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This chapter introduces four research methods that are not covered in the previous chapters. They are (1) non-parametric statistics, (2) interpretive structural modeling, (3) analytic hierarchy process, and (4) data envelopment analysis. The methods are discussed with examples. The discussion, however, is introductory; so we urge the reader to go through the pertinent references for details.

The provision of air transport services in small and regional airports is a major concern worldwide as regional aviation is challenged with inadequate revenues. This chapter aims to identify the driving factors for sustaining regional airlines and airports. Nine factors are identified through literature review and expert opinion. Fuzzy-total interpretive structural modeling is used to develop a hierarchical relationship among the factors. Truncated population of the region, national subsidies, and airport infrastructure development have been found to be the strongest drivers for promoting regional airports. This hierarchical model provides a logical structure to the factors.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.

Construction projects operate within a risky environment. It materialises as delays, which must be prevented or mitigated to avoid becoming amplified into late completion. But previous research has largely ignored how structural complexity of the underlying network schedules shapes their resilience.

This research hypothesizes that schedule structure plays a vital role in its ability to absorb or propagate delays. The impact of activity-level local risk factors is represented via activity duration distributions, i.e. probability density functions. The impact of project-level global risk factors is more challenging because they arise via interactions between multiple activities.

Modelling resilience to local and global risk factors can employ a matrix approach. Simulation shows that delay amplification depends on local structure, not global complexity.

Criticality had merely relied upon a single deterministic analysis of a network schedule to categorize activities as having zero or nonzero float from fixed relative duration a dependency structure. Repeated probabilistic analysis with sampled durations gives criticality indices of activities. This research limits itself to network schedules with point-wise relations between activities.

Managers can use this knowledge to develop schedules that protect their expected project duration with a suitable structural complexity.

Contributions to the body of knowledge are as follows: It converts the dependency structure into a reachability matrix and adds a correlation matrix to capture how the predecessor performance may impact its successors. It correlates criticality of activities with structural complexity indices. And it ranks activities objectively by their cruciality, i.e. potential delay propagation.

The purpose of this chapter is to identify and analyze various critical success factors (CSFs) that can facilitate retailing business in Thailand. This chapter further aims to understand the mutual interactions among these CSFs through identification of the hierarchical relationships among them. A framework for Thai retailers has been developed, wherein the hierarchical interrelationships between identified CSFs have been presented and interpreted using total interpretive structural modeling (TISM). Cross-impact matrix multiplication applied to classification analysis has been further employed to identify the driving power as well as dependence power of these CSFs. In the present research, 15 CSFs have been identified through literature review and expert opinions. The methodology employed in this study provides a mechanism to conduct an exploratory study by identifying the factors and analyzing their interactions through the development of a hierarchical framework. The proposed framework developed through qualitative modeling is an effort to understand relevant factors that can apply to the Thai retailers. This study makes a significant contribution in the literature of retailing business, which captures the perspective of different customers.

Introduction: Sustainability and Industry 4.0 have recently influenced the global economy. With the Industrial Revolution 4.0, there has been a significant focus on digital sustainability in enterprises. Micro, small and medium enterprises (MSMEs) are the most vulnerable sections regarding new transformations.

Purpose: Even during the COVID-19 pandemic, when all businesses had no option other than to adopt digitisation, MSMEs faced tremendous issues to make this shift. Despite the immense focus on digital sustainability, some deterrents exist to its adoption in MSMEs. Contemporary research focuses on determining the critical deterrents to digital sustainability in MSMEs.

Methodology: This chapter employs the interpretive structural modelling technique, and Matrice impacts Croises Multiplication Appliqué a classement (MICMAC) analysis to identify and further classify the deterrents to digital sustainability in MSMEs.

Findings: Legal barriers and firms’ economic conditions are identified as the major deterrents. Eliminating these deterrents would help minimise the minor ones too. To deliver global sustainability goals, digital sustainability must be duly adopted everywhere.

Purpose: Nowadays, many terms like computer vision, deep learning, and machine learning have all been made possible by recent artificial intelligence (AI) advances. As new types of employment have risen significantly, there has been significant growth in adopting AI technology in enterprises. Despite the anticipated benefits of AI adoption, many businesses are still struggling to make progress. This research article focuses on the influence of elements affecting the acceptance procedure of AI in organisations.

Design/Methodology/Approach: To achieve this objective, propose a hierarchical paradigm for the same by developing an Interpretive Structural Modelling (ISM). This paper reveals the barriers obstructing AI adoption in organisations and reflects the contextual association and interaction amongst those barriers by emerging a categorised model using the ISM approach. In the next step, cross-impact matrix multiplication is applied for classification analysis to find dependent, independent and linkages.

Findings: As India is now focusing on the implementation of AI adoption, therefore, it is essential to identify these barriers to AI to conceptualise it systematically. These findings can play a significant role in identifying essential points that affect AI adoption in organisations. Results show that low regulations are the most critical factor and functional as the root cause and further lack of IT infrastructure is the barrier. These two factors require the most attention by the government of India to improve AI adoption.

Implications: This study may be utilised by organisations, academic institutions, Universities, and research scholars to fill the academic gap and faster implementation of AI.

This study attempts to recognize obstacles and barriers to financial inclusion in the agriculture sector, propose a framework based on the inter-contextual link between the barriers and understand the financial exclusion in the agriculture sector at the grassroots level. Previously published research articles were used to identify the barriers to financial inclusion, followed by informal interviews and collaborative discussions with the local farmers of the Sonipat district of Haryana and expert interviews using a structured questionnaire. TISM and MICMAC analysis are used to decern the nature of the relationship among the barriers discovered. The authors find that inadequate financial literacy, a shortage of financial awareness and the reluctance of various financial institutions are significant linkage barriers to strong driving and dependence power. High transaction costs and poor infrastructural support are the independent barriers. The paper identifies these new barriers to financial inclusion in the Indian agriculture sector and the framework depicting financial exclusion in India. This paper only gives a framework of barriers and does not quantify the effect of any relationship identified, but strongly emphasizes granting the Indian agriculture sector broad and simple financial access to advance and strengthen the nation's sustainable, inclusive economic growth.

Purpose – This study assesses the extent to which four features of work – supervision, autonomy, creativity, and skill – are associated with different structural forms of social capital. Social capital may enhance actors’ access to diverse information and resources or it may foster mutual commitment and trust. Actors’ draw on these social connections, and the resources embedded therein, when they engage in work activities. The study considers how dense and diverse network structures serve complementary functions to actors engaged in creative and autonomous jobs or for reproducing inequality within firms.Methodology – The analysis uses nationally representative survey data and the position-generator approach to social capital measurement to determine the relationship between three social capital constructs – diversity, hierarchy, and density – and respondents’ work characteristics.Findings – Supervisory, autonomous, creative, and highly skilled workers all have more diverse social networks. Supervisors and skilled workers also have access to high-status contacts. Finally, creative and autonomous workers have more dense social networks.Originality/value – Findings suggest that density and diversity are useful to actors engaged in self-directed or creative work tasks. These findings support theories of complementary network structures that combine access to unique information with the collective ability to pursue goals.