Sensing the Future: A Design Framework for Context-Aware Predictive Systems

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Avital, Michel; Chatterjee, Samir; Furtak, Szymon

Abstract: Sensors embedded in smart objects, smart machines, and smart buildings produce ... Expand

Abstract: Sensors embedded in smart objects, smart machines, and smart buildings produce ever-growing streams of contextual data that convey information of interest about their operating environment. Although an increasing number of industries have embraced the utilization of sensors in routine operations, no clear framework is available to guide designers who aim to leverage contextual data collected from these sensors to develop predictive systems. In this paper, we applied design science research methodology to develop and evaluate a general framework that can help designers build predictive systems utilizing sensor data. Specifically, we developed a framework for designing context-aware predictive systems (CAPS). We then evaluated the framework through its application in MAN Diesel & Turbo, which served as a case company. The framework can be generalized into a class of demand-forecasting problems that rely on sensor-generated contextual data. The CAPS framework is unique and can help practitioners make better-informed decisions when designing context-aware predictive systems. Collapse

Topics: data quality evaluation criteria knowledge base systems design systems development

Methods: machine learning design science design artifact data transformation design process

The social fabric framework: steps to eliciting the social making of organisations in the digital age

2023 | European Journal of Information Systems | Citations: 0

Authors: Avital, Michel; Jensen, Tina Blegind; Dyrby, Signe

Abstract: The proliferation of enterprise social media generates an ever-growing record of ... Expand

Abstract: The proliferation of enterprise social media generates an ever-growing record of digital traces that provides ample opportunities to study the social making of organisations. Subsequently, we present the social fabric framework, which comprises a structured five-step approach for eliciting, interpreting, and representing the situated social idiosyncrasies and underlying patterns of the social making of organisations. The paper focuses on the application of the social fabric framework as a research method. However, the framework also lends itself to practice as a diagnostic tool that can detect emergent changes in the social fabric of an organisation as well as support organisational development and change. Moreover, by providing a vocabulary for articulating the social making of organisations, the framework can help organisation members reify their dispositions, make sense of the social dynamics, and enable a constructive discussion at the grassroots level about any controversy or aspiration. Collapse

Topics: social media information exchange accounting enterprise social media

Methods: longitudinal research cluster analysis qualitative coding cross sectional research time series analysis

A temporal graph framework for intelligence extraction in social media networks

2023 | Information & Management | Citations: 0

Authors: Chung, Wingyan; Lai, Vincent S.

Abstract: Social media networks (SMNs) are increasingly used in professional management of ... Expand

Abstract: Social media networks (SMNs) are increasingly used in professional management of knowledge workers and related assets. However, the factors affecting behavioral trends and activity levels in these networks are not well understood. Although social and cognitive theories can help to explain human behavior in traditional social networks, their application to SMNs has not been validated. Traditional social network modeling techniques may not accurately predict real-world SMN activities. This research developed a temporal graph framework for in­ telligence extraction in SMNs. Theory-based, data-driven models (Conformity Model (COM), Recency-Primacy Model (REM), Trend Interaction Model (TIM), Periodic Interaction Model (PIM)) were developed based on the framework to capture various aspects of user behavior: conformity effect, recency, primacy, periodicity, and dynamic trend. The models capture the activity history and dynamically combine pricing information to enhance predictive accuracy. Using data of 83,536 GitHub software repositories on cryptocurrency, this article reports the results of experiments that compare the models’ performance in predicting SMN activities over time. Experi­ mental results show that the model (REM) that captures recency/primacy effects of human cognitive processing outperformed other models in 9 (out of 18) measures pertaining to engagement, contribution, influence, and popularity. Primacy plays a dominant role in predicting engagement, contribution, and popularity, whereas recency plays a key role in predicting influence. Short-term trend (modeled with TIM) was found to yield significantly better performance on predicting user contribution. The models also outperformed an integrated machine learning (IML) model by most measures. Overall, the effects modeled by REM and TIM were found to be more significant than the effects modeled by COM, PIM, and IML. The research contributes to enhancing un­ derstanding of SMN behavior, developing new models to simulate and predict SMN activities, and designing new artifacts for information systems practitioners to manage knowledge assets and to extract SMN intelligence. Collapse

Topics: cryptocurrency social network systems development situation awareness user behavior

Methods: experiment machine learning longitudinal research computational algorithm time series analysis

Theories: psychological theory social contagion theory social interactionist theory social impact theory social sciences theory

A Theory-based Deep-Learning Approach to Detecting Disinformation in Financial Social Media

2023 | Information Systems Frontiers | Citations: 0

Authors: Chung, Wingyan; Zhang, Yinqiang; Pan, Jia

Abstract: The spreading of disinformation in social media threatens cybersecurity and unde ... Expand

Abstract: The spreading of disinformation in social media threatens cybersecurity and undermines market efficiency. Detecting disinformation is challenging due to large volumes of social media content and a rapidly changing environment. This research developed and validated a theory-based, novel deep-learning approach (called TRNN) to disinformation detection. Grounded in social and psychological theories, TRNN uses deep-learning and data-centric augmentation to enhance disinformation detection in financial social media. Temporal and contextual information is encoded as specific knowledge about human-validated disinformation, which was identified from our unique collection of 745,139 financial social media messages about four U.S. high-tech company stocks and their fine-grained trading data. TRNN uses multiple series of long short-term memory (LSTM) recurrent neurons to learn dynamic and hidden patterns to support disinformation detection. Our experimental findings show that TRNN significantly outperformed widely-used machine learning techniques in terms of precision, recall, F-score and accuracy, achieving consistently better classification performance in disinformation detection. A case study of Apple Inc.’s stock price movement demonstrates the potential usability of TRNN for secure knowledge management. The research contributes to developing novel approach and model, producing new information systems artifacts and dataset, and providing empirical findings of detecting online disinformation. Collapse

Topics: disinformation social media Twitter fake news usability

Methods: machine learning experiment design artifact deep learning recurrent neural network

Theories: psychological theory

Appreciating the Performance of Neuroscience Mining in NeuroIS Research: A Case Study on Consumer's Product Perceptions in the Two UI Modes-Dark UI vs. Light UI

2023 | HICSS | Citations: 0

Authors: Costello, F.; Kim, M.; Lee, K.

Abstract: The goal of the current study was to explore the potential of neuroscience mini ... Expand

Abstract: The goal of the current study was to explore the potential of neuroscience mining (NSM) for comprehending NeuroIS paradigms. NSM is an interdisciplinary field that combines neuroscience and business mining, which is the application of big data analytics, computational social science, and other areas to business problems. NSM makes it possible to apply predictive models to NeuroIS datasets, such as machine learning and deep learning, to find intricate patterns hidden by conventional regression-based analysis. First, we predicted 28 individual EEG power spectra separated brainwave data using a Random Forest (RF) model. Next, we used NSM to precisely predict how consumers would perceive a product online, depending on whether a light or dark user interface (UI) mode was used. The model was then used to extract more precise results that could not be obtained using more conventional linear-based analytical models using sensitivity analysis. The benefits of using NSM in NeuroIS research are as follows: (1) it can relieve the burden of the threehorned dilemma described by Runkel and McGrath; (2) it can enable more temporal data to be directly analyzed on the target variables; and (3) sensitivity analysis can be performed on a condition/individual basis, strengthening the rigor of findings by reducing sample bias that can be lost in grand averaging of data when analyzed with methods like GLM. Collapse

Topics: user interface big data usability tablet artificial intelligence

Methods: electroencephalography NeuroIS computational algorithm sensitivity analysis machine learning

Of Founders and Contributors: The Construction of Authority through Personal Data Digitalization

2023 | International Conference on Information Systems | Citations: 0

Authors: Curto-Millet, Daniel; Tona, Olgerta

Abstract: ... Expand

Abstract: Collapse

Topics: open source open source software project digital platform decision making Linux

Methods: archival research literature sample digital trace data theory development case study

Rising Above vs. Falling Below: When and Why Status Change Affects Interpersonal Helping in Workgroups

2023 | Organization Science | Citations: 0

Authors: Doyle, Sarah P.; Lount, Robert B.

Abstract: The current research sheds new light on how and why status hierarchies impact in ... Expand

Abstract: The current research sheds new light on how and why status hierarchies impact interpersonal helping by examining people’s reactions to recently experienced status change. Specifically, we incorporate findings from research on the self-serving attributional bias to theorize about how the direction of status change (i.e., a gain or a loss) can shape the extent to which people accept or deflect personal responsibility for their change in status, which we argue will then impact other-concern and, thus, their willingness to help. Further, we identify status change legitimacy as a key contingency that will strengthen or weaken the psychological and behavioral effects of status change. Among firefighter teams (Study 1), participants in the laboratory (Studies 2 and 3), and student teams (Study 4), we show that (1) status change impacts interpersonal helping through its impact on changes in other-concern and (2) status change legitimacy moderates the effect of status change on both other-concern and interpersonal helping. Additionally, we document an asymmetry with regards to the effects of status change on both other-concern and helping behavior (i.e., with the negative impact of a status loss being stronger than the positive impact of a status gain). Theoretical and practical implications are discussed. Collapse

Topics: team productivity missing data accounting

Methods: survey polynomial regression parametric test survey design two-way ANOVA

Exploring the Darkverse: A Multi-Perspective Analysis of the Negative Societal Impacts of the Metaverse

2023 | Information Systems Frontiers | Citations: 0

Authors: Dwivedi, Yogesh K.; Kshetri, Nir; Hughes, Laurie; Rana, Nripendra P.; Baabdullah, Abdullah M.; Kar, Arpan Kumar; Koohang, Alex; Ribeiro-Navarrete, Samuel; Belei, Nina; Balakrishnan, Janarthanan; Basu, Sriparna; Behl, Abhishek; Davies, Gareth H.; Dutot, Vincent; Dwivedi, Rohita; Evans, Leighton; Felix, Reto; Foster-Fletcher, Richard; Giannakis, Mihalis; Gupta, Ashish; Hinsch, Chris; Jain, Animesh; Jane Patel, Nina; Jung, Timothy; Juneja, Satinder; Kamran, Qeis; Mohamed AB, Sanjar; Pandey, Neeraj; Papagiannidis, Savvas; Raman, Ramakrishnan; Rauschnabel, Philipp A.; Tak, Preeti; Taylor, Alexandra; tom Dieck, M. Claudia; Viglia, Giampaolo; Wang, Yichuan; Yan, Meiyi

Abstract: The Metaverse has the potential to form the next pervasive computing archetype t ... Expand

Abstract: The Metaverse has the potential to form the next pervasive computing archetype that can transform many aspects of work and life at a societal level. Despite the many forecasted benefits from the metaverse, its negative outcomes have remained relatively unexplored with the majority of views grounded on logical thoughts derived from prior data points linked with similar technologies, somewhat lacking academic and expert perspective. This study responds to the dark side perspectives through informed and multifaceted narratives provided by invited leading academics and experts from diverse disciplinary backgrounds. The metaverse dark side perspectives covered include: technological and consumer vulnerability, privacy, and diminished reality, human–computer interface, identity theft, invasive advertising, misinformation, propaganda, phishing, financial crimes, terrorist activities, abuse, pornography, social inclusion, mental health, sexual harassment and metaverse-triggered unintended consequences. The paper concludes with a synthesis of common themes, formulating propositions, and presenting implications for practice and policy. Collapse

Topics: virtual reality privacy unintended consequence artificial intelligence IT security

Methods: digital trace data computational algorithm design guideline synthesis ethnography

Exploring the Synergies in Human-AI Hybrids: A Longitudinal Analysis in Sales Forecasting

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Fahse, Tobias; Schmitt, Anuschka

Abstract: Despite the promised potential of artificial intelligence (AI), insights into r ... Expand

Abstract: Despite the promised potential of artificial intelligence (AI), insights into real-life human-AI hybrids and their dynamics remain obscure. Based on digital trace data of over 1.4 million forecasting decisions over a 69-month period, we study the implications of an AI sales forecasting system's introduction in a bakery enterprise on decision-makers' overriding of the AI system and resulting hybrid performance. Decisionmakers quickly started to rely on AI forecasts, leading to lower forecast errors. Overall, human intervention deteriorated forecasting performance as overriding resulted in greater forecast error. The results confirm the notion that AI systems outperform humans in forecasting tasks. However, the results also indicate previously neglected, domain-specific implications: As the AI system aimed to reduce forecast error and thus overproduction, forecasting numbers decreased over time, and thereby also sales. We conclude that minimal forecast errors do not inevitably yield optimal business outcomes when detrimental human factors in decision-making are ignored. Collapse

Topics: artificial intelligence task productivity decision making IT productivity usability

Methods: digital trace data longitudinal research theory development ordinary least square qualitative interview

Theories: technology acceptance model socio technical theory

On the Same Page? What Users Benefit from a Desktop View on Mobile Devices

2023 | Information Systems Research | Citations: 0

Authors: Fink, Lior; Papismedov, Daniele

Abstract: The presentation of online content is routinely adapted to the smaller screens o ... Expand

Abstract: The presentation of online content is routinely adapted to the smaller screens of mobile devices by implementing an information architecture (IA) that hierarchically restructures the information being presented to users, in conjunction with layout and size adaptations. Despite the understanding that users of mobile devices are likely to experience lower usability and higher load without such adaptations, a recent trend is to offer these users the ability to opt out of content adaptation and revert to a desktop view. The prevailing justification for this trend is the existence of websites that fail to provide an optimal mobile experience. In this work, we show that a desktop view may benefit mobile users by increasing the accuracy of their decisions. Focusing on the difference between a desktop IA, where information is presented on a single page, and a mobile IA, where information is hierarchically distributed across pages, we hypothesize that mobile users exhibit lower decision accuracy when a mobile IA is implemented. We theoretically anchor this hypothesis in the effort-accuracy framework, which views decision behavior as contingent on the trade-off between effort and accuracy. This hypothesis is supported in two laboratory experiments in which participants are faced with the multiattribute choice task of making an online hotel reservation. The experiments also highlight a boundary condition for the negative effect of the mobile IA on decision accuracy, depending on whether the mobile IA is congruent with user preferences. The findings suggest that content adaptation on mobile devices deserves greater research attention, particularly from the perspective of adaptive decision behavior.History: Anandasivam Gopal, Senior Editor; Likoebe Maruping, Associate Editor.Funding: The authors thank the Israel Science Foundation [Grant 604/18] for providing funding in support of this research.Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1140. Collapse

Topics: mobile system information storage and retrieval system decision making website smartphone

Methods: experiment descriptive statistic regression analysis method survey statistical hypothesis test

Theories: theory of bounded rationality