Comparing Mobile Phone and Computer Use in the Influence of False Headlines on Investor Judgment: Revenue Increase vs. Profit Decrease
Keywords:
Investor judgment, special headlines, mobile phone, computer, behavioral finance, financial technologyAbstract
The objective of this study is to examine how Special financial headlines—revenue increase and profit decrease—differentially affect investor judgment when information is accessed through a mobile phone versus a computer. This study employed a descriptive-survey design with a quasi-experimental structure. Participants consisted of graduate and postgraduate students in accounting and financial management who were randomly assigned to conditions representing two device types (mobile phone vs. computer) and two special headline framings ( revenue increase vs. profit decrease). After reviewing the financial statements of a hypothetical company, participants completed a researcher-designed questionnaire using a five-point Likert scale. Data were analyzed using descriptive statistics and two-way ANOVA to evaluate the main and interaction effects of device type and headline condition on investor judgment. Results showed that in the revenue-increase model, device type significantly affected judgment scores (F = 2.76, p < .001), and the special headline produced a large and significant effect (F = 51.3, p < .001). The interaction term was also significant (F = 4.35, p = .039). In the profit-decrease model, device type (F = 14.5, p < .001), headline framing (F = 10.9, p = .001), and their interaction (F = 4.3, p = .040) were all statistically significant. Overall, mobile phone users showed stronger positive reactions to revenue-increase headlines and stronger negative reactions to profit-decrease headlines compared with computer users. The findings demonstrate that the digital device used to access financial information significantly shapes the impact of misleading headlines on investor judgment. Mobile phones amplify emotional responses and increase susceptibility to headline-based distortions, emphasizing the necessity of careful financial information design in smart digital platforms.
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Afriany, A. N., Putranti, L., Herdiany, H. D., & Tisya, V. A. (2022). Covid 19: Investment Decisions of Individual Investor Based on Behavioral Finance Factors. 89-101. https://doi.org/10.2991/978-2-494069-39-8_9
Bahaghighat, E., & Esmailzadeh, A. (2024). Presenting a Causal Model to Investigate Investors' Emotional Behavior Using Fuzzy DEMATEL Method. Judgment and Decision Making in Accounting.
Cen, X. (2018). Going Mobile, Investor Behavior, and Financial Fragility. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3424405
Daragmeh, A., Lentner, C., & Sági, J. (2021). FinTech Payments in the Era of COVID-19: Factors Influencing Behavioral Intentions of “Generation X” in Hungary to Use Mobile Payment. Journal of Behavioral and Experimental Finance, 32, 100574. https://doi.org/10.1016/j.jbef.2021.100574
Deck, C., Lee, J., & Reyes, J. A. (2013). Investing Versus Gambling: Experimental Evidence of Multi-Domain Risk Attitudes. Applied Economics Letters, 21(1), 19-23. https://doi.org/10.1080/13504851.2013.835470
Eyshi Ravandi, M., Moeinaddin, M., Taftiyan, A., & Rostami Bashmani, M. (2024). Investigating the Impact of Investor Sentiment and Liquidity on Stock Returns of the Iranian Stock Exchange. Dynamic Management and Business Analysis, 3(1), 40-52. https://doi.org/10.22034/dmbaj.2024.2038046.1068
Fan, L. (2021). Mobile Investment Technology Adoption Among Investors. The International Journal of Bank Marketing, 40(1), 50-67. https://doi.org/10.1108/ijbm-11-2020-0551
Goh, C. (2024). Analysts' Earnings per Share Forecasts: The Effects of Forecast Uncertainty and Forecast Precision on Investor Judgements. A Journal of Accounting, Finance and Business Studies, 60(1), 172-204. https://doi.org/10.1111/abac.12302
Gupta, S., & Dey, D. K. (2023). Risk Perception and Adoption of Digital Innovation in Mobile Stock Trading. Journal of Consumer Behaviour, 23(2), 639-654. https://doi.org/10.1002/cb.2225
Han, W. (2024). Application of Data Mining Technology in Precise Investment Analysis of Distribution Networks. https://doi.org/10.4108/eai.8-12-2023.2344735
Hasanzadeh, I., Sheikh, M. J., Arabzadeh, M., & Farzinfar, A. A. (2023). The Role of Economic Policy Uncertainty in Relation to Financial Market Instability and Stock Liquidity in Tehran Stock Exchange Companies. Dynamic Management and Business Analysis, 2(3), 163-178. https://doi.org/10.22034/dmbaj.2024.2031971.2315
Heidari, Z., & Mashayekh, S. (2025). The Consequences of Disclosing Key Audit Matters on Investor Judgment and Decision-Making. Empirical Accounting Research, 15(1), 53-84.
Hoffmann, A. O. I., Post, T., & Pennings, J. M. E. (2015). How Investor Perceptions Drive Actual Trading and Risk-Taking Behavior. Journal of Behavioral Finance, 16(1), 94-103. https://doi.org/10.1080/15427560.2015.1000332
Kalda, A., Loos, B., Previtero, A., & Hackethal, A. (2021). Smart(Phone) Investing? A Within Investor-Time Analysis of New Technologies and Trading Behavior. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3772602
Lamichhane, P. (2022). Financial Literacy and Individual Investors’ Stock Market Participation in Nepal. Tribhuvan University Journal, 37(02), 89-103. https://doi.org/10.3126/tuj.v37i02.51653
Le, M. T. (2021). Examining Factors That Boost Intention and Loyalty to Use Fintech Post-Covid-19 Lockdown as a New Normal Behavior. Heliyon, 7(8), e07821. https://doi.org/10.1016/j.heliyon.2021.e07821
Nair, P. S., Shiva, A., Yadav, N., & Tandon, P. (2022). Determinants of Mobile Apps Adoption by Retail Investors For online Trading in Emerging Financial Markets. Benchmarking an International Journal, 30(5), 1623-1648. https://doi.org/10.1108/bij-01-2022-0019
Nurhidayah, N. (2022). Social Media, Financial Risk Tolerance, and Indonesian Millennial Generation Investor Behavior. International Journal of Finance & Banking Studies (2147-4486), 11(4), 17-23. https://doi.org/10.20525/ijfbs.v11i4.1840
Shrestha, A. K., Vassileva, J., Joshi, S., & Just, J. (2021). Augmenting the Technology Acceptance Model With Trust Model for the Initial Adoption of a Blockchain-Based System. Peerj Computer Science, 7, e502. https://doi.org/10.7717/peerj-cs.502
Vinay, H. V., Rao, D., Kumar, C., Rao, K., & Mahadevaswamy, R. M. (2024). Impact of Demographic Factors on Emotional Behavioral Biases of the Individual Investors: Empirical Study on Indian Stock Market. Migration Letters, 21(S6), 1648-1662. https://doi.org/10.59670/ml.v21is6.8381
Yang, M., Mamun, A. A., Mohiuddin, M., Nawi, N. C., & Zainol, N. R. (2021). Cashless Transactions: A Study on Intention and Adoption of E-Wallets. Sustainability, 13(2), 831. https://doi.org/10.3390/su13020831
Yazdan Panah, M., & Ahmadi Mousavi, S. M. (2023). Legal Supervision Styles for Capital Market Health. Dynamic Management and Business Analysis, 2(3), 196-207. https://doi.org/10.22034/dmbaj.2024.2037208.1063
Zhang, H., Chen, Y.-C., & Shi, J. (2018). The Influence of Cranial Nerves on the Financial Risk Selection and Trading Behavior of Investors. NeuroQuantology, 16(6). https://doi.org/10.14704/nq.2018.16.6.1575
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