Explaining the intention to use social trading platforms: an empirical investigation

Reith, Riccardo; Fischer, Maximilian; Lis, Bettina

doi:10.1007/s11573-019-00961-2

Cited by 15 publications

(11 citation statements)

References 92 publications

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“…Overall, participants were ultimately less satisfied with their performance resonating with the previous findings that more experienced investors derive more utility from performance related to their own success of being ahead of others (see Kirchler, Lindner & Weitzel;Reith, Fischer & Lis, 2020;Schoenberg & Haruvy, 2012;Vostroknutov, Tobler & Rustichini, 2012). Two conditions did not differ in terms of final earnings, and, in contrast to tournament experiments, one's compensation was independent of that of other participants.…”

Section: Discussionsupporting

confidence: 69%

“…Also, the activity of the reward center of the brain is stronger in response to the performance generated by one's skills and effort than when the performance is a result of luck (Vostroknutov, Tobler & Rustichini, 2012), while previous experience with asset trading is linked to the expectation to obtain high performance on social trading platforms (Reith, Fischer & Lis, 2020). Therefore, financially literate participants attributing their performance to their trading rather than to luck would perceive their performance less rewarding when facing upward social comparison.…”

Section: Psychological Drivers Of Goal-directed Behaviormentioning

confidence: 99%

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The Influence of Upward Social Comparison on Retail Trading Behavior

Andraszewicz¹,

Kaszás²,

Zeisberger³

et al. 2022

Preprint

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The environment in which people make investments is changing substantially. Digitization made financial investing easily accessible to anyone with internet access and some disposable capital. Online investing is often facilitated by social media, where the information of peer top performers is often widely accessible and distributed. We investigate the impact of upward social comparison on risk taking, on trading activity and on investor satisfaction using a tailored experiment with 807 experienced retail investors. We find that investors presented with an upward social comparison take more risk and trade more actively, and they report significantly lower satisfaction with their own performance. These effects are triggered by a cognitive response to social influence, moderated by affect. Our findings demonstrate the pitfalls of modern and growing digital investment platforms with peer information and social trading. The widespread implications of this study also provide guidelines for improving retail investor satisfaction and protection.

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Section: Discussionsupporting

confidence: 69%

Section: Psychological Drivers Of Goal-directed Behaviormentioning

confidence: 99%

The Influence of Upward Social Comparison on Retail Trading Behavior

Andraszewicz¹,

Kaszás²,

Zeisberger³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Words are classified based on similarity, and the greater the similarity, the more likely they are to be grouped together. This step is the process of selecting and implementing the clustering algorithm and the input data model [13].…”

Section: Research Methods and Materialsmentioning

confidence: 99%

Function Extraction Based on CFPS and Digital Financial Index: Data Mining Techniques for Prognosis of Operational Risks of Financial Institutions

2022

Journal of Sensors

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Financial deregulation, financial globalization, and the increasing variety and technological sophistication of the commodities offered by financial services have made the operations of financial institutions more complex. Compared with credit risk and market risk, financial institutions’ transaction risk management plays an increasingly important role in financial practice. As an emerging technology, big data mining technology has a unique advantage in optimizing the processing and management of large amounts of data. Big data mining technology not only has the common functions of finding, comprehensively managing all kinds of information, collecting and analyzing data, and conducting statistics but also should have the ability to process information that is hidden and useful in the database through data mining technology. Based on CFPS and data mining technology, this paper analyzes the operational risk of financial institutions, analyzes the causes of the operational risk of financial institutions, discusses the measures to avoid the operational risk of financial institutions, and draws corresponding conclusions.

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“…Performance expectancy has been identified as key constructs and strongest predictor for intention behavior of a user (Mehra et al, 2022;Jaiswal et al, 2022;Maita et al, 2022). The findings of Reith et al (2020) concluded that any of the characteristics that are related with the performance of an individual are considered to be an effective factor for behavior intention. Another study by Goswami and Dutta (2016) explored that persons who have higher performance expectancy utilize mobile stock trading more frequently.…”

Section: Literature Review and Hypotheses Development 21 Theories Of ...mentioning

confidence: 99%

Determinants of mobile apps adoption by retail investors for online trading in emerging financial markets

Nair

Shiva

Yadav

et al. 2022

BIJ

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PurposeThe purpose of this study is to investigate the influence of mobile applications on investment decisions by retail investors in stocks and mutual funds. This study focuses on how mobile technologies are applied on mobile apps by retail investors for e-trading in emerging financial markets.Design/methodology/approachThe study explored predictive relevance for the adoption behavior of retail investors under the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Further, goal contagion theory was applied to investigate the adoption behavior of investors towards e-trading. An adapted questionnaire was used to collect the date from April to June 2021 and data analysis was performed on 507 usable responses. The methodology adopted in this study is variance based partial least square structural equational modelling (PLS-SEM). Additionally, the study explains important and performing constructs based on the response of retail investors towards mobile app usage for investment decisions.FindingsThe study shows that effort expectancy, performance expectancy followed by perceived return were the primary determinants of behavioral intentions to use mobile applications by retail investors for e-trading. Further, habit of investors determined the adoption behavior of investors towards mobile apps. Additionally, the study revealed that perceived risk is not an important aspect for retail investors in comparison to perceived return.Research limitations/implicationsThe study in future can address to the aspect of personality traits of retail investors for technology adoption for investment decisions. Further investigation is required on addressing unobserved heterogeneity of retail investors towards technology adoption process in emerging financial markets.Practical implicationsThe study provides theoretical and practical implications for retail investors, financial advisors and technology companies to understand the behavioral pattern and mobile apps adoption behavior of retail investors in emerging financial market. The findings in the study will help broking firms to sensitize their clients for effective use of their respective mobile apps for e-trading purposes. The study will strengthen the knowledge of financial advisors to understand investment behavior of retail investors in emerging financial markets.Originality/valueThis study unfolds a novel framework of research to understand the technology adoption pattern of retail investors for e-trading by mobile applications in emerging financial markets. The present study provides significant understanding in the domain of technology adoption by retail investors under behavioral finance environment.

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Explaining the intention to use social trading platforms: an empirical investigation

Cited by 15 publications

References 92 publications

The Influence of Upward Social Comparison on Retail Trading Behavior

The Influence of Upward Social Comparison on Retail Trading Behavior

Function Extraction Based on CFPS and Digital Financial Index: Data Mining Techniques for Prognosis of Operational Risks of Financial Institutions

Determinants of mobile apps adoption by retail investors for online trading in emerging financial markets

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