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

Q. Jia; Fu Xin; Dr Yue Guo; Stuart J. Barnes

doi:10.1109/ICRIIS.2017.8002483

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

Q. Jia, Fu Xin, Dr Yue Guo, Stuart J. Barnes

King's Business School

Hohai University

Research output: Chapter in Book/Report/Conference proceeding › Other chapter contribution › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	5th International Conference on Research and Innovation in Information Systems: Social Transformation through Data Science, ICRIIS 2017
Publisher	IEEE Computer Society Press
ISBN (Electronic)	9781509030354
DOIs	https://doi.org/10.1109/ICRIIS.2017.8002483
Publication status	Published - 3 Aug 2017
Event	5th International Conference on Research and Innovation in Information Systems, ICRIIS 2017 - Langkawi, Kedah, Malaysia Duration: 16 Jul 2017 → 17 Jul 2017

Conference

Conference	5th International Conference on Research and Innovation in Information Systems, ICRIIS 2017
Country/Territory	Malaysia
City	Langkawi, Kedah
Period	16/07/2017 → 17/07/2017

Keywords

E2.0 adoption
IS continuance
Logistic regression
Predictive model

Access to Document

10.1109/ICRIIS.2017.8002483

Cite this

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 Article 8002483 IEEE Computer Society Press. https://doi.org/10.1109/ICRIIS.2017.8002483

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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.",

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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

A firm and individual characteristic-based prediction model for E2.0 continuance adoption. / Jia, Q.; Xin, Fu; Guo, Dr Yue et al.
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 proceeding › Other chapter contribution › peer-review

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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.

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KW - Logistic regression

KW - Predictive model

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

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