Next Article in Journal
Authentic Romanian Gastronomy—A Landmark of Bucharest’s City Center
Previous Article in Journal
Building the Framework for Sustainable Tourism in Príncipe Island
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Psychometrics of Emotional Intelligence in Hospitality: A Cross-Sectional Study on Human Capital and Quality of Service

Alberto Sanz-García
Almudena Ros-Martínez
Ana López-Navas
1 and
Miguel Puig-Cabrera
Faculty of Medicine, Catholic University of Murcia—UCAM, 30107 Murcia, Spain
Research Centre for Tourism, Sustainability and Well-Being (CinTurs), Universidade do Algarve, 8005-200 Faro, Portugal
Author to whom correspondence should be addressed.
Tour. Hosp. 2024, 5(1), 237-250;
Submission received: 6 February 2024 / Revised: 15 March 2024 / Accepted: 19 March 2024 / Published: 21 March 2024


Little research has been conducted specifically on emotional intelligence (EI) and perceived quality of service in the hospitality industry. The main goal of this research consists of providing empirical evidence related to EI regarding its importance in quality of hospitality service (QHS). To do so, a hypothetical and conceptual model was tested by Partial Least Square-Structural Equation Modeling (PLS-SEM) techniques based on a sample of 408 employees in the hospitality sector in the region of Murcia (Spain). The findings of this work reveal the incidence that EI has on the QHS of human capital, despite a scenario that is characterized by a high rate of talent shortage worldwide and an increasing digitalization that could reduce human interaction. Also, implications are given so that EI could be more deeply analyzed for EI strategy-building within organizational behavior and human resource management areas from a practical approach, increasing engagement and preventing the so-called quality service sabotage.

1. Introduction

Emotional Intelligence (EI), as conceptualized by ref. [1], denotes the capability to identify, comprehend, and manage one’s own emotions and those of others. This capacity has garnered increasing recognition within the workplace due to its demonstrated impact on job performance, job satisfaction, and other organizational outcomes. EI’s relevance extends across various workplace outcomes, including leadership effectiveness, decision-making processes, and interpersonal relations [2], signifying its integral role in the professional realm.
The importance of emotional management is also an essential part of professions with a high degree of human interaction and attention like hospitality [3,4], including not only food and beverages but also tour guiding [5] and even the services provided in the lodging industry [4]. This is similar to other professional services such as healthcare [6,7] or education [8], among others.
According to one of the most accepted definitions of quality service, it is defined as the overall excellence of a service based on the discrepancy between customers’ expectations and perceptions [9].
However, little research has been conducted specifically on emotional intelligence (EI) in the hospitality industry, and, specifically, on contrasting its relationship with quality service [10].
On the one hand, EI is analyzed as a process in which customer incivility indirectly leads to employee incivility by increasing employees’ burnout that can be mitigated with EI [11]. Ref. [12] suggests that this also becomes an opportunity for hospitality managers to build competitive advantages and to differentiate a hospitality business based on human attention.
On the other hand, EI is supposed by the literature to contribute to the regulation of the emotions of hospitality workers, affecting their job performance and satisfaction [13,14] and also affecting the quality of hospitality service (QHS) [15,16].
Despite the apparent importance of EI in enhancing service quality and organizational performance, the hospitality industry faces challenges such as talent shortages that could compromise QHS [17,18]. Addressing these challenges requires a nuanced understanding of how EI can be leveraged for employees to not only navigate but thrive in such a competitive landscape. This study aims to contribute to this understanding by examining the direct effects of EI on QHS, employing the Trait Meta-Mood Scale (TMMS-24) for EI assessment [1] and the SERVQUAL model for measuring QHS [9], through a Partial Least Square-Structural Equation Modeling (PLS-SEM) approach based on data from 408 hospitality sector employees in the region of Murcia, Spain.
The findings of this work reveal the incidence that EI has on QHS, despite a scenario that is characterized by a high rate of talent shortage worldwide and an increasing digitalization that could reduce human interaction. Also, implications are given so that EI could be more deeply analyzed for EI strategy-building within organizational behavior and human resource management areas from a practical approach, increasing engagement and preventing the so-called quality service sabotage [19].

2. Literature Review

2.1. Socio-Labor Dimensions and EI Interrelationships

EI and socio-labor characteristics have been widely analyzed in literature research. Ref. [20] suggests a correlation between experience, age, professional category, and specific job positions with EI. These authors argue that managerial decisions are emotionally influenced, with leadership roles and professional status enhancing service quality through strategic decision-making, thereby establishing a direct and significant relationship between EI and service quality. This is echoed by ref. [21], which also finds experience and professional categories to align with EI attributes. Ref. [22] remarks on the relevance of age, educational level, years of experience, and job tasks, asserting a correlation within the hospitality sector, especially noting a positive association between non-university education, experience, and years worked in the sector with EI.
Recent investigations delve into how age affects EI and emotional behavior in the workplace [23], with findings indicating that emotional regulation and handling emotionally challenging situations effectively develop with age [24]. Additionally, older workers tend to adopt a more positive life outlook, potentially enhancing their job performance and satisfaction [25]. Ref. [26] highlights the support provided by older, more experienced hospitality workers and managers to lower-ranking, less-compensated employees, fostering professional growth and service quality improvement both individually and as a team. Ref. [5] concurs that age influences EI’s attentional capacities and the resultant service quality.
The relationship between salary and EI has also piqued research interest. Higher salaries may offer financial security and alleviate economic-related stress, positively affecting self-efficacy and job satisfaction [27]. Yet, the precise nature of the salary–EI relationship remains debated, calling for more targeted research designs.
Regarding gender and EI, findings are mixed. While some studies suggest women exhibit greater emotion recognition and expression capabilities than men, others find insufficient evidence to assert significant gender differences in EI [28]. This discussion must consider the influence of societal gender roles and expectations on emotional expression and management in the workplace. Refs. [15,29] note gender differences in EI and quality outcomes, with women showing higher EI levels in their research. Similarly, refs. [7,20] report stronger EI development among women, potentially mitigating counterproductive work behaviors.
Education level and work experience are also relevant factors, with higher education possibly offering additional skills for emotional understanding and management, while work experience promotes EI skills through real-world practice and feedback [30].
Given the nuanced interplay between EI and socio-labor characteristics (age, experience, gender, and educational background) and their collective impact on organizational outcomes and service quality, we propose the following hypothesis:
Hypothesis 1.
Socio-labor characteristics of hospitality employees are significantly interrelated with their EI.
This hypothesis is grounded in the premise that socio-labor characteristics of hospitality influence EI development and expression, which in turn affects service quality and customer satisfaction. Understanding these dynamics allows for targeted improvements in HR practices and service strategies, aligning with the overarching goal of elevating hospitality experiences through enriched emotional intelligence.

2.2. EI and QHS Interrelationships

Recent studies across various sectors spotlight the role of EI in influencing job outcomes and quality of service. Ref. [31] explored, within an Israeli public hospital framework, the intricate relationship between EI and job outcomes, shedding light on the nuanced ways in which EI contributes to quality of service. Similarly, a study involving 78 real estate agents in Kosovo [32] corroborated the positive impact of EI on job performance, highlighting its significance across diverse occupational landscapes. Ref. [33] suggests that an individual’s EI influences organizational performance, and it becomes a crucial factor to better guarantee quality of service.
The ability of an individual to regulate emotions and mood has especially significant effects on job performance in the hospitality sector [34]. Ref. [35] hypothesizes the relationship of team emotional intelligence and knowledge-sharing on team performance in several organizations. Thus, team EI could also be analyzed as an accumulative sum of both individuals’ EI and job outcomes.
In the hospitality sector, where emotional regulation and mood management are essential, the effects of EI on job performance become even more pronounced [34]. The guest–host relationship during the service encounter combines several tangible and intangible aspects that are related to the customers’ judgment of the service experience [10]. The service encounter is diagnosed as being emotionally charged through discussing tourism offerings, emotional labor performance, and tourist attributions [13]. For example, individuals with advanced EI are skilled at identifying the emotions of those around them and using this understanding to inform their behavior. Such abilities significantly improve their chances of precisely interpreting customers’ emotional needs and adjusting their emotional expressions to meet those needs effectively [3].
The hypothesis proposed by ref. [35] regarding the synergy between team EI and knowledge-sharing describes the potential for collective EI to enhance team performance across various organizations. This perspective suggests that team EI might be conceptualized as the aggregate of individual members’ EI, contributing significantly to job outcomes and, by extension, to organizational success.
Ref. [12] conducts a qualitative study on middle-level managers in UK-based tourism and hospitality organizations revealing EI’s substantial impact on staff satisfaction, motivation, and overall business productivity. By highlighting the importance of quality relationships among staff and the role of emotionally intelligent middle management, their work proposes EI as a competitive advantage for organizations on QHS.
Ref. [16] suggests there is a need for hospitality organizations to measure the level of EI among workers to foster affection toward their job and, thus, to assure a proper QHS standard. Despite EI and human capital being important from the supply and demand sides, the hospitality sector is suffering from a talent shortage that could endanger QHS [17,18]. Thus, working on EI organizational programs could become an opportunity for fostering a higher engagement rate once an optimal hypothetical and conceptual model can be validated.
As a result of this research landscape, our second hypothesis was formulated as follows:
Hypothesis 2.
EI of hospitality employees has a significant effect on perceived QHS of customers.
This hypothesis remarks on the anticipated direct impact of EI on enhancing the service quality within the hospitality industry, suggesting that EI’s role transcends mere interpersonal effectiveness to significantly influence customer satisfaction and organizational performance.

3. Materials and Methods

According to the main goal of this research, a hypothetical conceptual model was tested to measure the effect of EI hospitality employees on perceived QHS by customers in line with H2, after testing H1 (Figure 1).
On the one hand, to measure the EI of hospitality employees, the Trait Meta-Mood Scale (TMMS-24) was taken as reference.
On the other hand, the perceived quality of the QHS of customers was measured based on the so-called SERVQUAL questionnaire [9].
The Partial Least Square-Structural Equation Model (PLS-SEM) [36] was the used methodology to test this hypothetical conceptual model. This is one of the most accepted techniques for theory-building [36,37], as it analyzes the cause–effect relationships between latent constructs that measure theoretical concepts through proxy variables.
This research focuses on the case study of the hospitality sector in the region of Murcia (Spain) and includes both employees and customers regarding EI and QHS.

3.1. Sampling Process and Data Collection

Between 15th January and 30th September 2023, data were collected from a total of 47 hospitality establishments including bars, coffee shops, and restaurants located in the region of Murcia (Spain). The typology of the establishments is mixed, including both hospitality chains and small and medium food places. A sample of this work includes hospitality employees of these establishments.
Before collecting data, the owners of the establishments were contacted to validate the purpose of the research to gain permissions and offer them practical implications once the data were collected.
In this work, hospitality workers were included in the analysis to measure their EI. A total of 710 questionnaires were distributed by an external data manager (DM) to be fulfilled anonymously within a team-building campaign. The response rate was 57%; thus, 408 questionnaires were returned as valid examples.
The inclusion criteria of this sample were being a member of the staff. The exclusion criteria consisted of having at least 1 year of experience at the company and a minimum of 20 working hours contracted.
On the other hand, the perception of quality service was measured according to the perception of hospitality customers regarding the performance of each staff member in these establishments. To do so, an electronic questionnaire was given to customers when paying the bill. Each ticket had a specific code that was attributed to individuals of the sample so that the scoring could be attributed to the corresponding staff member.
Given the difficulties of obtaining data during the initial stage, a 15% discount for certain products was offered to respondents. Despite this measure, the response rate was still limited. Finally, a total of 2083 valid questionnaires were obtained concerning the perception of QHS to be attributable to each individual of the sample.
The data manager collected data once the service was provided to the customer.
All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Catholic University of Murcia (protocol code CE051503).

3.2. Constructs Measurement

3.2.1. Emotional Intelligence (EI)

The EI construct was measured through the Trait Meta-Mood Scale (TMMS-24) designed by ref. [1]. This questionnaire evaluates the meta-knowledge of the emotional states of hospitality employees. This scale consists of 24 items with 5 options of valuation or conformity by means of a Likert Scale. The test is grouped into 3 dimensions: (1) emotional attention; (2) clarity of feelings; and (3) emotional repair.
The TMMS-24 has already been used in other contexts to test EI among several collectives and/or groups [38] according to age ranges or professional sectors, among others.
The socio-labor characteristics of hospitality employees in this work also includes factors such as gender, salary, years of experience, working hours, professional category, and establishment type in which that individual is working.

3.2.2. Perceived Quality of Hospitality Service (QHS)

The perceived quality of QHS was measured through the so-called SERVQUAL questionnaire [9]. This tool allows for measuring the QHS by means of standardized questions and items. A Likert-type scale was used for data collection, measurement, evaluation, and segmentation. The test discriminates between the dimensions of perception versus expectations for measuring service in hospitality establishments, including the following: (1) service reliability; (2) responsiveness; (3) assurance; (4) empathy; and (5) tangibles. This questionnaire includes a total of 24 items.
The Measured Latent Marker Variable Method (MLMV) [39] was applied to address any potential common method variance (CMV) that could exist in the collected data.

4. Data Analysis and Results

4.1. Sample Profiles

This section presents the socio-demographic and professional profiles of the hospitality employees who participated in our study. Table 1 shows the sample description, consisting of 408 employees from various establishments in the region of Murcia, Spain, encompassing a wide range of ages, education levels, years of experience, job categories, and types of establishments.
According to a total of 408 hospitality customers (Table 1), the age ranges of the respondents were as follows: 44.1% of the respondents were between 18 and 30 years old; 42.9% between 31 and 45; 12% between 46 and 60; and respondents of more than 60 years old represented 1%.
According to the education level, 13.3% did not hold a degree, 34% held a secondary degree, and 37.7% a technical one. Finally, 15% of the respondents held a university degree.
The sample was also chosen in relation to the years of experience. A total of four categories were found: <1 year of experience (16.3%); 1–3 years (24.3%); 4–8 years (24.8%); and more than 8 years (34.7%).
Regarding the category of the employees in the enterprise: 13% of respondents were managers; 22.1% were in intermediate position; 36.6% were technical; and 28.3% were assistants.
The kind of establishment represented by respondents is divided in four groups: bar/coffee shop (39.2%); restaurants (35.2%); events (16.7%); and hotels (8.9%).
Regarding the number of working hours, four categories were represented: <10 h/week (11.1%); 10–20 h/week (9.4%); 20–30 h/week (10.6%); and full time (68.8%).
According to the salary range, most of the respondents earned between EUR 800 and 1100 per month (51.1%). However, other ranges were also represented: less than EUR 800 per month (26.6%); EUR 1100–1400 per month (17.6%); and more than EUR 1400 per month (4.7%).

4.2. The Relationship between EI and SL

First, an initial correlational analysis was performed concerning EI and the socio-labor characteristics of hospitality (Table 2).
EI seems to have a significant and direct relationship with the years of experience of hospitality workers. Specifically, the emotional attention (r = 0.085), clarity of feelings (r = 0.126), and emotional repair (r = 0.132) seem to become higher as professionals gain years of experience. These findings could reveal that the hospitality sector tends to build EI among workers.
Regarding the professional category of workers, emotional attention (r = 0.185) does not seem to be a determinant for the position and responsibility of workers. However, clarity of feelings (e = 0.145) and emotional repair (r = 0.115) tend to increase as these responsibilities also grow in the hospitality sector. Also, female hospitality professionals tend to have more EI than male hospitality professionals.
On the other hand, the type of hospitality establishments, salary, and working hours did not reveal any correlation with EI. Thus, H1 is partially supported.

4.3. The Measurement Model

According to Table 3, relationships between EI and QHS were revealed:
Convergent validity [36] was analyzed according to outer loadings and Average Variance Extracted (AVE). All outer loadings of the reflective constructs should be above 0.70 to confirm their belonging to their corresponding construct [40].
However, items with loads 0.40> and <0.70 were retained in the model, after checking that the AVE and Composite Reliability (CR) did not increase [41].
The AVE values are above 0.5, which means that the construct explains more than half of the variance of its indicators, including convergent validity [40].
Internal consistency reliability is assessed using Cronbach´s Alpha and composite CR [23]. While Cronbach’s alpha tends to underestimate the internal consistency reliability, the CR overestimates them; thus, it is appropriate to consider both indicators.
According to Cronbach´s Alpha, all constructs show values above 0.70 [42]. Regarding CR, all constructs show values above 0.70, which implies that they have high levels of internal consistency reliability.
On the other hand, no value is higher than 0.90, which means that none of the variables measure the same aspect and, thus, there are no redundant constructs [43].
The Heterotrait–Monotrait (HTMT) ratio of correlations was used to test discriminant validity. With all HTMT values lower than 0.85, validity is confirmed.
The results of analyzing the measurement model confirm that all evaluation criteria are valid and that the constructs and indicators must be included in the nomogram.

4.4. Hypothesis Testing

First, the Variance Inflation Factors (VIF) of EI as predictors of QHS (H2) were examined to discard any collinearity problem with a VIF 5> [44].
To evaluate the predictive power of the proposed model, the goodness of fit of the variables (R2) and Stone–Geisser’s criterion (Q2) were observed [45,46]. Ref. [47] established that R2 measures of 0.67, 0.33, and 0.19 should be classified as substantial, moderate, and weak, respectively. However, the values of R2 vary according to the complexity of the model and the research discipline [36].
In this model, the R2 value of the endogenous construct is 0.471 for EI toward QHS. Furthermore, since Q2 > 0 for all constructs, the model has an acceptable prediction power, establishing the predictive importance of the endogenous construct in the model [44].
The significance of the proposed hypothesis was evaluated with a bootstrap resampling technique that included 5000 resamples. Thus, none of the confidence intervals included value 1 [36].
The p-value and t-value were used to test the hypotheses under two necessary conditions: p-value being less than 0.05 and t-value being above 1.96.
The results contained in Table 4 confirm a direct and significant incidence of the EI of employees on perceived QHS (β = 0.465, t-value = 9.845, p < 0.001). Thus, H2 is supported, and EI becomes a direct factor on the customers’ perception about QHS. Figure 2 represents the tested structural model.

4.5. Discussion

The importance of EI on QHS was proved. Firstly, this work found direct and significant relationships between EI and the social and labor characteristics of customers such as years of experience, the managerial category of workers, and gender. This subsidiary analysis makes it possible to analyze EI as a significant factor that could also be conditioned by several factors.
Some studies suggest similar findings regarding the direct and significant effects of EI with years of experience [21]. This is consistent with the findings of this work, as professionals could gain EI while gaining years of experience.
Regarding variables such as gender, ref. [29] analyzes the capabilities of men and women in terms of working performance and supervision of a wide variety of jobs, and how these can influence clients’ perception of quality service. This work suggests that female hospitality workers could have more EI than male workers, thus having a bigger effect on QHS.
Other characteristics such as years of experience and the managerial category of hospitality jobs become explaining factors of EI in this sector [21]. However, other variables such as salary are still controversial, being direct [48] or inverse [49] to EI-building. This work did not reveal any existing correlation regarding EI, type of hospitality establishments, salary, age, education, or working hours.
On the other hand, regarding the tested hypothetical and conceptual model, the EI of hospitality employees was revealed as a significant factor on perceived QHS.
This is in line with other works on EI, job performance, and quality of service.
Ref. [31] focuses on an Israeli public hospital to test the relationship between EI and job outcomes. A direct effect was revealed between EI and the job outcomes of a total of 200 nurses in that hospital. Also, ref. [32], based on a total of 78 real estate agents in Kosovo, proves how EI affects job performance in a direct way.
Ref. [34] is based on a sample of 166 hospitality employees and found a positive and significant effect on job performance. However, it is also suggested that both EI and cultural intelligence in hospitality have a direct effect on service satisfaction [8]. It implies that individuals’ EI can enhance the better performance of an organization, but it becomes necessary to obtain an optimal team emotional intelligence. This is consistent with the findings of ref. [35].
According to ref. [50], the hospitality sector could be prevented from the so-called service sabotage if corresponding EI strategies are built for human capital. Furthermore, engagement also becomes one of the main challenges in the hospitality sector, given its low rate of talent retention [51].
Finally, despite EI and human capital being important in QHS, the hospitality sector is suffering from a talent shortage that could endanger QHS [17,18]. Specifically, in the case of Spain, there is a strong gap between demand and supply in the hospitality industry job market. The lack of qualifications and experience of hospitality staff implies that a significant number of jobs are filled with untrained people, thus making employment become more precarious in this sector [52].

5. Conclusions, Practical Implications, Limitations, and Future Directions

5.1. Conclusions and Practical Implications

The main goal of this research consisted of providing empirical evidence related to the effects of EI on QHS. To do so, a theoretical and conceptual model was tested including by Partial Least Square-Structural Equation Modeling (PLS-SEM) techniques based on a sample of 47 establishments, including 408 employees in the hospitality sector in the region of Murcia (Spain).
First, a positive correlation was found between the years of experience of hospitality workers and their EI capabilities, such as emotional attention, clarity of feelings, and emotional repair, suggesting that EI develops with professional growth in the hospitality sector. However, while professional category impacts EI, factors like establishment type, salary, and working hours showed no significant correlation, leading to partial support for H1.
The results of this work also reveal that the EI of hospitality employees has a significant direct power on job performance and, thus, on the perceived QHS of customers. Given the low rate of talent retention in this sector as well as the high number of jobs that are not filled in Spain, hospitality companies should work on dignifying these professions by building effective EI and engagement strategies, in line with H2.
First, according to the relation between high- and low-responsibility positions, a cooperative culture could be fostered to increase team collaboration, thus increasing team emotional intelligence and team job performance for a better QHS. As stated by ref. [53], the construction of a friendly work environment contributes to increased work engagement. To do so, team-building events should be organized additionally with several informal events.
Secondly, an individual’s perception of rewards and recognition should be developed based on a corporate system that improves performance outcomes at individual and organizational levels, as well as making staff members become an important part of the whole. Also, unfair situations should be corrected and solved in order to prevent employees from lack of satisfaction in their jobs. This is also consistent with the findings of ref. [39].
Thirdly, leadership programs should be developed so that optimal EI mechanisms can be made between upper- and lower-responsibility positions regarding job performance so that work barriers can be overcome together. A short-, medium-, and long-term goals system could be taken as a reference to do so.
Fourthly, working conditions such as salary and working hours should be standardized according to other sectors that become more competitive from an employee point of view. Additionally, career programs and other emotional salary items could be used, such as professional courses, holiday flexibility, corporate discounts and offers, or the enlargement of maternity and paternity allowance, among others. This could be the only way to overcome such a structural problem in hospitality like talent capture and retention.

5.2. Limitations and Future Studies

This study has some limitations that should be noted. First, the novelty of this work implies that the number of applied models about EI and QHS in hospitality is limited. This implies that the tested hypothetical and conceptual model is a pioneering one, yet can serve as an initial theoretical output that could be sharpened with other EI and QHS models already validated in the literature. Thus, the TMMS-24 and the SERVQUAL models are proxy variables to measure both EI and QHS that could be replaced with other empirically valid references.
Secondly, according to the two subsamples of this work and data collection, some data inconsistencies were found and treated. While reviewing the responses from employees and customers, questionnaires with incoherent answers where discarded. However, this fact suggests that other possible inconsistencies could exist under a false real answer that a customer or employee could give to align with universally accepted premises and which may not be the real perception.
Another limitation relates to the cross-sectional study, which could make predictive power diffuse and needs to be contrasted with other case studies to continue the testing of the model that was proved in this work.
In relation to future studies, the hypothetical and conceptual model tested should be contrasted with other proxy variables that measure EI and QHS, as well as other cross-sectional studies that contribute to give a holistic approach to the analyzed phenomenon.
Additionally, in the transition from traditional to smart hospitality management, the essential part that EI brings to both the supply and demand sides should be contrasted with the effects of artificial intelligence (AI) penetration [13].
Also, in a near future, emotionally intelligent workers could represent a new competitive advantage for hospitality businesses [54,55], according to the degree of adaption of customers to the digitalization of this sector. In that scenario, the coexistence between humans and humanoids in a hospitality organization could make the findings of this work differ, while QHS remains directly related to EI and human capital interaction. Thus, business strategies should focus on making a proper transition from a traditional to a smart hospitality business based on EI integration with artificial intelligence (AI) and with the new business models and processes.
Finally, it could also be an important factor to contrast the role of EI and human capital in QHS regarding the new digital era, and to know whether the perception of the customers in the hospitality sector is transformed at the same pace that the digitalization is reaching hospitality businesses.

Author Contributions

Conceptualization, A.S.-G., A.R.-M., A.L.-N. and M.P.-C.; methodology, A.S.-G., A.R.-M. and A.L.-N.; validation, A.R.-M., A.L.-N. and M.P.-C.; formal analysis, A.S.-G. and M.P.-C.; investigation, A.S.-G., A.R.-M., A.L.-N. and M.P.-C.; resources, A.S.-G.; data curation, A.S.-G.; writing—original draft preparation, A.S.-G., A.R.-M. and M.P.-C.; writing—review and editing, A.S.-G. and M.P.-C.; visualization, M.P.-C.; supervision, A.R.-M. and A.L.-N.; project administration, A.S.-G.; funding acquisition, M.P.-C. All authors have read and agreed to the published version of the manuscript.


This research was funded by National Funds provided by FCT—Foundation for Science and Technology through project UIDP/04020/2020 (DOI: 10.54499/UIDB/04020/2020) and European Research Executive Agency grant number 101071300 Sustainable Horizons (HORIZON).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Catholic University of Murcia (protocol code CE051503 that approved this research project on 29th May 2015).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.


  1. Salovey, P.; Mayer, J.D. Emotional intelligence. Imagin. Cogn. Personal. 1990, 9, 185–211. [Google Scholar] [CrossRef]
  2. Goleman, D. Emotional Intelligence: Why It Can Matter More than IQ; Bantam Books: New York, NY, USA, 1995. [Google Scholar]
  3. Koc, E.; Boz, H. Development of hospitality and tourism employees’ emotional intelligence through developing their emotion recognition abilities. J. Hosp. Mark. Manag. 2020, 29, 121–138. [Google Scholar] [CrossRef]
  4. Lam, R.; Cheung, C.; Lugosi, P. The impacts of cultural and emotional intelligence on hotel guest satisfaction: Asian and non-Asian perceptions of staff capabilities. J. China Tour. Res. 2021, 17, 455–477. [Google Scholar] [CrossRef]
  5. Gültekin, S.; Icigen, E. A research on professional tour guides emotional intelligence and problem-solving skills. J. Qual. Assur. Hosp. Tour. 2019, 20, 230–258. [Google Scholar] [CrossRef]
  6. Dimitrov, Y.; Vazova, T. Developing capabilities from the scope of emotional intelligence as part of the soft skills needed in the long-term care sector: Presentation of pilot study and training methodology. J. Prim. Care Community Health 2020, 11, 2150132720906275. [Google Scholar] [CrossRef] [PubMed]
  7. Kitsios, F.; Papageorgiou, E.; Kamariotou, M.; Perifanis, N.A.; Talias, M.A. Emotional intelligence with the gender perspective in health organizations managers. Heliyon 2022, 8, e11488. [Google Scholar] [CrossRef]
  8. Chen, J.; Guo, W. Emotional intelligence can make a difference: The impact of principals’ emotional intelligence on teaching strategy mediated by instructional leadership. Educ. Manag. Adm. Leadersh. 2020, 48, 82–105. [Google Scholar] [CrossRef]
  9. Zeithaml, V.A.; Parasuraman, A.; Berry, L.L. Delivering Quality Service: Balancing Customer Perceptions and Expectations; Simon and Schuster: New York, NY, USA, 1990. [Google Scholar]
  10. Baker, M. Service quality and emotional intelligence. In Emotional Intelligence in Tourism and Hospitality; Koc, E., Ed.; CABI: Wallingford, UK, 2019; pp. 108–112. [Google Scholar]
  11. Kim, H.; Qu, H. Employees’ burnout and emotional intelligence as mediator and moderator in the negative spiral of incivility. Int. J. Contemp. Hosp. Manag. 2019, 31, 1412–1431. [Google Scholar] [CrossRef]
  12. Stoyanova-Bozhkova, S.; Paskova, T.; Buhalis, D. Emotional intelligence: A competitive advantage for tourism and hospitality managers. Tour. Recreat. Res. 2022, 47, 359–371. [Google Scholar] [CrossRef]
  13. Prentice, C.; Dominique, L.; Wang, X. Emotional intelligence or artificial intelligence—An employee perspective. J. Hosp. Mark. Manag. 2020, 29, 377–403. [Google Scholar] [CrossRef]
  14. Wen, J.; Huang, S.S.; Hou, P. Emotional intelligence, emotional labor, perceived organizational support, and job satisfaction: A moderated mediation model. Int. J. Hosp. Manag. 2019, 81, 120–130. [Google Scholar] [CrossRef]
  15. Miao, C.; Humphrey, R.H.; Qian, S. Emotional intelligence and job performance in the hospitality industry: A meta-analytic review. Int. J. Contemp. Hosp. Manag. 2021, 33, 2632–2652. [Google Scholar] [CrossRef]
  16. Zhu, D.; Kanjanakan, P.; Kim, P.B. The impact of emotional intelligence on hospitality employees’ work outcomes: A systematic and meta-analytical review. J. Hosp. Mark. Manag. 2022, 31, 326–347. [Google Scholar] [CrossRef]
  17. Jooss, S.; Lenz, J.; Burbach, R. Beyond competing for talent: An integrative framework for coopetition in talent management in SMEs. Int. J. Contemp. Hosp. Manag. 2022, 35, 2691–2707. [Google Scholar] [CrossRef]
  18. Morosan, C.; Bowen, J. Labor shortage solution: Redefining hospitality through digitization. Int. J. Contemp. Hosp. Manag. 2022, 34, 4674–4685. [Google Scholar] [CrossRef]
  19. Tao, C.W.W.; Jang, J.; Kwon, J. Understanding the role of emotional intelligence and work status in service sabotage: Developing and testing a three-way interaction model. J. Hosp. Tour. Manag. 2019, 41, 51–59. [Google Scholar] [CrossRef]
  20. Alzoubi, H.M.; Aziz, R. Does Emotional Intelligence Contribute to Quality of Strategic Decisions? The Mediating Role of Open Innovation. J. Open Innov. Technol. Mark. Complex. 2021, 7, 130. [Google Scholar] [CrossRef]
  21. Kundi, Y.M.; Badar, K. Interpersonal Conflict and Counterproductive Work Behavior: The Moderating Roles of Emotional Intelligence and Gender. Int. J. Confl. Manag. 2021, 32, 514–534. [Google Scholar] [CrossRef]
  22. Scott-Halsell, S.A.; Blum, S.C.; Huffman, L. A Study of Emotional Intelligence Levels in Hospitality Industry Professionals. J. Hum. Resour. Hosp. Tour. 2008, 7, 135–152. [Google Scholar] [CrossRef]
  23. Khetjenkarn, S.; Agmapisarn, C. The Effects of Emotional Labour on the Outcomes of the Job and the Organization: Do the Differences in Age and the Manager’s Emotional Intelligence Have Any Impact in the Hotel Business? Eur. J. Tour. Res. 2020, 25, 2504. [Google Scholar] [CrossRef]
  24. Birditt, K.S.; Fingerman, K.L. Age and Gender Differences in Adults’ Descriptions of Emotional Reactions to Interpersonal Problems. J. Gerontol. Ser. B Psychol. Sci. Soc. Sci. 2003, 58, P237–P245. [Google Scholar] [CrossRef]
  25. Stutesman, M.G.; Frye, D.A. Affective Theory of Mind in Late Adulthood: The Role of Emotion Complexity and Social Relatedness. Exp. Aging Res. 2023, 49, 472–500. [Google Scholar] [CrossRef]
  26. Nguyen, Q.; Ladkin, A.; Osman, H. What Helps or Hinders the Emotional Intelligence Practice? A Study of Vietnamese Hotel Workers. Int. J. Contemp. Hosp. Manag. 2022, 34, 534–554. [Google Scholar] [CrossRef]
  27. Espinosa, A.; Rudenstine, S. The Contribution of Financial Well-Being, Social Support, and Trait Emotional Intelligence on Psychological Distress. Br. J. Clin. Psychol. 2020, 59, 224–240. [Google Scholar] [CrossRef]
  28. Van der Linden, D.; Pekaar, K.A.; Bakker, A.B.; Schermer, J.A.; Vernon, P.A.; Dunkel, C.S.; Petrides, K.V. Overlap between the General Factor of Personality and Emotional Intelligence: A Meta-Analysis. Psychol. Bull. 2017, 143, 36. [Google Scholar] [CrossRef]
  29. Koc, E. Do women make better in tourism and hospitality? A conceptual review from a customer satisfaction and service quality perspective. J. Qual. Assur. Hosp. Tour. 2020, 21, 402–429. [Google Scholar] [CrossRef]
  30. Miao, C.; Humphrey, R.H.; Qian, S. A Meta-Analysis of Emotional Intelligence Effects on Job Satisfaction Mediated by Job Resources, and a Test of Moderators. Personal. Individ. Differ. 2017, 116, 281–288. [Google Scholar] [CrossRef]
  31. Levitats, Z.; Vigoda-Gadot, E. Yours emotionally: How emotional intelligence infuses public service motivation and affects the job outcomes of public personnel. Public Adm. 2017, 95, 759–775. [Google Scholar] [CrossRef]
  32. Hoxha, V.; Zeqiraj, E. The role of emotional intelligence on the performance of real estate agents in Prishtina, Kosovo. Prop. Manag. 2022, 40, 739–757. [Google Scholar] [CrossRef]
  33. Supramaniam, S.; Singaravelloo, K. Impact of emotional intelligence on organisational performance: An analysis in the Malaysian Public Administration. Adm. Sci. 2021, 11, 76. [Google Scholar] [CrossRef]
  34. Koronios, K.; Kriemadis, A.; Dimitropoulos, P.; Douvis, I.; Papadopoulos, A.; Manousaridou, G. Emotional intelligence of employees in the hospitality sector: Exploring its effects on job satisfaction and performance. In Smart Tourism as a Driver for Culture and Sustainability: Fifth International Conference IACuDiT, Athens 2018; Springer International Publishing: Berlin, Germany, 2019; pp. 515–525. [Google Scholar]
  35. Jamshed, S.; Majeed, N. Relationship between team culture and team performance through lens of knowledge sharing and team emotional intelligence. J. Knowl. Manag. 2019, 23, 90–109. [Google Scholar] [CrossRef]
  36. Hair, J.F., Jr.; Matthews, L.M.; Matthews, R.L.; Sarstedt, M. PLS-SEM or CB-SEM: Updated guidelines on which method to use. Int. J. Multivar. Data Anal. 2017, 1, 107–123. [Google Scholar] [CrossRef]
  37. Carneiro, M.J.; Eusébio, C.; Caldeira, A. The influence of social contact in residents’ perceptions of the tourism impact on their quality of life: A structural equation model. J. Qual. Assur. Hosp. Tour. 2018, 19, 1–30. [Google Scholar] [CrossRef]
  38. Gómez-Núñez, M.I.; Torregrosa, M.S.; Inglés, C.J.; San Martín, N.G.L.; Sanmartín, R.; Vicent, M.; García-Fernández, J.M. Factor invariance of the Trait Meta-Mood Scale–24 in a sample of Chilean adolescents. J. Personal. Assess. 2018, 102, 231–237. [Google Scholar] [CrossRef] [PubMed]
  39. Tehseen, S.; Johara, F.; Halbusi, H.A.; Islam, M.A.; Fattah, F.A.M.A. Measuring dimensions of perceived business success among Malaysian and Bangladeshi SME owners. Rajagiri Manag. J. 2021, 17, 102–124. [Google Scholar] [CrossRef]
  40. Hulland, J. Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strateg. Manag. J. 1999, 20, 195–204. [Google Scholar] [CrossRef]
  41. Chin, W.W. How to write up and report PLS analyses. In Handbook of Partial Least Squares; Springer: Berlin/Heidelberg, Germany, 2010; pp. 655–690. [Google Scholar]
  42. Aibinu, A.A.; Al-Lawati, A.M. Using PLS-SEM technique to model construction organizations' willingness to participate in e-bidding. Autom. Constr. 2010, 19, 714–724. [Google Scholar] [CrossRef]
  43. Hayduk, L.A.; Littvay, L. Should researchers use single indicators, best indicators, or multiple indicators in structural equation models? BMC Med. Res. Methodol. 2012, 12, 159. [Google Scholar] [CrossRef]
  44. Benítez, J.; Henseler, J.; Castillo, A.; Schuberth, F. How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Inf. Manag. 2020, 57, 103168. [Google Scholar] [CrossRef]
  45. Geisser, S. The predictive sample reuse method with applications. J. Am. Stat. Assoc. 1975, 70, 320–328. [Google Scholar] [CrossRef]
  46. Stone, M. Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc. Ser. B (Methodol.) 1974, 36, 111–133. [Google Scholar] [CrossRef]
  47. Chin, W.W. The partial least squares approach for structural equation modeling. In Modern Methods for Business Research; Marcoulides, G., Ed.; Lawrence Erlbaum Associates: Mahwah, NJ, USA, 1998; pp. 295–336. [Google Scholar]
  48. Urquijo, I.; Extremera, N.; Azanza, G. The contribution of emotional intelligence to career success: Beyond personality traits. Int. J. Environ. Res. Public Health 2019, 16, 4809. [Google Scholar] [CrossRef]
  49. De Haro, J.M.; Castejón, J.L. Does trait emotional intelligence predict unique variance in early career success beyond IQ and personality? J. Career Assess. 2014, 22, 715–725. [Google Scholar]
  50. Choi, H.M.; Mohammad, A.A.; Kim, W.G. Understanding hotel frontline employees’ emotional intelligence, emotional labor, job stress, coping strategies and burnout. Int. J. Hosp. Manag. 2019, 82, 199–208. [Google Scholar] [CrossRef]
  51. Liu, X.; Yu, J.; Guo, Q.; Li, J. Employee engagement, its antecedents and effects on business performance in hospitality industry: A multilevel analysis. Int. J. Contemp. Hosp. Manag. 2022, 34, 4631–4652. [Google Scholar]
  52. Turijobs. 7 Reasons Why It Is so Hard to Find Talent in the Tourism Sector This Summer. 20 July 2021. Available online: (accessed on 1 February 2023).
  53. Yan, C.H.; Ni, J.J.; Chien, Y.Y.; Lo, C.F. Does workplace friendship promote or hinder hotel employees’ work engagement? The role of role ambiguity. J. Hosp. Tour. Manag. 2021, 46, 205–214. [Google Scholar] [CrossRef]
  54. Flores, E.; Xu, X.; Lu, Y. Human Capital 4.0: A workforce competence typology for Industry 4.0. J. Manuf. Technol. Manag. 2020, 31, 687–703. [Google Scholar] [CrossRef]
  55. Tajpour, M.; Moradi, F.; Jalali, S.E. Studying the influence of emotional intelligence on the organizational innovation. Int. J. Hum. Cap. Urban Manag. 2018, 3, 45–52. [Google Scholar]
Figure 1. Conceptual framework for emotional intelligence (EI) and hospitality service (QHS).
Figure 1. Conceptual framework for emotional intelligence (EI) and hospitality service (QHS).
Tourismhosp 05 00016 g001
Figure 2. Representation of the structural model.
Figure 2. Representation of the structural model.
Tourismhosp 05 00016 g002
Table 1. Hospitality employees.
Table 1. Hospitality employees.
Education level
No degree5413.3
Years of experience
Middle management8822.1
Establishment type
Bar/Coffee shop15539.2
Working hours
<10 h/week4511.1
10–20 h/week389.4
20–30 h/week4310.6
Full time27868.8
<EUR 80010726.6
EUR 800–110020651.1
EUR 1100–14007117.6
>EUR 1400194.7
Table 2. Correlations between EI and social and labor characteristics.
Table 2. Correlations between EI and social and labor characteristics.
Emotional AttentionClarity of FeelingsEmotional Repair
Years of experiencer = 0.085
(p= 0.009)
r = 0.126
(p= 0.002)
r = 0.132
(p= 0.009)
Categoryr = 0.185
(p= 0.440)
r = 0.145
(p= 0.026)
r = 0.115
(p= 0.028)
Middle management
Establishment typer = 0.185
(p= 0.692)
r = 0.244
(p = 0.676)
r = 0.197
(p = 0.268)
Bar/Coffee shop
Working hoursr = 0.185
(p= 0.692)
r = 0.244
(p = 0.676)
r = 0.197
(p = 0.268)
<10 h/week
10–20 h/week
20–30 h/week
Full time
Salaryr = 0.298
(p = 0.174)
r = 0.238
(p = 0.879)
r = 0.008
(p = 0.345)
<800 €
800–1.100 €
1.100–1.400 €
>1.400 €
Maler = 0.432r = 0.384r = 0.008
Female(p = 0.001)(p = 0.003)(p = 0.001)
Table 3. Summary of results for the Measurement Model.
Table 3. Summary of results for the Measurement Model.
Latent VariableItemsConvergent ValidityInternal Consistency ReliabilityDiscriminant Validity
Outer LoadingAVECRCronbach’s AlphaConfidence Intervals of HTMT Do Not Include Value 1
Emotional Intelligence (EI)EA1.0.4680.7430.7640.74Yes
CF8. 0.454
ER8. 0.761
Perceived QHSSR10.7190.8920.8520.804Yes
Table 4. Model path coefficients of SEM and hypotheses test results.
Table 4. Model path coefficients of SEM and hypotheses test results.
Hypotheses βInfluence PathPath Coefficientst ValueResults
H2EI → QHS0.465 ***9.845Supported
Note: *** p < 0.001.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Sanz-García, A.; Ros-Martínez, A.; López-Navas, A.; Puig-Cabrera, M. Psychometrics of Emotional Intelligence in Hospitality: A Cross-Sectional Study on Human Capital and Quality of Service. Tour. Hosp. 2024, 5, 237-250.

AMA Style

Sanz-García A, Ros-Martínez A, López-Navas A, Puig-Cabrera M. Psychometrics of Emotional Intelligence in Hospitality: A Cross-Sectional Study on Human Capital and Quality of Service. Tourism and Hospitality. 2024; 5(1):237-250.

Chicago/Turabian Style

Sanz-García, Alberto, Almudena Ros-Martínez, Ana López-Navas, and Miguel Puig-Cabrera. 2024. "Psychometrics of Emotional Intelligence in Hospitality: A Cross-Sectional Study on Human Capital and Quality of Service" Tourism and Hospitality 5, no. 1: 237-250.

Article Metrics

Back to TopTop