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This study aims to develop a predictive model using machine learning techniques to forecast construction dispute outcomes, thereby minimizing economic and social losses and promoting amicable settlements between parties.

This study develops a novel conceptual model incorporating project characteristics, root causes, and underlying causes to predict construction dispute outcomes. Utilizing a dataset of arbitration cases in Türkiye, the model was tested using five machine learning algorithms namely Logistic Regression, Support Vector Machines, Decision Trees, K-Nearest Neighbors, and Random Forest in a Python environment. The performance of each algorithm was evaluated to identify the most accurate predictive model.

The analysis revealed that the Support Vector Machine algorithm achieved the highest prediction accuracy at 71.65%. Twelve significant variables were identified for the best model namely, work type, root causes, delays from a contractor, extension of time, different site conditions, poorly written contracts, unit price determination, penalties, price adjustment, acceptances, delay of schedule, and extra payment claims. The study’s results surpass some existing models in the literature, highlighting the model’s robustness and practical applicability in forecasting construction dispute outcomes.

This study is unique in its consideration of various contract, dispute, and project attributes to predict construction dispute outcomes using machine learning techniques. It uses a fact-based dataset of arbitration cases from Türkiye, providing a robust and practical predictive model applicable across different regions and project types. It advances the literature by comparing multiple machine learning algorithms to achieve the highest prediction accuracy and offering a comprehensive tool for proactive dispute management.

Purpose: This chapter aims to evaluate the significant impact of Gandhian values on sustainable consumption behaviour (SCB) by applying the value-attitude-behaviour (VAB) framework. This chapter contributes by incorporating Gandhian values as one influencing factor for SCB.

Need for the Study: Values are considered as guiding principles in people’s lives. Studies suggest that values and other social and psychological factors can be vital in determining consumers’ behaviour towards sustainable consumption. There needs to be more empirical research on consumer behaviour facets of sustainable consumption for markets in India.

Methodology: The study uses partial least square structural equation modelling to empirically test proposed hypotheses and the research model of the relationship. The study results are based on data collected by administering a survey through a questionnaire confined to India.

Findings: The results indicated that Gandhian values, attitude, and sustainable consumption intention significantly influence SCB. Intention acts as a mediator between both outward and inward environmental attitudes and behaviour. The study provides directions for further research.

Practical Implications: This research study is helpful for researchers, marketers, and policymakers.