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A building deteriorates over time due to aging, wear and tear, and inadequate maintenance. Building diagnosis requires a sound knowledge of engineering, building defects, and detection tools to assess the condition of a building. The physical deterioration of a building reduces its ability to perform its intended function, while environmental deterioration influences the comfort and health of building occupants. This study presents a multi-tiered framework for the inspection of building elements and the environmental conditions of a building.

A three-tiered building inspection framework is proposed in this study, which consists of the following: Tier-I—a preliminary inspection, Tier-II—a detailed inspection, and Tier-III—an expert investigation. Each tier of inspection assesses the severity of building defects using different technologies for different levels of inspection.

Proposed multi-tier inspection framework is tested and implemented on a case study. Results were promising, with organized data management on a common platform for both physical and environmental condition inspection having the potential to save time.

The application program developed for the implementation of structured multi-tiered building inspection provides better documentation and data management for building inspection data that can save time involved in manual data operations in traditional paper-based processes.

The purpose of this paper is to identify and develop a hierarchical model for the barriers affecting the growth of mobile commerce (M-commerce). Based on the model developed, the authors’ objective is to identify those variables that are of strategic nature and are the root cause of the issue.

Variables considered as barriers are identified, and utilizing the interpretive structural model approach, a relationship model is developed. Further, the impact matrix cross-reference multiplication applied to a classification approach is used to analyze the effect and dependence among these factors.

The research in the area of M-commerce in the Arab world and related to the strategic aspect is limited in the extant literature. The present study tries to fill this gap by investigating the variables that inhibit the growth of M-commerce in Qatar. The research shows that there exists a group of barriers having a high driving power and low dependence requiring maximum attention and of strategic importance, while another group consists of those variables that have high dependence and are the resultant actions.

There is a growing concern that although the numbers of mobile subscribers are increasing at a very fast pace in Qatar, actual M-commerce activities in the country remain low. The findings of this study can be used to understand the differences between the independent and dependent variables and their mutual relationships. The study would also help the policy makers to develop suitable strategies to facilitate growth of M-commerce in the country.

This research was the first attempt to investigate the relationships among the variables inhibiting the growth of M-commerce in a fast-growing economy in a Gulf Cooperation Council (GCC) region. Given that there is limited research on M-commerce in the GCC context, the study can be viewed as an investigation that provides a good understanding of the variables and their interrelationships affecting M-commerce proliferation.

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.