Factors influencing acceptance of Robo-Advisors for wealth management in Malaysia

Abstract

Since the 2008 global financial crisis, many innovations have emerged in the financial sector as investors started to look for alternative methods to eliminate irrational decision-making in wealth management, and Robo-advisors is among those. Nine years after the first launching of Robo-advisors in the U.S. in 2008, the Securities Commission Malaysia has been issuing licenses to seven Robo-advisor platforms. The current COVID-19 outbreak has made this industry more in demand, increasing 763% in registration in 2020. However, much skepticism about Robo advisors’ ability and reliability in providing a similar quality or better advisory service compared to human-financial advisors. Therefore, this study examines the factors influencing the acceptance of Robo-advisors in wealth management in Malaysia. Adopting some factors from various established technology acceptance models, an online survey with 122 respondents was conducted using convenience sampling. Findings show that Relative Advantage, Effort Expectancy, and Social Influence significantly positive influence the Malaysian Behavioral Intention to Accept Robo-Advisors. On the contrary, there is no significant relationship between Perceived Risk and Malaysian Behavioral Intention to Accept Robo-Advisors. The study provides a positive insight into factors influencing the acceptance of Robo-Advisors in Malaysia.

Keywords:

• Robo-Advisors
• behavioral intention
• wealth management
• financial technology
• technology acceptance

PUBLIC INTEREST STATEMENT

With the demand for relatively more automated methods to eliminate irrational decision-making by humans in wealth management, Robo-Advisors are in demand worldwide. In Malaysia, the development of the Robo-advisor industry is relatively slower than in other peers such as Singapore and Hong Kong. However, the industry is expected to have around US$22.49 billion in AUM, and each user will have an average of US$12,000 in 2023. The industry is expected to grow at an annual rate of 11.72%, and its AUM and number of users will reach US$35.03 billion and 2.2 million by 2027, respectively. Although there are lucrative opportunities for the Robo-Advisory market expansion, there are plenty of doubts about the ability and reliability of Robo-advisory to provide a similar quality or better advisory service compared to human-financial advisors. Therefore, this study provides insight into factors influencing the acceptance of Robo-advisors in wealth management among Malaysians.

1. Introduction

The 2008 global financial crisis slapped the Western banking system with an estimated fine of around US$242 billion (Mainelli, 2015) for their misuse of collateralized debt obligations (CDOs) that separated the credit risk of underlying loans from their loan originator. The crisis caused a severe decline in public trust in the banking system and wealth loss for many individuals who invested in CDOs. The 2008 global financial crisis was said to be a perfect storm that gave birth to many financial technologies, including Robo-Advisors, that are expected to disrupt traditional financial services in the coming years (Phoon & Koh, 2017).

With the demand for relatively more automated methods to eliminate irrational decision-making by humans in wealth management, the world’s first Robo-Advisors, Betterment, was launched in 2008 (Reddavide, 2018). Since then, the industry has shown no sign of slowing down. According to Statista’s report, as of February 2023, the Robo-Advisor industry had grown from US$0.19 trillion to US$2.42 trillion in assets under management (AUM) from 2017 to 2022, implying an annual growth rate of 66%.

Robo-Advisors assist investors in making investment decisions through their personal financial goals, risk appetite, and financial situations. Using mathematical algorithms and artificial intelligence, Robo-Advisors are expected to perform services similar to human advisors and may outperform them. Under some guided rules, Robo-Advisors are more objective-driven and less emotionally influenced than human financial advisors; thus, their investment performances are unbiased and successfully achieved (Au et al., 2021). The Robo-Advisors will automatically create an appropriate asset allocation strategy based on personal investment preferences such as the initial capital and time horizon (D’Acunto & Rossi, 2021; Vanguard, 2017). In other words, individual investors’ portfolios are tailored and managed by configured algorithms accordingly. As a digital wealth advisor, tasks such as portfolio optimization or securities selection will be automated to mitigate most risks caused by human factors and lower costs because the same automated process can be run for many customers concurrently in real time (D’Acunto & Rossi, 2021; Grealish & Kolm, 2021). In short, Robo-Advisors offer investors attractive services with sounder advice at lower costs through economies of scale.

In Malaysia, the development of the Robo-advisor industry is relatively slower than in other peers such as Singapore and Hong Kong, given that this innovation landed in Malaysia only in 2017 when the Securities Commission Malaysia announced the “Digital Investment Management” framework (KPMG, 2021). Seven licensed Robo-advisory platforms operate in Malaysia, including Akru Now, BEST Invest, myTHEO, Raiz, StashAway, Versa, and Wahed Invest. Among these platforms, StashAway has represented the largest market share by recording over US$1 billion (RM4.05 billion) in AUM in January 2021 (Tan, 2021), while MyTheo recorded US$650 million and Wahed US$39 million (MyPF, 2021). The COVID-19 outbreak disrupted many businesses, including financial advisory services, due to the implementation of movement controls worldwide. Although traditional financial advice providers still occupy the market, Robo-Advisors are the only option for investors to turn to for advice. In 2020, Robo-advisor platforms in Malaysia experienced a user registration spike from 23,083 to 199,224 accounts, an approximate 763% increase (SC, 2020). According to Statista’s report, as of February 2023, the Malaysian Robo-Advisor industry will continue growing in both AUM and the number of users in the coming years. The industry is expected to have around US$22.49 billion in AUM, and each user will have an average of US$12,000 in 2023. The industry is expected to grow at an annual rate of 11.72%, and its AUM and number of users will reach US$35.03 billion and 2.2 million by 2027, respectively.

Although there are lucrative opportunities for the Robo-Advisory market expansion, there are plenty of doubts about the ability and reliability of Robo-advisory to provide a similar quality or better advisory service compared to human-financial advisors. There are doubts that the machine might fail to provide high-quality personal guidance as it needs to fully understand clients’ financial status with a limited risk assessment (FINRA, 2016). Moreover, it is not flexible to accommodate a unique situation as it needs to comply with some guided rules for the algorithms (Singh & Kaur, 2017). Furthermore, Data-Driven advisors like Robo-Advisors use past and current data to analyze and develop investment solutions, creating very little innovation but rather trying to fix what needs to be fixed (Herbers, 2021). Thus, it is understandable if resistance to the adoption of this technology exists among investors.

While this technology continuously gains market presence among investors globally, the Malaysian Robo-advisor industry is still in its infancy. Therefore, this paper examines factors influencing the acceptance of Robo-advisors in wealth management among Malaysians. The findings of this study are essential to help with the further development of the industry.

The structure of the paper is as follows: Section 2 explains some relevant literature reviews and theoretical development; Section 3 provides the conceptual framework, hypothesis development and methodology; Section 4 discusses the analysis result; and Section 5 provides conclusions.

2. Literature review and hypothesis development

Various definitions for Robo-Advisors have existed since its emergence. However, there appears to be a lack of agreement in past studies about the scope of services provided by Roo-Advisors (Darskuviene & Lisauskiené, 2021). For instance, Seo (2016) defines Robo-Advisory as financial advisory services that minimize human involvement with the help of well-developed software. Bruckes et al. (2019) define Robo-Advisory as a platform that assists customers in investment decision-making by implementing an algorithm system. Au et al. (2021) define Robo-Advisors as fully automated online investment services offered to individual and institutional investors. Using Abraham et al. (2019) ‘s definition, Ivantchev (2022) describes the features of Robo-Advisors as follows. They are said to initially define individual’s investment strategy based on their investment goals and risk profiles. Based on the person’s financial goals and time horizon, investment strategies, including preparing for a retirement fund, saving for large expenditures, establishing a rainy-day fund, or generating income to cover expenses, will be provided by Robo-Advisors (D’Acunto & Rossi, 2021).

Although the design for technology like Robo-Advisors aims to benefit their users, deploying such technology may only sometimes be successful (Davis, 1989; Venkatesh et al., 2003). When adopting a technology, user behaviour is the main deciding factor (Hu et al., 1999). Many models or theories related to the acceptance of technology in information systems management are the Technology Acceptance Model (TAM), Innovation Diffusion Theory (IDT), the model of PC utilization (MPCU), the Unified Theory of Acceptance and Use of Technology (UTAUT), and it has later extended version known as UTAUT 2. Important factors adopted from the above models will be discussed as follows.

2.1. Relative advantage

According to Rogers (1983)’s Innovation Diffusion Theory (IDT), a technology perceived as better than the existing solutions is more likely to be widely accepted in economic or subjective terms. In other words, a technology with a significant relative advantage will likely be adopted. In addition, a significant relationship between the relative advantage and the willingness to accept new technology is found for e-learning and mobile banking in Cheah et al. (2011), respectively. Echoing those studies, Milani (2019) revised the traditional UTAUT model (Venkatesh et al., 2003) by replacing the expected performance with relative advantage.

In this study, relative advantage refers to how Robo-Advisors will benefit individuals from using it. Providing a 24-hour advisory service with a much cheaper fee while maintaining transparency than the traditional financial advisor is a good reason for adopting it (Darskuviene & Lisauskiené, 2021; Singh & Kaur, 2017). Therefore, it is hypothesized as follows:

H1: Relative Advantage positively influences the Behavioural Intention to Accept Robo-Advisors.

2.2. Effort expectancy

In Venkatesh et al. (2003) ‘s UTAUT model, Effort Expectancy is described as “the degree of ease associated with the use of technology”. Effort expectancy is interrelated with performance expectancy, as users think technology is more valuable when it is easier to use (Mehta, Morris, Swinnerton, & Homer, 2019). Many past studies found a significant positive relationship between effort expectancy and the intention to use technology. For instance, Kim, Shin, and Lee (2009) found that user interfaces, designs, and features that affect user-friendliness can positively influence the adoption of an innovation. Chau and Hui (1998) found that personal innovativeness adverts an individual’s desire to embrace change and voluntarily try the latest things. According to Thatcher and Perrewe (2002), the stability and situation of individuals often affect how they adopt and use new technology. In addition, Cheah et al. (2011) also found that personal innovativeness significantly affects the intention to adopt mobile banking.

When individuals feel that using Robo-Advisors to manage their assets and wealth requires less effort as compared to using human advisors, the intention to adopt Robo-Advisors will increase. In this study, effort expectancy refers to ease while using Robo-Advisors.

Therefore, it is hypothesized as follows:

H2: Effort Expectancy positively influences the Behavioural Intention to Accept Robo- Advisors.

2.3. Social influence

Social influence (SI) is defined as how an individual perceives the importance that other believes he or she should use the new system (Venkatesh et al., 2003). Venkatesh and Zhang (2010) found that social influence significantly impacts women’s behavioural intention, especially older ones. However, findings on the influence of SI on the adoption of various technologies are mixed, where some found a significant favourable influence (Talukder et al., 2014; S. Yang et al., 2012), while others (Baabdullah, Alalwan, Rana, Kizgin & Patil, 2019) find none.

In this study, social influence is expected to be one of the essential factors affecting the adoption of Robo advisors, given that people are becoming more reliant on social communication. In this digital era, they can receive voluntary or non-voluntary feedback from their relatives, friends, or strangers through social platforms.

Moreover, Milani (2019) found that social influence significantly correlates with an individual’s attitude towards Robo-Advisory. In other words, people will have a high possibility of having favourable attitudes toward Robo-Advisors if someone close or essential to them recommends this technology. Therefore, our hypothesis based on the previous literature is:

H3: Social influence positively influences the Behavioural Intention to Accept Robo- Advisors.

2.4. Perceived risk

Using new technology in financial services always arouses users’ risk awareness as it involves the possibility of monetary losses. Perceived risk is an additional factor adopted in this study that was excluded from the UTAUT model. The degree of uncertainty arising from using technologies defines this factor.

The uncertainty refers to a different context, including the potential loss caused by the outcome (Gerrard & Cunningham, 2003) and security issues (Cruz et al., 2010). According to Featherman and Pavlou (2003), the potential risks can be categorized into several aspects: performance risk, financial risk, psychological Risk, privacy risk, and overall Risk. These risk factors can affect the user’s willingness to accept a technology (To & Trinh, 2021; M. Yang et al., 2021). In a survey conducted by Rossi and Utkus (2020) of 3,000 respondents, including individuals who are either unadvised or advised by human advisors or Robo-advisors, 33% said they do not trust investment algorithms in Robo-Advisor. Potential loss caused by a failed algorithm system is the most concern for this group of individuals.

Prior studies have proven a negative relationship between Perceived Risk and the adoption of financial innovations. For example, a study by Baek and King (2011) found that the uncertainty caused by mobile banking services might influence consumers’ perceptions of them. In 2015, a study by Slade et al. (2015) showed that perceived risk significantly negatively affects remote mobile payments (RMP) acceptance intention. Bhatia et al. (2021) found that risks such as data security, trust and behavioural biases are among the factors that Indian investors are sceptical about Robo-Advisors services. Bhatia et al. (2021) believe that advice made by Robo-Advisors may be biased when handling investors’ emotions; thus, human advisors cannot be replaced by Robo-Advisors for the Indian Stock market.

This study focuses on the privacy, security, and financial risks of using Robo-advisory services. Therefore, our hypothesis based on the previous literature is:

H4: Perceived Risk negatively influences the Behavioral Intention to Accept Robo- Advisors.

2.5. Behavioral intention (BI)

According to Bagozzi et al. (1992), every person’s strength in the intention-behaviour relationship differs from personality traits. Behaviour intention can directly impact technology usage (Venkatesh & Zhang, 2010), where the greater intention a person must accept new technology, the easier for his/her to accept the technology. This study’s dependent variable will be Users’ Behavioural Intention to Accept Robo-Advisors.

3. Research methodology

3.1. Theoretical framework

The below theoretical framework (see Figure 1) consists of four independent variables: Relative Advantage (RA), Effort Expectancy (EE), Social Influence (SI), and Perceived Risk (PR), which will influence the dependent variable, Behavioural Intention to Accept Robo-Advisors (BIA).

Figure 1. Theoretical Framework of Study.

3.2. Sampling Method

As per SC’s regulations, the minimum age to open an account on the Robo-advisors platform is 18. Thus, only 18 years and above Malaysians are invited to participate in this study. A sample of 122 respondents is obtained via the convenient sampling method for three months, i.e., from March 1 to 30 May 2022.

As an exploratory factor analysis, sample to item ratio is used to determine the adequate sample size of this study (Memon et al., 2020). Suhr (2006) recommends a minimum subject-to-item ratio of 5:1. However, higher ratios are generally better. A total of 20 items (questions) will be tested to measure the independent variables in this study. Therefore, 100 responses should be collected to represent the population and formulate a reasonable conclusion. Thus, the obtained sample of 122 respondents is reasonable and accepted for this study. Among the 122 respondents, 40% are females, and 60% are males. Most respondents are in the working age groups, where 48% are below 25, 27% are between 25 and 35, 17% are between 35 and 45, and only 6% are between 46 and 55.

3.3. Research instrument

A self-administered questionnaire of factors influencing behavioural intention to accept Robo-advisors was developed for primary data collection. The questionnaire was administered to respondents face-to-face and on various online social communication platforms such as WhatsApp, Facebook Messenger, and Telegram.

The independent variables of this study are Relative Advantage (RA), Effort Expectancy (EE), Social Influence (SI) and Perceived Risk (PR). The dependent variable of this study is Behavioural Intention to Accept Robo-Advisors (BIA). A closed-ended questionnaire was developed for this study. The survey questionnaire contains three sections. The first section - Section A - includes questions about respondents’ demographic information. Section B contains questions asking respondents’ opinions regarding the dependent variable acceptance of Robo Advisors for wealth management in Malaysia. The last section -Section C - contains questions about factors that may affect the acceptance of the Robo-Advisors services in wealth management, such as Relative Advantages, Effort Expectancy, Social Influence, and Perceived Risk. Most questions designed for the questionnaire used for this study are adapted from well-established studies, i.e. (relative advantage, perceived risk, and the acceptance of Robo advisory), Davis (1989) (effort expectancy), Moore and Benbasat (1991) (effort expectancy), Tan, Ooi, Chong and Hew (2014) (social influence), Nicole, Morgan, Adrian and Anita (2015) (social influence), Davis (1989) (social influence), Forsythe et al. (2006) (perceived risk), Venkatesh et al. (2012) (the acceptance of Robo advisory). Both Section B and Section C contain close-ended multiple-choice questions where respondents need to rate their agreement based on a 5-point Likert scale ranging from (1) strongly agree, (2) agree, (3) neutral, (4) disagree, and (5) strongly disagree.

A pilot study was conducted by distributing 30 survey questionnaires randomly to 18 years old and above individuals for the questionnaire validation purpose. The refined questionnaire was then used to collect data for this study.

3.4. Method of analysis

The survey’s data is analyzed using Statistical Package for the Social Sciences (SPSS 22) and SmartPLS 3.0. Partial Least Square “PLS” is a structural equation modelling (SEM) technique that can simultaneously test relationships between constructs.

4. Discussion of findings

4.1. Reliability and validity analysis of the proposed model

For a structural model to be validated, its measured variables must pass various tests to avoid bias in analysis later. Based on the data collected from 122 respondents, a reliability test was then conducted through SPSS. Table 1 shows the result of the reliability test of this study. A study’s acceptable Cronbach’s Alpha needs to be at least 0.65 (Hair et al., 2006), where Cronbach’s Alpha above 0.80 indicates the data set is excellent and reliable. According to Table 1, Cronbach’s Alpha in this study ranges from 0.819 to 0.919. For independent variables, Perceived Risk (PR) records the highest Cronbach’s Alpha of 0.919, followed by Relative Advantage (RA) (0.853), Social Influence (SI) (0.851), and Effort Expectancy (EE) with a value of 0.822. The overall Cronbach’s Alpha for all variables indicates that the model is reliable regarding the consistency of the results. According to Hair et al. (2010), the internal consistency reliability is acceptable for all four independent variables. Test of discriminant validity where HTMT confidence interval should not include is satisfied for all model constructs. Furthermore, the results of indicator reliability for all indicators shown in Table 1 are satisfactory and above the threshold of 0.5.

Table 1. Convergent Validity, Internal Consistency Reliability, and Discriminant Validity

Latent Variable	Indicators	Convergent Validity			Internal Consistency Reliability		Discriminant Validity
		Loadings	Indicator Reliability	AVE	Composite Reliability	Cronbach’s Alpha
		>0.7	>0.5	>0.5	0.6–0.9	0.6–0.9	HTMT confidence interval does not include 1
BIA	AC1	0.937	0.878	0.845	0.916	0.819	Yes
	AC2	0.902	0.814
EE	EE2	0.935	0.874	0.848	0.918	0.822	Yes
	EE4	0.906	0.821
PR	PR1	0.904	0.817	0.797	0.94	0.919	Yes
	PR2	0.888	0.789
	PR3	0.850	0.723
	PR5	0.927	0.859
RA	RA1	0.872	0.760	0.697	0.901	0.853	Yes
	RA2	0.890	0.792
	RA3	0.771	0.594
	RA4	0.800	0.640
SI	SI1	0.717	0.514	0.695	0.901	0.852	Yes
	SI2	0.868	0.753
	SI3	0.864	0.746
	SI4	0.876	0.767

In short, all evaluation criteria for the model have been met; thus, the model is fit for further path coefficient estimation.

4.2. Path coefficient analysis

To produce reliable results, a bootstrapping with 5,000 subsamples randomly drawn from the original sample of this study is carried out. Table 2 shows the results of the path coefficients obtained from the bootstrapping test for the proposed structural model. Path coefficients of three out of four independent variables (EE, RA, and SI) are significantly related to the BIA variable, with p-values of less than 0.05 and t-statistics of more than 1.65 (see, Table 2). Except for the path coefficient for the PR variable, the path coefficients of the other three independent variables (EE, RV, and SI) are positively significant at the 5% level, with path coefficient values of 0.217, 0.405, and 0.34, respectively. In short, the relation (PR) is rejected, and three hypotheses (H1, H2, and H3) are accepted at a significant level of 5%.

Table 2. Significance Testing Results of the Structural Model Path Coefficients

	Original Sample (O)	T Statistics (|O/STDEV|)	P-values	95% Confidence Interval	Significant (p < 0.05)?
EE -> BIA	0.217	2.700	0.007	[0.072, 0.393]	Yes
PR -> BIA	−0.091	1.010	0.312	[−0.236, 0.119]	No
RA -> BIA	0.405	3.843	0.000	[0.174, 0.587]	Yes
SI -> BIA	0.340	3.776	0.000	[0.165, 0.523]	Yes

PLS Predict output was then produced using a 10-fold process to generate and evaluate predictions from PLS path model estimations (Shmueli et al., 2016). In Table 3, the predictive power and predictive relevance assessment of the proposed model are presented. As shown in Table 4, the effect sizes of the four variables, i.e. EE, PR, RA, and SI, on the BIA are small. Regarding predictive power, the R-squared value of 0.636 implies that EE, RA, SI, and PR can predict 63.6% and rebuild 49.6% of BIA variance.

Table 3. Predictive Power and Predictive Relevance Assessment of The Proposed Model

	Predictive Power		Predictive Relevance
	R Square	Status	Q Square	Status
BIA	0.636	Moderate	0.496	Large

Table 4. Predictive Relevance and Size

	q^2 Effect Sizes	Size of Predictive Relevance
EE—> BIA	−0.0018	Small
PR—> BIA	0.0000	None
RA—> BIA	0.0018	Small
SI—> BIA	−0.0037	Small

Firstly, relative advantage refers to a new technology solution that works better than the existing solution. This result means that investors are more probably to accept and use a Robo-Advisors service if the features of this technology are better in terms of usefulness, convenience, and efficiency than a traditional financial advisor. This finding is supported by Hsbollah (2009) and Cheah et al. (2011), which found that relative advantage positively influences the acceptance of specific technologies such as E-learning and mobile banking. Malaysian investors might be attracted by Robo-Advisors’ advantages, such as advanced technology, lower fees, and more rational investment decisions, which the existing human advisors need to improve. In addition, the existing Robo-Advisors seem to adopt a conservative approach, providing higher stability, liquidity, and return in the long term (Phoon & Koh, 2017).

Next, this study found that effort expectancy has a positive and significant relationship with the Malaysians’ behavioural intention to accept Robo-Advisors. The easier the way to operate a Robo-Advisors service, the higher the possibility for the investors to accept and use this technology in wealth management. The study by Ghalandari (2012) shared a similar view in which the ease of using certain technologies can influence the intention of potential users toward using them. Moreover, Chuang et al. (2016) also mentioned that users have a high chance of accepting a new technology that requires little time and labour. Given that the Robo-Advisors service can operate 24- hours, it is convenient for investors to receive advisory services anywhere, anytime through accessing the platforms, without making an appointment with a financial advisor for face-to-face discussion.

Social influence is also found to positively and significantly influence users’ behavioural intention to accept Robo-Advisors for wealth management. A possible explanation is that people will have a positive attitude towards using Robo-Advisors if essential people such as family members and friends use it or recommend it. This result shared the same views as previous research by Teo and Pok (2003), Ghalandari (2012), and Milani (2019). It is believed that when consumers have low familiarity and no user experiences with Robots, they tend to rely on others’ opinions floating freely in the mass media (Bhattacherjee, 2001). In addition, most respondents in this research are categorized as young adults, an age group whose behaviours are easily influenced by the reference group.

In terms of perceived risk, this study revealed that Perceived Risk has no significant relationship with the Malaysians’ behavioural intention to accept Robo-Advisors. Based on the result, the potential threats to using Robo-Advisors, including financial losses, hacking, and information leakages, have no direct effects on Malaysians’ behavioural intention to accept Robo-Advisors. Although many previous studies found the opposite result, some research supports the insignificance of perceived risk results, such as Lema (2017) and Rühr et al. (2019). It claimed that users might perceive similar risk levels on both investing through Robo-Advisors and human advisors. The ambiguous results might be due to the different perceptions of investors towards risk. Each investor has a different risk appetite, and risk is theoretically known as a necessary component of the majority investment portfolio.

5. Conclusion

This study explores indicators influencing Malaysians’ behavioural intention to accept Robo-Advisors in wealth management. Four factors were identified to test what influences individual Malaysian intention to accept Robo-Advisors, i.e., Relative Advantage, Effort Expectancy, Social Influence, and Perceived Risk. Results found that Relative Advantage, Effort Expectancy, and Social Influence are essential in influencing Malaysian behavioural intention to accept Robo-Advisors. These three factors significantly and positively influence Malaysian behavioural intention to accept Robo-Advisors, while no significant evidence was found for Perceived Risk.

The study provides a positive insight into factors influencing the acceptance of Robo-Advisors. The findings suggest that Malaysians who intend to use Robo-Advisors think that they benefit individuals, i.e. cheaper fees, 24-hour advisory service, and transparency. Respondents who intend to use Robo-Advisors also think that Robo-Advisors require less effort than human advisors. This may hinder the Robo-Advisors platform’s straightforward and convenient user interface from attracting new customers, especially those unfamiliar with new technology and the elderly. Moreover, respondents said they would highly intend to use Robo-Advisors if people close or essential to them recommended this technology. Interestingly, respondents’ intention to accept Robo-Advisors is not significantly negatively affected by risks related to losses caused by the failed algorithm system of Robo-Advisors. As the study results were produced from a sample of 122, further research may be extended with more respondents and other possible factors that may influence further Malaysian Robo-Advisors once the local market has grown to a more sizable market.

Correction

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

Thi Phuong Lan Nguyen

Nguyen Thi Phuong Lan is a senior lecturer at the Faculty of Management of Multimedia University, Malaysia. She teaches and conducts research in finance areas, including investment, risk management, portfolio management, hedge funds, and FinTech.

Li Woon Chew

Chew Li Woon is a final year Bachelor of Financial Engineering student at Multimedia University who is passionate about exploring economics, investment, and Fintech fields.

Saravanan Muthaiyah

Saravanan Muthaiyah is currently a full Professor attached to Multimedia University, Malaysia. He teaches and conducts research in Semantic Web Algorithms, Web 4.0, Data Analytics, Blockchain, Cryptocurrencies and FinTech.

Boon Heng Teh

Teh Boon Heng is currently a senior lecturer at Multimedia University, Malaysia. Besides teaching at the faculty of Management, he carried out many research projects related to sustainability, finance, auditing, and accounting.

Tze San Ong

Tze San Ong is a professor at the School of Business and Economics at the University Putra, Malaysia. Her research interests include corporate sustainability, corporate performance measurement system, and corporate governance.