Innovation is the ultimate force that drives the development of society. In this dissertation, I examine the economic and organizational outcomes of technological innovations. In my first paper, I study how Artificial intelligence (AI) technology innovation replaces the intermediary role of real estate agents by reducing information asymmetry through delegation mechanisms. I found that consumers are more likely to delegate to AI algorithms as an alternative information source over real estate agents and this effect leads to the reduction of real estate agents’ employment. In my second paper, I studied technology innovation-led remote workforce settings from a cybersecurity risk perspective. Remote workforces are becoming more common due to technological advancements such as blockchain, and cybersecurity risks are documented to be higher for such remote workforces due to reduced monitoring and interactions with peers. I built and tested a model to explain cybersecurity behaviors in remote settings and found that determinants such as social influence differ from determinants in in-office settings. Both studies have implications for helping us better embrace the benefits of technology while controlling its negative effects.
Hebat Allah Adel, sayed abdelgaber, Wessam H. El-Behaidy
Ensuring transparency and security in digital recruitment systems remains a critical challenge. This study proposes BC-XAIA, a unified framework that integrates blockchain, smart contracts, explainable artificial intelligence (XAI), and agile methodology to enable consistent, secure, and traceable recruitment decision-making. Smart contracts, implemented in Solidity and deployed using the Remix Ethereum IDE, automate key processes such as identity verification, data access control, and behavior monitoring, reducing reliance on centralized intermediaries. To support intelligent decision-making, multiple machine learning models, including Random Forest, Logistic Regression, and Support Vector Machine (SVM), were trained and evaluated on a recruitment dataset, with Random Forest achieving the highest performance, reaching an accuracy of 93%. To enhance transparency, SHAP and LIME were employed to provide both global and local interpretability of model predictions. Furthermore, agile methodology is embedded to drive continuous adaptation, iterative development, and stakeholder feedback throughout the recruitment lifecycle. Unlike existing recruitment systems that treat blockchain, AI, and explainability separately, BC-XAIA unifies these technologies within an agile and decentralized architecture. Overall, BC-XAIA establishes a secure, transparent, and explainable decentralized recruitment ecosystem that enhances trust, fairness, and intelligent decision-making in next-generation HR systems.