This exploratory study, grounded in agency theory, employs quantitative analyses to investigate the simultaneous pursuit of accountability for regulatory compliance, financial benefits, and societal impacts within artificial intelligence (AI) projects.An agent-principal matrix was developed, synthesizing knowledge from the AI stakeholder model into 11 accountability indicators.These indicators establish a standard of responsibility among project actors for regulatory compliance, ethical practices, and financial benefits.Using quantitative methods, we analyzed survey data on accountability and defined the scope of AI systems under development.We identified two clusters of AI systemsautonomous and non-autonomous-based on seven features.We then examined how these two types of systems, as well as the importance of sustainability and fairness, impact the promotion of accountability.Results indicate that accountabilities shift based on the scope of the AI system and the project role.Regulatory compliance, financial benefits, and societal impacts are not mutually exclusive project goals and coexist.The findings quantify subjective and theoretical speculation about accountabilities within AI projects.Additionally, the study contributes empirical data to the literature on AI, ethics, and project management.

The literature proves that agile projects have a higher success rate in stakeholder satisfaction and overall success than projects managed with a plan-driven methodology such as waterfall.However, little corresponding literature examines whether that success extends to the target benefits.This study identifies the mechanisms-actions, decisions, or entities-that enable agile and plan-driven projects to deliver target benefits.It uses real options analysis to quantify and model the differences between project methods and builds a management decisionmaking framework.The framework includes real option types, mechanisms, and locations; project roles and processes; risk scores and failure rates; a computational model; and a binomial tree for visual analysis.The study contributes a novel framework to the project management literature on agile projects and benefits realization.

The algorithms implemented through artificial intelligence (AI) and big data projects are used in life-anddeath situations.While research exists to address varying aspects of moral decision-making with algorithms, the definition of project success is not readily available.Nevertheless, researchers place the burden of responsibility for ethical decisions from AI systems on the system developers.Using a systematic literature review, this research identified 71 AI project success factors in 14 groups related to moral decision-making with algorithms.It contributes to project management literature, specifically for AI projects.Project managers and sponsors can use the results during project planning and execution.

Projects offer learning opportunities and digital data that can be analyzed through a multitude of theoretical lenses.They are key vehicles for economic and social action, and they are also a primary source of innovation, research, and organizational change.This research involves a survey of digital assets available through a project; specifically, it identifies sources of data that can be used for practicing data-driven, context-specific project management, or for project-based academic research.It identified four categories of data sourcescommunications, reports/records, model representations, and computer systems --and 51 digital assets.The list of digital assets can be inputs in the creation of project artifacts and sources for monitoring and controlling project activities and for sensemaking in retrospectives or lessons learned.Moreover, this categorization is useful for decision support and artificial intelligence systems model development that requires real-world data.

Recent studies have confirmed the efficacy of agile methodologies in project success.However, can projects skip several project management tasks and still deliver the expected results?How are traditional project managers engaged in agile projects?The results from this study quantify subjective and theoretical speculation on who performs the project management tasks in agile projects.Project managers are engaged in agile projects and the team, the product owner, and project sponsor are significantly involved in project management tasks.The agile coach is not a substitute for the project manager.The study identifies that agile and traditional methodologies should be updated to clarify team, product owner, and agile coach responsibilities.

Decision support systems such as big data, business intelligence (BI), and analytics offer firms capabilities to generate new revenue sources, increase productivity and outputs, and improve competitiveness.However, the field is crowded with terminology that makes it difficult to establish reasonable project scopes and to staff and manage projects.This study clarifies the terminology around the data science, computation social science, big data, business intelligence, and analytics and describes their meaning relative to decision support projects.For BI and big data projects, it identifies the critical success factors, empirically classifies the project scopes, and investigates the similarities and differences between the project types.This comparative analysis provides unique insights into the factors and criteria that influence BI and big data project success.These results should inform project sponsors and project managers of the contingency factors to consider when preparing project charters and plans.