Mạnh Linh Trần
| Bá Hùng Lê* | Quỳnh Ngân Nguyễn
© 2026 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
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Gig economy - a modern employment model, connecting labor supply and demand through digital platforms with the form of payment by task or short-term time, without formal contracts or employment benefits, is a labor’s trend in the world. The research sample includes 400 responses from participants aged 18-60, collected through an online survey of people who have participated in the gig economy in Vietnam. The research illustrates that gig job attitude, gig self-efficacy and gig social norm (GSN) impact the intention to participate in gig economy of labor; in which there are 5 factors affecting gig job attitude including need for achievement, locus of control, risk taking, proactiveness and distress tolerance. Remarkably, involvement was found to significantly increase the direct and indirect relationships between proactiveness, gig attitudes, and gig job intention. Based on the findings of this research, some practical implications are provided. This study offers a novel perspective by extending and integrating the Theory of Planned Behaviour and Self-Determination Theory (SDT) to provide empirical evidence on the moderated mediation effects of involvement and three Theory of Planned Behavior (TPB) dimensions on the relationship between five personal traits and gig job intention, contributing to the academic understanding of the gig economy.
gig economy, gig job attitude, gig job intention, involvement, personality traits, Theory of Planned Behavior
Employers tend to adopt lay-off policy more because of advanced artificial intelligence (AI) [1]. Companies across industries, from media to consulting, are following cutting-jobs strategy to minimize cost and improve work efficiency [2]. In this situation, gig platform-an on-demand, online platform economy [3], is a new labour market for this unemployment [4]. In the context of digital transformation and globalization, the gig economy is emerging as an indispensable component of the modern labor market structure [5]. In the academic context, definitions of gig economy have become diversified [6]. According to the UK government, the gig economy is a place where workers can earn money through digital platforms on a short-term and payment-by-task basis [7]. The gig economy is also understood as an on-demand, digital, crowd or online platform economy [3] with a variety of occupations and digital, remote working opportunities in many different fields [5]. Concluding for the gig economy’s definition used in this research is an employment model that quickly matches labor supply and demand through a digital market supported by on-demand commerce [8, 9] with task-based compensation or short-term time-based, but without formal labour contracts or employment benefits [6]. However, gig work also exposes Generation Z (Gen Z) to insecure and precarious conditions, characterized by a lack of social security, algorithmic control, limited bargaining power, stifled career advancement opportunities, and intense competition that can reduce wages and decent work standards. Gig economy, therefore, has dual and contradictory impacts on sustainable labor practices, promoting flexibility and inclusion while also raising concerns about precarity and worker protection [10].
From the perspective of today's Millennials and Gen Z workers - the generation that tends to emphasize creativity, freedom, independence, non-linearity in career choices and the ability to control personal income, the gig economy helps them escape the constraints of traditional jobs, while helping them make good use of technology platforms to find and do work anytime, anywhere [11]. According to a survey by Deloitte [12], with the desire to prioritize flexibility, work-life balance, autonomy in time and workspace, more than 40% of Gen Z and 33% of Millennials participate in the gig economy as part of a long-term career strategy, instead of sticking to a fixed full-time job. At the same time, the strong development of global digital platforms such as Upwork, Fiverr or Uber has broken down spatial barriers and allowed workers to pursue the trend of human resource globalization, freely connecting directly with customers around the world [13]. From the perspective of organizations, the gig economy provides an optimal solution for the need for temporary labor and short-term expertise [14]. With its flexible characteristics, the gig economy allows businesses to easily access a workforce with the right skills in a short time, thereby saving fixed costs and increasing mobility in project implementation [15]. This model is especially effective in the context of a constantly fluctuating market, when businesses need to adjust their staff size according to peak-off-peak cycles [16]. At the same time, the gig economy also opens up opportunities for hiring highly skilled experts for specialized tasks, without having to recruit full-time [3]. In fact, businesses are shifting to using gig workers to increase flexibility, save costs and respond quickly to fluctuations, instead of hiring full-time [17]. During and after the COVID-19 pandemic, which disrupted labor markets and reduced workers’ income [18], the gig economy has become popular as a “flexible safety net”, helping many people maintain basic income during the crisis [19]. Not only that, this model also helps workers easily access jobs without requiring degrees or complicated administrative procedures, thereby reducing the burden on the social welfare system [20]. In the long term, gig work also promotes workers' resilience, through diversifying income sources and accessing globalization opportunities [21]. For individuals who value independence and flexibility, these conditions can strengthen their internal motivation, as gig work is more likely to align with their personal preferences [22]. The gig economy is not only a short-term response but also has a structural role in reshaping the world's post-pandemic labor market, a global career trend. Therefore, businesses need to grasp and understand the factors that influence workers' intention to participate in the gig economy to build effective human resource strategies [20].
In recent years, along with the rapid development of the global gig economy, the number of studies aimed at understanding the factors affecting the intention to participate in this model has increased significantly. However, the studies are still scattered in terms of approach, underlying theory and research context, especially in the context of each country and each specific industry [23]. Most of the works focus on qualitative research, describing the phenomenon and characteristics of gig workers, while quantitative studies with a clear behavioral theoretical framework to explain the intention to participate are still relatively limited, especially in developing countries [24]. Moreover, the shift from traditional employment to gig work is influenced by intricate psychological and social factors that have not been thoroughly examined [25]. Previous studies focusing on conventional employment may not be directly applicable to gig work due to its unique nature [23]. Gig workers often lack legal protection, financial benefits, and career advancement opportunities compared to conventional employees [26]. In addition, several studies have applied measurement scales and hypotheses developed initially in the context of entrepreneurial intentions to examine gig intentions [27, 28], as several characteristics of gig work closely resemble entrepreneurial activities [29, 30], such as autonomy, financial instability, and limited access to social safety nets [31-34]. Self‑Determination Theory posits that human motivation is driven not only by external contingencies but also by the satisfaction of three basic psychological needs: autonomy, competence, and relatedness. When these needs are fulfilled, individuals are more likely to internalize external regulations and act with a greater sense of volition and self‑endorsement, which in turn supports sustained engagement and well-being [35]. In the context of the gig economy, platform‑based work typically offers high levels of temporal and task flexibility, which can foster worker's sense of autonomy if they perceive genuine choice over when and how to work [36, 37]. At the same time, gig work provides opportunities to develop and display skills, thereby satisfying competence needs when workers feel capable of meeting platform demands and client expectations [38]. Social interactions with customers, peers, and online communities around gig platforms may also fulfill relatedness needs, strengthening workers’ identification with gig work as a meaningful career option [39]. Seen through a Self-Determination Theory (SDT) lens, gig workers’ motivation to engage in gigs will thus depend on the extent to which the gig environment supports these three basic psychological needs.
Building on this foundation, the present study applies the theoretical framework of Theory of Planned Behavior (TPB) to analyze factors that affect gig job intention. Five personality traits (PT) are examined for their indirect effects on gig job intention (GJI) through gig job attitude (GJA), while involvement (INV) work as a moderator. By doing so, this study seeks to fill the theoretical gap and contribute both academically and practically to understanding gig job intention in the context of a rapidly evolving labor market.
2.1 Personality traits influence gig job intention through gig job attitude
Personality traits are defined as relatively stable patterns of thought, emotion, and behavior that differentiate individuals from one another [40-42]. These traits represent consistent psychological dispositions that shape how people interact with their environment, both cognitively and behaviorally [43]. They offer a solid foundation for explaining variations in behavior across different contexts [44]. Empirical evidence supports this pathway, showing that traits like need for achievement (NFA), locus of control (LOC), and risk taking (RT) affect intention primarily through their influence on entrepreneurial attitudes [45, 46]. This study adopts five core PT which have shown relevance in entrepreneurial and freelance contexts. These traits are particularly suitable for explaining career intentions in environments that demand self-management, tolerance for uncertainty, and independent decision-making [47-49]. Building on SDT, personality traits can also be viewed as individual characteristics that shape how people experience need satisfaction in the gig context. For example, individuals high in proactiveness, need for achievement, or internal locus of control are more likely to perceive gig work as a domain where they can exercise autonomy and demonstrate competence, which, in turn, fosters more self‑determined forms of motivation toward gig work [50-52]. Likewise, risk‑taking and distress tolerance may help individuals cope with the uncertainty and income volatility inherent in gig work, allowing them to maintain feelings of personal control and effectiveness [53, 54]. These traits are expected to translate into more positive gig job attitudes and stronger intentions to participate in the gig economy.
Specifically, NFA refers to an individual's drive to perform better than others or to exceed their past accomplishments [55, 56]. Prior studies have shown that NFA positively influences entrepreneurial attitudes, which subsequently drive intention [46]. A high need for achievement may motivate individuals to engage in gig work as a means of self-directed goal attainment, even in the absence of clear career advancement pathways [57]. Locus of control reflects whether individuals perceive life outcomes as resulting from their own internal efforts or from external forces [58, 59]. Those with an internal LOC believe that success is shaped by their actions and capabilities and are more inclined to pursue autonomous career paths [60]. Empirical evidence supports the indirect influence of internal LOC on entrepreneurial intention through attitude [61]. Risk taking is defined as an individual's willingness to engage in actions involving uncertainty and potential failure [62, 63]. Although entrepreneurs are often portrayed as risk takers, they are in fact calculated risk managers who avoid extreme uncertainty [60, 64]. Given that gig work involves financial instability, lack of long-term contracts, and unpredictable workloads [65], individuals high in risk tolerance are more likely to develop positive attitudes toward this form of employment. Risk-taking propensity has becomes particularly salient given the inherent instability and uncertainty of income and employment conditions [66]. Multiple studies confirm that RT influences entrepreneurial intention via its effect on attitude [49, 61]. Proactiveness (PR) is characterized by the tendency to initiate change, seize opportunities, and act ahead of anticipated demands [67]. Proactive individuals tend to shape their environments rather than merely respond to them. This personality trait helps individuals to actively identify and pursue opportunities in a fragmented and continuously changing labor market [68]. This trait has been shown to influence both attitude toward entrepreneurship and intention [69, 70]. Distress tolerance (DT) refers to the ability to function effectively under conditions of uncertainty, pressure, or psychological strain [62]. Gig workers often face income instability, lack of social protection, and ongoing client acquisition stress [48, 71]. Hence, we proposed:
H1a: NFA positively influences GJI through GJA.
H1b: Internal LOC positively influences GJI through GJA.
H1c: RT positively influences GJI through GJA.
H1d: PR positively influences GJI through GJA.
H1e: DT positively influences GJI through GJA.
2.2 Influence of Theory of Planned Behavior components (attitude, self-efficacy, social norm) on gig job intention
The Theory of Planned Behavior is one of the most widely utilized frameworks for explaining how behaviors are formed. Therein, intention is the most immediate predictor of behavior and is influenced by three key factors [72]. Attitude refers to the extent to which an individual evaluates the behavior in question as favorable or unfavorable. Social norm represents the perceived social pressure from important others regarding whether the behavior should be performed. Self-efficacy (GSE) is defined as an individual’s internal belief in their capacity to succeed in specific tasks or contexts [73]. Individuals with high self-efficacy are more likely to demonstrate resilience, adaptability, and motivation when approaching uncertain or complex tasks [74]. This makes self-efficacy especially relevant in work contexts that require autonomy and problem-solving, such as gig employment.
There were several research conducted applied the TPB framework to forecast career intentions [25, 27, 75, 76]. In the context of the rapidly growing gig economy, understanding the psychosocial factors that influence the intention to participate in gig work has become an important research priority [77]. The strength of the influence of the three TPB components on intention may vary across research contexts, target populations, and national settings [72, 78]. Moreover, the explanatory power of the TPB framework can be enhanced when integrated with additional psychological constructs, such as self-efficacy, to provide a more comprehensive understanding of intention formation [27, 79].
In TPB model, PT are conceptualized as background factors that do not directly predict intentions but instead influence them indirectly by shaping the belief structures that underlie the TPB components [27, 49, 59, 80]. It is widely recognized that PT affects career choice, and they shape entrepreneurial intention indirectly through the evaluative processes that form attitudes [81]. Based on this reasoning, the present study hypothesizes that GJA mediates the relationship between PT and GJI. Hence, we proposed:
H2: Gig job attitude has a positive effect on GJI
H3: Gig social norms (GSN) have a positive effect on GJI.
H4: Gig self-efficacy has a positive effect on GJI.
2.3 Involvement
The concept of involvement is a key theoretical construct in social psychology, consumer behavior, and leisure studies. A substantial body of research has been developed regarding "enduring involvement", also known as "leisure involvement" or "ego involvement" in the literature [82]. Enduring involvement refers to customers developing knowledge, familiarity, and expertise about a specific product or service category over time [83]. Many studies have clearly distinguished between two main types of involvement with distinct characteristics and mechanisms. "Enduring involvement" - a long-term and relatively stable commitment to a particular product or activity, and "situational involvement" - a temporary interest in a particular situation [84]. Behaviors associated with enduring involvement remain stable over time, whereas behaviors resulting from situational involvement decline as the situation causing involvement changes. In the field of leisure studies, Kyle et al. [85] developed the Leisure Involvement Scale with five main factors: attraction, centrality, social bonding, identity affirmation, and identity expression. Recent empirical research by Tükel et al. [86] demonstrated a positive relationship between leisure involvement and entrepreneurial-oriented traits (risk-taking, innovativeness, proactiveness) in Turkish women, expanding the understanding of the role of involvement in shaping psychological characteristics and occupational behaviors.
Empirical research has consistently underscored the significant role that INV plays in shaping various dimensions of human behavior. Numerous studies indicate that heightened levels of INV are associated with more profound information processing [87], comprehensive information searches, and a stronger commitment to attitudes or intentions [88, 89]. Contemporary research highlights the need to differentiate various types of INV and develop measurement methods tailored to specific contexts, including consumer behavior, leisure, and career orientation. A recent study on ecotourism by Ting et al. [90] provided "empirical support for the importance of tourist involvement in strengthening the relationship between ecotourism attitudes and behavioral intentions", confirming the important moderating role of INV. Encourage personal involvement by creating profound and meaningful experiences, alongside establishing a loyalty program to strengthen their commitment to desired behaviors [90]. In addition, INV also plays a moderating role in the relationship between customers' attitudes toward KOLs and their purchase intentions from products recommended by KOLs. This means that customers who are highly involved are more likely to have stronger purchase intentions when they hold a positive attitude toward KOLs as representatives of a brand or product [88]. Therefore, these arguments lead to the following hypotheses:
H5: INV positively moderates (reinforces) the positive relationship between GJA and GJI.
Additionally, we also contend that the mediating role of attitude in the relationship between individual proactiveness and GJI will be strengthened when enduring involvement is high in the gig economy. Proactiveness - a critical aspect of decision-making intention, is the ability to anticipate future opportunities and challenges and proactively address them [91]. By promoting proactivity through leisure involvement, entrepreneurs can better position themselves to identify and exploit business opportunities, thereby increasing the likelihood of success in their ventures. Leisure involvement, far from being just for relaxation, plays a significant role in promoting a proactive approach in many aspects of life, not just in work-related contexts, thereby influencing behavioral intentions [86]. Indeed, high INV will influence personal proactiveness, which is expected to strengthen the mediating effect of GJA on GJI.
H6: The mediating effect of GJA on the relationship between PR and GJI is significantly enhanced by INV. Particularly, the mediating effect of attitude on proactiveness and GJI is enhanced when INV is high.
2.4 Proposed research model
Based on proposed hypotheses, Figure 1 presents the theoretical model illustrating the relationships.
This study adopts a quantitative, cross-sectional design to investigate gig job intention through TPB model, personality traits and involvement. Structural Equation Modeling using Partial Least Squares (SEM-PLS) was employed to examine direct, mediated, and moderated relationships among constructs in the conceptual model. Data were collected from Vietnam between 15th June to 28th August 2025 using online survey via Google Forms. A convenience sampling technique was used to recruit respondents. Missing data and invalid responses were removed. A total of 400 valid responses were retained for analysis, which satisfies the recommended threshold for SEM-PLS as suggested by Hair [92]. The demographic composition of the sample included 46% male and 54% female, with most respondents aged between 18-28.
All constructs were measured with multi-item scales adapted from validated instruments in prior studies, with slight modifications to fit the gig economy context. Respondents rated all items using a five-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree. PT were operationalized through five constructs: NFA (6 items), Internal LOC (3 items), and RT (3 items) were all adapted from Patre [27]. PR (4 items) was measured using items adapted from the study [93]. PT (4 items) was adapted from the study [94]. The four constructs from the TPB model, include GJA (5 items), GSE (4 items), GSN (4 items), and GJI (5 items) were adapted from Patre [27]. INV (14 items) was adapted from Kyle et al. [85]. The dataset was then analyzed using SmartPLS v.4 for SEM-PLS, and SPSS for descriptive statistics. The measurement model was assessed for reliability (Cronbach’s alpha, composite reliability), convergent validity (average variance extracted, AVE), and discriminant validity (heterotrait-monotrait ratio (HTMT), Fornell–Larcker criterion). The structural model was evaluated to test the hypothesized relationships among constructs.
4.1 Descriptive analysis
The study had a sample size of n= 400, in which women accounted for 54%, equivalent to 216 people, and men accounted for 46%, equivalent to 184 people. The results illustrate that 59% of survey participants were aged Gen Z (18-28 years old), 24% Generation Y (29-44 years old) and 17% were Generation X (45-60 years old). Regarding occupation, accounting for the largest part of the total survey participants were fresh graduate students with more than 41%, equivalent to 164 people, followed by the university students with 34% or 136 people, and experienced workers with 25% or 100 people.
4.2 Measurement model
This study utilized SPSS and SmartPLS v.4 for checking reliability by outer loadings, Cronbach's alpha, Composite reliability, average variance extracted, Heterotrait-monotrait and Fornell-Larcker criterion; and Partial Least Squares Structural Equation Modeling. This allowed the identification of factors. Composite reliability (CR) and average variance extracted (AVE) were used to confirm the validity and reliability of the measurement model, followed by structural equation modeling (SEM).
Prior to testing the structural model, the measurement model was assessed for reliability, convergent validity, and discriminant validity. All outer loadings of the indicators on their respective constructs exceeded the recommended threshold of 0.70 (Table 1), demonstrating satisfactory indicator reliability [95]. Internal consistency was confirmed as both Cronbach's alpha and composite reliability (rho_c) values for all constructs were above the 0.70 benchmark (Table 1) [96]. Convergent validity was established, with the average variance extracted (AVE) (Table 1) for each construct surpassing the 0.50 criterion (Table 2) [97]. Furthermore, discriminant validity was supported using two methods. The square root of the AVE for each construct was greater than its correlations with other constructs (Fornell-Larcker criterion). Additionally, all heterotrait-monotrait (HTMT) ratios were below the conservative threshold of 0.90 (Table 3) [98]. These results confirm that the measurement model is reliable and valid for testing the proposed structural relationships.
Table 1. Items, convergent validity and composite reliability of constructs
|
Construct |
Item |
Outer Loadings |
AVE |
CR (rho_a) |
CR (rho_c) |
Cronbach’s Alpha |
|
Distress Tolerance adapted from Garner et al. [94] |
DT1: My feelings of distress are so intense that they completely take over. |
0.801 |
0.635 |
0.813 |
0.874 |
0.809 |
|
DT2: Being distressed or upset is always a major ordeal for me. |
0.796 |
|||||
|
DT3: I can’t handle feeling distressed or upset. |
0.773 |
|||||
|
DT4: I’ll do anything to stop feeling distressed or upset. |
0.818 |
|||||
|
Gig job attitude adapted from Patre [27] |
GJA1: Gig jobs are meant for freshers/people who are entering the workforce. |
0.796 |
0.618 |
0.846 |
0.890 |
0.845 |
|
GJA2: Gig jobs leave workers financially insecure. |
0.796 |
|||||
|
GJA3: Gig jobs have the potential to make a career. |
0.758 |
|||||
|
GJA4: Gig jobs are great for people who prefer a flexible schedule. |
0.772 |
|||||
|
GJA5: Gig jobs are meant for the senior and the aged who do not want to work full-time anymore. |
0.807 |
|||||
|
Gjg job intention adapted from Patre [27] |
GJI1: I am prepared to do anything to be a gig worker. |
0.811 |
0.633 |
0.855 |
0.896 |
0.855 |
|
GJI2 My professional goal is to become a gig worker. |
0.790 |
|||||
|
GJI3: I am keen to start and continue gig work. |
0.799 |
|||||
|
GJI4: I am determined to start a gig career in future. |
0.772 |
|||||
|
GJI5: I strongly believe that I will start a gig career someday. |
0.806 |
|||||
|
Gig self-efficacy adapted from Patre [27] |
GSE1: I am good at making decisions which will help me select the right gig projects. |
0.774 |
0.640 |
0.815 |
0.877 |
0.813 |
|
GSE2: I know the necessary practical details to take up a gig career. |
0.819 |
|||||
|
GSE3: I am good at networking with people who will help me get gig projects continuously. |
0.806 |
|||||
|
GSE4: If I try to take up a gig career, I will have a high probability of success. |
0.801 |
|||||
|
Gig social norm adapted from Patre [27] |
GSN1: If I were to take up a gig career, my family would be supportive. |
0.770 |
0.638 |
0.817 |
0.876 |
0.811 |
|
GSN2: If I were to take up a gig career, my close friends would be supportive. |
0.807 |
|||||
|
GSN3: My parents’ opinion is very important to me. |
0.819 |
|||||
|
GSN4: I care about the opinion of my friends and am influenced by them. |
0.798 |
|||||
|
Involvement adapted from Kyle et al. [85] |
INV1: I believe working in the gig economy would be one of the most enjoyable things I could do. |
0.816 |
0.580 |
0.953 |
0.951 |
0.944 |
|
INV2: I'm really interested in participating in the gig economy. |
0.751 |
|||||
|
INV3: The idea of participating in the gig economy is very important to me. |
0.754 |
|||||
|
INV4: I find a lot of my life organized around gig work. |
0.745 |
|||||
|
INV5: The idea of participating in the gig economy occupies a central role in my future career plans. |
0.795 |
|||||
|
INV6: To change my preference from the gig economy to another career path would require major rethinking. |
0.728 |
|||||
|
INV7: I enjoy discussing about gig jobs with my friends. |
0.776 |
|||||
|
INV8: Most of my friends are in some way connected with gig jobs. |
0.764 |
|||||
|
INV9: Participating in gig economy provides me with an opportunity to be with friends. |
0.788 |
|||||
|
INV10: When I participate in the gig economy, I feel I can really be myself. |
0.721 |
|||||
|
INV11: I identify with the people and image associated with the gig economy. |
0.745 |
|||||
|
INV12: You can tell a lot about a person by seeing them participate in the gig economy. |
0.752 |
|||||
|
INV13: Participating in the gig economy would say a lot about who I am. |
0.754 |
|||||
|
INV14: When I participate in gig economy, others see me the way I want them to see me. |
0.765 |
|||||
|
Locus of control adapted from Patre [27] |
LOC1: When I get what I desire, it is because of my hard work. |
0.827 |
0.717 |
0.804 |
0.884 |
0.802 |
|
LOC2: My own actions determine my life. |
0.859 |
|||||
|
LOC3: In general, I can control what will happen in my life. |
0.854 |
|||||
|
Need for achievement adapted from Patre [27] |
NFA1: I know exactly what I want out of life. |
0.752 |
0.598 |
0.875 |
0.899 |
0.866 |
|
NFA2: Everyday, I try to accomplish something worthwhile. |
0.816 |
|||||
|
NFA3: I almost always feel that I must do the best at what I am doing. |
0.736 |
|||||
|
NFA4: I always do my best whether I am alone or with someone. |
0.789 |
|||||
|
NFA5: I very often find myself doing or saying something for the pleasure of it, rather than because it serves some purpose. |
0.770 |
|||||
|
NFA6: I try harder to content with myself than to be successful. |
0.772 |
|||||
|
Proactiveness adapted from Parker and Sprigg [93] |
PR1: No matter what the odds, if I believe in something I will make it happen. |
0.818 |
0.657 |
0.827 |
0.885 |
0.826 |
|
PR2: I love being a champion for my ideas, even against others' opposition. |
0.798 |
|||||
|
PR3: If I believe in an idea, no obstacle will prevent me from making it happen. |
0.822 |
|||||
|
PR4: I am excellent at identifying opportunities. |
0.805 |
|||||
|
Risk taking adapted from Patre [27] |
RT1: I am enthusiastic to take more risks for greater outcomes. |
0.859 |
0.711 |
0.803 |
0.881 |
0.797 |
|
RT2: I am not bothered if the earning is less as long as it is assured. |
0.839 |
|||||
|
RT3: I never fear moving into a new venture which is unknown to me. |
0.831 |
(Source: Author’s research)
Table 2. Fornell-Larcker criterion
|
Hypothesis |
DT |
GJA |
GJI |
GSE |
GSN |
INV |
LOC |
NFA |
PR |
RT |
|
DT |
0.797 |
|||||||||
|
GJA |
0.516 |
0.786 |
||||||||
|
GJI |
0.357 |
0.604 |
0.796 |
|||||||
|
GSE |
0.224 |
0.308 |
0.579 |
0.800 |
||||||
|
GSN |
0.347 |
0.294 |
0.415 |
0.261 |
0.799 |
|||||
|
INV |
0.038 |
0.068 |
0.237 |
0.137 |
0.119 |
0.761 |
||||
|
LOC |
0.269 |
0.540 |
0.429 |
0.235 |
0.238 |
0.029 |
0.847 |
|||
|
NFA |
0.070 |
0.305 |
0.121 |
0.021 |
0.065 |
0.019 |
0.034 |
0.773 |
||
|
PR |
0.322 |
0.558 |
0.419 |
0.270 |
0.282 |
0.018 |
0.339 |
0.031 |
0.811 |
|
|
RT |
0.248 |
0.518 |
0.388 |
0.320 |
0.230 |
0.075 |
0.260 |
0.056 |
0.247 |
0.843 |
(Source: Author’s research)
Table 3. Heterotrait-monotrait ratio (HTMT)-matrix
|
Hypothesis |
DT |
GJA |
GJI |
GSE |
GSN |
INV |
LOC |
NFA |
PR |
RT |
|
DT |
||||||||||
|
GJA |
0.621 |
|||||||||
|
GJI |
0.429 |
0.711 |
||||||||
|
GSE |
0.274 |
0.371 |
0.693 |
|||||||
|
GSN |
0.438 |
0.356 |
0.493 |
0.319 |
||||||
|
INV |
0.061 |
0.091 |
0.252 |
0.153 |
0.135 |
|||||
|
LOC |
0.331 |
0.655 |
0.517 |
0.289 |
0.295 |
0.070 |
||||
|
NFA |
0.106 |
0.348 |
0.138 |
0.047 |
0.111 |
0.058 |
0.060 |
|||
|
PR |
0.389 |
0.668 |
0.499 |
0.325 |
0.342 |
0.053 |
0.413 |
0.072 |
||
|
RT |
0.303 |
0.627 |
0.469 |
0.406 |
0.286 |
0.092 |
0.324 |
0.081 |
0.294 |
Source: Author’s research
4.3 Structural model and direct hypotheses
The structural model was evaluated for collinearity, path coefficients, and predictive power. The variance inflation factor (VIF) values for all predictor constructs were well below 5⁵, indicating that multicollinearity was not a concern in the model [99]. The results of the bootstrapping procedure (5000 samples) revealed that all direct path coefficients were statistically significant (p < 0.05). Specifically, DT (β = 0.252, p < 0.001), LOC (β = 0.286, p < 0.001), NFA (β = 0.252, p < 0.001), PR (β = 0.301, p < 0.001), and RT (β = 0.293, p < 0.001) exerted significant positive effects on GJA. In turn, GJA (β = 0.244, p < 0.001), GSE (β = 0.287, p < 0.001), and GSN (β = 0.121, p = 0.005) were found to be significant positive antecedents of GJI. Thus, hypotheses H1a to H1e and H2 to H4 were supported (Table 4).
Table 4. Path coefficients
|
Hypothesis |
Original Sample (O) |
Sample Mean (M) |
Standard Deviation (STDEV) |
T Statistics (|O/STDEV|) |
P Values |
|
H1e |
0.252 |
0.251 |
0.034 |
7.483 |
0.000 |
|
H2 |
0.244 |
0.246 |
0.040 |
6.063 |
0.000 |
|
H4 |
0.287 |
0.290 |
0.040 |
7.176 |
0.000 |
|
H3 |
0.121 |
0.122 |
0.043 |
2.786 |
0.005 |
|
H1b |
0.286 |
0.285 |
0.030 |
9.645 |
0.000 |
|
H1a |
0.252 |
0.254 |
0.033 |
7.737 |
0.000 |
|
H1d |
0.301 |
0.301 |
0.030 |
10.015 |
0.000 |
|
H1c |
0.293 |
0.293 |
0.034 |
8.617 |
0.000 |
|
H6 |
0.348 |
0.344 |
0.037 |
9.458 |
0.000 |
(Source: Author’s research)
The analysis of specific indirect effects was conducted to test the mediating role of GJA. The bootstrapping results indicated that all indirect paths were significant, as the 95% bias-corrected confidence intervals did not include zero. Specifically, GJA significantly mediated the relationships between DT, LOC, NFA, PR, RT and GJI. These findings provide strong support for the mediating relationship suggesting the influence of these factors on GJI (Figure 2).
4.4 Moderation analysis
The proposed moderating effect of INV on the relationship between GJA and GJI was examined. The bootstrapping results revealed that the path coefficient of the interaction term (INV*GJA) was positive and statistically significant (β = 0.348, p < 0.001) (Table 4). This finding provides strong support for Hypothesis H5, indicating that the level of individual INV significantly moderates the strength of the relationship between attitude and intention. The positive sign of the interaction term suggests that the positive effect of GJA on GJI becomes stronger for individuals who report higher levels of INV.
The analysis using PROCESS model 14 [100] reveals that the relationship between PR and GJI is both direct and indirect through the mediating variable GJA, with INV acting as a moderator (H6). The moderation analysis demonstrates that INV significantly strengthens the effect of GJA on GJI. The interaction term GJA*INV is positive and highly significant, whereas the main effect of INV is not significant. Conditional effects reveal that at low INV, the impact of GJA on GJI is negative and significant; at the mean level, the effect turns positive; and at high INV, it becomes strongly positive. Bootstrap results further indicate that the indirect effect PR → GJA → GJI varies across INV levels: −0.094 at low INV, 0.113 at the mean, and 0.320 at high INV. The index of moderated mediation is significant, confirming that the mediation pathway is contingent upon the level of INV.
The results in Table 5 and Figure 3 show that the interaction effects of INV moderating factors have positive values and are statistically significant; thus, it can be concluded that the INV factor has positively promoted the relationship among PR, GJA and GJI.
Table 5. Results of the moderation model for involvement factor
|
Hypothesis |
Linkage |
P-Value |
Results |
|
H5 |
GJA*INV -> GJI |
< .001 |
Supported |
|
H6 |
(PR -> GJA)*INV -> GJI |
< .001 |
Supported |
(Source: Author’s research)
4.5 Model strength
The explanatory power of the structural model was substantial. The independent variables explained 67.7% of the variance in GJA (R² adjusted = 0.677) and 68.4% of the variance in GJI (R² adjusted = 0.684) (Table 6), indicating a high level of predictive accuracy. The effect sizes (f²) for the paths were assessed. PR, RT, and LOC showed medium-to-large effects on GJA, while GSE demonstrated a medium effect on GJI. Furthermore, the model's predictive relevance was confirmed through the blindfolding procedure. The Q² values for both GJA (0.414) and GJI (0.431) were significantly above zero, providing support for the model's out-of-sample predictive power at a medium level [101].
Table 6. R-square
|
Construct |
R-square |
R-square Adjusted |
|
GJA |
0.681 |
0.677 |
|
GJI |
0.688 |
0.684 |
(Source: Author’s research)
The results indicated that all five PT exerted significant indirect effects on GJI through GJA which accords with the theoretical view that PT in the TPB framework function as background factors [59], and echoes empirical evidence from prior studies where they influenced intention via attitudes [49, 27, 61]. The mediating mechanism can be understood in light of the inherent features of gig work, which combines high flexibility, autonomy, and task variety with income insecurity, platform dependence, and psychological strain [23, 102, 103].
GJA, GSE, and GSN have significantly predicted GJI, thereby providing support for H2-H4. This outcome is consistent with the foundational propositions of TPB [25, 27, 72]. Nonetheless, the relative strength of these predictors varied, with GJA and GSE emerging as stronger determinants than GSN. This pattern resonates with Ajzen’s [72] assertion that the relative importance of the three antecedents of intention may differ across behaviors and contexts, as well as with the cross-national findings of Engle et al. [78]. The relatively weaker role of social norms may be explained by the individualized and flexible nature of gig jobs in Vietnam, which are often pursued for pragmatic motives (income or autonomy), and relatively new and weakly institutionalized career path, which make strong normative expectations have yet to develop.
Finally, the results confirmed that INV significantly moderated the relationship between GJA and GJI, thereby supporting H5. This is consistent with prior research demonstrating that higher levels of INV strengthen the predictive link between attitudes and behavioral intentions [88, 90]. This can be explained by the fact that higher involvement enhances the salience and stability of attitudes toward gig jobs, thereby making them more influential in guiding intention formation. When individuals perceive platforms and opportunities as personally relevant, their favorable evaluations of such work exert stronger influence on intention compared to those with low involvement. In contrast, when involvement is low, gig work does not occupy a central role in an individual’s lifestyle and is not integrated into their self-concept [104]. Although individuals may form a generally positive attitude toward gig work, it is not personally meaningful to them, which weakens the attitude–intention linkage and creates a disconnect between favorable evaluation and actual behavioral intention [87, 105]. Under these conditions, the indirect effect through attitude can become negative, reflecting that favorable evaluations alone are insufficient when personal engagement is minimal.
H6 was also supported, as INV amplified the indirect pathway through which PR influenced GJI via GJA. Proactive individuals tend to evaluate gig work positively because its flexible and opportunity-driven nature resonates with their preference for initiative-taking and the pursuit of new challenges. These favorable attitudes are more effectively translated into strong intentions when accompanied by high involvement with gig platforms and opportunities.
6.1 Theoretical implications
These findings contribute significantly to the theoretical advancement of behavioral intention research. First, this study focuses on the intention to participate in the gig economy, a relatively new area of research. The confirmation of the direct relationship between attitudes, social norms, self-efficacy, and intention (H1-H3) provides new empirical validation in the recently emerging field of gig work. Second, and more importantly, the mediating effects of GJA on the relationship between individual PT and GJI (H4a-H4e) extend SDT by illustrating how intrinsic motivational factors operate through cognitive appraisals to influence behavioral intentions. This is also a rare study that uses all five core personality traits to assess the impact on behavioral intentions through attitude. Third, the most critical and theoretically unique contribution lies in the moderating effect of INV, which strengthens the positive relationship between GJA and GJI (H5), and the finding of a moderated mediation effect in which involvement increases the mediating effect of attitude on the proactiveness - intention relationship (H6). These findings advance involvement theory by demonstrating its mechanisms in behavioral prediction models. Furthermore, these results provide further theoretical support and extension to the Elaboration Likelihood Model by showing that involvement enhances the proactive processing of information relevant to attitudes, leading to the formation of stronger, more sustainable intentions. These theoretical contributions provide a deeper understanding of when and how distinct individual characteristics translate into behavioral intentions in flexible working arrangements, thus enriching both the TPB and involvement literatures in specific contexts.
6.2 Practical implications
The findings provide significant practical guidance for a wide range of stakeholders in the gig economy ecosystem, particularly for organizational managers, platform developers, and policymakers seeking to optimize participation in gig work. For organizational leaders, the strong mediating role of attitudes suggests that promoting positive perceptions of gig work should take precedence over direct persuasion efforts. Organizations should focus on highlighting the benefits of flexibility, autonomy, and skill development opportunities that align with workers’ intrinsic motivation, especially for individuals with high proactiveness and achievement orientation. The significant moderating effect of INV suggests that talent recruitment and retention strategies should emphasize creating meaningful connections between workers and their roles, as high engagement amplifies the conversion of positive attitudes into actual GJI. In practice, this can be achieved through personalized job matching, individualized skill development programs, and community-building initiatives that enhance workers’ psychological investment in their gig activities. Platform designers should integrate features that enhance user involvement, such as goal-setting tools, progress tracking, and social networking capabilities, to strengthen the link between attitudes and intentions. For HR professionals managing their workforce, these findings suggest that high-involvement work practices combined with training that support autonomy, emotion management, relationship maintenance, and attention can significantly improve proactive behaviors and the ability to self-manage flexible work in a volatile work environment. Policymakers should consider these psychological mechanisms when designing regulations and support systems for gig workers, ensuring that these policies enhance rather than undermine workers’ intentions to engage in and positive attitudes toward flexible work practices. In addition, the strong role of self-efficacy implies that training and certification programs should focus not only on skill development but also on building self-efficacy and behavioral control, which are essential factors for maintaining engagement in gig work.
6.3 Limitation and future research
Despite the contributions of this study, several limitations should be acknowledged, which also provide directions for future research. The cross-sectional design and online survey restrict the ability to observe changes in intentions over time and also exclude individuals with limited internet access. The focus on Vietnam and the lack of distinction between different types of gig jobs limit the generalizability of the findings. In addition, the model emphasizes individual factors, while contextual influences such as platform characteristics, economic conditions, and policy support were not examined, though they may also play an important role in shaping GJI. Future research should address these gaps by using longitudinal designs, comparing different gig job types or incorporating broader contextual variables.
This research is funded by National Economics University, Hanoi, Vietnam.
[1] Suter, T. (2025). 4 in 10 companies planning job cuts due to AI: Survey. The Hill. https://thehill.com/policy/technology/5076325-companies-shrinking-workforce-artificial-intelligence-survey/.
[2] Layne, R. (2024). Layoffs surging in a strong economy? Advice for navigating uncertain times. Harvard Business School. https://www.library.hbs.edu/working-knowledge/layoffs-surging-in-strong-economy-advice-for-navigating-uncertain-times.
[3] Ashford, S.J., Caza, B.B., Reid, E.M. (2018). From surviving to thriving in the gig economy: A research agenda for individuals in the new world of work. Research in Organizational Behavior, 38: 23-41. https://doi.org/10.1016/j.riob.2018.11.001
[4] Huang, N., Burtch, G., Hong, Y., Pavlou, P.A. (2020). Unemployment and worker participation in the gig economy: Evidence from an online labor market. Information Systems Research, 31(2): 431-448. https://doi.org/10.1287/isre.2019.0896
[5] Srihita, R.H., Goli, G., M, R., Gobinath, R. (2025). Transformative dynamics of the gig economy: Technological impacts, worker well-being and global research trends. International Journal of Engineering Business Management, 17: 1-27. https://doi.org/10.1177/18479790241310362
[6] Li, M.M., Hu, X.Y., Jin, K.Y., Han, J.C. (2025). Exploring factors influencing entry into the gig economy: A study of Chinese workers. Acta Psychologica, 259: 105301. https://doi.org/10.1016/j.actpsy.2025.105301
[7] Lepanjuuri, K., Wishart, R., Cornick, P. (2018). The characteristics of those in the gig economy: Final report. APO. https://apo.org.au/node/244361.
[8] De Stefano, V. (2015). The rise of the “just-in-time workforce”: On-demand work, crowd work and labour protection in the “gig-economy”. Comparative Labor Law and Policy Journal, 37: 471. https://doi.org/10.2139/ssrn.2682602
[9] Johnston, H., Land-Kazlauskas, C. (2018). Organizing on-demand representation, voice, and collective bargaining in the gig economy. International Labour Organization. https://ideas.repec.org/p/ilo/ilowps/994981993502676.html.
[10] Sannon, S., Cosley, D. (2022). Toward a more inclusive gig economy: Risks and opportunities for workers with disabilities. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2): 1-31. https://doi.org/10.1145/3555755
[11] Al-Mamary, Y.H.S., Alraja, M.M. (2022). Understanding entrepreneurship intention and behavior in the light of TPB model from the digital entrepreneurship perspective. International Journal of Information Management Data Insights, 2(2): 100106. https://doi.org/10.1016/j.jjimei.2022.100106
[12] Deloitte. (2022). The Deloitte Global 2022 Gen Z and Millennial survey. Deloitte Global. https://www.deloitte.com/global/en/issues/work/genzmillennialsurvey-2022.html.
[13] Kässi, O., Lehdonvirta, V. (2018). Online labour index: Measuring the online gig economy for policy and research. Technological Forecasting and Social Change, 137: 241-248. https://doi.org/10.1016/j.techfore.2018.07.056
[14] Chitte, S. (2025). The gig economy: Navigating complexities in human resource management. International Journal of Research and Innovation in Social Science, VII(XII): 1-12.
[15] Kuhn, K.M., Maleki, A. (2017). Micro-entrepreneurs, dependent contractors, and instaserfs: Understanding online labor platform workforces. Academy of Management Perspectives, 31(3): 183-200. https://doi.org/10.5465/amp.2015.0111
[16] Manyika, J., Lund, S., Bughin, J., Robinson, K., Mischke, J., Mahajan, D. (2016). Independent work: Choice, necessity, and the gig economy. McKinsey & Company. https://www.mckinsey.com/featured-insights/employment-and-growth/independent-work-choice-necessity-and-the-gig-economy.
[17] Lund, S., Madgavkar, A., Manyika, J., Smit, S., Ellingrud, K., Robinson, O. (2021). The future of work after COVID-19. McKinsey & Company. https://www.mckinsey.com/featured-insights/future-of-work/the-future-of-work-after-covid-19.
[18] Dang, H.A., Huynh, T.L.D., Nguyen, M.H. (2024). Does the COVID-19 pandemic disproportionately affect the poor? Evidence from a six-country survey. Journal of Economics and Development, 26(1): 2-18. https://doi.org/10.1108/JED-06-2023-0107
[19] International Labour Organization. (2021). World employment and social outlook 2021: The role of digital labour platforms in transforming the world of work. https://www.ilo.org/publications/flagship-reports/role-digital-labour-platforms-transforming-world-work.
[20] Kuhn, K.M., Meijerink, J., Keegan, A. (2021). Human resource management and the gig economy: Challenges and opportunities at the intersection between organizational HR decision-makers and digital labor platforms. Research in Personnel and Human Resources Management, 39(1): 1-46. https://doi.org/10.1108/s0742-730120210000039001
[21] Kässi, O., Lehdonvirta, V., Stephany, F. (2021). How many online workers are there in the world? A data-driven assessment. Open Research Europe, 1: 53. https://doi.org/10.12688/openreseurope.13639.4
[22] Jabagi, N., Croteau, A.M., Audebrand, L.K., Marsan, J. (2019). Gig-workers' motivation: Thinking beyond carrots and sticks. Journal of Managerial Psychology, 34(4): 192-213. https://doi.org/10.1108/JMP-06-2018-0255
[23] Ray, B., Sengupta, A., Varma, A. (2024). The gig verse: Building a sustainable future. International Journal of Organizational Analysis, 32(10): 2275-2298. https://doi.org/10.1108/ijoa-08-2023-3946
[24] Davidson, A., Gleim, M.R., Johnson, C.M., Stevens, J.L. (2023). Gig worker typology and research agenda: Advancing research for frontline service providers. Journal of Service Theory and Practice, 33(5): 647-670. https://doi.org/10.1108/jstp-08-2022-0188
[25] Perdana, Y., Syahrul, A.R., Sudono, A., Kurnia, D., Susanto, E. (2025). The gig economy and young graduates' career preferences: Between freelancing and the entrepreneurial mindset. Journal Integration of Management Studies, 3(1): 73-85. https://doi.org/10.58229/jims.v3i1.325
[26] Sharma, N., Chillakuri, B.K. (2022). Positive deviance at work: A systematic review and directions for future research. Personnel Review, 52(4): 933-954. https://doi.org/10.1108/pr-05-2020-0360
[27] Patre, S. (2023). Gig intentions in management students: Integrating JD-R in an extended TPB model. Management and Labour Studies, 48(1): 76-97. https://doi.org/10.1177/0258042x221118482
[28] Gandhi, A., Hidayanto, A.N., Sucahyo, Y.G., Ruldeviyani, Y. (2018). Exploring people’s intention to become platform-based gig workers: An empirical qualitative study. In 2018 International Conference on Information Technology Systems and Innovation (ICITSI), Bandung, Indonesia, pp. 266-271. https://doi.org/10.1109/icitsi.2018.8696017
[29] Bogatyreva, K., Verkhovskaya, O., Makarov, Y. (2023). A springboard for entrepreneurs? Gig and sharing economy and entrepreneurship in Russia. Journal of Entrepreneurship in Emerging Economies, 15(4): 698-726. https://doi.org/10.1108/jeee-03-2021-0128
[30] Szaban, J., Skrzek-Lubasińska, M. (2018). Self-employment and entrepreneurship: A theoretical approach. Central European Management Journal, 26(2): 89-120. https://doi.org/10.7206/jmba.ce.2450-7814.230
[31] Pollio, F. (2022). Gig-workers’ motivations and their entrepreneurial mindset. Master Thesis U.S.E., Utrecht University. https://studenttheses.uu.nl/handle/20.500.12932/42384.
[32] Bajwa, U., Gastaldo, D., Di Ruggiero, E., Knorr, L. (2018). The health of workers in the global gig economy. Globalization and Health, 14: 124. https://doi.org/10.1186/s12992-018-0444-8
[33] McMullen, J.S., Bagby, D.R., Palich, L.E. (2008). Economic freedom and the motivation to engage in entrepreneurial action. Entrepreneurship Theory and Practice, 32(5): 875-895. https://doi.org/10.1111/j.1540-6520.2008.00260.x
[34] Caza, B.B., Reid, E.M., Ashford, S.J., Granger, S. (2022). Working on my own: Measuring the challenges of gig work. Human Relations, 75(11): 2122-2159. https://doi.org/10.1177/00187267211030098
[35] Ryan, R.M., Deci, E.L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1): 54-67. https://doi.org/10.1006/ceps.1999.1020
[36] Jarrahi, M.H., Sutherland, W., Nelson, S.B., Sawyer, S. (2020). Platformic management, boundary resources for gig work, and worker autonomy. Computer Supported Cooperative Work (CSCW), 29: 153-189. https://doi.org/10.1007/s10606-019-09368-7
[37] Gagné, M., Parent-Rocheleau, X., Bujold, A., Gaudet, M.C., Lirio, P. (2022). How algorithmic management influences worker motivation: A self-determination theory perspective. Canadian Psychology/Psychologie Canadienne, 63(2): 247-260. https://doi.org/10.1037/cap0000324
[38] Shulga, L.V. (2021). Change management communication: The role of meaningfulness, leadership brand authenticity, and gender. Cornell Hospitality Quarterly, 62(4): 498-515. https://doi.org/10.1177/1938965520929022
[39] Jacobs, S., De Vos, A., Stuer, D., Van der Heijden, B.I.J.M. (2019). "Knowing me, knowing you" the importance of networking for freelancers' careers: Examining the mediating role of need for relatedness fulfillment and employability-enhancing competencies. Frontiers in Psychology, 10: 2055. https://doi.org/10.3389/fpsyg.2019.02055
[40] Roberts, B.W., Wood, D., Caspi, A. (2008). The development of personality traits in adulthood. In Handbook of Personality: Theory and Research, pp. 375-398. https://psycnet.apa.org/record/2008-11667-014.
[41] Sanchez-Roige, S., Gray, J.C., MacKillop, J., Chen, C.H., Palmer, A.A. (2017). The genetics of human personality. Genes, Brain and Behavior, 17(3): e12439. https://doi.org/10.1111/gbb.12439
[42] McCrae, R.R., Costa, P.T. (2003). Personality in Adulthood: A Five-Factor Theory Perspective. Guilford Press.
[43] Mount, M.K., Barrick, M.R., Scullen, S.M., Rounds, J. (2005). Higher-order dimensions of the Big Five personality traits and the Big Six vocational interest types. Personnel Psychology, 58(2): 447-478. https://doi.org/10.1111/j.1744-6570.2005.00468.x
[44] De Feyter, T., Caers, R., Vigna, C., Berings, D. (2012). Unraveling the impact of the Big Five personality traits on academic performance: The moderating and mediating effects of self-efficacy and academic motivation. Learning and Individual Differences, 22(4): 439-448. https://doi.org/10.1016/j.lindif.2012.03.013
[45] Luthje, C., Franke, N. (2003). The 'making' of an entrepreneur: Testing a model of entrepreneurial intent among engineering students at MIT. R&D Management, 33(2): 135-147. https://doi.org/10.1111/1467-9310.00288
[46] Farrukh, M., Alzubi, Y., Shahzad, I. A., Waheed, A., Kanwal, N. (2018). Entrepreneurial intentions: The role of personality traits in perspective of theory of planned behaviour. Asia Pacific Journal of Innovation and Entrepreneurship, 12(3): 399-414. https://doi.org/10.1108/apjie-01-2018-0004
[47] Gurel, E., Altinay, L., Daniele, R. (2010). Tourism students' entrepreneurial intentions. Annals of Tourism Research, 37(3): 646-669. https://doi.org/10.1016/j.annals.2009.12.003
[48] Keith, M.G., Harms, P., Tay, L. (2019). Mechanical Turk and the gig economy: Exploring differences between gig workers. Journal of Managerial Psychology, 34(4): 286-306. https://doi.org/10.1108/jmp-06-2018-0228
[49] Ahmed, T., Klobas, J.E., Ramayah, T. (2021). Personality traits, demographic factors and entrepreneurial intentions: Improved understanding from a moderated mediation study. Entrepreneurship Research Journal, 11(4): 20170062. https://doi.org/10.1515/erj-2017-0062
[50] Yang, C., Bao, Y., Zhang, Z. (2024). More autonomy, more proactive? The (in)congruence effects of autonomy on proactive behaviour. Management Decision, 62(5): 1560-1575. https://doi.org/10.1108/MD-05-2023-0867
[51] Van den Broeck, A., Vansteenkiste, M., De Witte, H., Soenens, B., Lens, W. (2010). Capturing autonomy, competence, and relatedness at work: Construction and initial validation of the Work-related Basic Need Satisfaction scale. Journal of Occupational and Organizational Psychology, 83(4): 981-1002. https://doi.org/10.1348/096317909X481382
[52] Ng, T.W.H., Sorensen, K.L., Eby, L.T. (2006). Locus of control at work: A meta-analysis. Journal of Organizational Behavior, 27(8): 1057-1087. https://doi.org/10.1002/job.416
[53] Horswill, M.S., McKenna, F.P. (2006). The effect of perceived control on risk taking. Journal of Applied Social Psychology, 29(2): 377-391. https://doi.org/10.1111/j.1559-1816.1999.tb01392.x
[54] Seligman, N. (2017). Cognitive emotion regulation, distress tolerance, and perceived control predict perceived effectiveness. Master's thesis, Fordham University. https://research.library.fordham.edu/dissertations/AAI10618201/.
[55] Hansemark, O.C. (2003). Need for achievement, Locus of control and the prediction of business start-ups: A longitudinal study. Journal of Economic Psychology, 24(3): 301-319. https://doi.org/10.1016/s0167-4870(02)00188-5
[56] McClelland, D.C. (1961). Achieving Society (Vol. 92051). Simon and Schuster.
[57] Putra, H.M.M., Martono, B.A., Said, J., Setyaningrum, R.P. (2025). What drives gig worker success? Investigating the impact of relevant experience and self-directed learning. Jurnal Manajemen Bisnis, 16(2): 460-479. https://doi.org/10.18196/mb.v16i2.26861
[58] Rotter, J.B. (1966). Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs: General and Applied, 80(1): 1-28. https://doi.org/10.1037/h0092976
[59] Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4): 314-324. https://doi.org/10.1002/hbe2.195
[60] Mueller, S.L., Thomas, A.S. (2001). Culture and entrepreneurial potential: A nine country study of locus of control and innovativeness. Journal of Business Venturing, 16(1): 51-75. https://doi.org/10.1016/S0883-9026(99)00039-7
[61] Rosique-Blasco, M., Madrid-Guijarro, A., García-Pérez-de-Lema, D. (2018). The effects of personal abilities and self-efficacy on entrepreneurial intentions. International Entrepreneurship and Management Journal, 14: 1025-1052. https://doi.org/10.1007/s11365-017-0469-0
[62] Rauch, A., Frese, M. (2007). Let's put the person back into entrepreneurship research: A meta-analysis on the relationship between business owners' personality traits, business creation, and success. European Journal of Work and Organizational Psychology, 16(4): 353-385. https://doi.org/10.1080/13594320701595438
[63] Jackson, D.N. (1994). Jackson personality inventory- revised. Sigma Assessment Systems, Research Psychologists Press Division. https://www.sigmaassessmentsystems.com/assessments/jackson-personality-inventory-revised/.
[64] Chye Koh, H. (1996). Testing hypotheses of entrepreneurial characteristics: A study of Hong Kong MBA students. Journal of Managerial Psychology, 11(3): 12-25. https://doi.org/10.1108/02683949610113566
[65] Heeks, R. (2017). Decent work and the digital gig economy: A developing country perspective on employment impacts and standards in online outsourcing, crowdwork, etc. Development Informatics Working Paper no. 71. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3431033.
[66] Nieß, C., Biemann, T. (2014). The role of risk propensity in predicting self-employment. Journal of Applied Psychology, 99(5): 1000-1009. https://doi.org/10.1037/a0035992
[67] Lumpkin, G.T., Dess, G.G. (1996). Clarifying the entrepreneurial orientation construct and linking it to performance. The Academy of Management Review, 21(1): 135-172. https://doi.org/10.5465/amr.1996.9602161568
[68] Jiang, Z., Wang, Y., Li, W.L., Peng, K.Z., Wu, C.H. (2022). Career proactivity: A bibliometric literature review and a future research agenda. Applied Psychology, 72(1): 144-184. https://doi.org/10.1111/apps.12442
[69] Obschonka, M., Hakkarainen, K., Lonka, K., Salmela-Aro, K. (2017). Entrepreneurship as a twenty-first century skill: Entrepreneurial alertness and intention in the transition to adulthood. Small Business Economics, 48: 487-501. https://doi.org/10.1007/s11187-016-9798-6
[70] Seibert, S.E., Kraimer, M.L., Crant, J.M. (2006). What do proactive people do? A longitudinal model linking proactive personality and career success. Personnel Psychology, 54(4): 845-874. https://doi.org/10.1111/j.1744-6570.2001.tb00234.x
[71] Blyth, D.L., Jarrahi, M.H., Lutz, C., Newlands, G. (2022). Self-branding strategies of online freelancers on upwork. New Media & Society, 26(7): 4008-4033. https://doi.org/10.1177/14614448221108960
[72] Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2): 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
[73] Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2): 122-147. https://doi.org/10.1037/0003-066x.37.2.122
[74] Enongene, G.N., Isoh, A.V.N. (2024). The mediating role of self-efficacy on students' entrepreneurship education and entrepreneurship intention in Buea Municipality. Business Management and Strategy, 15(2): 352-373. https://doi.org/10.5296/bms.v15i2.22432
[75] Kolvereid, L. (1996). Prediction of employment status choice intentions. Entrepreneurship Theory and Practice, 21(1): 47-58. https://doi.org/10.1177/104225879602100104
[76] Shalihah, F., Alviah, S., Shob'ron, I.A. (2023). The wages in employment relations in the tourism sector in Yogyakarta in justice perspective. Substantive Justice International Journal of Law, 6(2): 138-162. https://doi.org/10.56087/substantivejustice.v6i2.261
[77] Sirakaya, Y. (2024). Psychological and economic dimensions of gig economy and freelancing: Challenges and opportunities in modern working models. International Journal of Social Science Humanity & Management Research, 3(12): 1613-1621. https://doi.org/10.58806/ijsshmr.2024.v3i12n08
[78] Engle, R.L., Dimitriadi, N., Gavidia, J.V., Schlaegel, C., Delanoe, S., Alvarado, I., He, X., Buame, S., Wolff, B. (2010). Entrepreneurial intent: A twelve-country evaluation of Ajzen's model of planned behavior. International Journal of Entrepreneurial Behavior & Research, 16(1): 35-57. https://doi.org/10.1108/13552551011020063
[79] Schlaegel, C., Koenig, M. (2014). Determinants of entrepreneurial intent: A meta-analytic test and integration of competing models. Entrepreneurship Theory and Practice, 38(2): 291-332. https://doi.org/10.1111/etap.12087
[80] Fishbein, M., Ajzen, I. (2010). Predicting and Changing Behavior: The Reasoned Action Approach. Psychology Press. https://doi.org/10.4324/9780203838020
[81] Caliendo, M., Fossen, F., Kritikos, A.S. (2014). Personality characteristics and the decisions to become and stay self-employed. Small Business Economics, 42: 787-814. https://doi.org/10.1007/s11187-013-9514-8
[82] Havitz, M.E., Mannell, R.C. (2005). Enduring involvement, situational involvement, and flow in leisure and non-leisure activities. Journal of Leisure Research, 37(2): 152-177. https://doi.org/10.1080/00222216.2005.11950048
[83] Sanchez-Franco, M.J. (2009). The moderating effects of involvement on the relationships between satisfaction, trust and commitment in e-Banking. Journal of Interactive Marketing, 23(3): 247-258. https://doi.org/10.1016/j.intmar.2009.04.007
[84] Richins, M.L., Bloch, P.H. (1986). After the new wears off: The temporal context of product involvement. Journal of Consumer Research, 13(2): 280-285. https://doi.org/10.1086/209067
[85] Kyle, G., Absher, J., Norman, W., Hammitt, W., Jodice, L. (2007). A modified involvement scale. Leisure Studies, 26(4): 399-427. https://doi.org/10.1080/02614360600896668
[86] Tükel, Y., Akçakese, A., Demirel, M., Torun, G. (2025). The role of leisure involvement in fostering entrepreneurship orientation among Turkish women: A resource-based view. Leisure Studies, 1-16. https://doi.org/10.1080/02614367.2025.2495260
[87] Johnson, B.T., Eagly, A.H. (1989). Effects of Involvement on persuasion: A meta-analysis. Psychological Bulletin, 106(2): 290-314. https://doi.org/10.1037/0033-2909.106.2.290
[88] Thuy, D.C., Quang, N.N., Huong, L.T., Phuong, N.T.M. (2024). The moderating effects of involvement on the relationships between key opinion leaders, customer's attitude and purchase intention on social media. Cogent Business & Management, 11(1). https://doi.org/10.1080/23311975.2024.2400600
[89] Lee, T.H. (2011). How recreation involvement, place attachment and conservation commitment affect environmentally responsible behavior. Journal of Sustainable Tourism, 19(7): 895-915. https://doi.org/10.1080/09669582.2011.570345
[90] Ting, C.T., Mao, Y.H., Huang, Y.S., Yen, Y.W. (2025). Exploring the influence of ecotourism attitudes and involvement on behavioral intentions. Journal of Management World, 2025(4): 34-40. https://doi.org/10.53935/jomw.v2024i4.1146
[91] Ward, P.T., Leong, G.K., Boyer, K.K. (1994). Manufacturing proactiveness and performance. Decision Sciences, 25(3): 337-358. https://doi.org/10.1111/j.1540-5915.1994.tb00808.x
[92] Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM).
[93] Parker, S.K., Sprigg, C.A. (1999). Minimizing strain and maximizing learning: The role of job demands, job control, and proactive personality. Journal of Applied Psychology, 84(6): 925-939. https://doi.org/10.1037//0021-9010.84.6.925
[94] Garner, L.E., Van Kirk, N., Tifft, E.D., Krompinger, J.W., et al. (2017). Validation of the distress tolerance scale-short form in obsessive compulsive disorder. Journal of Clinical Psychology, 74(6): 916-925. https://doi.org/10.1002/jclp.22554
[95] Hulland, J. (1999). Use of Partial Least Squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2): 195-204. http://www.jstor.org/stable/3094025.
[96] DeVellis, R.F., Thorpe, C.T. (2021). Scale Development: Theory and Applications. SAGE Publications Ltd.
[97] Fornell, C., Larcker, D.F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3): 382-388. https://doi.org/10.1177/002224378101800313
[98] Henseler, J., Ringle, C.M., Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43: 115-135. https://doi.org/10.1007/s11747-014-0403-8
[99] Hair, J.F., Ringle, C.M., Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2): 139-152. https://doi.org/10.2753/MTP1069-6679190202
[100] Hayes, A.F. (2013). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. New York: The Guilford Press. https://psycnet.apa.org/record/2013-21121-000.
[101] Hair, J.F., Risher, J.J., Sarstedt, M., Ringle, C.M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1): 2-24. https://doi.org/10.1108/EBR-11-2018-0203
[102] Hajiheydari, N., Delgosha, M.S. (2024). Investigating engagement and burnout of gig-workers in the age of algorithms: An empirical study in digital labor platforms. Information Technology & People, 37(7): 2489-2522. https://doi.org/10.1108/itp-11-2022-0873
[103] Watson, G.P., Kistler, L.D., Graham, B.A., Sinclair, R.R. (2021). Looking at the gig picture: Defining gig work and explaining profile differences in gig workers' job demands and resources. Group & Organization Management, 46(2): 327-361. https://doi.org/10.1177/1059601121996548
[104] McIntyre, N. (1989). The personal meaning of participation: Enduring involvement. Journal of Leisure Research, 21(2): 167-179. https://doi.org/10.1080/00222216.1989.11969797
[105] Liñán, F., Kurczewska, A. (2017). Why are some individuals willing to pursue opportunities and others aren’t? The role of individual values. In Research Handbook on Entrepreneurial Opportunities, pp. 263-284. https://doi.org/10.4337/9781783475445.00019