An ISM-Based Framework for Open Innovation Enablers Driving the Adoption of Industry 4.0 in Small Manufacturing Firms in Caribbean Small Island Developing States
Journal Title: International Journal of Knowledge and Innovation Studies - Year 2024, Vol 2, Issue 3
Abstract
This study examines the role of Open Innovation (OI) in facilitating the adoption of Industry 4.0 (I4.0) technologies by small manufacturing enterprises in the non-energy sector of Caribbean Small Island Developing States (SIDS). These firms encounter significant challenges, including limited resources, inadequate infrastructure, and underdeveloped innovation ecosystems, which necessitate the adoption of tailored OI practices. A comprehensive literature review was conducted to identify the key enablers of OI, which led to the development of a conceptual framework. Insights gained from structured interviews with industry experts were used to assess the influence of these enablers on I4.0 adoption. Pairwise comparisons were employed to explore the interrelationships among these factors, culminating in the construction of a reachability matrix and a hierarchical model through Interpretive Structural Modelling (ISM) to analyse the dependencies and causal relationships among them. The study identified “Competitive Pressure,” “Customer Pressure,” and “Managerial Dynamic Capabilities” as the primary enablers driving OI and influencing the adoption of I4.0 technologies. Intermediate factors, such as “Digital Trust,” “R&D Investment Capabilities,” and “Collaborative Networks,” were found to mediate the relationship between the primary enablers and the outcome of “Adaptation to Global Best Practices.” Despite the fact that OI practices are often driven by external pressures, the adoption of I4.0 technologies was found to be strongly supported by managerial dynamic capabilities, highlighting the importance of both push and pull factors. The adaptation to global best practices is significantly shaped by managerial capabilities, competitive pressures, and customer demands. Furthermore, environmental scanning was identified as an essential tool for aligning managerial dynamic capabilities with market conditions, facilitating agile decision-making for technology adoption through collaboration. Strategic interventions to support intermediary factors are crucial for small firms to navigate external pressures, sustain innovation, and build internal capabilities for I4.0. The findings contribute to the development of a networked ecosystem framework, which offers a pathway to strengthening stakeholder alliances, implementing customer-centric open OI practices, and enhancing management effectiveness. It is concluded that the successful adoption of I4.0 technologies is achievable through strategic, managerial, and policy-driven frameworks that align with global standards and address competitive and customization demands.
Authors and Affiliations
Annusha Bridglal, Kit Fai Pun
Keywords
Related Articles
- EP ID EP756128
- DOI https://doi.org/10.56578/ijkis020304
- Views 2
- Downloads 0
Racism and Hate Speech Detection on Twitter: A QAHA-Based Hybrid Deep Learning Approach Using LSTM-CNN
Twitter, a predominant platform for instantaneous communication and idea dissemination, is often exploited by cybercriminals for victim harassment through sexism, racism, hate speech, and trolling using pseudony-mous acc...
Enhanced Global Image Segmentation: Addressing Pixel Inhomogeneity and Noise with Average Convolution and Entropy-Based Local Factor
In the field of computer vision and digital image processing, the division of images into meaningful segments is a pivotal task. This paper introduces an innovative global image segmentation model, distinguished for its...
Assessing the Urban Competitiveness of European Cities Using LOPCOW-RAWEC Methodologies
Urban competitiveness is an essential determinant of the long-term sustainability and economic development of cities, influencing not only local prosperity but also national growth. The accurate measurement of urban comp...
Development and Evaluation of a Parallel K-means Algorithm for Big Data Analysis in Google MapReduce Environment
The challenge of executing iterative big data analysis algorithms within the Google Cloud MapReduce environment has been addressed by developing a parallel K-means algorithm capable of leveraging the distributed computin...
Utilizing Edge Cloud Computing and Deep Learning for Enhanced Risk Assessment in China’s International Trade and Investment
Amidst a transformative economic milieu in China, domestic enterprises are venturing into the global market, exposing them to intensified perils in international trade and investment. This research elucidates the interna...