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The Australian Government is Justified in Establishing a Single Disciplinary Body

Angelique McInnes
Angelique McInnes
   | 19. März 2024
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Online veröffentlicht: 19. März 2024
Seitenbereich: 40 - 73
Eingereicht: 17. Juni 2020
Akzeptiert: 20. Nov. 2020
DOI: https://doi.org/10.2478/fprj-2020-0003
© 2020 Angelique McInnes, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1:

Adapted from the works of McInnes (2020) is the framework supporting the implementation of the single disciplinary body
Adapted from the works of McInnes (2020) is the framework supporting the implementation of the single disciplinary body

Figure 2:

Frequency of sample gender, location, AR status, age, qualifications, and licensee status [n = 262] adapted from the works of McInnes (2020)
Frequency of sample gender, location, AR status, age, qualifications, and licensee status [n = 262] adapted from the works of McInnes (2020)

Figure 3:

Confirmatory factor analysis model cum Common Latent Factor adapted from McInnes (2020)
Confirmatory factor analysis model cum Common Latent Factor adapted from McInnes (2020)

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
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