Factors influencing big data decision-making quality

Janssen, Marijn; Voort, Haiko van der; Wahyudi, Agung

doi:10.1016/j.jbusres.2016.08.007

Cited by 543 publications

(464 citation statements)

References 26 publications

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“…The researchers found a significant correlation between stock returns and individual's reactions. In fact, valuable data in the domain of stock market should include several features like time, location, targeted audience, brand, and kind of service, but the most important for the decision makers who are looking to invest in the stock market are time, brand and location (Janssen, van der Voort, & Wahyudi, 2017;Alkubaisi et al, 2017).…”

Section: Feature Selectionmentioning

confidence: 99%

Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers

Alkubaisi¹,

Kamaruddin²,

Husni³

2018

CIS

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Sentiment analysis has become one of the most popular process to predict stock market behaviour based on consumer reactions. Concurrently, the availability of data from Twitter has also attracted researchers towards this research area. Most of the models related to sentiment analysis are still suffering from inaccuracies. The low accuracy in classification has a direct effect on the reliability of stock market indicators. The study primarily focuses on the analysis of the Twitter dataset. Moreover, an improved model is proposed in this study; it is designed to enhance the classification accuracy. The first phase of this model is data collection, and the second involves the filtration and transformation, which are conducted to get only relevant data. The most crucial phase is labelling, in which polarity of data is determined and negative, positive or neutral values are assigned to people opinion. The fourth phase is the classification phase in which suitable patterns of the stock market are identified by hybridizing Naïve Bayes Classifiers (NBCs), and the final phase is the performance and evaluation. This study proposes Hybrid Naïve Bayes Classifiers (HNBCs) as a machine learning method for stock market classification. The outcome is instrumental for investors, companies, and researchers whereby it will enable them to formulate their plans according to the sentiments of people. The proposed method has produced a significant result; it has achieved accuracy equals 90.38%.

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Section: Feature Selectionmentioning

confidence: 99%

Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers

Alkubaisi¹,

Kamaruddin²,

Husni³

2018

CIS

View full text Add to dashboard Cite

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“…[13]. Furthermore, in the process of using Big Data to make decision, the quality of data collection, analysis, and transmission would affect the decision quality [14]. Meanwhile, Big Data analysis ability had also effects on supply chain performance and organization benefits [15][16][17].…”

Section: Literature Reviewmentioning

confidence: 99%

Pricing Strategies of a Three-Stage Supply Chain: A New Research in the Big Data Era

Liu¹

2017

Discrete Dynamics in Nature and Society

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In the Big Data era, Data Company as the Big Data information (BDI) supplier should be included in a supply chain. In the new situation, to research the pricing strategies of supply chain, a three-stage supply chain with one manufacturer, one retailer, and one Data Company was chosen. Meanwhile, considering the manufacturer contained the internal and external BDI, four benefit models about BDI investment were proposed and analyzed in both decentralized and centralized supply chain using Stackelberg game. Meanwhile, the optimal retail price and benefits in the four models were compared. Findings are as follows. (1) The industry cost improvement coefficient, the internal BDI investment cost of the manufacturer, and the added cost of the Data Company on using Big Data technology have different relationships with the optimal prices of supply chain members in different models. (2) In the retailer-dominated supply chain model, the optimal benefits of the retailer and the manufacturer are the same, and the optimal benefits of the Data Company are biggest in all the members.

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“…Policy-making quality is influenced by the data and the process to make sense of the data [4]. This process should be analyzed to better understand its consequences on the decisions and policies that are based on this data.…”

Section: Further Research Directionsmentioning

confidence: 99%

“…The development of infrastructures have become a key component of many governments to facilitate reuse and development of new applications. New applications are often development in an agile manner [3] and organizations need to be adaptive [4].…”

Section: Introductionmentioning

confidence: 99%

Introduction to Open Data, Information Processing, and Datification in Government Minitrack

Janssen¹,

Charalabidis

Krcmar

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Factors influencing big data decision-making quality

Cited by 543 publications

References 26 publications

Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers

Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers

Pricing Strategies of a Three-Stage Supply Chain: A New Research in the Big Data Era

Introduction to Open Data, Information Processing, and Datification in Government Minitrack

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