Analysis of Influencing Factors of Big Data Adoption in Chinese Enterprises Using DANP Technique

Lei, Wang; Yang, Mengke; Pathan, Zulfiqar Hussain; Salam, Shafaq; Shahzad, Khuram; Zeng, Jianqiu

doi:10.3390/su10113956

Cited by 30 publications

(22 citation statements)

References 33 publications

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“…BDA can be defined as the technology that can extract valuable information from the big volume, velocity, and variety of data [5,15]. e study of Yasin et al [32] has defined BDA as a system that has the ability to gather, store, analyze, and communicate the big database.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is important to highlight the factors of concern that might influence the adoption of innovative technologies such as BDA with respect to healthcare organizations, particularly in developing countries like Pakistan. Countries around the globe are shi ing from traditional ways of analyzing data to BDA for decision making, even in healthcare [5]. In SAARC countries, Pakistan was the first that launched e-government in 2002 [33] and also in the countries that are the largest contributor in IT.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Several studies provided evidence that factors of TAM are a powerful predictor of the adoption of different innovative technologies like healthcare record system, online shopping, e-government, and mobile shopping apps [23,[39][40][41]. Meanwhile, the studies applied TAM model in organizational context to identify the employees' intentions to use the innovative technologies [4,5,42,43] e studies also used the TAM model to investigate the adoption mechanism of BDA and proved that PU and PEOU are powerful characteristics of IU [4,5,42], however, ignored the important part of external variables that can strongly influence the both PU and PEOU. It is confirmed from the literature that TAM is insufficient to predict the individual intentions to adopt a technology, and suggested that TAM should extend with other factors to increase the specificity and predictive power [44].…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to a survey, 76% of big companies are investing in BDA [5], and healthcare organizations have big ration in it. Meanwhile, existing literature elaborates that organizations are in the adoption phase, therefore, it is essential for the healthcare industry to have an adoption model of BDA before implementation to understand the most important factors in the adoption [6].…”

Section: Introductionmentioning

confidence: 99%

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Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare

et al. 2020

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The big data analytics (BDA) has dragged tremendous attention in healthcare organizations. Healthcare organizations are investing substantial money and time in big data analytics and want to adopt it to get potential benefits. Thus, this study proposes a BDA adoption model in healthcare organizations to explore the critical factors that can influence its adoption process. The study extends the technology acceptance model (TAM) with the self-efficacy as an external factor and also includes gender and resistance to change (RTC) as moderators to strengthen the research model. The proposed research model has been tested on 283 valid responses which were collected through a structured survey, by applying structural equation modeling. Our results portray that self-efficacy is a strong predictor of intention to use BDA along with other TAM factors. Moreover, it is confirmed by the results that RTC dampens the positive relationship between intention to use and actual use of BDA in healthcare organizations. The outcomes revealed that male employees as compared to female employees are dominant towards the positive intention to use BDA. Furthermore, females create more RTC than males while adopting BDA in healthcare organizations. Theoretical and practical implications, limitations, and future research directions also underlined in this study.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare

et al. 2020

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“…The model has the greatest degree of openness and inclusiveness, and it provides an effective framework for us to analyze the dynamics of behavior. Because it has good stability and predictability of behavior, the model is not only suitable for the interpretation of information receiving behavior, but also has a wide range of applications in the fields of public management, social capital, human resource management and knowledge management [12]. This study uses the MOA model to analyze the driving factors of the firms' green ICT practice as well.…”

Section: The Moa Frameworkmentioning

confidence: 99%

Which Factors Drive Firms’ Green Information Technology Practices?—an Integrated DEMATEL–ANP Approach

Wang¹,

Wang²,

Wu³

2020

Proceedings of the International Academic Conference on Frontiers in Social Sciences and Management Innovation (IAFSM 2019)

Self Cite

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Scholars have identified various factors for adopting green IT by organizations from different perspectives. However, the studies about the priority of those factors and the interdependent relations among them are relatively lack. To address this problem, we use the Motivation-Opportunity-Ability (MOA) framework to establish a systematic model of the factors which drive firms' green IT practices. Furthermore, an integrated Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP) approach has been adopted to analyze the complex relations among different factors and determine the weight distribution of them. The results show that top managers supports, policy and regulation, institutional pressure and human resources are key factors driving firms' green IT practices. This study provides the implications for organizations and policy makers to understand and develop strategies to implement green IT. IntroductionInformation technology (IT) is the cornerstone for the development of modern society. The dependence of various industries on IT is rising [1]. But at the same time, the energy consumption, carbon dioxide emissions and increased e-waste brought about by IT cause great anxiety. Therefore, green energy conservation emerges as the main theme of IT development since it reduces the negative impact of IT on the environment [2]. Green IT is defined as "the research and practice of design, construction and use of hardware, software and information technology with a positive impact on the environment" [3]. The application of green IT not only brings benefits to enterprises but also plays an important role in supporting environmentally sustainable growth [4,5]. The term "Green IT" has become more and more popular and the number of related studies increases rapidly [6].However, there are still several gaps between the research and practice of green IT [1]. As a young field, a unified comprehensive framework for green IT is lacked [3] which causes each firm has its own criteria about implementing green IT [5]. Furthermore, there are few studies focusing on the relations and the quantitative evaluation of those driving factors. Through addressing the question, our study contributes to green IT from two aspects. Theoretically, this paper develops the Motivation-Opportunity-Ability (MOA) framework to establish a new systematic model of the factors which drive green IT practices. Empirically, on the basis of the model, we analyze the relations among those factors and identify the key factors by using an integrated Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP) approach.The remainder of the paper is divided into four sections. Section 2 establishes the systematic model based on literature review. Section 3 introduces the methodology, data collection and analysis procedure. Section 4 explains the relations among various factors and discusses the analysis results. In section 5, the conclusions and limitations of the article are presented. Theoretic...

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Big Data Analytics Adoption Model for Malaysian SMEs

Tien

Ali

Miskon

et al. 2019

Advances in Intelligent Systems and Computing

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Analysis of Influencing Factors of Big Data Adoption in Chinese Enterprises Using DANP Technique

Cited by 30 publications

References 33 publications

Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare

Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare

Which Factors Drive Firms’ Green Information Technology Practices?—an Integrated DEMATEL–ANP Approach

Big Data Analytics Adoption Model for Malaysian SMEs

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