Factors influencing the acceptance of healthcare information technologies: A meta-analysis

Chong, Alain Yee‐Loong; Blut, Markus; Zheng, Shuning

doi:10.1016/j.im.2022.103604

Cited by 36 publications

(21 citation statements)

References 101 publications

(121 reference statements)

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“…Furthermore, in order to develop an inclusive integrated conceptual model, we grouped constructs carrying similar conceptualizations and meaning. For instance, PU and performance expectancy had similar conceptualization; and thus, they were grouped as 'perceived usefulness' (Yee et al, 2022;Zhao et al, 2018). Similarly, effort expectancy and perceived convenience resemble PEOU (Jadil et al, 2021).…”

Section: Coding Proceduresmentioning

confidence: 99%

Antecedents of intention to use chatbots in service encounters: A meta‐analytic review

Gopinath

Kasilingam

2023

Int J Consumer Studies

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The trend of using artificial intelligence based chatbots for service encounters has seen a significant spike in recent times. Many studies have attempted to explain the adoption of the technology for various service applications; this study synthesizes the literature through a meta‐analysis, for which data from 70 independent studies, with a combined sample size of 20,026 was considered, to understand the influencers of the attitude and intention to use chatbots for service encounters. In addition, this study also sought to understand the moderating role of the culture, the technological characteristics captured through the variables transactional/non‐transactional, and informative/entertaining of the hypothesized relationships. Meta structural equation modelling (meta‐analytic SEM) was used to analyse the data. The results indicate playfulness, attitude, usefulness, facilitating condition and social influence of the chatbots are the key factors in influencing user adoption. Further, technological characteristics and culture were found to significantly moderate certain relationships in the model. Practical and theoretical implications have also been discussed.

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Section: Coding Proceduresmentioning

confidence: 99%

Antecedents of intention to use chatbots in service encounters: A meta‐analytic review

Gopinath

Kasilingam

2023

Int J Consumer Studies

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“…Advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), Block chain, Predictive Analytical tools, etc., have significantly affected various aspects of the healthcare ecosystems 1 . As a result, technology usage in the healthcare industry has advanced tremendously 2 . During the early years (before the 1980s), technology had limited use and was mainly employed to increase pharmaceutical production.…”

Section: Introductionmentioning

confidence: 99%

“…These techniques can uncover many previously unknown associations 11,12 . They can also help in generating newer hypotheses 2,13 . This will also help draw inferences from multiple datasets of different people, and one can open up many new interrelationships.…”

Section: Introductionmentioning

confidence: 99%

Novel 4A Framework for Measuring Outcomes of Technological Implementation in The Healthcare Industry

Rajaram

Pandey

Saproo³

et al. 2023

Int J Life Sci Pharm Res

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The healthcare industry is ever-evolving, and advancements in this industry are happening on multiple fronts, i.e. scientific, medical, regulatory etc. This industry has witnessed significant changes concerning technology usage, which has changed the overall complexion. The advent of advanced computing tools, such as AI, ML etc., has significantly augmented the capabilities within the industry and is making this industry future ready. The healthcare industry has been reactive, i.e. it treats individuals after they get the disease; however, with the application of the latest tools, the healthcare industry is becoming more predictive, i.e. it can predict the risk profile. This is the most significant impact advanced tools have brought to this industry. Advanced computing tools are being applied to multiple functions across the industry, such as drug development, patient treatment, diagnosis, insurance reimbursement etc. As there are multitudes of applications projecting different kinds of benefits, it becomes essential that we have a tool or framework which can measure the impact. This study aimed to develop a framework to evaluate the benefits of implementing these advanced computing tools. The primary objective of this technology application should be to impact the primary outcomes, i.e. accessibility, speed and accuracy, patient compliance and affordability. We must have a framework that objectively assesses the impact and provides guidance to all the stakeholders. This paper proposes a novel 4A framework to help all stakeholders assess the effects. The central idea in healthcare is to improve a patient's life. One can do that by making medicines more productive, reducing adverse effects, improving predictability, reducing cost, improving access etc. This paper provides a strategic framework to assess the impact on the suitable parameters. Novel 4A framework measures the effect on these critical parameters, i.e. access, accuracy, affordability and adherence. As this industry touches patient's life, there are multiple concerns and the primary amongst them, especially for the technology applications, are the ones related to legal and ethical. The overall archetype proposed in this research paper takes into account and provides a framework for the impact assessment. The novel 4A framework presented in this article will serve as a tool to evaluate the impact advanced computing tools will have on the healthcare industry.

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Section: Introductionmentioning

confidence: 99%

“…The analysis of systematic review articles published on technology acceptance in healthcare also showed that the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) models are the most prevalent models in explaining what affects the acceptance of various healthcare technologies through different user groups—health-care professionals and patients (also older adults), settings, and countries [ 39 , 44 , 45 , 46 , 47 ]. Moreover, nowadays, the UTAUT model is still one of the commonly-used theories for technology adoption studies in various other areas as well as in the medical sector [ 39 , 47 ]. In fact, internationally, this methodology has been applied many times in surveys conducted in the healthcare sector, confirming the acceptability of the UTAUT model in different applications of eHealth by also taking different perspectives, either health centers [ 35 ] or healthcare professionals [ 48 , 49 , 50 , 51 , 52 , 53 ] or both patients’ and health professionals’ acceptance [ 54 ] or general users of an eHealth [ 55 ], including older adults [ 56 , 57 , 58 , 59 ].…”

Section: Introductionmentioning

confidence: 99%

What Determines the Acceptance and Use of eHealth by Older Adults in Poland?

Rój

2022

IJERPH

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An aging population is considered a major challenge for governments and healthcare planners. eHealth is perceived as a tool with the potential to ensure efficient healthcare. Moreover, eHealth services may help older adults to maintain longer life in good health. However, there are still several challenges to the large-scale implementation of these solutions among older adults. Therefore, the aim of this study was to explore determinants of the acceptance and use of eHealth by older adults in Poland. Data was collected by the questionnaire, and the UTAUT model was employed. This research covered older adults aged 60 to 69. The analysis of the results using nested regression analysis showed that performance expectancy has a strong significance on the older adults’ acceptance and use of eHealth, followed by effort expectancy and social influence. In contrast, facilitating conditions do not have a significant influence on the acceptance and use of eHealth. These findings may also be beneficial for the government to provide relative policies to support the development and usage of eHealth services as well as for the healthcare devices industry to design more older adult-oriented products.

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Factors influencing the acceptance of healthcare information technologies: A meta-analysis

Cited by 36 publications

References 101 publications

Antecedents of intention to use chatbots in service encounters: A meta‐analytic review

Antecedents of intention to use chatbots in service encounters: A meta‐analytic review

Novel 4A Framework for Measuring Outcomes of Technological Implementation in The Healthcare Industry

What Determines the Acceptance and Use of eHealth by Older Adults in Poland?

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