Using Factor Analysis to Determine the Factors Impacting Learning Python for Non-Technical Business Analytics Graduate Students

This pioneering research represents a unique and singular study conducted within the United States, with a specific focus on non-technical graduate students pursuing degrees in business analytics. The primary impetus behind this study stems from the escalating demand for data-driven professionals, the diverse academic backgrounds of students, the imperative for adaptable pedagogical methods, the ever-evolving landscape of curriculum designs, and the overarching commitment to fostering educational equity. To investigate these multifaceted dynamics, we employed a data collection method that included the distribution of an online survey on platforms such as LinkedIn. Our survey reached and engaged 74 graduate students actively pursuing degrees in Business Analytics within the United States. This comprehensive research is the first and only one of its kind conducted in this context, and it serves as a vanguard exploration into the challenges and influences that shape the learning journey of Python among non-technical graduate Business Analytics students. The analytical insights derived from this research underscore the pivotal role of hands-on learning strategies, exemplified by practice exercises and assignments. Moreover, the study highlights the positive and constructive influence of collaboration and peer support in the process of learning Python. These invaluable findings significantly augment the existing body of knowledge in the field of business analytics. Furthermore, they offer an essential resource for educators and institutions seeking to optimize the educational experiences of non-technical students as they acquire essential Python skills.

Managing Knowledge in Shared Spaces

Information has always been an integral component of business strategy. However, with the rise of information technology (IT) and globalization of businesses, information has taken a far more important role in explaining business performance. IT can be used to enhance research activities of the firm;and its product knowledge can be used to improve business productivity. In fact, scholars and researchers have indicated that knowledge can be a source of sustainable advantages for the firm. However, not much research has been done that explains what kind of knowledge is important for sustainable advantages. In this paper, we present three kinds of knowledge: core, complementary, and peripheral. We also develop an idea of shared space along three dimensions: physically shared space, technically shared space, and cognitively shared space. The main theme of this paper is to show the relevance and importance of these different kinds of knowledge along the shared spaces that create sustainable advantages for the firm.

Shaping the Next Generation Pharmaceutical Supply Chain Control Tower with Autonomous Intelligence

This paper summarizes the findings of an industry panel study evaluating how new Autonomous Intelligence technologies,such as artificial intelligence and machine learning,impact the system and operational architecture of supply chain control tower (CT) implementations that serve the pharmaceutical industry.Such technologies can shift CTs to a model in which real-time information gathering,analysis,and decision making are possible.This can be achieved by leveraging these technologies to better manage decision complexity and execute decisions at levels that cannot otherwise be managed easily by humans.Some of the key points identified are in the areas of the fundamental capabilities that need to be supported and the improved level of decision visibility that they provide.We also consider some the challenges in achieving this,which include data quality and integrity,collaboration and data sharing across supply chain tiers,cross-system interoperability,decision-validation and organizational impacts,among others.