ABSTRACT
The adoption of Big Data Analysis (BDA) has become popular among firms since it creates evidence for decision-making by managers. However, the adoption of BDA continues to be poor among small and medium enterprises (SMEs). Therefore, this study adopted the Technology-Organization-Environment (TOE) framework to identify the drivers of readiness to adopt BDA among SMEs. Chi-square automatic interaction detection (CHAID), Bayesian network, neural network, and C5.0 algorithms of data mining were utilized to analyze data collected from 240 Vietnamese managers of SMEs. The evaluation model identified the C5.0 algorithm as the best model, with accurate results for the prediction of factors influencing the readiness to adopt BDA among SMEs. The findings revealed management support, data quality, firm size, data security and cost to be the fundamental factors influencing BDA adoption readiness. Moreover, the results identified the service sector as having a higher level of readiness toward the adoption of BDA compared to the manufacturing sector. The findings are imperative for the enhancement of the decision-making process and advancement of comprehension of the determinants of BDA adoption among SMEs by researchers, managers, providers and policymakers.
Subject(s)
Computer Security , Data Analysis , Algorithms , Bayes Theorem , Data MiningABSTRACT
Discrimination of highly valued and non-hepatotoxic Cinnamomum species (C. verum) from hepatotoxic (C. burmannii, C. loureiroi, and C. cassia) is essential for preventing food adulteration and safety problems. In this study, we developed a new method for the discrimination of four Cinnamomum species using physico-functional properties and chemometric techniques. The data were analyzed through principal component analysis (PCA) and multiclass discriminant analysis (MDA). The results showed that the cumulative variability of the first three principal components was 81.70%. The PCA score plot indicated a clear separation of the different Cinnamomum species. The training set was used to build the discriminant MDA model. The testing set was verified by this model. The prediction rate of 100% proved that the model was valid and reliable. Therefore, physico-functional properties coupled with chemometric techniques constitute a practical approach for discrimination of Cinnamomum species to prevent food fraud.
ABSTRACT
BACKGROUND: The popularity of the internet aggravated by its excessive and uncontrolled use has resulted in psychological impairment or addiction. Internet addiction is hypothesized as an impulse-control disorder of internet use having detrimental impacts on daily life functions, family relationships, and emotional stability. The goal of this review is to provide an exhaustive overview of the empirical evidence on internet addiction and draw attention to future research themes. METHODS: We performed a literature search on ScienceDirect and PubMed to review original research articles with empirical evidence published on peer-reviewed international journals from 2010 to 2019. Eight hundred and 26 articles were eligible for analysis. Frequency and descriptive statistics were calculated by Microsoft Excel. RESULTS: A substantial contribution has been coming from researchers from China, Turkey, Korea, Germany, and Taiwan respectively. Despite controversies regarding its definition and diagnostic procedures, internet addiction has become the focal point of a myriad of studies that investigated this particular phenomenon from different exposures. Given observed literature review data regarding research design, data acquisition, and data analysis strategies, we proposed the 3C paradigm which emphasizes the necessity of research incorporating cross-disciplinary investigation conducted on cross-cultural settings with conscientious cross-validation considerations to gain a better comprehension of internet addiction. CONCLUSIONS: The findings of the present literature review will serve both academics and practitioners to develop new solutions for better characterize internet addiction.
