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The strategic learning perspective has attracted increased interest among strategic management scholars, yet the operationalisation of this concept is still in its infancy. The aim of this study is to develop a multidimensional understanding of the strategic learning process and to build an instrument to measure this concept.

The article confirms the validity of the developed measurement instrument with expert evaluations and quantitative data from the analysis of 206 Finnish software companies. Structural equation modelling was the primary statistical technique used.

The results of the validation study suggest that strategic learning is a multidimensional construct that is manifested through the sub‐processes of strategic knowledge creation, distribution, interpretation, and implementation. The results demonstrate that the reliability and validity of the developed measurement model is satisfactory, thus enabling its use in further studies.

Although the validation study and the use of a panel of expert judges present substantial support for the developed construct, future research is necessary to continue to examine and refine the measure in other industries and cultural contexts.

Executives and practitioners can use the developed tool to identify potential areas for improvement and thus bring focus to organisational development efforts to enhance collective strategic learning.

This study contributes to strategic management research by developing and validating a measurement method for the concept of strategic learning. To date, the empirical research of strategic learning has been mainly limited to descriptive case studies, and the literature lacks a comprehensive measurement tool.

This research aims to outline the key factors responsible for industry 4.0 (I4.0) application in industries and establish a factor stratification model.

This article identifies the factor pool responsible for I4.0 from the extant literature. It aims to identify the set of key factors for the I4.0 application in the manufacturing industry and validate, classify factor pool using appropriate statistical tools, for example, factor analysis, principal component analysis and item analysis.

This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries from the factor pool. This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries. Strategy, leadership and culture are found key elements of transformation in the journey of I4.0. Additionally, design and development in the digital twin, virtual testing and simulations were also important factors to consider by manufacturing firms.

The proposed I4.0 factor stratification model will act as a starting point while designing strategy, adopting readiness index for I4.0 and creating a roadmap for I4.0 application in manufacturing. The I4.0 factors identified and validated in this paper will act as a guide for policymakers, researchers, academicians and practitioners working on the implementation of Industry 4.0. This work establishes a solid groundwork for developing an I4.0 maturity model for manufacturing industries.

The existing I4.0 literature is critically examined for creating a factor pool that further presented to experts to ensure sufficient rigor and comprehensiveness, particularly checking the relevance of subfactors for the manufacturing sector. This work is an attempt to identify and validate major I4.0 factors that can impact its mass adoption that is further empirically tested for factor stratification.