Modeling of Continuity and Change Forces in Private Higher Technical Education Using Total Interpretive Structural Modeling (TISM)

2011 ◽  
Vol 12 (3-4) ◽  
pp. 31-39 ◽  
Author(s):  
Umesh Chandra Prasad ◽  
R. K. Suri
Keyword(s):  
Technical Education ◽  
Structural Modeling ◽  
Interpretive Structural Modeling ◽  
Continuity And Change ◽  
Change Forces

Critical success factors for next generation technical education institutions

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Smita Kashiramka ◽  
Mahim Sagar ◽  
Amlendu Kumar Dubey ◽  
Amit Mehndiratta ◽  
Sushil Sushil
Keyword(s):  
Critical Success Factors ◽  
Success Factors ◽  
Technical Education ◽  
Research Output ◽  
Structural Modeling ◽  
Next Generation ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Amplification Factors ◽  
Critical Success

Purpose The purpose of this paper is to create a hierarchy of critical success factors affecting the higher technical education institutions, taking a case study of India. Using total interpretive structural modeling (TISM), the paper attempts to establish the inter-linkages among ten critical success factors for enhancing the performance of these institutions. Design/methodology/approach The paper employs Total Interpretive Structural Modeling (TISM) to understand the hierarchy of the factors and their interplay using response from 18 experts in the domain. Findings The findings reveal that autonomy and accountability coupled with availability of sustainable funds are the driving factors for the success of the institutions. Infrastructural facilities and establishment of centers of excellence act as amplification factors. Introduction of new programs and their accreditation, improvement in faculty quality, research output and improvement in performance of academically weak students emerge as process factors that drive the output factors, namely, academic performance and student placement. Research limitations/implications The major limitation of this study is the scope that was limited to 191 institutions, as mandated in the project. Practical implications This study has important implications for the institutions as well as the policy makers to channelize their focus and efforts on driving and amplification factors that would ultimately lead to enhanced performance of the next generation higher technical education institutions. Originality/value This paper is a part of pan India project carried out to assess the performance of higher technical education institutions in India.

scholarly journals Factors Affecting the Lut Desert Tourism in Iran: Developing an Interpretive-Structural Model

2021 ◽  
Vol 13 (13) ◽  
pp. 7245
Author(s):  
Beniamino Murgante ◽  
Mohammad Eskandari Sani ◽  
Sara Pishgahi ◽  
Moslem Zarghamfard ◽  
Fatemeh Kahaki
Keyword(s):  
Sampling Method ◽  
Structural Model ◽  
Tourism Development ◽  
Cost Effective ◽  
Structural Modeling ◽  
Interpretive Structural Modeling ◽  
Factors Affecting ◽  
Purposive Sampling ◽  
Interpretive Structural Model ◽  
Lut Desert

The Lut desert is one of the largest and most attractive deserts in Iran. The value of desert tourism remains unclear for Iran’s economy and has only recently been taken into consideration by the authorities, although its true national and international value remains unclear. This study was aimed at investigating the factors that influence tourism development in the Lut desert. Data collected through the purposive sampling method was analyzed using Interpretive Structural Modeling and the MICMAC Analysis. According to the results, cost-effective travel expenses, security, and safety provided in the desert, together with appropriate media advertising and illustration of the Lut desert (branding) are the leading factors that influence tourism in the Lut desert in Iran. This paper highlighted the importance of desert tourism, especially in this region.

Analysis of third party reverse logistics provider using interpretive structural modeling

2012 ◽  
Vol 140 (1) ◽  
pp. 204-211 ◽  
Author(s):  
Kannan Govindan ◽  
Murugesan Palaniappan ◽  
Qinghua Zhu ◽  
Devika Kannan
Keyword(s):  
Reverse Logistics ◽  
Structural Modeling ◽  
Third Party ◽  
Interpretive Structural Modeling
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