Can Enterprises Achieve Shared Benefits from Digital Transformation?-A Study on Employee Benefits Expenditures in Chinese Listed Companies

Can Enterprises Achieve Shared Benefits from Digital Transformation?-A Study on Employee Benefits Expenditures in Chinese Listed Companies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Can Enterprises Achieve Shared Benefits from Digital Transformation?-A Study on Employee Benefits Expenditures in Chinese Listed Companies jin yao, zengwen wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8111385/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract With the rapid advancement of digital technologies, firms are increasingly embracing digital transformation to improve productivity and competitiveness. However, it remains unclear whether and how this technological shift yields shared benefits for employees. Using panel data of Chinese A-share listed companies (2014–2023), this study examines the effect of firm-level digitalization on employee benefits spending. We develop an analytical framework linking value creation to internal distribution and focus on three channels (innovation, efficiency, and labor composition). The results show that more digitalized firms allocate significantly more to employee benefits. Mechanism tests indicate that digitalization raises firms’ R&D intensity and R&D personnel share (boosting innovation-generated surplus), enhances total factor productivity and profit margins (strengthening efficiency rents), and shifts the workforce toward higher-skilled, more educated employees (heightening retention incentives). Heterogeneity analysis finds the positive effect is strongest among state-owned enterprises, larger firms, and those in more developed cities. These findings provide new insights into how technology-driven gains translate into improved employee benefits at the firm level, with practical implications for managers and policymakers promoting people-centered digital transformation. Business and commerce/Business and management Social science/Business and management Business and commerce/Economics Social science/Economics Earth and environmental sciences/Environmental social sciences Business and commerce/Information systems and information technology Digital Transformation Employee Benefits Chinese Listed Firms Figures Figure 1 1. Introduction Contemporary economies are undergoing a major transformation driven by digital technologies. Artificial intelligence, big data, cloud computing, and the Internet of Things are increasingly integrated into production and social systems, reshaping industries and global competition. The digital economy has become not only an important engine for post-crisis recovery but also a strategic priority for national growth and competitiveness (Nambisan et al., 2021; Plekhanov, 2023; Chen et al., 2024). Within this transformation, firms play a central role as key actors in market economies. The speed and effectiveness of digital transformation determine how well national digital strategies are implemented and how vibrant business activity is at the micro level. By leveraging digital technologies to improve production, update business models, and enhance management efficiency, firms strengthen their competitiveness and contribute to higher-quality economic growth (Warner & Wäger, 2019; Zhang & Li, 2023). However, the success of digital transformation should not be measured solely by profitability or productivity. Its broader goal is to promote inclusive and sustainable social welfare. International organizations such as the ILO and OECD stress that digitalization must remain people-centered; otherwise, it may worsen skill polarization, wage inequality, and job insecurity (Frey & Osborne, 2017; Autor et al., 2023). At the firm level, digital transformation changes how production is organized and managed, and has wide-ranging effects on workers. These effects go beyond employment levels or skill structures to include wages, benefits, training, and job quality (Han et al., 2024; Zhou et al., 2025). On the one hand, digital technologies can boost productivity and innovation, generating surplus value that enables higher pay, better working conditions, and greater investment in employee development. This in turn helps firms share the gains from technological progress more equitably (Wang et al., 2024). On the other hand, automation, task substitution, and the rapid pace of skill change may lead to income instability and job insecurity, especially for less-skilled workers. Whether digital transformation ultimately improves employee benefits or creates new risks is an open empirical question. Understanding how digitalization affects employee benefits is crucial (Li & Xu, 2025). It helps firms align technological upgrading with social responsibility, guides policymakers in designing effective labor protections, and contributes to balancing efficiency gains with fairness in labor-market outcomes. Furthermore, while existing literature has extensively examined the impact of digitalization on wages, employee benefits remain a less explored but critical component of total compensation. Benefits, such as pensions, housing funds, and healthcare, often reflect a firm’s long-term commitment to its workforce and its discretionary effort in sharing surplus, making them a pivotal lens through which to assess the ‘shared’ nature of digital gains. Building on these motivations, this study examines whether and how corporate digital transformation influences employee benefits at the firm level. Using data from Chinese listed companies, we empirically analyze the relationship between digitalization and employee benefits. The study aims to shed light on how technology-driven changes within firms affect labor outcomes. It also contributes to the discussion on income distribution in the digital economy and provides insights for policymakers and firms seeking to implement a people-oriented digital transformation. 2. Theoretical framework and hypothesis development Digital technologies are fundamentally transforming business models and production systems. Corporate digital transformation, defined as the organization-wide adoption of technologies such as artificial intelligence, big data, and cloud computing to redesign processes, business models, and organizational structures, aims to enhance efficiency, foster innovation, and build new sources of competitive advantage (Xu et al., 2024; Lu et al., 2025). Empirical studies show that digital transformation can increase total factor productivity, improve profitability, and stimulate innovation (Heiko et al., 2020; Yu & Meng, 2024). Recent firm-level and cross-country evidence further documents that the diffusion of data-driven decision-making and new ICT capital has been an important contributor to measured productivity gains, strengthening the claim that digitalization creates measurable economic surplus at the firm level (Brynjolfsson & McElheran, 2016; Graetz & Michaels, 2018). This economic surplus is the necessary precondition for firms to consider expanding discretionary compensation such as employee benefits. Simultaneously, digital adoption is reshaping labor demand. A growing body of research explores its impact on hiring, demand for skills, and wage structures (Scholz, 2016; Pulignano et al., 2024; Kim, 2024). As a form of skill-biased technological change, digital adoption tends to increase demand for high-skilled workers while replacing routine tasks, leading to occupational polarization and higher wage premiums for skilled labor (Deming, 2017; Acemoglu & Restrepo, 2020). Beyond wages, several studies suggest that technological change also affects the distribution of total labor remuneration (labor share) and the composition of compensation: automation and ICT can alter the balance between wage and non-wage components of pay, and the scarcity of digitally capable employees strengthens retention incentives (Karabarbounis & Neiman, 2014; Bessen, 2019). Therefore, the changing skill composition creates strategic pressure on firms to deploy retention-oriented instruments (e.g., enhanced benefits, training allowances), not only across pay scales but also across pay components. However, existing research has primarily examined wages as the channel through which digitalization affects income distribution, giving less attention to another key but more discretionary margin: discretionary benefits. Unlike relatively rigid wages, discretionary benefits reflect firms’ strategic choices in sharing productivity gains with employees, and are influenced by profitability, human capital strategy, and the institutional environment (Gerhart & Rynes, 2003; Bessen, 2019). At the same time, organizational and HR literature documents that adopting data-driven HR and IT systems enhances firms’ ability to target compensation, including benefits, to specific employee groups through performance analytics and differentiated schemes (Aral, Brynjolfsson & Wu, 2012; Bloom et al., 2014; Cappelli & Tavis, 2018). Yet, systematic evidence linking digital transformation to firm-level discretionary benefit allocation remains limited, which motivates our integrated analytical framework below. To address this gap, we propose an integrated analytical framework that specifically models employee benefits as a distinct and strategic margin of distribution. This framework not only links digitalization-driven value creation to distribution but also explicates why firms might prioritize increasing benefits over adjusting wages or reinvesting all surplus, particularly in the Chinese institutional context. The framework consists of two complementary dimensions. The first dimension concerns value creation: how digitalization generates economic surplus. Two key channels underpin this process. The efficiency channel builds on neoclassical and efficiency-improvement arguments: automation, data analytics, and process redesign can increase total factor productivity, reduce costs, and generate efficiency rents that can be shared with employees (Chiroleu-Assouline & Fodha, 2005; Zareie et al., 2024). The innovation channel follows Schumpeterian logic: by reducing experimentation costs and expanding technological possibilities, digitalization promotes R&D investment and successful innovations that generate monopoly profits, creating a sustainable source of surplus. (Youtie et al., 2018; Bodrožić & Adler, 2021). This created surplus constitutes the potential pool of resources for distribution. The second dimension involves distribution mechanisms, focusing specifically on why this surplus may be allocated to employee benefits. We posit two central mechanisms that make benefits a strategically favored channel for sharing digital gains, distinct from wages or reinvestment. The incentive mechanism is rooted in human capital and efficiency wage theories, but we refine it by emphasizing the relative flexibility and long-term commitment signaled by benefits. In China's dynamic labor market, base wages are often downwardly rigid due to institutional norms and regulatory expectations, and upward adjustments create permanent fixed costs. In contrast, certain benefits, such as enterprise annuities, training allowances, and high-end medical insurance, offer greater managerial discretion. Digital transformation increases the value of firm-specific human capital. To retain these critical employees and motivate them to acquire and apply new digital skills, firms use enhanced benefits as a strategic, long-term investment. This is not merely about reducing supervision costs but about locking in key talent with deferred and status-enhancing compensation that is less immediately portable than cash wages. The power mechanism, drawn from bargaining theory, highlights that skill-biased digitalization increases the scarcity and bargaining power of high-skilled workers (Acemoglu & Restrepo, 2022). We extend this by arguing that these workers' demands may specifically target benefits. High-skilled employees, often in prime earning years, have strong preferences for future-oriented compensation that addresses housing security, retirement planning, and family welfare. Consequently, firms are pressured to allocate surplus to benefits not only to match market offers but also to meet the specific composition of compensation demanded by the talent they need to attract and retain. Taken together, this framework (see Fig. 1 ) suggests that a firm’s capacity to create distributable surplus (through efficiency and innovation) and its willingness to share it (driven by incentive and power mechanisms) jointly determine employee benefit spending, with the distinct properties of benefits, including their discretion, long-term orientation, and alignment with high-skilled worker preferences, which makes them a strategically salient outlet for distributing digital gains. Based on this reasoning, we propose the following hypotheses: H1. Corporate digital transformation has a positive effect on employee benefits spending. Corporate digital transformation increases employee benefits spending via multiple channels: H2a. By enhancing firms’ innovation capacity, digital transformation raises benefit spending. H2b. By improving firms’ profitability, digital transformation raises benefit spending. H2c. By altering firms’ labor composition in favor of higher-skilled workers, digital transformation raises benefit spending. 3. Research design 3.1 Sample Selection and Data Processing To test the proposed hypotheses, this study uses panel data from A-share listed firms in Shanghai and Shenzhen for the period 2014 to 2023. Considering the availability and consistency of firm-level indicators for digital technology adoption and employee benefits, the sample is refined through several steps. First, firms in the financial industry are excluded because their business models and accounting practices differ substantially from those of non-financial firms (Beck et al., 2021). Second, companies labeled as ST or ST* during the sample period are removed to avoid distortions caused by abnormal operations. Third, all continuous variables are winsorized at the 1st and 99th percentiles to reduce the influence of outliers. After these adjustments, the final sample contains 36,789 firm-year observations. The regression sample is determined by the availability of data for all variables included in models. Consequently, the final sample size varies across different regression specifications due to missing values in specific variables, and the exact observation count for each regression is explicitly reported in its corresponding results table. Firm-level data are obtained from the CSMAR database, and city-level indicators are collected from various editions of the China Statistical Yearbook. 3.2 Variable Construction ( 1 ) Dependent Variable: employee benefits. The dependent variable measures firms’ spending on employee benefits. Specifically, it is proxied by annual expenditures reported under the “Employee Compensation Payable” account in financial statements. To reduce skewness, the natural logarithm of this value is used in the analysis. This account comprehensively captures cash and non-cash benefits paid to employees, providing a holistic measure of a firm's financial commitment to its workforce beyond base salaries. ( 2 ) Key Independent Variable: Digital Transformation. Digital transformation refers to a systematic and organization-wide process of integrating digital technologies into production, management, and decision-making. It involves upgrading production equipment, investing in human capital, and restructuring business models and management systems. Following Wu et al. (2021), this study measures firms’ digital transformation by calculating the frequency of digital-related keywords disclosed in annual reports. This text-based measure effectively captures the strategic emphasis and managerial attention devoted to digitalization, which is a precursor and companion to substantial tangible investments. Word frequencies associated with five major digital domains, including artificial intelligence, blockchain, cloud computing, big data, and digital applications, are summed for each firm-year. Because the distribution of this measure is right-skewed, one is added to the total frequency before taking the natural logarithm to construct the proxy for digital transformation. ( 3 ) Control Variables. At the firm level, control variables include firm size (lnsize), financial leverage (lev), Tobin’s Q (Tobin q), cash flow (CF), ownership balance (Balance), and state ownership (SOE). At the city level, we control for the logarithm of per capita GDP (lnGDP) and the fiscal capacity (FC) of the city where the firm is located. These controls account for fundamental firm characteristics that influence both the capacity to digitalize and the ability to pay benefits, as well as regional economic conditions that may affect labor costs and standards (Fan et al., 2020). 3.3 Model Specification To estimate the effect of digital transformation on employee benefits, we construct the following panel regression model: (1) where ,, denote firm, city and year, respectively. represents firm employee benefits investment;denotes the degree of digital transformation; and is a vector of control variables. 、、 represent firm, year, and city fixed effects, respectively, and is the idiosyncratic error term. The firm fixed effects control for time-invariant unobserved firm heterogeneity, year fixed effects absorb macroeconomic shocks common to all firms, and city fixed effects account for persistent regional differences in policy and development. Descriptive statistics for all variables are reported in Table 1 . Table 1 Descriptive Statistics of Variables Variable Obs Mean Std Min Max Benefits 24927 15.9174 1.5514 4.4121 23.0737 Digital 35288 1.6827 1.4285 0 6.3801 lnSize 35462 22.1474 1.4462 14.9416 31.3101 Lev 31740 1.2840 0.8519 0.2953 6.8550 Tobin-q 34920 2.0414 1.3215 0.8288 8.5638 CF 36441 0.8886 1.3514 0.0170 8.5360 Balance 35421 0.7988 0.6319 0.0031 4 SOE 36770 0.3084 0.4618 0 1 lnGDP 31011 11.6710 0.7884 9.3249 21.1475 FC 31374 0.1102 0.0420 0.0053 0.2143 4. Analysis and results 4.1 Baseline regression results Table 2 presents the baseline estimates of the effect of firm digitalization (Digital) on employee benefits (Benefits). Column ( 1 ) shows a simple specification that includes only the core explanatory variable and a constant. Column ( 2 ) adds firm-level control variables, while Columns ( 3 ) to ( 5 ) sequentially introduce firm, year, and city fixed effects to account for unobserved heterogeneity. In all specifications, the coefficient on Digital is positive and statistically significant. In the preferred specification (with firm, year, and city fixed effects), the coefficient on Digital is 0.0246 (statistically significant at the 5% level). Since both variables are logged, this implies that a 1% increase in Digital is associated with a 0.0246% increase in Benefits. These results support H1, providing initial evidence that firm digitalization can lead to higher employee benefits. Table 2 Baseline Regression Results ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) Benefits Benefits Benefits Benefits Benefits Digital 0.0700 *** 0.0293 *** 0.0442 *** 0.0290 *** 0.0246 ** (0.00701) (0.00560) (0.0102) (0.0104) (0.0103) lnSize 0.799 *** 0.682 *** 0.616 *** 0.602 *** (0.00664) (0.0199) (0.0266) (0.0242) Lev -0.129 *** -0.0273 *** -0.0235 ** -0.0232 ** (0.00969) (0.0104) (0.0103) (0.0102) Tobin-q 0.0300 *** -0.0144 * -0.0162 * -0.00888 (0.00646) (0.00813) (0.00973) (0.00779) CF -0.0599 *** -0.0193 ** -0.0202 ** -0.0181 ** (0.00616) (0.00803) (0.00817) (0.00803) Balance -0.00792 0.0116 0.00800 0.0145 (0.0125) (0.0279) (0.0282) (0.0285) SOE 0.138 *** -0.00964 -0.0418 -0.0426 (0.0185) (0.0497) (0.0495) (0.0497) lnGDP 0.0130 0.00850 0.00165 0.000590 (0.0108) (0.00663) (0.00673) (0.00673) FC -1.999 *** -0.869 1.275 * 0.868 (0.193) (0.639) (0.733) (0.796) Firm FE N N Y Y Y Year FE N N N Y Y City FE N N N N Y Cons 15.84 *** -1.517 *** 0.936 ** 2.158 *** 2.511 *** (0.0153) (0.191) (0.459) (0.581) (0.523) N 23991 17041 16934 16934 16934 R 2 0.004 0.562 0.903 0.904 0.907 4.2 Robustness check 1: alternative variable measures To examine whether the baseline findings are sensitive to different variable definitions, we conduct two robustness tests: ( 1 ) Re-specifying the independent variable (AI-Investment) First, we measure firm digitalization by focusing on a key technological input: firms’ investment in artificial intelligence. We construct AI-Investment as the natural logarithm of firm-level spending on AI-related projects (or AI capital investment), as this directly reflects the intensity of a firm’s commitment to digital transformation and the depth of technology adoption. Replacing Digital with AI-Investment produces a positive and statistically significant coefficient, consistent with the baseline results. This reduces concerns that the original text-frequency measure may bias the estimated relationship. ( 2 ) Re-specifying the dependent variable (Housing Provident Fund contributions) Second, we refine the dependent variable to better capture discretionary employee benefits. In China, social insurance contributions are largely mandatory and therefore do not reflect firms’ voluntary benefits decisions. By contrast, employee contributions to the Housing Provident Fund (HPF) and enterprise annuity better indicate discretionary benefits choices. The HPF is a particularly salient benefit in China, directly impacting employees’ ability to purchase housing, and firms have some discretion in setting the contribution ratio within a government-mandated range. Given the low coverage of enterprise annuities among listed firms in our sample (about 13.68%), we use the firm’s annual employee HPF contributions as an alternative dependent variable, measured in natural logarithms (HPF). This measure is widely available across firms and closely linked to employees’ perceived benefits needs in the Chinese housing context. Table 3 Robustness Check 1: Alternative Variable Measures ( 1 ) ( 2 ) Benefits HPF AI-Investment 0.0646 *** (0.00904) Digital 0.0284 *** (0.00748) Control Variables Y Y Firm FE Y Y Year FE Y Y City FE Y Y Cons 2.947 *** 1.879 *** (0.538) (0.414) N 15326 17100 R 2 0.908 0.965 Table 3 shows that the positive relationship remains robust under alternative measures. Whether we use AI-Investment for digitalization or HPF contributions for benefits, the main finding holds: greater digitalization is associated with higher employee benefits. 4.3 Robustness check 2: sample restrictions We further examine the robustness of the baseline results by applying sample restrictions to reduce potential sources of endogeneity and measurement bias. Specifically, we perform three complementary sample exclusions and re-estimate the baseline specification: First, we exclude firms in the information sector. Firms whose main business is the production of digital technologies or services naturally mention digital activities more frequently in annual reports. As a result, text-based measures of digitalization may overstate their actual transformation compared with non-digital firms. More importantly, this study focuses on industrial digitization, meaning the adoption of digital technologies by non-digital industries, rather than digital industrialization. Keeping information-sector firms could therefore mix two conceptually distinct processes and bias the estimated effect. Second, we exclude firms located in four leading Chinese cities: Beijing, Shanghai, Guangzhou, and Shenzhen. These cities have concentrated digital infrastructure, early policy pilots, and abundant technology supply, which reduce adoption costs and create labor-market conditions that differ from other regions. By removing observations from these top-tier cities, we test whether the results are influenced by regional differences in infrastructure, policy, or labor markets. Third, we convert the original unbalanced panel into a balanced panel by keeping only firms with continuous, non-missing observations over the entire sample period. This step addresses potential selection bias caused by firm entry, delisting, or intermittent reporting, which could otherwise affect the estimated relationship. Table 4 Robustness Check 2: Sample Restrictions ( 1 ) Exclude Information Industry Company ( 2 ) Exclude super-tier cities ( 3 ) Exclude missing samples Benefits Benefits Benefits Digital 0.0203 * 0.0547 *** 0.0327 ** (0.0107) (0.0134) (0.0131) Control Variables Y Y Y Firm FE Y Y Y Year FE Y Y Y City FE Y Y Y Cons 2.192 *** 15.45 *** 0.897 (0.582) (0.139) (0.742) N 14184 11685 11039 R 2 0.913 0.896 0.899 Table 4 presents the results. Across all restricted samples-the sample excluding information-sector firms, the sample excluding the four leading cities, and the balanced-panel sample-the coefficient on Digital remains positive and statistically significant. These findings indicate that the main result, that greater firm digitalization is associated with higher employee benefits, is robust to different sample structures and is not driven by the presence of specialized digital firms, mega-city effects, or panel unbalance. 5. Heterogeneity analysis We examine heterogeneity along three dimensions: ownership, firm size, and the economic development level of the city. For each dimension, we re-estimate the baseline specification and interpret the results considering organizational logic and institutional constraints. 5.1 Ownership Splitting the sample by ownership shows that the positive effect of digitalization on benefits is significant only for state-owned enterprises (SOEs), not for non-state-owned firms. As reported in Table 5 , this difference suggests that ownership structure shapes how digital gains are redistributed. Two mechanisms likely explain this pattern. First, SOEs face both commercial and social objectives and are subject to internal rules or external expectations that link performance to benefits adjustments. In China’s institutional context, SOEs operate under a ‘total wage and benefit quota’ system regulated by the State-owned Assets Supervision and Administration Commission (SASAC). Documented efficiency gains from digitalization provide a legitimate and financially viable justification for SOEs to apply for an increase in their total compensation quota, thereby facilitating higher employee benefits. As a result, productivity or profitability gains from digitalization are more likely to be partly allocated to benefit enhancements. Second, non-state-owned firms focus on profit maximization and cost control, so digital gains are more often reinvested or used for market expansion rather than for increasing employee benefits. Table 5 Heterogeneity Analysis: Ownership ( 1 ) State-owned enterprises ( 2 ) non-state-owned enterprises Benefits Benefits Digital 0.0328 ** 0.0199 (0.0162) (0.0131) Control Variables Y Y Firm FE Y Y Year FE Y Y City FE Y Y Cons 1.106 2.995 *** (0.866) (0.670) N 5569 11324 R 2 0.928 0.880 5.2 Firm size Subsample analysis by firm size (large, medium, small) shows that the positive effect is limited to large firms; medium and small firms show no significant response. As reported in Table 6 , this pattern likely reflects two factors: resource constraints and less formalized benefits systems in smaller firms. Large firms have greater financial capacity to invest in substantial digital projects and to sustain ongoing benefit costs. They also tend to have formal compensation systems that link benefits spending to performance, which facilitates converting digital gains into employee benefits. In contrast, smaller firms usually make more modest digital investments that do not significantly improve efficiency or profitability. They also operate with minimal discretionary benefits budgets, so any savings from digital adoption are more likely used to ease cash-flow pressures or support survival-oriented investments rather than to raise benefits. This finding underscores a potential ‘digital divide’ in the distribution of gains, where larger, more resourceful firms are better positioned to both capture and share the rewards of transformation. Table 6 Heterogeneity Analysis: Firm Size ( 1 ) large-sized enterprises ( 2 ) medium-sized enterprises ( 3 ) low-sized enterprises Benefits Benefits Benefits Digital 0.0630 *** 0.0491 0.0576 (0.0119) (0.0359) (0.111) Control Variables Y Y Y Firm FE Y Y Y Year FE Y Y Y City FE Y Y Y Cons 15.69 *** 14.75 *** 14.96 *** (0.152) (0.395) (1.444) N 12777 2964 384 R 2 0.887 0.755 0.633 5.3 Urban economic development level To examine regional heterogeneity, we split the sample by city per-capita GDP (above versus below the sample mean). As reported in Table 7 , the positive effect of Digital on Benefits is significant in the high-development subgroup but not in the low-development subgroup. Two mechanisms likely explain this difference. First, developed cities provide better digital infrastructure, denser technology ecosystems, and larger pools of skilled labor, which make it easier to turn digital investments into productivity and profitability gains, increasing the resources available for distribution. Second, labor-market institutions and competitive pressures in developed cities increase compliance requirements and emphasize talent retention, giving firms stronger incentives to allocate part of digital gains to benefits improvements. By contrast, firms in less developed areas face tighter resource constraints and may prioritize operational resilience and reinvestment over discretionary benefits spending. This regional heterogeneity points to a ‘spatial mismatch’ in the inclusive benefits of digitalization, potentially exacerbating existing regional inequalities. Table 7 Heterogeneity Analysis: City Economic Development Level ( 1 ) high per capita GDP ( 2 ) low per capita GDP Benefits Benefits Digital 0.0299 * 0.0152 (0.0155) (0.0143) Control Variables Y Y Firm FE Y Y Year FE Y Y City FE Y Y Cons 3.383 *** 1.675 (0.796) (1.515) N 9361 7280 R 2 0.908 0.925 Across these three dimensions, the benefits-enhancing effect of firm digitalization is not uniform. It is strongest among SOEs, concentrated in large firms, and most evident in economically advanced urban areas. These heterogeneous patterns highlight important boundary conditions for the generalizability of our baseline finding: converting digital gains into improved employee benefits depends not only on technological investments but also on ownership incentives, organizational capacity, and the broader regional context. 6. Mechanism analysis The baseline results show a positive relationship between firm digitalization and employee benefits. To understand how digitalization translates into benefits, we examine three potential channels rooted in the value-creation and distribution processes within firms: innovation, efficiency, and human capital composition. Evidence for these channels is presented in Tables 8 to 10 . Table 8 Innovation Channel ( 1 ) ( 2 ) Percentage of R&D Investment Percentage of R&D Personnel Digital 0.0686 ** 0.189 ** (0.0336) (0.0846) Control Variables Y Y Firm FE Y Y Year FE Y Y City FE Y Y Cons -1.487 14.73 *** (1.720) (4.554) N 21659 20129 R 2 0.889 0.923 6.1 Innovation channel Digitalization is linked to greater innovation activity, which can expand the pool of distributable surplus. Table 8 shows that higher digitalization is associated with increased R&D intensity and a larger share of R&D staff. Successful innovation may produce new products, technologies, or business models that generate returns above industry norms. These innovation-derived gains provide an incremental and discretionary source of surplus that firms can allocate to employee benefits. The positive coefficient on R&D personnel share further suggests that the innovation process itself creates a cadre of high-value employees whom the firm has a strong incentive to retain through better benefits. 6.2 Efficiency channel Digital investments also enhance operational efficiency. As reported in Table 9 , firms with higher digitalization exhibit significantly higher total factor productivity (TFP). Productivity improvements reduce unit costs and increase profit margins, thereby enlarging distributable surplus without necessarily crowding out productive reinvestment. In this way, efficiency gains provide a sustainable profit base that makes higher benefit spending possible. This channel underscores that digital transformation is not a zero-sum game; it can create a larger economic pie, from which slices can be allocated to workers without diminishing shareholder returns. Table 9 Efficiency Channel ( 1 ) ( 2 ) TFP_LP TFP_OP Digital 0.0285 *** 0.0168 *** (0.00659) (0.00635) Control Variables Y Y Firm FE Y Y Year FE Y Y City FE Y Y Cons -3.738 *** -2.300 *** (0.350) (0.344) N 21882 21882 R 2 0.941 0.913 6.3 Human Capital channel Digitalization changes firms’ skill requirements, increasing the proportion of technical and highly educated employees. Table 10 shows a positive association between digitalization and the share of technical staff and tertiary-educated workers. This shift in workforce composition affects bargaining dynamics and raises retention pressures. To attract and retain high-value, mobile talent, firms have stronger incentives to allocate part of digitalization gains to employee benefits. This channel highlights the dual role of human capital in digital transformation: it is both a key driver of value creation and a powerful claimant on the value created. Table 10 Human-Capital Channel ( 1 ) ( 2 ) Percentage of technical personnel Percentage of higher-education personnel Digital 0.00480 *** 0.00536 *** (0.00127) (0.00146) Control Variables Y Y Firm FE Y Y Year FE Y Y City FE Y Y Cons 0.0789 0.0320 (0.0637) (0.0761) N 24020 23804 R 2 0.898 0.928 These three channels are complementary and mutually reinforcing, forming a logical sequence from technology adoption to benefit distribution. Digitalization often drives innovation while shifting labor toward higher-skilled roles, both of which enhance productivity. The surplus generated through innovation and efficiency, combined with the distributional pressures associated with an upgraded workforce, increases the likelihood that firms allocate a portion of this surplus to employee benefits. Conceptually, this sequence links technology investment to surplus creation and ultimately to surplus distribution through mechanisms related to human capital composition, thereby supporting H1 and the mechanism hypotheses H2a to H2c. 7 Conclusion and policy implications This study examines how firm-level digitalization affects employee benefits. We develop an integrated framework that links value creation to internal distribution and test it using data from Chinese A-share listed firms. Our main finding is robust: higher digitalization is associated with increased employee benefit spending. Mechanism tests provide evidence for three complementary channels. First, digitalization is linked to greater innovation activity and higher R&D intensity, which can generate additional rents and increase the surplus available for distribution. Second, digital investments raise total factor productivity and reduce unit costs, expanding durable profit margins that make higher benefit spending feasible. Third, digitalization shifts firms’ human capital toward a larger share of technical and highly educated employees, increasing retention pressures and creating stronger incentives to allocate part of the gains to benefits. These results support hypotheses H1 and H2a-H2c. Heterogeneity analysis shows that the benefits-enhancing effects of digitalization are concentrated in state-owned enterprises, in large firms, and in firms located in economically advanced cities, highlighting important boundary conditions for generalization. The findings have both theoretical and practical implications. Theoretically, the study extends the literature on digitalization by linking productivity gains to internal distribution decisions and by showing how organizational incentives and regional context shape how firms share technological gains. Empirically, it provides micro-level evidence from a major emerging market, documenting the conditions under which technology-driven gains translate into improved employee benefits. From a policy and managerial perspective, the results suggest several targeted actions to promote an inclusive digital transformation: 7.1 For firms Firms, especially private and smaller enterprises, should view digital transformation not only as a cost-reduction tool but also as an opportunity to align human capital strategy. Complementing technology investments with targeted talent policies and firm-sponsored training can help convert productivity gains into sustainable benefits improvements for employees. Managers could consider establishing formal ‘Digital Gain-Sharing Programs’ that transparently link a portion of the quantified savings or profits from digital projects to employee benefit funds or special bonuses, making the distribution of gains tangible and motivating further employee engagement in the transformation process. 7.2 For policymakers Policymakers should strengthen labor-market institutions and create incentives that encourage firms to share technological gains. This could include supporting collective bargaining, incorporating broader benefits indicators into corporate responsibility assessments, and offering fiscal incentives or subsidies to reduce the effective cost of employee benefits for resource-constrained firms. For instance, designing a ‘Digital Transformation Benefit Tax Credit’ for SMEs that demonstrably link profit increases from digitalization to higher voluntary benefit expenditures could help level the playing field. At the regional level, investing in digital infrastructure and skills development can narrow disparities in digital capacity, enabling local firms to realize productivity gains and support employee benefits. Ultimately, ensuring that the benefits of digitalization reach workers requires coordinated action. Firms need to adopt people-centered digital strategies and pair technological upgrades with human capital initiatives, while governments should provide targeted, context-sensitive support to complement firm-level efforts. Together, strategic corporate behavior and effective public policy can guide digitalization toward outcomes that enhance productivity while promoting inclusive benefits. Declarations Competing interests The authors declare no competing interests. Ethical approval This article does not contain any studies with human participants performed by any of the authors. Informed consent This article does not contain any studies with human participants performed by any of the authors. Author Contribution jin yao and zengwen wang wrote the main manuscript text . All authors reviewed the manuscript Acknowledgements This research was supported by the project of National Natural Science Foundation of China. [grant number 72574171]. Data Availability The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request. References Acemoglu D, Restrepo P (2020) The wrong kind of AI? Artificial intelligence and the future of labour demand. J Economic Perspect 34(4):3–30 Acemoglu D, Restrepo P (2022) Automation and new tasks: How technology displaces and reinstates labor. J Econ Lit 60(2):394–449 Autor D, Katz L, Kearney M (2023) The polarization of job opportunities in the digital era. Am Econ Rev 113(4):1234–1275 Bessen JE (2019) AI and jobs: The role of demand. J Economic Perspect 33(2):97–120 Beck T, Demirgüç-Kunt A, Maksimovic V (2021) Financial and legal constraints to growth: Does firm size matter? J Banking Finance 127:106010 Bloom N, Sadun R, Van Reenen J (2014) Does management matter in schools? Quart J Econ 129(1):1–51 Bodrožić Z, Adler PS (2021) Alternative futures for the digital transformation: A macro-level Schumpeterian perspective. Organ Sci 33(1):105–125 Brynjolfsson E, McElheran K (2016) Digitalization and innovation: The role of data-driven decision making. Manage Sci 62(10):1452–1471 Cappelli P, Tavis A (2018) HR goes digital. Harvard Business Rev 96(5):124–131 Chen Y, Liu X, Wang H (2024) Digital transformation and firm performance: Evidence from China, vol 188. Technological Forecasting & Social Change, p 122565 Chiroleu-Assouline M, Fodha M (2005) Double dividend with involuntary unemployment: Efficiency and intergenerational equity. Environ Resour Econ 31(4):389–403 Deming DJ (2017) The growing importance of social skills in the labor market. Quart J Econ 132(4):1593–1640 Fan J, Wong TJ, Zhang T (2020) Institutions and organizational structure: The case of state-owned and non-state-owned enterprises in China. J Financ Econ 137(2):325–342 Frey CB, Osborne MA (2017) The future of employment: How susceptible are jobs to computerisation? Technol Forecast Soc Chang 114:254–280 Gebauer H, Fleisch E, Lamprecht C, Wortmann F (2020) Growth paths for overcoming the digitalization paradox. Bus Horiz 63(3):313–323 Gerhart B, Rynes SL (2003) Compensation: Theory, Evidence, and Strategic Implications. Sage Graetz G, Michaels G (2018) Robots at work. Econ J 128(608):205–232 Han Y, Yang J, Ying L, Niu Y (2024) The impact of corporate digital transformation on labor employment. Finance Res Lett 60:104888 Karabarbounis L, Neiman B (2014) The global decline of the labor share. Quart J Econ 129(1):61–103 Kim H (2024) The impact of robots on labor demand: Evidence from job vacancy data in South Korea. Empirical Economics 67(3):1185–1209 Li Q, Xu R (2025) Digital adoption and employee benefits: Empirical evidence from Chinese firms. J Bus Res 181:115–130 Lu M, Han Q, Hao Q (2025) Can digital transformation help alleviate corporate financial redundancy? Int Rev Econ Finance 97:103772 Nambisan S, Wright M, Feldman M (2019) The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes. Res Policy 48(8):103773 Plekhanov D, Franke H, Netland TH (2023) Digital transformation: A review and research agenda. Eur Manag J 41(6):821–844 Pulignano V, Grimshaw D, Domecka M, Vermeerbergen L (2024) Why does unpaid labour vary among digital labour platforms? Exploring socio-technical platform regimes of worker autonomy. Hum Relat 77(9):1243–1271 Scholz T (2016) Uberworked and Underpaid: How Workers Are Disrupting the Digital Economy. Polity, Cambridge Singer SJ, Pfeffer J, Nikolov MC (2025) An absence of accountability: Evidence of employers' failure to measure and manage employee health benefits administration. Soc Sci Med 377:118131 Wang Y, Huang B, Pan Y, Shao P (2024) Which groups benefit more? Evidence from the impact of the digital economy on the gender wage gap. Appl Econ 56(58):8462–8480 Warner KS, Wäger M (2019) Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Plann 52(3):326–349 Wu F, Hu HZ, Lin HY et al (2021) Enterprise digital transformation and capital market performance: Empirical evidence from stock liquidity[J]. Manage World 37(07):130–144 Xu M, Zhang Y, Sun H, Tang Y, Li J (2024) How digital transformation enhances corporate innovation performance: The mediating roles of big data capabilities and organizational agility. Heliyon 10, e34905 Youtie J, Shapira P, Roper S (2018) Exploring links between innovation and profitability in Georgia manufacturers. Econ Dev Q 32(4):271–287 Yu J, Meng S (2023) How does digital development affect firm innovation and who can benefit more? Technol Anal Strateg Manag 36(6):1–21 Zareie M, Attig N, Ghoul E, Fooladi S, I (2024) Firm digital transformation and corporate performance: The moderating effect of organizational capital. Finance Res Lett 61:105032 Zhang L, Li J (2023) Digitalization and firm competitiveness: Evidence from manufacturing firms. Res Policy 52(1):104619 Zhou Y, Shi X (2025) How does digital technology adoption affect corporate employment? Evidence from China. Econ Model 147:107045 Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003eContemporary economies are undergoing a major transformation driven by digital technologies. Artificial intelligence, big data, cloud computing, and the Internet of Things are increasingly integrated into production and social systems, reshaping industries and global competition. The digital economy has become not only an important engine for post-crisis recovery but also a strategic priority for national growth and competitiveness (Nambisan et al., 2021; Plekhanov, 2023; Chen et al., 2024). Within this transformation, firms play a central role as key actors in market economies. The speed and effectiveness of digital transformation determine how well national digital strategies are implemented and how vibrant business activity is at the micro level. By leveraging digital technologies to improve production, update business models, and enhance management efficiency, firms strengthen their competitiveness and contribute to higher-quality economic growth (Warner \u0026amp; W\u0026auml;ger, 2019; Zhang \u0026amp; Li, 2023).\u003c/p\u003e \u003cp\u003eHowever, the success of digital transformation should not be measured solely by profitability or productivity. Its broader goal is to promote inclusive and sustainable social welfare. International organizations such as the ILO and OECD stress that digitalization must remain people-centered; otherwise, it may worsen skill polarization, wage inequality, and job insecurity (Frey \u0026amp; Osborne, 2017; Autor et al., 2023). At the firm level, digital transformation changes how production is organized and managed, and has wide-ranging effects on workers. These effects go beyond employment levels or skill structures to include wages, benefits, training, and job quality (Han et al., 2024; Zhou et al., 2025). On the one hand, digital technologies can boost productivity and innovation, generating surplus value that enables higher pay, better working conditions, and greater investment in employee development. This in turn helps firms share the gains from technological progress more equitably (Wang et al., 2024). On the other hand, automation, task substitution, and the rapid pace of skill change may lead to income instability and job insecurity, especially for less-skilled workers.\u003c/p\u003e \u003cp\u003eWhether digital transformation ultimately improves employee benefits or creates new risks is an open empirical question. Understanding how digitalization affects employee benefits is crucial (Li \u0026amp; Xu, 2025). It helps firms align technological upgrading with social responsibility, guides policymakers in designing effective labor protections, and contributes to balancing efficiency gains with fairness in labor-market outcomes. Furthermore, while existing literature has extensively examined the impact of digitalization on wages, employee benefits remain a less explored but critical component of total compensation. Benefits, such as pensions, housing funds, and healthcare, often reflect a firm\u0026rsquo;s long-term commitment to its workforce and its discretionary effort in sharing surplus, making them a pivotal lens through which to assess the \u0026lsquo;shared\u0026rsquo; nature of digital gains.\u003c/p\u003e \u003cp\u003eBuilding on these motivations, this study examines whether and how corporate digital transformation influences employee benefits at the firm level. Using data from Chinese listed companies, we empirically analyze the relationship between digitalization and employee benefits. The study aims to shed light on how technology-driven changes within firms affect labor outcomes. It also contributes to the discussion on income distribution in the digital economy and provides insights for policymakers and firms seeking to implement a people-oriented digital transformation.\u003c/p\u003e"},{"header":"2. Theoretical framework and hypothesis development","content":"\u003cp\u003eDigital technologies are fundamentally transforming business models and production systems. Corporate digital transformation, defined as the organization-wide adoption of technologies such as artificial intelligence, big data, and cloud computing to redesign processes, business models, and organizational structures, aims to enhance efficiency, foster innovation, and build new sources of competitive advantage (Xu et al., 2024; Lu et al., 2025). Empirical studies show that digital transformation can increase total factor productivity, improve profitability, and stimulate innovation (Heiko et al., 2020; Yu \u0026amp; Meng, 2024). Recent firm-level and cross-country evidence further documents that the diffusion of data-driven decision-making and new ICT capital has been an important contributor to measured productivity gains, strengthening the claim that digitalization creates measurable economic surplus at the firm level (Brynjolfsson \u0026amp; McElheran, 2016; Graetz \u0026amp; Michaels, 2018). This economic surplus is the necessary precondition for firms to consider expanding discretionary compensation such as employee benefits.\u003c/p\u003e \u003cp\u003eSimultaneously, digital adoption is reshaping labor demand. A growing body of research explores its impact on hiring, demand for skills, and wage structures (Scholz, 2016; Pulignano et al., 2024; Kim, 2024). As a form of skill-biased technological change, digital adoption tends to increase demand for high-skilled workers while replacing routine tasks, leading to occupational polarization and higher wage premiums for skilled labor (Deming, 2017; Acemoglu \u0026amp; Restrepo, 2020). Beyond wages, several studies suggest that technological change also affects the distribution of total labor remuneration (labor share) and the composition of compensation: automation and ICT can alter the balance between wage and non-wage components of pay, and the scarcity of digitally capable employees strengthens retention incentives (Karabarbounis \u0026amp; Neiman, 2014; Bessen, 2019). Therefore, the changing skill composition creates strategic pressure on firms to deploy retention-oriented instruments (e.g., enhanced benefits, training allowances), not only across pay scales but also across pay components.\u003c/p\u003e \u003cp\u003eHowever, existing research has primarily examined wages as the channel through which digitalization affects income distribution, giving less attention to another key but more discretionary margin: discretionary benefits. Unlike relatively rigid wages, discretionary benefits reflect firms\u0026rsquo; strategic choices in sharing productivity gains with employees, and are influenced by profitability, human capital strategy, and the institutional environment (Gerhart \u0026amp; Rynes, 2003; Bessen, 2019). At the same time, organizational and HR literature documents that adopting data-driven HR and IT systems enhances firms\u0026rsquo; ability to target compensation, including benefits, to specific employee groups through performance analytics and differentiated schemes (Aral, Brynjolfsson \u0026amp; Wu, 2012; Bloom et al., 2014; Cappelli \u0026amp; Tavis, 2018). Yet, systematic evidence linking digital transformation to firm-level discretionary benefit allocation remains limited, which motivates our integrated analytical framework below.\u003c/p\u003e \u003cp\u003eTo address this gap, we propose an integrated analytical framework that specifically models employee benefits as a distinct and strategic margin of distribution. This framework not only links digitalization-driven value creation to distribution but also explicates why firms might prioritize increasing benefits over adjusting wages or reinvesting all surplus, particularly in the Chinese institutional context. The framework consists of two complementary dimensions.\u003c/p\u003e \u003cp\u003eThe first dimension concerns value creation: how digitalization generates economic surplus. Two key channels underpin this process. The efficiency channel builds on neoclassical and efficiency-improvement arguments: automation, data analytics, and process redesign can increase total factor productivity, reduce costs, and generate efficiency rents that can be shared with employees (Chiroleu-Assouline \u0026amp; Fodha, 2005; Zareie et al., 2024). The innovation channel follows Schumpeterian logic: by reducing experimentation costs and expanding technological possibilities, digitalization promotes R\u0026amp;D investment and successful innovations that generate monopoly profits, creating a sustainable source of surplus. (Youtie et al., 2018; Bodrožić \u0026amp; Adler, 2021). This created surplus constitutes the potential pool of resources for distribution.\u003c/p\u003e \u003cp\u003eThe second dimension involves distribution mechanisms, focusing specifically on why this surplus may be allocated to employee benefits. We posit two central mechanisms that make benefits a strategically favored channel for sharing digital gains, distinct from wages or reinvestment.\u003c/p\u003e \u003cp\u003eThe incentive mechanism is rooted in human capital and efficiency wage theories, but we refine it by emphasizing the relative flexibility and long-term commitment signaled by benefits. In China's dynamic labor market, base wages are often downwardly rigid due to institutional norms and regulatory expectations, and upward adjustments create permanent fixed costs. In contrast, certain benefits, such as enterprise annuities, training allowances, and high-end medical insurance, offer greater managerial discretion. Digital transformation increases the value of firm-specific human capital. To retain these critical employees and motivate them to acquire and apply new digital skills, firms use enhanced benefits as a strategic, long-term investment. This is not merely about reducing supervision costs but about locking in key talent with deferred and status-enhancing compensation that is less immediately portable than cash wages.\u003c/p\u003e \u003cp\u003eThe power mechanism, drawn from bargaining theory, highlights that skill-biased digitalization increases the scarcity and bargaining power of high-skilled workers (Acemoglu \u0026amp; Restrepo, 2022). We extend this by arguing that these workers' demands may specifically target benefits. High-skilled employees, often in prime earning years, have strong preferences for future-oriented compensation that addresses housing security, retirement planning, and family welfare. Consequently, firms are pressured to allocate surplus to benefits not only to match market offers but also to meet the specific composition of compensation demanded by the talent they need to attract and retain.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTaken together, this framework (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) suggests that a firm\u0026rsquo;s capacity to create distributable surplus (through efficiency and innovation) and its willingness to share it (driven by incentive and power mechanisms) jointly determine employee benefit spending, with the distinct properties of benefits, including their discretion, long-term orientation, and alignment with high-skilled worker preferences, which makes them a strategically salient outlet for distributing digital gains. Based on this reasoning, we propose the following hypotheses:\u003c/p\u003e \u003cp\u003eH1. Corporate digital transformation has a positive effect on employee benefits spending.\u003c/p\u003e \u003cp\u003eCorporate digital transformation increases employee benefits spending via multiple channels:\u003c/p\u003e \u003cp\u003eH2a. By enhancing firms\u0026rsquo; innovation capacity, digital transformation raises benefit spending.\u003c/p\u003e \u003cp\u003eH2b. By improving firms\u0026rsquo; profitability, digital transformation raises benefit spending.\u003c/p\u003e \u003cp\u003eH2c. By altering firms\u0026rsquo; labor composition in favor of higher-skilled workers, digital transformation raises benefit spending.\u003c/p\u003e"},{"header":"3. Research design","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sample Selection and Data Processing\u003c/h2\u003e \u003cp\u003eTo test the proposed hypotheses, this study uses panel data from A-share listed firms in Shanghai and Shenzhen for the period 2014 to 2023. Considering the availability and consistency of firm-level indicators for digital technology adoption and employee benefits, the sample is refined through several steps. First, firms in the financial industry are excluded because their business models and accounting practices differ substantially from those of non-financial firms (Beck et al., 2021). Second, companies labeled as ST or ST* during the sample period are removed to avoid distortions caused by abnormal operations. Third, all continuous variables are winsorized at the 1st and 99th percentiles to reduce the influence of outliers. After these adjustments, the final sample contains 36,789 firm-year observations. The regression sample is determined by the availability of data for all variables included in models. Consequently, the final sample size varies across different regression specifications due to missing values in specific variables, and the exact observation count for each regression is explicitly reported in its corresponding results table. Firm-level data are obtained from the CSMAR database, and city-level indicators are collected from various editions of the China Statistical Yearbook.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Variable Construction\u003c/h2\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Dependent Variable: employee benefits. The dependent variable measures firms\u0026rsquo; spending on employee benefits. Specifically, it is proxied by annual expenditures reported under the \u0026ldquo;Employee Compensation Payable\u0026rdquo; account in financial statements. To reduce skewness, the natural logarithm of this value is used in the analysis. This account comprehensively captures cash and non-cash benefits paid to employees, providing a holistic measure of a firm's financial commitment to its workforce beyond base salaries.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Key Independent Variable: Digital Transformation. Digital transformation refers to a systematic and organization-wide process of integrating digital technologies into production, management, and decision-making. It involves upgrading production equipment, investing in human capital, and restructuring business models and management systems. Following Wu et al. (2021), this study measures firms\u0026rsquo; digital transformation by calculating the frequency of digital-related keywords disclosed in annual reports. This text-based measure effectively captures the strategic emphasis and managerial attention devoted to digitalization, which is a precursor and companion to substantial tangible investments. Word frequencies associated with five major digital domains, including artificial intelligence, blockchain, cloud computing, big data, and digital applications, are summed for each firm-year. Because the distribution of this measure is right-skewed, one is added to the total frequency before taking the natural logarithm to construct the proxy for digital transformation.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Control Variables. At the firm level, control variables include firm size (lnsize), financial leverage (lev), Tobin\u0026rsquo;s Q (Tobin q), cash flow (CF), ownership balance (Balance), and state ownership (SOE). At the city level, we control for the logarithm of per capita GDP (lnGDP) and the fiscal capacity (FC) of the city where the firm is located. These controls account for fundamental firm characteristics that influence both the capacity to digitalize and the ability to pay benefits, as well as regional economic conditions that may affect labor costs and standards (Fan et al., 2020).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Model Specification\u003c/h2\u003e \u003cp\u003eTo estimate the effect of digital transformation on employee benefits, we construct the following panel regression model:\u003c/p\u003e \u003cp\u003e (1)\u003c/p\u003e \u003cp\u003ewhere ,, denote firm, city and year, respectively. represents firm employee benefits investment;denotes the degree of digital transformation; and is a vector of control variables. 、、 represent firm, year, and city fixed effects, respectively, and is the idiosyncratic error term. The firm fixed effects control for time-invariant unobserved firm heterogeneity, year fixed effects absorb macroeconomic shocks common to all firms, and city fixed effects account for persistent regional differences in policy and development. Descriptive statistics for all variables are reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics of Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenefits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.9174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.4121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.0737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.3801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.1474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.9416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.3101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.8550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTobin-q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.5638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.5360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBalance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.6710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.3249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.1475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Analysis and results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Baseline regression results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the baseline estimates of the effect of firm digitalization (Digital) on employee benefits (Benefits). Column (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) shows a simple specification that includes only the core explanatory variable and a constant. Column (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) adds firm-level control variables, while Columns (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) to (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) sequentially introduce firm, year, and city fixed effects to account for unobserved heterogeneity. In all specifications, the coefficient on Digital is positive and statistically significant. In the preferred specification (with firm, year, and city fixed effects), the coefficient on Digital is 0.0246 (statistically significant at the 5% level). Since both variables are logged, this implies that a 1% increase in Digital is associated with a 0.0246% increase in Benefits. These results support H1, providing initial evidence that firm digitalization can lead to higher employee benefits.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Regression Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenefits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenefits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBenefits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBenefits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBenefits\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0700\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0293\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0442\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0290\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0246\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.00701)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00560)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0103)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003elnSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.799\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.682\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.616\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.602\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00664)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0266)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0242)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.129\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0273\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0235\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0232\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00969)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0102)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTobin-q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0300\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0144\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0162\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00646)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.00813)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.00973)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.00779)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0599\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0193\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0202\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0181\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00616)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.00803)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.00817)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.00803)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBalance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0279)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0282)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0285)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSOE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.138\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0497)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0495)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0497)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003elnGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000590\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.00663)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.00673)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.00673)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.999\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.275\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.193)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.639)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.733)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.796)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.84\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.517\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.936\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.158\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.511\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.459)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.581)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.523)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Robustness check 1: alternative variable measures\u003c/h2\u003e \u003cp\u003eTo examine whether the baseline findings are sensitive to different variable definitions, we conduct two robustness tests:\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Re-specifying the independent variable (AI-Investment)\u003c/p\u003e \u003cp\u003eFirst, we measure firm digitalization by focusing on a key technological input: firms\u0026rsquo; investment in artificial intelligence. We construct AI-Investment as the natural logarithm of firm-level spending on AI-related projects (or AI capital investment), as this directly reflects the intensity of a firm\u0026rsquo;s commitment to digital transformation and the depth of technology adoption. Replacing Digital with AI-Investment produces a positive and statistically significant coefficient, consistent with the baseline results. This reduces concerns that the original text-frequency measure may bias the estimated relationship.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Re-specifying the dependent variable (Housing Provident Fund contributions)\u003c/p\u003e \u003cp\u003eSecond, we refine the dependent variable to better capture discretionary employee benefits. In China, social insurance contributions are largely mandatory and therefore do not reflect firms\u0026rsquo; voluntary benefits decisions. By contrast, employee contributions to the Housing Provident Fund (HPF) and enterprise annuity better indicate discretionary benefits choices. The HPF is a particularly salient benefit in China, directly impacting employees\u0026rsquo; ability to purchase housing, and firms have some discretion in setting the contribution ratio within a government-mandated range. Given the low coverage of enterprise annuities among listed firms in our sample (about 13.68%), we use the firm\u0026rsquo;s annual employee HPF contributions as an alternative dependent variable, measured in natural logarithms (HPF). This measure is widely available across firms and closely linked to employees\u0026rsquo; perceived benefits needs in the Chinese housing context.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness Check 1: Alternative Variable Measures\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenefits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHPF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAI-Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0646\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.00904)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0284\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00748)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.947\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.879\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.538)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.414)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the positive relationship remains robust under alternative measures. Whether we use AI-Investment for digitalization or HPF contributions for benefits, the main finding holds: greater digitalization is associated with higher employee benefits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Robustness check 2: sample restrictions\u003c/h2\u003e \u003cp\u003eWe further examine the robustness of the baseline results by applying sample restrictions to reduce potential sources of endogeneity and measurement bias. Specifically, we perform three complementary sample exclusions and re-estimate the baseline specification:\u003c/p\u003e \u003cp\u003eFirst, we exclude firms in the information sector. Firms whose main business is the production of digital technologies or services naturally mention digital activities more frequently in annual reports. As a result, text-based measures of digitalization may overstate their actual transformation compared with non-digital firms. More importantly, this study focuses on industrial digitization, meaning the adoption of digital technologies by non-digital industries, rather than digital industrialization. Keeping information-sector firms could therefore mix two conceptually distinct processes and bias the estimated effect.\u003c/p\u003e \u003cp\u003eSecond, we exclude firms located in four leading Chinese cities: Beijing, Shanghai, Guangzhou, and Shenzhen. These cities have concentrated digital infrastructure, early policy pilots, and abundant technology supply, which reduce adoption costs and create labor-market conditions that differ from other regions. By removing observations from these top-tier cities, we test whether the results are influenced by regional differences in infrastructure, policy, or labor markets.\u003c/p\u003e \u003cp\u003eThird, we convert the original unbalanced panel into a balanced panel by keeping only firms with continuous, non-missing observations over the entire sample period. This step addresses potential selection bias caused by firm entry, delisting, or intermittent reporting, which could otherwise affect the estimated relationship.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness Check 2: Sample Restrictions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Exclude Information Industry Company\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Exclude super-tier cities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Exclude missing samples\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenefits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenefits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBenefits\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0203\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0547\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0327\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0131)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.192\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.45\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.582)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.139)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.742)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results. Across all restricted samples-the sample excluding information-sector firms, the sample excluding the four leading cities, and the balanced-panel sample-the coefficient on Digital remains positive and statistically significant. These findings indicate that the main result, that greater firm digitalization is associated with higher employee benefits, is robust to different sample structures and is not driven by the presence of specialized digital firms, mega-city effects, or panel unbalance.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Heterogeneity analysis","content":"\u003cp\u003eWe examine heterogeneity along three dimensions: ownership, firm size, and the economic development level of the city. For each dimension, we re-estimate the baseline specification and interpret the results considering organizational logic and institutional constraints.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Ownership\u003c/h2\u003e \u003cp\u003eSplitting the sample by ownership shows that the positive effect of digitalization on benefits is significant only for state-owned enterprises (SOEs), not for non-state-owned firms. As reported in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, this difference suggests that ownership structure shapes how digital gains are redistributed. Two mechanisms likely explain this pattern. First, SOEs face both commercial and social objectives and are subject to internal rules or external expectations that link performance to benefits adjustments. In China\u0026rsquo;s institutional context, SOEs operate under a \u0026lsquo;total wage and benefit quota\u0026rsquo; system regulated by the State-owned Assets Supervision and Administration Commission (SASAC). Documented efficiency gains from digitalization provide a legitimate and financially viable justification for SOEs to apply for an increase in their total compensation quota, thereby facilitating higher employee benefits. As a result, productivity or profitability gains from digitalization are more likely to be partly allocated to benefit enhancements. Second, non-state-owned firms focus on profit maximization and cost control, so digital gains are more often reinvested or used for market expansion rather than for increasing employee benefits.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity Analysis: Ownership\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) State-owned enterprises\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) non-state-owned enterprises\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenefits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenefits\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0328\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0162)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0131)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.995\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.866)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.670)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Firm size\u003c/h2\u003e \u003cp\u003eSubsample analysis by firm size (large, medium, small) shows that the positive effect is limited to large firms; medium and small firms show no significant response. As reported in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, this pattern likely reflects two factors: resource constraints and less formalized benefits systems in smaller firms. Large firms have greater financial capacity to invest in substantial digital projects and to sustain ongoing benefit costs. They also tend to have formal compensation systems that link benefits spending to performance, which facilitates converting digital gains into employee benefits. In contrast, smaller firms usually make more modest digital investments that do not significantly improve efficiency or profitability. They also operate with minimal discretionary benefits budgets, so any savings from digital adoption are more likely used to ease cash-flow pressures or support survival-oriented investments rather than to raise benefits. This finding underscores a potential \u0026lsquo;digital divide\u0026rsquo; in the distribution of gains, where larger, more resourceful firms are better positioned to both capture and share the rewards of transformation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity Analysis: Firm Size\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) large-sized enterprises\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) medium-sized enterprises\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) low-sized enterprises\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenefits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenefits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBenefits\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0630\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0576\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0119)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0359)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.111)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.69\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.75\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.96\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.152)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.395)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.444)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Urban economic development level\u003c/h2\u003e \u003cp\u003eTo examine regional heterogeneity, we split the sample by city per-capita GDP (above versus below the sample mean). As reported in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the positive effect of Digital on Benefits is significant in the high-development subgroup but not in the low-development subgroup. Two mechanisms likely explain this difference. First, developed cities provide better digital infrastructure, denser technology ecosystems, and larger pools of skilled labor, which make it easier to turn digital investments into productivity and profitability gains, increasing the resources available for distribution. Second, labor-market institutions and competitive pressures in developed cities increase compliance requirements and emphasize talent retention, giving firms stronger incentives to allocate part of digital gains to benefits improvements. By contrast, firms in less developed areas face tighter resource constraints and may prioritize operational resilience and reinvestment over discretionary benefits spending. This regional heterogeneity points to a \u0026lsquo;spatial mismatch\u0026rsquo; in the inclusive benefits of digitalization, potentially exacerbating existing regional inequalities.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity Analysis: City Economic Development Level\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) high per capita GDP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) low per capita GDP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenefits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenefits\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0299\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0143)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.383\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.796)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.515)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAcross these three dimensions, the benefits-enhancing effect of firm digitalization is not uniform. It is strongest among SOEs, concentrated in large firms, and most evident in economically advanced urban areas. These heterogeneous patterns highlight important boundary conditions for the generalizability of our baseline finding: converting digital gains into improved employee benefits depends not only on technological investments but also on ownership incentives, organizational capacity, and the broader regional context.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Mechanism analysis","content":"\u003cp\u003eThe baseline results show a positive relationship between firm digitalization and employee benefits. To understand how digitalization translates into benefits, we examine three potential channels rooted in the value-creation and distribution processes within firms: innovation, efficiency, and human capital composition. Evidence for these channels is presented in Tables\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e to \u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInnovation Channel\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of R\u0026amp;D Investment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of R\u0026amp;D Personnel\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0686\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.189\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0336)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0846)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.73\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.720)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.554)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Innovation channel\u003c/h2\u003e \u003cp\u003eDigitalization is linked to greater innovation activity, which can expand the pool of distributable surplus. Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows that higher digitalization is associated with increased R\u0026amp;D intensity and a larger share of R\u0026amp;D staff. Successful innovation may produce new products, technologies, or business models that generate returns above industry norms. These innovation-derived gains provide an incremental and discretionary source of surplus that firms can allocate to employee benefits. The positive coefficient on R\u0026amp;D personnel share further suggests that the innovation process itself creates a cadre of high-value employees whom the firm has a strong incentive to retain through better benefits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Efficiency channel\u003c/h2\u003e \u003cp\u003eDigital investments also enhance operational efficiency. As reported in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, firms with higher digitalization exhibit significantly higher total factor productivity (TFP). Productivity improvements reduce unit costs and increase profit margins, thereby enlarging distributable surplus without necessarily crowding out productive reinvestment. In this way, efficiency gains provide a sustainable profit base that makes higher benefit spending possible. This channel underscores that digital transformation is not a zero-sum game; it can create a larger economic pie, from which slices can be allocated to workers without diminishing shareholder returns.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEfficiency Channel\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTFP_LP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTFP_OP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0285\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0168\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.00659)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00635)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.738\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.300\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.350)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.344)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Human Capital channel\u003c/h2\u003e \u003cp\u003eDigitalization changes firms\u0026rsquo; skill requirements, increasing the proportion of technical and highly educated employees. Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows a positive association between digitalization and the share of technical staff and tertiary-educated workers. This shift in workforce composition affects bargaining dynamics and raises retention pressures. To attract and retain high-value, mobile talent, firms have stronger incentives to allocate part of digitalization gains to employee benefits. This channel highlights the dual role of human capital in digital transformation: it is both a key driver of value creation and a powerful claimant on the value created.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHuman-Capital Channel\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of technical personnel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of higher-education personnel\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00480\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00536\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.00127)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00146)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0637)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0761)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23804\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese three channels are complementary and mutually reinforcing, forming a logical sequence from technology adoption to benefit distribution. Digitalization often drives innovation while shifting labor toward higher-skilled roles, both of which enhance productivity. The surplus generated through innovation and efficiency, combined with the distributional pressures associated with an upgraded workforce, increases the likelihood that firms allocate a portion of this surplus to employee benefits. Conceptually, this sequence links technology investment to surplus creation and ultimately to surplus distribution through mechanisms related to human capital composition, thereby supporting H1 and the mechanism hypotheses H2a to H2c.\u003c/p\u003e \u003c/div\u003e"},{"header":"7 Conclusion and policy implications","content":"\u003cp\u003eThis study examines how firm-level digitalization affects employee benefits. We develop an integrated framework that links value creation to internal distribution and test it using data from Chinese A-share listed firms. Our main finding is robust: higher digitalization is associated with increased employee benefit spending.\u003c/p\u003e \u003cp\u003eMechanism tests provide evidence for three complementary channels. First, digitalization is linked to greater innovation activity and higher R\u0026amp;D intensity, which can generate additional rents and increase the surplus available for distribution. Second, digital investments raise total factor productivity and reduce unit costs, expanding durable profit margins that make higher benefit spending feasible. Third, digitalization shifts firms\u0026rsquo; human capital toward a larger share of technical and highly educated employees, increasing retention pressures and creating stronger incentives to allocate part of the gains to benefits. These results support hypotheses H1 and H2a-H2c. Heterogeneity analysis shows that the benefits-enhancing effects of digitalization are concentrated in state-owned enterprises, in large firms, and in firms located in economically advanced cities, highlighting important boundary conditions for generalization.\u003c/p\u003e \u003cp\u003eThe findings have both theoretical and practical implications. Theoretically, the study extends the literature on digitalization by linking productivity gains to internal distribution decisions and by showing how organizational incentives and regional context shape how firms share technological gains. Empirically, it provides micro-level evidence from a major emerging market, documenting the conditions under which technology-driven gains translate into improved employee benefits. From a policy and managerial perspective, the results suggest several targeted actions to promote an inclusive digital transformation:\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e7.1 For firms\u003c/h2\u003e \u003cp\u003eFirms, especially private and smaller enterprises, should view digital transformation not only as a cost-reduction tool but also as an opportunity to align human capital strategy. Complementing technology investments with targeted talent policies and firm-sponsored training can help convert productivity gains into sustainable benefits improvements for employees. Managers could consider establishing formal \u0026lsquo;Digital Gain-Sharing Programs\u0026rsquo; that transparently link a portion of the quantified savings or profits from digital projects to employee benefit funds or special bonuses, making the distribution of gains tangible and motivating further employee engagement in the transformation process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e7.2 For policymakers\u003c/h2\u003e \u003cp\u003ePolicymakers should strengthen labor-market institutions and create incentives that encourage firms to share technological gains. This could include supporting collective bargaining, incorporating broader benefits indicators into corporate responsibility assessments, and offering fiscal incentives or subsidies to reduce the effective cost of employee benefits for resource-constrained firms. For instance, designing a \u0026lsquo;Digital Transformation Benefit Tax Credit\u0026rsquo; for SMEs that demonstrably link profit increases from digitalization to higher voluntary benefit expenditures could help level the playing field. At the regional level, investing in digital infrastructure and skills development can narrow disparities in digital capacity, enabling local firms to realize productivity gains and support employee benefits.\u003c/p\u003e \u003cp\u003eUltimately, ensuring that the benefits of digitalization reach workers requires coordinated action. Firms need to adopt people-centered digital strategies and pair technological upgrades with human capital initiatives, while governments should provide targeted, context-sensitive support to complement firm-level efforts. Together, strategic corporate behavior and effective public policy can guide digitalization toward outcomes that enhance productivity while promoting inclusive benefits.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed consent\u003c/strong\u003e \u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ejin yao and zengwen wang wrote the main manuscript text . All authors reviewed the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis research was supported by the project of National Natural Science Foundation of China. [grant number 72574171].\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcemoglu D, Restrepo P (2020) The wrong kind of AI? Artificial intelligence and the future of labour demand. J Economic Perspect 34(4):3\u0026ndash;30\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcemoglu D, Restrepo P (2022) Automation and new tasks: How technology displaces and reinstates labor. 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Econ Model 147:107045\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Digital Transformation, Employee Benefits, Chinese Listed Firms","lastPublishedDoi":"10.21203/rs.3.rs-8111385/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8111385/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the rapid advancement of digital technologies, firms are increasingly embracing digital transformation to improve productivity and competitiveness. However, it remains unclear whether and how this technological shift yields shared benefits for employees. Using panel data of Chinese A-share listed companies (2014\u0026ndash;2023), this study examines the effect of firm-level digitalization on employee benefits spending. We develop an analytical framework linking value creation to internal distribution and focus on three channels (innovation, efficiency, and labor composition). The results show that more digitalized firms allocate significantly more to employee benefits. Mechanism tests indicate that digitalization raises firms\u0026rsquo; R\u0026amp;D intensity and R\u0026amp;D personnel share (boosting innovation-generated surplus), enhances total factor productivity and profit margins (strengthening efficiency rents), and shifts the workforce toward higher-skilled, more educated employees (heightening retention incentives). Heterogeneity analysis finds the positive effect is strongest among state-owned enterprises, larger firms, and those in more developed cities. These findings provide new insights into how technology-driven gains translate into improved employee benefits at the firm level, with practical implications for managers and policymakers promoting people-centered digital transformation.\u003c/p\u003e","manuscriptTitle":"Can Enterprises Achieve Shared Benefits from Digital Transformation?-A Study on Employee Benefits Expenditures in Chinese Listed Companies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-08 16:42:44","doi":"10.21203/rs.3.rs-8111385/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3524c918-f001-41c5-a3ab-a5eae1abeb88","owner":[],"postedDate":"April 8th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-14T04:29:46+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65862927,"name":"Business and commerce/Business and management"},{"id":65862928,"name":"Social science/Business and management"},{"id":65862929,"name":"Business and commerce/Economics"},{"id":65862930,"name":"Social science/Economics"},{"id":65862931,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":65862932,"name":"Business and commerce/Information systems and information technology"}],"tags":[],"updatedAt":"2026-05-14T04:40:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-08 16:42:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8111385","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8111385","identity":"rs-8111385","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}