The Interplay of Information Processing, Mindfulness, and Affective State in Phishing Detection

2023 | International Conference on Information Systems | Citations: 0

Authors: Bera, Debalina; Kim, Dan

Abstract: This study investigates the effect of individuals' information processing modes ... Expand

Abstract: This study investigates the effect of individuals' information processing modes on phishing email detection mechanisms and accuracy. Drawing on the heuristic-systematic model of processing, mindful decision-making, and affect-and-persuasion literature, this study examines the antecedents (i.e., heuristic, systematic processing, general and domain mindfulness, and affective state) and behavioral consequences (i.e., detection accuracy) in phishing identification. Using a scenario-based survey experiment this study shows mindfulness and affectivity navigate information processing mechanisms, thus influence phishing detection accuracy. It aims to contribute to information security literature by examining novel associations between general, domain mindfulness and its effect on online users' information processing strategy and phishing detection accuracy. Furthermore, this study contributes to phishing training and awareness by identifying the function of cognitive-affective (affective states, trait mindfulness) and cognitive-behavioral (domain mindfulness) factors on the choice of information processing modes and phishing detection accuracy. Finally, the affective states might help improve the efficacy of automatic filters. Collapse

Topics: phishing mindfulness electronic mail email phishing data security

Methods: experiment survey partial least squares regression machine learning laboratory experiment

Theories: protection motivation theory

Decide wisely: Interactive videos as appealing educational element to attract students to information security

2023 | International Conference on Business Informatics | Citations: 0

Authors: Brehmer, Martin

Abstract: Educational videos play an important role in university education. However, the ... Expand

Abstract: Educational videos play an important role in university education. However, they are often not designed to foster active learning and the learning process is not immersive. Interactive digital storytelling is a promising game design element but requires a rigorous evaluation to avoid negative side effects. Therefore, we adopt a design science research approach to design and evaluate an interactive video that includes interactive storytelling and real-world recordings. The proposed artifact aims to raise information security awareness among bachelor students at a German university for malicious USB sticks and the reporting of incidents. In our evaluation, we focus on learning progress before and after using the video as well as qualitative feedback about the experience with the learning object. Our results show that videos that are based on interactive choice-based storytelling can foster an active and immersive process, and significant learning outcomes. Collapse

Topics: IT security training data security anonymity cybersecurity awareness user experience

Methods: parametric test Student's t-test survey design science descriptive statistic

Using machine learning to create and capture value in the business models of small and medium-sized enterprises

2023 | International Journal of Information Management | Citations: 0

Authors: Costa-Climent, Ricardo; Haftor, Darek M.; Staniewski, Marcin W.

Abstract: Start-ups have revolutionised many economic ecosystems, becoming innovation pion ... Expand

Abstract: Start-ups have revolutionised many economic ecosystems, becoming innovation pioneers around the world. Most are based on data-driven business models, particularly relying on machine learning technologies. However, not all start-ups that use machine learning technologies manage to create and capture value. The existing literature on the use value enabled by information technologies does not take into account the unique capabilities of machine learning. The theory of data network effects offers a promising explanation of how to create value using machine learning. However, it does not explicitly describe how to capture value using machine learning. In contrast, business model theory explains how companies use technologies to create and capture value, but not specifically through the use of machine learning technology. Therefore, this study aims to improve the theo­ retical understanding of the key drivers of value creation and capture in start-ups with business models driven by this kind of technology. Statistical techniques are used in a sample of 122 start-ups to explore the theoretical relationships between these two theories. The analysis reveals the link between specific value creation and capture factors of the two theories, such as efficiency, novelty, and performance expectancy. The study also provides evidence of the need to adopt a co-evolutionary perspective of value creation and capture through the use of machine learning. Collapse

Topics: value creation Twitter social media database system website

Methods: descriptive statistic theory development regression analysis method general multivariate regression model linear regression analysis

To Stem the Tide: Organizational Climate and the Locus of Knowledge Transfer

2023 | Organization Science | Citations: 0

Authors: Di Stefano, Giada; Micheli, Maria Rita

Abstract: Prior work has maintained that organizations benefit from managing the transfer ... Expand

Abstract: Prior work has maintained that organizations benefit from managing the transfer of proprietary knowledge. Transfer is often advantageous within organizational boundaries but may be harmful across them, because it might erode competitive advantage. Hence, we ask: How can organizations affect the direction in which knowledge flows? We examine the role of organizational climate as a governing mechanism for knowledge transfer. Our empirical strategy consists of a mixed-methods approach leveraging qualitative and experimental data over two cycles of theory building and theory testing. We start with an extensive field study of the European Organization for Nuclear Research (CERN), leveraging the insights from desk research, field observations, 53 interviews, and a laboratory-in-the-field experiment involving 518 physicists. We then provide a causal test of the emerging framework by means of two laboratory experiments with 389 participants. Our findings suggest employees are more likely to transfer knowledge to their colleagues when they identify as an integral part of the organization, but they would rather transfer knowledge to outside competitors when their organization encourages them to outperform coworkers. In the presence of an organizational climate that is unfavorable to preventing knowledge spillovers, we argue, organizations can redirect the locus of knowledge transfer internally by acting upon an individual employee’s job design and socialization regime.History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory.Funding: The authors acknowledge generous financial support from Bocconi University, the HEC Foundation of HEC Paris, and IESEG School of Management.Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.1551. Collapse

Topics: knowledge sharing electronic mail pandemic knowledge spillover strategic management

Methods: experiment qualitative interview field study theory development laboratory experiment

Theories: organizational theory

Promoting Learning Through Explainable Artificial Intelligence: An Experimental Study in Radiology

2023 | International Conference on Information Systems | Citations: 0

Authors: Ellenrieder, Sara; Kallina, Emma; Pumplun, Luisa; Gawlitza, Joshua Felix; Ziegelmayer, Sebastian; Buxmann, Peter

Abstract: The deployment of machine learning (ML)-based decision support systems (DSSs) i ... Expand

Abstract: The deployment of machine learning (ML)-based decision support systems (DSSs) in high-risk environments such as radiology is increasing. Despite having achieved high decision accuracy, they are prone to errors. Thus, they are primarily used to assist radiologists in their decision making. However, collaborative decision making poses risks to the decision maker, e.g. automation bias and long-term performance degradation. To address these issues, we propose combining findings of the research streams of explainable artificial intelligence and education to promote human learning through interaction with ML-based DSSs. We provided radiologists with explainable vs nonexplainable decision support that was high-vs low-performing in a between-subject experimental study to support manual segmentation of 690 brain tumor scans. Our results show that explainable ML-based DSSs improved human learning outcomes and prevented false learning triggered by incorrect decision support. In fact, radiologists were able to learn from errors made by the low-performing explainable ML-based DSS. Collapse

Topics: decision support system decision support explainable artificial intelligence decision making artificial intelligence

Methods: experiment qualitative interview magnetic resonance imaging parametric test ANOVA

The Power of Trust: Designing Trustworthy Machine Learning Systems in Healthcare

2023 | International Conference on Information Systems | Citations: 0

Authors: Fecho, Mariska; Zöll, Anne

Abstract: Machine Learning (ML) systems have an enormous potential to improve medical car ... Expand

Abstract: Machine Learning (ML) systems have an enormous potential to improve medical care, but skepticism about their use persists. Their inscrutability is a major concern which can lead to negative attitudes reducing end users trust and resulting in rejection. Consequently, many ML systems in healthcare suffer from a lack of user-centricity. To overcome these challenges, we designed a user-centered, trustworthy ML system by applying design science research. The design includes meta-requirements and design principles instantiated by mockups. The design is grounded on our kernel theory, the Trustworthy Artificial Intelligence principles. In three design cycles, we refined the design through focus group discussions (N1=8), evaluation of existing applications, and an online survey (N2=40). Finally, an effectiveness test was conducted with end users (N3=80) to assess the perceived trustworthiness of our design. The results demonstrated that the end users did indeed perceive our design as more trustworthy. Collapse

Topics: health information system artificial intelligence information privacy concern privacy collaborative work

Methods: survey design science kernel theory experimental group design artifact

Theories: trust theory

Effort as investment in healthcare dialogue-based agents: On the role of means-goal configurations

2023 | Decision Support Systems | Citations: 0

Authors: Ghasemi, Ehsan; Sheikh-Zadeh, Alireza; Song, Jaeki

Abstract: The recent pandemic and resultant increase in clinical caseload have brought dia ... Expand

Abstract: The recent pandemic and resultant increase in clinical caseload have brought dialogue-based agents (DBAs) to the forefront of attention in the healthcare industry. The ability of these agents to accomplish a variety of sequential and related tasks within a single application software has made them more effortful and complex. While effort requirements and complexity of information systems are generally considered negative attributes, the potential effect of user effort investment on user perceptions and decisions is less investigated. Within the framework of goal systems theory, this study explores how perceived effort could be appropriated to enhance users’ positive perceptions and willingness to accept healthcare DBAs outputs. We find evidence for the positive role both in the perceived effort before and after the actual use of healthcare DBAs as well as in user goal magnitude and means-goal configuration on perceptions of usefulness and probability of considering outputs. Collapse

Topics: technology adoption mobile application user experience decision making mobile phone

Methods: experiment regression analysis method experimental group linear regression analysis descriptive statistic

Theories: general systems theory

Overcoming Anchoring Bias: The Potential of AI and XAI-based Decision Support

2023 | International Conference on Information Systems | Citations: 0

Authors: Haag, Felix; Stingl, Carlo; Zerfass, Katrin; Hopf, Konstantin; Staake, Thorsten

Abstract: Information systems (IS) are frequently designed to leverage the negative effec ... Expand

Abstract: Information systems (IS) are frequently designed to leverage the negative effect of anchoring bias to influence individuals ' decision-making (e.g., by manipulating purchase decisions). Recent advances in Artificial Intelligence (AI) and the explanations of its decisions through explainable AI (XAI) have opened new opportunities for mitigating biased decisions. So far, the potential of these technological advances to overcome anchoring bias remains widely unclear. To this end, we conducted two online experiments with a total of N=390 participants in the context of purchase decisions to examine the impact of AI and XAI-based decision support on anchoring bias. Our results show that AI alone and its combination with XAI help to mitigate the negative effect of anchoring bias. Ultimately, our findings have implications for the design of AI and XAIbased decision support and IS to overcome cognitive biases. Collapse

Topics: decision support explainable artificial intelligence artificial intelligence task productivity decision making

Methods: experiment experimental group computational algorithm ordinary least square Student's t-test

Reorganization and Participation in Decentralized Platform Ecosystems: Evidence from Blockchain Forking

2023 | HICSS | Citations: 0

Authors: Hsieh, Ying-Ying; Andersen, Jonas

Abstract: Like any organizational system, platform ecosystems reorganize to update its ali ... Expand

Abstract: Like any organizational system, platform ecosystems reorganize to update its alignment with the internal and external environments. However, unlike reorganizations of centrally managed platforms performed by the owners, reorganizations of decentralized platforms ecosystems do not rely on formal authority. Instead, the network self-reorganizes to renew the structure, rules, and information to evolve. Little is known about how self-reorganizations influence the participation of various types of networks. In this study, we investigate nine reorganization events on Ethereum, a blockchain-based decentralized smart contract platform, to unpack how self-reorganization related to hard forking influence participation in the development, validation, transaction, and complementor networks. We find that, while participation increases across all networks show a small increase after hard forking events, more complex dynamics are at play within each network that builds on delicate trade-offs between participation structure, configuration, and incentives. Our findings have implications for blockchain research as well as for start-ups building decentralized applications on top of decentralized smart contract platforms. Collapse

Topics: blockchain smart contract Ethereum digital ecosystem organizational change

Methods: paired t-test post analysis longitudinal research

Empowering Users with Narratives: Examining the Efficacy of Narratives for Understanding Data-Oriented Conceptual Models

2023 | Information Systems Research | Citations: 0

Authors: Hvalshagen, Merete; Lukyanenko, Roman; Samuel, Binny M.

Abstract: With the ongoing digitalization of human society, regular employees (e.g., nonin ... Expand

Abstract: With the ongoing digitalization of human society, regular employees (e.g., noninformation technology experts) become increasingly autonomous and proactive in using organizational data and information technologies to facilitate data-driven actions and insights. This growing autonomy of regular employees (dubbed here, empowered users) in using information technologies creates new challenges. As most empowered users lack sophisticated information technology skills, they struggle to find and access relevant data, understand their meaning, and extract and adapt them to meet their needs. We propose a powerful way to support empowered users with a combination of conceptual models and narratives. A data narrative is a descriptive and textual representation of one or more aspects of data in a domain that is organized and presented as a connected sequence of sentences in a natural language. We assess the efficacy of narrative representations for conceptual modeling cardinality constraints, which are an essential part of understanding data. We conducted two laboratory experiments and found a positive and strong effect for narratives: cardinality constraints that were described in the narratives were better understood than constraints only represented in the model (i.e., script). This effect was robust across cardinalities, measures, script complexities, and familiarity levels with the conceptual modeling grammar. History: Manju Ahuja, Senior Editor; Choon-Ling Sia, Associate Editor. Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2022.1141. Collapse

Topics: data management data literacy database system IT workforce analytical information system

Methods: conceptual modelling experiment entity relationship modeling design science parametric test

Theories: cognitive load theory