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Article

Perfectionism and Workaholism as Barriers to Lifelong Learning and Occupational Sustainability: A Cross-Professional Analysis

by
Aniella Mihaela Vieriu
and
Simona Magdalena Hainagiu
*
The Department of Teacher Training and Social Sciences, The National University of Science and Technology POLITEHNICA, 060042 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(14), 6512; https://doi.org/10.3390/su17146512
Submission received: 4 June 2025 / Revised: 7 July 2025 / Accepted: 15 July 2025 / Published: 16 July 2025
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Abstract

Workaholism and perfectionism have increasingly been identified as significant obstacles to effective lifelong learning and skills development, ultimately undermining long-term career adaptability and organizational resilience. This study explores the predictive role of perfectionism and professional workaholism, with a particular focus on their implications for continuous education and occupational sustainability—defined as employees’ ability to remain adaptable and resilient over time. Using a cross-sectional quantitative design, data were collected from 105 participants (54 IT professionals and 51 nurses) who completed standardized measures of perfectionism and workaholism and reported their cognitive–emotional readiness for further training. Four regression models were employed to assess the impact of the three perfectionism dimensions and profession on overall workaholism and its subcomponents (excessive work, compulsive work, supplementary work). Socially prescribed perfectionism emerged as a strong predictor, accounting for over one-third of the variance in workaholism (β = 0.37; R2_adj = 0.368; p < 0.001), while self-oriented perfectionism significantly predicted excessive work (β = 0.25; p = 0.015). Professional domain had no significant effect, indicating the trans-professional nature of these psychological barriers. Additionally, workaholism was associated with reduced cognitive–emotional availability for ongoing training, highlighting its detrimental effects on lifelong learning. Limitations include the cross-sectional design and reliance on convenience sampling. From a practical perspective, the findings support interventions targeting maladaptive perfectionism, aiming to enhance engagement in continuous professional education and foster sustainable work environments, in line with the United Nations Sustainable Development Goals (SDG 4 and SDG 8).

1. Introduction

1.1. Context and Relevance

Amid rapid professional transformations and increasing mental health challenges, organizational and educational sustainability have emerged as strategic priorities within the framework of the UN 2030 Agenda for Sustainable Development. However, psychological factors that undermine individuals’ capacity for lifelong learning, particularly perfectionism and workaholism—remain underexplored in sustainability-oriented research. Gaining insight into how these internalized demands impair cognitive and emotional readiness for continuous learning is essential for promoting socially and educationally sustainable work environments.
Both constructs negatively impact employee well-being and organizational performance. For instance, recent data indicate that up to 50% of healthcare professionals and over one-third of IT workers experience clinically relevant burnout symptoms, often associated with dysfunctional perfectionist tendencies and compulsive overwork [1]. Supporting employee mental health is therefore vital to achieving SDG 3 (Good Health and Well-Being) and SDG 8 (Decent Work and Economic Growth). Moreover, widespread digitalization and the post-pandemic landscape have intensified workplace stress, exacerbating both perfectionism—characterized by excessively high personal standards and harsh self-evaluation—and workaholism—marked by an uncontrollable drive to invest time and effort in work beyond organizational expectations [2]. These traits pose a direct threat to sustainability goals by depleting the cognitive and emotional resources necessary for long-term adaptability and learning.
To clarify the conceptual scope of this study, three central constructs are defined.
Occupational sustainability refers to an individual’s ability to maintain a productive, meaningful, and health-preserving career over time, particularly in demanding professional settings. It involves the integration of employability, work–life balance, and adaptability to organizational change [3].
Continuous learning (or lifelong learning) denotes ongoing engagement in formal and informal educational processes throughout one’s career, ensuring skill renewal and long-term professional relevance [4].
Psychological well-being encompasses emotional stability, perceived competence, and self-determined motivation—dimensions shown to be reciprocally linked with depressive symptoms over time, and particularly vulnerable to erosion under conditions of perfectionism and overwork [5].
Recent empirical studies have reinforced the theoretical connection between these constructs. For example, Hill and Curran [6] conducted a meta-analysis demonstrating that maladaptive forms of perfectionism—particularly perfectionistic concerns—are significantly linked to burnout and emotional exhaustion in occupational settings. Jung et al. [7] found that workaholism substantially increases employee burnout and turnover intentions, especially among younger professionals. Furthermore, a 2022 MDPI study on workplace perfectionism indicated that self-oriented perfectionism plays a significant role in shaping job performance and interacts with perceptions of organizational justice to influence occupational outcomes [8]. These findings collectively suggest that perfectionist tendencies and workaholic behaviors compromise employee well-being and hinder sustainable learning trajectories.
Education for sustainable development emphasizes lifelong learning, the ongoing, self-motivated pursuit of knowledge and skills across one’s career, as a cornerstone of professional adaptability and resilience. When organizations promote continuous learning, they cultivate environments where employees can recover from stress, adapt to technological advances, and sustain long-term employability [2]. By examining how perfectionism and workaholism hinder participation in both formal and informal training, this study positions itself at the intersection of psychological well-being and educational sustainability.

1.2. Research Gap

Although perfectionism and workaholism have each been studied extensively, few investigations have examined their combined impact across distinct professional domains such as IT and healthcare [9,10]. For instance, Shin and Shin [11] analyzed determinants of workaholism among IT professionals, highlighting how job insecurity and organizational pressures contribute to excessive work behavior. Similarly, Barbosa et al. [12] conducted an integrative review showing that workaholism among nurses is strongly associated with burnout, stress, and anxiety, yet did not explore the role of perfectionism in these outcomes. Moreover, Falco et al. [13], using a longitudinal design, demonstrated that self-oriented perfectionism predicts increased workaholism over time under conditions of high workload in a mixed occupational sample; however, they neither compared professions nor assessed readiness for lifelong learning. To date, no study has simultaneously examined the three dimensions of perfectionism (self-oriented, other-oriented, and socially prescribed) alongside professional role (nurses vs. IT employees) in relation to workaholism and its downstream effects on engagement in lifelong learning. This represents a critical research gap at the intersection of occupational psychology and educational sustainability, especially given the rising importance of continuous learning in high-demand sectors.
Understanding how perfectionism contributes to workaholic tendencies—and how these tendencies, in turn, hinder readiness for continuous learning—is essential for designing interventions that support professional adaptability, emotional well-being, and long-term workforce sustainability. Although research on the psychosocial predictors of workaholism is growing, few studies have examined their combined impact on learning engagement and occupational sustainability across professional sectors.
This study addresses the following research question: How do the dimensions of perfectionism and professional role (IT vs. healthcare) interact to influence workaholism and engagement in lifelong learning? Given the increasing demands for continuous professional development and the importance of learning agility for workforce resilience, it is crucial to examine how psychological barriers—such as perfectionism and workaholism—may obstruct this process. While prior research has investigated these constructs in isolation, the present study adopts an integrated, cross-professional approach, which is increasingly necessary in the context of labor market volatility and rapid technological disruption.
By identifying the psychological mechanisms that undermine participation in lifelong learning, this study provides evidence-based guidance for promoting inclusive, sustainable, and psychologically healthy workplaces—key goals of the Sustainable Development Agenda. Accordingly, this research advances the understanding of psychological impediments to continuous learning and directly aligns with SDG 3 (Good Health and Well-Being), SDG 4 (Quality Education), and SDG 8 (Decent Work and Economic Growth).
Despite growing interest in these topics, no previous study has simultaneously explored how perfectionism dimensions and professional role interact to predict both workaholism and engagement in lifelong learning. Moreover, although the literature has demonstrated separate associations among these variables, integrative cross-professional models remain absent—an omission that is particularly relevant in the wake of digital transformation, post-pandemic recovery, and the intensifying global emphasis on sustainable workforce development, as addressed by SDG 3, SDG 4, and SDG 8.

1.3. Aim and Objectives

The primary objective of this study is to examine how the three dimensions of perfectionism (self-oriented, other-oriented, and socially prescribed) and professional role (nurse versus IT employee) influence workaholism and the propensity for lifelong learning—operationalized as cognitive and emotional availability to engage in formal or informal training activities. This research responds to the pressing need to foster mental well-being and sustainable learning within organizations, in alignment with SDG 3 (Good Health and Well-Being) and SDG 8 (Decent Work and Economic Growth).
This study also addresses a theoretical and practical gap by identifying how stress associated with perfectionism undermines employees’ readiness for upskilling—a critical condition for long-term employability and organizational sustainability.
The research objectives are as follows:
O1:
To evaluate the effect of each perfectionism dimension (self-oriented, other-oriented, socially prescribed) on overall workaholism.
O2:
To determine the impact of professional role (nurse vs. IT employee) on workaholic tendencies.
O3:
To examine whether perfectionism dimensions and professional role interact in predicting workaholism.
By integrating these objectives, this study advances our understanding of the psychosocial mechanisms underlying workaholic behaviors in high-demand professions and provides practical insights for promoting occupational health and sustainable lifelong learning. The following section presents the theoretical background on perfectionism, workaholism, and lifelong learning, and introduces the study’s hypotheses.

2. Theoretical Background

This section provides a structured theoretical framework that synthesizes and critically evaluates the key literature on perfectionism, workaholism, organizational well-being, and lifelong learning, thereby establishing the conceptual foundation of the present study.

2.1. Perfectionism and Workaholism—Impact on Psychological Sustainability

Building on cross-sectoral and meta-analytic findings, perfectionism is a multidimensional trait defined by excessively high personal standards and critical self-evaluation [14]. Its three facets—self-oriented, other-oriented, and socially prescribed—are associated with distinct mental health outcomes; socially prescribed perfectionism, in particular, is consistently linked with anxiety, depression, and burnout [15]. Workaholism—defined as an excessive, compulsive investment of time and effort in work—diminishes psychological sustainability, understood as the capacity to maintain mental health under persistent work pressure [16].
Recent meta-analytic research indicates that workaholism affects a significant portion of the workforce and is consistently linked with maladaptive perfectionism, especially through mechanisms such as chronic rumination and overcommitment [17]. Cross-cultural studies confirm that socially prescribed perfectionism is the most robust predictor of negative psychological outcomes, particularly in the context of high external demands [18]. Self-oriented perfectionism has a more nuanced role—sometimes adaptive in achievement contexts—but it becomes maladaptive under chronic stress due to its association with feelings of guilt and performance-based self-worth [19]. Other-oriented perfectionism, while less explored, is increasingly linked to interpersonal conflict and reduced collaboration in team-based environments, particularly in high-performance professions [20]. This theoretical distinction is crucial in understanding how perfectionistic subtypes contribute to emotional exhaustion and lower well-being.

2.2. Sustainable Organizational Health—Work–Life Balance

Drawing on longitudinal and contextualized evidence from multiple sectors, within the framework of social sustainability, organizational health requires a climate of psychosocial safety where employee well-being is prioritized [21]. Economic performance is thus inseparable from mental health and the ongoing adaptability of human resources.
Implementing Sustainable HRM practices—such as flexible work arrangements, supportive leadership, and workload monitoring—can significantly lower burnout risk and enhance job satisfaction [22]. However, the effectiveness of these measures depends on organizational culture, available resources, and demographic profiles. According to the realist “what works for whom and when” model, interventions must be systematically tailored to each organization’s specific context [23].
Recent empirical research demonstrates that cultivating a strong psychosocial safety climate (PSC)—defined as the shared perception that organizational leadership prioritizes psychological health—can significantly improve workplace outcomes [24]. Longitudinal studies in healthcare and education confirm that PSC acts as a moderator: in units with high demands, a stronger PSC mitigates exhaustion and fosters work–life balance and learning engagement [25].
Additionally, recent meta-analytic evidence indicates that decent work conditions—including safety, fairness, and job security—are significantly associated with improved employee well-being and enhanced career development outcomes across sectors [26].

2.3. Lifelong Learning (LLL) and Educational Sustainability

Lifelong learning (LLL) is central to organizational resilience: firms that implement continuous learning cycles double their innovation rate and increase their likelihood of maintaining a competitive advantage in volatile environments by 15–20% [27]. LLL also supports the educational sustainability objectives of SDG 4 (skill development across the lifespan) and SDG 8 (Decent Work and Economic Growth).
However, persistent psychobehavioral barriers—rooted in personality traits and habitual work patterns—continue to hinder access to LLL. Maladaptive perfectionism induces a fear of evaluation and undermines mastery motivation [28], while workaholism drains the time and psychological resources necessary for formal development, thereby reducing participation in educational programs [29].
Recent longitudinal evidence shows that workaholism, unlike work engagement, is associated with reduced cognitive and emotional resources, leading to lower participation in workplace learning and proactive training initiatives [30]. These capacity deficits—often sustained by rumination—undermine LLL engagement and weaken progress toward SDGs 4 and 8. To counteract this trend, research highlights three key interventions: (1) cultivating a psychosocially safe climate that frames errors as learning opportunities; (2) integrating micro-learning into daily routines to reduce time-related barriers for workaholics [31]; (3) promoting development-oriented job crafting, whereby employees reshape tasks to facilitate incremental skill renewal [32].
When implemented synergistically, these measures can embed lifelong learning as a continuous driver of educational and organizational sustainability.

2.4. Conceptual Model and Hypotheses

Building on the previously discussed constructs (perfectionism, workaholism, and lifelong learning), we propose the following theoretical model illustrating the expected relationships among them (Figure 1). In this model, each of the three perfectionism facets (self-oriented, other-oriented, and socially prescribed) is hypothesized to directly influence workaholism, which in turn negatively affects the propensity for lifelong learning (LLL).
This diagram encapsulates our main hypotheses:
H1. 
Perfectionism dimensions and professional role significantly predict workaholism.
H1a. 
They predict excessive work.
H1b. 
They predict compulsive work.
H1c. 
They predict supplementary work.
H2. 
There is a significant interaction between perfectionism dimensions and professional role in predicting workaholism.
H2a. 
Interaction predicts excessive work.
H2b. 
Interaction predicts compulsive work.
H2c. 
Interaction predicts supplementary work.
Thus, the model suggests that self-oriented perfectionism, other-oriented perfectionism, and socially prescribed perfectionism contribute to workaholism, which in turn diminishes the propensity for lifelong learning (LLL). To empirically examine these relationships, the following section details the methodology, sample, instruments, and analysis procedures.

3. Materials and Methods

3.1. Study Variables

To examine the influence of perfectionism and professional role on the development of workaholism, the study employs the following clearly defined variables (Table 1):

3.2. Sample

The study employed a non-probabilistic convenience sample of 105 voluntary participants in Romania, drawn from two high-demand professions: 54 IT specialists and 51 nurses. The gender distribution was relatively balanced (45 men, 60 women), with ages ranging from 20 to 59 years (M = 31.26, SD = X). Eighty-five respondents lived in urban areas and 20 in rural settings; marital status was reported as 64 single, 38 married, and 3 divorced. Convenience sampling may introduce self-selection bias—for example, IT employees with easy internet access or nurses who were more likely to participate due to flexible work schedules.
A moderate-sized, non-random sample was appropriate given the exploratory nature of this research and the practical constraints of accessing these professional groups. G*Power (version 3.1.9.7) analysis indicated that, for a multiple linear regression with three predictors and a medium effect size (f2 = 0.15) at α = 0.05, 77 participants would yield 80% power. Samples exceeding 100 are generally sufficient to detect medium effects in regression and factor analyses, particularly when examining psychosocial interactions [33]. Although generalizability is limited, this sample provides a robust empirical basis for hypothesis testing and offers guidance for future studies using probabilistic sampling and longitudinal designs to validate and extend findings on human resource sustainability and lifelong learning.

3.3. Procedure

Participants were recruited through institutional mailing lists and online professional networks (e.g., LinkedIn groups, field-specific forums) relevant to the IT and healthcare sectors. The invitation included information about the study’s purpose, guaranteed anonymity, and provided access to the questionnaire. Informed consent was obtained by participants voluntarily proceeding to the survey after reading this information. Eligibility criteria required participants to be actively employed in the IT or healthcare sectors. No exclusion criteria were specified. Participation was entirely voluntary and uncompensated.
Participants completed the DUWAS and MPS questionnaires electronically via Google Forms. Prior to participation, they were informed about the voluntary nature of the study, its objectives, and the confidentiality of their responses. The introductory instructions stated: “This study explores your agreement with statements about professional activities and personal perceptions. You will complete three questionnaire sections. There are no right or wrong answers; please respond honestly and spontaneously. All responses are anonymous and used solely for research purposes. Thank you for your contribution.”
After providing informed consent online, participants completed the measures in the following order: (1) DUWAS, to assess workaholism; (2) MPS, to measure dimensions of perfectionism; and (3) a socio-demographic questionnaire covering gender, age, profession, residence, contracted and overtime hours, and marital status. Each participant completed the survey individually and at their own pace, with an estimated completion time of 20–25 min. This protocol helped ensure response accuracy and minimized completion bias, while adhering to ethical standards in psychosocial research.

3.4. Data Collection, Research Instruments, and Statistical Analyses

Data collection was conducted in May 2025 using three validated instruments: a socio-demographic questionnaire, the Dutch Work Addiction Scale (DUWAS), and the Multidimensional Perfectionism Scale (MPS). The socio-demographic section recorded participants’ gender, age, place of residence (urban/rural), marital status, professional domain, contracted working hours per week, and average overtime. Although not a standardized scale, the socio-demographic questionnaire was constructed based on commonly used variables in occupational health research and was reviewed by two academic experts to ensure face validity. These covariates were selected based on their theoretical and empirical relevance to workaholism and learning engagement across occupational settings, as they are frequently included in psychosocial occupational studies. These variables were entered as covariates in the regression models to statistically control for potential confounding effects. Participants were informed of the anonymity of their responses and the confidentiality of their data, in accordance with ethical standards in psychological research and the principles outlined in the Declaration of Helsinki. Age and experience are known to influence learning engagement and psychological flexibility, with younger employees often reporting higher openness to training and adaptability to new technologies [34]. Gender differences in the expression of perfectionism have also been documented, with women more likely to score higher on socially prescribed perfectionism, which may interact with workaholism dimensions [35]. Marital status and overtime work have been associated with work–life conflict, a key factor contributing to compulsive work patterns [36]. Therefore, the inclusion of these covariates was theoretically and empirically justified, aiming to isolate the net effects of perfectionism dimensions and professional role on the outcomes of interest, in alignment with best practices in contemporary multivariate modeling.
The DUWAS [37] measures two core dimensions of workaholism—Working Excessively (9 items) and Working Compulsively (7 items)—plus four additional items on work hours. Subscale scores are computed as the mean of their respective items, and a total score above the 75th percentile indicates workaholism. The Romanian version demonstrated satisfactory internal consistency (Cronbach’s α = 0.85) [38]. The MPS developed by Hewitt & Flett [14] comprises 45 Likert-type items across three dimensions: self-oriented, other-oriented, and socially prescribed perfectionism. Reverse-coded items are included to control for response bias. In a pilot sample (n = 30), the scale yielded a Cronbach’s α of 0.83, indicating good reliability. All demographic covariates were initially included in multiple linear regression models. Covariates with non-significant associations (p > 0.05) were excluded via a stepwise procedure, retaining only significant predictors to enhance model parsimony and explanatory power.
Analyses were conducted using IBM SPSS Statistics 26. The dataset was first examined with descriptive statistics and Kolmogorov–Smirnov tests for normality. Spearman correlations were applied to non-normal variables, while Pearson correlations were used otherwise. Bonferroni corrections were employed to reduce the risk of Type I error in multiple comparisons. Predictive hypotheses were tested via enter-method multiple regression, supplemented by univariate ANOVAs and independent-samples t-tests where applicable. For interaction analyses, continuous predictors were mean-centered, and interaction terms (perfectionism × profession) were included to ensure accurate coefficient interpretation. Complete DUWAS and MPS item listings appear in Appendix A to ensure transparency and replicability.
Although the main focus was on perfectionism and professional role, demographic covariates—including gender, working hours, and marital status—were initially incorporated into the regression models. However, these variables did not exhibit significant effects (p > 0.05) and were excluded through a stepwise model reduction procedure to preserve parsimony. The availability of occupational health and wellness services was not explicitly assessed, however, future research should include such organizational factors to explore their potential moderating role in the relationship between workaholism and engagement in lifelong learning.

4. Results

All analyses were performed in IBM SPSS Statistics 26. Distribution normality was tested using the Kolmogorov–Smirnov test. Spearman correlations were applied to non-normal variables, and multiple linear regressions (Enter method) were conducted to test predictive hypotheses. Independent-samples t-tests and univariate ANOVAs were used to examine interaction effects. Statistical significance was set at α = 0.05. The models assessed both the main effects of psychosocial predictors (perfectionism dimensions and profession) and their interactions, in relation to workaholism, within the broader framework of organizational and educational sustainability (SDG 3, SDG 4, SDG 8).
Prior to regression, assumption checks confirmed:
▪
Multicollinearity: VIF values ranged from 1.12 to 1.38 and tolerance from 0.72 to 0.89, indicating no severe collinearity.
▪
Residual normality and homoscedasticity: Breusch–Pagan tests showed no heteroscedasticity (p > 0.10 for all models).
▪
Error independence: Durbin–Watson statistics ranged from 1.89 to 2.07, approximating the ideal value of 2.0.
▪
Spearman’s rho was employed for non-normal variables (supplementary work and profession).

4.1. Assessment of Distribution Normality

Kolmogorov–Smirnov tests were conducted to assess the normality of the main variables. Table 2 presents the D and p values for each measure. The results show that Working Excessively (D = 0.076, p = 0.200), Working Compulsively (D = 0.063, p = 0.200), and all three perfectionism dimensions were normally distributed (all p > 0.05). In contrast, Supplementary Work did not meet the assumption of normality (D = 0.189, p = 0.023), confirming its non-normal distribution.
Since all perfectionism and workaholism measures—except for Supplementary Work and Profession—met the assumption of normality (p > 0.05), multiple linear regression analyses were applied to those variables. For analysis involving Supplementary Work and Profession, Spearman’s rank-order correlation was employed to account for their non-normal distributions.

4.2. Bivariate Relationships: Perfectionism–Workaholism

Spearman’s rho correlations were computed for variables violating the normality assumption, while Pearson’s r was used for those normally distributed. Table 3 presents the full correlation matrix and reveals that self-oriented perfectionism was significantly associated with both Working Excessively (r = 0.43, p < 0.001) and Working Compulsively (r = 0.31, p = 0.002). Other-oriented perfectionism showed a moderate correlation with Working Compulsively (r = 0.29, p = 0.004), while socially prescribed perfectionism demonstrated a strong correlation with Working Compulsively (r = 0.47, p < 0.001). Due to its non-normal distribution, Supplementary Work was analyzed using Spearman’s rho and exhibited a significant correlation with socially prescribed perfectionism (ρ = 0.26, p = 0.008).
H1a–H1c were supported at the bivariate level: each perfectionism facet was positively correlated with all three workaholic behaviors. The strongest association was observed between self-oriented perfectionism and the total DUWAS score (r = 0.495), aligning with recent meta-analytic findings that identify self-oriented perfectionism as a robust predictor of workaholism [15].

4.3. Testing Hypothesis 1—Predictive Effects of Perfectionism and Profession on Workaholism

To test Hypothesis 1—that the dimensions of perfectionism and profession predict overall workaholism and its subcomponents—four multiple linear regression models were estimated: (1) total DUWAS score (overall workaholism); (2) Working Excessively; (3) Working Compulsively; and (4) supplementary work.
Each model tested one of the sub-hypotheses (H1a–H1c) and included the same set of four predictors: (1) self-oriented perfectionism (P-Self); (2) other-oriented perfectionism (P-Other); (3) socially prescribed perfectionism (P-Social); and (4) profession (coded 0 = IT employee; 1 = nurse).
A summary of regression results is provided in Table 4.
These findings indicate that socially prescribed perfectionism exerts the strongest predictive effect on workaholic tendencies across professions, highlighting the need for interventions aimed at reducing perceived external pressure.
The regression model predicting the overall DUWAS score was statistically significant (F(4,100) = 16.12, p < 0.001), accounting for 36.8% of the variance in workaholism. Both self-oriented (β = 0.25, p = 0.015) and socially prescribed perfectionism (β = 0.37, p < 0.001) emerged as significant predictors. Employees who set high personal standards and perceive strict social expectations report greater work dependency, underscoring the importance of interventions focused on boundary-setting and self-regulation.
In the model for Working Excessively, the predictors explained 29.2% of the variance, with both self-oriented and socially prescribed perfectionism significantly contributing to overwork. This suggests that excessive working hours are primarily driven by socially prescribed perfectionism, and supports the implementation of interventions targeting maladaptive social comparison beliefs.
The Working Compulsively model explained 27.1% of the variance, with only socially prescribed perfectionism remaining a significant predictor. This indicates that compulsive work behaviors are rooted more in external evaluative fears than in internal standards, reinforcing the need to reduce comparative performance pressures in organizational cultures.
The model for supplementary work accounted for 27.7% of the variance, with both socially prescribed and other-oriented perfectionism as significant predictors.
Overall, H1a (excessive work) and H1c (supplementary work) received full support, while H1b (compulsive work) was only partially supported. No significant perfectionism × profession interaction effects were found, thereby rejecting H2 and demonstrating the trans-professional generalizability of these psychological vulnerabilities. The statistical outcomes supporting these hypotheses are presented in Table 5.
These results identify key perfectionism-driven factors that may hinder employees’ engagement in continuous learning (SDG 4) and compromise their ability to sustain a healthy work–life balance (SDG 3 and SDG 8).
Profession was not a significant predictor in any model, suggesting that the perfectionism–workaholism mechanisms operate similarly in programmers and nurses.

4.4. Testing Hypothesis 2—Interactions Between Perfectionism and Profession in Predicting Workaholism

According to this hypothesis, the three dimensions of perfectionism (self-oriented, other-oriented, socially prescribed) were expected to interact with profession (IT vs. nursing) in predicting overall workaholism and its three subcomponents (Working Excessively, Working Compulsively, supplementary work).
Dichotomization of Predictors: Each perfectionism facet was dichotomized using a median split, an approach commonly employed in interaction analyses when distributions are approximately normal [39]. Profession was coded as a binary variable (0 = IT; 1 = nurse).
Analysis Model: For each 2 × 2 univariate ANOVA (profession × perfectionism), we report partial eta squared (η2p) and interpret effect sizes based on Cohen’s conventions (small ≥ 0.01; medium ≥ 0.06; large ≥ 0.14). Detailed results are presented in Table 6.
Global Workaholism (DUWAS)—The interaction between profession and self-oriented perfectionism approached statistical significance (F(1,101) = 3.25, p = 0.074), with a small effect size (η2p = 0.031). Interactions involving the other two perfectionism facets were non-significant (p > 0.18). The tendency for self-oriented perfectionists to develop workaholism appears marginally stronger among nurses than IT specialists, though the difference does not reach conventional significance. This finding supports Clark et al.’s (2016) meta-analytic conclusion that the impact of perfectionism on workaholism is largely independent of professional sector [41].
Working Excessively—None of the interactions reached or approached statistical significance (p ≥ 0.17). Across all subgroups, higher levels of perfectionism were consistently associated with increased excessive work, regardless of profession.
Working Compulsively—The interaction with self-oriented perfectionism was marginally significant (p = 0.065). Compulsive work tendencies appeared somewhat more pronounced among healthcare professionals, potentially due to stricter performance and error-tolerance standards, although the effect size remained small (η2p = 0.033).
Supplementary Work—Interaction effects did not reach p < 0.10. Overtime behavior seems primarily driven by socially prescribed perfectionism, with minimal variation across professions.
None of the twelve profession × perfectionism combinations yielded conventionally significant effects (α = 0.05), and all observed effect sizes were small (η2p ≤ 0.033). Consequently, Hypothesis 2 was not supported. These results indicate that perfectionism represents a comparable risk factor for workaholism among both nurses and IT specialists. This convergence suggests that, from an occupational-sustainability perspective, interventions aimed at reducing maladaptive perfectionism can be implemented transversally rather than tailored to specific sectors—a key insight relevant to SDG 3 (Mental Health) and SDG 8 (Decent Work).

4.5. Additional Analyses—Gender and Profession Differences

Independent-samples t-tests revealed no significant differences in workaholism levels between the two occupational groups (p = 0.420). Similarly, no significant gender differences were observed in workaholism scores (p = 0.420).

5. Discussion

5.1. Interpretation of H1a–H1c

This study aimed to investigate how perfectionism and professional role predict workaholism. The findings partially supported the hypothesis that perfectionism and profession influence workaholism, demonstrating a clear association between maladaptive personality traits and unsustainable occupational behaviors. Among the three perfectionism dimensions, socially prescribed perfectionism emerged as the most consistent and robust predictor across all facets of workaholism (total, excessive, compulsive, supplementary). This suggests that perceived social expectations for high performance can undermine employees’ work–life balance—a key element of psychological sustainability (SDG 3) and long-term organizational viability (SDG 8). These results are consistent with the recent empirical literature, which highlights the detrimental impact of perfectionism on employee mental health and well-being. For example, Kiziloglu et al. [42] reported a positive association between socially prescribed perfectionism and workaholism, reinforcing the view that external pressure for perfection poses significant psychological risks to workers.
Sub-hypotheses H1a–H1c were supported by multiple regression analyses, revealing differentiated prediction patterns for workaholic behaviors depending on the perfectionism type: excessive work was significantly associated with self-oriented perfectionism, suggesting an internalized drive for achievement and difficulties in setting personal limits. In contrast, compulsive work was predominantly predicted by socially prescribed perfectionism, reflecting a persistent preoccupation with fulfilling perceived external expectations—a mechanism consistently supported in recent empirical work [9,43].
These results highlight the importance of disaggregating the components of workaholism. Excessive work appears to be fueled predominantly by an internalized drive for achievement and self-validation—features commonly linked to self-oriented perfectionism. In contrast, compulsive work is characterized by intrusive cognitions and uncontrollable work-related rumination, mechanisms strongly associated with socially prescribed perfectionism [32,34]. From a theoretical standpoint, this distinction reinforces dual-process models of motivation, which propose that both autonomous and controlled drives can culminate in maladaptive outcomes when perfectionist standards are overly rigid. Clarifying these divergent psychological mechanisms is critical for designing targeted interventions that address the unique cognitive and emotional underpinnings of each workaholism subtype.

5.2. Confirmation of H2 and Trans-Sectoral Perspectives

Notably, professional role exerted no significant direct influence on workaholism scores, suggesting that psychosocial traits—particularly perfectionism—function as cross-professional predictors. This supports Clark et al. [41] who concluded that workaholism is shaped more by individual predispositions than by occupational context, and is further reinforced by findings indicating comparable levels of workaholism across sectors such as IT, healthcare, and education [44].
These results confirm Hypothesis 2, showing that perfectionism interacts with professional role in shaping specific workaholism dimensions—particularly compulsive overwork among socially prescribed perfectionists in demanding sectors. This result can be interpreted through the lens of personality theory, which posits that perfectionism represents a stable intrapersonal disposition that influences motivational and behavioral patterns regardless of structural conditions. For instance, self-oriented perfectionism is associated with excessive self-imposed standards and a compulsive drive for achievement, often linked to workaholism [37]. Meanwhile, socially prescribed perfectionism increases vulnerability to overwork through perceived external pressure and fear of evaluation [39]. These mechanisms explain why personality traits, rather than profession or workload alone, may shape persistent patterns of disengagement from learning and rest, ultimately affecting occupational sustainability.

5.3. Implications for SDG 3, SDG 4, SDG 8, and SDG 17

In the context of educational sustainability (SDG 4), workaholism’s inhibitory effect on participation in continuous learning is evident, as excessive and compulsive work restrict both the time and cognitive self-regulation necessary for training. Consequently, workaholic employees are less available for professional development, which undermines their ability to adapt to technological changes and enhance human capital [45].
These results have practical implications for sustainable human resource strategies. Organizations should implement targeted interventions for employees exhibiting maladaptive perfectionism, such as job crafting, integrated micro-learning, and initiatives that foster a psychosocially safe climate. Incorporating emotional self-regulation and personal-standards management into professional training can mitigate the risk of workaholism, thereby promoting a healthier and more sustainable organizational culture [46,47].
Beyond its theoretical contributions, this study offers specific, actionable insights for evidence-based public policy. Our results show that socially prescribed perfectionism is a consistent cross-professional predictor of workaholism, confirming prior evidence that links external performance pressure to emotional dysregulation and burnout [48]. These mechanisms reduce employees’ cognitive–emotional capacity for learning and limit their adaptability to change.
Self-determination theory explains this mechanism: external standards reduce autonomous motivation and resilience, especially among those who internalize others’ expectations, leading to compulsive overwork and disengagement from learning [49].
The limited impact of gender and working hours further suggests that stable personality traits may be stronger predictors of workaholic tendencies than structural variables, highlighting the need for targeted psychological interventions in occupational health and learning policy. Furthermore, self-oriented perfectionism is linked to work addiction via increased rumination, suggesting a distinct cognitive pathway, while other-oriented perfectionism may disrupt collaborative learning by fostering interpersonal pressure [50]. These findings call for tailored interventions: cognitive restructuring to support self-critical individuals and team coaching for those who impose high standards on others. These recommendations are directly derived from the differentiated empirical effects observed across perfectionism dimensions, enhancing their practical precision and theoretical alignment.
Beyond their relevance for organizational practice, these results carry substantial implications for policymaking. Specifically, the identification of perfectionism and workaholism as trans-sectoral risk factors supports the development of national strategies and workplace policies that integrate psychosocial risk assessment and employee well-being into labor, health, and education frameworks. For example, public policies could mandate psychological risk screening as part of occupational health standards, while educational policies may include emotional self-regulation training to foster sustainable learning pathways. Implementing such measures requires coordinated efforts among governments, educational institutions, and organizations, thereby aligning with SDG 17 (Partnerships for the Goals), which emphasizes multi-stakeholder collaboration for sustainable development. These measures would directly advance SDG 3 (Good Health and Well-Being), SDG 4 (Quality Education), SDG 8 (Decent Work and Economic Growth), and SDG 17 (Partnerships for the Goals).

5.4. Limitations and Future Directions

This study has several limitations that should be acknowledged. First, the relatively small sample size reduces the statistical power and generalizability of the findings. Therefore, the present research should be regarded as an exploratory or pilot investigation that provides preliminary evidence on how perfectionism and workaholism interact across professional groups. Although the sample included two distinct occupational sectors, its modest size may limit the applicability of the findings across diverse settings or cultures. Future studies should consider expanding the participant pool to include a wider range of professions, age groups, and cultural contexts. Additionally, longitudinal designs would allow for a more robust understanding of how perfectionist tendencies and workaholism evolve over time, and how they influence long-term engagement in lifelong learning.
Despite these limitations, the present study offers valuable insights into the psychological barriers to sustainable learning engagement, with implications for both future research and evidence-based occupational policy.

6. Conclusions

This study provides compelling evidence that maladaptive personality traits—particularly socially prescribed perfectionism—drive workaholic behaviors, with direct implications for employees’ psychological, occupational, and educational sustainability. These findings underscore the importance of an integrated psychosocial approach to sustainability, extending beyond purely organizational or economic models.
From an SDG 3 (“Good Health and Well-Being”) standpoint, normative performance pressure fosters psycho-emotional imbalance and compulsive overwork, undermining mental health. Socially prescribed perfectionism has been linked to depression and reduced life satisfaction, particularly among young women [51], while workaholism correlates with personal burnout even within dual-career couples [52]. Together, these insights highlight the need for systemic interventions focused on emotional self-regulation, pressure reduction, and a sustainable organizational culture.
In line with SDG 4 (“Quality Education”), the study highlights workaholism’s indirect but detrimental effect on continuous learning: perfectionism and overwork deplete the cognitive, emotional, and temporal resources essential for professional development. Workaholic employees tend to prioritize immediate tasks over long-term growth, compromising adaptability; recent research confirms that workaholism is associated with emotional exhaustion and reduced willingness to engage in self-directed training, especially in fast-paced industries [53]. Accordingly, sustainable educational and organizational policies must foster a culture that values ongoing learning, offering support for self-regulation and perfectionism mitigation [54], to position lifelong learning as a strategic component of global socio-economic sustainability.
Regarding SDG 8 (“Decent Work and Economic Growth”), our findings advocate for human-centered management practices that integrate psychological support, targeted interventions for maladaptive perfectionism, and personalized job crafting. The absence of significant differences in workaholism between professions underscores the trans-sectoral nature of these risks; thus, sustainable interventions should focus on employees’ psychological profiles rather than occupational categories. Recent studies demonstrate that job crafting—particularly in remote work settings—can enhance performance and mitigate technological strain, fostering sustainable work environments [55].
Overall, this research illustrates that achieving work sustainability requires a paradigm shift toward addressing the psychosocial drivers of organizational behavior. Our conclusions provide a foundation for public policies and institutional strategies that integrate mental health, continuous education, and work–life balance as core pillars of social sustainability. The findings also offer clear guidance for policymakers aiming to embed sustainable upskilling, mental health, and psychosocial safety into labor, education, and health agendas.
In sum, the study contributes to several Sustainable Development Goals: SDG 3 (Good Health and Well-Being), SDG 4 (Quality Education), SDG 8 (Decent Work and Economic Growth), and SDG 17 (Partnerships for the Goals). By offering a shared empirical foundation, it supports collaborative efforts between educational institutions, healthcare providers, and labor organizations to foster sustainable workforce development. Given the exploratory nature and modest sample size of this study, future large-scale investigations are needed to confirm and extend these findings.

Author Contributions

Conceptualization, A.M.V. and S.M.H.; methodology, A.M.V.; software, A.M.V.; validation, A.M.V. and S.M.H.; formal analysis, S.M.H.; investigation, A.M.V. resources, A.M.V. and S.M.H.; data curation, A.M.V. and S.M.H.; writing—A.M.V. and S.M.H.; writing—review and editing, A.M.V. and S.M.H.; visualization, A.M.V. and S.M.H.; supervision, A.M.V. and S.M.H.; project administration, A.M.V. and S.M.H.; funding acquisition, A.M.V. and S.M.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of The National University of Science and Technology Politehnica Bucharest. The ethical approval for the study was obtained from the Institutional Review Board, approval number 16040/23.05.2025.

Informed Consent Statement

All participants provided informed consent electronically prior to participation. They were presented with a written explanation of the study’s purpose, procedures, anonymity, and voluntary nature. By proceeding with the survey, participants confirmed their consent. No personally identifiable data was collected, and all responses were fully anonymized.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Appendix A.1. Dutch Work Addiction Scale (DUWAS)

The following 20 statements describe how you may feel at work. Please read each statement carefully and indicate how often you experience each sentiment in relation to your work. Select the option that best represents the frequency with which you feel that way. For example, if you never or almost never feel that way, circle 1; if you always or almost always feel that way, circle 4. There are no right or wrong answers; please respond honestly. For each statement, circle the number (1–4) corresponding to how frequently you feel that way at work. Your responses are confidential.
Response Option1234
1. I do not like overworking.
2. I wish I were not so dedicated to my work.
3. I seem to be racing against the clock.
4. I keep working even after my colleagues stop.
5. It is important for me to work hard, even when I do not enjoy what I am doing.
6. I stay busy and juggle multiple tasks.
7. I think about work even when I want to take a break.
8. I take on more tasks than I can handle.
9. I feel an internal drive to work hard—a sense that I have to do it, whether I want to or not.
10. I go to work even when I am not feeling well.
11. I pressure myself with self-imposed deadlines when I am working.
12. I feel something inside me forcing me to work hard.
13. I spend more time working than I do with friends or engaging in preferred or relaxing activities.
14. I feel guilty when I am not working on something.
15. I feel compelled to work hard even when it is not enjoyable.
16. I work on weekends.
17. I often do two or three things at once, such as eating lunch, writing an email, and talking on the phone.
18. I feel guilty when I take a break from work.
19. I find it difficult to relax when I am not working.
20. I take work home with me.
1 = (Almost Never); 2 = Sometimes; 3 = Often; 4 = (Almost) Always.

Appendix A.2. Multidimensional Perfectionism Scale (MPS)

Below are statements about personal characteristics and traits. Read each statement and indicate your level of agreement using a 1–7 scale (1 = Strongly disagree; 7 = Strongly agree). There are no right or wrong answers—please respond openly and honestly. Your responses are confidential. For each statement, circle the number (1–7) that best reflects your level of agreement.
ItemStatement1234567
1When I work on something, I cannot relax until it is perfect.
2It is not in my nature to criticize someone who gives up too easily.
3It does not matter to me if people close to me are successful.
4I rarely criticize my friends for settling for second-best.
5I find it difficult to accept others’ expectations of me.
6One of my goals is to be perfect in everything I do.
7Everything others do must be of the highest quality.
8I never strive for perfection in my work.
9Those around me easily accept that I can make mistakes.
10It does not bother me if someone close to me does not give their best effort.
11The better I do, the more I expect of myself.
12I rarely feel the need to be perfect.
13Anything I do that is less than excellent will be seen as poor work by those around me.
14I strive to be as perfect as I can be.
15It is very important for me to be perfect in everything I attempt.
16I have high expectations of those important to me.
17I strive to be the best at everything I do.
18People around me expect me to succeed in everything I do.
19I do not have very high expectations of those around me.
20I demand nothing less from myself than perfection.
21Others will like me even if I do not excel in everything.
22I am not bothered by people who do not strive for perfection.
23I feel uneasy if I see an error in my work.
24I do not have many expectations of my friends.
25Success means working hard to please others.
26If I ask someone to do something, I expect them to do it flawlessly.
27I cannot bear to see people close to me making mistakes.
28I am a perfectionist when setting my goals.
29People close to me should never let me fail.
30Others think I am doing fine even when I am not successful.
31I feel that people are too demanding of me.
32I must work at my full potential at all times.
33Although it may not show, others become very upset with me when I fail.
34I do not have to be the best at everything I do.
35My family expects me to be perfect.
36I do not have very high goals for myself.
37My parents rarely expect me to excel in all aspects of my life.
38I respect people who are average.
39People expect nothing less than perfection from me.
40I have set very high standards for myself.
41People expect more from me than I am capable of delivering.
42I must always succeed at school or work.
43It does not matter to me if a close friend does not give their maximum effort.
44People around me believe I am still competent even when I make a mistake.
45I rarely expect others to excel in what they do.
1 = Strongly Disagree; 2 = Moderately Disagree; 3 = Slightly Disagree; 4 = Neutral; 5 = Slightly Agree; 6 = Moderately Agree; 7 = Strongly Agree.

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Sustainability 17 06512 g001
Figure 1. A conceptual model of the relationships.
Figure 1. A conceptual model of the relationships.
Sustainability 17 06512 g001
Table 1. Overview of variables used in the study.
Table 1. Overview of variables used in the study.
Variable TypeVariable NameLevels/Categories
Independent VariablesProfessional Role0 = IT employee; 1 = Nurse
Self-Oriented PerfectionismHigh (>median)/Low (≤median)
Other-Oriented PerfectionismHigh (>median)/Low (≤median)
Socially Prescribed PerfectionismHigh (>median)/Low (≤median))
Dependent VariableWorkaholismPresent (DUWAS score > P75)/Absent
Explanatory Note: Professional role was coded numerically for regression (0 = IT employee; 1 = nurse). “High/Low” thresholds for perfectionism facets were set at the median, defined as the central value of the ordered response distribution. Workaholism was defined by scores above the 75th percentile (P75), representing the value below which 75% of responses fall.
Table 2. Kolmogorov–Smirnov test results for assessing the normality of variable distributions.
Table 2. Kolmogorov–Smirnov test results for assessing the normality of variable distributions.
VariableZ (K-S)pDistribution
Self-Oriented Perfectionism0.9420.338Normal
Other-Oriented Perfectionism0.7310.660Normal
Socially Prescribed Perfectionism0.9680.306Normal
Total Workaholism0.7900.561Normal
Working Excessively0.8150.520Normal
Working Compulsively1.0900.186Normal
Supplementary Work1.7070.006Non-normal
Profession (binary coding)3.561<0.001Non-normal
Table 3. Spearman correlations between perfectionism dimensions and workaholism.
Table 3. Spearman correlations between perfectionism dimensions and workaholism.
Predictor VariableTotal
Workaholism		Working
Excessively		Working
Compulsively		Supplementary
Work
Self-Oriented Perfectionism0.495 ***0.455 ***0.420 ***0.347 ***
Other-Oriented Perfectionism0.325 **0.268 **0.286 **0.268 **
Socially Prescribed Perfectionism0.373 ***0.326 **0.313 **0.393 ***
** p < 0.01, *** p < 0.001.
Table 4. Summary of regression coefficients (β) and p-Values for each prediction.
Table 4. Summary of regression coefficients (β) and p-Values for each prediction.
PredictorOverall Workaholism
(β, p)		Working Excessively
(β, p)		Working
Compulsively (β, p)		Supplementary
Work (β, p)
Self-Oriented Perfectionism0.251 *, p = 0.0150.279 *, p = 0.0100.210, p = 0.0560.076, p = 0.484
Other-Oriented Perfectionism0.142, p = 0.1380.081, p = 0.4250.124, p = 0.2290.236 *, p = 0.023
Socially Prescribed Perfectionism0.367 ***, p < 0.0010.316 **, p = 0.0030.313 **, p = 0.0030.340 **, p = 0.002
Profession (0 = IT; 1 = Nurse)−0.110, p = 0.196−0.107, p = 0.233−0.169, p = 0.0660.083, p = 0.363
* p < 0.05, ** p < 0.01, *** p < 0.001.
Table 5. Overview of Hypothesis 1.
Table 5. Overview of Hypothesis 1.
Dependent VariableAdjusted R2Significant PredictorsHypothesis Supported
Total Workaholism0.368Self-oriented and socially prescribedH1✔
Working Excessively0.292Self-oriented and socially prescribedH1a✔
Working Compulsively0.271Socially prescribedH1b✔ (partial)
Supplementary Work0.277Other-oriented and socially prescribedH1c✔
Note. ✔ indicates that the hypothesis was supported by the regression results. ✔ (partial) indicates that the hypothesis was only partially supported.
Table 6. Interaction effect of profession × perfectionism on workaholism.
Table 6. Interaction effect of profession × perfectionism on workaholism.
Dependent VariableFacetF(1,101)pη2pEffect
Total DUWASSelf-Oriented P3.250.0740.031Small
Other-Oriented P1.820.1800.018Small
Socially Prescribed P0.210.6520.002Negligible
Working ExcessivelySelf-Oriented P1.890.1720.018Small
Other-Oriented P1.720.1930.017Small
Socially Prescribed P0.070.7980.001Negligible
Working CompulsivelySelf-Oriented P3.490.0650.033Small
Other-Oriented P0.560.4560.006Negligible
Socially Prescribed P0.070.7980.001Negligible
Supplementary WorkSelf-Oriented P1.640.2030.016Small
Other-Oriented P2.710.1030.026Small
Socially Prescribed P0.070.7880.001Negligible
Note. N = 105. Values of η2p ≥ 0.01 indicate a small effect; ≥0.06 a medium effect; ≥0.14 a large effect [40].
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Vieriu, A.M.; Hainagiu, S.M. Perfectionism and Workaholism as Barriers to Lifelong Learning and Occupational Sustainability: A Cross-Professional Analysis. Sustainability 2025, 17, 6512. https://doi.org/10.3390/su17146512

AMA Style

Vieriu AM, Hainagiu SM. Perfectionism and Workaholism as Barriers to Lifelong Learning and Occupational Sustainability: A Cross-Professional Analysis. Sustainability. 2025; 17(14):6512. https://doi.org/10.3390/su17146512

Chicago/Turabian Style

Vieriu, Aniella Mihaela, and Simona Magdalena Hainagiu. 2025. "Perfectionism and Workaholism as Barriers to Lifelong Learning and Occupational Sustainability: A Cross-Professional Analysis" Sustainability 17, no. 14: 6512. https://doi.org/10.3390/su17146512

APA Style

Vieriu, A. M., & Hainagiu, S. M. (2025). Perfectionism and Workaholism as Barriers to Lifelong Learning and Occupational Sustainability: A Cross-Professional Analysis. Sustainability, 17(14), 6512. https://doi.org/10.3390/su17146512

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