EU Maritime Industry Blue-Collar Recruitment: Sustainable Digitalization

Abstract

This research identifies the maritime industry’s key manpower recruitment criteria and uses a specialized online platform to assess the impact of recruitment digital transformation. Through the analysis of 183 validated surveys of Romanian shipbuilding and ship-repair technicians using IBM SPSS Statistics, this study examines digital recruitment trends and potential outcomes. Additionally, it highlights a notable gap in recent literature on digital recruitment optimization within the EU shipbuilding and ship-repair sectors. The findings demonstrate that digitally integrating recruitment tools—such as technical interviews, verified performance evaluations, and machine-learning algorithms for candidate prioritization—could significantly enhance recruitment accuracy, transparency, and efficiency. The key potential outcomes include improved efficiency, reduced bias, scalability, and cost savings overall for the recruitment process—valuable insights for European maritime stakeholders seeking to remain competitive, while addressing increasing labor demands.

1. Introduction

The European Maritime Technology Industry includes both the shipyard market, covering newbuilding and repairs, and the marine supplies market. The EU shipyard newbuilding sector, together with the ship maintenance, repair, and conversion (SMRC) sector, employs approximately 312,000 people. These sectors form an essential pillar of the European maritime economy, significantly influencing global transport, security, and energy sustainability. Additionally, the EU marine supplies industry, which delivers materials, equipment, systems, and services across the value chain, employs about 320,000 people within more than 22,000 active companies [1].

Although Europe has the expertise to construct any type of commercial vessel, its global market share in shipbuilding has declined over recent decades. As of 2023, Europe accounted for only 7% of the global newbuild order book, compared to China’s 55% and South Korea’s 26%. Nonetheless, Europe continues to lead in constructing highly complex vessels, such as cruise ships, superyachts, dredgers, and offshore support vessels. The SMRC sector also remains a vital and growing component of the EU maritime industry [2].

The European shipbuilding and ship-repair sectors face unprecedented challenges, particularly in strategically and sustainably recruiting a highly skilled workforce [3]. A critical risk is the potential loss of essential competencies due to an ageing workforce and inadequate training and recruitment initiatives [1]. The European Economic and Social Committee has emphasized the urgent need for continuous evaluation, updating, and tracking of workers within the shipbuilding and SMRC sectors, warning that the sector’s competitiveness is at imminent risk [2]. Similarly, the Organization for Economic Co-operation and Development (OECD) has noted that labor and skills shortages are rapidly increasing, with 40% of the current workforce expected to retire within the next decade. Concurrently, the industry’s shift towards green and digital advancements is creating demand for new skill sets, making it increasingly difficult to attract and retain younger generations [1].

This study uncovers a seasonal emigration pattern of shipbuilding and ship repair technicians, who can be observed migrating from Romania to stronger northern and northwestern economies, such as Norway, Denmark, Belgium, France, and Germany [4,5]. This has established a specific labor market dynamic across the EU, characterized by the continuous mobility of thousands of technicians who pursue either long-term employment in major EU shipyards or temporary SMRC work, often for periods ranging from a few days to a few weeks at a time [5].

This requires research, particularly because the recruitment value chain in shipbuilding and ship repair is financially influenced by a multitude of market players, including local recruitment agencies, global recruitment agencies, and personnel leasing agencies. Each of these entities uses various manpower supply pipelines, such as maritime job boards or agents in different countries, to gain a competitive edge. This diversity in recruitment channels contributes to increased recruitment and hiring costs, which are ultimately passed to the final beneficiary [6].

Research using specific keywords in the Web of Science database shows that previous studies have extensively examined the economic contributions and technological advancements within the European maritime industry [7,8], while there is a notable lack of research addressing the critical issue of labor shortages and the evolving recruitment practices necessary to sustain the sector. Specifically, the gap lies in the investigation of digital recruitment strategies tailored to the maritime industry’s unique needs, especially within the shipbuilding and ship maintenance, repair, and conversion (SMRC) sectors. With the ongoing workforce aging and increasing demand for specialized skills [3], traditional recruitment methods are insufficient to meet the growing labor demands. Additionally, the literature search revels that seasonal migration patterns of skilled maritime workers have not been studied in terms of how they impact workforce stability and operational efficiency across EU shipyards [1,5], and this should require further research. This paper aims to address the gap by exploring how digital recruitment solutions in the context of cross-border labor exchanges can enhance the competitiveness of the EU maritime sector in the face of growing global labor competition.

This study’s Introduction outlines the broader context of the European maritime technology industry, highlighting labor shortages and recruitment challenges, and identifies the research gap in digital recruitment solutions tailored to the industry. The Literature Review examines existing research on digital recruitment, identifying trends and gaps relevant to the subject. The Methodology details the case study research design, including the survey of Romanian shipbuilding and ship-repair technicians and data analysis using statistical methods. The Discussion analyzes the results in relation to existing literature, emphasizing the potential of digital recruitment to improve efficiency and transparency. Conclusions summarize the key findings and contributions, offering recommendations for future research and industry practices.

2. Literature Review

The expansion of the global fleet, driven by growing international trade and the aging of existing vessels, has created a substantial demand for ship maintenance, conversions, and retrofit [1,4]. This demand is particularly pronounced in the context of the new global environmental, energy, and climate regulations aimed at reducing the maritime industry’s environmental footprint. The Ballast Water Convention and Annex IV of the MARPOL Convention set forth strict guidelines for fleet upgrades, particularly regarding the reduction in air and water pollution caused by maritime activities. In addition, the geopolitical landscape, notably the unprovoked invasion of Ukraine by Russia, has disrupted global energy supply chains, thereby increasing the demand for LNG vessels, which are essential for the transportation of natural gas across international markets [1]. As the global fleet evolves to meet the shifting demands of energy transport, the European maritime industry, especially its shipyards, is also experiencing increasing opportunities related to superyacht construction, autonomous ships, and the expansion of the ship recycling market [6].

These trends, while offering significant growth potential, also present numerous challenges [1,3,6]. For example, meeting the global climate targets set for 2050 will require the retrofitting of almost half of the world’s fleet with technologies that enable the use of renewable or low-carbon fuels. Retrofitting ships to meet these targets is a costly but necessary endeavor, as without significant upgrades, the maritime industry risks being one of the largest contributors to global greenhouse gas emissions. In this context, the efficient and targeted recruitment of skilled maritime engineers and technicians has emerged as a critical requirement for ensuring that European shipyards and related industries remain competitive in the global market [3,6].

As digitalization is now present in nearly all aspects of modern industry, digital recruitment solutions are being recognized as a key driver for the sustainable development of the European shipbuilding, maintenance, repair, and conversion (SMRC) sectors. The highly dynamic nature of the maritime industry, particularly in ship maintenance and repair, requires a flexible and agile recruitment system [4]. Shipyards involved in newbuilding projects generally experience more predictable manpower needs, but those engaged in repair and maintenance face significant fluctuations depending on the nature and scope of the projects at hand [2]. These activities, which typically involve short-term engagements in dry-docks lasting 10–15 days [2], demand a specialized, adaptable, and highly skilled workforce. Therefore, the ability to efficiently recruit the right personnel is crucial for maintaining operational efficiency and sustainability across the European shipbuilding and SMRC sectors [1,4].

To address these challenges, the European Commission has advocated for research into cross-sectoral exchanges of labor and skills, encouraging innovation in recruitment strategies through initiatives like the LeaderShip 2020 report [4]. This report, endorsed by the European Maritime Technology Industry, underscores the importance of sustainable and innovative manpower solutions. As the maritime industry adapts to these shifts, it must also enhance its recruitment practices to match the evolving demands of environmental regulations, technological advancements, and shifting global markets [1,4].

The literature consistently highlights the role of recruitment strategies as a critical factor in maintaining the competitiveness and sustainability of the maritime industry. One study explores how integrating department managers and supervisors into the recruitment process can align company expectations with customer needs, presenting a customer-oriented approach to human resource management as a strategic tool [7]. This approach is operationally significant and also strategically vital, as it helps companies maintain a workforce that can meet the immediate and long-term needs of their clients. Similarly, workforce qualifications and the continuity of knowledge transfer are identified as competitive factors critical to the sustainable development of shipyards [8]. This research emphasizes that a well-supported and strategically aligned workforce is essential for shipyards to remain competitive in an increasingly complex global market [1].

However, despite the growing importance of recruitment, significant challenges persist in the maritime sector, particularly in terms of workforce demographics. Another study discovered a growing oversupply of junior-level engineers and technicians, coupled with a looming shortage of senior personnel in the maritime transport sector [9]. This finding highlights the importance of developing recruitment strategies that not only address the current workforce needs but also anticipate future demand. The maritime industry, like many other sectors, faces a critical need to balance the recruitment of new talent with the retention and development of experienced professionals. The potential shortfall of senior personnel could have long-term implications for the industry, particularly as it seeks to innovate and adapt to new environmental and technological challenges.

It was found that the integration of AI technologies has significantly accelerated the digital transformation of organizations across the EU, particularly in HR management and recruitment [10]. Automated processes, such as CV evaluation, candidate classification, and interview scheduling, have improved operational efficiency and reduced the time-to-hire. These technologies streamline recruitment operations, allowing HR departments to manage a larger volume of applicants more effectively, while focusing on strategic decision making. However, the increasing reliance on automation has also raised concerns related to fairness, transparency, and data privacy. One study identified resistance to change and the need for strong leadership as critical factors in the successful implementation of digital recruitment processes [11]. Their findings suggest that the adoption of digital tools requires not only technical innovation but also a cultural shift within organizations, where leadership must actively promote the benefits of digital transformation, while addressing concerns related to job security, ethical considerations, and candidate data privacy.

In the maritime industry, where the demand for specialized skills and workforce mobility is great, digital recruitment tools are in growing demand. Researchers emphasize the growing digitalization gap [12] in developing maritime business environments, suggesting that ICT adoption is necessary to enhance digital capabilities and operational efficiency [13]. Their study underscores the need for maritime companies to invest in digital infrastructure and workforce training to remain competitive in a rapidly changing global market. Another study adds that the rapid pace of technological and environmental changes in the maritime industry is driving the need for workers to acquire new skills, particularly in sustainability, digitalization, and leadership [14]. Their research highlights four key trends shaping the maritime workforce: sustainability, cluster collaboration, digitalization, and new education models. These trends, particularly the adoption of ICT and sustainability practices, are crucial for preparing the maritime workforce to meet future challenges.

The ongoing digital transformation of recruitment processes raises several ethical and practical challenges. One study explores the ethical issues that arise when digital systems are implemented in recruitment processes, particularly regarding tensions between data privacy and the need for comprehensive candidate assessments [15]. The increasing reliance on data-driven tools such as AI raises questions about how organizations can ensure fairness and avoid biases in their hiring decisions [16]. Other researchers warn that people analytics—the use of AI and machine-learning algorithms in recruitment—can sometimes lead to biased outcomes if the algorithms are not properly designed or managed [17]. These findings highlight the potential risks of relying completely on automated decision-making processes in recruitment, where human judgment and understanding of individual contexts are sometimes irreplaceable. Moreover, another study stresses the importance of ensuring compliance with the General Data Protection Regulation (GDPR) in digital recruitment practices, especially when collecting and handling sensitive personal data from candidates [18]. Their study shows that ongoing communication with candidates, transparency in data handling practices, and strict adherence to GDPR guidelines are essential for maintaining candidate trust and protecting organizational integrity.

The explanatory framework for this study aims to explore the causal relationships between digital recruitment tools and recruitment outcomes, such as efficiency, transparency, meritocracy, and fairness in candidate selection. Digital recruitment tools, including machine-learning algorithms, offer a range of potential benefits [19], such as automating key recruitment processes. These tools could be particularly important in the maritime industry, where the demand for skilled labor is both urgent and fluctuating.

Through the integration of digital tools, the recruitment process can become more streamlined, allowing employers to address skill shortages, improve transparency in hiring practices, and reduce recruitment costs. Additionally, by incorporating technical interviews and performance evaluations into digital platforms, the recruitment process can be optimized to ensure that candidates meet the high standards required in the shipbuilding and SMRC sectors [1,3,6]. These digital tools are expected to contribute to the overall sustainability of the maritime workforce by ensuring that candidates are matched to roles based on objective criteria, such as past performance and verified qualifications, rather than subjective assessments [20].

Based on the explanatory framework, as shown in Figure 1, and the literature reviewed, the following hypotheses are proposed:

Hypothesis 1. 

Conventional recruitment based on traditional CV can be misleading, since false information can be showcased as true.

Traditional CV-based recruitment often presents a challenge in verifying the authenticity of candidate qualifications. Often job-seekers interpret job offers and adapt their CV to suit the job description to an extent where their assumptions are false [21]. Digital recruitment platforms can mitigate this issue by incorporating automated validation tools that cross-check credentials and employment history. This hypothesis reflects the need for recruitment practices that can provide more accurate and reliable assessments of candidate qualifications, thereby reducing the potential for mismatches between candidate profiles and job requirements.

Hypothesis 2. 

Including a technical interview in the account creation and vetting process of a digital recruitment platform helps ensure that candidates meet community standards and possess the necessary skills.

Technical interviews allow for more accurate assessments of candidate skills, particularly in specialized fields, such as shipbuilding [22]. By incorporating these interviews into digital platforms, employers can ensure that candidates possess the technical expertise required for specific roles, thereby improving the overall quality of the recruitment process.

Hypothesis 3. 

Performance evaluation coming from past employers and keeping track of past and future performance in a digital database can solve meritocracy issues amongst the shipbuilding and ship-repair technicians’ community.

Digital evaluations could contribute to fairer recruitment processes by ensuring that candidates are assessed based on their actual work history and achievements, rather than subjective criteria or biased AI decision making [23]. By maintaining a digital database of performance evaluations, recruitment platforms can provide a more merit-based system for candidate selection.

Hypothesis 4. 

Integrating the identified worker criteria into a digital recruitment platform powered by a machine-learning algorithm can offer a reliable and automated safeguard, ensuring a consistent and fair recruitment process for all stakeholders involved.

Machine-learning algorithms can help ensure that all candidates are evaluated based on objective, data-driven criteria, thereby reducing the potential for bias and ensuring that the recruitment process is fair for all stakeholders [23,24], as shown in Figure 1.

While digital recruitment tools offer significant potential to improve the recruitment process [25], their successful implementation depends on overcoming challenges related to fairness, transparency, and data privacy [26]. These tools can assess large volumes of applicants quickly, reducing the time to hire, while improving the overall match between candidates and job roles [27]. However, despite these advancements, challenges remain, particularly regarding the ethical use of AI in recruitment processes. Issues regarding fairness, transparency, and bias in AI decision making highlight the need for greater oversight and adherence to legal frameworks, such as the GDPR, especially in the European context [28]. Additionally, digital platforms and benchmarking methodologies are playing a key role in mitigating risks, such as modern slavery, in supply chains, thus reinforcing ethical recruitment practices [29]. In the maritime industry specifically, digitalization is not only impacting recruitment but also driving operational efficiencies through innovations, improving supply chain management and reducing costs [30]. Despite the progress, barriers such as high implementation costs and a lack of digital infrastructure remain challenges to full-scale digital transformation in this sector [31].

The explanatory framework provides a theoretical foundation for exploring how digital tools can impact key recruitment outcomes in the European maritime industry, particularly in the context of a highly skilled and mobile workforce. The hypotheses developed in this study reflect the expected benefits of digital recruitment solutions in addressing the labor market challenges faced by the shipbuilding and SMRC sectors, particularly as the industry seeks to meet evolving environmental and technological demands.

3. Methodology

To ensure a comprehensive analysis, an integrative approach was chosen, combining data collected from the survey with a systematic literature review.

To gain insight into digital recruitment practices and the current state of digital recruitment within the EU Maritime Technology Industry, a comprehensive literature review was conducted using the Web of Science Open Access database, focusing on research papers published in the last 5 years, to capture the most recent advancements in this area.

The Web of Science database was selected for this study due to its stringent selection criteria, which ensures the inclusion of only high-quality, peer-reviewed journals and publications [32,33]. The following keywords were used in the search:

• Maritime recruitment AND Shipbuilding;
• Maritime recruitment AND Digitalization;
• European Union AND Shipbuilding;
• European Union AND Ship repair;
• Digital recruitment AND Shipbuilding;
• Digital recruitment AND Shipyard;
• Digital recruitment AND Maritime;
• Digital recruitment AND Marine;
• Recruitment AND Digital transformation.

Given the limited results yielded by the initial keyword searches, the authors expanded the research scope by utilizing broader keyword combinations:

• Shipbuilding recruitment;
• Maritime recruitment;
• Shipbuilding manpower;
• Maritime manpower;
• Ship repair;
• Digital recruitment.

These keyword combinations were selected to maintain a relevant focus on the research topic. Although there is a notable scarcity of research specifically addressing the digital transformation of recruitment and human resource processes within the EU Maritime Technology Industry, a considerable body of literature on digital recruitment does exist. Broader keywords were employed to uncover emerging trends relevant to this study. Additionally, a thorough search of the European Union and European Commission official websites was conducted to identify all pertinent reports published over the past 15 years.

Furthermore, building on literature review insights and the authors’ operational experience gained while monitoring maritime technicians’ performance since 2016, a set of key competencies that are critical for ensuring both efficiency and sustainability in these industries were identified:

• Technical trade knowledge;
• On-the-job experience;
• Trade qualification training and diploma;
• Ability to integrate in multinational teams;
• English language skill at conversational level;
• Availability to work in various location as per employer need;
• Career advancement intentions.

Based on the identified criteria, the authors developed a questionnaire to gather individual opinions from Romanian shipbuilding and ship-repair technicians. The questionnaire was administered in Romanian using the specialized platform www.romcrew.com, as it specifically targeted Romanian professionals within these sectors. For the purposes of this study, the survey questions were translated into English, ensuring that the original meanings were preserved. The questionnaire underwent a validation process, where it was reviewed and tested by five industry experts. Following this validation, the questionnaire was made available to all 10,087 registered and validated users of the romcrew.com platform [34]. A “validated user” is defined as a technician who has successfully completed the registration and vetting process on the platform, which includes filling out a detailed online personal data and experience form, as well as passing a technical interview on the phone, designed to confirm their technical proficiency in accordance with industry and community standards.

To ensure a comprehensive and representative sample, the survey started in January 2024 and remained open until July 2024, when data collection was concluded, resulting in 183 validated responses that were processed. Respondents were assured of the confidentiality of their responses, and participation was entirely voluntary. When developing the questionnaire, the free and open market status among EU Member States was taken into account as a reference for this study, with all EU citizen rights implied, including the right to travel and work in any EU Member State without restrictions [35].

Raw survey data processing was performed using latest version of Excel for Microsoft 365 and statistical analysis was carried out using IBM SPSS Statistics v. 29.0.2. Raw questionnaire data were downloaded from the online platform and inserted into the statistics analysis software. The survey was divided into two main sections:

• Section 1: The first five questions aimed at understanding the technicians’ opinions on various aspects of professional experience and evaluations.
• Section 2: The next six questions that were answered using a 5-point Likert scale, ranging from “Not Important” to “Very Important”, to assess the perceived importance of specific job-related criteria.

3.1. Data Preparation

Prior to conducting any analyses, the raw data were meticulously prepared to ensure their suitability for statistical testing. This involved the following steps:

• Variable Coding: As shown in

  Table 1. Description of IBM SPSS Statistics variables used.

  Variable Code	Variable Description	Variable Label	Variable Value
  Group 1: Questions 1–5 (Opinions on Professional Experience and Evaluations)
  Q1	How much experience do you think technicians need to master their trade very well?	1+ years	1
  		2+ years	2
  		5+ years	3
  		10+ years	4
  Q2	Do you generally consider that the professional experience described in the CV is relevant for the job?	Yes, extensive experience is a guarantee of professionalism	1
  		No, some colleagues exaggerate their skills on their CV	2
  Q3	Do you think a technical interview during the candidate profile approval phase could be beneficial in filtering out candidates who tend to provide incorrect information in their CVs?	Yes, a technical interview is a good solution	1
  		No, it’s difficult to assess accuracy over the phone	2
  Q4	Do you think it is beneficial for employers to periodically send performance evaluations, which would then be attached to candidates’ profiles?	Yes	1
  		No, employer assessments can be wrong	2
  Q5	Do you think the selection for accessing future job offers should be based on performance evaluations from employers?	Yes, we’ve been waiting for this	1
  		No, evaluations are biased	2
  Group 2: Questions 6–11 (Importance of Job-Related Criteria—Likert Scale)
  Q6	How important is it for a technician to have the level of professional skill required by the job description?	Not Important	1
  		Slightly Important	2
  		Moderately Important	3
  		Important	4
  		Very Important	5
  Q7	How important is it for a technician to have extensive professional experience (over 5 years) to successfully fulfill the responsibilities of the position?	Not Important	1
  		Slightly Important	2
  		Moderately Important	3
  		Important	4
  		Very Important	5
  Q8	How important is it for a technician to possess a qualification diploma for the role they are applying for?	Not Important	1
  		Slightly Important	2
  		Moderately Important	3
  		Important	4
  		Very Important	5
  Q9	How important is it for a technician to know English at a conversational level?	Not Important	1
  		Slightly Important	2
  		Moderately Important	3
  		Important	4
  		Very Important	5
  Q10	How important is it for employers to give priority to candidates who are willing to work both onshore and offshore when needed?	Not Important	1
  		Slightly Important	2
  		Moderately Important	3
  		Important	4
  		Very Important	5
  Q11	How important do you think it is for applicants with the initiative to advance in their careers to Supervisor or Team Leader positions to have priority in accessing employers’ offers?	Not Important	1
  		Slightly Important	2
  		Moderately Important	3
  		Important	4
  		Very Important	5

  Source: designed based on authors’ research.

  , all survey responses were coded numerically, and the data were entered into IBM SPSS Statistics v. 29. Questions 1–5, which focused on technicians’ opinions on professional experience and evaluations, were measured on an ordinal scale and were appropriately coded. Similarly, Questions 6–11, which used a 5-point Likert scale, were also coded as ordinal variables.
• Grouping variables: Age group was categorized into three ranges: 18–30 years, 31–50 years, and 51–70 years. Similarly, experience group was categorized based on years of professional experience into three categories: less than 7 years, 8–20 years, and more than 20 years. These groupings enabled the analysis to compare responses across different age and experience levels, providing insights into how demographic factors influence opinions on recruitment and professional evaluations.

3.2. Data Analysis

3.2.1. Descriptive Statistics

Descriptive statistics were employed to provide a foundational summary of the survey data. This method was chosen to offer an initial overview, revealing central tendencies and variability across responses, which is essential for understanding the general trends before performing more complex analyses. Specifically, measures of central tendencies were calculated for all variables, including the ordinal responses from both sections of the questionnaire. Additionally, variability was assessed using standard deviation to observe the spread of responses. To further understand the distribution of responses, frequency distributions were generated for each question, highlighting the most common perceptions among the respondents.

3.2.2. Cross-Tabulation

Cross-tabulation was employed to explore relationships between demographic variables (such as age group and experience group) and responses to the questionnaire items. This analysis helps in identifying patterns and associations between different groups of technicians and their preferences or opinions on recruitment matters.

3.2.3. Analysis of Variance (ANOVA)

Analysis of variance (ANOVA) was performed to compare the mean responses across different demographic groups. This analysis helped determine whether there were significant differences in opinions based on variables, such as age or years of experience. Significant results from the ANOVA can indicate that there are statistically meaningful differences in recruitment-related opinions across different demographic groups, such as age or experience, which could reveal to interested stakeholders’ new methods to perform targeted recruitment strategies.

3.2.4. Hypothesis Testing Framework

To rigorously evaluate the four hypotheses central to this study, traditional statistical methodologies were employed. Descriptive statistics, cross-tabulation, and analysis of variance were selected to interrogate the survey data, providing a robust framework for hypothesis testing. These methods were chosen for their ability to reveal nuanced patterns, relationships, and differences across the dataset, ensuring the conclusions drawn are firmly anchored in empirical evidence.

4. Results

4.1. Survey Results

4.1.1. Descriptive Statistics Analysis

To understand the overall response patterns for this survey, descriptive statistics using IBM SPSS Statistics were calculated for each question. This analysis shown in

Table 2. Descriptive statistics for Group 1 (Questions 1–5).

Variable Code	N	Mean	Std. Deviation
Q1	183	2.89	0.798
Q2	183	1.40	0.492
Q3	183	1.14	0.350
Q4	183	1.16	0.371
Q5	183	1.32	0.467
Valid N (listwise)	183

Source: designed based on authors’ research.

,

Table 3. Frequency statistics for Group 1 (Questions 1–5).

Variable Code	Variable Value	Frequency	Percentage
Q1	1	11	6
	2	36	19.7
	3	98	53.6
	4	38	20.7
Q2	1	109	59.6
	2	74	40.4
Q3	1	157	85.8
	2	26	14.2
Q4	1	153	83.6
	2	30	16.4
Q5	1	125	68.3
	2	58	31.7
Valid N (listwise)	183

Source: designed based on authors’ research.

,

Table 4. Descriptive statistics for Group 2 (Questions 6–11).

Variable Code	N	Mean	Std. Deviation
Q6	183	4.55	0.617
Q7	183	4.42	0.758
Q8	183	3.99	1.112
Q9	183	4.39	0.817
Q10	183	4.31	0.760
Q11	183	4.01	0.896
Valid N (listwise)	183

and

Table 5. Frequency statistics for Group 2 (Questions 6–11).

Variable Code	Variable Value	Frequency	Percentage
Q6	1	0	0
	2	1	0.5
	3	9	4.9
	4	62	33.9
	5	111	60.7
Q7	1	1	0.5
	2	4	2.2
	3	12	6.6
	4	67	36.6
	5	99	54.1
Q8	1	5	2.7
	2	17	9.3
	3	32	17.5
	4	49	26.8
	5	80	43.7
Q9	1	0	0
	2	7	3.8
	3	18	9.8
	4	55	30.1
	5	103	56.3
Q10	1	1	0.5
	2	4	2.2
	3	15	8.2
	4	80	43.7
	5	83	45.4
Q11	1	4	2.2
	2	6	3.3
	3	30	16.4
	4	87	47.5
	5	56	30.6
Valid N (listwise)	183

Source: designed based on authors’ research.

gives insights into the central tendency and variability of responses. The descriptive statistics are summarized for each question across two main sections of the survey.

Question 1: Experience Needed to Master the Trade

Most respondents (53.6%) believe that 5+ years of experience are necessary to master the trade, which is reflected in the mean value of 2.89. This suggests that, while some respondents believe less experience may be enough to get the job done, the other opinions favor a more substantial amount of experience. The standard deviation (0.798) indicates a moderate level of variability in responses, showing that there is diversity of opinion about the amount of experience needed. Also, it is important to understand that 20.7% of the respondents consider 10+ years as required time to master the trade. This will be further investigated towards age and experience groups in cross-tabulation analysis.

Question 2: Relevance of Professional Experience in the CV

With a mean of 1.40, the results indicate that respondents lean towards believing that professional experience in CVs is relevant, but with a significant portion (40.4%) expressing concerns about exaggerations. The standard deviation of 0.492 suggests that, while the majority see CVs as reliable, a notable minority are skeptical.

Question 3: Benefit to Have a Technical Interview

The positive response to this question (mean = 1.16) indicates strong support for the inclusion of technical interviews as part of the recruitment process. The high agreement (85.8%) suggests that respondents see significant value in this method. The low standard deviation (0.350) highlights a strong agreement on this point, with very few respondents disagreeing.

Question 4: Employers Should Send Performance Evaluations

There is also strong agreement that employers should provide performance evaluations, with a mean of 1.16 and 83.6% of respondents in favor. This suggests a high level of trust in performance evaluations as a tool for ongoing assessment. The low standard deviation (0.371) indicates little variation in opinion.

Question 5: Selection Based on Performance Evaluations

While there is strong support for using performance evaluations in selection processes (mean = 1.32), the standard deviation is higher (0.467) than for the previous questions. This indicates variability in opinions, with 31.7% of respondents expressing concerns about potential biases in evaluations. The data suggest that, while performance evaluations are generally seen as a positive step, there is a significant minority who is concerned about the potential for these evaluations to be unfair or biased.

The data reflect a collective agreement for the necessity of professional experience, the implementation of technical interviews, and the use of performance evaluations to ensure a fair and meritocratic selection process. However, concerns about biases in performance evaluations make it hard to translate these criteria into a digital format.

Question 6: Importance of Professional Skill Level

The majority (60.7%) of respondents rate the professional skill level as “Very Important”, with a mean score of 4.55, indicating that this is a top priority for experienced shipbuilding and ship repair technicians. Agreement is strong, as shown by the low standard deviation of 0.617.

Question 7: Importance of Extensive Professional Experience

Extensive professional experience is highly valued, with 54.1% of respondents rating it as “Very Important” and a mean score of 4.42. This shows us that long-term experience in the field, which is probably seen as essential for mastering the trade of shipbuilding and ship repair. The presence of a notable minority (9.3%) who consider extensive experience as only slightly or moderately important suggests that some technicians may prioritize recent, relevant experience or skill development over length of service in the field.

Question 8: Importance of a Qualification Diploma

The qualification diploma is considered important, but there is significantly more variability in responses, with a mean of 3.99 and a higher standard deviation of 1.112. Only 43.7% of respondents view it as “Very Important”, which indicates that qualifications are respected but not essential. A considerable portion of respondents (29.5%) view diplomas as only moderately or slightly important, and 2.7% see no value in them at all. This suggests that in the practical field of shipbuilding and ship repair, proven ability to perform on the job may be valued over formal education diplomas by a significant minority.

Question 9: Importance of Conversational English Skills

Conversational English skills are seen as very important by a majority (56.3%), with a mean score of 4.39, reflecting the global nature of the maritime industry and the necessity of communication across diverse teams and international borders. While the majority holds English skills in high regard, a notable minority (13.6%) considers them only slightly or moderately important. This could reflect the realities of certain roles within shipbuilding and repair where technical skills outweigh the need for English proficiency.

Question 10: Importance of Willingness to Work Onshore and Offshore

The willingness to work both onshore and offshore is regarded as crucial by respondents, with 45.4% rating it as “Very Important” and a mean score of 4.31. This reflects the flexibility required in the maritime industry, where technicians often need to adapt to varying work environments. A minority (10.9%) views this flexibility as only slightly or moderately important, which may indicate a preference for stability or specialization in either onshore or offshore work rather than switching between both.

Question 11: Importance of Career Advancement Initiative

Career advancement is seen as important by a significant portion of respondents (47.5% rating it as “Important” and 30.6% as “Very Important”), with a mean score of 4.01. This suggests that, while many technicians are focused on advancing their careers, there is more variability in how this is prioritized compared to other criteria. A notable segment of respondents (21.9%) places only moderate or slight importance on career advancement, and a small percentage (2.2%) does not consider it important at all. This could reflect differing career goals, with some technicians perhaps content with their current roles or more focused on job stability.

The descriptive statistics presented in the tables have been visually represented through bar charts, as shown in Figure 2, highlighting the mean values and their respective standard deviations for each survey question. This image provides a clearer understanding of the central tendencies and variability in responses, offering a more accessible interpretation of the data.

4.1.2. Cross-Tabulation Analysis

To further explore the data, cross-tabulation analyses were conducted to examine the relationships between demographic variables (specifically age group and experience group) and the responses to specific questions.

The primary objective of this cross-tabulation analysis, as shown in

Table 6. Cross-tabulation: age group vs. Q4.

Age Group		Value	Label	Value	Label	Total
		1	Yes	2	No, Employers’ Assessments Can Be Wrong
18–30 years old	Count	10		0		10
	Expected count	8.4		1.6		10.0
	% within age group	100.0%		0.0%		100.0%
	% within Q4	6.5%		0.0%		5.5%
31–50 years old	Count	109		18		127
	Expected count	106.2		20.8		127.0
	% within age group	85.8%		14.2%		100.0%
	% within Q4	71.2%		60.0%		69.4%
51–70 years old	Count	34		12		46
	Expected count	38.5		7.5		46.0
	% within age group	73.9%		26.1%		100.0%
	% within Q4	22.2%		40.0%		25.1%
Total	Count	153		30		183
	Expected count	153.0		30.0		183.0
	% within age group	83.6%		16.4%		100.0%
	% within Q4	100.0%		100.0%		100.0%

Source: designed based on authors’ research.

, is to comprehensively examine how opinions on whether employers should provide performance evaluations vary across different age groups.

Respondents aged 18–30 years old reported the following:

• All 10 respondents (100%) in this age group agreed that employers should send performance evaluations.

Respondents aged 31–50 years old reported the following:

• Out of 127 respondents in this age group, 109 (85.8%) agreed that employers should send performance evaluations, while 18 respondents (14.2%) disagreed, expressing concerns that employer assessments might be wrong.

Respondents aged 51–70 years old reported the following:

• In this age group, 34 out of 46 respondents (73.9%) agreed that employers should send performance evaluations, while 12 respondents (26.1%) disagreed, indicating concerns about the reliability of these assessments.

The chi-square test analysis presented in

Table 7. Age group vs. Q4.Chi-square tests.

	Value	Df	Asymptotic Significance (2-Sided)
Pearson chi-square	5.571 ᵃ	2	0.062
Likelihood ratio	6.825	2	0.033
Linear-by-linear association	5.517	1	0.019
No. of valid cases	183

^a 1 cells (16.7%) have expected count less than 5. The minimum expected count is 1.64. Source: designed based on authors’ research.

shows the following:

• Pearson chi-square value: 5.571 with a significance level of 0.062, which is just above the threshold for statistical significance (0.05), indicates a relationship between age and opinions on whether employers should send performance evaluations;
• Likelihood ratio: 6.825 with a level of 0.033 suggests a significant relationship;
• Linear-by-linear association: 5.517 with a significance level of 0.019, indicates that there is a significant linear trend across the age groups.

The data show that younger respondents (18–30 years) support the idea of employers sending performance evaluations. As age increases, the level of agreement decreases, with older respondents (51–70 years) showing the highest level of skepticism. This suggests that, while younger professionals are more trusting of institutional evaluations, older workers may have concerns about the fairness and accuracy of such evaluations. Furthermore, the data uncovered a trend that shows us that the younger generation is actively looking for methods to prove that their performance can be at the community standard, and they consider performance reviews from employer representatives to be helpful in achieving this.

The cross-tabulation between experience group and responses to Question 2, which asks about the relevance of professional experience in CVs, is shown in

Table 8. Cross-tabulation: experience group vs. Q2.

Experience Group		1 (Yes, Extensive Experience Is a Guarantee of Professionalism)	2 (No, Some Colleagues Exaggerate Their Skills on Their CV)	Total
Less than 7 years	Count	5	7	12
	Expected count	7.1	4.9	12.0
	% within age group	41.7%	58.3%	100.0%
	% within Q4	4.6%	9.5%	6.6%
8–20 years	Count	54	49	103
	Expected count	61.3%	41.7%	103.0
	% within age group	52.4%	47.6%	100.0%
	% within Q4	49.5%	66.2%	56.3%
More than 20 years	Count	50	18	68
	Expected count	40.5	27.5	68.0
	% within age group	73.5%	26.5%	100.0%
	% within Q4	45.9%	24.3%	37.2%
Total	Count	109	74	183
	Expected count	109.0	74.0	183.0
	% within age group	59.6%	40.4%	100.0%
	% within Q4	100.0%	100.0%	100.0%

Source: designed based on authors’ research.

and provides insights into how different experience levels perceive the reliability of CVs:

• Respondents with less than 7 years of experience: among respondents with less than 7 years of experience, 41.7% believe that extensive experience in a CV is a guarantee of professionalism, while 58.3% express skepticism, suggesting that a majority of less experienced respondents are cautious about trusting CVs;
• Respondents with 8–20 years of experience: In this group, the opinions are more balanced, with 52.4% agreeing that CVs are reliable and 47.6% disagreeing. This split indicates that those in the mid-range of experience may be more ambivalent about the reliability of CVs, recognizing both the potential accuracy and exaggeration in such documents;
• Respondents with more than 20 years of experience: Among respondents with over 20 years of experience, 73.5% believe that extensive experience in a CV is a guarantee of professionalism, while 26.5% express skepticism. This indicates that most highly experienced respondents trust that CVs reflect true professionalism, yet a significant minority remains cautious, recognizing that even extensive experience can be misrepresented.

The chi-square test results (Pearson Chi-Square = 9.280, p = 0.010) presented in

Table 9. Experience group vs. Q2.Chi-square tests.

	Value	Df	Asymptotic Significance (2-Sided)
Pearson chi-square	9.280 ᵃ	2	0.010
Likelihood ratio	9.513	2	0.009
Linear-by-linear association	8.907	1	0.003
No. of valid cases	183

^a 1 cells (16.7%) have expected count less than 5. The minimum expected count is 4.85. Source: designed based on authors’ research.

indicate a statistically significant relationship between the experience level and the perception of CV reliability. The likelihood ratio (9.513, p = 0.009) and the linear-by-linear association (8.907, p = 0.003) further confirm the significance of this relationship.

The data reveal a subtle but significant generational divide in perceptions of CV reliability among shipbuilding and ship-repair professionals. More experienced respondents, particularly those with over 20 years in the field, show a marked tendency to trust the professional experience outlined in CVs as an indicator of professionalism. Yet, this same group also harbors greater skepticism, recognizing the potential for exaggeration more keenly than their less experienced counterparts.

4.2. Hypothesis Testing

Hypothesis 1. 

Conventional recruitment based on traditional CVs can be misleading, since false information can be showcased as true.

• Analysis: Descriptive statistics from Question 2 and cross-tabulation with the experience group were utilized to evaluate this hypothesis.

Table 2 shows that the mean response to Question 2 was 1.40, indicating a general skepticism towards the reliability of traditional CVs, with many respondents expressing doubt that extensive CV experience necessarily guarantees professionalism. The cross-tabulation analysis by experience level in Table 8 reveals that 41.7% of those with less than 7 years of experience believe in the reliability of extensive CVs, compared to 58.3% who are skeptical, suspecting that some colleagues may exaggerate their skills. Among respondents with 8–20 years of experience, 52.4% view extensive CV experience as a guarantee of professionalism, while 47.6% harbor doubts. This trust significantly increases among those with more than 20 years of experience, where 73.5% believe in the reliability of extensive CV experience, with only 26.5% expressing skepticism.

The results indicate that, while traditional CVs are generally viewed with skepticism, this skepticism decreases as the respondents’ experience increases. More experienced professionals are more likely to rely on the professional experience listed in CVs as an indicator of professionalism; although, a significant portion remains cautious. This dual perspective may reflect a deeper generational conflict: while older professionals value the traditional markers of expertise, they are also more wary of the gaps between paper qualifications and real-world skills. This behavior might indicate a common concern in the industry, where many technicians looking for jobs feel the need to exaggerate their skills and experience to stand out in the application process. This tendency is especially noticeable among older workers, who often show a distrust of the CVs of their peers or especially their younger colleagues.

This generational difference in trust highlights the need for supplementary evaluation methods, particularly in digital recruitment platforms, to ensure a more accurate assessment of candidates’ true capabilities. Hypothesis 1 is partially supported.

Hypothesis 2. 

Including a technical interview in the account creation and vetting process of a digital recruitment platform helps ensure that candidates meet community standards and possess the necessary skills.

• Analysis: Descriptive statistics from Question 3 and ANOVA across the age group and experience group were employed to test this hypothesis.

The mean response in Table 2 for Question 3 was 1.14, indicating strong support for the inclusion of technical interviews in the recruitment process, with the low mean value reflecting a broad agreement on their benefits. ANOVA analysis across different age groups and experience levels revealed no significant differences, as the p-value was greater than 0.05, suggesting consistent support for technical interviews across all demographic groups. This uniformity in responses demonstrates a broad consensus on the value of technical interviews in digital recruitment. The lack of significant differences across demographic groups further underscores the broad consensus on this issue, suggesting that technical interviews are seen as a valuable tool for ensuring that candidates possess the necessary skills, regardless of the respondents’ age or experience level. Hypothesis 2 is strongly supported.

Hypothesis 3. 

Performance evaluation coming from past employers and keeping track of past and future performance in a digital database can solve meritocracy issues among the shipbuilding and ship-repair technicians’ community.

• Analysis: Descriptive statistics from Question 4 and Question 5 and cross-tabulation with experience group were utilized to determine if respondents believe that digital performance evaluations can effectively address meritocracy issues.

The analysis reveals that the mean responses to Question 4 (1.16) and Question 5 (1.32), as per Table 2, suggest moderate support for using digital performance evaluations, with the relatively low means indicating that a significant portion of respondents agrees with their use, with some concerns about bias being present. A cross-tabulation by experience group shows that 100% of respondents with less than 7 years of experience support employers sending performance evaluations, with no concerns about bias (

Table 10. Cross-tabulation: experience group vs. Q4.

Experience Group		1 (Yes)	2 (No, Employer Assessments Can Be Wrong)	Total
Less than 7 years	Count	12	0	12
	Expected count	10.0	2.0	12.0
	% within age group	100.0%	0.0%	100.0%
	% within Q4	7.8%	0.0%	6.6%
8–20 years	Count	87	16	103
	Expected count	86.1	16.9	103.0
	% within age group	84.5%	15.5%	100.0%
	% within Q4	56.9%	53.3%	56.3%
More than 20 years	Count	54	14	68
	Expected count	56.9	11.1	68.0
	% within age group	79.4%	20.6%	100.0%
	% within Q4	35.3%	46.7%	37.2%
Total	Count	153	30	183
	Expected count	153.0	30.0	183.0
	% within age group	83.6%	16.4%	100.0%
	% within Q4	100.0%	100.0%	100.0%

Source: designed based on authors’ research.

).

In contrast, 84.5% of those with 8–20 years of experience agree, while 15.5% express concerns about potential biases. Among those with more than 20 years of experience, support decreases to 79.4%, with 20.6% expressing skepticism due to bias concerns. Statistics suggest a trend where more experienced respondents are more likely to question the fairness and potential biases inherent in these evaluations. While there is strong support for the use of digital performance evaluations, particularly among less experienced workers, there are concerns about their fairness, especially among more experienced workers. This finding suggests that, while performance tracking recorded into each workers’ digital profile can be a valuable tool, it must be implemented with transparency and safeguards to mitigate potential biases, ensuring trust and acceptance across all experience levels. Hypothesis 3 is strongly supported.

Hypothesis 4. 

Integrating the identified worker criteria into a digital recruitment platform powered by a machine-learning algorithm can offer a reliable and automated safeguard, ensuring a consistent and fair recruitment process for all stakeholders involved.

• Analysis: Descriptive analysis to evaluate the effectiveness of machine-learning algorithms in recruitment, specifically in terms of their perceived fairness and consistency, as assessed by Questions 6–11.

The descriptive analysis focuses on the perceived importance of various criteria for integrating into a machine-learning algorithm for digital recruitment and provides a comprehensive view of respondent opinions. The analysis reveals that professional skill level (mean = 4.55) and extensive professional experience (mean = 4.42) are considered the most critical factors by respondents, with a strong consensus, as indicated by relatively low variability in their ratings. Conversational English skills also received high importance (mean = 4.39), reflecting its significance in the recruitment process. In contrast, the importance of having a qualification diploma (mean = 3.99) and showing a career advancement initiative (mean = 4.01) were rated slightly lower, with more variability in responses. This suggests that, while most respondents view these factors as important, there is some division in how crucial they are considered to be relative to other criteria. Overall, the analysis highlights that professional skills, experience, and language proficiency are universally valued, while the importance of qualifications and career ambition may be more context-dependent. These insights can guide the development of machine-learning algorithms in digital recruitment platforms, ensuring that they emphasize the most universally valued attributes, while remaining flexible to account for varied perceptions of other criteria (

Table 11. Summary of hypotheses and results.

Hypotheses	Analysis	Result
H1	Descriptive statistics (Q2) and cross-tabulation with experience group. Skepticism towards traditional CVs is more common among those with less experience.	Partially supported
H2	Descriptive statistics (Q3) and ANOVA across age and experience groups. Strong and consistent support for technical interviews across all demographic groups.	Strongly supported
H3	Descriptive statistics (Q4, Q5) and cross-tabulation with experience group. Strong support, with concerns about bias among more experienced workers.	Strongly supported
H4	Descriptive statistics (Q6–Q11). Professional skills, experience, and language proficiency highly valued, with lower emphasis on diplomas and career ambition.	Partially supported

Source: designed based on authors’ research.

). This approach can help create a fair and consistent recruitment process that aligns with the priorities of experienced professionals in the maritime industry. Hypothesis 4 is partially supported.

In the context of this research, the terms “strongly supported” and “partially supported” reflect the degree to which the data align with the hypotheses. These terms are used to indicate the strength and consistency of the evidence provided by the analysis.

Strongly Supported Hypothesis: This occurs when there is broad agreement across respondents. For example, the responses of Hypothesis 3 (digital performance evaluations) are summarized below:

• All respondents with less than 7 years of experience supported the idea of performance evaluations.
• In the other experience groups, 84.5% of those with 8–20 years and 79.4% with over 20 years also supported it, with a small portion expressing concerns about bias.

The high level of agreement across all experience groups (over 79%) indicates strong support.

Partially Supported Hypothesis: This occurs when opinions are more divided. For Hypothesis 4 (machine-learning algorithms in recruitment), the responses are summarized below:

• In total, 60.7% rated professional skill level as “Very Important”, and 54.1% rated extensive experience highly.
• However, only 43.7% rated qualification diplomas as “Very Important”, with a significant variability in responses.

The significant variability in the importance assigned to certain criteria, particularly qualifications, suggests that, while some aspects of the hypothesis are supported, there is not universal agreement. As a result, Hypothesis 4 is only partially supported.

In summary, strong support is defined by high percentages of agreement (typically over 70%) with limited opposition, while partial support reflects more divided opinions, with lower percentages of agreement and greater variability across respondent groups.

5. Discussion

The findings of this study provide valuable insights into the evolving challenges and opportunities associated with the digitalization of recruitment within the European Maritime Technology Industry, particularly in the shipbuilding and ship-repair sectors. As the industry integrates digital tools into recruitment processes, the research highlights critical points that expand the current understanding of digital recruitment and its implications for maritime workforce management.

One significant contribution of this research is the identification of a fundamental issue within traditional recruitment methods: the reliability of conventional CVs. This study reveals that concerns about CV reliability are particularly acute in the maritime industry, where highly specialized skills are required. The skepticism expressed by many professionals, especially younger or mid-career candidates, regarding the potential for exaggerated or falsified qualifications highlights an ongoing challenge in ensuring fairness and transparency in recruitment. This finding shows that, while many managers may already be aware of potential misrepresentations in CVs, the depth of distrust within this industry calls for more robust solutions.

This research extends previous knowledge by demonstrating that more experienced professionals, despite relying on the professional experience listed in CVs as an indicator of a candidate’s credibility, still harbor concerns about the overall reliability of these documents. This generational divide in trust toward traditional CVs underscores the need for digital recruitment platforms to incorporate mechanisms that verify the accuracy of the information provided. The theoretical implication here is significant, as it moves beyond simple acknowledgment of CV inaccuracies and suggests specific, practical solutions for verification, such as integrating real-time data validation tools into recruitment platforms. This approach can bridge the gap between traditional recruitment expectations and modern digital solutions, offering EU maritime industry managers a clearer path to enhance recruitment fairness.

Moreover, the findings reinforce the importance of technical interviews as a crucial part of the recruitment process. The support for technical interviews across all demographic groups reveals that these assessments are seen as indispensable in ensuring that candidates have the necessary skills, directly addressing concerns related to false or exaggerated information on CVs. By directly linking technical interviews to the mitigation of CV-related risks, the study provides a more nuanced understanding of how technical evaluations can serve as both a validation tool and a trust-building measure in an industry that depends heavily on specialized skills.

Another key contribution refers to the role of archived performance evaluations. Findings suggest that, while there is strong potential for archived performance evaluations data to address meritocracy issues in the maritime industry, the successful implementation of such systems depends on transparency and objectivity. The caution expressed by more experienced professionals about the potential for bias in performance evaluations points to a critical concern that has not been sufficiently addressed in earlier research.

The use of performance evaluations as a ranking tool introduces the possibility of improving fairness, but it also raises concerns about how these evaluations are applied. This study offers a new perspective by highlighting that the transparency and accuracy of performance evaluations are as important as the evaluations themselves. The implication for theory is that digital recruitment tools must go beyond incorporating performance data—they must ensure that these data are used in a way that is seen as fair and unbiased by all stakeholders. This expands on earlier work by suggesting that trust in performance evaluation systems is not just about their technical use but also about how they are perceived by the workforce. For EU maritime industry managers, this means that digital platforms must offer clear, unbiased, and transparent evaluation mechanisms to prevent resistance from key demographic groups, particularly senior professionals who may be skeptical of algorithmic decision making.

The shift from traditional email-based recruitment to fully digital platforms presents its own set of challenges, particularly in terms of user adoption. This research highlights that, while the benefits of digital recruitment are recognized, candidates are likely to resist new platforms unless these systems can immediately provide a wide range of opportunities tailored to industry-specific needs. This observation builds on existing knowledge about digital transformation by emphasizing that the key to successful platform adoption is not just the digital tool itself, but how seamlessly it can be integrated into the existing recruitment ecosystem. This insight advances theoretical discussions on the adoption of digital platforms, specifically within industries like maritime, where recruitment often requires balancing short-term project-based work with longer-term employment needs. For managers, the implication is that digital platforms must be carefully designed to integrate with existing recruitment processes and should be adaptable enough to offer real-time value to both employers and candidates.

Machine-learning algorithms can prioritize candidates in recruitment, emphasizing the need for flexibility in weighting criteria like qualifications and career opportunities across different workforce segments. While these algorithms automate candidate selection, they must remain adaptable to ensure fairness and avoid bias. This provides practical insights for designing equitable digital recruitment systems.

The study’s findings on the importance of worker criteria for machine-learning algorithms provide new insights into how these tools can balance efficiency with fairness. By demonstrating that the flexible weighting of criteria is essential, the research offers a theoretical framework that can guide the development of more sophisticated recruitment algorithms. For managers in the EU maritime industry, this insight is critical, as it highlights the need for recruitment tools that not only streamline hiring processes but also ensure that candidates who genuinely rank higher based on key criteria are prioritized in a transparent and objective manner.

This research addresses several critical gaps in the existing literature on digital recruitment by providing a more detailed understanding of the digital recruitment challenges and opportunities within the EU maritime industry. It expands the theoretical discourse on recruitment by offering concrete recommendations for integrating digital tools in a way that aligns with both current industry needs and future workforce expectations. For EU maritime industry managers, the findings offer actionable insights into how to design and implement digital recruitment platforms that are not only efficient but also trusted by all stakeholders. By examining the specific context of the maritime sector, this study contributes new knowledge that can help managers better navigate the complexities of digital recruitment and ensure that their platforms are both fair and effective.

6. Conclusions

6.1. Theoretical Implications

This study makes several important contributions to the theoretical understanding of digital recruitment in the European maritime industry, particularly within the shipbuilding and ship-repair sectors. By integrating digital tools such as technical interviews, verified performance evaluations, and machine-learning-powered prioritization systems, this research adds to the existing body of knowledge on recruitment by offering a framework that improves both the accuracy and reliability of candidate selection processes.

From a theoretical perspective, the findings highlight a new dimension in recruitment theory specific to industries requiring highly specialized technical skills, such as maritime engineering. Traditional CV-based recruitment methods, while still valued, are insufficient for ensuring the trustworthiness of candidate qualifications. This insight challenges existing theories that have relied heavily on CVs as primary recruitment tools and suggests a shift towards a more verification-based recruitment model, particularly in sectors where skills misrepresentation can have significant operational impacts.

The study also contributes to the literature on performance evaluations in recruitment, demonstrating that, while digital tools such as archived performance reviews can be used to enhance meritocracy, their effectiveness is dependent on the transparency and fairness of these evaluations. This finding enriches the discussion on algorithmic decision making in recruitment, adding a layer of complexity that has not been thoroughly explored in prior studies. Theoretical implications also arise from the generational divide observed in how different groups perceive recruitment tools, suggesting the need for adaptive recruitment strategies that cater to varying levels of trust in digital processes.

6.2. Practical Implications

The practical implications of this study are particularly relevant to managers and HR professionals within the EU maritime industry. This research underscores the need for digital recruitment platforms that integrate technical interviews and real-time performance verification to address the challenge of CV misrepresentation. By incorporating these tools, as shown in Figure 3, organizations can ensure that candidates are selected based on their actual skills and experience, rather than misleading information presented on CVs. This practice not only enhances the fairness of the recruitment process but also helps maintain operational efficiency, especially in industries where specialized skills are critical to project success. The study also highlights the potential for machine-learning algorithms to improve recruitment efficiency by automating the candidate screening and prioritization processes. By reducing the need for manual reviews and shortlisting, organizations can cut administrative costs and improve time-to-hire, a key concern in high-volume hiring scenarios.

These systems offer the additional benefit of reducing operational delays caused by unfilled positions, saving companies significant resources. For employers in the European maritime sector, the implementation of these digital recruitment tools can help meet the growing demand for skilled labor, while maintaining merit-based hiring practices. Moreover, the cross-border applicability of this digital framework within the EU offers a streamlined solution for managing workforce mobility and ensuring consistency in recruitment practices across different regions.

However, the study also reveals concerns regarding the fairness of digital performance evaluations, particularly among more experienced professionals. This suggests that, while such tools offer numerous benefits, organizations must carefully design these systems to ensure unbiased evaluation processes that maintain the trust of all stakeholders. The study recommends that digital recruitment platforms include clear, transparent mechanisms for performance evaluations to foster the greater acceptance of these tools.

6.3. Limitations and Future Studies

While this study provides important insights into the development of digital recruitment solutions for the maritime industry, it has several limitations that should be addressed in future research. The research primarily focuses on data from Romanian shipbuilding and ship-repair technicians, which may limit the generalizability of the findings to other regions or sectors within the broader maritime industry. Future studies should aim to include a more diverse sample from different EU countries to validate the applicability of the proposed digital recruitment framework across various contexts.

Another limitation is the exploratory nature of the machine-learning algorithm proposed in this study. While the results suggest that such algorithms can improve recruitment fairness and efficiency, the specific design and effectiveness of these algorithms require further empirical validation. Future studies should focus on developing, testing, and refining the algorithm in real-world settings, measuring its impact on recruitment outcomes such as time-to-hire, cost savings, and candidate quality.

Additionally, future research should explore the long-term implications of digital performance evaluations on career growth and job mobility within the maritime industry. While the study highlights the potential of these tools to enhance meritocracy, it is important to understand how they affect employees’ career trajectories over time. Research could examine if these systems create new opportunities for career advancement or introduce unintended barriers to mobility, particularly for senior professionals.

In conclusion, this study provides valuable contributions to both the theoretical framework and practical applications of digital recruitment within the European maritime industry. By addressing the specific challenges of skills verification, performance evaluation, and candidate prioritization, this research lays the groundwork for more efficient, fair, and transparent recruitment practices. However, further studies are necessary to explore the broader applicability and long-term impact of these digital solutions across diverse contexts and regions.