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Effectiveness of Health Communication Technology on Compliance Disposition of Covid-19 Guidelines

2023 | Communications of the Association for Information Systems | Citations: 0
Authors: Bui, Quang “Neo”; Moriuchi, Emi

Abstract: Recent research has mostly examined the role of health communication technology ... Expand

Abstract: Recent research has mostly examined the role of health communication technology (HCT) in patient empowerment and in producing patient-focused outcomes. This study examines HCT in a larger context where it is used as a tool to improve public health. The objective is to examine how HCT is used to monitor Covid-19's spread, and how social factors affect individual assessment of HCT and individual compliance disposition of Covid-19 guidelines. Analyzing data collected from 360 HCT users suggests that the information and system quality of HCT indeed impact users' assessment of its effectiveness and their compliance disposition. However, such effects are strongly mediated by social factors: Peer influence and government trust can sway an individual's cognitive judgments of Covid-19 regardless of HCT's impacts. The findings highlight the importance of social factors in pandemic management and the need to socialize health informatics to make them more effective. Collapse

Semantic filters: health belief model

Topics: social influence data quality digital dashboard system quality mobile application

Methods: survey mediation analysis partial least squares path modeling post analysis path analysis

Theories: health belief model information systems success model
Becoming a blockchain user: understanding consumers’ benefits realisation to use blockchain-based applications
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2023 | European Journal of Information Systems | Citations: 0
Authors: Raddatz, Nirmalee; Coyne, Joshua; Menard, Philip; Crossler, Robert E

Abstract: Data breaches and cyber incidents are on the rise, and companies continually res ... Expand

Abstract: Data breaches and cyber incidents are on the rise, and companies continually research new technologies to defend against attacks and protect customer data. The blockchain is a data store designed to promote data privacy, as well as transaction integrity. Enterprises in several industries, especially banking, have investigated the implementation of blockchain-based databases to replace centralised databases as one mechanism for protecting customers’ data by separating transactional data from personally identifiable information. Despite the blockchain’s privacy protections, consumers remain largely unaware of these benefits. Building on the Health Belief Model (HBM), we include privacy concerns and inertia as critical factors that influence consumers’ perceptions of blockchain-based databases’ benefits. Using a sample of 304 respondents, we test a theoretical model incorporating these factors. Our study results indicate threat severity, threat susceptibility, awareness, and inertia significantly influence the perceived benefits of blockchain, which has a significant positive influence on consumers’ intention to switch to blockchain-based applications. Although consumers’ comfort with the status quo of traditional banking mechanisms is a significant barrier to the realisation of blockchain banking applications benefits, additional awareness of consumer privacy protections can persuade customers to use the blockchain-based applications, especially if they exhibit heightened privacy concerns. Collapse

Semantic filters: health belief model

Topics: blockchain database system information privacy concern privacy banking sytem

Methods: survey survey design chi squared test structural equation modeling expert panel

Theories: health belief model loss aversion
Giving to Get Well: Patients’ Willingness to Manage and Share Health Information on AI-Driven Platforms
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2023 | Communications of the Association for Information Systems | Citations: 0
Authors: Raddatz, Nirmalee; Kettinger, William; Coyne, Joshua

Abstract: The digitalization of healthcare makes for the widespread availability of patie ... Expand

Abstract: The digitalization of healthcare makes for the widespread availability of patient-provided data. Artificial Intelligence (AI) relies on this data. In this information-intensive environment, it is imperative to understand the contributing factors of an individual's willingness to manage and share personal health information (PHI). Drawing from the health belief model, we identify the factors that motivate individuals to manage and share their PHI in an AI-driven health platform to obtain its intended benefits. We recognize security risks and present the use of a blockchain database as a representative means of securely managing and controlling an individual's PHI. Data collected from a nationally representative sample of allergy sufferers indicate that the health belief model strongly predicts willingness to share PHI on a personalized AI-supported platform. Our study makes significant contributions by investigating the factors that motivate patients to use an AI-driven health platform to manage their health.Artificial intelligence (AI) advances are taking shape and promising to transform the healthcare industry. According to a study by Accenture, AI in the U.S. healthcare industry is expected to have a compound annual growth rate of 46.1% and reach $95.65 billion by 2028 (Vantage Market Research, 2022). AI can offer a more personalized healthcare experience that optimizes the time it takes to gather information for more efficient decision-making, ultimately allowing better patient care. AI is making its presence known across the medical profession, and medical professionals, researchers, patients, and healthcare providers that do not embrace this technology could be left behind.Ultimately, the success of AI-based platforms for medical purposes depends heavily on individual willingness to use the technology. 1 AI can collect, combine, and analyze large amounts of data from various sources, increasing information-gathering and processing capabilities tremendously (Deane, 2018). This capability offers numerous benefits but also potential risks. For example, according to Rocher, Hendrickx, and de Montjoye (2019), AI can profile, identify, and track individuals by de-anonymizing once anonymized information. Some AI only needs 15 data points about an individual to identify the person. Because of security risks, people may be reluctant to share their personal health information (PHI) with AIbased platforms (Deane, 2018).Achieving the potential benefits of AI from patient platforms will depend on (1) the development of intelligent systems that can collect and analyze vast datasets and (2) the willingness of data owners to share and manage their data with these intelligent systems (Agarwal & Khuntia, 2009). Within healthcare, these conditions are (1) the development of systems that can collect and process PHI and provide or augment diagnostics as/more efficiently and effectively than a traditional doctor-patient profile and (2) patients' ability and willingness to manage and share their own PHI. AI developers work to satisfy the first condition but often take the second condition for granted with the attitude, "If we build it, they will come." However, the first condition is certainly not sufficient to satisfy the second. Without convincing many individuals to share their PHI, an AI-driven health platform cannot serve its purpose. As a result, the question arises as to how to increase individuals' willingness and ability to manage and share their PHI with AI-powered health platforms. Thus, the purpose of our investigation is to discover how to begin to satisfy this second condition by addressing the following questions:Are patients willing to share their PHI with AI-driven health platforms despite security risks? Are patients empowered and ready to manage their PHI, thus, in large part, bringing the control of information to the individual?While numerous studies focus on the design aspects of AI-based predictive models, a significant lack of studies investigate the behavioral aspects pertaining to the usability of these models by patients and clinicians. We adapt Rosenstock's (1974) health belief model (HBM) and expand it to identify the determinants of our test subjects' willingness to share PHI with an AI-driven health platform that uses a blockchain-based database. We apply the HBM to a patient profile in which patients do not solely rely on traditional healthcare provider information channels to get diagnostic and treatment information. Instead, they are empowered to use their PHI to get diagnostic and treatment information from an AI platform. As a result, our application of the HBM moves beyond a patient's willingness to follow a proactive health behavior to considering how patients might proactively use (manage and share) their personal health information to help them receive health-related guidance and insights. This patient information management and information sharing behavior represent a future path of the healthcare industry, and we wish to determine whether this well-regarded health-centered model explains behavior under this new "informationally empowered" patient health profile.Because we care about the practical usability of AI-driven health platforms rather than purely hypothetical usability, we model our research instrument on an actual AI-driven health platform (i.e., doc.ai). 2 This platform uses AI for diagnostic and treatment information and a blockchain-based database to store PHI in 1 We define an AI-based healthcare platform as a software application that (1) collects and stores data for analysis by artificial intelligence (e.g., machine learning) and (2) makes the results of that data analysis available to patients and healthcare professionals in order to guide diagnostic and treatment decisions. We base this definition on an actual AI-based healthcare platform (doc.ai) that we describe in section 2. 2 We provide more specific information regarding this health platform in sections 2 and 3. Collapse

Semantic filters: health belief model

Topics: artificial intelligence self efficacy healthcare data blockchain database system

Methods: survey survey design SEM tool chi squared test machine learning

Theories: health belief model technology acceptance model
Digital Follow-up after Surgery – A Randomized Controlled Trial and its Effects on Patients’ Adherence and Doctor-Patient Relationship

2023 | International Conference on Business Informatics | Citations: 0
Authors: Seerig, Kirsten Hannah; Finze, Nikola; Yang, Cui; Gewald, Heiko

Abstract: ... Expand

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Semantic filters: health belief model

Topics: usability ease of use self efficacy mobile health smartphone

Methods: qualitative interview qualitative content analysis personal interview experimental group longitudinal research

Theories: health belief model
CARING ABOUT EMPLOYEES’ HEALTH@WORK – UNDERSTANDING HEALTH BELIEFS AND THE USE OF DIGITAL HEALTH INTERVENTIONS

2023 | European Conference On Information Systems | Citations: 0
Authors: Weißenfels, Silke; Kappler, Karolin; Smolnik, Stefan

Abstract: Employees find contemporary ways of working very demanding. Blurred boundaries ... Expand

Abstract: Employees find contemporary ways of working very demanding. Blurred boundaries between life and work cause technostress and considerable mental strain. To overcome these shortcomings, digital health interventions (DHIs) possess the power to prevent employees from suffering health-related issues at and due to their work. We developed a theoretically sound instrument employing a unified conceptualization of health beliefs, technology acceptance, privacy concerns, and autonomy to predict preventive health and technology use behavior at work. Accordingly, we conducted a survey-based study to investigate the factors influencing DHIs use for preventive health behavior at work. Our empirical results emphasize that employees undertake a cost-benefit analysis, valuing the benefits of reducing health threats and the perceived usefulness of DHIs for taking preventive actions at work. We confirm that privacy concerns and autonomy play a vital role. Our research outlines new avenues in DHIs use by contributing a valid theoretical framework for healthier work environments. through behavior change approaches (Pagoto and Bennett, 2013). For instance, wearables for selftracking (Giddens et al., 2017) that enable self-care and autonomy at work, enhance employees' health and well-being (Lupton, 2016). Against this background, we ask which beliefs and perceptions are important in evaluating the outcome of a particular behavior (Agarwal and Karahanna, 2000) as, for example, using DHIs for preventive health behavior at work. Surprisingly, despite the radical challenges of work practices with severe implications for employees, and the grown work around health interventions, the discourse on research referring to DHIs as facilitators of preventive health behavior in organizations is still in its infancy (Melzner et al., 2014;Yassaee and Mettler, 2019;Oesterle et al., 2019;Howarth et al., 2018). We shift our attention from technology use only and move toward an understanding of preventive health and technology use behavior, supported by DHIs in occupational settings. To address our research objectives, we refer to and extend previous research on preventive health behavior and technology use with multiple technological options beyond a narrow focus on which technology to use. Our work is rooted in research on health beliefs, based on the health belief model (HBM) (Rosenstock, 1974) considering the potential to initiate and change health behavior patterns (Abraham and Sheeran, 2005), and assuming that individuals attempt to avoid perceived health threats (Abraham and Sheeran, 2005;Rosenstock, 1974;Champion and Skinner, 2008). Additionally, we investigate employees' behavior in DHIs use at work. Interestingly, the decision-making process regarding technology use for health-related reasons differs from similar processes in other contexts (Sun et al., 2013). Hence, to understand employees' preventive health behavior supported by technology use, we expect that they intend to use DHIs, which, according to the technology acceptance model (TAM) (Davis, 1989;Venkatesh et al., 2003), is a result of systemrelated characteristics that we combine with health-related variables. Further, in offering voluntary DHIs use at work to encourage employees' empowerment, we have to consider their privacy concerns and autonomy, to engage them in healthy behavior at work (Roossien et al., 2021). As it is not clear whether or how health beliefs influence preventive health and technology use behavior at work, nor whether there is a link in-between, this paper seeks to answer the following research question: What impact do health beliefs, privacy concerns, and autonomy have on employees' DHIs use for preventive health behavior at work? More specifically, our research objectives are (1) to explore whether health beliefs influence employees' preventive health behavior at work, (2) to predict employees' technology use behavior of DHIs, (3) to empirically validate the link between health beliefs, DHIs use, and preventive health behavior, and (4) to theorize the influence of privacy concerns and autonomy on DHIs use at work. Our study follows the call to unfold and predict preventive health behavior through complex causal models (Champion and Skinner, 2008) and to demonstrate the interventions' effectiveness (Abraham and Sheeran, 2005). While DHIs have the potential to intervene in unhealthy (working) behavior, we suggest an analysis of the health-related beliefs and factors related to technology use. Therefore, our theoretical framework is to develop a deeper understanding of HBM and TAM as theoretical lenses and their interplay and to offer new perspectives that could ensure healthier work environments. In a quantitative research setting, we use an online-based survey to evaluate our hypothesized relations empirically. Our findings provide compelling evidence that technology use and preventive health behavior do not emerge in isolation. More precisely, our study demonstrates the relationship between health beliefs and system-related characteristics in DHIs use at work. These results help organizations to understand the conditions under which DHIs are likely to have an impact on employees' preventive health behavior and well-being in occupational settings, which can in turn develop into a meaningful quality of work (Rongen et al., 2013). The paper is structured as follows: Section 2 introduces the theoretical background for research on preventive health and technology use behavior and the development of our theoretical conceptualization for the quantitative analysis. Section 3 introduces our survey-based approach to address the research question. We present our analysis in Section 4. Our results and implications are summarized in Section 5 together with a discussion of related literature. Section 6 concludes the paper. Collapse

Semantic filters: health belief model

Topics: information system use information privacy concern perceived usefulness decision making technology adoption

Methods: survey partial least squares regression card sorting theory development factor analysis

Theories: technology acceptance model measurement theory health belief model
BLOCKCHAIN-BASED DATA SHARING SYSTEM: AN EXPERIMENTAL ANALYSIS OF BEHAVIOURAL FEATURES AFFECTING INTER-ORGANISATIONAL COOPERATION

2022 | European Conference On Information Systems | Citations: 0
Authors: Ballatore, Marta; Toumi, Dr Mira; Arena, Lise

Abstract: This paper investigates the role of managers' behavioural features on Blockchai ... Expand

Abstract: This paper investigates the role of managers' behavioural features on Blockchain Technology (BCT) appropriation within an online scenario-based behavioural experiment. At the intersection betweenManagement of Information Systems and Experimental Economics, the scenario describes a BCT setup as a new governance mechanism facilitating data sharing among organisations. The experiment involves BCT experts who performed two different Public Good Games (PGG): the first, reflecting a traditional data sharing system while the second was concerned with an exogenous minimum contribution level representing a BCT-based system. Results reveal that experts' beliefs play a positive role in BCT early appropriation at an ecosystem level. When BCT-based data-sharing is established, and information about others' cooperation is available, early appropriation level still affects the managers' contribution and relative appropriation, moderating the role of behavioural features, such as beliefs and generalised trust.1 Xceed project is an inter-organisational collaborative project around connected vehicles involving Renault Group and its major automotive industry partners like Continental, Faurecia, Plastic Omnium, Saint-Gobain. The project aims to adopt a distributed ledger as a unique "source of truth" from vehicle design to production -XCEED project launched in 2019. A blockchain-based record-keeping would provide reliable information on each subcomponent that boosts regulatory conformity in the automotive industry. The distributed ledger is seen as the basis for a trusted network for sharing data and information. This project significantly inspired the design of our experiment. In fact, it exemplifies a BCT consortium which, contrarily to private blockchains, operates under the leadership of a group of entities allowing collaborative business transformation among organizations (Dib et a., 2018). In our context, the consortium structure helps newly arrived organizations in a BCT ecosystem to directly share information and to help all participants to find solutions together and save development costs. Collapse

Semantic filters: health belief model

Topics: blockchain public good decision making privacy mobile system

Methods: experiment qualitative interview survey Mann Whitney U test nonparametric test

Theories: behavioral economics health belief model technology acceptance model decision theory theory of bounded rationality
Towards a Model for Inclusive Healthcare Access post COVID-19
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2022 | HICSS | Citations: 0
Authors: Bhatt, Paras; Akanfe, Oluwafemi; Guo, Yuanxiong; Gong, Yanmin

Abstract: The devastation caused by the COVID-19 pandemic has exposed years of cyclic ineq ... Expand

Abstract: The devastation caused by the COVID-19 pandemic has exposed years of cyclic inequalities faced by disadvantaged and minority communities. Unequal access to healthcare and a lack of financial resources further exacerbates their suffering, especially during a pandemic. In such critical conditions, information technology-based healthcare services can be an efficient way of increasing access to healthcare for these communities. In this paper, we put forward a decision model for guiding the distribution of IT-based healthcare services for racial minorities. We augment the Health Belief Model by adding financial and technology beliefs. We posit that financial inclusion of minority populations increases their ability to access technology and, by extension, IT-based healthcare services. Financial inclusion and the use of secure private technologies like federated learning can indeed enable greater access to healthcare services for minorities. Therefore, we incorporate financial, health, and technology tools to develop a model for equitable delivery of healthcare services and test its applicability in different use-case scenarios. Collapse

Semantic filters: health belief model

Topics: information system use self efficacy privacy healthcare data information privacy concern

Methods: conceptual modelling federated learning literature study computational algorithm survey

Theories: health belief model
Understanding the adoption of the mask-supply information platforms during the COVID-19
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2022 | Electronic Markets | Citations: 0
Authors: Huang, Cheng-Kui; Chen, Shin-Horng; Hu, Chia-Chen; Lee, Ming-Ching

Abstract: Since late 2019, coronavirus disease 2019 (COVID-19) has led to a significant in ... Expand

Abstract: Since late 2019, coronavirus disease 2019 (COVID-19) has led to a significant increase in the demand for medical resources. To publish data on face mask supplies, the Taiwanese government collaborated with program developers to construct a mask-supply information transitional platform (MITP). To comprehend the adoption of MITP, the study proposes a research model that integrates the health behavior model (HBM) and IS/IT continuance model for examining the factors affecting intention to use an MITP. Survey data collected from 524 respondents indicated that (1) intention to use an MITP is directly influenced by perceived threat of COVID-19 and beliefs toward using the MITP; (2) cues to action directly influence the perceived threat of COVID-19; and (3) perceived ease of use of MITP is a significant determinant of perceived usefulness of MITP. These results provide practical guidelines for health authorities and government to develop health information systems and strategies to control pandemics. Collapse

Semantic filters: health belief model

Topics: usage intention pandemic perceived usefulness perceived ease of use health information system

Methods: survey partial least squares regression computational algorithm PLS tool structural equation modeling

Theories: health belief model
Comparison of Voluntary versus Mandatory Vaccine Discussions in Online Health Communities: A Text Analytics Approach
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2021 | HICSS | Citations: 2
Authors: Alazazi, Massara; Wang, Bin

Abstract: Vaccines are vital health interventions. However, they are controversial and som ... Expand

Abstract: Vaccines are vital health interventions. However, they are controversial and some people support them while others reject them. Social media discussion and big data are a rich source to understand people’s insights about different vaccines and the related topics that concern most of them. This study aims to explore the online discussions about mandatory and voluntary vaccines using text analysis techniques. Reddit social platform is popular in online health discussion and thus data from Reddit is analyzed. The results show that different aspects are discussed for different types of vaccines. The discussion of mandatory vaccines is more interactive and is focused on the risks associated with them. Voluntary vaccines’ discussion is focused on their effectiveness and whether to get them or not. The study have important implications for health agencies and researchers as well as for healthcare providers and caregivers. Collapse

Semantic filters: health belief model

Topics: Reddit electronic health online community misinformation social media

Methods: sentiment analysis natural language processing qualitative content analysis topic model latent dirichlet allocation

Theories: health belief model
Designing Information Systems to Break Habits and Promote Preventive Behaviours During Large-Scale Disease Outbreaks
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2021 | HICSS | Citations: 0
Authors: Chung, Alexander; Lessard, Lysanne; Andreev, Pavel; O'Reilly, Philip

Abstract: Adhering to preventive behaviours, like social distancing and wearing a mask, ca ... Expand

Abstract: Adhering to preventive behaviours, like social distancing and wearing a mask, can help reduce the spread of some transmissible diseases; however, doing so can be a challenge as it requires people to break established habits. This challenge will be most evident for organisations as they need to ensure that all stakeholders adhere to preventive behaviours to resume in-person business operations. While various information systems (IS) have emerged to address this challenge, they remain limited in scope and fall short of helping users navigate the evolving practices and guidelines of a pandemic. To address this shortcoming, we adopt the design science research approach to derive design principles for IS supporting the breaking of established habits and promotion of preventive behaviours. The design principles are rigorously anchored in the habit alteration knowledge base and the Health Belief Model. We demonstrate how the design principles can be applied using an illustrative case. Collapse

Semantic filters: health belief model

Topics: knowledge base systems design

Methods: design principle kernel theory design science design artifact design process

Theories: health belief model theory of change

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Found 39 articles

Effectiveness of Health Communication Technology on Compliance Disposition of Covid-19 Guidelines

Becoming a blockchain user: understanding consumers’ benefits realisation to use blockchain-based applications

Giving to Get Well: Patients’ Willingness to Manage and Share Health Information on AI-Driven Platforms

Digital Follow-up after Surgery – A Randomized Controlled Trial and its Effects on Patients’ Adherence and Doctor-Patient Relationship

CARING ABOUT EMPLOYEES’ HEALTH@WORK – UNDERSTANDING HEALTH BELIEFS AND THE USE OF DIGITAL HEALTH INTERVENTIONS

BLOCKCHAIN-BASED DATA SHARING SYSTEM: AN EXPERIMENTAL ANALYSIS OF BEHAVIOURAL FEATURES AFFECTING INTER-ORGANISATIONAL COOPERATION

Towards a Model for Inclusive Healthcare Access post COVID-19

Understanding the adoption of the mask-supply information platforms during the COVID-19

Comparison of Voluntary versus Mandatory Vaccine Discussions in Online Health Communities: A Text Analytics Approach

Designing Information Systems to Break Habits and Promote Preventive Behaviours During Large-Scale Disease Outbreaks