Developing a Formal Navy Knowledge Management Process

Prepared for: Chief of Naval Operations, N1Organization tacit and explicit knowledge are required for high performance, and it is imperative for such knowledge to be managed to ensure that it flows rapidly, reliably and energetically. The Navy N1 organization has yet to develop a formal process for knowledge management (KM). This places N1 in a position of competitive disadvantage, particularly as thousands of people change jobs every day, often taking their hard earned job knowledge out the door with them and leaving their replacements with the need to learn such knowledge anew. Building upon initial efforts to engage with industry and conceptualize a Navy KM strategy, the research described in this study employs a combination of Congruence Model analysis, Knowledge Flow Theory, and qualitative methods to outline an approach for embedding a formal Navy KM process. This work involves surveying best tools and practices in the industry, government and nonprofit sectors, augmented by in depth field research to examine two specific Navy organizations in detail. Results are highly promising, and they serve to illuminate a path toward improving Navy knowledge flows as well as continued research along these lines.Chief of Naval Operations, N1Chief of Naval Operations, N1.Approved for public release; distribution is unlimited

Analytics and Intuition in the Process of Selecting Talent

In management, decisions are expected to be based on rational analytics rather than intuition. But intuition, as a human evolutionary achievement, offers wisdom that, despite all the advances in rational analytics and AI, should be used constructively when recruiting and winning personnel. Integrating these inner experiential competencies with rational-analytical procedures leads to smart recruiting decisions

Human-AI Interaction – Investigating the Impact on Individuals and Organizations

Artificial intelligence (AI) has become increasingly prevalent in consumer and business applications, equally affecting individuals and organizations. The emergence of AI-enabled systems, i.e., systems harnessing AI capabilities that are powered by machine learning (ML), is primarily driven by three technological trends and innovations: increased use of cloud computing allowing large-scale data collection, the development of specialized hardware, and the availability of software tools for developing AI-enabled systems. However, recent research has mainly focused on technological innovations, largely neglecting the interaction between humans and AI-enabled systems. Compared to previous technologies, AI-enabled systems possess some unique characteristics that make the design of human-AI interaction (HAI) particularly challenging. Examples of such challenges include the probabilistic nature of AIenabled systems due to their dependence on statistical patterns identified in data and their ability to take over predictive tasks previously reserved for humans. Thus, it is widely agreed that existing guidelines for human-computer interaction (HCI) need to be extended to maximize the potential of this groundbreaking technology. This thesis attempts to tackle this research gap by examining both individual-level and organizational-level impacts of increasing HAI. Regarding the impact of HAI on individuals, two widely discussed issues are how the opacity of complex AI-enabled systems affects the user interaction and how the increasing deployment of AI-enabled systems affects performance on specific tasks. Consequently, papers A and B of this cumulative thesis address these issues. Paper A addresses the lack of user-centric research in the field of explainable AI (XAI), which is concerned with making AI-enabled systems more transparent for end-users. It is investigated how individuals perceive explainability features of AI-enabled systems, i.e., features which aim to enhance transparency. To answer this research question, an online lab experiment with a subsequent survey is conducted in the context of credit scoring. The contributions of this study are two-fold. First, based on the experiment, it can be observed that individuals positively perceive explainability features and have a significant willingness to pay for them. Second, the theoretical model for explaining the purchase decision shows that increased perceived transparency leads to increased user trust and a more positive evaluation of the AI-enabled system. Paper B aims to identify task and technology characteristics that determine the fit between an individual's tasks and an AI-enabled system, as this is commonly believed to be the main driver for system utilization and individual performance. Based on a qualitative research approach in the form of expert interviews, AI-specific factors for task and technology characteristics, as well as the task-technology fit, are developed. The resulting theoretical model enables empirical research to investigate the relationship between task-technology fit and individual performance and can also be applied by practitioners to evaluate use cases of AI-enabled system deployment. While the first part of this thesis discusses individual-level impacts of increasing HAI, the second part is concerned with organizational-level impacts. Papers C and D address how the increasing use of AI-enabled systems within organizations affect organizational justice, i.e., the fairness of decision-making processes, and organizational learning, i.e., the accumulation and dissemination of knowledge. Paper C addresses the issue of organizational justice, as AI-enabled systems are increasingly supporting decision-making tasks that humans previously conducted on their own. In detail, the study examines the effects of deploying an AI-enabled system in the candidate selection phase of the recruiting process. Through an online lab experiment with recruiters from multinational companies, it is shown that the introduction of so-called CV recommender systems, i.e., systems that identify suitable candidates for a given job, positively influences the procedural justice of the recruiting process. More specifically, the objectivity and consistency of the candidate selection process are strengthened, which constitute two essential components of procedural justice. Paper D examines how the increasing use of AI-enabled systems influences organizational learning processes. The study derives propositions from conducting a series of agent-based simulations. It is found that AI-enabled systems can take over explorative tasks, which enables organizations to counter the longstanding issue of learning myopia, i.e., the human tendency to favor exploitation over exploration. Moreover, it is shown that the ongoing reconfiguration of deployed AI-enabled systems represents an essential activity for organizations aiming to leverage their full potential. Finally, the results suggest that knowledge created by AI-enabled systems can be particularly beneficial for organizations in turbulent environments

Analytics and Intuition in the Process of Selecting Talent

In management, decisions are expected to be based on rational analytics rather than intuition. But intuition, as a human evolutionary achievement, offers wisdom that, despite all the advances in rational analytics and AI, should be used constructively when recruiting and winning personnel. Integrating these inner experiential competencies with rational-analytical procedures leads to smart recruiting decisions

AI for social good: social media mining of migration discourse

The number of international migrants has steadily increased over the years, and it has become one of the pressing issues in today’s globalized world. Our bibliometric review of around 400 articles on Scopus platform indicates an increased interest in migration-related research in recent times but the extant research is scattered at best. AI-based opinion mining research has predominantly noted negative sentiments across various social media platforms. Additionally, we note that prior studies have mostly considered social media data in the context of a particular event or a specific context. These studies offered a nuanced view of the societal opinions regarding that specific event, but this approach might miss the forest for the trees. Hence, this dissertation makes an attempt to go beyond simplistic opinion mining to identify various latent themes of migrant-related social media discourse. The first essay draws insights from the social psychology literature to investigate two facets of Twitter discourse, i.e., perceptions about migrants and behaviors toward migrants. We identified two prevailing perceptions (i.e., sympathy and antipathy) and two dominant behaviors (i.e., solidarity and animosity) of social media users toward migrants. Additionally, this essay has also fine-tuned the binary hate speech detection task, specifically in the context of migrants, by highlighting the granular differences between the perceptual and behavioral aspects of hate speech. The second essay investigates the journey of migrants or refugees from their home to the host country. We draw insights from Gennep's seminal book, i.e., Les Rites de Passage, to identify four phases of their journey: Arrival of Refugees, Temporal stay at Asylums, Rehabilitation, and Integration of Refugees into the host nation. We consider multimodal tweets for this essay. We find that our proposed theoretical framework was relevant for the 2022 Ukrainian refugee crisis – as a use-case. Our third essay points out that a limited sample of annotated data does not provide insights regarding the prevailing societal-level opinions. Hence, this essay employs unsupervised approaches on large-scale societal datasets to explore the prevailing societal-level sentiments on YouTube platform. Specifically, it probes whether negative comments about migrants get endorsed by other users. If yes, does it depend on who the migrants are – especially if they are cultural others? To address these questions, we consider two datasets: YouTube comments before the 2022 Ukrainian refugee crisis, and during the crisis. Second dataset confirms the Cultural Us hypothesis, and our findings are inconclusive for the first dataset. Our final or fourth essay probes social integration of migrants. The first part of this essay probed the unheard and faint voices of migrants to understand their struggle to settle down in the host economy. The second part of this chapter explored the viability of social media platforms as a viable alternative to expensive commercial job portals for vulnerable migrants. Finally, in our concluding chapter, we elucidated the potential of explainable AI, and briefly pointed out the inherent biases of transformer-based models in the context of migrant-related discourse. To sum up, the importance of migration was recognized as one of the essential topics in the United Nation’s Sustainable Development Goals (SDGs). Thus, this dissertation has attempted to make an incremental contribution to the AI for Social Good discourse

Succession Planning in the Federal Government

Succession planning is a term that refers to the systematic and methodological efforts an organization uses to plan for organizational stability and proficiency. Organizations must provide employees the training, experiences, and knowledge required to assume positions of increased responsibility when those jobs are vacated. Agencies should strive to create a diversified pool of qualified candidates to avoid a talent gap, workforce shortages, or a loss of agency knowledge. Over the past fifteen years, the Federal Government has continued to highlight the need to take a proactive approach to succession planning by first identifying the skill sets needed for critical positions and then developing their future leaders. With a limited number of new employees entering civil service and projected retirements over the next several years, it is essential that agencies quickly prioritize succession planning strategies to train and prepare employees to assume critical acquisition positions, such as the Contracting Officer (CO) role. COs are the only individuals with authority to procure goods and services on the Government’s behalf and therefore occupy positions classified as inherently governmental functions. This research study explored the lack of succession planning at DoN agencies in Southern MD and the impact of the failure to create a multi-generational pipeline of qualified candidates who can compete for CO positions as they are vacated

Developing unbiased artificial intelligence in recruitment and selection : a processual framework : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Management at Massey University, Albany, Auckland, New Zealand

For several generations, scientists have attempted to build enhanced intelligence into computer systems. Recently, progress in developing and implementing Artificial Intelligence (AI) has quickened. AI is now attracting the attention of business and government leaders as a potential way to optimise decisions and performance across all management levels from operational to strategic. One of the business areas where AI is being used widely is the Recruitment and Selection (R&S) process. However, in spite of this tremendous growth in interest in AI, there is a serious lack of understanding of the potential impact of AI on human life, society and culture. One of the most significant issues is the danger of biases being built into the gathering and analysis of data and subsequent decision-making. Cognitive biases occur in algorithmic models by reflecting the implicit values of the humans involved in defining, coding, collecting, selecting or using data to train the algorithm. The biases can then be self-reinforcing using machine learning, causing AI to engage in ‘biased’ decisions. In order to use AI systems to guide managers in making effective decisions, unbiased AI is required. This study adopted an exploratory and qualitative research design to explore potential biases in the R&S process and how cognitive biases can be mitigated in the development of AI-Recruitment Systems (AIRS). The classic grounded theory was used to guide the study design, data gathering and analysis. Thirty-nine HR managers and AI developers globally were interviewed. The findings empirically represent the development process of AIRS, as well as technical and non-technical techniques in each stage of the process to mitigate cognitive biases. The study contributes to the theory of information system design by explaining the phase of retraining that correlates with continuous mutability in developing AI. AI is developed through retraining the machine learning models as part of the development process, which shows the mutability of the system. The learning process over many training cycles improves the algorithms’ accuracy. This study also extends the knowledge sharing concepts by highlighting the importance of HR managers’ and AI developers’ cross-functional knowledge sharing to mitigate cognitive biases in developing AIRS. Knowledge sharing in developing AIRS can occur in understanding the essential criteria for each job position, preparing datasets for training ML models, testing ML models, and giving feedback, retraining, and improving ML models. Finally, this study contributes to our understanding of the concept of AI transparency by identifying two known cognitive biases similar-to-me bias and stereotype bias in the R&S process that assist in assessing the ML model outcome. In addition, the AIRS process model provides a good understanding of data collection, data preparation and training and retraining the ML model and indicates the role of HR managers and AI developers to mitigate biases and their accountability for AIRS decisions. The development process of unbiased AIRS offers significant implications for the human resource field as well as other fields/industries where AI is used today, such as the education system and insurance services, to mitigate cognitive biases in the development process of AI. In addition, this study provides information about the limitations of AI systems and educates human decision makers (i.e. HR managers) to avoid building biases into their systems in the first place

Skilling up for CRM: qualifications for CRM professionals in the Fourth Industrial Revolution

The 4th industrial revolution (4IR) describes a series of innovations in artificial intelligence, ubiquitous internet connectivity, and robotics, along with the subsequent disruption to the means of production. The impact of 4IR on industry reveals a construct called Industry 4.0. Higher education, too, is called to transform to respond to the disruption of 4IR, to meet the needs of industry, and to maximize human flourishing. Education 4.0 describes 4IR’s impact or predicted impact or intended impact on higher education, including prescriptions for HE’s transformation to realize these challenges. Industry 4.0 requires a highly skilled workforce, and a 4IR world raises questions about skills portability, durability, and lifespan. Every vertical within industry will be impacted by 4IR and such impact will manifest in needs for diverse employees possessing distinct competencies. Customer relationship management (CRM) describes the use of information systems to implement a customer-centric strategy and to practice relationship marketing (RM). Salesforce, a market leading CRM vendor, proposes its products alone will generate 9 million new jobs and $1.6 trillion in new revenues for Salesforce customers by 2024. Despite the strong market for CRM skills, a recent paper in a prominent IS journal claims higher education is not preparing students for CRM careers. In order to supply the CRM domain with skilled workers, it is imperative that higher education develop curricula oriented toward the CRM professional. Assessing skills needed for specific industry roles has long been an important task in IS pedagogy, but we did not find a paper in our literature review that explored the Salesforce administrator role. In this paper, we report the background, methodology, and results of a content analysis of Salesforce Administrator job postings retrieved from popular job sites. We further report the results of semi-structured interviews with industry experts, which served to validate, revise, and extend the content analysis framework. Our resulting skills framework serves as a foundation for CRM curriculum development and our resulting analysis incorporates elements of Education 4.0 to provide a roadmap for educating students to be successful with CRM in a 4IR world

Big data for monitoring educational systems

This report considers “how advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sector”, big data are “large amounts of different types of data produced with high velocity from a high number of various types of sources.” Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the “macro perspective on governance on educational systems at all levels from primary, secondary education and tertiary – the latter covering all aspects of tertiary from further, to higher, and to VET”, prioritising primary and secondary levels of education