Purpose: This study explores how organizational innovation supports environmental sustainability through digital transformation, green technologies and operational efficiency amid technological, environmental and geopolitical challenges. Design/methodology/approach: A qualitative approach and secondary data analysis are used to examine innovation across technological, managerial and business model dimensions, with a focus on integrating sustainability into corporate strategy. Findings: The study finds that organizations can align profitability with sustainability by adopting green technologies and digital solutions that enhance efficiency and resilience. However, aligning innovation with sustainability goals remains challenging, particularly in complex regulatory and geopolitical environments. Embedding ecological civilization principles into strategy is vital for long-term competitiveness. Research limitations/implications: Reliance on secondary data and qualitative methods may limit generalizability. Future research should incorporate empirical studies and quantitative analyses to validate these findings. Practical implications: The study offers strategic insights for integrating sustainability into innovation processes. It highlights the need for adaptive corporate strategies to enhance resilience in volatile markets. Social implications: Promoting sustainability-driven innovation contributes to environmental protection, renewable energy adoption and responsible corporate behavior. Originality/value: This research provides theoretical and practical contributions by examining the role of digital transformation, AI analytics and green technologies in achieving sustainable innovation and competitive advantage.

Reliable person identification is a key factor for any safety measure. Unlike other biometrics such as the palm, retina, gait, face and fingerprints, the characteristic of the iris is stable in a person’s lifetime. Iris patterns are chaotically distributed and well suited for recognizing persons throughout their lifetime with a single conscription. This paper proposes a new approach to person recognition based on iris patterns, which works with indoor outdoor conditions, spectacles contact lens wearing persons and diseased eyes. A challenge-response method is used for eye aliveness checking that puts off artificial sources from entering the iris database. The proposed algorithm can work with 84 statistical iris features that are extracted from an individual. Space and time complexity of the proposed approach is lesser than the existing methods. This algorithm has been implemented and results have been analysed on 2500 different iris patterns acquired in India under different real-time conditions. Experimental results illustrate that the proposed method has been easily espoused in elections, bank transactions and other security applications.

Purpose: The study proposes a framework for integrating intelligent agents (IA) into human resource management (HRM) to improve recruitment, screening, training, and decision-making. It utilizes machine learning and pattern recognition to enhance candidate search accuracy and efficiency, addressing the limitations of traditional Boolean methods.Study design/methodology/approach: The study develops an Intelligent Agent AI (IAI) system for recruitment, using reinforcement learning and Naïve Bayes to optimize decision-making. It compares the IAI system to traditional methods like RecruitEm and Merlin, evaluating performance in accuracy, time efficiency, and resource management.Sample and data: The study customizes the IAI system, integrates historical candidate data, and conducts pilot testing in real-world HR settings. Job seeker data is used to train and test the system’s performance.Results: The IAI system significantly outperforms traditional methods in accuracy, time efficiency, and resource management. It also addresses issues like algorithmic bias and Boolean search limitations, improving candidate-job alignment and recruitment efficiency.Originality/value: The study provides a novel AI-powered solution to HR workflows, improving scalability, efficiency, and adaptability. It helps overcome traditional recruitment bias and enhances decision-making through data-driven insights.Research limitations/implications: Challenges include scalability, data bias, and integration issues. Future research should focus on addressing these limitations, refining AI processes, and optimizing performance in diverse HR contexts.

Background: Collaborative logistics strategies are vital in today’s business world, where logistics factors play a fundamental role in achieving success and sustainability. This study aims to use a hybrid SEM–ANN approach to reveal nonlinear mechanisms in multidimensional supply chain collaboration among Sudanese manufacturing companies. Methods: The current study used a descriptive methodology to examine the relationships between variables. A survey was the main data-gathering tool for a cross-sectional, non-probability sample of 246 manufacturing companies. A two-stage SEM–ANN approach was used for data analysis. Results: The findings confirm a partially positive relationship between supply chain collaborative strategy and the logistics relationship satisfaction of Sudanese manufacturing companies. The results indicate that commitment (β = 0.447, p < 0.001) and partnership (β = 0.282, p < 0.01) significantly influence information sharing. Information sharing positively affects relationship satisfaction (β = 0.04, p < 0.001). Information sharing partially mediates the relationship between collaboration dimensions and relationship satisfaction, while relationship quality exerts a significant negative moderating effect (β = −0.090, p = 0.036). The ANN analysis further confirms the relative importance of commitment as the most influential predictor. Conclusions: The dual-method approach of SEM and ANN captures both the linear mechanisms through which commitment and partnership act (SEM) and the nonlinear patterns of predictor importance (ANN).