Factors Affecting the Adoption of Big Data Analytics in Companies

Cabrera-Sánchez, Juan-Pedro; Ramos, Ángel Francisco Villarejo

doi:10.1590/s0034-759020190607

Cited by 24 publications

(42 citation statements)

References 67 publications

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“…consider the predicted performance in the UTAUT as the expected performance in the technology adoption model and consideration it as a critical part of the proposal to the technology acceptor . The statistically direct relationship in the context of performance expectancy and behaviour BDA intention is confirmed by several studies (Cabrera-Sánchez & Villarejo-Ramos, 2019;Chao, 2019;Queiroz, & Farias, 2019).…”

Section: Hypothesissupporting

confidence: 63%

“…The findings of the study show two key determinant factors of behavioral intention to use BDA in Malaysian government agencies. The assumption that the technology is going to yield decent results raises the performance expectancy, as seen in past studies (Cabrera-Sánchez & Villarejo-Ramos, 2019;Brünink, 2016;Chao, 2019;Queiroz, & Farias, 2019) who found that expected results have positive and significant repercussions on BDA adoption. This indicates perception of, as has been shown in previous studies, the implementation of BDA (better decision making, competitiveness, improve services, strategic planning, new innovation in services etc.)…”

Section: Discussionmentioning

confidence: 93%

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Determinants factors of intention to adopt big data analytics in Malaysian public agencies

Sahid

Sani

Noordin

et al. 2021

JIEM

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Purpose: This research objective was to analyze factors affecting IT practitioners’ behavioral intentions in adopting (BDA) using a combination of Initial Trust Model (ITM), Unified Theory Of Acceptance And Use Of Technology (UTAUT) AND Task-Technology Fit (TTF) Model acceptance models.Design/methodology/approach: Using a cross sectional survey, empirical data were collected. A total of 200 questionnaires were obtained and screened. 14 insufficient answers were subsequently discarded leaving 186 valid structured data review questionnaires. Data was analizad using the Partial Least Square Modeling of Structural Equations due to one of the best software for verifying structured data on structural equations modeling (SEM).Findings: Our findings show that two key factors determine behavioral intention to adopt BDA in government agencies. Firstly, the assumption that the technology is going to produce great results raises the expectation of performance. TTF was the second determinant factor. Surprisingly, Initial trust, on the other hand, had not been considered to be a key determinant of behavioural intention and was found to be adversely related to the BDA intentionResearch limitations/implications: These findings identify the crucial model, that would be useful to IT officers in public agencies in making investment choices and designing non-adopter-friendly outreach strategies because they have more barriers to acceptance than adopters and lead adopters in the reward ladder.Practical implications: The findings suggest significant recommendations and consequences for BDA systems practitioners and application developers, which could coincide with the successful implementation of BDA systems. This research gives practitioners an initial way to incorporate and endorse BDA activities in their organization to make the most of the advantages of revolutionary technology, particularly within public agencies.Originality/value: This study's model is helpful and promotes a theory for further BDA research. By utilizing the model, existing public agencies can know their own BDA capability, centered around activities with specifically tailored outcomes, organization dynamics, resources, skills and potential services.

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Section: Hypothesissupporting

confidence: 63%

Section: Discussionmentioning

confidence: 93%

Determinants factors of intention to adopt big data analytics in Malaysian public agencies

Sahid

Sani

Noordin

et al. 2021

JIEM

View full text Add to dashboard Cite

show abstract

“…Its precedents lie in perceived usefulness, extrinsic motivation and fit in the job. In addition to the original study (Venkatesh et al, 2003 ), this construct has been used extensively in later research (Chauhan and Jaiswal, 2016 ; Lakhal, 2017 ; Cabrera-Sánchez and Villarejo-Ramos, 2019 ; Kalinić et al, 2019 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Big Data Adoption in Companies With an Explanatory and Predictive Model

et al. 2021

Self Cite

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The purpose of this paper is to identify the factors that affect the intention to use Big Data Applications in companies. Research into Big Data usage intention and adoption is scarce and much less from the perspective of the use of these techniques in companies. That is why this research focuses on analyzing the adoption of Big Data Applications by companies. Further to a review of the literature, it is proposed to use a UTAUT model as a starting model with the update and incorporation of other variables such as resistance to use and perceived risk, and then to perform a neural network to predict this adoption. With respect to this non-parametric technique, we found that the multilayer perceptron model (MLP) for the use of Big Data Applications in companies obtains higher AUC values, and a better confusion matrix. This paper is a pioneering study using this hybrid methodology on the intention to use Big Data Applications. The result of this research has important implications for the theory and practice of adopting Big Data Applications.

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“…Big data is creating a new generation of decision support data management, other factors has been considered for a successful big data project as: organizational culture, data architecture, analytical tools, and personnel issues [15].…”

Section: A Big Datamentioning

confidence: 99%

Big Data Analysis Proposal for Manufacturing Firm

Valdez

Cortes

Vazquez

et al. 2021

EJECE

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The analysis of large volumes of data is an important activity in manufacturing companies, since they allow improving the decision-making process. The data analysis has generated that the services and products are personalized, and how the consumption of the products has evolved, obtaining results that add value to the companies in real time. In this case study, developed in a large manufacturing company of electronic components as robots and AC motors; a strategy has been proposed to analyze large volumes of data and be able to analyze them to support the decision-making process; among the proposed activities of the strategy are: Analysis of the technological architecture, selection of the business processes to be analyzed, installation and configuration of Hadoop software, ETL activities, and data analysis and visualization of the results. With the proposed strategy, the data of nine production factors of the motor PCI boards were analyzed, which had a greater incidence in the rejection of the components; a solution was made based on the analysis, which has allowed a decrease of 28.2% in the percentage of rejection.

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Factors Affecting the Adoption of Big Data Analytics in Companies

Cited by 24 publications

References 67 publications

Determinants factors of intention to adopt big data analytics in Malaysian public agencies

Determinants factors of intention to adopt big data analytics in Malaysian public agencies

Predicting Big Data Adoption in Companies With an Explanatory and Predictive Model

Big Data Analysis Proposal for Manufacturing Firm

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