Toward a Model Undergraduate Curriculum for the Emerging Business Intelligence and Analytics Discipline

Business intelligence (BI) combined with business analytics (BA) is an increasingly prominent strategic objective for many organizations. As a pedagogical subject, BI/BA is still in its infancy, and, in order for this to mature, we need to develop an undergraduate model BI/BA curriculum. BI/BA as an academic domain is emerging as a hybrid of disciplines, including information systems, statistics, management science, artificial intelligence, computer science, and business practice/theory. Based on IS 2010’s model curriculum constructs (Topi et al., 2010), we explore two curricular options: a BI/BA concentration in a typical IS major and a comprehensive, integrated BI/BA undergraduate major. In support, we present evidence of industry need for BI/BA, review the current state of BI/BA education, and compare anticipated requirements for BI/BA curricula with the IS 2010 model curriculum. For this initial phase of curricular design, we postulate a preliminary set of knowledge areas relevant for BI/BA pedagogy in a multi-disciplinary framework. Then we discuss avenues for integrating these knowledge areas to develop professionally prepared BI/BA specializations at the undergraduate level. We also examine implications for both AACSB and ABET accreditation and describe the next phase of applying the IS 2010 concept structure to BI/BA curriculum development

IS Faculty Engagement in Pedagogical Research

There is growing interest in pedagogical research in response to demands for accountability in higher education. Major accrediting bodies have also recognized its role in advancing the practice of teaching, with the goal of improving student learning. In this session, we will highlight the benefits of involvement in IS educational research across the full spectrum from reader to reviewer, author, and editorial board member. We will survey publication outlets for IS pedagogy in a range of genres. Guidelines for planning, implementing, and writing up instructional projects will also be discussed

Making Neural Networks FAIR

Research on neural networks has gained significant momentum over the past few years. Because training is a resource-intensive process and training data cannot always be made available to everyone, there has been a trend to reuse pre-trained neural networks. As such, neural networks themselves have become research data. In this paper, we first present the neural network ontology FAIRnets Ontology, an ontology to make existing neural network models findable, accessible, interoperable, and reusable according to the FAIR principles. Our ontology allows us to model neural networks on a meta-level in a structured way, including the representation of all network layers and their characteristics. Secondly, we have modeled over 18,400 neural networks from GitHub based on this ontology, which we provide to the public as a knowledge graph called FAIRnets, ready to be used for recommending suitable neural networks to data scientists

Management learning at the speed of life:Designing reflective, creative, and collaborative spaces for millenials

This paper introduces the concept of "management learning at the speed of life" as a metaphor to inspire millenials. Millenials may face three major problems in relation to management learning: lack of concentration, lack of engagement, and lack of socialization. Management learning at the speed of life addresses these potential problems through three dimensions: reflective, creative, and collaborative learning. This paper illustrates the benefits of reflective, creative, and collaborative spaces for millenials using practices from leadership and personal development courses that were offered over seven years in Canada, Turkey, and the UK. These courses incorporated the latest technology that brought the course activities up to the speed of life

Cellular Versus Process Layouts: An Analytic Investigation of the Impact of Learning and Productivity Improvement

Evidence from the literature on cellular manufacturing suggests that shops configured as manufacturing cells perform poorly compared to job shops. However, cellular shops, being conducive to the use of teams in the assignment of production activities, have the potential to yield higher productivity than a job shop. Productivity differentials, and in particular, differences in the rates at which processing times can be reduced, have been largely overlooked in prior comparisons of cellular and job shops. This paper uses queuing theory to illustrate the relationship between processing time learning rates and flow time performance in cellular and job shops. Models are developed that make it possible to estimate the learning rate required in a cellular shop in order for it to yield performance comparable to that of a job shop. Simulation is used to validate the models under dynamic conditions as opposed to the steady state conditions assumed by queuing theory. Results indicate that a cellular shop need only achieve a marginally higher learning rate than a job shop in order to perform at a comparable level

Cellular vs process layouts: an analytic investigation of the impact of learning on shop performance

Evidence from the literature on cellular manufacturing suggests that shops configured as manufacturing cells perform poorly compared to job shops. However, cellular shops, being conducive to the use of teams in the assignment of production activities, have the potential to yield higher productivity than a job shop. Productivity differentials, and in particular, differences in the rates at which processing times can be reduced, have been largely overlooked in prior comparisons of cellular and job shops. This paper uses queuing theory to illustrate the relationship between processing time learning rates and flow time performance in cellular and job shops. Models are developed that make it possible to estimate the learning rate required in a cellular shop in order for it to yield performance comparable to that of a job shop. Simulation is used to validate the models under dynamic conditions as opposed to the steady state conditions assumed by queuing theory. Results indicate that a cellular shop need only achieve a marginally higher learning rate than a job shop in order to perform at a comparable level.Cellular manufacturing Group technology Learning curve Queuing theory Simulation