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Accelerating the influences of external stakeholders in any construction project is inevitable. Studies on external stakeholder influence on construction projects and literature on external stakeholder management in irrigation infrastructure projects executed with donor funds are scarce. Thus, this study aimed to investigate how to manage the external stakeholders' influence on donor-funded irrigation infrastructure projects effectively.

A mixed approach consisting of 17 semi-structured interviews and two rounds of questionnaire surveys was adopted to rank the following: the types of external stakeholders who can significantly influence irrigation infrastructure projects, significant influencing strategies used by those stakeholders, and significant strategies that can be adopted to manage external stakeholder influence on the projects.

In total, 12 of external stakeholders who can significantly influence irrigation infrastructure projects were identified; 17 significant influencing strategies used by external project stakeholders and 22 significant strategies used to manage external stakeholder influence on the projects were identified. The influencing/management strategies specific to each external stakeholder type and those that are common to all external stakeholder types were identified separately. The grievance redress mechanism should be activated for managing external stakeholder influence on donor-funded irrigation infrastructure projects.

This study contributes to theory by identifying significant strategies that can be used to manage external stakeholder influence on donor-funded irrigation infrastructure projects during the planning and design stages. The study will help project teams to handle external stakeholder influence on the projects successfully, accomplish project objectives, and make maximum utilization of the donor funds received.

This research employed two neurophysiological techniques (electroencephalograms (EEG) and galvanic skin response (GSR)) and machine learning algorithms to capture and analyze relationship-oriented leadership (ROL) and task-oriented leadership (TOL). By grounding the study in the theoretical perspectives of transformational leadership and embodied leadership, the study draws connections to the human body's role in activating ROL and TOL styles.

EEG and GSR signals were recorded during resting state and event-related brain activity for 52 study participants. Both leadership styles were assessed independently using a standard questionnaire, and brain activity was captured by presenting subjects with emotional stimuli.

ROL revealed differences in EEG baseline over the frontal lobes during emotional stimuli, but no differences were found in GSR signals. TOL style, on the other hand, did not present significant differences in either EEG or GSR responses, as no biomarkers showed differences. Hence, it was concluded that EEG measures were better at recognizing brain activity associated with ROL than TOL. EEG signals were also strongest when individuals were presented with stimuli containing positive (specifically, happy) emotional content. A subsequent machine learning model developed using EEG and GSR data to recognize high/low levels of ROL and TOL predicted ROL with 81% accuracy.

The current research integrates psychophysiological techniques like EEG with machine learning to capture and analyze study variables. In doing so, the study addresses biases associated with self-reported surveys that are conventionally used in management research. This rigorous and interdisciplinary research advances leadership literature by striking a balance between neurological data and the theoretical underpinnings of transformational and embodied leadership.