Empowering Users to Create Augmented Reality-Based Solutions – Deriving Design Principles for No-Code AR Authoring Tools

2023 | International Conference on Information Systems | Citations: 0

Authors: Bräker, Julia; Hertel, Julia; Semmann, Martin

Abstract: Grounded on an experimental study with 18 participants, we derive 15 design pri ... Expand

Abstract: Grounded on an experimental study with 18 participants, we derive 15 design principles for no-code AR authoring tools in an organizational setting. The study consists of two distinct treatments that aim to augment lightweight processes with AR. The outcomes are two interactive tutorials utilizing AR instructions. Following the no-code approach, the participants were empowered to create relevant AR content using a reduced interface and no need for advanced configurations or coding. The study thus combines two research streams with the aim of better understanding mechanisms for AR use in a professional context. As prior work has shown, despite the potential benefits, the adoption of AR authoring tools is limited because ramping up AR to productive use is heavily dependent on consulting and custom software solutions. Our novel approach bears the potential to broaden application domains and empower professionals to apply AR. Collapse

Topics: mobile application usability user experience information system use augmented reality

Methods: design principle design artifact survey cognitive walkthrough design science

Excuse Me, Something Is Unfair! - Implications of Perceived Fairness of Service Robots

2023 | European Conference On Information Systems | Citations: 0

Authors: Büttner, Sebastian; Sourkounis, Cora Maria; Gutzmann, Jan; Prilla, Michael

Abstract: Fairness is an important aspect for individuals and teams. This also applies to ... Expand

Abstract: Fairness is an important aspect for individuals and teams. This also applies to human-robot interaction (HRI). Especially if intelligent robots provide services to multiple humans, humans may feel treated unfairly by robots. Most work in this area deals with the aspects of fair algorithms, task allocation, and decision support. This work focuses on a different, yet little-explored perspective, which looks at fairness in HRI from a human-centered perspective in human-robot teams. We present an experiment in which a service robot was responsible for distributing resources among competing team members -in one condition with an efficient (but rather unfair) distribution algorithm and in another condition with an algorithm that was less efficient but could be considered fair. We investigated how the different strategies of distribution influence the perceived fairness and how this perceived fairness influenced the perception of the robot as such. Our study shows that humans might perceive technically efficient algorithms executed by a robot as unfair, especially if humans personally experience negative consequences. Interestingly, this perceived unfairness had a negative impact on human perception of the robot as such, which should be considered in the design of future robots. Collapse

Topics: robotic user acceptance user interface artificial intelligence data quality

Methods: experiment nonparametric test computational algorithm Mann Whitney U test survey

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

Cognitive load during planned and unplanned virtual shopping: Evidence from a neurophysiological perspective

2023 | International Journal of Information Management | Citations: 0

Authors: Kakaria, Shobhit; Saffari, Farzad; Z. Ramsøy, Thomas; Bigné, Enrique

Abstract: Rapid adoption of virtual-reality-assisted retail applications is inadvertently ... Expand

Abstract: Rapid adoption of virtual-reality-assisted retail applications is inadvertently reshaping consumer buying pat­ terns, making it crucial for businesses to enhance their shopping experience. This new scenario challenges marketers with unique hurdles in both the commercialization of products and in managing information cues derived via VR retailing. Therefore, this study examined consumers’ impulsive behavior and unplanned pur­ chases in a virtual retail store, using self-reports and electroencephalography. Borrowing assorted perspectives from retailing, virtual reality, and neuromarketing literature, we extended the stimulus-organism-response framework to evaluate how unplanned behavior evolves through conscious and unconscious measures. We found that consumers’ impulsiveness was significantly associated with their unplanned expenditure and the number of unplanned purchases. Using mediation analysis, we observed that flow experience during shopping partially mediated the relationship between the sense of presence and the desire to stay longer in a virtual shopping store. Desire to stay in the virtual store positively influenced store satisfaction, basket-size deviation, and budget deviation. Additionally, cognitive workload obtained via electroencephalogram revealed significant differences during both planned and unplanned purchases. These findings provide fresh opportunities for re­ tailers to leverage the disruptive potential of immersive and interactive virtual technology to transform consumer shopping experiences. Collapse

Topics: virtual reality decision making psychological ownership social media marketing management

Methods: electroencephalography self reported survey longitudinal research neurophysiology experiment

AI Washing: The Framing Effect of Labels on Algorithmic Advice Utilization

2023 | International Conference on Information Systems | Citations: 0

Authors: Leffrang, Dirk; Mueller, Oliver

Abstract: Many researchers and practitioners see artificial intelligence as a game change ... Expand

Abstract: Many researchers and practitioners see artificial intelligence as a game changer compared to classical statistical models. However, some software providers engage in "AI washing", relabeling solutions that use simple statistical models as AI systems. By contrast, research on algorithm aversion unsystematically varied the labels for advisors and treated labels such as "artificial intelligence" and "statistical model" synonymously. This study investigates the effect of individual labels on users' actual advice utilization behavior. Through two incentivized online within-subjects experiments on regression tasks, we find that labeling human advisors with labels that suggest higher expertise leads to an increase in advice-taking, even though the content of the advice remains the same. In contrast, our results do not suggest such an expert effect for advice-taking from algorithms, despite differences in self-reported perception. These findings challenge the effectiveness of framing intelligent systems as AI-based systems and have important implications for both research and practice. Collapse

Topics: artificial intelligence decision making individual characteristic user experience user behavior

Methods: computational algorithm experiment survey longitudinal research within subject experiment

The Influence of Citizen Involvement on the Intention to Use Smart Traffic Management Applications

2023 | International Conference on Information Systems | Citations: 0

Authors: Lorenz, Alisa; Madeja, Nils; Leyh, Christian

Abstract: Smart traffic management initiatives are a growing trend in many cities. While ... Expand

Abstract: Smart traffic management initiatives are a growing trend in many cities. While they have the potential to fundamentally improve traffic in urban areas, their success is determined by the citizens' use of the applications developed in related projects. However, citizens are rarely involved in the requirements elicitation or development process encompassed within such initiatives, even despite research showing the high potential of user-centric software development. In a multiple-methods approach combining focus group discussions and a quantitative survey, we investigate how citizens can be integrated in the specification process of data-driven traffic management applications and whether their involvement can influence the intention to use those applications in the future. With our findings, we contribute to the field of user-centric software design in information systems research applied to smart cities and offer insights into citizen involvement in that context. Collapse

Topics: requirements elicitation usage intention smart city systems design systems development

Methods: focus group survey group interview Wilcoxon signed-rank test nonparametric test

Theories: unified theory of acceptance and use of technology

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

Unveiling and Unraveling Aggregation and Dispersion Fallacies in Group MCDM

2023 | Group Decision and Negotiation | Citations: 0

Authors: Mohammadi, Majid; Tamburri, Damian A.; Rezaei, Jafar

Abstract: Priorities in multi-criteria decision-making (MCDM) convey the relevance prefere ... Expand

Abstract: Priorities in multi-criteria decision-making (MCDM) convey the relevance preference of one criterion over another, which is usually reflected by imposing the non-negativity and unit-sum constraints. The processing of such priorities is different than other unconstrained data, but this point is often neglected by researchers, which results in fallacious statistical analysis. This article studies three prevalent fallacies in group MCDM along with solutions based on compositional data analysis to avoid misusing statistical operations. First, we use a compositional approach to aggregate the priorities of a group of DMs and show that the outcome of the compositional analysis is identical to the normalized geometric mean, meaning that the arithmetic mean should be avoided. Furthermore, a new aggregation method is developed, which is a robust surrogate for the geometric mean. We also discuss the errors in computing measures of dispersion, including standard deviation and distance functions. Discussing the fallacies in computing the standard deviation, we provide a probabilistic criteria ranking by developing proper Bayesian tests, where we calculate the extent to which a criterion is more important than another. Finally, we explain the errors in computing the distance between priorities, and a clustering algorithm is specially tailored based on proper distance metrics. Collapse

Topics: decision making

Methods: descriptive statistic computational algorithm cluster analysis Wilcoxon signed-rank test nonparametric test

Theories: SERVQUAL

A decision support framework to incorporate textual data for early student dropout prediction in higher education

2023 | Decision Support Systems | Citations: 0

Authors: Phan, Minh; De Caigny, Arno; Coussement, Kristof

Abstract: Managing student dropout in higher education is critical, considering its substa ... Expand

Abstract: Managing student dropout in higher education is critical, considering its substantial impacts on students’ lives, academic institutions, and society as a whole. Using predictive modeling can be instrumental for this task, as a means to identify dropouts proactively on the basis of student characteristics and their academic performance. To enhance these predictions, textual student feedback also might be relevant; this article proposes a hybrid decision support framework that combines predictive modeling with student segmentation efforts. A real-life data set from a French higher education institution, containing information of 14,391 students and 62,545 feedback documents, confirms the superior performance of the proposed framework, in terms of the area under the curve and top decile lift, compared with various benchmarks. In contributing to decision support system research, this study (1) proposes a new framework for automatic, data-driven segmentation of students based on textual data; (2) compares multiple text representation methods and confirms that incorporating student textual feedback data improves the predictive performance of student dropout models; and (3) establishes useful insights to help decision-makers anticipate and manage student dropout behaviors. Collapse

Topics: decision support decision support system electronic mail

Methods: machine learning doc2vec data transformation BERT computational algorithm

Adaptive Conversational Agents: Exploring the Effect of Individualized Design on User Experience

2023 | European Conference On Information Systems | Citations: 0

Authors: Riefle, Lara; Alonso, Sebastian Alamo; Jungnitz, Mauricio; Rothmaier, Alexander; Wiegand, Henrik; Herrera, Ricardo; Benz, Carina

Abstract: Conversational agents (CA) offer a range of benefits to firms and users, yet us ... Expand

Abstract: Conversational agents (CA) offer a range of benefits to firms and users, yet user experiences are often unsatisfying. An explanation might be that individual differences of users are only insufficiently addressed in today's CA design. Drawing on communication accommodation theory, we develop a research model and study design to investigate how adapting CA design to users' individual characteristics influences the user experience. In particular, we develop text-based CAs (i.e., chatbots) that are adapted to users' rational/intuitive cognitive style or need for interaction, and compare the user experience to non-adapted CAs. Initial results from our pilot study (n=37) confirm that individualized CA design can enhance the user experience. We expect to contribute to the growing research field of adaptive CA design. Moreover, our results will provide guidance for developers on how to facilitate a pleasing user experience by adapting the CA design to users. Collapse

Topics: user experience usage intention individual characteristic user requirement Google+

Methods: experiment experimental group survey chatbot statistical power analysis