Introduction
Literature suggests that most students who choose to enroll in computer courses generally do so because they have the intention to become entrepreneurs in the future (McArthur, 2020; Smith et al., 2020). It is essential that computer college students be exposed to entrepreneurial ideas and be inspired to pursue their own ventures (Doboli et al., 2010). There is also a call to provide computer college students with lifelong financial literacy skills by integrating financial education within the context of skills training (Paterson Community Technology Center, 2002). Furthermore, prior research suggests that computer college students who are taught entrepreneurial skills can develop into potential entrepreneurs (Daimi & Rayess, 2008). According to Lawler and Joseph (2011), computer college students should be exposed to financial technology entrepreneurship as a crucial component of the curriculum. McArthur (2020) observed that it is necessary to teach computer college students the importance of applying business strategies to the creation and development of their own ideas.
Entrepreneurial risk attitudes such as financial risk tolerance and attitude towards financial risk are critical concepts that are addressed within the framework of entrepreneurship program and research (Bayar et al., 2020; Park & Yao, 2016; Saurabh & Nandan, 2018). According to the International Organization for Standardization (2006), risk tolerance refers to the degree to which one is willing to experience a less favorable outcome for a more pleasing outcome. Financial risk tolerance is defined as the amount of uncertainty one is willing to accept when making financial decisions that could result in a loss (Cordell, 2001). Financial risk tolerance can be assessed by objective and subjective methods, but because each method has its own advantages and disadvantages, the evidence on which is better has proved to be mixed (Ramudzuli, 2016). Risk attitude refers to the way people respond to uncertainty which is determined by their perception of it (Hillson & Murray-Webster, 2006). Financial risk attitude refers to the disposition to react positively or negatively to uncertainties that may affect one’s financial and entrepreneurial goals (Hyll & Irrek, 2015; Park & Yao, 2016).
It has been shown that online interventions are effective in assisting students towards developing positive attitudes (Oloidi et al., 2022; Pordelan et al., 2020). Current intervention research indicates that online business coaching program based on the rational-emotive and cognitive behavior therapy coaching (RE-CBT coaching) approach can enhance entrepreneurial risk attitudes such as tolerance and attitudes related to financial risk (Onyeanu et al., 2022). An online business coaching intervention to promote risk tolerance and attitudinal changes related to financial risk among Nigerian computer college students may have implications for college educators and administrators. In their study, Chikezie & Sabri (2017) reported that Nigerian students demonstrated poor financial wellbeing with 36.3% being on the low range, 50.7% demonstrated negative financial behavior while 11.50% and 23.92% exhibited feelings of obsession and inadequacy respectively in terms of their attitudes. The study by Ramudzuli and Muzindutsi (2018) showed that students’ age does not significantly affect their financial risk tolerance, and students’ sex may have a positive effect on their financial risk tolerance. As reported by Ramudzuli (2016), age and education level did not contribute significantly to differences in students’ financial risk tolerance. However, there is less information available about the effect of online business coaching based on the RE-CBT coaching on Nigerian college students’ entrepreneurial risk attitudes like tolerance and attitudes related to financial risk. Accordingly, the aim of this study was to bridge this research gap by investigating if online business coaching based on RE-CBT coaching could improve entrepreneurial risk attitudes (financial risk tolerance and attitudes) of Nigerian computer college students.
RE-CBT Coaching and Financial Risk
In the present study, the online business coaching program based on RE-CBT coaching approach is an entrepreneurship-based program designed to improve the students’ financial risk tolerance and attitude towards financial risk. Its philosophy is based on the RE-CBT coaching approach as it emphasizes modifying students’ thoughts, attitudes and behaviors related to entrepreneurial risk attitudes (e.g. Kodish, 2002; Onyeanu et al., 2022; Otu, 2020). In their research, Si and Zhang (2017) advocated for the use of the rational emotive behavior intervention approach to help individuals who have a low tolerance for frustrating situations. Onyeanu et al. (2022) also advocated for the use of rational emotive and cognitive behavior therapy coaching method in view of its utility in helping individuals who were intolerant of financial risk and displayed unhelpful attitudes towards it. The RE-CBT coaching approach aims to elevate individual’s entrepreneurial risk tolerance level. Theoretically, Ellis (1962) points to the tolerance of risk being needed for positive mental health. According to Ellis, as a rational person, one should strive to stand on their own two feet and to possess the ability to think and act for oneself rather than relying upon others or hypothetical abstractions. An individual who is rational would strive to achieve several concrete goals, including the ability to take risks (Ellis, 1962). Among the most essential things any rational person should recognize is that failure to accomplish certain goals is not inherently terrible and awful; that humans may learn mostly by failing; and that one’s failures have nothing to do with one’s personal worth as a human being (Ellis, 1962). A rational person would continue striving to attain whatever they desire in life, even if the probability of getting it is often low, and would adopt the philosophy that it is better to take risks and make mistakes of their own choosing, rather than selling their soul for the needless assistance of others (Ellis, 1962). Therefore, elevated risk tolerance is seen as a good characteristic for an individual to possess because it helps one to develop the ability to think and act independently, become committed to creative pursuits (Dryden, 1994), and reduces the probability of unnecessary reliance on others or hypothetical abstractions for aid. In order to formulate the current research hypotheses, we further drew insights from Onyeanu et al.’s (2022) theoretical elucidation of RE-CBT coaching that individuals have the capacity to change their low financial risk tolerance and negative attitudes towards financial risk to high financial risk tolerance and positive attitudes towards financial risk because they have the autonomy to either express functional or dysfunctional emotions and behaviors.
It is often the case that clients come and remain in therapy for two specific reasons: (i) healing, and (ii) growth (Ellis, 1962). Therefore, it is possible for a person who has been healed - that is, induced to surrender much of his intense and crippling anxiety or hostility - to still significantly grow as a human - that is, to re-examine and reduce some of his less intense and less crippling negative emotions, and to learn to take higher risks (Ellis, 1962, p.113). Three forms of changes may be attainable in a RE-CBT intervention and these include philosophical change, inferential change and behavioral change (Dryden & Bond, 1994). For a philosophical change to take place, the client must focus on their dogmatic views and then work steadily in order to transform these views to non-dogmatic preferences (Dryden & Bond, 1994). In addition, an inferential change occurs when people refute their inferences concerning their experiences rather than attempting to evaluate them as they actually occurred (Dryden & Bond, 1994). Finally, Ellis believes that clients have the ability to change their behavior. Thus, behavioral change usually aims to alter the activating event - that is, the “A” in the ABC model. As Dryden and Ellis (1988) argue, achieving philosophical change is often a necessary step toward change in inference or behavior. Conversely, inference and behavior are often less likely to lead to philosophical change. This is why Ellis has always emphasized that it is of the utmost importance to attempt to achieve philosophical change in therapy (Dryden, 1994; Dryden and Bond, 1994).
In order to achieve philosophical change which is crucial for therapy to be considered successful, the client must first realize that they are to a large extent responsible for creating their own psychological disturbances. The second thing they need to realize is that they have the capability to modify these disturbances in a significant way. The third step requires that they understand that the origin of emotional and behavioural disturbances is largely due to irrational, absolutistic beliefs. Moreover, they must be able to recognize their irrational beliefs and dissociate them from their rational beliefs. The fifth step is for them to use logical and empirical methods of scientific reasoning to dispute these irrational beliefs. Additionally, it is also important to employ cognitive, behavioural, and emotional methods of change in order to internally integrate their new rational beliefs. Lastly, they must continue to challenge irrational beliefs and use multi-modal methods of change for the rest of their lives in order to create lasting change (Dryden & Bond, 1994).
Study Rationale
Research show that individuals who engage in specific cognitive and emotional processes related to uncertainty and risk can readily accept such uncertainty and risk if they perceive reality in optimistic rather than pessimistic terms, and orient themselves towards benefits rather than threats (Singh et al., 2022; Yeşilkaya & Yıldız, 2022). There is increasing evidence to suggest that individual attitudes, cognitive and affective processes play a role in entrepreneurial risk attitudes. For instance, Zaleskiewicz et al. (2020) note that people who engage in such activities are more risk tolerant than others and they also accept risks more than the average person. Furthermore, Zaleskiewicz et al. (2020) suggest that a person’s willingness to take entrepreneurial risks can be explained by the way they perceive threats and opportunities rather than by their general risk-taking orientation. Other studies show that individuals that display entrepreneurial spirits have a more opportunity-oriented risk perception when they analyze business scenarios than those who do not (Momeni et al., 2022; Pratiwi et al., 2022). Such individuals tend to focus on strengths and benefits rather than weaknesses and threats when evaluating a business scenario. It has been suggested that entrepreneurial risk can be skewed by overly optimistic interpretations of reality, illusory feelings of control, and overconfidence taken together, which together form a positive bias (Cervellati et al., 2022; Chen et al., 2022). The positive bias is believed to result not only from cognitive, but also from emotional processes (Zaleskiewicz, Bernady & Traczyk, 2020). Wang and Poutziouris (2010) earlier found, for instance, that the patterns of entrepreneurs’ affective reactions change with experience, with an increased focus on positive emotions such as hope and happiness over time.
The report by the U.S. Bureau of Labor Statistics (2022) shows that about one-fifth of start-up businesses fail within the first two years of operation, and nearly 45% become bankrupt within five years. Further, the data indicate that most start-ups (65%) will not survive for more than a decade. It also estimates that only one-fourth of start-up businesses will survive beyond 15 years. The report shows that historically, start-up businesses are at a high risk of bankruptcy within few years of operation. In such an atmosphere, inadequate risk-taking results from people not having sufficient information about how, when and what options they have for dealing with a business problem, and what likely outcomes they expect (Gomes & Lopes, 2022; Infante & Mardikaningsih, 2022) which may be improved through business coaching based on the RE-CBT coaching approach. Although a number of people can offer RE-CBT coaching intervention, it is imperative that the coach is selected in line with the needs of the coachees (Kodish, 2002). An RE-CBT coach can identify an individual’s concerns and provide them with necessary supports through the help of online interactive tools like telegram, WhatsApp and electronic mail (Koledoye et al., 2021; Onyeanu et al., 2022). Also, in consideration of the statistical limitation of an RE-CBT coaching intervention that aimed to improve participants’ tolerance and attitudes towards financial risk (Onyeanu et al., 2022), a superior statistical technique has been adopted for the analysis of the current research data to shed more light on the utility of online business coaching program based on the rational-emotive and cognitive behavior therapy coaching approach in enhancing individual’s entrepreneurial risk attitudes. The rationale is to address statistical dependence issue that has been criticized to affect group-administered interventions (Baldwin, Stice & Rohde, 2008). To our knowledge, this is the first study to report the dataset on the use of online business coaching to improve computer college students’ entrepreneurial risk attitudes (tolerance for financial risk and attitude toward financial risk). We also added to the knowledge about coaching intervention that aimed to improve participants’ entrepreneurial risk attitudes by examining the dataset on interaction effects of group, sex and age of the students on these constructs.
Research Hypotheses
The following research hypotheses informed the current study:
1.
Online business coaching based on RE-CBT coaching approach will significantly improve college students’ financial risk tolerance.
2.
Online business coaching based on RE-CBT coaching approach will significantly improve college students’ attitudes towards financial risk.
3.
There will be significant interaction effects of group, sex and age on the students’ financial risk tolerance.
4.
There will be significant interaction effects of group, sex and age on the students’ attitudes towards financial risk.
Method
Study Design and Ethical Approval Statement
The researchers used an open label, group randomized control design to obtain the datasets examined in this study for computer college students from August 2021 to February 2022. There are several benefits of using an open label, group randomized control design, including the flexibility of assigning participants at random to one of two or more treatment conditions, comparing treatments, and obtaining further information about the effects of the treatment on the intended research participants (Concert Pharmaceuticals, 2019; Kendall, 2003; Poland et al., 2013). It also provides both the participants and researchers with the opportunity to know what kind of treatment is being delivered (Nair, 2019; Poland et al., 2013). The Faculty of Education Research Ethics Committee at the University of Nigeria approved this study [REC/UNN/FE/2021/00132]. Online informed consent was required from all participants.
Study Area, Population and Sample
The population of this study comprised all students in computer training colleges in Enugu State, Nigeria. In this study, students were selected from various computer training colleges in Enugu State, which lies in the southeast region of Nigeria. Based on Gpower sample sensitivity calculation (Faul et al., 2007), 80% statistical power resulted in the required minimum sample size (n = 105; effect size = 0.40; p = .05) for this study. This study employed a multi-step sampling approach that included purposive, convenience, and simple random sampling techniques. As a first step, purposive sampling was used to select eight colleges with large student populations, a good number of functional training facilities, and accreditation by regulatory government bodies to award advanced and diploma certificates in computer science. Second, four colleges (Digital Dreams ICT Academy, n = 27; Prince Computer School, n = 27; BMS Computer School, n = 27; and Ultimate Computer Training Institute, n = 26) whose management consented to allow their students to participate in the study were selected by convenience sampling. Third, simple random sampling was used to allocate 107 study participants from the four colleges into two study groups (see Fig. 1). It is argued that by utilizing simple random sampling, researchers will be able to eliminate any possibility of bias from their study (Horton, 2022; West, 2016). Taking into account that the individuals that comprise the sample of the target population are selected randomly, it follows that each individual within the target population has an equal chance of being chosen (Horton, 2022; Lund Research, 2012). This method may lead to a sample size that is balanced and has the highest chance of providing a good representation of the target population (Horton, 2022; Lund Research, 2012). The online business coaching group had 54 participants, while the untreated control group had 53 based on computer-generated random numbers (Saghaei, 2004). According to Beene (2020) and Sutton (2022), 10 to 15 participants are enough for a group coaching program. The choice of college students is due to the fact most students who attend computer colleges often do so because they intend to become entrepreneurs one day (McArthur, 2020; Paterson Community Technology Center, 2002; Smith et al., 2020).
Fig. 1
Study flow diagram
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Inclusion and Exclusion Criteria
To be considered for inclusion in this study, colleges must have substantial student populations, a good number of functional training facilities, and accreditation by the government; they must also give their informed consent for their students to participate in the research. For students to be considered eligible to participate in the study they must provide their informed consent in writing; all included students must demonstrate a low tolerance to financial risk and a negative attitude toward financial risk at baseline; and should not be in receipt of other forms of coaching for the same concerns. The research excluded colleges and students that did not meet all these criteria.
Outcome Assessment
Participants’ characteristics were collected using a sociodemographic form containing their age and sex. In the pre-test, post-test, and follow-up phases, the measures used to collect data from the study participants are as follows:
Grable-Lytton Risk Tolerance Scale (GL-RTS) (Grable & Lytton, 1999): The GL-RTS comprises 13 items on a response scale of 1 to 4, where 1 represents most risk-averse and 4 represents most willing to take risks. Summing the individual risk-tolerance scores gives the total risk-tolerance score. Low risk tolerance is indicated by scores less than or equal to 18, moderate risk tolerance by scores 19–35, and high risk tolerance by scores greater than or equal to 36. In terms of internal consistency, GL-RTS is adequate (Grable & Lytton, 1999). The GL-RTS had a test-rest reliability of 0.91 in a Nigerian bank customers’ sample (Onyeanu et al., 2022). Internal consistency reliability of this scale in our current sample was 0.87 Cronbach’s alpha.
Attitude Toward Financial Risk Questionnaire (ATFRQ)
In 2018, Metzger and Fehr developed a questionnaire that modified Keller and Siegrist’s (2006) attitude towards financial risk questionnaire. The questionnaire consists of six items rated on a five-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). The questionnaire is a valid predictor of actual investment behavior, as demonstrated by Metzger and Fehr (2018). Test-retest reliability was 0.90 in a Nigerian bank customers’ sample (Onyeanu et al., 2022). Internal consistency reliability of this questionnaire in our current sample was 0.86 Cronbach’s alpha.
Pearson’s r was used to establish evidence of test-retest reliability (Heidel, 2022a) for the GL-RTS and ATFRQ respectively. Data showed that scores for the GL-RTS were highly correlated across time (Pearson’s r = .76, p = .000). Data also showed that scores for the ATFRQ were highly correlated across time (Pearson’s r = .77, p = .000). According to Heidel (2022a), researchers have evidence of test-retest reliability if the p-value is less than 0.05 and the Pearson correlation coefficient is greater than 0.7. The one-way random effect intra-class correlation (ICC) was used to ascertain the inter-observer reliability (Landers, 2015; McLeod, 2007; Shrout & Fleiss, 1979) for the GL-RTS and ATFRQ respectively. Data showed an ICC value of 0.79 for the GL-RTS and an ICC value of 0.78 for the ATFRQ, both scores indicating evidence of excellent inter-observer reliability. Koo and Li (2016) assert that ICC values below 0.5 represent poor reliability, those between 0.5 and 0.75 represent moderate reliability, those between 0.75 and 0.9 represent good reliability, and those greater than 0.90 represent excellent reliability. Test-retest reliability and inter-observer reliability enable researchers to establish external validity, according to McLeod (2007). For internal validity, the split-half method was applied to establish that all parts of the instruments contributed equally to the attributes that are being measured (Heidel, 2022b; McLeod, 2007); the data showed a Guttman Splif-Half of 0.84 for the GL-RTS while a Guttman Split-Half coefficient of 0.82 was shown for the ATFRQ. Heidel (2022b) notes that a Guttman Split-Half coefficient above 0.80 can be considered an acceptable reliability.
Study Procedure
The coaching group received online business coaching, while the control group experienced no treatment. Twice a week, the online coaching meetings took place between 4 and 5 pm. An additional two-week, four-session follow-up took place three months after the coaching concluded. Based on Otu’s (2020) coaching program package, the study conducted its online business coaching within the framework of rational-emotive and cognitive behavior coaching approach. In order to improve the financial risk tolerance and attitude of students enrolled in computer colleges, online business coaching was delivered through telegram and email over an eight-week period.
Description of the Online Coaching Program
The online coaching program began with an introduction, acquainting participants with the concept of coaching, describing the responsibilities of online business coaches and coachees, and discussing the concepts of financial risk tolerance and attitudes during the first and second weeks of the program.
We used coaching techniques to redress the participants’ lack of ability to deal with financial risk and disputing of their unhelpful risk attitudes in the third and fourth weeks. A focus was also on enhancing the experiences of the participants and their commitment to fostering financial risk tolerance and exhibiting positive financial risk attitudes.
Fifth and sixth weeks focused on abilities and skills that promote healthy attitudes toward financial risk. Through online business coaching, participants learned how to identify and apply effective strategies to modify their financial risk tolerance and attitudes. Additionally in these phases, the coaches attempted to prepare participants for the role of becoming their own business coaches as well as inspire them to apply what they have learned throughout the coaching process.
After the coaching period, the seventh and eighth weeks, which were the follow-up phases, provided an opportunity to appreciate the participants’ efforts and assess whether any positive changes related to their financial risk tolerance and attitudes had been sustained.
Online coaching techniques used were goal setting and alliance building (Kodish, 2002). In addition, the intervention also drew a number of other techniques from Si and Zhang (2017) such as cognitive reframing, functional disputes, empirical disputes, and adversity coping statements that they advocated for modifying clients’ intolerance to adverse situations. In their study, Zaleskiewicz et al. (2020) suggested using mental imagery for determining willingness to take entrepreneurial risks. It was demonstrated in the authors’ work that a person would deliberately try to simulate the future and generate images that visualize the positive and negative consequences of their entrepreneurial decisions. Images that may be considered positive and pleasant would include earning money and developing the business. In other words, visualizing consequences can be interpreted in terms of a sort of pre-decisional ’trying out’ of various options for what might happen depending on the course of action taken (Ji et al., 2016). As a result, mental imagery technique was employed by the therapists in this coaching program to improve students’ financial risk tolerance and attitudes towards financial risk. Each session ended with take-home assignments for the students.
Therapists’ Training and Qualifications
Two female and two male coaches specializing in RE-CBT delivered the online sessions, and each had a doctorate degree in counselling psychology as well as a minimum of five years of cognate experience in online business coaching.
Statistical Procedures and Data Analyses
In light of the statistical dependences of data resulting from group-based interventions which could increase Type I errors if overlooked, the repeated measures multivariate analysis of covariance was employed for data evaluation at 0.05 probability level. Participants’ sex and age were used as covariates. With regard to the multivariate analyses, Wilks’ lambda (Λ) test was used to analyze the data. Partial η2 was reported as the estimate of the coaching intervention’s effect size. Sidak’s post hoc was conducted for students’ financial risk tolerance and financial risk attitude by Group x Time interaction. Mauchly’s Test of Sphericity indicated that the assumption of sphericity was violated for students’ financial risk tolerance data, χ2(2) = 80.438, p = .000, Mauchly’s W = 0.454. Also, Mauchly’s Test of Sphericity indicated that the assumption of sphericity was violated for the research data on students’ attitudes towards financial risk, χ2(2) = 93.39, p = .000, Mauchly’s W = 0.407. The Greenhouse-Geisser correction was applied to both datasets since the estimated epsilon (ε) were 0.647 and 0.628 (< 0.75). A Huynd-Feldt correction is suggested in cases where the estimated epsilon (ε) exceeds 0.75 (Lund Research, 2018). In the data processing and analyses, the major statistical application used was IBM Statistical Package for the Social Sciences (IBM SPSS, version 22).
Results
Data in Table 1 showed that there were no significant differences between groups in terms of mean age [p = .90], and sex [p = .21].
Table 1
Participants’ characteristics by group
Characteristics | Online Coaching Group (n, %) | Control Group (n, %) | Test value |
p
| |
|---|---|---|---|---|---|
Sex | Male | 29 (53.7%) | 22 (45.1%) | ||
Female | 25 (46.3%) | 31 (58.5%) | 1.59 | 0.21 | |
Age* | 25.11 ± 3.66 | 25.21 ± 4.22 | − .126a | .90a | |
The descriptive statistics (mean and standard deviation scores) concerning students’ financial risk tolerance and attitudes towards financial risk are shown in Table 2.
Table 2
Descriptive statistics (means and SDs) according to the study variables and study groups
Time | Group | GL-RTS Mean (SD) | ATFRQ Mean (SD) |
|---|---|---|---|
Time 1 | Control group | 15.89 (1.19) | 13.26 (1.11) |
Coaching group | 15.98 (1.27) | 13.54(0.64) | |
Time 2 | Control group | 15.54 (0.96) | 14.47(2.82) |
Coaching group | 35.97 (1.49) | 24.22(1.08) | |
Time 3 | Control group | 15.29 (0.88) | 14.34 (2.83) |
Coaching group | 36.09 (1.56) | 24.56 (1.18) |
As shown in pretest data in Table 3, between-subjects effects revealed a non-significant difference in students’ financial risk tolerance scores between the online business coaching group and control group [F(1,106) = 0.508, p = .478, Partial η2 = 0.007]. Posttest data based on the test of between-subjects effects revealed that online business coaching program based on RE-CBT coaching approach was significantly helpful in the improvement of students’ financial risk tolerance compared to control group [F(1, 103) = 5164.103, p = .000, Partial η2 = 0.980] (see Table 3). The effect of Time [F(1.294, 133.288) = 50.647, p = .000, Partial η2 = 0.330] and interaction effects of Time and Group [F(1.294, 133.288) = 4056.845, p = .000, Partial η2 = 0.975] on students’ financial risk tolerance were significant. Follow-up data based on the test of between-subjects effects further revealed that the significantly helpful effects of online business coaching program based on RE-CBT coaching approach on students’ financial risk tolerance was sustained when compared to control group [F(1,106) = 4364.135, p = .000, Partial η2 = 0.983] (see Table 3).
Table 3
Between subjects and interaction effects test for intervention effects on financial risk tolerance as measured with Grable-Lytton Risk Tolerance Scale
Source | Dependent Variable | Type III Sum of Squares | df | Mean Square | F | Sig. | Partial η2 |
|---|---|---|---|---|---|---|---|
Group | GLRTS-Time 1 | 0.895 | 1,106 | 0.895 | 0.508 | 0.478 | 0.007 |
GLRTS-Time 2 | 6324.978 | 1,103 | 14968.683 | 5164.103 | 0.000 | 0.980 | |
GLRTS- Time 3 | 6651.370 | 1,106 | 6651.370 | 4364.135 | 0.000 | 0.983 | |
Time | GLRTS | 92.691 | 1.294, 133.288 | 71.628 | 50.647 | 0.000 | 0.330 |
Time x Group interaction | GLRTS | 7424.660 | 1.294, 133.288 | 5737.498 | 4056.845 | 0.000 | 0.975 |
The multivariate analysis in Table 4 further suggest that the online business coaching program based on RE-CBT coaching approach had significant effects on the students’ financial risk tolerance [F(3,74) = 1694.778, p = .000, Partial η2 = 0.986, Wilks’ Λ = 0.014]. The moderating effects of Age [F(27, 216.760) = 1.167, p = .268, Partial η2 = 0.124, Wilks’ Λ = 0.673] and Sex [F(3,74) = 1.209, p = .312, Partial η2 = 0.047, Wilks’ Λ = 0.953] on the students’ financial risk tolerance were not significant. There were no significant interaction effects between Group and Age [F(18, 209.789) = 1.253, p = .222, Partial η2 = 0.092, Wilks’ Λ = 0.749], Group and Sex [F(3,74) = 1.551, p = .209, Partial η2 = 0.059, Wilks’ Λ = 0.941] as well as Group and Age and Sex [F(18, 209.789) = 0.741, p = .766, Partial η2 = 0.056, Wilks’ Λ = 0.840] on students’ financial risk tolerance.
Table 4
Wilks’ Lambda multivariate analyses of students’ financial risk tolerance following online business coaching program based on RE-CBT coaching
Effect Multivariate Test | Value | F | Hypothesis df | Error df | Sig. | Partial η2 | |
|---|---|---|---|---|---|---|---|
Group | Wilks’ Λ | 0.014 | 1694.778 | 3 | 74 | 0.000 | 0.986 |
Age | Wilks’ Λ | 0.673 | 1.167 | 27 | 216.760 | 0.268 | 0.124 |
Sex | Wilks’ Λ | 0.953 | 1.209 | 3 | 74 | 0.312 | 0.047 |
Group * Age | Wilks’ Λ | 0.749 | 1.253 | 18 | 209.789 | 0.222 | 0.092 |
Group * Sex | Wilks’ Λ | 0.941 | 1.551 | 3 | 74 | 0.209 | 0.059 |
Group * Age * Sex | Wilks’ Λ | 0.840 | 0.741 | 18 | 209.789 | 0.766 | 0.056 |
As further shown in Table 5, pretest data of between-subjects effects revealed a non-significant difference in students’ attitudes towards financial risk scores between the online business coaching group and control group [F(1,106) = 1.487, p = .226, Partial η2 = 0.019]. Posttest data based on the test of between-subjects effects revealed that online business coaching program based on RE-CBT coaching approach was significantly helpful in the improvement of students’ attitudes towards financial risk compared to control group [F(1, 103) = 586.164, p = .000, Partial η2 = 0.851] (see Table 5). The effect of Time [F(1.256, 129.323) = 17.434, p = .000, Partial η2 = 0.145] and interaction effects of Time and Group [F(1.256, 129.323) = 429.790, p = .000, Partial η2 = 0.807] on students’ attitudes towards financial risk were significant. Follow-up data based on the test of between-subjects effects revealed that the significantly helpful effects of online business coaching program based on RE-CBT coaching approach on students’ attitudes towards financial risk was sustained when compared to control group [F(1,106) = 422.134, p = .000, Partial η2 = 0.847] (see Table 5).
Table 5
Between-subjects and interaction effects test for the interventions’ effect on financial risk attitudes as measured with Attitude toward Financial Risk Questionnaire
Source | Dependent Variable | Type III Sum of Squares | Df | Mean Square | F | Sig. | Partial η2 |
|---|---|---|---|---|---|---|---|
Group | ATFRQ-Time 1 | 1.172 | 1, 106 | 1.172 | 1.487 | 0.226 | 0.019 |
ATFRQ-Time 2 | 3514.806 | 1,103 | 3514.806 | 586.164 | 0.000 | 0.851 | |
ATFRQ-Time 3 | 1288.904 | 1,106 | 1288.904 | 422.134 | 0.000 | 0.847 | |
Time | ATFRQ | 67.171 | 1.256, 129.323 | 53.499 | 17.434 | 0.000 | 0.145 |
Time x Group interaction | ATFRQ | 1655.971 | 1.256, 129.323 | 1318.912 | 429.790 | 0.000 | 0.807 |
The multivariate analysis in Table 6 further suggest that the online business coaching program based on RE-CBT coaching approach had significant effects on the students’ attitudes towards financial risk [F(3,74) = 143.313, p = .000, Partial η2 = 0.853, Wilks’ Λ = 0.147]. There was a significant moderating effect of Age [F(27, 216.760) = 1.143, p = .293, Partial η2 = 0.122, Wilks’ Λ = 0.678] and a non-significant moderating effect of Sex [F(3,74) = 3.567, p = .018, Partial η2 = 0.126, Wilks’ Λ = 0.874] on the students’ attitudes towards financial risk. Except for Group and Sex [F(3,74) = 4.888, p = .004 Partial η2 = 0.165, Wilks’ Λ = 0.835], there were no significant interaction effects between Group and Age [F(18, 209.789) = 1.391, p = .138, Partial η2 = 0.101, Wilks’ Λ = 0.727], as well as Group and Age and Sex [F(18, 209.789) = 1.367, p = .150, Partial η2 = 0.099, Wilks’ Λ = 0.731] on students’ attitudes towards financial risk.
Table 6
Wilks’ Lambda multivariate analyses of students’ attitudes towards financial risk following online business coaching program based on RE-CBT coaching
Effect Multivariate Test | Value | F | Hypothesis df | Error df | Sig. | Partial η2 | |
|---|---|---|---|---|---|---|---|
Group | Wilks’ Λ | 0.147 | 143.313 | 3 | 74 | 0.000 | 0.853 |
Age | Wilks’ Λ | 0.678 | 1.143 | 27 | 216.760 | 0.293 | 0.122 |
Sex | Wilks’ Λ | 0.874 | 3.567 | 3 | 74 | 0.018 | 0.126 |
Group * Age | Wilks’ Λ | 0.727 | 1.391 | 18 | 209.789 | 0.138 | 0.101 |
Group * Sex | Wilks’ Λ | 0.835 | 4.888 | 3 | 74 | 0.004 | 0.165 |
Group * Age * Sex | Wilks’ Λ | 0.731 | 1.367 | 18 | 209.789 | 0.150 | 0.099 |
According to Sidak’s post hoc output on the data for the financial risk tolerance score of students in the online business coaching group was significantly comparable to that of the control group at Time 1 (Mean difference=-0.13, standard error = 0.33, p = .704, 95%CI: − 0.78,0.53) (see Table 7). On the other hand, the financial risk tolerance score of students in the online business coaching group was significantly higher than that of the control group at Time 2 (Mean difference = 20.56, standard error = 0.29, p = .000, 95%CI: 19.98,21.14). A further result show that at Time 3, the financial risk tolerance score of students in the online business coaching group remained significantly higher than their counterpart in the control group (Mean difference = 21.02, standard error = 0.31, p = .000, 95%CI: 20.41,21.63) (see Table 7).
Table 7
Sidak’s post hoc analysis for students’ financial risk tolerance and financial risk attitude by Group x Time
Dependent Variable | (I) Group | (J) Group | Mean Difference (I-J) | Std. Error | Sig.b | 95% CIb | |
|---|---|---|---|---|---|---|---|
GLRTS-Time 1 | Coaching group | Control group | − 0.13 | 0.33 | 0.704 | − 0.78,0.53 | |
Control group | Coaching group | 0.13 | 0.33 | 0.704 | − 0.53,0.78 | ||
GLRTS-Time 2 | Coaching group | Control group | 20.56* | 0.29 | 0.000 | 19.98,21.14 | |
Control group | Coaching group | -20.56* | 0.29 | 0.000 | -21.14,-19.98 | ||
GLRTS-Time 3 | Coaching group | Control group | 21.02* | 0.31 | 0.000 | 20.41,21.63 | |
Control group | Coaching group | -21.02* | 0.31 | 0.000 | -21.63,-20.41 | ||
ATFRQ-Time 1 | Coaching group | Control group | 0.24 | 0.22 | 0.273 | − 0.19, 0.68 | |
Control group | Coaching group | − 0.24 | 0.22 | 0.273 | − 0.68, 0.19 | ||
ATFRQ-Time 2 | Coaching group | Control group | 9.18* | 0.44 | 0.000 | 8.30, 10.06 | |
Control group | Coaching group | -9.18* | 0.44 | 0.000 | -10.06,-8.30 | ||
ATFRQ-Time 3 | Coaching group | Control group | 9.46* | 0.43 | 0.000 | 8.59,10.32 | |
Control group | Coaching group | -9.46* | 0.43 | 0.000 | -10.32, -8.59 | ||
According to Sidak’s post hoc output on the data for the students’ attitudes towards financial risk in the online business coaching group was significantly comparable to that of the control group at Time 1 (Mean difference = 0.24, standard error = 0.22, p = .273, 95%CI: − 0.19, 0.68) (see Table 5). On the other hand, at Time 2, students’ attitudes towards financial risk in the online business coaching group was significantly better than that of the control group (Mean difference = 9.18, standard error = 0.44, p = .000, 95%CI: 8.30, 10.06). A further result show that at Time 3, students’ attitudes towards financial risk in the online business coaching group remained significantly better than their counterpart in the control group (Mean difference = 9.46, standard error = 0.43, p = .000, 95%CI: 0.59,10.32) (see Table 7).
Discussion
The study findings demonstrate improved entrepreneurial risk attitudes (financial risk tolerance and attitudes) among computer college students following the coaching program. Data from this study are consistent with current research on coaching intervention with RE-CBT coaching approach which showed that it enhances entrepreneurial risk attitudes especially financial risk tolerance and attitude towards financial risk of participants (Onyeanu et al., 2022). Also research findings by Ugwoke et al. (2022a, b) also lend credence to these results on the use of RE-CBT coaching approach as an efficient coaching model for promotion of clientele’s entrepreneurial attitudes and financial risks behaviour in that the magnitude of changes observed in these studies aligns with this present report. Similar effects have been reported in previous coaching intervention studies (David & Cobeanu, 2015). Similarly, Mahfar and Senin (2015) reported that coaching program is an effective way to cope with worries (in this case, financial risks intolerance and unhelpful attitude). In line with our data report, studies had shown that people who come into contact with coaching strategies are better able to manage negative attitudes (Eseadi et al., 2017a, b). The study further showed that there were no significant interaction effects between group and age and sex on students’ financial risk tolerance. The implication is that age and sex did not explain significant differences in students’ financial risk tolerance. This is consistent with the report by Ramudzuli (2016) that age is not a significant factor for explaining differences in students’ financial risk tolerance. The data also supports the research outcome by Ramudzuli and Muzindutsi (2018) which showed that students’ age did not significantly affect their financial risk tolerance; however, it did not support their finding that students’ sex could have an impact on students’ ability to manage financial risk. Other than the effects of sex and group interaction, the interaction effects of students’ sex with age and group of online coaching participants could not explain significant differences in students’ attitudes toward financial risk. Therefore, taking students’ sex into consideration may be necessary in future online business coaching interventions specifically designed to improve students’ attitudes toward financial risk. Based on the results of the study which found that online business coaching based on RE-CBT coaching approach significantly improved students’ attitudes toward financial risk and risk tolerance, college proprietors should consider promoting online business coaching based on RE-CBT coaching paradigm as a method for improving students’ attitudes and tolerance towards financial risk.
Limitations and Strengths of the Study
However, there are some limitations in the current research. The study limitation included the fact that its sample was only made up of Nigerian students attending computer colleges. Using solely self-report data, the study also analyzed the effectiveness of online business coaching without considering qualitative factors. There were no direct behavioral means of financial risk tolerance as dependent measures, only the self-report tests were used. This study is germane, however, because it examined how computer college students could increase their tolerance and attitude related to financial risk. With the aid of a randomized trial design, the intervention was able to demonstrate the effectiveness of online business coaching in improving financial risk attitudes of computer college students. This can be seen as strength of the study.
Theoretical Significance and Contributions
From this research, it can be inferred that participants’ irrational life views that lead to inadequate risk tolerance can be vigorously challenged in therapy until they give up such views through the use of online business coaching based on RE-CBT coaching. Particularly, therapists can vigorously attack participants’ idiotic philosophies, for instance, that having elevated entrepreneurial risk attitudes are awful and dreadful; that high financial risk tolerance is only suitable for affluent people in society; and that anyone who fails to reach their entrepreneurial goals will be seen as utterly inept and insignificant as a human being. In order for therapists to achieve the rational beliefs they hope participants will come to have to assist them in adjusting their beliefs that might lead to appropriate risk taking, they can help the participants to see that greater risk tolerance can contribute to positive mental health and financial independence; and that failing at times in an entrepreneurial endeavor is a normal part of human living and implies nothing whatever about a person’s self-worth or value. The therapist should not only work to reveal the origins of the participant’s fears and to help them understand why they should no longer be afraid to take entrepreneurial risks, regardless of how much they may have feared taking them at one time, but also, and just as important, encourage, persuade, and impel them to take appropriate entrepreneurial risks definitively and ultimately see that they were not actually fearful in the first place. Therapy needs to be a lot more eclectic, exhortative-persuasive, and activity-oriented in order for clients to achieve their goals (Ellis, 1962).
Implications of the Study
A wide range of future directions and implications arose from this study for educators and college administrators. The findings indicate that online business coaching program for computer college students based on the rational-emotive and cognitive behavior therapy coaching paradigm should be incorporated into the school curriculum in order to promote appropriate risk taking and positive financial well-being among this population. This study can be useful to educators in accounting towards investigating how online business coaching may enhance students’ money attitudes. Additionally, educators in accounting must be aware of and apply models like online business coaching to get the message across to school owners about how college students’ financial risk intolerance affects them and how they can employ similar models to improve college students’ wellbeing and perceptions. Educators have to take up the responsibility of offering entrepreneurial support services to college students by utilizing business coaching models that have a significant link with coaching approaches like RE-CBT coaching. For educators in accounting, it is also essential to look at the key tenets of online business coaching based on RE-CBT coaching approach and use them to shed light on how college students’ tolerance and attitudes in relation to financial risks are influencing their entrepreneurial intentions. According to our study, students who are unable to manage financial risk and show unhelpful risk attitudes need RE-CBT coaching to cope with such circumstances. Educators in accounting with business coaching experience should not hesitate to help college students alter unhelpful financial risk attitudes and intolerance through the use of online business coaching strategies based on RE-CBT coaching approach. The impact of school and community factors on online business coaching programs should be examined in future research. In the future research, it would be useful to examine the impact of factors like school culture and school climate on the effectiveness of online business coaching, as well as financial wellbeing and literacy of students.
Conclusion
The improvement in computer college students’ entrepreneurial risk attitudes (financial risk tolerance and attitudes toward financial risk) were reported in this study following an online business coaching program based on RE-CBT coaching approach. The study dataset indicates that online business coaching based on RE-CBT coaching approach has the potential to enhance the financial risk tolerance and attitudes toward financial risk of computer college students. The study dataset also showed that group, age, and sex interactions did not affect students’ financial risk tolerance. Students’ attitudes toward financial risk could not be explained by the interaction effect of sex with age and group, other than by sex and group interaction. There is a need therefore to consider the sex of students in future interventions specifically designed to improve students’ attitudes toward financial risk. In order to promote entrepreneurial risk attitudes among other groups of college students, we suggest that an RE-CBT coaching program using an online business coaching model be implemented.
Declarations
Conflict of Interest
There is no conflict of interest to disclose.
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