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This chapter aims to identify, analyse, classify and rank the sustainability indices and internationalisation challenges of the footwear industry in the emerging economy of Iran. This would provide deeper decision-making insights into Iranian footwear businesses. First, a list of sustainability indices and internationalisation challenges was obtained by reviewing the literature. Then, a combination of multi-criteria decision-making (MCDM) approaches was implemented. The initial sustainability indices and internationalisation challenges were screened using the fuzzy Delphi method, keeping a total of 14 criteria. The best–worst method (BWM) was employed to weigh and rank the criteria. The interpretive structural modelling (ISM) technique and cross-impact matrix applied in MICMAC were employed to visualise the conceptual model based on the levels and classification of the important criteria for the internationalisation of the Iranian footwear industry. The 14 criteria were demonstrated to be important in internationalisation. The most critical sustainability indices were reducing hazardous substances in leather tanning and labour education and training. In contrast, exchange rate instability in Iran’s economy and strict chemical regulations for clothing and footwear were found to be the most important internationalisation challenges. Hence, these criteria should be considered in the internationalisation strategies of the Iranian footwear industry. A combined multilayer sustainable decision-making approach was used to analyse the Iranian footwear industry’s essential sustainability indices and internationalisation challenges. Furthermore, implications and insights are offered to footwear businesses for future decision-making.

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 chapter assists in Iran’s start-ups swift internationalisation from the onset. Indeed, it sheds in-depth qualitative and quantitative insights into analysing the propelling factors towards entrepreneurial internationalisation. To accomplish this feat, a mixed method of Systematic Literature Review (SLR), Fuzzy-Delphi (FD) and Fuzzy-DEMATEL (Decision Making Trial and Evaluation Laboratory) – ISM (Interpretive Structural Modelling) – MICMAC (Matrix-based Multiplication Applied to a Classification) (FDIM), along with a multi-scenario analysis have innovatively been applied. As a result, entrepreneur characteristics and an accessible qualified workforce, even in foreign countries, are the most prominent drivers. Most probably, the institutional voids, interconnected benchmarking and the advent of new disruptive technologies form the independent factors which can sharply influence the whole system, particularly the entrepreneur characteristics as a dependent one. Moreover, social media, customer orientation and the domestic market cover autonomous drivers, which can moderately be affected or influence the abovementioned factors.

This study deals with the examination, classification and ranking of the food industry supply chain in Mazandaran Free Economic Zone from a sustainability perspective and its effect on international entrepreneurship. The findings can help international decision-makers and entrepreneurs better understand their decision-making processes. Interpretive structural modelling (ISM) and Matrix-based Multiplication Applied to Classification (MICMAC) methods is an export-oriented method. The indicators are determined based on the definitive opinion of experts. Indicators in this research were selected using fuzzy Delphi with 99% accuracy. A combination of ISM–MICMAC methods were used based on sustainable facts in the food supply chain. The developed classify the factors practical on the sustainable supply chain. The findings showed that minimising the total cost, improvement of health and safety, environmentally friendly packaging, use of environmentally friendly materials and minimising the use of synthetic fertilisers were among the linkage factors. Factors that reduce energy costs, the possibility of immediate customer response and social services were among the dependent factors. Since free economic zones have been established to attract international investment, exploit new technologies, create new job opportunities and increase export capacity, the focus on reducing costs and the ability to respond immediately to customers and providing social services can result in the creation of a suitable business position for international investment and attracting international entrepreneurs.

This study aims to identify the factors and propose a conceptual framework for the civil aviation industry from the sustainability perspective with the participation of international entrepreneurs. Based on the results of this study, international decision-makers and entrepreneurs in the civil aviation transportation industry will better understand their decision-making processes. A combination of interpretive structural modelling (ISM) and matrix-based multiplication applied to a classification (MICMAC) was used to classify practical factors to depict a conceptual model based on their level and classification in the sustainable supply chain (SSC) of the civil aviation transportation industry. In this study, special attention has been paid to the issue of sustainability as an essential mechanism for developing international entrepreneurship in the civil aviation transportation industry. The factor of flexibility in service production was identified as the driver factor; the factors of organisational commitment to a SSC were found to have the highest driver-dependent power that can attract international entrepreneurs in this field.

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.

The environmental performance of organizations has come under public policy limelight since the phenomenal increase of natural resource degradation and industrial waste. Thus, green concepts have been put forward, but the implementation of green practices faces many barriers and challenges. These barriers require attention as organizational practices are negatively affecting the environment leading to global warming and climate change. Therefore, this chapter systematically identifies four internal barriers including inadequate management commitment and support, insufficient technology competence and infrastructure, financial constraints, the uncertainty of economic benefits, and eight sub barriers within an organization concerning green concepts implementation. Moreover, this chapter also identifies four external barriers including lack of stakeholder's interest, inadequate environmental administrative support, scarce academic research, and lack of green collaborative practices, and eight sub barriers outside an organizational context concerning green concept implementation. The barriers in this research were identified by reviewing the existing literature on the topic. This chapter advances the green literature by identifying multiple barriers and challenges to the successful implementation of green concepts in organizations. This is of significance as if these barriers are tackled strategically, it would reduce environmental degradation problems and help make financial gains. Moreover, this research can help managers understand the key barriers to green concept implementation and provide guidance to them when attempting to implement green practices in their organizations. This research would also motivate researchers to extend further investigation on how to overcome such barriers and find out strategies to mitigate the barriers to green concept implementation to effectively address environmental issues.