The determinants of the use of process control mechanisms in FDI decisions in headquarters-subsidiary relationships

The paper addresses how FDI motivations (resource-seeking vs. market-seeking) and technological resource commitment shape headquarters’ use of behavioral control—specifically process control—over subsidiaries in multinational corporations. Situated within agency theory (principal–agent goal incongruence and information asymmetry) and the resource dependence perspective (bargaining power from critical resources), the study argues that control mechanisms vary with MNC strategies, multiple objectives, and the distribution of technological resources across HQs, subsidiaries, and local partners. Highlighting gaps in prior work that often examined equity control or single-party resource commitments, the authors focus on behavioral (process) control and consider three technological resource sources simultaneously. Using Taiwanese HQs and their Chinese subsidiaries (NIE to emerging economy context), the study tests whether resource-seeking motivations increase HQ process control, whether market-seeking motivations reduce it, and how technological resource commitments by HQs, subsidiaries, and partners respectively moderate HQs’ propensity to use process control. The study posits five hypotheses (H1–H5) linking motivations and resource commitments to process control and underscores the importance of designing fit-for-purpose control in contexts characterized by information asymmetry and institutional differences.

The literature review integrates agency theory with resource dependence theory to explain HQ–subsidiary control. Prior classifications distinguish equity ownership and behavioral control; this study emphasizes behavioral process control (monitoring and influencing operations), which can be flexibly adapted compared to equity-based control. Agency theory suggests that when monitoring costs are lower, goal conflicts are fewer, and tasks are programmable, HQs favor process control; otherwise, they rely more on output control. Motivations for FDI influence integration-responsiveness trade-offs: resource-seeking (e.g., labor, raw materials) often entails centralized integration and tighter control, while market-seeking emphasizes local responsiveness, potentially reducing process control due to higher information asymmetry and the need for local agility. The resource dependence perspective highlights bargaining power arising from technological resource commitments. HQ-contributed technological assets can increase HQ’s control to protect know-how and ensure effective transfer; subsidiary-developed resources can empower subsidiaries, reducing HQ process control; partner contributions (e.g., in JVs or local networks) may require autonomy and intense partner–subsidiary communication, further reducing HQ process control. The review formalizes five hypotheses: H1 resource-seeking motivation positively relates to HQ process control; H2 market-seeking motivation negatively relates to HQ process control; H3 HQ technological resource commitment positively relates to HQ process control; H4 subsidiary technological resource commitment negatively relates to HQ process control; H5 partners’ technological resource commitment negatively relates to HQ process control.

Design and data: Cross-sectional study using a national survey database maintained by the Statistics Bureau, Ministry of Economic Affairs (Taiwan). Population: 1541 Taiwanese manufacturing firms engaged in foreign investments; sample: 1015 HQ–subsidiary relationships where HQ is in Taiwan and subsidiary is in China (year 2003). Non-manufacturing subsidiaries (n=311) were excluded by SIC. The context—Taiwan (NIE) investing in China (emerging economy) around China’s early WTO period—supports testing agency and resource dependence mechanisms under high information asymmetry. Dependent variable: Process control (Pcontrol) measures the extent of HQ influence over subsidiary operations via five items: business strategy, pricing, marketing, personnel policy, and financial strategy. Coding: 3 = HQ determined; 2 = jointly determined; 1 = subsidiary determined. Sum of five items yields 5–15 (higher = more process control). Reliability: Cronbach’s alpha = 0.91; single factor confirmed by factor analysis (loadings≈0.80–0.92). Independent variables:

• Resource-seeking motivation (Resource): sum of three binary items (0/1) on ease of land acquisition, inexpensive/plentiful raw materials, plentiful low-wage labor; range 0–3.
• Market-seeking motivation (Market): sum of three binary items on market potential, host government incentives, avoiding high tariffs/trade barriers; range 0–3. Technological resource commitment measures (binary items summed):
• HQ technological resource commitment (Htrc): HQ provides key technologies, manufacturing equipment, raw materials, components/semi-finished goods; range 0–4.
• Subsidiary technological resource commitment (Strc): subsidiary-developed key technology, local sourcing of raw materials and components, own R&D department, own design department; range 0–5.
• Partners’ technological resource commitment (Ptrc): partners provide key technologies, learned from partners, co-developed with partners; range 0–3. Control variables: MNC size (Msize; ln employees), MNC international experience (MIexp; ln foreign sales ratio), MNC R&D expenses (MR&D; ln amount), entry mode (Emode; WOS>95% coded 1, else JV coded 0), subsidiary fixed asset investment intensity (SFAII), subsidiary experience (Sexp; years since establishment), importance of subsidiary in group (ISG; asset ratio), host-country barriers: GRIM (import/export restrictions), GRE (equity restrictions), DSCBP (differences in customs/practices), ILG (local government inefficiency), ILI (insufficient infrastructure), NQETM (lack of qualified expertise/technicians), UHCLF (uncertainty of home country legal framework). Industry dummies: Metal & Machinery (MM), Chemicals & Plastics (CP), Food/Textile/Others (FTO), with Information & Electronics (IE) as reference. Estimation: Hierarchical linear regression with mean-centered predictors to reduce multicollinearity (Aiken & West, 1991). Model 1 includes controls; Model 2 adds motivations; Model 3 adds technological resource commitments; Model 4 includes all. Robustness checks: split samples by firm size (median sales) and by industry (IE vs. non-IE); alternative size proxy (total assets, log). Results were consistent across robustness tests.

• Hypotheses supported with expected signs:
  • H1 (Resource-seeking → higher process control): β≈0.08 in Model 2; β≈0.09 in Model 4; p<0.001.
  • H2 (Market-seeking → lower process control): β≈−0.12 in Model 2; β≈−0.09 in Model 4; p<0.001.
  • H3 (HQ tech resource commitment → higher process control): β≈0.16 in Model 3; β≈0.15 in Model 4; p<0.001.
  • H4 (Subsidiary tech resource commitment → lower process control): β≈−0.14 in Model 3; β≈−0.15 in Model 4; p<0.001.
  • H5 (Partners’ tech resource commitment → lower process control): β≈−0.11 in Model 3; β≈−0.10 in Model 4; p<0.001.
• Model fit improves with added constructs: R² increases from 0.13 (controls) to 0.15 (motivations), 0.18 (resource commitments), and 0.19 (full model); all hierarchical F-tests significant (p<0.001).
• Notable control effects: Emode (WOS) positively associated with process control (β≈0.15–0.18, p<0.001); GRE (equity restrictions) positive (β≈0.07–0.08, p≤0.01); ILG (local government inefficiency) positive (p≈0.02–0.08); subsidiary experience (Sexp) negative (β≈−0.12 to −0.15, p<0.001); industry dummies MM, CP, FTO negative vs. IE (p values mostly ≤0.01).
• Robustness checks: results stable across firm size and industry splits; alternative size (assets) yielded consistent findings.

Findings confirm agency-theoretic expectations that HQs increase process control when resource-seeking motivates FDI—consistent with the need for integration, protection of know-how, and efficient production coordination in emerging markets—where lower monitoring costs and greater task programmability are feasible. Conversely, market-seeking FDI entails higher information asymmetry and the need for local responsiveness, leading HQs to reduce process control and delegate autonomy to subsidiaries. Resource dependence logic is supported: HQ technological resource commitments increase HQ bargaining power and the need to safeguard and efficiently deploy proprietary assets, thus elevating process control; subsidiary-developed resources shift bargaining power toward the subsidiary, decreasing HQ process control; partner technological contributions necessitate intensive partner–subsidiary interaction and alignment, and often expand the subsidiary’s local network power, making reduced HQ process control more effective. Results underscore the NIE-to-emerging-market context, where control choices may differ from patterns observed in developed-to-developed settings, and suggest that HQs may substitute toward output controls when process control is less suitable in market-seeking scenarios. Overall, the evidence shows that both FDI motivation and the locus of technological resources jointly shape behavioral control choices in HQ–subsidiary relations.

The study contributes by integrating agency theory and resource dependence perspectives to explain when and why HQs employ process control over subsidiaries. Using Taiwanese manufacturing MNCs with Chinese subsidiaries, it demonstrates that resource-seeking FDI and HQ technological resource commitments increase process control, while market-seeking FDI and technological commitments from subsidiaries and partners reduce it. Managerially, NIE MNCs operating in emerging markets should align control mechanisms with their FDI motivations and the origin of critical technological resources, balancing knowledge protection with the need for local agility. Future research should broaden contexts beyond Taiwan–China, incorporate multiple control types (process and output) simultaneously, examine resource transfer/leverage processes, link control to subsidiary role mandates, include service sectors, and integrate performance and longitudinal data to assess dynamics and outcomes of control choices.

• Context specificity: sample restricted to Taiwanese manufacturing HQs with Chinese subsidiaries (2003), limiting generalizability to other countries, periods, sectors, or broader cultural distance settings.
• Focus on HQ FDI motivations in emerging markets; does not include motivations for developed-market investments or subsidiaries’ own motivations.
• Dependent variable limited to process control; output control and other control forms were not simultaneously modeled.
• Mechanisms of resource transfer and leverage were not directly examined.
• Potential linkages between control mechanisms and subsidiary role positioning were not analyzed.
• Sample excludes service industries.
• Secondary, cross-sectional data without sales/financial performance measures; lack of longitudinal design raises potential time-lag concerns and limits causal inference.