No abstract

Knowledge strategy is the set of guidelines that shape the decisions that an organization makes regarding the acquisition, storage, manipulation, and application of its knowledge base. The purpose of this study is to identify and describe the influence knowledge strategy has on the manner in which an organization's knowledge is organized. Using semi-structured interviews of upper level executives from various industries, relationships are established between certain characteristics of knowledge strategy types (e.g. proactive or reactive knowledge acquisition) and the organization of knowledge within the organization. Results indicate that certain aspects of a knowledge strategy are linked to certain approaches to knowledge organization, though organizational characteristics such as structure and industry type also play a major role.

The healthcare industry has a growing record of using big data-related technologies such as data analytics, internet of things, and machine learning, especially in the clinical areas. However, healthcare institutions must also perform all of the administrative processes just as any other organization. Thus, like many other industries, healthcare has begun to apply these same technologies to improve their understanding of these internal operations and use them to make better decisions and run a more effective operation. This study takes a structured literature review approach to describe the current state of this literature and identify the major themes and priorities of both the research community and the healthcare industry as a whole. The contribution made by this study is to provide a comprehensive analysis of the state of the literature to use as a foundation for the future research opportunities noted in the paper.

Anecdotal evidence suggests that there is a stark contrast between the project management tools and techniques taught in schools and those actually used in industrial settings. This exploratory study provides evidence of those differences in the form of qualitative data collected from project managers in the information technology areas of major firms from the banking, cosmetics, and electronics manufacturing industries. Evidence suggests that there is a significant variation in which the formal project management processes taught in traditional project management curricula are used, if any. The study explores the reasons behind these differences and suggests possible approaches to ease the strain for new hires and current employees.

The purpose of this article is to report on a systematic review performed on the literature describing the research on the use of big data technologies in pharmaceutical manufacturing. Big data technologies refer to a variety of technologies that are associated with big data such as data analytics, machine learning, and data mining. The systematic review uses a set of search terms that describe the topic under study and performs searches on major databases. The returns from these searches are subjected to content analysis to determine their primary topic and those that are focused on pharmaceutical manufacturing are then analyzed to reveal research emphasis, tools used, and major findings. The literature is then organized to reveal the structure of the body of literature. A total of 64 papers met the inclusion requirements for the study and they represent four themes: Manufacturing Specific Products, General Manufacturing Process Control, Safety/QC, and Manufacturing Management. A review such as this provides guidance to researchers to expand their current research.