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Stages of User Engagement on Social Commerce Platforms: Analysis with the Navigational Clickstream Data

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Stages of User Engagement on Social Commerce Platforms : Analysis with the Navigational Clickstream Data. / Kumar, Ashish; Salo, Jari; Li, Hongxiu.

julkaisussa: INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE, Vuosikerta 23, Nro 2, 03.04.2019, s. 179-211.

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Harvard

Kumar, A, Salo, J & Li, H 2019, 'Stages of User Engagement on Social Commerce Platforms: Analysis with the Navigational Clickstream Data', INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE, Vuosikerta. 23, Nro 2, Sivut 179-211. https://doi.org/10.1080/10864415.2018.1564550

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Author

Kumar, Ashish ; Salo, Jari ; Li, Hongxiu. / Stages of User Engagement on Social Commerce Platforms : Analysis with the Navigational Clickstream Data. Julkaisussa: INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE. 2019 ; Vuosikerta 23, Nro 2. Sivut 179-211.

Bibtex - Lataa

@article{76cffaad9eea43c78dda389dd256e3e2,
title = "Stages of User Engagement on Social Commerce Platforms: Analysis with the Navigational Clickstream Data",
abstract = "Social commerce platforms have gained prominence in e-commerce, as social media has become an integral part of users’ online activities. Therefore, firms have been either developing or utilizing social commerce platforms to increase user engagement by adding social shopping facility onto their electronic commerce platforms. However, managing user engagement and user interaction becomes complex when e-commerce platforms are transformed into social commerce platforms. In this study, we operationalize four distinct stages of the social commerce platform, namely, social identification, social interaction, social shopping, and transaction based on salience theory. Using clickstream data, we empirically measure user engagement in these four states by modeling users’ incidence and time spent. Drawing from the PageRank algorithm, we capture the importance of ranking and distance on user engagement. The model also accounts for the effects of situational variables such as weekend; holiday; time of day; and user characteristics, such as gender and social media setting. Our results suggest that ranking and distance have significant effects on users’ incidence as well as time spent on social commerce platforms. The insights from this study can be helpful in designing the social commerce platform effectively using only the customers’ path navigational clickstream data from the parent social commerce platform.",
keywords = "Clickstream data, Dijkstra’s shortest path algorithm, hierarchical Bayesian method, multivariate type-2 Tobit, online communities, online platforms, online shopping, PageRank algorithm, social commerce platforms",
author = "Ashish Kumar and Jari Salo and Hongxiu Li",
year = "2019",
month = "4",
day = "3",
doi = "10.1080/10864415.2018.1564550",
language = "English",
volume = "23",
pages = "179--211",
journal = "INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE",
issn = "1086-4415",
publisher = "Taylor & Francis",
number = "2",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Stages of User Engagement on Social Commerce Platforms

T2 - Analysis with the Navigational Clickstream Data

AU - Kumar, Ashish

AU - Salo, Jari

AU - Li, Hongxiu

PY - 2019/4/3

Y1 - 2019/4/3

N2 - Social commerce platforms have gained prominence in e-commerce, as social media has become an integral part of users’ online activities. Therefore, firms have been either developing or utilizing social commerce platforms to increase user engagement by adding social shopping facility onto their electronic commerce platforms. However, managing user engagement and user interaction becomes complex when e-commerce platforms are transformed into social commerce platforms. In this study, we operationalize four distinct stages of the social commerce platform, namely, social identification, social interaction, social shopping, and transaction based on salience theory. Using clickstream data, we empirically measure user engagement in these four states by modeling users’ incidence and time spent. Drawing from the PageRank algorithm, we capture the importance of ranking and distance on user engagement. The model also accounts for the effects of situational variables such as weekend; holiday; time of day; and user characteristics, such as gender and social media setting. Our results suggest that ranking and distance have significant effects on users’ incidence as well as time spent on social commerce platforms. The insights from this study can be helpful in designing the social commerce platform effectively using only the customers’ path navigational clickstream data from the parent social commerce platform.

AB - Social commerce platforms have gained prominence in e-commerce, as social media has become an integral part of users’ online activities. Therefore, firms have been either developing or utilizing social commerce platforms to increase user engagement by adding social shopping facility onto their electronic commerce platforms. However, managing user engagement and user interaction becomes complex when e-commerce platforms are transformed into social commerce platforms. In this study, we operationalize four distinct stages of the social commerce platform, namely, social identification, social interaction, social shopping, and transaction based on salience theory. Using clickstream data, we empirically measure user engagement in these four states by modeling users’ incidence and time spent. Drawing from the PageRank algorithm, we capture the importance of ranking and distance on user engagement. The model also accounts for the effects of situational variables such as weekend; holiday; time of day; and user characteristics, such as gender and social media setting. Our results suggest that ranking and distance have significant effects on users’ incidence as well as time spent on social commerce platforms. The insights from this study can be helpful in designing the social commerce platform effectively using only the customers’ path navigational clickstream data from the parent social commerce platform.

KW - Clickstream data

KW - Dijkstra’s shortest path algorithm

KW - hierarchical Bayesian method

KW - multivariate type-2 Tobit

KW - online communities

KW - online platforms

KW - online shopping

KW - PageRank algorithm

KW - social commerce platforms

U2 - 10.1080/10864415.2018.1564550

DO - 10.1080/10864415.2018.1564550

M3 - Article

VL - 23

SP - 179

EP - 211

JO - INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE

JF - INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE

SN - 1086-4415

IS - 2

ER -