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The recently developed resource orchestration theory studies the processes by which managers handle resources to create competitive advantages. According to this theory, it is the way that resources interact with each other that results in such advantages. Resource integration, i.e. the alignment, or fit between resources, is one important outcome of resource orchestration processes. This paper aims to develop a scale and outline approaches to measuring such resource integration.

Using a typology of five types of resources derived from value theory, the authors develop a scale for measuring the fit between resource types, i.e. the degree of resource integration. The authors illustrate the method using a case example of an IT company and demonstrate how a variety of statistical methods including hierarchical cluster analysis, structural equation modeling, social network analysis and methods from biostatistics can provide measures of resource integration.

The authors develop a scale and associated measures that can help scholars systematically measure and identify firms with a high or low level of resource integration capability. This makes it possible to investigate further these companies and reconstruct how they support dynamic capabilities, as well as commonalities across firms with high and low levels of this capability.

Existing studies on resource orchestration have failed to provide us with a reliable measurement instrument that can be used both in cross-sectional work, and in repeated or time-series studies, allowing us to assess the degree to which a wider range of resources in an organization are integrated. The authors develop and demonstrate such an instrument.

The aim of this paper is to move toward a holistic model of organizational interpretation under uncertainty. This paper makes a series of novel conceptual propositions regarding the associations between state, effect and response uncertainty and the organizational interpretation process.

This conceptual paper extends existing conceptual work by distinguishing between general and issue-specific scanning and linking the interpretation process to three different types of perceived uncertainty: state, effect and response uncertainty.

It is proposed that environmental scanning leads to lower state and effect uncertainty, i.e. less uncertainty regarding the estimation of probabilities of events occurring in the external environment of the organization and of their consequences. It is further proposed that scanning leads to higher levels of perceived control over events and that the actual interpretation of events, in opportunity/threat terms, drives irregular issue-specific scanning and organizational reactions to such events.

The paper suggests a way to test links between organizational interpretation and uncertainty that might help explain and untangle some of the conflicting empirical results found in the extant literature. The paper illustrates how the literature could benefit from re-conceptualizing the perceived environmental uncertainty construct to take into account different types of uncertainty.

For practitioners, this paper emphasizes the importance of environmental scanning and how scanning practices can lead to general alertness, to more positive event interpretations and how interpretations form responses to opportunities in the environment.

This paper extends on existing work by linking the interpretation process to three different types of uncertainty (state, effect and response uncertainty) with several novel and testable propositions. The paper also differentiates clearly general (regular) scanning from issue-specific (irregular) scanning. Finally, the paper provides a unifying view, piecing together in one picture elements that have so far been dispersed in the literature.