Big data-assisted social media analytics for business model for business decision making system competitive analysis
2
School of Economics and Management, Chongqing Metropolitan College of Science and Technology, Chongqing 402167, China
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5
Tsinghua-Berkeley Shenzhen Institute (China)
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Publication type: Journal Article
Publication date: 2022-01-01
scimago Q1
wos Q1
SJR: 2.062
CiteScore: 18.6
Impact factor: 6.9
ISSN: 03064573, 18735371
Computer Science Applications
Library and Information Sciences
Information Systems
Management Science and Operations Research
Media Technology
Abstract
• Intelligent, detection, design phases to analyze the social media message. • The sentiment analysis methodology is created to analyze praises and complaints. • Development and implementation of integrative management information system. Business is based on manufacturing, purchasing, selling a product, and earning or making profits. Social media analytics collect and analyze data from various social networks such as Facebook, Instagram, and Twitter. Social media data analysis can help companies identify consumer desires and preferences, improve customer service and market analytics on social networks, and smarter product development and marketing investments. The business decision-making process is a step-by-step process that enables employees to resolve challenges by weighing evidence, evaluating possible solutions, and selecting a route. In this paper, Big Data-assisted Social Media Analytics for Business (BD-SMAB) Model increases awareness and affects decision-makers in marketing strategies. Companies can use big data analytics in many ways to enhance management. It can evaluate its competitors in real-time and change prices, make deals better than its competitors' sales, analyze competitors' unfavorable feedback and see if they can outperform that competitor. The proposed method examines social media analysis impacts on different areas such as real estate, organizations, and beauty trade fairs. This diversity of these companies shows the effects of social media and how positive decisions can be developed. Take better marketing decisions and develop a strategic approach. As a result, the BD-SMAB method enhance customer satisfaction and experience and develop brand awareness.
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102
Total citations:
102
Citations from 2024:
61
(59.81%)
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GOST
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Zhang H. et al. Big data-assisted social media analytics for business model for business decision making system competitive analysis // Information Processing and Management. 2022. Vol. 59. No. 1. p. 102762.
GOST all authors (up to 50)
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Zhang H., Zang Z., ZHU H., Uddin M. I., Amin M. A. Big data-assisted social media analytics for business model for business decision making system competitive analysis // Information Processing and Management. 2022. Vol. 59. No. 1. p. 102762.
Cite this
RIS
Copy
TY - JOUR
DO - 10.1016/j.ipm.2021.102762
UR - https://doi.org/10.1016/j.ipm.2021.102762
TI - Big data-assisted social media analytics for business model for business decision making system competitive analysis
T2 - Information Processing and Management
AU - Zhang, Honglei
AU - Zang, Zhenbo
AU - ZHU, HONGJUN
AU - Uddin, M. Irfan
AU - Amin, M. Asim
PY - 2022
DA - 2022/01/01
PB - Elsevier
SP - 102762
IS - 1
VL - 59
SN - 0306-4573
SN - 1873-5371
ER -
Cite this
BibTex (up to 50 authors)
Copy
@article{2022_Zhang,
author = {Honglei Zhang and Zhenbo Zang and HONGJUN ZHU and M. Irfan Uddin and M. Asim Amin},
title = {Big data-assisted social media analytics for business model for business decision making system competitive analysis},
journal = {Information Processing and Management},
year = {2022},
volume = {59},
publisher = {Elsevier},
month = {jan},
url = {https://doi.org/10.1016/j.ipm.2021.102762},
number = {1},
pages = {102762},
doi = {10.1016/j.ipm.2021.102762}
}
Cite this
MLA
Copy
Zhang, Honglei, et al. “Big data-assisted social media analytics for business model for business decision making system competitive analysis.” Information Processing and Management, vol. 59, no. 1, Jan. 2022, p. 102762. https://doi.org/10.1016/j.ipm.2021.102762.