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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: measurement theory

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
When Constructs Become Obsolete: A Systematic Approach to Evaluating and Updating Constructs for Information Systems Research

2022 | Management Information Systems Quarterly | Citations: 10
Authors: Compeau, Deborah; Correia, John; Thatcher, Jason

Abstract: In this paper, we confront a paradox in the IS literature that even though our f ... Expand

Abstract: In this paper, we confront a paradox in the IS literature that even though our field focuses on the rapid pace of technological change and the dramatic scale of technology-enabled organizational and societal changes, we sometimes find ourselves studying these changes using—largely without question—constructs that were developed in a vastly different IT, user, and organizational environment. We provide guidelines to help assess whether an existing construct warrants updating and to structure the updating task if it is undertaken. Our three-step process provides for a theoretically grounded and comprehensive method that ensures we balance the need for construct updating against the need to sustain our cumulative tradition. We illustrate our guidelines using computer self-efficacy (CSE) as a case study. We document each of the steps involved in analyzing, reconceptualizing, and testing the revised construct information technology self-efficacy (ITSE). Our analyses show that the new construct better explains both traditional and contemporary constructs with a traditional (postal survey) and contemporary (online panel) sample. We discuss the implications of our work both for research on self-efficacy and more broadly for future updating of other important constructs. Collapse

Semantic filters: measurement theory

Topics: self efficacy information system use anonymity information privacy concern technological change

Methods: survey case study survey design theory development descriptive statistic

Theories: identity theory measurement theory social cognitive theory
A Double-Edged Sword of Involvement: On the Tension Between Customers’ Group Value and Self-Interest in Data Breach Response Processes

2022 | International Conference on Information Systems | Citations: 0
Authors: Diesterhöft, Till Ole; Masuch, Kristin; Greve, Maike; Trang, Simon

Abstract: As data breaches continue to rise, customers exhibit heterogeneous expectations ... Expand

Abstract: As data breaches continue to rise, customers exhibit heterogeneous expectations regarding the company's response. Universal responses can show backfire effects since they fail to meet the expectations. Thus, the challenge arises that customer expectations must be known to mitigate the consequences while time is limited to publish the data breach announcement. By drawing on service failure, data breach, and justice research, we theorize that customer involvement provides a viable approach to this challenge. We argue that active customer involvement allows customers to formulate their expectations. Thus, enabling companies to leverage these expectations to provide tailored data breach responses. We test our hypotheses in a digital experiment (n=304). Our results provide a first indication that active customer involvement in a data breach drives positive group value and negative self-interest effects. We contribute to the data breach literature by revealing that customer involvement constitutes a suitable mechanism for identifying customer expectations. Collapse

Semantic filters: measurement theory

Topics: data breach customer satisfaction individual impact

Methods: experiment partial least squares path modeling theory development literature study post-hoc analysis

Theories: procedural justice theory measurement theory justice theory
Re-Examining the Impact of Multidimensional Trust on Patients’ Online Medical Consultation Service Continuance Decision
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2022 | Information Systems Frontiers | Citations: 0
Authors: Yang, Ming; Jiang, Jinglu; Kiang, Melody; Yuan, Fangyun

Abstract: Recent years have witnessed the rapid growth of an emerging digital healthcare s ... Expand

Abstract: Recent years have witnessed the rapid growth of an emerging digital healthcare service – online medical consultation (OMC). Despite its popularity, many OMC platforms have encountered issues in initial adoption and continuance use among patients. We posit that many of the hesitation and resistance may arise from a lack of trust toward OMC, which is a complex phenomenon that involves both interpersonal and technological-oriented considerations. This study seeks to clarify the conceptualization of online trust in the context of OMC. It compares two plausible explanations (i.e., trust as a direct cause vs. trust as a moderator) regarding how interpersonal and technological trust contributes to the service continuance decision in OMC. By contextualizing the valence framework, we identify the critical factors in making the risk-benefit assessment of patients’ OMC decision. We conduct an online survey of 365 experienced OMC users and analyze our structural model using a partial least square approach. Our results show that the multidimensional conceptualization approach, which incorporates both interpersonal and technological aspects of trust, is superior to the unitary approach. Besides, our findings suggest that the role trust plays in determining service continuance decisions in OMC is more of a direct cause than a qualifier that buffers the impacts of risk-benefit evaluation. We believe the findings can help both researchers and practitioners recognize the multidimensional perspective of trust and better understand the role trust plays in OMC and other online healthcare delivery problems. Collapse

Semantic filters: measurement theory

Topics: decision making information system use digital platform privacy individual characteristic

Methods: regression analysis method partial least squares regression survey mediation analysis Student's t-test

Theories: measurement theory
Cybersecurity compliance behavior: Exploring the influences of individual decision style and other antecedents
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2020 | International Journal of Information Management | Citations: 11
Authors: Donalds, Charlette; Osei-Bryson, Kweku-Muata

Abstract: Recent information and cybersecurity research have focused on improving individu ... Expand

Abstract: Recent information and cybersecurity research have focused on improving individuals’ security compliance behavior. However, improved security performance remains a challenge since individuals often fail to comply with security best practices. In this study, we investigate a new individual cybersecurity compliance behavior model proposed by Donalds and Osei-Bryson (2017). Specifically, we investigate the influence of individual decision styles on their cybersecurity compliance behavior and other antecedents of such behavior. To empirically validate the hypotheses in the Donalds & Osei-Bryson model, we used data collected from 248 individuals and then use multiple regression to examine the assertions of the model. Our findings confirm that individual’s decision styles, specifically, dominant orientation and dominant decision style, influence their individual cybersecurity compliance behavior and other antecedents of such behavior. Our research offers new dimensions for investigating individual cybersecurity compliance behavior and new insights into factors that may influence individual’s cybersecurity compliance behavior. Collapse

Semantic filters: measurement theory

Topics: IT security self efficacy password decision making information system use

Methods: survey regression analysis method theory development decision tree classification data transformation

Theories: theory of abduction measurement theory
Can the development of a patient’s condition be predicted through intelligent inquiry under the e-health business mode? Sequential feature map-based disease risk prediction upon features selected from cognitive diagnosis big data
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2020 | International Journal of Information Management | Citations: 0
Authors: Liu, Xin; zhou, yanju; Zongrun, Wang

Abstract: The data-driven mode has promoted the researches of preventive medicine. In pred ... Expand

Abstract: The data-driven mode has promoted the researches of preventive medicine. In prediction of disease risks, physicians’ clinical cognitive diagnosis data can be used for early prevention of diseases and, therefore, to reduce medical cost, to improve accessibility of medical services and to lower medical risk. However, researches involved no physicians’ cognition of patients’ conditions in intelligent inquiry under e-health business mode, oﬀered no diagnosis big data, neglected the values of the fused text information generated by joint activities of online and oﬄine medical data, and failed to thoroughly analyze the phenomenon of redundancy-complementarity dispersion caused by high-order information shortage from the online inquiry data-driven perspective. Besides, the risk prediction simply based on oﬄine clinical cognitive diagnosis data undoubtedly reduces prediction precision. Importantly, relevant researches rarely considered temporal relationships of diﬀerent medical events, did not conduct detailed analysis on practical problems of pattern explosion, did not oﬀer a thought of intelligent portrayal map, and did not conduct relevant risk prediction based on the sub-maps obtained from the map. In consequence, the paper presents a disease risk prediction method with the model for redundancy-complementarity dispersion-based feature selection from physicians’ online cognitive diagnosis big data to realize features selection from the cognitive diagnosis big data of online intelligent inquiry; the obtained features were ranked intelligently for subsequent high-dimensional information shortage compensation; the compensated key feature information of the cognitive diagnosis big data was fused with oﬄine electronic medical record (EMR) to form the virtual electronic medical record (VEMR). The formed VEMR was combined with the method of the sequential feature map for modelling, and a sequential feature map-based model for disease risk prediction was presented to obtain online users’ medical conditions. A neighborhood-based collaborative prediction model was presented for prediction of an online intelligent medical inquiry user’s possible diseases in the future and to intelligently rank the risk probabilities of the diseases. In the experiments, the online intelligent medical inquiry users’ VEMRs were used as the foundation of the simulation experiments to predict disease risks in chronic obstructive pulmonary disease (OCPD) population and rheumatic heart disease (RHD) population. The experiments demonstrated that the presented method showed relatively good metric performances in the VEMR and improved disease risk prediction. Collapse

Semantic filters: measurement theory

Topics: big data electronic health record missing data healthcare data information management

Methods: experiment regression analysis method machine learning computational algorithm support vector machine

Theories: measurement theory
How Much Method-in-Use Matters? A Case Study of Agile and Waterfall Software Projects
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2020 | Journal of the Association for Information Systems | Citations: 0
Authors: Thummadi, Babu Veeresh; Lyytinen, Kalle

Abstract: Development methods are rarely followed to the letter, and, consequently, their ... Expand

Abstract: Development methods are rarely followed to the letter, and, consequently, their effects are often in doubt. At the same time, information systems scholars know little about the extent to which a given method truly influences software design and its outcomes. In this paper, we approach this gap by adopting a routine lens and using a novel methodological approach. Theoretically, we treat methods as (organizational) ostensive routine specifications and deploy routine construct as a feasible unit of analysis to analyze the effects of a method on actual, “performed” design routines. We formulated a research framework that identifies method, situation fitness, agency, and random noise as main sources of software design routine variation. Empirically, we applied the framework to examine the extent to which waterfall and agile methods induce variation in software design routines. We traceenacted design activities in three software projects in a large IT organization that followed an objectoriented waterfall method and three software projects that followed an agile method and then analyzed these traces using a mixed-methods approach involving gene sequencing methods, Markov models, and qualitative content analysis. Our analysis shows that, in both cases, method-induced variation using agile and waterfall methods accounts for about 40% of all activities, while the remaining 60% can be explained by a designer’s personal habits, the project’s fitness conditions, and environmental noise. Generally, the effect of method on software design activities is smaller than assumed and the impact of designer and project conditions on software processes and outcomes should thus not be understated. Collapse

Semantic filters: measurement theory

Topics: waterfall model systems development agile software development IT project requirements elicitation

Methods: design methodology design process qualitative interview Markov chain cluster analysis

Theories: measurement theory
Toward a Framework for Cooperation Behavior of Start-Ups: Developing a Multi-Item Scale from an Empirical Perspective

2019 | HICSS | Citations: 0
Authors: Garidis, Konstantin; Rossmann, Alexander

Abstract: Many start-ups are in search of cooperation partners to develop their innovative ... Expand

Abstract: Many start-ups are in search of cooperation partners to develop their innovative business models. In response, incumbent firms are introducing increasingly more cooperation systems to engage with start-ups. However, many of these cooperations end in failure. Although qualitative studies on cooperation models have tried to improve the effectiveness of incumbent start-up strategies, only a few have empirically examined start-up cooperation behavior. Considering the lack of adequate measurement models in current research, this paper focuses on developing a multi-item scale on cooperation behavior of start-ups, drawing from a series of qualitative and quantitative studies. The resultant scale contributes to recent research on start-up cooperation and provides a framework to add an empirical perspective to current research. Collapse

Semantic filters: measurement theory

Topics: database system business model

Methods: survey survey design data transformation confirmatory factor analysis partial least squares regression

Theories: measurement theory
Developing a Quality of Experience (QoE) model for Web Applications
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2019 | Information Systems Journal | Citations: 2
Authors: Lycett, Mark; Radwan, Omar

Abstract: Web-based service providers have long been required to deliver high quality serv ... Expand

Abstract: Web-based service providers have long been required to deliver high quality services in accordance with standards and customer requirements. Increasingly, however, providers are required to think beyond service quality and develop a deeper understanding of their customers' Quality of Experience (QoE). Although models exist that assess the QoE of Web Application, significant challenges remain in: (1) Defining QoE factors from a Web engineering perspective, (2) quantifying the relationship between so-called “objective” and “subjective” factors of relevance, and (3) dealing with limited data available in relation to subjective factors. In response, the work here presents a novel model (and associated software instantiation) that integrates factors through Key Performance Indicators and Key Quality Indicators. The mapping is incorporated into a correlation model that assesses the QoE of Web Applications, with a consideration of defining the factors in term of quality requirements derived from web architecture. The data resulting from the mapping is used as input of the proposed model to develop artefacts that quantify and predict QoE using Machine Learning. The development of proposed model is framed and guided by Design Science Research approach with the purpose of enabling providers to more informed decisions regarding QoE and/or to optimise resources accordingly. Although the work is oriented at developing an artefact that has clear utility for practice, the nascent design theory underpinning the work is developed and discussed. Collapse

Semantic filters: measurement theory

Topics: service quality web application usability Oracle database customer relationship management system

Methods: design theory computational algorithm design artifact descriptive statistic design process

Theories: measurement theory co-evolution theory
Implications of Technological Progress for the Measurement of Technology Acceptance Variables: The Case of Self-efficacy

2017 | International Conference on Information Systems | Citations: 0
Authors: Compeau, Deborah; Correia, John; Thatcher, Jason

Abstract: Despite decades of technological and organizational change, our research on tech ... Expand

Abstract: Despite decades of technological and organizational change, our research on technology adoption and acceptance continues to use measures that were developed during the late 1980s and early 1990s. In this paper, we examine one such measure, computer-selfefficacy. We consider the implications of changing technologies and context and propose a new direction for conceptualizing and measuring self-efficacy. We present an updated conceptualization and measurement for a new construct called Technology Self-efficacy. We validated the measure using a survey of 285 technology users. Our analysis shows the scale to be unidimensional and reliable, to exhibit discriminant validity from related constructs, and to possess better predictive validity in relation to outcome expectations and use than the current measure of computer self-efficacy. We conclude with a description of our plans for further validation and an exploration of the implications of our work for self-efficacy theory in information systems, and for construct measurement more broadly. Collapse

Semantic filters: measurement theory

Topics: self efficacy information system use technology adoption technostress spreadsheet

Methods: survey chi squared test survey design exploratory factor analysis confirmatory factor analysis

Theories: self efficacy theory measurement theory social cognitive theory

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Found 27 articles

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

When Constructs Become Obsolete: A Systematic Approach to Evaluating and Updating Constructs for Information Systems Research

A Double-Edged Sword of Involvement: On the Tension Between Customers’ Group Value and Self-Interest in Data Breach Response Processes

Re-Examining the Impact of Multidimensional Trust on Patients’ Online Medical Consultation Service Continuance Decision

Cybersecurity compliance behavior: Exploring the influences of individual decision style and other antecedents

Can the development of a patient’s condition be predicted through intelligent inquiry under the e-health business mode? Sequential feature map-based disease risk prediction upon features selected from cognitive diagnosis big data

How Much Method-in-Use Matters? A Case Study of Agile and Waterfall Software Projects

Toward a Framework for Cooperation Behavior of Start-Ups: Developing a Multi-Item Scale from an Empirical Perspective

Developing a Quality of Experience (QoE) model for Web Applications

Implications of Technological Progress for the Measurement of Technology Acceptance Variables: The Case of Self-efficacy