Information Systems Research on Digital Platforms for Knowledge Work: A Scoping Review

Digital platforms for knowledge work, such as Upwork, Freelancer.com and Amazon Mechanical Turk connect clients with millions of workers for a range of tasks, including software development, virtual assistance, and micro-tasks. Information systems research on this emergent phenomenon has gained traction in recent years regarding publication volume and research diversity. To identify relevant papers, to distinguish them from related types of digital platforms, and to guide future research, we conducted a scoping review, focusing on the information systems literature. Results are structured according to a theoretical framework of the knowledge work process, covering three phases: Worker-client matching, committing for future action, and executing commitments. While the first phase has been analyzed extensively, we contend that the main phases of the knowledge work process have received scant attention. In this emergent stage of extant research, our review identifies promising research directions to guide prospective studies

Explaining and Distinguishing Scientific Impact in Information Systems Research: A Study of Review Articles and Design Science Research

Since its inception, the Information Systems discipline has been striving to develop impactful papers that contribute to cumulative knowledge development. Yet, there is a surprising lack of insights on how scientific impact can be accomplished and to which extent this impact represents a substantial engagement with, and extension of the knowledge contributions of the original papers. Especially for review articles and design science research, there are both competing conceptions of what makes these papers impactful and a lack of empirical evidence that would inform this debate. Furthermore, there is a latent skepticism as to whether this sometimes staggering impact of review articles actually represents knowledge development. In a similar way, it is unclear how and to which extent design science research has stimulated meaningful, cumulative knowledge development in information systems. The goal of this thesis is therefore to (1) explain and to (2) distinguish the scientific impact of review articles and design science research. Specifically, the first goal considers overall scientific impact as the dependent variable whose association with antecedent factors is analyzed by regression methodologies. The second goal zooms in on the concept of scientific impact and considers it as a relation between citing and cited papers that is explored through methodologies of manual content analysis and machine learning classification. With Paper 1, I develop the foundation of knowledge development through review articles by crystallizing their contributions and aligning them with their underlying knowledge conversion processes in an overarching framework. This framework is based on the abstraction and codification of knowledge and thereby integrates two essential dimensions of knowledge development. Overall, the foundation developed in the first paper informs the underlying conception of knowledge development of both review articles and citing papers. Addressing the first goal, Papers 2 and 3 develop and test scientometric impact models explaining the scientific impact of review articles and design science research, respectively. Beyond common control variables related to the journal and author level, they offer distinct insights for each type of paper. For review articles, I identify strong effects related to methodological transparency and the development of a research agenda, which vary depending on the type of review. For design science research, I show that theorization and novelty drive scientific impact. Concerning the second goal, Papers 4 and 5 distinguish different types of scientific impact of review articles and design science research, respectively. To analyze the different types of impact that review articles have on their overwhelming number of citing papers, I develop machine learning classifiers. Specifically, I distinguish ideational impact, which corresponds to a substantial engagement with and development of the knowledge contributions of the review article, from perfunctory impact, which corresponds to more trivial connections to the review article. In a similar, though not automated way, I analyze the types of impact of information systems design theories, a particular type of design science research. These analyses primarily focus on whether follow-up research tests and extends these theories. Based on our content analysis, I identify an alarming paucity of follow-up research in this area and develop specific guidelines for the design science community to address this challenge. The thesis concludes with an overview of the research contributions, implications for research practice, future research opportunities, and final remarks

Theory of Knowledge for Literature Reviews: An Epistemological Model, Taxonomy and Empirical Analysis of IS Literature

Literature reviews play an important role in the development of knowledge. Yet, we observe a lack of theoretical underpinning of and epistemological insights into how literature reviews can contribute to knowledge creation and have actually contributed in the IS discipline. To address these theoretical and empirical research gaps, we suggest a novel epistemological model of literature reviews. This model allows us to align different contributions of literature reviews with their underlying knowledge conversions - thereby building a bridge between the previously largely unconnected fields of literature reviews and epistemology. We evaluate the appropriateness of the model by conducting an empirical analysis of 173 IS literature reviews which were published in 39 pertinent IS journals between 2000 and 2014. Based on this analysis, we derive an epistemological taxonomy of IS literature reviews, which complements previously suggested typologies

Prospective Physicians’ Intention to Adopt Artificial Intelligence: A Configurational Perspective

Artificial intelligence (AI) drives transformation across medical specialities, requiring current and future generations of physicians to navigate ever-changing digital environments. In this context, prospective physicians will play a key role in adopting and applying AI-based health technologies, underlining the importance of understanding their knowledge, attitudes, and intentions toward AI. To dissociate corresponding profiles, we adopted a configurational perspective and conducted a two-stage survey study of 184 (t_0) and 138 (t_1) medical students at a Canadian medical school. Our principal findings corroborate the existence of distinct clusters in respondents’ AI profiles. We refer to these profiles as the AI unfamiliar, the AI educated, and the AI positive, showing that each profile is associated with different intentions towards future AI use. These exploratory insights on the variety of AI profiles in prospective physicians underline the need for targeted and adaptive measures of education and outreach

A Knowledge Development Perspective on Literature Reviews: Validation of a new Typology in the IS Field

Literature reviews (LRs) play an important role in developing domain knowledge in all fields. Yet, we observe insufficient insights into the activities with which LRs actually develop knowledge. To address this important gap, we 1) derive knowledge-building activities from the extant literature on LRs, 2) suggest a knowledge-based LR typology that complements existing typologies, and 3) apply the typology in an empirical study that explores how LRs with different goals and methodologies have contributed to knowledge development. In analyzing 240 LRs published in 40 renowned information systems (IS) journals between 2000 and 2014, we draw a detailed picture of knowledge development that one of the most important genres in the IS field has achieved. With this work, we help to unify extant LR conceptualizations by clarifying and illustrating how they apply different methodologies in a range of knowledge-building activities to achieve their goals with respect to theory

Factors Affecting the Scientific Impact of Literature Reviews: A Scientometric Study

Standalone literature reviews are fundamental in every scientific discipline. Their value is reflected by a profound scientific impact in terms of citations. Although previous empirical research has shown that this impact has a large variance, it is largely unknown which specific factors influence the impact of literature reviews. Against this background, the purpose of our study is to shed light on the driving factors that make a difference in the scientific impact of literature reviews. Our analysis of an exhaustive set of 214 IS literature reviews reveals that factors on the author level (e.g., expertise, collaboration, and conceptual feedback) and on the article level (e.g., methodological rigor) are significant and robust predictors of scientific impact over and above journal level factors. These insights enhance our understanding of what distinguishes highly cited literature reviews. In so doing, our study informs future guidelines on literature reviews and provides insights for prospective authors

Forecasting IT Security Vulnerabilities - An Empirical Analysis

Today, organizations must deal with a plethora of IT security threats and to ensure smooth and uninterrupted business operations, firms are challenged to predict the volume of IT security vulnerabilities and allocate resources for fixing them. This challenge requires decision makers to assess which system or software packages are prone to vulnerabilities, how many post-release vulnerabilities can be expected to occur during a certain period of time, and what impact exploits might have. Substantial research has been dedicated to techniques that analyze source code and detect security vulnerabilities. However, only limited research has focused on forecasting security vulnerabilities that are detected and reported after the release of software. To address this shortcoming, we apply established methodologies which are capable of forecasting events exhibiting specific time series characteristics of security vulnerabilities, i.e., rareness of occurrence, volatility, non-stationarity, and seasonality. Based on a dataset taken from the National Vulnerability Database (NVD), we use the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to measure the forecasting accuracy of single, double, and triple exponential smoothing methodologies, Croston's methodology, ARIMA, and a neural network-based approach. We analyze the impact of the applied forecasting methodology on the prediction accuracy with regard to its robustness along the dimensions of the examined system and software package "operating systems", "browsers" and "office solutions" and the applied metrics. To the best of our knowledge, this study is the first to analyze the effect of forecasting methodologies and to apply metrics that are suitable in this context. Our results show that the optimal forecasting methodology depends on the software or system package, as some methodologies perform poorly in the context of IT security vulnerabilities, that absolute metrics can cover the actual prediction error precisely, and that the prediction accuracy is robust within the two applied forecasting-error metrics. (C) 2019 Elsevier Ltd. All rights reserved

Assessing Data Quality - A Probability-based Metric for Semantic Consistency

We present a probability-based metric for semantic consistency using a set of uncertain rules. As opposed to existing metrics for semantic consistency, our metric allows to consider rules that are expected to be fulfilled with specific probabilities. The resulting metric values represent the probability that the assessed dataset is free of internal contradictions with regard to the uncertain rules and thus have a clear interpretation. The theoretical basis for determining the metric values are statistical tests and the concept of the p-value, allowing the interpretation of the metric value as a probability. We demonstrate the practical applicability and effectiveness of the metric in a real-world setting by analyzing a customer dataset of an insurance company. Here, the metric was applied to identify semantic consistency problems in the data and to support decision-making, for instance, when offering individual products to customers

Literature Reviews in IS Research: What Can Be Learnt from the Past and Other Fields?

Literature reviews (LRs) are recognized for their increasing impact in the information systems literature. Methodologists have drawn attention to the question of how we can leverage the value of LRs to preserve and generate knowledge. The panelists who participated in the discussion of “Standalone Literature Reviews in IS Research: What Can Be Learnt from the Past and Other Fields?” at ICIS 2016 in Dublin acknowledged this significant issue and debated 1) what the IS field can learn from other fields and where IS-specific challenges occur, 2) how the IS field should move forward to foster the genre of LRs, and 3) the best practices to train doctoral IS students in publishing LRs. This paper reports the key takeaways of this panel discussion. We provide guidance for IS scholars on how to conduct LRs that contribute to the cumulative knowledge development in and across the IS field to best prepare the next generation of IS scholars