NYIT AKILA HOW TO

NYIT AKILA HOW TO

Our main findings provide a deeper understanding of the enablers of BDA adoption through the development of a framework that includes direct and moderating constructs, as well as recommendations to practitioners on how to enhance BDA adoption based on eight BDA enablers. Furthermore, the study has a number of theoretical and practical implications. The study provides fresh findings on determinants of intention to adopt big data analytics, actual use, and moderating role of perceived risk within the model to develop sustainability. However, the moderating influence of perceived risk on the relationship between intention and actual use of big data has not been proved. The results reveal that there is a significant relationship between top management support and competitive pressures and intentions to adopt big data analytics. Moreover, this study employed the Partial Least Square Structural Equation Modelling (PLS-SEM) technique and analyses the data collected from 172 respondents working in different organizations in Amman and Jordan. Therefore, this study aims to examine the readiness of manufacturing firms in adopting big data analytics in sustainable development.

However, managing, analysing, and utilizing the vast amount of data for sustainability decision are not easy. Information and communication technology make it easier for managers to gather customer data quickly and efficiently. Furthermore, this study employs on survey method through questionnaire survey in the data collection stage The final result finding will help to extend the better understanding of adoption of big data by private companies in Malaysia and will help to expand the contribution to literature review and contribute to the new knowledge on theory based on the integrated TAM (Technology Acceptance Model) and DOI (Diffusion of Innovation) for adoption of big data. The model being proposed clarifies the structural relationships of the seven constructs which consists of perceived usefulness, perceived benefit, predictive analytics accuracy, perceived ease-of-use, perceived risk, training and adoption of big data which were analysed through method of statistical analysis with SPSS and Structural Equation Modeling using AMOS were utilized to obtain an appropriate model of adoption to explain the relationship between key determinants and adoption of big data in the proposed conceptual framework. This study help establish the model which are helpful in examining these determinants. The objective of this study was to bridge the gap by examining the key determinants affecting the adoption of big data in Malaysia by applying the integrated Technology Acceptance Model and Diffusion of Innovation approach and to establish a model for the adoption of big data by private companies in Malaysia. This study explores on the adoption of big data in Malaysia. It seeks to provide understanding of the benefit and challenges for addressing key determinants in the adoption of big data by private companies in Malaysia. The review of literature and report from international industry analyst were being analyzed on the trends of utilization in development of big data regionally and globally. Large private companies had shown interest in adopting big data technologies.

Nyit akila