Impact of CEO's scientific research background on the enterprise digital level

The paper situates enterprise digitalization within the broader rise of the digital economy, highlighting rapid advances in cloud computing, big data, blockchain, and artificial intelligence. It argues that digitalization reshapes industries, organizational forms, and value creation, while improving internal controls and decision-making efficiency at the firm level. The study asks how firms can enhance digitalization and emphasizes the role of scarce digital talent and leadership. Drawing on upper-echelon theory and imprinting theory, it posits that CEOs’ personal characteristics and formative experiences influence strategic choices including digital transformation. The paper focuses on whether CEOs’ scientific research backgrounds (from learning and work experiences) affect enterprise digitalization, considering China’s unique institutional context. The authors hypothesize that CEOs with research backgrounds better grasp digital strategy, have the capability and persistence to implement complex technological transformations, and thus drive higher digitalization levels. They also consider moderating roles of regional digital economy development, ownership (state vs. non-state), and regional integration, and propose innovation drive as a transmission channel.

The related literature section reviews the digital economy’s impact on resource allocation, productivity, and organizational redesign, and notes the long-term investments required for digital transformation. Using upper-echelon theory, the paper emphasizes that managerial characteristics shape cognition and decisions, while imprinting theory suggests specific formative experiences leave lasting effects. Prior work shows CEO and executive traits (education, tenure, gender, political orientation, functional expertise, military/financial background) affect firm outcomes such as CSR, innovation, and risk-taking. In China’s transforming economy, leaders’ cognition can substitute for institutional gaps. The authors synthesize studies linking executives’ academic/research backgrounds to decision quality and innovation, and research connecting regional environments (digital economy development, knowledge spillovers subject to spatial constraints) to firm behavior. They develop four hypotheses: H1, CEOs with research backgrounds increase firm digitalization; H2, regional digital economy level strengthens the positive effect; H3, the effect is weaker in state-owned enterprises; H4, higher regional integration strengthens the effect.

Sample: A-share firms listed on Shanghai and Shenzhen Stock Exchanges, 2011–2022. Exclusions include firms with missing data and ST/PT firms, yielding 4,969 firms and 25,229 firm-year observations. Data sources: Firm location from office address; city-level digital economy inputs from China City Statistical Yearbook; provincial economic and legal indicators from China Statistical Yearbook and Wang et al. (2017) with extrapolation; remaining variables from CSMAR. All continuous variables winsorized at 1st and 99th percentiles.

Dependent variable (Firm_Digi): Firm digitalization level measured via text analysis of annual reports. The authors construct a digital vocabulary covering AI, blockchain, cloud computing, big data, and digital application terms. Steps: (1) define digital lexicon; (2) extract and count keywords in annual reports (2011–2022) for five aspects; (3) set base year 2011 for cross-year comparison; (4) standardize counts by aspect per firm-year using Digit_x = (Number_it,x − Number_xmin^0)/(Number_xmax^0 − Number_xmin^0)×6+1; (5) sum standardized scores across five aspects to obtain Firm_Digi.

Independent variable: Sciback = 1 if CEO has an academic background and prior R&D/design experience (scientific research background), else 0.

Moderators: City_Digi (city-level digital economy index from PCA of internet development and digital financial inclusion indicators); SOE (1 if ultimately government-controlled); Area (1 if located in Yangtze River Delta, Pearl River Delta, or Beijing-Tianjin-Hebei regions).

Controls: Macro (LnGDP, Law, IC, EPU); Firm-level (Size, Lev, Boardsize, BI, Cash, Gap, INS, MH, TAT, Company_Age); CEO-level (Gender, Age, Seaback, Finback, Tenure). Industry and year fixed effects included.

Baseline model (OLS): Firm_Digi_it = a + a1·Sciback_it + Σ Controls_it + Industry FE + Year FE + ε_it.

Empirical strategy: Baseline OLS regressions, cross-sectional splits by City_Digi (high/low), ownership (SOE vs. non-SOE), and regional integration (Area high/low). Mediation analysis tests whether innovation enthusiasm (Inno_E = R&D expenditure / operating income) mediates Sciback → Firm_Digi. Robustness: alternative dependent variable (digital assets ratio), alternative independent variable (CEO digital background, Databack), PSM (1:1 nearest neighbor), IV (2SLS) using number of provincial “211” universities (Uni_Num) as instrument for Sciback, lagged Sciback, additional fixed effects (individual/year), exclusion of real estate and utilities, DID around CEO changes, and adding Tobin’s Q control.

• Descriptive and correlations: Firm_Digi mean 5.9690 (median 5.0938), with wide dispersion; Sciback mean 0.3915. Sciback positively correlates with Firm_Digi.
• Baseline OLS (Table 4): Sciback coefficient 0.0773 (t=2.0538), significant at 5% with full controls and FE, supporting H1 that CEOs with research backgrounds raise firm digitalization.
• Moderation by city digital economy (Table 5): In high City_Digi subsample, Sciback = 0.1467 (t=2.4215, p<0.05); in low City_Digi subsample, not significant. Supports H2.
• Ownership (Table 6): Non-SOE subsample: Sciback = 0.1057 (t=2.0616, p<0.05); SOE subsample: not significant. Supports H3 (weaker in SOEs).
• Regional integration (Table 7): High-integration cities: Sciback = 0.1791 (t=3.0657, p<0.01); low-integration cities: not significant. Supports H4.
• Mediation (Table 8): Sciback → Firm_Digi = 0.0773 (p<0.05); Sciback → Inno_E = 0.4891 (p<0.01); with both in model, Sciback becomes insignificant while Inno_E remains positive and significant, indicating full mediation via innovation enthusiasm (Inno_E).
• Robustness checks:
  • Alternative dependent variable (digital assets ratio, Table 9): Sciback = 0.0003 (t=3.9828, p<0.01), consistent with main results.
  • Alternative independent variable (Databack, Table 10): Databack = 0.6560 (t=6.6172, p<0.01).
  • PSM (Table 11): Sciback = 0.0911 (t=1.7409, p<0.10).
  • IV 2SLS with Uni_Num (Table 12): First stage Uni_Num significant (p<0.01); second stage Sciback significant (p<0.01); Cragg-Donald = 14.882 (>12), Kleibergen-PaaprkLM = 14.529 (p=0.0001), alleviating reverse causality concerns.
  • Additional tests: control individual and year fixed effects (Sciback = 0.2880, p<0.01; Table 13); exclude real estate/utility firms (0.0886, p<0.05; Table 14); DID around CEO change (0.3419, p<0.10; Table 15); add Tobin’s Q (0.0830, p<0.05; Table 16).

Findings indicate that CEOs with scientific research backgrounds materially enhance enterprise digitalization, aligning with upper-echelon and imprinting theories: such CEOs better perceive, value, and execute complex digital strategies. The effect is stronger where external conditions are favorable—cities with advanced digital economies and higher regional integration—suggesting environmental complements that reduce costs, expand talent supply, and normalize digital adoption. The effect is attenuated in SOEs, likely due to administrative appointment systems, constrained incentives, and risk-averse promotion criteria that dampen willingness to undertake high-cost, long-horizon digital initiatives. Mediation results suggest the mechanism operates through cultivating innovation enthusiasm (higher R&D intensity), which then translates into higher digitalization. Overall, the results answer the central question by demonstrating that the CEO’s research background is an intrinsic micro-level driver of digital transformation, and they delineate boundary conditions and the innovation channel through which the effect occurs.

The study shows that Chinese listed firms led by CEOs with scientific research backgrounds achieve higher levels of digitalization. The positive effect is stronger in regions with more advanced digital economies and higher regional integration and is weaker in state-owned enterprises. Mechanistically, CEOs’ research background promotes innovation enthusiasm, which fully mediates the impact on digitalization. Contributions include: (1) extending digital transformation research by identifying a managerial experiential determinant of firm digitalization; (2) integrating macro-regional and micro-firm perspectives to construct a refined text-analysis-based metric of firm digitalization; and (3) providing evidence on contextual moderators and a mediating innovation channel. Practical implications highlighted include cultivating open-minded executive leadership, internal rotations and training, recruiting research-oriented talent, and fostering university–industry collaboration; and at the policy level, tailoring support to regional and ownership contexts and enhancing incentives for SOE digital transformation.