Product Diffusion Pattern Analysis Model Based on User’s Review of E-Commerce Application

Aggrawal, Niyati; Arora, Anuja; Jain, Ankit Kumar; Rathor, Dharmesh

doi:10.1007/978-3-319-65139-2_10

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…al., 2017;2018;Shiau et al 2017;2018) have brought number of decision making challenges for digital marketing industries. Specifically, Social media platforms have become essentially a medium not only for communication among individual but also for several aspects of business sectors which includes decision making process (Choi, 2017), knowledge-based decision support systems (Chen et al, 2012;Ibrahim et al, 2016), brand promotions (Kaplan & Haenlein, 2010;Lipsman et al, 2012), brand marketing (Aggrawal et al, 2017a;Kapoor et al, 2018), brand and product co-creation (Kamboj et al 2018;Rathore et al, 2016), product diffusion (Aggrawal et al, 2017b) etc. In the current scenario where there is a constant race for content promotion and propagation, organizations are leveraging the power of social media for reaching out to the masses (Hanna et al, 2011;Kietzmann et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Measuring social media influencer index- insights from facebook, Twitter and Instagram

Arora

Bansal²,

Kandpal³

et al. 2019

Journal of Retailing and Consumer Services

Self Cite

408

198

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The growth of social media has completely revamped the way people interact, communicate and engage. These platforms play a key role in facilitating greater outreach and influence. This study proposes a mechanism for measuring the influencer index across popular social media platforms including Facebook, Twitter, and Instagram. A set of features that determine the impact on the consumers are modelled using a regression approach. The underlying machine learning algorithms including Ordinary Least Squares (OLS), K-NN Regression (KNN), Support Vector Regression (SVR), and Lasso Regression models are adapted to compute a cumulative score in terms of influencer index. Findings indicate that engagement, outreach, sentiment, and growth play a key role in determining the influencers. Further, the ensemble of the four models resulted in the highest accuracy of 93.7% followed by the KNN regression with 93.6%. The study has implications across various domains of e-commerce, viral marketing, social media marketing and brand management wherein identification of key information propagators is essential. These influencer indices may further be utilized by e-commerce portals and brands for the purpose of social media promotion and engagement for larger outreach.

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Section: Introductionmentioning

confidence: 99%

Measuring social media influencer index- insights from facebook, Twitter and Instagram

Arora

Bansal²,

Kandpal³

et al. 2019

Journal of Retailing and Consumer Services

Self Cite

408

198

View full text Add to dashboard Cite

show abstract

“…Failure has been studied extensively in the field of projects (Lu, Liu, & Ye, 2010;Pinto & Mantel, 1990;Yeo, 2002), however, limited attention has been given to failure of digital services such as failure in online booking of tickets, playing online games (Huang, Cheng, Huang, & Teng, 2018), online banking services (Alalwan, Dwivedi, Rana, & Williams, 2016;Alalwan, Dwivedi, Rana, & Algharabat, 2018;Sharma & Sharma, 2019), online marketing (Aggrawal, Ahluwalia, Khurana, & Arora, 2017;Aggrawal, Arora, Jain, & Rathor, 2017;Mittal, Kaul, Gupta, & Arora, 2017) paying of taxes and utility bills online and mismatch of citizen data sanctity in and across government systems (Dwivedi, Kapoor, Williams, & Williams, 2013. According to Buchanan and McMenemy (2012), digital services are "services or resources accessed and/or provided via digital transaction".…”

Section: Introductionmentioning

confidence: 99%

Understanding the impact of digital service failure on users: Integrating Tan’s failure and DeLone and McLean’s success model

Mustafa

Kar

Janssen

2020

International Journal of Information Management

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Although information systems (IS) success has been given much attention in IS literature, failure has received lesser attention. This study empirically validates a model of digital service failure for consumers by integrating three dimensions from Tan's failure model and one dimension from DeLone and Mclean's Information Success model. The factors have been mapped to Expectation Disconfirmation Theory (EDT). Experiential survey approach has been used to collect primary data from information systems users who have experienced digital service failure. Structural equation modeling (SEM) has been used for model testing and validation to measure the impact of information, system, functional and service failure of digital service on end users. The findings suggests that information, functional, system and service failure has an impact on the consumers through the outcome, process, cost and user satisfaction of digital services.

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Lifecycle forecast for consumer technology products with limited sales data

Yin

Manrique

et al. 2021

International Journal of Production Economics

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Product Diffusion Pattern Analysis Model Based on User’s Review of E-Commerce Application

Cited by 3 publications

References 19 publications

Measuring social media influencer index- insights from facebook, Twitter and Instagram

Measuring social media influencer index- insights from facebook, Twitter and Instagram

Understanding the impact of digital service failure on users: Integrating Tan’s failure and DeLone and McLean’s success model

Lifecycle forecast for consumer technology products with limited sales data

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