Digital Job Demands and Resources: Digitization in the Context of the Job Demands-Resources Model.

This study comprehensively investigates the effects of digitization in the workplace, with a specific focus on white-collar employees, using the job demands-resources (JD-R) model as a theoretical framework. By examining the intricate interplay between digital job demands and digital job resources, the research offers valuable insights to help organizations navigate the complexities caused by technological advancements. Utilizing a qualitative triangulation approach, the research combines a systematic literature review with a thematic analysis of 15 interdisciplinary expert interviews. Thereby, the study establishes a robust theoretical foundation for exploring stress, motivation, and the organizational consequences arising from integrating technology in the workplace. The JD-R model is extended to incorporate digital job demands and resources, enabling a thorough examination of both the positive and negative aspects of digitization within organizations. Moreover, the study highlights the necessity for the consistent adaptation of the JD-R model across diverse job contexts in the ever-evolving digital landscape. It advocates for organizations to effectively leverage digital resources and proactively manage job demands, aiming to transform digitization into a valuable job asset while preventing the onset of overwhelming burdens. In conclusion, the research encourages organizations to embrace the vast potential of digitization while prioritizing digital health in the workplace.

Predicting mHealth Acceptance Using the UTAUT2 Technology Acceptance Model: A Mixed-Methods Approach.

BACKGROUND: Mobile health (mHealth) apps are increasingly used in healthcare to support people with chronic diseases such as diabetes. mHealth acceptance is crucial for using them. Due to acceptance problems, however, mHealth apps are not used by all chronic disease patients. To predict user acceptance, technology acceptance models such as UTAUT2 are used. However, UTAUT2 was not explicitly developed for the mHealth context. OBJECTIVES: This study investigates if additional health-related constructs could increase the predictive power of the UTAUT2 model. METHODS: A mixed-methods design, comprising an initial qualitative methods triangulation study that consisted of a literature search, expert interviews, and patient interviews, and a subsequent quantitative cross-sectional survey with 413 patients was used. RESULTS: The mixed-methods study revealed and validated two new constructs relevant for predicting mHealth acceptance not represented in the UTAUT2 model: "perceived disease threat" and "trust". CONCLUSION: The UTAUT2 model was successfully extended by two new constructs relevant to the mHealth context.

Extension of the Unified Theory of Acceptance and Use of Technology 2 model for predicting mHealth acceptance using diabetes as an example: a cross-sectional validation study.

OBJECTIVES: Mobile health applications are instrumental in the self-management of chronic diseases like diabetes. Technology acceptance models such as Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) have proven essential for predicting the acceptance of information technology. However, earlier research has found that the constructs "perceived disease threat" and "trust" should be added to UTAUT2 in the mHealth acceptance context. This study aims to evaluate the extended UTAUT2 model for predicting mHealth acceptance, represented by behavioral intention, using mobile diabetes applications as an example. METHODS: We extended UTAUT2 with the additional constructs "perceived disease threat" and "trust". We conducted a web-based survey in German-speaking countries focusing on patients with diabetes and their relatives who have been using mobile diabetes applications for at least 3 months. We analysed 413 completed questionnaires by structural equation modelling. RESULTS: We could confirm that the newly added constructs "perceived disease threat" and "trust" indeed predict behavioural intention to use mobile diabetes applications. We could also confirm the UTAUT2 constructs "performance expectancy" and "habit" to predict behavioural intention to use mobile diabetes applications. The results show that the extended UTAUT2 model could explain 35.0% of the variance in behavioural intention. DISCUSSION: Even if UTAUT2 is well established in the information technologies sector to predict technology acceptance, our results reveal that the original UTAUT2 should be extended by "perceived disease threat" and "trust" to better predict mHealth acceptance. CONCLUSION: Despite the newly added constructs, UTAUT2 can only partially predict mHealth acceptance. Future research should investigate additional mHealth acceptance factors, including how patients perceive trust in mHealth applications.

Suitability of the Unified Theory of Acceptance and Use of Technology 2 Model for Predicting mHealth Acceptance Using Diabetes as an Example: Qualitative Methods Triangulation Study.

BACKGROUND: In recent years, the use of mobile health (mHealth) apps to manage chronic diseases has increased significantly. Although mHealth apps have many benefits, their acceptance is still low in certain areas and groups. Most mHealth acceptance studies are based on technology acceptance models. In particular, the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model was developed to predict technology acceptance in a consumer context. However, to date, only a few studies have used the UTAUT2 model to predict mHealth acceptance and confirm its suitability for the health sector. Thus, it is unclear whether the UTAUT2 model is suitable for predicting mHealth acceptance and whether essential variables for a health-related context are missing. OBJECTIVE: This study aims to validate the suitability of UTAUT2 for predicting mHealth acceptance. METHODS: In this study, diabetes was used as an example as mHealth apps are a significant element of diabetes self-management. In addition, diabetes is one of the most common chronic diseases affecting young and older people worldwide. An explorative literature review and guided interviews with 11 mHealth or technology acceptance experts and 8 mHealth users in Austria and Germany were triangulated to identify all relevant constructs for predicting mHealth acceptance. The interview participants were recruited by purposive sampling until theoretical saturation was reached. Data were analyzed using structured content analysis based on inductive and deductive approaches. RESULTS: This study was able to confirm the relevance of all exogenous UTAUT2 constructs. However, it revealed two additional constructs that may also need to be considered to better predict mHealth acceptance: trust and perceived disease threat. CONCLUSIONS: This study showed that the UTAUT2 model is suitable for predicting mHealth acceptance. However, the model should be extended to include 2 additional constructs for use in the mHealth context.