Investigating the Use of Augmented Reality and Machine Learning in Electrical Engineering Courses

2023 | International Conference on Business Informatics | Citations: 0

Authors: Al Mehdawi, Miles; Dreesbach, Tobias; Gösling, Henning; Brinkmann, Marcel

Abstract: The use of augmented reality (AR) in education and training is growing increasi ... Expand

Abstract: The use of augmented reality (AR) in education and training is growing increasingly. However, applications to integrate augmented reality learning content into training for basic electrical engineering courses are scarce. The individual learning objectives of trainees complicate the digitalization of learning content, particularly for the drawing of circuit diagrams. To increase trainees' learning outcome while simultaneously relieving instructors of classroom supervision, we designed and developed an AR-based prototype, to enhance hand-drawn circuit diagrams in vocational training. The context sensitivity is achieved by combining AR with image recognition. In an experiment with twelve participants, a positive impact of the prototype on trainees' learning outcomes was observed, in comparison to a control group that received instructions without the prototype. Collapse

Topics: augmented reality smart device artificial intelligence smart glass extended reality

Methods: design artifact experimental group qualitative interview survey parametric test

Gender, Pay Transparency, and Competitiveness: Why Salary Information Sometimes, but Not Always, Mitigates Gender Gaps in Salary Negotiations

2023 | Group Decision and Negotiation | Citations: 0

Authors: Bear, Julia B.; Pinkley, Robin; Barsness, Zoe; Mazei, Jens; Bhatia, Nazli; Sleesman, Dustin J.

Abstract: Although pay transparency is a widely accepted remedy for the gender pay gap, re ... Expand

Abstract: Although pay transparency is a widely accepted remedy for the gender pay gap, research has devoted little attention to the specific types of salary information that are available to job seekers and whether the framing of this information moderates gender differences in negotiation outcomes. We first conducted an exploratory study to investigate whether men and women differ in how much they rely on and perceive the usefulness of various sources of salary information. A survey of experienced MBA students revealed that, relative to women, men tend to place greater value on sources that involve direct social comparisons (e.g., professional networks) as opposed to more aggregate, general sources (e.g., websites). We then conducted a controlled experiment using a hypothetical job offer negotiation to determine whether framing salary information in terms of social comparison moderates gender gaps in salary requests. Our results supported a moderated mediation model, revealing that men requested higher salaries than women when presented with upward social comparison information (i.e., what a more qualified employee earns), but not downward (i.e., what a less qualified employee earns) or lateral comparison information (i.e., what a similarly qualified employee earns). These effects were driven by heightened competitiveness among men. We also discovered that both men and women experienced the greatest boost in competitiveness and salary requests when presented with downward social comparison information. Overall, salary information is effective in reducing gender gaps, but the effects are nuanced, as they depend on the social comparison inherent to the information. Collapse

Topics: IT career website

Methods: archival research parametric test ANOVA theory development experiment

The social dilemma of big data: Donating personal data to promote social welfare

2023 | Information and Organization | Citations: 0

Authors: Hillebrand, Kirsten; Hornuf, Lars; Müller, Benjamin; Vrankar, Daniel

Abstract: When using digital devices and services, individuals provide their personal data ... Expand

Abstract: When using digital devices and services, individuals provide their personal data to organizations in exchange for gains in various domains of life. Organizations use these data to run technologies such as smart assistants, augmented reality, and robotics. Most often, these organizations seek to make a profit. Individuals can, however, also provide personal data to public databases that enable nonprofit organizations to promote social welfare if sufficient data are contributed. Reg­ ulators have therefore called for efficient ways to help the public collectively benefit from its own data. By implementing an online experiment among 1696 US citizens, we find that individuals would donate their data even when at risk of getting leaked. The willingness to provide personal data depends on the perceived risk level of a data leak but not on a realistic impact of the data on social welfare. Individuals are less willing to donate their data to the private industry than to academia or the government. Finally, individuals are not sensitive to whether the data are pro­ cessed by a human-supervised or a self-learning smart assistant. Collapse

Topics: database system economic surplus IS economics health information system data breach

Methods: archival research experiment nonparametric test Kruskal Wallis test computational algorithm

Theories: prospect theory privacy calculus theory

Improved learning performance for small datasets in high dimensions by new dual-net model for non-linear interpolation virtual sample generation

2023 | Decision Support Systems | Citations: 0

Authors: Lin, Liang-Sian; Lin, Yao-San; Li, Der-Chiang; Liu, Yun-Hsuan

Abstract: The number of reliable samples obtained in early decision-making activity is usu ... Expand

Abstract: The number of reliable samples obtained in early decision-making activity is usually relatively small. Due to variable distribution and incomplete structure of tiny datasets, it is challenging to create reliable and robust predictive modeling using classic statistical and machine learning models in small sample settings. The virtual sample generation (VSG) technique improves model learning accuracies for minimal datasets across diverse applications. Virtual samples on independent variables were generated using established VSG methods predi­ cated on the assumption of a probability distribution or a membership function to fill data gaps. However, in the actual world, non-linear function interactions between variables are common. To address this issue, this paper developed a novel VSG method called Dual-VSG, which generates non-linear interpolation virtual samples using a self-supervised learning (SSL) framework to improve learning performance on small datasets. We generated non-linear interpolation virtual samples without labels by estimating non-linear functions and transforming them into a high-dimensional space using the proposed dual-net model. The weights of the dual-net model are transferred to a downstream task to generate virtual sample labels. To demonstrate the effectiveness of the suggested strategy, this research employed five datasets. On the Backpropagation Neural Networks (BPNN) predictive model, we compared the suggested method’s prediction performance to two state-of-the-art VSG approaches. To assess prediction performance on a regression dataset, the Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE) are used. Furthermore, the classification accuracy (ACC) and the Fl measure are used to assess classification capability on classification datasets. In addition, the paired t-test was utilized to see if the suggested Dual-VSG approach differed significantly from the other VSG methods in terms of RMSE, MAPE, accuracy (ACC), or F1 score. For short datasets, the suggested Dual-VSG method out­ performs those VSG methods, according to our experimental results. Collapse

Topics: Python programming language

Methods: data transformation t-distributed stochastic neighbor embedding experiment machine learning parametric test

Uncertainty Analysis in Group Decisions through Interval Ordinal Priority Approach

2023 | Group Decision and Negotiation | Citations: 0

Authors: Mahmoudi, Amin; Javed, Saad Ahmed

Abstract: In multiple criteria decision-making (MCDM) problems, ranking alternatives is us ... Expand

Abstract: In multiple criteria decision-making (MCDM) problems, ranking alternatives is usually the end. However, in real life, it is rarely an end in itself and serves as a means to an end. Further, in real-life problems, the selection of alternatives is usually made with the aid of unique experts, and thus minimizing the influence of the uncertainty of their subjective judgements on the optimality of the decisions is a critical issue. The current study proposes a novel Interval Ordinal Priority Approach to objectively solve these and other issues by allowing uncertainty analysis and quantification. The study argues that when the model’s input contains uncertainty (even if represented by crisp numbers), expecting the output to be free from uncertainty is an unrealistic conjecture. Therefore, unlike the conventional MCDM models producing crisp weights, the proposed approach yields interval weights with the length of the interval representing the uncertainty (inconsistency among the experts’ judgements). Also, instead of resorting to the subjective measurement of thresholds to qualify or disqualify a set of inputs based on the degree of uncertainty, a novel objective measure of threshold is put forward. The validity of the proposed method is demonstrated through illustrative examples and comparative analysis. Later, the study is concluded with the implications for real-world decision-making. Collapse

Topics: decision making decision support system

Methods: descriptive statistic cross sectional research statistical hypothesis test trade-off analysis method Grey relational analysis

Theories: mathematical theory

HUMAN-AI COLLABORATION IN CONCEPTUALIZING DESIGN SCIENCE RESEARCH STUDIES: PERCEIVED HELPFULNESS OF GENERATIVE LANGUAGE MODEL'S SUGGESTIONS

2023 | European Conference On Information Systems | Citations: 0

Authors: Memmert, Lucas; Cvetkovic, Izabel; Bittner, Eva

Abstract: Solving complex problems was named a new challenge for research on human-AI col ... Expand

Abstract: Solving complex problems was named a new challenge for research on human-AI collaboration. In our study we focus on a particular means to solving complex problems: design science research (DSR). We investigate whether AI, more specifically, generative language models (GLM), can support an individual in conceptualizing DSR studies by making helpful suggestions. To do so we use extracts of a published DSR study and have GPT-3, a GLM provide suggestions for aspects of this study. These suggestions are then evaluated in a survey (n=33) regarding their helpfulness. Results show that GLM suggestions are perceived to be helpful, with some variation depending on expertise. Reported interest in using such a tool in the future was high. Describing how GLMs can offer helpful suggestions we contribute toward a DSR tool support ecosystem and, more generally, towards knowledge on how humans and (generative) AI systems can team up to solve complex problems. Collapse

Topics: artificial intelligence human computer interaction human AI collaboration generative AI system support

Methods: survey design requirement design principle correlation analysis descriptive statistic

From Opaque to Accountable Governance: Investor Activism for Transparency in Social Media Amid Disruption in Cryptocurrency

2023 | International Conference on Information Systems | Citations: 0

Authors: Myeong, Jaeho; Jung, Miyeon; Auh, Seigyoung; Han, Sang Pil

Abstract: ... Expand

Abstract: Collapse

Topics: Twitter social media cryptocurrency blockchain Bitcoin

Methods: proportional hazards model survival analysis mediation analysis longitudinal research econometric modeling

Theories: uncertainty reduction theory

Control-style ambidexterity and information systems project performance: an expanded view of control activities

2023 | European Journal of Information Systems | Citations: 0

Authors: Syed, Tahir Abbas; Wiener, Martin; Mehmood, Fahad; Abdelrahman, Mahmoud

Abstract: Information systems (IS) projects represent key building blocks of large-scale d ... Expand

Abstract: Information systems (IS) projects represent key building blocks of large-scale digital transformation and innovation initiatives. As a result, IS projects have become increasingly ambitious in terms of both goals and scale, making it even more challenging for managers to exercise control over such projects. While prior research focused primarily on the direct and interactive effects of formal and informal control modes on IS project performance, recent research directs attention to the importance of considering project managers’ control styles (i.e., how managers interact with controlees to enact controls). Corresponding studies also indicate that “either/or” control approaches – as opposed to “both/and” approaches – are no longer viable in today’s complex environment. As such, our study draws on an ambidexterity perspective to theoretically develop and empirically test the direct and interactive effects of control-style ambidexterity on IS project performance. Using matched-pair data from 146 IS projects (from 146 high-tech firms), we find that control-style ambidexterity improves project performance – directly and in combination with both formal and informal control. The study results contribute novel insights regarding the effective control of IS projects in the digital era and help explain mixed findings in prior literature, thereby facilitating continued theory development in the research area. Collapse

Topics: IT project project productivity IT project team database system innovation management

Methods: survey theory development partial least squares path modeling post-hoc analysis survey design

Theories: ambidexterity theory

The Role of Resources in the Success or Failure of Diverse Teams: Resource Scarcity Activates Negative Performance-Detracting Resource Dynamics in Social Category Diverse Teams

2023 | Organization Science | Citations: 0

Authors: Yu, Siyu; Greer, Lindred L.

Abstract: Increasing the social category diversity of work teams is top of mind for many o ... Expand

Abstract: Increasing the social category diversity of work teams is top of mind for many organizations. However, such efforts may not always be sufficiently resourced, given the numerous resource demands facing organizations. In this paper, we offer a novel take on the relationship between social category diversity and team performance, seeking to understand the role resources may play in both altering and explaining the performance dynamics of diverse teams. Specifically, our resource framework explains how the effects of social category diversity on team performance can be explained by intrateam resource cognitions and behaviors and are dependent on team resource availability. We propose that in the face of scarcity in a focal resource (i.e., budget), diverse (but not homogenous) teams generalize this scarcity perception to fear that all resources (i.e., staff, time, etc.) are scarce, prompting performance-detracting power struggles over resources within the team. We find support for our model in three multimethod team-level studies, including two laboratory studies of interacting teams and a field study of work teams in research and development firms. Our resource framework provides a new lens to study the success or failure of diverse teams by illuminating a previously overlooked danger in diverse teams (negative resource cognitions (scarcity spillover bias) and behaviors (intrateam power struggles)), which offers enhanced explanatory power over prior explanations. This resource framework for the study of team diversity also yields insight into how to remove the roadblocks that may occur in diverse teams, highlighting the necessity of resource sufficiency for the success of diverse teams. Collapse

Topics: team productivity research and development decision making task interdependence human resource management

Methods: survey field study structural equation modeling theory development laboratory experiment

Unintended Emotional Effects of Online Health Communities: A Text Mining-Supported Empirical Study

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Zhou, Jiaqi; Zhang, Qingpeng; Zhou, Sijia; Li, Xin; Zhang, Xiaoquan (Michael)

Abstract: Online health communities (OHCs) play an important role in enabling patients to ... Expand

Abstract: Online health communities (OHCs) play an important role in enabling patients to exchange information and obtain social support from each other. However, do OHC interactions always benefit patients? In this research, we investigate different mechanisms by which OHC content may affect patients' emotions. Specifically, we notice users can read not only emotional support intended to help them but also emotional support targeting other persons or posts that are not intended to generate any emotional support (auxiliary content). Drawing from emotional contagion theories, we argue that even though emotional support may benefit targeted support seekers, it could have a negative impact on the emotions of other support seekers. Our empirical study on an OHC for depression patients supports these arguments. Our findings are new to the literature and have critical practical implications since they suggest that we should carefully manage OHC-based interventions for depression patients to avoid unintended consequences. We design a novel deep learning model to differentiate emotional support from auxiliary content. Such differentiation is critical for identifying the negative effect of emotional support on unintended recipients. We also discuss options to alter the intervention volume, length, and frequency to tackle the challenge of the negative effect. Collapse

Topics: web forum website application programming interface web page online community

Methods: machine learning deep learning robustness check instrumental variables estimation statistical hypothesis test

Theories: social contagion theory appraisal theory