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Identifying Messenger Platform Preferences using Multiple Linear Regression and Conjoint Analyses

Journal of Information Systems Engineering and Business Intelligence · Vol. 8 No. 2 (2022) · pp. 119–129

DOI
10.20473/jisebi.8.2.119-129
ISSN
2443-2555, 2598-6333
Published
2022-10-29
Publisher
Universitas Airlangga
Language
eng
License
creativecommons.org/licenses/by/4.0

Abstract

Background: The rapid development of telecommunication technology has prompted the creation of various messenger applications. The competition among social messengers to gain market share is becoming tighter. Objective: This study aims to capture user preferences for messenger platforms and inform software development companies to improve their products based on user needs. Methods: This research uses quantitative methods, i.e., categorical analysis and multiple linear regression analysis, to extend the results from qualitative methods that identify the preferences in past studies. The data were obtained through a questionnaire. Results: The results show that customers are influenced by accessibility, flexibility, effectiveness and chat history. Meanwhile, users are influenced by responsiveness, user-friendly interface, performance, personal needs, privacy and security, and customer services. Conclusion: The research can identify the indicators to guide the creation of an ideal messenger platform based on customer and user preferences. Keywords: Conjoint, Messenger Platform, Multiple Linear Regression, Preference

Collaboration network of the authors

9 authors · 20 co-authorship links · 8 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Institut Teknologi Dan Bisnis STIKOM Bali
  • Rajamangala University of Technology
  • National Taiwan University of Science and Technology
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Dechrit ManeethamRajamangala University of Technology2
Padma Nyoman CrisnapatiRajamangala University of Technology1
I Komang Agus Ady AryantoRajamangala University of Technology1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://e-journal.unair.ac.id/JISEBI/oai, first harvested 29 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Identifying Messenger Platform Preferences using Multiple Linear Regression and Conjoint Analyses</dc:title>
	<dc:creator>Triandini, Evi</dc:creator>
	<dc:creator>Wijaya, I Gusti Ngurah Satria</dc:creator>
	<dc:creator>Wulandari, Riza</dc:creator>
	<dc:creator>Pratami , Ni Wayan Cahya Ayu </dc:creator>
	<dc:creator>Suniantara, I Ketut Putu  </dc:creator>
	<dc:creator>Ahmadi, Candra</dc:creator>
	<dc:description xml:lang="en-US">Background: The rapid development of telecommunication technology has prompted the creation of various messenger applications. The competition among social messengers to gain market share is becoming tighter.
Objective: This study aims to capture user preferences for messenger platforms and inform software development companies to improve their products based on user needs.
Methods: This research uses quantitative methods, i.e., categorical analysis and multiple linear regression analysis, to extend the results from qualitative methods that identify the preferences in past studies. The data were obtained through a questionnaire.
Results: The results show that customers are influenced by accessibility, flexibility, effectiveness and chat history. Meanwhile, users are influenced by responsiveness, user-friendly interface, performance, personal needs, privacy and security, and customer services.
Conclusion: The research can identify the indicators to guide the creation of an ideal messenger platform based on customer and user preferences.
 
Keywords: Conjoint, Messenger Platform, Multiple Linear Regression, Preference</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2022-10-29</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://e-journal.unair.ac.id/JISEBI/article/view/36966</dc:identifier>
	<dc:identifier>10.20473/jisebi.8.2.119-129</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Information Systems Engineering and Business Intelligence; Vol. 8 No. 2 (2022): October; 119-129</dc:source>
	<dc:source>2443-2555</dc:source>
	<dc:source>2598-6333</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/JISEBI/article/view/36966/22811</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 The Authors. Published by Universitas Airlangga.</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>