Figure 1:
Figure 2:
![Frequency of sample gender, location, AR status, age, qualifications, and licensee status [n = 262] adapted from the works of McInnes (2020)](https://sciendo-parsed.s3.eu-central-1.amazonaws.com/65f98a5e812d8816c96adecd/j_fprj-2020-0003_fig_002.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=ASIA6AP2G7AKJ62MB4CR%2F20260124%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Date=20260124T170111Z&X-Amz-Expires=3600&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEEcaDGV1LWNlbnRyYWwtMSJHMEUCIQCnNOOUYuTbcvHMMkGTNfeGWwS8M9bitg6IUbf0QC1XugIgCjUDEE9d1TwCPGAWrvY5t9cDBWgWJW9aqzChBUMpesEqvAUIEBACGgw5NjMxMzQyODk5NDAiDNQxFkIU7YQvrza5ayqZBQ%2FwU5D9bel%2FKDNrYo1Ao0BhYQ9X%2FheZgnx671QB2hjhPpisssZiUzQvpzg1NRduxzRh3FSSmRZDulvucdSgpMUNX1xj3wpcicxTPV9tFSdZ2Khyr7KKg4uSaa%2BxWUlt%2BbKoN8daNK42QDNeo7oAeOU8T3ArbPY%2FD32rp3Gvq6KmR7UgYsb22teWNaOivT2fCPU1C%2B9fGw6ty1zrxYf7y3KX0rEunPbHpZ0kPpZaaUt%2BonC8%2Bk1wtep%2FJN5dap9o6lgKjEqEx7aqutxtR72SCYPsshK1KELaHXRjCwnKxUJy7Y6N6h4yW6HY%2BYs3qXYP1EfnX5bQrOkSY1zPnN%2BgPkqDYsWMkYdcfcr23Q%2BwUOpkxweMbDA9PAgRa9lBzVUI8WGptnCeJgixKzL%2B1oXbkml8N8p9iYPrKLkoXgP4nAwf2k4Yg6VkkJgU1hZDSBZrwQ%2BDOsyO6n7yrEpULGoZ1u%2BxDv%2BvUA1kbrSzN%2B1SZoaoWDLC2XF0iNaVtsCH%2Bqkgyrmm2w5xOL3nJFu3sXfZALxIQ%2B7jt03NFGIJA%2Fcm%2B3O0eTRqsQxm2JeILsCd4QPEWLsmkrhSvT5y64YyV7NpyISa2I15vu0BM73RANuPuHuXrFXY%2BouQ1T9Rr8U6pyaPXRf3LIoqTW03AYZhWdEpgMHO09Ma8al6XCQr8eGmZhQvpmVuMjh64YxvubWhK4Y9rCMeBV9f8CVVS1hPfF7Zn75Ht6tzub2uC9wLl77c7KgPwD%2BQxWIESTXIQj7qxhZI5yvxaUdO5fKVuNFXdsh9gpQ588Ko18cWig33oAFLxght5rJqyf2zmwgQfw28cZqKFQzzbPoP3w2ak8w4X6fw9SexPTD3KD9dgq7iAH8iGGFS8IW5L3wa6mPIMKC308sGOrEBSNiHCfBWPFkhK9T7Mk%2Fbs%2F3q6%2B56vZmhzAuwNo2X7LRrhYZouXoHV83Nk7wW7rs79hWZDM%2BARgcFNRWgrwGU9jhnlAeCSarAfY0ihBh%2FuBvfjyyC8Cb4FPpHQHw8Y5o8Yi0XaZkpONSD7rnguiZsTXBOPde679lVbEU9oeasgynMsNogjBR3VTEBt3NUUy8WWF8kowzgdH9Q5lPW6m9gWXk4TdZ1Twtt63DEH461nVGe&X-Amz-Signature=9369688d5942f983cdd0853cceb53a46cb1455b69fe0c18549f941888e80af65&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject)
Figure 3:
Question 3: To what extend do financial advisers agree the current licensee-adviser licensing model is legitimate based on Suchman’s theoretical legitimacy framework extended and applied to financial planning theory? Adopted from the works of McInnes (2020)
| LITERATURE REVIEW Illegitimacy of adviser licensing | SUB-HYPOTHESES | EVIDENCE RW CR p-value SMC M [95% CI] MSE CR p-value |
|---|---|---|
| Regulative illegitimacy: Perception of activities/rules/laws operating within some socially acceptable system (Suchman 1995; Chen & Roberts 2010) | a9: Licensing increases risks of unintentional breaches of the Act (Bitektine 2011; Chelli, Durocher & Richard 2014) | .727 15.207 p = *** 0.628 48 [43, 52] 2.337 20.365 p= 0.10 |
| Consequential normative [moral] illegitimacy: Perception of specific morals/values/ethics of socially value outputs/outcomes (Suchman 1995) | a10: Licensees’ commercial interests compromise clients’ best interests (Smith 2009; Moran 2014; Maclean & Behnam, 2010) | .794 19.416 p = *** 0.768 63 [59, 58] 2.264 28.111 p=0.10 |
| Procedural normative [moral] illegitimacy: Perception of socially acceptable practices, standards & procedures (Suchman 1995) | a11: Licensees’ sales policies window-dressed to comply with the Act (Valentine & Hollingworth 2015; Newnham 2012; Sampson 2010; West 2009; Valentine 2013) | .781 13.844 p = *** 0.687 61 [56, 66] 2.356 25.956 p=0.10 |
| Structural normative [moral] illegitimacy: Perception of adopting formal structures acceptable to society (Suchman 1995) | a4: Conflicts of interests from association/affiliation/ownership exists (Steen, McGrath & Wong 2016; Smith 2009; Commonwealth of Australia 2009; Valentine 2013) | .740 9.073 p = ***0.574 75 [70, 78] 2.041 36.477 p=0.10 |
| Personal normative [moral] Illegitimacy: Perception of leaders’ roles to exert their personal influence to dismantle/create existing/new bodies (Suchman 1995; Carnegie & O’Connell 2012; Goretzki, Strauss & Weber 2013) | a13: Aligned leaders aim to protect their product distribution channels (Bird & Gilligan 2015; Sampson 2010) | .679 5.193 p = *** 0.463 78 [75, 82] 1.797 43.594 p=0.10 |
| Cultural-cognitive illegitimacy: Shared understanding to perpetuate an institutional order based on cognition or awareness (Santana 2012; Meyer 2007; Suchman 1995; Kury 2007) | a14: Clients-advisers’ shared understanding as to advisers’ identity - independent/conflicted (Zimmerman & Zeitz 2002; Scott 2014). The public cannot clearly distinguish between s923A independent from product conflicted advisers (Morris 2013) | .682 3.817 p = *** 0.502 62 [58, 66] 2.268 27.401 p = 0.10 |
Question 1: To what extend do financial advisers agree the current licensee-adviser licensing model makes advisers double agents creating conflicts of interest by association? Adopted from the works of McInnes (2020)
| LITERATURE REVIEW Advisers are double agents | SUB-HYPOTHESES | EVIDENCE RW CR p-value SMC M [95% CI] MSE CR p-value |
|---|---|---|
| Licensee-adviser (Gor 2005; Smith & Walter 2001) & adviser-client relationship (Corones & Galloway 2013) | a1: Advisers are double agents | .604 2.676 p = .007 0.448 77 [73, 80] 1.912 40.266 p=0.10 |
| Advisers serve the interests of licensees & clients, simultaneously (Kingston & Weng, 2014) | a2: Advisers serve clients’ best interests & licensees’ commercial interests simultaneously | .689 marker p = *** 0.47 0.481 62 [57, 66] 2.188 28.234 p=0.10 |
| Double role creates a conflict of interest (Kingston & Weng, 2014) | a3: Advisers generate revenue for their licensees, while serving clients best interests | .375 3.642 p = *** 0.143 78 [75, 82] 1.767 44.416 p=0.10 |
Question 2: To what extend do financial advisers agree the current licensee-adviser licensing model achieves objectives of the Act 2001? Adopted from the works of McInnes (2020)
| LITERATURE REVIEW Objectives of the Act | SUB-HYPOTHESES | EVIDENCE RW CR p-value SMC M [95% CI] MSE CR p-value |
|---|---|---|
| Manage, control or avoid conflicts of interests (Tuch 2005; Schwarcz 2009, Valentine 2008; 2013) | a6: Unavoidable conflicts of interests is present | .773, 15.101.169 p = *** 0.688 65 [61, 69] 2.315 28.137 p=0.10 |
| Ensure compliance of the statutory fiduciary duty (Banister et al. 2013) | a7: At risk of unintentionally breaching best interests’ duty | .821, marker p = *** 0.839 59 [54, 63] 2.288 25.717 p=0.10 |
Question 4: To what extend do financial advisers agree the current licensee-adviser licensing model should be replaced with a single disciplinary body? Adopted from the works of McInnes (2020)
| LITERATURE REVIEW Professional individual licensing | SUB-HYPOTHESES | EVIDENCE RW CR p-value SMC M [95% CI] MSE CR p-value |
|---|---|---|
| Lack of trust & confidence (Morgan & Levine 2015) prevents the public from seeking advice (Balasubramnian, Brisker & Gradisher 2014) | a16: Individual licensing will improve public trust & confidence | .745, marker p = *** 0.754 64 [60, 68] 2.327 27.386 p=0.10 |
| Institutional commercial licensee favoured over individual professional adviser (Sanders & Roberts 2015), which leads to problems (O'Brien & Gilligan 2014). Individual licensing to disconnect advisers from product issuers may lead to a culture shift (Steen, McGrath & Wong 2016) to independence (North 2015; Kingsford Smith, Clarke & Rogers 2017) | a17: Individual license will promote independence from conflicted licensees | .662, 11.035 p = *** 0.541 65 [61, 69] 2.230 29.147 p=0.10 |
| Financial advisers have been likened to other professionals (Ap 2011; Bruce 2012; Burke et al. 2015) Professional regulation evident in law/medicine is critical to the proper functioning of financial services industry (Omarova 2010) | a18: Individual license should be modelled on other professions [accounting, legal and medical] | .711, 11.211 p = *** 0.694 69 [64, 73] 2.244 30.618 p=0.10 |
| Individual license (Hoyle 2017; Sanders & Roberts 2015; Commonwealth of Australia 2014; Commonwealth of Australia 2009) via single monopoly body = most effective way to regulate the future financial planning profession (Kingsford Smith 2014) | a19: Individual license regulated through a single independent registration, competency, education, conduct, standards, and disciplinary board preferred | .695, 12.075 p = *** 0.623 68 [63, 72] 2.198 30.969 p=0.10 |
| Conflicts of interests by association due to licensees-advisers acting as co-workers (Money Management 2014) lead to institutional- professional conflicts (Smith 2009). Government’s policy objective is to eliminate conflicts of interest (Millhouse 2019) | a21: Individual licensing will eliminate conflicts of interests from association | .536, 8.625 p = *** 0.39 52 [48, 57] 2.167 24.188 p=0.10 |
Goodness of fit indices adapted from the works of McInnes (2020)
| Measure | Estimate Ex CLF | Cum CLF | Definition of measures | Thresholds for good fit |
|---|---|---|---|---|
| CMIN | 222.131 | 128.339 | Chi-square fit index shows the sample and estimated matrix are the same. | |
| CMIN DF | 119 | 101 | Chi-square fit index degrees of freedom. | |
| CMIN P | 0 | 0.034 | Chi-square fit index p-value. | p>0.01 |
| PCMIN/DF | 1.867 | 1.271 | Relative or normed chi-square fit index measures the difference between the population’s true covariance structure and the target model. | <3 |
| GFI | 0.915 | 0.95 | Goodness of fit index measures the relative amount of variance and covariance in the sample matrices jointly explained by the population matrices. | >0.95 good; >0.90 permissible; 0 [no fit] to 1 [perfect fit] |
| AG Fl | 0.878 | 0.915 | Adjusted goodness of fit index for the degrees of freedom value. | >0.95 to >0.80; 0 [no fit] to 1 [perfect fit] |
| CFI | 0.964 | 0.991 | Comparative fit index is an incremental fit index comparing the hypothesised model against some standard baseline independence and null model. Measures the overidentification condition. | >0.95 good; >0.90 permissible; 0 [no fit] to 1 [perfect fit] |
| Tl l/NNΠ | 0.954 | 0.986 | Tucker-Leis fit/Non-normed fit index compares the hypothesised model with null [no] model. Measures over-identification condition. | close to 0.95; 0 [no fit] to 1 [perfect fit] |
| NFI | 0.927 | 0.958 | Normed fit index. | close to 0.95; 0 [no fit] to 1 [perfect fit] |
| PCFI | 0.75 | 0.654 | Parsimony comparative fit index measures whether the estimated parameter is robust against others. | 0 [no fit] to 1 [perfect fit] |
| AIC | 326.131 | 268.339 | Akaike information criteria compares alternative models. A value as low as possible is better. Should be smaller than the saturated and independence models. | < saturated [342] & independence [3,073] |
| BIC | 511.685 | 518.123 | Bayesian information criteria compares alternative models. A value as low as possible is better. Should be smaller than the saturated and independence models to be more generalisable. | < saturated [952] & independence [3,137] |
| SMSR | 0.0688 | 0.0318 | Average error in the model is minimal. | <0.09 good; 1 [no fit] to 0 [perfect fit] |
| RMSEA | .058 | .032 | Root mean square error of approximation measures whether the population matrix is the same as the sample matrix within a 90% Confidence Interval [Cl], Lower discrepancy between matrices the better. | <0.05 good; 0.05 to 0.10 moderate; >0.10 poor |
| RMSEA 90% Cl | [.046;. 069] | [.009;.048] | Root mean square error of approximation confidence interval. | <0.05 good; 0.05 to 0.10 moderate; >0.10 poor |
| PCLOSE | 0.139 | 0.971 | Closeness of fit. If less than 0.05, then RMSEA fails the test of minimal discrepancy between observed and predicted covariance matrix. | >0.05 |