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In this chapter, we present our ongoing efforts in developing and sustaining interdisciplinary STEM undergraduate programs at North Carolina A&T State University (NCA&T) – a state-supported HBCU and National Science Foundation (NSF) Historically Black Colleges and Universities Undergraduate Program (HBCU-UP) Institutional Implementation Project grantee. Through three rounds of NSF HBCU-UP implementation grants, a concerted effort has been made in developing interdisciplinary STEM undergraduate research programs in geophysical and environmental science (in round 1), geospatial, computational, and information science (in round 2), and mathematical and computational biology (in round 3) on NCA&T campus. We first present a brief history and background information about the interdisciplinary STEM undergraduate research programs developed and sustained at NCA&T, giving rationales on how these programs had been conceived, and summarizing what have been achieved. Next we give a detailed description on the development of undergraduate research infrastructure including building research facilities through multiple and leveraged funding sources, and engaging a core of committed faculty mentors and research collaborators. We then present, as case studies, some sample interdisciplinary research projects in which STEM undergraduate students were engaged and project outcomes. Successes associated to our endeavor in developing undergraduate research programs as well as challenges and opportunities on implementing and sustaining these efforts are discussed. Finally, we discuss the impact of well-structured undergraduate research training on student success in terms of academic performance, graduation rate and continuing graduate study, and summarize many of the learnings we have gained from implementation and delivery of undergraduate research experiences at HBCUs.

The expansion of marketing data is encouraging the growing use of deep learning (DL) in marketing. I summarize the intuition behind deep learning and explain the mechanisms of six popular algorithms: three discriminative (convolutional neural network (CNN), recurrent neural network (RNN), and Transformer), two generative (variational autoencoder (VAE) and generative adversarial networks (GAN)), and one RL (DQN). I discuss what marketing problems DL is useful for and what fueled its growth in recent years. I emphasize the power and flexibility of DL for modeling unstructured data when formal theories and knowledge are absent. I also describe future research directions.

Our preoccupation with the Repertory Grid Technique has left little time and attention to the core ideas articulated in Kelly’s (1955) Theory of Personal Constructs. After more than 20 years engaging with the method, I have (re)discovered his theorizing about man’s quest for knowing, to be the most insightful. This chapter shares my reflections/reflexions about the crucial role he placed on the notion of “anticipation.” I position this importance within the context of the challenges of our times and advocate that his “psychology of the unknown” is just as important today as it was 62 years ago.

All good social science researchers recognise that each investigative paradigm and their respective research tools (e.g. experiments, surveys, interviews, observations, questionnaires) is limited in its exploratory power but especially by its dependence on the interests, perspectives and skills of the researchers employing it. They determine the research questions pursued and the questions asked of the respondents. Constructivist researchers however, though they too focus on an area they deem deserving of research and carry their own biases, seek to explore the respondents' perspectives of those domains, what they consider important and construe from their knowledge and life experiences and why they act as they do. Participants in research also bring to the task their own orientations, including their suspicions about researchers' motives, their willingness to take part in the investigation and their personal gains from doing so. When the aim of the research is agentic change, these are critical considerations for research design, data analysis and interpretation. This chapter, after summarising the main tenets of personal and social constructivist theory, explores and evaluates constructivist methodology, illustrating benefits and limitations through examples from research practice of its tools and analytical procedures from research conducted by the authors and their doctoral researchers across and between disciplines and levels in higher education. Techniques used include repertory grids, narrative and pictorial methods used to study, inter alia, staff development issues and transformative doctoral learning. Caveats emerging from practice will be included that limit and focus appropriately the collection, analysis and interpretation of results. Finally, arguments are presented for why, when and how to adopt, or indeed to avoid, these approaches and methods within such research.

Big Data is one of the most promising area where it can be applied to make a change is health care. Healthcare analytics have the potential to reduce the treatment costs, forecast outbreaks of epidemics, avoid preventable diseases, and improve the quality of life. In general, the lifetime of human is increasing along world population, which poses new experiments to today’s treatment delivery methods. Health professionals are skillful of gathering enormous volumes of data and look for best approaches to use these numbers. Big data analytics has helped the healthcare area by providing personalized medicine and prescriptive analytics, medical risk interference and predictive analytics, computerized external and internal reporting of patient data, homogeneous medical terms and patient registries, and fragmented point solutions. The data generated level within healthcare systems is significant. This includes electronic health record data, imaging data, patient-generated data, etc. While widespread information in health care is now mostly electronic and fits under the big data as most is unstructured and difficult to use. The use of big data in health care has raised substantial ethical challenges ranging from risks for specific rights, privacy and autonomy, to transparency and trust.

Introduction: New ideas and concepts of big data have emerged in recent years in response to the astounding growth of data in many industries. Furthermore, the phenomenal increase in the use of the internet and social media has added enormous amounts of data to conventional data processing systems. Still, it has also created challenges for traditional data processing.

Purpose: A significant characteristic of the insurance sector is critically dependent on information. This sector generates a great deal of structured and unstructured data, which traditional data processing techniques cannot handle. As compared to conventional insurance data processing and decision-making requirements, this lesson shows an analysis of data technology’s value additions.

Research methodology: The author assesses the primary use of cases for data in the insurance industry via a case study analysis. From the perspective of the insurance sector, this chapter examines the concepts, technologies, and tools of big data. A few analytical reviews by the insurance company are also provided, which justified several gains gained either through inefficient processing of massive, diverse data sets or by supporting better decisions.

Findings: This chapter demonstrates the importance of adopting new business models that allow insurers to move beyond understand and protect and become more predictive and preventative by using the tools and technologies of big data technology.