DOI: 10.28934/jwee23.pp58-75 JEL: O3, A2, D63, E24, H5 ORIGINAL SCIENTIFIC PAPER 3BThe Effects of Digitalization and Skills on Women’s Labor Market Inclusion- Serbian Gap Study Boris Jevtić1 1F1 University Union, Belgrade, Serbia, Computing faculty RAF, Belgrade, Serbia] Milos Vucekovic12 F2 Singidunum University, Belgrade, Serbia Svetlana Tasić1 3F3 Belgrade Business and Arts Academy of Applied Studies, Belgrade, Serbia A B S T R A C T In this paper, women's social inclusion in the labor market as a long-lasting employment problem is researched under the new digitalization requirements. Furthermore, the importance of the factors is explored, including those connected to the digital knowledge and skills of women (WDKS) and the institutional support of digitalization (DIS), for women's inclusion in the labor market (WLMI) and employment in a digital age. Using the Likert scale instrument for 15 claims within the specified variables of the study conducted in Serbia in 2022, 224 women participated in the research and rated the influence. Findings showed that the knowledge, digital skills, and competencies of girls and women (WDKS) are strongly correlated with their social inclusion in the labor market (WLMI). The impact of the DIS, institutional measures, and policies as a set of digital infrastructure, networks, and low-level adjustments for the digitization process's development is also important as a part of national digital ecosystem development. 1 E-mail: boris.jevtic@digit-star.com 2 Corresponding author, e-mail: milosvu@gmail.com 3 E-mail: ceceatasic1@gmail.com Boris Jevtić, Milos Vucekovic, Svetlana Tasić 59 These findings contribute to the literature on digitization and new knowledge development, the SDG goals on gender divide and equality, and further feminist economics scientific works. KEYWORDS: digitalization, education, digital skills, sustainability, institutional support, SDGs Introduction This research focuses on the labor market opportunities that arise from digitalization in the new normal. COVID-19 has significantly changed the working patterns in Europe and Serbia in favor of home-based and flexible jobs on the platform. The platform or gig economy via applications includes crowdsourcing and on-demand work. ILO has published several studies on non-standard employment, agency work, and temporary and dependent self-employment, with discussions on open issues of employment relationships and hidden employment relationships (McKinsey Global Institute, 2015; 2017; ILO, 2016; WEF, 2016a; 2016c; Bertrand, 2011; UN Women, 2005). Many studies of platform work assume that atypical employment relationships involving casual, daily, or seasonal contracts are not covered or are covered only to a limited extent by traditional employment protections (Nambisan, Wright, & Feldman, 2019; Nambisan, Siegel, & Kenney, 2018; Balsmeier& Woerter, 2019; Ahlstrom, 2020). Platform female workers are usually legally treated as self-employed, and many EU countries have modernized labor law and made the labor market more flexible by modifying common law (Leahy & Wilson, 2014; Radović-Marković, Kočović & Grozdanić, 2013; Radović-Marković, Grozdanić & Jevtić, 2017). The paper also respects the SDGs concerning sustainable employment of women and gender equality (Worthington, 2014), which are connected to: − Economic benefits of women, SDG 5 − Unemployment and wages of women, SDG 8 − Gender-sensitive policies addressing environmental sustainability challenges (SDG 12–17) Based on the literature (Jacobides, Cennamo, & Gawer, 2018; Frenken et al., 2020; Hinings, Gegenhuber, & Greenwood, 2018; Brynjolfsson & 60 Journal of Women’s Entrepreneurship and Education (2023, Special Issue, 58-75) McAfee, 2014; Faik, Barrett, & Oborn, 2020), the authors defined the further research question (RQ) for this study as “Are digital skills and knowledge of women (WDSK) and institutional support for digitalization development (DIS) significantly impacting the labor market inclusion of women (WLMI)?” In selecting digital job opportunities for women, the authors include the SDG pillars of sustainability, equality issues, and the increase of women's employment rates faster than men's by the end of the decade (Diesendorf, 2000; Dunphy, 2000; EU Treaty for Equality Promotion by 2030). Both traditional and modern industries can be considered the demand side of the labor market for female employment in the digital era (Sorgner et al., 2017; Acemoglu & Autor, 2011; Popović & Jevtić, 2020; Mitić et al., 2020; 2020a; Grozdanić, Radović-Marković & Vučić, 2008). The paper is structured through the introduction, the case study gap, the materials and methods with the main findings, the conclusions, and the references used in the study. Serbian Gap Analysis The number of employed persons in Serbia decreased to 2,888,700 in 2022 from 2,942,000 in 2021 (SORS, 2023) (Figure 1). The labor force participation rate in Serbia remained unchanged at 55.80 percent in 2022. Figure 1: The trend of employment/unemployment rates (in %), population 2017-2022. Source: SORS (2020) https://publikacije.stat.gov.rs/G2023/PdfE/G20231047.pdf https://publikacije.stat.gov.rs/G2023/PdfE/G20231047.pdf Boris Jevtić, Milos Vucekovic, Svetlana Tasić 61 The number of unemployed persons in Serbia decreased to 427.15 thousand in December 2022 from 428.96 thousand in November 2022 (Table 1). Table 1: Serbia, some indicators of labor market trend, 2022 2022 Changes relative to the previous year (in 000) (in 000) % Population 15+* 5769.8 -68.5 -1.2 1. Active 3179.8 -54.3 -1.7 1.1 Employed 2888.7 -28.7 -1.0 1.1.1 Formally employed 2519.2 0.4 0.0 1.1.2 Informally employed 369.5 -29.1 -7.3 2. Unemployed 291.1 -25.6 -8.1 3. Outside the labor force* 2590.0 -14.2 -0.5 Source: Statistical Release on Labour Force Survey for the fourth quarter 2022 available at: http://publikacije.stat.gov.rs/G2023/Xls/G20231047.xlsx. Female unemployment in Serbia was reported at 11% in 2021, according to the World Bank development indicators (Figure 2). Although the youth unemployment rate in Serbia decreased from 25.40 percent in the third quarter of 2022 to 24.30 percent in the fourth quarter of 2022, it is still very high. Figure 2: The female unemployment rate for Serbia, 2014-2021. Source: SORS, 2022. available at: G20231047.pdf (stat.gov.rs) http://publikacije.stat.gov.rs/G2023/Xls/G20231047.xlsx https://publikacije.stat.gov.rs/G2023/PdfE/G20231047.pdf 62 Journal of Women’s Entrepreneurship and Education (2023, Special Issue, 58-75) The reason for taking this country for the gap study lay in the fact that Serbia is among the ten economies in the world by the number of freelancers in terms of population. About 100,000 freelancers were employed, mostly equally men and women, in 2021. According to SORS (2022), most freelancers work for foreign companies, and most of them did not sign any contract, and if they did, such a document is not recognized in the Serbian legal system as the platform company is not recognized as an employer. Furthermore, the research found the following insights: − More men are working in the technology sector than women. − Men are mostly better paid than women. − Women usually offer language courses, translation, textile design, writing, fashion, and similar services over online platforms (Vučeković et al., 2021). − The number of women joining the "gig" economy is growing, and the feedback is usually positive. − Freelancing is the main source of revenue for many women, as the gross value of such income is double the average gross wage in Serbia. In the time of the digital transformation of work in Serbia, legal structures were not changed and adapted to new circumstances of employment and new work patterns. Stronger labor and social protection regulatory rules have to be developed, and policymakers in Serbia would have to work to shape the transformation of regulations. Concerning the ICT sector in Serbia, its impact on economic and social influence on new business models is expanding, and it can be said that the industry opportunities are growing each year with more support from the national ecosystem (Špiler et al., 2023). Illustrative could be the positions of Serbia among 144 other countries in: − 125th in firm-level technology absorption, 134th in the extent of staff training, 131st in capacity for innovation in ICT, 97th in B-C use of the Internet, 86th in business-to-business ICT usage, 101st in FDI and technology transfer, and 107th in access to basic services by ICTs, ICT use, and government efficiency; − 72nd in technology readiness; Internet access in schools, 89th; Internet bandwidth, 88th; fixed internet, 46th; mobile broadband subscriptions per 100 people, 55th position. Boris Jevtić, Milos Vucekovic, Svetlana Tasić 63 − But in terms of labor market efficiency, Serbia is in the lower positions: the country's capacity to retain and attract talent is low, ranking 79th in women in the labor force in ratio to men, 68th in pay and productivity, with 46% of knowledge-intensive jobs in the workforce, and 80th out of 144 in hiring and firing practices. − 65% of individuals using the Internet have digital skills (more men); 68% use virtual social networks; in the E-Participation Index, 78th position (WEF, 2022). Methods and Materials This research is part of a wider investigation by authors across Serbia about the success triggers of female employment opportunities and barriers in the age of new technologies. To answer the research question, an empirical analysis was conducted in Serbia in 2022, which included 224 unemployed female participants (T. 2). One main and two auxiliary hypotheses are defined in the hypothetical research model given in Figure 3. Figure 3: Hypothetical research model Research variables are: 1. Independent variable: digital competencies and skills of women (WDSC) , 64 Journal of Women’s Entrepreneurship and Education (2023, Special Issue, 58-75) 2. Independent variables: institutional support of digitalization development (DDIS), and one 3. dependent variable: labor market inclusion of women (WLMI) The main hypothesis of the research defined based on the literature review is that H0 = digital knowledge and skills of women (WDKC), and institutional support for digitalization development (DIS) significantly impacts the labor market inclusion of women (WLMI), or 𝐻𝐻0 = C is not impacted by A and B. 𝐻𝐻𝑟𝑟 = C is impacted by A and B. Two auxiliary hypotheses are also defined as follows: 𝐻𝐻1= A does not impact C. 𝐻𝐻𝑟𝑟1= A impacts C. 𝐻𝐻2= B does not impact C. 𝐻𝐻𝑟𝑟2 =B impacts C. A mixed-methods research design was adopted, with the online quantitative data collected through 15 claims to identify the participants’ attitudes and correlation and regression analysis realized in SAS JMP 17. The sample includes women and girls from all over the country. In terms of demographic characteristics, 33.93% of participants are between the ages of 18 and 24, and 29.91% are between the ages of 25 and 40, which makes up the majority of the sample (young people, 63.84% of the total number). Most have secondary and high education levels: 168 girls and women (65.00%). Employment status shows that 89 participants are temporally employed, 78 are not employed, and 43 lost their jobs because of being laid off (technological decline, job reduction, company closing), which makes 54.02% of women unemployed for both reasons. 6.25% of participants were employed on full-time contracts in various industries (Table 2). Boris Jevtić, Milos Vucekovic, Svetlana Tasić 65 Table 2: Sample characteristics Respondents age range N Column % (18-24) 76 33.93% (25-40) 67 29.91% (41-50) 55 24.55% (51-65) 26 11.61% All 224 100.00% Education level N Column % Primary 33 14.73% Secondary school 123 54.91% High education 45 20.09% Without education 23 10.27% All 224 100.00% Social status/Employment N Column % Not employed 78 34.82% Temporally employed 89 39.73% Laid off - technological decline, job reduction, company closing 43 19.20% Full employed 14 6.25% All 224 100.00% Research Findings The defined impact of 3 variables is analyzed through 15 further claims: Claims A. Independent variable, Digital knowledge and skills of women (abbr. WDKS) a11 Helping women with new job opportunities, helping companies in digital positions provision to women, and helping SDGs goals fulfillment. a12 Digital skills' literacy (skills gaps and shortages) of women working online on a platform, freelancing a13 Investing in the professional learning and capabilities in basic knowledge and new technologies of women a14 Entrepreneurship Training, Mentoring, and Support for self- employment a15 Education and mentoring for entrepreneurs Claims B. Independent variable, Institutional support for digitalization development (abbr. WDIS) b11 Gender-sensitive legislation for social inclusion of women 66 Journal of Women’s Entrepreneurship and Education (2023, Special Issue, 58-75) b12 Government bodies coordination policy for the promotion of WDIS b13 Inclusive supply chains & linkages, public procurement targeting women entrepreneurs. b14 Women and girls’ better approach to technology infrastructure, the internet, and networks. b15 Legislation and administration adjustments to digital platforms development and women’s digital work opportunities Claims C. Dependent variable, Labor market inclusion of women (abbr. WLMI) c11 Employment/reemployment (public enterprises and institutions) c12 Employment/reemployment in SMEs, businesses, ICT firms c13 Self-employment, technology entrepreneurship, Online Outsourcing c14 Working online over the platform, freelancing c15 Informal work, micro work The standard deviation and mean scores for the respondents' attitudes toward the stated claims within the variables are given in Tables 3 and 4. Table 3: Factors and values for the (A, B & C) A Mean Std Dev a11 4.21875 0.8789548676 a12 4.5267857143 0.7452067631 a13 3.9866071429 1.1063611859 a14 4.3616071429 0.7801052632 a15 4.3214285714 0.8386866277 B Mean Std Dev b11 4.2678571429 0.8882102007 b12 4.28125 0.8398196742 b13 4.4017857143 0.8413082001 b14 4.3258928571 0.9113382006 b15 4.7142857143 0.6271663289 C Mean Std Dev c11 4.4955357143 0.6828965469 c12 4.2321428571 0.9515825182 c13 4.4642857143 0.9320267845 c14 4.1071428571 1.1431774055 c15 4.5401785714 0.7624553 Boris Jevtić, Milos Vucekovic, Svetlana Tasić 67 Table 4: Factors and values for the (A, B & C) Mean Std Dev A 4.2830357 0.7217801 B 4.3982143 0.6198484 C 4.3678571 0.6046478 Variable (AC) Correlation analysis The hypothetical model of A & C is given in the further Figure. Figure 4: Hypothetical model (A & C) The coefficient of determination value is 0.548503. It showcases that the (C) variable can be described by 54.85% of the (A) variable. Also, 0.74061 is the coefficient of correlation between two variables, which means a strong relationship between them (Figure 5). Figure 5: (A & C) hypothetical model contribution sizes/Standard (up) and non-standard (down) [F (1, 222) = 269.6979, p <0.0001] statistical significance assessment is given in (Table 5). 68 Journal of Women’s Entrepreneurship and Education (2023, Special Issue, 58-75) Table 5: (A & C) variables ANOVA Source DF Sum of Squares Mean Square F Ratio Model 1 44.718688 44.7187 269.6979 Error 222 36.809884 0.1658 Prob > F C. Total 223 81.528571 <0.0001 According to the findings, the first hypothesis H1 = a does not impact C cannot be confirmed (the alternative one is accepted, Ha1: that A impacts C). 4.2830357 is the mean score for the A variable. The multiple regression equation reads (Formula 1): 𝐶𝐶 = 1.7105676 + 0.620422 ∙ 𝐴𝐴 (1) (A & C) diagram is presented in further Figure (6). Figure 6: Diagram for A & C variables (BC) variable correlations The hypothetical research model (BC), composed by one independent (B) variable, and (C) dependent one (Figure 7). Boris Jevtić, Milos Vucekovic, Svetlana Tasić 69 Figure 7: Theoretical system model (B & C) The coefficient of determination is 0.478716, and the variable (C) is 47.87% described by the (B) variable. 0.691893 is found as a value of the coefficient of correlation between the variables, showing a weaker relationship (Figure 8). Figure 8: (B & C) system model contribution sizes- Standard (up) and non- standard (down) [F (1, 222) = 203.8714, p <0.0001] is statistical importance, presented in Table 6. Table 6: (B & C) variables ANOVA Source DF Sum of Squares Mean Square F Ratio Model 1 39.029019 39.0290 203.8714 Error 222 42.499553 0.1914 Prob > F C. Total 223 81.528571 <0.0001 According to the findings, HE=B does not impact C, so that can be accepted as alternative hypothesis Ha2: that B impacts C. The mean score for the (B) is 4.39282143. A multiple regression equation, based on the given data, can be defined, and it reads (2): 70 Journal of Women’s Entrepreneurship and Education (2023, Special Issue, 58-75) 𝐶𝐶 = 1.3993898 + 0.6749256 ∙ 𝐵𝐵 (2) Figure 9: The regression equation diagram (B & C) is presented. (A, B & C) Variable Multiple Correlations The hypothetical research model (ABC) is shown in Figure 3. 0.612228 presents the determination indicator, meaning that with 61.22% (C), it can be closer defined by two variables, AB. By analyzing the variables ABC together, a strong correlation is found. Among A and C, the correlation is the highest, 0.7406. The largest variance size is 0.519, and it is the size of the A variable. The smallest variance of 0.382 is found for the dependent variable (C). Figure 10: (A, B & C) system model standard contribution sizes [F (2, 221) = 174, 4616, p <0.0001] is the assessment of statistical significance, presented in the further Table. Boris Jevtić, Milos Vucekovic, Svetlana Tasić 71 Table 7: (A, B & C) ANOVA Source DF Sum of Squares Mean Square F Ratio Model 2 49.914109 24.9571 174.4616 Error 221 31.614462 0.1431 Prob > F C. Total 223 81.528571 <0.0001 Source: Authors According to the findings, the main hypothesis cannot be confirmed (H0: A and B do not impact C), and the alternative one, Ha: A and B impact C, can be accepted. Figure 11 presents the non-standard contribution values set system model. A positive correlation between variables (A) and (B) was found. The 0.502397 is the variable (A) with the highest impact on (C). Variable (B) has a lower impact of 0.347089. The 0.686 is found as the mean value of the impact of the deviations of two variables (A) and (B) from their respective means. Figure 11: (A, B & C) System model A multiple regression equation is formulated, and it reads (3): 𝐶𝐶 = 1.0761355 + 0.4208663 ∙ 𝐴𝐴 + 0.3385774 ∙ 𝐵𝐵 (3) The (A, B & C) multiple regression equation diagrams are given in (Figure 12). 72 Journal of Women’s Entrepreneurship and Education (2023, Special Issue, 58-75) Figure 12: (A, B & C) variables multiple regression equation diagrams Conclusion The inclusion of women in the Serbian labor market depends on educational factors, digital capabilities, skills, and government support with labor laws and other regulations. The main hypothesis is supported by the correlation analysis provided in the paper. To support educational efforts in Serbia, investing in training and reskilling opportunities for women, mid-career, or those returning to the workplace is needed and recommended (Radović-Marković et al., 2022). It would mean that the country subsidized transition costs for selected occupations and sectors, increased transparency on labor demand trends, and launched informational campaigns targeting women (Grozdanić, Radović-Marković & Jevtić, 2013). To address labor mobility constraints, women need support in balancing family care and work obligations, and finally, they need help reducing stereotypes about gendered occupations. Positive movements are found with institutional support for nurturing changes in digital transformation, as they provide an open platform for the sustainability of women's employment in the global workforce. The research also showcased the levels of the digital divide and social inclusion issues (SDGs) in the "gig" economy. Furthermore, the paper's results contribute to the current literature regarding women's human and labor rights, education, flexible work patterns, and the digital transformation of work. 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