Enablers of satisfaction with coworking spaces: assessing the influence of users’ personality and experience

Abstract

Purpose

This study analysed the extent to which differences in personality traits and coworking experiences affect coworkers’ satisfaction with coworking spaces (CWS).

Design/methodology/approach

The present study is based on employee-workplace alignment theory (Appel-Meulenbroek et al., 2021). This approach addresses people’s ability to do their jobs in a certain work environment and studies job satisfaction as an outcome variable. We used a dataset of 135 CWS members grouped in the Spanish Association of Flexible Office Spaces, Prowork Spaces. The regression models were fitted using satisfaction with coworking as the dependent variable.

Findings

The findings support the idea that some psychological traits of coworkers, such as extroversion and agreeableness, have a significant influence on their satisfaction with coworking. Our study also shows that the relationship between coworking experience and satisfaction is curvilinear.

Originality/value

This study contributes to the advancement of employee-workplace alignment theory by showing that some personality traits are relevant variables for person-organisation fit in CWS. While extroversion and agreeableness are traditionally associated with prosocial outcomes, we found that agreeable coworkers were not more satisfied with CWS. In addition, the study identified a nonlinear relationship between experience and satisfaction with CWS, which has not been detected in previous studies.

研究目的

本研究擬探討共同工作空間用戶的性格特質和使用共同工作空間的經驗會如何影響他們對共同工作空間的滿意度, 進而了解這影響的程度.

研究設計/方法/理念

研究人員基於員工工作場所調整理論 (Appel-Meulenbroek et al., 2021) 進行分析和探討。這個研究理念用來了解人們在某種工作環境裏完成工作的能力, 並探討作為結果變數的工作滿意度。研究人員使用的數據集, 包括西班牙柔性辦公空間、團隊協同空間協會 (此為直譯) 內被分類的135個共同工作空間成員, 研究人員以對共同工作空間的滿意度為因變數而設置回歸模型.

研究結果

研究結果確認了共同工作者的諸如外向性和友善等的心理特徵會顯著地影響他們對合作辦公的滿意度。研究結果亦顯示, 合作辦公的經驗與滿意度成曲線的關聯.

研究的原創性

本研究會幫助推進員工工作場所調整理論, 因研究結果顯示, 有些性格特質, 就共同工作空間的人與組織間之可容納性而言是相關的變數。研究人員發現, 雖然外向性和友善在傳統上被認為與親社會結果有所關聯, 但友善的共同空間用戶對共同工作空間不是更為滿意的; 而且, 研究人員確認了一個過去的研究均未曾探測過的關聯, 那就是合作辦公的經驗與對共同工作空間的滿意度之間的關聯是非線性的.

Citation

Rodríguez-Ruiz, Ó., Labrado-Antolín, M., Fernández-Menéndez, J. and Delgado-Piña, I. (2024), "Enablers of satisfaction with coworking spaces: assessing the influence of users’ personality and experience", European Journal of Management and Business Economics, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/EJMBE-06-2024-0198

1. Introduction

The phenomenon of coworking has acquired significant international relevance in recent years (Dell’Aversana and Miglioretti, 2024). Today, public and private sector organisations use coworking spaces (CWS) as an alternative to home-based telework. It is estimated that there will be approximately 42,000 such spaces in the world by the end of 2024 (Servín, 2024). User satisfaction is an outcome derived from the coworking experience related to retention and performance (Rådman et al., 2023). Therefore, satisfaction with coworking has emerged as an important topic of study.

This study analysed the extent to which differences in personality traits and coworking experience affect coworkers’ satisfaction with CWS. A CWS represents a cross-sectional working community with opportunities for social interaction (Bouncken and Reuschl, 2018). Users at these locations can be entrepreneurs, freelancers, self-employed individuals, or people working on contracts for larger companies (Clifton et al., 2022). The literature has shown that specific workplaces affect worker satisfaction. In the case of CWS users, satisfaction is not determined only by the physical and functional characteristics of their location. The innate characteristics of subjects, such as personality traits, are also relevant (Hartog et al., 2018). It is reasonable to assume that an individual’s personality predisposes him or her to like or dislike CWS. Moreover, as more experience accumulates over time, users may better adjust to coworking dynamics.

Research on the different drivers of work satisfaction in CWS is centred on aspects such as design and infrastructure. There is a clear gap in the understanding of why some coworkers experience greater levels of satisfaction. Some explanatory approaches, such as the employee-workplace alignment theory (Appel-Meulenbroek et al., 2021), have not been explored in the field of coworking. Additionally, there is a lack of empirical evidence on personality-based fit among CWS. Our research objective was to study the Big Five personality traits (Norman, 1963) as variables that condition the degree of satisfaction with a new way of working. In this line, this study attempted to cover this gap and expand research on the explanatory factors of user satisfaction by answering two research questions: (1) How do coworkers’ personalities influence satisfaction with CWS? (2) Does member satisfaction with coworking activities vary as their coworking experience increases?

CWS hosts must understand users’ personalities to foster their satisfaction, which is crucial for explaining their intention to leave the space (Orel et al., 2024). To study the correlation between personality and satisfaction with coworking, we assumed that individuals would experience more satisfaction when the coworking environment fits with their personality. The five-factor personality model provided the conceptual basis for our study. Extraversion, agreeableness, conscientiousness, emotional stability, and openness to experience have been used to analyse personality traits in numerous studies (Judge and Zapata, 2015). We utilised a dataset comprising 135 Spanish CWS users. Spain is particularly interesting as coworking has experienced rapid growth in recent years. Considering that it is the fourth country in the world for coworking in terms of numbers (1,400 spaces) and capacity (Universitat Oberta de Catalunya, 2024), the coworking surface has increased by one million square metres since 2014.

Our study makes several novel contributions to the literature. First, it adopts a person-centred perspective. While most studies on place in management have attempted to understand how workplace characteristics impact workers (Ashforth et al., 2024), we explored how coworkers’ personalities and experiences influence satisfaction with the work environment. The person-environment fit literature highlights the idea that there is an interaction between employee characteristics, environmental work characteristics, and job attitudes. Values and skills are studied as employee characteristics. However, the role of personality predispositions has not yet been considered. We add to the fit theories by exploring a specific form of person-environment fit related to the Big Five personality traits as employee characteristics. This study focuses specifically on understanding satisfaction with coworking. Comparative measures of satisfaction with other workplaces are not used. Our results show that extroverted individuals tend to have a more positive attitude towards coworking and challenge the common idea that agreeableness is key to adjusting to environments characterised by social and group activities. We also add to the knowledge on new workplaces by showing a nonlinear association between experience as a function of time and satisfaction with CWS.

2. Theoretical framework

2.1 Previous studies about satisfaction in CWS

The literature has explored some motivational aspects of coworking. Fuzi (2015) conducted an eight-month study of two CWS in South Wales to examine their motivations and expected outcomes. Kojo and Nenonen (2017) found that attractiveness and work–life balance are important drivers for the use of these spaces. Recently, Dell’Aversana and Miglioretti (2024) noted that satisfying social relationships is one of the benefits that motivates workers to use CWS. However, studies about satisfaction of members of CWS are limited and diverse. Gerdenitsch et al. (2016) hypothesised that social support from coworkers in CWS was positively related to satisfaction. Avdikos and Kalogeresis (2017) described the profile of those working in third places and their level of satisfaction, in contrast to those working from home and formal offices. Robelski et al. (2019) examined overall satisfaction with CWS, noting that 8 out of 10 respondents reported higher job satisfaction in the coworking space than in the home office. In a study on multi-tenant offices, Hartog et al. (2018) found that users who were extroverted, open to new experiences, and more agreeable were generally more satisfied with these working arrangements. Along a different line, Lashani and Zacher (2021) pointed out that the congruence between coworkers’ needs and supplies by CWS relates to job satisfaction. Recently, Berdicchia et al. (2023) showed that CWS positively impacted happiness when workers were more proactive. Orel et al. (2024) highlighted the impact of CWS on the satisfaction and well-being of microentrepreneurs.

2.2 Conceptual approach

Various theoretical approaches have been used to study worker satisfaction in work environments. The underlying assumption of these visions is that different people require different environments. The person-environment fit framework (Edwards, 2008) is a prominent research field that focuses on the outputs of the adjustment between workers and environmental characteristics (Ghetta et al., 2020). This congruence occurs when the characteristics of the individuals and work environment are well matched (Kristof-Brown et al., 2005). Within this framework, some theories have paid special attention to the workplace. For instance, the employee-workplace alignment theory (Appel-Meulenbroek et al., 2021) addresses people’s ability to do their job in a certain work environment and studies job satisfaction as an outcome variable. Satisfaction is determined by the technical, functional, and behavioural attributes of the workplace (Armitage and Amar, 2021). It is possible to distinguish between the three types of employee-workplace alignment. First, alignment between the person and the workplace is achieved when the needs of the users are satisfied. The second alignment occurs when individuals enter a space and respond to the environment in a manner that is consistent with their personality traits. Finally, employee-workplace alignment depends on the social environment and organisational culture of employees. This study is based on the second type of alignment, according to which personality influences how well workers adapt to the workplace environment. Following this reasoning, we suggest that innate characteristics such as personality traits influence the level of satisfaction with coworking activities. Additionally, we advocate that acquired characteristics, such as experience, can contribute to a better fit with the work environment, increasing the level of satisfaction.

2.3 Hypotheses

The notion of person-environment fit indicates that job satisfaction is higher when there is a match between the person and the environment. Personality traits influence users’ beliefs and attitudes and may predispose them to engage in CWS.

One of the most validated personality taxonomies is the five-factor model. It parsimoniously represents the human personality with five traits: extroversion, openness, conscientiousness, agreeableness, and mental stability (Bozionelos, 2004). According to this model, extroversion refers to warmness, excitement seeking, and positive emotions (Seddigh et al., 2016). Individuals who score high in openness tend to be open to feelings and emotions. Conscientiousness can be understood as a tendency towards duty and competence (Shi et al., 2018). People high in conscientiousness are organised and responsible. Agreeableness refers to sympathy, tolerance, and cooperation (McCrae and Costa, 1987). Lastly, a lack of mental stability (neuroticism) is described as a source of negative affectivity and stress (Judge et al., 2002).

These five personality traits affect people’s workplace engagement in two ways. First, each personality feature predisposes a person to behave in a specific manner. Second, personality traits may facilitate adaptation to the workplace environment. Thus, we can expect significant relationships between the Big Five personality traits and satisfaction with CWS. Our main assumption was that personality traits determine work behaviour, which in turn influences individual satisfaction with the coworking space.

H1.

Satisfaction with CWS varies depending on the personality traits of the coworker.

Those with extraversion tend to be more sociable. This includes characteristics such as activity assertiveness and positive emotions (John and Srivastava, 1999). This trait has been found to be positively correlated with job satisfaction (Smith et al., 2018). Extroverted people tend to be affectionate and desire interaction with others (Özbek et al., 2014). Shared workspaces, such as coworking centres, can answer extroverted users’ demands for recognition and social belonging (Merkel, 2015). Therefore, we argue the following:

H1.1.

Coworkers high on extraversion can be expected to be more satisfied with CWS.

Openness is characterised by imagination and curiosity (Peeters et al., 2006). Open-minded individuals tend to be behaviourally flexible, independent, and interested in variety (Özbek et al., 2014). Openness is also one of the main characteristics of CWS (Berdicchia et al., 2023). Co-workers who are open to experience may appreciate the contributions of other users of the space, which could affect their satisfaction (Hofeditz et al., 2020). Thus, we hypothesised the following:

H1.2.

Coworkers high on openness can be expected to be more satisfied with CWS.

Conscientiousness refers to the extent to which a person is organised and the work is involved (Matzler and Renzl, 2007). Conscientious individuals tend to be careful and disciplined, showing higher levels of goal-directed behaviour (Özbek et al., 2014). As CWS facilitate a structured organisational environment (Orel et al., 2024), conscientious workers can fit within these enclaves, which are an ideal setting for stimulating challenges (Berdicchia et al., 2023). Coworking can offer users self-determination and proactivity, which are essential for satisfaction (Waters-Lynch and Duff, 2021). Thus, we propose the following hypothesis:

H1.3.

Coworkers high on conscientiousness can be expected to be more satisfied with CWS.

Agreeableness is a personality trait which includes courtesy and friendliness (Peeters et al., 2006). Individuals with high levels of agreeableness are assumed to be cooperative and helpful (Özbek et al., 2014). Some studies have found a positive relationship between agreeableness and user satisfaction in multi-tenant offices (Hartog et al., 2018). This relationship can be explained by the fact that agreeable individuals interact with others. Members of CWS who prefer socialising may have higher levels of satisfaction. Thus, we can assume the following:

H1.4.

People high in agreeableness can be expected to be more satisfied with CWS.

Emotional stability is another factor that affects work experience. Employees with high or low levels of this feature experience different levels of stress and anxiety. Emotionally stable individuals are calm, secure, and better able to cope with job changes (Kang and Malvaso, 2023). Conversely, emotional instability (neuroticism) has been consistently found to be negatively related to job satisfaction. While workers with high neuroticism experience less control in exposed workspaces, individuals with emotional stability can fit CWS better. Thus, we hypothesise as follows:

H1.5.

Coworkers high in emotional stability can be expected to be more satisfied with CWS.

The coworking experience may positively influence member satisfaction. First, individuals with experience place a greater emphasis on adaptation (Kristof-Brown et al., 2002). Second, previous experience guides workers towards work environments in which they find value congruence. Additionally, time is an essential element in developing routines and perceiving a coworking space as a social place (Endrissat and Leclercq-Vandelannoitte, 2021). Individuals become aware of the match between their attributes and work environment over time. People who work more hours in multi-tenant offices have been found to be more satisfied with the office climate (Hartog et al., 2018). When users cooperate more intensively, they have a better understanding of coworking dynamics and perceive benefits, such as social integration (Lescarret et al., 2022). In this vein, it is possible to say that experience generally leads to a better fit with the workplace facilitating the process of “learning to cowork” (Butcher, 2018). The longer coworkers work in a coworking space, the higher their degree of adjustment. Thus, we hypothesise the following:

H2.

Previous coworking experience increases satisfaction with coworking.

3. Methods

3.1 Data sample

The study population was professionals who were independent or part of an enterprise and who had experience carrying out their duties from the premises of a coworking space located in Spain. Finding reliable data on the number of CWS users worldwide and in Spain is difficult (Martínez-Navarrete and Sánchez-Hernández, 2016). The size and characteristics of this group of individuals is not easy to explore and it is possible to consider them a “hard-to-reach population”. Accessing these populations is complicated and probability sampling is not possible. Conversely, purposive sampling is a valid option to understand these groups. Different authors have used this type of sample in the coworking literature (e.g. Dell’Aversana and Miglioretti, 2024; Orel et al., 2024).

In this study, we used “facility-based sampling” (Shaghaghi et al., 2011). This technique recruits members of a target population from various facilities. We signed an agreement with Prowork Spaces of the Spanish Association of Flexible Office Spaces. In the first phase, the association requested collaboration from the CWS to disseminate the survey. Fifty-two centres agreed to place a QR code on their premises to respond. In the second phase, some users of each centre decided to answer the questionnaire. With this type of sampling, we were exposed to two potential sources of self-selection bias at the CWS and coworker levels. Dealing with self-selection bias requires additional information about non-respondent units. In the case of CWS, after several semi-structured interviews with managers, we noticed that there could be an over-representation of larger centres in the sample. This bias seems to be small as most coworkers were concentrated in the largest CWS. Regarding self-selection bias at the coworker level, anecdotal evidence shows that coworkers who spend more time in CWS tend to pay more attention to QR codes and respond. The higher representation of more intensive coworkers is mostly unproblematic, as these individuals are the most interesting to analyse.

Another potential risk in our sample is the presence of common method variance (Podsakoff et al., 2003). Respondents who provided information about the dependent variable (satisfaction with the CWS) were the same as those answering questions related to the independent variables (e.g. extroversion, openness, agreeableness, etc.). We can expect that those who declare that they are very satisfied with coworking will also tend to proclaim that they are more extroverted or agreeable. To control for the tendency to emphasise consistency over reality, we separated personality measures from satisfaction measures in the questionnaire. We also conducted Harman’s single-factor test, loading the variables suspected to cause bias into an exploratory factor analysis. We found that by loading the variables satisfaction, extroversion, agreeableness, and openness, the null hypothesis that a single factor is sufficient was rejected, with a p-value of 0.026. When adding the two other personality traits, conscientiousness and mental stability, the null hypothesis was also rejected, with a p-value that became nearly equal to zero. Therefore, there is no indication that our data were affected by common method variance.

After fieldwork, 135 valid responses were obtained. The average age of respondents was 37 years. The sex composition was balanced. One out of two respondents held a master’s degree or PhD.

3.2 Data collection

We used a web survey for data collection. Participation was voluntary and anonymous. No reward was offered for participation.

3.3 Empirical methodology

Our research fitted regression models using satisfaction with coworking as a dependent variable. Regression analysis has been found to be a suitable method for understanding person-environment fit (Edwards et al., 1998).

3.4 Variables

According to the employee-workplace alignment theory (Appel-Meulenbroek et al., 2021), compatibility between an individual and the work environment is the key to satisfaction. In this study, we analysed different variables that could explain adjustment to the workplace. For instance, certain psychological traits are better suited for working in a CWS. Moreover, time and experience are needed for coworkers to successfully engage with CWS dynamics.

The dependent variable was coworker satisfaction (SATISFACTION). Overall satisfaction is commonly used in workplace studies and reflects perceptions of the value of the work environment. This measure attempts to capture user satisfaction as an outcome of coworking activity (Clifton et al., 2022). According to a meta-analysis conducted by Wanous et al. (1997), single-item measures of job satisfaction perform comparably to multi-item measures. Consequently, we measured satisfaction with a single item that reflects an overall assessment of the coworking experience (“Taking everything into consideration, how satisfied do you feel about your coworking activity?”). The items were anchored to a 5-point Likert-type scale ranging from 1 (“very unsatisfied” to 5 “very satisfied”). Our measure is similar to that of Lashani and Zacher (2021).

The main explanatory variables were extroversion (EXTROVERTED), agreeableness (NICE), conscientiousness (DILIGENT), openness (OPEN_NEW), emotional stability (STABLE), and experience (TIME). The first five variables refer to the Big Five personality traits, which are considered valid and robust frameworks for studying personality traits. Traditionally, personality trait measures have consisted of multi-item scales which can provoke fatigue among respondents. Following Gosling et al. (2003), we developed items that captured the definition of five prototypical adjectives (for example, “Do you consider yourself an extroverted person? Extroverted: sociable, assertive, talkative, active, not reserved or shy”). Each item was rated on a 5-point scale from 1 (“strongly disagree”) to 5 (“strongly agree”).

Experience (TIME) was a numerical variable which records the length of time spent in the coworking space. Participants were asked to indicate how long they had been working in their current coworking space (“less than 6 months”, “between 6 and 12 months”, “between 13 and 18 months”, and “more than 18 months”). The literature on remote work has frequently used quantitative measures (days, weeks, months, or years) to reflect individuals’ experiences (see, for instance, Labrado-Antolín et al., 2024).

We also included a set of control variables. For instance, age (AGE), gender (GENDER), and coworker profile (POSITION) have been found to influence self-reported job satisfaction (Danielsson and Bodin, 2008). Educational attainment (STUDIES), payment of a fee by the user (FEE), virtual applications for the coworking space (USERAPP), and the number of activities for social interaction (COMMINT) can interact with each other and influence the explained variable. The size (SIZE) of the firm where users work is a relevant measure, but was not the focus of our study.

Table 1 describes these variables briefly.

4. Results

To test our hypotheses, we used a regression model with satisfaction (SATISFACTION) as the dependent variable and five personality traits (EXTROVERTED, NICE, DILIGENT, STABLE, and OPEN_NEW) and experience (TIME) as explanatory variables. We also included several control variables as regressors: age (AGE), gender (GENDER), educational level (STUDIES), professional profile (POSITION), membership fees (FEE), firm size (FIRM_SIZE), face-to-face social events (COMMINT), and digital platforms (USERAPP).

Each coworking space defines a group of observations that share certain characteristics and a correlation between them was expected. The regression model must consider the correlated nature of the observations. A simple and effective way to achieve this is to introduce a random effect into the regression model, which is the realisation of a random variable that takes a value for each group in the data. The random effect reflects the two-stage nature of the sampling process through which observations were obtained. In the first step, we obtained a sample of CWS, and in the second step, we obtained a sample of coworkers within each centre.

With the random effect the regression model becomes as follows:

SATISFACTION=α+bi+β1⋅PERS TRAITS+β2⋅TIME+β3⋅POSITION+controls+εj

In this model, there are two random variables, the usual error term εj, and the random effect bi. We first fit two linear regression models: a baseline model with only the control variables and a full model that included our variables of interest, different personality traits, and experience. To consider the clustered nature of the data, we fit two other regression models with a random effect that was added to the intercept of the linear models. Table 2 shows the estimated coefficients of the fixed effects (i.e. βs in the regressions) and their standard errors (in parentheses) for the four models. Random-effects models were fitted using the lme function in the nlme R package (Pinheiro et al., 2023).

In Table 2, the values of the columns of the models with and without random effects are identical. This may seem surprising, but looking at the variance components of the full random effects model, the between-group variability is negligible. Thus, it is not necessary to use a random-effects model.

To account for the potential effects of outliers, high leverage, and influential observations, whose presence could distort the estimated regression coefficients, we performed a robust linear regression (see Table 2). A robust regression model was fitted using the rlm function of the MASS package in R (Venables and Ripley, 2002). This function uses an M-estimator for the model coefficients. This estimator underweighs the residuals based on their size; thus, the effect of observations with large residuals is attenuated. As shown in Table 2, the robust model is quite similar to the other models, which is a clear sign that there were no outlier observations in the data that could compromise the validity of the results.

With respect to the different personality traits, Table 2 shows that EXTROVERTED and NICE were significant but not the other traits (Hypotheses 1.2, 1.3, and 1.5). The more extroverted a coworker is, the more satisfied he/she will be with the coworking space (Hypothesis 1.1). Agreeableness was also a significant negative predictor of satisfaction with coworking spaces. This contradicts Hypothesis 1.4. The results obtained are interesting for the progress of employee-workplace alignment theory. According to the notion of person-environment fit, workplace satisfaction arises as a result of the alignment between employee characteristics such as skills and values, and environmental characteristics. Our findings reveal the need to include personality traits among employee characteristics that have a strong relationship with subjective wellbeing. This contribution mitigates the lack of empirical evidence on personality-based fit among CWS. Our analysis advances our understanding of the effects of personality on satisfaction with coworking. Two of the five personality traits (extroversion and agreeableness) were related to changes in the dependent variables. Conversely, openness, conscientiousness, and mental stability were not significant predispositions for adjusting to CWS.

Hypothesis 2 stated that satisfaction with CWS depends on how long one has worked in a coworking space. TIME represents coworking experience in months. As the form of the relationship between coworking experience and satisfaction is not clear, we tested a linear trend for this relationship, as well as quadratic, cubic, and so on. The linear, quadratic, and cubic terms were significant; the higher-order terms were not significant, and we discarded them.

Therefore, the relationship between experience and satisfaction can be described using a third-order polynomial. This complex functional relationship can be better understood through graphing. This is done in Figure 1, which shows how the effect of TIME (x-axis) on SATISFACTION (y-axis) has a “sine wave” shape. Satisfaction increases when coworking experience starts, reaches a peak after a few months (about six), then decreases, and after about a year of experience, increases again. Because coworking experience is very recent, we have no information to predict its long-term effect on satisfaction beyond a couple of years. The non-linear relationship between experience and satisfaction points to the dynamic nature of the alignment between employees and the workplace environment. Person-environment fit theories are process theories, and it is important to study the effects of an acquired variable, such as experience, at different points in time.

It is noteworthy that the control variable POSITION (entrepreneur, worker, manager) was significant. Specifically, the results showed that among CWS users, workers and managers were more satisfied with coworking than entrepreneurs.

5. Discussion

The findings of this study support the idea that some psychological traits of coworkers, such as extroversion and agreeableness, influence their satisfaction with coworking. Previous studies have highlighted that both traits are important for job satisfaction in social occupations (Judge et al., 2002). Conversely, no evidence was found of a significant relationship between openness, conscientiousness, mental stability, and the dependent variable. Our study also showed that satisfaction varies depending on the coworking experience.

5.1 Explanation of the results for Hypotheses 1.1 to 1.5

Regarding personality traits, extroversion was associated with a higher level of satisfaction with coworking (Hypothesis 1.1). This result is in line with the study by Hartog et al. (2018), who noted that extroverted users are more satisfied with the features of CWS. In contrast, the buzz of CWS activities may not be adequate for introverted people who have a lower need for relatedness. Extroverted people are characterised by sociability. This feature fits with the idea of CWS as places to “meet people” (Dell’Aversana and Miglioretti, 2024). Conversely, introverted individuals appreciate privacy and seek fewer daily social interactions. Our results also showed that agreeableness had a significant negative impact on coworkers’ satisfaction (Hypothesis 1.4). This intriguing result requires reflection. Several studies found that agreeableness positively influences employee satisfaction (e.g. Matzler and Renzl, 2007). Logic suggests that those who score high on agreeableness are more satisfied with the synchronous forms of communication that take place in CWS. However, the prosocial behaviour of agreeable individuals could collide with the competitive dimensions of the coworking environment. Other personality traits did not have an important effect on work satisfaction. Openness is a distinctive characteristic of the coworking movement. Apparently, open-minded people should be more attracted to the receptivity to new ideas often seen in CWS (Hypothesis 1.2). However, this was not the case in the present study. High openness scores were not correlated with satisfaction. Conscientiousness reflects a greater need for order and proactivity. In this vein, we could expect that individuals with high scores on conscientiousness would be more satisfied with coworking because it is an adequate environment for self-determination (Hypothesis 1.3). However, this association was not supported by our data. The lack of a relationship between mental stability and satisfaction among CWS is noteworthy (Hypothesis 1.5). The literature presents emotional support as an advantage of coworking (Kopplin et al., 2022). Nevertheless, our findings contradicted the idea that individuals who score low on emotional stability (neuroticism) can engage in coworking to learn how to cope with uncertainty.

5.2 Explanation of results for Hypothesis 2

The main assumption of Hypothesis 2 was that satisfaction with coworking increases with accumulated coworking experience. We found a nonlinear relationship between experience and satisfaction. This means that increasing experience in the low-experience phase (nine months) has a positive effect on satisfaction. During this early stage of coworking, it is possible that people are keener to adapt to themselves. However, acquiring more experience during the medium-experience phase has detrimental effects. Coworkers can fail to adapt to changes, and excessively accumulated experience harms satisfaction. Finally, when users have a longer coworking experience (one year), they become accustomed to the coworking centre. In this case, additional increases in experience are typically accompanied by increased satisfaction. Although this result is difficult to explain, our data clearly indicated that user satisfaction with CWS is not uniform as users gain more experience. In this vein, we can say that the effects of “learning to cowork” do not emerge linearly. After a “honeymoon effect”, it is possible to identify a decline and a recovery of satisfaction.

6. Conclusion

The literature has not sufficiently considered the connection between the personal characteristics of coworkers and their satisfaction with coworking. We addressed this knowledge gap with the initial assumption that personality traits and accumulated coworking experience influence satisfaction with coworking. Our findings showed that the level of satisfaction varies depending on the users’ extroversion, agreeableness, and coworking experience. The open environment of CWS was well-suited to extroverts. However, agreeable individuals were less satisfied with coworking.

6.1 Implications for scholars

Our research broadens the scope of employee-workplace alignment theory by confirming that some personality traits are relevant variables for person-environment fit in CWS. Employees with personality characteristics matching the requirements of the CWS environment were more satisfied with this arrangement. Thus, we identified extroversion and agreeableness as predispositions that affect satisfaction as an output of coworking. The nonlinear relationship between the coworking experience and satisfaction with coworking has implications for the vision of coworking as a gradual learning process. Our findings open horizons for future research. For instance, it would be interesting to use other methodologies such as structural equation modelling with a larger sample size. In addition, we could compare our results with those obtained from other EU countries. An analysis of coworkers based on control variables could also complement the findings of the current study.

6.2 Implications for managers

Our research found empirical evidence to support the decisions of coworking managers. Congruence between personality traits and CWS is crucial for attracting motivated people. These centres must tailor their offers to seek new customers. Hosts that are aware of user predispositions can adapt, improve their coworking experience, and deliberately favour particular types of individuals. Conscientiousness, openness, and mental stability did not explain a significant proportion of the variance of satisfaction with coworking. However, members’ satisfaction with coworking spaces increased if he or she is more extroverted. Paradoxically, agreeable individuals may not be satisfied with these enclaves. This study challenges the widely accepted notion that agreeableness is a key personality trait for adjusting to social or group-based environments.

6.3 Implications for society

Modern work environments, such as CWS, are not ideal for everyone as innate predispositions and experience condition the adjustment to these locations. Understanding the factors that contribute to coworkers’ satisfaction is essential for coworking managers. Our findings have implications for workers seeking new workplaces that fit their personalities. This work also offers insights for policymakers interested in encouraging innovation and entrepreneurship through CWS.

6.4 Limitations

This study has some limitations. First, it was a cross-sectional analysis, and CWS users were not studied over time. There is also a risk of self-selection as respondents can be convinced coworkers. CWS are heterogeneous in terms of location, spatial setting, amenities, and atmosphere. These characteristics also influence coworkers’ satisfaction. In addition, we treated the experience as a function of time, focusing on the quantity of experience without considering quality.

Figures

Figure 1

Effect of time on satisfaction

Table 1

Variables

Dependent variable	SATISFACTION_COWORKING	Satisfaction with coworking: Degree of satisfaction with coworking activity 1 (Very high) 5 (very low)
	EXTROVERTED	Self-definition as an extroverted person: Agreement with the statement “you are an extroverted person.” 1 (Strongly disagree) 5 (very low)
	NICE	Self-definition as an agreeable person: Agreement with the statement “you are an agreeable person.” 1 (Strongly disagree) 5 (very low)
Independent variables	DILIGENT	Self-definition as a conscientious person: Agreement with the statement “you are a conscientious person.” 1(Strongly disagree) 5 (very low)
	OPEN_NEW	Self-definition as a person open to new experiences: Agreement with the statement “you are a person open to new experiences.” 1 (Strongly disagree) 5 (very low)
	STABLE	Self-definition as a person emotionally stable: Agreement with the statement “you are an emotionally stable person.” 1 (Strongly disagree) 5 (very low)
	TIME	Coworking experience: 1 (Less than one month) 2 (6–12 months) 3 (13–18 months) 4 (More than 19 months)
Control variables	AGE	Age in Years
	GENDER	Declared gender: Male Female Other
	STUDIES	Educational attainment: Completed primary Upper secondary College degree Master PhD
	FEE	Payment of a fee by the user: Yes No
	USERAPP	Virtual application for the coworking space: Yes No
	COMMINT	Number of activities for social interaction: 1 (None) 2 (1 in a month) 3 (2–4 in a month) 4 (More than 4 in a month)
	FIRM SIZE	Number of employees of the firm: 1 (one) 2 (2–10) 3 (11–50) 4 (51–250) 5 (251 more)
	POSITION	Profile of the coworker: 1 (Entrepreneur) 2 (Corporate employee, manager) 3 (Rest of corporate employees)

Source(s): Authors’ own work

Table 2

Statistical models

	Baseline linear model	Full linear model	Robust linear model	Random eff. Model	Random eff. Full model
(Intercept)	3.8598***	1.3526	1.4879	3.8598***	1.3526
	(0.6543)	(1.2492)	(1.2265)	(0.6543)	(1.2492)
AGE	−0.0026	0.0001	0.0012	−0.0026	0.0001
	(0.0069)	(0.0070)	(0.0069)	(0.0069)	(0.0070)
GENDERMan	0.1763	0.4056	0.3968	0.1763	0.4056
	(0.4647)	(0.4645)	(0.4561)	(0.4647)	(0.4645)
GENDERWoman	0.0801	0.2369	0.2260	0.0801	0.2369
	(0.4611)	(0.4578)	(0.4495)	(0.4611)	(0.4578)
STUDIES	−0.0462	−0.0845	−0.0722	−0.0462	−0.0845
	(0.0816)	(0.0848)	(0.0833)	(0.0816)	(0.0848)
FEEyes	0.5002*	0.5814*	0.5134*	0.5002*	0.5814*
	(0.2312)	(0.2307)	(0.2266)	(0.2312)	(0.2307)
FIRM_SIZE	−0.1292+	−0.1902**	−0.1723*	−0.1292+	−0.1902**
	(0.0682)	(0.0681)	(0.0669)	(0.0682)	(0.0681)
COMMINT	0.2568*	0.2520*	0.2541*	0.2568+	0.2520+
	(0.1215)	(0.1205)	(0.1183)	(0.1215)	(0.1205)
USERAPP	0.4890*	0.5944**	0.5746**	0.4890*	0.5944*
	(0.2212)	(0.2242)	(0.2201)	(0.2212)	(0.2242)
POSITIONmanager	0.9133***	0.9496***	0.8244**	0.9133***	0.9496***
	(0.2628)	(0.2561)	(0.2515)	(0.2628)	(0.2561)
POSITIONworker	0.7756**	0.8304**	0.7178**	0.7756**	0.8304**
	(0.2662)	(0.2598)	(0.2550)	(0.2662)	(0.2598)
EXTROVERTED		0.2016*	0.1942*		0.2016*
		(0.0909)	(0.0893)		(0.0909)
NICE		−0.3346**	−0.3303**		−0.3346**
		(0.1188)	(0.1167)		(0.1188)
DILIGENT		0.1487	0.1431		0.1487
		(0.1155)	(0.1134)		(0.1155)
STABLE		−0.0834	−0.0707		−0.0834
		(0.0994)	(0.0976)		(0.0994)
OPEN_NEW		0.0853	0.0867		0.0853
		(0.1066)	(0.1047)		(0.1066)
TIME		4.0269*	3.7852*		4.0269*
		(1.6792)	(1.6488)		(1.6792)
TIMEˆ2		−1.9428*	−1.8267*		−1.9428*
		(0.7745)	(0.7605)		(0.7745)
TIMEˆ3		0.2760*	0.2596*		0.2760*
		(0.1062)	(0.1042)		(0.1062)
R2	0.1761	0.2918
Adj. R2	0.0992	0.1631
Num. obs.	118	118	118	118	118
AIC	240.3836	238.5259		242.3836	240.5259
Log likelihood				−108.1918	−99.2629
Num. groups: centre				16	16

Note(s): ^***p < 0.001; ^**p < 0.01; ^*p < 0.05; ⁺p < 0.1

Source(s): Authors’ own work