Introduction to the Minitrack on Reports from the Field: Knowledge and Learning Applications in Practice

Welcome to this, the 53rd Hawaii International Conference on System Sciences (HICSS) conference. This Reports from the Field Minitrack, under the Knowledge Innovation and Entrepreneurial Systems Track, proudly brings you the latest research focused on the application of innovation of knowledge management issues as reported by both researchers and practitioners alike. With the focus on application, this minitrack helps practioners and theorists alike. It is here we examine how theory informs and impacts practice as well as how practice can influence theory from the practioner perspective

Celebrating HICSS50: The Past, Present, and Future of HICSS

The Hawaii International Conference on System Sciences (HICSS) celebrated its 50th anniversary (HICSS-50) in January, 2017. To mark the occasion and to pay respect to the significant standing of this conference in the global IS community, the Communications of the Association for Information Systems (CAIS) organized a special section on “Celebrating HICSS50: The Past, Present, and Future of HICSS Conference”. In this editorial, we share the guest editors’ perspectives on HICSS and summarize the three papers in the special section

Evaluation of Variability Concepts for Simulink in the Automotive Domain

Modeling variability in Matlab/Simulink becomes more and more important. We took the two variability modeling concepts already included in Matlab/Simulink and our own one and evaluated them to find out which one is suited best for modeling variability in the automotive domain. We conducted a controlled experiment with developers at Volkswagen AG to decide which concept is preferred by developers and if their preference aligns with measurable performance factors. We found out that all existing concepts are viable approaches and that the delta approach is both the preferred concept as well as the objectively most efficient one, which makes Delta-Simulink a good solution to model variability in the automotive domain.Comment: 10 pages, 7 figures, 6 tables, Proceedings of 48th Hawaii International Conference on System Sciences (HICSS), pp. 5373-5382, Kauai, Hawaii, USA, IEEE Computer Society, 201

Knowledge, Innovation, and Entrepreneurial Systems at HICSS

This paper presents an overview and history of the knowledge, innovation, and entrepreneurial systems (KIES) track and the knowledge and related systems research community at the Hawaii International Conference on System Sciences (HICSS). This community began as a task force that examined organizational memory in HICSS-27. It has since evolved into a mini-track, a research cluster, and, finally, a full research track that encompasses research knowledge, innovation, and entrepreneurial systems. In this paper, we acquaint knowledge system researchers with a research community that has leveraged HICSS to develop a rich history of high-quality scholastic inquiry in the knowledge system, knowledge management, innovation systems, entrepreneurial systems, organizational memory, and organizational learning research areas

Biometric Technologies and the Law: Developing a Taxonomy for Guiding Policymakers

Despite the increasing adoption of biometric technologies, their regulation has not kept up with the same pace, particularly with regard to safeguarding individuals' privacy and personal data. Policymakers may struggle to comprehend the technology behind biometric systems and their potential impact on fundamental rights, resulting in insufficient or inadequate legal regulation. This study seeks to bridge this gap by proposing a taxonomy of biometric technologies that can aid in their effective deployment and supervision. Through a literature review, the technical characteristics of biometric systems were identified and categorised. The resulting taxonomy can enhance the understanding of biometric technologies and facilitate the development of regulation that prioritises privacy and personal data protection.Comment: 11 pages, 1 figure, submitted to 57th Hawaii International Conference on System Sciences (HICSS-57

Stopping rules from a multilevel perspective of IS design

Paper presented at The HICSS Multi-Disciplinary System Design Knowledge workshop, Hawaii International Conference on System Sciences (HICSS-38).Traditionally, design is viewed as conforming to Simon’s (1960; 1973; 1981) model of problem-solving, in which intelligence about the problem is gathered, alternatives are evaluated, and a solution is chosen and acted upon. But the persistence of this model of the design process imposes serious constraints on how we manage design. These constraints are discussed here, to understand how we may manage design more effectively if we view it as a multi-layered process

Android Anti-forensics: Modifying CyanogenMod

Mobile devices implementing Android operating systems inherently create opportunities to present environments that are conducive to anti-forensic activities. Previous mobile forensics research focused on applications and data hiding anti-forensics solutions. In this work, a set of modifications were developed and implemented on a CyanogenMod community distribution of the Android operating system. The execution of these solutions successfully prevented data extractions, blocked the installation of forensic tools, created extraction delays and presented false data to industry accepted forensic analysis tools without impacting normal use of the device. The research contribution is an initial empirical analysis of the viability of operating system modifications in an anti-forensics context along with providing the foundation for future research.Comment: Karlsson, K.-J. and W.B. Glisson, Android Anti-forensics: Modifying CyanogenMod in Hawaii International Conference on System Sciences (HICSS-47). 2014, IEEE Computer Society Press: Hawai

The Six Pillars of Knowledge Economics

The purpose of this paper is to extend our earlier work on the contributions to the mini-track on Knowledge Economics at the Hawaii International Conference on System Sciences (HICSS). In the present work, we analyze 16 contributions from 2012 to 2016 and based on our analysis, we propose the Six Pillars of Knowledge Economics framework. The proposed framework articulates that six elements are essential to generate knowledge outputs: Innovation Capability, Leadership, Human Capital, Information Technology Resources, Financial Resources, and Innovation Climate. Additional major findings are that organizations are the most common unit of analysis, while the individual level is hardly considered. Journals represent the major source of citations. Conference proceedings were less cited, though more current. We recommend major conferences to be indexed by services like Scopus and provide open access to peer-reviewed proceedings

Emerging Digital Frontiers for Service Innovation

This paper examines emerging digital frontiers for service innovation that a panel discussed at a workshop on this topic held at the 48th Annual Hawaii International Conference on System Sciences (HICSS). The speakers and participants agreed that that service systems are fundamental for service innovation and value creation. In this context, service systems are related to cognitive systems, smart service systems, and cyber-physical systems and depend on the interconnectedness among system components. The speakers and participants regarded humans as the central entity in all service systems. In addition, data, they saw personal data in particular as key to service systems. They also identified several challenges in the areas of cognitive systems, smart service systems, cyber-physical systems, and human-centered service systems. We hope this workshop report helps in some small way to cultivate the emerging service science discipline and to nurture fruitful discussions on service innovation

How well can machine-generated texts be identified and can language models be trained to avoid identification?

With the rise of generative pre-trained transformer models such as GPT-3, GPT-NeoX, or OPT, distinguishing human-generated texts from machine-generated ones has become important. We refined five separate language models to generate synthetic tweets, uncovering that shallow learning classification algorithms, like Naive Bayes, achieve detection accuracy between 0.6 and 0.8. Shallow learning classifiers differ from human-based detection, especially when using higher temperature values during text generation, resulting in a lower detection rate. Humans prioritize linguistic acceptability, which tends to be higher at lower temperature values. In contrast, transformer-based classifiers have an accuracy of 0.9 and above. We found that using a reinforcement learning approach to refine our generative models can successfully evade BERT-based classifiers with a detection accuracy of 0.15 or less.Comment: This paper has been accepted for the upcoming 57th Hawaii International Conference on System Sciences (HICSS-57