Step 6. Types of sensor
technology, in turn, generate certain types of sensor
information for the MASS operator (Step 8). This type-
based approach enables the adaptive and technology-
open nature of the STISA methodology (gaps F and L).
It is important to note that only the types of sensor
information are relevant for the creation of the MASS
operators' situational awareness (ROSA, NOSA). The
types of sensor information required by MASS
operators vary depending on the operational context,
objectives and purpose of the MASS operation. In other
words, a MASS operator requires certain setups of
sensor information with different combinations of
types of sensor information (gaps D and G). Within the
hypothetical model setups of sensor information is
identified as the second dimension of the Core System
Variable. Consequently, an initial part of the analysis
of the interview data involves the description of setups
of sensor information that correspond to the
Operational Envelope (Step 10). The types of sensor
information are used to define the extracted setups of
sensor information in more detail (Step 9). Combining
Step 9 with Step 10 ensures that the STISA
methodology only includes those types of sensor
information that influence the situational awareness
within the Operational Envelope. In Step 12, the types
of sensor information are allocated to the System
Variables (NOSA, ROSA or coordinating activities). It
should be noted that the system component Modes of
Operation is considered in the dimension setups of
sensor information.
Within the hypothetical model, the system
component operational challenges is identified as the
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third dimension of the Core System Variable. Rostek
and Baldauf [30] conclude that it is not possible to
derive operational challenges from specific sensor
limitations alone. However, the types of sensor
limitations can be utilised to define operational
challenges with greater precision. Prior to this step, the
operational challenges are extracted from the interview
data using defined indicators (Step 13). Each
operational challenge is then defined by assigning
case-specific MASS sensor technologies (Step 14) and
subsequently described by incorporating information
about the types of sensor limitations that contribute to
the operational challenge (Step 11, gaps B and H).
Based on the relationship between types of sensor
information and types of sensor limitations described
above, operational challenges are assumed to depend
on certain types of sensor information. This
relationship is represented in a concluding analytical
step (Analytical step). Whether a particular operational
challenge impacts the situational awareness of a MASS
operator is analysed in Step 15 (gap A).
3.5 STISA model
Based on the developed STISA methodology, a
hypothetical conceptual model has been derived: the
STISA model. It reduces complexity by focusing on the
relationships between the key elements most relevant
for practical application. The STISA model is shown in
Figure 3 and consists of four columns, A to D.
Figure 3. Generalised visualisation of the STISA model.
As explained above, it is only the type of sensor
information that is relevant for the creation of the
MASS operators' situational awareness. Therefore, the
influence of the types of sensor information on the
System Variables NOSA, ROSA and coordinating
activities is shown in columns A and B of the model.
The underlying considerations correspond to path 2 of
the STISA methodology (see Figure B1). Arrows
between columns B and C illustrate the dependency of
operational challenges on certain types of sensor
information. The impact of a particular operational
challenge on situational awareness is indicated by an
arrow between columns C and D, corresponding to
path 1. In this way, the STISA model allows for the
inclusion of all the identified system components:
situational awareness, sensor technologies, operational
challenges, and Modes of Operation. At the same time,
the adaptive nature of the model enables a case-specific
modification to different attributes of the system
components according to the Operational Envelope
(gap L).
At this point, it should be noted that the parameters
of the Operational Envelope, such as the navigational
phase, environmental limitations or traffic conditions,
affect other System Variables. These parameters are
included in the comprehensive hypothetical model as
High-Level System Variables (see Figure 2). To limit
the complexity of this paper, the High-Level System
Variables are not discussed in detail. However, when
validating the model and applying it in practice, their
influence must be taken into account. Once the STISA
model has been created using the STISA methodology,
its reduction to relevant elements will enable a rapid
assessment of the impact of all system components on
situational awareness.
4 DISCUSSION AND CONCLUSION
This study develops the STISA methodology in
response to the limited specificity of existing
competency frameworks for MASS operators. Two
main steps, each targeting a research question,
structured the research design. First, an SLR was used
to identify the conceptual and methodological gaps
that explain why existing competency frameworks
remain too general and merely describe areas of
competency. The SLR indicates that current research
often lacks task- and process-level foundations, relies
on methods that are only partly suitable for a future-
oriented field with limited theoretical foundations,
insufficiently considers future MASS sensor
technologies, and does not fully account for different
Modes of Operation. These gaps support the need for a
methodology that does not attempt to define
competencies directly but first establishes a structured
basis for developing MASS operator work processes.
In answering the second research question, the
identified gaps were translated into methodological
requirements to guide the abductive development of
the STISA methodology. The result is an adaptive
analytical structure that differs from existing
competency frameworks in several ways. One major
contribution is the incorporation of all four identified
system components: sensor technologies, situational
awareness, operational challenges, and Modes of
Operation. In response to another identified gap, it
links MASS sensor information and limitations to
possible impacts on ROSA, NOSA and coordinating
activities. In this way, the STISA methodology
provides a bridge between sensor-related constraints
and future operator work processes.
A further contribution is the mixed-methods
structure of the methodology, which increases the
validity and scope of the model results [20]. Data from
an SLR provides the basis for identifying sensor
technologies and their fields of application, as well as
sensor limitations. In parallel, expert interviews
provide case-specific insights into MASS operator
roles, setups of sensor information and operational
challenges. This combination reduces the risk of
relying only on expert assumptions about future MASS
operations.
Beyond the validation function, the mixed-methods
design also supports a technological perspective, with
its adaptive, type-based structure forming a central
pillar of the methodology. By distinguishing between
types of sensor technologies, types of sensor
limitations, and types of sensor information, the STISA
methodology remains open to future technological
advancement. This is important because MASS sensor
technologies are still developing and fixed technology-
specific competencies may become obsolete. With its
adaptive approach to Modes of Operation and the
766
flexibility for new and innovative sensor technologies,
the STISA methodology meets the technology-open
requirements of the consolidated MASS Code.
With every requirement implemented, the STISA
methodology has become more complex. The STISA
model offers an alternative that focuses on the key
elements relevant to practical application: System
Variables (NOSA, ROSA and coordinating activities),
operational challenges, and types of sensor
information. The considerations for the STISA
methodology also apply to the STISA model. For this
reason, the term “STISA” is proposed for use in future
work when referring to aspects that apply to both the
methodology and the reduced model.
However, the study has limitations. STISA is
conceptual and has not yet been empirically validated.
Future research must apply, test, and refine the
methodology in relevant MASS operational contexts.
In particular, future studies should examine whether
the assumed relationships between sensor information,
sensor limitations, operational challenges, and
situational awareness can be confirmed through
empirical data. A more specific focus should be placed
on identifying the operational challenges that affect the
ability of Remote Operators and onboard navigators to
establish situational awareness and on explaining how
these challenges arise from technical limitations
inherent in emerging MASS sensor technologies.
The scope of this paper is also limited to the sensor-
related section of the overarching hypothetical model.
This does not imply that other variables are irrelevant.
Rather, the sensor-related focus represents an initial
analytical layer. Future studies should extend the same
methodological logic to further System Variables of the
hypothetical model and their interactions. The
methodological design is intended to be adaptable to
different Operational Envelopes, although its practical
application still requires validation.
From a practical perspective, STISA may support
maritime researchers, MET institutions, shipping
companies, and MASS developers by offering a
structured way to analyse which sensor information is
needed, which limitations matter, and how these may
affect MASS operator situational awareness. It does not
yet deliver final work processes or validated
competencies. Instead, it establishes the conceptual
and methodological basis from which such work
processes can be derived in future research. Once
validated, STISA may become a cornerstone in the
development of detailed and measurable
competencies.
APPENDIX A
Table A1 provides an overview of the studies included
in the full analysis and coding. The table summarises
the focus, research method and contribution to the gap
analysis of each study.
Table A1. Articles included in the full analysis and coding.
Ref.
Year
Study focus
Method
Gap
[2]
2025
MASS skills and
competencies
SLR / bibliometric analysis
D; I; J;
K
[10]
2021
Training needs for
autonomous ship
operation
SLR
D; I; E
[11]
2023
Skills for future MASS
operators
Qualitative interview
study
D; J;
K
[13]
2024
MASS operator
competency framework
Conceptual / framework
study
D; G;
I
[15]
2024
STCW and MASS
operator competencies
Conceptual feasibility
analysis
D; G;
I
[14]
2024
Challenges in competency
framework design
SLR
D; I; J;
K
[21]
2024
Remote Operator key
competencies
Analytic Hierarchy
Process / quantitative
prioritisation
D; G;
K
[24]
2024
Remote Operator
competency model
Conceptual model
development
D; G;
I
[18]
2023
ROC operator
competencies
Task-oriented competency
analysis
D; G;
I
[1]
2022
Industry 4.0 and seafarer
skills
Conceptual / qualitative
analysis
D; E; I
[34]
2021
Competencies of MASS
navigators
Mixed-methods
competency analysis
D; E;
G; I; J
[35]
2019
STCW competency
framework in MASS
STCW competency
framework analysis
D; G;
I; K
[36]
2021
MET implications of
autonomous shipping
Review / conceptual study
D; E; I
[9]
2020
MET in the digital era
Conceptual / literature-
based study
D; E; I
[40]
2019
Training for autonomous
systems and ships
Conceptual study
D; E; I
[33]
2018
MASS impact on seafarer
careers and MET
Report / impact analysis
D; E; I
[28]
2020
Future maritime skills
and competence needs
Industry report / needs
analysis
D; E; I
[42]
2020
Remote Operator
competency and ship
sense
Goal-based gap analysis
A; D;
G; L
[29]
2014
RCC information needs
and situational awareness
Conceptual analysis
A; G;
L
[8]
2019
Future skills requirements
in the maritime industry
Literature-based study
D; E; I
[4]
2022
Competency and training
implications for MASS
Empirical / educational
study
D; E; I
[3]
2021
Integration of
autonomous ships into
MET
Conceptual / curriculum-
oriented study
D; E; I
[31]
2021
Competency
requirements for Remote
Operators
Competence mapping /
framework-oriented
analysis
D; G;
I; J
[22]
2020
STCW competency under
MASS
Analytic Hierarchy
Process
D; I;
K
[41]
2020
MET methods for MASS-
related innovation
Case-study analysis /
literature-based analysis
D; E; I
Note: Gaps B, C, F, and H are not directly addressed
in the reviewed studies. Their absence highlights the
identified gap in sensor technology integration,
demonstrating the need for a methodology that links
sensor limitations to the situational awareness of
MASS operators.
5 APPENDIX B
Figure B1 presents an extended version of the STISA
methodology. Compared with the overview in the
main text, the figure includes additional explanatory
notes to improve traceability of the methodological
steps.
767
Figure B1. Extended STISA methodology.
REFERENCES
[1] P. Baum-Talmor and M. Kitada, "Industry 4.0 in shipping:
Implications to seafarers' skills and training,"
Transportation Research Interdisciplinary Perspectives,
vol. 13, 01/20 2022, doi: 10.1016/j.trip.2022.100542.
[2] M. Belabyad, C. Kontovas, R. Pyne, and C.-H. Chang,
"Skills and competencies for operating maritime
autonomous surface ships (MASS): a systematic review
and bibliometric analysis," Maritime Policy &
Management, 03/04 2025, doi:
10.1080/03088839.2025.2475177.
[3] B. Belev, A. Penev, D. Mohovic, and H. A. Peric,
"Autonomous ships in maritime education model course
7.01," (in No Linguistic Content), Pomorstvo (Online)
Pomorstvo, vol. 35, no. 2, pp. 388-394, 2021.
[4] K. Bogusławski, M. Gil, J. Nasur, and K. Wróbel,
"Implications of autonomous shipping for maritime
education and training: the cadet’s perspective,"
Maritime Economics & Logistics, vol. 24, no. 2, pp. 327-
343, 2022/06/01 2022, doi: 10.1057/s41278-022-00217-x.
[5] H.-C. Burmeister. "Maritime Unmanned Navigation
through Intelligence in Networks (Reporting)."
Fraunhofer Center for Maritime Logistics and Services.
https://cordis.europa.eu/project/id/314286/reporting
(accessed 05 July 2024.
[6] C.-J. Chae, "MASS and IMO Works," in Maritime
Autonomous Surface Ships (MASS) - Regulation,
Technology, and Policy: Three Dimensions of Effective
Implementation, C.-J. Chae and R. Baumler Eds. Cham:
Springer Nature Switzerland, 2024, pp. 9-27.
[7] J. P. Chan, R. Norman, K. Pazouki, and D. Golightly,
"Autonomous maritime operations and the influence of
situational awareness within maritime navigation,"
WMU Journal of Maritime Affairs, 2022/03/02 2022, doi:
10.1007/s13437-022-00264-4.
[8] K. Cicek, E. Akyuz, and M. Celik, "Future Skills
Requirements Analysis in Maritime Industry," Procedia
Computer Science, vol. 158, pp. 270-274, 01/01 2019, doi:
10.1016/j.procs.2019.09.051.
[9] E. Demirel, "Maritime Education and Training in the
Digital Era," Universal Journal of Educational Research,
vol. 8, no. 9, pp. 4129-4142, 2020, doi:
10.13189/ujer.2020.080939.
[10] G. R. Emad, H. Enshaei, and S. Ghosh, "Identifying
seafarer training needs for operating future autonomous
ships: a systematic literature review," Australian Journal
of Maritime & Ocean Affairs, pp. 1-22, 2021, doi:
10.1080/18366503.2021.1941725.
[11] G. R. Emad and S. Ghosh, "Identifying essential skills
and competencies towards building a training framework
for future operators of autonomous ships: a qualitative
study," WMU Journal of Maritime Affairs, 2023/04/18
2023, doi: 10.1007/s13437-023-00310-9.
[12] M. Endsley, "Toward a Theory of Situation Awareness in
Dynamic Systems," The Journal of the Human Factors
and Ergonomics Society, vol. 37, pp. 32-64, 03/01 1995,
doi: 10.1518/001872095779049543.
[13] S. Ghosh and G. R. Emad, "Developing and
Implementing a Skills and Competency Framework for
MASS Operators: Opportunities and Challenges,"
ICMAR NAV, 2024, doi: 10.29007/z3mc.
[14] S. Ghosh and G. R. Emad, "Identifying challenges in
designing and implementing a skills and competency
framework for future seafarers: a systematic literature
review," Australian Journal of Maritime & Ocean Affairs,
vol. 17, pp. 1-14, 05/22 2024, doi:
10.1080/18366503.2024.2356365.
[15] S. Ghosh and G. R. Emad, "Skills and Competency
Framework for Future Autonomous Ship Operators: A
Feasibility Study for STCW Code Revision," ICMAR
NAV, 2024, doi: 10.29007/fb7s.
[16] J. Gläser and G. Laudel, Experteninterviews und
qualitative Inhaltsanalyse als Instrument
rekonstruierender Untersuchungen, 2nd ed. Wiesbaden:
VS Verlag für Sozialwissenschaften, 2006.
[17] IMO. "MSC 109/5 - DEVELOPMENT OF A GOAL-
BASED INSTRUMENT FOR MARITIME
AUTONOMOUS SURFACE SHIPS (MASS): Report of the
Intersessional MASS Working Group." IMO.
https://www.imokorea.org/upfiles/board/92.%20ISWG-
MASS%203%C2%F7%20%B0%E1%B0%FA%BA%B8%B0
%ED%BC%AD%28%BF%B5%B9%AE%29.pdf (accessed
12 May 2025.
[18] T. Jung, M.-C. Harre, N. Rousselle, A. Luedtke, and M.
Saager. "CMOROC Identification of Competences for
MASS Operators in Remote Operation Centres." EMSA.
https://www.emsa.europa.eu/publications/reports/item/
5089-cmoroc-mass.html (accessed 20 May 2025.
[19] J. Kansal and S. Singhal, "Development of a competency
model for enhancing the organisational effectiveness in a
knowledge-based organisation," International Journal of
Indian Culture and Business Management, vol. 16, p. 287,
01/01 2018, doi: 10.1504/IJICBM.2018.090909.
[20] U. Kelle, Die Integration qualitativer und quantitativer
Methoden in der empirischen Sozialforschung:
Theoretische Grundlagen und methodologische
Konzepte, 2nd ed. Wiesbaden: Verlag für
Sozialwissenschaften, 2008.
[21] J. Kim, "A Fundamental Study of the Sustainable Key
Competencies for Remote Operators of Maritime
Autonomous Surface Ships," Sustainability, vol. 16, no.
12, p. 4875, 2024. [Online]. Available:
https://www.mdpi.com/2071-1050/16/12/4875.
[22] T.-e. Kim and S. Mallam, "A Delphi-AHP study on STCW
leadership competence in the age of autonomous
maritime operations," WMU Journal of Maritime Affairs,
vol. 19, no. 2, pp. 163-181, 2020/06/01 2020, doi:
10.1007/s13437-020-00203-1.
[23] C. Kooij, M. Loonstijn, R. Hekkenberg, and K. Visser,
"Towards autonomous shipping: Operational challenges
of unmanned short sea cargo vessels," in Marine Design
XIII, P. Kujala and L. Lu Eds., 1st ed. London: Taylor &
Francis, 2018, pp. 871-880.
[24] T. Kuntasa and T.-C. Lirn, "A conceptual model of
autonomous ship remote operators' competency," Journal
of Navigation, vol. 76, pp. 1-22, 12/16 2024, doi:
10.1017/S0373463324000055.
[25] X. Li and K. F. Yuen, "A human-centred review on
maritime autonomous surfaces ships: impacts, responses,
and future directions," Transport Reviews, vol. 44, no. 4,
768
pp. 791-810, 2024/07/03/ 2024, doi:
https://doi.org/10.1080/01441647.2024.2325453.
[26] M. Lützhöft and J. Earthy, Human-Centred Autonomous
Shipping. Abingdon: CRC Press, 2024.
[27] D. Moher, A. Liberati, J. Tetzlaff, D. G. Altman, and P. G.
The, "Preferred Reporting Items for Systematic Reviews
and Meta-Analyses: The PRISMA Statement," PLOS
Medicine, vol. 6, no. 7, p. 1000097, 2009, doi:
10.1371/journal.pmed.1000097.
[28] A. Oksavik et al., Future skill and competence needs.
SkillSea, 2020.
[29] T. Porathe, J. Prison, and Y. Man, "Situation Awareness
in Remote Control Centres for Unmanned Ships,"
Proceedings of the 5th International Conference on
Applied Human Factors and Ergonomics, 2014, doi:
10.3940/rina.hf.2014.12.
[30] D. Rostek and M. Baldauf, "Technologies for situational
awareness in autonomous shipping and their impact on
maritime training and education," INTED - International
Technology, Education and Development Conference,
2024, doi: 10.21125/inted.2024.1322.
[31] R. Saha, "Mapping competence requirements for future
shore control center operators," Maritime Policy &
Management, pp. 1-13, 2021, doi:
10.1080/03088839.2021.1930224.
[32] P. M. Salmon, N. A. Stanton, and K. L. Young, "Situation
awareness on the road: review, theoretical and
methodological issues, and future directions," Theoretical
Issues in Ergonomics Science, vol. 13, no. 4, pp. 472-492,
2012/07/01 2012, doi: 10.1080/1463922X.2010.539289.
[33] Shanghai Maritime University, "Research on the Impacts
of Marine Autonomous Surface Ship on the Seafarer’s
Career and MET," 2018. [Online]. Available:
http://www.mnoghk.org/mnoghk/wp-
content/uploads/2018/12/自主船舶-英文-终稿.pdf.
[34] A. Sharma and T.-e. Kim, "Exploring technical and non-
technical competencies of navigators for autonomous
shipping," Maritime Policy & Management, pp. 1-19,
2021, doi: 10.1080/03088839.2021.1914874.
[35] A. Sharma, T.-e. Kim, and S. Nazir, Catching up with
time? Examining the STCW competence framework for
autonomous shipping. 2019.
[36] A. Sharma, T.-E. Kim, and S. Nazir, "Implications of
Automation and Digitalization for Maritime Education
and Training," in Sustainability in the Maritime Domain:
Towards Ocean Governance and Beyond, A. Carpenter,
T. M. Johansson, and J. A. Skinner Eds. Cham: Springer
International Publishing, 2021, pp. 223-233.
[37] S. Thombre et al., "Sensors and AI Techniques for
Situational Awareness in Autonomous Ships: A Review,"
IEEE Transactions on Intelligent Transportation Systems,
vol. 23, no. 1, pp. 64-83, 2022, doi:
10.1109/TITS.2020.3023957.
[38] UNIDO. "United Nations Industrial Development
Organization (UNIDO) Competencies, Part 1:
Strengthening Organizational Core Values and
Managerial Capabilities." UNIDO Human Resource
Management Branch. https://www.sabourtinat.com/wp-
content/uploads/2023/02/UNIDO.pdf (accessed 08 June
2025.
[39] E. Veitch and O. Alsos, "A systematic review of human-
AI interaction in autonomous ship systems," Safety
Science, vol. 152, 04/13 2022, doi:
10.1016/j.ssci.2022.105778.
[40] P. Vidan, M. Bukljaš, I. Pavić, and S. Vukša,
"Autonomous Systems & Ships -Training and Education
on Maritime Faculties," International Maritime Science
Conference, 2019, doi:
https://www.researchgate.net/publication/354922331_Au
tonomous_Systems_Ships_-
Training_and_Education_on_Maritime_Faculties.
[41] Yamada and Haruto, "Development of maritime
education and training methods with technological
innovation: Japan as a case study focusing on MASS,"
World Maritime University Dissertationsz, 2020.
[Online]. Available:
https://commons.wmu.se/all_dissertations/1374
[42] M. Yoshida, E. Shimizu, M. Sugomori, and A. Umeda,
"Regulatory Requirements on the Competence of Remote
Operator in Maritime Autonomous Surface Ship:
Situation Awareness, Ship Sense and Goal-Based Gap
Analysis," Applied Sciences, vol. 10, no. 23, p. 8751, 2020.
[Online]. Available: https://www.mdpi.com/2076-
3417/10/23/8751.