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The dynamic business environment and intense competition have mandated agility in operations for manufacturing firms. Effective inter-organizational collaboration can make operations more agile. This paper develops an interpretive model to explore the effect of supply chain collaboration (SCC) on supply chain agility (SCA) and, subsequently, on business performance.

A hierarchical model that illustrates the relationship between SCC, SCA and firm performance components is developed using total interpretative structural modeling (TISM). Also, statistical validation of the model has been performed.

The results indicate that the vision and alertness of the firm on the strategic front promote collaboration between supply chain partners. This creates operational agility, helping the firm to absorb fluctuations in demand, thereby enhancing business performance.

The opinion of most respondents was considered to develop the TISM framework over the fuzzy one, which necessitates a significantly more extensive data set. However, multiple discussions with participants can eliminate the prejudice of the majority approach. Also, the paper's development and validation were restricted to Indian manufacturing small and medium-sized enterprises (SMEs). The model can potentially be evaluated in large organizations to provide further insights.

The study blends the factors of SCC and SCA in a novel way to explain their combined impact on business performance. The TISM model addresses the “why” of theory development in addition to the “what” and “how” of it. Using triangulation in combination with the interpretative tool, this study additionally offers methodological value.

Tech start-ups (TSs) functioning in different domains have a responsibility of ensuring that domestic knowledge and capabilities are leveraged to minimize dependence on foreign organizations. Despite the growth of the ecosystem, while numerous TSs emerge, very few of them are able to survive, and of those that survive, very few scale up. The aim of this study is to identify the factors influencing the competitiveness of technological start-ups and to study the interrelationship and interdependence of these factors.

Modified total interpretative structural modeling (m-TISM) was employed for the current research. The analysis of what factors have an effect on competitiveness, how they affect it and why they affect it should be explored. The study begins by developing the list of factors through literature search, and further it is validated by expert opinion. A hierarchical model has been developed using m-TISM and MICMAC analysis to analyze the driving and dependency power of factors at each level.

Results show that the competitiveness of TSs is affected by organizational agility and internationalization. Factors present at the bottom level, namely entrepreneurial intensity, act as a strong driver for TSs. Team member commitment, transformational leadership, strategic alliances, knowledge sharing and organizational ambidexterity are middle-level factors.

This study is among the few articles that have explored competitiveness of TSs in the Indian context.

Intense competition in every sector has put administrators under tremendous pressure to develop strategies for survival, and the educational sector is no exception. This paper aims to explore the dynamics between the factors that affect the quality of engineering institutes by developing an interpretive model.

The factors that affect the quality of engineering institutes are identified through a thorough review of extant literature, and the dynamics between them are studied using the total interpretive structural modeling (TISM) technique. The developed model has also been statistically validated.

Results indicate that top management leadership, location and infrastructure drive academics, research and consultancy, industry collaboration and placements, resulting in accreditation from global agencies, thereby improving the institute’s quality.

The TISM framework was developed based on the suggestions of the majority of respondents rather than using a fuzzy one, which requires a much larger data set. Nevertheless, the bias of the majority approach can be removed by multiple conversations with respondents. Secondly, the model development and validation are based on the perceptions of faculty members working at engineering institutes in India. Scholars can extend the work in the future by identifying additional factors and considering administrators’ perceptions.

The study integrates the factors that impact the quality of engineering institutes in a unique way to understand their combined impact. The developed framework will assist policymakers in identifying and dedicating adequate resources to essential factors that drive the other factors, thereby enhancing the institute’s ranking.