Effects of Analytics Large Data Set on Decision-Making and Organizational Performance: A Study on Chinese Manufacture Sector
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F25%3A50022618" target="_blank" >RIV/62690094:18450/25:50022618 - isvavai.cz</a>
Result on the web
<a href="https://ojs.istp-press.com/jait/article/view/788" target="_blank" >https://ojs.istp-press.com/jait/article/view/788</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.37965/jait.2025.0788" target="_blank" >10.37965/jait.2025.0788</a>
Alternative languages
Result language
angličtina
Original language name
Effects of Analytics Large Data Set on Decision-Making and Organizational Performance: A Study on Chinese Manufacture Sector
Original language description
In today’s data-driven environment, Big Data Analytics (BDA) plays a vital role in enhancing decision-making quality and organizational performance. However, limited empirical research exists on how the five characteristics of big data (5Vs: Volume, Velocity, Variety, Veracity, and Value) influence decision-making effectiveness in China’s industrial sector. Addressing this gap, the present study builds on Simon’s decision-making theory and the information processing perspective to develop and test a research model linking BDA to decision-making and performance outcomes. Using a self-designed structured survey, data were collected from 312 managers across medium and large-sized manufacturing firms in China. Structural equation modeling (SEM) was employed to examine the relationships among constructs. The results show that all five BDA characteristics significantly enhance the quality and efficiency of decision-making, which in turn positively impacts organizational performance. Furthermore, multi-group analysis revealed no significant difference in the BDA–decision-making relationship between medium and large enterprises. This study contributes theoretically by integrating BDA with decisionmaking theory and practically by offering managers evidence-based insights on how to leverage big data for more informed and effective decision-making across industrial operations. © 2025 Elsevier B.V., All rights reserved.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Artificial Intelligence and Technology
ISSN
2766-8649
e-ISSN
2766-8649
Volume of the periodical
5
Issue of the periodical within the volume
September
Country of publishing house
US - UNITED STATES
Number of pages
11
Pages from-to
365-375
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-105018182511