Effects of Analytics Large Data Set on Decision-Making and Organizational Performance: A Study on Chinese Manufacture Sector

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Effects of Analytics Large Data Set on Decision-Making and Organizational Performance: A Study on Chinese Manufacture Sector

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Effects of Analytics Large Data Set on Decision-Making and Organizational Performance: A Study on Chinese Manufacture Sector

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Artificial Intelligence and Technology

ISSN

2766-8649

e-ISSN

2766-8649

Svazek periodika

5

Číslo periodika v rámci svazku

September

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

365-375

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105018182511