Research on Big Data Information Processing Model of Management Communication Under the Background of Big Data

Hongtao Mao

2022

Abstract

Management under the background of big data is faced with the application of big data technology and the change of thinking mode, and how to better integrate the two is the purpose of the research. Through the analysis of relevant theoretical models, the qualitative method, inductive method, deductive method and comparative analysis method are used to analyze the significance of management communication effectiveness in the context of big data, and focus on providing the effectiveness strategy of management communication in the context of big data. In the context of big data, the effectiveness of management communication is not based on a simple upgrade of technology, but more based on the upgrade of management thinking in the context of big data, with the ability of big data information processing.

Paper Citation

in Harvard Style

Mao H. (2022). Research on Big Data Information Processing Model of Management Communication Under the Background of Big Data. In Proceedings of the 2nd International Conference on Public Management and Big Data Analysis - Volume 1: PMBDA; ISBN 978-989-758-658-3, SciTePress, pages 299-307. DOI: 10.5220/0012074100003624

in Bibtex Style

@conference{pmbda22,
author={Hongtao Mao},
title={Research on Big Data Information Processing Model of Management Communication Under the Background of Big Data},
booktitle={Proceedings of the 2nd International Conference on Public Management and Big Data Analysis - Volume 1: PMBDA},
year={2022},
pages={299-307},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012074100003624},
isbn={978-989-758-658-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Public Management and Big Data Analysis - Volume 1: PMBDA
TI - Research on Big Data Information Processing Model of Management Communication Under the Background of Big Data
SN - 978-989-758-658-3
AU - Mao H.
PY - 2022
SP - 299
EP - 307
DO - 10.5220/0012074100003624
PB - SciTePress