Does robo-advisory help reduce the likelihood of carrying a credit card debt? Evidence from an instrumental variable approach

Bai, Zefeng

doi:10.1016/j.jbef.2021.100461

Cited by 11 publications

(17 citation statements)

References 84 publications

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“…All the interviews are conducted in the Malay language and by two researchers independently. Subsequently, the transcribed data is translated into English and checked by appointed language proficient (Van Ooijen & van Rooij, 2016;Bai, 2021;Wilson, 2019). Six informants have been approached and interviewed.…”

Section: Research Methods and Materialsmentioning

confidence: 99%

Debt management literacy among credit cards users

2021

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This research investigates the debt management literacy among credit card users. Based on thematic analysis through semi-structured interviews with debt management experts, particularly in credit cards, this research sheds light on the positive and negative to the causal of debt management literacy, including awareness and experiences. Meanwhile, the negative causal is ignorance and the economy. These themes depict that credit cards users are prone to develop knowledge in debt management for several causes. Taken together, the research findings highlight the importance of understanding the nature of debt management, which has received relatively little attention to date.

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Section: Research Methods and Materialsmentioning

confidence: 99%

Debt management literacy among credit cards users

2021

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“…There are two ways to mitigate the endogeneity issue. One possible way is to use the panel regression estimation technique with a fixed-effects model (Bai, 2021). Another way is to use instrumental variable regression, which helps to control for the potential endogeneity issue.…”

Section: Instrumental Variable Regression Modelmentioning

confidence: 99%

“…Based on the existing literature (Bai, 2021;Brenner and Meyll, 2020), we control several factors, such as demographics, financial literacy and financial education. A detailed explanation of the measurement of control variables is given in Table 1.…”

Section: Control Variablesmentioning

confidence: 99%

“…In the NFCS data, age, income and a number of children were collected into categories; hence, we do not have data in a ratio level for these variables. All the studies based on the NCFS data face this limitation where possibly ratio variables are reported into categories (Bai, 2021;Brenner and Meyll, 2020). For the financial literacy scale, we have given the detailed questions in Appendix.…”

Section: Control Variablesmentioning

confidence: 99%

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Does robo-advisory increase retirement worry? A causal explanation

Chhatwani

2022

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PurposeArtificial intelligence and robo-advisory have become prevalent in the finance industry, and many people rely on robots instead of humans for financial advice. This study aims to examine whether robo-advisory increases retirement worry based on agency theory and rational choice theory.Design/methodology/approachThe present study investigate whether relying on robots for financial advice increases retirement-related worry in the present study. Using a sample of 1915 investors from the National Financial Capability Study (NCFS) survey, the author conducted instrumental variable regression analysis to examine the causal linkage.FindingsUsing fear of financial fraud as an instrument variable, the study provides a causal explanation of the linkage between robo-advisory usage and retirement worry. After controlling for sociodemographic and financial literacy-related variables, it is found that robo-advisory increases retirement worry.Originality/valueFindings of the study emphasize on downsides of the artificial intelligence-enabled robo-advisory for financial planning. This article provides evidence that a lack of human involvement in financial planning may lead to increased worry among investors, which calls for attention from the regulators and policymakers.

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“…Individuals with higher levels of financial education are found to have the tendency to seek for financial advice (Litterscheidt and Streich, 2020) to better allocate their financial resources in order to make sounder financial decisions (Lusardi et al, 2017) especially in managing their retirement drawdowns (Lusardi and Mitchell, 2014). These individuals tend to have lower credit card debt (Bai, 2021;Allgood and Walstad, 2016) because they may have accumulated assets and pension (Piotrowska, 2019) to support their spending, or they may have better financial planning (Koh et al, 2018) compared with their counterparts with less financial literacy.…”

Section: Financial Knowledgementioning

confidence: 99%

Retirement Preparedness and Subjective Well-Being: Evidence from Malaysia

2021

QAS

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Organizations offer sufficient preparation to prepare their employees to succeed at work, but very few, if any, prepare their employees for life after work. A cross-sectional survey was undertaken, with a total of 514 Malaysian employees participating in the study. The Partial Least Squares-Structural Equation Modelling estimation technique was employed to analyse the data. The results revealed that retirement preparedness in orgnizations via financial knowledge, retirement goals and retirement confidence significantly enhances subjective well-being of employees. The study serves to discuss current policy issues that affect the government, financial institutions, the employees, the elderly and their families.

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Does robo-advisory help reduce the likelihood of carrying a credit card debt? Evidence from an instrumental variable approach

Cited by 11 publications

References 84 publications

Debt management literacy among credit cards users

Debt management literacy among credit cards users

Does robo-advisory increase retirement worry? A causal explanation

Retirement Preparedness and Subjective Well-Being: Evidence from Malaysia

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