Leveraging Digital Trace Data to Investigate and Support Human-Centered Work Processes
Barbara Weber, University of St.Gallen, Switzerland
On the Relation Between Gender and Software Engineering
Letizia Jaccheri, Norwegian University of Science and Technology, Norway
Enterprise-Level IS Research: Challenges and Potentials of Looking Beyond Enterprise Solutions
Robert Winter, University of St. Gallen, Switzerland
Leveraging Digital Trace Data to Investigate and Support Human-Centered Work Processes
Barbara Weber
University of St.Gallen
Switzerland
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Brief Bio
Barbara Weber is Full Professor for Software Systems Programming and Development and Director at the Institute of Computer Science at the University of St. Gallen (HSG), Switzerland since 2019. Since February 2024 she is additionally Vice-President for Studies and Teaching at HSG. Before joining HSG, Barbara held a full professorship at the Technical University of Denmark and led the Section for Software and Process Engineering for 3 years. Before moving to Denmark, Barbara worked for over 15 years for the University of Innsbruck where she started her research career and obtained her doctorate and habilitation degrees. Barbara’s research interests include human and cognitive aspects in software and process engineering, process modeling and mining. Together with her team, she focusses on the development and evaluation of software artifacts. This includes topics in the areas of source code analysis, the Internet of Things, and process mining to study and build event-driven software systems that adapt based on the user’s behavior and context. On these and other topics, Barbara published around 200 peer-reviewed papers and articles in scientific journals. Barbara is part of the BPM and CAiSE Steering Committee and served as PC chair for BPM 2013, CAiSE 2019, EASE 2021, ICPM 2022 and was general chair of BPM 2015.
Abstract
The ongoing digitization of processes in all domains of everyday life driven by IT systems shows great potential for process automation, analysis, and optimization. In the last decade process mining has advanced to an important and mature discipline of computer science research and has been widely adopted in industry. More recently—acknowledging the huge potential of digital trace data to study process—process science has been introduced as an interdisciplinary field studying how processes unfold over time. In my keynote presentation I will discuss the potential that arises when using digital trace data to investigate human-centered (work) processes and elaborate on associated challenges. Examples range from healthcare processes to process analysts performing process mining tasks and software engineers reading software artifacts like source code and process models.
On the Relation Between Gender and Software Engineering
Letizia Jaccheri
Norwegian University of Science and Technology
Norway
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Brief Bio
Letizia Jaccheri (PhD from Politecnico di Torino, Italy) is a Professor at the Department of Computer Science of the Norwegian University of Science and Technology (link www.ntnu.edu), Norway. Jaccheri has been teaching courses in software engineering at various levels and acted as one of the independent directors of Reply S.p.A., the largest Italian IT company with 9059 employees. She has supervised and she is supervising several master and PhD students. From 2013 to 2017 she was department head for the Computer Science department at NTNU. She is ACM Distinguished speaker (link https://speakers.acm.org/speakers/jaccheri_10303). She in involved in several research projects, and she leads the COST Action CA19122 Gender Balance in Informatics EUGAIN (link https://eugain.eu/) with 155 members from 39 European countries.
Jaccheri has plans to continue to contribute to address the issue of diversity in computer science.
Abstract
Women are underrepresented in Computer Science disciplines at all levels, from undergraduate and graduate studies to participation and leadership in academia and industry. Increasing female representation in the field is a grand challenge for academics, policymakers, and society. The lack of women is among the core reasons for the huge skills and talent gap existing between the number of graduates in higher education institutions and the number of job positions available in the ICT Industry in Europe.
The main questions are: How to have more girls choosing computer science as their higher education studies and profession; How to retain female students and assure they finish their studies and start successful careers in the field; How to encourage more female Ph.D. and postdoctoral researchers to remain in the academic career and apply for professorships in computer science departments; How to support and inspire young women in their careers and help them to overcome the main hurdles that prevent women from reaching senior positions in industry and public sector. Which communication and dissemination strategy to adopt in this field.
The lecture presents statistics about female presence in education, research and software engineering industry. Moreover, it presents research issues from projects at NTNU and other from other international partners mainly in EUGAIN.
Enterprise-Level IS Research: Challenges and Potentials of Looking Beyond Enterprise Solutions
Robert Winter
University of St. Gallen
Switzerland
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Brief Bio
Robert Winter is a full professor of business and information systems engineering at the University of St. Gallen (HSG) and director of HSG’s Institute of Information Management. He was founding academic director of HSG’s Executive Master’s of Business Engineering program and academic director of HSG’s PhD in Management program. He received master’s degrees in business administration and business education as well as a doctorate in social sciences from Goethe University, Frankfurt, Germany. He served as vice editor-in-chief of Business; Information Systems Engineering as well as senior editor at the European Journal of Information Systems and currently serves on several editorial boards including MIS Quarterly Executive. His research interests include design science research methodology and all aspects of enterprise-level IS research, including enterprise architecture management, design and governance of digital platforms, corporate data management, and design and governance of enterprise transformation.
Abstract
For more than 40 years, enterprise solutions, specifically enterprise systems, allowed companies to integrate enterprises’ operations throughout. Enterprise solutions facilitate cooperation and coordination of work across functional and organizational silos, thereby enabling significant efficacy and efficiency gains. Starting with integrating core operational functions, the integration scope of enterprise solutions has increasingly widened, now often covering customer activities, activities along supply chains, and business analytics.
IS research has contributed a wide range of explanatory and design knowledge dealing with this class of IS. During the last two decades, however, not only technological innovations (e.g., cloud and in-memory computing, digital platforms), but also managerial / organizational innovations (e.g., decentral control, ecosystem-level management) not only extend the affordances of enterprise solutions, but also challenge traditional approaches to their design and coordination. Particularly in large enterprises or complex business ecosystems, many IT/business alignment issues have not yet been fundamentally addressed, and novel, more decentralized (aka agile) forms of coordination have not yet been integrated with mainstream IS design and management practice. At the same time, IS complexity is not harnessed at all, and is increasingly threatening to impose limits to IS efficiency and flexibility gains.
This talk presents a cross-solution (= enterprise-level) perspective on IS, discusses the challenges of complexity and coordination for IS design and management, presents selected enterprise-level insights for IS coordination and governance, and explores avenues towards a more comprehensive body of knowledge on this important level of analysis.