Hybrid SEM-neural networks for predicting electronics logistics information system adoption in Thailand healthcare supply chain

Kunnapapdeelert, Siwaporn; Pitchayadejanant, Krittipat

doi:10.1504/ijbpscm.2020.108887

Cited by 10 publications

(4 citation statements)

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“…The findings suggest that adoption strategies cannot exclude extensive stakeholder involvement in the roll-out of e-procurement within the Ghanaian milieu. In contrast to other studies (El-Masri and Tarhini, 2017; Aranyossy, 2018; Kunnapapdeelert and Pitchayadejanant, 2020), this study revealed that the construct H has a negative influence on the adoption decision of e-procurement systems in a developing country. The use of e-commerce is still relatively new, and government e-commerce is at its nascent stage in Ghana.…”

Section: Discussioncontrasting

confidence: 99%

Predicting the adoption of e-procurement in construction project delivery in Sub-Saharan Africa: an application of UTAUT2

Addy

Addo

Kwofie

et al. 2022

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Purpose The substantial benefits of e-procurement to organizations and institutions have, over the past two decades, become central to supply chain management. This study aims to identify the factors that facilitate the adoption of e-procurement system using modified Unified Theory of Acceptance and Use of Technology (UTAUT2). Design/methodology/approach Using a quantitative approach, the factors identified in the UTAUT2 are operationalized and examined carefully. The investigation has been conducted using a survey questionnaire. The data set has been obtained from 205 construction professionals in Ghana. Findings The research evinced that the decision by individuals to adopt e-procurement is mainly influenced by performance expectancy and social influence. By implication, the study found out that the respondents are more likely to adopt e-procurement if it is seen to be user-friendly and useful, and when they have a community of people supporting its use. Overall, the findings suggest that the model can stimulate a high adoption and intention to use e-procurement technologies, given its good predictive potential and robustness. Practical implications The structures uncovered in the study show that not all UTAUT2 factors are applicable within the study area. The findings provide a good backdrop in the development of policy and a roadmap for e-procurement implementation in Ghana. This is particularly useful for system development and implementation purposes for government agencies, contracting organizations, professionals and users of e-procurement technologies. Originality/value The original contribution and value of the paper is the use of UTAUT2 to provide evidence on their significance to e-procurement adoption among construction professionals.

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Section: Discussioncontrasting

confidence: 99%

Predicting the adoption of e-procurement in construction project delivery in Sub-Saharan Africa: an application of UTAUT2

Addy

Addo

Kwofie

et al. 2022

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“…Topic 2 (green – UTAUT Innovations: Fitness Apps, Wearables and M-Health Adoption) groups studies putting UTAUT and UTAU2 models to the test, employing advanced analytical techniques such as neural network analysis, as in the case of Kunnapapdeelert and Pitchayadejanant (2020), Sabbir et al. (2021a, b) and Yee-Loong Chong et al.…”

Section: Resultsmentioning

confidence: 99%

“…The undeniable influence of seminal contributions by Venkatesh et al (2003Venkatesh et al ( , 2012 in this cluster marks the inception of the UTAUT model and its subsequent extension, UTAUT2. Surprisingly, only one contribution, namely Kunnapapdeelert and Pitchayadejanant (2020), directly adopts the UTAUT model without extensions. This rarity raises intriguing questions about the perceived limitations or evolving needs that might drive researchers to extend these models in various ways.…”

Section: Discussionmentioning

confidence: 99%

“…In summary, Topic 1 encompasses diverse research strands within the overarching theme of healthcare digitalization and its significance during the COVID-19 pandemic, highlighting the distinct but interconnected interests of healthcare professionals and end-users in the realm of ATI. Topic 2 (green -UTAUT Innovations: Fitness Apps, Wearables and M-Health Adoption) groups studies putting UTAUT and UTAU2 models to the test, employing advanced analytical techniques such as neural network analysis, as in the case of Kunnapapdeelert and Pitchayadejanant (2020), Sabbir et al (2021a, b) and Yee-Loong Chong et al (2015). Researchers extended the models by introducing additional dimensions.…”

Section: Topic Modelingmentioning

confidence: 99%

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Combining topic modeling and bibliometric analysis to understand the evolution of technological innovation adoption in the healthcare industry

Cobelli,

Blasi

2024

EJIM

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PurposeThis paper explores the Adoption of Technological Innovation (ATI) in the healthcare industry. It investigates how the literature has evolved, and what are the emerging innovation dimensions in the healthcare industry adoption studies.Design/methodology/approachWe followed a mixed-method approach combining bibliometric methods and topic modeling, with 57 papers being deeply analyzed.FindingsOur results identify three latent topics. The first one is related to the digitalization in healthcare with a specific focus on the COVID-19 pandemic. The second one groups up the word combinations dealing with the research models and their constructs. The third one refers to the healthcare systems/professionals and their resistance to ATI.Research limitations/implicationsThe study’s sample selection focused on scientific journals included in the Academic Journal Guide and in the FT Research Rank. However, the paper identifies trends that offer managerial insights for stakeholders in the healthcare industry.Practical implicationsATI has the potential to revolutionize the health service delivery system and to decentralize services traditionally provided in hospitals or medical centers. All this would contribute to a reduction in waiting lists and the provision of proximity services.Originality/valueThe originality of the paper lies in the combination of two methods: bibliometric analysis and topic modeling. This approach allowed us to understand the ATI evolutions in the healthcare industry.

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Artificial Neural Network and Structural Equation Modeling Techniques

Zaidan¹,

Arianpoor²

2022

Artificial Neural Networks and Structural Equation Modeling

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Hybrid SEM-neural networks for predicting electronics logistics information system adoption in Thailand healthcare supply chain

Cited by 10 publications

References 0 publications

Predicting the adoption of e-procurement in construction project delivery in Sub-Saharan Africa: an application of UTAUT2

Predicting the adoption of e-procurement in construction project delivery in Sub-Saharan Africa: an application of UTAUT2

Combining topic modeling and bibliometric analysis to understand the evolution of technological innovation adoption in the healthcare industry

Artificial Neural Network and Structural Equation Modeling Techniques

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