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Article

Unveiling the Nexus Between Use of AI-Enabled Robo-Advisors, Behavioural Intention and Sustainable Investment Decisions Using PLS-SEM

by
Nargis Mohapatra
1,
Sameer Shekhar
1,
Rubee Singh
2,
Shahbaz Khan
3,
Gilberto Santos
4,* and
Sandro Carvalho
5
1
School of Economics and Commerce (KSEC), Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Bhubaneswar 751024, India
2
Institute of Business Management, GLA University, Mathura 281406, India
3
Department of Industrial Engineering, University of Tabuk, Tabuk 71491, Saudi Arabia
4
Design School, Polytechnic Institute Cavado Ave, Campus do IPCA, 4750-810 Barcelos, Portugal
5
Technological School, 2Ai—Applied Artificial Intelligence Laboratory Polytechnic Institute of Cavado and Ave, 4750-810 Barcelos, Portugal
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(9), 3897; https://doi.org/10.3390/su17093897
Submission received: 11 February 2025 / Revised: 22 March 2025 / Accepted: 26 March 2025 / Published: 25 April 2025
(This article belongs to the Special Issue Innovative Technologies and Management Practices for Sustainable Development)
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Abstract

:
The study examines the nexus between AI-driven technology, i.e., robo-advisors, and the behavioural intention of investors towards sustainable investment decisions considering government regulations and sustainable investment awareness as the moderating variables. A total of 372 responses were collected from across India through a structured questionnaire along identified variables from the TAM and UTAUT theories under the select constructs, i.e., trust, perceived risk, user-friendliness, perceived usefulness, and emotional arousal. This is with reference to the use of robo-advisors to unearth the extent to which they influence the behavioural intention and finally the sustainable investment decisions taking into account government regulations and sustainable investment awareness as the moderating variables. The results derived by using PLS-SEM reveal that all the five factors are having a significant impact on the behavioural intention for sustainable investment decisions of the investors. Further, both sustainable investment awareness and government regulations have been found to have a moderating impact on shaping the behavioural intention of the investors with respect to most of the variables. The results of the study come up with significant suggestions for the government, financial institutions, and the investors as well as the academicians, and therefore, have policy implications, managerial implications, and theoretical implications. The constructs and moderating variables considered here can further be used for studying the behavioural intentions. The robo-advisory service providers may emphasize developing the algo ensuring trust, usability, and friendly interface in a manner that tends to minimize the perceived risk and emotional arousal leading to the use of robo-advisors pushing the intention of the investors towards sustainable investment.

1. Introduction

The recent technological innovations in the financial services sector have introduced automated financial advisors, widely known as robo-advisors. The reasons behind the success of robo-advisors are mainly the generation of new investors, and advantages of robo-advisors over the traditional financial advisors [1]. The robo-advisors are considered less vulnerable to potential conflicts of interest as they usually provide significantly lower and more transparent cost structures for investments, compared to human financial advisors who often misguide the investors to avail incentive-based compensation schemes [2]. The robo-advisors provide suitable answers to the queries of the investors and encourage them to invest in the most appropriate channels and thus, it is also regarded as an efficient way to increase sustainable investments by making the investors aware [3]. Sustainable investments are one of the rapidly growing investment strategies in recent years. Such investments ensure sustainable development through the integration of financial benefits along with long-term environmental, social, and governance (ESG) factors for making investment decisions [4]. It symbolizes the investors’ desire to optimize their risk–return profile while adhering to the corporate ethics and doing social good by investing in socially responsible investments (SRI), sustainable and responsible investments, or ESG investments [5]. Responsible investors usually avoid investing in the stocks that are involved in promoting harmful products like tobacco, alcohol, and weapons and rather favour the firms involved in promoting best practices that comply with enhancing community relations, environmental sustainability, and labour conditions [6]. However, the investor’s intention to invest in sustainable investments depends on their perception, personal benefits, and effective decision-making skills [7].
Even though the SRI funds have a multitude approach, it follows three basic strategies which include screening, shareholder advocacy, and community investing [8]. The screening and assessment of sustainable investments is tedious and complicated work for the investors. Thus, the robo-advisors that provide strategies for sustainable investments are preferred by the investors as it saves the time for screening work, reduces the complexity of the investments, and also makes the uninformed investors aware about these investment opportunities, thereby helping to overcome key barriers to sustainable investing and elevating investors’ interest towards the sustainable investment market [3,9]. Therefore, it is important to study the factors that affect the behavioural intention of the investors to use robo-advisors for making sustainable investments. Thus, the study aims at assessing the impact of the use of robo-advisors on sustainable investment decisions.
  • Research Questions: The study has been conducted around the following research questions:
  • What are the factors related to robo-advisors that influence the behavioural intention of the investors to make sustainable investments?
  • Does government regulation act as a moderator for assessing the impact of robo-advisor use on behavioural intention towards sustainable investment decisions?
  • Does sustainable investment awareness act as a moderator for assessing the impact of robo-advisor use on behavioural intention towards sustainable investment decisions?
  • Research Objectives: The following objectives have been framed to undertake the study:
  • To analyze the impact of factors affecting the behavioural intention of investors to make sustainable investments while using robo-advisors.
  • To examine the moderating role of government regulations on the impact of robo-advisor use on behavioural intention towards sustainable investment decisions.
  • To examine the moderating role of awareness about sustainable investment on the impact of robo-advisor use on behavioural intention towards sustainable investment decisions.

2. Literature Review

Technological advancement has greatly accelerated human progress in recent years with remarkable breakthroughs in fields like artificial intelligence (AI) [10,11], biotechnology [12], and nanotechnology [13,14]. Among these fast-growing technologies, AI has emerged as a transformative force by revolutionizing data-driven innovation and decision-making capabilities across various sectors [15]. From self-driving cars to voice-activated home assistance devices, AI has taken over the daily tasks that were previously carried out by humans [16]. AI technologies like predictive analytics [17,18], augmented and virtual reality [19,20,21], natural language processing (NLP) [22], generative AI [23,24], and AI-powered robotics [25] are some of the rapidly developing advancements across these sectors. Wirtz et al. [26] have discussed the effective changes made by the AI technologies and service robots in the services sector, which enhances customer satisfaction and productivity as well as is cost effective. This makes it more crucial to study the implementation of AI technologies in the services industry as it involves customer-centric activities across sectors like hospitality and tourism [27], banking and insurance [28], education [29,30], retailing [31], and entertainment [32]. The NLP technologies like chatbots [33], robo-advisors [34], and voice assistants [35] are being widely accepted by the customers among various service sectors. In online shopping and the entertainment sector, chatbots are being widely used by customers for gathering information [36,37]. The banking industry is often regarded as one of the key service sectors as it deals with a variety of customers with different backgrounds, who avail the same services. AI in banking has attracted more customers to become involved in banking activities and making more investments as the chatbots and robo-advisors have made the customer interaction services easier, convenient, and time saving [38].

2.1. AI and Investment

AI has changed the trajectory of the financial industry by automating difficult tasks like accumulating information from various sources, intellectualizing the risk pricing models, standardizing investment decision-making process, automating customer interaction services, and many more core financial functions which results in improved efficiency and reduced cost benefits [39]. This rigorous advancement in the financial sector led by AI technologies has encouraged many researchers to conduct significant studies in this field. Cao [40] studied the new generation AI and data science (AIDS) techniques used in finance that integrates methods like deep learning, cognitive computing, and quantum computing with AI, which provides data-driven analytical results to enhance the smartness of financial services and further gives personalized financial solutions for a broad range of financial products and services. AI-driven predictive analytics in finance is also transforming the sector with advanced risk management and decision-making capabilities through machine learning algorithms [41]. AI is providing intelligent analysis and decision-making capabilities in investment management as well by getting involved in stock market prediction, financial risk identification, process automation, and ESG investment [42]. Further, Chua et al. [43] revealed that the investor’s intention to adopt AI technologies for making investment decisions is greatly affected by their trust, attitude, and perceived accuracy of the system. In quantitative investment, the application of AI technologies can significantly improve the prediction accuracy with the help of machine learning and deep learning, automate the trading decisions, optimize the risk assessment process, provide real-time market information, reduce transaction costs, and provide personalized investment advice through robo-advisors [44]. The AI-driven interventions and behavioural insights can improve financial decision making and reduce costs for users, which influences the investment behaviour of the individuals [45]. Amongst the various AI technologies used in financial markets, robo-advisors have garnered significant attention in the wealth management industry which is caused by increasing technical know-how of the investors and their preference to have active and hassle-free control over their investments in a diversified portfolio [46].

2.2. Robo-Advisors and Sustainable Investment

Financial robo-advisors are services that offer automated and algorithmic online advice on private asset management without requiring human participation [47]. Robo-advisors are characterized by investment types, such as low capital thresholds, lower investment knowledge requirements, cost effectiveness, and high real-time reactivity to the market [48]. Unlike the traditional method of obtaining investment advice from human advisers, financial robo-advisors utilize algorithms to quickly filter data and simulate asset portfolios to give personalized investment advice [49]. Further, Mugerman et al. [50] has highlighted that the method of payment for financial advice influences the investors’ investment decisions and willingness to pay for advisory services, where he concluded that the timing of payment is more influential than loss or gain associated with the payment and investors tend to pay more when payments are linked to better outcomes. Abudy et al. [51] stated that financial advisors have to change their clients’ investment options according to any change in the regulation policy to provide better results. Subsequently, Brenner and Meyll [2] found that the cause behind the investor’s shift from human financial advisors to robo-advisors is their fear of falling prey to investment scams and thus the study suggests that robo-advisory is a sound alternative for those potential investors who want to avoid conflict of interests with human advisors. Syed and Janamolla [52] found that the use of robo-advisors in investment decision making enhances accuracy, personalization, and accessibility but faces certain challenges related to algorithmic transparency, data privacy, and regulatory compliance. Moreover, investors today are inclined towards value-based or sustainable investments as these are a potential solution for ecological and social issues which can transform the financial markets to have a more accountable impact [53]. Marti et al. [54] concluded portfolio screening, shareholder engagement, and field building as the major impact of sustainable investing that influence corporate sustainability. Studies reveal that accessing and screening information related to sustainability in confirming assets is a tedious task for the investors but the robo-advisors reduce this complexity and also spread sustainable investment awareness among the uninformed investors. It helps in overcoming major hindrances to sustainable investment, and motivating the non-responsible investors to make sustainable investments [3].

2.3. Behavioural Finance Theories and Investment

Behavioural finance highlights the connection between investor sentiment and stock returns, which has received considerable attention in recent years in the finance literature. In contrast to traditional financial theory, behavioural finance theory contends that investors are frequently swayed by herd behaviour, depending on sentiment and noise rather than market fundamentals [55]. Behavioural finance can explain stock price movements that are not explained by classic financial theory based on strong rational investor assumptions [56]. Zouaoui et al. [57] found that investor emotions significantly impacted the 2008 financial crisis, particularly in capital markets prone to herd behaviour and exaggeration. Thus, investor’s behavioural patterns greatly influence the intention to make an investment. To conduct such studies, many researchers have adopted a variant of models having different constructs to justify their argument. Abdeldayem and Aldulaimi [58] found herding theory, prospect theory, and heuristics theory to be accountable for variance in investor’s choices in the Gulf Cooperation Council countries’ cryptocurrency market. Brunen and Laubach [3] studied the non-investment-related sustainable behaviour of investors using robo-advisors to assess their investment decisions taking social preferences, robo-advisory experience, investment skills, and socio-demographic characteristics as control variables. Over the years, researchers have applied various behavioural finance theories like unified theory of acceptance and use of technology (UTAUT) [59], technology acceptance model (TAM) [60], theory of planned behaviour (TPB) [61,62], theory of reasoned action (TRA) [63], and behavioural reasoning theory [64] to study the behavioural intention of individuals towards different technologies.
The present study has been conducted around the variables selected from the existing literature based on the following behavioural finance theories:

2.3.1. Unified Theory of Acceptance and Use of Technology (UTAUT)

UTAUT is considered as one of the most powerful theories of technology acceptance, which was developed to study the users’ ability to accept and intention to adopt a new technology [65]. Further, the study reveals that this model is developed by integrating the most essential characteristics of eight old technology acceptance theories, making it a unified form of these theories. Venkatesh et al. [66] classified the core constructs of the UTAUT model as performance expectancy, effort expectancy, social influence, and facilitating conditions, which are formed by combining certain relevant constructs like perceived usefulness, relative advantage, perceived ease of use, complexity, subjective norms, and compatibility obtained from other theories. Several studies related to exploring the behavioural intention among the users towards the adoption of robo-advisors in various sectors have applied the UTAUT model by taking performance expectancy, effort expectancy, social influence, facilitating conditions, and attitude as the independent variables [63,67,68]. Roh et al. [63] used a combination of UTAUT and TRA models to study the intention of Fintech users towards the adoption of robo-advisors by integrating perceived security, perceived privacy, and trust with the core UTAUT variables. Further, Nazmi et al. [67] found that advisory transparency has a significant mediating effect on the UTAUT variables towards adoption of robo-advisors in Malaysia.

2.3.2. Technology Acceptance Model (TAM)

TAM has become a dominant model for investigating the factors affecting the acceptance of a technology among the users [69]. Further, the study reveals that TAM assumes perceived ease of use and perceived usefulness to have a mediating role in determining the complex relation between the external variables and the usage or adoption of a potential technology. Previous studies evaluating the behavioural intention towards adoption of robo-advisors have used the TAM model to derive the results by taking trust, anxiety, performance expectancy, preference to human advisors, perceived ease of use, perceived usefulness, perceived convenience, transparency, customization, social pressure, and user control as the constructs [70,71,72,73]. Kwon et al. [72] combined the TAM and innovation resistance model (IRM) to investigate the factors influencing the intention to use robo-advisors by taking perceived usefulness, perceived complexity, and perceived safety as the moderating variables. Belanche et al. [73] found that perceived usefulness and attitude towards robo-advisors have a slightly higher influence on the customers than the perceived ease of use.
In addition to TAM and UTAUT, the previous studies have also applied other behavioural finance theories, i.e., TRA [63] and IRM [72], but the present study focuses on the constructs derived from TAM and UTAUT as they are found to be more significant for the study. Although the existing studies related to TAM have considered various constructs, the present study has considered trust, perceived risk, user friendliness, perceived usefulness, and emotional arousal as the constructs by integrating the UTAUT model as given by Venkatesh et al. [66]. Through the literature review, sustainable investment awareness and government regulations have also been identified as the relevant constructs for the study. Based on these constructs, the conceptual model and the hypotheses for the study have been designed, which are discussed in the following section.

3. Conceptual Framework

Based on the previous literature, the conceptual model for the study has been framed which is shown in Figure 1.
Figure 1 shows the conceptual model developed for the study, where trust, perceived risk, user friendliness, perceived usefulness, and emotional arousal are used as the independent variables to measure the behavioural intention of investors towards the use of robo-advisors to make sustainable investments with government regulations and sustainable investment awareness as the moderating variable.
Trust: The willingness of users to adopt new technologies is highly influenced by trust [74]. In context of service, trust implies that the technology provides more security and reliability as compared to the traditional investment methods [75]. Previous studies have highlighted the significant role of trust as a factor in influencing the adoption of AI technologies. Bedué and Fritzsche [76] stated that trust in AI technology can be measured by assessing the ability, integrity, and benevolence of the AI system. Lack of trust of users on AI makes them uncertain and they refuse to provide their personal information to the banks, thus the customer’s or investor’s assessment of trust about security mitigates uncertainty, which consequently stimulates acceptance of the technology [77,78]. Following the hypotheses tested by other related studies that have proposed trust to be a factor impacting the behavioural intention of individuals to adopt AI technology, the following hypothesis for the study has been developed:
H1: 
Trust on robo-advisors has a significant impact on behavioural intention to make sustainable investments.
Perceived Risk: The uncertainty of the users regarding their ability to derive expected outcomes while using a technology and the fear that they might incur a loss because of their imparity with the technology is often regarded as the perceived risk [79]. This includes privacy risks, financial risks, and performance risks. Previous studies have mostly considered complexity and security concerns related to the AI system as perceived risk for AI adoption [80,81,82]. Studies on AI reveal that users perceive the potential risks might affect their experience while using AI, which undermines their willingness to use the technology [83]. This makes it necessary to study the perceived risk of the investors relating to robo-advisors and thus the following hypothesis has been developed:
H2: 
Perceived risk related to robo-advisors has a significant impact on behavioural intention to make sustainable investments.
User Friendliness: The AI technology used must be easy to use and understand so that the users do not get confused or face difficulty while using it. The users will be satisfied with a technology only if the interface and structure is easy to use and understand [84]. Further, Lee et al. [85] stated that the support of AI makes the users feel like it is a friendly and safe interaction just like having a real face-to-face communication environment in the context of banking apps. Thus, it can be perceived that using robo-advisors for making investment decisions can help to create a friendly environment and boost the interest of the investors to make more investments. Based on this conclusion from the previous studies, the following hypothesis has been developed:
H3: 
User friendliness of robo-advisors has a significant impact on behavioural intention to make sustainable investments.
Perceived Usefulness: Perceived usefulness is the extent to which the users think that a technology can improve their potential and skill to perform a job effortlessly [86]. The intention of users to adopt AI chatbots is significantly predicted by their perception towards the usefulness and helpfulness of the technology [87]. Several studies reveal that users’ attitude towards chatbots and robo-advisors strongly depends on its perceived usefulness [88,89], and to show this relationship, the following hypothesis has been developed for the study:
H4: 
Perceived usefulness of robo-advisors has a significant impact on behavioural intention to make sustainable investments.
Emotional Arousal: Emotional characteristics of the users can affect their willingness to use and adopt a technology. Positive emotions usually strengthen the AI users’ willingness to use the system [83]. Researchers have found that users’ emotional state affects their attitude and willingness towards using a technology [90]. Further, Yim et al. [91] has stated that the emotional support established between the customer and service provider significantly affects the behavioural intention of the customer. Thus, it confirms that emotional arousal of the users affects their attitude and intention to use an AI technology. Based on this, the following hypothesis has been developed for the use of robo-advisors:
H5: 
Emotional arousal while using robo-advisors has a significant impact on behavioural intention to make sustainable investments.
Sustainable Investment Awareness: Investment awareness help people in making sound and effective decisions for investment activities [92]. Sustainable investment has gained attention in recent years by shifting investors’ preferences towards environmental, social, and governance issues. The sustainable practices have achieved remarkable attention in recent years, and thus, it is important to scan the awareness among the investors about sustainable investments [93]. The awareness about sustainable investments can act as a catalyst to boost the behavioural intention of the investors to select sustainable investment options over other investments while using robo-advisors. To verify this moderation effect, the following hypothesis has been developed:
H6: 
The effect of trust, perceived risk, user friendliness, perceived usefulness, and emotional arousal on behavioural intention will be moderated by sustainable investment awareness.
Government Regulations: The government regulations supporting sustainable investment practices by providing certain tax incentives and subsidies can boost the interest of the investors towards making sustainable investments [94]. The Income Tax Act, 1961 provides certain tax incentives and subsidies for investing in specific sustainable investments like green bonds and solar panels. to encourage investors to make sustainable investment decisions. These incentives can have a moderating impact on the behavioural intention of the investors. So, to verify this moderation effect, the following hypothesis has been developed:
H7: 
The effect of trust, perceived risk, user friendliness, perceived usefulness, and emotional arousal on behavioural intention will be moderated by government regulations.

4. Methodology

The study is exploratory in nature which has been conducted along primary data. To collect primary data, a structured questionnaire was designed by modifying the measurement items identified through a literature review of relevant constructs related to the use of robo-advisors. The questionnaire includes multiple sections consisting of the basic information and demographics in the first section, which incorporates age, gender, educational qualification, occupation, annual income, types of investments held, and other basic information. The following sections include questions to measure investors’ perception using a 5-point Likert scale, where 1 means “strongly agree”, 2 means “agree”, 3 means “neutral”, 4 means “disagree”, and 5 means “strongly disagree”. The constructs and measurement items used for the study are shown in Table 1.

4.1. Sampling and Data Collection

The study has been conducted on the data collected from the investors across India through an online floated structured questionnaire. For collecting data from the investors, a simple random sampling technique and snow-ball sampling was used. A total of 550 questionnaires were distributed among the respondents in a mixed-method design. In total, 70 questionnaires were distributed in hard copy, and 480 questionnaires were distributed through an online mode using google form. A total of 432 responses were collected including online and offline mode, and then were put through data refinement and filtration during which 60 responses were found to be either incomplete or with inappropriate information. Thus, a total of 372 responses were found to be valid for conducting the study. This sample size has been finalized taking into account the rule of thumb given by Hair et al. [99] for the partial least squares method of structural equation modelling (PLS-SEM) analysis which rationalized 10 times of the total indicators used under the defined constructs of the study. Thus, the sample size of 372 has been used for reaching the inference by using PLS-SEM as the minimum required sample size for this study and 280 along the 28 items used for the study, i.e., 28 multiplied by 10.

4.2. PLS-SEM

PLS-SEM is one of the widely used methods for multivariate data analysis. It has a causal-predictive approach to SEM that emphasizes prediction in estimating those statistical models, whose structures are designed to provide causal explanations [100]. The study used PLS-SEM analysis to test the structural equation model. The regression equation formulated for PLS-SEM analysis is
BI = β0 + β1 (Trust) + β2 (Perceived Risk) + β3 (User Friendliness) +
β4 (Perceived Usefulness) + β5 (Emotional Arousal) + ϵ
where, β1, β2, β3, β4, β5 = Path coefficients that represent the strength of the relationship between the independent variables and the dependent variable.
ϵ = Error term (residuals) that accounts for variance not explained by the independent variables.

5. Results

To derive the results for the formulated research objectives, PLS-SEM analysis was conducted along the formulated regression equation using Smart-PLS 4 to validate and test the structural model framed for the study.

5.1. Demographic Analysis

The demographic variables used in the study are the age, gender, educational qualification, occupation, annual income, professional qualification on investment management, and types of investments held by the investors. The demographics of the respondents collected through the questionnaire are shown in Table 2.
Table 2 reveals that the majority of the respondents are in the age group of 18 to 25 (47.1%), followed by the age group of 25 to 35 (37.1%). The percentage of the female participants is higher than the male participants. The educational qualification of the respondents shows that 64.3% of them are post-graduates. The demographics show that the major share of respondents are private sector employees (41.4%) and students (31.4%). A total of 50% of the respondents have an annual income below 500,000. Further, the results show that stocks are the most preferred type of investment chosen by 70% of the respondents, followed by fixed deposits with 67.1% and mutual funds with 57.1%. Finally, it shows that 71.4% of the respondents are already aware of sustainable investments.

5.2. Construct Validity and Reliability

The reliability of the constructs was checked using Cronbach’s Alpha and composite reliability (CR). Hair et al. [100] has stated that both the Cronbach’s Alpha and CR must be greater than 0.70 for the data to be reliable and consistent. Further, the factor loadings and average variance extracted (AVE) must be greater than 0.50 for ensuring the validity of the data. The results derived from the reliability and validity test are shown in Table 3.
As shown in Table 3, all the constructs have Cronbach’s alpha and CR values greater than 0.70, hence satisfying the construct reliability. Further, the results of factor loadings and AVE of all constructs are also satisfactory as the values are greater than the threshold value of 0.50. So, both the convergent validity and the reliability of the model are satisfactory.
In addition to the convergent validity, discriminant validity ensures that each construct is different from the other constructs. The study used Fornell–Larcker criterion for measuring the discriminant validity. Fornell and Larcker [101] stated that the discriminant validity can be assessed by comparing the square root of AVE of each construct with the correlation coefficients of other constructs. Discriminant validity is achieved when the correlations of other constructs are less than the square root of AVE of that construct. The calculated results of discriminant validity for the study are given in Table 4.
Table 4 shows that discriminant validity for all constructs has been ensured as it satisfies the criterion given by Fornell–Larcker, i.e., the square root of AVE of each construct is greater than its correlation with the other constructs.

5.3. Structural Model Analysis and Hypotheses Testing

Following the accomplishment of validity and reliability of the measurement model, the structural model was analyzed to test the validation (acceptance or rejection) of the hypotheses framed for the study. To assess the explanatory power of the structural model, the coefficient of determination (R2) was used in PLS-SEM. The R2 value can range from 0 to 1, with the values 0.75, 0.50, and 0.25 considered as substantial, moderate, and weak [100]. Further, the hypotheses can be tested with the help of a t-value. A t-value is deemed statistically significant for two-tailed testing when its corresponding p-value is less than 0.05 and it is outside the range of −1.96 and +1.96 at 95% confidence level. Table 5 reveals the summary of the results derived by bootstrapping the structural model to test the hypotheses.
Table 5 gives a summary of the structural model including R2 value, coefficients (Beta), t-statistics, and p-values (significance). The R2 of the model is 0.501, which can be considered satisfactory as there is moderate explanatory power of the model. The coefficient values show that there is a negative impact of emotional arousal and perceived risk on behavioural intention of the investors whereas trust, user friendliness, and perceived usefulness have a positive impact. The table also shows the p-values of the constructs which is further illustrated in Figure 2.
Figure 2 also illustrates the structural model used in the study along with the p-values of each relation. The first five alternative hypotheses are accepted as there is a significant impact of trust, perceived risk, user friendliness, perceived usefulness, and emotional arousal on the behavioural intention of the investors to use robo-advisors for making sustainable investments with the p-values being less than 0.05 and the t-statistic falling outside the range of −1.96 and +1.96. This shows that the factors related to robo-advisors make the investors interested in investing in sustainable investments as they have a perceived trust and emotional arousal towards robo-advisors in making decisions. Sustainable investment awareness significantly moderates trust, perceived risk, user friendliness, and emotional arousal whereas it has no significant moderation on perceived usefulness. The perceived usefulness is about the benefits related to the robo-advisory technology and thus the presence or absence of sustainable investment awareness does not show any significant impact on the usefulness of the technology as the p-value for the relation is 0.300 and the t-statistic is 1.037.
The investors are willing to use robo-advisors based on the perceived usefulness that can be derived from it irrespective of their awareness about sustainable investments. Further, government regulations significantly moderate perceived risk, user friendliness, perceived usefulness, and emotional arousal but have no significant moderation on trust. In general, the trust in a technology is enhanced by the support of government regulations regarding the safety and security of the technology but in this study, the moderator, government regulations, is about the government regulations related to sustainable investments which include tax incentives and subsidies. Thus, the trust of investors on robo-advisors is not affected by the government regulations safeguarding sustainable investments with a statistical p-value 0.605 and t-statistic 0.517.

6. Implications

The study provides certain managerial as well as academic implications as it has been conducted to analyze the behavioural intention of the investors towards using robo-advisors while making sustainable investments. For this, the study has adapted a new facet by using selected factors from behavioural finance theories and based on the results derived, the following managerial and academic implications can be made.

6.1. Policy Implication

The results of the study clearly show that the government and financial organizations must spread more awareness about the prospects and benefits of sustainable investments for generating interest among the investors. The government may come up with a set of regulations including tax benefits and subsidies influencing the usefulness and psychological inclination, along-with moderating the perceived risks in shaping the behavioural intention for sustainable investment.

6.2. Managerial Implication

The management should focus on designing the robo-advisors in a manner that may ensure user friendliness, secured personal data management, relevance of information, and accuracy to avoid risk and build trust for strong behavioural intention among the investors. The result also indicates that the interface should be very transparent and capable of enriching the confidence for using robo-advisors to strengthen the behavioural intention of the investors.

6.3. Academic Implication

The results of the study also lead to academic implications as it has considered several factors related to robo-advisors in integration which were part of the separate study earlier assessing their impact on sustainable investment decisions. Therefore, the framework in the study provides multifaceted dimensions for conducting such a study using a structural equation model. Future research could utilize the newly identified set of variables to assess their impact on other technological adoption mechanisms. They may be used along with other methodologies and behavioural theories.

7. Conclusions

The study assesses the impact of the use of robo-advisors for making sustainable investments. Through a literature review, trust, perceived risk, user friendliness, perceived usefulness, and emotional arousal on the use of robo-advisors are used as the independent variables along with sustainable investment awareness, and government regulations on sustainable investments as the moderating variables that affect sustainable investment intention of the investors. The conceptual model and hypotheses for the study were constructed around these variables which were tested by conducting PLS-SEM analysis. The result of the analysis shows that trust, perceived risk, user friendliness, perceived usefulness, and emotional arousal related to robo-advisors have a significant impact on the behavioural intention of investors towards sustainable investments as the suggestions given by robo-advisors are accepted as trustworthy and useful that makes them more interested in selecting sustainable investments. The variables of perceived risk and emotional arousal have a negative impact on the behavioural intention whereas trust, user friendliness, and perceived usefulness have a positive impact on the behavioural intention. Sustainable investment awareness significantly moderates all other independent variables except for perceived usefulness as the usefulness of the robo-advisory technology cannot be accelerated by the awareness about sustainable investments. Further, government regulations have a significant moderation over the independent variables except for trust as the trust on robo-advisors and government regulations related to sustainable investment cannot affect each other. Thus, it can be stated that the use of robo-advisors is having a significant influence, both positive and negative, on the behavioural intention of investors for preferring sustainable investments which safeguards our environment and society.

8. Limitations

The study is limited to analyzing the behavioural intentions of the investors towards using robo-advisors for making sustainable investments. Therefore, the study does not reveal any sectoral division of the investments made by the individual investors. Thus, future studies can include the sector-wise or industry specific division of the investments made with the help of robo-advisors by the investors.

Author Contributions

The idea was conceived by N.M. and S.S. and the literature survey was conducted by R.S. and S.K. to identify the constructs along which the questionnaire was constructed and administered by N.M., S.S. and R.S. The analysis was conducted by N.M., S.C. and S.K. The acquired results from the analysis were used to write the paper by N.M. and G.S. The original draft was edited by S.C. and S.S. The project was administrated by G.S. All authors have read and agreed to the published version of the manuscript.

Funding

The research work has not received any funding from any organization or academic institution.

Institutional Review Board Statement

According to the Portuguese law and Indian legislation, this study was carried out in the “Sustainable Investment Decision” field, which does not require pre-approval of an Ethical Committee.

Informed Consent Statement

The authors inform that participation in responding to the questionnaire was voluntary and the data were treated anonymously.

Data Availability Statement

The data presented are available on request from the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 17 03897 g001
Figure 1. Conceptual research model.
Figure 1. Conceptual research model.
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Figure 2. Result of structural model.
Figure 2. Result of structural model.
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Table 1. Constructs and measurement items.
Table 1. Constructs and measurement items.
ConstructItemsSource
TrustI believe the robo-advisor gives accurate information.[70]
Robo-advisors are transparent in sharing information when I enquire for specific investment purpose or grievance.[70]
The recommendations given by robo-advisor for sustainable investment caters for more productivity and safety.[83]
The information about sustainable investment provided by Robo-advisor is consistently reliable.[83]
The robo-advisor acts in my best interest to suggest right sustainable investment options.[83]
Perceived RiskI fear that any mistake made by the robo-advisor may negatively impact my investment choices.[83]
I believe that using robo-advisory service for sustainable investment may be risky.[83]
I feel concerned that using robo-advisory services may lead to financial losses in my sustainable investments.[83]
I worry about the security of my personal data while using robo-advisors.[63]
User FriendlinessI can easily learn and remember how to use robo-advisors for making sustainable investments.[70]
I find the robo-advisor’s interface quite easy to use.[95]
I find the robo-advisor’s features to be intuitive and easy to understand.[70]
Using robo-advisors requires less mental effort.[95]
Perceived UsefulnessThe robo-advisor enhances my efficiency to make better sustainable investment decisions.[83]
The robo-advisor can improve the overall performance of my sustainable investment portfolio.[83]
I believe that the robo-advisor provides useful insights into sustainable investment opportunities.[68]
The robo-advisor is helpful in aligning my investment strategy with sustainability.[63]
Emotional ArousalI get nervous while using a robo-advisor for taking sustainable investment decisions.[83]
I get a sense of confusion with the use of robo-advisors.[96]
I feel frustrated while using the robo-advisor to make sustainable investments.[97]
I feel anxious about the success of my sustainable investments while using robo-advisors.[98]
Sustainable Investment AwarenessAdvice given by financial experts on sustainable investment opportunities influences my investment decisions.[92]
The knowledge about the difference between traditional and sustainable investments increases my interest towards sustainable investments.[92]
Environmental, social, and governance (ESG) measures adopted by a firm influences my sustainable investment decision.[93]
Government RegulationsThe government’s tax incentives for sustainable investment impact my investment decisions.[94]
The subsidies provided by the government on sustainable investment have an impact on my investment choices.[94]
Behavioural IntentionI intend to keep using robo-advisors for making sustainable investment decisions.[83]
I would recommend others to use robo-advisors for making sustainable investments.[70]
Table 2. Demographic analysis of respondents.
Table 2. Demographic analysis of respondents.
VariablesCategoriesPercentage (%)
Age18–2547.1
25–3537.1
35–4515.7
GenderFemale52.9
Male47.1
Educational qualification12th Standard (Intermediate)1.4
Under-graduate27.1
Post-graduate64.3
PhD/and above7.1
OccupationGovernment sector employee7.1
Private sector employee41.4
Student31.4
Self-employed10
Unemployed10
Annual incomeBelow 500,00050
500,000–1,000,00017.1
1,000,000–2,000,00015.7
2,000,000 and above17.1
Professional qualification on investment managementYes18.6
No81.4
Types of investmentsStocks70
Bonds35.7
Fixed deposit or CDs (Certificates of Deposit)67.1
Mutual funds57.1
Real estate17.1
Commodities (e.g., gold, silver)34.3
ETFs (Exchange Traded Funds)7.1
Cryptocurrencies11.4
Alternative investments (e.g., hedge funds, collectibles)4.3
Sustainable investment awarenessYes71.4
No28.6
Source: Authors’ calculation.
Table 3. Results of reliability and validity test.
Table 3. Results of reliability and validity test.
ConstructItemsFactor LoadingsCronbach’s AlphaAVECR
Trust (T)T10.9410.9580.8560.963
T20.932
T30.926
T40.912
T50.915
Perceived risk (PR)PR10.7970.9050.7791.002
PR20.957
PR30.887
PR40.881
User friendliness (UF)UF10.9480.9650.9040.972
UF20.940
UF30.959
UF40.956
Perceived usefulness (PU)PU10.9080.9360.8400.938
PU20.896
PU30.938
PU40.924
Emotional arousal (EA)EA10.7690.9040.7720.953
EA20.895
EA30.933
EA40.908
Sustainable investment awareness (SIA)SIA10.9440.9170.8570.928
SIA20.899
SIA30.933
Government regulations (GR)GR10.9490.8440.8620.895
GR20.907
Behavioural intention (BI)BI10.9400.7840.8170.862
BI20.867
Source: Authors’ calculation.
Table 4. Results of discriminant validity.
Table 4. Results of discriminant validity.
VariableBIEAGRPRPUSIATUF
BI0.904
EA0.3350.879
GR0.5140.7800.929
PR0.1550.4350.4870.882
PU0.3840.8660.8290.4490.917
SIA0.2920.5320.6860.2920.5920.926
T0.3450.5670.4410.1200.4410.1650.925
UF0.2610.1550.145−0.0410.1640.2100.1320.951
Source: Authors’ calculation.
Table 5. Summary of structural model assessment.
Table 5. Summary of structural model assessment.
ConstructsCoefficientt-ValueSig.
EA→BI−0.7215.2780.000 ***
GR→BI0.93111.5160.000 ***
PR→BI−0.1252.330.020 **
PU→BI0.292.7930.005 ***
SIA→BI0.3053.9940.000 ***
T→BI0.192.3480.019 **
UF→BI0.2275.4130.000 ***
SIA ×PU→BI0.1851.0370.300
GR × PR→BI0.2992.8390.005 ***
SIA × EA→BI0.6693.3560.001 ***
GR × UF→BI0.2693.4480.001 ***
SIA × T→BI0.374.2390.000 ***
GR × EA→BI−0.8584.7560.000 ***
SIA × PR→BI0.5644.8650.000 ***
GR × PU→BI0.382.3080.021 **
SIA × UF→BI0.2012.6340.008 ***
GR × T→BI0.0620.5170.605
R20.501
*** p < 0.01, ** p < 0.05. Source: Authors’ calculation.
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Mohapatra, N.; Shekhar, S.; Singh, R.; Khan, S.; Santos, G.; Carvalho, S. Unveiling the Nexus Between Use of AI-Enabled Robo-Advisors, Behavioural Intention and Sustainable Investment Decisions Using PLS-SEM. Sustainability 2025, 17, 3897. https://doi.org/10.3390/su17093897

AMA Style

Mohapatra N, Shekhar S, Singh R, Khan S, Santos G, Carvalho S. Unveiling the Nexus Between Use of AI-Enabled Robo-Advisors, Behavioural Intention and Sustainable Investment Decisions Using PLS-SEM. Sustainability. 2025; 17(9):3897. https://doi.org/10.3390/su17093897

Chicago/Turabian Style

Mohapatra, Nargis, Sameer Shekhar, Rubee Singh, Shahbaz Khan, Gilberto Santos, and Sandro Carvalho. 2025. "Unveiling the Nexus Between Use of AI-Enabled Robo-Advisors, Behavioural Intention and Sustainable Investment Decisions Using PLS-SEM" Sustainability 17, no. 9: 3897. https://doi.org/10.3390/su17093897

APA Style

Mohapatra, N., Shekhar, S., Singh, R., Khan, S., Santos, G., & Carvalho, S. (2025). Unveiling the Nexus Between Use of AI-Enabled Robo-Advisors, Behavioural Intention and Sustainable Investment Decisions Using PLS-SEM. Sustainability, 17(9), 3897. https://doi.org/10.3390/su17093897

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