Visit Your Therapist in Metaverse - Designing a Virtual Environment for Mental Health Counselling

2023 | International Conference on Information Systems | Citations: 0

Authors: Turan Akdag, Merve; Jacquemin, Philippe; Wahl, Nihal

Abstract: The emergence of the metaverse as a virtual world platform has opened up new po ... Expand

Abstract: The emergence of the metaverse as a virtual world platform has opened up new possibilities for the use of immersive technologies in healthcare. This paper aims to explore the potential of the metaverse for healthcare and show how metaverse should be designed. We conduct a study based on design science research and derive design principles for the designing of a virtual environment for mental health counselling. We evaluate each of these design principles and describe how they can be applied in a practical solution. The results indicate that the metaverse holds significant promise for improving healthcare delivery and enhancing patient outcomes. Our study thus contributes to the emerging field of metaverse in healthcare by providing a design approach for the development of applications that can serve as a virtual environment for therapeutic sessions between medical therapists and patients. Collapse

Topics: virtual reality health information system privacy intrinsic motivation usage intention

Methods: qualitative interview design science group interview design principle design artifact

Theories: hedonic motivation system adoption model

Prejudiced against the Machine? Implicit Associations and the Transience of Algorithm Aversion

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Turel, Ofir; Kalhan, Shivam

Abstract: Algorithm aversion is an important and persistent issue that prevents harvestin ... Expand

Abstract: Algorithm aversion is an important and persistent issue that prevents harvesting the benefits of advancements in artificial intelligence. The literature thus far has provided explanations that primarily focus on conscious reflective processes. Here, we supplement this view by taking an unconscious perspective that can be highly informative. Building on theories of implicit prejudice, in a preregistered study, we suggest that people develop an implicit bias (i.e., prejudice) against artificial intelligence (AI) systems, as a different and threatening "species," the behavior of which is unknown. Like in other contexts of prejudice, we expected people to be guided by this implicit bias but try to override it. This leads to some willingness to rely on algorithmic advice (appreciation), which is reduced as a function of people's implicit prejudice against the machine. Next, building on the somatic marker hypothesis and the accessibility-diagnosticity perspective, we provide an explanation as to why aversion is ephemeral. As people learn about the performance of an algorithm, they depend less on primal implicit biases when deciding whether to rely on the AI's advice. Two studies (n1 = 675, n2 = 317) that use the implicit association test consistently support this view. Two additional studies (n3 = 255, n4 = 332) rule out alternative explanations and provide stronger support for our assertions. The findings ultimately suggest that moving the needle between aversion and appreciation depends initially on one's general unconscious bias against AI because there is insufficient information to override it. They further suggest that in later use stages, this shift depends on accessibility to diagnostic information about the AI's performance, which reduces the weight given to unconscious prejudice. Collapse

Topics: artificial intelligence decision making accounting intrinsic motivation explainable artificial intelligence

Methods: computational algorithm parametric test longitudinal research statistical hypothesis test analysis of covariance

Responses to ambivalence toward social networking sites: A typological perspective

2023 | Information Systems Journal | Citations: 2

Authors: Turel, Ofir; Qahri-Saremi, Hamed

Abstract: A common assumption in prior research on social networking sites (SNS) has been ... Expand

Abstract: A common assumption in prior research on social networking sites (SNS) has been that users' orientations toward SNS use are positioned somewhere along a bipolar, univalent continuum, stretching from negative to positive orientation. However, considering recent findings unfolding the intricacy and variety of SNS use patterns, such a linear conceptualization of users' orientations is too simplistic with limited ability to explain the intricate patterns of SNS use. To alleviate this deficiency in this paper, we draw on the ambivalence literature and explain that users can simultaneously experience both positive and negative orientations toward SNS use based on the positive and negative aspects of their SNS use experience. Focusing on post- adoptive SNS use context, we theorise archetypes of SNS users' attitudinal responses to ambivalence, and their associated behavioural outcomes in terms of SNS use patterns. We first follow a typological perspective and develop typologies of attitudinal and behavioural responses to ambivalence toward SNS use. Then, we offer six hypotheses that explain the relations between the archetypes of attitudinal responses to ambivalence toward SNS use and users' SNS use patterns. Lastly, we empirically test our hypotheses using latent profile analysis and ANCOVA applied to two-wave data collected from 370 ambivalent SNS users. The findings support the hypotheses and validate our typologies. The findings ultimately point to likely choices from a range of post-adoption SNS use patterns as plausible outcomes of SNS users' attitudinal responses to ambivalence. Collapse

Topics: usage intention information system use Facebook user behavior systems implementation

Methods: analysis of covariance statistical hypothesis test post-hoc analysis survey literature study

MDSCAN: AN EXPLAINABLE ARTIFICIAL INTELLIGENCE ARTIFACT FOR MENTAL HEALTH SCREENING

2023 | European Conference On Information Systems | Citations: 0

Authors: Tutun, Salih; Bhattacherjee, Anol; Topuz, Kazim; Tosyali, Ali; Li, Gorden

Abstract: This paper presents a novel artifact called MDscan that can help mental health ... Expand

Abstract: This paper presents a novel artifact called MDscan that can help mental health professionals quickly screen a large number of patients for ten mental disorders. MDscan uses patient responses to the SCL-90-R clinical questionnaire to create a full-color image, similar to radiological images, which identifies which disorder or combination of disorders may afflict a patient, the severity of the disorder, and the underlying logic of this prediction, using an explainable artificial intelligence (XAI) approach. While prior artificial intelligence (AI) tools have seen limited acceptance in clinical practice because of the lack of transparency and interpretability in their "black box" models, the XAI approach used in MDscan is a "white box" model that elaborates which patient feature contributes to the predicted outcome and to what extent. Using patient data from a mental health clinic, we demonstrate that MDscan outperforms current (expert-based) clinical practice by an average of 20%. Collapse

Topics: explainable artificial intelligence shapley additive explanation local interpretable model agnostic explanation IT security artificial intelligence

Methods: computational algorithm machine learning data transformation t-distributed stochastic neighbor embedding survey

A Responsible AI Framework for Mitigating the Ramifications of the Organ Donation Crisis

2023 | Information Systems Frontiers | Citations: 0

Authors: Tutun, Salih; Harfouche, Antoine; Albizri, Abdullah; Johnson, Marina E.; He, Haiyue

Abstract: Thousands of people die while waiting for organ transplants due to a significant ... Expand

Abstract: Thousands of people die while waiting for organ transplants due to a significant gap between demand and supply. This gap often leads to illegal activities and ethical issues such as illicit organ trade and auctions. Therefore, to increase the organ supply and procure more organs, organizations must understand the causes of families who refuse to consent to donate their loved one's organs. Furthermore, such organizations must better identify those families most likely to consent to organ donation. We propose a responsible AI framework that integrates network science and artificial intelligence to identify consent outcomes for organ donation. The proposed framework includes three phases: (1) collecting and pre-processing data, (2) creating new features and identifying root causes of family refusal, and (3) training and testing models to predict the probability of families granting consent for organ donation. The designed artifact included collaborative decisions and network measures, increasing explainability through network science. It integrated human reviews and assessment of risks which increases correct and interpretable predictions. Results can help encourage organ donations and reduce the illegal organ trade. The experimental results show that the designed artifact outperformed previous studies identifying factors affecting consent outcomes. This framework integrates network science and artificial intelligence to reduce maleficence, solve the lack of transparency (i.e., increase trustworthiness) and improve accountability of the model that aims to predict consent outcomes. Collapse

Topics: responsible artificial intelligence artificial intelligence explainable artificial intelligence procurement management authorization

Methods: machine learning computational algorithm support vector machine experiment classification method

Theories: justice theory

An AI-based Decision Support System for Predicting Mental Health Disorders

2023 | Information Systems Frontiers | Citations: 0

Authors: Tutun, Salih; Johnson, Marina E.; Ahmed, Abdulaziz; Albizri, Abdullah; Irgil, Sedat; Yesilkaya, Ilker; Ucar, Esma Nur; Sengun, Tanalp; Harfouche, Antoine

Abstract: Approximately one billion individuals suffer from mental health disorders, such ... Expand

Abstract: Approximately one billion individuals suffer from mental health disorders, such as depression, bipolar disorder, schizophrenia, and anxiety. Mental health professionals use various assessment tools to detect and diagnose these disorders. However, these tools are complex, contain an excessive number of questions, and require a significant amount of time to administer, leading to low participation and completion rates. Additionally, the results obtained from these tools must be analyzed and interpreted manually by mental health professionals, which may yield inaccurate diagnoses. To this extent, this research utilizes advanced analytics and artificial intelligence to develop a decision support system (DSS) that can efficiently detect and diagnose various mental disorders. As part of the DSS development process, the Network Pattern Recognition (NEPAR) algorithm is first utilized to build the assessment tool and identify the questions that participants need to answer. Then, various machine learning models are trained using participants’ answers to these questions and other historical data as inputs to predict the existence and the type of their mental disorder. The results show that the proposed DSS can automatically diagnose mental disorders using only 28 questions without any human input, to an accuracy level of 89%. Furthermore, the proposed mental disorder diagnostic tool has significantly fewer questions than its counterparts; hence, it provides higher participation and completion rates. Therefore, mental health professionals can use this proposed DSS and its accompanying assessment tool for improved clinical decision-making and diagnostic accuracy. Collapse

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

Methods: computational algorithm support vector machine design process lasso regression ridge regression

Development of an Agile Requirements Risk Prioritization Method: A Design Science Research Study

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

Authors: Tuunanen, Tuure; Vartiainen, Tero; Kainulainen, Sanna; Ebrahim, Mehdi

Abstract: The practice of information systems development (ISD) has changed during the pa ... Expand

Abstract: The practice of information systems development (ISD) has changed during the past two decades from very structured approaches to agile ISD methods. However, in the literature many methods available for managing requirements-related risks follow a structured approach to ISD. Few, if any, available methods offer solutions that prioritize requirements risks for agile ISD projects based on recognizing requirements-related risks and patterns in order to mitigate these. To fill this gap in the literature, we apply the design science research methodology to develop an agile requirements risk prioritization method together with industry experts (n=54) in Finland and New Zealand in a multi-year study. The method was developed by applying contingency theory, and our study makes an artifactual contribution to the literature. The method helps practitioners prioritize the overall requirements-related risks for ISD projects.The remainder of the paper is structured as follows. First, we review relevant literature on ISD risk management and agile requirements risk prioritization. Then, we depict the applied DSR approach, and different qualitative research methods, to develop our method. Then, we present the developed artifacts 611 Development of an Agile Requirements Risk Prioritization Method: A Design Science Research Study Volume 52 10.17705/1CAIS.05226 Paper 26and the demonstration and evaluation process we used with our industry participants. Finally, we discuss the implications of our work and conclude the paper. Managing ISD Project Risks and Moving Towards Agile Requirements PrioritizationThe following sections review the literature on managing ISD risks in general. Thereafter, we review the literature on agile in requirements risks management and, finally, we review how contingency theory offers a solution to prioritize requirements according to the identified risks.Communications of the Association for Information Systems 630 Volume 52 Collapse

Topics: electronic mail risk management communication service infrastructure agile software development systems development

Methods: focus group qualitative interview delphi study design science qualitative content analysis

Theories: contingency theory

Improving Performance by Encouraging Users to Consider Different Levels of Action Identification (LAI)

2023 | International Conference on Information Systems | Citations: 0

Authors: Tzur, Aviram; Te’Eni, Dov; Zalmanson, Lior

Abstract: Action Identification Theory proposes that individuals perceive their actions a ... Expand

Abstract: Action Identification Theory proposes that individuals perceive their actions at different levels of abstraction and how this perception can significantly impact their behavior. The paper argues that prompting users to shift between different levels of action identification during their interaction with an information system can improve their performance. The experimental work includes a laboratory experiment and a think-aloud study that explores the effect of users' attention to different levels of action identification and the cognitive fit between on-screen representation and mental models on performance improvement. The discussion analyzes the results and outlines future research plans and expected contributions to the field. This study highlights the importance of considering the user's cognitive processes when designing information systems and suggests potential ways to enhance their performance. Collapse

Topics: human computer interaction usability decision making problem solving

Methods: experimental group experiment cognitive walkthrough laboratory experiment statistical hypothesis test

Theories: cognitive fit theory

Polarization in Online Social Networks: A Review of Mechanisms and Dimensions

2023 | European Conference On Information Systems | Citations: 0

Authors: Tölle, Laura; Trier, Matthias

Abstract: The extensive use of social media has sparked a controversial debate regarding ... Expand

Abstract: The extensive use of social media has sparked a controversial debate regarding interactions on social media platforms and their influence on network polarization, a phenomenon that still misses conceptual clarity and consensus. By conducting a systematic literature review on empirical findings concerning network polarization, we take first steps to a more systematic conceptualization of polarization. We do this by identifying (a) the different dimensions on which polarization manifests itself, and (b) the relevant influence factors associated with the emergence of polarization phenomena in online social networks. We identified Attitude Extremity, Topic Diversity, Social Fragmentation, and Linguistic Usage as the dimensions on which polarization can be observed. Furthermore, we derive an integrated research framework covering a comprehensive set of mechanisms associated with polarization on social media, hence serving as a useful starting point for further investigation of polarization phenomena in IS research and the unintended consequences of ICT usage. Collapse

Topics: social network filter bubble social media website unintended consequence

Methods: literature study structured literature research literature sample meta analysis co-citation analysis

The AI-based Transformation of Organizations: The 3D-Model for Guiding Enterprise-wide AI Change

2023 | HICSS | Citations: 0

Authors: Uba, Chikaodi; Lewandowski, Tom; Böhmann, Tilo

Abstract: Artificial Intelligence (AI) is increasingly gaining importance for organization ... Expand

Abstract: Artificial Intelligence (AI) is increasingly gaining importance for organizations due to its immense potential for value creation and growth. However, companies struggle to tap this potential, as many AI projects fail in the early stages because of lacking guidance and best practices. To shed light on how AI adoption and transformation can be approached and what challenges organizations face, we analyzed eleven organizations of varying sizes and industries. Drawn on these insights, we identify four transformation types distinguished by different AI transformation stages and journeys. Furthermore, we develop a 3D-Model to guide enterprise-wide AI change and propose concrete recommendations for action on each dimension. Our findings help practitioners navigate, manage, and (re)evaluate their AI strategy for an enterprise-wide transformation. Collapse

Topics: artificial intelligence IT manager IS maturity electronic commerce Chief Information Officer

Methods: focus group qualitative interview company material multiple case study qualitative coding