Social media influencers: a systematic review using PRISMA

被引:0
|
作者
Dhingra, Vippa [1 ]
Keswani, Sarika [2 ]
Sama, Ramzan [3 ]
Qureshi, Mohamed Rafik Noor Mohamed [4 ]
机构
[1] SVKMs Narsee Monjee Inst Management Studies NMIMS, Sch Commerce, Indore, Madhya Pradesh, India
[2] Symbiosis Int Deemed Univ SIU Mouza Wathoda, Symbiosis Ctr Management Studies SCMS, Nagpur, Maharashtra, India
[3] Jaipuria Inst Management, Jaipur, India
[4] King Khalid Univ, Coll Engn, Ind Engn Dept, Abha, Saudi Arabia
来源
COGENT BUSINESS & MANAGEMENT | 2024年 / 11卷 / 01期
关键词
Artificial intelligence; deep learning; social media influencers; TWITTER; BRANDS; FACEBOOK; NUMBER; IMPACT;
D O I
10.1080/23311975.2024.2368100
中图分类号
F [经济];
学科分类号
02 ;
摘要
Social Media Influencers (SMI) is one of the favorite domains for many researchers because of its powerful ability to connect stakeholders. The importance of this topic has dynamically evolved that attracted the attention of researchers, academics, manufacturers, consumers, and practitioners to exploit social media. The present research attempts to cover the dynamic changes that the SMI has undergone in the period 2011-2023. It further critically analyzes the most relevant 61 pieces of research published in different journals in the SMI domain. The identified researcher were critically analyzed on various basis like year of study, research design adopted, data analysis technique used, industry sector under study, and country. This enabled the exploration of the SMI, its growth, tapping the knowledge gaps, and identification of future research opportunities in the SMI. The most important finding suggests that the study field is dominated by qualitative and quantitative studies for solution-based approaches for society and consumers. The analysis also shows that there is sufficient room to broaden the research domain. Several prospective options should be looked at for instance quantitative modeling, the application of advanced techniques, and the development of efficient algorithms using machine learning, deep learning, and artificial intelligence.
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页数:21
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