Exploring the Relationships between Virtual Client Meetings, Financial Anxiety, and Trust in Financial Planning

This paper proposes a theoretical framework that integrates the theory of polymedia (Madianou & Miller 2013) and the TAM (Davis 1989) to provide theoretically informed considerations for fostering trust when working virtually with clients. These theories are appropriate as clients and financial planners can now communicate through a variety of channels, including face-to-face meetings, phone calls, emails, and videoconferencing. For example, it is not uncommon for planners to email, have a phone call, and meet with the same clients all within the same week. The theory of polymedia (Madianou & Miller 2013) posits that the choice of communication channel will have a direct impact on the planner–client relationship (Archuleta et al. 2021; Collier et al. 2024). These multiple touchpoints can facilitate the working alliance between the professional and the client (Keller 2021). Yet, the choice of communication needs to be intentional as Brown (2020) warns that clients want communications that are relevant and personalised. As each channel will have unique characteristics that result in unintended social and emotional consequences, there is a moral responsibility to use the different types of media appropriately (Madianou & Miller 2013). For example, if the financial planner needs to discuss a spouse’s estate plan with their client, a text message is probably not the best channel choice. The potential for emotional and social consequences generated from various media options lies at the centre of the modality choice—and movement amongst media options—in the financial planner–client relationship. Our paper examines how trust (a socio-emotional consequence) is facilitated or hindered using virtual client meetings.

To provide more insights into how virtual client meetings impact trust, a second theory was added to the theoretical framework—the TAM (Davis 1989). TAM (Davis 1989) is a theoretical framework used to explain and predict users’ adoption and acceptance of technologies such as video conferencing. The TAM model is rooted in reasoned action theories. The theory of reasoned action by Fishbein and Ajzen (1975) and the theory of planned behaviour by Ajzen (1991) both emphasise the role of antecedents (attitudes, subjective norms, perceived behavioural control) on behavioural intentions, which in turn influences actual behaviour. The TAM simplified the antecedents into two key determinants: PEU and perceived usefulness. Both determinants are theorised to shape behavioural intention, which predicts the actual use of the technology (this is how it aligns with the broader tradition of reason action theories). Our study focuses on the client’s report of how easy it is to use their virtual meeting platforms from their perspective. Users are more likely to accept and adopt a technology if they perceive it as easy to use, as this reduces the cognitive and physical efforts required to engage with the technology. We chose not to include perceived usefulness, as research has found fintech and virtual meetings do not decrease the time needed to meet with clients or produce efficiencies (Tharp et al. 2021), which is the key feature in the perceived usefulness measure (Teo, Luan & Sing 2008).

This research focuses on the relationship between clients’ perceptions of technology’s ease of use and their satisfaction with virtual meetings. To link this concept of PEU with the theory of polymedia, we hypothesise that if clients find virtual meetings easy to use and enjoyable, then that channel of communication will create a positive socio-emotional experience, which will facilitate client trust (Davis 1989; Madianou & Miller 2013).

While research has explored how PEU leads to technology adoption in other professions, the financial-planning literature has yet to examine how tele-financial planning relates to client outcomes (Archuleta et al. 2021). There are many important client outcomes, but a trusting relationship is critical for client engagement and follow-through (Alyousif & Kalenkoski 2017; Pullen & Rizkalla 2003). Tele-financial planning and financial anxiety are the two primary predictors of client trust in this study. There are many components to tele-financial planning, but this study primarily focuses on PEU, virtual meetings, whether a client has a choice in selecting the meeting format, and their satisfaction with the virtual meeting. Financial anxiety has been shown to influence planner–client relationships in various ways, such as avoiding meetings with financial planners (e.g., Gerrans & Hershey 2017). More on each of these elements is discussed below.

In 2016, Cull and Sloan created the most comprehensive review to date of trust in financial planning highlighting that its multifaceted nature necessitates recognising both the affective and cognitive aspects of trust. The affective dimension is associated with the emotional side of trust (e.g., the sense of security and benevolence in the other person) whereas the cognitive dimension aligns with the competency aspect (e.g., the belief in the other person’s expertise and dependability) (Cull & Sloan 2016). This study measures client trust using a scale developed by Sharpe et al. (2007), which defined trust as “the belief that the financial planner can be relied on to behave in such a manner that the long-term interests of the client will be served” (p. 7). This is similar to the definition of trust developed by Cull (2016, p. 17), which states that in a financial planning context, “adviser (trustee) can be relied on to act honestly, competently and in the best interests of the client (trustor) and thereby reduce the trustor’s risk of loss.”

Building trust with clients is a worthwhile endeavour in financial planning, whether meeting in person or virtually. Research has shown that trust is essential for improving the likelihood of clients being honest about their financial situation, encouraging clients to seek a financial planner for advice (Alyousif & Kalenkoski 2017), and promoting the follow-through of financial recommendations (Hunt, Brimble & Freudenberg 2012). However, building trust is not easy for several reasons. First, there is still a taboo against talking about money openly in many cultures (Alsemgeest, 2016). According to McCoy, White and Chen (2019), 83% of individuals in America had not spoken to anyone about their finances in the previous year. Furthermore, the client may experience difficulties understanding the complexities of the financial planning process, which creates a sense of uneasiness (Pullen & Rizkalla 2003). Sharpe et al. (2007) found that nonverbal and verbal communication skills were primary antecedents of client trust. Nonverbal communication can refer to facial expressions or posture from either the planner or the client, while verbal communication refers to the planner’s ability to restate or summarise what the client said (Sharpe et al. 2007). Communication skills may be especially tested during virtual meetings, where it is difficult to make out nonverbal cues and connection issues may prevent effective verbal communication (Oeppen, Shaw & Brennan 2020).

The relationship between virtual meetings and financial planner–client trust still needs to be established (Archuleta et al. 2021), but research in virtual teams and telehealth can provide some initial direction. Virtual teams also may have more difficulty building trust (Hacker et al. 2019). Research from MIT’s Human Development Lab (Pentland 2012) found that in a team setting, face-to-face interactions were the most valuable method of communicating, with videoconferencing and phone conversations being the next best methods. According to Springer et al. (2020), it takes more to build trust through a screen than in person due to the previously described challenges. There may be initial discomfort or anxiety when meeting virtually with a professional, which can inhibit building rapport and trust (Springer et al. 2020). In their systematic literature review, Simpson and Reid (2014) determined that a therapeutic alliance, which refers to the bond between therapist and client, can be developed during videoconferences. Patients generally reported similar therapeutic alliance levels for in-person and virtual meetings, and Simpson and Reid (2014) suggest this could be due to therapists making additional preparations and putting in extra effort to improve outcomes. However, without these intentional efforts to improve communication and mitigate technology issues, virtual meetings may inhibit patient trust (O’Reilly-Jacob, Vicini & Duggan 2022).

Fox and Bartholomae (2020) interviewed financial planners who expressed their reliance on Zoom during the COVID-19 pandemic for client meetings. For some financial planners, this was a positive shift as they were already aiming to use more digital tools, while others lost some of their clientele and struggled to build relationships virtually (Fox & Bartholomae 2020). From a client perspective, adopting and trusting financial technology may be tied to their PEU (Nangin et al. 2020; Prastiawan, Aisjah & Rofiaty 2021). Studies from related fields support the efficacy of virtual mediums to increase client engagement—while maintaining client satisfaction—compared to face-to-face interactions (Sensenig et al. 2020). The benefits of virtual sessions must be weighed against an increase in training needs, dropouts, privacy concerns (Sensenig et al. 2020), and other challenges such as “Zoom fatigue” (Shoshan & Wehrt 2022). Virtual meetings can provide evidence beyond client satisfaction. For example, an Australian study on technology and the client–planner relationship found that while the COVID-19 pandemic presented challenges for comforting clients in person during a time they needed it most, technology adaptation provided opportunities for both cost savings and time savings, which extended beyond the global crisis (Loy et al. 2021).

Client choice may be an important component in determining whether clients experience satisfaction with virtual meetings (Hanna 2012). In telehealth, trust is related to patients’ perception of the ease of the technology used in the session and their ability to choose between in-person sessions and virtual sessions (Schnall, Higgins & Brown 2015; Zhou et al. 2018). In a study with medical students, there was evidence that video conferencing meetings themselves did not result in a substantial difference in the overall experience for the students (Zhou et al. 2018). However, in the qualitative comments, students stressed the importance of being allowed the option to choose their meeting type, which the authors suggest could improve satisfaction, and others reported frustrations with technical challenges (Zhou et al. 2018). Interestingly, McCoy et al. (2024) found that less than half of financial planning clients are given the option of whether their meetings are in person or virtual, even though most reported preferring in-person meetings. While virtual meetings are becoming more accepted in many fields such as education and health, in-person meetings are often a preferred method (Savary et al. 2022; Sorensen et al. 2020).

Financial anxiety has been shown to influence client–planner relationships, particularly client communication and engagement (Grable et al. 2015; Lim et al. 2014). There is preliminary evidence that financial anxiety also influences client trust (Machiz et al. under review). It is important to differentiate between financial anxiety and financial stress. Financial stress is induced by an external financial event or stressor that puts pressure on one’s ability to make ends meet, such as an unexpected bill, losing one’s job, or moving expenses (Britt et al. 2016; Lurtz 2020). Financial stress is usually time-bound; once the individual can address the financial concern, the stress will decrease or potentially disappear. Financial anxiety is different in that it does not have to be directly related to a financial stressor event and does not dissipate in the absence of a stressor event. It is marked by the sensation of worrying about one’s finances, even if, objectively speaking, they may have enough to meet their needs (Lurtz 2020). This study uses a financial anxiety scale derived from the generalised anxiety disorder diagnostic criteria (Archuleta, Dale & Spann 2013).

Research has shown that financial planners tend to underestimate the amount of financial anxiety their clients are experiencing (Anderson et al. 2022). Outcomes of financial anxiety include reduced comprehension of financial concepts and unhealthy financial behaviours like overspending or misuse of credit (Shapiro & Burchell 2012; Sages, Britt & Cumbie 2013). It has been noted that financially anxious individuals tend to avoid dealing with financial situations as a defence mechanism (Porges 2011; Shapiro & Burchell 2012). This avoidance of one’s finances may manifest in avoiding the financial planning session or the planner, which would reduce trust and commitment. Experiencing financial anxiety may also hinder the ability to use higher-level coping skills such as logical thinking because physiological arousal creates stress hormones like adrenaline or cortisol (Pittman & Carle 2015). Therefore, the client may not fully comprehend what the planner is saying during the sessions (Grable 2015). Considering the evidence that a client’s financial anxiety is a crucial factor in client–planner relationships and engagements, this study also seeks to examine whether financial anxiety impedes client trust.

Based on the guiding theoretical framework and the prior literature, we have developed the following hypotheses for this study:

H1. PEU will be positively associated with virtual meetings.

A survey was administered to clients of financial planners in Canada via Precision Data in June of 2023. The central focus of this research was to examine how tele-financial planning impacts client trust. From the initial set of 2,925 respondents, discrepancies in data pertaining to key screening questions reduced the sample size to 2,783. Within this group, 1,246 respondents indicated they did not engage with a financial planner. Conversely, 1,537 acknowledged interactions with financial planners. A follow-up question was posed to this subset of 1,537 to obtain further details on their experiences. Six respondents abstained from this question, resulting in a sample of 1,531. The data for the current study focused on those who use financial planners for more than only investment services (e.g., they also see their planner for other services such as retirement or tax planning) resulting in a final sample size of 633.

This study uses items and scales to measure client trust, virtual interactions, and financial anxiety. The control variables for this study include a variety of client demographics such as age, gender, education, marital status, race, household income, and self-reported financial knowledge. The number of years a client has worked with their financial planner, whether their financial planner has a designation, and the services clients see their financial planner for are also controlled. This information is presented in Table 1.

The dependent variable measures client trust by creating a latent variable using a validated five-item trust scale developed by Sharpe et al. (2007). This scale asked respondents to rate on a scale of 1 (disagree) to 5 (agree) their: (a) confidence in their financial planner’s recommendations, (b) confidence in their financial planner’s integrity, (c) confidence in their financial planner’s financial skills and expertise, (d) ability to rely on their financial planner to follow through on their commitments, and (e) trust that their financial planner will act in their best interests. Confirmatory factor analysis (CFA) revealed a good fit for the latent construct (χ² [3] = 1.15, p = 0.766; RMSEA = 0.00; CFI = 1.00; SRMR = 0.00; TLI = 1.00) with standardised factor loadings ranging from 0.74 to 0.89. Alpha for the construct was 0.94.

The independent variables were constructs that were predicted to influence client trust, either directly or indirectly.

Perceived ease of use (PEU). Clients’ PEU toward video conferencing was measured using an adaptation of the PEU Scale by Teo et al. (2008). The three items asked respondents to rate on a scale of 1 (disagree) to 5 (agree) whether (a) their interaction with video conferencing is clear and understandable, (b) they find it easy to get video conferencing to do what they want it to do, and (c) they find video conferencing easy to use. The specific software chosen is not included in this measure. Initial CFA revealed a good fit for the latent construct with standardised factor loadings ranging from 0.86 to 0.91. Given the three-item measure, it was just identified, and thus fit stats are not available (Kline 2016). Alpha for the construct was 0.92.

Virtual meeting choice. Clients were asked whether they had a choice to meet virtually or in person. The responses included (1) no, (2) asked preference for the first meeting, and (3) can choose virtual or in person for each meeting.

Client satisfaction. The client’s satisfaction with the frequency of virtual interactions was measured with a single item that asked clients whether they were satisfied with the frequency with which their planner interacted with them virtually. This was a binary yes/no variable.

Frequency of virtual meetings. The frequency of virtual meetings was collapsed into a binary variable to include those who met virtually at least once a year and those who never met virtually.

Financial anxiety. Financial anxiety was measured by the financial anxiety scale developed by Archuleta et al. (2013). Respondents were asked to rate on a scale of 1 (never) to 5 (always) their experience of the following: (a) feel anxious about their financial situation, (b) have difficulty sleeping because of their financial situation, (c) have difficulty concentrating on their studies/work because of their financial situation, (d) are more irritable because of their financial situation, (e) have difficulty controlling their worries about their financial situation, (f) their muscles feel tense because of worries about their financial situation, and (g) they feel fatigued because they worry about their financial situation. Initial CFA revealed a decent fit for the latent construct (χ² [14] = 160.98, p < 0.001; RMSEA = 0.13; CFI = 0.97; SRMR = 0.02; TLI = 0.95) with standardised factor loadings ranging from 0.79 to 0.90. Modification indices indicated that stress should be correlated with the other indicators of fatigue, worry, sleep, and irritability and that irritability should be correlated with sleep, and fatigue with feeling tense. With these correlations in place, the fit for the latent construct improved greatly (χ² [8] = 14.67, p = 0.066; RMSEA = 0.04; CFI = 1.00; SRMR = 0.01; TLI = 0.99). A chi-square difference test proved this to be true (χ² diff [6] = 146.31, p < 0.05). Final standardised factor loadings ranged from 0.75 to 0.92. Alpha for the construct was 0.96. Figure 1 depicts the conceptual model for these relationships.

Figure 1.

Conceptual model. 

After coding and preparing the data in STATA/SE 17.0, this analysis employed structural equation modelling (SEM) to analyse the results using MPlus 8.8. Two variables used in this study were binary, for which weighted least square means and theta parameterisation were used to estimate missing data (Muthén & Muthén 2018). The analysis for this study constituted three steps. First, descriptive statistics for the dependent and independent variables are presented. Second, a measurement model using CFA was used to evaluate factor loadings and model fit. Finally, a full structural model was used to test the effects as depicted in Figure 1 on client trust.

To test model fit statistics, MPlus 8.8 was employed to analyse the unconstrained model chi-square test of model fit, standardised root mean square residual (SRMR), the root mean square error of approximation (RMSEA), Tucker Lewis Index (TLI), and the comparative fit index (CFI). Values of less than .05 for SRMR and RMSEA indicate excellent model fit (Kenny 2015; Kline 2016), while values of 0.95 or greater for TLI and CFI are considered excellent for model fit (Kenny 2015), and a non-significant p-value (> 0.05) is considered a good fit when the sample size is greater than 400 (Kenny 2015).

The descriptive statistics and each latent variable indicator are presented in Table 2. The respondents’ trust scores were high across all five of the items in the client trust scale, with mean scores above four. The three items for PEU with video conferencing skewed slightly higher, with the highest mean score of 3.81 (SD = 1.11) for clear and understandable video conferencing interactions. Only 30% of clients were never given the choice to meet virtually or in person, and nearly 90% of clients said they were satisfied with the frequency of virtual meetings. The sample was nearly evenly split between those who met with their planner virtually at least once a year (48%) and those who never met with their planner virtually (52%). Finally, clients reported low levels of financial anxiety for each of the items. Feeling anxious about their financial situation had the highest mean score of 1.36 (SD = 1.09).

Three latent variables were created to measure PEU, financial anxiety, and client trust. The measurement model exhibited good model fit statistics (χ² [79] = 160.16, p < 0.001; RMSEA = 0.04; CFI = 0.99; SRMR = 0.03; TLI = 0.99). The modification indices indicated a strong correlation between two of the items in the financial anxiety scale (difficulty with concentrating and difficulty sleeping). These were correlated and improved model fit (χ² [77] = 149.37, p < 0.001; RMSEA = 0.04; CFI = 0.99; SRMR = 0.03; TLI = 0.99). A chi-square difference test indicated significant improvement of the revised model over the null model (χ² diff [2] = 10.79, p < 0.001). Each of the CFAs exhibited strong factor loadings and were significant (p < 0.001). See Table 3 for the factor loadings. The full structural model was then run considering that the CFAs and model fit were deemed satisfactory (Kline 2016).

Figure 2 depicts the full structural model for this study, with PEU, financial anxiety, and trust all regressed on the control variables. The model fit statistics indicated acceptable model fit (χ² [343] = 411.93, p = 0.006; RMSEA = 0.03; CFI = 0.96; SRMR = 0.09; TLI = 0.95). The model explained 63.7% of the variance in client trust (R² = 0.637, SE = 0.06, p < 0.001).

Figure 2.

Full structural model for the effects of … on trust.

Note: Standardised estimates. For clarity, the paths from the control variables are not shown, but were included in the analysis.

Solid lines = significant paths; dashed lines = non-significant paths.

Model fit indices: χ² (343) = 411.929, p < .01; TLI = .95; CFI = .96; RMSEA = .03; SRMR = .09.

*p < .05. **p < .01. ***p < .001 (two-tailed).

Table 4 reports the standardised and unstandardised results of the full structural model. The standardised results are discussed herein. Among the control variables, only age (β = 0.18, p = 0.021), education (β = −0.14, p = 0.012), and budgeting (β = 0.13, p = 0.007) were significantly related to client trust.

PEU was positively related to whether a client met with their planner virtually at least once a year (β = 0.27, p < 0.001). PEU was also directly and positively related to client trust (β = 0.27, p < 0.001). Those who saw their planner for retirement and estate planning also reported higher PEU scales. Clients who were given a choice on whether to meet with their planner virtually were more likely to meet virtually at least once a year (β = 0.37, p < 0.001), but it was not related to their level of satisfaction with the frequency of virtual meetings.

Both client satisfaction with virtual meetings and whether clients met virtually at all were directly related to their trust in their planners. Clients who reported greater levels of satisfaction with how frequently their planner met with them virtually reported greater trust (β = 0.44, p < 0.001). Interestingly, compared to those who never met virtually with their planner, those who met virtually at least once a year were more likely to report lower trust in their planner (β = −0.15, p = 0.012).

Age and subjective financial knowledge were negatively related to financial anxiety, too. Remarkably, those who saw their planner for budgeting and insurance had higher levels of financial anxiety. Financial anxiety was negatively related to client trust (β = −0.33, p < 0.001).

Planners appear to recognise which clients have greater PEU, as those clients were more likely to meet virtually. Improving PEU could be accomplished by fostering technology self-efficacy, which refers to the belief a person has in how well or how successful they will be able to use technology (Huffman, Whetten & Huffman 2013). Continuous exposure to technology could help promote technology self-efficacy (McCoy 2010). There may also be virtual planning tools and software available that clients may find easier to use or more secure, which would increase trust. Financial planners could create a quick “how-to” video for clients showcasing the tools they use, how to connect, etc., so that clients have something to walk through prior to the meeting actually starting. However, planners should consider how to find optimal technology solutions that are compliant with existing laws and regulations, as not every possible communication method is permitted.

Clients who are more satisfied with the frequency of their virtual interactions also reported greater levels of trust. More research is needed to understand what leads to this satisfaction. Financial planners should consider ways to improve satisfaction with virtual interactions, such as clear meeting expectations or testing technology ahead of time (Archuleta et al. 2021). In any case, financial planners should not let their own biases or preferences (about virtual versus face-to-face engagements) dictate the platform of the financial planning meeting. Future studies may also want to examine methods of training financial planners to effectively use virtual tools with their clients to increase trust and satisfaction. Furthermore, Ludwig & Bennetts (2023) suggest that while AI is unlikely to replace financial advisors, those who adeptly incorporate it into their practice may gain an advantage over those who do not. For example, there are AI tools (e.g., Fathom, Fireflies, Otter, and more) that can automatically record and take notes while also providing a summary of action items for all parties (client and planner), further reinforcing the value of embracing technology in financial planning.

It is possible that the negative association between virtual meetings and trust points to the challenge of building strong client relationships through a virtual medium compared to face-to-face meetings (Oeppen et al. 2020; Sensenig et al. 2020). Although challenging, financial planners’ awareness of this could encourage the investment of additional time in rapport building within the online environment (Archuleta et al. 2021). Focusing on improving virtual communication skills (Sharpe et al. 2007) could mitigate the negative relationship between virtual meetings and client trust. It may also be helpful for planners to go slower and repeat important messages during online meetings to ensure the client can hear them. Moreover, planners may benefit from talking to their clients ahead of time about what technology they use and what their internet connection is like (Archuleta et al. 2021). This could prevent some of the frustrating connection issues that may impede communication (and ultimately trust).

The finding that financial anxiety is negatively related to trust aligns with previous studies and adds additional evidence for this phenomenon (e.g., Grable et al. 2015; Machiz et al. under review). To effectively foster trust in clients, planners need to attend to their client’s financial anxiety. As previous research has pointed out, planners underestimate their client’s level of financial anxiety (Anderson et al. 2022). Some clients may seem actively nervous (e.g., legs shaking or pressured speech), however, others may present with anger, somatic issues, or even avoidance (Stein et al. 2021). Thus, it may be important to have a formal financial anxiety measurement, such as the Financial Anxiety Scale (Archuleta, Dale & Spann 2013) integrated into assessment packages along with other financial assessments (e.g., risk tolerance and cash flow analysis). According to the theory of polymedia (Madianou & Miller 2013), our choice of communication outlet has socio-emotional impacts, which may shape our interpretations of what is being said (Mazor et al. 2013).

Several limitations must be noted due to the convenience and size of the sample, and the cross-sectional nature of the study. The sample size was relatively small, and therefore, it is recommended that future studies be conducted with larger sample sizes to increase the generalisability of the results (Brown & Lee 2019). Furthermore, the study population was specific to Canadians, while the theories and literature reviewed used in the study are American-centric, making broad generalisations difficult. As with many datasets, the demographic population skewed heavily white. Due to survey length constraints, only a subjective assessment of financial literacy was included. Future studies may want to include objective measures as there may be a disconnect between these objective and subjective measures (Xin et al. 2024). Respondents in the survey reported high levels of trust on average, which may indicate the sample is more trusting of their planners than the general population. Lastly, one of the control variables included in this study was whether their advisor had a professional designation. Clients may not always know whether their financial planner has a designation.

More research could clarify the nature of these empirical relationships, such as the antecedents to virtual meetings. Hacker et al. (2019) lists multiple possible antecedents to trust in virtual settings such as cultural values and interpersonal relationships that may apply in the financial planning field. Future studies may also want to further examine meeting choice, and client satisfaction with specific types of technology such as videoconferencing, emailing, etc. Other elements of tele-financial planning could influence client trust, such as the method of communication, examining more meeting frequencies (i.e., annually versus semi-annually), or the client’s perception of their planner’s empathy. Client tenure should also be considered as it, and the type of meeting (annual review, tax review, estate review etc.), could also have an impact on client trust. These same factors may also relate to other client outcomes such as client commitment or shared values. A replication and comparative study on the US population or another country would provide robust information. Finally, as software, hardware and the model of advice changes over time, future research could create a repeated measure design to explore changes over time.

Although previous research shows the significant limitations of data-gathering within video calls (Britt, Lawson & Haselwood 2016), the ever-improving research technology landscape might lend insight into the biological and nonverbal drivers behind video calls. For example, future research could use biometric and eye tracking technology to gather novel data from clients within the virtual environment. This novel data, in conjunction with insights gathered in this study, could give a better overall picture of what compels (or repels) engagement with virtual financial planning and its corresponding outcomes.