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Overview of Attempts to Measure The Gig Economy with Considering The Role of Data in Making Managerial Decisions Cover

Overview of Attempts to Measure The Gig Economy with Considering The Role of Data in Making Managerial Decisions

Foundations of Management
Volume 16 (2024): Issue 1 (January 2024)
By:   
Open Access
|Dec 2024
Figures & tables

Figures & Tables

Figure 1.

Current and expected value of the global gig economy market in 2023–2028 (in USD billions)

(Source: Own compilation based on Business Research Insights data1)

Figure 2.

Chosen countries' share of employee participation in the global platform economy (2017–2021)

(Source: Own compilation based on Ostoj, 2020, p.350)

Figure 3.

Activity of gigers in chosen European countries (ever, during the last year, month, and week)

(Source: Own compilation based on Piasna, Zwysen and Drahokoupil, 2022, p.16)

Table 1.

Data regarding gig economy in Europe – variance

(Source: Own compilation based on Piasna, Zwysen and Drahokoupil, 2022, p.16)

CountryAntytime (a)At least once during the last year (b)At least once during the last month (c)At least once during the last week (d)Part of total income €
Austria28.10%17.10%10.80%5.10%2.30%
Bulgaria31.20%19.10%9.80%5.40%2.90%
The Czech Republic33.80%20.10%13.60%8.80%3.60%
Estonia24.40%15.00%8.60%4.90%2.30%
France25.90%16.10%11.50%6.90%2.60%
Germany30.50%16.90%11.20%5.70%2.30%
Greece27.50%15.70%9.90%3.50%2.50%
Hungary20.90%13.30%9.60%3.20%4.60%
Ireland31.40%18.70%13.20%6.50%4.30%
Italy25.00%12.40%8.90%5.30%2.40%
Poland37.30%19.40%7.80%5.20%4.10%
Romania19.20%9.90%4.90%3.30%1.50%
Slovakia43.30%25.20%14.30%10.00%3.60%
Spain33.60%18.60%10.40%5.10%2.50%
------
Average29.44%16.96%10.32%5.64%2.96%
Variance0.00414810.00141720.00060740.00037560.0000839
Std deviation0.06440570.03764530.02464570.01938120.0091619
Table 2.

Selected methods for measuring the scale of activity in the gig economy

(Source: Own compilation based on: Murtin, 2021)

Research methodShort descriptionStrengthsWeaknesses
Information and communication technologies (ICT) research (ICT usage surveys)Computer, personal, or phone surveys

  • Good data comparability

  • Research conducted mainly in countries where the gig economy market is highly developed

  • Small size of the research sample

  • Much of the existing data comes from the USA, which is quite unique in terms of its labor market, characterized by relatively low levels of employment stability and a large number of gig workers. As a result, attempting to apply the results obtained for the USA to the global labor market may lead to incorrect conclusions

  • Research implementation is limited to a few selected countries

  • Risk of subjective replies from free lancers

Web scrapingThe process of data extraction which involves collecting information from online resources for later analysis. This data can be processed using big data techniques

  • Possibility of collecting data in real time

  • Possibility of comparative analysis of data in time

  • The issue of ethics is debatable

  • Legal data collection via web scraping requires consent from individual users. This means that research conducted using this method may not include freelancers who do not consent to the analysis of their data

Tax dataAnalysis of data collected by public administration, which is facilitated by the use of ICT systems

  • Generally, the study applies to all participants earning income in a given country (although it does not include people operating in the gray market)

  • Focusing solely on numerical data, completely disregarding qualitative data. Such a study does not take into account, for example, issues related to the type of platforms used or technological development

  • Differences between countries resulting from different tax solutions. This factor may seriously hamper the comparative analysis of data between individual countries

Big data analysisAnalysis of large data sets enables ex post research and ex ante estimation of future phenomena, thanks to usage of forecasting methods and technique known as data science

  • Wide possibilities of data analysis and prediction

  • Difficulties in obtaining complete data

  • The data analysis process may be time-consuming

  • Focus on quantitative data, difficulty in analyzing qualitative information

Table 3.

Data regarding gig economy and gigers in making managerial decisions

(Source: Own compilation based on: Freelancing w Polsce 2023, Useme Report; UK HM Government, The experiences of individuals in the gig economy; Ernst&Young, GIG on, Nowy Ład na rynku pracy)

EntityWhere data can be used by management staff?
UsemeHR planning, adaptation to project requirements
Analysis of employment costs, adjustment of salary strategies
Identification of market areas, analysis of potential clients
Assessment of employee competencies
Evaluation of marketing effectiveness, analysis of the job market
Ernst&YoungInformation useful for shaping the company's legal strategy
Identification of areas for technology investment
Better understanding of gig workers’ expectations
Analysis of the employment structure in the company
Anticipation of market trends
Financial planning, adjustment of compensation strategies
Assessment of cost-effectiveness of employing gig workers
UK GovernmentEvaluation of gigers’ skills
HR planning, adjustment of recruitment strategy
Identification of market areas for expansion
Analysis services cost
Understanding the expectations of gig workers
Assessment of the effectiveness of recruitment platforms
Adjustment of compensation and employment condition strategies
Table 4.

Examples of measuring the size of the gig economy

(Source: Own compilation based on: Ostoj, 2020, pp.34-35)

YearResearch subjectResearch areaPros and cons of the chosen method
2009–2010Activity of freelancers registered on a selected digital platform (Amazon Mechanical Turk)Amazon Mechanical Turk (digital platform)Amazon Mechanical Turk (digital platform)
2010Individual interviews conducted with analysts, journalists, managers, entrepreneursGig economy in IT and internet marketingPros: In-depth individual interviews
Cons: Exclusion of freelancers themselves, focusing on managers’, etc. point of view
2009–2012Activity of freelancers registered on a selected digital platform (Upwork)Upwork (digital platform)Pros: Study conducted in different countries
Cons: Limited exclusively to one digital intermediary platform (Upwork)
2013Expert interviews with representatives of firms offering online outsourcing servicesFreelancers working onlinePros: Interviews conducted with experts
Cons: Limited emphasis on obtaining opinions from freelancers
2012–2015Study of large datasets from various online platforms30 English-language digital platformsPros: Large research sample (study included about 1 million service buyers and about a quarter of a million performers)
Cons: Lack of in-depth expert interviews
2015Survey conducted on freelancers as part of Research ANd Development (RAND) the Rise and Nature of Alternative Work Arrangements in the USA)FreelancersPros: Coverage of offline work in the study;
Cons: Relatively small research group (just under 4000 respondents)
2015Activity of freelancers registered on a selected digital intermediary platform (Up-work)Upwork (digital platform)Pros: Big data analysis
Cons: Limited analysis exclusively to one digital intermediary platform (Upwork)
2016–2017Survey of freelancers from seven European countriesDigital platformsPros: Analysis covering gig workers engaged in both online and offline activities from various countries
Cons: Focusing only the highly developed countries
Table 5.

Criteria for freelancer membership: author's proposal

(Source: Own compilation)

CriterionDescriptionPotential data sources
Average time of cooperationAverage time of cooperation with all ordering parties in a given time period (e.g., a year)Data collected by tax offices
Number of ordering partiesThe number of ordering parties in a given period of time exceeding a specified value (e.g., more than X ordering parties commissioning work in a year)Data collected by tax offices
Activity on a digital intermediary platformActivity on the digital intermediary platform may be considered, for example, in relation to

  • working time (e.g., activity for at least X months a year),

  • income level (e.g., income from activity on platforms exceeding X% of all income earned in a given tax year)

  • Working time: activity reported by digital intermediary platforms

  • Income level: data collected by tax offices

Profession typeQualifying a given contractor to a list of freelance professions created for this purposeA catalog of freelance professions that would be developed and published in the form of a legal act
Contract typeCivil law contracts, B2B contractsData collected by tax offices
Table 6.

Criteria for freelancer membership: author's proposal

(Source: Own compilation)

QuestionReplies
1Mean: 37.30
Standard error: 0.60
Median: 38
Kurtosis: -1.22
Minimum: 25
Maximum: 50
Mode: 32
2Micro enterprise (up to nine employees): 12.90%
Small enterprise (10–49 employees): 32.26%
Medium enterprise (50–249 employees): 41.94%
Large enterprise (over 250 employees): 12.90%
3Mean: 5.58
Standard error: 0.25
Median: 5
Kurtosis: 1.15
Minimum: 1
Maximum: 15
Mode: 5
4Secondary: 3.23%
Bachelor’s degree: 32.26%
Master’s degree: 64.52%
5Yes: 58.06%
No: 41.94%
6Mean: 2.35
Median: 2.00
Kurtosis: -1.70
Minimum: 0.00
Maximum: 5.00
Mode: 4.00
7, 8Mean: 0.68
Median: 0
Kurtosis: 1.07
Minimum: 0
Maximum: 3
Mode: 0		Mean: 8.29
Median: 6
Kurtosis: 12.59
Minimum: 2.00
Maximum: 60.00
Mode: 6
9YesYes – this is possible through state institutions that already have the appropriate data (e.g., tax data)23.26%
Yes – but this will only be possible when a law is developed that clearly defines who is a giger16.28%
Yes – the scale of the market can be assessed based on existing data from the statistical office and private sector entities that research the labor market16.28%
NoIt is not possible because it is impossible to clearly define who is a freelancer44.19%
QuestionReplies
10-Response rate (in %)
Reply12345
a. Short-term nature of cooperation0025.8148.3925.81
b. The existence of a legal act regulating the work of this professional group03.2319.3561.2916.13
c. Performing work via digital platforms (e.g., Uber)0029.0341.9429.03
d. The existence of trade unions/industry organizations that bring together gig workers016.1345.1629.039.68
e. Flexibility in terms of working hours0025.8138.7135.48
f. Regularity of income earned03.2325.8135.4835.48
g. Being subject to state health and social insurance obligations09.6838.7129.0322.58
h. Type of contract concluded09.6838.7132.2619.35
11a. Yes – this is determined by the temporary nature of the cooperation22.5835.4816.1325.810
b. Yes – it depends on the freelancer himself19.3538.7119.3522.580
c. Yes – a freelancer is anyone employed under a civil law contract22.5835.4816.1325.810
d. Yes – anyone registered on the digital intermediary platform is a freelancer22.5832.2619.3522.580
e. No, because it is so fluid that it is difficult to define when you are a freelancer6.4516.136.4545.1625.81
f. No, because there are no appropriate legal
regulations that govern who is a freelancer		6.4516.136.4545.1625.81
12a. Public authorities (e.g., statistical offices or tax authorities)03.239.6845.1641.94
b. Private sector entities03.236.4554.8435.48
c. Academic centers03.236.4554.8435.48
13a. It is not really clear who is a freelancer03.236.4558.0632.26
b. Not every civil law contract means that its party is a freelancer009.6854.8435.48
c. Freelancers can also work in the gray zone03.2322.5841.9432.26
d. Information on this topic is not needed3.233.2338.7132.2622.58
e. Freelancers may not want to share information about their professional activity09.6816.1341.9432.26
14a. Negotiating gig workers' salaries006.4558.0635.48
b. Choosing the type of agreement governing cooperation or the use of intermediary digital platforms09.6819.3551.6119.35
c. Negotiating working hours0012.9061.2925.81
d. Planning cooperation with giger, for example, regarding its duration0016.1358.0625.81
e. Assessing whether you can find freelancers with specific qualifications3.2319.3529.0341.946.45
Table 7.

Correlation and R2 coefficients between replies

(Source: Own research)

Question123456789
11.00------
20.141.00-------
30.060.691.00------
40.130.440.291.00-----
50.03–0.01–0.080.001.00---
60.02–0.030.010.06–0.921.00--
70.080.01–0.010.180.16–0.151.00--
8–0.030.23–0.010.16–0.280.35–0.021.00-
90.05–0.04–0.06–0.04–0.270.250.130.401.00
R2 coefficients
1-----0.000.010.000.00
2-----0.000.000.050.00
3-----0.000.000.000.00
4-----0.000.030.020.00
5-----0.000.030.020.00
Table 8.

Correlation and R2 coefficients between replies

(Source: Own research)

Question1234510a10b10c10d10e10f10g10h
11.00------------
20.141.00-----------
30.060.691.00----------
40.130.440.291.00---------
50.03–0.01–0.080.001.00--------
10a0.020.150.090.080.271.00-------
10b–0.020.250.320.24–0.070.461.00------
10c–0.03–0.100.040.000.170.770.43------
10d–0.05–0.15–0.370.20–0.090.100.110.101.00----
10e–0.18–0.080.150.010.060.460.440.710.051.00---
10f–0.050.060.210.160.270.680.390.640.160.571.00--
10g0.090.040.030.170.040.480.300.630.300.400.501.00-
10h0.050.020.06–0.110.290.350.300.330.160.050.350.491.00
R2 coefficients
1-----0.000.000.000.000.030.000.010.00
2-----0.020.060.010.020.010.000.000.00
3-----0.010.100.000.130.020.050.000.00
4-----0.010.060.000.040.000.030.030.01
5-----0.070.000.030.010.000.070.000.08
Table 9.

Correlation and R2 coefficients between replies

(Source: Own research)

Question1234511a11b11c11d11e11f
11.00----------
20.141.00---------
30.060.691.00--------
40.130.440.291.00-------
50.03–0.01–0.080.001.00------
11a–0.05–0.22–0.36–0.350.011.00-----
11b–0.02–0.24–0.29–0.31–0.050.951.00----
11c–0.02–0.22–0.34–0.30–0.050.950.951.00---
11d–0.09–0.12–0.01–0.240.190.700.630.731.00--
11e0.010.080.150.150.28–0.77–0.84–0.82–0.531.00-
11f0.010.080.150.150.28–0.77–0.84–0.82–0.531.001.00
R2 coefficients
1-----0.000.000.000.010.000.00
2-----0.050.060.050.010.010.01
3-----0.130.090.110.000.020.02
4-----0.120.100.090.060.020.02
5-----0.000.000.000.030.080.08
Table 10.

Correlation and R2 coefficients between replies

(Source: Own research)

Question1234512a12b12c
11.00-------
20.141.00------
30.060.69------
40.130.440.291.00----
50.03-0.01-0.080.001.00---
12a0.01-0.31-0.08-0.220.141.00--
12b0.060.010.12-0.190.100.551.00-
12c0.120.01-0.10-0.190.030.600.361.00
R2 coefficients
1-----0.000.000.01
2-----0.100.000.00
3-----0.010.010.01
4-----0.050.040.04
5-----0.020.010.00
Table 11.

Correlation and R2 coefficients between replies

(Source: Own research)

Question1234513a13b13c13d13e
11.00---------
20.141.00--------
30.060.691.00-------
40.130.440.291.00------
50.03–0.01–0.080.001.00-----
13a0.130.200.130.200.331.00----
13b0.070.210.070.100.280.791.00---
13c0.150.07–0.210.17–0.030.560.621.00--
13d–0.06–0.25–0.210.010.010.380.360.461.00-
13e0.02–0.02–0.080.230.100.510.570.550.461.00
R2 coefficients
1-----0.020.000.020.000.00
2-----0.040.050.000.060.00
3-----0.020.010.050.040.01
4-----0.040.010.030.000.05
5-----0.110.080.000.000.01
Table 12.

Correlation and R2 coefficients between replies

(Source: Own research)

Question1234514a14b14c14d14e
11.00---------
20.141.00--------
30.060.691.00-------
40.130.440.291.00------
50.03–0.01–0.080.001.00-----
14a–0.010.260.270.350.251.00----
14b0.050.100.460.11–0.040.371.00---
14c0.020.470.480.250.140.530.291.00--
14d0.020.420.470.29–0.030.710.440.711.00-
14e–0.07–0.34–0.040.090.150.140.42-0.180.061.00
R2 coefficients
1-----0.000.000.000.000.00
2-----0.070.010.220.180.12
3-----0.070.210.230.220.00
4-----0.120.010.060.080.01
5-----0.060.000.020.000.02
DOI: https://doi.org/10.2478/fman-2024-0022 | Journal eISSN: 2300-5661 | Journal ISSN: 2080-7279
Journal RSS Feed
Language: English
Page range: 359 - 378
Published on: Dec 31, 2024
Published by: Warsaw University of Technology
In partnership with: Paradigm Publishing Services
Keywords:
gig economy,
freelancing economy,
measurement of gig economy
JEL:
D11,
D23,
M13,
M31
Related subjects:
Computer sciences,
Business computing,
Business and economics,
Business management,
Management accounting, financial controlling, cost calculation, investment

© 2024 Emil ZELMA, published by Warsaw University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 16 (2024): Issue 1 (January 2024)
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