JOURNAL ARTICLE
The effect of the information channel on the investment decision: The bull and bear market and investment experience as a moderator.
Published In: Australian Journal of Management (Sage Publications Ltd.), 2025, v. 50, n. 1. P. 246 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Koç Ustali, Nesrin; Kaya, Ahmet; Gürler, Hasan Emin; Buyukdag, Naci 3 of 3
Abstract
This article investigates how different information channels—Telegram, Twitter, friend/peer recommendations, brokerage houses, and investors' own research—affect individual investors' attitudes and intentions toward stock market investments, considering the moderating roles of investment experience and market conditions (bull and bear markets). Through three experimental studies, the research finds that investors’ own research consistently has the strongest positive influence on investment attitudes and intentions, especially among experienced investors, while inexperienced or potential investors rely more on private messaging platforms like Telegram. Additionally, investors generally prefer to invest during bull markets, and the impact of information channels varies with market trends and prior investment experience. The study contributes to behavioral finance literature by highlighting the nuanced effects of information sources and experience on investment decision-making and suggests practical implications for investor education and regulatory oversight of information channels.
Additional Information
- Source:Australian Journal of Management (Sage Publications Ltd.). 2025/02, Vol. 50, Issue 1, p246
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2025
- ISSN:0312-8962
- DOI:10.1177/03128962231184659
- Accession Number:182791704
- Copyright Statement:Copyright of Australian Journal of Management (Sage Publications Ltd.) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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