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Factors that Affect Perceptions of Gig Workers

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11 May 2023

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11 May 2023

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Abstract
Just 2 years ago, 35% of Americans were involved with gig work. According to Statista, over half of Americans will be freelancing through online gig work by 2027. Research on how consumers select gig workers, or taskers, is still mostly underexplored. The purpose of this study is to investigate the potential impact of a tasker's gender and self-presentation in their profile picture on consumer perceptions and choices. We proposed that taskers who have a professional headshot as the profile picture will receive higher ratings on competence, warmth, and trustworthiness than taskers whose profile picture is a selfie. We also proposed that taskers who smile in their profile picture will receive higher ratings on competency, warmth, and trustworthiness than taskers who do not smile. Furthermore, we predicted that professionalism has a stronger positive impact on perceptions of male taskers than on female taskers for our third hypothesis. Lastly, we hypothesized that gender bias exists on gig platforms, such that smiling will have a stronger positive effect on female taskers’ competence, warmth, and trustworthiness than on male taskers. Our study had 292 Kennesaw State University students who were tested online through Qualtrics. Our first and second hypotheses are not supported but our last two have partial support. There is a significant main effect of gender, and we find that male taskers are rated significantly higher on competence, warmth, and trustworthiness, compared to female taskers. Professional photos give male taskers an advantage over professional female taskers and female taskers are punished more for not smiling than male taskers. Future directions include looking into factors such as Race, Age, or background setting could be investigated. Conducting similar tests in a larger and more externally valid setting could yield more concrete results. Continuation into this new area of gig work could lead the way into revolutionizing how freelancers conduct their public appearance or how gig work platforms could handle this new market.
Keywords: 
Subject: Social Sciences  -   Psychology

Factors that Affect Perceptions of Gig-Workers

The term “Gig Economy” was originally created by the former New Yorker editor Tina Brown in 2009 (Hasija, 2021). A gig worker is traditionally defined as an individual who freelances or is contracted by an organization such as TaskRabbit, Uber, or Fiverr to carry out a temporary task, or gig, through their platform. Within the US, the gig economy grew by 33% in 2020 and as of 2021, 35% of Americans are involved in gig work (Zgola, 2022). Additionally, a projected 86.5 million people, or 50.9% of Americans, will be freelancing through an online gig platform by 2027 (Statista Research Department, 2022).
This study focuses on the individual aspects that affect user perceptions when hiring workers on gig platforms such as Fiverr, TaskRabbit, Airbnb, and Uber. The impact of understanding these factors can shape the way these contractors structure their online visual presence and create a more understanding environment for the implicit biases that customers make when hiring others. In this experiment, we manipulated gig workers’ Gender, Professionalism, and Smiling and examined their effects on their perceived attractiveness, competence, warmth, and trustworthiness.
There has been very little research done on factors affecting their perceptions when being hired by potential customers (Keith, 2019, as cited in Spreitzer et al., 2017, p. 480). However, existing research on social perception regarding other online platforms may shed some light on how potential customers perceive gig workers’ profiles and make their hiring decision. For example, a study that focused on the perceptions of smiling on online platforms by comparing smiling and neutral photos found a significant positive effect between smiling on sincerity, sociability, and competence (Reis et al., 1990).
Additionally, it has been found that profile photos of a smiling individual, regardless of gender, are seen as more honest than nonsmiling ones which have a positive correlation on hire likelihood (Kyrs et al., 2023). Smiling individuals are also seen to be more reliable; with a female smiling photograph to be more competent as well (Otta et al., 1994). On the topic of competency, a study conducted on 133 college students in Hong Kong showed a significant positive correlation between smiling and perceived intelligence. The study also showed this correlation to be stronger for the male smiling stimulus than the female smiling stimulus which will be touched on later (Lau, 1982).
Looking at the results between smiling and nonsmiling individuals on an online platform shows a preference between the two. An Airbnb host study was conducted and found that it is not enough to simply have a photo of oneself on the profile, but to have a positive facial expression is key to increasing perceived trust and booking rates as opposed to the absence of positive facial expressions (Banerjee et al., 2022). The presence of a positive facial expression is so important that in a study conducted about Airbnb preference showed that, on average, the second most important quality in an Airbnb booking is facial expression with an importance value of 32.9% following the primary quality of price at 50.4% (Fagerstrøm et al., 2017). Therefore, I hypothesize that taskers with a smiling profile picture should be perceived more positively than those who do not smile in the profile picture (Hypothesis 1).
In addition to facial expressions, the level of professionalism of the profile picture may also impact customers’ impression of the taskers. A study reviewing profile pictures of Airbnb hosts shows a significant positive correlation between photo quality and perceived trustworthiness. Perceived attractiveness was also found to be higher with photo quality which indirectly raised the perceived trustworthiness of the host (Ert & Fleischer, 2020). A study conducted in 2014 investigated the bias between professional photos and unprofessional photos, or selfies, when employers are looking for hire candidates. The results of the study showed evidence towards a preference of professional photos instead of selfies when employers are looking at potential hire candidates (Mazza et al., 2014). Therefore, I propose that taskers whose profile picture looks professional will be perceived more favorably than those who use a selfie (Hypothesis 2).
Gender is an important factor to consider when studying gig worker and the gig economy. Women in the online freelance market have only diminished with time leaving them as a minority in the pool of potential workers. Through a survey in 2020, during the COVID-19 pandemic, researchers found that there was a significant decrease in the number of women in the online work market due primarily for two reasons (Dunn et al., 2021). Firstly, the pandemic magnified the uncertainty of earnings as many lost their jobs and flocked to gig work en masse, making the market vastly more competitive (Zgola, 2022). Secondly, the loss of flexibility in allocating time and resources during the pandemic. Many women reported an inability to work since their kids were at home more often. The traditional gender role of women is to take care of the children and home with the majority in agreeance with this responsibility to this day (Schnurr et al., 2020). Because of this increased constraint, many women have had to give up on their gig work or decrease their activity to support their spouse or family.
As gender plays a significant role in joining the online gig-work market, there also is a presence of gender inequality within the market as well. Previous research shows that the perception of being a woman on TaskRabbit results in a 10% decrease in reviews than a man due to a decrease in perceived competency and capability (Hannák et al., 2017). Furthermore, women are reported to charge less than men by 16.7% for their gig work despite working more part-time than full-time compared to men (Foong & Gerber, 2021).
Based on traditional gender roles, women are expected to have more warmth and trustworthiness while men are expected to be competent and more logical (Heilman, 2012). Thus, it is likely that smiling is more important for female taskers than for their male counterparts because smiling is linked to higher trustworthiness (Schmidt et al., 2012) and warmth (Wang et al., 2017). It has also been found that women, in general, tend to smile more than their male counterpart. In relation to Cohen (1977), effect sizes of 0.20, 0.50, and 0.80 indicate small, medium, and large effects. A medium effect size, d = 0.41, was seen when comparing smiling frequency between men and women (LaFrance et al., 2003). A professional image, on the other hand, may have a stronger positive impact on male taskers because professionalism is associated with authoritativeness and competence as the main two factors with trustworthiness, creativity, and friendliness being the least three influential factors (Cardon & Okoro, 2009).

Methods

Participants

In this study, the sample size was 292 participants. The age of the sample had a mean of 19.16 (SD = 2.42). Of the sample size, 59.9% of participants were female and 38.014% of participants were male. Additionally, 2.1% of participants were Non-Binary/Third Gender. Within the sample size, 43.8% were White/Caucasian, 38.1% were Black/African American, and 18.2% were Other. When asked about their current living situation, 57.5% of participants were living with a roommate(s), 36.6% were living with a family member(s) and 5.8% were other. When asked about their previous experience with Gig Workers, 25.7% of participants had Good Experience, 43.2% had Average Experience, and the remaining 31.2% had no experience.
The sampling method we took was through the PREX/SONA System. The system allowed first-year undergraduate psychology students at Kennesaw State University to take part in the study online through Qualtrics.

Procedure

The online study conducted through Qualtrics on the PREX/SONA System was measured mostly by a Likert scale from 1(Strongly Disagree) to 7(Strongly Agree). The study was a 2x2x2 Factorial ANOVA comparing Male/Female, Smiling/No Smiling, and Professional Photo/Selfie. The study was counterbalanced, and participants were given demographic questions, a Propensity to Trust measure (Schoorman et al., 1996), and a Mini IPIP test (Donnellan et al., 2006) before being split into 1 of 16 different modules for the study. Each module had one of the different possibilities of our factors.
In each module, the participant was shown a profile picture of the Experimental Gig Worker and they would answer scales on their Perceived Physical Attractiveness (McCroskey & McCain, 1974), Perceived Competence and Warmth (Cuddy & Fiske, 2004), and Perceived Trustworthiness (adapted from Ma et al., 2017) before answering how likely they would hire the worker for Cleaning, Shopping, and Moving. Then the participants are shown the Control Gig Worker, or Experimental if it is the counterbalanced version, and they will go through the same process until the end where they will compare the two and choose which they would prefer to hire for the three aforementioned jobs.

Materials

Each study variable was measured on a 7-point Likert scale ranging from strongly disagree to strongly agree.
Perceived Competence (α = .93) was measured with a 4-item scale developed by Cuddy & Fiske (2004). A sample item is “This person is capable. 1(Strongly Disagree) - 7 (Strongly Agree)”.
Perceived Warmth (α = .92) was measured with a 4-item scale developed by Cuddy & Fiske (2004). A sample item is “This person is good-natured. 1(Strongly Disagree) - 7 (Strongly Agree)”.
Perceived Trustworthiness (α = .87) was measured with a 6-item scale developed by Ma et al. (2017). A sample item is “This person will not intentionally harm, overcharge, or scam me. 1(Strongly Disagree) - 7 (Strongly Agree)”.
Control variables include participants’ age, gender, ethnicity, living situation, experience with gig workers, and employment status. In order to rule out the impact of gig workers’ physical attractiveness on how participants would perceive them, we also included perceived physical attractiveness of the gig worker as a control variable, which is measured by a 10-item scale developed by McCroskey & McCain (1974). A sample item is “This person is very well groomed. 1(Strongly Disagree) - 7 (Strongly Agree)”.

Results

Descriptive Statistics

I first conducted descriptive statistical analyses on the studied variables. The means, standard deviations, and Pearson’s correlation coefficients are displayed in Table A1.

Hypothesis Testing

To test the effects of the three proposed factors and their interactions on gig workers’ perceived competence, warmth, and trustworthiness, I conducted a 2 (Gender of gig workers: male vs. Female) x 2 (Smiling: Yes vs. No) x 2 (Professionalism: Professional vs. Selfie) factorial MANCOVA. The covariates include participant’s gender, ethnicity, current living situation, current employment status, previous experience with gig workers, and participant-rated physical attractiveness of the gig worker.
Based on the multivariate test results, gender of the gig worker was significantly related to the outcomes (F = 4.33, Wilks’ Lambda = 0.94, p = .006). The interaction between gig worker gender and smiling was also statistically significant (F = 2.94, Wilks’ Lambda = 0.96, p = .035). However, smiling (F = 0.93, Wilks’ Lambda = 0.99, p = .427), professionalism (F = 0.99, Wilks’ Lambda = 0.24, p = .867), gender x professionalism (F = 0.99, Wilks’ Lambda = 0.42, p = .742) interaction were not statistically significant.
Univariate tests revealed that gig worker gender was a significant predictor of gig workers’ ratings. Specifically, participants perceived the male gig worker to be significantly more competent (Mean difference = .45, p = .001), warmer (Mean difference = .28, p = .041), and more trustworthy (Mean difference = .33, p = .008) than the female gig worker. Smiling gig workers did not score significantly higher on perceived competence (Mean difference = -0.01, p = .910), warmth (Mean difference = -.17, p = .185), and trustworthiness (Mean difference < -0.01, p = .229) than non-smiling ones. Therefore, Hypothesis 1 was not supported. Similarly, there was no significant difference between gig workers with a professional headshot and those with a selfie in terms of perceived competence (Mean difference = -0.08, p = .540), warmth (Mean difference = -0.03, p = .837), and trustworthiness (Mean difference = .02, p = .892). Thus, Hypothesis 2 was not supported.
Furthermore, male gig workers, regardless of whether they have a professional headshot or a selfie as their profile photo, received significantly higher ratings on perceived competence than their female counterparts (Professional: p = .017; Selfie: p = .010). Additionally, participants also rated male gig workers with a professional profile photo as more trustworthy than female workers with a professional photo ((Mean difference = .41, p = .012), but male and female gig workers with a selfie did not receive different ratings on trustworthiness (Mean difference = .25, p = .163). That is, having a professional profile photo gave males a disadvantage regarding trustworthiness, but it did not help female gig workers, partially supporting Hypothesis 3.
There was no significant difference between smiling female and male gig workers in terms of their perceived competence (Mean difference = .34, p = .062) and warmth (Mean difference = .16, p = .388). However, non-smiling male gig workers were rated significantly higher on competence (Mean difference = .57, p = .001) and warmth (Mean difference = .39, p = .032) than non-smiling females. Therefore, not smiling in the profile photo put female gig workers at a disadvantaged position compared to their male counterparts, which partially supports Hypothesis 3. Smiling male workers were perceived as more trustworthy than smiling females (Mean difference = .48, p = .005), but there was no significant difference on trustworthiness between non-smiling male and female workers (Mean difference = .17, p = .295).

Discussion

In this study, we looked at the different factors that could affect hire rates among gig workers based on profile pictures. Our first hypothesis was that Taskers who have a professional headshot as the profile picture will receive higher ratings on competence, warmth, and trustworthiness than taskers whose profile picture is a selfie. Our second hypothesis was that Taskers who smile in their profile picture will receive higher ratings on competency, warmth, and trustworthiness than taskers who do not smile. This was not seen in our results and was not supported. Both were not found to be supported in our findings. However, albeit not initially hypothesized, we found that male taskers received significantly more favorable ratings compared to their female counterparts, indicating potential gender bias on online labor platforms (insert references).
Our findings showed partial support for our third hypothesis that professionalism will have a stronger positive impact on perceptions of male taskers than on female taskers, and our fourth hypothesis that smiling will have a stronger positive effect on female taskers’ competence, warmth, and trustworthiness than those of male taskers. For our third hypothesis, we found that men were seen as more trustworthy than female taskers when both appeared professional while gender on selfie yielded no significant difference between perceived competency, warmth, or trustworthiness between genders.
We found that there was no difference on smiling between genders. However, non-smiling women were punished significantly more than men, thus partially supporting our fourth hypothesis. These findings align with previous studies from Heilman (2012) and Schmidt et al., (2012) that smiling complements typical traits of women such as warmth and trustworthiness.

Limitations and Future Research

In this study, we had limitations in our methodology. Firstly, the sample size may not be large enough for us to detect all the interaction effects we hypothesized. Additionally, our sample came exclusively from Kennesaw State University undergraduates with limited life experiences, which could result in low external validity of our findings. In our online survey, we had two attention check questions to detect if subjects were reading the questions given. The checks were failed by almost a third of the sample. Lastly, we suspect our manipulation of professionalism may not have been strong enough to garner accurate results between headshots and selfies which may have hindered our results.
There is a plethora of future directions to build upon this research. Providing a more empirical format for selfies and headshots could lead to results we had not found. Looking into factors such as race, age, or background setting could be investigated. Conducting similar tests in a larger and more externally valid setting could yield more concrete results. Continuation into this new area of gig work could lead way into revolutionizing how freelancers conduct their public appearance or how gig work platforms could handle this burgeoning market.

Practical Implications

The applications for this research could reveal what is important for gig workers or applicants that utilize profile pictures to appear more trustworthy, warm, or competent. Utilizing the evidence we found, Gig platforms could implement helpful hints to their contractors to gain more use on the platform. Furthermore, these platforms can use this information to counterbalance the gender inequality in the gig market to create a more inclusive environment. The use of these factors has a significant effect on getting hired in the gig economy and it is up to the worker to utilize them.

Appendix A

On a scale from 1(Strongly Disagree) to 7(Strongly Agree), rate your position on the following terms:
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Figure A1. Perceived physical attractiveness (McCroskey & McCain, 1974).
Figure A1. Perceived physical attractiveness (McCroskey & McCain, 1974).
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On a scale from 1(Strongly Disagree) to 7(Strongly Agree), rate your position on the following terms:
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Figure A2. Perceived competence and warmth (Cuddy & Fiske, 2004).
Figure A2. Perceived competence and warmth (Cuddy & Fiske, 2004).
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On a scale from 1(Strongly Disagree) to 7(Strongly Agree), rate your position on the following terms:
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Figure A3. Perceived trustworthiness (adapted from Ma et al., 2017).
Figure A3. Perceived trustworthiness (adapted from Ma et al., 2017).
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Figure A4. Experimental Profile Photos.
Figure A4. Experimental Profile Photos.
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Table A1. Descriptive statistical analyses on the studied variables.
Table A1. Descriptive statistical analyses on the studied variables.
Variable M SD 1 2 3 4 5 6 7 8 9 10
1. Age 19.17 2.415 - -.09 -.07 .13* -.04 -.26** .12* -.07 -.05 -.06
2. Gender 1.64 .522 -.09 - <-.01 -.16** -.16** .02 -.07 .02 -.05 -.04
3. Ethnicity 2.16 1.635 -.07 -.07 - .20** .08 -.02 <.01 -.02 .02 .02
4. Living situation 1.82 .993 .13* -.16** .20** - .10 -.20** .94 -.06 .03 <.01
5. Gig experience 2.80 1.141 -.04 .16** .08 .10 - .11 -.08 -.01 .03 .01
6. Employment status 3.06 1.550 -.26** .02 -.03 -.20** .11 - .04 .06 <-.01 .03
7. Attractiveness 4.19 .673 .12* -.07 <.01 .09 -.08 .04 - .30** .28** .30**
8. Competence 5.19 1.007 -.07 .02 -.02 -.06 -.01 .06 .30** - .54** .67**
9. Warmth 4.78 1.002 -.05 -.05 .02 .03 .03 <-.01 .28** .54** - .63**
10. Trustworthiness 4.94 .899 -.06 -.04 .02 <.01 .01 .03 .30** .67** .63** -
Note. * p < .05, ** p < .01.

References

  1. Bajwa, U.; Gastaldo, D.; Di Ruggiero, E.; Knorr, L. The health of workers in the global gig economy. Glob. Health 2018, 14, 124. [Google Scholar] [CrossRef]
  2. Banerjee, S.; Lens, M.; Pal, A. Put on your sunglasses and smile: The secret of airbnb hosts’ profile photos? Int. J. Hosp. Manag. 2022, 103, 103219. [Google Scholar] [CrossRef]
  3. Cardon, P.W.; Okoro, E.A. Professional characteristics communicated by formal versus casual workplace attire. Bus. Commun. Q. 2009, 72, 355–360. [Google Scholar] [CrossRef]
  4. Cohen, J. The concepts of power analysis. Stat. Power Anal. Behav. Sci. 1977, 1–17. [Google Scholar] [CrossRef]
  5. Cuddy, A.J.; Fiske, S.T.; Glick, P. When professionals become mothers, warmth doesn't cut the ice. J. Soc. Issues 2004, 60, 701–718. [Google Scholar] [CrossRef]
  6. David Schoorman, F.; Mayer, R.C.; Davis, J.H. Empowerment in veterinary clinics: The role of trust in delegation. J. Trust. Res. 2016, 6, 76–90. [Google Scholar] [CrossRef]
  7. Donnellan, M.B.; Oswald, F.L.; Baird, B.M.; Lucas, R.E. The mini-IPIP scales: Tiny-yet-effective measures of the big five factors of personality. Psychol. Assess. 2006, 18, 192–203. [Google Scholar] [CrossRef] [PubMed]
  8. Dunn, M.; Munoz, I.; Sawyer, S. Gender differences and lost flexibility in online freelancing during the COVID-19 pandemic. Front. Sociol. 2021, 6. [Google Scholar] [CrossRef] [PubMed]
  9. Ert, E.; Fleischer, A. What do airbnb hosts reveal by posting photographs online and how does it affect their perceived trustworthiness? Psychol. Mark. 2019, 37, 630–640. [Google Scholar] [CrossRef]
  10. Fagerstrøm, A.; Pawar, S.; Sigurdsson, V.; Foxall, G.R.; Yani-de-Soriano, M. That personal profile image might jeopardize your rental opportunity! on the relative impact of the seller's facial expressions upon buying behavior on Airbnb™. Comput. Hum. Behav. 2017, 72, 123–131. [Google Scholar] [CrossRef]
  11. Foong, E.; Gerber, E. (2021). Understanding gender differences in pricing strategies in online labor marketplaces. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. [CrossRef]
  12. Hannák, A.; Wagner, C.; Garcia, D.; Mislove, A.; Strohmaier, M.; Wilson, C. (2017). Bias in online freelance marketplaces. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. [CrossRef]
  13. Hasija, S. Will the Pandemic Push Knowledge Work into the Gig Economy? Harvard Business Review. 9 September 2021. Available online: https://hbr.org/2020/06/will-the-pandemic-push-knowledge-work-into-the-gig-economy (accessed on 15 February 2023).
  14. Heilman, M.E. Gender stereotypes and workplace bias. Res. Organ. Behav. 2012, 32, 113–135. [Google Scholar] [CrossRef]
  15. Keith, M.G.; Harms, P.; Tay, L. Mechanical turk and the Gig Economy: Exploring differences between gig workers. J. Manag. Psychol. 2019, 34, 286–306. [Google Scholar] [CrossRef]
  16. Krys k Hansen, K.; Xing, C.; Espinosa, A.D.; Szarota, p.; Morales, M.F. It is Better to Smile to Women: Gender Modifies Perception of Honesty. Available online: https://www.semanticscholar.org/paper/It-is-better-to-smile-to-women%3A-gender-modifies-of-Krys-Hansen/b92fcf4e487df3e317669b408fa976f7e00927d0 (accessed on 23 March 2023).
  17. LaFrance, M.; Hecht, M.A.; Paluck, E.L. The contingent smile: A meta-analysis of sex differences in smiling. Psychol. Bull. 2003, 129, 305–334. [Google Scholar] [CrossRef] [PubMed]
  18. Lau, S. The effect of smiling on person perception. J. Soc. Psychol. 1982, 117, 63–67. [Google Scholar] [CrossRef]
  19. Ma, X.; Hancock, J.T.; Lim Mingjie, K.; Naaman, M. (2017). Self-disclosure and perceived trustworthiness of Airbnb host profiles. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. [CrossRef]
  20. Mazza, F.; Da Silva, M.P.; Le Callet, P. (2014). Would you hire me? selfie portrait images perception in a recruitment context. SPIE Proceedings. [CrossRef]
  21. McCroskey, J.C.; McCain, T.A. The measurement of Interpersonal Attraction. Speech Monogr. 1974, 41, 261–266. [Google Scholar] [CrossRef]
  22. Otta, E.; Lira, B.B.; Delevati, N.M.; Cesar, O.P.; Pires, C.S. The effect of smiling and of head tilting on person perception. J. Psychol. 1994, 128, 323–331. [Google Scholar] [CrossRef]
  23. Published by Statista Research Department, & 30, S. (2022, September 30). Gig economy: Projected gross volume 2023. Statista. Available online: https://www.statista.com/statistics/1034564/gig-economy-projected-gross-volume/ (accessed on 22 March 2023).
  24. Reis, H.T.; Wilson, I.M.; Monestere, C.; Bernstein, S.; Clark, K.; Seidl, E.; Franco, M.; Gioioso, E.; Freeman, L.; Radoane, K. What is smiling is beautiful and good. Eur. J. Soc. Psychol. 1990, 20, 259–267. [Google Scholar] [CrossRef]
  25. Schmidt, K.; Levenstein, R.; Ambadar, Z. Intensity of smiling and attractiveness as facial signals of trustworthiness in women. Percept. Mot. Ski. 2012, 114, 964–978. [Google Scholar] [CrossRef] [PubMed]
  26. Schnurr, S.; Zayts, O.; Schroeder, A.; Le Coyte-Hopkins, C. ‘it's not acceptable for the husband to stay at home': Taking a discourse analytical approach to capture the gendering of work. Gend. Work. Organ. 2020, 27, 414–434. [Google Scholar] [CrossRef]
  27. Wang, Z.; Mao, H.; Jessica Li, Y.; Liu, F. Smile big or not? effects of smile intensity on perceptions of warmth and competence. J. Consum. Res. 2016, 43. [Google Scholar] [CrossRef]
  28. Zgola, M. Council Post: Will the Gig Economy Become the New Working-Class Norm? Forbes. 9 November 2022. Available online: https://www.forbes.com/sites/forbesbusinesscouncil/2021/08/12/will-the-gig-economy-become-the-new-working-class-norm/ (accessed on 15 February 2023).
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