research article on employee satisfaction

The impact of healthy workplaces on employee satisfaction, productivity and costs

Journal of Corporate Real Estate

ISSN : 1463-001X

Article publication date: 25 November 2021

Issue publication date: 20 February 2023

This paper aims to explore the added value of healthy workplaces for employees and organizations, in particular regarding employee satisfaction, labour productivity and facility cost.

Design/methodology/approach

The paper is based on a narrative review of journal papers and other sources covering the fields of building research, corporate real estate management, facilities management, environmental psychology and ergonomics.

The review supports the assumption of positive impacts of appropriate building characteristics on health, satisfaction and productivity. Correlations between these impacts are still underexposed. Data on cost and economic benefits of healthy workplace characteristics is limited, and mainly regard reduced sickness absence. The discussed papers indicate that investing in healthy work environments is cost-effective.

Originality/value

The findings contribute to a better understanding of the complex relationships between physical characteristics of the environment and health, satisfaction, productivity and costs. These insights can be used to assess work environments on these topics, and to identify appropriate interventions in value-adding management of buildings and facilities.

Productivity
Satisfaction
Added value

Voordt, T.v.d. and Jensen, P.A. (2023), "The impact of healthy workplaces on employee satisfaction, productivity and costs", Journal of Corporate Real Estate , Vol. 25 No. 1, pp. 29-49. https://doi.org/10.1108/JCRE-03-2021-0012

Emerald Publishing Limited

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

The WHO defines health as “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity”. As such, a healthy workplace can be defined as a workplace that contributes to the physical, mental and social well-being of its users. Health is the result of a complex interaction between the physiological, psychological, personal and organizational resources available to individuals and the stress placed upon them by their physical and social environment at work and home ( Clements-Croome, 2018 ). Well-being reflects one’s feelings about oneself in relation to the world, personal feelings about motivation, competence, aspirations and degree of personal control.

1.1 Impact of the physical environment on health and well-being

The past decades show a growing awareness of the impact of the physical environment on peoples’ health and well-being, both in academic research and in professional publications. This may be because of the shift from a one-sided focus on cost reduction to a more holistic and integrated value-based approach and an optimal balance between costs and benefits of interventions in buildings, facilities and services ( Jensen and Van der Voordt, 2017 ). Besides, people have become more aware of the impact of health and well-being on our quality of life and the risk of health complaints, illness or – in worst cases – burnout ( Appel-Meulenbroek et al. , 2020 ). The relationship between physical workplace characteristics and health and well-being has been explored by a variety of studies, using reviews of the literature ( Forooraghi et al. , 2020 ; Van der Voordt, 2021 ), surveys ( Cordero et al. , 2020 ), case studies ( Bauer, 2020 ) and conducting short-term experiments using mobile devices ( Nelson and Holzer, 2017 ).

It appears that in particular a poor indoor climate, noise and distraction have a negative impact on employees’ health and well-being, whereas appropriate opportunities to communicate and to concentrate and contact with nature contribute to a healthy workplace. In a survey of 2,000 office workers, occupants reported preferences for lots of natural light, access to outdoor spaces, contemplation spaces, support from colleagues and private as well as collaborative spaces, whereas the main irritants were noise in open-plan areas, lack of natural light, lack of colour, lack of greenery, lack of artwork, lack of fresh air, no personal control of temperature, lack of privacy, clutter and inflexible space ( British Council for Offices, 2018 ).

Another frequently assessed factor is office type. A literature review by Colenberg et al. (2020) on the relationship between interior office space (layout, furniture, light, greenery, controls and noise) and employees’ physical, psychological and social well-being showed that open-plan offices, shared rooms and higher background noise are negatively related to health. Positive relationships were found between physical well-being and aspects that encourage physical activity; between physical/psychological well-being and (day)light, individual control and real/artificial greenery; and between social well-being and small shared rooms.

Other influencing factors on health and well-being are important as well, such as the context (cultural, social, economic, political), personal characteristics (age, gender, lifestyle), organizational issues (leadership, personal support) and job characteristics (work load, (mis)fit between demands and resources). The European Agency for Safety and Health at Work (2014) warns for a disbalance between high job demands and available job resources. Too little time, too much work and tight deadlines are the most widely recognized risk factors, resulting in sleep disturbance, changes in mood, fatigue, headaches and stomach irritability.

1.2 Relationship between healthy workplaces and other values

Healthy workplaces that support employees’ health and well-being can be a goal in itself, but may also have intended or unintended effects on other values, such as employee satisfaction, productivity, costs, corporate image and risk. Vice versa, values such as sustainability may contribute to health and well-being. For instance, green buildings are supposed to be healthier than non-green buildings, because of its focus on the triple P of people, planet and profit. Interrelationships between healthy workplaces and other values are much less studied. This paper aims to reduce this gap in our knowledge, and to answer two research questions: What is the relationship between healthy workplaces and employee satisfaction, productivity and costs? And which evidence is available for these relationships?

These three values turned out to be most frequently prioritized in interviews with corporate real estate and facility managers ( Van der Voordt and Jensen, 2014 ). It is hypothesized that health, satisfaction and productivity go hand in hand. Furthermore, because of the high staff costs compared to facility costs, it is hypothesized that health-supportive interventions are cost-effective. Figure 1 visualizes the key topics of this paper in blue.

Because of a limited number of available publications, it was decided to select a number of leading journals in the field and to conduct a narrative review ( Green et al. , 2006 ; Ferrari, 2015 ). In our earlier review of environmental impact factors on healthy workplaces ( Jensen and Van der Voordt, 2020 ), we checked four facilities management and corporate real estate management oriented journals in a 10-year period, covering 2008–2017: Journal of Corporate Real Estate , Corporate Real Estate Journal , Facilities and the Journal of Facilities Management . For the current paper, we extended our search to the period 2018–2021 and to other journals, based on paper citations and journal titles. We also screened the last six volumes of Applied Ergonomics , Building and Environment , Building Research and Information , Environment and Behavior , Ergonomics , Intelligent Buildings International and Journal of Environmental Psychology on the keywords workplace, health, well-being, satisfaction, productivity and cost.

All papers from the screened journals that discuss health in connection to workplace characteristics and satisfaction, productivity and/or cost were included in this review. This has resulted in a selection of 45 papers on health and satisfaction and/or productivity. Because very few scientific papers related to facility cost were found, we have included relevant industry reports and other publications. Papers that discuss the relationship between the physical environment and either health, satisfaction, productivity or cost, without discussing any interrelationships between these variables, have been excluded.

3. Findings on the added value of healthy workplaces

3.1 employee satisfaction.

Table 1 summarizes the research topics, methods and findings of eight papers that discuss relationships between physical characteristics of the built environment, health and satisfaction, ranked by year and per year in alphabetical order of the first author. Five out of eight studies investigate the impact of office type and workspaces. The other studies focus on environmental conditions, sense of coherence or green buildings. The findings show positive but also contradictory connections between office type; health and well-being; and employee satisfaction. Open-plan seems to have a negative impact, which can be partly compensated by improved environmental conditions. High density and poor acoustics affect health and satisfaction in a negative way. The green building study showed mixed results. Personal characteristics make a difference as well. Employees with high need for concentration report more distraction in all office types, except in cell, and more cognitive stress in all office types except cell and flex-offices. People suffering from claustrophobia perceive stronger effects.

3.2 Labour productivity

The findings on relationships between health and well-being and labour productivity are summarized in Table 2 . Four studies focus on office type and workplace concept (open-plan, work pattern–office type fit, high-performance hub, variety of workplaces). Five studies investigated the impact of indoor air quality (IAQ) and related issues such as thermal comfort and look-and-feel. Four studies focus on sit-stand/adjustable workstations. The other studies show a variety of research topics, i.e. the influence of a healing office design concept, wind-inducing motion of tall buildings, green buildings, workplace safety, biophilia, plants and time spent in the office. The findings show significant positive but also mixed impacts of IAQ, “green” buildings and sit–stand work on both health and productivity. Health and productivity are usually discussed separately; correlations between health and productivity were only explored in two studies. Interrelationships are affected by job demands and job stress

3.3 Satisfaction and productivity

Table 3 summarizes the findings from 17 studies on health and well-being and both satisfaction and productivity. Independent variables include office types, non-territorial workspaces, proximity, impact of break out areas, storage space, adopting the WELL criteria, indoor environmental quality (IEQ), shading conditions, sit–stand workstations and plants. Here, too, health, satisfaction and productivity are mainly discussed separately and less regarding possible correlations. In general, activity-based workplaces are perceived to have a positive impact on satisfaction, partly because of better technical qualities regarding IEQ. Searching for a workplace needs time and reduces productivity. Personal control, easiness of interaction and communication, availability of break out areas, windows, sit–stand workstations, comfort of furnishing, attractive IEQ, modern shading systems and applying to the WELL standard show to have a positive impact on both health and satisfaction, whereas distraction and lack of privacy are important predictors of productivity loss.

All presented studies on health in connection to satisfaction and/or productivity originate from Europe, USA, Australia and New Zealand.

3.4 Applied research methods to study health and satisfaction and/or productivity

The discussed papers on health and satisfaction and/or productivity show a variety of research designs and research methods ( Table 4 ). Ten studies conducted a before–after study; four studies used an experiment in a lab setting. About 80% of the presented studies used a questionnaire survey, some of them as part of a mixed-methods approach with interviews and observations, identifying healthy or unhealthy office design qualities, scores on the WELL standard and data about toxic substances in the air. Measuring physical conditions such as the heart rate or skin temperature is rather rare.

3.5 Financial costs and benefits

Clements-Croome (2018) mentions a return on investment of €5.7 for every euro invested in well-being. However, not much quantitative data was found about the financial impact of changing the spatial layout, supporting new ways of working, providing more contact with nature or the introduction of sit–stand desks. This may be because of the difficulties to quantify the results of healthy workplaces. Various papers discuss the monetary costs and benefits of health-promoting programs such as stop-smoking programs or providing sports facilities and healthier nutrition. However, these topics are not related to physical characteristics of workplaces and are beyond the scope of this paper. Table 5 summarizes the findings from 11 publications. Different research methods are used, such as literature reviews, surveys and analysis of sickness absence data (8 out of 11 studies) and costs. Some studies focus on the impact of stress, without clear links to physical characteristics. Not all project data on financial costs and benefits has been tested scientifically on reliability and validity.

4. Discussion and conclusions

The discussed studies show a huge variety in environmental characteristics that influence health and well-being, employee satisfaction and labour productivity, such as office type, proximity, density, IEQ of IAQ, furniture (ergonomics, sit–stand desks), plants and personal control. Some studies focus on specific building types such as certified green buildings, WELL-certified buildings and tall wind-excited building, specific building components such as shading systems or specific interior elements such as sit–stand desks and furniture comfort. Research methods range from questionnaire surveys to before–after studies and laboratory experiments. Measuring physical conditions such as heart rates and skin temperature is still underexposed. Remarkably, most discussed papers present findings on health and satisfaction and/or productivity without discussing correlations between health, satisfaction and productivity.

The reviewed studies indicate positive but also mixed and contradictory effects of healthy workplaces on satisfaction and productivity. Overall, a healthy IAQ, opportunities for communication, concentration and privacy, availability of break-out rooms, an attractive look-and-feel, ergonomic furniture, contact with nature and plants go hand-in-hand with higher employee satisfaction and perceived productivity. Large open-plan offices and centrally controlled air condition show a negative effect on health, satisfaction and productivity. There is some evidence that workplaces in green buildings are healthier than workplaces in conventional buildings. Adjustable workstations with sit–stand desks show to have beneficial effects for comfort and labour productivity. Practitioners should take these findings into account in their design and management activities.

What constitutes a healthy workplace is much dependent on the workstyles and the preferences of the users. The degree to which the workplace has impact on satisfaction is in particular dependent on user preferences in relation to privacy versus social contact. The impact on productivity is in particular dependent on the specific workstyle and how well the workplace supports the work activities. Involving the users in the planning process and change management during implementation is crucial.

Scientific research on monetary cost and benefits of healthy workplaces is limited. Overall, the data indicate a positive impact of healthy workplaces on the reduction of sickness absence.

Because of the impact of many interrelated variables, it is difficult to trace cause–effect relationships between characteristics of healthy work environments and support of other value dimensions. Usually, various interventions are conducted simultaneously. Furthermore, employees’ health not only depends on what the workplace does to employees, but also on what workers bring with them to the workplace.

The mixed findings make it hard to provide a sound business case for physical interventions to improve health and well-being. On the one hand, taking care of healthy work environments is a matter of moral responsibility and has in general a positive effect on employee satisfaction and labour productivity and on society as a whole. These advantages have to be balanced with the costs of interventions to provide more healthy environments. An obstacle for a more integrated, holistic business case may be that the cost of interventions and its resulting output and outcomes are not always easy to measure in a quantitative way. Another difficulty is that some outcomes might be experienced in the short term and perhaps only temporarily, while others might be sustained, reduced or only experienced in the long term. One solution is to base business cases not only on quantitative data but to take into account well-argued qualitative considerations as well. As such, we plea for a so-called value based business case or “value case”.

4.1 Suggestions for further research

Additional research is needed to get a deeper, holistic and evidence-based knowledge of the added value of healthy workplaces and interrelationships between health, satisfaction and productivity and financial impacts that integrate different research topics and research methods. A next step can be to use the research findings as input to follow-up transdisciplinary research by academics from different fields, including corporate real estate management, facilities management, human resource management, environmental psychology and work and organizational psychology. Reflections on data by an interdisciplinary team and experimenting with particular interventions may be helpful as well.

Other topics for future research are extension of this literature review with papers from other journals and databases such as Scopus and PubMed, and to conduct additional empirical research with before–after studies of particular interventions and data-collecting techniques such as workshops, group interviews, pilot projects and self-measurement of health and health-supportive behaviour, e.g. by using wearables and apps. Cost studies should not only focus on data analysis of sickness absence, but extend their scope to self-reported health risks and health conditions, to get a better understanding of what drives health costs and lost productivity ( Jinnett et al. , 2017 ). Besides, more studies are needed into the costs of particular interventions and return on investment.

A particular topic for further research is the use and experience of offices in the post Covid-19 period. Increased “infection risk mitigation” will affect the presence in the office, number of people per m 2 , need for fresh air access, etc. The Covid-19 crisis has resulted in a drastic increase in home working and this experience is likely to have profound implications for office work in the future.

research article on employee satisfaction

Key topics of this paper

Health and well-being and satisfaction (eight studies)

Health and well-being and labour productivity (20 studies)

Health and well-being, satisfaction and labour productivity (17 studies)

Applied research methods in the presented studies

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Job Satisfaction and the ‘Great Resignation’: An Exploratory Machine Learning Analysis

Original Research
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Published: 30 October 2023
Volume 170 , pages 1097–1118, ( 2023 )

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Mehmet Güney Celbiş ORCID: orcid.org/0000-0002-2790-6035 1 , 2 ,
Pui-Hang Wong ORCID: orcid.org/0000-0001-6818-983X 2 , 3 ,
Karima Kourtit ORCID: orcid.org/0000-0002-7171-994X 4 , 5 &
Peter Nijkamp ORCID: orcid.org/0000-0002-4068-8132 4 , 5

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Labor market dynamics is shaped by various social, psychological and economic drivers. Studies have suggested that job quit and labor market turnover are associated with job satisfaction. This study examines the determinants of job satisfaction using a large survey dataset, namely the LISS Work and Schooling module on an extensive sample of persons from the Netherlands. To handle these big data, machine learning models based on binary recursive partitioning algorithms are employed. Particularly, sequential and randomized tree-based techniques are used for prediction and clustering purposes. In order to interpret the results, the study calculates the sizes and directions of the effects of model features using computations based on the concept of Shapley value in cooperative game theory. The findings suggest that satisfaction with the social atmosphere among colleagues, wage satisfaction, and feeling of being appreciated are major determinants of job satisfaction.

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Avoid common mistakes on your manuscript.

1 Introduction

Though the global health crisis has ended, its economic impacts have only started to ripple over the global labor market. In the United States, about four million workers voluntarily quit their jobs in April 2021 (Reuters, 2019 ). This so-called Great Resignation is observed in other advanced economies as well. In the Netherlands, it is reported that nearly one out of five people have switched their jobs in 2022 (Algemeen Dagblad, 2023 ). A macroeconomic explanation maintains that labor shortages in some sectors lead workers to believe that they may find a better offer to compensate the drops in real wage under a tight labor market, which may explain the high turnovers (Duval et al., 2022 ). While this explanation has some truth in it, research on organizational behaviors may argue that the phenomenon can be driven by low job satisfaction. Unhappy workers are more likely to quit their jobs (Green, 2010 ), especially during and after the pandemic period. For instance, Martin et al. ( 2022 ) found that remote working has increased job stress and reduced job satisfaction in Luxembourg. Demirkaya et al. ( 2022 ) reported that the feeling of entrapment is significantly correlated with job quits in Turkey.

Departing from the job satisfaction perspective, this article evaluates an array of individual-level factors that influence job satisfaction of Dutch workers using newly available Dutch household survey data. This study does not seek to explain the occurrence of Great Resignation. It highlights that job satisfaction has escaped from the discussion. Based on the multifaceted approach of job attitudes (Judge & Kammeyer-Mueller, 2012 ), an economic explanation would suggest that satisfaction of pay and of work conditions are major drivers of the behaviors of workers. In this study we examine to what extent economic concerns predict overall job satisfaction during 2022 using a novel machine-learning approach. Findings from this study should help us to assess which facets of job satisfaction are associated with the ‘Great Resignation’ in the Dutch labor market.

2 Job Satisfaction and the Utility Approach

Job satisfaction is commonly referred to as employees' affect and attitude toward their job. Emphasized on the affective dimension, Locke ( 1976 ) defined the concept as “a pleasurable or positive emotional state resulting from the appraisal of one's job or job experiences” (p. 1300). Focusing on the cognitive aspect of job satisfaction, Weiss ( 2002 ) defined the concept as “a positive (or negative) evaluative judgment one makes about one's job or job situation” (p. 6). The affective view sees affect at work as an indicator of job satisfaction, while the cognitive, evaluative approach considers affective experiences on the job as a source of job satisfaction.

In economics, job satisfaction is frequently treated as a unidimensional variable and a function of wage (Borjas, 1979 ; Hamermesh, 1977 ). Footnote 1 An implication of this formulation is that, when members of certain social groups (e.g., women) are discriminated in the labor markets, they would experience a lower level of job satisfaction because of a lower wage or other non-wage benefits (Bartel, 1981 ). Interestingly, while a gender wage gap is found to exist (Blau and Kahn, 2017 ), studies have shown that women usually report a higher level of job satisfaction than men, a stylized fact that contradicts what the theory predicts (Blanchflower & Oswald, 1999 ; Clark, 1997 ; Sloane & Ward, 2001 ). However, when factors such as age and job expectations are controlled for, the gap in job satisfaction between the two genders diminishes, suggesting that job satisfaction can depend on some often-overlooked demographic factors.

The utility function approach also implies that satisfaction is, at least partly, driven by extrinsic incentives. This early conceptualization is based on the utility theory that labor supply is a rational choice after a careful deliberation to the trade-off between leisure and consumption backed by the paycheck. Although studies have found that salary is an important determinant of job satisfaction, many studies have documented that workers do value a wide range of nonpecuniary characteristics of a job, which include job security, autonomy, shorter and more flexible work hour (Berger et al., 2019 ; Clark, 2001 ; Lepinteur, 2019 ; Origo & Pagani, 2009 ). A study by Lange ( 2012 ) further showed that these job characteristics could be even more influential than individual-specific factors like personality traits and values.

If people are happy with their jobs, they should be less likely to switch jobs. Some studies use quit data to analyze impacts of pecuniary and non-pecuniary factors on job satisfaction. Clark ( 2001 ) not only showed that job satisfaction is a powerful predictor of job change, it also found that job security and pay are the most important determinants of quit. Although not been emphasized in the literature, analyses of quit data may suggest that factors contributing to satisfaction and dissatisfaction may not be identical and their impacts could also be asymmetric. These asymmetric effects, however, have not been highlighted and emphasized in the existing literature.

One major conclusion from the job satisfaction literature is that happy workers are more productive (Oswald et al., 2015 ). But what beneath is the belief that a well-designed reward system should improve workers productivity. The starting point of personnel economics is the principal-agent conceptualization (Laffont & Martimort, 2002 ) that workers may shirk (i.e., moral hazard) and productive workers are costly to recruit (i.e., adverse selection). Clever economic mechanisms are required to identify these workers ex ante and to induce their efforts ex post. From this perspective, many of the human resource management practices such as performance pay, promotion, and job autonomy can be seen as performance optimizing mechanisms. Although financial and non-pecuniary rewards are parts of the job satisfaction equation (Cassar & Meier, 2018 ; Cornelissen et al., 2011 ; Ellingsen & Johannesson, 2007 ; Gosnell et al., 2020 ; Jones et al., 2009 ), from this cynical view, job satisfaction is nothing more than a happy by-product of human resources policies or a means to productivity and performance.

3 Individual and Social Dimensions of Job Satisfaction

An underlying assumption behind the utility approach is that income induces satisfaction and/or happiness. Although studies have found a significant but weak relationship between the two variables, behavioral studies have challenged this fundamental assumption (Clark et al., 2005 ; Easterlin, 1995 ). While some studies suggest that the relationship may be causal (Powdthavee, 2010 ), three decades of economics of happiness research have contested this finding (Clark et al., 2008 ). One major conclusion from the literature is that subjective well-being does not always increase with income. When examined the dynamic relationship between income and subjective well-being, Easterlin ( 2001 ) argued that income increases happiness initially. But aspirations grow as one climbs the income ladder. Over a life cycle, people's level of happiness remains stable and does not increase along with salary. Increase in income has only a short-term effect on happiness. The same mechanism may explain why job satisfaction may not catch up with income.

Another explanation to the weak statistical correlation between money and satisfaction over time is related to social comparisons. Taking a geographical approach, Luttmer ( 2005 ) found that people who earn an income lower than the local average feel worse off. A large amount of economics research in job satisfaction has been testing this hypothesis. Using data on British workers, Clark and Oswald ( 1996 ) found a similar relationship between job satisfaction and expected income. In an experimental study, Card et al. ( 2012 ) showed that the knowledge about their earnings below the median income of their peers significantly reduces job satisfaction and increases job search intentions. People form expectations as well as inspirations based on their peers, work conditions, and wage history (Diriwaechter & Shvartsman, 2018 ; McBride, 2001 ; Poggi, 2010 ). When the expectation-aspiration spiral is kick-started, an increase in aspirations can negatively affect people's satisfaction levels (Mcbride, 2010 ). What remains unclear is whether job dissatisfaction is due to pure social comparisons or fairness concerns (Card et al., 2012 ; d’Ambrosio et al., 2018 ; Smith, 2015 ). Interestingly, in a study using matched employer–employee panel data in Denmark, Clark et al. ( 2009 ) found that job satisfaction is positively correlated with their co-workers', a finding that counters many of the existing research in this area. They interpret that this relationship can be related to people's expectations about their future earnings. A higher average salary level leads the thinking that their potential wage may increase soon. All in all, while studies have shown that expectations and aspiration matter, it remains unclear how they are formed based on social comparisons and wage profiles.

One major assumption behind the neoclassical formulation of the utility function is that people gain utility mainly from consumption but not from the job per se. While the personnel economics literature covers aspects such as reward systems from a well-grounded, humanized (i.e., incentive-based) perspective, it is not unrealistic to think that under certain conditions, work can lead to a sense of achievement, which in turns shapes aspirations and hence job satisfaction (Genicot & Ray, 2020 ). Another stylized fact in the literature is that entrepreneurs and the self-employed are found to have a higher level of job satisfaction (Lange, 2012 ; Millán et al., 2013 ). The risk-adjusted returns and job security of entrepreneurship is known to be low. The existence of intrinsic motivations, however, offers a plausible explanation to the surprising, stylized fact (Carree & Verheul, 2012 ). In a laboratory setting, Ariely et al. ( 2008 ) and Chandler and Kapelner ( 2013 ) manipulated the perceived meaningfulness of a task and found that meaningfulness influences effort and labor supply behaviors. This lends support to the idea that the meaning of work could be part of the utility function in its own right (Cassar & Meier, 2018 ). Drakopoulos and Theodossiou ( 1997 ) considered a hierarchical utility function, in which increase in earnings, up to a certain point, ceases to induce utility, and the marginal utility of other work-related variables becomes much higher thereafter. Although the proposed modification does not directly speak to the fulfillment mechanism, the modification formally speaks to human's intrinsic motivations and the feeling of satisfaction.

If intrinsic motivations involve meaning making, a job that is connected to a person's educational background, skill sets, and competency should be more fulfilling. In fact, Nikolova and Cnossen ( 2020 ) showed that intrinsic motivations, measured by perceived job autonomy and competence, matter even more than extrinsic rewards. Feeling competent is pleasant and induces satisfaction (Loewenstein, 1999 ). Nevertheless, some studies have shown that the competence concern may backfire. García-Mainar and Montuenga-Gómez ( 2020 ) found that, in terms of education, overqualified workers tend to dissatisfy with their jobs. The same applies to horizontal educational mismatch—when graduates are employed in an occupation unrelated to their fields of study (Levels et al., 2014 )—and skill mismatches (Vieira, 2005 ). However, there is only little evidence on the effects of skill obsolescence and gaps on job satisfaction (McGuinness et al., 2018 ).

One fundamental criticism on the study of job satisfaction is that many findings from these empirical studies may not be easily fed into neoclassical economic theories. To some economists, if job satisfaction is nothing more than yet another term in a utility function, it can only be assumed but not explained. Accordingly, this leads to a proposal that, because job satisfaction is unobserved and is related to some volatile, external factors like economic fluctuations and labor market policies (Pilipiec et al., 2020 , 2021 ; Ravid et al., 2017 ), and is unstable even within-individual (Bryson & MacKerron, 2017 ), instead of fixating on job satisfaction and treating it as a dependent variable, a more fruitful approach is to treat it as an explanatory variable to study worker behaviors such as quits and labor market functioning like employee turnover (Hamermesh, 2004 ).

Although the general working environment and coworker relationship has been an important dimension of job satisfaction in organizational psychology (Jolly et al., 2021 ; Kinicki et al., 2002 ; Smith et al., 1969 ), the variable has received relatively little attention in economics literature, probably due to its difficultly in incorporating into the existing theoretical framework. The same also seems to hold in organizational studies (Judge & Kammeyer-Mueller, 2012 ). Cassar and Meier ( 2018 ) discussed feelings of relatedness in the context of meaning of work and productivity, but they did not articulate how the concept can be related to job satisfaction. They also mentioned social comparison and fairness, which was discussed above, but they clearly did not relate it to coworker relationship. Intuitively it is easy to understand why collegial relationship may influence job satisfaction. Karlsson et al. ( 2004 ) explained that social extensions such as a family and work ameliorate feelings of inconsequentiality, and people could find meaning in one's life. But it is not obvious how it can be related to the meaning of a job (Nikolova & Cnossen, 2020 ). One possibility is about the pursuit of common (organizational) goals in a team setting. On a material level, cooperation makes success more likely and helps to achieve higher output or goals which cannot be completed alone. On the social level, contributing to a common goal can create a warm-glow effect when individuals consider working with or “helping” their colleagues as altruistic acts and gain utility from that (Andreoni, 1990 ). From this perspective, a friendly work environment can be considered as a public good. Even for pure egoists, they can gain utility from contributing to the building of a constructive work environment and creating positive spillovers simultaneously. In this regard, an affable work environment can be considered as a by-product rather than a source of job satisfaction.

In our study, the relative importance of different facets of job satisfaction will be tested using a predictive, machine learning approach. Many psychometric tests have been developed to assist in clinical diagnoses. Thus, a predictive approach is well-established in psychology. In fact, predictive validity is a core property of psychometric measures (Mulder et al., 2014 ). Linearity is commonly assumed in traditional statistical measures and tools such as correlation coefficients and structural equation modeling when linear algebra is used. However, higher dimension interactions and linearity are plagued in many relations. The machine learning approach employed in this study is able to capture nonlinearity which is not easily modeled using traditional regression methods (James et al., 2013 ). Additionally, as argued above, there are two major limitations in traditional studies using regression analysis: (1) a variable that influences job satisfaction necessarily affects dissatisfaction, and (2) the effects of a variable on job satisfaction are symmetric. An analytical advantage of the machine learning approach is its ability to reveal potential asymmetry between variables in a relationship. The notion of nonlinearity would become clearer when it is discussed in the result section.

Data in our analysis were drawn from the Work and Schooling module of the Dutch Longitudinal Internet studies for the Social Sciences (LISS; Streefkerk & Centerdata, 2022 ). The LISS panel is administered by Centerdata research institute based in Tilburg University. Based on the population register of the national statistical office of the Netherlands, a random, nationally representative sample was drawn. In this study the fifteenth wave of the LISS survey was used, which is the most recent one after the pandemic. The online survey was conducted between the 4th of April 2022 and the 31st of May 2022. The cross-sectional dataset that was used consists of 420 variables with 5775 responses.

One advantage of the machine learning approach is its ability to include in an analysis a large number of variables, an approach which is seldom adopted in a typical regression analysis due to the concern of multicollinearity. Instead of pre-selecting variables, which can involve personal bias, the machine learning method takes a data-driven approach and includes as many of context relevant variables as possible. The method, however, involves a trade-off: the inclusion of additional variables usually reduces the sample size because of the missing value problem. Therefore, following Celbiş et al. ( 2023 ), an algorithmic process is implemented in order to optimize the number of observations. In each iteration, a simple regression tree analysis is conducted, and the root mean squared error (RMSE) is recorded. In the next step, the algorithm searches for the variable that, if excluded, would cause the greatest reduction in the number of observations. Then the variable is dropped from the dataset and the process is repeated. The data matrix that keeps most of the observations and has smaller impact on accuracy is used for the final analysis. Figure 6 (in the Appendix ), visualizes the observation-feature trade-off of the procedure. In each iteration, represented by the x-axis, either one or more variables are dropped. Identifying the variable(s) to be dropped in each iteration is done through generating an UpSet plot developed by Lex et al. ( 2014 ). Footnote 2 In Fig. 7 (in the Appendix ) we present a sample UpSet plot built in the 4th iteration as an example. According to this UpSet plot, the intersection of the features cw22o582, cw22o583, cw22o584, cw22o585, and cw22o586 account for the largest loss in observation as shown by the first vertical bar, suggesting that these features may represent connected or follow-up questions in the survey which are usually entered as missing in conjunction. Within the iteration, a regression tree is fitted into the subset of the training data which omits the above specified variables and its RMSE is noted. In the next iteration, the highest horizontal bar will correspond to the variable cw22o510, as the five features with higher bars below it will have already been dropped. In this new iteration, cw22o510 would account for the largest decrease in observations by itself unlike the earlier dropped group of variables. Therefore, the iteration will only drop cw22o510. Footnote 3 A new regression tree then is fit into this new subset of the training data and a new RMSE value will be computed. The recursive steps continue until the features of the dataset are exhausted or until the dataset has no observations left with missing values. In our case, this corresponds to iteration 54 (as shown in Fig. 6 ) where the y-axis represents the percentage of observations (persons) or variables that is left in this iteration. As variables responsible for high missing values are dropped, the percentage of observations retained increases. The purpose of this procedure is to identify the optimum combination of the number of observations and variables that is likely to yield the highest prediction performance in our subsequent main empirical implementations of machine learning models. However, the case at hand suggests that RMSE (not represented by any axis) is not sensitive to this trade-off as suggested by the nearly flat RMSE curve in Fig. 6 . As a result, the choice of the desired observation-variable balance becomes somewhat subjective. We selected the combination which retains a balance in this trade-off such that the difference between the percentage of persons retained and the percentage of variables retained is at a minimum. This corresponds to iteration 30. Prior to implementing the above outlined steps, variables that are completely consisted of missing information, variables with no variation (i.e., same value reported for all persons), administrative variables coded into the questionnaire (e.g., start date of the interview, duration of the interview) were dropped. After extensive data cleaning and validation procedure, which involved the above algorithmic trimming of the dataset, the final dataset consists of 1878 individuals and 89 variables. 30% of this data is randomly selected and set aside as the test dataset. All models are applied on the remaining training data. The results are assessed by evaluating the root mean squared error of the models using the test data. The definitions of the top ten features selected by the model in addition to the dependent variable (job satisfaction) are presented in Table 1 .

5 Empirical Models

The empirical analysis takes on two steps: prediction and interpretation. The prediction step is based on a variation of the Gradient Boosting Machine (GBM) technique by Friedman ( 2001 , 2002 ). GBM is applied using the Extreme Gradient Boosting (XGBoost) algorithm by Chen and Guestrin ( 2016 ). XGBoost extends the usability of GBM by allowing regularization and adding further randomization options. The prediction also relies, to a lesser extent, on the Random Forest (Breiman, 2001 ) technique for clustering. Both XGBoost and Random Forest are collections of weak learners based on the binary recursive partitioning algorithm by Breiman et al. ( 1984 ). Hence, randomized (Random Forest) and sequential and randomized (XGBoost) tree-based ensemble machine learning models are used in this study. The XGBoost algorithm allows for cross validation for regularization and determining the optimum model parameters including the learning rate. We partitioned the training sample into 10 subsamples (i.e., internal validation sets) to decide parameters pertaining to tree complexity (i.e., the maximum tree depth and minimum number of observations in terminal nodes) and the learning rate through cross validation. While regression trees are normally pruned through n-fold cross validation, the random forest model produces unpruned trees. However, while cross validation is absent from the random forest proximity clustering, unbiasedness is achieved through the use of the out-of-bag (OOB) observations. The resulting Shapley Additive Explanations (SHAP) values are derived from the above mentioned cross validated gradient boosting model.

XGBoost and Random Forest present several advantages thanks to their ability to consider all possible interactions and nonlinearities as the algorithms are based on binary recursive partitioning (James et al., 2013 ; Varian, 2014 ). The aggregation of many classification trees with high variance but low bias (due to their unpruned structures), built by taking repeated samples from the training data, can significantly improve prediction accuracy while reducing the variance on the prediction function (Breiman, 1996 , 2001 ; Friedman, 2001 ; Friedman et al., 2001 ; James et al., 2013 ). However, as trees built using the same training dataset are expected to be highly correlated, the benefits of using an ensemble would be limited (Aldrich & Auret, 2013 ; Breiman, 2001 ; Friedman et al., 2001 ). The random forest algorithm aims to cope with this correlation by introducing randomized restrictions on the feature space at each iteration (i.e., it randomly selects input features in each tree) (Breiman, 2001 ; Breiman & Cutler, 2020 ; Friedman et al., 2001 ; James et al., 2013 ). Therefore, in addition to the reduction in variance through aggregation, further reduction is made possible compared to bootstrap aggregation which is an ensemble model with correlated trees (James et al., 2013 ). However, gradient boosting does not decorrelate trees. Instead, each tree is a modified version of the previous one (Friedman, 2001 , 2002 ). Nevertheless, GBM embodies randomization like Random Forest but also introduces regularization which is not present in Random Forest. As a result, a group of weak learners with low variance are chained sequentially and modified with learning steps in between leading to the bias in prediction being lowered gradually in each iteration (Friedman, 2001 , 2002 ; Friedman et al., 2001 ).

A random forest with 500 unregularized regression trees is generated for predicting the individual job satisfaction level for the \(N\) persons in the training data. At each iteration, some individuals are left out of the computation, because the bootstrap aggregation algorithm on which a random forest is based draws random subsamples of 2/3 of the size of the training dataset (Breiman, 2001 ; James et al., 2013 ). Further randomization is applied by selecting a split feature from a random subset of size 1/3 of the feature set at each split (Breiman, 2001 ). A random forest proximity matrix ( \(N\times N\) ) is produced where the proximity score of two persons is increased by 1 each time when they are predicted to fall into the same terminal unpruned regression tree node in a random forest iteration in which they were out-of-bag (i.e., randomly left out). The matrix is divided by 500 (the number of trees) and the additive inverse is computed (Aldrich & Auret, 2013 ; Breiman & Cutler, 2020 ; Friedman, 2001 ).

The exploration of clusters is performed based on the random forest results. Random Forest uses the proximity scores among the observations in the training dataset in order to detect cluster structures (Aldrich & Auret, 2013 ; Cutler et al., 2009 ; Friedman, 2001 ). The distance measures used in conventional clustering techniques such as hierarchical and k -means clustering are prone to be dominated by uninformative features that may cloud the effects of the important model features (Cutler et al., 2012 ). In this regard, the main advantage of the random forest proximity matrix is due to its randomization procedure which aims the aforementioned decorrelation process. In addition, unlike classic clustering approaches, feature selection in random forest proximity plots is based on the underlying model which employs algorithmic selection (Xu & Tian, 2015 ). Furthermore, the random forest proximity plot used for clustering in the present study is generated using OOB observations pair-wise frequencies of sharing a terminal node, which is an internal validation procedure, leading to improvements in out-of-sample performance (Breiman & Cutler, 2020 ; Friedman, 2001 ).

The random forest proximity plots tend to detect and represent one class in one arm of a star shaped visual where pure class regions of out-of-bag observations in the training data are grouped towards the extremities of an arm due to the tree-based structure of the underlying algorithm (Hastie et al., 2009 ; Aldrich & Auret, 2013 ; Cutler et al., 2009 ). In this regard Friedman et al., ( 2001 , p. 595) state that “The idea is that even though the data may be high-dimensional, involving mixed variables, etc., the proximity plot gives an indication of which observations are effectively close together in the eyes of the random forest classifier”.

The interpretation of the machine learning findings is an essential part of any study in social sciences, as predictions alone—regardless of their success—cannot provide clear information and policy implications. The main interpretable machine learning tool employed in this paper is the computation and assessment of Shapley Additive Explanations (SHAP) values (Lundberg & Lee, 2017 ) based on the cooperative game theoretical approach by Shapley ( 1953 ). SHAP values have been introduced recently to the machine learning fields. As opposed to older approaches such as calculating variable importance scores (Lundberg & Lee, 2017 ; Molnar, 2019 ), the SHAP values approach can measure both the sizes and the directions of the relationships. The computation of SHAP values is preformed using the “SHAPforxgboost” module by Liu and Just ( 2020 ). A remarkable advantage of the SHAP value approach is that the calculations of the effect sizes are done by considering many different values and (theoretically all) combinations of model features (Celbiş, 2022 ; Lundberg & Lee, 2017 ; Molnar, 2019 ). Consequently, when computing the effect of a given feature for a given data instance (i.e., individual) all other variables are not held constant as is usually done in traditional econometric approaches. The departure from the ceteris paribus restriction leads to more realistic assessments of effect sizes, as in the real-world other factors can never be held constant in the context of a social science research. Finally, because considering all possible feature combinations and values is computationally not feasible, an approximation formulated by Štrumbelj and Kononenko ( 2013 ) is used in this study.

Among the included variables, both satisfaction with coworker relations and the pay are important features in predicting job satisfaction. The random forest proximity plot shown in Fig. 1 visualizes the clusters based on proximities in prediction between individuals. Footnote 4 The plot suggests the existence of about three clusters based on the roles of model features in explaining job satisfaction.

highly satisfied by colleagues atmosphere (yellow).

moderately satisfied by colleagues atmosphere (green).

poorly satisfied by colleagues atmosphere (dark green).

Clusters detected by random forest predictions

In the plot, larger-sized circles indicate individuals with lower wage satisfaction. These individuals are grouped towards the intersection of the “arms”, suggesting that the model has a harder time in distinguishing them (i.e., in iterations in which they were out of sample, people with low wage satisfaction often fell into same terminal nodes when run down the tree). The clear formation of the arms as separate clusters suggests a successful differentiation of the individuals through their inherent similarities. We also observe that people who are poorly satisfied with their jobs are not all part of the same cluster; suggesting that job dissatisfaction may arise from a diverse set of factors.

Regarding the SHAP analysis, a grid search procedure yielded the following optimal parameters for the XGBoost model except for subsample and number of trees, where the former is decided based on the finding by Friedman ( 2002 ) and the latter on computational restrictions:

Learning rate: 0.01

Maximum tree depth: 10

Minimum number of observations in terminal nodes: 1

Subsample ratio in each iteration: 0.5

Feature subsample ratio in each iteration: 0.5

Number of Trees: 10,000

The model is run on the training data, which consists of 70% of the observations randomly sampled from the full dataset. The job satisfaction levels of the individuals in the remaining test data are predicted with a RMSE of 1.14. The SHAP values that are computed based on the XGBoost predictions are visualized in Fig. 2 , where each dot represents an individual. The values show the contribution of each feature value—where features are on the y-axis and higher values are represented with darker colors—on the deviation of a specific individual's predicted value from the mean prediction (the point 0 on the x-axis). The top ten features with the highest SHAP importance values, listed next to the variable name, are presented in the figure. The features that affect job satisfaction the strongest are the first three variables, as the SHAP importance values of the remaining features are all less than 0.1.

SHAP values

The SHAP analysis summarized in Fig. 2 suggests that a high satisfaction of the atmosphere among colleagues has a positive effect on job satisfaction. Furthermore, this variable has the highest importance in the prediction of job satisfaction. The relationship is shown in more detail in the SHAP dependence plot in Fig. 3 (a slight amount of jitter is used for better representation). A value of 10 for this variable alone can account to up to more than 1 point (in Likert scale) positive deviation from the mean job satisfaction value in the training data.

SHAP feature dependence plots

Focusing on wage satisfaction, we observe that high wage satisfaction has a positive effect on job satisfaction. While this is not surprising, it should be highlighted that the importance of wage satisfaction is less than half of that of satisfaction in colleagues. The interaction and dependence between these two top variables are further visualized in the form of a two-way partial dependence plot (PDP) in panel A of Fig. 4 . Unlike the SHAP dependence plots, the PDPs represent joint predictions by holding constant all other features, except one or two features of interest (Friedman, 2001 ). The predictions are recalculated for each value of the features(s) of interest and averaged over the individuals in the training data (Friedman, 2001 ). However, some predictions suggested by the PDPs may be implausible for features that have high correlation with variables that are held constant (Friedman, 2001 ; Molnar, 2019 ). The PDP for Colleagues and Wage Satisfaction suggests that high wage satisfaction, while making a difference, does not predict truly high job satisfaction levels in the absence of high satisfaction in the atmosphere with colleagues. A similar outcome can be deducted from panel B of Fig. 4 which shows that high wage satisfaction without feeling of appreciation does not predict high job satisfaction. We also observe in panel C of Fig. 4 that higher wage satisfaction is associated with high job satisfaction, but the effect is stronger for older individuals. Finally, panel D suggests that high wage satisfaction without work freedom does not predict high job satisfaction.

Two-way patrial dependence plots

The feature Appreciate is ranked third in Fig. 2 . This result suggests that the perception that people get the appreciation they deserve for their work has a positive effect on job satisfaction. Feeling unappreciated has a slightly stronger negative effect than the positive effect of feeling appreciated.

The SHAP values of the variable Birth Year are quite symmetrically spread about zero and indicate that younger individuals in the dataset tend to be less satisfied with their jobs. The effect of this variable and the remaining ones are relatively small compared to those of Colleagues , Wage Satisfaction , and Appreciate .

The remaining features in the set of top ten variables with the highest SHAP importance values do not determine job satisfaction to a high extent individually, but collectively they affect the prediction. We briefly summarize the suggestions of their SHAP values. Lack of freedom in organizing one's work has a negative effect on job satisfaction. However, the effect is not symmetric: the positive effect of high freedom is less than the negative effect of lower freedom. Furthermore, the perception of getting enough support in difficult situations has a positive effect. Similar to the freedom variable, the effect of perceived support is also not symmetric.

The income variable is among the top used variables by the algorithm in predicting satisfaction. However, its effect is small and not clear. In elaborating personal economic gain, we instead focus on the wage satisfaction feature.

Preferring to work less hours than current is negatively associated with job satisfaction, but the effect is not very pronounced. Another feature in relation to work hours, Work Home Hours suggest that more hours worked from home has a negative effect on job satisfaction. Again, the effect is not symmetric. Finally, for travel time the effect is weak, but it is evident that long travel duration has a negative impact on job satisfaction.

Further information is given by the SHAP dependence plots. In panels A and B of Fig. 3 we observe that the positive effect of Colleagues and Wage Satisfaction begin after about a score of 7, and that there exist individuals for whom their high satisfaction with their colleagues make a big impact on their job satisfaction despite low wage satisfaction. On the other hand, panel C shows that feeling appreciated has a positive effect that becomes stronger if the person agrees with this opinion stronger. Finally, in panel D we observe that younger persons (born after around 1985) tend to have lower job satisfaction even if they may have high wage satisfaction. The effect is more negative the younger the person is.

Whereas the above discussed partial dependence computations visualize the mean predictions of individual job satisfaction level, individual conditional expectation (ICE) plots (Goldstein et al., 2015 ) visualize the predicted change in everyone’s job satisfaction level by plotting individual curves. Centered ICE plots provide a more explicit presentation by anchoring each individual curve at a given y-intercept value (Goldstein et al., 2015 ; Molnar, 2019 ). The ICE plots shown in Fig. 5 visualize the predicted paths for each individual in the training data where each line represents an individual. The plots for Colleagues , Wage Satisfaction , Appreciate , and Birth Year respectively suggest that the direction of the effects is mostly similar for individuals, although a small amount of heterogeneity in expectations exists.

Individual conditional expectation plots

7 Discussion and Conclusion

Welfare maximization is generally, in standard economic textbooks, regarded as a respectable economic objective or driver in any society. But the empirical measurement of welfare (including life satisfaction) is still fraught with many hurdles and uncertainties. In practice, GDP per capita—or, in a labor market context, wage rates—are often regarded as signposts for economic performance. However, this measuring rod has many serious shortcomings, such as the neglect of distributional and equity aspects, the exclusive focus on income to the detriment of essential consumption categories (such as human health, food, safety, education, green environment, quality of life), the bias caused by the presence of negative externalities or social costs (e.g., climate change, social stress, environmental decay), or the omission of the welfare implications of the worker’s balance between leisure time and working time. The welfare of a society depends among other aspects on how satisfied laborers in that society are with their work and work environments. The present study has aimed to shed further light on the individual dimension of social welfare.

The present study used the most recent wave available of the Work and Schooling module of the LISS survey on individuals in the Netherlands. The empirical analysis was mostly founded on tree-based sequential ensemble prediction algorithms. The predictions were elaborated in detail using interpretable machine learning techniques to quantify the strengths and directions of the relationships between the survey features and the level of job satisfaction of an individual.

The main result is that wage satisfaction alone is not sufficient to ensure job satisfaction for the analyzed sample of individuals from the Netherlands. Being satisfied with the atmosphere among one's colleagues and feeling appreciated are also essential for job satisfaction. While low wage satisfaction can have a strong negative effect on job satisfaction, high satisfaction with colleagues has a stronger potential positive effect on job satisfaction compared to the effect of wage satisfaction. Among other results, we also observe that younger people are less satisfied with their jobs.

It is believed that facet-based measures such as the Job Descriptive Index (JDI) predict overall job satisfaction well (Judge & Klinger, 2008 ). These measures cover similar dimensions of job satisfaction (Dunham et al., 1977 ; Kinicki et al., 2002 ). For instance, the JDI examines job satisfaction in five dimensions: work, supervision, coworkers, pay, and promotion. The Minnesota Satisfaction Questionnaire (MSQ; Weiss et al., 1967 ) measures job satisfaction in terms of compensation, advancement, coworkers, and supervisor human relations. The Index of Organizational Reactions (IOR; Smith, 1976 ) looks at supervision, the kind and amount of work, finance, coworkers, physical conditions, career prospects, and company identification. Most if not all related variables are featured in our machine learning analysis. Results from our machine learning approach generally support the construct of these popular job satisfaction measurements. However, based on the SHAP importance values of features, using the value of 0.1 as a cut-off point, it is found that three features predict job satisfaction particularly well: coworker atmosphere, pay satisfaction, and recognition. And among the top three factors, the coworker dimension performs the best. Based on results from our study, simple measures like the JDI and MSQ perform reasonably well. Nevertheless, the lower predictive power of some features suggests that looking only at few features may not be sufficient capturing the overall picture. More is not necessarily better given the lower predictive performance of some other indicators.

Relatedly, features that capture job characteristics do not perform particularly well. This challenges the job characteristics model (Hackman & Oldham, 1976 ), the dominant approach in job satisfaction research (Judge et al., 2017 ). Although the social dimension of job satisfaction is well recognized and is featured in all major measures, coworker relations is understudied when compared with other dimensions such as work conditions and pay satisfaction (Judge & Kammeyer-Mueller, 2012 ). Our findings also suggest that if an objective of the many traditional human resources management policies is to improve job satisfaction, some of the focus on, for example, skills mismatch and training, could be less important than the cultivation of a supportive and collegial working environment. Future research should focus more on the social aspect. There are several prominent theories related to the social environment in workplace (Jolly et al., 2021 ). The conservation of resources theory (Halbesleben et al., 2014 ; Hobfoll, 1989 ) maintains that, as a resource, (perceived) social supports from colleague and job supervisors helps workers to regulate resources in times of difficulty to prevent (mental) resource loss such as burnout. Focusing on job performance and engagement, the job demands–resources theory (Bakker & Demerouti, 2007 ; Gerich & Weber, 2020 ) suggests that social resources in workplace could help workers to improve their performance. Given the general interpretation of the survey question, it remains unclear in which way coworker atmosphere in our analysis entails. Future research is still required to disentangle the mechanism behind the finding. Furthermore, since support has been picked up by a separate top-ranked variable, and work pressure, while included, was not a highly relevant variable in predicting job satisfaction. One interpretation is that work pressure has been captured by other variables, which have lower importance, one may favor the job demands–resources theory which emphasizes the role of support on performance. Another interpretation is that work pressure has a lower importance because part of it has been captured by social support. If this interpretation is correct, the conservation of resources theory might be more important. Which explanation of the finding is correct remains a topic of future research.

What do our findings suggest about the role of job satisfaction in the Great Resignation in the Netherlands? The importance of pay satisfaction is partly consistent with the explanation about the tightness of the labor market in the pandemic era. Under high inflation, workers are predictably unsatisfied with their wages. A tight labor market would favor workers to shift jobs and to ask for a high bid. However, the predictive power of collegial atmosphere out-weights that of wage satisfaction by a lot. It is likely that job satisfaction, mainly through its impact on coworker relations, plausibly related to remote working and home office, has a greater impact on job shifts when compared with the wage factor. As the Netherlands is a developed economy, the hierarchy of needs for individuals are likely to be different from middle and low income countries. The importance of coworker relationship may have relatively less importance compared to wages and other features pertaining to living standards in countries when working individuals are considerably more concerned about their socioeconomic well-being. While our evidence could shed light on the research question at hand with respect to high-income countries, empirical research on data from lower income countries may point towards different results.

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The authors acknowledge the grant from the Romanian Ministry of Research, Innovation and Digitisation, CNCS—UEFISCDI, project number PN-III-P4-PCCE-2021-1878, within PNCDI III, project—Institutions, Digitalisation and Regional Development in the EU.

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Iterations and the feature-observation trade-off

A sample UpSet plot from the fourth iteration

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Main article content, effects of employees’ perception of performance appraisal fairness on job satisfaction, ewnetu tadesse, habtamu dadi, mesfin lemma.

This study investigates the effect employees’ perception of fairness in the performance appraisal system on job satisfaction of an employee. The perceived fairness in appraisal system is discussed with the help of organizational justice theory which was principally derived from Adam’s equity theory and used by many researchers in organizational research. The perception of fairness in performance appraisal system consists of three main factors: Distributive justice, procedural justice, interactional justice and are used as independent variables and job satisfaction of an employee as dependent variable. Using a random sample of 297 employees from a total of 1624 population the required data is obtained through structured questionnaires. Descriptive statistics, independent sample t-test, one way- ANOVA, correlation analysis and multiple regression analysis were performed. The independent sample t-test shows that there is significant difference between genders in fairness perception in performance appraisal system, however there is no significant difference between genders in distributive and procedural fairness perception. The one way- NOVA test shows that there is significant difference among work experience groups and age level groups, however no significant difference found among educational level groups. The descriptive finding of the study shows that in ASTU employees had low level of fairness perception towards the existing performance appraisal practice, and low level of job satisfaction. The correlation analysis result also indicates that distributive, procedural and interactional fairness in the appraisal system had positive and significant relationship with job satisfaction. Whereas the finding of multiple regression analysis indicates that distributive, procedural and interactional fairness in the appraisal system had positive and significant influence job satisfaction. The human resource management of the University should create organizational climate that enhance positive perception among employees regarding distributive, procedural, and interactive justices of performance appraisal more than ever.

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Published: 29 April 2024

Professional attractiveness among long-term care workers in nursing homes in China: a cross-sectional study

Xiaojing Qi 1 na1 ,
Ziyan Dong 2 na1 ,
Wen Xie 2 ,
Liuqing Yang 2 &

BMC Health Services Research volume 24 , Article number: 548 ( 2024 ) Cite this article

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The population aging trend and the shortage of elderly care workers require the long-term care profession to become more attractive. However, the professional attractiveness among long-term care workers has yet to be extensively studied. This study aims to identify the factors that influence the attractiveness of the long-term care profession for nursing home (NH) care workers..

A cross-sectional study was conducted in more than 50 NHs. Perception of professional attractiveness among long-term care workers and potential associated factors were measured using the Attractive Work Questionnaire (AWQ) and structural instruments including the Fraboni Scale of Ageism (FSA) and the Maslach Burnout Inventory (MBI). A multiple linear regression method was employed to explore the influence of potential independent variables on professional attractiveness.

The overall response rate was 99%. The results showed the score of professional attractiveness (185.37 ± 20.034), as well as the scores of each component (99.26 ± 11.258 for work condition, 30.13 ± 3.583 for work content, and 55.99 ± 7.074 for job satisfaction). Findings of multiple linear regression analysis indicated that age( β = 0.129, p<.05 ), years of work( β = 0.156, p<.05 ), 12-hour shifts( β = 0.185, p<.05 ), and training times per year( β = 0.148, p<.05 ) positively associated with long-term care workers perceived professional attractiveness. Whereas only ageism( β=-0.267, p<.05 ) significantly and negatively influenced professional attractiveness.

Conclusions

The perceived professional attractiveness of long-term care workers in NHs was acceptable. Age, years of work, shifts, training opportunities, and ageism contributed to the professional attractiveness of nursing home care workers in China. Target intervention measures should be taken to enhance the attractiveness of long-term care careers so as to avoid the shortage of long-term care workers.

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Population aging has become an inevitable trend that could pose serious health challenges worldwide. The proportion of older persons in the world is anticipated to increase from the current level of 9% to nearly 16% by 2050, with 1.6 billion older people [ 1 ]. According to the National Bureau of Statistics of China data [ 2 ], the number of elderly adults aged 60 years and over reached 264 million in 2020, accounting for 18.7% of the total Chinese population. Elderly people experience a decline in cognition and function as they age, threatening independence. An estimated 47 million disabled or semi-disabled older adults live in China in 2020 [ 3 ]. Hence, there is an urgent need to develop long-term care (LTC) to address the challenges of aging and rising disabled populations.

LTC refers to care provided by carers or care workers for individuals with declines in intrinsic capacity or functional ability, which can last for an extended period [ 4 ]. LTC services generally occur in non-institutional settings such as homes and communities or institutional settings such as LTC facilities (i.e., nursing homes (NHs)). China’s long-term care service system consists of three main types of services: home-based, community-based, and institution-based, and shows a pattern of “9073”, namely, 90% of the elderly ageing at home, 7% receiving community-based services and 3% receiving institutionalized services [ 5 ]. Home care, with family members as the primary caregivers, has long been the traditional mode of care in China [ 6 ]. However, decreased fertility rates, shrinking family sizes, and frequent internal immigration from rural to urban have shifted China’s traditional family-based care model for the elderly to the current trend toward greater reliance on NHs [ 7 ].

The development of NHs faces several challenges, including low staffing ratios and high staff turnover combined with low salaries and unfavorable social attitudes toward LTC workers [ 8 ]. The National Occupational Standard for Elderly Caregivers (2011), formulated by China’s Ministry of Labor and Social Security, stipulates that the occupation of elderly care workers has four grades: junior, intermediate, senior and technician. Subsequently, in the 2019 revision, the entry requirements for elderly care workers have been further relaxed to meet the challenge of a lack of elderly caregivers, and the number of years of experience required for the promotion of care worker professional qualifications has been shortened. The new standard adjusts the educational requirement for care workers from “graduation from junior high school” to “no educational requirement”; and adjusts the declaration requirement for junior workers is adjusted from “more than 2 years of continuous apprenticeship in this occupation” to “more than 1 year (inclusive) of cumulative work in this occupation or related occupations”. For caregivers who have already obtained the intermediate elderly care worker vocational qualification certificate and want to obtain the senior vocational qualification, the number of years of employment required has been reduced from 4 to 2 years [ 9 ]. Most long-term care workers in NHs are middle-aged women with low education levels and hardly accept formal pre-employment training [ 10 , 11 , 12 ]. Moreover, it is frustrating that students and skilled nurses show a somewhat sluggish interest in devoting themselves to LTC [ 13 , 14 ]. Previous studies examining the quality of LTC facilities have shown that many problems are linked to caregiver shortages, such as falls, pressure ulcers, and medication errors [ 15 ]. The shortage of skilled care workers reduces a unit’s ability to meet the diverse nursing requirements of the aged for physical, psychological, and rehabilitation.

The definition of professional attractiveness varies across different occupations and lacks consistency in the literature. Ateg defines “attractive work” as work that possesses positive characteristics and attracts the attention of job seekers and current employees in a positive manner [ 16 ]. Professional attractiveness of long-term care workers had previously been described based on the Walker and Avant’s classical concept analysis method [ 17 ]. The definition is that LTC facilities possess the ability to attract excellent staff, not only in terms of eliciting the willingness of potential applicants to work but in terms of retaining and motivating the current long-term care workers. According to our interpretation, professional attractiveness also reflects a psychological tendency of employees working in LTC facilities, which was driven by the work experiences and perceptions (e.g. satisfaction, turnover intention, and burnout) that various job features bring to employees.

The experience of work tends to be affected on many levels. Previous studies have demonstrated numerous individual characteristics, such as age, health status, working years, and education levels, could influence long-term care workers’ job satisfaction and turnover intention [ 18 , 19 ]. Staff participation in supportive management had a positive relationship with their job satisfaction in LTC facilities [ 20 ], which might endow care workers with more confidence and sense of accomplishment. Besides, several organizational factors have been reported that might exert an influence on job satisfaction and turnover intention. Foa found that positive relationships with colleagues and training favor job satisfaction, while workload, lack of training, and reduction of rest time create dissatisfaction and even increase burnout [ 21 ]. Lee et al. reported that several work conditional factors like wage, working hours, and working intensity influence the turnover intention of home care workers [ 22 ]. Additionally, intrapersonal factors, including ageism, burnout, and mood, have been associated with job satisfaction and intention to leave directly or indirectly. Ageism, referring to the prejudice of one age group against another [ 23 ], is associated with LTC workers’ intention to leave and plays a mediating role in job satisfaction and intention to stay [ 24 ]. Actually, discrimination against the aged based on age, resources, and contribution leads to a negative image of geriatric nursing [ 25 ]. White found that those nurses who work in LTC facilities with higher burnout were more likely to leave their jobs [ 26 ]. It appears that burnout would make LTC less attractive. Long-term care workers who are dissatisfied with their current job situation or even have a desire to leave may perceive that their job has become less desirable.

There have been some studies exploring job attractiveness in the healthcare profession [ 27 , 28 ], indicating that determinants of professional attractiveness are work engagement and age. However, few studies probed into professional attractiveness in long-term care workers directly. This study aimed to investigate the status of professional attractiveness among long-term care workers working in NHs in China using structural questionnaires and explore the impact of potential factors on it. We hypothesized that demographics, job security factors (including wage and training), and individual subjective factors (including job burnout and ageism) would affect long-term care workers’ professional attractiveness.

Study design and participants

This was a cross-sectional descriptive study conducted from June 2021 to June 2022. The sample size was grossly calculated based on the requirement of a multiple linear regression model, in which cases should be more than 5–10 times the number of independent variables [ 29 ]. The number of independent variables in this study was 15 and considering 10% invalid questionnaires, the required sample size should be 83–165 at least. The inclusion criteria for the current study were as follows: (1) care workers who work in NHs and take care of the elderly directly, (2) have worked in LTC facilities for at least three months, and (3) are volunteered to participate in this survey. The participants were excluded if they were: (1) unable to complete the questionnaire or (2) reluctant to participate in the study.

Data collection

The study was approved by the ethics committee of Huazhong University of Science and Technology. Subjects were selected using a convenience sampling strategy from 57 NHs in Wuhan, Hubei Province, and Kaifeng, Henan Province, China. All participants were informed about the content and purpose of the study in detail and signed an informed consent form. Structural questionnaires were delivered to participants on-site by trained investigators, and confidentiality was emphasized. We recruited 396 care workers and the responsiveness of the sample was 99%.

Variables and measurements

Independent variables, demographics and job institutional security factors.

Long-term care workers’ basic demographics and job security data were obtained using a questionnaire designed by researchers, which included age, marital status, gender, education, employment form, working years, certificate level, shift mode, number of care objects, number of care objects with a disability, and whether to participate in management. Income and training opportunities belong to job security.

The Fraboni scale of ageism (FSA) was used to measure cognitive and affective aspects of ageism [ 30 ]. The FSA consists of 29 items related to three dimensions of counterstatement, avoidance, and denial. Each item is evaluated on a Likert scale from 1 (definitely do not agree) to 4 (definitely agree), with higher scores suggesting greater ageism. The Chinese version of the FSA was revised previously and confirmed as a valid and reliable tool among medical students with a Cronbach’s alpha of 0.81 and a content validity of 0.93 [ 31 ]. To ensure accurate results, we have verified the reliability of FSA in long-term care workers with a Cronbach’s alpha of 0.856 and a KMO value of 0.786.

Job burnout

The Maslach Burnout Inventory (MBI) is one of the best-known tools for evaluating job burnout, which consists of three dimensions of emotional exhaustion, depersonalization, and personal accomplishment [ 32 ]. The Chinese version of MBI adapted by Li et al. [ 33 ] has been used widely and was constitutive of 15 items on a 7-point Likert scale ranging from 1 (never) to 7 (every day). The overall reliability of MBI in long-term care workers is acceptable with a Cronbach’s alpha of 0.882.

Professional attractiveness

The Attractive Work Questionnaire (AWQ), created by Ateg and Hedlund [ 34 ], was used to assess employees perceived professional attractiveness. For the purpose of the study, the Chinese version was used to assess the professional attractiveness of long-term care workers in the Chinese context after cross-cultural adjustment. The revised AWQ has 46 items, including three areas of work conditions, work contents, and job satisfaction. All items were rated on a 5-point Likert scale ranging from not at all to entirely , with higher scores indicating that the job is more attractive to the employee. The Cronbach’s alpha of the three components of the Chinese version of AWQ ranges from 0.816 to 0.920, indicating good reliability. As well as the KMO values of the three components are 0.822, 0.685, and 0.848 respectively, indicating good validity.

Statistical analysis

Standard descriptive statistics were used to describe participants’ characteristics. Mean values and standard deviation (SD) (for symmetric distribution) or median and quartiles (for skewed distribution) were calculated for continuous variables, while frequency and percentage were used for categorical variables. We generated the mean total score for professional attractiveness and the mean score for each domain. A series of Spearman’s correlation coefficients, the Wilcoxon rank-sum test, and the Kruskal-Wallis H test were calculated to examine the association between demographics, job-related characteristics, ageism, burnout, and professional attractiveness. When the p-value was less than 0.05, it was considered to be statistically significant. The multiple linear regression method was employed to analyze the influence of many variables that were significant in the univariable analysis of professional attractiveness. All of the data were analyzed by the SPSS, version 24.0.

Characteristics of sample

Table 1 showed the summary characteristics of the long-term care workers. The participants’ age ranged between 18 and 74 years. We found that most workers were middle-aged and had been working in LTC services for 3–7 years according to analysis. The overwhelming majority of the long-term care workers included in the analysis were female (89.3%), married (87.8%), and with a degree below associate (73%). The proportion of contract (49%) and temporary (51%) workers was comparable. Merely about one-fifth of the care workers in the sample had intermediate or advanced professional qualifications for elderly care workers. About half of the workers (47.4%) work eight-hour shifts and only around one-third of the staff are involved in management. Results indicated that the typical ageism scores ranged from 51 to 63 on a total score of 129, and the typical job burnout scores ranged from 45 to 55 on a total score of 105 in participants.

The professional attractiveness of long-term care workers

Table 2 showed the results of the analysis for the AWQ questionnaire regarding the three aspects of professional attractiveness: work conditions, work contents, and job satisfaction. The total mean score for the AWQ was 185.37 ± 20.034 points, among which the score of work condition, work content, and job satisfaction was 99.26 ± 11.258, 30.13 ± 3.583 and 55.99 ± 7.074, respectively. The sub-dimensional statistical results are shown in Table 2 , respectively.

Predictors of long-term care workers perceived professional attractiveness

Spearman correlation, Mann-Whitney U test, and Kruskal-Wallis H test were used to examine the professional attractiveness related to all variables and the results are shown in Table 3 . In terms of demographics, the results showed that the differences in total professional attractiveness score by age, marital status, gender, education level, employment mode, years of work experience, nursing assistant certificate level, shift status, average daily care number, and average daily care disabled number were statistically significant ( p < .05).

As for job institutional security and subjective perception factors, it was discovered that the more salary ( r = .139, p = .006) and training opportunities ( r = .226, p = .000) a long-term care worker gets and the higher perceived professional attractiveness, and the higher ageism in long-term care workers is associated with lower professional attractiveness ( r =-.315, p = .000). However, there was no significant relationship between job burnout and professional attractiveness.

Five significant variables in univariate analysis were entered into the regression equation and accounted for 26.9% of the variance in the scores of professional attractiveness.

As shown in Table 4 , age( β = 0.129, p <.05), years of work experience( β = 0.156, p <.05), and 12-hour shifts( β = 0.185, p <.05) were found to have a positive relationship with long-term care workers perceived professional attractiveness. Training times per year( β = 0.148, p <.05), the only significant job institution security factor, was positively associated with the professional attractiveness. Whereas the ageism is higher for decreasing professional attractiveness total score( β =-0.267, p <.05).

The present study has been the first to quantitatively describe the professional attractiveness among long-term care workers who work in NHs and explore the determinants from multiple aspects, such as job institutional security and subjective perception.

In this study, the culturally adapted AWQ was first used to evaluate the professional attractiveness of long-term care workers. Compared with the mean score of the whole questionnaire and each component, the findings suggested that the overall perceived professional attractiveness, as well as each component, were superior among long-term caregivers, which indicated that NH care workers find their profession as a decent attraction. This result was similar to previous study. Liu et al. employed a self-designed questionnaire on the attractiveness of the elderly care service talents and found that the attractiveness of elderly caregivers was generally acceptable [ 35 ]. Similarly, studies have shown that in the United States, low-income older workers report that working in long-term care is attractive. However, they preferred to work in a home environment compared to a nursing home or other institutional setting [ 36 ]. However, the results of another study suggest that care workers in LTC facilities do not rate their jobs highly due to low wages and intense job content [ 37 ]. Long-term care workers in Chinese NHs tend to be middle-aged and older women who have retired, been laid off, or migrated to cities to work, it is not easy to find a suitable job [ 38 ]. In the 1990s, many workers in state-owned factories were laid off in the course of market reforms and were left in a precarious situation due to the lack of re-employment programs. Many of these workers entered the care sector as a last resort due to the high demand and low barriers to employment in the care sector in urban areas [ 39 ]. They are satisfied with possessing a job, so that the LTC industry is relatively more desirable to them. Besides, the flexible shifts in NHs allow care workers relative freedom to arrange their own time, which might be sufficient for long-term care workers to have a better appreciation of their work.

Our study found that long-term care workers who are older and have longer working years perceived higher professional attractiveness. These findings are consistent with previous studies [ 40 ]. This may be attributable to the fact that care workers in NHs who are older and with longer work tenure already have a wealth of theoretical knowledge and practical solid ability to care for the elderly and are familiar with to work environment and content.

Besides, higher professional attractiveness is obtained when long-term care workers on 12-hour shifts compared to 8-hour shifts. Previous literature points out that when wards are on 12-hour nursing shifts, there is even decreased turnover intention in the LTC industry [ 41 ], which is similar to our findings. Whereas other studies indicated that longer shifts are associated with job burnout, dissatisfaction, and turnover intention [ 42 ]. This may endorse that too frequent shifts make it difficult for care workers in NHs to balance their work and life. Along with the disruption of circadian rhythms, short shifts in LTC facilities tend to prevent the formation of everyday routines and even lead to work-family conflict among care workers [ 10 ]. While working too many hours in a row easily fatigues employees and increases the risk of work errors, which could bring unpleasant experiences to care workers [ 43 ].

According to our study, long-term care workers who receive more training opportunities per year perceive greater attractiveness. Similarly, previous studies reported that the absence of training to enhance professionalism, as a characteristic of elderly care work, is associated with turnover [ 44 ]. The lack of professional training leaves care workers with less expertise in caring for the elder with or without disability or dementia, which puts them under tremendous pressure practically and psychologically. As a result, care workers may be dissatisfied with work condition nursing homes provided and even think about leaving their job, indicating that a career in LTC is no longer attractive to them [ 11 ]. Instead, training facilities capacity enhancement and increases accomplishment, which is an essential factor affecting work enthusiasm [ 45 ]. It has been suggested that satisfying the needs of knowledge acquisition of health professionals involved in the care of older adults is helpful in increasing work engagement and reducing job burnout [ 46 ]. The lack of professionalism and preparedness among care workers in long-term care sector reinforced the importance of training to create more promotion opportunities [ 40 ].

Additionally, our findings suggest that there is a decrease in professional attractiveness when care workers in NHs exhibit higher ageism. Ageism as a stereotype is prevalent in LTC facilities, and it may be linked to cognitive prejudice resulting from the low qualifications and dissatisfaction caused by low wages among LTC workers [ 47 , 48 , 49 ]. Age-based discrimination from care workers lead to decreased quality of care as well as psychological distress for the elderly [ 50 ]. LTC workers showing high levels of ageism tend to be reluctant to stay with and provide comprehensive care to older people, indicating that their jobs become less appealing and desirable [ 24 ].

Limitations

This study has several limitations. Firstly, participants were selected only from two cities in China, which could introduce potential bias due to regional disparity and limit the generalizability of our results. Another important limitation is that the study did not consider the potential influence of varying characteristics of LTC facilities (e.g. business nature of LTC facilities), which could lead to incomplete results. Besides, the current study was conducted in the midst of the COVID-19 epidemic, and previous studies have demonstrated that changes in patterns of care brought about by COVID-19 epidemics can increase the workload of nursing home care workers [ 51 ]. In contrast, our findings cannot exclude the impact of the epidemic on the perceived professional attractiveness of nursing home care workers, which also implies that further investigations are necessary during non-epidemic periods. Additionally, it is impossible to establish causality due to the cross-sectional design.

Implications

Measures such as developing professional education programs, lowering entry barriers, and providing monetary incentives have all been taken by the Chinese government in order to attract talent and solve the problem of labor shortage [ 52 ]. However, to maximize the attractiveness and retention of talent in the elderly care industry while improving the quality of care of LTC facilities, these measures are far from sufficient. By exploring the potential influences on the perceived professional attractiveness of long-term care workers, the current study provides a direction for policymakers and managers of long-term care institutions to consider improvements. Training programs, not only on nursing skills but also on knowledge of aging, should be prioritized to improve the ability of care workers to provide quality care and to mitigate ageism, potentially reversing unattractive career prospects. This will require greater government investment in education and training, as well as monitoring of the effective implementation of policies in LTC facilities. Additionally, managers of LTC institutions should consider a series of effective coping and managing mechanisms to attract talent. Career promotion systems and incentive mechanisms that go beyond the traditional ones should be put in place, with pay scales set according to the level of vocational skills qualification, years of experience, and working performance, to enhance the enthusiasm of elderly care workers. We have highlighted the importance of suitable working hours for ensuring care workers that are more likely to behave in high efficiency. Hence, administrators should optimize shift settings and try to avoid employees working too long hours in a row, maximizing job satisfaction of care workers while ensuring the quality of care, which in turn increases their perceived professional attractiveness.

Future research should include larger cross-regional samples and longitudinal studies to further verify relevant predictors and examine relationships across time. Besides, other LTC settings beyond nursing homes need to be considered, such as LTC hospitals. Additionally, future studies should explore professional attractiveness of long-term care workers from a qualitative perspective.

This study examines the current state of professional attractiveness among LTC care workers in nursing homes. It also assessed the factors associated with professional attractiveness. This study provides insight into the plight faced by China’s LTC industry and strategies to improve the attractiveness of LTC industry to retain and appeal to healthcare professionals.

Data availability

The datasets analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Nursing Home

Long-term Care

Fraboni Scale of Ageism

The Attractive Work Questionnaire

The Maslach Burnout Inventory

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Acknowledgements

Thanks to Huazhong University of Science and Technology for providing the referral letter. Thanks to all the nursing home directors in Wuhan and Kaifeng for their support during the survey.

This work was supported by Humanities and social science foundation of Ministry of Education of China (grant number 20YJZAH054) and National Key Research and Development Program of China [grant number 2023YFC3806503].

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Xiaojing Qi and Ziyan Dong are co-first authors.

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Department of Nursing, Peking Union Medical College Hospital, No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China

Xiaojing Qi

School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

Ziyan Dong, Wen Xie, Liuqing Yang & Jie Li

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Qi X.J, Dong Z.Y conceived the study. Qi X.J conducted literature retrieval, screening and data collection, data analysis, and data interpretation. Dong Z.Y drafted and revised the manuscript. Dong Z.Y, Xie W, and Yang L.Q helped data collection. Li J helped revise the manuscript. All authors have read and approved the manuscript.

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Correspondence to Jie Li .

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The study used human data and was approved by the ethics committee of Huazhong University of Science and Technology (approval number S028). All participants were informed about the content and purpose of the study in detail and signed an informed consent form.

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Qi, X., Dong, Z., Xie, W. et al. Professional attractiveness among long-term care workers in nursing homes in China: a cross-sectional study. BMC Health Serv Res 24 , 548 (2024). https://doi.org/10.1186/s12913-024-11023-x

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Professional attractiveness among long-term care workers in nursing homes in China: a cross-sectional study

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Study design and participants

Data collection

Variables and measurements

Job burnout

Statistical analysis

Characteristics of sample

The professional attractiveness of long-term care workers

Predictors of long-term care workers perceived professional attractiveness

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Data availability

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