Should I play or should I go? Individuals' characteristics and preference for uncertainty

Saraiva, Tânia; Gonçalves, Tiago Cruz Article Should I play or should I go? Individuals' characteristics and preference for uncertainty Games Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Saraiva, Tânia; Gonçalves, Tiago Cruz (2022) : Should I play or should I go? Individuals' characteristics and preference for uncertainty, Games, ISSN 2073-4336, MDPI, Basel, Vol. 13, Iss. 2, pp. 1-14, https://doi.org/10.3390/g13020031 This Version is available at: https://hdl.handle.net/10419/329942 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Games 2022,13, 31. https://doi.org/ 10.3390/g13020031 Academic Editors: Lacra Pavel and Ulrich Berger Received: 10 March 2022 Accepted: 11 April 2022 Published: 13 April 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). games Article Should I Play or Should I Go? Individuals’ Characteristics and Preference for Uncertainty Tânia Saraiva 1and Tiago Cruz Gonçalves 2,* 1ISCAL—Lisbon Accounting and Business School, 1069-035 Lisbon, Portugal; [email protected] 2ISEG Lisbon School of Economics & Management, Universidade de Lisboa, Advance/CSG, 1200-781 Lisbon, Portugal *Correspondence: [email protected] Abstract: This paper presents an incentivized experiment analyzing the role of demographic characteristics in individual decision-making under uncertainty. Reactions to a natural source of uncertainty, payoffs in a TV game show, were measured using Fuzzy-set Qualitative Comparative Analysis (fsQCA), allowing us to identify multiple configurations of causal conditions that are sufficient for individuals to prefer an uncertain payoff to a sure gain, and, thus, lower risk aversion. This paper found evidence of preference for uncertainty, measured as willingness to play for an uncertain payoff, in individuals with characteristics most commonly present in the literature: being male; young; childless; with studies in finance or similar areas. This paper also shows that conditions that would not justify the preference for uncertainty according to the literature (an older individual or having children), when combined with other conditions, change contestants’ behavior regarding preference for uncertainty. Individuals that are both older and single, and individuals that have children combined with education in finance, show an inverse effect on preference for uncertainty. Keywords: preference for uncertainty; individuals’ characteristics; risk aversion; socio-demographics; fsQCA; TV game show JEL Classification: D81 1. Introduction Decision-making is a complex process that usually involves several levels of uncertainty. Given the importance of decision-making and its consequences, it is essential to understand how individuals make their decisions, and how individual characteristics affect the preference for uncertainty. Several empirical studies analyze the relationship between decision-making under uncertainty and the characteristics of individuals ([ 1 – 6 ]). The characteristics previously proposed and investigated as possible determinants of the individuals’ decision-making under uncertainty include, among others: gender, age, marital status, number of dependents, and education. Similarly, other variables can influence the decision-making under uncertainty, such as ethnicity ([7]), geographic location ([1]), or wealth ([1,2]). We defined preference for uncertainty when an individual chooses a stochastic payoff rather than a guaranteed gain. In our study, preference for uncertainty occurs when a contestant decides to Game, having a 25% chance of winning a higher payoff if the answer is correct, and a 75% chance of lowering his current payoff if the answer is incorrect, instead of opting for No Game, guaranteeing a payoff, with a lower certain value. This concept is associated with risk-seeking behavior under uncertainty in the existing literature. The aim of this paper is to disentangle the role that individuals’ characteristics can play in individual decision-making under uncertainty, more specifically, to assess the complexity of the relationships between the variables and the preference for uncertainty. Based on Games 2022,13, 31. https://doi.org/10.3390/g13020031 https://www.mdpi.com/journal/games Games 2022,13, 31 2 of 14 the analysis of 401 episodes of the JOKER television contest, we measured the impact of individual characteristics in decision-making under uncertainty. Results show evidence of preference for uncertainty when the contestant is male; young; without children; and with studies in finance. This paper also demonstrates that combinations that were not expected to justify the preference for uncertainty, according to the literature, namely being older or having children, when combined with being single or having studies in finance, respectively, show a behavior of preference for uncertainty. 2. Literature Review This section presents a review of the relevant literature, which is divided into subsections with a brief review of each individual characteristics that affect a preference for uncertainty. 2.1. Gender Many empirical studies explored gender differences to explain the preference for uncertainty in terms of decision-making ([ 1 , 8 – 12 ]). Most empirical literature is consensual regarding the fact that women are more averse to uncertainty than men ([6,13–18]). Two of the first empirical studies that looked at the differences between women and men in decision-making under uncertainty were conducted by Cohn et al. [ 19 ], who collected data from a survey to clients of an American brokerage firm, and Riley and Chow [ 1 ], who used data on the actual allocation of assets of a random sample of the population of the United States of America. Both studies concluded that men tend to prefer investments with more uncertain returns. Subsequent studies, such as the one by Bajtelsmit and Vanderhei [ 8 ], concluded that, even when controlling for variables such as wealth and age, women generally choose pension fund options with less uncertainty of return. Similarly, Sunden and Surette [ 9 ] used data from the Survey of Consumer Finances—between 1992 and 1995—and reported that women tend to choose less risky pension funds. Warner and Cramer [ 20 ] explored the saving behaviors of baby boomers and found similar results. Based on a laboratory experiment, where individuals are asked to choose between five alternative options that differ in terms of expected return and variance, Eckel and Grossman [ 21 ] concluded that men choose, on average, riskier options with higher expected returns. Motivated by the work of Kahneman and Tversky [ 22 ], the former authors also demonstrate that the highest average level of risk aversion in women remains both in the domain of losses and in the domain of gains. Jianakoplos and Bernasek [ 14 ] explored the gender difference in the preference for uncertainty in asset allocation, and concluded that single families headed by women, or couples, are more risk averse than families headed by single men. Men tend to have higher levels of confidence than women, and, in this sense, are willing to take more risk in their decisions ([ 23 – 25 ]). Byrne and Worthy [ 26 ] suggest that men and women differ in decision-making because women tend to decide according to the frequency of the reward, or they search for options that offer consistent rewards even if smaller, whereas men look for options with the highest possible returns. These conclusions are also corroborated by the study developed by Barber and Odean [ 23 ], in which they concluded that men have higher levels of confidence than women, and, in this sense, tend to opt for the preference for uncertainty in their investments. This study demonstrates, therefore, that men are more confident regarding investments, which makes them assume a riskier profile. Dwyer et al. [ 27 ] demonstrate that gender differences in decision-making under uncertainty can be driven by a financial knowledge divergence between men and women. These authors conducted a study with a sample of 2000 investors in mutual funds and found that women take less risk than men in their investment decisions. However, when monitoring knowledge about financial markets and investments, the differences between genders become significantly reduced, with a greater risk-taking being shown by the female gender. A similar conclusion is presented in the study by Bajtelsmit and VanDerhei [ 8 ], and even Games 2022,13, 31 3 of 14 though they show that women invest a relatively larger fraction in low-risk assets, when compared to men, the authors justify that this difference between genders can be explained by the socioeconomic status, wealth, or financial knowledge, which the authors were unable to monitor in their study. However, Jetter and Walker [ 28 ] tested for how gender might influence performance and preference for uncertainty in contests, concluding that women appear to be more aggressive and (marginally) more competitive, as well as taking on more risk. 2.2. Age Several studies focused on another demographic—age—and concluded that older individuals are more averse to uncertainty when compared to young individuals ([ 3 , 29 – 34 ]). According to Aren and Canikli [ 34 ], the main explanatory factors are the fact that older individuals prefer investments without uncertainty, assuming lower but consistent returns, due to the reduction of their cognitive abilities in terms of information analysis regarding investments with uncertain returns. However, the literature is not always consistent. Some authors reveal that the choice for uncertain investments increases until a certain age ([ 1 , 14 , 35 ]). Jianakoplos and Bernasek [ 14 ] found that single women have a decline in tolerance for uncertainty when they reach the ages between 36 and 40 years old. Bougherara et al. [ 36 ] found that older and female individuals make more uncertainty-avoiding and more skewness-seeking choices. Other authors have shown that older individuals behave similarly to young individuals under uncertainty (for example, [ 33 , 37 – 39 ]). Clark and Strauss [ 37 ] obtained evidence that both older and younger individuals have the same level of propensity for uncertainty; however, middle-aged individuals tend to be more conservative. According to the authors, this conclusion can be explained by the accumulation of wealth that compensates for uncertainty in investments for older individuals. Mata et al. [ 33 ] found that older individuals make decisions under uncertainty in gain scenarios and decide in favor of gain certainty in loss scenarios. As these authors demonstrated, applying the Tversky and Kahneman framework effect [ 40 ], older individuals are more sensitive to utility in the gain domain than in the loss domain. Best and Charness [ 41 ] also concluded that the difference in decision-making under uncertainty between younger and older individuals only occurs when the decision is based on low values. In these cases, older individuals are less likely than young individuals to choose investments with uncertain returns, as a way to guarantee the maximization of their usefulness, and they are also more likely to maintain their current resources. The authors report that this age effect disappears when the values at stake are substantially high. Sproten et al. [ 42 ] observed that the decision-making behavior of older individuals differs in part from the behavior of younger individuals. Under conditions of uncertainty, older individuals behave like young individuals, only if the a priori probabilities apply. Young individuals are more averse to uncertainty if they do not get any feedback, and older individuals are more averse to uncertainty when it comes to estimates. 2.3. Marital Status Several studies also point to marital status as a factor that influences uncertaintyaversion in decision-making. Some of these studies report that single individuals are more prone to uncertainty than married individuals ([3,19,43–46]). Nosita et al. [ 47 ] present, as a main reason, the levels of responsibility of married people compared to those of unmarried individuals. This is in line with the results of research by Roszkowski et al. [ 43 ] concluding that responsibility increases along with marriage and having children. On the onset, a couple has greater financial responsibility, especially when there are dependents; therefore, they have less tolerance for uncertainty. In addition, the authors also claim that couples can face greater social risks, such as deprivation of their social status, due to the loss of an investment. Games 2022,13, 31 4 of 14 However, other studies reveal that married individuals are more tolerant to uncertainty, since they have greater financial margins to absorb negative results ([ 2 , 48 ]). Schooley and Worden [ 49 ] justify these results with the fact that married individuals usually have two incomes, whereas other studies found no significant relationship between marital status and decision-making under uncertainty ([1,44,50–52]). Chaulk [ 53 ] finds that, with an increase in disposable income, both single and married individuals increase their tolerance to uncertainty. This increase is more pronounced for married individuals when compared to single individuals. For both groups, an increase in income justifies a greater margin to recover from unexpected losses. 2.4. Number of Dependents The number of dependents is also a studied variable in terms of understanding the behavior in decision-making under uncertainty. However, most of the empirical literature present statistically non-significant results. This variable is sometimes integrated into the analysis of the marital status variable, where it is assumed that married people with dependents have less preference for uncertainty (for example, Roszkowski et al. [43]). Daly and Wilson [ 54 ] suggest that the responsibilities accompanying marriage and children make individuals less tolerant to uncertainty. Warner and Cramer [ 20 ] compared individuals without children to individuals with children, and the latter were less tolerant to uncertainty, which led to the assumption that they demand certainty in the return of their investments. Chaulk [ 53 ] also concluded that individuals with children are less likely to be tolerant to uncertainty than those who do not have children, regardless of their age or gender. In addition, Nosita et al. [ 47 ] observed that when married couples have children, they will be more concerned about uncertainty, and try to reduce risk by transferring those risks, namely by buying health insurance, education insurance, and life insurance. The risk transfer is an effort to reduce uncertainty in the future. On the other hand, Jianakoplos and Bernasek [ 14 ] reported that as the number of young dependents increased in a family, the greater the proportion of assets with uncertain returns was. However, in single women’s households, investment in assets with uncertain returns decreases as the number of children increases. It should be noted that in the study by Jianakoplos and Bernasek [ 14 ], they considered the number of individuals under the age of 18 as an independent variable and recognized that the dependents could be younger siblings or relatives. Xiao [ 55 ] also achieved similar results in families with small children. Most do not invest in assets with uncertain returns. However, families with children aged between 6 and 11 years old, and in their teens (between 12 and 17 years old), tend to prefer uncertainty in their investments. Hallahan et al. [ 3 ] found no evidence linking the existence of children in a family with attitudes towards preference for uncertainty. Schooley and Worden [ 49 ] found that the relationship between the number of dependents and tolerance to uncertainty is insignificant. Therefore, the literature shows contradictory results regarding this variable, and it is still not possible to define whether the number of dependents can significantly affect the individuals’ preferences for uncertainty. 2.5. Education Another factor that seems to influence an individual’s tolerance for uncertainty is their level of education. Several studies have shown a positive relationship between tolerance for uncertainty and the level of education. The argument is that individuals with higher levels of education are better prepared to assess the risk–return relationship, which leads to a greater tolerance for uncertainty ([1–3,5,44,47,56–58]). Baker and Haslem [ 56 ] argue that less educated individuals prefer less-volatile assets. Riley and Chow [ 1 ] concluded that aversion to uncertainty decreases with the level of education, because the individual’s understanding of complex financial products increases. Sung and Hanna [ 57 ] reinforce the idea of Riley and Chow [ 1 ] due to the individual’s ability to understand the differences in the nature of the risk. Grable [ 2 ] concludes that Games 2022,13, 31 5 of 14 individuals with a higher level of education are less averse to uncertainty, i.e., they have higher levels of financial knowledge. Hallahan et al. [ 3 ] also found a positive relationship between education and the degree of risk tolerance. These authors assessed the wealth of individuals over 60 and concluded that approximately half of the millionaires had a higher education degree and only 9% did not complete their secondary education. Booij et al. [58] state that less-educated individuals show greater aversion to uncertainty. Bayar et al. [59] affirm that as the awareness of individuals of financial knowledge increases, they tend to have a preference for uncertainty. Although, it is evident that income level is also an important determinant of high financial risk tolerance. However, Sjoberg and Engelberg [ 60 ] demonstrated that this aversion to uncertainty, in addition to being related to the level of education, also depends on the degree of specialization. Finance students demonstrated a greater degree of tolerance to uncertainty than the general population. Besides, according to Gaudecker [ 5 ], the role of financial knowledge is quite relevant, and is related to the returns. Families with above average financial literacy tend to get higher returns than families with lower levels of financial literacy. Based on the theoretical background, the following testable hypotheses are formulated: Hypothesis 1 (H1). Gender preference. Men are more likely to prefer uncertainty when compared to women. Hypothesis 2 (H2). Age preference. The preference for uncertainty decreases with age. Hypothesis 3 (H3). Marital status preference. Single people are more likely to choose uncertain options than married people. Hypothesis 4 (H4). Child preference. Individuals without children have a greater preference for uncertainty in their decisions. Hypothesis 5 (H5). Education preference. Individuals with an education in finance or similar show a greater preference for uncertainty in their decision-making. 3. Model of the TV Game Show This section presents the model of the game show “JOKER” that may be useful to interpret this paper’s results. However, the non-interested reader can jump directly to the methodology at the end of this section. The game model we present here is responsible for the dynamic structure of the whole game. Initially, we present a simplified version of the game in which we assume the contestants are neutral in terms of uncertainty and, therefore, maximisers of the expected result. Next, we consider aversion to uncertainty in a very direct way, but that allows us to maintain the structure presented. prepresents the probability of a contestant (with a certain level of knowledge) answering the question correctly, where pis a realization of the random variable pwith a cumulative distribution function F. A contestant may reach the last question (the only question where he/she must decide with uncertainty whether or not to play) at any of the levels, that is, n= 7, . . . , 1. So, a n is the representation of the monetary values accumulated up to the twelfth question. The monetary value at stake in the last question can correspond to any of the game levels because it depends on the contestant’s performance in the previous eleven questions, where {an}7 n=1={50,000, 10,000, 3000, 1000, 500, 200, 0} . Likewise, a n+3 is the value of the fall from the previous gain level, that is, the gains that can be maintained in the event of a wrong answer. So, if in the twelfth question the contestant is at the 3000 euros level, the value of continuing the game is given by: V3000(p)=max {a4,p(f2)+a6}(1) Games 2022,13, 31 6 of 14 Thus, we can generalize this model, so that when there are npossible levels in the twelfth question, it can be successfully answered with a pprobability, and the value of the game is: Vn(p)=an+1,p(fn−1)+an+3(2) fn−1=E[Vn−1(P)] (3) We define F 0 =a 1 . It is important to note that the decision to play or not in the last question is made after the question is presented. There is a critical value for p, pn=an−1−an+3 (fn−1)−an+3(4) for a value maximiser, so that if p≤pn, the individual gives up the game and, therefore: Vn(p)=an−1(5) On the other hand, if p>pn , and a contestant answers the question, the value of the game is: Vn(p)=p(fn−1)+an+3(6) The following equation represents the relation for {fn}: fn−1−fn=(fn−1−an+3)Z1 pn F(p)dp (7) To obtain the probability, we need to assess the probability of gain. The probability of a contestant deciding to answer the twelfth question, with a preview of it, is: Pr0Game0=1−F(pn)≡F(pn)(8) The probability that a contestant will give the correct answer, after making the decision to answer, is given by: Pr[0Win0|0Game0] = Z1 pn pdF(p) 1−F(pn)≡G(pn) F(pn)(9) where the probability of answering correctly is simply: Pr0Win0=G(pn)(10) The extension to allow a preference for uncertainty, as opposed to neutrality, is accommodated simply by allowing the premiums to be measured in terms of utility, that is, we consider a0 n=u(an), n= 1 . . . 7, for some increasing utility function u(x) with u(0) = 0. 4. Materials and Methods This section presents the details about data and the methodology used for examining the role of the individuals’ characteristics in preference for uncertainty. Data were collected using recordings of the programs, which were obtained from RTP 1, the broadcaster of the JOKER TV game. We examined 401 games shown between 27 August 2018 and 3 October 2020. The JOKER television contest consists of a game in which contestants answer twelve general knowledge questions that vary in difficulty and time to answer according to the level of the payoff. In each question, the contestant has 4 possible answers (25% probability per option). If a correct answer is chosen, the contestant climbs one level in the payoff ladder; with each incorrect answer, they drop three levels in the payoff ladder. The game ends with the twelfth and final question, where contestants can choose to either Game or No Game. If they opt for Game, and answer correctly, they Games 2022,13, 31 7 of 14 win a higher payoff. If they answer incorrectly, they win a lower payoff (three levels below). If they opt for No Game, they receive an insurance payoff (one level below). We collected the sociodemographic characteristics of the contestants because these characteristics have significant explanatory power in the study of the preference for uncertainty. Any of the JOKER’s contestants have their own demographic characteristics, life experience, a certain level of knowledge, areas of interest, and other pre-game factors that are determinant in their decisions during the game and may affect the decision under uncertainty. The characteristics we were able to analyze in advance, and that are the individual characteristic variables, are Gender as a dummy and binary variable, which is equal to 0 if the contestant is male and 1 if female; Age, which we defined as a categorical variable with three categories, according to what the vast majority of previous studies present (see metadata of Best and Charness [ 41 ]): 0—if young up to 30 years old, 1—if adult up to 59 years old, and 2—if older than 59 years old; Marital Status, as a binary variable, which is equal to 0 if the individual is married (includes common law unions), and 1 if the individual is single (includes divorced, separated, or widowed); the Number of Dependents was defined as a binary variable, which is equal to 0 if the individual does not have children, and 1 if the individual has children; and Education as a binary variable, which is equal to 0 if the individual has an education in finance or similar areas, and 1 if they have an education in other areas. It is important to note that some of the variables were estimated based on the contestants’ physical appearance and the information provided in the contestants’ introductory presentation. For example, in some cases, the contestants’ level of education was not explicitly mentioned, although that was often clarified by the declared profession. We estimated the missing values for age based on the physical appearance of the contestants and on the information revealed in their presentation, such as, for example, the age of their children, how long they have been working, or have been married, among others. In addition, we used the education variable to analyze the area of study, since approximately 99% of the individuals being studied had a higher education degree, and, therefore, it would not be feasible to evaluate this variable as the level of academic education, as most studies consider it. Therefore, based on the results of previous empirical investigations (for example, Sjoberg and Engelberg [ 60 ]), we hypothesize a greater preference for uncertainty in individuals with studies in finance or similar. To test the hypotheses regarding the influence of the individual characteristics on decision-making under uncertainty, we estimated the decision to play the last question as a series of independent samples t-tests and one-way ANOVA tests on the explanatory variables. The remaining demographic characteristics are not statistically significant, although they present the correct signal. These results may appeal to the idea of complex relationships between individual characteristics and a preference for uncertainty. To strengthen the analysis of the demographic characteristics, we used the Fuzzy-set Qualitative Comparative Analysis (fsQCA) methodology, which allowed us to present a more qualitative analysis of these variables. The fsQCA is particularly suitable for our study as it allows us to identify multiple configurations of causal conditions that are sufficient for a given outcome, that is, when a contestant has a preference for uncertainty in the twelfth question ([ 61 – 64 ]). We used fsQCA over conventional regression analysis because it allows us to identify the configurations of causal conditions ([ 65 , 66 ]) that are present or absent when a given result occurs. The fsQCA also allows us to verify if the causal conditions present or absent in the occurrence of a given result (to play the Game) are different and asymmetrical from the causal conditions that are present or absent when the same result does not occur (not playing the Game) ([61,65–68]). Games 2022,13, 31 8 of 14 5. Results 5.1. Summary Statistics In terms of individual characteristics, we concluded that 69% of the contestants in the JOKER contest are male. Regarding the age group, adults (between 31 and 59 years old) are the most representative group, accounting for approximately 72% of the contestants. As for marital status, married contestants are the group with the highest representation (63% of the contestants). In terms of the number of children, we have a very bipolar sample, with 54% of the contestants having no children, and 46% having children. Regarding the contestants’ area of education, 13% of contestants have degrees in finance or similar, and 87% have degrees in a different area. 5.2. Bivariate Analysis In Table 1, we present the estimated marginal effects and the p-values for each of the individual characteristic variables. Table 1. Bivariate analysis: demographic characteristics. Demographic Characteristics Test Groups N Mean Std Dev. Coeff. Gender t-student Male 275 0.74 0.440 0.000 Female 126 0.74 0.441 Age ANOVA Young 69 0.72 0.450 0.287 Adult 290 0.73 0.442 Older 42 0.79 0.415 Marital status t-student Single 148 0.79 0.408 14.738 * Married 253 0.71 0.456 Children t-student No 218 0.75 0.435 0.891 Yes 183 0.73 0.447 Education t-student Financial 52 0.79 0.412 3.716 Other 349 0.73 0.444 Note: * Significant at the 1% level. The preference for uncertainty, regardless of whether the contestant is male or female, is exactly the same (0.74); consequently, the difference in the averages is not statistically significant to explain the decision to play the game (p-value > 0.10), assuming an equality in variances for both groups. This is a result similar to the studies by Bajtelsmit and VanDerhei [ 8 ] and Dwyer et al. [ 27 ], in which, when monitoring knowledge in an area, the differences between genders are reduced or non-existent. The result is similar for the variable age of the contestants, where the average of preference for uncertainty is similar across all age groups, being slightly higher among older people (0.79). According to the ANOVA test, there are no differences in the averages between the three age groups (p-value > 0.10). This result is in line with studies by several authors showing that older individuals behave similarly to young individuals under uncertainty (for example, Clark and Strauss [ 37 ]; Henninger et al. [ 38 ]; Mata et al. [ 33 ]; Tymula et al. [ 39 ]). In addition, Sproten et al. [ 42 ] found that, in conditions of uncertainty, older individuals behave like young individuals when the a priori probabilities apply, as in the JOKER contest where the probabilities of gain or loss are known beforehand. Both married contestants (0.71) and single ones (0.79) show a preference for uncertainty higher at the center of the scale, which is slightly higher for singles. The difference in the averages is statistically significant (p-value = 0.00). Therefore, the idea that single contestants are more likely to play in the 12th question is valid and similar to other studies that report that single individuals are more prone to uncertainty than married individuals (Cohn et al., 1975 [ 18 ]; Roszkowski et al., 1993 [ 42 ]; Grable and Lytton, 1998 [ 43 ]; Hallahan et al., 2004 [3]; Yao and Hanna, 2005 [44]; Roussanov and Savor, 2014 [45]).