Borrowers’ credit risk severity – The effect of Data Smog

1. Borrowers’ credit risk severity – The effect of Data Smog Simangaliso Biza-Khupe Faculty of Business and Informatics 18th Annual Southern African Finance Association Conference, Graduate School of Business, University of Cape Town, 14 – 16th January 2009

2. Objectives The study adopts the theory of consumer economic behaviour* to: • propose a testable structural equation model in the area of consumer credit • explore the effect of perceived data smog on borrowers’ perception to credit risk severity • explore the antecedents of perceived data smog. *(Claxton, Fry, & Portis, 1974; Punj & Staelin, 1983; Furse, Punj, & Stewart, 1984; Beatty & Smith, 1987; Srinivasan & Ratchford, 1991; Moorthy, Ratchford, & Talukdar, 1997; Punj & Brookes, 2002)

3. Introduction Motivation for the study: • The alarming increase in consumer debt and bankruptcy rates. In most Organisation for Economic Cooperation and Development (OECD) countries the ratio of total household debt to income rose from 80% or lower two decades ago, to the recent levels of 120% - 180%. (Reserve Bank of Australia, 2003; Worthington, 2006) • The paucity of studies in consumer credit behaviour

4. Introduction cont.. • The few studies conducted, notably Chang & Hanna (1992), Worden & Sullivan (1987), and Lee & Hogarth (2000), have: • not proposed a comprehensive typology of borrowers’ credit behaviour • limited their approaches to bivariate analysis. SEM is a better technique (Cheng, 2001) • not accounted for financial information clutter within consumer credit markets, inspite of growing evidence (OECD, 1992; Waddington, 1997; Garman & Forgue, 2000; Malbon, 2001)

5. Review of Literature Borrower Perceived Risk Severity • ‘Chance’ and ‘consequence’ of a loss are the dual dimensions associated with consumer perceived risk. • ‘Chance’ encapsulate the aspect of the probability of a loss or undesired outcome (perceived financial risk), • conceptualised as the subjective probability of financial difficulties attributable to acquisition of credit (Peter & Tarpey, 1975; Srinivasan & Ratchford, 1991) • ‘Consequence’ represents the element of the severity of that loss or undesired outcome (Peter & Tarpey, 1975).

6. Review of Literature cont.. • Perceived risk severity is a measure of how weighty a potential financial loss would be to an individual as a consequence of acquiring credit • Perceived risk severity implicitly accounts for the financial wellness of an individual, while perceived financial risk does not

7. Review of Literature cont.. • Individuals are generally risk averse (Brigham & Houston, 1998; Brealey et al., 2007) • Consumers are therefore motivated to assess risk associated with their financial decision and engage appropriate strategies directed at offsetting it (Beatty & Smith, 1987; Srinivasan & Ratchford, 1991; Schmidt & Spreng, 1996) • A necessary condition of any risk-reducing strategy is effective access to information and the ability to cognitively synthesise the information (Stigler, 1961; Murray, 1991; Malbon, 2001).

8. Information Regulation • Evidence of lack of knowledge, aptitude and skills base necessary to become questioning and informed borrowers is attributed to the global regime of financial information regulation, truth in lending (OECD, 1992, Lee & Hogarth, 1999; Malbon, 2001) • The wisdom of the approach lies in creating transparency in the operations of lenders on matters of service availability, price and lending terms and conditions • Thereby arming consumers with information that would enable them to choose financial services judiciously

9. Perceived Data Smog BUT the plethora of information produced, coupled with technological innovations that have made the production, retrieval and distribution of information easy, has created information oversupply* (data smog) • Consumers are cognitively overwhelmed by the aggregate mass of information disclosures • Data smog is conceptualised as the perceived proliferation of credit information and products and the associated degree of confusion *(Mandell, 1973; Truelstrup, 1974; O’Mara, 1991; Mitchell, 1992; Avram, 1997; Waddington, 1997; Duggan & Lanyon, 1999; J. Lee & Hogarth, 1999; Malbon, 2001)

10. Hypotheses • Hypothesis 1: Perceived data smog is positively related to time constraint (time constraint exacerbates pressure to the cognitive process during financial decision making; too much information – too little time!) • Hypothesis 2: Perceived data smog is negatively related to prior memory structure (PMS is self-report confidence in the adequacy of one’s knowledge level about credit facilities) • Hypothesis 3: Risk severity is positively related to perceived data smog.

11. Methodology • Targeted population – individuals who bought a household appliance on credit terms in the preceding 12-month period • A questionnaire was developed and personally-administered to a sample frame of Melbourne residents • Data was collected from patrons of a diverse range of community centres in the city of Melbourne between November & December 2007 • A total of 245 usable responses were collected • Average credit amount AU$ 1,008 • 56% females and 44% males • Most respondents (34.4%) fell in 31-40yrs age-group

12. Construct Reliability and Validity • Interitem stability and consistency measured by Cronbach’s coefficient alpha: range 0.75 - 0.92 • Convergent validity was tested using CFA (AMOS 7). Standardised factor loadings ranged 0.52 - 0.93, all items hypothesised to measure a latent construct had statistically significant factor loadings by the critical ratio test (> ± 1.96, p < .05)

13. Model Fit • Model fit was ascertained using both the absolute goodness-of-fit and incremental fit indexes • The results are within the targeted benchmark index values *(Joreskog & Sorbom, 1984; Browne & Cudeck, 1993; Segars & Grover, 1993; Hair, Anderson, Tatham, & Black, 1995; Hoyle, 1995; McCullum, Browne, & Sugawara, 1996; Chau, 1997; Ho, 2006)

14. Results Standardised regression weights (factor loadings) Squared Multiple Correlations () .80 Notime Time Constraint DiffIns DiffExp DiffPay .84 .30 Urgent .93 .91 .84 Financial Risk (.09) .57 .49 RedCr NS .77 Risk Severity (.33) .89 IncrInc .81 RedExp .23 Data Smog (.35) -.14 Know1 .87 .92 .89 .68 Prior Memory Structure .76 Know2 Conf1 Conf2 Conf3 .52 Acess

15. Hypothesis Testing • H1:a positive path coefficient statistically significant at the p < 0.001 was found between perceived data smog and time constraint. Supported • Thus, borrowers who are time constrained find it difficult to effectively synthesise the plethora of data generated by financial markets

16. Hypotheses testing cont.. • H2: a negative path coefficient significant at the p < 0.05 level was found between perceived data smog and prior memory structure. Supported • Thus, borrowers with increased levels of prior memory structure are better cognitively equipped to filter through the masses of financial information, hence less susceptible to data smog

17. Hypothesis testing • H3: a positive path coefficient statistically significant at the p < 0.001 level was found between perceived risk severity and perceived data smog. Supported • Thus, borrowers overwhelmed by financial information clutter are more uncertain about the prudence of their financial decisions, and hence perceive the severity of a credit decision as high

18. Conclusion • Information is imperative to the rationalisation of credit by borrowers • More imperative, is how information is disseminated • To policy makers and marketers, information clutter ≠ better informed consumer • Consumer education on personal finance, thus improving knowledge base (prior memory structure), mitigates perceived data smog

19. Study limitations & further research • The sampling frame may not be representative, thereby limiting the generalisability of the findings (exploratory) • Comparative analysis on the different types of credit, i.e. store credit vs cash credit • Research on large credit, i.e. auto loans

20. Thank you