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The purpose of this paper is to identify and screen continuous innovation capability enablers (CICEs) in Indonesia’s manufacturing sectors, develop a relationship among these enablers and determine their driving power and dependence power in the sector.

The initial CICEs identification process is based on a literature review, while a fuzzy Delphi method (FDM) was used for the screening process of CICEs. Total interpretive structural modelling (TISM) was used to develop contextual relationships among various CICEs. The results of the TISM are used as an input for the matrix of cross-impact multiplications applied to classification (MICMAC) to classify the driving power and dependence powers of the CICEs.

This paper selected 16 CICEs classified in seven dimensions. TISM results and MICMAC analysis show that leadership, as well as climate and culture, are enablers with the highest driving power and lowest dependence powers; followed by information technology. The results of this study indicate that efforts to continuously develop innovation capabilities in the Indonesian manufacturing industries are strongly influenced by their leadership capability, climate and culture, also information technology-related capability.

The framework assessed in this study provides business managers and policymakers to obtain a bigger picture in developing policies with evidence-based strategy and priority in regard to continuous innovation capability.

The results will be useful for business managers and policymakers to understand the relationship between CICEs and identify key CICEs in Indonesia’s manufacturing sectors, which were previously non-existent.

The purpose of this paper is to develop a comprehensive continuous innovation capability (CIC) measurement model in manufacturing sectors.

The development of this CIC model was conducted through three stages of research, i.e. identification of manufacturing continuous innovation measures (MCIMs), development of measurement model, followed by model evaluation and validation. MCIMs were identified using systematic literature review and focus group discussion. Selection process for MCIMs employed the fuzzy Delphi method. To develop measurement model, contextual relationships between MCIMs were assessed using total interpretive structural modeling, followed by measurements of MCIMs weight with the analytical network process method. Then, assessment indicators for each MCIM and criteria were determined as well as mathematical model to measure CIC scores. Model evaluation and validation were performed in two case studies: in an automotive company and an electronics company.

This research produced 50 criteria and 103 assessment indicators, as well as mathematical model to measure CIC scores. The validation process showed that currently developed model was deemed valid.

The results of this research are expected to provide a practical input for manufacturing company managers in managing their innovation activities systematically and comprehensively.

The CIC model is a new comprehensive measurement model; it integrates three fundamental elements of CI capability measurement, considering all important dimensions in a company and also able to explain contextual relationships between measured factors.