<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Online forums are becoming increasingly important in influencing customers’ buying decision process, hence understanding customers’ likelihood to rely on online forums while making buying decisions is of major concern for marketers. The companies should also be aware of how reliance-likelihood differs with the introversion/extraversion nature of customers. Using factor analysis as data reduction technique and classification and regression tree as machine learning technique, the current study categorizes customers and builds decision rules based on their self-perception related to their inter-intra communication comfort level (introversion/extraversion level). Based on customers’ self-perception of their inter-intra communication comfort level, four groups were identified as: Extroverts, Introverts, Socially Active and Vacillators. Analysis of the data collected from 209 respondents revealed that being socially active is a common trait for both introverts and extroverts in being influenced by online forums while making buying decisions. The current study will be useful for companies in understanding the effect of the level of introversion-extraversion in making customers more likely to be influenced by online forums for making buying decisions and hence will help firms in formulating more effective strategies and better predictive models related to online forums.</p>
</abstract>

Online forums are becoming increasingly important in influencing customers’ buying decision process, hence understanding customers’ likelihood to rely on online forums while making buying decisions is of major concern for marketers. The companies should also be aware of how reliance-likelihood differs with the introversion/extraversion nature of customers. Using factor analysis as data reduction technique and classification and regression tree as machine learning technique, the current study categorizes customers and builds decision rules based on their self-perception related to their inter-intra communication comfort level (introversion/extraversion level). Based on customers’ self-perception of their inter-intra communication comfort level, four groups were identified as: Extroverts, Introverts, Socially Active and Vacillators. Analysis of the data collected from 209 respondents revealed that being socially active is a common trait for both introverts and extroverts in being influenced by online forums while making buying decisions. The current study will be useful for companies in understanding the effect of the level of introversion-extraversion in making customers more likely to be influenced by online forums for making buying decisions and hence will help firms in formulating more effective strategies and better predictive models related to online forums.

Influence of Online Forums on Customers’ Buying Decisions: A Machine Learning Approach

Studies in Business and Economics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Online forums are becoming increasingly important in influencing customers’ buying decision process, hence understanding customers’ likelihood to rely on...

Influence of Online Forums on Customers’ Buying Decisions: A Machine Learning Approach

Publié en ligne: 11 janv. 2024

Pages: 5 - 23

DOI: https://doi.org/10.2478/sbe-2023-0042

Mots clés
Online forums, Buying Decision, Introversion, Extraversion, Machine learning

© 2023 Reeti Agarwal et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Influence of Online Forums on Customers’ Buying Decisions: A Machine Learning Approach

Publié en ligne: 11 janv. 2024

Pages: 5 - 23

DOI: https://doi.org/10.2478/sbe-2023-0042

Mots clésOnline forums, Buying Decision, Introversion, Extraversion, Machine learning

© 2023 Reeti Agarwal et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Online forums, Buying Decision, Introversion, Extraversion, Machine learning