Responsible access to credit for sole-traders and micro-organizations under unstable market conditions with psychometrics

Figures & data

Figure 1. Conceptual PSM attributes with their hypothesized relationships with arrears (overdue payments).

Table 1. Hypotheses model with core literature per PSM attribute.

Hypothesis number	Hypothesis	Hypothesized relationship	Core Literature
H0	H0: Psychometric credit scoring models do not increase responsible access to finance for small businesses in unstable market conditions		Goel and Rastogi (2023); Van Thiel, Goedee, and Leenders (2023); Djeundje et al. (2021); Liberati and Camillo (2018); Dlogosch, Klinger, and Frese (2017)
H0a	Lower levels of financial skills increases arrears	Negative relation	Struckell et al. (2022); Lusardi, Hasler, and Yakoboski (2021); Klapper and Lusardi (2020); Lusardi (2019)
H0b	Higher levels of self-efficacy (confidence) increases arrears	Positive relation	Nosić & Weber (2010); Yang, Markoczy, and Qi (2007); Dans and Haberman (2007); Seaward and Kemp (2000)
H0c	Positive debt-attitudes increases arrears	Positive relation	Almenberg et al. (2021); Haultain, Kemp, and Chernyshenko (2010)
H0d	Higher levels of materialism increases arrears	Positive relation	Garðarsdóttir & Dittmar (2012); Pham, Yap, and Dowling (2012); Donnelly, Iyer, and Howell (2012); Pirog and Roberts (2007); Ponchio and Aranha (2008); Richins (2011); Norvilitis et al. (2006); Richins and Dawson (1992); Belk (1984)
H0e	Higher levels of immediate gratification increases arrears.	Positive relation	O'Connor et al. (2019); Stroömbäck et al. (2017); Khare (2016); Achtziger et al. (2015); Phau and Woo (2008); Joji and Raveendran (2008)
H0f	Higher levels of fluid intelligence decreases arrears	Negative relation	Małkiński and Mańdziuk (2022); Djeundje et al. (2021); Muñoz-Murillo, Álvarez-Franco, and Restrepo-Tobón (2020); Dlogosch, Klinger, and Frese (2017); Liberati and Camillo (2018)
H0g	Higher levels of social desirability increases arrears	Positive relation	Norvilitis et al. (2006); Ganster, Hennessey, and Luthans (1983)
H1	Psychometric credit scoring models increase responsible access to finance for small businesses in unstable market conditions		Goel and Rastogi (2023); Van Thiel, Goedee, and Leenders (2023); Djeundje et al. (2021); Liberati and Camillo (2018); Dlogosch, Klinger, and Frese (2017)

Table 2. Variable descriptions.

Age	Definition	Measurement	Source
Financial literacy	Knowledge, understanding of financial concepts and risks but also the skills, motivation, and confidence to apply such knowledge and understanding in order to make effective decisions across a range of financial contexts, to improve the financial well-being of individuals, their enterprises and society, and to enable participation in economic life	Psychometric Credit Score	OECD; Van Thiel, Goedee, and Leenders (2023); Lusardi and Mitchell (2007, 2008, 2011)
Dependent variable
Default	Any instance of late repayment of the loan or a portion thereof	Actual loan repayment data	Lender; AdviceRobo
Independent personality variables
Financial skills	The practical abilities and competencies entrepreneurs possess to manage their finances effectively. These skills encompass various aspects of financial management, including budgeting, saving, investing, debt management, financial planning, and the ability to make informed and wise financial decisions. Financial skills enable individuals to navigate complex financial situations and work towards their financial goals, ultimately contributing to their financial well-being	5-point Likert scale	Lusardi and Mitchell (2007, 2008, 2011)
Self-efficacy	Self-efficacy refers to a sense of personal agency, the belief that one can achieve and succeed at a given task and is related to self-confidence, motivation, optimism, and the belief that one can cope with a variety of life’s challenges	5-point Likert scale	Bandura (2012)
Materialism	The importance an entrepreneur places on possessions and their acquisition as a necessary or desirable form of conduct to reach desired end states	5-point Likert scale	Richins (2017, 1992)
Gratification	The process in which an entrepreneur foregoes engaging in immediate reward behaviour to receive a larger reward later	5-point Likert scale	Norvilitis et al. (2006)
Fluid intelligence	The innate ability of an entrepreneur to respond to complex and unexpected situations	6-point categorical	Muñoz-Murillo, Álvarez-Franco, and Restrepo-Tobón (2020)
Social desirability	Social desirability refers to the tendency to present oneself, one's enterprise and one’s social context in a way that is perceived to be socially acceptable, but not wholly reflective of one’s reality	Dichotomous variable	Bergen and Labonté (2020)
Independent attitude variables
Debt attitude	An entrepreneur's emotional repsonse to debt	5-point Likert scale	Almenberg et al. (2021)
Independent demographic variables
Age	The number of years an entrepreneur has been alive since birth	Continous variable

Table 3. Psychometric attributes and their questions.

Attribute	Question
Financial skills	I review my bills for accuracy
	Sometimes I forget to pay my bills on time
	If I have an incidental expense, I have enough savings to cover the expense
Self-efficacy	It is easy for me to stick to my aims and accomplish my goals
	I can stay calm if I have problems because I trust myself that I can handle it well
	When I am confronted with a problem, I can usually find several solutions
Debt attitude	Taking out a loan can be a good thing, if it helps people to enjoy their life
	For many people, debt is a normal part of life.
	In today’s society, it is almost impossible to live without debts
Materialism	I admire people who own expensive homes, cars, and clothes
	I place much emphasis on the amount of material objects people own as a sign of success
	I like to own things that impress people
Gratification	I am guided by my immediate desires and do not dwell on what the future may look like.
	My convenience is a big factor in the decisions I make or the actions I take
	I don't think about future problems because I think the future will take care of itself
Fluid intelligence	(1) What is the logical image in the open space?
	(2) What is the logical image in the open space?
Social desirability	I sometimes feel resentful when I don't get my way
	There have been occasions when I felt like smashing things
	There have been times when I was quite jealous of the good fortune of others

Table 4. Descriptive data.

	Variable	Coding	N	Mean	Min	Max
1.	Entity type	(0 = small business; 1 = sole trader)	2884	0.57	–	–
2.	Psychometric score	Sum of the finanical psychometric attributes/ total of attributes	2884	0.43	0.02	0.97
3.	Entity status	1 = accepted; 0 = rejected	826	0.29	–	–
4.	Default	1 = default; 0 = no default	68	0.24	–	–
5.	Financial skills	1 = very low to 5 = very high	2884	3.74	0	5
6.	Self-efficacy	1 = .very low to 5 = very high	2884	4.21	1	5
7.	Debt attitude	1 = very low to 5 = very high	2884	2.85	1	5
8.	Materialism	1 = very low to 5 = very high	2884	2.45	1	5
9.	Gratification	1 = very low to 5 = very high	2884	2.17	1	5
10.	Fluid intelligence	Choose the right pattern	2884	2.79	0	6
10.	Social desirability	0 = no social desirability; 1 = social desirability	2884	0.60	0	1
11.	Period	0 = pre-covid period; 1 = covid period	2884	0.48	-	-
12.	Age	18 – 76	2884	35.09	18	76

Table 5. Overview of correlations between PSM attributes and arrears.

Variable	Arrears	r	p <
Financial skills	−0.57***	−0.55	0.001
Self-efficacy	−0.25***	0.23	0.001
Debt attitude	0.22**	0.28	0.001
Materialism	0.11	0.12	0.08
Delay of gratification	0.08	0.04	0.58
Fluid intelligence	−0.12	0.10	0.17
Social desirability	−0.31***	0.28	0.001
Age	−0.28***	0.28	0.001

***Significant at < 0.001 level.

**Significant at <0.01 level

Figure 2. ROC-curve total period (August 2019–February 2022).

Table 6. Confusion Matrices PSM versus TCSM total period.

Confusion matrices total period
PSM	Cut off threshold: 0.33
Predicted \ Actual		Default	No default	Total
	Default	40	341	381
	No default	28	417	445
	Total	68	758	826
Traditional model	Cut off threshold: 0.33
Predicted \ Actual		Default	No default	Total
	Default	0	0	0
	No default	68	758	826
	Total	68	758	826

Table 7. PSM metrics. Overall, per period and per customer segment.

	Micro-businesses + self employed		Micro-businesses only		Self-employed only
PSM	Total period	COVID-period	Total period	COVID-period	Total period	COVID-period
AUC	0.61	0.59	0.58	0.40	0.62	0.69
Gini	0.22	0.18	0.16	0	0.24	0.38
Accuracy	0.55	0.51	0.51	0.44	0.58	0.57
Precision	0.87	0.91	0.84	0.92	0.88	0.90
Recall	0.55	0.51	0.51	0.44	0.58	0.57
F1	0.65	0.63	0.60	0.59	0.68	0.67
N	826	356	319	169	507	187
Arrears	68	20	31	6	37	14

Figure 3. ROC-curve period of ‘state of emergency’ (March 2020–November 2020).

Table 8. Confusion matrices PSM versus TCSM state-of-emergency period.

PSM COVID-period	Cut off threshold: 0.33
Predicted \ Actual		Default	No default	Total
	Default	12	166	178
	No default	8	170	178
	Total	20	336	356
Traditional model	Cut off threshold: 0.33
Predicted \ Actual		Default	No default	Total
	Default	0	0	0
	No default	20	336	356
	Total	20	336	356

Table 9. Acceptance rates PSM & TCSM total period and state of emergency period.

Overall	PSM				Traditional score
	State	Predicted			State	Predicted
Total Period	Actual	No default	Default		Actual	No default	Default
	No default	1049	1767	2816	no default	727	/
	Default	28	40	68	default	88	/
	Total	1077	1807	2884	Total	815	2069	2884
	Acceptance rate			37.34%	Acceptance rate			28.26%
	PSM				Traditional score
		Predicted				Predicted
State-of-emergency	Actual	No default	Default		Actual	No default	Default
	No default	526	849	1375	No default	336	/
	Default	8	12	20	Default	20	/
	Total	534	861	1395	Total	356	1039	1395
	Acceptance rate			38.28%	Acceptance rate			25.52%

Table 10. Default rates PSM & TCSM total period and state-of emergency.

	PSM				Traditional score
	Predicted				State	Predicted
Total period	Actual	No default	Default		Actual	No default	Default
	No default	417	341	758	No default	758	/
	Default	28	40	68	Default	68	/
	Total	445	381	826	Total	826		826
	Default rate			6.29%	Default rate			8.23%
		PSM				Traditional score
		Predicted				Predicted
State-of-emergency	Actual	No default	Default		Actual	No default	Default
	No default	170	166	336	No default	336	/
	Default	8	12	20	Default	20	/
	Total	170	178	356	Total	356		356
	Default rate			4.49%	Default rate			5.62%

Table 11. Acceptance rates per sector PSM TCSM total period and state-of-emergency.

		PSM				Traditional score
		State	Predicted			State	Predicted
Total Period	Micro-organisation	Actual	No default	Default		Actual	No default	Default
		No default	671	934	1605	No default	288	/
		Default	16	21	37	Default	31	/
		Total	687	955	1642	Total	319	1323	1642
		Acceptance rate			41.84%	Acceptance rate			19.43%
	Sole Trader	Actual	No default	Default		Actual	No default	Default
		No default	378	833	1211	No default	439	/
		Default	12	19	31	default	68	/
		Total	390	852	1242	Total	507	735	1242
		Acceptance rate			31.40%	Acceptance rate			40.82%
		PSM				Traditional score
State-of-emergency	Micro-organisation	Actual	No default	Default		Actual	No default	Default
		No default	247	449	696	No default	163	/
		Default	4	2	6	Default	6	/
		Total	251	451	702	Total	169	533	702
		Acceptance rate			35.75%				24.07%
	Sole Trader	Actual	No default	Default		Actual	No default	Default
		No default	279	400	679	No default	173	/
		Default	4	10	14	Default	14	/
		Total	283	410	693	Total	187	506	693
		Acceptance rate			40.84%	Acceptance rate			26.98%

Table 12. Default rates PSM & TCSM per sector for both periods.

		PSM				Traditional score
		State	Predicted			State	Predicted
Total Period	Micro-organisation	Actual	No default	Default		Actual	no default	Default
		No default	274	196	470	No default	470	/
		Default	16	21	37	Default	37	/
		Total	290	217	507	Total	507		507
		Bad rate			5.52%	Bad rate			7.30%
	Sole Trader	Actual	No default	Default		Actual	No default	Default
		No default	143	145	288	No default	288	/
		Default	12	19	31	Default	31	/
		Total	155	164	319	Total	319		319
		Bad rate			7.74%	Bad rate			9.72%
			PSM				Traditional score
			Predicted				Predicted
State-of-emergency	Micro-organisation	Actual	No default	default		Actual	No default	default
		No default	73	90	163	No default	163	/
		Default	4	2	6	default	6	/
		Total	77	92	169	Total	169		169
		Bad rate			5.19%	Bad rate			3.55%
	Sole Trader	Actual	No default	Default		Actual	No default	Default
		No default	97	76	173	No default	173	/
		Default	4	10	14	Default	14	/
		Total	101	86	187	Total	187		187
		Bad rate			3.96%	Bad rate			7.49%

Table 13. Research findings.

Hypothesis number	Hypothesis	Conclusion	Result 1	Result 2	Literature
H0	There is no significant difference between the psychometric attributes and arrears	Rejected	H0a/ H0c accepted	H0b/ H0 g partly accepted	Goel and Rastogi (2023); Djeundje et al. (2021); Liberati and Camillo (2018); Dlogosch, Klinger, and Frese (2017)
H0a	Low levels of financial skills increases arrears	Accepted	Negative relation	Significant correlation	Struckell et al. (2022); Lusardi, Hasler, and Yakoboski (2021); Klapper and Lusardi (2020); Lusardi (2019)
H0b	High levels of self-efficacy (confidence) increases arrears	Partly accepted	Negative relation	Significant correlation	Nosić and Weber (2010); Yang, Markoczy, and Qi (2007); Dans and Haberman (2007); Seaward and Kemp (2000)
H0c	Positive debt-attitudes increases arrears	Accepted	Positive relation	Significant correlation	Almenberg et al. (2021); Haultain, Kemp, and Chernyshenko (2010)
H0d	High levels of materialism increases arrears	Rejected	Positive relation	Low correlation	Garðarsdóttir and Dittmar (2012); Pham, Yap, and Dowling (2012); Donnelly, Iyer, and Howell (2012); Pirog and Roberts (2007); Ponchio and Aranha (2008); Richins (2011); Norvilitis et al. (2006); Richins and Dawson (1992); Belk (1984)
H0e	Immediate gratification increases arrears	Rejected	Positive relation	Low correlation	O'Connor et al. (2019); Strömbäck et al. (2017); Khare (2016); Achtziger et al. (2015); Phau and Woo (2008); Joji and Raveendran (2008)
H0f	Higher levels of fluid intelligence decreases arrears	Rejected	Negative relation	Low correlation	Małkiński and Mańdziuk (2022); Djeundje et al. (2021); Muñoz-Murillo, Álvarez-Franco, and Restrepo-Tobón (2020); Dlogosch, Klinger, and Frese (2017); Liberati and Camillo (2018);
H0g	Higher levels of social desirability increases arrears	Partly accepted	Negative relation	Significant correlation	Norvilitis et al. (2006); Ganster, Hennessey, and Luthans (1983)
H1	Psychometric credit scoring increases the financial inclusion of small businesses in unstable Central European markets	Accepted	Acceptable discrimination	Acceptable discrimination in stress test period	Goel and Rastogi (2023); Djeundje et al. (2021); Liberati and Camillo (2018); Dlogosch, Klinger, and Frese (2017)

Goel, A., and S. Rastogi. 2023. “Credit Scoring of Small and Medium Enterprises: A Behavioural Approach.” Journal of Entrepreneurship in Emerging Economies 15 (1): 46–69.

 Web of Science ®Google Scholar

Van Thiel, D., J. Goedee, and R. T. A. J. Leenders. 2023. “Leveraging Financial Personality for Inclusive Credit Scoring Amidst Global Volatility.” Journal of Risk Management in Financial Institutions 17 (1): 22–42.

 Google Scholar

Djeundje, V. B., J. Crook, R. Calabrese, and M. Hamid. 2021. “Enhancing Credit Scoring with Alternative Data.” Expert Systems with Applications 163:113766.

 Web of Science ®Google Scholar

Liberati, C., and F. Camillo. 2018. “Personal Values and Credit Scoring: New Insights in the Financial Prediction.” Journal of the Operational Research Society 69 (12): 1994–2005.

 Web of Science ®Google Scholar

Dlogosch, T. J., B. Klinger, and M. Frese. 2017. “Personality-based Selection of Entrepreneurial Borrowers to Reduce Credit Risk: Two Studies on Prediction Models in Low and High-Stakes Settings in Developing Countries.” Journal of Organisational Behaviour 39:612–628.

 Web of Science ®Google Scholar

Struckell, E. M., P. C. Patel, D. Ojha, and P. Oghazi. 2022. “Financial Literacy and Self Employment – The Moderating Effect of Gender and Race.” Journal of Business Research 139: 639–653.

 Web of Science ®Google Scholar

Lusardi, A., A. Hasler, and P. J. Yakoboski. 2021. “Building up Financial Literacy and Financial Resilience.” Mind & Society 20:181–187.

 Google Scholar

Klapper, L., and A. Lusardi. 2020. “Financial Literacy and Financial Resilience: Evidence from Around the World.” Financial Management 49 (3): 589–614.

 Web of Science ®Google Scholar

Lusardi, A. 2019. “Financial Literacy and the Need for Financial Education: Evidence and Implications.” Swiss Journal of Economics and Statistics 155 (1): 1–8.

 Google Scholar

Nosić, A., and M. Weber. 2010. “How Riskily do I Invest? The Role of Risk Attitudes, Risk Perceptions, and Overconfidence.” Decision Analysis 7 (3): 282–301.

 Web of Science ®Google Scholar

Yang, S., L. Markoczy, and M. Qi. 2007. “Unrealistic Optimism in Consumer Credit Card Adoption.” Journal of Economic Psychology 28 (2): 170–185.

 Web of Science ®Google Scholar

Dans, S. M., and H. R. Haberman. 2007. “Teen Financial Knowledge, Self-efficacy, and Behaviour: A Gendered View.” Journal of Financial Counseling and Planning 18 (2): 13. https://ssrn.com/abstract=222840.

 Google Scholar

Seaward, H. G., and S. Kemp. 2000. “Optimism Bias and Student Debt.” New Zealand Journal of Psychology 29 (1): 17–19.

 Web of Science ®Google Scholar

Almenberg, J., A. Lusardi, J. Säve-Söderbergh, and R. Vestman. 2021. “Attitudes Toward Debt and Debt Behaviour.” The Scandinavian Journal of Economics 123 (3): 780–809. https://doi.org/10.1111/sjoe.12419.

 Google Scholar

Haultain, S., S. Kemp, and O. S. Chernyshenko. 2010. “The Structure of Attitudes to Student Debt.” Journal of Economic Psychology 31 (3): 322–330. https://doi.org/10.1016/j.joep.2010.01.003.

 Web of Science ®Google Scholar

Garðarsdóttir, R. B., and H. Dittmar. 2012. “The Relationship of Materialism to Debt and Financial Well-Being: The Case of Iceland’s Perceived Prosperity.” Journal of Economic Psychology 33 (3): 471–481. https://doi.org/10.1016/j.joep.2011.12.008.

 Web of Science ®Google Scholar

Pham, T. H., K. Yap, and N. A. Dowling. 2012. “The Impact of Financial Management Practices and Financial Attitudes on the Relationship Between Materialism and Compulsive Buying.” Journal of Economic Psychology 33 (3): 461–470.

 Web of Science ®Google Scholar

Donnelly, G., R. Iyer, and R. T. Howell. 2012. “The Big Five Personality Traits, Material Values, and Financial Well-Being of Self-Described Money Managers.” Journal of Economic Psychology 33 (6): 1129–1142.

 Web of Science ®Google Scholar

Pirog, S. F., and J. A. Roberts. 2007. “Personality and Credit Card Misuse among College Students: The Mediating Role of Impulsiveness.” Journal of Marketing Theory and Practice 15 (1): 65–77.

 Google Scholar

Ponchio, M. C., and F. Aranha. 2008. “Materialism as a Predictor Variable of Low Income Consumer Behaviour When Entering into Installment Plan Agreements.” Journal of Consumer Behaviour: An International Research Review 7 (1): 21–34.

 Google Scholar

Richins, M. L. 2011. “Materialism, Transformation Expectations, and Spending: Implications for Credit use.” Journal of Public Policy & Marketing 30 (2): 141–156.

 Web of Science ®Google Scholar

Norvilitis, J. M., M. M. Merwin, T. M. Osberg, P. V. Roehling, P. Young, and M. M. Kamas. 2006. “Personality Factors, Money Attitudes, Financial Knowledge, and Credit-Card Debt in College Students.” Journal of Applied Social Psychology 36 (6): 1395–1413. https://doi.org/10.1111/j.0021-9029.2006.00065.x.

 Web of Science ®Google Scholar

Richins, M. L., and S. Dawson. 1992. “A Consumer Values Orientation for Materialism and Its Measurement: Scale Development and Validation.” Journal of Consumer Research 19 (3): 303–316.

 Web of Science ®Google Scholar

Belk, R. W. 1984. “Three Scales to Measure Constructs Related to Materialism: Reliability, Validity, and Relationships to Measures of Happiness.” In Advances In Consumer Research, edited by T. Kinnear, 291–297. Provo, UT: Association for Consumer Research.

 Google Scholar

O'Connor, G. E., C. E. Newmeyer, N. Y. C. Wong, J. B. Bayuk, L. A. Cook, Y. Komarova, C. Loibl, L. L. Ong, and D. Warmath. 2019. “Conceptualizing the Multiple Dimensions of Consumer Financial Vulnerability.” Journal of Business Research 100:421–430.

 Web of Science ®Google Scholar

Strömbäck, C., T. Lind, K. Skagerlund, D. Västfjäll, and G. Tinghög. 2017. “Does Self-Control Predict Financial Behaviour and Financial Well-Being?” Journal of Behavioural and Experimental Finance 14: 30–38.

 Google Scholar

Khare, A. 2016. “Money Attitudes, Credit Card and Compulsive Buying Behaviour: A Study on Indian Consumers.” International Journal of Business Competition and Growth 5 (1-3): 49–71.

 Google Scholar

Achtziger, A., M. Hubert, P. Kenning, G. Raab, and L. Reisch. 2015. “Debt out of Control: The Links Between Self-Control, Compulsive Buying, and Real Debts.” Journal of Economic Psychology 49:141–149.

 Web of Science ®Google Scholar

Phau, I., and C. Woo. 2008. “Understanding Compulsive Buying Tendencies among Young Australians: The Roles of Money Attitude and Credit Card Usage.” Marketing Intelligence & Planning 26 (5): 441–458.

 Google Scholar

Joji, N. A., and P. T. Raveendran. 2008. “Does Compulsive Buying Affect Credit Card Defaults?” Vision 12 (4): 23–32.

 Google Scholar

Małkiński, M., and J. Mańdziuk. 2022. “A Review of Emerging Research Directions in Abstract Visual Reasoning.” Information Fusion.

 Google Scholar

Muñoz-Murillo, M., P. B. Álvarez-Franco, and D. A. Restrepo-Tobón. 2020. “The Role of Cognitive Abilities on Financial Literacy: New Experimental Evidence.” Journal of Behavioural and Experimental Economics 84:101482.

 Web of Science ®Google Scholar

Ganster, D. C., H. W. Hennessey, and F. Luthans. 1983. “Social Desirability Response Effects: Three Alternative Models.” Academy of Management Journal 26 (2): 321–331.

 Web of Science ®Google Scholar

Lusardi, A., and O. S. Mitchell. 2007. “Financial Literacy and Retirement Planning: New Evidence from the Rand American Life Panel.” Michigan Retirement Research Center Research Paper No. WP, 157. https://doi.org/10.2139/ssrn.1095869.

 Google Scholar

Lusardi, A., and O. S. Mitchell. 2008. “Planning and Financial Literacy: How do Women Fare?” American Economic Review 98 (2): 413–417. https://doi.org/10.1257/aer.98.2.413.

 Web of Science ®Google Scholar

Lusardi, A., and O. S. Mitchell. 2011. “Financial Literacy and Planning: Implications for Retirement Wellbeing (No. w17078).” National Bureau of Economic Research.

 Google Scholar

Bandura, A. 2012. “On the Functional Properties of Perceived Self-efficacy Revisited.” Journal of Management 38 (1): 9–44.

 Web of Science ®Google Scholar

Richins, M. L. 2017. “Materialism Pathways: The Processes That Create and Perpetuate Materialism.” Journal of Consumer Psychology 27 (4): 480–499.

 Web of Science ®Google Scholar

Richins, M. L. 1992. “Media Images, Materialism, and What Ought to be: The Role of Social Comparison.” Advances in Consumer Research 19: 202–206.

 Google Scholar

Bergen, N., and R. Labonté. 2020. ““Everything is Perfect, and We Have no Problems”: Detecting and Limiting Social Desirability Bias in Qualitative Research.” Qualitative Health Research 30 (5): 783–792.

 PubMed Web of Science ®Google Scholar