多样性(控制论)
大数据
格式塔心理学
数据科学
知识管理
情感(语言学)
描述性统计
心理学
计算机科学
人工智能
数据挖掘
统计
数学
沟通
神经科学
感知
作者
Maryam Ghasemaghaei,Goran Calic
标识
DOI:10.1016/j.jbusres.2019.07.006
摘要
Abstract Grounded in gestalt insight learning theory and organizational learning theory, we collected data from 280 middle and top-level managers to investigate the impact of each big data characteristic (i.e., data volume, data velocity, data variety, and data veracity) on firm innovation competency (i.e., exploitation competency and exploration competency), mediated through data-driven insight generation (i.e., descriptive insight, predictive insight, and prescriptive insight). Findings show that while data velocity, variety, and veracity enhance data-driven insight generation, data volume does not impact it. Additionally, results of the post hoc analysis indicate that while descriptive and predictive insights improve innovation competency, prescriptive insight does not affect it. These results provide interesting and unique theoretical and practical insights.
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