Competencies, work-related interests and person-job fit in on-demand gig platform work for physical services

The fast-paced, demanding nature of today's work environment is driven by global competition, the relentless need for innovation, and a shift towards project- and task-based work, which requires more flexible work arrangements than traditional employment contracts (Toth et al., 2020). The extant literature distinguishes three groups of people who engage in non-standard work arrangements: high-skilled freelancers, permanent employees who engage in platform work as additional work, and low-wage service workers (Spreitzer et al., 2017). The complexity of tasks and the qualifications required of workers can vary significantly across the jobs offered on platforms (Bérastégui, 2021). At the same time, the digital platform economy (also known as the gig economy) is growing rapidly (European Commission, 2023), with on-demand physical services being the most prevalent type of digital platform (Bérastégui, 2021). These platforms use location-based applications to assign service-oriented tasks to individuals within a specific geographic area. They fulfill online consumer orders by leveraging a readily available pool of workers who perform offline services. Common examples include ride-hailing (e.g. Uber) and food delivery (e.g. Deliveroo) platforms. The COVID-19 pandemic accelerated the adoption of platform work, pushing it into the mainstream, largely due to a surge in food and grocery deliveries. This trend has turned platform work into a significant driver of innovation and job creation (European Commission, 2023).

This study focuses on workers who provide the above-characterized on-demand physical services, work exclusively for platform companies, and can be classified as low-wage service workers in the typology by Spreitzer et al. (2017). Previous studies on on-demand platform work have focused on its characteristics, such as unpredictability, work and income insecurity, low wages, and the use of algorithmic management (Lenaerts et al., 2022; Shapiro, 2018), which lead to high employee exposure to stress (Bérastégui, 2021; Vieira, 2023). Platform workers have even been associated with precarious workers (Doan and Diehl, 2025; Umney et al., 2024; Wang and Meng, 2024). However, other research shows that on-demand platform work can be associated with high-quality work for couriers who highly value autonomy, freedom, and a favorable work-life balance (Kusk and Bossen, 2022; Švagan, 2023). Platform work typically requires minimal qualifications or experience, making it easier for individuals – especially young people – to enter the workforce and earn an income. It also allows workers to choose their working hours and locations, which supports better work–life balance, particularly for those managing other commitments such as education or caregiving (Nuckols, 2025). Collectively, these findings suggest that platform work encompasses aspects that can simultaneously support and challenge employees' work experiences. The simultaneous presence of autonomy and control, as well as flexibility and pressure, underscores the need to investigate person–job fit (P–JF) within the context of platform work.

P–JF is usually defined as the degree to which an individual's attributes match job requirements (Brkich et al., 2002). It comprises two dimensions: demands–abilities fit (alignment of competencies with job requirements) and needs–supplies fit (alignment of employee needs with what the job provides). Together, they determine the overall quality of P–JF (Kristof-Brown et al., 2002). Most previous studies have excessively examined the outcomes of P–JF (Liu et al., 2020). While it is generally recognized that P–JF is crucial for job satisfaction (Peng and Mao, 2015), organizational commitment (Berisha and Lajçi, 2020) and well-being (Akanni et al., 2020), ultimately leading to lower turnover intention (Berisha and Lajçi, 2020), higher job performance (Kaur and Kaur, 2023) and better quality of customer service (Alqhaiwi et al., 2023), understanding the factors that influence this fit is particularly important given that platform work has emerged as a new mechanism for matching the supply and demand for paid labor. Research on this phenomenon is scarce, with only one study exploring the roles of virtual community trust and work engagement on P–JF of high-skilled platform workers (freelancers) (Toth et al., 2020). Thus, this study aims to fill this research gap through identifying factors influencing P–JF among on-demand platform workers.

As argued by Kristof-Brown et al. (2002), competencies are key factors in determining the quality of P–JF. Recently, Piwowar-Sulej and Bąk-Grabowska (2024) called for research on future competencies of non-standard employees with particular emphasis on platform workers. Competencies – in general – are psychosocial resources (including knowledge, skills, and attitudes) and individual abilities to meet complex demands in a particular context (OECD, 2005), whereas future competencies are the basics of individual adaptation in an unstable work environment (Piwowar-Sulej and Bąk-Grabowska, 2024). Although past studies indicated some competencies which can be required from platform workers offering on-demand services (Piwowar-Sulej and Bąk-Grabowska, 2024; Usabiaga et al., 2022), they did not identify which competencies most effectively corresponds to high P–JF among platform workers. Additionally, as Brkich et al. (2002) noted, aligning individual employee contributions with job requirements has become increasingly complex due to the evolving needs and expectations of both employees and organizations. Importantly, the extent to which employee needs align with the employer's offerings is a key determinant of person–job fit (Brkich et al., 2002). Although some studies (e.g. Polkowska (2024) and Qi et al. (2023a, b)) provide valuable insights into the needs of platform workers, empirical evidence remains scarce, particularly regarding how different needs contribute to high P–JF among individuals performing on-demand platform work. Therefore, to fill the above-presented research gaps, this study aims to answer the following research question: What are the employee competency-related and interest-related profiles that most effectively align with P–JF of platform workers?

This study was conducted in three phases. The first phase involved a literature review on the main phenomena. During this phase, the authors gained deeper insight into existing research on platform work, which helped reveal its core characteristics. The authors also analyzed studies on P–JF, competencies, and interests presented in the past literature, with particular emphasis on platform work. In the second phase, the authors invited business and academic experts to design the final lists of competencies and interests. In the third phase, they conducted a survey among 280 platform workers offering on-demand services (taxi drivers, food deliverers, couriers) working in Poland. The authors used linear regression modeling and Ordinary Least Squares (OLS) regression modeling in the statistical analyses.

Poland is a Central European country that is a member of the European Union (EU), where non-standard employment is the most prevalent form of employment. In this country, non-standard employment is associated with “company–worker” arrangements that are not governed by the Labor Code (Piwowar-Sulej and Bąk-Grabowska, 2024). Additionally, most non-standard forms of employment in Poland involve temporary, low-wage jobs, growing social insecurity, and the curtailment of workers' rights (Muszyński, 2019). Research by the Polish Central Labor Inspectorate on the precarity of platform work showed that physical workers, i.e. those providing on-demand services, scored 1.85 on the precarity scale ranging from 1 to 4 (Stachura-Krzyształowicz and Barańska, 2022). Past academic research on platform workers' experiences was conducted in US (Davidson et al., 2023), Malaysia (Fang et al., 2022), China (Chen and Chen, 2025), Taiwan (Cheng et al., 2024), however they did not address the above-presented research question. Accordingly, this study fills a contextual gap by exploring the factors that stimulate P–JF among platform workers in the European country.

This study contributes to theory in the following ways. Firstly, it extends studies on platform work, focusing on on-demand services and going beyond legal and well-being issues. Secondly, it responds to the call by Liu et al. (2020) to recognize the factors that influence P–JF in general. This study covers both skill-related and needs-related employee characteristics and the assessment of P–JF by currently working employees, whereas previous studies focused on the possible match between candidates' competencies and job attributes (presented in job offers) (Saeid et al., 2024; Wang et al., 2022), personality characteristics and the description of different occupations (Yuan et al., 2023), personality traits and perceived P–JF among representatives of different occupations (Xu and Li, 2020). More specifically, this study extends the P–JF theory in terms of platform work. Examining employee attributes that lead to P–JF increases the knowledge needed for effective staffing processes in platform work organizations. Considering the fact that P–JF results in better career adaptation (Odo et al., 2022), this study also provides employees with guidelines in their choices related to undertaking platform vs. other types of work. Thirdly, to increase the reliability of the results, this study uses data collected from people who work exclusively for platform companies and can be associated with low-wage service workers. However, following the recommendation of Schor et al. (2020), this study uses empirical research conducted among representatives from different platforms. A multi-platform study has the potential to develop a more general theory in contrast to highly Uber-centric past literature (Schor et al., 2020). Finally, this study is conducted in Poland, a member of the EU, where platform work is growing rapidly yet remains poorly regulated, creating significant gaps in workers' rights, job security, and social protection (Council of The European Union, 2025). Understanding the needs of platform workers is crucial for developing fair labor policies, ensuring decent working conditions, and protecting fundamental rights in the evolving digital economy.

Platform work is defined as “a form of employment in which organizations or individuals use an online platform to access other organizations or individuals to solve specific problems, or to provide specific services in exchange for payment” (European Commission, 2023). It has the following main characteristics: “three parties are involved: the platform, the client and the employee; the work is outsourced or contracted out; jobs are broken into tasks; services are provided on demand” (de Groen et al., 2018). This type of work reflects non-standard, flexible employment which is based on civil law contracts (Piwowar-Sulej and Bąk-Grabowska, 2024).

Although platform work can be performed by high-skilled professionals offering legal and IT services (Pais et al., 2021), this study focuses on on-demand platform work, which typically involves physical or service-oriented tasks such as transportation and delivery. Most extant studies on such understood platform work have focused on regulatory issues (Fredman et al., 2025), employees' well-being (Koivusalo et al., 2024), and precariousness (De Andres et al., 2024). Platforms were acknowledged for allowing women to participate in paid work while spending more time with their children daily and weekly, as well as decreasing their reliance on costly childcare services required in their previous jobs. However, participants also noted continuing and emerging work-life conflicts, frequent instances of unpaid overtime, difficulty in disconnecting from work, and the contradictory nature of work-life balance (James, 2024). Flexibility of when to work has also been appreciated by drivers (Lenaerts et al., 2022). Other research shows that autonomy, freedom, and a favorable work-life balance were platform work characteristics perceived by couriers (Švagan, 2023). By contrast, Schor et al. (2020) demonstrated that individuals who worked solely as platform workers, felt more pressure to go the extra mile to earn more money. Furthermore, Wang et al. (2024) revealed that algorithmic control used by platform work providers leads to ego depletion and employees' customer-directed deviant behavior. At the same time, Mannan and Pek (2024) showed that platform companies, to some extent, use human resource management practices typical for standard employers, such as giving the employees opportunities to socialize (attending virtual meetings, participating in projects, and participating in virtual communities), vote, and serve as a company representative.

Taken together, these findings indicate that platform work combines elements that can both enhance and undermine employees' work experiences. This coexistence of autonomy and control, flexibility and pressure, highlights the importance of examining P–JF in the platform work context.

P–JF is about the match between an individual's attributes and job characteristics (Brkich et al., 2002). Many studies have provided evidence that employees with low P–JF fit are likely to exhibit counterproductive work behavior (Khan et al., 2022) and to have an intention to quit (Berisha and Lajçi, 2020). In turn, positive outcomes of P–JF include: psychological capital and voice behavior (Yang et al., 2017), intrinsic motivation, innovative behavior (Alqhaiwi et al., 2023), employee well-being (Huang et al., 2023), job and career satisfaction (Cable and DeRue, 2002), career success (Li et al., 2020), organizational commitment (Kim et al., 2020), organizational citizenship behavior (Pattanawit and Charoensukmongkol, 2022). Qi et al. (2023a, b) provided evidence that flexibility-related P–JF increases innovative behavior among employees who work flexibly. However, flexible working conditions (i.e. flexible working hours, workplace, and methods of task performance) can be offered within the standard employment contract (Piwowar-Sulej and Bąk-Grabowska, 2024). Therefore, their research does not capture the essence of platform work. In turn, Guduru and Santhanam (2026) demonstrated that work-life balance and job performance are outcomes of P–JF among location-based platform workers, who provide not only passenger transport, food, and goods delivery, but also cleaning, childcare, and other personal or professional services.

The above has been explained mainly based on social exchange theory and the theory of congruence (Kaur and Kaur, 2023). Regarding job-related phenomena, social exchange theory holds that when employees' expectations are fulfilled in the workplace, they reciprocate with expected behaviors (Blau, 1964). In turn, when their needs are unmet, they engage in negative exchanges and exhibit behaviors that are not acceptable to the employer (Qureshi et al., 2022). As Kaur and Kaur (2023) emphasized, the condition that must be met to reciprocate positive work-related behaviors is also the fit between an employee's skills and the job profile. In turn, based on the theory of congruence, the positive perception of P–JF is associated with high employee performance (Kristof-Brown et al., 2023). In the view of conservation of resources theory, P–JF can be treated as a source of gain that elicits further investments (expected behaviors) to gain resources (Alqhaiwi et al., 2023).

Less research exists on the antecedents of P–JF. Supportive company strategies (Neuenschwander and Hofmann, 2022), leader's positive emotions, psychological safety (Liu et al., 2020), workplace spirituality (Pattanawit and Charoensukmongkol, 2022), gender (with female gender reducing the perceived P–JF) (Yousaf et al., 2023), emotional intelligence (Akanni et al., 2020), mental illnesses (Hennekam et al., 2021), personal connections (Chen et al., 2024), job crafting (Abbas et al., 2023), perceived overqualification (Yu et al., 2025), and self-efficacy (Neuenschwander and Hofmann, 2022) have been proven to influence P–JF, however proactive personality stimulated needs-supplies fit only (Soares et al., 2025). Past research about the antecedents of P–JF were conducted among students (Neuenschwander and Hofmann, 2022), Chinese state-owned companies (Liu et al., 2020) or different industries (Chen et al., 2024), Pakistani banking sector employees (Yousaf et al., 2023), university staff in Nigeria (Akanni et al., 2020), hospitality employees in US (Yu et al., 2025), master's internship trainees (Soares et al., 2025), geographically and industrially dispersed individuals with mental illnesses (Hennekam et al., 2021), full-time employees in a five-star Egyptian hotel (Abbas et al., 2023). Although Toth et al. (2020) demonstrated the positive impact of virtual community trust and work engagement on P–JF of high-skilled platform workers (freelancers), an unanswered question remains regarding the factors that influence P–JF among on-demand platform workers.

P–JF – the degree of alignment between an individual's attributes and the characteristics or requirements of a job – is critical for shaping employees' work-related outcomes. In this alignment, employee competencies constitute a key factor shaping the quality of the fit (Kristof-Brown et al., 2002). Accordingly, the literature has long emphasized that job performance depends on the alignment of competencies with the requirements of a specific occupation or position (Converse et al., 2004). The influence of competencies on job outcomes is often explained through the ability–motivation–opportunity (AMO) theory, which posits that employees perform well when they possess the necessary abilities, are motivated to apply them, and are given opportunities by the work environment to use their competencies effectively (Bos-Nehles et al., 2023). From this perspective, a good P–JF occurs when the individual's competencies correspond directly to the demands of the job, creating the conditions for optimal performance. Furthermore, the importance of competencies extends beyond the individual level and is equally crucial for the success of the entire organization. This perspective is emphasized in the competence-based theory of the firm (Freiling, 2004). In addition, a growing body of research analyzes job-specific competence and skill requirements by examining the content of job postings (e.g. Nickson et al. (2017), Usabiaga et al. (2022)). Such studies highlight how employers signal the competencies they value most and how these competency profiles vary across occupations and industries. In this context, it is important to note that the gig economy is often organized in terms of tasks rather than jobs (Gundert and Leschke, 2024). Moreover, developing universal competency profiles for individuals working under non-standard employment arrangements (including platform workers) is challenging, as they perform a wide variety of jobs and tasks (Bérastégui, 2021; Piwowar-Sulej et al., 2025).

Because on-demand platform work – being the central focus of this paper – is closely connected with digitalization, it is worth emphasizing that many previous studies focused on employees' digital competencies as antecedents of digital transformation (Piwowar-Sulej et al., 2024). Moreover, these competencies are required in almost all professions and are treated as components of future competencies (Piwowar-Sulej and Bąk-Grabowska, 2024). Undoubtedly, platform workers such as taxi drivers, food deliverers, and couriers must be familiar with applications used by platform companies (Mwakatumbula and Moshi, 2020). However, digital skills are not enough to be effective in platform work. In addition to having a solid technical understanding and expertise in their field, employers frequently specify a range of soft (interpersonal) skills they expect from service workers (Usabiaga et al., 2022). They believe these skills will enable employees to perform their roles to the highest standard (Piwowar-Sulej and Bąk-Grabowska, 2024). They can also be referred to as on-demand platform work which is based on serving people (Frenzel-Piasentin et al., 2022). Furthermore, many other competencies are shown in the literature as the fundamentals of individual adaptation in an uncertain and complex work environment (Piwowar-Sulej and Bąk-Grabowska, 2024). This is particularly relevant for platform work, where workers must frequently navigate unpredictable conditions. Such competencies include, among others, creative thinking skills, complex problem solving, and the ability to adapt. The open question remains which competencies most effectively enhance P–JF among platform workers.

Beyond competencies, many authors emphasize that successful human resource practices align with employees' needs (expectations, interests) (Piwowar-Sulej and Cierniak-Emerych, 2024). The issue of employees' interests is notably emphasized by scholars studying work motivation. Various motivational theories highlight different types of employee needs. For example, Maslow's hierarchy of needs organizes human needs into five levels: physiological, safety, belonging, esteem, and self-actualization. In contrast, Adams's Equity Theory stresses needs related to justice, fairness, and esteem, focusing on employees' perceptions of equitable treatment in the workplace (Bandhu et al., 2024). Many studies have shown links between employees' interests and their future job searches, educational choices, current job positions, and even their job satisfaction (Harackiewicz and Hulleman, 2010; Hidi and Renninger, 2006).

Piwowar-Sulej and Cierniak-Emerych (2024) explored how the employees' needs have been measured in the past. Some studies used the Strong Interest Inventory (Prince, 1998), however, this tool functions more as a career assessment model, designed to identify the professions that best align with a person's interests. It was designed many years ago, does not cover the specifics of platform work, and is used mainly in the field of psychology and vocational counseling (Leierer et al., 2008). Most previous studies considered individual interests separately or explored a narrow set of interests. For example, Munnich et al. (2025) explored hybrid-working needs, while Qi et al. (2023a, b)focused on expected feedback quality. Scholars specializing in self-determination theory use the set of basic psychological needs, which cover: the need for autonomy, the need for belongingness, and the need for competence (Ryan and Deci, 2000). Yang et al. (2021) conducted factor analysis and prepared the following list of expectations of transport industry employees: fair employment and promotion, health and safety at work, provision of training, attractive compensation, and counseling for dismissed employees. Hitka et al. (2023) developed a set of employees' needs related to rewards (e.g. basic salary, fair HR appraisal), career paths (e.g. education and personal development, individual decision-making, recognition, self-fulfillment), relations between people at work (e.g. communication at work, attitude of leaders), social needs (e.g. company reputation, company-environment relationship) and working conditions (e.g. job security, mental effort, physical work demands). Finally, Piwowar-Sulej and Cierniak-Emerych (2024) designed a scale consisting of 22 items loaded into five dimensions of employees' needs such as: support and development at the level of the enterprise, participation, employee support and development at the departmental level, employment security, and conditions of work and remuneration.

The above-presented studies referred to standard employment. However, Stich (2021) argues that the fulfillment of flexibility needs is vital to obtain P–JF in non-standard work arrangements. Some researchers explored the specific needs of platform workers. For example, Daba-Buzoianu et al. (2025) explored the needs of gig workers in creative industries. From the perspective of the aim of the current study, based on a sample of different platform workers, Santabarbara et al. (2025) revealed that financial needs encouraged workers to enter platform work – either as a main or supplementary source of income – in the UK food delivery. Deng et al. (2025) found that discrepancies between expected and perceived algorithmic autonomy support lead to increased incivility among platform workers, with this relationship being mediated by work-related stress. Among surveyed platform workers (couriers and drivers) in Poland, autonomous motivation prevailed, which means that they demonstrated the need for autonomy, competence, and connection with others by doing platform work (Polkowska, 2024). Qi et al. (2023a, b) also emphasized that platform workers are those who need autonomy in their work. Despite these valuable insights, existing studies examine needs in isolation and do not explain how different needs relate to high P–JF among platform workers offering on-demand services. Thus, the question of which needs most effectively align with platform work characteristics and enhance P–JF remains unanswered.

The first step aimed to create the initial lists of employees' competencies and interests. The authors used the deductive-inductive approach here. Deductive analysis offers objective, universal results based on a literature review (Gelfand et al., 2007). In this case, reference was made to studies that reviewed and developed lists of employees' future competencies and expectations. Following Creswell's and Miller's (2000) recommendation to apply inductive logic, the authors collected the opinions of five expert-professionals (human resource management – HRM – specialists) on the competencies needed from platform workers and possible expectations of platform workers. By eliminating redundancies (items collected via literature search and from experts, high similarity in the meaning of a competence) the authors prepared two lists, which were the subjects of evaluation in terms of the face and content validity. The first list contained 21 competencies, whereas the second – 22 employees' interests.

In the second step, the initial lists were given to five academic experts specializing in HRM. The authors followed the procedure suggested by Farh et al. (2004) and applied the “sum-score decision rule”, which means that they collected the total score for an item across five judges. The latter is considered an effective method for predicting whether an item should be included in the measurement instrument (Morgado et al., 2018). Each judge evaluated an item using an ordinal rating scale reflecting its representativeness. The ratings were assigned numerical values as follows: 3 points – completely representative, 2 points – somewhat representative, and 1 point – not representative. An item was considered to have passed the evaluation if it obtained a sum-score of at least 10 points, which implies that all five judges had to rate the item as at least somewhat representative for it to be retained. Not all items were assessed as valid, thus the authors removed them from the lists. The final lists contained 16 competencies and 14 employees' interests shown in Table 1.

As far as the measurement of P–JF is concerned, past literature mainly used one question to measure this fit. It was a question of to what extent the respondent's new job aligns with the type of job they were seeking (Cable and DeRue, 2002). There is also a unidimensional approach to the measurement of P–JF (e.g. (Huang et al., 2023)). However, Edwards (1991) introduced two dimensions of P–JF: needs-supplies fit and demands-abilities fit. Needs-supplies fit refers to how well an employee's interests (needs, desires) align with what the job provides. Conversely, demands-abilities fit describes the alignment between the job's requirements and the employee's competencies (Kristof-Brown et al., 2005). The authors used scales to measure the two dimensions of P–JF proposed and verified by Cable and DeRue (2002).

The academic experts recommended using the following well-known levels of competencies: (1) novice (I require close supervision and clear instructions to use the competence), (2) advanced beginner (I begin to connect theoretical knowledge with practical application, need occasional guidance and feedback), (3) competent (I have a solid understanding in the area, can handle unexpected situations with some support), (4) proficient (I have a deep understanding in the area, can see the bigger picture and anticipate future challenges), (5) expert (I have exceptional and comprehensive knowledge and experience, can act as a role model) (Filomeno et al., 2024). In the case of respondents' assessment of interests, the authors applied a five-point scale used by Piwowar-Sulej and Cierniak (2024), where 1 meant “totally unimportant interest”, and 5 – “very important interest”.

The academic experts also recommended testing whether certain demographic factors influence the relationship between competencies/interests and perceived demands-abilities fit/needs-supplies fit, providing a more nuanced understanding of the factors affecting fit. For competencies and demands-abilities fit, respondents were grouped by age, gender and education level, while for interests and needs-supplies fit, they were categorized by age, gender, education level, personal status (single vs. in a relationship), and parental status (having children vs. not having children). The duration of engagement in platform-based work was also used as a variable that can have an impact in both cases.

Likert scales are considered highly useful in social science research because they offer insights into employees' perceptions based on their self-reported feelings (Iqbal and Piwowar-Sulej, 2023). Since more detailed Likert scales (e.g. covering seven points) may negatively affect the quality of data by causing a cognitive burden (Iqbal and Piwowar-Sulej, 2023), the authors used a five-point Likert scale to assess P–JF (from 1 – “I totally disagree” to 5 – “I totally agree”).

The total number of people working in the delivery and transport sector through platforms in Poland may range from several tens of thousands to even around 100,000. This number includes both drivers (Uber, Bolt, FreeNow) and couriers (Glovo, Pyszne.pl, Uber Eats), who deliver full-time or as part-time work (Mokrzycka, 2021). However, there are no official statistics on how many people work solely for platforms. The authors employed a research agency to collect data via the computer-assisted telephone interview (CATI) method. This agency contacted 489 platform workers, of whom 280 people were identified as working exclusively for platforms.

Prior to data collection, each platform worker was informed about the objective of this study. They were asked to participate on a volunteer basis if they were interested. The research agency also ensured that the data collection was only for the research purpose and would be shared with any third party, and that the respondents' identities would be disclosed at any stage of this study. Furthermore, participants were informed that there were no correct or incorrect responses, aiming to reduce social desirability bias.

The majority of workers used a single platform (65.7%), with Barbora (32.5%) and Glovo (31.07%) being the most popular. The workforce was predominantly male (72.86%), and most respondents (84.64%) were Polish citizens. The largest age group was 25–34 (36.07%), followed by 18–24 (32.14%), 35–44 (19.64%), 45–54 (9.29%), and 55–64 (2.86%). A significant majority (79.29%) had a secondary school education, while 20.71% had a vocational school education. Only 12.86% held a bachelor's degree, 2.86% a master's degree, and 1.79% had completed only primary school. Over half of the workers (54.64%) were single, and 70% did not have children. 57.14% of respondents had been employed as platform workers for more than 2–3 years, 29.28% for more than 1–2 years, and the remaining 13.57% for between 6 months and 1 year.

The aim of this study was to find the competencies and interests that suit P-JF the best. The authors listed individual competencies and interests. This study is not a “general quantitative hypothesis-testing research” but rather an “exploratory quantitative research”, in which research questions are necessary, and hypotheses are unjustified (Lund, 2022). Exploratory research proposed here assumes that the research question corresponds to a set of possible solutions, and that the methodological strategy is designed and implemented to identify the best “candidate” among these possible solutions (Lund, 2022).

This study examined the relationship between individual competencies and demands-abilities fit, as well as personal interests and needs-supplies fit, using linear regression analysis (Maulud and Abdulazeez, 2020). Linear regression was selected because the study focuses on estimating average linear associations between competency- and interest-related measures and perceived fit outcomes, while allowing for transparent interpretation of effect sizes and straightforward comparison across predictors. Moreover, Ordinary Least Squares (OLS) regression is commonly applied to composite and index-based measures derived from survey data, particularly when the objective is explanatory analysis rather than prediction (Singh et al., 2023). Internal consistency reliability of the demands-abilities fit and needs-supplies fit constructs was assessed using Cronbach's alpha. This procedure was applied to evaluate the extent to which items within each scale consistently measure the same underlying latent construct. The obtained reliability estimates were used to support the validity of the constructs.

Baseline linear regression models were first estimated to assess the direct (main) effects of competencies and interests on demands-abilities fit and needs-supplies fit for the full sample. To evaluate the relative importance of each factor, regression coefficients were normalized to enable direct comparison across variables measured on different scales (Więckowski et al., 2023). As the study adopted an exploratory perspective and involved a relatively large set of potential predictors, an equal-weight benchmark was used as a heuristic selection criterion. The benchmark represents the average contribution that would be expected if all predictors had the same influence on the outcome. Therefore, predictors were retained for subsequent analyses only when their normalized coefficients exceeded this reference level, indicating a relative importance greater than the uniform contribution assumptions (Li and Mao, 2023). This procedure was intended to identify the most influential factors while preserving the exploratory character of the analysis. Accordingly, the threshold was set at 0.0625 for the “competencies – demands-abilities fit” model and 0.0714 for the “interests –needs-supplies fit” model.

In the second step, OLS regression models incorporating interaction terms were estimated to examine whether group membership has an influence on the relationships between the filtered competencies/interests and the respective fit measures (Bun and Harrison, 2019). This two-stage modeling strategy allowed for a clear distinction between baseline effects and additional effects.

To enhance robustness and transparency, 95% confidence intervals were calculated for OLS results. Multicollinearity was assessed using the Variance Inflation Factor (VIF), with all values remaining below conventional thresholds. Effect size measures were also reported. For models that include interaction terms, changes in R² between the baseline and extended models were computed to assess the incremental explanatory power of conditional associations across demographic groups. These interaction effects are interpreted as indicating group-based differences in the strength or direction of associations between competencies/interests and person–job fit, rather than as evidence of causal moderation. This approach complements the interpretation of normalized coefficients derived from the baseline linear regression models (Wei et al., 2019).

Table 2 shows the results of linear regression analysis. It can be seen that the three competencies that align most with demands-abilities fit are: interpersonal skills (weight: 01,761), ability to adapt (weight: 01,534), and creative thinking (weight: 01,267). In addition, the demands-abilities fit construct demonstrated good internal consistency reliability (Cronbach's alpha = 0.783, 95% CI [0.735, 0.824]), supporting its reliability.

In the OLS analysis of competencies and demands-abilities fit by the duration of engagement in platform-based work, age and gender, no significant differences were found across subgroups. However, when grouped by education level, several significant differences emerged (see Table 3).

Table 3 presents the results of the OLS analysis for relevant variables. Participants with secondary school education showed significant differences from those with primary school education (p = 0.004). The 95% confidence intervals (CI) for the education-related coefficients confirmed the precision of these findings, with the interaction term between competencies and education exhibiting a significant negative effect (CI: −0.79 to −0.15). Diagnostic tests for multicollinearity revealed no issues, with all VIF values below the threshold of 5. The R² change due to the interaction term was 0.0390, indicating a small but statistically significant effect. Further analysis revealed differences between vocational school and primary school participants (p = 0.033). The negative interaction coefficient (−1.55) suggests a weaker effect of competencies on demands-abilities fit in the vocational school group, with the 95% CI ranging from −2.98 to −0.13. The R² score was 0.0681, suggesting a moderate effect in this group. For primary school and bachelor's/engineering degrees, p = 0.039, with a negative interaction coefficient (−0.76), indicating a weaker effect in the primary school group (95% CI: −1.48 to −0.04, suggesting a precise estimate). The R² score was 0.1060, pointing to a more considerable effect in this case. In contrast, for master's degrees and primary school, p = 0.039, with the positive coefficient for the interaction term (1.98) indicating a stronger effect in the master's degree group (95% CI: 0.12 to 3.85). The R² score was 0.3813, showing a substantial contribution in this group. These results suggest that education level plays a significant role in shaping how competencies influence demands-abilities fit.

When grouping respondents by gender for the interests aspect, significant differences were observed between males and females with p = 0.012. The negative interaction coefficient (−0.4342) suggests a weaker relationship between competencies and demands-abilities fit for women, with the 95% CI ranging from −0.7722 to −0.0962. The R² change due to this interaction was 0.0244, indicating a relatively small but significant contribution to explaining variability in demands-abilities fit through gender and competencies.

The three interests that most strongly predict a high needs–supplies fit are: social needs (weight: 02,026), access to high-quality tools (weight: 01,450) and regular feedback (weight: 01,255), as shown in Table 4. The needs-supplies fit construct demonstrated very good internal consistency reliability (Cronbach's alpha = 0.837, 95% CI [0.801, 0.868]), which supports the validity of this construct.

With the OLS analysis, no significant differences were found in interests and needs-supplies fit based on the duration of engagement in platform-based work, personal or parental status, indicating no influence on their relationship. However, significant differences were found between the 35–44 years old and 55–64 years old groups (p = 0.044), indicating that age impacts the relationship between interests and needs-supplies fit. The OLS results are summarized in Table 5.

The negative interaction coefficient (−0.34) suggests a weaker relationship between interests and needs-supplies fit in the 35–44 years old group, with the 95% CI ranging from −0.68 to −0.01. The R² score was 0.0635, suggesting a moderate impact of age on this relationship.

Participants with bachelor's/engineering degrees showed significant differences from those with secondary school education (p = 0.040). The positive interaction coefficient (0.454) suggests a stronger relationship between competencies and needs-supplies fit in the secondary school group, with the 95% CI ranging from 0.0198 to 0.8892. The R² change due to this interaction was 0.0171, which is a small but statistically relevant effect in the model. For bachelor's/engineering degrees and vocational school participants (p = 0.049), the negative interaction coefficient (−0.5469) suggests a weaker relationship in the vocational education group, with the 95% CI ranging from −1.0921 to −0.0016. The R² change was 0.0361, showing a moderate effect in this context. For the comparison between bachelor's/engineering degrees and primary school education (p = 0.042), the interaction coefficient (−0.5125) shows a weaker relationship for primary school education (95% CI: −0.9526 to −0.0725). The R² change due to the interaction was 0.1081, reflecting a stronger contribution of education level in this case. These results underscore the significant role of education in shaping the relationship between interests and needs-supplies fit.

This study aimed to answer the question of what makes individuals suitable for platform work, thus facilitating P–JF. In particular, it examined the individual's competencies and interests that match the corresponding dimensions of P–JF. Below, the authors presented a discussion of the results, organized according to the different dimensions of P–JF considered in this study.

Considering competencies needed for platform work, this study found that three competencies that most align with demands-abilities fit are: interpersonal skills, the ability to adapt, and creative thinking. However, skills on how to maintain good physical health, general digital skills and continuous learning skills are important as well.

The above findings support Frenzel-Piasentin's et al. (2022) statements that strong non-technical skills, such as communication, adaptability, and problem-solving, are increasingly important among platform workers who serve people. These skills help platform workers manage client relationships. The adaptation to various platform demands is also necessary (Alasoini et al., 2023; Sutherland et al., 2020). This study emphasizes that knowledge and skills on maintaining good physical health are particularly important for platform workers due to the nature of their work, which often involves physical tasks, long hours, and irregular working conditions. This finding appears to extend past literature that focused on gig workers' digital (Mwakatumbula and Moshi, 2020) and interpersonal skills (Frenzel-Piasentin et al., 2022).

Although platform workers use digital tools, according to the study's results, they do not have to have strong digital skills as indicated in past literature on a gig economy (Kim and Sawyer, 2023). The possible reason for is that many platforms are designed with user-friendly, intuitive interfaces that do not require advanced digital skills. For example, the process of accepting tasks or communicating with clients is often simplified to make it accessible to a broader range of people, including those with limited digital proficiency.

This study found that education level was associated with variations in the relationship between competencies and demands-abilities fit. For example, command of foreign languages strongly predicted demands-abilities fit among platform workers with primary education. In turn, complex problem-solving predicts demands-abilities fit among platform workers with a master's degree. In platform work – especially in service-oriented roles – effective communication with customers, colleagues, and supervisors is crucial. Foreign language proficiency may be related to better perceived alignment of job performance, increased access to better opportunities within the platform, and may also help workers navigate diverse work environments. As a result, those with strong language skills may experience a better match between their abilities and job demands, even if their formal education level is lower. These findings extend studies that show the positive impact of foreign language skills on work-related outcomes (Gazzola and Mazzacani, 2019). In turn, master's degree holders may be better equipped to assess situations, adapt strategies, and optimize their work processes, leading to a stronger alignment between job demands and their abilities. Their advanced cognitive skills help them navigate unpredictable work environments, making them more efficient and adaptable in meeting platform requirements. These findings can be interpreted through the lenses of human capital theory (Eide and Showalter, 2010) which posits that individuals accumulate competencies that enhance their productivity and labor market outcomes. From this perspective, foreign language proficiency represents a valuable form of human capital for platform workers with lower levels of formal education, enabling them to communicate effectively, access a broader range of tasks, and better meet job requirements. Similarly, among workers holding a master's degree, complex problem-solving skills may constitute an advanced form of human capital that facilitates adaptation to changing work demands and improves the alignment between individual abilities and job requirements.

Although numerous studies have shown that cognitive abilities change with age, which means the decline in reasoning abilities and growth of knowledge and expertise (Beier et al., 2020), this study did not find the impact of age on the “competencies – demands-abilities fit” relationship. One plausible interpretation is that platform work may standardize tasks and reduce the influence of age-related differences, making competencies more universally applicable across age groups. Furthermore, the study sample might not have had enough variation in age-related work experience to detect significant differences.

Considering employee interests, this study found that three interests predict high needs-supplies fit most: social needs, access to high-quality tools, and regular feedback. However, active participation, clear employer expectations, autonomy, and job security are also important. These findings partially support previous research showing that autonomy, competence, and connection with others are most important to platform workers in Poland (Polkowska, 2024). However, for surveyed platform workers, access to training (the need for competence) wasn't important. One explanation for this finding is that platform workers have the flexibility to choose when, where, and how they work. This independence can mean they are less reliant on employers to develop new skills or competencies. Instead, many platform workers take a self-directed approach to improving their competencies, learning independently or through external sources such as online courses or peer networks (Kim and Sawyer, 2023).

Although platform work is often characterized by its lack of stable, long-term employment and the absence of traditional benefits associated with permanent jobs (like job security, pensions, or health insurance) (De Andres et al., 2024), according to this study's results, job security can still play an important role in determining platform workers' P–JF. Workers in platform jobs may not have long-term contracts or guarantees about their work hours or income. However, having a certain level of job security (e.g. predictable work availability, clear contracts, or a reliable platform) can make the work feel less precarious and more aligned with their personal needs, especially for those seeking a balance between stability and flexibility.

Age, education level, and gender were observed to influence the relationship between interests and needs-supplies fit. For the younger generation (35–44), access to training was more important than for older workers (55–64), which is in line with research conducted by Cloud Assess, emphasizing that Millennials and Gens Z prioritize training and development more than other generations (Moore, 2023).

High-quality tools were particularly important for workers graduating from vocational schools compared to other educational groups. Vocational education typically emphasizes practical, technical, and hands-on skills that depend on adequate equipment for effective job performance. In contrast, workers with higher academic degrees tend to rely more on cognitive and analytical competencies. From the perspective of career development theory, which explains how individuals' interests, abilities, and skills are aligned with occupational choices and how careers are shaped through planning, skill development, and work experiences (Yates, 2025), these differences reflect the way educational pathways influence skill utilization and resource needs over the life course. Accordingly, vocational graduates benefit more directly from material and instrumental support provided by the platform. When equipped with high-quality tools, they experience greater perceived support and a stronger alignment between their needs and platform resources, which in turn enhances job performance and supports their career development in platform work. In turn, highly educated workers are likely to assess this fit primarily through the lens of whether their roles enable them to apply and develop advanced cognitive skills.

The need for a positive corporate culture was found to predict needs-supplies fit more strongly for male platform workers than for females. This result is somewhat surprising, as many previous studies indicate that women tend to place greater importance on workplace atmosphere and relational aspects of work, whereas men's preferences are more strongly oriented toward higher pay (Seehuus, 2023). Since platform work can be associated with challenging emotional labor (Caza et al., 2022), a supportive culture may enhance men's job satisfaction, increasing their perceived fit.

As this study shows, personal status and children status did not affect the “interests – needs-supplies fit” relationship. It means that platform workers' core work-related needs (e.g. social needs) may be more strongly influenced by other factors rather than personal life circumstances. Furthermore, platform work is associated with flexible working hours, which accommodate diverse personal circumstances, minimizing the effect of personal and parental status on the relationship between workers' interests and what the organization provides.

The first contribution of this study lies in shifting the focus of platform work research from predominantly regulatory, well-being, and precariousness perspectives (e.g. Doan and Diehl (2025), Fredman et al. (2025), Koivusalo et al. (2024)) toward a P–JF lens applied to on-demand platform work. While prior studies have extensively documented both the enabling (e.g. autonomy, flexibility, work–life balance) (e.g. Kusk and Bossen (2022)) and constraining (e.g. algorithmic control, income pressure, work-life conflicts) (e.g. Lenaerts et al. (2022)) features of platform work, they have largely examined these aspects in isolation. This study integrates these contradictory characteristics by conceptualizing platform work as a context in which positive and negative work features coexist, making P–JF a particularly suitable analytical framework.

Secondly, as the subject literature highlights, when employees' skills and interests align with their job roles, they tend to feel more satisfied and fulfilled in their work, thus achieving better performance (Kristof-Brown et al., 2005). Building on this well-established relationship, the present study responds to the call by Liu et al. (2020) to identify the factors that shape P–JF. Specifically, this study shows that research on the antecedents of P–JF remains limited and has largely focused on standard employment settings. In addition, this study extends P–JF theory to the context of platform work, considering that workers who provide on-demand physical services, work exclusively for platform companies, and can be categorized as low-wage service workers represent a new approach to matching supply and demand for paid labor.

Thirdly, this study utilized empirical research conducted among representatives working solely for platforms, however for different platforms, contributing to a more general theory of P–JF in platform work. It found that interpersonal skills, the ability to adapt and creative thinking skills have the highest impact on demands-abilities fit. Social needs, access to high-quality tools and regular feedback were the most associated with needs-supplies fit. Past literature focused on the antecedents of P–JF among representatives of different occupations (Xu and Li, 2020) rather than new forms of work. In turn, past research on platform work was high-skilled workers-oriented (van Slageren and Herrmann, 2024) or Uber-centric (Schor et al., 2020), and did not examine competencies and interest-related predictors of P–JF. Furthermore, this study was conducted among currently working employees, thus extending research approaches that focused on the possible match between candidates' attributes and job offers (Saeid et al., 2024; Wang et al., 2022).

Fourthly, this study was conducted in the EU member state where platform work is expanding rapidly but continues to be insufficiently regulated, resulting in notable shortcomings in worker protection, employment security, and access to social benefits (Stachura-Krzyształowicz and Barańska, 2022). In this context, understanding the competencies and needs of platform workers – key factors shaping P–JF in non-standard, on-demand work – both advances theoretical knowledge and provides practical insights. These findings offer a foundation for further academic research on platform work and inform actionable recommendations for platform workers, platform companies, and local and EU-level policymakers. The resulting practical implications are elaborated below.

Through examining employee attributes that lead to P–JF, this study increases the knowledge needed for effective staffing processes in platform work organizations that offer on-demand physical services. These platforms have faced criticism for offering poor working conditions due to the absence of employment benefits, job stability, and opportunities for training or career advancement (Kost et al., 2020).

Firstly, as revealed by Mattijssen et al. (2020), in the context of non-standard employment, employers are more likely to base hiring decisions on individual-level skills rather than occupation-level skill profiles. In this regard, the current study shows that to achieve higher levels of P–JF among workers, the platform companies should employ people who have interpersonal skills, the ability to adapt, creative thinking skills, and communication skills. This necessitates recruitment efforts similar to those of traditional employers, such as incorporating these requirements into job postings and utilizing tests to assess the competency levels of candidates for platform work. Platform companies should also offer the possibility of fulfilling social needs and job security, since these interests were the most correlated with needs-supplies fit and P–JF. They may organize virtual or physical events for platform workers to connect, share experiences, and build a sense of community. The provision of online forums or communication channels for collaboration and social interaction – as indicated by Mannan and Pek (2024) – is also recommended. To increase job security, companies should provide transparent contracts outlining terms, expectations, and rights for workers.

Considering the fact that P–JF results in better career adaptation (Odo et al., 2022), this study also provides employees with guidelines in their choices related to undertaking platform vs. other types of work. A good fit between job demands and employee abilities ensures that tasks are performed more efficiently and effectively, and employees are satisfied with their work. In turn, misalignment between an employee's abilities and job demands can lead to stress and job dissatisfaction. This study shows the competencies that guarantee higher levels of P–JF. Employees who struggle with skills such as interpersonal communication may find platform work challenging and should consider focusing on developing these competencies to improve their job fit and overall career outcomes (Zwettler et al., 2024).

This study also provides knowledge for educational institutions and policymakers. There is an academic discussion on micro-credentials as a good solution for gig economy competency requirements (Wheelahan and Moodie, 2022), however these credentials mainly address technical skills. As this study shows, training on soft competencies is needed to increase the P–JF of platform workers. An implication for policymakers is the need to create regulations that ensure greater job security for platform workers. Without strengthening job security, platform work may lead to a long-term decline in job quality, increased inequality, and higher social costs.

The present study has certain limitations, which open avenues for future research. Firstly, this research focused exclusively on platform workers in Poland. Additionally, the sample is predominantly male and largely composed of individuals with secondary or vocational education, reflecting the demographic profile of platform workers in Poland (Bryła, 2023). Workforce diversity and cultural contexts vary considerably across countries, raising questions about the generalizability of the findings beyond the Polish setting. The gender imbalance may have influenced the findings, as competencies, interests, and work-related attitudes can vary across genders. Likewise, the overrepresentation of respondents with lower and medium levels of education may have affected the results, given that educational attainment is often associated with differences in skills, career aspirations, and access to employment opportunities. Future studies should therefore examine the antecedents of person–job fit among platform workers in different national and cultural contexts to assess the robustness and transferability of the proposed relationships. Secondly, the authors employed a cross-sectional approach, gathering data from platform workers at a single point in time. Because this study is based on cross-sectional and correlational data, the findings do not allow for causal inference. The relationships identified should be interpreted as associations rather than evidence of direct cause-and-effect mechanisms. Employees' interests are dynamic and influenced by numerous factors, such as experience, personality, and community influence (Piwowar-Sulej and Cierniak-Emerych, 2024). Therefore, future studies should consider longitudinal designs to capture these evolving interests.

Thirdly, although both objective and subjective fits pertain to aligning attributes with job demands, individuals do not always perceive this fit accurately. The relationship between the two is causally connected but often imperfect due to factors such as cognitive distortions, incomplete information, or limited access to objective data (Stich, 2021). Thus, the authors recommend further research to examine potential mechanisms and contextual factors influencing the relationship between competencies, interests, and P–JF. Finally, on-demand platform work exhibits characteristics of traditional occupations (e.g. courier work) performed using modern technologies. This makes it possible to assess P–JF. In contrast, evaluating person–organization fit (alignment between an individual's personal values and the values upheld by gig platform organizations (Kristof-Brown et al., 2023)) is more challenging due to the loose relationships between workers and platform companies. Therefore, in the literature, for platform work – which is task-based rather than job-based and involves collaboration with multiple organizations – it has been proposed to measure a new construct: person–skill fit (Chalutz-Ben Gal, 2023). Research in this area represents an interesting avenue for future studies.