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Static program analysis aims to analyse program behavior without executing it, and has traditionally been implemented as offline tools running on desktop or server machines. As embedded and edge platforms gain compute capability, however, parts of these analyses are increasingly expected to run in new contexts—for example, inside Integrated Development Environments (IDEs), where developers require continuous, low-latency feedback as code evolves, and on embedded systems and hardware, where integrating selected analysis tasks can improve performance or enable new functionality. These settings impose strict constraints—low latency, limited memory, and energy efficiency—which favor demand-driven and incremental analyses over batch-oriented processing. A major obstacle to deploying static analyses in such settings is their reliance on multi-stage graph construction and fixed-point iteration; for example, computing control dependencies commonly involves transforming the program into a node-based representation, constructing a Control Flow Graph (CFG), and deriving a post-dominator tree (PDT), and materializing these intermediate structures increases both latency and memory footprint. This thesis enables the use of static program analysis techniques in such constrained environments through two distinct and complementary directions: first, it reduces unnecessary computation in unstructured code by eliminating full fixed-point iteration and avoiding complete construction of CFGs and PDTs; second, it introduces a syntax-directed method for computing control dependencies without constructing intermediate graphs or reformulating the program into additional node-based representations. The method is single-pass, requires only a constant number of auxiliary in-memory entities, and eliminates graph overhead; experimental evaluation shows that it preserves accuracy while substantially reducing memory usage and improving performance—typically by 6–12× compared to representative graph-based baselines—for well-structured programming languages. Although these two directions are distinct, they are complementary: the syntax-directed method is designed to be integrated into the demand-driven approach.

The rapid adoption of digital technologies has transformed the organizational structures of multinational corporations (MNCs) and altered how they coordinate geographically dispersed knowledge activities, centralizing some value‑chain activities while decentralizing others. Yet, despite growing scholarly interest, IB research continues to conceptualize digitalization largely as a unidimensional phenomenon, overlooking the heterogeneous and sometimes opposing effects of different forms of digital technologies on intra‑MNC coordination and knowledge transfer. This dissertation addresses this conceptual and empirical gap by examining how distinct forms of digital technology adoption trigger different organizational conditions and, in turn, influence subsidiaries’ capacity to transfer knowledge within the MNC network and their market-facing agility to contribute strategically. It comprises four papers: one conceptual study and three empirical studies based on two datasets.

The first empirical study builds the theoretical and methodological foundation for this dissertation by conceptualizing and operationalizing a firm’s digital technology adoption from a functional perspective. It develops a psychometrically validated 19-item scale manifested in four core dimensions: digital communication technology (DCT), digital in situ technology (DST), digital data analytics technology (DDAT), and digital networking technology (DNT), which reflect the coordination, optimization, data access, and location access functions that digital technologies perform within firms. The resulting measurement instrument, based on survey data from 113 technology-intensive Swedish firms, offers scholars a precise means to examine how digital technologies influence organizational processes. 

The second study advances a theoretical argument suggesting that MNCs’ adoption of distinct forms of digital technologies shapes value-chain activities through opposing centrifugal and centripetal forces associated with DCTs and DSTs, respectively. These opposing dynamics generate differentiated organizational conditions, leading to variations in subsidiaries’ capacity to disseminate knowledge to headquarters.

The third and fourth studies empirically demonstrate how MNCs’ adoption of different forms of digital technologies affects lateral knowledge transfer and subsidiaries’ responsiveness to market changes, drawing on data from 100 foreign subsidiaries of 14 Swedish MNCs. The findings reveal that while DST alone enhances operational efficiency but limits knowledge transfer, a combination of DST and DNT reduces a focal subsidiary’s dependence on local embeddedness and enhances lateral outward knowledge transfer via digital platforms. Moreover, lateral inward knowledge transfer and DDAT adoption enable subsidiaries to interpret market insights and respond rapidly to emerging opportunities.

Overall, this dissertation contributes to the IB literature by demonstrating that MNCs’ adoption of different digital technologies has heterogeneous organizational effects on knowledge flows and subsidiary roles. The findings offer guidance for managers assessing digital technology investments and strategic positioning.