Resource capabilities and sustainable export performance: An application of m-TISM for Indian manufacturing MSMEs

Arora, Kashika; Siddiqui, Areej Aftab

doi:10.1080/10686967.2022.2034493

Cited by 5 publications

(2 citation statements)

References 120 publications

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“…Future researchers are also encouraged to use m-TISM as a simpler and more convenient way of constructing structural explanatory models to validate their results and findings if there are fewer variables, the field of study is well established and the relevant literature is extensive. [78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93]…”

Section: Limitations and Future Workmentioning

confidence: 99%

Artificial intelligence healthcare service resources adoption by medical institutions based on TOE framework

2022

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Objectives This study used the Technology-Organization-Environment (TOE) framework to identify the factors involved in the decisions made by integrated medical and healthcare organizations to adopt artificial intelligence (AI) elderly care service resources. Method This study identified the Decision-making Trial and Evaluation Laboratory-Interpretive Structural Modeling (DEMATEL-ISM) method was used to construct a multilayer recursive structural model and to analyze the interrelationships between the levels. A MICMAC quadrant diagram was used for a cluster analysis. Results The ISM recursive structural model was divided into a total of seven layers. The bottom layer contained the four factors of High risk of data leakage (T1), Lack of awareness of the value and benefits of AI healthcare technology (T5), Lack of management leadership support (O1), and Government policies (E1). Having a low dependency but high driving force, these factors are the root causes of adoption by healthcare organizations. The topmost layer contained the most direct factors, which had a high dependency but the low driving force, influencing adoption: Competitive pressures (E2), Lack of patient trust (E5), and Lack of excellent partnerships (E7). Healthcare organizations are more concerned with technology and their environments when deciding to adopt intelligent healthcare resources. Conclusion The combination of the three methods of DEMATEL-ISM-MICMAC construction models provides new ideas for smart healthcare services for hospitals. The DEMATEL method favors the construction dimension of the micro-model, while the ISM method favors the construction dimension of the macro-model. Combining these two methods may reduce the loss of information within the system, simplify the matrix calculation workload, and improve the efficiency of operations while decomposing the complex problems into several sub-problems in a more comprehensive and detailed way. Conducting cluster analysis of the adoption determinants utilizing MICMAC quadrant diagrams may provide strong methodological guidance and decision-making recommendations for government departments, senior decision-makers in healthcare organizations, and policy-makers in associations in the senior care industry.

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Section: Limitations and Future Workmentioning

confidence: 99%

Artificial intelligence healthcare service resources adoption by medical institutions based on TOE framework

2022

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“…Environmental research adopted this method to identify solutions to reduce energy consumption and pollution [81,82]. Nevertheless, this approach was applied to knowledge management disciplines, human resource management, and technologyrelated risk management [83][84][85][86], including analysis of factors affecting the effectiveness of e-learning [87].…”

Section: Tism and Its Relation With Open Innovationmentioning

confidence: 99%

An Innovation Framework of Medical Organic Cannabis Traceability in Digital Supply Chain

Pookkaman

Samanchuen

2022

Journal of Open Innovation: Technology, Market, and Complexity

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Cannabis is increasingly accepted by medical organizations for medicinal and research purposes. A traceability system is required for monitoring and controlling the use of cannabis. This work aimed to investigate the relationship between critical success factors (CSFs) for creating the innovation framework that affects the implementation of the cannabis traceability system. These factors are identified based on the digital supply chain by structural modeling. Additionally, the issue of patients’ safety is a crucial factor that needs to be considered. Total Interpretive Structural Modeling (TISM) and fuzzy MICMAC analysis techniques were applied to investigate the relationship between CSFs. Finally, the 14 CSFs affecting cannabis traceability systems were determined. The research results discovered that system reliability is the most significant key factor with the greatest influence and contribution to the achievement. The five CSFs based on TISM comprise harmonized strategic orientation, inter-organization collaboration, standardization, government regulations, and customer awareness. According to the conclusive dependence–driving power diagram, they have the most driving power in the implementation and operation traceability system. The innovation framework helps to establish the traceability system in a way that works well. This will build trust in the supply chain and prevent abuse and substandard products.

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Exploring the challenges to cope with ripple effects in the perishable food supply chain considering recent disruptions: Implications for urban supply chain resilience

Aziz,

Arman,

Karmaker

et al. 2025

Journal of Open Innovation: Technology, Market, and Complexity

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Resource capabilities and sustainable export performance: An application of m-TISM for Indian manufacturing MSMEs

Cited by 5 publications

References 120 publications

Artificial intelligence healthcare service resources adoption by medical institutions based on TOE framework

Artificial intelligence healthcare service resources adoption by medical institutions based on TOE framework

An Innovation Framework of Medical Organic Cannabis Traceability in Digital Supply Chain

Exploring the challenges to cope with ripple effects in the perishable food supply chain considering recent disruptions: Implications for urban supply chain resilience

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