“Understanding the women’s digital employment intentions: The role of policies and values”

The digital economy has enforced women’s employment and provided more possibilities for promoting employment for gender equality (SGD5). In order to achieve SDG5, the study aims to explore the role of digital employment policy and digital employment value on digital employment intention based on the support alliance theory and employment behavior theory and to build a model of digital employment for gender equality. 492 women with digital work experience from China participated in the survey. The results reveal that digital employment policy (β = 0.327, p < 0.001) and digital employment value (β = 0.454, p < 0.001) predict digital employment intention. Digital employment policy plays an active role in determining digital employment value (β = 0.546, p < 0.001). At the same time, the study claims the intermediary role of digital employment value in the structural model. This study can inspire the government and relevant departments to design more scientific and diversified employment policies for women, including policy support in economic, educational, and social aspects. Furthermore, women in the digital era should actively participate in training, improve their digital skills, understand the possibilities that digitalization brings to their work and life, and adapt themselves to the development of the digital society. This study encourages women to integrate into the digital society and actively improve their values, thus achieving SDG5.


INTRODUCTION
The digital economy has brought subversive changes to people's production and lifestyle.Women's employment in the digital era has undergone profound changes (Webster, 2014).It is worth noting that the digital gender gap that may aggravate will also cause women's employment trouble (Antonio & Tuffley, 2014).Improving women's competitiveness in employment has become an essential issue of the world's attention to gender equality (SDG5) (Coe et al., 2019).
Countries worldwide attach great importance to the impact of the digital economy on the labor market, especially the employment and entrepreneurship of different groups (Laukkanen & Tura, 2020).Driven by digital technology, digital managers, artificial intelligence engineers, cloud computing engineers, prominent data engineers, and other new professions have emerged on the digital platform, opening a new window for women's employment (Rani & Furrer, 2021).
Relying on the digital labor platform ensures labor anonymity and avoids gender discrimination against women in the traditional employment market (Graham et al., 2017).In 2022, women's labor participation rate in China will exceed 70%, which is higher than that of the United States, Japan, Germany, and other major economies worldwide (Verma et al., 2021).China has made remarkable achievements in promoting women's equal exercise of rights following the law, participating in economic and social development, sharing development achievements, ensuring women's equal enjoyment of economic rights and interests, and eliminating gender discrimination in employment (Dai & Menhas, 2020).The digital economy has created 57 million jobs for Chinese women and digital gender dividends to expand women's value in the labor market, reduce women's vulnerability, and supply new employment spaces for women (Olsson & Bernhard, 2021).Women's career development is an important yardstick to measure the quality of labor market operation and an essential mechanism for the healthy operation of society (Novkovic, 2022).
Employment and gender equality are not strange topics.However, only some quantitative research topics exist on employment policy and employment psychology in digital employment.Although traditional employment behavior research has provided much evidence, there may be new results based on the background of the digital economy.

LITERATURE REVIEW
This study uses support alliance theory and employment behavior theory and discusses policies, values, and intentions in the context of digital employment, thereby proposing research aims and hypotheses.
Although the policy process is full of various intuitive, practical interests, these interests must be conceptualized as people's policy preferences or policy objectives if they want to exert a beneficial influence on the policy, and specific policy preferences or policy objectives are the reflections of specific belief systems (Vrydagh, 2022).Moreover, interests can only be clearly defined in a specific theoretical system -an interconnected cognitive system.In essence, it is a specific belief system that is more inclusive and verifiable (Badcock et al., 2019).
The employment behavior theory results from the long-term application of the planned behavior theory to discussing employment situations (Meira & Hancer, 2021).Employment behavior is a social interaction process conducted by people with labor needs, capabilities, and aspirations to be met, including the first employment behavior oriented to employment opportunity acquisition and the multiple employment behavior oriented to ideal job acquisition (Enciso-Santocildes et al., 2021).
The theory of employment behavior believes that the influence of age, profession, education, abili-ty, personality, and values may predict workers' behavior (Sürücü et al., 2019).It focuses on the employment intention of labor suppliers and the matching degree of individual characteristics and occupational needs (Schor & Attwood-Charles, 2017).Employment intention is an essential category in the theory of employee behavior.Social capital, employment policy, economic structure, and culture often affect employment intention and behavior as external factors (Jayachandran, 2021).
The purpose of digital employment policy is to promote the development of digital employment and the digital economy and provide convenience for employment from the perspectives of education, investment, training, and policy (Chetty et al., 2020).Women's primary employment in Africa and the Middle East has recently been widely mentioned (Jayashree et al., 2020).Many countries attach importance to the opportunities for women to continue to develop, so they actively increase investment in education, knowledge, and skills and even actively change the concept of family employment to promote sustainable economic development (Pu & Jiang, 2021).Therefore, the digital employment policy that this study focuses on is investigated from this dimension, which shows the connotation of digital employment policy from multiple dimensions.
Employment values are an essential part of values, and employment values, like other individual values, constitute life values (Sugiarti et al., 2021).Therefore, the theoretical clarification of employment values should return to people's understanding of the external world they adapt to and transform (Klein & Todesco, 2021).Establishing the value ideal of digital employment can effectively stimulate the incentive and cohesion function of value orientation to obtain the courage and continuous power to overcome difficulties and achieve the unity of personal and social values (Sahut et al., 2021).
The proposal of employment intention can be traced back to the early 20th century (Lee & Chao, 2013).Due to the different personality characteristics of each person, the differences between individuals are universal.After understanding their professional characteristics, individuals can analyze the requirements of each occupation with the help of an instructor and then choose an occupation that closely matches their characteristics (Lee & Chao, 2013).
The theory of employment behavior believes that individuals will be affected by interest and personality factors when selecting careers (Hodder, 2020).It emphasizes that people can fully realize themselves at the career stage.Digital employment intention is a new sublimation of the research on employment intention under the digital background (Serkova, 2020).Although the current research on employment intention is wealthy, the discussion on digital employment intention needs to be more profound.
Women's employment intention in the era of the digital economy refers to the vague and straightforward needs of women in the process of digital employment choice (Alon et al., 2020).It is uncertain and will be affected by personal subjective consciousness, but it may also evolve into reality in the future.Women's digital employment intention can be narrow and broad (Jayachandran, 2021).The narrow sense refers specifically to women's direction of digital employment.The broad sense includes not only the narrow sense of digital employment intention, that is, the choice of digital career, but also the choice of digital employment area and the choice of digital employment salary expectation (Webster, 2014).
The new technology provides new ideas for employment and another more convenient and efficient way for workers to obtain employment and self-employment (Hodder, 2020).Many policies on digital employment provide a framework for employment in the 21st century to embrace digitalization and establish a relationship of sustainable employment.
The theory of planned behavior is often used to test the factors that affect job-hunting intention (Chansuk et al., 2022).The policy is already the premise that affects job-hunting self-efficacy, subjective norms, and job-hunting attitude (Jayachandran, 2021).At the same time, the support alliance framework theory suggests that policy is the pre-factor that affects employment (Hudson et al., 2019).
The development of digital infrastructure has increased the number of digital jobs, which are essential for the digital transformation of employees in the SME sector (Aly, 2022).Encouraged by the female policy dividend, female workers may be more inclined to enter small and medium-sized enterprises for employment (Sovbetov, 2018).Evidence from the employment survey of female college students in China shows that governments at all levels, from the bottom to the top, actively release their official accounts to show their work interestingly to promote women's employment and cultivate their positive intention of employment.
China has an important strategic position in the global labor market.The talent introduction policy and the preferential policies for car and house purchases have apparent potential to attract employment (Chiang, 2019).The employment policy under the digital economy differs from the content proposed in the traditional sense (McConnell & Hart, 2019).The consistency of the digital employment policy, including the compensation method, employment status, and work schedule, may positively affect women's employment intention and retention.
The development of employment behavior theory insists that employment value is one of the factors of employment choice (Chansuk et al., 2022).At the same time, the support alliance theory determines the critical role of policy on employment psychological factors (Hudson et al., 2019).The employment protection policy allows more people to see the significance of current employment, such as corporate cash-holding decisions, integra-tion into joint corporate development, and even efficient appreciation plans (Enciso-Santocildes et al., 2021).Economic growth and urbanization are the main achievements of the digital economy, which has significantly reduced the poverty rate (Graham et al., 2017).Because employees have found employment policy's value in changing their status quo, they should try to move toward digital employment or entrepreneurship (Galperin & Arcidiacono, 2021).
All EU member states have deployed various policies to increase the total employment rate of various industries (Graham et al., 2017).Due to the influence of the environmental economy, economic policies in different political backgrounds aim to reduce entrepreneurship's direct and indirect impact on domestic income inequality by solving employment problems (Akter et al., 2017).Therefore, no matter what the favorable employment policy makes the employees feel the value of employment, the actual and preferred employment policy is optimistic for the employment of multiple industries (Banerjee, 2019).The relationship between digital employment policy and employment value may also be the same in the digital economy.
The relationship between value and behavior intention has been widely studied in employment behavior theory and its development (Zhang et al., 2019).Based on the situation of employment behavior research, employment intention may be affected by employment value and attitude (Al-Jubari et al., 2019).Based on this, one of the determinants of female doctors' employment behavior is the practitioners' perception of the value of medical skills (Munir et al., 2019).This conclusion helps more women be willing to engage in medical work and take active medical assistance actions (Akter et al., 2017).Although, in recent years, there has been much discussion on the impact of digitalization on employment, there needs to be more discussion on the value and intention of employment as a driving force for employment (Galperin & Arcidiacono, 2021).
The study of the digital economy has clarified the brand employment value and the willingness to promote the digital brand promotion industry (Litvinenko, 2020).In the field of agricultural engineering, the value of environment-friendly ag-riculture has become the primary basis for the intention of female employees to engage in environment-friendly agricultural products (Akter et al., 2017).
As an intermediary, value is essential to discussion and verification based on behavioral theory, carried out in multiple contexts (Jiang et al., 2022).The value of digital technological innovation has produced many new results under capitalist policies, which formally guide the employment of the opportunity to cultivate employment intention (Lee & Chao, 2013).Spain paid particular attention to the new technology policy's impact on Libyan workers' employment and self-employment (Lieu et al., 2020).It determined the role of the economic value brought by technology on employment intention.China's industrial policy has had a massive impact on the digital economy (Chiang, 2019).
However, the employment opportunities brought by economic value have increased significantly, and the promotion of industrial policy is of great significance to the employment value (Laukkanen & Tura, 2020).Employment education and training enable more women to pay attention to the possibility and opportunity of future employment (Jiang et al., 2022).Different employment values often lead to different degrees of employment intention (Lee & Chao, 2013).
This study aims to discuss the role of digital employment policy on digital employment intention based on the research status of digital female employment and further verify the intermediary role of digital employment value.Moreover, based on hypotheses verification, it constructs a structural equation model of digital employment for gender equality, which inspired the development of women's digital employment, the government's digital employment behavior, and public policy.
Hence, the study proposes the following hypotheses: H1: Digital employment policies positively affect digital employment intention.
H2: Digital employment policies positively affect digital employment value.

METHODS
The study uses an online questionnaire to obtain 492 valid responses.means that many students who may be in school are also engaged in digital part-time jobs for a long time.Finally, the majority of participants with a bachelor's degree (133) and below (204) means that digital work is not a privilege of a high degree in the era of the digital economy.On the contrary, the proportion of master's and doctor's degree participants is insignificant.Of course, this may be related to the level of national education, and a master's degree or above is not a large group in social development.
The studies were done using random sampling, so the distribution of demographics was not uniform.However, demographic differences could also help further investigate the role of demographic variables on the digital employment intention of women.
The questionnaire design originates from discussions on related topics, including four parts.Firstly, the study surveys the respondent's basic information, including age, annual family income, location, digital work experience, and education level.After the data collection, SPSS 26 and Amos 26 were the main tools for statistical data analysis.Firstly, SPSS was used to complete the descriptive statistical analysis and the questionnaire's overall reliability and validity analysis.Then Amos 26 helps the study to complete the confirmatory factor analysis, model fit analysis, aggregation validity and discrimination validity analysis, path analysis, and finally, build a diagram of the structural equation model of digital employment intention for women.

RESULTS
Reliability refers to the stability and consistency of the questionnaire results when the same method is used to survey the same object, that is, wheth-er the measuring tool (questionnaire or scale) can measure the measured things or variables stably (Griffin et al., 2022).Cronbach's α value is the most commonly used reliability coefficient at present.Generally speaking, Cronbach's α greater than 0.7 is acceptable; Cronbach's α > 0.9 means the scale has good validity (Griffin et al., 2022).Table 2 found that the scale included 20 questions with Cronbach's α = 0.933, meaning the scale has good validity.Validity refers to the degree to which measuring tools or means can accurately assess the things.Generally, KMO > 0.6 indicates a correlation, and Sig < 0.05 has significance, which means that the scale meets the requirements of factor analysis (Griffin et al., 2022).If KMO > 0.9 and Sig = 0.000, the internal consistency of the questionnaire data is relatively good.The selection of all questions in each dimension is the same, and the dimension division is relatively good.Table 3 illustrates that KMO = 0.984 (> 0.9) and Sig = 0.000, indicating that the data in the questionnaire are valid and suitable for factor analysis..000 Aggregation validity is the degree of similarity when measuring the same feature using different measurement methods.CR value greater than 0.7 is acceptable.AVE is the calculation of the average explanatory power of latent variables.The higher the AVE value, the higher the convergence validity of the latent variable; the AVE value should be greater than 0.5, and the AVE value is 0.5 acceptable threshold between 0.36-0.50(Mueller & Hancock, 2018).
Confirmatory factor analysis uses composite reliability (CR) and mean-variance extraction (AVE) as the evaluation criteria of convergence validity.When the CR value of each factor is more significant than 0.7 and the AVE value is greater than 0.50, the convergence validity is considered good.The criterion for passing the discrimination validity is that the square root value of each factor AVE is greater than the correlation coefficient of this and other factors (Mueller & Hancock, 2018).Tables 4 and 5 show the aggregation and differentiation validity; AVE, CR, and other indicators meet the standard.Figure 1 shows the results of the confirmatory factor analysis.Hence, the study deletes the three items for optimal fitting.Among the model-relevant indicators, χ 2/df < 3, GFI and CFI are always smaller than 1.The model fitting effect is good when they are more significant than 0.9.RMSEA less than 0.1 indicates a good fit; below 0.05, the fitting is good (Mueller & Hancock, 2018).However, Table 7 indicates that χ 2/df, GFI, and NFI do not meet the standard.
Table 8 presents the results of the aggregate validity analysis after deleting the items.In terms of convergence validity, the AVE value extracted from the mean-variance of each variable is 0.567-0.618,which is greater than the standard of 0.5, and CR is 0.874-0.907,which is more than 0.7, indicating that the convergence validity is reliable.
Source: Authors' elaboration.Table 9 claims that the absolute value of the correlation coefficient between any two factors is less than the square root of the corresponding AVE factor after deleting the question.This means there is a certain degree of discrimination between the three factors in the study.Hence, the discrimination validity of the deleted scale is reliable.Based on reliability and validity analysis and validation of factor analysis results, the study continued to conduct path analysis.After the establishment of the structural equation model (SEM), through the model fitting of the soft-ware, the estimated value of the detection path, the standardized path coefficient, the standard error (SE), CR value, and the significance p-value are obtained.

Figure 1. Confirmatory factor analysis
In general, if CR is more significant than 1.96, the p-value is less than 0.05, it can be considered that the path coefficient can pass the significance test within the 95% confidence interval, indicating that the corresponding path hypothesis of the preset model is valid; otherwise the assumption is untenable (Mueller & Hancock, 2018).Table 10 describes the test results.
Digital employment policy has a significant positive impact on DEV (β = 0.546, p < 0.001).Thus, H1 is supported.Digital employment value has a significant positive impact on digital employment intention (β = 0.454, p < 0.001).Thus, H2 is confirmed.Finally, digital employment policy significantly positively affects digital employment intention (β = 0.327, p < 0.001), supporting H3.After verifying the direct effects, the study further tests the intermediary effect.Table 11 summarizes the results of the indirect effect.The study used the boosting method in Amos 26.0, set the confidence interval at 95%, and conducted the indirect effect test.The study found that: • The 95% upper and lower range of the total effect of "DEP → DEI" is [0.495, 0.644], excluding 0, indicating that the total effect between DEP and DEI is significant, with the practical value of 0.575; According to the results of the path analysis, Figure 2 constructs SEM of women's digital employment intention, which fully demonstrates the intermediary role of digital employment value in digital employment policy and digital employment policy.Hence, H4 is supported.

DISCUSSION
The study explored a model that may provide more possibilities for women's digital employment, namely the structural equation model of digital employment for gender equality.In this model, the paper emphasizes the role of digital employment policy and digital employment value on digital employment intention, reflecting the intermediary role of digital employment value.
Digital employment is no longer a strange topic.The digital economy provides many possibilities for digital employment (Graham et al., 2017).Therefore, improving digital skills has become an essential task at present.In order to achieve the goal of the digital economy, the literature on employment institutions, employment directions, and skills from the perspective of employment has gradually emerged (Galperin & Arcidiacono, 2021).Moreover, actively encouraging the combination of digital employment and artificial intelligence to promote economic development has become the economic goal that the world is pursuing (Hodder, 2020).
On the other hand, digital employment services significantly affect digital employment's transformation, and providing rich and diverse digital Although the results make some breakthroughs, limitations have also emerged.First, from the perspective of the samples, the analysis only focuses on the issue of women's digital employment equality.These samples are mainly from online surveys collected via the random sampling method.Therefore, future research can become more universal by choosing other sampling approach.Secondly, the results announced the potential of social support alliance and employment behavior theory to explain digital employment behavior.However, other variables involved in these two theories have not been proposed or verified in the paper, which provides opportunities for future research.
Future studies may continue to add more scientific and accurate sampling methods to women's employment research and even compare women's digital employment in different countries.Further refinement and verification of other variables that may have an impact can enrich the energy of social support alliance and employment behavior theory on digital employment behavior.Finally, it may further clarify the meaning of digital employment for gender equality, which can frame and process this macro concept.

CONCLUSION
This study aims to uncover the relationship between digital employment policy, digital employment value, and digital employment intention based on the social support alliance and employment behavior theory and construct a digital employment model for gender equality, which verifies the intermediary role of digital employment value.Finally, the study put forward proposals for the government and women to promote digital employment and inspire their possibilities in digital employment.
The digital employment policy emphasizes the impact of macro and micro economic policies on digital employment.The education policy aims to discuss how to provide more women with digital skills training and development opportunities.Employment policy is a broad concept essential for society to promote employee development and solve employment problems.Therefore, a scientific employment policy is conducive to making more groups aware of the importance of women's employment.
The value of digital employment makes women realize the possibilities of digital work, including increasing income, having a good working environment, making more friends, and mastering more skills.These women in the digital economy era have more possibilities and opportunities.
The study encourages them to enter the digital society actively, but this is also an essential part of current school education and enterprise development.
Improving women's digital employment intention is a channel conducive to achieving gender equality.Therefore, multi-dimensional and multifaceted attention to women's digital employment intention should become an essential field of scientific research.In the process of promoting the realization of sustainable development goals, digital empowerment has become one of the main ways to solve the employment problem.The digital economy industry is one of the emerging industries in the world, and digital employment is a significant breakthrough in promoting high-quality economic development.As one of the main components of digital employment, women have created tremendous value for developing the digital economy and sustainable development.Therefore, it has become the responsibility of all parts of society to actively attach importance to women's digital employment behavior.
The second part refers to a survey of digital employment policies fromRhodes et al. (2015), consisting of seven questions.Then, the study investigated the digital employment value using six questions derived fromKenney et al. (2019).The fourth part aims to measure digital employment intention based on seven questions ofWang et al. (2020).The study is adapted and modified based on the original scale to study digital employment intention.The questions use a 7-point Likert scale, from 1-7, "strongly disagree -strongly agree."

Table 1 .
Demographics of the respondents ipants are not old, their work experience is not inferior because there are 244 participants with more than three years of work experience, accounting for nearly 50% of the total population.Furthermore, it

Table 2 .
Reliability statistics

Table 4 .
Aggregate validity test Note: DEP: digital employment policy; DEV: digital employment value; DEI: digital employment intention.

Table 5 .
Differentiation validity test Note:The diagonal line is the square root of the corresponding AVE dimension.DEP: digital employment policy; DEV: digital employment value; DEI: digital employment intention.

Table 6
reveals the output results of modification indices.By sorting the MI values from the largest to the smallest, the residuals e5, e15, e18, and oth-er latent variables corresponding to DEP5, DEI3, and DEI6 have high MI values.

Table 8 .
Aggregate validity test (after deletion and correction) Note: DEP: digital employment policy; DEV: digital employment value; DEI: digital employment intention.

Table 10 .
Structural equation model path test

Table 7 .
Confirmatory factor analysis model fitting index

Table 11 .
Summary of mediation effect Figure 2. Structural equation model of digital employment for gender equality