Decision models play an important role in various areas of information systems research, including system analysis and design, compliance management, and various application domains. Decision models must be effectively presented so that analysts can assure their correctness and completeness. So far, empirical research on the cognitive effectiveness of decision models has provided partially inconclusive results. Our paper provides novel insights into the drivers of decision model comprehension by moving from a classification based on representation archetypes to granular representation features. Using an experimental research design, we discover that the decision model type must be assessed in conjunction with other representational factors, such as representation structure (expanded vs. frugal) and representation design (monochromatic vs. colours). In this way, we extend prior arguments of cognitive fit theory by demonstrating that colour can be used to compensate for a misfit of decision model and task, and structural features can further increase model comprehension. We further studied the root causes of the observed effects by using eye-tracking. Our findings have implications for both cognitive information systems research and practice, as they can be used to guide decision model users and tool vendors.

Abstract Question-and-answer platforms such as Stack Overflow are an important way for software developers to share and retrieve knowledge. However, reusing poorly understood code can lead to serious problems, such as bugs or security vulnerabilities. To better understand how code comments affect the perceived helpfulness of Stack Overflow answers, we conducted an online experiment simulating a Stack Overflow environment (n=91). The results indicate that both block and inline comments are perceived as significantly more helpful than uncommented source code. Moreover, novices rated code snippets with block comments as more helpful than those with inline comments. Interestingly, other surface features, such as the position of an answer and its answer score, were considered less important. Moreover, the content of Stack Overflow has been a major source for training large language models. AI-based coding assistants such as GitHub Copilot, which are based on these models, are changing the way Stack Overflow is used. However, our findings have implications beyond Stack Overflow. First, they may help to improve the relevance also of other community-driven platforms, which provide human advice and explanations of code solutions, complementing AI-based support for software developers. Second, since chat-based AI tools can be prompted to generate code in different ways, knowing which properties influence perceived helpfulness can lead to more targeted prompting strategies to generate readable code snippets.

A critical part of flow-based conceptual modeling, such as process modeling, is visualizing the logical and temporal sequence in which activities in a process should be completed. While there are established standards and recommendations, there is limited empirical research examining the influence of process model layout on model comprehension. To address this research gap, we conducted a controlled eye-tracking experiment with 70 participants comparing different layouts. The experimental results confirm that the visibility of control flow patterns is critical for assisting users with visual processing, particularly attentional allocation, when comprehending process models for both local comprehension tasks and tasks requiring cognitive integration of model components. In models with more directional changes, users’ visual attention is more drawn to irrelevant regions, but comprehension is less affected as long as patterns remain visible. Our findings not only elucidate how cognitive fit between a visual representation and a task can manifest itself and the perceptual benefits it brings, but they can also guide the automated layout of models in tools and complement practical process modeling guidelines.

The user expects webpages of specific categories to have a look-and-feel specific to that category. For example, unlike university homepages, online-shop webpages typically feature relatively little text, a long grid-like structure listing products, and numerous functional elements for product search, filtering, and recommendation. Ensuring that a webpage meets user expectations enhances its prototypicality and has a positive impact on the user’s impression of the webpage. Despite the potential impact on users, the concept of webpage prototypicality has not been fully explored or extensively employed in Human-Computer Interaction (HCI). This paper addresses this gap by conducting a user study with 1530 participants to investigate webpage prototypicality. The study revealed a strong correlation between prototypicality and webpage visual aesthetics, perceived pre-use usability, and trustworthiness. Notably, the direct effect of prototypicality on trustworthiness outweighed the indirect effects through aesthetics and usability. Overall, prototypicality, aesthetics, and usability collectively accounted for 29% to 68% of the variance in trustworthiness, depending on the webpage category. These findings underscore the importance of embracing prototypicality within the field of HCI, encouraging its wider adoption.

The main aim of this study is to investigate human understanding of process models and to develop an improved understanding of its relevant influence factors. Aided by assumptions from cognitive psychology , this article attempts to address specific deductive reasoning difficulties based on process models. The authors developed a research model to capture the influence of two effects on the cognitive difficulty of reasoning tasks: (i) the presence of different control-flow patterns (such as conditional or parallel execution) in a process model and (ii) the interactivity of model elements. Based on solutions to 61 different reasoning tasks by 155 modelers, the results from this study indicate that the presence of certain control-flow patterns influences the cognitive difficulty of reasoning tasks. In particular, sequence is relatively easy, while loops in a model proved difficult. Modelers with higher process modeling knowledge performed better and rated subjective difficulty of loops lower than modelers with lower process modeling knowledge. The findings additionally support the prediction that interactivity between model elements is positively related to the cognitive difficulty of reasoning. Our research contributes to both academic literature on the comprehension of process models and practitioner literature focusing on cognitive difficulties when using process models.

As the complexity and variety of systems and software products have increased, the ability to manage their variability effectively and efficiently became crucial. To this end, variability can be specified either as an integral part of the development artifacts or in a separate orthogonal variability model. Lately, orthogonal variability models attract a lot of attention due to the fact that they do not require changing the complexity of the development artifacts and can be used in conjunction with different development artifacts. Despite this attention and to the best of our knowledge, no empirical study examined the comprehensibility of orthogonal variability models.

Most business process modeling languages are visual languages. Yet, very little attention has been devoted to the choice of symbols used. This article contributes to the visual languages literature by providing an enriched understanding about how symbol choice affects the ease of memorizing and recalling visual diagrams. Specifically, the experiment reported here was designed to compare the effect of three different symbol sets of visual languages from the business process modeling domain on diagram recall. Quantitative analysis based on data collected from 37 business school students, revealed that symbol choice significantly influences memory of visual aspects of the diagrams as layout. However, the study did not find a significant effect of symbol choice on recognition accuracy of the semantic content of the diagrams. Overall, the findings underscore the relevance of symbol choice for visual language design.

Peer review is used as an effective quality assurance measure in many contexts, including science, business, programming or education. In education, several studies confirmed the positive effects of peer reviewing on student learning. Based on recent research concerning the role of media in the peer review process this study investigates how students perceive the process, content and effects of peer reviews. We also analyze students' opinions on different modes of peer reviewing activities, e.g. online vs. face-to-face reviewing. In the context of a computer science course on scientific writing, these research questions were addressed by administering an online questionnaire (n=38) and analysis using quantitative and qualitative methods. Results indicate that students value the peer review activity, take peer reviews seriously and provide comprehensive and constructive reviews. Findings also show that students prefer written online reviews with the possibility of oral follow-up questions to reviewers.