The effect of emotional intelligence on the acceptance and use of technologies used İn occupational health and safety: A systematic literature review

Abstract

Background

While the complexity of today's business environments links the adoption of occupational health and safety (OHS) technologies with individual and institutional factors, emotional intelligence (EI) plays an important role in this process. It contributes to the development of a safety culture.

Objective

This study aims to analyze the effect of EI on the acceptance and use of OHS technologies using the systematic literature review (SLR) method.

Methods

This systematic literature review selected and evaluated 39 academic studies examining the relationship between OHS and EI in the context of technology acceptance and use between 2010 and 2025 using PRISMA and Mixed Methods Assessment Tool (MMAT) methods in Scopus, PubMed, Web of Science, and SpringerLink databases with the specified keywords.

Results

While the 39 studies evaluated according to the MMAT criteria generally offered high methodological quality and consistent analyses, it was observed that the themes of EI, occupational health and safety, and technology adaptation came to the fore, especially with the increasing number of publications after 2021.

Conclusions

This systematic review demonstrates that EI is a key determinant of accepting and using OHS technologies. Improvements are needed in methodological consistency and contextual diversity. Integrating EI as a mediating structure in technology acceptance models can enhance the effectiveness of OHS practices. Developing employee EI skills accelerates technology adoption within the Unified Technology Acceptance and Use Model 2 (UTAUT2) model, strengthening individual and organizational OHS performance.

Keywords

Occupational health safety management emotional intelligence technology risk assessment occupational accidents information technology attitude to computers

Introduction

Technological developments with the industrial revolutions have led to changing working conditions in our country and the emergence of new occupational groups, robotic applications, technological terms, management approaches, and business platforms. While the advancement of technology and its integration with human beings cause new occupational accidents and diseases, occupational accidents and diseases experienced in the past are evolving in parallel with this transformation. In order to effectively combat changing working conditions and emerging occupational health and safety (OHS) problems, it is necessary to consider employee health and safety as a fundamental element of OHS services. In this context, the priority objectives should be developing decent working conditions and providing sustainable, healthy, and safe working environments. Preventing and controlling occupational accidents and diseases today and in the future both facilitates working life and strengthens occupational health and safety, thanks to the effective use of technological systems in the field of OHS. Although technological tools in OHS applications aim to prevent occupational accidents and diseases with a proactive approach, it has been understood that safety cannot be ensured only by technological solutions, and the human factor should also be considered. Although traditional safety models emphasize the adaptation of technological systems to the individual, it is seen that organizations try to overcome technical deficiencies by focusing on the human factor. However, it is argued that security culture should develop not only with individuals but also with organizational and technological components. Unlike the intelligence quotient (IQ), a fixed characteristic, EI stands out as a competency that is open to change and can be improved, strengthens safety culture, increases business performance, and supports organizational sustainability. In the context of industrial production, technology refers to the use of machinery and automation systems that enable high-capacity production instead of traditional methods based on muscle power, while the technological change in enterprises is embodied by the integration of robots that can perform certain tasks faster and more efficiently than human labor into production processes.¹ While OHS aims to protect the social, mental and physical well-being of employees as a multidisciplinary field covering disciplines such as engineering, sociology, psychology, medicine, physics, chemistry, law and social policy,² the use of technology in this field is becoming increasingly important in order to increase health and safety in workplaces, ensure compliance with laws and standards, and prevent worker deaths and injuries.³ These technological systems utilize digital transformation and innovative technologies to improve risk assessment, training, and health and safety practices monitoring. Radio Frequency Identification (RFID) technology is important in improving OHS by monitoring employees’ physiological values through wearable equipment. Internet of Things (IoT)-based hard hats detect air quality and levels of hazardous gases in the mining industry and identify risky situations in advance, while optimized hard hats in the construction industry aim to minimize impact injuries.^4,5 Furthermore, pneumatic wall-climbing robots offer a safer solution than traditional inspection methods by reducing the safety risks of workers in inspection tasks.⁶

Innovations in digitized Personal Protective Equipment (PPE) include integrated sensors that monitor exposure to hazardous conditions and ensure compliance with safety regulations.⁷ Wearable devices that can monitor environmental factors and human health, IoT applications that reduce the rate of errors or accidents by providing IoT data and improving the functioning of the machine with high efficiency and accuracy,⁸ developing a predictive maintenance platform based on a cyber-physical system (CPS) thanks to the use of sensors,⁹ big data technologies that collect, store, process and analyze large and complex datasets.¹⁰ Digital Transformation Tools benefit building a culture of safety awareness by increasing user engagement in safety education and hazard management through interactive digital platforms.¹¹ Modern OHS systems use data analytics to monitor workplace conditions and predict potential hazards, enabling proactive safety.⁷ Artificial intelligence (AI)-based systems are used to identify and track objects and analyze the environmental conditions employees interact with. Wearable technologies and smart devices continuously monitor employee health indicators and environmental parameters, providing real-time alerts to mitigate risks.¹² For example, systems that use computer vision and machine learning algorithms to detect whether a worker is wearing a hard hat and gloves generate alerts in cases of non-compliance, enabling rapid corrective actions.¹³ Smart PPE can improve worker safety and reduce musculoskeletal disorders through innovative approaches such as continuous monitoring of worker health conditions, analysis of behavioral patterns, and virtual ergonomic analysis.¹⁴ While these technological advances have significantly improved OHS practices, challenges remain in ensuring their widespread adoption and integration into existing systems. Adapting to emerging technologies and regulatory changes is essential to maximize their effectiveness in protecting worker health and safety.

The term “EI” was popularized by Daniel Goleman's book “Emotional Intelligence” published in 1995.¹⁵ However, the pioneers Peter Salovey and John Mayer, who identified the roots of this type of intelligence, defined EI as a system of mental skills for perceiving, understanding, and regulating emotions necessary for solving problems.¹⁶ This skill allows individuals to achieve positive outcomes by influencing their behavior, social interactions, and personal decisions.¹⁷ EI consists of five core skills: self-awareness, self-motivation, empathy, relationship management, and effective communication. Self-awareness involves recognizing emotions and accepting their changeability; self-motivation involves acting with inner strength to achieve goals; empathy involves deeply understanding others’ emotions; and relationship management involves effective communication and problem-solving skills. Effective communication requires appropriate expression of emotions and accurate interpretation of nonverbal signals. By enabling the rational management of emotions, EI offers a teachable skill set to help individuals cope with life stressors and play an important role in human health and safety.¹⁸ Originating from social intelligence and evolving into multiple intelligences and modern ability/mixed models, EI is now considered one of the key factors of individual and societal success. Different theoretical methods and models are used to understand and measure EI.¹⁹ Although the definitions of these models have some similarities in how they are measured and applied, their basic structure and emphasis differ from one perspective. Since the emergence of the concept, the field of EI has been handled with different theoretical frameworks; In this context, Salovey and Mayer's (1990, 1997) ability model, Bar-On's (1997) mixed model, Goleman's (1995, 1998, 2012) leadership-oriented mixed model, Cooper and Sawaf's (1997) applied approach, Weisinger's (1998) studies linking EI with stress management, Higgs and Dulewicz's (1999) organizational competence dimensional model, and Petrides and Furnham's (2001) trait-based approach stand out as the main models in the literature.^19,20 Goleman's leadership-focused hybrid model integrates EI with leadership and work-oriented soft skills such as self-awareness, self-management, social awareness, and relationship management.²¹ The key components of the model include the individual's capacity for recognition (self-awareness), managing (self-management), understanding the emotions of others (social awareness), and establishing effective relationships (relationship management).²²

With the rapid development of information technologies, individuals and organizations are increasingly forced to use these technologies. Considering that users’ knowledge and experience levels regarding information technologies are different, it is inevitable that the processes of accepting technology will also vary. Users’ adoption and acceptance of technology are two basic concepts often discussed in the literature, but they have different meanings. Acceptance refers to the user's attitude and intention towards technology, while adoption refers to a broader process encompassing technology's continuous and effective use.²³ The distinction between technology adoption and adoption is crucial to understanding users’ interactions with technology. Technology adoption focuses on attitudes and intentions, while technology adoption emphasizes the resulting practical engagement and satisfaction. This distinction highlights the complexity of users’ interactions with technology, arguing that successful adoption requires more than just positive attitudes; It suggests that it requires constant interaction and satisfaction. Several models have been developed to understand the factors driving technology adoption, emphasizing the importance of user needs and psychological components.²⁴ In the literature, the Technology Acceptance Model (TAM) has been developed as a theoretical framework to explain why individuals adopt or reject new technologies. In terms of disseminating the use of technology, it is important to understand the factors that affect the intentions of individuals to adopt and use new technologies. Therefore, to increase the usage rates of new technologies, it is necessary to increase the level of technology acceptance of individuals and to analyze the future effects of their intentions to use these technologies.²⁵

With the rapid development of information technologies, individuals and organizations are faced with the necessity of using these technologies. However, as users vary in their knowledge and experience with information technologies, their acceptance of these technologies also varies. In the last three decades, various theoretical models have been developed to evaluate and explain individuals’ adoption of information technologies, and one of the most widely used frameworks in this context is the Technology Acceptance Model (TAM). While TAM provides a basic theoretical structure explaining why individuals adopt or reject new technologies,²⁶ the Unified Technology Acceptance and Use Model 2 (UTAUT2) provides an advanced framework that comprehensively addresses technology acceptance by combining the elements missing in previous models.^26,27 UTAUT2 includes seven sub-dimensions that influence technology use: Social influence refers to the user's level of influence on the perception of technology by those around them; performance expectancy refers to the belief that technology will enhance job performance; effort expectancy refers to the perceived ease of use; facilitating conditions refer to the existence of infrastructure and resources that support technology; hedonic motivation refers to the pleasure and satisfaction derived from technology; cost value refers to the evaluation of costs and benefits; and habit refers to regular technology use. These dimensions provide a basic framework for explaining the technology adoption processes of individuals and organizations.^28,29

The literature shows that various psychological and cultural variables are integrated into the UTAUT model. For example, in a study conducted in Australia with the integration of perceived safety into UTAUT, it was determined that individuals’ demographics and previous experiences significantly influenced the adoption of technology towards autonomous vehicles.³⁰ Similarly, it reveals that EI is decisive in social relationships and the intention to use technology. Shanab et al. 2022 in their research conducted with 268 university students, found that EI dimensions explained the intention to use technology by 47.8% through self-efficacy and performance expectation.³¹ These findings suggest that EI interacts with technology acceptance models. Manko (2023) in his study carried out in multinational production organizations, has observed that individual characteristics such as instrumental leadership, leader's EI level, and computer gaming significantly affect employees’ perceptions and acceptance levels of Industry 4.0 technologies.³² These findings suggest that EI interacts with cognitive variables in technology adoption processes, enhancing the model's explanatory power. These studies show that traditional technology acceptance models (e.g., UTAUT2) explain technology adoption predominantly through cognitive expectations and behavioral intentions; however, they largely ignore the emotional elements that shape decision-making processes in high-stakes work environments. This study treats EI not as a static personality trait but as a dynamic mediating construct that situationally influences safety-oriented judgments, integrating it into the model's internal dynamics rather than adding it as an external variable to UTAUT2. This approach expands the framework of utility-based rational decision-making. It reveals that EI shapes individuals’ perceptions of responsibility and risk awareness, offering a complementary and independent contribution to technology adoption from existing psychological constructs. Thus, EI makes visible its unique role in supporting the cognitive and behavioral components in UTAUT2 and offers a holistic perspective on the applicability and sustainable use of OHS technologies.

Accordingly, as a systematic literature review, this study aims to analyze research examining the effects of EI on technology adoption and to evaluate the implications of this relationship for the acceptance and use of technological systems in the field of OHS.

Methods

This study uses a qualitative research methodology to understand the impact of EI on the acceptance and use of technologies in occupational health and safety. The approach is to gain an in-depth and comprehensive understanding of the impact of EI on the acceptance and use of technologies used in occupational health and safety. Qualitative research was chosen to facilitate a holistic understanding of the participants’ EI and their experiences and perceptions of the technologies. The study utilized a Systematic Literature Review (SLR) to review all articles in the international literature. SLR is a structured methodology for comprehensively and rigorously synthesizing existing research in a particular field. This method systematically reviews, selects, evaluates, and interprets relevant literature based on identified research questions.³³ The SLR method offers a systematic and reproducible methodology that increases the transparency and reliability of the research process by minimizing bias.³⁴ SLR offers a transparent and reproducible process of literature synthesis, providing a holistic analysis of the body of knowledge. However, the scientific validity and reliability of the review depend on the rigor of the selection process and the clarity of the research questions.³³

Guided by the PRISMA Preferred Reporting Items for Systematic Review and Meta-Analysis³⁵ framework, the SLR process encompasses five key stages: question formulation, study discovery, study selection and evaluation, analysis and synthesis, and reporting and using results. Each stage must ensure a comprehensive and transparent review.³⁶ Bibliometric analysis graphs were made on the data using the bibliometrix³⁷ library of the R Studio program.

The studies that were included were evaluated using the Mixed Methods Assessment Tool (MMAT). A total of 39 studies were analyzed. MMAT 2018 Edition is a critical assessment tool designed to assess the quality of studies using mixed methods by combining qualitative and quantitative research. It was developed to address the challenges of evaluating various study designs in systematic reviews. It was evaluated with the MMAT, a widely used tool that provides quality assessment of quantitative, qualitative, and mixed-method studies.^38,39 MMAT is a tool that allows for systematic assessment of the quality of mixed-methods research. However, because evaluation is a subjective process, two independent evaluators were included in the evaluation process. Both independent evaluators evaluated the articles separately using MMAT criteria and compared their findings. This approach aims to increase the objectivity of the evaluation and minimize possible biases. Inter-rater consistency was assessed using Cohen's Kappa coefficient (κ = 0.79), which was calculated using IBM SPSS Statistics 29.0 qualitative data analysis software. According to the Landis and Koch (1977)⁴⁰ classification, this value indicates a “significant level of agreement” among raters. This finding supports the methodological reliability of the quality assessment. The evaluation process provided a comprehensive review, considering the articles’ methodological soundness, data collection and analysis processes, the consistency of the findings, and the validity of the results.

Search terms and databases

Scopus, Pubmed, Web of Science, and SpringerLink databases were searched to identify the articles to be included in the study. The search databases and terms are shown in Table 1. Databases were searched between these dates: 26.07.2025–06.08.2025. The main criteria used to select studies to be accepted for systematic review are shown in Table 2.

Table 1.

Search databases and terms.

Search Databases	Search Terms
Scopus PubMed Web of Science SpringerLink	(“occupational health and safety” OR “occupational safety and health” OR “occupational safety” OR “occupational health”) AND (“emotional intelligence” OR “emotional quotient”) AND (“technology acceptance and use” OR “technology acceptance” OR “technology use” OR “technology adoption”)

Table 2.

Selection criterias.

Inclusion criteria	Exclusion criteria
Being a research article Publication year: 2010–2025 Are the purposes of the articles clear? Open access and full-text studies published on the subject	Reviews, theses, dissertations, books and letters to the editor, study protocols, manuals, pilot studies, congress proceedings on Occupational Health and Safety, Emotional Intelligence or Technology Acceptance and Use were not included. Studies that are not open access and not available in full text Articles published in languages other than English.

Study selection

Research questions (RQ) were formulated to determine the purpose of the SLR, which aimed to extract relevant data through systematically evaluating all articles. In this context, various research questions were developed to facilitate the data collection and analysis. These research questions are as follows:

RQ1: How does EI influence individual differences, the adoption, the use, and the behavioral outcomes related to OHS technologies?

RQ2: How does EI affect OHS behaviors and the likelihood of engaging in risky behaviors?

RQ3: How do employees’ levels of EI shape their perceptions of OHS technologies and their intentions to adopt and use these technologies?

RQ4: How does the sectoral context moderate or differentiate the impact of EI on OHS practices?

The selection of articles according to the PRISMA flowchart is depicted in Figure 1.

Figure 1.

SLR flow diagram based on PRISMA protocol stages.

Duplicate records were automatically identified using the Mendeley Reference Management Program and removed from the data set. Following this process, 1823 unique records were examined within the scope of a systematic literature review. After the preliminary examination at the title and abstract level, 456 full-text reports were tried to be reached; However, 50 of them could not be accessed. The remaining 406 full-text articles were evaluated in detail according to the determined eligibility criteria. As a result of the full-text review, 367 articles were excluded due to a lack of methodological competence, relevance to the subject, or lack of data. Ultimately, 39 studies were included in the systematic review and taken to the synthesis stage.

Results

General characteristics of the included studies

An overview of the articles addressing the interaction of EI, OHS, and technology can be found in Table 3.

Table 3.

Characteristics of the included studies (N = 39).

No	Author	Sample size	Gender	Age	Population	Study location
1	Lal et al., 2023 ⁴¹	29 employees	15 Male 14 Female	25–65 years old	Knowledge workers (software engineers, consultants, system developers, etc.)	UK, India, USA, Germany, Nepal, Canada and Luxembourg
2	Hoştut et al., 2023 ⁴²	179 reports (at the level of accounts)	—	—	Sustainability reports of 26 companies traded on Borsa Istanbul	Turkey
3	Paterson et al., 2024 ⁴³	43 studies	—	—	43 process assessment studies on workplace intervention programs in various sectors	Majority UK
4	Chabane et al., 2023 ⁴⁴	Theoretical framework	—	—	Industrial systems, asset management framework	The work is universal,
5	Krijgsheld et al., 2022 ⁴⁵	Systematic review based on 92 studies	—	—	Professionals working in different health institutions (doctors, nurses, medical personnel, etc.)	Studies of health systems in Europe, America, Asia
6	Babatunde et al., 2017 ⁴⁶	26 articles	—	—	Physiotherapist and occupational therapist practices in the field of musculoskeletal rehabilitation	ABD, Avustralya, Kanada, İngiltere, çeşitli ülkeler<
7	Pozo-Rico et al., 2020 ⁴⁷	141 teachers	54.6% Female45.4% Male	Average age: 38.4	Primary school teachers	Spain
8	Sioula et al., 2020 ⁴⁸	81 IT workers	39 Female42 Male	Average age: 34.9	Information technology professionals	Greece
9	Nauman et al., 2023 ⁴⁹	373 employees	59.50% Female45.4% Male	Average age: 31.52	Information technology workers (supervisor–subordinate dyad)	—
10	Garavan et al., 2022 ⁵⁰	Based on 305 executive employees	42% Female58% Male	Average age: 38,2	Executive employees from 15 different Indian information and communication technology (ICT) organizations	India
11	Chaudhary et al., 2022 ⁵¹	810 international IT workers	568 Male242 Female	Average age: 40	Employees of international information technology companies	USA, UK, Denmark, India, Australia
12	Yekinni et al. 2025 ⁵²	179 students	136 Male43 Female	Average age: 22.5	Electrical/Electronic Technology students	Nigeria
13	Mwita et al. 2025 ⁵³	217 Human Resource Management	122 Male95 Female	The majority are between the ages of 31–40	Human Resources Staff	Tanzania
14	Li et al. 2025 ⁵⁴	3195 pre-school teachers in 268 schools	3127 Female68 Male	—	Preschool teachers in China	China
15	Anwar et al. 2025 ⁵⁵	366 faculty members	210 Male156 Female	Average age: 33,2	Academic staff in private higher education institutions in Pakistan	Pakistan
16	Hu et al. 2025 ⁵⁶	376 Employees	—	—	Healthcare and e-commerce employees	China
17	Tat Ho et al. 2024 ⁵⁷	327 Documents saved to Scopus resources	—	—	327 articles on the relationship between “EI and job performance” in Scopus	International: Studies published in the US, UK, Australia, India, Malaysia, China and other countries
18	Nasaj et al. 2025 ⁵⁸	540 employees	366 Male174 Female	The majority are 31–40 years old	Multinational construction companies	Denmark, Germany, Netherlands, Norway, Portugal, Spain, Sweden and the United Kingdom
19	Zheng et al. 2025 ⁵⁹	362 young employees	164 Males198 Female	22–35 years old (young employees)	Young white-collar workers in China	China
20	Gavic et al. 2024 ⁶⁰	589 dentists	110 Male479 Female	Average age: 35,71	Dental specialists	Croatia, Serbia, Bosnia and Herzegovina
21	Iftikhar et al. 2021 ⁶¹	500 employees (service sectors)	—	—	Civil servants/employees in Pakistan's service sector	Pakistan
22	Weeratunga et al. 2019 ⁶²	Generation Y employees, exact number of participants not specified	—	Generation Y range (births 1980–1993)	Millennials working in Malaysia's information and communication technology sector	Malaysia
23	Muthu Kumarasamy et al. 2015 ⁶³	1 566 police officers	66.90% Male 33.10% Female	The majority of them are 26–30 years old	Employees of the Malaysian Police Service	Malaysia
24	Yanyu et al. 2023 ⁶⁴	300 employees	300 Male	Most of them have an estimated age of between 30 and 50 years old.	Miners working in state mines in Shanxi, Shaanxi and Sichuan provinces in China	China – State mines in Shanxi, Shaanxi and Sichuan provinces
25	Liang et al. 2023 ⁶⁵	295 nurses	8 Men287 Female	Average age: 32.42	Full-time nurses at a 400-bed teaching hospital in Taiwan	Taiwan
26	Giorgi et al., 2016 ⁶⁶	326 employees	259 employees were male and 56 were female, and 11 employees did not specify their gender.	—	Individuals working in two Italian privately owned firms (manufacturing and transport sectors)	Italy
27	Weng et al., 2011 ⁶⁷	110 internal medicine specialists and feedback of 2872 patients	85.4% Male14.6% Female	Average age: 40.78	Internal medicine specialist doctors and patients undergoing treatment in Taiwan	Taiwan
28	Zhang et al., 2025 ⁶⁸	1 479 employees	727 Male752 Female	Average age: 31,2	Employees in the tourism industry in China	China
29	Győri et al., 2025 ⁶⁹	4539 employees	1079 Male3460 Female	The majority of them are between 35 and 50 years old	Family/child welfare, social work and pedagogical professionals in Hungary	Hungary
30	Zhang et al. 2025 ⁷⁰	206 employees	55.34% Male44.7% Female	Average age: 31.90	Workers in information and communication technologies in China	China
31	Riedl et al., 2025 ⁷¹	178 software developers	Male: 166 Female: 50 Miscellaneous: 1	The majority of them are between the ages of 25 and 34	Software development teams in Austria	Austria
32	Song et al. 2022 ⁷²	219,483 individuals (of European descent)	50.5% Male49.5% Female	Average age: 54.4	UK Biobank database	United Kingdom
33	Tawfik et al. 2021⁷³	15 505 health workers	—	—	Medical staff working in 31 hospitals in Michigan, USA	USA
34	Edmund et al. 2023 ⁷⁴	699 oil and gas workers	72.9% Male27.1% Female	The majority are 20–40 years old	Employees of government oil and gas institutions in Ghana	Ghana
35	Alsulami et al., 2023 ⁷⁵	265 construction workers	—	The majority are under the age of 30	Construction workers in Saudi Arabia	Saudi Arabia
36	Ifelebuegu et al. 2019 ⁷⁶	124 contributors	58% Male48% Female	—	Professionals in the oil and gas sector in Nigeria	Nigeria
37	Charuvil Elizabeth et al. 2024 ⁷⁷	1000 incident qualitative classification + 45,603 incident machine learning analysis	—	—	Workers in the industrial construction industry in Canada	Canada
38	Graham et al. 2025 ⁷⁸	Nearly hundreds of thousands of Twitter and Reddit comments	—	—	Social media users (general consumers)	Global social media platforms (Twitter/X and Reddit)
39	Clarke et al. 2015 ⁷⁹	441 vet clinic clients (104 replies)	82% Female18% Male	Average age: 49	Clinical client base serving veterinary technologists (those with paraveterinary interaction experience)	Australia

As seen in Table 3, studies addressing the interaction of EI, OHS, and technology have been conducted in various sectors and geographical regions. This situation reveals the subject's interdisciplinary nature and the importance of cultural contexts; it allows research findings to be generalized to broader contexts. A significant part of the studies in the literature include individuals between the ages of 31 and 50, and this reflects the employee population that takes an active role in the use of technology. The spread of participant samples across different age groups and sectors suggests that the relationships between EI and technology use may vary according to age and occupational context. Sectoral diversity indicates that the impact of EI on technology adoption, especially in terms of OHS, may differ depending on factors such as occupational risk perception, communication skills, and stress management. The wide distribution of sample sizes allows for in-depth analysis at the micro level and monitoring trends at the macro level. This diversity is important to understand the decisive role of the EI level in adopting technology-based solutions in OHS applications and to test these roles in different contexts.

Between 2011 and 2025, a significant upward trend was observed when the publications on EI, OHS, and technology interactions were examined (Figure 2). In the 2011–2017 period, a limited number of publications were realized, and this process was considered a preparatory phase in which the subject started to gain a new place in the literature. The number of publications, which increased after 2019, gained momentum, especially between 2022 and 2025, reaching 12 articles in the first eight months of 2025. This increase points to the growing importance of EI in digitalization, AI-powered OHS applications, and stress management. The place of EI in the OHS literature is getting stronger, and more comprehensive models are predicted to develop in line with interdisciplinary approaches.

Figure 2.

Temporal distribution of reviewed studies (2010–2025).

The word cloud given in Figure 3 visually represents the prominent conceptual foci in academic publications analyzed within the scope of the systematic literature review. The dimensions of the expressions in the image reflect the relative frequencies of the relevant terms in the compiled texts. In particular, multi-word concepts such as “emotional intelligence”, “occupational health”, and “technology adoption” are analyzed together in order to emphasize the thematic structures that are frequently encountered in the literature. In addition, singular but semantically decisive keywords such as “technology”, “motivation”, and “acceptance” are also prominently included in the image. This reveals that the research area centers on intersecting concepts such as emotional intelligence, occupational health and safety, and technological adaptation. Generally accepted expressions (“study”, “data”, “methods”, etc.) were excluded from the analysis, ensuring that only contextually meaningful terms were included. Thus, it is aimed to visualize the main themes, methodological contributions, and research trends in the literature more clearly. This approach emphasizes the conceptual density contained in the words and allows for a quick and effective interpretation of the basic tendencies in the literature.

Figure 3.

Word cloud of keywords in reference articles (made with R Studio program).

Quality assessment outcome

Within the scope of this systematic review, 39 studies were selected and evaluated in terms of methodological quality according to the MMAT tool. MMAT screening questions: “S1. Are there clear research questions?” and “S2. Do the collected data allow us to address the research questions?” Positive answers to the questions were the basis for the detailed quality review of the study. According to the research designs, qualitative studies (Table 4), randomized controlled studies (Table 5), non-randomized studies (Table 6), quantitative descriptive studies (Table 7), and mixed methods studies (Table 8). The relevant tables systematically present the findings of each group regarding the MMAT evaluation criteria and comparatively present the quality levels of the included studies.

Table 4.

MMAT assessment results for included qualitative studies.

No	Author	Type of study	1.1	1.2	1.3	1.4	1.5
			Is the qualitative approach appropriate to answer the research question?	Are the qualitative data collection methods adequate to address the research question?	Are the findings adequately derived from the data?	Is the interpretation of results sufficiently substantiated by data?	Is there coherence between qualitative data sources, collection, analysis and interpretation?
1	Lal et al., 2023 ⁴¹	Qualitative	Yes	Yes	Yes	Yes	Yes
2	Hoştut et al., 2023 ⁴²	Qualitative	Yes	Yes	Yes	Yes	Yes
3	Paterson et al., 2024 ⁴³	Qualitative	Yes	Yes	Yes	Yes	Yes
4	Chabane et al., 2023 ⁴⁴	Qualitative	Yes	Yes	Yes	Yes	Yes
5	Krijgsheld et al., 2022 ⁴⁵	Qualitative	Yes	Yes	Yes	Yes	Yes
6	Babatunde et al., 2017 ⁴⁶	Qualitative	Yes	Yes	Yes	Yes	Yes

Table 5.

MMAT assessment results for included randomized controlled trials.

No	Author	Type of study	2.1	2.2	2.3	2.4	2.5
			Is randomization appropriately performed?	Are the groups comparable at baseline?	Are there complete outcome data?	Are outcome assessors blinded to the intervention provided?	Did the participants adhere to the assigned intervention?
7	Pozo-Rico et al., 2020 ⁴⁷	Randomized Controlled Trials	Yes	Yes	Yes	Can't tell	Yes
8	Sioula et al., 2020 ⁴⁸	Randomized Controlled Trials	Yes	Yes	Can't tell	Yes	Yes

Table 6.

MMAT assessment results for included non-randomized studies.

No	Author	Type of study	3.1	3.2	3.3	3.4	3.5
			Are the participants representative of the target population?	Are measurements appropriate regarding both the outcome and intervention (or exposure)?	Are there complete outcome data?	Are the confounders accounted for in the design and analysis?	During the study period, is the intervention administered (or exposure occurred) as intended?
9	Nauman et al., 2023 ⁴⁹	Non-Randomized Studies	Yes	Yes	Yes	Yes	Yes
10	Garavan et al., 2022 ⁵⁰	Non-Randomized Studies	Yes	Yes	Yes	Can't tell	Yes
11	Chaudhary et al., 2022 ⁵¹	Non-Randomized Studies	Yes	Yes	Yes	Can't tell	Yes

Table 7.

MMAT assessment results for included quantitative descriptive studies.

No	Author	Type of study	4.1	4.2	4.3	4.4	4.5
			Is the sampling strategy relevant to address the research question?	Is the sample representative of the target population?	Are the measurements appropriate?	Is the risk of nonresponse bias low?	Is the statistical analysis appropriate to answer the research question?
12	Yekinni et al. 2025 ⁵²	Quantitative	Yes	Yes	Yes	Can't tell	Yes
13	Mwita et al. 2025 ⁵³	Quantitative	Yes	Yes	Yes	Can't tell	Yes
14	Li et al. 2025 ⁵⁴	Quantitative	Yes	Yes	Yes	Can't tell	Yes
15	Anwar et al. 2025 ⁵⁵	Quantitative	Yes	Yes	Yes	Can't tell	Yes
16	Hu et al. 2025 ⁵⁶	Quantitative	Yes	Yes	Yes	Can't tell	Yes
17	Tat Ho et al. 2024 ⁵⁷	Quantitative	Yes	Yes	Yes	Yes	Yes
18	Nasaj et al. 2025 ⁵⁸	Quantitative	Yes	Yes	Yes	Can't tell	Yes
19	Zheng et al. 2025 ⁵⁹	Quantitative	Yes	Yes	Yes	Can't tell	Yes
20	Gavic et al. 2024 ⁶⁰	Quantitative	Yes	Yes	Yes	Can't tell	Yes
21	Iftikhar et al. 2021 ⁶¹	Quantitative	Yes	Yes	Yes	Can't tell	Yes
22	Weeratunga et al. 2019 ⁶²	Quantitative	Yes	Yes	Yes	Can't tell	Yes
23	Muthu Kumarasamy et al. 2015 ⁶³	Quantitative	Yes	Yes	Yes	Can't tell	Yes
24	Yanyu et al. 2023 ⁶⁴	Quantitative	Yes	Yes	Yes	Can't tell	Yes
25	Liang et al. 2023 ⁶⁵	Quantitative	Yes	Yes	Yes	Can't tell	Yes
26	Giorgi et al., 2016 ⁶⁶	Quantitative	Yes	Yes	Yes	Can't tell	Yes
27	Weng et al., 2011 ⁶⁷	Quantitative	Yes	Yes	Yes	Can't tell	Yes
28	Zhang et al., 2025 ⁶⁸	Quantitative	Yes	Yes	Yes	Yes	Yes
29	Győri et al., 2025 ⁶⁹	Quantitative	Yes	Yes	Yes	Yes	Yes
30	Zhang et al. 2025⁷⁰	Quantitative	Yes	Yes	Yes	Can't tell	Yes
31	Riedl et al., 2025 ⁷¹	Quantitative	Yes	Yes	Yes	Can't tell	Yes
32	Song et al. 2022 ⁷²	Quantitative	Yes	Yes	Yes	Yes	Yes
33	Tawfik et al. 2021 ⁷³	Quantitative	Yes	Yes	Yes	Can't tell	Yes
34	Edmund et al. 2023 ⁷⁴	Quantitative	Yes	Yes	Yes	Yes	Yes
35	Alsulami et al., 2023 ⁷⁵	Quantitative	Yes	Yes	Yes	Yes	Yes
36	Ifelebuegu et al. 2019 ⁷⁶	Quantitative	Yes	Yes	Yes	Yes	Yes

Table 8.

MMAT assessment results for included mixed methods studies.

No	Author	Type of study	5.1	5.2	5.3	5.4	5.5
			Is there an adequate rationale for using a mixed methods design to address the research question?	Are the different components of the study effectively integrated to answer the research question?	Are the outputs of the integration of qualitative and quantitative components adequately interpreted?	Are divergences and inconsistencies between quantitative and qualitative results adequately addressed?	Do the different components of the study adhere to the quality criteria of each tradition of the methods involved?
37	Charuvil Elizabeth et al. 2024 ⁷⁷	Mixed Methods Studies	Yes	Yes	Yes	Can't tell	Yes
38	Graham et al. 2025 ⁷⁸	Mixed Methods Studies	Yes	Yes	Yes	Can't tell	Can't tell
39	Clarke et al. 2015 ⁷⁹	Mixed Methods Studies	Yes	Yes	Yes	Can't tell	Yes

Research questions assessment result

The contributions of 39 articles examined within the scope of this systematic review to the four main research questions are presented in Table 9. The table shows the extent to which EI affects the adoption, use, and behavioral outcomes of OHS technologies (RQ1), its impact on OHS behaviors and risky behaviors (RQ2), and the extent to which it provides findings on employee perception and intention to use technology (RQ3). In addition, the impact of sectoral context on EI and OHS relations (RQ4) was systematically evaluated. Thus, Table 9 presents the level of contribution of the studies included in the review to the research questions from a holistic perspective, providing the opportunity to determine the scope of the existing knowledge in the literature and possible research gaps.

Table 9.

Evaluation table of the contribution levels of the examined articles to the research questions.

No	Author	Title	RQ1	RQ2	RQ3	RQ4
1	Lal et al., 2023 ⁴¹	Working from Home During Covid-19: Doing and Managing Technology-enabled Social Interaction With Colleagues at a Distance	✓	✓	✓	X
2	Hoştut et al., 2023 ⁴²	Who's in and who's out? Reading stakeholders and priority issues from sustainability reports in Turkey	✓	X	X	✓
3	Paterson et al., 2024 ⁴³	Barriers and facilitators to implementing workplace interventions to promote mental health: qualitative evidence synthesis	✓	✓	X	X
4	Chabane et al., 2023 ⁴⁴	Barriers and facilitators to implementing workplace interventions to promote mental health: qualitative evidence synthesis	✓	✓	X	✓
5	Krijgsheld et al., 2022 ⁴⁵	Job performance in healthcare: a systematic review	✓	X	X	✓
6	Babatunde et al., 2017 ⁴⁶	Characteristics of therapeutic alliance in musculoskeletal physiotherapy and occupational therapy practice: a scoping review of the literatüre	✓	✓	X	✓
7	Pozo-Rico et al., 2020 ⁴⁷	Teacher training can make a difference: tools to overcome the impact of COVID-19 on primary schools. An experimental study	✓	✓	X	✓
8	Sioula et al., 2020 ⁴⁸	An 8-week Stress Management Program in Information Technology Professionals and the Role of a New Cognitive Behavioral Method: a Pilot Randomized Controlled Trial	✓	✓	✓	✓
9	Nauman et al., 2023 ⁴⁹	Late-night smartphone usage and job performance: unlocking the missing links	✓	X	X	X
10	Garavan et al., 2022 ⁵⁰	Type A/B personality, work–family, and family–work conflict: The moderating effects of emotional intelligence	✓	✓	X	X
11	Chaudhary et al., 2022 ⁵¹	Impact of leader's e-competencies on employees’ wellbeing in global virtual teams during COVID-19: the moderating role of emotional intelligence	✓	✓	X	X
12	Yekinni et al. 2025 ⁵²	Mediating role of psychological adjustment ability on emotional intelligence-workshop/laboratory safety behaviour relationship among Electrical/Electronic Technology students	✓	✓	X	X
13	Mwita et al. 2025 ⁵³	Potential benefits and challenges of artificial intelligence in human resource management in public institutions	✓	X	✓	X
14	Li et al. 2025 ⁵⁴	When technology meets emotions: Exploring preschool teachers’ emotion profiles in technology use	✓	X	✓	X
15	Anwar et al. 2025 ⁵⁵	Unveiling the role of emotional intelligence as a mediator between digital leadership and employee cynicism: a study in the private higher educational institutes	✓	✓	X	X
16	Hu et al. 2025 ⁵⁶	Artificial Intelligence Symbolic Leadership in Small and Medium-Sized Enterprises: Enhancing Employee Flexibility and Technology Adoption	✓	X	✓	X
17	Tat Ho et al. 2024 ⁵⁷	Mapping emotional intelligence and job performance: A bibliometric and thematic analysis	✓	✓	X	X
18	Nasaj et al. 2025 ⁵⁸	Collective AI anxiety and team innovative behaviour: the role of coping strategies	✓	✓	X	✓
19	Zheng et al. 2025 ⁵⁹	Mindfulness and work addiction among young employees: the mediating roles of cognitive reappraisal and perfectionism	✓	✓	X	X
20	Gavic et al. 2024 ⁶⁰	The association of empathy and the work–family conflict in doctors of dental medicine	✓	✓	X	X
21	Iftikhar et al. 2021 ⁶¹	Impact of multifaceted workplace bullying on the relationships between technology usage, organisational climate and employee physical and emotional health	✓	✓	✓	✓
22	Weeratunga et al. 2019 ⁶²	Influence of emotional and spiritual intelligence towards intention to stay by generation y employees in the information and communication technology sector in Malaysia: The mediating role of perceived organizational support	✓	X	✓	X
23	Muthu Kumarasamy et al. 2015 ⁶³	Individual, organizational and environmental factors affecting work-life balance	✓	X	X	X
24	Yanyu et al. 2023 ⁶⁴	The effect of emotional intelligence on unsafe behavior of miners: the role of emotional labor strategies and perceived organizational support	✓	✓	X	✓
25	Liang et al. 2023 ⁶⁵	The relationships among overcommitment, effort-reward imbalance, safety climate, emotional labour and quality of working life for hospital nurses: a structural equation modelling	✓	✓	X	✓
26	Giorgi et al., 2016 ⁶⁶	Detrimental Effects of Workplace Bullying: Impediment of Self-Management Competence via Psychological Distress	✓	✓	X	X
27	Weng et al., 2011 ⁶⁷	Associations between emotional intelligence and doctor burnout, job satisfaction and patient satisfaction	✓	✓	X	✓
28	Zhang et al., 2025 ⁶⁸	The mediating and moderating effects of mindfulness and emotional intelligence on the emotional labor strategies and career resilience of tourism employees	✓	✓	X	✓
29	Győri et al., 2025 ⁶⁹	Psychosocial risks and their predictors in human service occupations: the case of Hungary	✓	✓	X	X
30	Zhang et al. 2025⁷⁰	Supervisory support for subordinates’ use of information and communication technologies: development and preliminary validation of a scale in a Chinese context	X	X	✓	X
31	Riedl et al., 2025 ⁷¹	How an Agile Software Process Increases Developers’ Job Satisfaction: A Stress Perspective Based on the Effort-Reward-Imbalance Model	✓	✓	X	X
32	Song et al. 2022 ⁷²	Genetic basis of job attainment characteristics and the genetic sharing with other SES indices and well-being	✓	X	X	X
33	Tawfik et al. 2021⁷³	Frustration With Technology and its Relation to Emotional Exhaustion Among Health Care Workers: Cross-sectional Observational Study	X	X	✓	✓
34	Edmund et al. 2023 ⁷⁴	Emotional intelligence as a conduit for improved occupational health safety environment in the oil and gas sector	✓	✓	X	✓
35	Alsulami et al., 2023 ⁷⁵	Impact of emotional intelligence on the stress and safety of construction workers’ in Saudi Arabia	✓	✓	X	✓
36	Ifelebuegu et al. 2019 ⁷⁶	The Role of Emotional Intelligence Factors in Workers’ Occupational Health and Safety Performance-A Case Study of the Petroleum Industry	✓	✓	X	✓
37	Charuvil Elizabeth et al. 2024 ⁷⁷	Reducing serious injuries and fatalities in industrial construction-application of machine learning to analyze emotional intelligence and psychosocial factors	✓	✓	X	✓
38	Graham et al. 2025 ⁷⁸	Consumer perceptions of AI chatbots on Twitter (X) and Reddit: an analysis of social media sentiment and interactive marketing strategies	✓	X	✓	X
39	Clarke et al. 2015 ⁷⁹	Client perspectives on desirable attributes and skills of veterinary technologists in Australia: Considerations for curriculum design	✓	X	X	✓

Note: In this table, the ✓ symbol denotes compliance (“Yes”), the X symbol denotes non-compliance (“No”).

Discussion

This systematic review is the first to comprehensively address the factors influencing the role of EI in the acceptance and use of OHS technologies. The included 39 studies were conducted with large and diverse samples in different sectors and geographies, including adults aged 20–50. This diversity allows for the understanding and testing of EI's decisive impact on technology adoption processes in the OHS field in different contexts.

Studies by Lal et al. (2023),⁴¹ Pozo-Rico et al. (2020),⁴⁷ and Hu et al. (2025)⁵⁶ show that self-awareness and self-management competencies (Goleman, 1995)²² are directly related to the performance expectancy and effort expectancy dimensions of the UTAUT2 model (Venkatesh et al., 2016),²⁸ and that this relationship is strong. Furthermore, individual safety behaviors play an important role in supporting technology use. In addition, it shows that EI competencies play an intermediary or regulatory role in the processes of employees managing technology-based social interactions, adopting technology in the context of education, and adapting to AI leadership in small and medium-sized enterprises. This supports EI's contribution to technology adoption and usage intent, allowing for validation and application of existing theoretical models.

Studies by Nauman et al. (2023),⁴⁹ Edmund et al. (2023),⁷⁴ and Ifelebuegu et al. (2019)⁷⁶ show that the social awareness and relationship management sub-dimensions of EI interact with the social influence and facilitating conditions dimensions of the UTAUT2 model. This interaction strengthens intra-team communication in OHS practices, contributing to the widespread adoption of a technology-based safety culture. It is also emphasized that social interactions and organizational support mechanisms play a decisive role in the effective adoption of OHS technologies, thus increasing employee confidence in safety technologies and ensuring the sustainability of organizational processes.

Hedonic motivation and habituation are particularly prominent in the continuity and internalization of technology use. These sub-dimensions explain why users prefer technology, user experience, and convenience for the task at hand. Studies such as Graham et al. (2025)⁷⁸ and Charuvil Elizabeth et al. (2024)⁷⁷ have shown that users’ EI skills and positive attitudes toward technology on digital platforms effectively increase OHS awareness.

This relationship has been observed extensively across various sectors. In different fields such as health (Liang et al., 2023⁶⁵; Tawfik et al., 2021⁷³), informatics (Garavan et al., 2022⁵⁰; Riedl et al., 2025⁷¹), construction (Alsulami et al., 2023⁷⁵; Charuvil Elizabeth et al., 2024⁷⁷), oil and gas (Edmund et al., 2023⁷⁴; Ifelebuegu et al., 2019⁷⁶), education (Pozo-Rico et al., 2020⁴⁷), and tourism (Zhang et al., 2025⁶⁸), the effect of EI on technology acceptance positively shapes OHS performance. Risk perception in high-risk sectors (e.g., mining, oil and gas, construction) is accelerating the adoption of safety-focused technologies by strengthening the adoption of safety-focused technologies through EI's performance expectation and enabling conditions.^74–76 Corporate culture and social interactions in the health, education, and service sectors shape the acceptance of technology by clarifying the social impact and hedonic motivation dimensions of EI.^47,65,73 In addition, in business lines with high stress levels, self-awareness and self-management skills facilitate technology adoption by supporting the dimensions of effort expectation and habit.^49,56 These cases show that individual competencies based on EI affect the adoption of OHS technologies in a multidimensional way through UTAUT2 dimensions in interaction with sectoral dynamics. Therefore, the role of EI in technology acceptance and the sector's risk profile differ according to organizational values, psychosocial conditions, and employee interaction styles. Accordingly, the impact of EI on technology adoption should be considered in relation to sectoral dynamics.

The findings of this systematic review comprehensively reveal the role of EI in the acceptance and use of OHS technologies, which aligns with different research questions. The studies of Lal et al. (2023)⁴¹ and Hoştut et al. (2023)⁴² provide strong evidence under RQ1, highlighting the impact of EI on individual differences and technology adoption behaviors. In addition, Hoştut et al. (2023)⁴² contribute to RQ4 by emphasizing the role of the organizational context in shaping OHS practices through sustainability reports. Paterson et al. (2024)⁴³ and Chabane et al. (2023)⁴⁴ support RQ2 by demonstrating the determinism of EI in OHS behaviors and risk management processes. In the sectoral context, Babatunde et al. (2017)⁴⁶ and Chabane et al. (2023)⁴⁴ presented meaningful findings for RQ4 by comparatively evaluating the effects of EI on OHS practices in different sectors. The studies of Pozo-Rico et al. (2020)⁴⁷ and Sioula et al. (2020),⁴⁸ on the other hand, contribute to RQ1 and RQ2 by strengthening the sub-dimensions of EI such as stress management and self-awareness; in the sectoral context (education and information technologies) it makes an indirect contribution to RQ4. Studies conducted by Nauman et al. (2023),⁴⁹ Garavan et al. (2022),⁵⁰ and Chaudhary et al. (2022)⁵¹ highlight the regulatory impact of EI on employee behavior. Yekinni et al. (2025)⁵² and Anwar et al. (2025)⁵⁵ provide strong evidence under RQ1 and RQ2, revealing how EI shapes safe behaviors and organizational attitudes through individual awareness and psychological adjustment.

The studies of Li et al. (2025)⁵⁴ and Hu et al. (2025)⁵⁶ make a significant contribution to RQ3 by showing the impact of EI on technology perception and adoption. Studies such as Mwita et al. (2025)⁵³ and Tat Ho et al. (2024)⁵⁷ indirectly address the relationship between EI and technology use, and in this respect, RQ1 makes a limited contribution. Studies such as Weng et al. (2011),⁶⁷ Edmund et al. (2023),⁷⁴ and Alsulami et al. (2023)⁷⁵ make a significant contribution to these areas by revealing the regulatory role of EI on burnout, stress, and safety behaviors. These findings suggest that EI significantly influences safe behaviors (RQ1) and risky attitudes (RQ2) through individual awareness, self-regulation, and emotional processing capacity. Nasaj et al. (2025)⁵⁸ and Zheng et al. (2025)⁵⁹ highlighted the regulatory role of EI on stress management and innovation behaviors, revealing significant findings, especially in the context of RQ1 and RQ2. Gavic et al. (2024)⁶⁰ comprehensively analyze the effects of EI on organizational and individual functioning. On the other hand, Weeratunga et al. (2019)⁶² contribute to RQ1 and RQ3 by examining the role of EI on employee engagement in the information technology sector. Hu et al. (2025)⁵⁶ provide evidence for the impact of EI on technology adoption and behavioral outcomes, revealing that using AI as a symbolic leadership tool in small and medium-sized businesses increases employee flexibility and technology adoption (RQ1, RQ3). Graham et al. (2025)⁷⁸ provide indirect awareness of the perceptual processes that shape technology adoption by examining social media perceptions of AI interactions (RQ3), while Iftikhar et al. (2021)⁶¹ highlight the negative effects of workplace bullying on technology use and employee health and point to the potential role of EI in mitigating these effects (RQ1, RQ2, RQ3). However, these studies offer limited implications in the context of RQ4, as they do not compare sectoral differences.

At the individual level, EI facilitates adaptation to technology, encourages innovative behaviors, and improves occupational performance (Nasaj et al., 2025⁵⁸; Weng et al., 2011⁶⁷; Zhang et al., 2025⁶⁸; Clarke et al., 2015⁷⁹). High EI has been shown to reduce risky behaviors, lower stress levels, and improve safety performance in different contexts, from mining to construction and the petroleum industry (Yanyu et al., 2023⁶⁴; Alsulami et al., 2023⁷⁵; Ifelebuegu et al., 2019⁷⁶; Charuvil Elizabeth et al., 2024⁷⁷), providing consistent evidence under RQ2. Negative emotional responses to technology can be mitigated through EI, and their adoption and use intentions can be strengthened (Tawfik et al., 2021⁷³; Iftikhar et al., 2021⁶¹), which overlaps with RQ3. However, even though the studies provide data from a wide range of sectors such as health, mining, tourism, petroleum, construction, and veterinary medicine, the lack of analyses that directly compare the regulatory role of the sectoral context in EI–OHS interactions creates a significant gap in RQ4. Therefore, while the existing literature reveals the important role of EI in improving the effectiveness of OHS technologies at the individual and organizational level, the systematic examination of cross-sectoral differences is a priority requirement for future research.

Overall, the studies reviewed show that EI plays a multidimensional role in adopting and using OHS technologies; It reveals that it is effective at the individual, behavioral, and organizational levels. However, the limited empirical studies on integrating EI into technology acceptance models and the fact that sectoral differences have not been adequately tested reveal the necessity of further research in this field. The impact of EI on technology adoption in OHS applications has not yet been adequately experimentally tested.^30,80 The impact of cultural and demographic factors on technology adoption is highlighted, but cross-sectoral comparative studies are lacking.⁸⁰ The findings show that EI should be considered a strategic variable in the effective use of OHS technologies and developing safe workplace behaviors. Employees with a high EI are better able to manage their emotions and the emotions of others, showing higher risk perception and adherence to safety practices. This leads to the adoption of safe behaviors and a reduction in occupational accidents.^74–76 EI plays an important role not only at the individual level, but also in organizational culture and leadership. Leaders and teams with high EI levels facilitate the adoption of security practices and reduce workplace stress.^74,75 EI is an important factor for accepting and effectively using new OHS technologies. Employees’ EI facilitates adaptation to technological changes and integrating safety technologies into the daily workflow.^74,76

Although emerging OHS technologies (e.g., AI, IoT, augmented/virtual reality (AR/VR), big data, robotic systems, wearable devices, and ergonomic exoskeletons) were excluded from the scope of this study, these tools provide significant opportunities for future research. In particular, EI may act as a mediating or moderating factor influencing variables such as technology acceptance, intention to use, adoption, psychosocial adaptation, organizational culture, and employees’ perceptions of stress and risk.

Limitations

This systematic literature review is limited to the period between 2010 and 2025, specific databases, publication language (English), and published articles. This study included academic papers published exclusively in English, which introduced the risk of possible language bias. Furthermore, excluding grey literature (theses, technical reports, industry reports, etc.) can lead to underrepresenting observational data across different sectors. These limitations may limit the generalizability of the findings obtained. In future research, it is recommended to include multilingual publications and data sources that include gray literature to provide a more comprehensive and balanced assessment. Due to the increase in relevant studies in the field of OHS and the acceleration of technological developments, the systematic literature review has been limited to publications published after 2010.

Furthermore, since data extraction and quality assessment (MMAT) processes are based on investigator interpretations, subjectivity may be involved. Due to differences in measurement between EI scales and the variety of types of technologies studied, the effect sizes could not be combined at the statistical level. Therefore, the findings were analyzed descriptively. This study is limited to research that measures EI over basic EI models and addresses technology adoption within the framework of UTAUT2 in order to maintain conceptual coherence. For this reason, studies involving digital emotional intelligence, post-pandemic technology adoption models, or new generation OHS technologies such as AI, IoT, big data, robotic systems, wearable devices, AR/VR applications, and ergonomic exoskeleton solutions are excluded. This is one of the study's main limitations regarding the generalizability of the findings.

Conclusion and suggestions

This systematic review presents studies examining the adoption of OHS technologies by EI. The publications for 2011–2025 examined in the study reflect the growing academic interest in the context of EI's OHS and technology interactions. The significant increase in the number of publications, which was limited in the early period, especially after 2021, shows that theoretical and applied studies in this field have gained momentum. The findings reveal that EI has started to be at the center of interdisciplinary approaches to improving security performance in digitalized business environments. Based on the findings obtained from the systematic literature review, the word cloud supports the main trends in the research field by visually showing that the themes of EI, OHS, and technological adaptation are prominent conceptual foci in the literature. This trend shows that academic interest in relevant literature and applied research has increased recently, and interdisciplinary collaborations and methodological diversification have strengthened. In this context, it is recommended that the studies to be carried out in this field in the future should proceed not only by considering the quantitative increase, but also by considering the methodological depth, reporting transparency, and contextual diversity. Thus, the transfer of the findings to both theoretical knowledge and practice will be ensured more strongly and sustainably.

The overall methodological quality of the studies evaluated according to MMAT criteria was high. The vast majority of studies conducted in qualitative, randomized, controlled, non-randomized, quantitative, and mixed method designs (Tables 4 –8) are strong in terms of clear definition of research questions, use of appropriate and valid data collection tools, methodological integrity of analysis processes, and reporting of findings consistent with data.

The effect sizes could not be statistically aggregated due to differences in measurement between EI scales and the variety of technology types studied. However, according to the table showing the contribution levels of the 39 studies examined to the research questions (Table 9), descriptive analyses reveal that the effect of EI on technology adoption is moderately to highly positive in the vast majority of studies. This trend reveals that EI can be considered a psychosocial resource supporting individual attitude and behavior intention and a complementary regulatory element interacting with UTAUT2 dimensions. Widespread findings observed in the literature suggest that strengthening EI skills accelerates using OHS-focused technology in high-risk work environments, thus positioning it as a strategic element that supports safety culture. Therefore, it is recommended that technology acceptance models should be transformed into a holistic structure that includes the capacity for emotional regulation, rather than being limited to rational frameworks based solely on cognitive expectations. Future research should consider the EI-technology acceptance relationship comparatively across different sectors and risk profiles and systematically test regulatory or mediating variables. In this context, EI is considered a strategic variable that supports the sustainable use of OHS technologies and a theoretical component that can be repositioned as an intermediary structure in technology adoption.

As a result, considering EI as a strategic variable in adopting OHS technologies and developing safe workplace behaviors improves safety performance at both the individual and organizational levels. From a theoretical perspective, integrating EI as a mediating construct into existing technology acceptance and use models offers the potential to enrich conceptual frameworks in OHS. Such initiatives can facilitate more effective adoption and sustainable use of emerging OHS technologies. Methodological integrity, sample representativeness, advanced analytical techniques, and integrating theory and practice will produce high-quality studies that significantly contribute to the literature at both theoretical and applied levels. Finally, studies should provide theoretical contributions and produce actionable results for field applications. In this context, developing concrete and feasible proposals, especially for practitioners in the field of OHS, will increase the practical benefit of the research.

Footnotes

Acknowledgments

The authors would like to express their deepest gratitude to all those who participated in this study.

Ethical approval

Not applicable. Ethics committee approval was not required since this study is a literature review.

Informed consent

Not applicable.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

ORCID iD

Melih Engin

References

Doğan

Varoğlu

Tuncer

. General Business Information. Ankara, Turkey: Political Bookstore, 2022.

Hämäläinen

Takala

Saarela

. Global estimates of occupational accidents. Saf Sci 2006; 44: 137–156.

Glanz

Rimer

Viswanath

. Health behavior and health education: Theory, research, and practice. 4. Jossey-Bass/Wiley, 2008.

Roja

Srihari

. IOT Based smart helmet for air quality used for the mining industry. Themed Section: Engineering and Technology 2018; 4(8): 514–521.

Pan

Wimer

, et al. Finite element simulations of the head–brain responses to the top impacts of a construction helmet: effects of the neck and body mass. Proc Inst Mech Eng H 2017; 231: 58–68.

Schmidt

Berns

. Climbing robots for maintenance and inspections of vertical structures—A survey of design aspects and technologies. Rob Auton Syst 2013; 61: 1288–1305.

Trus

Berezovskyi

Prokopenko

. Innovative approaches to ensuring occupational health and safety: challenges and opportunities. Municipal Econ Cities 2024; 3: 245–249.

Alotaibi

. A new internet of things (IoT) framework for public sectors. Int J Inf Technol Web Eng 2022; 17: 1–15.

Yang

F-N

Lin

H-Y

. Development of A predictive maintenance platform for cyber-physical systems. 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS). IEEE; 2019, p. 331–335. https://doi.org/10.1109/ICPHYS.2019.8780144 .

10.

Nuryanto

Basrowi

Quraysin

. Big data and IoT adoption in shaping organizational citizenship behavior: the role of innovation organizational predictor in the chemical manufacturing industry. Int J Data Netw Sci 2024; 8: 225–268.

11.

Abd Rani

Yusoff

Osman

, et al. Cybergogy tools and technologies aided awareness in safety and health management. Jurnal Mekanikal 2025; 48(1): 139–148. https://doi.org/10.11113/jm.v48.565 .

12.

Flor-Unda

Fuentes

Dávila

, et al. Innovative technologies for occupational health and safety: a scoping review. Safety 2023; 9: 35.

13.

Snehalakshmi

Sridar

Devanathan

, et al. Smart safety surveillance system: personal protective equipment and drowsiness detection in industrial environments. 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE; 2024, p. 1–6. https://doi.org/10.1109/ICCCNT61001.2024.10724032 .

14.

Zorzenon

Lizarelli

de A. Moura

DBA

. What is the potential impact of industry 4.0 on health and safety at work? Saf Sci 2022; 153: 105802.

15.

Tiffin

Paton

. When I say … emotional intelligence. Med Educ 2020; 54: 598–599.

16.

Salovey

Mayer

. Emotional intelligence. Imagin Cogn Pers 1990; 9: 185–211.

17.

Bradberry

Greaves

. Emotional Intelligence 2.0. Istanbul: Sola Publications, 2017.

18.

Castelino

Mendonca

. Emotional intelligence in nursing: the heart of the art – A review. J Compl Altern Med Res 2021; 14(3): 1–6. https://doi.org/10.9734/jocamr/2021/v14i330244 .

19.

Decker

Landaeta

Kotnour

. Exploring the relationships between emotional intelligence and the use of knowledge transfer methods in the project environment. Knowl Manag Res Pract 2009; 7: 15–36.

20.

Siegling

Furnham

Petrides

. Trait emotional intelligence and personality: gender-invariant linkages across different measures of the big five. J Psychoeduc Assess 2015; 33: 57–67.

21.

Patel

Wadia

. How emotional intelligence helps teachers: a review of different models. Int J Manage Dev Stud 2025; 14: 05–10.

22.

Mokhtar

Krishnan

. A Proposed Framework for the Relationship between Emotional Intelligence and Employee’s Performance: Mediating Role of Job Demand. Information Management and Business Review 2023; 15(4): 200–207. https://doi.org/10.22610/imbr.v15i4(SI)I.3593.

23.

Gürses

. Adoption of e-government from the perspective of administrators and citizens in local governments in Turkey: The case of Bursa Metropolitan Municipality. Uludag University/Institute of Social Sciences, Bursa, Turkey, 2016.

24.

Reza

Salmassi

Kazemi

, et al. A review of most applicable theories and models of technology acceptance. Journal of Health Sciences 2022; 6(7): 6022–6032.

25.

Holden

Karsh

B-T

. The technology acceptance model: its past and its future in health care. J Biomed Inform 2010; 43: 159–172.

26.

Tetik

Türkeli

Pinar

, et al. Health information systems with technology acceptance model approach: a systematic review. Int J Med Inform 2024; 190: 105556.

27.

Lee

Ramasamy

Subbarao

. Understanding psychosocial barriers to healthcare technology adoption: A review of TAM and UTAUT frameworks. Public Health and Healthcare 2025: 1–29. https://doi.org/10.20944/preprints202501.0074.v1 .

28.

Venkatesh

Thong

. Unified theory of acceptance and use of technology: a synthesis and the road ahead. J Assoc Inf Syst 2016; 17: 328–376.

29.

Hakimi

Jaafar

Mohamad

, et al. Unified theory of acceptance and use of technology (UTAUT) applied in higher education research: a systematic literature review and bibliometric analysis. Multidisc Rev 2024; 7: 2024303.

30.

Chen

Khan

Shiwakoti

, et al. Integrating perceived safety and socio-demographic factors in UTAUT model to explore Australians’ intention to use fully automated vehicles. Res Transp Bus Manage 2024; 56: 101147.

31.

Shanab

EAA

Shanab

. The influence of emotional intelligence on technology adoption and decision-making process. Int J Appl Dec Sci 2022; 15: 04.

32.

Manko

. The role of leadership and emotional intelligence in times of adopting new Industry 4.0 technologies. 2023.

33.

Eka Pratiwi

Meiliasari

. Systematic literature review: pengaruh model pembelajaran terhadap kemampuan pemahaman konsep matematis. Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika Dan Statistika 2024; 5: 2142–2156.

34.

Gualtieri

Rauch

Vidoni

. Emerging research fields in safety and ergonomics in industrial collaborative robotics: a systematic literature review. Robot Comput Integr Manuf 2021; 67: 101998.

35.

Page

McKenzie

Bossuyt

, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Br Med J 2021; 372: n71. https://doi.org/10.1136/bmj.n71 .

36.

Dominic

Mahamed

Abdullah

, et al. Preferred reporting items for a systematic review and meta-analysis (Prisma): The impact of COVID-19 on University reputation and comparative evaluation. Int J Acad Res Acc, Fin Manage Sci 2022; 12: 214–237. https://doi.org/10.6007/IJARAFMS/v12-i3/14694 .

37.

Aria

Cuccurullo

. Bibliometrix : an R-tool for comprehensive science mapping analysis. J Informetr 2017; 11: 959–975.

38.

Hong

Fàbregues

Bartlett

, et al. The mixed methods appraisal tool (MMAT) version 2018 for information professionals and researchers. Educ Inf 2018; 34: 285–291.

39.

Nha Hong

Pluye

Fàbregues

, , et al. Mixed methods appraisal tool (MMAT) version 2018 user guide. n.d.

40.

Landis

Koch

. The measurement of observer agreement for categorical data. Biometrics 1977; 33: 159–174.

41.

Lal

Dwivedi

Haag

. Working from home during COVID-19: doing and managing technology-enabled social interaction with colleagues at a distance. Inf Syst Front 2023; 25: 1333–1350.

42.

Hoştut

van het Hof

Aydoğan

, et al. Who’s in and who’s out? Reading stakeholders and priority issues from sustainability reports in Turkey. Humanit Soc Sci Commun 2023; 10: 73.

43.

Paterson

Leduc

Maxwell

, et al. Barriers and facilitators to implementing workplace interventions to promote mental health: qualitative evidence synthesis. Syst Rev 2024; 13: 52.

44.

Chabane

Komljenovic

Abdul-Nour

. Converging on human-centred industry, resilient processes, and sustainable outcomes in asset management frameworks. Environ Syst Decis 2023; 43: 663–679.

45.

Krijgsheld

Tummers

Scheepers

. Job performance in healthcare: a systematic review. BMC Health Serv Res 2022; 22: 49.

46.

Babatunde

MacDermid

MacIntyre

. Characteristics of therapeutic alliance in musculoskeletal physiotherapy and occupational therapy practice: a scoping review of the literature. BMC Health Serv Res 2017; 17: 75.

47.

Pozo-Rico

Gilar-Corbí

Izquierdo

, et al. Teacher training can make a difference: tools to overcome the impact of COVID-19 on primary schools. An experimental study. Int J Environ Res Public Health 2020; 17: 8633.

48.

Sioula

Tigani

Artemiadis

, et al. An 8-week stress management program in information technology professionals and the role of a new cognitive behavioral method: a pilot randomized controlled trial. J Mol Biochem 2020; 9: 22–31.

49.

Nauman

Zheng

Ali

, et al. Late-night smartphone usage and job performance: unlocking the missing links. Behav Inf Technol 2023; 42: 1704–1719.

50.

Garavan

Srivastava

Madan

, et al. Type A/B personality, work–family, and family–work conflict: the moderating effects of emotional intelligence. Hum Resour Dev Q 2022; 33: 223–247.

51.

Chaudhary

Rohtagi

Singh

, et al. Impact of leader’s e-competencies on employees’ wellbeing in global virtual teams during COVID-19: the moderating role of emotional intelligence. Employee Relations: The International Journal 2022; 44: 1048–1063.

52.

Yekinni

Ogbuanya

. Mediating role of psychological adjustment ability on emotional intelligence-workshop/laboratory safety behaviour relationship among electrical/electronic technology students. Discover Educ 2025; 4: 40.

53.

Mwita

Kitole

. Potential benefits and challenges of artificial intelligence in human resource management in public institutions. Discover Global Soc 2025; 3: 35.

54.

Bai

Liu

. When technology meets emotions: exploring preschool teachers’ emotion profiles in technology use. Comput Educ 2025; 236: 105355.

55.

Anwar

Saraih

Soomro

. Unveiling the role of emotional intelligence as a mediator between digital leadership and employee cynicism: a study in the private higher educational institutes. Int J Org Anal 2025; 33: 1180–1202.

56.

Mohi Ud Din

Tahir

. Artificial intelligence symbolic leadership in small and Medium-sized enterprises: enhancing employee flexibility and technology adoption. Systems 2025; 13: 16.

57.

Tat Ho

Phan Thanh

Nguyen

T-H

, et al. Mapping emotional intelligence and job performance: a bibliometric and thematic analysis. Probl Persp Manage 2024; 23: 1–22.

58.

Nasaj

Badi

Murtagh

, et al. Collective AI anxiety and team innovative behaviour: the role of coping strategies. Construction Innovation 2025. https://doi.org/10.1108/CI-08-2024-0222.

59.

Zheng

Sheng

Wang

, et al. Mindfulness and work addiction among young employees: the mediating roles of cognitive reappraisal and perfectionism. Front Psychiatry 2025; 16: 1–12. https://doi.org/10.3389/fpsyt.2025.1631792 .

60.

Gavic

Jerkovic

Ambarkova

, et al. The association of empathy and the work–family conflict in doctors of dental medicine. Acta Odontol Scand 2024; 83: 356–361.

61.

Iftikhar

Qureshi

Qayyum

, et al. Impact of multifaceted workplace bullying on the relationships between technology usage, organisational climate and employee physical and emotional health. Int J Environ Res Public Health 2021; 18: 3207.

62.

Weeratunga

Singh

JSK

. Influence of emotional and spiritual intelligence towards intention to stay by generation Y employees in the information and communication technology sector in Malaysia: the mediating role of perceived organizational support. Int J Eng Adv Technol 2019; 8: 954–961.

63.

A P Muthu Kumarasamy

Pangil

Faizal Mohd Isa

. Individual, organizational and environmental factors affecting work-life balance. Asian Soc Sci 2015; 11(25): 111–123. https://doi.org/10.5539/ass.v11n25p111 .

64.

Yanyu

Jizu

. The effect of emotional intelligence on unsafe behavior of miners: the role of emotional labor strategies and perceived organizational support. Int J Occup Saf Ergon 2023; 29: 515–527.

65.

Liang

Tseng

Dai

, et al. The relationships among overcommitment, effort-reward imbalance, safety climate, emotional labour and quality of working life for hospital nurses: a structural equation modeling. BMC Nurs 2023; 22: 04.

66.

Giorgi

Perminienė

Montani

, et al. Detrimental effects of workplace bullying: Impediment of self-management competence via psychological distress. Front Psychol 2016; 7(60): 1–11. https://doi.org/10.3389/fpsyg.2016.00060 .

67.

Weng

H-C

Hung

C-M

Liu

Y-T

, et al. Associations between emotional intelligence and doctor burnout, job satisfaction and patient satisfaction. Med Educ 2011; 45: 835–842.

68.

Zhang

Zhao

, et al. The mediating and moderating effects of mindfulness and emotional intelligence on the emotional labor strategies and career resilience of tourism employees. Sci Rep 2025; 15: 24268.

69.

Győri

Balogh

Ádám

, et al. Psychosocial risks and their predictors in human service occupations: the case of Hungary. Curr Psychol 2025; 44: 14409–14421. https://doi.org/10.1007/s12144-025-08159-2 .

70.

Zhang

van Eerde

Liu

, et al. Supervisory support for subordinates’ use of information and communication technologies: development and preliminary validation of a scale in a Chinese context. Humanit Soc Sci Commun 2025; 12: 46.

71.

Riedl

Oettl

Stangl

, et al. How an Agile software process increases Developers’ job satisfaction: a stress perspective based on the effort-reward-imbalance model. Bus Info Syst Eng 2025; 67: 83–107.

72.

Song

W-D

, et al. Genetic basis of job attainment characteristics and the genetic sharing with other SES indices and well-being. Sci Rep 2022; 12: 8902.

73.

Tawfik

Sinha

Bayati

, et al. Frustration with technology and its relation to emotional exhaustion among health care workers: cross-sectional observational study. J Med Internet Res 2021; 23: e26817.

74.

Edmund

NNK

Suxia

Ebenezer

, et al. Emotional intelligence as a conduit for improved occupational health safety environment in the oil and gas sector. Sci Rep 2023; 13: 19698.

75.

Alsulami

Serbaya

Rizwan

, et al. Impact of emotional intelligence on the stress and safety of construction workers’ in Saudi Arabia. Engineering. Constr Archit Manage 2023; 30: 1365–1378.

76.

Ifelebuegu

Martins

Theophilus

, et al. The role of emotional intelligence factors in Workers’ occupational health and safety performance—A case study of the petroleum industry. Safety 2019; 5: 30.

77.

Charuvil Elizabeth

Sattari

Lefsrud

, et al. Reducing serious injuries and fatalities in industrial construction-application of machine learning to analyze emotional intelligence and psychosocial factors. Int J Constr Manage 2024; 25(12): 1–11. https://doi.org/10.1080/15623599.2024.2417629 .

78.

Graham

Stough

. Consumer perceptions of AI chatbots on Twitter (X) and Reddit: an analysis of social media sentiment and interactive marketing strategies. J Res Inter Market 2025; 19(7): 1096–1124. https://doi.org/10.1108/JRIM-05-2024-0237 .

79.

Clarke

Al-Alawneh

Pitt

, et al. Client perspectives on desirable attributes and skills of veterinary technologists in Australia: considerations for curriculum design. J Vet Med Educ 2015; 42: 217–231.

80.

Audrin

. More than just emotional intelligence online: introducing “digital emotional intelligence”. Front Psychol 2023; 14: 1–12. https://doi.org/10.3389/fpsyg.2023.1154355 .