Book Review by Edward Hess Learn or Die: Using Science to Build a Leading-Edge Learning Organization

The article of record as published may be found at http://dx.doi.org/10.1108/TLO-01-2021-260Reviewed: Learn or Die: Using Science to Build a Leading-Edge Learning Organization by Edward Hes

Learning to Commit: Examining the Predictive Relationship of Learning Culture Upon Employee Commitment

This quantitative, correlational study investigated if a predictive relationship exists between an organization’s learning culture and organizational commitment. The research design for this quantitative study was three bivariate linear regressions as the statistical tool to test three null hypotheses (p \u3c0.017). The predictor variable for this study were the organization’s learning culture scores, as measured by the Dimensions of the Learning Organization Questionnaire-Abbreviated (DLOQ-A), and the criterion variables for this study was the organization’s employee commitment profile, comprising (1) affective commitment scores, (2) normative commitment scores, and (3) continuance commitment scores, as measured by the Revised Version of the Three Component Model (TCM) of the Employee Commitment Survey. The population for this study was a diverse, cross-functional employee workforce at a medium-sized, information technology-centered public-sector organization numbering 430 employees. Data collection occurred through asynchronous virtual interaction through web survey methodology during the Coronavirus/COVID-19 global pandemic. The results demonstrate a positive predictive relationship between learning culture scores and affective and normative commitment, respectively; and a negative predictive relationship between learning culture scores and continuance commitment. More research is needed to investigate other factors that may account for the remaining variability in predicting learning cultures and employee commitment. Furthermore, research needs to be done to explore how the learning organization impacts employee commitment

The Fourth Industrial Revolution’s Wave Crashes Upon the Shores of Accounting: The Value Metric for the Fourth Industrial Revolution

Financial Management / Faculty ReportAcquisition Research Program Sponsored Report SeriesSponsored Acquisition Research & Technical ReportsAlmost 30 years ago, Elliott (1992) shared several critical insights about the inadequacies of the field of accounting to account for radical changes in the ways businesses develop and execute strategy based on the fundamental opportunities that had come about due to information age technology. Accounting has remained virtually unchanged for over 500 years and society has now entered what Schwab (2015) referred to as the “Fourth Industrial Revolution” where technology advancements follow an exponential growth curve introduces a reality that combines technology across the physical, digital, and biological domains. The Fourth Industrial Revolution has the potential to change both public and private sector organizations, and society itself, however, the accounting practices are not positioned to take advantage of these changes. With this phenomenon in mind, this study seeks to address a gap in the literature that the current accounting practices are insufficient to meet the challenges of the Fourth Industrial Revolution as they do not provide a raw, non-monetized common unit of value, that can measure productivity on a ratio scale for non-profit organizations or at the sub-corporate level in for-profit organization. Through a discussion guided by the literature, this study seeks to generate a scholastic dialogue on how to address this problem.Approved for public release; distribution is unlimited

and Cost/Benefits Opportunities

Acquisition Research Program Sponsored Report SeriesSponsored Acquisition Research & Technical ReportsThe acquisition of artificial intelligence (AI) systems is a relatively new challenge for the U.S. Department of Defense (DoD). Given the potential for high-risk failures of AI system acquisitions, it is critical for the acquisition community to examine new analytical and decision-making approaches to managing the acquisition of these systems in addition to the existing approaches (i.e., Earned Value Management, or EVM). In addition, many of these systems reside in small start-up or relatively immature system development companies, further clouding the acquisition process due to their unique business processes when compared to the large defense contractors. This can lead to limited access to data, information, and processes that are required in the standard DoD acquisition approach (i.e., the 5000 series). The well-known recurring problems in acquiring information technology automation within the DoD will likely be exacerbated in acquiring complex and risky AI systems. Therefore, more robust, agile, and analytically driven acquisition methodologies will be required to help avoid costly disasters in acquiring these kinds of systems. This research provides a set of analytical tools for acquiring organically developed AI systems through a comparison and contrast of the proposed methodologies that will demonstrate when and how each method can be applied to improve the acquisitions lifecycle for AI systems, as well as provide additional insights and examples of how some of these methods can be applied. This research identifies, reviews, and proposes advanced quantitative, analytically based methods within the integrated risk management (IRM)) and knowledge value added (KVA) methodologies to complement the current EVM approach. This research examines whether the various methodologies—EVM, KVA, and IRM—could be used within the Defense Acquisition System (DAS) to improve the acquisition of AI. While this paper does not recommend one of these methodologies over the other, certain methodologies, specifically IRM, may be more beneficial when used throughout the entire acquisition process instead of within a portion of the system. Due to this complexity of AI system, this research looks at AI as a whole and not specific types of AI.Approved for public release; distribution is unlimited.Approved for public release; distribution is unlimited

Analyzing the U.S. Marine Corps Enterprise Information Technology Framework for IT Acquisition and Portfolio Governance

This research examined the ongoing development of a Marine Corps-wide, enterprise architecture (EA) approach for assessing the IT planning and investment process, including IT-related programs of record. The EA-approach to an architecture known as the Marine Corps Information Enterprise Technology Strategy (MCIENT-S) is intended to transition Marine Corps into the 21st century by providing Marine Corps leadership with superior decision support. This study evaluated planning and implementation strategies against Return on Investment (ROI) and requirements-based Capabilities Based Assessment (CBA) processes in their contrasting measures of effectiveness. By analyzing the current and proposed additional IT investment performance metrics to enhance the enterprise architecture, the study learned of the need to conduct an organizational analysis of the Marine Corps IT development and portfolio management process. The study begins with a baseline understanding of the current financial environment of EA; from the initial and rapid growth in defense-specific IT acquisitions since 9/11 into the current fiscally constrained environment of FY2013. The rising trend of the last decade of defense (IT) investment yields its own unintended consequences. One noted conclusion is that some procurements have unfortunately occurred outside the intended parameters of the enterprise architecture framework and the DoD acquisition process and thereby created consequences in the IT governance. One recommendation for the Marine Corps leadership is to develop a systematic process to link the MCIENT-S and its two primary ROI processes, Capital Planning Investment Control (CPIC) and Information Technology Steering Group (ITSG), to the Marine Corps Combat Development Command (MCCDC) requirements based CBA process.http://archive.org/details/analyzingusmarin1094517460Captain, United States Marine Corps;Captain, United States Marine CorpsApproved for public release; distribution is unlimited

A Comparative Analysis of Advanced Methodologies to Improve the Acquisition of Information Technology in the Department of Defense for Optimal Risk Mitigation and Decision Support Systems to Avoid Cost and Schedule Overruns

This study examines five advanced decision support methodologies—Lean Six Sigma (LSS), Balanced Score Card (BSC), Integrated Risk Management (IRM), Knowledge Value Added (KVA), and Earned Value Management (EVM)—in terms of how each can support the information technology (IT) acquisition process. In addition, the study provides guidance on when each methodology should be applied during the acquisition life cycle of IT projects. This research includes an in-depth review of each methodology in the context of the acquisition life cycle. All acquisition projects within the Department of Defense must go through the acquisition life cycle. While each acquisition project is unique, all must pass a series of common hurdles to succeed. Understanding how and when the methodologies can be applied to an IT acquisition is fundamental to its success. The study concludes with a set of recommendations for the use of each methodology in the acquisition life cycle of IT projects.Prepared for the Naval Postgraduate School, Monterey, CA 93943.Naval Postgraduate SchoolApproved for public release; distribution is unlimited.Approved for public release; distribution is unlimited

Acquiring Artificial Intelligence Systems: Development Challenges, Implementation Risks, and Cost/Benefits Opportunities

The acquisition of artificial intelligence (AI) systems is a relatively new challenge for the U.S. Department of Defense (DoD). Given the potential for high-risk failures of AI system acquisitions, it is critical for the acquisition community to examine new analytical and decision-making approaches to managing the acquisition of these systems in addition to the existing approaches (i.e., Earned Value Management). In addition, many of these systems reside in small start-up or relatively immature system development companies, further clouding the acquisition process due to their unique business processes when compared to the large defense contractors. This can lead to limited access to data, information, and processes that are required in the standard DoD acquisition approach. The well-known recurring problems in acquiring information technology automation within the DoD will likely be exacerbated in acquiring complex and risky AI systems. Therefore, more robust, agile, and analytically driven acquisition methodologies will be required to help avoid costly disasters in acquiring AI systems. This research provides a set of analytical tools for acquiring organically developed AI systems through a comparison and contrast of the proposed methodologies that will demonstrate when and how each method can be applied to improve the acquisitions life cycle for AI systems.Prepared for the Naval Postgraduate School, Monterey, CA 93943.Naval Postgraduate SchoolApproved for public release; distribution is unlimited.Approved for public release; distribution is unlimited

Developing Artificial Intelligence in Defense Programs, A Live Webinar

The Department of Defense is moving to incorporate arti!cial intelligence in its programs, using the power of curated data and high-speed computing power to save time, make better decisions, and maintain a competitive advantage across all war!ghting domains. This webinar presents original research that explores the challenges and opportunities of acquiring, developing, and utilizing AI in defense programs