Exponential Random Graph Models (ERGMs) to analyze the online shop networking in Instagram

Bekti, Rokhana Dwi and Pratiwi, noviana and Sutanta, Edhy and Nurnawati, Erna Kumalasari (2020) Exponential Random Graph Models (ERGMs) to analyze the online shop networking in Instagram. In: The 5th International Conference on Technology and Vocational Teachers (ICTVT 2019), 14-15 Desember 2020, Yogyakarta.

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Abstract

Social media is one of the important media for entrepreneurs in marketing their products, one of the popular is Instagram. Account activity on Instagram greatly influences marketing and sales levels, such as network, number of follower, following or posting. This study applies the Exponential Random Graph Models (ERGMs) to analyze how the network structure, follower, following, and total posts were affect to the online shop network accounts on Instagram. The data analyzed were 28 samples Instagram accounts which domiciled in DIY and Central Java Province. The predictor variables are non-directed network structure (edges, 2-star) and individual activeness (number of followers, number of following, and total post). The network structure graph constructed a directed network with 41 nodes and 104 edges. Based on ERGMs and significance test with α=5%, the network structure and characteristic of account have a significant influence on the networks between the online shop. The parameter estimation results can be interpreted as follows, an online shop does not take the initiative to make friends or network with another account, because focused on making network with the consumers. If there are more followers, then there is a high probability to get a connection between accounts. This result can be used as a marketing strategy through social media.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Fakultas Teknologi Industri > Informatika (S1)
Depositing User:	Erna Kumalasari Nurnawati
Date Deposited:	18 Apr 2023 02:46
Last Modified:	18 Apr 2023 02:46
URI:	http://eprints.akprind.ac.id/id/eprint/1842

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