Antecedents of Information Adoption of Sharing Mobile Social Commerce Experience: The Mediation Role of Trust

2023 | European Conference On Information Systems | Citations: 0

Authors: Acikgoz, Fulya; Busalim, Abdelsalam; Lynn, Theo

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Topics: data quality mobile business mobile application information privacy concern electronic commerce

Methods: survey partial least squares path modeling partial least squares regression theoretical contribution qualitative interview

An Investigation of Domain-based Social Influence on ChatGPT-Related Twitter Data

2023 | International Conference on Information Systems | Citations: 0

Authors: Ahangama, Supunmali

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Topics: Twitter social influence social media social identity robotic

Methods: chatGPT sentiment analysis multiple linear regression term frequency–inverse document frequency NLTK

THE IMPACT OF ONLINE ADVERTISMENT PERSONALIZATION AND TRANSPARENCY ON INDIVIDUAL DEFENSIVE RESPONSES AND ENGAGMENT

2023 | European Conference On Information Systems | Citations: 0

Authors: Al Helaly, Yasser; Dhillon, Gurpreet; Oliviera, Tiago

Abstract: This study identifies the different individual defensive responses that are tri ... Expand

Abstract: This study identifies the different individual defensive responses that are triggered by ad personalization. We investigate how these responses impact user engagement behavior on social media. The study also examines the role of transparency in advertising and the perceived benefits of personalization on user engagement in Europe. To conduct our study, we are theoretically grounded in the defensive response model. A multi-step quantitative approach constituting two experiments and a survey is used. With 154 and 199 participants each, the experiments allowed us to define the theoretical model. The experiment identified paradoxical findings. We indent surveying online ads-users to examine the impact of personalization on individual defensive responses and the role transparency plays in predicting user engagement. Our research contributes to the theory by identifying defensive responses that explain the engagement behavior, and the impact of transparency on personalized advertising. Collapse

Topics: personalization usability Facebook social media privacy

Methods: experiment survey Student's t-test statistical hypothesis test parametric test

Advancing the Understanding of the Role of Responsible AI in the Continued Use of IoMT in Healthcare

2023 | Information Systems Frontiers | Citations: 1

Authors: Al-Dhaen, Fatema; Hou, Jiachen; Rana, Nripendra P.; Weerakkody, Vishanth

Abstract: This paper examines the continuous intention by healthcare professionals to use ... Expand

Abstract: This paper examines the continuous intention by healthcare professionals to use the Internet of Medical Things (IoMT) in combination with responsible artificial intelligence (AI). Using the theory of Diffusion of Innovation (DOI), a model was developed to determine the continuous intention to use IoMT taking into account the risks and complexity involved in using AI. Data was gathered from 276 healthcare professionals through a survey questionnaire across hospitals in Bahrain. Empirical outcomes reveal nine significant relationships amongst the constructs. The findings show that despite contradictions associated with AI, continuous intention to use behaviour can be predicted during the diffusion of IoMT. This study advances the understanding of the role of responsible AI in the continued use of IoMT in healthcare and extends DOI to address the diffusion of two innovations concurrently. Collapse

Topics: usage intention artificial intelligence responsible artificial intelligence innovation management online learning system

Methods: SEM tool survey descriptive statistic structural equation modeling survey design

Investigating Students’ Satisfaction with Online Collaborative Learning During the COVID-19 Period: An Expectation-Confirmation Model

2023 | Group Decision and Negotiation | Citations: 0

Authors: Cheng, Xusen; Bao, Ying; Yang, Bo; Chen, Sihua; Zuo, Yiting; Siponen, Mikko

Abstract: The recent outbreak of COVID-19 posed discontinuous disruption to traditional le ... Expand

Abstract: The recent outbreak of COVID-19 posed discontinuous disruption to traditional learning modes worldwide. In order to keep social distance, online collaborative learning has become a necessity during the pandemic. However, our understanding of students’ well-being and satisfaction with online collaborative learning is limited, especially during the COVID-19 period. Leveraging expectation confirmation theory, this study focuses on the triggers and inhibitors of students’ cognitive load during online collaborative learning process and their subsequent satisfaction with the learning mode during the pandemic. We used a mixed-method approach in this study. We conducted a qualitative study with interview data and a quantitative study with surveys. The results indicate several psychological and cognitive antecedents of students’ cognitive load during online collaborative learning. Findings also indicate that a high level of cognitive load will decrease students’ perceived usefulness of the online learning platform and expectation confirmation, thus leading to a low level of satisfaction with online collaborative learning. This study can provide theoretical and practical implications for a better understanding of online student groups’ satisfaction with online collaborative learning during the COVID-19 period. Collapse

Topics: online learning system pandemic perceived usefulness anonymity social network

Methods: survey mixed method qualitative interview statistical hypothesis test partial least squares path modeling

Theories: expectation confirmation theory

Unveiling the paradox of technostress: Impacts of technology-driven stressors on the elderly's avoidance behaviors

2023 | Information & Management | Citations: 0

Authors: Cheng, Xusen; Huang, Xiaowen; Yang, Bo; Xiao, Yuting

Abstract: This study reveals the paradox of technostress on the elderly due to the large-s ... Expand

Abstract: This study reveals the paradox of technostress on the elderly due to the large-scale COVID testing during the postCOVID-19 period. Drawing upon the stressor-strain-outcomes framework, we examined how technology-driven stressors affect the elderly’s avoidance behaviors. Results indicate that two types of technology-driven stressors exert opposite influences on the elderly’s avoidance behaviors through the mediating effect of technology anxiety. Specifically, the elderly’s technology-driven challenge stressors exert a positive influence on their avoidance behaviors, while the elderly’s technology-driven hindrance stressors exert a negative influence. Moreover, stigma consciousness plays a moderating role in the relationship between the elderly’s technologydriven hindrance stressors and technology anxiety by strengthening the positive influence of the elderly’s technology-driven hindrance stressors on technology anxiety. Our findings deepen the understanding of the good and bad of technostress and provide implications for older adults, technology developers, and the government. Collapse

Topics: smartphone technostress pandemic self efficacy anonymity

Methods: qualitative interview survey SEM tool mixed method qualitative coding

Moral Foundations, Himpathy, and Punishment Following Organizational Sexual Misconduct Allegations

2023 | Organization Science | Citations: 0

Authors: Dodson, Samantha J.; Goodwin, Rachael D.; Graham, Jesse; Diekmann, Kristina A.

Abstract: We build on deontic justice and moral foundations theories to shed light on resp ... Expand

Abstract: We build on deontic justice and moral foundations theories to shed light on responses to sexual misconduct at work by proposing a model that explains why some third parties punish accusing victims and support alleged perpetrators. We theorize that when third parties are given conflicting he-said, she-said information, they intuitively evaluate organizational injustice based on moral values. We further theorize that binding moral foundations (loyalty, authority, purity) give rise to sympathy toward men accused of sexual misconduct and anger toward female accusers. Across five studies (total n = 5,413) utilizing archival, field, and vignette designs, we examined third-party responses to sexual misconduct accusations ranging in severity across several industries. Third-party endorsement of binding moral foundations was linked to increased perpetrator-directed sympathy and victim-directed anger (Studies 1–4). These emotions jointly mediated the relationship between binding values and credibility perceptions of the accusing victim and the alleged perpetrator (Studies 2 and 3). Moreover, victim credibility was negatively associated with social sanctions and punishment severity levied toward the accusing victim, and perpetrator credibility was negatively associated with the same punishment outcomes for the alleged perpetrator (Studies 3 and 4). In Study 5, we found that managers framing the accusing victim as disloyal exacerbated negative judgments and emotions toward the victim and positive judgments and emotions toward the perpetrator for individuals who highly ascribe to binding moral foundations. We discuss the theoretical contributions and practical implications of moral concerns on third parties’ emotions, judgments, and motivations to punish actors involved in sexual misconduct allegations. Supplemental Material: The e-companion is available at https://doi.org/10.1287/orsc.2022.1652. Collapse

Topics: Twitter electronic mail social media IT career Facebook

Methods: theory development survey statistical hypothesis test structural equation modeling mediation analysis

Theories: justice theory big five model organizational behavior theory

A Task-Technology-Identity Fit Model of Smartwatch Utilisation and User Satisfaction: A Hybrid SEM-Neural Network Approach

2023 | Information Systems Frontiers | Citations: 0

Authors: El-Masri, Mazen; Al-Yafi, Karim; Kamal, Muhammad Mustafa

Abstract: Smartwatches are wearable devices intended to be smartphone companions that capt ... Expand

Abstract: Smartwatches are wearable devices intended to be smartphone companions that capture health data and ease access to notifications. They have also become personalisable standing as a fashion statement. This combination resulted in staggering adoption rates recently leading to question whether smartwatch users’ choice and use satisfaction emerge from utility features or from its fashion characteristics. This paper proposes and validates a fit theory to investigate the antecedents of adopters’ satisfaction. Besides evaluating fit with identity, the model assesses both perceived and actual task-technology fit of smartwatches. A questionnaire-based quantitative approach is used to collect data from about 300 smartwatch users in Qatar. To test the proposed model, data is analysed using structural equation modeling (SEM) and artificial neural networks (ANN). Furthermore, ANN sensitivity analysis ranks the importance of the fit factors affecting users’ choice during pre- and post-adoption stages. Both task-technology and technology-identity fit factors are quasi-equally important in explaining 62% of satisfaction variance. ANN analysis revealed that post-adoption satisfaction is primarily attributed to smartwatches’ ability to fit with users’ identity and secondarily to its perceived fit with tasks. Nevertheless, pre-adoption choice of smartwatches is mainly guided by their functionality. This paper is the first to propose and validate an integrated task-technology-identity fit model to explain smartwatch utilization and users’ satisfaction. The originality also lies in assessing actual task-technology fit and as perceived by users. Employing two modes of analysis revealed extra insights too. Collapse

Topics: smart watch mobile system user satisfaction personality smart wearable device

Methods: structural equation modeling data transformation survey factor analysis principal component analysis

Theories: theory of task technology fit unified theory of acceptance and use of technology technology acceptance model

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