Navigating post-pandemic challenges through institutional research networks and talent management

The study addresses how research and educational institutions can navigate post-COVID-19 constraints on physical research activities by leveraging research collaboration networks to support global talent management and enhance team scientific and technological performance. Grounded in the context of disrupted mobility and lab access during the pandemic, the paper posits that institutional intellectual capital—specifically human capital (skills, techniques, knowledge) and structural capital (processes, IT, databases)—drives team-level scientific and technological outcomes when channeled through collaboration platforms. It emphasizes the role of top management support in incentivizing knowledge sharing, adoption of collaboration systems, and sustained research productivity. Research objectives: (1) investigate strategies for hunting and managing global talent in research/educational institutions; (2) analyze the impact of human and structural capital on team scientific and technological performance via a research collaboration system; (3) test the moderating effect of top management support on these relationships.

The study integrates Resource-Based View (RBV) and institutional theory to explain adoption and effectiveness of research collaboration networks post-pandemic. RBV (Barney, 1991) frames human capital (HUC) and structural capital (STC) as valuable, rare, inimitable, and non-substitutable resources that underpin competitive advantage and performance. Institutional theory (Scott, 2008; Oliver, 1997) explains how norms, legitimacy, and top management support shape adoption of collaboration platforms and knowledge-sharing practices under mobility restrictions. Literature highlights: cross-border collaboration supports knowledge transfer and talent management but faces rising political/economic nationalism; research collaboration platforms help sustain knowledge exchange during/after COVID-19; HUC (skills, techniques, expertise) and STC (processes, IT infrastructure, databases, intellectual property) jointly enable knowledge sharing and innovation; top management support fosters resources, incentives, and alignment with institutional norms, strengthening knowledge sharing and performance. Hypotheses: H1: HUC positively impacts team scientific and technological performance (STP) using a research collaboration system. H2: STC positively impacts STP using a research collaboration system. H3a: Top management support (TMS) moderates the HUC→STP relationship (stronger under high TMS). H3b: TMS moderates the STC→STP relationship (stronger under high TMS).

Design: Three-wave time-lagged survey in China's research and academic sector to reduce common method variance. Sampling and procedure: T1 (baseline): HUC, STC, demographics; T2 (after one month): STP; T3 (after another month): TMS. Responses: contacted 450; usable questionnaires—T1: 417; T2: 403; T3: 363 (final sample). Demographics: 63.4% male, 36.6% female; ages 25–30 (6.6%), 31–35 (57%), 36–40 (19.8%), >40 (16.5%); experience 1–5 years (45.7%), 6–10 (39.4%), 11–15 (11.3%), >16 (3.6%); education: bachelor 4.1%, master 11.6%, PhD 78.8%, postdoc and above 5.5%. Measures: 5-point Likert (1=strongly disagree to 5=strongly agree). HUC: 8 items (Kim et al., 2016). STC: 7 items (Nezam et al., 2013). TMS: 7 items (Singh et al., 2021). STP: 4 items (Gonzalez-Mulé et al., 2016). Controls: age, gender, education, experience. Analysis: AMOS (v24) for CFA and structural modeling; reliability and validity assessed via factor loadings (>0.60), Cronbach’s alpha and Composite Reliability (>0.70), AVE (>0.50). Reported CR: HUC 0.938; STC 0.901; TMS 0.921; STP 0.911. AVE: HUC 0.629; STC 0.622; TMS 0.648; STP 0.639. Discriminant validity supported via cross-loadings, Fornell-Larcker criteria (AVE > squared inter-construct correlations), and HTMT (<0.85). CFA model fit (four-factor): χ²=414.650, df=198, χ²/df=2.094, TLI=0.95, CFI=0.93, RMSEA=0.06, SRMR=0.04. Hypothesis testing used bootstrapping with 5,000 samples to estimate path coefficients, p-values, and confidence intervals. Correlations: HUC with STC r=0.594**, TMS r=0.456**, STP r=0.517**; STC with TMS r=0.893**, STP r=0.853**; TMS with STP r=0.859** (p<0.001).

• Measurement model exhibited strong reliability and validity: CR ≥ 0.901; AVE ≥ 0.622; HTMT < 0.85; CFA fit acceptable (χ²/df=2.094, TLI=0.95, CFI=0.93, RMSEA=0.06, SRMR=0.04). - Direct effects: HUC → STP: B=0.476, SE=0.042, p=0.001 (H1 supported). STC → STP: B=0.877, SE=0.028, p=0.001 (H2 supported). - Moderation by top management support: HUC×TMS → STP: B=−0.1310, SE=0.0323, p=0.001; STC×TMS → STP: B=−0.1415, SE=0.0294, p=0.001 (H3a and H3b supported as significant moderating effects). Plots indicate stronger positive HUC→STP and STC→STP relationships under higher TMS. - Descriptive stats showed moderately high means for HUC (M=3.979), STC (M=3.742), TMS (M=3.832), and STP (M=3.971). - Substantial positive inter-construct correlations (all p<0.001) among HUC, STC, TMS, and STP, consistent with the hypothesized model.

Findings demonstrate that strengthening intellectual capital—both human and structural—within research and academic institutions enhances team-level scientific and technological performance, particularly when collaboration platforms facilitate knowledge exchange across borders. The results align with RBV by showing internal resources (skills, processes, IT infrastructure) drive performance. Institutional theory is supported through the demonstrated role of top management support in legitimizing and resourcing collaboration practices, which amplifies the performance benefits of intellectual capital. Practically, organizations can achieve higher productivity and innovation by investing in researcher capabilities, robust organizational systems, and active managerial support to promote adoption of research collaboration networks, especially in contexts of travel restrictions or future disruptions.

The study advances understanding of how organizations can navigate post-pandemic challenges by leveraging research collaboration systems to convert intellectual capital into superior team scientific and technological performance. It empirically confirms positive impacts of human and structural capital on performance and identifies top management support as a critical boundary condition that strengthens these effects. Contributions include integrating RBV and institutional perspectives with team performance metrics in a global talent management context. Future research should expand to larger, more diverse samples and explore additional moderators/mediators and longitudinal dynamics to further generalize and unpack mechanisms.

• Potential common method bias due to single-source, self-reported measures, despite time-lag design. - Final sample limited to 363 respondents from ten Chinese research and academic institutions, constraining generalizability. - Cross-sectional time-lagged design limits causal inference; fully longitudinal approaches recommended. - Future work should use larger, more diverse samples and examine additional boundary conditions to enhance model robustness and external validity.