A Dynamic Resource-Bourcing Framework for Entrepreneurial Opportunity Sensing

Thesis Abstract

This study addresses a critical gap in entrepreneurship research by presenting a Dynamic Resource-Bourcing Framework (DRBF) for entrepreneurial opportunity sensing, emphasizing how temporal shifts in resource access and partner networks influence opportunity identification, evaluation, and mobilization. The problem addressed is that traditional opportunity sensing models often assume static resource pools and linear information flows, which inadequately capture the rapid resource contingencies faced by new ventures in dynamic environments. The aim is to develop and validate a framework that articulates how dynamic resource acquisition processes interact with sensemaking mechanisms to enhance recognition and exploitation of opportunities. Specific objectives include (1) theorizing a dynamic resource-bourcing mechanism linking resource heterogeneity, network fluidity, and opportunity signals; (2) identifying antecedents and moderators (e.g., founder improvisational capability, connective social capital, and institutional context) that shape sensing accuracy; (3) operationalizing key constructs into measurable indicators and constructing a parsimonious predictive model of sensing outcomes; (4) assessing the framework's predictive validity across multiple industries and geographies; and (5) delineating managerial implications for early-stage ventures and policy recommendations for entrepreneurial ecosystems. The methodology adopts a mixed-methods design, integrating a cross-sectional survey with a longitudinal panel component and qualitative case studies. The population comprises 4,000 early-stage ventures across technology, manufacturing, and service sectors in five regional innovation ecosystems. A stratified random sample of 1,200 ventures is drawn for the survey, with a 28-month longitudinal follow-up for 600 firms to capture dynamic resource sourcing and sensing outcomes. Data collection employs structured questionnaires measuring dynamic resource-bourcing capabilities (frequency of reconfiguring resource bundles, time-lagged resource acquisition, partner diversification), opportunity sensing accuracy (convergence between sensed opportunities and subsequent venture pivots or launches), and cognitive and network factors (sensemaking intensity, partner trust, social capital). Instruments incorporate validated scales adapted from prior work in dynamic capabilities, opportunity recognition, and social network analysis, supplemented by in-depth semi-structured interviews with 40 founders or top executives to triangulate survey data. Validity and reliability are established through pilot testing, confirmatory factor analysis (CFA) for construct validity, Cronbach’s alpha above 0.80 for internal consistency, and test-retest reliability over the longitudinal interval. Analytical approaches include partial least squares structural equation modeling (PLS-SEM) to test the proposed DRBF relationships and moderation effects, complemented by time-series cross-sectional analyses to capture dynamic effects. Multilevel modeling will assess cross-level interactions between individual founders and ecosystem-level variables. The qualitative component employs thematic analysis of interview transcripts, guided by the Resource-Based View (RBV) and Dynamic Capabilities framework, to elucidate the micro-foundations of resource-bourcing processes and sensemaking in opportunity detection. A subset of 12 case studies will be selected for in-depth narrative analysis to illustrate mechanism pathways and boundary conditions. Expected findings include (a) a positive association between dynamic resource-bourcing capabilities and sensing accuracy, moderated by founder improvisational ability and high-quality social networks; (b) evidence that rapid reconfiguration of resource portfolios shortens the time from opportunity signal to venture action, particularly in technologically turbulent sectors; (c) identification of distinct sensing pathways—high-velocity signal integration versus slow, deliberative analysis—differing in industry and institutional context; (d) validation of a parsimonious DRBF model with strong predictive power for venture formation, pivot decisions, and subsequent performance indicators over the 28-month period. The study contributes to knowledge by integrating dynamic resource-sourcing concepts with opportunity sensing theory within a coherent framework, extending the RBV and Dynamic Capabilities literature by detailing how real-time resource acquisition and network reconfiguration shape entrepreneurial cognition and action. Practically, the DRBF provides a diagnostic tool for founders and incubators to assess and enhance resource-bourcing readiness, and informs policymakers on supporting mechanisms that strengthen ecosystem liquidity and connective infrastructure. The main conclusion posits that dynamic resource-bourcing is a critical precursor to effective opportunity sensing, with sustainability of advantage contingent on continuous resource reconfiguration and adaptive sensemaking. Recommendations encompass strategic resource diversification, investment in accelerators that foster network permeability, and policy interventions to reduce frictions in resource exchange across regional ecosystems.