Anita Kolnhofer-Derecskei, Ph.D, Obuda University Keleti Faculty of Business and Management Budapest, Hungary 1084 Budapest Tavaszmező st. 15-17
[email protected] +36 1 666 5260
RISK TAKER AND NON RISK TAKER FOREIGN STUDENTS (RISK PERCEPTIONS AND RISK PREFERENCES AMONG DIFFERENT CULTURES)
Abstract: Risk becomes a relevant part of business life and our society as well (Vasvári, 2015) The main purpose of this research was to examine whether systematic cross-national differences in risk preferences (mainly in the context of financial and gambling decisions). For this I used the validated DOSPERT Scale’s Financial /Investment /Gambling part measuring foreign students’ risk perceptions and risk preferences (n=56). Comparing personal traits with Hofstede’s cultural Uncertainty Avoidance Index helps us to understand deeper cultural influences. Keywords: Risk, DOSPERT Scale
1
Risk assessment
Risk can be judged in many various ways, like probability or certainity of a risky event, as I have found earlier (KolnhoferDerecskei & Nagy, 2016) subjects defined risk with certainity or income of an event and connected it with dangerous situtation and did not used difficult mathematical expressions in their minds like in case of theoratical literatures. However in a former research students ranked entrepreneurship (which carrys a big risk) to be the most important among 17 technical skills. (Farkas & Nagy, 2008)
Figure 1 Vasvári’s risk definition (2015, p. 36)
I agree with Vasvári (2015) that rather than absolute probabilities, the result is a subjective expected value based on perceived probabilities. Risk perceptions and preferences are based on personal traits but personalities are coloured by cultural impacts and backgrounds. For this I have chosen DOSPERT Scale. Weber and her colleagues (Weber, et al., 2002) suggested a validated (i.e. scientifically approved) scale for measurement of risk. In their framework, people’s preference for risky options is assumed to reflect a tradeoff between an option’s expected benefit, usually equated to expected value, and its riskiness. In 2006 a new version was developed which contains only 30 items i.e. risk interpretations or statements on risk classified into 5 domains. All items have to be evaluated in three different dimensions, as the following (see table 1.): Domain subscales or life domains
Ethical
Items number
Risk-taking (How respondents engage in risky activities.)
Risk perception (How respondents assess the level of risk in each activities.)
6 sentences
Instruction: “For each of
Instruction: “we are
Expected Benefits of risk (what kind of benefit respondents obtain in each risky situations.) Instruction: “For each of
Financial (Investment/Gambling)
6 sentences
Health/Safety
6 sentences 6 sentences
Recreational Social
6 sentences
5 categories
30 items
the following interested in the following statements, your gut level statements, please assessment of please indicate the how risky indicate the likelihood that each situation benefits you you would or behavior would obtain engage in the is.” from each described 7 points situation.” activity or ranking scale 7 points behavior if ranking scale you were to find yourself in that situation.” 7 points ranking scale 30 items (from 5 categories) have to be evaluated 3 times = 90 scales
Table 1 DOSPERT 30 (Own table based on Centre for Decision Sciences, Columbia Business School)
This test contains 30 statements, the five subscales have 6-6 statements and as the table shows in three different context i.e. scales. Each response scale uses the same items from the five domain subscales or categories. In this study I only focused on the highlighted part of the DOSPERT Scale. All among others also Vasvári (2015) handled the impact of different cultural backgrounds on risk taking. Earliest comparison between cultural differences could be connected with Hofstede’s work. Based on Hofstede’s research, attitudes to uncertainty avoidance, and consequently judgments of risk, can be assumed to differ by culture. Hofstede defines uncertainty avoidance as the following “the way that a society deals with the fact that the future can never be known: should I try to control the future or just let it happen. This ambiguity brings with it anxiety and different cultures have learnt to deal with this anxiety in different ways. The extent to which the members of a culture feel threatened by ambiguous or unknown situations and have created beliefs and institutions that try to avoid is reflected in the score on Uncertainty Avoidance.” (Hofstede Centre, 2017) It has to be underlined that Hofstede focused on uncertainty (i.e. “The Uncertainty Avoidance dimension expresses the degree to which the members of a society feel uncomfortable with uncertainty and ambiguity.”) and not on risk, because risk is mainly a personal trait how the probability of an event’s positive or negative outcome can
be managed. Due to this I used Hofstede’s UA Indices comparing cultural differences.
Figure 1 UAIndex of Samples’ Home countries (based on Hofstede Centre’s data)
Also Hsee and Weber (1999) tested cultural differences, they explored whether there are systematic cross-national differences in choice-inferred risk preferences between Americans and Chineses. Our sample did not contain American or Chinese students. According to them Chineses were more risk seeker in various financial factors than Americans.
2 Research 2.1 Method and samples These findings were carried out at Obuda University in Budapest, all subjects got online questionnaires. Descriptive statistics about respondents can be found in the appendix 2. The survey contained more parts, in this paper I just observed one about financial risk attitudes. Three statements focused on investment attitude and three on gambling (betting). The six sentences were measured in two different point of view. Firstly, the personal traits of respondents (How respondents engage in risky activities). Secondly, the risk perception (How respondents assess the level of risk in each activities). Altogether 12 scales (7 points ranking scales) were observed. Used methods will be detailed later, after any results.
2.2
Results
Firstly, descriptive statistics (i.e. frequencies of scales) draws out the average judgements of respondents. In Table 1. can be seen any details. In nutshell, I just underline some findings (1) respondents perceived the meaning of any mentioned risky situation and they valued higher Risk Perceptions than Risk Taking, so they try to avoid risk, mostly in case Investment. But they would take risk in gamble or betting likely than on financial market.
RTI2Investing 10% of your annual income in a moderate growth mutual fund.]
RPI2[Investing 10% of your annual income in a moderate growth diversified fund.]
RTI3[Investing 10% of your annual income in a new business venture.]
RPI3[Investing 10% of your annual income in a new business venture.]
RTB2[Betting a day’s income on the outcome of a sporting event]
RPB2[Betting a day’s income on the outcome of a sporting event.]
56
56
56
56
56
56
56
56
56
56
0
0
0
0
0
0
0
0
0
0
0
0
Mean
3,8036
4,5000
4,0714
3,5714
4,3036
4,1607
2,6607
4,2321
3,9821
4,0893
2,8571
4,4821
Median
4,0000
5,0000
4,0000
4,0000
5,0000
4,0000
2,0000
4,0000
4,0000
4,5000
2,0000
5,0000
5,00
4,00
3,00 a
4,00
5,00
4,00 a
2,00
4,00
6,00
5,00
1,00
5,00
Valid Missing
Mode Std. Deviation
RPB3[Betting a day’s income at a high-stake poker game.]
RTB3[Betting a day’s income at a high-stake poker game.]
RPI1[Investing 5% of your annual income in a very speculative stock.]
56
N
RPB1 [Betting a day’s income at the horse races.]
RTI1[Investing 5% of your annual income in a very speculative stock.]
RTB1[Betting a day’s income at the horse races.]
Statistics
56
1,77272 1,79899 1,94335 1,74624 1,79818 1,52288 1,63236 1,68405 2,05816 1,78149 1,97649 1,84874
Minimum
1,00
1,00
1,00
1,00
1,00
1,00
1,00
1,00
1,00
1,00
1,00
1,00
Maximum
7,00
7,00
7,00
7,00
7,00
7,00
7,00
7,00
7,00
7,00
7,00
7,00
Table 2 Descriptive statistics of any statements (Own sources)
Secondly, I focused on cultural differences. Although UAI showed sharp differences among nations, in my research the number of respondents was not satisfying. That is why non parametric k independent sample hypothesis (so called Kruskal-Wallis) test was used (p=0,95) but there were no statements with significant differences between countries.
Chi15,522 14,792 13,065 Square df 10 10 10 Asymp. ,114 ,140 ,220 Sig. a. Kruskal Wallis Test b. Grouping Variable: Home_country
Figure 2 Moderated UAI&ART&ARP Indexes of Samples’ Home countries (Own sources)
RPI3[Investing 10% of your annual income in a new business venture.]
RPB2[Betting a day’s income on the outcome of a sporting event.]
RPI1[Investing 5% of your annual income in a very speculative stock.]
RPB3[Betting a day’s income at a high-stake poker game.]
RPI2[Investing 10% of your annual income in a moderate growth diversified fund.]
RPB1 [Betting a day’s income at the horse races.]
RTB3[Betting a day’s income at a high-stake poker game.]
RTB2[Betting a day’s income on the outcome of a sporting event]
RTB1[Betting a day’s income at the horse races.]
RTI3[Investing 10% of your annual income in a new business venture.]
RTI2Investing 10% of your annual income in a moderate growth mutual fund.]
RTI1[Investing 5% of your annual income in a very speculative stock.]
Test S tatistics a,b
10,260 10,641 14,358 13,526 8,573 15,947 15,984 9,597
10 10 10 10 10 10 10 10 10
,418 ,386 ,157 ,196 ,573 ,101 ,100 ,477 ,401
10,466
Table 3 Comparison statistics of scales regarding home countries (Own sources)
Due to this above mentioned reasons I used graphs observing countries’ comparison. For this, I calculated the average mean of Risk Taking and Risk Perception data grouped by home countries. After that, I used relative (percentages) indices which is a kind of easy standardization (skip out unit differences). The ranking order is the following:
It was slightly new finding that UA Indices were higher than personal Risk Indices. Perhaps, the young new generation handle risk differently. Finally, I did some directional measures. Eta value shows the connection between interval data (like DOSPERT Scales) and nominal data (like home countries or genders). In Table 4. I structured the results. Risk Perceptions and Risk Taking attitudes linked with home countries (i.e. cultural background of subjects) UAI was a test value, because UA Index depends on countries (see Eta which was 1). Directional Measures
Directional Measures Value
Nominal by Eta Home_coun Interval try Dependent Average risk taking Dependent Directional Measures
Value
Nominal by Eta Gender ,756 Interval Dependent
,501
Average risk taking Dependent Directional Measures
Value Nominal by Eta Home_coun Interval try Dependent Average risk perception Dependent Directional Measures
Average risk perception Dependent Directional Measures
Value Nominal by Eta Home_coun Interval try Dependent UAI Dependent
,699
,030
Value
Nominal by Eta Gender ,934 Interval Dependent
1,000
,033
Value
Nominal by Eta Gender ,661 Interval Dependent
,488
,567
UAI Dependent
,390
,055
Table 4 Directional measures regarding home country and gender (Own sources)
Gender differences can be also important and this was treat by Hofstede’s indices, as well. The strongest relationship was in case of risk perceptions, women perceived risky situations differently than men.
3
Conclusion
The current research shows that risk preference is among the variables that seem to have some systematic cross-national variation. Respondents valued Risk Perceptions Scales of any items higher than Risk Taking, that means they were clear with risky situations but they were not really a risk-taker group. Respondents avoid to invest money in a risky financial situation but they would take risk in area of gamble and betting. Unfortunatelly, the main limitation of this research was the sample size. Heterogenity of sample influenced testing, but I hope that this work will inspire further research to better determine the crossnational differences in risk preference. At the same time, I also hope that my findings will help decision makers and negotiators in practical applications. But in this research I could not observed representative responds so the predictions of a foreigner's risk preferences based on stereotypes might be take into consideration. Acknowledgement This paper SUPPORTED BY THE ÚNKP-16-2/I. NEW NATIONAL EXCELLENCE PROGRAM OF THE MINISTRY OF HUMAN CAPACITIES.
References [1]
[2]
[3]
[4] [5]
[6] [7]
[8]
Blais, A.-R. & Weber, E. U., 2006. A Domain-Specific Risk-Taking (DOSPERT) scale for adult populations. Judgement and Decision Making, Vol 1. No 1., pp. 3347. Centre for Decision Sciences, DOSPERT ORG. [Online] Available at: https://sites.google.com/a/decisionsciences.columbia.edu/dospert/ [October 2016]. Hofstede, G., 2017. The Hofstede Centre [Online] Available at: https://geert-hofstede.com [February 2017]. Hsee, C. K. & Weber, E. U. 1999. Cross-National Dierences in Risk Preference and Lay Predictions. Journal of Behavioral Decision Making 12: pp. 165-179 Kolnhofer-Derecskei, A., & Nagy, V. 2016. Under Risk. Proceedings of FIKUSZ ’16 Symposium for Young Researchers, pp. 161-172 Online] Available at: https://kgk.uni-obuda.hu/sites/default/files/16_Derecskei_Nagy.pdf Vasvári, T., 2015. Risk, Risk Perception, Risk Management – a Review of the Literature. Public Finance Quarterly, pp. 29-48. Weber, E. U., Blais, A.-R. & Betz, N., 2002. A domain-specific risk attitude scale: Measuring risk perceptions and risk behaviors. Journal of Behavioral Decision Making, 15., pp. 263-290. Farkas, A. & Nagy, V. 2008. Student Assessment of Desirable Technical Skills: A Correspondence Analysis Approach. ACTA POLYTECHNICA HUNGARICA Volume:5 Issue Number:2 pp. 43-57.
Appendix Financial (Investment/Gambling) statements (from DOSPERT 30) 12. Investing 5% of your annual income in a very speculative stock. 4. Investing 10% of your annual income in a moderate growth mutual fund. 18. Investing 10% of your annual income in a new business venture. 3. Betting a day’s income at the horse races. 14. Betting a day’s income on the outcome of a sporting event. 8. Betting a day’s income at a high-stake poker game. Risk taking scale Risk Perception scale 1 - Extremely unlikely 1 - Not at all 2 - Moderately unlikely 2 - Slightly risky 3 - Somewhat unlikely 3 - Somewhat risky 4 - Not sure 4 - Moderately risky 5 - Somewhat likely 5 - Risky 6 - Moderately likely 6 - Very risky 7 - Extremely likely 7 - Extremely risky
Gender Frequency Valid
female male Total
Valid
20,00 21,00 22,00 23,00 24,00 25,00 26,00 27,00 28,00 29,00 30,00 32,00 Total
53,6 46,4
53,6 46,4
56 Age
100,0
100,0
1 2 13 4 8 13 7 1 1 3 1 2
1,8 3,6 23,2 7,1 14,3 23,2 12,5 1,8 1,8 5,4 1,8 3,6
1,8 3,6 23,2 7,1 14,3 23,2 12,5 1,8 1,8 5,4 1,8 3,6
100,0
100,0
56 Home_country
Albania France Germany Hungary Italy Poland Romania Serbia Spain Turkey Unkraine Total
Valid Percent
30 26
Frequency Valid
Percent
Percent
Valid Percent
2 4 8 17 2 12 5 1 1 1 3
3,6 7,1 14,3 30,4 3,6 21,4 8,9 1,8 1,8 1,8 5,4
3,6 7,1 14,3 30,4 3,6 21,4 8,9 1,8 1,8 1,8 5,4
56
100,0
100,0
25 29 2
44,6 51,8 3,6
44,6 51,8 3,6
56
100,0
100,0
Cumulative Percent 53,6 100,0
1,8 5,4 28,6 35,7 50,0 73,2 85,7 87,5 89,3 94,6 96,4 100,0
Cumulative Percent 3,6 10,7 25,0 55,4 58,9 80,4 89,3 91,1 92,9 94,6 100,0
Actual_study Valid
BA/BSC M A/M SC PHD Total
44,6 96,4 100,0
Main_faculty Valid
M issing Total
Business/economy
33
58,9
94,3
94,3
Engineering/IT Total System
2 35 21 56
3,6 62,5 37,5 100,0
5,7 100,0
100,0
Appendix 2 Frequency tables of sample
