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A firm and individual characteristic-based prediction model for E2.0 continuance adoption

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A firm and individual characteristic-based prediction model for E2.0 continuance adoption. / Jia, Q.; Xin, Fu; Guo, Dr Yue; Barnes, Stuart J.

5th International Conference on Research and Innovation in Information Systems: Social Transformation through Data Science, ICRIIS 2017. IEEE Computer Society Press, 2017. 8002483.

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

Harvard

Jia, Q, Xin, F, Guo, DY & Barnes, SJ 2017, A firm and individual characteristic-based prediction model for E2.0 continuance adoption. in 5th International Conference on Research and Innovation in Information Systems: Social Transformation through Data Science, ICRIIS 2017., 8002483, IEEE Computer Society Press, 5th International Conference on Research and Innovation in Information Systems, ICRIIS 2017, Langkawi, Kedah, Malaysia, 16/07/2017. https://doi.org/10.1109/ICRIIS.2017.8002483

APA

Jia, Q., Xin, F., Guo, D. Y., & Barnes, S. J. (2017). A firm and individual characteristic-based prediction model for E2.0 continuance adoption. In 5th International Conference on Research and Innovation in Information Systems: Social Transformation through Data Science, ICRIIS 2017 [8002483] IEEE Computer Society Press. https://doi.org/10.1109/ICRIIS.2017.8002483

Vancouver

Jia Q, Xin F, Guo DY, Barnes SJ. A firm and individual characteristic-based prediction model for E2.0 continuance adoption. In 5th International Conference on Research and Innovation in Information Systems: Social Transformation through Data Science, ICRIIS 2017. IEEE Computer Society Press. 2017. 8002483 https://doi.org/10.1109/ICRIIS.2017.8002483

Author

Jia, Q. ; Xin, Fu ; Guo, Dr Yue ; Barnes, Stuart J. / A firm and individual characteristic-based prediction model for E2.0 continuance adoption. 5th International Conference on Research and Innovation in Information Systems: Social Transformation through Data Science, ICRIIS 2017. IEEE Computer Society Press, 2017.

Bibtex Download

@inbook{3197a09da7f64ed2b6c3af64f9f8a0ed,
title = "A firm and individual characteristic-based prediction model for E2.0 continuance adoption",
abstract = "Enterprise-level 2.0 applications (E2.0) built on cloud computing Web 2.0 infrastructure offer promising new business models. However, recent studies show that most E2.0 firms experience a low free-to-paid conversion rate. Based on accumulated archival data and literature on predictive models and data mining, in this paper, we develop a logit model to predict the likelihood of E2.0 user continuance. The proposed model includes firm-specific and individual characteristics and estimates coefficients relating predictor variables to E2.0 continuance decisions. The sample includes information on 575 paid customers (i.e. firms) with 65,407 individual users and 2,286 previous customers with 99,807 individual users from 2011-2016. The resulting model can help business managers of E2.0 service providers to identify effectively reliable customers, optimize their sales efforts, and increase the free-to-paid conversion rate.",
keywords = "E2.0 adoption, IS continuance, Logistic regression, Predictive model",
author = "Q. Jia and Fu Xin and Guo, {Dr Yue} and Barnes, {Stuart J.}",
year = "2017",
month = "8",
day = "3",
doi = "10.1109/ICRIIS.2017.8002483",
language = "English",
booktitle = "5th International Conference on Research and Innovation in Information Systems: Social Transformation through Data Science, ICRIIS 2017",
publisher = "IEEE Computer Society Press",

}

RIS (suitable for import to EndNote) Download

TY - CHAP

T1 - A firm and individual characteristic-based prediction model for E2.0 continuance adoption

AU - Jia, Q.

AU - Xin, Fu

AU - Guo, Dr Yue

AU - Barnes, Stuart J.

PY - 2017/8/3

Y1 - 2017/8/3

N2 - Enterprise-level 2.0 applications (E2.0) built on cloud computing Web 2.0 infrastructure offer promising new business models. However, recent studies show that most E2.0 firms experience a low free-to-paid conversion rate. Based on accumulated archival data and literature on predictive models and data mining, in this paper, we develop a logit model to predict the likelihood of E2.0 user continuance. The proposed model includes firm-specific and individual characteristics and estimates coefficients relating predictor variables to E2.0 continuance decisions. The sample includes information on 575 paid customers (i.e. firms) with 65,407 individual users and 2,286 previous customers with 99,807 individual users from 2011-2016. The resulting model can help business managers of E2.0 service providers to identify effectively reliable customers, optimize their sales efforts, and increase the free-to-paid conversion rate.

AB - Enterprise-level 2.0 applications (E2.0) built on cloud computing Web 2.0 infrastructure offer promising new business models. However, recent studies show that most E2.0 firms experience a low free-to-paid conversion rate. Based on accumulated archival data and literature on predictive models and data mining, in this paper, we develop a logit model to predict the likelihood of E2.0 user continuance. The proposed model includes firm-specific and individual characteristics and estimates coefficients relating predictor variables to E2.0 continuance decisions. The sample includes information on 575 paid customers (i.e. firms) with 65,407 individual users and 2,286 previous customers with 99,807 individual users from 2011-2016. The resulting model can help business managers of E2.0 service providers to identify effectively reliable customers, optimize their sales efforts, and increase the free-to-paid conversion rate.

KW - E2.0 adoption

KW - IS continuance

KW - Logistic regression

KW - Predictive model

UR - http://www.scopus.com/inward/record.url?scp=85029922378&partnerID=8YFLogxK

U2 - 10.1109/ICRIIS.2017.8002483

DO - 10.1109/ICRIIS.2017.8002483

M3 - Other chapter contribution

AN - SCOPUS:85029922378

BT - 5th International Conference on Research and Innovation in Information Systems: Social Transformation through Data Science, ICRIIS 2017

PB - IEEE Computer Society Press

ER -

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