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• Published: 22 May 2020

Assessing the Big Five personality traits using real-life static facial images

• Alexander Kachur   ORCID: orcid.org/0000-0003-1165-2672 1 ,
• Evgeny Osin   ORCID: orcid.org/0000-0003-3330-5647 2 ,
• Denis Davydov   ORCID: orcid.org/0000-0003-3747-7403 3 ,
• Konstantin Shutilov 4 &
• Alexey Novokshonov 4  

Scientific Reports volume  10 , Article number:  8487 ( 2020 ) Cite this article

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• Human behaviour

There is ample evidence that morphological and social cues in a human face provide signals of human personality and behaviour. Previous studies have discovered associations between the features of artificial composite facial images and attributions of personality traits by human experts. We present new findings demonstrating the statistically significant prediction of a wider set of personality features (all the Big Five personality traits) for both men and women using real-life static facial images. Volunteer participants (N = 12,447) provided their face photographs (31,367 images) and completed a self-report measure of the Big Five traits. We trained a cascade of artificial neural networks (ANNs) on a large labelled dataset to predict self-reported Big Five scores. The highest correlations between observed and predicted personality scores were found for conscientiousness (0.360 for men and 0.335 for women) and the mean effect size was 0.243, exceeding the results obtained in prior studies using ‘selfies’. The findings strongly support the possibility of predicting multidimensional personality profiles from static facial images using ANNs trained on large labelled datasets. Future research could investigate the relative contribution of morphological features of the face and other characteristics of facial images to predicting personality.

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Introduction.

A growing number of studies have linked facial images to personality. It has been established that humans are able to perceive certain personality traits from each other’s faces with some degree of accuracy 1 , 2 , 3 , 4 . In addition to emotional expressions and other nonverbal behaviours conveying information about one’s psychological processes through the face, research has found that valid inferences about personality characteristics can even be made based on static images of the face with a neutral expression 5 , 6 , 7 . These findings suggest that people may use signals from each other’s faces to adjust the ways they communicate, depending on the emotional reactions and perceived personality of the interlocutor. Such signals must be fairly informative and sufficiently repetitive for recipients to take advantage of the information being conveyed 8 .

Studies focusing on the objective characteristics of human faces have found some associations between facial morphology and personality features. For instance, facial symmetry predicts extraversion 9 . Another widely studied indicator is the facial width to height ratio (fWHR), which has been linked to various traits, such as achievement striving 10 , deception 11 , dominance 12 , aggressiveness 13 , 14 , 15 , 16 , and risk-taking 17 . The fWHR can be detected with high reliability irrespective of facial hair. The accuracy of fWHR-based judgements suggests that the human perceptual system may have evolved to be sensitive to static facial features, such as the relative face width 18 .

There are several theoretical reasons to expect associations between facial images and personality. First, genetic background contributes to both face and personality. Genetic correlates of craniofacial characteristics have been discovered both in clinical contexts 19 , 20 and in non-clinical populations 21 . In addition to shaping the face, genes also play a role in the development of various personality traits, such as risky behaviour 22 , 23 , 24 , and the contribution of genes to some traits exceeds the contribution of environmental factors 25 . For the Big Five traits, heritability coefficients reflecting the proportion of variance that can be attributed to genetic factors typically lie in the 0.30–0.60 range 26 , 27 . From an evolutionary perspective, these associations can be expected to have emerged by means of sexual selection. Recent studies have argued that some static facial features, such as the supraorbital region, may have evolved as a means of social communication 28 and that facial attractiveness signalling valuable personality characteristics is associated with mating success 29 .

Second, there is some evidence showing that pre- and postnatal hormones affect both facial shape and personality. For instance, the face is a visible indicator of the levels of sex hormones, such as testosterone and oestrogen, which affect the formation of skull bones and the fWHR 30 , 31 , 32 . Given that prenatal and postnatal sex hormone levels do influence behaviour, facial features may correlate with hormonally driven personality characteristics, such as aggressiveness 33 , competitiveness, and dominance, at least for men 34 , 35 . Thus, in addition to genes, the associations of facial features with behavioural tendencies may also be explained by androgens and potentially other hormones affecting both face and behaviour.

Third, the perception of one’s facial features by oneself and by others influences one’s subsequent behaviour and personality 36 . Just as the perceived ‘cleverness’ of an individual may lead to higher educational attainment 37 , prejudice associated with the shape of one’s face may lead to the development of maladaptive personality characteristics (i.e., the ‘Quasimodo complex’ 38 ). The associations between appearance and personality over the lifespan have been explored in longitudinal observational studies, providing evidence of ‘self-fulfilling prophecy’-type and ‘self-defeating prophecy’-type effects 39 .

Fourth and finally, some personality traits are associated with habitual patterns of emotionally expressive behaviour. Habitual emotional expressions may shape the static features of the face, leading to the formation of wrinkles and/or the development of facial muscles.

Existing studies have revealed the links between objective facial picture cues and general personality traits based on the Five-Factor Model or the Big Five (BF) model of personality 40 . However, a quick glance at the sizes of the effects found in these studies (summarized in Table  1 ) reveals much controversy. The results appear to be inconsistent across studies and hardly replicable 41 . These inconsistencies may result from the use of small samples of stimulus faces, as well as from the vast differences in methodologies. Stronger effect sizes are typically found in studies using composite facial images derived from groups of individuals with high and low scores on each of the Big Five dimensions 6 , 7 , 8 . Naturally, the task of identifying traits using artificial images comprised of contrasting pairs with all other individual features eliminated or held constant appears to be relatively easy. This is in contrast to realistic situations, where faces of individuals reflect a full range of continuous personality characteristics embedded in a variety of individual facial features.

Studies relying on photographic images of individual faces, either artificially manipulated 2 , 42 or realistic, tend to yield more modest effects. It appears that studies using realistic photographs made in controlled conditions (neutral expression, looking straight at the camera, consistent posture, lighting, and distance to the camera, no glasses, no jewellery, no make-up, etc.) produce stronger effects than studies using ‘selfies’ 25 . Unfortunately, differences in the methodologies make it hard to hypothesize whether the diversity of these findings is explained by variance in image quality, image background, or the prediction models used.

Research into the links between facial picture cues and personality traits faces several challenges. First, the number of specific facial features is very large, and some of them are hard to quantify. Second, the effects of isolated facial features are generally weak and only become statistically noticeable in large samples. Third, the associations between objective facial features and personality traits might be interactive and nonlinear. Finally, studies using real-life photographs confront an additional challenge in that the very characteristics of the images (e.g., the angle of the head, facial expression, makeup, hairstyle, facial hair style, etc.) are based on the subjects’ choices, which are potentially influenced by personality; after all, one of the principal reasons why people make and share their photographs is to signal to others what kind of person they are. The task of isolating the contribution of each variable out of the multitude of these individual variables appears to be hardly feasible. Instead, recent studies in the field have tended to rely on a holistic approach, investigating the subjective perception of personality based on integral facial images.

The holistic approach aims to mimic the mechanisms of human perception of the face and the ways in which people make judgements about each other’s personality. This approach is supported by studies of human face perception, showing that faces are perceived and encoded in a holistic manner by the human brain 43 , 44 , 45 , 46 . Put differently, when people identify others, they consider individual facial features (such as a person’s eyes, nose, and mouth) in concert as a single entity rather than as independent pieces of information 47 , 48 , 49 , 50 . Similar to facial identification, personality judgements involve the extraction of invariant facial markers associated with relatively stable characteristics of an individual’s behaviour. Existing evidence suggests that various social judgements might be based on a common visual representational system involving the holistic processing of visual information 51 , 52 . Thus, even though the associations between isolated facial features and personality characteristics sought by ancient physiognomists have emerged to be weak, contradictory or even non-existent, the holistic approach to understanding the face-personality links appears to be more promising.

An additional challenge faced by studies seeking to reveal the face-personality links is constituted by the inconsistency of the evaluations of personality traits by human raters. As a result, a fairly large number of human raters is required to obtain reliable estimates of personality traits for each photograph. In contrast, recent attempts at using machine learning algorithms have suggested that artificial intelligence may outperform individual human raters. For instance, S. Hu and colleagues 40 used the composite partial least squares component approach to analyse dense 3D facial images obtained in controlled conditions and found significant associations with personality traits (stronger for men than for women).

A similar approach can be implemented using advanced machine learning algorithms, such as artificial neural networks (ANNs), which can extract and process significant features in a holistic manner. The recent applications of ANNs to the analysis of human faces, body postures, and behaviours with the purpose of inferring apparent personality traits 53 , 54 indicate that this approach leads to a higher accuracy of prediction compared to individual human raters. The main difficulty of the ANN approach is the need for large labelled training datasets that are difficult to obtain in laboratory settings. However, ANNs do not require high-quality photographs taken in controlled conditions and can potentially be trained using real-life photographs provided that the dataset is large enough. The interpretation of findings in such studies needs to acknowledge that a real-life photograph, especially one chosen by a study participant, can be viewed as a holistic behavioural act, which may potentially contain other cues to the subjects’ personality in addition to static facial features (e.g., lighting, hairstyle, head angle, picture quality, etc.).

The purpose of the current study was to investigate the associations of facial picture cues with self-reported Big Five personality traits by training a cascade of ANNs to predict personality traits from static facial images. The general hypothesis is that a real-life photograph contains cues about personality that can be extracted using machine learning. Due to the vast diversity of findings concerning the prediction accuracy of different traits across previous studies, we did not set a priori hypotheses about differences in prediction accuracy across traits.

Prediction accuracy

We used data from the test dataset containing predicted scores for 3,137 images associated with 1,245 individuals. To determine whether the variance in the predicted scores was associated with differences across images or across individuals, we calculated the intraclass correlation coefficients (ICCs) presented in Table  2 . The between-individual proportion of variance in the predicted scores ranged from 79 to 88% for different traits, indicating a general consistency of predicted scores for different photographs of the same individual. We derived the individual scores used in all subsequent analyses as the simple averages of the predicted scores for all images provided by each participant.

The correlation coefficients between the self-report test scores and the scores predicted by the ANN ranged from 0.14 to 0.36. The associations were strongest for conscientiousness and weakest for openness. Extraversion and neuroticism were significantly better predicted for women than for men (based on the z test). We also compared the prediction accuracy within each gender using Steiger’s test for dependent sample correlation coefficients. For men, conscientiousness was predicted more accurately than the other four traits (the differences among the latter were not statistically significant). For women, conscientiousness was predicted more accurately, and openness was predicted less accurately compared to the three other traits.

The mean absolute error (MAE) of prediction ranged between 0.89 and 1.04 standard deviations. We did not find any associations between the number of photographs and prediction error.

Trait intercorrelations

The structure of the correlations between the scales was generally similar for the observed test scores and the predicted values, but some coefficients differed significantly (based on the z test) (see Table  3 ). Most notably, predicted openness was more strongly associated with conscientiousness (negatively) and extraversion (positively), whereas its association with agreeableness was negative rather than positive. The associations of predicted agreeableness with conscientiousness and neuroticism were stronger than those between the respective observed scores. In women, predicted neuroticism demonstrated a stronger inverse association with conscientiousness and a stronger positive association with openness. In men, predicted neuroticism was less strongly associated with extraversion than its observed counterpart.

To illustrate the findings, we created composite images using Abrosoft FantaMorph 5 by averaging the uploaded images across contrast groups of 100 individuals with the highest and the lowest test scores on each trait. The resulting morphed images in which individual features are eliminated are presented in Fig.  1 .

Composite facial images morphed across contrast groups of 100 individuals for each Big Five trait.

This study presents new evidence confirming that human personality is related to individual facial appearance. We expected that machine learning (in our case, artificial neural networks) could reveal multidimensional personality profiles based on static morphological facial features. We circumvented the reliability limitations of human raters by developing a neural network and training it on a large dataset labelled with self-reported Big Five traits.

We expected that personality traits would be reflected in the whole facial image rather than in its isolated features. Based on this expectation, we developed a novel two-tier machine learning algorithm to encode the invariant facial features as a vector in a 128-dimensional space that was used to predict the BF traits by means of a multilayer perceptron. Although studies using real-life photographs do not require strict experimental conditions, we had to undertake a series of additional organizational and technological steps to ensure consistent facial image characteristics and quality.

Our results demonstrate that real-life photographs taken in uncontrolled conditions can be used to predict personality traits using complex computer vision algorithms. This finding is in contrast to previous studies that mostly relied on high-quality facial images taken in controlled settings. The accuracy of prediction that we obtained exceeds that in the findings of prior studies that used realistic individual photographs taken in uncontrolled conditions (e.g., selfies 55 ). The advantage of our methodology is that it is relatively simple (e.g., it does not rely on 3D scanners or 3D facial landmark maps) and can be easily implemented using a desktop computer with a stock graphics accelerator.

In the present study, conscientiousness emerged to be more easily recognizable than the other four traits, which is consistent with some of the existing findings 7 , 40 . The weaker effects for extraversion and neuroticism found in our sample may be because these traits are associated with positive and negative emotional experiences, whereas we only aimed to use images with neutral or close to neutral emotional expressions. Finally, this appears to be the first study to achieve a significant prediction of openness to experience. Predictions of personality based on female faces appeared to be more reliable than those for male faces in our sample, in contrast to some previous studies 40 .

The BF factors are known to be non-orthogonal, and we paid attention to their intercorrelations in our study 56 , 57 . Various models have attempted to explain the BF using higher-order dimensions, such as stability and plasticity 58 or a single general factor of personality (GFP) 59 . We discovered that the intercorrelations of predicted factors tend to be stronger than the intercorrelations of self-report questionnaire scales used to train the model. This finding suggests a potential biological basis of GFP. However, the stronger intercorrelations of the predicted scores can be explained by consistent differences in picture quality (just as the correlations between the self-report scales can be explained by social desirability effects and other varieties of response bias 60 ). Clearly, additional research is needed to understand the context of this finding.

We believe that the present study, which did not involve any subjective human raters, constitutes solid evidence that all the Big Five traits are associated with facial cues that can be extracted using machine learning algorithms. However, despite having taken reasonable organizational and technical steps to exclude the potential confounds and focus on static facial features, we are still unable to claim that morphological features of the face explain all the personality-related image variance captured by the ANNs. Rather, we propose to see facial photographs taken by subjects themselves as complex behavioural acts that can be evaluated holistically and that may contain various other subtle personality cues in addition to static facial features.

The correlations reported above with a mean r = 0.243 can be viewed as modest; indeed, facial image-based personality assessment can hardly replace traditional personality measures. However, this effect size indicates that an ANN can make a correct guess about the relative standing of two randomly chosen individuals on a personality dimension in 58% of cases (as opposed to the 50% expected by chance) 61 . The effect sizes we observed are comparable with the meta-analytic estimates of correlations between self-reported and observer ratings of personality traits: the associations range from 0.30 to 0.49 when one’s personality is rated by close relatives or colleagues, but only from −0.01 to 0.29 when rated by strangers 62 . Thus, an artificial neural network relying on static facial images outperforms an average human rater who meets the target in person without any prior acquaintance. Given that partner personality and match between two personalities predict friendship formation 63 , long-term relationship satisfaction 64 , and the outcomes of dyadic interaction in unstructured settings 65 , the aid of artificial intelligence in making partner choices could help individuals to achieve more satisfying interaction outcomes.

There are a vast number of potential applications to be explored. The recognition of personality from real-life photos can be applied in a wide range of scenarios, complementing the traditional approaches to personality assessment in settings where speed is more important than accuracy. Applications may include suggesting best-fitting products or services to customers, proposing to individuals a best match in dyadic interaction settings (such as business negotiations, online teaching, etc.) or personalizing the human-computer interaction. Given that the practical value of any selection method is proportional to the number of decisions made and the size and variability of the pool of potential choices 66 , we believe that the applied potential of this technology can be easily revealed at a large scale, given its speed and low cost. Because the reliability and validity of self-report personality measures is not perfect, prediction could be further improved by supplementing these measures with peer ratings and objective behavioural indicators of personality traits.

The fact that conscientiousness was predicted better than the other traits for both men and women emerges as an interesting finding. From an evolutionary perspective, one would expect the traits most relevant for cooperation (conscientiousness and agreeableness) and social interaction (certain facets of extraversion and neuroticism, such as sociability, dominance, or hostility) to be reflected more readily in the human face. The results are generally in line with this idea, but they need to be replicated and extended by incorporating trait facets in future studies to provide support for this hypothesis.

Finally, although we tried to control the potential sources of confounds and errors by instructing the participants and by screening the photographs (based on angles, facial expressions, makeup, etc.), the present study is not without limitations. First, the real-life photographs we used could still carry a variety of subtle cues, such as makeup, angle, light facial expressions, and information related to all the other choices people make when they take and share their own photographs. These additional cues could say something about their personality, and the effects of all these variables are inseparable from those of static facial features, making it hard to draw any fundamental conclusions from the findings. However, studies using real-life photographs may have higher ecological validity compared to laboratory studies; our results are more likely to generalize to real-life situations where users of various services are asked to share self-pictures of their choice.

Another limitation pertains to a geographically bounded sample of individuals; our participants were mostly Caucasian and represented one cultural and age group (Russian-speaking adults). Future studies could replicate the effects using populations representing a more diverse variety of ethnic, cultural, and age groups. Studies relying on other sources of personality data (e.g., peer ratings or expert ratings), as well as wider sets of personality traits, could complement and extend the present findings.

Sample and procedure

The study was carried out in the Russian language. The participants were anonymous volunteers recruited through social network advertisements. They did not receive any financial remuneration but were provided with a free report on their Big Five personality traits. The data were collected online using a dedicated research website and a mobile application. The participants provided their informed consent, completed the questionnaires, reported their age and gender and were asked to upload their photographs. They were instructed to take or upload several photographs of their face looking directly at the camera with enough lighting, a neutral facial expression and no other people in the picture and without makeup.

Our goal was to obtain an out-of-sample validation dataset of 616 respondents of each gender to achieve 80% power for a minimum effect we considered to be of practical significance ( r  = 0.10 at p < 0.05), requiring a total of 6,160 participants of each gender in the combined dataset comprising the training and validation datasets. However, we aimed to gather more data because we expected that some online respondents might provide low-quality or non-genuine photographs and/or invalid questionnaire responses.

The initial sample included 25,202 participants who completed the questionnaire and uploaded a total of 77,346 photographs. The final combined dataset comprised 12,447 valid questionnaires and 31,367 associated photographs after the data screening procedures (below). The participants ranged in age from 18 to 60 (59.4% women, M = 27.61, SD = 12.73, and 40.6% men, M = 32.60, SD = 11.85). The dataset was split randomly into a training dataset (90%) and a test dataset (10%) used to validate the prediction model. The validation dataset included the responses of 505 men who provided 1224 facial images and 740 women who provided 1913 images. Due to the sexually dimorphic nature of facial features and certain personality traits (particularly extraversion 1 , 67 , 68 ), all the predictive models were trained and validated separately for male and female faces.

Ethical approval

The research was carried out in accordance with the Declaration of Helsinki. The study protocol was approved by the Research Ethics Committee of the Open University for the Humanities and Economics. We obtained the participants’ informed consent to use their data and photographs for research purposes and to publish generalized findings. The morphed group average images presented in the paper do not allow the identification of individuals. No information or images that could lead to the identification of study participants have been published.

Data screening

We excluded incomplete questionnaires (N = 3,035) and used indices of response consistency to screen out random responders 69 . To detect systematic careless responses, we used the modal response category count, maximum longstring (maximum number of identical responses given in sequence by participant), and inter-item standard deviation for each questionnaire. At this stage, we screened out the answers of individuals with zero standard deviations (N = 329) and a maximum longstring above 10 (N = 1,416). To detect random responses, we calculated the following person-fit indices: the person-total response profile correlation, the consistency of response profiles for the first and the second half of the questionnaire, the consistency of response profiles obtained based on equivalent groups of items, the number of polytomous Guttman errors, and the intraclass correlation of item responses within facets.

Next, we conducted a simulation by generating random sets of integers in the 1–5 range based on a normal distribution (µ = 3, σ = 1) and on the uniform distribution and calculating the same person-fit indices. For each distribution, we generated a training dataset and a test dataset, each comprised of 1,000 simulated responses and 1,000 real responses drawn randomly from the sample. Next, we ran a logistic regression model using simulated vs real responses as the outcome variable and chose an optimal cutoff point to minimize the misclassification error (using the R package optcutoff). The sensitivity value was 0.991 for the uniform distribution and 0.960 for the normal distribution, and the specificity values were 0.923 and 0.980, respectively. Finally, we applied the trained model to the full dataset and identified observations predicted as likely to be simulated based on either distribution (N = 1,618). The remaining sample of responses (N = 18,804) was used in the subsequent analyses.

Big Five measure

We used a modified Russian version of the 5PFQ questionnaire 70 , which is a 75-item measure of the Big Five model, with 15 items per trait grouped into five three-item facets. To confirm the structural validity of the questionnaire, we tested an exploratory structural equation (ESEM) model with target rotation in Mplus 8.2. The items were treated as ordered categorical variables using the WLSMV estimator, and facet variance was modelled by introducing correlated uniqueness values for the items comprising each facet.

The theoretical model showed a good fit to the data (χ 2  = 147854.68, df = 2335, p < 0.001; CFI = 0.931; RMSEA = 0.040 [90% CI: 0.040, 0.041]; SRMR = 0.024). All the items showed statistically significant loadings on their theoretically expected scales (λ ranged from 0.14 to 0.87, M = 0.51, SD = 0.17), and the absolute cross-loadings were reasonably low (M = 0.11, SD = 0.11). The distributions of the resulting scales were approximately normal (with skewness and kurtosis values within the [−1; 1] range). To assess the reliability of the scales, we calculated two internal consistency indices, namely, robust omega (using the R package coefficientalpha) and algebraic greatest lower bound (GLB) reliability (using the R package psych) 71 (see Table  4 ).

Image screening and pre-processing

The images (photographs and video frames) were subjected to a three-step screening procedure aimed at removing fake and low-quality images. First, images with no human faces or with more than one human face were detected by our computer vision (CV) algorithms and automatically removed. Second, celebrity images were identified and removed by means of a dedicated neural network trained on a celebrity photo dataset (CelebFaces Attributes Dataset (CelebA), N > 200,000) 72 that was additionally enriched with pictures of Russian celebrities. The model showed a 98.4% detection accuracy. Third, we performed a manual moderation of the remaining images to remove images with partially covered faces, those that were evidently photoshopped or any other fake images not detected by CV.

The images retained for subsequent processing were converted to single-channel 8-bit greyscale format using the OpenCV framework (opencv.org). Head position (pitch, yaw, roll) was measured using our own dedicated neural network (multilayer perceptron) trained on a sample of 8 000 images labelled by our team. The mean absolute error achieved on the test sample of 800 images was 2.78° for roll, 1.67° for pitch, and 2.34° for yaw. We used the head position data to retain the images with yaw and roll within the −30° to 30° range and pitch within the −15° to 15° range.

Next, we assessed emotional neutrality using the Microsoft Cognitive Services API on the Azure platform (score range: 0 to 1) and used 0.50 as a threshold criterion to remove emotionally expressive images. Finally, we applied the face and eye detection, alignment, resize, and crop functions available within the Dlib (dlib.net) open-source toolkit to arrive at a set of standardized 224 × 224 pixel images with eye pupils aligned to a standard position with an accuracy of 1 px. Images with low resolution that contained less than 60 pixels between the eyes, were excluded in the process.

The final photoset comprised 41,835 images. After the screened questionnaire responses and images were joined, we obtained a set of 12,447 valid Big Five questionnaires associated with 31,367 validated images (an average of 2.59 images per person for women and 2.42 for men).

Neural network architecture

First, we developed a computer vision neural network (NNCV) aiming to determine the invariant features of static facial images that distinguish one face from another but remain constant across different images of the same person. We aimed to choose a neural network architecture with a good feature space and resource-efficient learning, considering the limited hardware available to our research team. We chose a residual network architecture based on ResNet 73 (see Fig.  2 ).

Layer architecture of the computer vision neural network (NNCV) and the personality diagnostics neural network (NNPD).

This type of neural network was originally developed for image classification. We dropped the final layer from the original architecture and obtained a NNCV that takes a static monochrome image (224 × 224 pixels in size) and generates a vector of 128 32-bit dimensions describing unique facial features in the source image. As a measure of success, we calculated the Euclidean distance between the vectors generated from different images.

Using Internet search engines, we collected a training dataset of approximately 2 million openly available unlabelled real-life photos taken in uncontrolled conditions stratified by race, age and gender (using search engine queries such as ‘face photo’, ‘face pictures’, etc.). The training was conducted on a server equipped with four NVidia Titan accelerators. The trained neural network was validated on a dataset of 40,000 images belonging to 800 people, which was an out-of-sample part of the original dataset. The Euclidean distance threshold for the vectors belonging to the same person was 0.40 after the training was complete.

Finally, we trained a personality diagnostics neural network (NNPD), which was implemented as a multilayer perceptron (see Fig.  2 ). For that purpose, we used a training dataset (90% of the final sample) containing the questionnaire scores of 11,202 respondents and a total of 28,230 associated photographs. The NNPD takes the vector of the invariants obtained from NNCV as an input and predicts the Big Five personality traits as the output. The network was trained using the same hardware, and the training process took 9 days. The whole process was performed for male and female faces separately.

Data availability

The set of photographs is not made available because we did not solicit the consent of the study participants to publish the individual photographs. The test dataset with the observed and predicted Big Five scores is available from the openICPSR repository: https://doi.org/10.3886/E109082V1 .

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Acknowledgements

We appreciate the assistance of Oleg Poznyakov, who organized the data collection, and we are grateful to the anonymous peer reviewers for their detailed and insightful feedback.

Contributions

A.K., E.O., D.D. and A.N. designed the study. K.S. and A.K. designed the ML algorithms and trained the ANN. A.N. contributed to the data collection. A.K., K.S. and D.D. contributed to data pre-processing. E.O., D.D. and A.K. analysed the data, contributed to the main body of the manuscript, and revised the text. A.K. prepared Figs. 1 and 2. All the authors contributed to the final version of the manuscript.

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Correspondence to Alexander Kachur or Evgeny Osin .

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A.K., K.S. and A.N. were employed by the company that provided the datasets for the research. E.O. and D.D. declare no competing interests.

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Kachur, A., Osin, E., Davydov, D. et al. Assessing the Big Five personality traits using real-life static facial images. Sci Rep 10 , 8487 (2020). https://doi.org/10.1038/s41598-020-65358-6

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Big five personality traits and performance: A quantitative synthesis of 50+ meta-analyses

Affiliation.

• 1 Department of Psychology, University of North Carolina at Greensboro, Greensboro, North Carolina, USA.
• PMID: 34687041
• DOI: 10.1111/jopy.12683

Objective: The connection between personality traits and performance has fascinated scholars in a variety of disciplines for over a century. The present research synthesizes results from 54 meta-analyses (k = 2028, N = 554,778) to examine the association of Big Five traits with overall performance.

Method: Quantitative aggregation procedures were used to assess the association of Big Five traits with performance, both overall and in specific performance categories.

Results: Whereas conscientiousness yielded the strongest effect (ρ = 0.19), the remaining Big Five traits yielded comparable effects (ρ = 0.10, 0.10, -0.12, and 0.13 for extraversion, agreeableness, neuroticism, and openness). These associations varied dramatically by performance category. Whereas conscientiousness was more strongly associated with academic than job performance (0.28 vs 0.20), extraversion (-0.01 vs 0.14) and neuroticism (-0.03 vs -0.15) were less strongly associated with academic performance. Finally, associations of personality with specific performance outcomes largely replicated across independent meta-analyses.

Conclusions: Our comprehensive synthesis demonstrates that Big Five traits have robust associations with performance and documents how these associations fluctuate across personality and performance dimensions.

Keywords: Big Five; meta-analysis; performance; personality.

© 2021 Wiley Periodicals LLC.

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Big Five personality traits in the workplace: Investigating personality differences between employees, supervisors, managers, and entrepreneurs

1 Imperial College London, London, United Kingdom

Kreisha Lou Guzman

2 R&D, Macro Health Research Organization Inc., Quezon City, Philippines

Antonio Malvaso

3 Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy

Associated Data

Publicly available datasets were analyzed in this study. This data can be found here: https://www.understandingsociety.ac.uk .

Personality relates to employment status. Previous studies have mainly compared the difference between entrepreneurs and managers. It remains unknown how personalities differ in entrepreneurs, managers, supervisors, and employees. In this research, we answer the questions by analyzing data from Understanding Society: the UK Household Longitudinal Study (UKHLS) that consisted of 2,415 entrepreneurs, 3,822 managers, 2,446 supervisors, and 10,897 employees. By using a multivariate analysis of variance (MANOVA) and ANOVA, we found that employment status has a significant multivariate effect on personality traits ( F (5, 17,159) = 172.51, p < 0.001) after taking account into demographics. Moreover, there were also significant univariate effects for Neuroticism ( F (3,19502) = 16.61, P < 0.001), Openness ( F (3,19502) = 3.53, P < 0.05), Agreeableness ( F (3,19502) = 66.57, P < 0.001), Conscientiousness ( F (3,19502) = 16.39, P < 0.001), and Extraversion ( F (3,19502) = 31.61, P < 0.001) after controlling for demographics. Multiple comparisons revealed that entrepreneurs are characterized by low Neuroticism, high Openness, high Conscientiousness, and high Extraversion while managers had low Neuroticism, low Agreeableness, high Openness, high Conscientiousness, and high Extraversion. Finally, supervisors are associated with high Conscientiousness. Implications and limitations are discussed.

Introduction

Criterion-related validity studies strongly supported the role of personality in predicting employee job performance ( Ones et al., 2007 ; Chamorro-Premuzic and Furnham, 2010 ). Literature agrees that there is a significant relationship between personality and job performance across all occupational groups, managerial levels, and performance outcomes ( Barrick and Mount, 1991 ; Hurtz and Donovan, 2000 ; Barrick et al., 2001 ). Although higher Conscientiousness and lower Neuroticism were associated with higher job performance across most types of jobs, the relationship between Extraversion, Openness, and Agreeableness with job performance was found to be more context-dependent ( Barrick et al., 2001 ). Thus, it is important to understand how personality differs in different job positions.

Over the years, more and more people have found success in creating their businesses and working on their terms. With the number of successful entrepreneurs on the rise, researchers have become more interested in specific characteristics of entrepreneurs and how they affect their performance ( Kerr et al., 2018 ). A notable number of studies comparing the differences in Big Five personality traits (i.e., Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) between entrepreneurs and managers emerged between 1960 and 2000 ( Kerr et al., 2018 ). Managers were often compared to entrepreneurs (e.g., Zhao and Seibert, 2006 ), given the need of both groups to direct workers and manage multiple tasks. Both are crucial positions crucial in the company’s operations, but their roles are completely different. An entrepreneur is described as an individual who is “instrumental to the conception of the idea of an enterprise and its implementation” ( Kets de Vries, 1996 ) and “an innovator and a catalyst of change who continuously does things that were not done before and do not fit established societal patterns” ( Schumpeter, 1965 ). Meanwhile, a manager is defined as “the one who sets goals, plans and organizes the activities, motivates human resources, and controls the overall procedures.” ( Tovmasyan, 2017 ). Another important player in the organizational structure is the supervisor. According to Stevens and Ash (2001) , the supervisor is responsible for ensuring that the work of his subordinates is completed on time and at a satisfactory level of quality. Although the terms’ manager and supervisor are sometimes used interchangeably with managers, they are not the same. Managers are higher-level and higher-paid leaders whereas supervisors are closer to day-to-day activities of their teams to ensure the manager’s goals are met.

These observed characteristic differences in employment status are attributed to the “attraction-selection-attrition model” by Schneider (1987) . According to this model, “first, individuals are attracted to jobs commensurate with their personality traits (i.e., attraction). Second, organizational selection procedures result in the selection of individuals with similar personality scale scores for a particular job (i.e., selection). Finally, individuals who take jobs to which personality traits are not suited are more likely to leave their jobs (i.e., attrition)” ( Ones and Viswesvaran, 2003 ).

Specifically, combined evidence from the meta-analysis conducted by Zhao and Seibert (2006) reported that entrepreneurs were more open to experience, more conscientious, less agreeable, less neurotic, and but have similar levels of Extraversion compared to managers. However, many individual studies showed different patterns. One example is from a Canadian survey of 218 entrepreneurs and managers by Envick and Langford (2000) , and they found that entrepreneurs were significantly less conscientious, less agreeable, and less extroverted than managers.

Entrepreneurs were also consistently found to be more open than managers. Researchers hypothesized that an entrepreneur is likely to be attracted to constantly changing environments and the novelty of new challenges in a business venture ( Zhao and Seibert, 2006 ; Kerr et al., 2018 ). Individuals who thrived on challenges and novel environments presented creative solutions, business models, and products, and the Openness of entrepreneurs may help these functions ( Kerr et al., 2018 ). Meanwhile, managers are usually chosen by their superiors to execute and deliver high-quality results for a set of directives rather than for seeking novel solutions. Thus, it is hypothesized that an entrepreneur’s environment and job requirements might be more suitable for those who were more open ( Kerr et al., 2018 ).

Zhao and Seibert (2006) suggested that higher Conscientiousness, which is a composite of achievement motivation and dependability, is the most significant difference between entrepreneurs and managers. Their study also found that entrepreneurs and managers are similar in dependability, but entrepreneurs score significantly higher than managers in the achievement motivation facet. A meta-analysis by Collins et al. (2004) concluded that individuals who pursue entrepreneurial careers were significantly higher in achievement motivation than individuals who pursue other types of careers. Stewart and Roth (2007) similarly concluded that entrepreneurs have a higher need for achievement than managers. It is often hypothesized that achievement-oriented individuals set goals, maintain high standards, and have a strong sense of ownership. In contrast, there is insufficient evidence on whether entrepreneurs score higher than managers on Extraversion. Extraversion is a trait that measures the extent to which one is dominant, energetic, active, talkative, and enthusiastic ( Costa and McCrae, 1992 ). Several studies found that Extraversion is more fundamental for entrepreneurs than managers since entrepreneurs act as salespeople for their ideas to investors, partners, employees, and customers. However, no reliable difference was observed in the literature according to Zhao and Seibert (2006) . Further, Envick and Langford (2000) found that entrepreneurs are less extroverted than managers, suggesting that many entrepreneurs may run small businesses from their homes to be away from large bureaucracies that demand one to be relentlessly sociable.

Thus, although the personality differences between entrepreneurs and managers have been extensively studied and compared, much less is known about how personality would differ in entrepreneurs, managers, and supervisors from normal employees. Moreover, previous studies used a small sample size, which could be biased due to their reduced power. Understanding the personality differences between different employment statuses is important because understanding the personality trait differences between different employment statuses may have the potential to contribute to the established personality-job choice-job performance relationship, and thus contribute to employee selection. The aim of our study is to understand the personality difference between them by analyzing data on a large scale. We hypothesized that Openness and Conscientiousness are positively related to the employment status hierarchy (i.e., employee- > supervisor- > manager- > entrepreneur), Neuroticism and Agreeableness are negatively related to the employment status hierarchy, and Extraversion has little association with the employment status hierarchy.

Materials and methods

Sample and data collection.

Data were from Understanding Society: the UK Household Longitudinal Study (UKHLS), which has been collecting annual information from the original sample of UK households since 1991 (when it was previously known as The British Household Panel Study (BHPS). Data were ethically collected from this sample from 2011 to 2012. This data collection has been approved by the University of Essex Ethical Committee by letter dated 17 December 2010. Samples included (1) The General Population Sample (GPS), which is a clustered and stratified probability sample of approximately 24, 000 households living in the Great Britain and a sample of approximately 2000 households in the Northern Ireland in 2009, (2) The Ethnic Minority Boost Sample (EMBS), which consists of approximately 4000 households chosen from areas with high ethnic minorities, and (3) The British Household Panel Survey sample (BHPS), which is consisted of around 8000 households. Please refer to Lynn (2009) for more details. Each household is visited each year to collect relevant information. Interviews are conducted face-to-face in participants’ homes by trained interviewers or they completed a survey online. We excluded participants who were under the age of 18 or who were above the age of 99, and those who had missing fields in relevant variables. Thus, a total number of 19,580 participants remained in our analysis from the original 49,693 participants.

Measurement and analysis

Personality was measured using the 15-item version (3 items for each personality trait) of the Big Five Inventory with a Likert scale ranging from 1 (“disagree strongly”) to 5 (“agree strongly”). Personality scores were reversed when appropriate. The mean scores averaged across the three items for assessing each personality trait were used to represent scores for each personality trait. These shorter forms of personality measures have been approved to have good internal consistency, test-rest reliability, and convergent and discriminant validity ( Hahn et al., 2012 ; Soto and John, 2017 ). Participants also responded to questions regarding if they are entrepreneurs, managers, supervisors, or employees if they were workers. Demographics information was collected from participants as well ( Table 1 ). All analyses were conducted using a customized script on MATLAB 2018a. We used the mean scores of relevant items to represent each personality trait. A multivariate analysis of variance (MANOVA) and ANOVA were used to see the effect of employment status on personality traits in general and in detail with employment status and demographics as predictors. A multiple comparison test was used to assess the specific differences in each personality trait in different employment statuses.

Descriptive statistics of sociodemographic variables and personality traits.

Demographics can be found in Table 1 . Employment status had a significant multivariate effect on personality traits ( F (5, 17159) = 172.51, p < 0.001) after taking account into demographics. Moreover, there were also significant univariate effects for Neuroticism ( F (3,19502) = 16.61, P < 0.001), Openness ( F (3,19502) = 3.53, P < 0.05), Agreeableness ( F (3,19502) = 66.57, P < 0.001), Conscientiousness ( F (3,19502) = 16.39, P < 0.001), and Extraversion ( F (3,19502) = 31.61, P < 0.001) after controlling for demographics ( Table 2 ).

The results of the ANOVA for A. Neuroticism, B. Agreeableness, C. Openness, D. Conscientiousness, and E. Extraversion respectively.

Multiple comparison tests showed that entrepreneurs are less neurotic than normal employees (mean difference = −0.16, [95% CI: −0.24, −0.08], p < 0.001). Managers had lower Neuroticism scores than employees (mean difference = −0.16, [95% CI: −0.24, −0.08], p < 0.001) and supervisors (mean difference = −0.09, [95% CI: −0.18, 0.00], p < 0.05). Managers were less agreeable than supervisors (mean difference = −0.07, [95% CI: −0.14, 0.00], p < 0.05) and employees (mean difference = −0.06, [95% CI: −0.12, 0.00], p < 0.05). Regarding Openness, entrepreneurs were more open than managers (mean difference = 0.17, [95% CI: 0.09, 0.25], p < 0.001), supervisors (mean difference = 0.29, [95% CI: 0.20, 0.38], p < 0.001), and employees (mean difference = 0.37, [95% CI: 0.30, 0.45], p < 0.001). Similarly, managers had higher Openness scores than supervisors (mean difference = 0.20, [95% CI: 0.14, 0.27], p < 0.001) and employees (mean difference = 0.08, [95% CI: 0.01, 0.15], p < 0.05). Conscientiousness scores in entrepreneurs (mean difference = 0.11, [95% CI: 0.05, 0.16], p < 0.001), in managers (mean difference = 0.11, [95% CI: 0.06, 0.16], p < 0.001), in supervisors (mean difference = 0.11, [95% CI: 0.05, 0.17], p < 0.001) were significantly higher than that of in employees. Finally, entrepreneurs were more extroverted than supervisors (mean difference = 0.23, [95% CI: 0.14, 0.33], p < 0.001) and employees (mean difference = 0.25, [95% CI: 0.17, 0.32], p < 0.001). Managers were also more extraverted than supervisors (mean difference = 0.15, [95% CI: 0.07, 0.24], p < 0.001) and employees (mean difference = 0.17, [95% CI: 0.10, 0.24], p < 0.001; Figure 1 ).

The bar graph shows differences in personality traits between different employment statuses with standard error.

Token together, our study compared the personality differences between employees, supervisors, managers, and entrepreneurs using multivariate and univariate ANOVA after controlling for demographics with multiple comparison tests to assess specific differences between groups. Our study is the first study that compared the Big Five personality differences between these groups according to the best of our knowledge although previous studies have compared this difference between entrepreneurs and managers. A detailed discussion is provided in the following paragraphs.

Results showed that entrepreneurs and managers exhibit lower Neuroticism compared to employees. These findings were consistent with existing studies suggesting entrepreneurs are less neurotic ( Zhao and Seibert, 2006 ; Kerr et al., 2018 ). Lower levels of Neuroticism are described as having emotional stability that allows entrepreneurs to deal with stress and uncertainty, and develop a good working relationship with others ( Etemad et al., 2013 ). Another study done by Yitshaki (2021) also highlighted the need for entrepreneurs to keep their emotions in control because their firm’s growth might depend on how they manage these. Similarly, managers have to be emotionally stable to fulfill management duties. However, we did not find a significant difference in Neuroticism between entrepreneurs and managers ( Zhao and Seibert, 2006 ).

Similarly, we found a significant effect of employment status on Agreeableness. People with high Agreeableness were found to be more prosocial ( Costa and McCrae, 1992 ), and it seems to be crucial for the success of entrepreneurs to gain external resources from other organizations with the help of maintained relationships ( Street and Cameron, 2007 ). Specifically, we found that managers were less agreeableness than supervisors and employees. Indeed, although high Agreeableness may lead one to be considered trustworthy and build positive work relationships, it may prevent managers to drive hard bargains, look out for one’s own self-interest, and influence other people for one’s own advantage. All of these characteristics made it not desirable for managers because they may interfere with the manager’s ability to make difficult decisions which may affect subordinates and coworkers ( Zhao and Seibert, 2006 ).

Similarly, we found a significant effect of employment status on Openness, which is a trait that has been often characterized by creativity, being attracted to changing environments, and prefer variety over routine ( Kerr et al., 2018 ). Specifically, we found that managers were less open than supervisors and employees. Indeed, the goal of a manager is to control the whole procedure and ensure goals are met rather than being very creative and innovative, which requires less degree of Openness although managers’ Openness may be positively associated with organizational success ( Kay and Christophel, 1995 ).

This study also found that Openness in entrepreneurs is higher than that of managers, supervisors, and employees. Specifically, entrepreneurs were more open than managers, supervisors, and employees. Similarly, managers were more open than supervisors and employees. Entrepreneurs are characterized by their emphasis on innovation ( Zhao and Seibert, 2006 ). Creating a new venture may require the entrepreneur to come up with new or novel ideas, use creativities to solve problems that have not been encountered before, and make innovative products, business models, or strategies. Interestingly, we also found that managers are more open than supervisors and employees, which may indicate that even though enforcing the rules is important, being innovative in establishing policies and making strategies is also critical for the success of the manager as well.

Conscientiousness is described as a person’s ability to control their impulses, develop long-term goals, and consistently work on these goals to achieve them. In this study, we found that entrepreneurs, managers, and supervisors have higher Conscientiousness scores than normal employees. Despite mixed results of previous studies ( Envick and Langford, 2000 ; Collins et al., 2004 ; Stewart and Roth, 2007 ; Cantner et al., 2011 ), the role of Conscientiousness is generally considered important in entrepreneurship which was stressed by Ciavarella et al. (2004) as the positive link between long-term venture survival. Additionally, Hough and Oswald (2000) reported that Conscientiousness is the strongest predictor of managerial performance. Ülgen et al. (2016) discussed the relationship between Conscientiousness and management styles and found significant effects of Conscientiousness on management styles that require rational decision-making like authoritarian, protective, supporter, and laissez-faire styles but not on the unionized styles.

We also found that Extraversion scores in entrepreneurs and managers are significantly higher than that of supervisors or employees. Individuals with Extraversion tend to be dominant, energetic, talkative, and enthusiastic ( Costa and McCrae, 1992 ). Entrepreneurs are most likely to get involved in activities that require a high level of social skills, it is expected that they exhibit higher levels of Extraversion, which is heavily supported by our results. Thus, having jobs not requiring much interaction with other people could explain why average employees had the lower level of Extraversion among the other statuses of employment. The finding that entrepreneurs do not have higher Extraversion scores than managers seemed to be consistent with one previous study ( Awwad and Al-Aseer, 2021 ) but contradictory to others (e.g., Zhao and Seibert, 2006 ).

There are some limitations in this study. First, we used cross-sectional data and all the relationships in the current study were associative, which makes it hard to identify the causal effect. Thus, it remains unclear regarding if certain personality traits cause people to be in certain employment status or if employment status causes changes in personality traits. Second, we measured employment status in general, it is unclear how personality in a different occupation and in different employment statuses would differ. For instance, a salesman’s personality could totally differ from an assembly line worker as the main activity of a salesman is to engage with other people, which requires more social skill and thus have different personality traits. Moreover, compared to personality traits, characteristics such as general or emotional intelligence, temperament or motivation, or interests and aspirations may be more important in differentiating occupational positions ( McManus et al., 2003 ; Cheng and Furnham, 2012 ; Stoll et al., 2017 ).

This study provided novel insights and further understanding of how the Big Five personality traits vary across different employment statuses. A deeper comprehension of the connection between personality and employment status has the possibility to be useful in several practical fields. Although theories of vocational choice have found considerable application in the context of career counseling, different employment status as a career path has received less consideration in this literature. Our findings offer proof of the personality traits that set someone who is likely to be drawn to, chosen for, and stay in a different employment status. With this knowledge, people will be better able to match their strengths to the risks and opportunities presented by a professional career. The decisions made by venture capitalists, government funding organizations, and others on their support for certain employment status may be influenced, at least in part, by their own theories and models of employment status and personality. Decision-makers may become more realistic and modest in the implementation of their own implicit ideas if they are aware of the true relationship between personality and employment status. Large firms frequently work to foster innovation by choosing staff members who will act as internal entrepreneurs (intrapreneurs) and elevating them to important positions. The study’s findings can be used to create suitable selection and placement standards for such choices. Furthermore, this study has consequences for how people interested in entrepreneurship should be trained. Even though the Big Five fundamental personality traits are generally stable, many of the behaviors connected to them can be learned with experience and effort. For instance, research by Barrick et al. (1993) revealed that people who scored highly on Conscientiousness were more likely to develop and stick to goals, which was then linked to their better job performance. Both the person seeking to pursue different positions and society at large may find training intended to promote the behaviors associated with employment status to be very useful. We don’t believe that personality theory offers a comprehensive theory of employment status or even covers all the possible themes. Instead, our findings demonstrate that personality must be taken into account as one significant element in a multidimensional model of the variables, processes, and contextual factors influencing employment status and the establishment of new ventures.

Data availability statement

Ethics statement.

The studies involving human participants were reviewed and approved by University of Essex. The patients/participants provided their written informed consent to participate in this study.

Author contributions

WK: conceptualization, data curation, formal analysis, investigation, methodology, resources, software, writing – original draft, and writing – review and editing. KG: writing – original draft. AM: writing – review and editing. All authors contributed to the article and approved the submitted version.

Funding Statement

This work was supported by the Imperial Open Access Fund.

Conflict of interest

KG was employed by the company Macro Health Research Organization Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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• DOI: 10.1007/978-3-030-10576-1_300044
• Corpus ID: 241995225

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Five-Factor Model of Personality

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• Michael S. Chmielewski 3 &
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Big five, The

The five-factor model (also referred to as “The Big Five”) is the most widely used and empirically supported model of normal personality traits. It consists of five main traits: Neuroticism, Extraversion, Openness (to experience), Agreeableness, and Conscientiousness.

Description

The five-factor model (FFM; Digman, 1990 ), or the “Big Five” (Goldberg, 1993 ), consists of five broad trait dimensions of personality. These traits represent stable individual differences (an individual may be high or low on a trait as compared to others) in the thoughts people have, the feelings they experience, and their behaviors. The FFM includes Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness. Neuroticism is the tendency to experience negative emotions (e.g., sadness, anxiety, and anger) and to have negative thoughts (e.g., worry, self-doubt). In general, Neuroticism represents the predisposition to experience psychological distress. It has been...

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References and Readings

Digman, J. M. (1990). Personality structure: Emergence of the five-factor model. Annual Review of Psychology, 41 , 417–440.

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Chmielewski, M.S., Morgan, T.A. (2013). Five-Factor Model of Personality. In: Gellman, M.D., Turner, J.R. (eds) Encyclopedia of Behavioral Medicine. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1005-9_1226

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ORIGINAL RESEARCH article

The relationship between big five personality and social well-being of chinese residents: the mediating effect of social support.

• 1 School of Public Finance and Management, Yunnan University of Finance and Economics, Kunming, China
• 2 Tourism and Cultural Industry Research Institute, Yunnan University of Finance and Economics, Kunming, China
• 3 International College, National Institute of Development Administration, Bangkok, Thailand
• 4 National Centre for Borderlands Ethnic Studies in Southwest China at Yunnan University (NaCBES), Yunnan University, Kunming, China

Previous studies have noted that personality traits are important predictors of well-being, but how big five personality influences social well-being is still unknown. This study aims to examine the link between big five personality and five dimensions of social well-being in the Chinese cultural context and whether social support can play the mediating effect in the process. This study included 1,658 participants from different communities in China, and regression analyses were conducted. Results revealed that five personality traits were significantly related to overall social well-being; extraversion was significantly related to social integration; agreeableness was positively related to all five dimensions of social well-being; conscientiousness was positively related to social actualization, social coherence, and social contribution; neuroticism was negatively related to social integration, social acceptance, social actualization, and social coherence; openness was positively related to social integration, social acceptance, social coherence, and social contribution. Social support plays mediating roles in the relationships between extraversion/agreeableness/conscientiousness/neuroticism/openness and social well-being, respectively.

Introduction

Personality variables are strong predictors of well-being, a large body of research has explored the associations between big five personality and subjective well-being ( DeNeve and Cooper, 1998 ; Gutiérrez et al., 2005 ). Unfortunately, the psychological construct of well-being portrays adult well-being as a primarily private phenomenon largely neglecting individuals’ social lives ( Keyes, 2002 ; Hill et al., 2012 ). Individuals are embedded in social structures and communities; as such, it is necessary to evaluate one’s circumstance and functioning in a society; more attention needs to be devoted on the topic of social well-being ( Keyes, 1998 ). Previous studies focused on the social well-being from the perspective of interpersonal factors, such as sense of community ( Sohi et al., 2017 ), and civic engagement ( Albanesi et al., 2010 ). However, less work has examined social well-being from the level of the individual ( Keyes and Shapiro, 2004 ).

Although there are few studies focusing on the relationship between five personality traits and social well-being ( Hill et al., 2012 ; Joshanloo et al., 2012 ), their data come from United States or Iran; Chinese cultural background has been conducted to a lesser extent. Different countries have different cultural traditions. Personality is created through the process of enculturation ( Hofstede and McCrae, 2004 ). The interplay of personality and cultural factors was found to predict residents’ well-being significantly ( Diener and Diener, 1995 ). Confucius culture has embedded itself in the daily life of the Chinese, however, studies about the relationship between personality and social well-being under the context of Chinese culture are largely overlooked.

In addition, present studies ( Hill et al., 2012 ; Joshanloo et al., 2012 ) examine only the direct effect of personality on social well-being. The mechanism between big five personality and five dimensions of social well-being has been neglected. Additionally, social support can help individuals protect against the health consequences of life stress and increase their well-being ( Cobb, 1976 ; Siedlecki et al., 2014 ). Thus, following a social support perspective, the present study examined not only the relationship between five personality traits and domains of social well-being, but also whether social support can play a mediating effect in the relationship between big five personality and social well-being.

Literature Review and Hypothesis

Big five personality and social well-being.

The big five personality consists of five general traits: extraversion, neuroticism, openness, agreeableness, and conscientiousness ( John and Srivastava, 1999 ). Extraversion refers to the degree to which one is energetic, social, talkative, and gregarious. Agreeableness reflects the extent to which one is warm, caring, supportive, and cooperative and gets along well with others. Conscientiousness involves the extent to which one is well-organized, responsible, punctual, achievement-oriented, and dependable. Neuroticism means the degree to which one is worry, anxious, impulsive, and insecure. Openness reflects the degree to which one is imaginative, creative, curious, and broad-minded ( Barrick et al., 2001 ; Funder and Fast, 2010 ). Many scholars assessed personality under different culture context by a combined emic–etic approach ( John and Srivastava, 1999 ; Cheung et al., 2001 ). Even if there were researches that demonstrated several unique dimensions of personality under the Chinese culture ( Cheung et al., 2001 ; Cheung, 2004 ), the generalizability of the big five trait taxonomy in China is still confirmed ( Li and Chen, 2015 ; Minkov et al., 2019 ). Previous studies have consistently demonstrated that the big five are associated with subjective well-being ( DeNeve and Cooper, 1998 ; Gutiérrez et al., 2005 ), however, the findings are mixed under different cultural context. For instance, Ha and Kim (2013) found openness has a positive effect on subjective well-being in South Korea residents, whereas another study by Hayes and Joseph (2003) in England found that openness was not associated with each of the three measures of subjective well-being.

Culture variables can explain differences in mean levels of well-being ( Diener et al., 2003 ). With the uniqueness of Confucian cultural tradition and social setting, it is noteworthy to discuss the relationship between personality and well-being in Chinese cultural background, especially social-well-being.

Individuals are embedded in social structures. They need to face social challenges and evaluate their life quality and personal functioning by comparison to social criteria ( Keyes and Shapiro, 2004 ). However, the research about social well-being has been almost completely neglected in the hedonic and psychological well-being models ( Keyes, 2002 ; Joshanloo et al., 2012 ). Keyes (1998) proposed social well-being, which indicates to what degree individuals are functioning well in the social world they are embedded in. Social well-being can be described on multiple dimensions, including social integration, social contribution, social acceptance, social coherence, and social actualization. Social integration is the extent to which people feel commonality and connectedness to their neighborhood, community, and society. Social contribution refers to a value evaluation that one can provide to the society. Social acceptance entails a positive view of human nature and believes that people are kind. Social coherence refers to the perception of the quality and operation of the social world and reflects a belief that society is meaningful. Social actualization is the evolution of the potential and of society and includes a sense that social potentials can be realized through its institutions and citizens. In summary, social well-being emphasizes individuals’ perceptions of and attitudes toward the whole society. Prior studies have found the effect of sense of community ( Sohi et al., 2017 ), and social participation ( Albanesi et al., 2010 ) on social well-being, Also, some studies have shown the outcomes of social well-being, such as anxiety problems ( Keyes, 2005 ), general mental and physical health ( Zhang et al., 2011 ), and prosocial behaviors ( Keyes and Ryff, 1998 ). Personality traits and cultural factors are important predictors of well-being ( Diener et al., 2003 ). However, the only studies about personality and social well-being were conducted in Iran or United States. It is still not known whether the association would be similar in a different cultural context ( Hill et al., 2012 ; Joshanloo et al., 2012 ). For example, with the data from the MIDUS sample, Hill et al. (2012) found social well-being is positively related to extraversion, agreeableness, conscientiousness, emotional stability, and openness. In addition, previous studies did not test the correlation between five personality traits and five domains of social well-being entirely ( Joshanloo et al., 2012 ). Personality shapes many of the attitudes and behaviors that form Keyes’ different dimensions of social well-being. Thus, certain personalities would predict social well-being; for example, extraverted persons should be more socially integrated, whereas agreeable individuals should possess higher levels of social acceptance. Based on the above, we hypothesize the following:

Hypothesis 1 a : Extraversion is positively related to social well-being.

Hypothesis 1 b : Agreeableness is positively related to social well-being.

Hypothesis 1 c : Conscientiousness is positively related to social well-being.

Hypothesis 1 d : Neuroticism is negatively related to social well-being.

Hypothesis 1 e : Openness is positively related to social well-being.

The Mediating Effect of Social Support

Social support refers to individuals’ psychological or material resources from their own social networks that can assist them to cope with stressful challenges in daily lives ( Cohen, 2004 ). It comes from a variety of sources, such as friends, family, and significant others ( Taylor, 2011 ). Social support comprised both received and perceived social support ( Oh et al., 2014 ; Hartley and Coffee, 2019 ). However, many studies showed that perceived social support is more effective at predicting residents’ mental health than the received social support ( Cohen and Syme, 1985 ). Perceived social support indicates recipients’ perceptions concerning the general availability of support ( Sarason et al., 1990 ), which fosters a sense of social connectedness in a network and provides resources with which to overcome obstacles in their lives ( Lee et al., 2001 ; Chen, 2013 ). Social support theory emphasizes that social support is an important resource that can help individuals protect against life stress and increase their quality of lives ( Cobb, 1976 ; Cohen and Wills, 1985 ). Numerous studies have explored the associations between social support and well-being, including subjective well-being ( Brannan et al., 2013 ; Siedlecki et al., 2014 ) and psychological well-being ( Jasinskaja-Lahti et al., 2006 ; Wong et al., 2007 ). Although Inoue et al. (2015) found social support mediated the effect of team identification on community coherence, little research has addressed the effect of social support on social well-being. The benefits of social support come into play when individuals have to deal with social challenges and problems. Individuals with high level of social supports will better face social tasks ( Cox, 2000 ). Harmonious social relationships can help residents to satisfy their social needs, better understand, and be confident of the social world. Therefore, their social well-being will increase.

Personality traits are stable predictors of social support ( Swickert et al., 2010 ; Udayar et al., 2018 ; Barańczuk, 2019 ). Big five personality traits are found to be related to social support. Individuals with high levels of neuroticism report greater vulnerability to stress and negative affectivity, which could decrease the availability of social support ( Ayub, 2015 ). Individuals who score high on extraversion always seek social interactions and tend to be cheerful and friendly. The positive emotions could increase their social support ( Swickert et al., 2010 ). Individuals with high openness to experience are characterized by greater openness to emotions, appreciation of art and beauty, intellect, and liberalism. These characteristics would be significantly related to social support ( Barańczuk, 2019 ). Agreeableness characteristics, such as modesty, compliance, and trust, may facilitate individuals building a more extensive social support network ( Barańczuk, 2019 ). Conscientiousness are characterized by achievement-striving, self-discipline, orderliness, and dutifulness. These tendencies can help individuals better cope with life stress, so it is positively related to social support ( Ayub, 2015 ). Culture is an important moderator between big five personality traits and social support association, but it has been largely overlooked in previous studies ( Barańczuk, 2019 ). Therefore, studies about the relationship between five personality traits and social support under Chinese background are needed.

Previous studies discuss only the direct effect of personality on social well-being, but it remains unknown what mechanism(s) may explain this relation. Social support plays an important stress-buffering role when individuals are under high levels of life stress ( Cohen, 2004 ). Individuals with different levels of personality traits (extraversion, agreeableness, conscientiousness, neuroticism, openness) will form different types of social support network. Further, social support will help individuals cope with social challenges and increase their social well-being. Based on the above, we hypothesize the following:

Hypothesis 2 a : Social support mediates the relationship between extraversion and social well-being.

Hypothesis 2 b : Social support mediates the relationship between agreeableness and social well-being.

Hypothesis 2 c : Social support mediates the relationship between conscientiousness and social well-being.

Hypothesis 2 d : Social support mediates the relationship between neuroticism and social well-being.

Hypothesis 2 e : Social support mediates the relationship between openness and social well-being.

Materials and Methods

Participants and procedure.

Community residents from five different districts in Kunming, Yunnan Province, were selected as participants by stratified random sampling technique. Four hundred questionnaires were distributed to each district. Participants would complete the questionnaires in a face-to-face interaction with an enumerator who helped them to answer the questionnaire that was in paper format. When we administered the survey, we emphasized that the data were collected for research purposes. Participants were encouraged to answer all the questions honestly and were reminded that their responses would be anonymous. Upon completion of answering the questionnaire, participants received a small gift (e.g., tissue) as compensation for their participation. A total of 2,000 questionnaires were distributed, and 1,721 responded. After dropping incomplete and invalid data, 1,658 respondents remained. The final sample consisted of 932 females (56.2%) and 726 males (43.8%), aged 18–81 years (mean = 30.73 years, SD = 11.98 years).

Big Five Personality

The 44-item Big Five Inventory (BFI; John et al., 1991 ) was used to measure the five broad personality traits. All items were evaluated on a 5-point Likert scale, ranging from “strongly disagree” to “strongly agree.” Coefficient α reliabilities for the five trait scales in the present study were 0.707 for extraversion, 0.712 for agreeableness, 0.729 for conscientiousness, 0.706 for neuroticism, and 0.733 for openness. The Chinese version of BFI we used had been translated from English using common back-translation procedures ( Brislin, 1970 ; Li and Chen, 2015 ), and the validity had been conformed in previous studies ( Zhou, 2010 ; Li and Chen, 2015 ).

Social Support

Participants rated their social support from Chen and Yu (2019) using scales that ranged from 1 (strongly disagree) to 5 (strongly agree). The measure comprised three items, such as “It is easy for me to find someone to help when I meet with difficulties.” The entire survey demonstrated good reliability (α = 0.733).

Social Well-Being

Social well-being was measured through Keyes’s (1998) 15-item scale composed of five dimensions: social actualization, social integration, social acceptance, social contribution, and social coherence. Responses to this measure were assessed on a 5-point scale, from “strongly disagree” to “strongly agree.” An example of measure items was “I believe that people are kind.” The reliabilities of five dimensions were good (ranging from 0.702 to 0.725), and overall α reliability for the present sample was 0.791. Previous studies had confirmed the validity of social well-being measurement of Chinese version we used ( Miao and Wang, 2009 ; Chen and Yu, 2019 ; Chen et al., 2020 ).

The Common Method Bias Examination

As one of the main sources of measurement error, common method variance is a potential problem, which may be a threat to the validity of the conclusions. We tested for common method bias with a single-factor measurement model by combining all items into a single factor ( Podsakoff et al., 2003 ; Rhee et al., 2017 ). Results showed a poor model fit [Comparative Fit Index (CFI) = 0.763, Tucker-Lewis Index (TLI) = 0.695, Goodness-of-Fit Index (GFI) = 0.719, Root Mean square Residual (RMR) = 0.025, Root Mean Square Error of Approximation (RMSEA) = 0.109]. The above results suggested that there was no common method bias effect.

Descriptive Statistics and Correlations Between the Study Variables

There is no significant difference between the five different districts in Kunming. The correlation coefficients, means, and standard deviations are shown in Table 1 . All the big five personality traits were correlated significantly with social support and five domains of social well-being (expect agreeableness and social coherence). Extraversion, agreeableness, conscientiousness, and openness were correlated positively with domains of social well-being (expect agreeableness and social coherence) and social support, whereas neuroticism correlated negatively with domains of social well-being and social support.

Table 1. Correlations, means, and standard deviations of all study variables.

Regression Analyses

Statistical analyses were conducted with the Statistical Package for Social Sciences (SPSS, version 22.0). Based on preliminary analyses, multiple regression analyses were conducted to assess the relationship between the big five personality domains and dimensions of social well-being. Both gender and age were statistically controlled during the regression analysis, because there is evidence to show that social well-being likely increases with one’s age ( Chen and Li, 2014 ) and that men generally score higher on well-being than women do ( Miao and Wang, 2009 ). OLS regression was used to test the hypothesis. In each regression analysis, one social well-being dimension was entered as the dependent variable; gender, age, and all five personality domains were entered as potential predictors. Results of the regression analyses are presented in Table 2 . Five personality traits were significant predictors of overall social well-being. Extraversion (β = 0.052, p ≤ 0.05), agreeableness (β = 0.197, p ≤ 0.001), conscientiousness (β = 0.138, p ≤ 0.001), and openness (β = 0.156, p ≤ 0.001) are positively related to social well-being, whereas neuroticism (β = −0.171, p ≤ 0.001) is negatively related to social well-being. H1 a , H1 b , H1 c , H1 d , and H1 e are supported. Extraversion (β = 0.118, p ≤ 0.001), agreeableness (β = 0.162, p ≤ 0.001), neuroticism (β = −0.065, p ≤ 0.05), and openness (β = 0.086, p ≤ 0.001) were significant predictors of social integration. Agreeableness (β = 0.268, p ≤ 0.001), neuroticism (β = −0.102, p ≤ 0.001), and openness (β = 0.089, p ≤ 0.001) were significantly associated with social acceptance. Agreeableness (β = 0.168, p ≤ 0.001), conscientiousness (β = 0.111, p ≤ 0.001), and neuroticism (β = −0.110, p ≤ 0.001) predicted social actualization significantly. Agreeableness (β = −0.088, p ≤ 0.001), conscientiousness (β = 0.060, p ≤ 0.05), neuroticism (β = −0.241, p ≤ 0.001), and openness (β = 0.125, p ≤ 0.001) were found to be predicting social coherence. Agreeableness (β = 0.120, p ≤ 0.001), conscientiousness (β = 0.191, p ≤ 0.001), and openness (β = 0.164, p ≤ 0.001) were found to be predictors of social contribution.

Table 2. Results of regression analyses for five personality traits predicting dimensions of social well-being.

Mediation Analyses

Further, mediation analysis was performed to determine whether the effect of big five personality on social well-being was mediated by social support. Mediation analyses were conducted following the recommendations of Preacher and Hayes (2004) , using the PROCESS macro (version 3.0), developed by Hayes (2013) . The current study used 5,000 bootstrapped samples with a 95% confidence interval. The results of this analysis are shown in Table 3 . The results suggested five personality traits are related to social support significantly, and social support is positively related to social well-being. In addition, social support mediated the relationship between five personality traits and social well-being. H2 a , H2 b , H2 c , H2 d , and H2 e are supported.

Table 3. Summary of mediation analyses on five personality traits and social well-being (5,000 bootstraps).

Discussion and Conclusion

The results obtained from the survey of 1,658 Chinese residents demonstrated the effects of five personality traits on five dimensions of social well-being and the mediating role of social support in the associations between big five personality and social well-being.

Theoretical Contributions

Research on linkages between big five personality domains and five dimensions of social well-being conducted in China will likely contribute to the extant personality and well-being literature. First, this study provides empirical evidence about the relationship between big five personality and social well-being. The association between the big five personality and social well-being was evidenced in our study. However, our research also showed some inconsistencies with previous researches ( Joshanloo et al., 2012 ). From our results, extraversion was significantly related to social integration; agreeableness was positively related to all five dimensions of social well-being; conscientiousness was positively related to social actualization, social coherence, and social contribution; neuroticism was negatively related to social integration, social acceptance, social actualization, and social coherence; openness was positively related to social integration, social acceptance, social coherence, and social contribution. This inconsistency may be explained by the fact that the differences between Iran and China. For instance, Iran is a non-Arab Muslim country; the interactions in Iran are regulated partly by religious norms ( Joshanloo et al., 2012 ). In China, with the Reform and Opening, the way of thinking and behavior of Chinese are becoming more and more open and innovative ( Ma, 2013 ). The goal of community construction in China is to establish the autonomous system of community residents ( Fei, 2002 ). Community residents’ committee is an important organization of residents’ self-governing and self-service ( Sun, 2016 ). Thus, most community residents can participate in community management and satisfy their own service needs via residents’ committee, which will benefit residents’ life quality.

Second, the study highlights the effect of social support on social well-being. The existing literature has shown the relationship between social support and subjective well-being or psychological well-being ( Jasinskaja-Lahti et al., 2006 ; Brannan et al., 2013 ). Further, our study demonstrated social support is positively related to social well-being. Well-being is increasingly being associated with social and cultural relationships ( Helliwell and Putnam, 2004 ). Community in China is increasingly becoming a place for residents to integrate into urban society ( Chen et al., 2020 ). One of the most important responsibilities of the community is to achieve the society reconstruction ( Fei, 2002 ). Thus, during the development of community, the Chinese government was committed to improving the quality of community services, which may provide more opportunities for residents to get more social support. Individuals having high social support means they had selected and built large and effective social networks, which can help to overcome difficulties in lives. With the help from their social relations, they will give a high appraisal to their circumstances and functioning in society; their social well-being also increases.

Third, the mediating effects were found for social support for relation between extraversion/agreeableness/conscientiousness/neuroticism/openness and social well-being. This may contribute to the literature on the relationship between big five personality and social well-being ( Hill et al., 2012 ; Joshanloo et al., 2012 ). Previous studies neglected to examine the relationship and the mechanism between big five personality and social well-being from the perspective of the community. Community is an important place for residents’ daily activities. Individuals with different personality traits may build their social relations in different ways. Friends or family or neighbors around them may behave with different reactions. The different levels of social support will influence their evaluation of the social world, which may cause different levels of social well-being.

Practical Implications

Our study provides valuable insight into how individuals of different traits to improve their social well-being. Social support serves as a mediator in the relationship between big five personality and social well-being. The results also affirm the importance of social support that can enhance social well-being. When one’s psychological, social, and/or resource needs are met, one is likely to experience greater social support, which is important for their well-being. Therefore, it is possible for residents to promote social support. Individuals should spend more time participating in community public affairs or other social activities that could offer opportunities for them to establish meaningful relationship with neighbors or friends.

Limitations and Future Research

Despite these findings, our research is not without limitations. First, culture is an important factor that can influence both personality traits and well-being ( Diener et al., 2003 ; Hofstede and McCrae, 2004 ). Our study just discussed the mediating effect of social support between personality and social well-being. Future research should explore the effects of different cultural variables (such as power distance, collectivism/individualism etc.,). In addition, comparative studies among different countries or regions are needed. Second, the cross-sectional design means that no causal conclusions for the found relationship can be made. Consequently, future researches should adopt longitudinal or experimental design to ascertain the relationship. Third, social support has usually been classified into several specific forms, such as informational support, emotional support, perceived social support ( Taylor, 2011 ). In current study, we just regarded perceived social support as the mediating variable. So, future research should examine the effects of different forms of social support.

The research used a sample drawn from 1,658 Chinese residents to investigate the relationship between big five personality and social well-being and the mediating effect of social support in the relationship between big five personality and social well-being. Results of this study support previous studies that highlighted the relationship between big five personality and social support ( Swickert et al., 2010 ; Barańczuk, 2019 ). In addition, this study demonstrated the effects of five personality traits on five dimensions of social well-being. Lastly, the results demonstrated the mediating role of social support in the associations between extraversion/agreeableness/conscientiousness/neuroticism/open ness and social well-being, respectively.

Data Availability Statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Yunnan University of Finance and Economics. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author Contributions

YY, YZ, and JL designed the research and wrote the manuscript. YY and YZ are co-first authors of the article. All authors planned and conducted the data collection. YZ, JZ, and DL analyzed the data and revised the manuscript. All authors listed have made direct and intellectual contribution to the article and approved the final version for publication.

This study was supported by the Chinese National Natural Science Fund (72064042), the Post-project of Chinese Ministry of Education (18JHQ080), the Philosophy and Social Science Research Project in Yunnan Province (QN202026), and the Science Research Fund of Yunnan Provincial Department of Education (2020J0384).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords : big five personality, social support, social well-being, China, mediating effect

Citation: Yu Y, Zhao Y, Li D, Zhang J and Li J (2021) The Relationship Between Big Five Personality and Social Well-Being of Chinese Residents: The Mediating Effect of Social Support. Front. Psychol. 11:613659. doi: 10.3389/fpsyg.2020.613659

Received: 03 October 2020; Accepted: 31 December 2020; Published: 05 March 2021.

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Copyright © 2021 Yu, Zhao, Li, Zhang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jiewei Li, [email protected]

† These authors share first authorship

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• Open access
• Published: 19 February 2023

Big five model personality traits and job burnout: a systematic literature review

• Giacomo Angelini 1  

BMC Psychology volume  11 , Article number:  49 ( 2023 ) Cite this article

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Job burnout negatively contributes to individual well-being, enhancing public health costs due to turnover, absenteeism, and reduced job performance. Personality traits mainly explain why workers differ in experiencing burnout under the same stressful work conditions. The current systematic review was conducted with the PRISMA method and focused on the five-factor model to explain workers' burnout risk.

The databases used were Scopus, PubMed, ScienceDirect, and PsycINFO. Keywords used were: “Burnout,” “Job burnout,” “Work burnout,” “Personality,” and “Personality traits”.

The initial search identified 3320 papers, from which double and non-focused studies were excluded. From the 207 full texts reviewed, the studies included in this review were 83 papers. The findings show that higher levels of neuroticism (r from 0.10** to 0.642***; β from 0.16** to 0.587***) and lower agreeableness (r from − 0.12* to − 0.353***; β from − 0.08*** to − 0.523*), conscientiousness (r from -0.12* to -0.355***; β from − 0.09*** to − 0.300*), extraversion (r from − 0.034** to − 0.33***; β from − 0.06*** to − 0.31***), and openness (r from − 0.18*** to − 0.237**; β from − 0.092* to − 0.45*) are associated with higher levels of burnout.

Conclusions

The present review highlighted the relationship between personality traits and job burnout. Results showed that personality traits were closely related to workers’ burnout risk. There is still much to explore and how future research on job burnout should account for the personality factors.

Peer Review reports

Introduction

Burnout: origin, evolution, and definition.

Since the 1970s, when most research in occupational health psychology was focused on industrial workers, studies on burnout have seen a substantial increase. Initially considered a syndrome exclusively linked to helping professions [ 1 , 2 , 3 , 4 ], burnout has been adopted by a broader range of human services professionals [ 5 , 6 ]. Job burnout’s construct has undergone considerable conceptual and methodological attention in the last fifty years. Nowadays, job burnout is considered a multidimensional construct closely referred to as repeated exposure to work-related stress (e.g., [ 7 ]). According to the original theoretical framework, job burnout is defined chiefly as referring to feelings of exhaustion and emotional fatigue, cynicism, negative attitudes toward work, and reduced professional efficacy [ 6 ].

While the relationship between socio-demographic, organizational, and occupational factors and burnout syndrome have received significant attention, the relationship between burnout and individual factors, such as personality, is less explored (for a meta-analysis, see [ 8 ]).

Therefore, it is interesting to investigate whether there is sufficiently convincing evidence to indicate that personality factors play a role in predictors of job burnout. Investigating to what extent personality factors predict job burnout could include a measure of these factors in the selection processes of workers. At the same time, it could also allow preventive actions to support all those at risk of job burnout. This literature review involved a search for cohort studies published since 1993, which used self-report measures of personality traits and job burnout and investigated the relationships between these variables.

Personality and job burnout

In the past, research on this issue has been chiefly haphazard and scattered ([ 9 , 10 ] for a meta-analysis; [ 11 ]). Indeed, personality has often been evaluated in terms of positive or negative affectivity (respectively, e.g., [ 12 , 13 ]), adopting the type A personality model (e.g., [ 14 ]), or the concept of psychological hardiness [ 15 ]. More recently, burnout research focused on the relationship between workers’ personalities measured by the Big Five personality model and their burnout syndrome [ 16 , 17 ]. More specifically, neuroticism (e.g., [ 18 , 19 ]) and extraversion personalities (e.g., [ 20 ]) were abundantly investigated in the scientific panorama (for review; [ 21 ]).

Personality traits according to the five-factor model (FFM)

Since the twentieth century, scholars and researchers have increasingly dedicated themselves to studying this topic, given the importance assumed by personality in the psychological panorama. One of the most famous and relevant approaches to the study of character is the five-factor model (FFM) of personality traits (often referred to as the “Big Five”) proposed by McCrae & Costa [ 22 , 23 ]. As a multidimensional set, personality traits include individuals’ emotions, cognition, and behavior patterns [ 23 – 26 ]. Furthermore, the FFM is the most robust and parsimonious model adopted to understand personality traits and behavior reciprocal relationships [ 27 ] due to two main reasons: its reliability across ages and cultures [ 28 , 29 ] and its stability over the years [ 30 ]. According to several scholars, the FFM consists of five personality traits: agreeableness, conscientiousness, extraversion, neuroticism, and openness [ 23 , 25 , 26 , 31 ]. Agreeableness refers to being cooperative, sympathetic, tolerant, and forgiving towards others, avoiding competition, conflict, pressuring, and using force [ 32 ]. Conscientiousness is reflected in being precise, organized, disciplined, abiding by principles and rules, and working hard to achieve success [ 33 ]. Extraversion is related to the quantity and intensity of individual social interaction characteristics. It is displayed through higher degrees of sociability, assertiveness, talkativeness, and self-confidence [ 32 ]. Neuroticism reflects people’s loss of emotional balance and impulse control. It is characterized by a prevalence of negative feelings and anxiety that are attempted to cope with through maladaptive coping strategies, such as delay or denial [ 29 , 34 ]. Openness is reflected in intellectual curiosity, open-mindedness, untraditionality and creativity, the preference for independence, novelty, and differences [ 33 , 35 ]. In the last thirty years, the Big Five model has been recognized as a primary representation of salient and non-pathological aspects of personality, the alteration of which contributes to the development of personality disorders [ 36 – 40 ], such as antisocial, borderline, and narcissistic personality disorders [ 41 ].

Although the role of the work environment as a predictor of burnout has been broadly documented (e.g., [ 5 , 6 , 11 ]), it cannot be neglected the effect that personality has on the development of this syndrome. Even reducing or eliminating stressors related to the work environment, some people may still experience high levels of burnout (e.g., [ 42 ]). For this reason, it is necessary to know the associations between personality traits and job burnout to identify the workers most prone to burnout and implement more risk-protection activities. Consequently, based on the literature presented above, this PRISMA review aimed to shed some light on the role that personality traits according to the Five Factors Model—Agreeableness, Conscientiousness, Extraversion, Neuroticism, and Openness—play in the development of job burnout.

Protocol and registration

The systematic analysis of the relevant literature for this review followed procedures based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) process [ 43 – 45 ], a checklist of 27 items which together with a flow-chart (see Fig.  1 ) constitute the most rigorous guide to systematic reviews with or without meta-analysis. The systematic analysis of the relevant literature for this review followed procedures based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) process [ 43 – 45 ].

Diagram flow of information through the different phases of a systematic review

The PRISMA method intends to provide a checklist tool for creating systematic reviews of quality literature.

Eligibility criteria

The study was conducted by extensively searching articles published before June 30th, 2021 (time of research), limited to papers in journals published in English. Review articles, meta-analyses, book chapters, and conference proceedings were excluded. Articles investigating the relationship between personality traits and job burnout in any field of employment, except athletic and ecclesiastical, were included.

Information sources

The databases PsycINFO, PubMed, Scopus, and ScienceDirect, were used for the systematic search of relevant studies applying the following keywords:

* Burnout * AND * Personality *

* Burnout * AND * Personality traits *

* Job burnout * AND * Personality *

* Work burnout * AND * Personality *

* Job burnout * AND * Personality traits *

* Work burnout * AND * Personality traits *

The initial search identified 3320 papers. The details (title; author/s; year of publication; journal) of the documents identified for inclusion across all inquiries were placed in a separate excel document. After removing duplicates, reviewing titles, and reading abstracts (see Fig.  1 ), the papers were reduced to 207, of which full-text records were read. Studies selected in total for inclusion in this review were limited to the five dimensions of the Big Five Factor model [ 46 ] and were 83 papers.

Study selection

As shown by the Prisma Diagram flow (Fig.  1 ), a total of 83 studies were identified for inclusion in the review. Via the initial search process have been identified total of 3320 studies (Scopus, n = 1339; PubMed, n = 515; ScienceDirect, n = 181; PsycInfo, n = 1285). After excluding duplicates, the remaining studies were 1455 of these 1421 records analyzed, and 1195 were discarded. After reviewing the abstracts, these papers did not meet the criteria. Of the remaining 226 full texts, the 207 papers available were examined in more detail, and it emerged that 112 studies did not meet the inclusion criteria as described. Furthermore, to ensure that only studies that had received peer review and met certain quality indicators were included, the SCImago Journal Rank (SJR) was inspected. SCImago considers the reputation and quality of a journal on citations, based on four quartiles used to classify journals from the highest (Q1) to the lowest (Q4). As suggested by Peters and colleagues [ 47 ], SCImago represents a widely accepted measure of the quality of journals and reduces the possibility of including in systematic reviews papers that do not meet certain quality indices. Based on this, 12 papers were excluded. Finally, 83 studies were included in the systematic review that met the inclusion criteria. Of the articles included in the review, more than half (60%) are published in journals indexed as Q1. The others were in Q2 (28%), Q3 (5%), and finally Q4 (7%).

Study characteristics

Participants.

The included studies have involved 36,627 participants. Based on the inclusion criteria, all reviewed studies included (1) adult samples (18 years or older), (2) workers from the general population rather than clinical samples, (3) regardless of the type of work, and for most studies (4) more female participants than male (female, 57.79%; male, 42.21%). Six studies did not include participants’ demographic information [ 48 – 53 ]. The above percentages refer to the available data (n = 33,299).

The sample consisted of about 26% Teachers or Professors, 22% Nurses, 11% Physicians with various specializations, 10% Policemen, 10% Health professionals, 8% Clerks, of which about 5% worked with IT. Furthermore, the sample was made up of almost 3% Drivers, and less than 2% ICT Manager and Firefighters. Finally, about 9% of the sample carried out different types of jobs.

Countries of collecting data

The 83 articles included in this review have been published between 1993 and 2021 (see Fig.  2 ). In terms of geographic dispersion, more than half of the studies (n = 45; 54.21%) were conducted in Europe (France, Belgium, Bulgaria, Croatia, Germany, Greece, Italy, Netherland, Norway, Poland, Romania, Serbia, Spain, Sweden, Switzerland, and the UK). In contrast, the others were conducted either in America (n = 18; Canada, Jamaica, and the USA), Asia (n = 13; China, India, Iran, Israel, Jordan, and Singapore), Africa (n = 6; Nigeria, South Africa, and Turkey) and Oceania (n = 1; Australia).

Research records achieving the inclusion criteria from 1993 to June 30th, 2021

A summary of information about the general characteristics and main methodological properties of all included 83 studies is reported in Table 1 .

Concerning the key methodological features of studies, all studies reviewed involved empirical and quantitative research design. Most of the papers included (n = 73; 88%) in this review were cross-sectional and descriptive studies, except nine (11%) papers presenting longitudinal studies [ 50 , 54 – 61 ]. Furthermore, one paper (1%; [ 62 ]) presented two different studies within it, one cross-sectional and the other longitudinal.

Most of the studies, 84% (n = 70), assessed job burnout via the Maslach Burnout Inventory, both in the original version (MBI; [ 3 , 63 ]), and in the subsequent versions [ 64 , 65 ], or its adaptation [ 66 ]. The other studies, 16% (n = 13), used tools other than MBI, but which share with it the theoretical approach to job burnout and the dimensions of (emotional) exhaustion, depersonalization or cynicism, and reduced personal or professional accomplishment (see Table 1 ). Five papers used the Shirom-Melamed Burnout Measure (SMBM; [ 67 ]), four the Oldenburg burnout inventory (OLBI; [ 68 , 69 ]), one the Bergen Burnout Indicator (BBI; [ 70 ]), one the Brief Burnout Questionnaire (CBB; [ 71 ]), one the Burnout Measure [ 72 ] and one the Short Burnout Measure (SBM; [ 73 ]).

According to the Big Five model, the outcome of the analyzed studies was the correlational and regressive between work burnout and personality traits. The data of the models in which the personality traits mediated or moderated the relationships with other variables, which were not the study’s object, were not considered in this review. Concerning personality, all included studies were compatible with the "Big Five" model [ 74 , 75 ] and investigated traits of Agreeableness, Conscientiousness, Extraversion, Neuroticism, and Openness.

In detail, about 28% (n = 23) of the studies used the NEO Five-Factor Inventory (NEO-FFI; [ 33 , 76 – 79 ]), 17% (n = 14) have used the Big Five Inventory (BFI; [ 31 , 75 , 80 – 83 ]), one of which is the 10-item version [ 84 ]. Yet, 10% (n = 8) used the Eysenck Personality Questionnaire (EPQ; [ 85 , 86 ]), with one study with the revised version [ 87 ], and four studies with the revised and short version [ 88 ]. Furthermore, 7% (n = 6) involved the International Personality Item Pool (IPIP; [ 89 , 90 ]), with two studies adopting the mini version [ 91 ], while another 7% (n = 6) involved the NEO-Personality Inventory (NEO-PI; [ 81 ]), with five studies adopting the revised version. About 5% (n = 4) has used the Ten-Item Personality Inventory (TIPI; [ 92 ]), 4% (n = 3) has used the Big Five mini markers scale [ 93 ], and 4% (n = 3) involved the Big Five Questionnaire (BFQ; [ 94 ]) Finally, about 2% (n = 2) has submitted the Five Factor Personality Inventory (FFPI; [ 95 ]), and 2% (n = 2) used the Mini Markers Inventory [ 93 ].

The remaining studies, about 14% (n = 12), used the following tools: the Basic Character Inventory (BCI; [ 96 ]), the Big Five factor markers [ 90 ], the Big Five measure-Short version [ 32 , 97 ], the Big Five Plus Two questionnaire-Short version [ 98 ], the Brief Big five Personality Scale [ 92 ], the Basic Traits Inventory (BTI; [ 99 ]), the Comprehensive Personality and Affect Scales (COPAS; [ 100 ]), the Eysenck Personality Inventory (EPI; [ 101 ]), the Freiburg Personality Inventory (FPI; [ 102 ]), the M5-120 Questionnaire [ 103 ], the Minimal Redundant Scales (MRS-30; [ 104 ][ 104 ]), and the Personality Characteristics Inventory (PCI; [ 105 , 106 ]).

All instruments included in the studies were in line with the “Big Five” domains [ 26 ], such as e.g., the NEO-FFI and the NEO-PI, widely used measures of the Big Five [ 81 ], the dimensions of the TIPI and the IPIP [ 89 , 92 ], or the factors of the EPQ and the EPI, compatible with the Big Five model [ 107 , 108 ].

Risk of bias in individual studies

Study design, sampling, and measurement bias were assessed regarding the evaluation risk of bias in each study. Table 2 summarizes the limits reported in each study. Where not registered, no limitations related to the study were referred by the authors of the original studies.

Study design bias

Although most of the studies (89%) have a cross-sectional design, this review reported in the table (see Table 2 ) this bias only on the studies that highlighted this as a weakness (50%). Cross-sectional methods are cheap to conduct, agile for both the researcher and the participant, and can give answers to many research questions [ 109 ]. At the same time, however, since it is a one-time measurement, it does not allow us to test dynamic and progressive effects to conclude the causal relationships among variables.

Three longitudinal studies reported a shortness [ 56 , 58 ] or longness [ 55 ] time-lag between the first and successive administrations. The time length between the study’s waves is an essential issue in longitudinal research methodology. The time interval between the first and following measurements should correspond with the underlying causal lag (e.g., [ 110 ]). If the time lag is too short, probably the antecedent variable does not affect the outcome variable. If, on the contrary, the time lag is too long, the effect of the antecedent variable may already have disappeared. In both cases, the possibility of detecting the impact of the antecedent variable on the outcome variable may decrease.

Furthermore, it is possible that in the period between the first and subsequent measurements, several events may occur affecting the outcome. Finally, the same participant in the sample could change the condition under study (to know more, [ 177 ]). Especially in work-related studies, employees may be subject to changes in context, needs, and working hours [ 178 ]. Despite this, longitudinal designs offer substantial advantages over cross-sectional methods in examining the causal links between the variables [ 177 ].

Sampling bias

About 29% of the studies (n = 24) reported the small samples as limitation. Among these, one study that had two different samples reported a small sample only in second one [ 62 ], while another study, in investigating differences, highlighted that certain groups have a relatively small sample size and reported this as a limitation [ 140 ]. Additionally, about 10% of the studies reported having received an inadequate response rate. About 18% of the studies reported a non-probabilistic sampling as a limitation, and 6% of studies examined reported having a gender-biased sample (male/female). Other studies (13%) reported collecting data in a single organization, country, or an imbalance among workers’ categories. Finally, three studies [ 154 , 168 , 170 ] reported a cultural or geographical bias. To sum up, studies’ limitations regarding the sample characteristics may significantly impact scores’ reliability [ 179 , 180 ]. Specifically, this research’s limits prevent to generalize the findings.

Measurement and response bias

Since inclusion evaluated burnout and personality traits through self-reports that respected the previously illustrated models, all the studies examined used self-report measures. Again, only 40% report this as a limitation. Using perceptual measures, one could be subject to the Common Method Bias (CMB; [ 181 ]). The CMB occurs when the estimated relationships among variables are biased due to a unique-measure method [ 182 ]. This bias may be due to several factors, including response trends due to social desirability, similar responses of respondents due to proximity and wording of items, and similarity in the conditions of time, medium, and place of measurements [ 183 – 185 ]. These variations in responses are artificially attributed to the instrument rather than to the basic predispositions of the participants [ 181 , 186 , 187 ]. Suppose the systematic method variance is not contained. In that case, it can result in an incorrect evaluation of the scale's reliability and convergent validity, inflating the reliability estimates of correlations [ 188 ] and distorting the estimates of the effects of the predictors in the regressions [ 184 ].

Furthermore, about 5% of studies reported using single-item measures. Personality characteristics were often measured through self-reports with single items and assessed through a Likert scale [ 189 ]. This type of assessment is susceptible to social desirability (SDR; [ 184 , 185 ]), i.e., the tendency to respond coherently with what others perceive as desirable [ 190 ]. Furthermore, this type of assessment is also susceptible to acquiescent responding (ACQ; [ 191 ]), i.e., the tendency to prefer positive scores on the Likert scale, regardless of the meaning of the item [ 192 ]. Response-style-induced errors can influence reliability estimates (e.g., [ 193 , 194 ]) and overestimate or underestimate the relationships between the variables examined [ 195 ]. Despite these response biases, widely documented in the literature [ 184 – 186 , 196 – 198 ], it appears that this bias is overstated in psychological research [ 185 ]. Indeed, self-reports would seem to be the most valid measurement method for evaluating personality factors because the same participant is the most suitable person to report their personality and level of burnout [ 42 ]. Other studies (10%) reported using a poor reliability scale: employing imprecise psychometric procedures in a study is likely to distort the outcome, therefore not allowing to make inferences about an individual and creating a response bias [ 199 ]. Finally, about 16% of the studies examined reported that the study did not review all the variables relating to the constructs investigated. Table 2 also identifies some specific limitations of the studies examined, such as, e.g., the comparison between non-numerically equivalent samples [ 174 ], the long compilation time required [ 165 ], and the lack of a control group [ 57 , 138 ]. Furthermore, some studies have used tools that evaluate only a total score of burnout [ 17 ] or personality [ 54 ] Finally, other studies have focused only on individual factors, leaving out job-related and organizational factors [ 147 ].

This systematic review was conducted to identify, categorize, and evaluate the studies investigating the relationship between job burnout and personality traits addressed to date. Specifically, the interest of this review was to explore the role of personality traits as individual factors related to job burnout. To do this, only studies that analyzed the direct relationship between personality traits and job burnout were included, leaving out all those studies that investigated additional variables that could in any way mediate or moderate this relationship.

Results of the studies included

Table 3 summarizes the results, the correlation and regression indices, and the power of significance of the studies included in this review.

The results of the included studies based on the five personality traits and the association with a dimension of job burnout are discussed below. The correlations between the personality trait and the size of the job burnout report first, while subsequently those of the regressions, presenting the cross-sectional studies first, which are most of them, and then also the longitudinal ones.

As seen previously, job burnout is a multidimensional construct that consists of the individual response to stressors at work [ 3 , 9 ]. The literature has long investigated the association between organizational and occupational factors and burnout. However, a recent meta-analysis shows that there is a bidirectional relationship between occupational stressors and burnout [ 200 ]. Because the research on individual factors has been less systematic, partial, and contradictory [ 113 ], this review aimed to synthesize research evidence about the role that FFM personality traits play in the development of job burnout. To do this, 83 independent studies that used different tools to assess both job burnout and personality traits while maintaining the same reference theory were identified. The most investigated personality traits were, in order, neuroticism, extraversion, agreeableness, conscientiousness, and openness to experience.

The present review extracted data from the reviewed studies, including (1) main characteristics of participants (including job type), (2) data collected country, (3) personality traits related to job burnout, (4) risk of bias in individual studies, and (5) methodological features of studies. As for the participants, all reviewed studies included (1) adult samples, (2) workers from the general population rather than clinical samples, (3) regardless of the type of work, and for most studies (4) more female participants than male. Based on these observations, future studies examining personality traits and work burnout should employ other samples (e.g., clinical samples) to enhance external validity.

This systematic review focused exclusively on personality traits and the relationship between them and job burnout. Results of the included studies confirmed a relationship between job burnout and the five distinct personality traits of the Big Five model [ 46 ] and that some of these were risk factors for job burnout (although not always in the same direction). A descriptive picture of the relationship between the five personality traits and job burnout will be discussed.

Agreeableness

A negative association between Agreeableness and job burnout was reported (range, r from − 0.12* to − 0.353***; β from − 0.08*** to − 0.523*). Longitudinal studies also suggest a role of Agreeableness as a protective factor of dimensions of Emotional Exhaustion, Depersonalization, and reduced Professional Accomplishment (EE; β, − 0.83*; β, − 0.48*; D; β, − 0.31*; PA; β, − 0.22*; rPA; β, − 0.28**). As seen previously, the Agreeableness trait has been described as a sense of cooperation, tolerance, and avoidance of conflict on problematic issues [ 32 ]. Agreeable individuals are warm, supportive, and good-natured [ 201 , 202 ], protecting them from feelings of frustration and emotional exhaustion [ 113 ]. Indeed, their tendency towards a positive understanding of others, coupled with interpersonal relationships based on feelings of affection and warmth [ 201 ], could protect them from developing job burnout and greater depersonalization [ 8 , 203 ]. Although most of the studies found a negative relationship between Agreeableness and job burnout, in some studies Agreeableness was positively correlated with Emotional exhaustion [ 159 ], and reduced Professional Accomplishment [ 50 , 62 ].

Conscientiousness

A negative association between Conscientiousness and job burnout was reported (range, r from − 0.12* to − 0.355***; β from − 0.09*** to − 0.300*). Longitudinal studies also suggest the role of Conscientiousness as a protective factor against Burnout (B; β, -0.21*). As seen previously, the Conscientiousness trait is reflected in precise, organized, and disciplined individuals who respect the rules and work hard to achieve success [ 33 ]. Their perseverance in work and success orientation would protect these people from developing emotional exhaustion [ 76 , 204 ] and poor personal accomplishment, as they are unlikely to perceive themselves as unproductive. Although most studies found a negative relationship between Conscientiousness and job burnout dimensions, some studies pointed out an unexpected inverse correlation between Conscientiousness and reduced Professional Accomplishment [ 60 , 62 , 143 , 159 , 166 ]. Furthermore, Conscientiousness was positively associated with Emotional exhaustion and Depersonalization [ 131 ]. This result would be due to the greater commitment and effort employed in their work, which would have greater levels of exhaustion and depersonalization [ 131 ]. Finally, another longitudinal study [ 56 ] attributes Conscientiousness as a negative predictor role for the dimensions of Personal/Professional Accomplishment. However, the authors do not provide reasons for this discordant result from the literature.

Extraversion

A negative association between Extraversion and job burnout was reported (range, r from − 0.034** to − 0.33***; β from − 0.06*** to − 0.31***). Longitudinal studies also suggest the role of Extraversion as a protective factor against burnout and its dimension of Exhaustion (B; β, − 0.16*; EE; β, − 0.26*). As seen previously, the Extraversion trait has been identified as the intensity of social interaction and the level of self-esteem of individuals [ 32 ]. People with higher levels of extraversion appear positive, cheerful, optimistic, and have more likely to experience positive emotions [ 206 ]. This positive view of their level of job-related self-efficacy [ 207 ], often associated with the interpersonal bonds they tend to create [ 208 ] can protect outgoing individuals from experiencing high levels of emotional exhaustion. On the contrary, introverted individuals tend to experience greater feelings of helplessness and lower levels of ambition [ 204 ], which instead results in a risk factor for job burnout. Although the negative association is the most frequent, some studies have found a directly proportional association between Burnout and Extraversion [ 54 ], Cynicism [ 127 , 173 ], and reduced Professional Accomplishment [ 50 , 60 , 62 , 143 , 146 , 159 ]. Again, the authors do not provide reasons for this discordant result from the literature.

Neuroticism

A positive association between Neuroticism and job burnout was reported (range, r from 0.10** to 0.642***; β from 0.16** to 0.587***). Longitudinal studies also suggest a role of Neuroticism as a predictor of Burnout and its extent of Exhaustion, while predicting a decrease in Professional Accomplishment (B; β, 0.21*; EE; β, 0.31***; β, 0.15**; β, 0.19**; PA; β, − 0.23**). As seen previously, it is possible to define Neuroticism as the inability of people to control their impulses and manage their emotional balance. Neurotic people experience a series of feelings of insecurity, anxiety, anger, and depression [ 25 , 76 , 204 ] that they try to manage through maladaptive coping strategies, such as delay or denial [ 29 , 34 ]. These characteristics of the personality trait of Neuroticism would interfere with job functioning and satisfaction, operating a negative "filter" that magnifies the impact of adverse events (see [ 209 ]) and constitutes a significant risk factor for job burnout [ 8 , 174 ]. Feelings of anxiety and nervousness could lead them more easily to experience higher levels of emotional exhaustion, and by focusing on more aspects of their work, they are more likely to manifest depersonalization. Although most studies report a positive association between Neuroticism and Burnout [ 164 ], Burnout [ 159 , 169 ], Depersonalization [ 133 , 159 ], and reduced Professional Accomplishment [ 60 , 62 , 126 ]. Ye and colleagues [ 164 ] tie this result to the Chinese cultural situation, whereby the observed greater sense of responsibility and discipline could reduce the effects of extroversion on job burnout. Farfán and colleagues [ 169 ], on the contrary, link this result to the tendency of the neurotic personality trait to use rationalization as a defense against job burnout. Unlike most of the studies included in this review, some results show a negative association between Neuroticism and Burnout [ 159 , 164 ], Emotional exhaustion, and Depersonalization [ 155 ]. Furthermore, a study indicates that Neuroticism is positively associated with reduced Personal/Professional Accomplishment [ 131 ]. Finally, in the longitudinal study by Armon and colleagues [ 54 ], Neuroticism even seems to protect against Emotional exhaustion. The authors explain the association over time of Neuroticism with job burnout as due to an underrepresentation in the measurement scales used or the moderating effect of gender on these associations [ 159 ].

A negative association between Openness and job burnout was reported (range, r from − 0.18*** to − 0.237**; β from − 0.092* to − 0.45*). Longitudinal studies have suggested the role of Openness as a protective factor of reduced Professional Accomplishment (rPA; β, 0.10*). As seen previously, individuals with high levels of Openness tend to be more intellectually curious about novelty and open-minded and have a predisposition to independence [ 35 , 76 , 202 ]. These characteristics protect individuals from experiencing discomfort, experiencing novelty and failures as opportunities [ 203 ], and protecting them from job burnout from emotional exhaustion. Conversely, when faced with stressors at work, less open individuals can adopt quick but suboptimal strategies, such as depersonalization [ 8 ]. Although most of the studies found a negative relationship between Openness and job burnout, five studies found a positive correlation between Openness and Emotional exhaustion [ 54 , 122 ] and Depersonalization [ 159 ], while negative with Personal/Professional Accomplishment [ 62 , 131 , 159 ]. The authors do not provide reasons for this discordant result from the literature. Other studies instead have found a positive association between Openness and all dimensions of Burnout [ 116 ]: Exhaustion [ 131 , 173 ], Depersonalization [ 131 ], and reduced Personal/Professional Accomplishment [ 142 ]. Finally, the longitudinal study by Ghorpade and colleagues [ 120 ] attributes Openness to the role of the positive predictor of Emotional exhaustion. According to the authors, this result could be attributed to the work of the professors (Professors) which, requiring a greater openness to listening to students' different problems and encouraging different positions in them, could increase emotional exhaustion.

The findings of most of the studies reviewed indicate that individuals who have higher levels of neuroticism and lower agreeableness, conscientiousness, extraversion, and openness to experience are more prone to experiencing job burnout. However, the few studies that show other results than this theoretical line cannot explain the conflicting results. Some authors adduce these results to a measurement bias (e.g., [ 159 ]) or sample characteristics (e.g., [ 120 ]) but fail to explain the reason for this relationship and believe that it is due to further variables to be explored.

Limitations

Although the literature review was conducted as rigorously as possible, the search strategy was limited to four scientific search engines. Furthermore, it was impossible to find all the relevant studies if the search terms were not mentioned in the articles' titles, abstracts, or keywords. Therefore, some related papers might be missed due to the selected terms. Furthermore, the search included only studies published in English, thus excluding relevant studies in other languages. Additionally, gray literature was not included in the study, and therefore, it may not have been considered essential data contained in non-peer-reviewed studies, unpublished theses, and dissertation studies. Furthermore, one of the exclusion criteria was the journal ranking of SCImago. Although this is a widely accepted and recognized measure to reduce the possibility of including in systematic reviews papers that do not meet certain quality indices [ 47 ], they may not have been considered relevant data. In addition, the Big Five model [ 46 ] was used as a conceptual model of reference to compare the results of the studies on job burnout. Studies that did not include the Big Five models or that explored the relationship between Burnout and personality disorders (e.g., Antisocial Personality Disorder, Narcissistic Personality Disorder, Borderline Personality Disorder, etc.) were therefore not examined in this study. Restricting studies to a single conceptual model of personality was necessary to focus the review, but at the same time, it limited our investigation. Furthermore, the heterogeneity of the study samples' work type, burnout measurement tools, and personality traits prevented comparing results across studies. Finally, despite precautions to reduce selection bias, confounding, and measurement bias, no studies have addressed reverse causality problems in the relationship between personality traits and burnout. Although the cross-sectional research design does not allow us to investigate the causal links between personality and burnout, an answer to the existence of this link is offered by the longitudinal studies included in the review. This type of study demonstrates that personality traits play a role in the development of burnout, but future research must investigate this relationship, especially with the help of longitudinal studies that can reduce the problems related to reverse causality.

The findings obtained in the present review highlight the importance of examining the role of personality traits in the development of job burnout syndrome. At the same time, it is possible to observe how scientific evidence places us in front of a picture that is not fully defined. In line with Guthier's meta-analysis [ 200 ], the findings of this review highlight the need for expanding job stress theories focusing more on the role that personality plays in burnout.

I am convinced of the value of this review in directing future empirical research on job burnout, especially in the light of new approaches to burnout as a multi-component factor (see [ 210 , 211 ]). Even more future research will have the task of encouraging the use of methodologies that evaluate personality traits in work contexts. An assessment of personality traits and continuous monitoring of occupational stress levels (e.g., [ 212 ]) could help identify the people who are most likely to develop burnout syndrome to prevent or limit its damage. Future research should improve understanding and intervention on burnout, too often limited by universal approaches that have neglected the uniqueness of the antecedents of burnout [ 213 ]. Some traits related to burnout predict work outcomes such as job performance, job satisfaction, and turnover [ 203 , 214 – 218 ]. It is, therefore, necessary to investigate the antecedents of Burnout to provide implications practices for jobs and organizations.

Availability of data and materials

As this is a systematic review of the literature, this study indicates the information to obtain all data analyzed in the databases used. However, the datasets used during the current study remain available from the corresponding author upon reasonable request.

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Angelini, G. Big five model personality traits and job burnout: a systematic literature review. BMC Psychol 11 , 49 (2023). https://doi.org/10.1186/s40359-023-01056-y

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Big Five Inventory (BFI-2)

The Big Five Inventory 2 (BFI-2) is a self-report inventory designed to measure the Big Five dimensions (which we label Extraversion, Agreeableness, Conscientiousness, Negative Emotionality, and Open-Mindedness) and 15 more-specific facet traits. It is quite brief for a multidimensional personality inventory (60 items total), and consists of short phrases with relatively accessible vocabulary.

Downloading the BFI-2

Christopher J. Soto and I hold the copyright to the BFI-2 and it is not in the public domain per se. However, it is freely available for researchers to use for non-commercial research purposes. Please keep us posted on your findings.

• To take the BFI-2 online and receive instant feedback on your personality profile, click here .
• To download the BFI-2 for research purposes , please click here which will direct you to the BFI-2 download page. We are trying to create a database for BFI-2 users of publications, relevant findings, and translations of the BFI-2 in an effort to make the scale more useful for users. Thus, before downloading a copy of the BFI-2 and the scoring instructions, please complete a short survey to let us know a little more about who you are and why you want to use the measure. All information will be kept strictly confidential.
• If you are interested in using the BFI-2 for commercial purposes , please submit a request to [email protected]. At this time, the BFI-2 is for non-commercial uses only.

You should reference these articles in manuscripts using the BFI-2:

• John, O. P., Naumann, L. P., & Soto, C. J. (2008). Paradigm Shift to the Integrative Big-Five Trait Taxonomy: History, Measurement, and Conceptual Issues. Handbook of personality: Theory and research (pp. 114-158). New York, NY: Guilford Press.
• Soto, C. J., & John, O. P. (2017). The next Big Five Inventory (BFI-2): Developing and assessing a hierarchical model with 15 facets to enhance bandwidth, fidelity, and predictive power. Journal of Personality and Social Psychology, 113, 117-143.

Scoring the BFI-2

Scoring instructions are downloadable from this website after completing the survey.

There is no official BFI-2 manual with published norms. However, the following paper contains means from age 20 to age 60. You might want to look at it (download here) for an American sample; scores were converted to POMP (percentage of maximum possible) metric and graphed by gender and age for each Big Five dimension.

Srivastava, S., John, O. P., Gosling, S. D., & Potter, J. (2003). Development of personality in early and middle adulthood: Set like plaster or persistent change? Journal of Personality and Social Psychology, 84, 1041-1053

Different Versions of the BFI-2

Yes, there is an abbreviated 11-item version available here. However, given that the entire BFI-2 consists of only 60 very short phrases and takes only 5-7 minutes to complete, we do not recommend using the short 11-item version unless there are exceptional circumstances.

Yes, there is a version of the BFI where items with wording difficult for children have been modified. This version has also been adapted so that parents may fill it out for their children. Both versions are available after completing the survey.

Yes, the BFI has been translated into the following languages:

If you translate the BFI-2, an expert bilingual (someone who is American or has lived in the U.S.A.) should perform back-translation procedures with your newly-translated version. In other words, after you have translated the BFI-2 into the new language, an expert bilingual should translate it back into English. Next, you should compare that back-translated English version to the original English version of the BFI. Please send us a copy of the version that you are going to use so that we may add it to our collection.

Finally, it's much more important to capture the *total meaning* of the item than to translate any of its parts literally. For example, the translation of "calm" into German often comes out as "ruhig" but that could be N- as well as E- (as in still, quiet--even in English, "calm waters" are still and quiet, too!). So, an N item that has calm as a part is better translated into German using the negation "nicht nervoes" than the more literal translation "ruhig".

Psychometric Questions

For several items, such as "being relaxed, handles stress well, the typical understanding would be that the second phrase provides an elaboration of the first concept, so its understood as "being relaxed, in the sense of being good at handling stress". Specifically, "relaxed" is typically a low-Neuroticism item and means "not anxious, not easily upset or stressed out." But some people might misunderstand "relaxed" to mean "easy-going, having fun" which would be an high-Extraversion item. Thus, to rule out this misinterpretation, we use "handles stress well" to elaborate what we mean by "being relaxed".

There are different approaches in the literature, varying in complexity. If a lot of item responses are missing, you may not want to use that person's scores. With only a few responses missing (6 or less), I try to use either the response to the closest synonym (similar) item or I compute the scale score (as an average item response) without the missing item(s) and then use that score (rounded to an integer) as the substitute item score (when you do that, be careful not to get confused with reverse items).

For an introduction to the conceptual and measurement issues surrounding the Big Five personality factors, a good place to start is the recent John, Naumann, & Soto (2008) Handbook of Personality chapter.

The chapter covers a number of important issues including the scientific origins and history of the Big Five, theoretical accounts of the Big Five, and comparisons of different measurement instruments. The chapter also includes a conceptual and empirical comparison of three measurement instruments: the Big Five Inventory (BFI), Costa and McCrae's NEO Five Factor Inventory (NEO-FFI), and Goldberg's set of 100 trait-descriptive adjectives. There is no one-size-fits-all measure, but the chapter includes our recommendations on which instrument(s) you should use for different applications.

Has anyone used the scale without response 3 (neither agree or disagree)? i.e. use only the remaining 4 item scale to force respondents to choose an answer? Have there been any psychometrics done on this?

You are implying that you will give the BFI-2 in an interview format--will you? If so, you could give an instruction that says "first, consider whether you agree with this item, or disagree--choose one way.” Then have them rate *how much* they agree (strongly or a little) or disagree (strongly or a little). And tell them that they should/can respond "neither agree nor disagree, right in the middle" only in those rare instances when they are really right in the middle.

If the BFI-2 is self-administered, in that participants read the items by themselves and record their answers in writing (the way we usually administer the BFI-2), then yes, you could simply give the scale as ranging from 1 to 4, with the middle-response option omitted. If you have strong reasons to do that, it's ok with me, but you will end up sacrificing the opportunity to compare your means and SDs to other research, all of which has used the standard 5-point scale. If the problem is fatigue-related, I would rather have them take a little break!

I cannot locate the John & Donahue, 1998, The Big Five Inventory: Studies of reliability and validity article referenced in Benet-Martinez & John 1998. Was it published?

No, it was not published. Please refer to Rammstedt & John 2007, Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German. Journal of Research in Personality, 41, 203-212. This article focuses on a shorter 10-item version that includes information on external validity via peer ratings for the full, 44-item BFI as well. Note that we do not recommend using the short 10-item version unless there are exceptional circumstances.

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