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Visual Social Networking Sites (SNSs) enable users to present themselves favorably to gain likes and the attention of others. Especially, Instagram is known for its focus on beauty, fitness, fashion, and dietary topics. Although a large body of research reports negative weight-related outcomes of SNS usage (e.g., body dissatisfaction, body image concerns), studies examining how SNS usage relates to these outcomes are scarce. Based on the visual normalization theory, we argue that SNS content facilitates normalization of so-called thin- and fit-ideals, thereby leading to biased perceptions of the average body weight in society. Therefore, this study tests whether Instagram use is associated with perceiving that the average person weighs less. Responses of 181 survey participants confirm that Instagram use is negatively related to average weight perception of both women and men. These findings contribute to the growing body of research on how SNS use relates to negative weight-related outcomes.
The management of knowledge in organizations considers both established long-term
processes and cooperation in agile project teams. Since knowledge can be both tacit and explicit, its transfer from the individual to the organizational knowledge base poses a challenge in organizations. This challenge increases when the fluctuation of knowledge carriers is exceptionally high. Especially in large projects in which external consultants are involved, there is a risk that critical, company-relevant knowledge generated in the project will leave the company with the external knowledge carrier and thus be lost. In this paper, we show the advantages of an early warning system for knowledge management to avoid this loss. In particular, the potential of visual analytics in the context of knowledge management systems is presented and discussed. We present a project for the development of a business-critical software system and discuss the first implementations and results.
Artificial intelligence (AI)-based technologies can increasingly perform knowledge work tasks, such as medical diagnosis. Thereby, it is expected that humans will not be replaced by AI but work closely with AI-based technology (“augmentation”). Augmentation has ethical implications for humans (e.g., impact on autonomy, opportunities to flourish through work), thus, developers and managers of AI-based technology have a responsibility to anticipate and mitigate risks to human workers. However, doing so can be difficult as AI encompasses a wide range of technologies, some of which enable fundamentally new forms of interaction. In this research-in-progress paper, we propose the development of a taxonomy to categorize unique characteristics of AI-based technology that influence the interaction and have ethical implications for human workers. The completed taxonomy will support researchers in forming cumulative knowledge on the ethical implications of augmentation and assist practitioners in the ethical design and management of AI-based technology in knowledge work.
The envy spiral
(2020)
On Social Networking Sites (SNS) users disclose mostly positive and often self-enhancing information. Scholars refer to this phenomenon as the positivity bias in SNS communication (PBSC). However, while theoretical explanations for this phenomenon have been proposed, an empirical proof of these theorized mechanisms is still missing. The project presented in this Research-in-Progress paper aims at explaining the PBSC with the mechanism specified in the self-enhancement envy spiral. Specifically, we hypothesize that feelings of envy drive people to post positive and self-enhancing content on SNS. To test this hypothesis, we developed an experimental design allowing to examine the causal effect of envy on the positivity of users’ subsequently posted content. In a preliminary study, we tested our manipulation of envy and could show its effectiveness in inducing different levels of envy between our groups. Our project will help to broaden the understanding of the complex dynamics of SNS and the potentially adverse driving forces underlying them.
The devil in disguise
(2021)
Envy constitutes a serious issue on Social Networking Sites (SNSs), as this painful emotion can severely diminish individuals' well-being. With prior research mainly focusing on the affective consequences of envy in the SNS context, its behavioral consequences remain puzzling. While negative interactions among SNS users are an alarming issue, it remains unclear to which extent the harmful emotion of malicious envy contributes to these toxic dynamics. This study constitutes a first step in understanding malicious envy’s causal impact on negative interactions within the SNS sphere. Within an online experiment, we experimentally induce malicious envy and measure its immediate impact on users’ negative behavior towards other users. Our findings show that malicious envy seems to be an essential factor fueling negativity among SNS users and further illustrate that this effect is especially pronounced when users are provided an objective factor to mask their envy and justify their norm-violating negative behavior.
During the outbreak of the COVID-19 pandemic, many people shared their symptoms across Online Social Networks (OSNs) like Twitter, hoping for others’ advice or moral support. Prior studies have shown that those who disclose health-related information across OSNs often tend to regret it and delete their publications afterwards. Hence, deleted posts containing sensitive data can be seen as manifestations of online regrets. In this work, we present an analysis of deleted content on Twitter during the outbreak of the COVID-19 pandemic. For this, we collected more than 3.67 million tweets describing COVID-19 symptoms (e.g., fever, cough, and fatigue) posted between January and April 2020. We observed that around 24% of the tweets containing personal pronouns were deleted either by their authors or by the platform after one year.
As a practical application of the resulting dataset, we explored its suitability for the automatic classification of regrettable content on Twitter.
Many prediction tasks can be done based on users’ trace data. This paper explores divergent and convergent thinking as person-related attributes and predicts them based on features gathered in an online course. We use the logfile data of a short Moodle course, combined with an image test (IMT), the Alternate Uses Task (AUT), the Remote Associates Test (RAT), and creative self-efficacy (CSE). Our results show that originality and elaboration metrics can be predicted with an accuracy of ~.7 in cross-validation, whereby predicting fluency and RAT scores perform worst. CSE items can be predicted with an accuracy of ~.45. The best performing model is a Random Forest Tree, where the features were reduced using a Linear Discriminant Analysis in advance. The promising results can help to adjust online courses to the learners’ needs based on their creative performances.
Data sharing requires researchers to publish their (primary) data and any supporting research materials. With increased attention on reproducibility and more transparent research requiring sharing of data, the issues surrounding data sharing are moving beyond whether data sharing is beneficial, to what kind of research data should be shared and how. However, despite its benefits, data sharing still is not common practice in Information Systems (IS) research. The panel seeks to discuss the controversies related to data sharing in research, specifically focusing on the IS discipline. It remains unclear how the positive effects of data sharing that are often framed as extending beyond the individual researcher (e.g., openness for innovation) can be utilized while reducing the downsides often associated with negative consequences for the individual researcher (e.g., losing a competitive advantage). To foster data sharing practices in IS, the panel will address this dilemma by drawing on the panelists’ expertise.
Openness indicators for the evaluation of digital platforms between the launch and maturity phase
(2024)
In recent years, the evaluation of digital platforms has become an important focus in the field of information systems science. The identification of influential indicators that drive changes in digital platforms, specifically those related to openness, is still an unresolved issue. This paper addresses the challenge of identifying measurable indicators and characterizing the transition from launch to maturity in digital platforms. It proposes a systematic analytical approach to identify relevant openness indicators for evaluation purposes. The main contributions of this study are the following (1) the development of a comprehensive procedure for analyzing indicators, (2) the categorization of indicators as evaluation metrics within a multidimensional grid-box model, (3) the selection and evaluation of relevant indicators, (4) the identification and assessment of digital platform architectures during the launch-to-maturity transition, and (5) the evaluation of the applicability of the conceptualization and design process for digital platform evaluation.
Missing out on life
(2020)
Mobile devices have become an integral part of everyday life due to
their portability. As literature shows, technology use is not only beneficial but also has dark sides, such as addiction. Parents face the need to balance perceived benefits and risks of children’s exposure to mobile technologies. However, no study has uncovered what kind of benefits and concerns parents consider when implementing technology-related rules. We built on qualitative responses of 300
parents of children aged two to thirteen to explore concerns about, and perceived benefits of children’s smartphone and tablet usage, as well as the rules parents have developed regarding technology use. Findings point to concerns regarding children’s development, as well as benefits for both children and parents, and ultimately to new insights about mobile technology mediation. These results provide practical guidance for parents, physicians and mobile industry
stakeholders, trying to ensure that children are acting responsibly with mobile technology.
The metaverse is envisioned as a virtual shared space facilitated by emerging technologies such as virtual reality (VR), augmented reality (AR), the Internet of Things (IoT), 5G, artificial intelligence (AI), big data, spatial computing, and digital twins (Allam et al., 2022; Dwivedi et al., 2022; Ravenscraft, 2022; Wiles, 2022). While still a nascent concept, the metaverse has the potential to “transform the physical world, as well as transport or extend physical activities to a virtual world” (Wiles, 2022). Big data technologies will also be essential in managing the enormous amounts of data created in the metaverse (Sun et al., 2022). Metaverse technologies can offer the public sector a host of benefits, such as simplified information exchange, stronger communication with citizens, better access to public services, or benefiting from a new virtual economy. Implementations are underway in several cities around the world (Geraghty et al., 2022). In this paper, we analyze metaverse opportunities for the public sector and explore their application in the context of Germany’s Federal Employment Agency. Based on an analysis of academic literature and practical examples, we create a capability map for potential metaverse business capabilities for different areas of the public sector (broadly defined). These include education (virtual training and simulation, digital campuses that offer not just online instruction but a holistic university campus experience, etc.), tourism (virtual travel to remote locations and museums, virtual festival participation, etc.), health (employee training – as for emergency situations, virtual simulations for patient treatment – for example, for depression or anxiety, etc.), military (virtual training to experience operational scenarios without being exposed to a real-world threats, practice strategic decision-making, or gain technical knowledge for operating and repairing equipment, etc.), administrative services (document processing, virtual consultations for citizens, etc.), judiciary (AI decision-making aids, virtual proceedings, etc.), public safety (virtual training for procedural issues, special operations, or unusual situations, etc.), emergency management (training for natural disasters, etc.), and city planning (visualization of future development projects and interactive feedback, traffic management, attraction gamification, etc.), among others. We further identify several metaverse application areas for Germany's Federal Employment Agency. These applications can help it realize the goals of the German government for digital transformation that enables faster, more effective, and innovative government services. They include training of employees, training of customers, and career coaching for customers. These applications can be implemented using interactive learning games with AI agents, virtual representations of the organizational spaces, and avatars interacting with each other in these spaces. Metaverse applications will both use big data (to design the virtual environments) and generate big data (from virtual interactions). Issues related to data availability, quality, storage, processing (and related computing power requirements), interoperability, sharing, privacy and security will need to be addressed in these emerging metaverse applications (Sun et al., 2022). Special attention is needed to understand the potential for power inequities (wealth inequity, algorithmic bias, digital exclusion) due to technologies such as VR (Egliston & Carter, 2021), harmful surveillance practices (Bibri & Allam, 2022), and undesirable user behavior or negative psychological impacts (Dwivedi et al., 2022). The results of this exploratory study can inform public sector organizations of emerging metaverse opportunities and enable them to develop plans for action as more of the metaverse technologies become a reality. While the metaverse body of research is still small and research agendas are only now starting to emerge (Dwivedi et al., 2022), this study offers a building block for future development and analysis of metaverse applications.
Looking for participation
(2022)
A stronger learner orientation through participatory learning increases learning motivation and results. But what does participatory learning mean? Where do learning factories and fabrication laboratories (FabLabs) stand in this context, and how can didactic implementation be improved in this respect? Using a newly developed analytical framework, which contains elements of the stage model of participation and general media didactics, we compare a FabLab and a learning factory example concerning the degree of participation. From this, we derive guidelines for designing participative teaching and learning processes in learning factories. We explain how FabLabs can be an inspiration for the didactic design of learning factories.
Living in a world of plenty?
(2020)
Inequality in the distribution of economic wealth within populations has been rising steadily over the past century, having reached unprecedented highs in many Western societies. However, this development is not reflected in people’s perceptions of wealth inequality, as the public tends to underestimate it. Research suggests that inequality estimates are derived from personal reference groups, which, as we propose, are expanded by social network site (SNS) use. As content on SNSs frequently revolves around events of consumption, signaling enhanced overall population wealth, this study tests the hypothesis that SNS use distorts inequality perceptions downward, i.e., increases the perception of societal equality. Responses of 534 survey participants in the United States confirm that SNS use negatively predicts perceived inequality. The relationship is stronger the more SNS users perceive the content they encounter online as real, supporting the assumption that observing other people’s behavior online lowers estimates of nationwide wealth inequality. These findings provide novel insights on inequality misperceptions by suggesting individuals’ SNS use as a new predictor of perceived wealth inequality.
With larger artificial neural networks (ANN) and deeper neural architectures, common methods for training ANN, such as backpropagation, are key to learning success. Their role becomes particularly important when interpreting and controlling structures that evolve through machine learning. This work aims to extend previous research on backpropagation-based methods by presenting a modified, full-gradient version of the backpropagation learning algorithm that preserves (or rather crystallizes) selected neural weights while leaving other weights adaptable (or rather fluid). In a design-science-oriented manner, a prototype of a feedforward ANN is demonstrated and refined using the new learning method. The results show that the so-called crystallizing backpropagation increases the control possibilities of neural structures and interpretation chances, while learning can be carried out as usual. Since neural hierarchies are established because of the algorithm, ANN compartments start to function in terms of cognitive levels. This study shows the importance of dealing with ANN in hierarchies through backpropagation and brings in learning methods as novel ways of interacting with ANN. Practitioners will benefit from this interactive process because they can restrict neural learning to specific architectural components of ANN and can focus further development on specific areas of higher cognitive levels without the risk of destroying valuable ANN structures.
We welcome you to the 54th Hawaii International Conference on System Sciences (HICSS-54) conference. This is the fifth year for the Organizational Learning Minitrack which has had the usual growing pains: two years ago, we added the topic of Unlearning and joined with the Intentional Forgetting Minitrack - as these topics are all organizationally-based knowledge management issues. We proudly bring you the latest research focused on the methods to develop and maintain organizational learning within the Knowledge Innovation and Entrepreneurial Systems Track. The ability to update, change and use current knowledge effectively, especially in light of the ongoing knowledge explosion, can be costly for any organization. Organizations that consider themselves “learning” or “knowledge-based” organizations must develop a competent workforce using KM strategies. Success in organizations involves developing a variety of human factors for changing competencies. With technological change, modification and revisions, many skills require updating for a competitive advantage in the marketplace. The focus on new techniques and insights into how individuals and organizations use their knowledge is our focus for the improvement of organizational
learning in this Minitrack.
We welcome you to the 53rd Hawaii International Conference on System Sciences (HICSS) conference. After joining with Intentional Forgetting Minitrack last year, this is the fourth year of the Organizational Learning Minitrack. We add Unlearning, and Intentional Forgetting to proudly bring you the latest research focused on organizational learning issues within the Knowledge Innovation and Entrepreneurial Systems Track. The ability to update, change and use current knowledge effectively, especially in light of the ongoing knowledge explosion, can be costly for any organization. Organizations that consider themselves “learning” or “knowledge-based” organizations must develop a competent workforce using KM strategies. Success in organizations involves developing a variety of human factors for changing competencies. With technological change, modification and revisions, many skills require updating for a competitive advantage in the marketplace. The focus on new techniques and insights into how individuals and organizations use their knowledge is our focus for the improvement of organizational learning in this Minitrack.
Virtual reality can have advantages for education and learning. However, it must be adequately designed so that the learner benefits from the technological possibilities. Understanding the underlying effects of the virtual learning environment and the learner’s prior experience with virtual reality or prior knowledge of the content is necessary to design a proper virtual learning environment. This article presents a pre-study testing the design of a virtual learning environment for engineering vocational training courses. In the pre-study, 12 employees of two companies joined the training course in one of the two degrees of immersion (desktop VR and VR HMD). Quantitative results on learning success, cognitive load, usability, and motivation and qualitative learning process data were presented. The qualitative data assessment shows that overall, the employees were satisfied with the learning environment regardless of the level of immersion and that the participants asked for more guidance and structure accompanying the learning process. Further research is needed to test for solid group differences.
How messy is your news feed
(2020)
Social Networking Sites (SNSs) are pervasive in our daily lives. However, emerging reports suggest that people are increasingly dissatisfied with their experience of SNSs News Feeds. Motivated by the cognitive load theory, the paper postulates that arrangement and presentation of information are important constituents of one’s Facebook News Feed experience. Integrating these factors into the novel concept of ‘perceived disorder’, this paper hypothesizes that the perception of disorder elicited by the Facebook News Feed plays an important role in causing discontinuance intentions. Drawing on the Stressor-Strain-Outcome Model, we suggest that perceived disorder leads to SNS discontinuance intention and is partially mediated by SNS fatigue. The paper uses the responses of 268 Facebook users to investigate these relationships and introduces perceived disorder as a novel stressor. Besides adding to the existing body of literature, these insights are of relevance to internet service providers, policy makers and SNS users.
Die 8. Fachtagung für Hochschuldidaktik der Informatik (HDI) fand im September 2018 zusammen mit der Deutschen E-Learning Fachtagung Informatik (DeLFI) unter dem gemeinsamen Motto „Digitalisierungswahnsinn? - Wege der Bildungstransformationen“ in Frankfurt statt.
Dabei widmet sich die HDI allen Fragen der informatischen Bildung im Hochschulbereich. Schwerpunkte bildeten in diesem Jahr u. a.:
- Analyse der Inhalte und anzustrebenden Kompetenzen in Informatikveranstaltungen
- Programmieren lernen & Einstieg in Softwareentwicklung
- Spezialthemen: Data Science, Theoretische Informatik und Wissenschaftliches Arbeiten
Die Fachtagung widmet sich ausgewählten Fragestellungen dieser Themenkomplexe, die durch Vorträge ausgewiesener Experten und durch eingereichte Beiträge intensiv behandelt werden.