Adoption of interorganizational system standards in supply chains

Chong, Alain Yee‐Loong; Ooi, Keng‐Boon

doi:10.1108/02635570810868371

Cited by 135 publications

(34 citation statements)

References 24 publications

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“…Furthermore, the technology readiness of organizations has positive influence on the adoption of cloud computing; the more readiness organization has, the more intention to adopt cloud computing [75]. The findings of this study were supported by those of references [48] [52] and [88]. This is mainly because organizations with high technological readiness are aware of current IT infrastructure potential and limitations and are willing to provide adequate training to enable the skills need to adopt cloud computing.…”

Section: Discussionsupporting

confidence: 76%

Factors Affecting the Intention of Adopting Cloud Computing in Jordanian Hospitals

Harfoushi¹,

Akhorshaideh²,

Aqqad³

et al. 2016

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The purpose of this study is to examine the different factors that are expected to influence the intention of hospitals to adopt cloud computing in Jordan. This study is conducted using quatititative methodology. 223 questionnaires were distributed to the IT departments of different hospitals to evaluate their ability and willingness to adopt cloud computing. The data were tested using multiple regression in order to determine whether Technology, Organizational, and Environmental factors (TOE) played a role in hospitals' decision to consider cloud computing as a beneficial investment. The findings of this study showed that all the factors had a significant positive impact on the intention of hospitals to adopt cloud computing, with the Technological factor having the most impact on the decision made.

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Section: Discussionsupporting

confidence: 76%

Factors Affecting the Intention of Adopting Cloud Computing in Jordanian Hospitals

Harfoushi¹,

Akhorshaideh²,

Aqqad³

et al. 2016

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“…This study will examine eWOM using Amazon.com, and the products include electronic devices such as camera, television, Hi-Fi, notebook, etc. Unlike books, which have been examined by Chevalier and Mayzlin (2006), electronic products have shorter product shelf lives, and it would be interesting to see how the relationships between factors such as price, promotion and online reviews influence product sales (Chong and Ooi 2008). Similar to another study (Lu et al 2013), we used Amazon.com solely as we are unable to include dispersion in our study.…”

Section: Research Context and Datamentioning

confidence: 97%

Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews

Chong

Ch’ng

Liu

et al. 2015

International Journal of Production Research

153

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This study aims to investigate the contributions of online promotional marketing and online reviews as predictors of consumer product demands. Using electronic data from Amazon.com, we attempt to predict if online review variables such as valence and volume of reviews, the number of positive and negative reviews, and online promotional marketing variables such as discounts and free deliveries, can influence the demand of electronic products in Amazon.com. A Big Data architecture was developed and Node.JS agents were deployed for scraping the Amazon.com pages using asynchronous Input/Output calls. The completed Web crawling and scraping data-sets were then preprocessed for Neural Network analysis. Our results showed that variables from both online reviews and promotional marketing strategies are important predictors of product demands. Variables in online reviews in general were better predictors as compared to online marketing promotional variables. This study provides important implications for practitioners as they can better understand how online reviews and online promotional marketing can influence product demands. Our empirical contributions include the design of a Big Data architecture that incorporate Neural Network analysis which can used as a platform for future researchers to investigate how Big Data can be used to understand and predict online consumer product demands.

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“…The remaining 27 focused on the IT field. For example, Internet Protocol version 6 [ 22 - 24 ], RosettaNet [ 25 - 27 ], and electronic data interchange [ 28 - 30 ]. Only 13 articles comprehensively considered the 3 dimensions of technology, organization, and environment [ 12 , 13 , 18 , 22 , 25 , 26 , 28 , 30 - 35 ], whereas one of them also included interorganizational determinants [ 28 ].…”

Section: Resultsmentioning

confidence: 99%

Factors Influencing the Adoption of Health Information Standards in Health Care Organizations: A Systematic Review Based on Best Fit Framework Synthesis

Han¹,

Liu²,

Song³

et al. 2020

JMIR Med Inform

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Background Since the early 1970s, health care provision has experienced rapid growth in the investment and adoption of health information technologies (HITs). However, the development and deployment of HITs has often been conducted in silos, at different organizational levels, within different regions, and in various health care settings; this has resulted in their infrastructures often being difficult to manage or integrate. Health information standards (ie, the set norms and requirements that underpin the deployment of HITs in health care settings) are expected to address these issues, yet their adoption remains to be frustratingly low among health care information technology vendors. Objective This study aimed to synthesize a comprehensive framework of factors that affect the adoption and deployment of health information standards by health care organizations. Methods First, electronic databases, including Web of Science, Scopus, and PubMed, were searched for relevant articles, with the results being exported to the EndNote reference management software. Second, study selection was conducted according to pre-established inclusion and exclusion criteria. Finally, a synthesized best fit framework was created, which integrated a thematic analysis of the included articles. Results In total, 35 records were incorporated into the synthesized framework, with 4 dimensions being identified: technology, organization, environment, and interorganizational relationships. The technology dimension included relative advantage, complexity, compatibility, trialability, observability, switching cost, standards uncertainty, and shared business process attributes. The organization dimension included organizational scale, organizational culture, staff resistance to change, staff training, top management support, and organizational readiness. The environment dimension included external pressure, external support, network externality, installed base, and information communication. Finally, the interorganizational relationships dimension included partner trust, partner dependence, relationship commitment, and partner power. Conclusions The synthesized framework presented in this paper extends the current understanding of the factors that influence the adoption of health information standards in health care organizations. It provides policy and decision makers with a greater awareness of factors that hinder or facilitate their adoption, enabling better judgement and development of adoption intervention strategies. Furthermore, suggestions for future research are provided.

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Adoption of interorganizational system standards in supply chains

Cited by 135 publications

References 24 publications

Factors Affecting the Intention of Adopting Cloud Computing in Jordanian Hospitals

Factors Affecting the Intention of Adopting Cloud Computing in Jordanian Hospitals

Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews

Factors Influencing the Adoption of Health Information Standards in Health Care Organizations: A Systematic Review Based on Best Fit Framework Synthesis

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