consideration if:
1. They answered less than five questions.
2. They failed both “attention check” questions.
3. They responded to questions on experience using a freelancing platform
inconsistently (e.g., they reported that they had used a freelancing platform before, then later reported that they had never used a freelancing platform).
4. They provided answers to the open-ended questions that did not make sense in context.
This process resulted in 402 responses being removed, leaving a total of 658 legitimate responses for the analysis. IBM’s SPSS statistics 22 was used to analyse the responses, and the tables come from the SPSS output. Unless otherwise noted, all Likert scale questions are reported on a scale from 1 to 5.
7.3 Descriptive Statistics Respondents
61
Collector Frequency Percent
Web Link 1 Facebook 49 7.4
The majority of responses came from MTurk (n = 602), with Facebook making up most of the other responses (n = 49).
Gender Frequency Percent
Male 325 49.4
Female 247 37.5
Missing 86 13.1
Total 658 100.0
Table 4: Survey results; Gender Distribution
About half of the respondents reported their gender as male (49.4%). Nearly three out of four reported that they were female (37.5%) and the rest did not indicate a gender (13.1%).
Age Range Frequency Percent
Under 18 1 .2
Table 5: Survey results; Age Range
The majority of respondents fell within the range of 18 to 39 (63.0%). The largest group was the 25 to 29 group (21.9%).
Country Frequency Percent
Algeria 1 .2
62
Table 6: Survey results; Respondents country of origin
63 The majority of respondents indicated the United States (56.4%) or India (7.9%) as the country in which they resided, with another 4.1% from Germany, 2.3% from Canada, and 2.1% from the United Kingdom. About 13% did not report what country they resided in.
Independent Worker Status Frequency Percent
No 247 37.5
Yes 326 49.5
Missing 85 12.9
Total 658 100.0
Table 7: Survey results; Independent worker status
Nearly half of respondents reported that they were an independent worker of some kind, whether full-time, part-time, or to complement another job (49.5%). About one out of every eight respondents (12.9%) did not respond to the question.
Experience with Cryptocurrency and Blockchain Assets
Experience with Cryptocurrency Frequency Percent
No 451 68.5
Yes 186 28.3
Not sure 17 7.1
Missing 4 .6
Total 334 100.0
Table 8: Survey results; Experience with Cryptocurrency
Only 28.3% of respondents reported that they had used cryptocurrency before, while nearly 69% indicated that they had not.
Experience with Blockchain Assets Frequency Percent
No 455 69.1
Yes 172 26.1
Not sure 23 3.5
Missing 8 1.2
Total 334 100.0
Table 9: Survey results; Experience with Blockchain Assets
Similarly, just over a quarter of respondents (26.1%) reported that they had invested in blockchain assets before, while about 69% had not.
Experience with Freelancing Platforms
64
Experience with Freelancing Platform(s) Frequency Percent
Both 66 10.0
Buyer 145 22.0
Seller 202 30.7
Neither 245 37.2
Total 658 100.0
Table 10: Survey results; Experience with Freelancing Platform(s)
Most respondents also reported that they had used a freelancing platform before, as a buyer, a seller, or both (62.7%). Nearly a third had only used a platform as a seller (30.7%), while 22.0% had only used a platform as a buyer. About 10% had used a platform as both a buyer and a seller.
Table 11: Survey results; Threat of bad sellers
Generally, respondents felt that bad sellers (sellers who do not deliver on their promises) were only a small problem (54.4%). About a quarter (28.0%) felt that bad sellers were a big problem, and only 16.4% felt they were not a problem at all.
Bad Purchasers Frequency Percent
Not a problem 103 15.7
Small problem 296 45.0
Big problem 256 38.9
Missing 3 .5
Total 658 100.0
Table 12: Survey results; Threat of bad purchasers
On the other hand, more respondents felt that bad purchasers (purchasers who would reject competent work or refuse to pay after receiving service) were a big problem (38.9%).
Worry About Security Frequency Percent
Not at all worried 29 4.4
Not very worried 106 16.1
Somewhat worried 111 16.9
Very worried 166 25.2
65
Extremely worried 145 22.0
Missing 101 15.3
Total 658 100.0
Table 13: Survey results; worry about security
Nearly half of all respondents were very worried (25.2%) or extremely worried (22.0%) about the security of their transactions on freelancing platforms. Only 4.4% were not at all worried.
Worry as Purchaser Frequency Percent
Not at all worried 13 2.0
Not very worried 40 6.1
Somewhat worried 98 14.9
Very worried 190 28.9
Extremely worried 156 23.7
Missing 161 24.5
Total 658 100.0
Table 14: Survey results; worry as purchaser
Similarly, over half of respondents reported that they were very worried (28.9%) or extremely worried (23.7%) about the quality of the service they will receive as buyers on a freelancing platform. Only 2.0% were not at all worried.
Value of Features
Table 15: Survey results; value of features
Generally, respondents found ratings and reviews (mean = 4.24) and social security benefits (mean = 4.23) the most valuable in terms of SKILLITY’s features. On the other hand, respondents were least interested in customer relationship management (CRM) tools (mean = 3.86).
About half of respondents felt that the ratings and reviews (47%) and social security benefits (52%) were extremely valuable. About three-quarters of respondents felt that the escrow
66 function (73%), matchmaking with local providers (75%), certification of service quality (75%), and low transaction fees (73%) were either very valuable or extremely valuable.
Comfort Using Cryptocurrency
Figure 3: Comfort using Cryptocurrency
Respondents are generally at least somewhat comfortable using cryptocurrency, although a little over a third (36%) are hesitant about accepting a cryptocurrency as payment (not at all comfortable or not very comfortable with it).
Question N Minimum Maximum Mean Std. Deviation
Interest Selling 657 1.0 5.0 3.170 1.1410
Interest Buying 653 1.0 5.0 2.866 1.2597
Interest Investing 651 1.0 5.0 2.348 1.2201
Appeal of Coop Ownership 656 1.0 5.0 3.710 .9794
Ease of Finding Sellers 658 1.0 5.0 2.860 .9765
Table 16: Survey results; interest indication
Respondents are somewhat interested in selling on SKILLITY (mean = 3.17), but less interested in buying from (mean = 2.87) or investing in SKILLITY (mean = 2.35). They also generally find the idea of cooperative ownership very appealing (mean = 3.71). Respondents agree that it is not a huge problem finding local sellers, but that it is not easy either (mean = 2.86).
Question N Minimum Maximum Mean Std. Deviation
Brand Overall 600 2.0 5.0 3.998 .7501
Table 17: Survey results; comfort with Brand, Logo and Tokens 12%
Not at all comfortable Not very comfortable Somewhat comfortable Very comfortable Extremely comfortable
67 Overall, respondents were very positive about the brand as described (mean = 4.00). They also responded positively to the logo (mean = 3.87). However, respondents were only somewhat likely to exchange (mean = 2.73) or pay in HEART tokens (mean = 2.74), and a bit less likely to purchase (mean = 2.65) or accept HEART tokens as payment (mean = 2.66).
Skills & Services Respondents Would Consider Purchasing on SKILLITY
Skill/Service Frequency Percent
Caretaking 147 22.3
Cleaning 268 40.7
Custom-made Art 143 21.7
Handyman Service/Repairs 280 42.6
Live Music 92 14.0
Photography 182 27.7
Special Delivery and Transportation 182 27.7
Tour Guide 99 15.0
Translation 119 18.1
Tutoring or Teaching 218 33.1
Other 15 2.3
Table 18: Survey results; demanded services
A little over a third of respondents would consider using SKILLITY to purchase cleaning (40.7%), handyman/repair (42.6%), or tutoring/teaching services (33.1%). Fewer indicated that they might use SKILLITY to purchase live music (14.0%) or tour guide (15.0%) services.
About 2% indicated that they might purchase other sills that involved office assistance, tax and financial services, legal guidance, graphic design, job finding, picking up/dropping off people, planning small activities, software skills and services, fitness and sport training/coaching, and voiceover artistry.
68 7.4 T-Tests
Gender and value of features
Group Statistics
Gender N Mean Std. Deviation Std. Error Mean
Mean Value Male 316 4.0212 .65103 .03662
Female 240 4.1762 .63545 .04102
Table 20: t-test group statistics
A t-test to assess differences between males and females on the overall value of SKILLITY’s features revealed that females were slightly more likely to feel the features were valuable than males (p = .005).
Independent Samples Test Levene's Test
for Equality of
Variances t-test for Equality of Means
F Sig. t df
Table 19: Independent Samples Test
69 Gender and Comfort/Likelihood of Using Cryptocurrency/HEART Tokens
Table 21: t-test with gender differences; Independent Samples Test Independent Samples Test
Levene's Test for Equality of
Variances t-test for Equality of Means
F Sig. t df
ComfortPayingCrypto Equal variances assumed 3.793 .052 5.364 568 .000 .5513 .1028 .3495 .7532
Equal variances not assumed 5.461 557.947 .000 .5513 .1010 .3530 .7496
ComfortAcceptingCrypto Equal variances assumed .449 .503 5.723 569 .000 .6161 .1077 .4047 .8276
Equal variances not assumed 5.800 552.354 .000 .6161 .1062 .4075 .8248
LikelyPayInHEARTTokens Equal variances assumed .004 .950 3.880 565 .000 .4005 .1032 .1978 .6033
Equal variances not assumed 3.904 536.733 .000 .4005 .1026 .1990 .6021
70
Table 22: t-test with gender differences; Independent Samples Test
However, males were more likely to feel comfortable paying in and accepting cryptocurrency, exchanging HEART token, accepting HEART tokens as payment, and making payments in HEART tokens. In some cases, the difference was relatively large (e.g., mean comfort accepting cryptocurrency as payment for males was 3.090, but only 2.538 for females).
Experience with Blockchain and Comfort/Likelihood of Using Cryptocurrency/HEART Tokens
Levene's Test for Equality of
Variances t-test for Equality of Means
F Sig. t df
Equal variances not assumed -3.416 364.924 .001 -.2771 .0811
ComfortPayingCrypto Equal variances assumed 24.391 .000 -13.569 622 .000 -1.3331 .0983
Equal variances not assumed -14.774 363.462 .000 -1.3331 .0902
ComfortAcceptingCrypto Equal variances assumed 9.706 .002 -13.864 622 .000 -1.4341 .1034
Equal variances not assumed -14.684 345.198 .000 -1.4341 .0977
LikelyPurchaseHEARTTokens Equal variances assumed 2.884 .090 -6.981 551 .000 -.7453 .1068
Equal variances not assumed -7.080 248.718 .000 -.7453 .1053
LikelyExchangeHEARTTokens Equal variances assumed 1.932 .165 -8.721 561 .000 -.9685 .1111
Equal variances not assumed -8.903 260.505 .000 -.9685 .1088
LikelyAcceptHEARTTokens Equal variances assumed 2.846 .092 -7.679 563 .000 -.8637 .1125
Equal variances not assumed -7.851 260.629 .000 -.8637 .1100
LikelyPayInHEARTTokens Equal variances assumed 2.708 .100 -7.021 558 .000 -.8039 .1145
Equal variances not assumed -7.207 258.247 .000 -.8039 .1115
Table 23: t-test Blockchain experience and potential usage Group Statistics
Gender N Mean Std. Deviation Std. Error Mean
Appeal of Coop Ownership Male 323 3.895 1.0252 .0570
Female 247 3.838 .9096 .0579
ComfortPayingCrypto Male 323 3.090 1.2834 .0714
Female 247 2.538 1.1215 .0714
ComfortAcceptingCrypto Male 324 2.985 1.3272 .0737
Female 247 2.368 1.2021 .0765
LikelyPurchaseHEARTTokens Male 317 2.713 1.1541 .0648
Female 243 2.523 1.1109 .0713
LikelyExchangeHEARTTokens Male 323 2.892 1.2451 .0693
Female 247 2.462 1.1641 .0741
LikelyAcceptHEARTTokens Male 324 2.790 1.2364 .0687
Female 247 2.470 1.1748 .0748
LikelyPayInHEARTTokens Male 322 2.894 1.2411 .0692
Female 245 2.494 1.1861 .0758
71
ComfortPayingCrypto No 454 2.520 1.1425 .0536
Yes 170 3.853 .9463 .0726
ComfortAcceptingCrypto No 453 2.355 1.1897 .0559
Yes 171 3.789 1.0472 .0801
LikelyPurchaseHEARTTokens No 412 2.432 1.1020 .0543
Yes 141 3.177 1.0709 .0902
LikelyExchangeHEARTTokens No 418 2.452 1.1646 .0570
Yes 145 3.421 1.1160 .0927
LikelyAcceptHEARTTokens No 420 2.419 1.1808 .0576
Yes 145 3.283 1.1286 .0937
LikelyPayInHEARTTokens No 417 2.511 1.1972 .0586
Yes 143 3.315 1.1347 .0949
Table 24: t-test Blockchain experience and potential usage; group statistics
As suggested in other analyses, those who had experience investing in blockchain currencies were more likely to find cooperative ownership appealing, to be more comfortable in both paying with and accepting cryptocurrency, and to use HEART tokens (whether purchasing, exchanging, accepting, or paying with them).
Gender and Interest in Buying, Selling, and Investing in SKILLITY/HEART Tokens
Levene's Test for
Equality of Variances t-test for Equality of Means
F Sig. t df
Equal variances not assumed 2.377 502.203 .018 .2330 .0980
Interest Buying Equal variances assumed 1.635 .202 .433 565 .665 .0462 .1067
Equal variances not assumed .429 505.124 .668 .0462 .1077
Interest Investing
Equal variances assumed .797 .372 2.825 564 .005 .2929 .1037
Equal variances not assumed 2.850 541.342 .005 .2929 .1028
Table 25: t-test gender and investing interest
72
Table 26: t-test gender and investing interest, group statistics
Gender also had a significant effect on interest in selling and investing in SKILLITY and HEART tokens. For both cases, males were more likely than females to report interest (p = .016 and p
= .005, respectively); however, males and females reported the same interest in buying (p = .665).
7.5 ANOVA
Experience with Blockchain/Cryptocurrency and Feelings About Brand Overall
ANOVA Lower Bound Upper Bound
No 427 3.958 .7680 .0372 3.885 4.031 1.0 5.0
Yes 146 4.171 .6471 .0536 4.065 4.277 3.0 5.0
Total 573 4.012 .7443 .0311 3.951 4.073 1.0 5.0
Table 28: ANOVA Descriptives
The results of an Analysis of Variance (ANOVA) show that those who have invested in blockchain assets before are significantly more likely to feel positive about the brand overall (p = .003). The difference is relatively small, but those who have invested reported feeling slightly more positive (mean = 4.17) than those who have not (mean = 3.96).
73 7.6 Regression
Several regression analyses were conducted to determine the relationships between variables, including likelihood to use SKILLITY. This variable was collected on a scale from 0 (not likely) to 2 (likely)
Gender and Likelihood of Using SKILLITY
Coefficientsa
Table 29: Regression Coefficients, Gender and usage
Gender is significant in predicting whether someone can see themselves using SKILLITY (p
= .001), although the variance explained is small (R-square = .018). Males are more likely to indicate that they would use SKILLITY than females.
Age Range and Likelihood of Using SKILLITY
Analysis of Variance
Table 30: Regression Coefficients, Age Range and usage
74 Age Range is significant in predicting whether someone can see themselves using SKILLITY (p
= .018), such that younger individuals tend to be more likely to see themselves using SKILLITY;
however, the effect is very small (-.025 points for each “unit” of age range).
Independent Worker and Likelihood of Using SKILLITY
Analysis of Variance
Table 31: Regression Coefficients, Freelancing status and usage
Whether someone is a freelancer, self-employed, or small business owner (or other independent worker) is significant in predicting whether they can see themselves using SKILLITY or not (p
< .001). Those who are independent workers are more likely to report that they would consider using SKILLITY.
Experience with Cryptocurrency and Likelihood of Using HEART Tokens
Past Investment in Blockchain Assets
Likelihood of… Overall Model Variable
F-value p-value R-square t-value p-value
Purchasing HEART Tokens 48.74 <.001 .081 6.98 <.001
Exchanging HEART Tokens 76.06 <.001 .119 8.72 <.001
Accepting HEART Tokens as Payment 58.97 <.001 .095 7.68 <.001
Making Payments in HEART Tokens 49.29 <.001 .081 7.02 <.001
Table 32: Experience with Blockchain influencing the likelihood of token usage
75 Those who have invested in blockchain assets in the past are more likely to purchase HEART tokens than those who have not (p < .001). They are also more likely to exchange HEART tokens (p < .001), accept HEART tokens as payment (p < .001), and make payments in HEART tokens (p < .001).
Past Use of Cryptocurrency
Likelihood of… Overall Model Variable
F-value p-value R-square t-value p-value
Purchasing HEART Tokens 30.71 <.001 .052 5.54 <.001
Exchanging HEART Tokens 54.36 <.001 .087 7.37 <.001
Accepting HEART Tokens as Payment 35.54 <.001 .058 5.96 <.001
Making Payments in HEART Tokens 51.91 <.001 .084 7.20 <.001
Table 33: Experience with Cryptocurrency influencing the likelihood of token usage
Just as those with experience investing in blockchain assets are more likely to use HEART tokens than those without, the same is true of those with experience using cryptocurrency in the past (p
< .001).
7.7 Logit Model
Logistic regression (Logit Model) is used to model the relationship between the binary dependent variable and the independent variables. This analysis is undertaken in order to create a model (a regression formula) that can predict with at least some degree of accuracy which outcome is likely to follow a certain set of variables. In this case, we are attempting to determine whether someone is likely to use SKILLITY or not based on several demographic variables, experience with cryptocurrency and freelancing platforms, and attitudes about SKILLITY. The Likelihood to use SKILLITY is measured against all alternative solutions to offer or book services. These alternative methods include competitor platforms, local service companies or analogue ways to book freelancers (in person) and were not further specified in the questionnaire and analysis. For this
76 analysis, the likelihood of using SKILLITY was coded as 0 and 1, with “No” and “Maybe” coded as 0 and “Yes” coded as 1.
Predicting Likelihood to Use SKILLITY
Model Summary
Step -2 Log likelihood
Cox & Snell R Square
Nagelkerke R Square
1 592.888a .197 .271
a. Estimation terminated at iteration number 5 because parameter estimates changed by less than .001.
Table 34: Logit Model Summary Hosmer and Lemeshow Test
Step Chi-square df Sig.
1 14.933 8 .060
Table 35: Hosmer and Lemeshow Test Classification Tablea
Table 36: Logit Model Classification Table Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 1a Independent Worker .312 .213 2.144 1 .143 1.366
EverInvestedBlockchain .498 .248 4.035 1 .045 1.645
AppealofCoopOwnership .672 .123 29.617 1 .000 1.957
EverUsedBuyingorSelling 1.098 .229 23.050 1 .000 2.999
ComfortAcceptingCrypto .295 .089 11.069 1 .001 1.342
Constant -5.152 .580 78.892 1 .000 .006
Variable(s) entered on step 1: Independent Worker, EverInvestedBlockchain, EverUsedBuyingorSelling, AppealofCoopOwnership, ComfortAcceptingCrypto.
Table 37: Logit Model Variables
The best logistic regression model to predict likelihood to use SKILLITY uses the respondent’s answers on the following questions as independent variables:
• Whether the respondent is an independent worker or not
• Whether the respondent has ever invested in blockchain currency before
77
• Whether the respondent has ever used a freelancing platform as a buyer or seller before
• The appeal of cooperative ownership
• The comfort level of the respondent in accepting cryptocurrency as payment
All five are significant (p < .05) and the model predicts likelihood to use SKILLITY correctly 73.6% of the time. This means that we can be quite certain that these five variables have an impact on reported likelihood to use SKILLITY.
The Hosmer and Lemeshow Test is not significant at the p < .05 level, indicating that the model’s coefficients are not equal to zero (i.e., that the variables do have some impact on likelihood to use SKILLITY).
The coefficients show that for each response, the higher the value, the more likely the respondent is to use SKILLITY. For example, since the coefficient is positive for the experience with blockchain variable (EverInvestedBlockchain), this indicates that those who have previously invested in blockchain (variable value = 1) are more likely to use SKILLITY than those who have not (variable value = 0). The p-value for this coefficient is .045, making it a statistically significant finding.
However, although the model is significant, it explains a relatively small portion of the variance (Cox & Snell R square = .197; Nagelkerke R square = .271), indicating that this model explains an estimated 20% of the variance in likelihood to use SKILLITY. This suggests that there are other, unknown factors that are also important in predicting likelihood to use SKILLITY.
78 7.8 Cluster Analysis
Hierarchical Cluster Analysis
First, a hierarchical cluster analysis was conducted. However, the results were not entirely conclusive (see Appendix A for results). To further investigate the clusters in this data, several k-means cluster analyses were conducted and compared.
K-Means Cluster Analysis
Seven separate k-means cluster analyses were conducted, and the results compared. The analyses specified a different number of clusters each time, determined to be reasonable solutions to the problem of customer segmentation:
To determine which solution best fit the data, the sum of the mean error and the sum of the distance from the center of the cluster were calculated and graphed. The researcher used the “elbow method”
to determine which solution was best.
Figure 4: Sum of the Distance from the Cluster Center by Number of Clusters 650
79 This chart was helpful, but not conclusive. It seemed that the elbow occurred around the 6- or 7-cluster solutions but this was not definitive. Next, the researcher graphed the sum of the mean error to determine again where the “elbow” appeared.
Figure 5: Sum of the Mean Error by Number of Clusters
This chart was more conclusive on where the elbow appeared. Around the 6-cluster mark, the points levelled out to around 0.7. This suggested that the 6-cluster solution may be the solution that best fit the data.
K-Means Cluster Analysis – 6 Clusters
Once narrowed in on the 6-cluster solution, the next step was to make sure the 6 clusters were practical representations of customer segments.
The table below was put together to compare the clusters on all relevant variables. It shows some distinct differences between the clusters, on everything from demographics to experience with freelancing and cryptocurrency to the perceived value of SKILLITY’s benefits and likelihood to use SKILLITY and/or HEART tokens.
0 0.5 1 1.5 2 2.5
0 1 2 3 4 5 6 7 8 9
Sum of the Mean Error
80
6-Cluster Solution
Cluster Name Enthusiasts Sellers,
not Buyers Dual Users Accepting
Crypto Sceptics Dual Users Not
Accepting Crypto Non-Users
Ease of Finding Local Sellers (Higher number = greater difficulty)
3.1 3 3.1 3 2.9 3.1
Worry about Security of
Transactions
3.6 3.5 3.7 2.8 3.4 3.6
Worry about Quality of Work
as a Purchaser
3.9 3.8 3.8 3.6 4.2 3.9
81
Likelihood of Paying in HEART
Tokens
4.1 2.6 3.3 1.5 1.6 3
How Problematic are Bad Sellers? (Lower number = less of a problem)
82
Due to Bad Experiences Worried about Freelancing and
Feature 3 Mostly Male
and Early 30s Mostly Male
and Late 20s Mostly Male and Late
20s Half Male and
83 Services each cluster would
consider purchasing
Service 1 Tutoring or
teaching (49%) Handyman or
Repairs (44%) Cleaning (50%) Handyman or
Repairs (44%) Handyman or Repairs
(57%) Cleaning (43%)
Service 2 Cleaning (47%) Photography
(30%) Handyman or Repairs
(50%) Cleaning (40%) Cleaning (56%) Handyman or
Teaching (26%) Special Delivery and
Transportation (36%) Tutoring or Teaching (30%) Table 38: k-means 6-Cluster Overview
For a full listing of the services that each cluster would be interested in purchasing, see the table below.
Cluster Caretaking Cleaning Custom Art Handyman
or Repairs Live
Music Photography
Special Delivery and
Transportation Tour
Guide Translation Tutoring or
Teaching Other
84 The results of the cluster analysis indicated that there are six distinct groups of respondents:
1. Enthusiasts: those who are highly open to trying SKILLITY. They are generally experienced freelancers and many of them have used cryptocurrency and/or invested in blockchain assets before. They are optimistic about SKILLITY and do notbelieve that bad buyers or sellers are much of a problem in freelancing platforms. This group is the most likely of all groups to report intentions to use HEART tokens. They are mostly male (68%) and the largest age group is 30 – 34. According to the in chapter 5.1 presented McKinsey segmentation of independent workers, this cluster can best be described as “Free agents”. These respondents are experienced and professional freelancers. Professionality and economic independence can be expected, due to their high degree of blockchain experience and cryptocurrency usage.
2. Sellers: those who are interested in selling on SKILLITY but are unlikely to make purchases.
They are also experienced freelancers and at least somewhat familiar with cryptocurrency.
They are interested in hosting more skills or services than the other groups (mean = 3.2 skills) and they find low transaction fees to be the most valuable feature (mean = 4.2). They are also mostly male (58%) and are most often in their late 20s. According to the in chapter 5.1
presented McKinsey segmentation of independent workers, this cluster can best be described as “Reluctants”. These very experienced freelancers highly value monetary features and social security. Moreover, they rather prefer to be paid in local currency then in
presented McKinsey segmentation of independent workers, this cluster can best be described as “Reluctants”. These very experienced freelancers highly value monetary features and social security. Moreover, they rather prefer to be paid in local currency then in