An analysis of growth-accelerating factors for the Indian automotive industry using modified TISM

Meena, Abhilasha; Dhir, Sanjay; Sushil, Sushil

doi:10.1108/ijppm-01-2019-0047

Cited by 40 publications

(17 citation statements)

References 112 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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“…Coupling originally belongs to the category of physics, refers to two or more systems through the interaction of the formation of mutual influence and even the phenomenon of mutual union, in recent years has been constantly introduced to the economic disciplines, and refers to the role of certain conditions, two economic phenomena combined and the role of objective phenomena [9]. Industry experts believe that industrial coupling refers to the economic phenomenon that two or more systems composed of two or more types of industries of different nature, through their respective coupling elements, produce mutual effects and influence each other [10].…”

Section: The Related Workmentioning

confidence: 99%

The Innovation Ecological Model of Chinese Automotive Industry Based on Artificial Intelligence and Big Data Technology

Zhang

Liu

2022

Mathematical Problems in Engineering

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The digitalization of the automobile industry must depend on the information technology industry. The coupling development of the two creates conditions for the realization of industry 4.0 and intelligent manufacturing. The purpose of this paper is to make an empirical study on the possibility of coupling and the coupling degree, correlation degree, and innovation ability of China’s automobile industry and information technology industry. This paper takes the intelligent manufacturing of the automobile industry as the research object. According to the current situation of China’s automobile industry, this paper uses data to analyze and predict the development level of China’s automobile industry and the gap with the foreign automobile industry. Firstly, it summarizes and analyzes the development of China’s automobile industry and information technology industry. Then, the gray correlation analysis method is used to explore the factors affecting the innovation of China’s automobile industry. It is found that the relationship between capital investment and innovation is the most significant in “industrial innovation investment.” Finally, the coupling development model of the automobile industry and information technology industry is established, and the coupling degree of the two is analyzed by using the analytic hierarchy process and the Delphi method. The research of this paper mainly focuses on the automobile industry. Under the wave of internet, other industries such as the commerce and service industry also have a strong coupling relationship with the information technology industry, which will provide a new research direction for the follow-up research of experts and scholars.

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“…Researchers are using it more often now (e.g. Singh et al , 2019; Meena and Dhir, 2020). Thus, the MTISM leads to offer the same output with less efforts by respondents and researchers.…”

Section: Extension Of Ism As a Toolkitmentioning

confidence: 99%

Modelling the abstract knots in supply chains using interpretive structural modelling (ISM) approaches: a review-based comprehensive toolkit

Abbas¹,

Mehdi²,

Azad³

et al. 2022

BIJ

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PurposeThis study endeavours to (a) develop a comprehensive interpretive structural modelling (ISM) toolkit containing sufficient details about the suitability and procedural aspects of each ISM approach and offer points of reference for budding researchers, (b) highlight the compatibility of ISM approaches with other qualitative and quantitative approaches, and (c) chalk-out an agenda for future research.Design/methodology/approachThis study is based on an extensive review of 74 studies where researchers have used one or more ISM approaches. These studies span across the different industry sectors.FindingsThere exists a huge void in terms of the methodological synthesis of ISM approaches. ISM approaches are frequently used in sync with other qualitative and quantitative approaches. Furthermore, it highlights the need of improving the robustness of the proposed ISM models by sharing the critical details of research process.Research limitations/implicationsBeing a review-based work, it could not illustrate the discussed ISM approaches with real data. However, it offers a research agenda for the prospective researchers.Practical implicationsThe prerequisites, pitfalls, suitability and the procedural aspects of various ISM approaches contained in this toolkit are equally useful for the academicians as well as practitioners.Originality/valueIn the absence of a synthesized framework, this study contributes a comprehensive ISM toolkit which will help the researchers to choose a suitable ISM approach in a given case.

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An analysis of growth-accelerating factors for the Indian automotive industry using modified TISM

Cited by 40 publications

References 112 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

The Innovation Ecological Model of Chinese Automotive Industry Based on Artificial Intelligence and Big Data Technology

Modelling the abstract knots in supply chains using interpretive structural modelling (ISM) approaches: a review-based comprehensive toolkit

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