Dr. Jagtiani's teaching philosophy and practice is simple. He does not follow the book. He is a contrarian when it comes to teaching and research. He teaches at the student’s level – at every student’s level, even in a single classroom. For Dr. Jagtiani, teaching success is defined by student learning, personal growth, opportunities for problem-solving, motivation, inspiration, and engagement. Student adoption and active engagement provide fuel to his efforts. Dr. Jagtiani enjoys learning from his students through their experiences, work assignments, and course contributions. He leverages technology heavily to build an interactive learning environment with students (e.g., polling, surveys, interactive gaming, Q&A, and hands-on technical assignments). He draws on case studies as a matter of routine and will invite guest speakers in the classroom whenever possible. Dr. Jagtiani is never apprehensive about changing the course syllabus at any time during the semester. In fact, changing the program, in real-time, and in collaboration with his students is a regular occurrence in his classrooms. His goal is to have instruction remain aligned to the classroom environment and the changing needs of students. He always sticks to the learning objectives set forth at the start of the course.
The business world increasingly uses real-time data analytics to serve the needs of customers best while pursuing organizational objectives. Similarly, and for every class, Dr. Jagtiani alters his teaching style and areas of focus to suit the students while full-filling course objectives. Each student leaves his classroom with life-long memorable experiences while still adhering to the required learning objectives. Students feel more connected with the subject at hand, with their instructor, and with each other. The desired end-state is that students are more knowledgeable and inspired by the topics discussed by the end of each course. Dr. Jagtiani uses rubrics that are clear. The learning objectives are clear and measurable. The schedule of classes, course topics, and assignments are clear. From the first class itself, students know exactly how to earn a grade they desire for the course. There is no confusion. There is no ambiguity. Dr. Jagtiani takes student feedback very seriously and regularly solicits inputs throughout the semester.
Dr. Jagtiani maintains a perspective of research in academia especially as it relates to the study of information management and related sciences. While he values pure academic research, he feels that research in management sciences, and related fields are only valuable if it can be directly related to understanding or solving industry problems using methods that are practical, sustainable, and measurable. All too often management related research efforts fail to meet these objectives and therefore presenting an opportunity for future research to fill this gap. Dr. Jagtiani avoids getting distracted [or having his students get distracted] with isolated research that is void of suitable applicability. His students follow a structured, well-defined approach, in the classroom and on joint research projects.
Dr. Jagtiani's research interests are focused on improving the understanding of failures of software-build processes and improving success rates of related projects. He is specifically interested in studying failure data from business information systems initiatives. Recent studies continue to show alarming failure rates (e.g., $1M+ business software projects are still experiencing less than a 50% success rate on first attempt resulting in sub-optimal performance and business outcomes). Business analytics projects suffer the same failure rates, and the root cause is often not related to the technology components. The overarching motivation for his current research is to offer sustainable improvements in software technology management and delivery. The primary purpose is to discover and analyze the impact, role, and level of influence of various project related data on the ongoing management of technology projects. The study leverages open source data regarding software performance attributes. The goal is to temper the subjectivity currently used by project managers (PMs) with quantifiable measures when assessing project execution progress.
The specific objectives of phase 1 research plans are the following:
Examine the impact of real-time data on the quality of the software execution process.
Develop a predictive model leveraging unbiased software outcomes data (e.g., Open Source Software data).
Increase objectivity in software management by incorporating the use of quantitative and data-based approaches in project assessment processes.
Pending further outcomes about the viability of supporting data sources, the following blueprint represents Dr. Jagtiani's research goals for the next four years. The plan naturally assumes follow-on publication authorship and academic conference representation. Consistent with his research philosophy, Dr. Jagtiani intends to include field validation with industry clients. His doctoral dissertation and related research focuses on understanding how to use machine learning effectively and in novel ways to enhance project outcomes. The title of the thesis is “Enhancing Software Project Outcomes: Using Machine Learning and Open Source Data to Employ Software Project Performance Determinants.”