C. Dominik Güss Paul Fadil Stefan Strohschneider · International Business: Research, Teaching and Practice 2012 6(2) 13 Dynamic decision making (DDM) can follow various strategic

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International Business: Research, Teaching and Practice 2012 6(2)

THE INFLUENCE OF UNCERTAINTY AVOIDANCE ON DYNAMIC BUSINESS DECISION MAKING ACROSS CULTURES:

A GROWTH MIXTURE MODELING APPROACH

C. Dominik Güss*∗ Department of Psychology, University of North Florida

Jacksonville, FL 32224

Research Fellow at the Humboldt Foundation, Germany

Paul Fadil Department of Management, University of North Florida

Jacksonville, Florida 32224

Stefan Strohschneider Intercultural Business Communication

Friedrich-Schiller-Universität Jena, Germany

Key Words: Cross-cultural differences, dynamic decision making, complex problem solving, strategic planning, uncertainty avoidance, growth mixture modelling

∗ E-mail: [email protected]

Internat iona l Bus iness : Research , Teach ing and Prac t i c e 2012 6(2)

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Dynamic decision making (DDM) can follow various strategic patterns, one of them being stability versus flexibility. This paper explores the influence of uncertainty avoidance and expertise on stable versus flexible dynamic decision making. Participants were 40 German business students, 51 U.S. business students, and 66 U.S. psychology students. Every participant took the role of a manager in a computer-simulated company called CHOCO FINE and worked on the simulation individually over a period of 24 simulated months. Participants’ decisions were saved automatically in computer files and analyzed using growth mixture modeling in MPlus (GMM; Muthén & Muthén, 2006) which controls for interdependence of longitudinal data. Surprisingly, the German sample was more tolerant of ambiguity than the two U.S. samples, and uncertainty avoidance and intolerance of ambiguity did not predict DDM intensity and flexibility. The implications of this study are also discussed. In sum, results showed these unexpected cross-cultural differences, but no differences between novices and experts.

INTRODUCTION Decision making is a key demand for managers in organizations all around the world. Companies and managers have to adapt almost daily to changes within the organization and externally in the environment. The complexity of the many problems encountered and the uncertainty of the possible decision outcomes make decision making a difficult and unpredictable process. Research over the last decades has shown situational influences on decision making. How a decision problem is worded or framed influences what process and option the decision maker selects (e.g., Tversky & Kahneman, 1981). Recent research has shown cross-cultural differences affect how people approach complex problems and what strategies they use to make dynamic decisions (e.g., Güss, Tuason, & Gerhard, 2010; Strohschneider, 2001). An important question lies in what specific aspects of culture and enculturation are responsible for differences in dynamic decision making (DDM). Many studies have focused on cultural values such as individualism and collectivism (e.g., Hofstede, 1980, 2001; House, Hanges, Javidan, Dorfman, & Gupta, 2004; Triandis, 1995) to explain differences in decision making. Güss (2011), for example, showed in a study involving five countries that horizontal and vertical individualism and collectivism predicted the selection of dynamic decision-making strategies (action-orientation and planning) which in turn predicted performance. However, the explained variance of these values was relatively small. The purposes of this study are twofold: (1) to extend cross-cultural research on DDM and to investigate the influence of the cultural values uncertainty avoidance and intolerance of ambiguity on dynamic business decision making

Güss e t a l . Mode l ing o f Bus iness Dec i s ion Making across Cul tures

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across cultures; and (2) to investigate an alternative hypothesis stating that differences in professional-experiential culture would also influence DDM. Expertise research has shown significant decision making differences between novices and experts (Ericsson, Charness, Feltovich, & Hoffman, 2006). Based on different professional experiences, experts should show more flexible DDM patterns compared to novices. UNCERTAINTY AVOIDANCE Managers around the world have to deal with uncertainty in their work environments, yet different cultures have varying tolerance levels for unpredictable situations (Sully de Luque & Javidan, 2004). Uncertainty avoidance has been defined as “the extent to which the members of a culture feel threatened by uncertain or unknown situations” (Hofstede, 2001, p. 161). Hofstede (2001) describes three components of uncertainty avoidance: rule orientation, employment stability, and stress. High uncertainty avoidance is expressed in the acceptance of existing norms and rules, preference for employment stability, and the desire for low stress. When a manager with high uncertainty avoidance is confronted with a novel and uncertain situation, –we can expect–he or she will try to incorporate existing norms and rules into his or her decisions, and prefer decisions that guarantee stability and reduce stress. A few studies have investigated the influence of uncertainty avoidance on management decision making showing some contradicting results. Ayoun and Moreo (2008), for example, investigated empirically the relationship between uncertainty avoidance and business strategy. They argued that national culture would affect managers’ roles, values, attitudes, behavior, and business philosophy. Specifically, managers from high uncertainty-avoidant cultures would shy away from quick action and be more resistant to strategic change, whereas managers from low uncertainty-avoidant cultures would be “more comfortable with instability and are likely to engage in greater entrepreneurial activity” and ultimately be more open to strategic change (Ayoun & Moreo 2008, p. 67). Participants in their study were hotel managers from the United States and Malaysia (both low UA) and Thailand and Turkey (both high UA, following Hofstede’s classification). Participants answered surveys related to their openness to strategic change. Results showed “uncertainty avoidance was found to have little or no association with the degree of openness to strategic change” (Ayoun & Moreo, 2008, p. 70). Brinckmann, Grichni, and Kapsa (2010) conducted a meta-analysis including 47 studies investigating the relationship between business planning or strategy and performance taking uncertainty avoidance as a contextual variable into consideration. The authors found a positive relationship between business planning (both process and output) and performance which was moderated by uncertainty avoidance. Somewhat surprising, high levels of uncertainty avoidance in the national culture, such as in Germany (the authors used Hofstede’s scores), reduced the influence of business planning on performance. One possible


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explanation, given by the authors, could be greater experienced threat by ambiguous situations in high uncertainty-avoidant cultures and greater adherence to plans and regulations. As a consequence, these firms would be less flexible and open for changes to their business plans. Unforeseen events might be perceived as threats in high uncertainty-avoidant cultures whereas they might be perceived as opportunities in low uncertainty-avoidant cultures such as the United States. Geletkanycz (1997) investigated the influence of cultural value orientations on top executives’ resistance to change and commitment to the status quo (CSQ) in 20 nations. One of the four cultural values investigated was uncertainty avoidance. Counterintuitively, there was “a negative relationship between uncertainty avoidance and both leadership and strategy CSQ” Geletkanycz (1997, p. 627). In other words, executives from countries with low uncertainty avoidance values most often stuck to existing policies. Executives from countries with high uncertainty avoidance values, in contrast, showed greater openness toward change. One explanation given for this unexpected finding was that adherence to existing policies could often pose a greater risk than does change. Contradicting results were found in a study by van Oudenhoven, Mechelse, and de Dreu (1998). The authors investigated managerial conflict management in five European countries administering surveys to middle managers from one multinational company. Although, they predicted based on Hofstede’s uncertainty scores that Danish managers (low UA) should prefer constructive ways of conflict management much more so than Belgian managers (high UA), their results did not confirm this hypothesis. POSSIBLE REASONS FOR THE COUNTERINTUITIVE UNCERTAINTY AVOIDANCE RESULTS Although several studies predict an influence of uncertainty avoidance on stable decision making and a rejection of change, little empirical support has been found to support this reasoning. Possible methodological reasons for these inconclusive results could be the reliance on Hofstede’s UA scores (collected many decades ago) and the reliance on self-report measures. Most studies used Hofstede’s UA scores in their analyses and did not measure uncertainty avoidance. It is also questionable whether the selected cultural samples in the discussed studies represent the same UA scores as the one of the overall culture. Therefore, it would be beneficial to assess the cultural construct also on the individual level. In order to do so, the current study includes the value of intolerance of ambiguity (ITA). ITA is related to UA and refers to a manager’s ability to function and make decisions under uncertainty. Perceived ambiguity can be understood from a psychological point of view as the perceived absence of relevant information or information vagueness or lack of usage of present information (Venkatraman, Aloysius, & Davis, 2006). Researchers have determined that tolerance for ambiguity impacts performance in numerous business-related behaviors. Some of these include: decision making (e.g., Dollinger, Saxton, & Golden, 1995; Einhorn & Hogarth, 1986; Fox & Weber, 2002), entrepreneurial


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behavior (e.g., Schere, 1982), and the ability to deal with complexity (e.g., Gupta & Fogarty, 1993). Tolerance for ambiguity (for a review of the construct, see Furnham & Ribchester, 1995) has been researched in a number of cross-cultural studies (e.g., Ralston, Gustafson, Cheung, & Terpstra, 1993). Second, all of the studies on UA discussed used surveys with often low response rates. Surveys are more susceptible to response bias than behavioral decision data, especially culture-specific response-bias (see e.g., Johnson, Shavitt, & Holbrook, 2011; Sireci, 2011). How people perceive themselves is often not congruent with their behaviors. Therefore, the current study uses a microworld as research instrument to assess behavioral decision making. MICROWORLDS/BUSINESS SIMULATIONS

Microworlds are computer-simulated virtual environments that require sequences of decisions from participants (Brehmer & Dörner, 1993). Thus, it is possible to investigate decision making over time. Simulations have been quite common in the field of business (Keys & Wolfe, 1990; Lane, 1995). One of the main advantages of business/management simulations as a training instrument is that participants can engage in experiential learning (Faria & Nulsen, 1996). They can practice and learn from their mistakes in the virtual world, thereby potentially minimizing their decision failures in the real world (Thornton & Cleveland, 1990). Another advantage of simulations is that they allow individuals to track their decisions over time. Usually, all decisions participants make are saved in computer files. Not only in training, but also in basic research have simulations been applied, such as Markstrat (Stratx, 2011) to investigate “cross sectional organizational behavior, longitudinal organizational behavior, management, decision-making, forecasting, and marketing” (Dickinson, Gentry, & Burns, 2004, p. 346). Several studies have shown internal validity and external validity of management games (e.g., Wolfe & Roberts, 1986). EXPERTISE An alternative hypothesis to the cross-cultural uncertainty avoidance hypothesis for this study is related to expertise. Cognitive psychological research has shown that experts develop different knowledge structures and use different strategies than novices when they approach problems and make decisions. Chess experts, for example, can integrate more information on the chess board into chunks than novices (Chase & Simon, 1973). The deliberate practice theory (Ericsson, Krampe, & Tesch-Römer, 1993) suggests that experience allows people to become experts if they have many opportunities to repeat tasks, if the learner is provided with feedback, and if the learner has the chance to correct his/her errors. Experts do not have necessarily a bigger working memory capacity than novices, but they have stored their knowledge and experiences in long-term memory in such a way that this


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information is more easily accessible (Ericsson, Charness, Feltovich, & Hoffman, 2006). Based on different professional experiences, the two business student expert samples in the current study should show more flexible DDM patterns compared to the psychology novice student sample. HYPOTHESES

This study will contribute to the scientific literature in several ways. First, it will significantly add to the existing research stream on business decision making using self-reports by assessing actual decision making and by focusing on the process. Second, it will contribute to the field of cross-cultural psychology by unpacking culture and by exploring the role of uncertainty avoidance and intolerance of ambiguity values on decision-making stability versus flexibility in the cultural samples by assessing these values on the individual level. Finally, it will contribute to the area of dynamic decision making by using growth mixture modeling for the analysis of longitudinal data—which has been advocated recently (e.g., Proctor et al., 2011). The four hypotheses of the current study are:

Hypothesis 1: German students will show higher uncertainty avoidance and intolerance of ambiguity scores than U.S. students.

Hypothesis 2: In all samples, high uncertainty avoidance and intolerance of ambiguity will predict cautious and stable decision making. Low uncertainty avoidance and intolerance of ambiguity will predict intensive and flexible decision making.

Hypothesis 3: Cross-cultural hypothesis: German students will show more cautious and stable decision making compared to both U.S. samples.

Hypothesis 4: Expertise hypothesis: German and U.S. business students will show more flexible decision making than U.S. psychology students.

METHOD

PARTICIPANTS Participants were 40 undergraduate business students in Germany (medium high UA with 65, see Hofstede, 2001), 51 undergraduate business students in the United States (low UA with 46), and 66 psychology undergraduate students from the same U.S. university. In the German business sample, 45.0% were female, in the U.S. business sample, 29.4% were female, in the U.S. psychology sample, 77.4% were female. The age range was from 19 to 35 in the German sample (M = 24.33, SD = 7.56), from 19 to 55 years in the U.S. business sample (M = 25.17, SD = 8.13), and from 18 to 58 years in the U.S. psychology sample (M = 24.04, SD = 7.54). The three samples did not differ significantly regarding age, but


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differed regarding gender. Computer experience, handling the mouse, and socio-economic status were assessed as well and did not differ among the three samples. INSTRUMENTS CHOCO FINE simulation: Business decisions were assessed using the virtual chocolate-producing company CHOCO FINE (Dörner, 1993, 2000). It consists of over 1,000 simulated variables. The European Center for the Development of Vocational Training (Cedefop) and the Federal Institute for Vocational Education and Training in Germany (BIBB) have endorsed CHOCO FINE as a valid training system for “complex work-related situations where action is required” (BIBB, 2003). In a current study, we have validated the simulation in the U.S. with 160 participants and showed that performance was best for U.S. business owners, followed by U.S. graduate business students, followed by U.S. undergraduate business students, followed by U.S. psychology students (Güss & Fadil, in preparation). Participants took the role of managers and worked individually on the simulation for a period of 24 simulated months. The simulation lasted 2 hours. CHOCO FINE shows three main screens. The first screen provides basic data on sales, demand, etc. The second screen is the production screen. On this screen participants can decide how many chocolates of which kind should be produced on the machines. The third screen is the marketing screen. It shows the market and the segments of the market leaders and competitors. On this screen participants can conduct market research and make advertising and personnel decisions. Participants gathered information (e.g., about market, competitors, clients), made decisions (e.g., change production, advertise, hire) and went to the next month. As feedback, they saw a bar indicating the total monies for the respective month. Then they collected information again, made decisions, and proceeded to the next month. Every decision a participant made was saved automatically in computer files. The participants’ decision data can be grouped into the following four main areas: production, personnel, advertising, and market research and information gathering. Production decisions were not included in the following analyses, because the machines allow participants to produce chocolates only in a specified range, yet we were interested in variability of possible decisions. Uncertainty Avoidance (UA): The three Hofstede UA items refer to current work experiences and stress and do not seem appropriate for our student sample. UA was measured with a 3-item scale developed and validated by König et al. (2007). The items reflect possible problems at work, for example: “Imagine that one of your employees comes up with a new idea. His idea sounds promising, but its implementation would necessitate considerable changes in your business routines. What do you do?” Six answer options are presented from one extreme “You encourage your employee to try out his idea.” (1) Extremely, (2) very true of me, (3) somewhat; to the other extreme “You refuse to implement your employee’s


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idea. Changing your business routines is too risky to you.” (4) somewhat, (5) very true of me, (6) extremely. The UA score is the overall sum of the 3-item answers ranging from a minimum of 3 to a maximum of 18. Higher scores indicate higher UA. Cronbach’s alpha in our sample was .47 which is relatively low, but probably due to the low number of items. In order to calculate convergent validity we also used a survey assessing intolerance of ambiguity. Intolerance of Ambiguity (ITA) Scale: The Intolerance of Ambiguity scale was developed by Budner (1962). It consists of 16 items, half of them reversed, referring to ambiguous and uncertain situations. A sample item is: “It is more fun to tackle a complicated problem than to solve a simple one.” Each item has 6 answer options from strongly agree to strongly disagree. Each answer is coded with a score from 1 to 7 omitting 4, leading to a possible range from 16 to 112. High scores stand for high intolerance of ambiguity. Cronbach alpha reliability in our sample was .63. Convergent validity of the UA and ITA measures will be assessed inspecting intercorrelations. Demographic variables: Demographics such as gender, age, and socio-economic status were assessed and controlled. PROCEDURE

Participants received three pages of instructions explaining CHOCO FINE. Then they were allowed to familiarize themselves with the three screens and the possible commands during a 15-minute test version. This test version was exactly the same version they were playing afterwards for 2 hours. The experimenter answered questions regarding the control and commands of the simulation, but did not give strategic decision-making advice. Each participant click was saved in computer files. The saved data in participants’ files prevent experimenter or coder bias. After completion of the CHOCO FINE simulation, participants filled out the ITA scale, the UA survey, and the demographic survey. These survey instruments were translated into German using the translation-backtranslation procedure (Brislin, 1970). DATA ANALYSIS USING GROWTH MIXTURE MODELING

The recordings of CHOCO FINE in saved computer files result in a complex time-series type of data structure with many data points. Growth mixture modeling (GMM; Muthén, 2004) in the Mplus 5 software (Muthén & Muthén, 2006) was used to analyze the data. GMM has become a recognized method for identifying multiple homogeneous subpopulations within larger heterogeneous populations, and describing change over time among the main and the sub-populations (e.g., Jung & Wickrama, 2008; Ram & Grimm, 2009; Muthén, 2008). Additionally GMM does not assume that a given predictor influences all trajectories equally. The model for the current study consists of the following variables: The three basic decision area outcomes as continuous variables


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(advertising, personnel, and information gathering in market research), the continuous control variables (UA, ITA, age, socio-economic status), and the categorical control variable gender. For the current study, we theoretically postulated and assumed three different cultural groups, the U.S. business, the German business, and the U.S. psychology sample. We used the KNOWNCLASS command to define those three classes in the program. Our sample sizes would be too small to investigate possible within-country subgroups. Stability versus change/flexibility in the classes will be shown in the slopes and intercepts of the three lines of advertising, gathering of market information, and personnel. We modeled latent classes based on all 3 areas together (Tuellera & Lubke, 2010). Additionally, the control variables were examined to determine whether they influenced the trajectories. We included the covariates in the analyses because the covariates have direct effects on the growth factors and the outcome and thus change the individual classifications (Muthén, 2004). Mplus 5 generates a set of random start values to avoid local solutions and then runs through iterations with each set. For the initial set of iterations the expectation-maximization algorithm is used. Then the program takes the set with the highest log-likelihood and continues to iterate with that set until the convergence criteria are reached. Survey data and computer files were incomplete for 11 participants. Some participants were not able to complete the 24 months in the given time. We assumed missing decisions at random (Little & Rubin, 2002). In the next step, the identified trajectory classes were compared and the associations among the trajectories and the other background variables were investigated. The intercept indicates the intensity of decisions because it shows the distance from 0 to the point where a graph crosses the coordinate axis. The slope of a line (y = mx + b with “m” being the slope and “b” being the y-intercept) is the “steepness” and indicates flexibility or stability. A small slope indicates stable and high slope indicates changing/flexible decisions.

RESULTS Hypothesis 1 predicted that German students will show higher uncertainty avoidance and intolerance of ambiguity scores than U.S. students. Uncertainty avoidance UA and intolerance of ambiguity ITA means of the three samples (see Table 1) were compared in two one-way between groups ANOVAs. The three samples did not differ significantly regarding UA, F(2, 140) = 1.61, p = .20, partial eta squared = .02. The three samples did, however, differ significantly regarding ITA, F(2, 157) = 13.48, p < .001, partial eta squared = .15. Post-hoc tests showed that German students had significantly lower ITA scores compared to both U.S. samples, which did not differ significantly from each other. This finding is contrary to our expectations. It was hypothesized that German students would have higher UA and ITA scores.


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Table 1. Descriptive statistics of Uncertainty Avoidance (UA) and Intolerance of Ambiguity (ITA).

M-‐ITA SD-‐ITA M-‐UA SD-‐UA

German business students 3.06 .42 2.69 .44

U.S. business students 3.48 .61 2.58 .63

U.S. psychology students 3.69 .69 2.79 .67

Total 3.46 .65 2.69 .60

The Pearson correlation of UA and ITA was .32, p < .001 for the overall sample. Thus, high ITA scores correlated positively with high UA scores demonstrating convergent validity of the two scales. The Pearson correlation was .16 for the German sample (p = .32), .33 for the U.S. business student sample (p = .02), and .38 for the U.S. psychology student sample (p = .004). Hypothesis 2 stated that in all samples, high uncertainty avoidance and intolerance of ambiguity will predict cautious and stable decision making. Low uncertainty avoidance and intolerance of ambiguity will predict intensive and flexible decision making. To test this hypothesis growth mixture modeling was conducted. Besides ITA and UA, we also included the covariates age, gender, and SES, and investigated their effects on the intercepts and slopes of the three decision curves over the 24 months (expenses in advertising, personnel, and market research). Neither ITA nor UA and none of the covariates predicted significantly any slope or intercept overall and in any class. Hypothesis 3 was based on cross-cultural psychological findings and predicted that German students will show more cautious and stable decision making compared to both U.S. samples. Figure 1 shows the distribution of participants’ decisions in the three decision areas expenses for market research, personnel, and advertising over the period of 24 months. To test Hypothesis 3, the means and variances of the intercepts (I) and slopes (S) of the three decision areas personnel, advertising, and market research (see Table 2) were compared in Mplus using growth mixture modeling without including the covariates. For the comparison among classes, we used the following formula: z = [(mean slope of latent class a - mean slope of latent class b)/ square root (variance of mean slope of latent class a + variance of mean slope of latent class b - 2*covariance (mean slope of latent class a, mean slope of latent class b)]. Class 1 was compared with class 2; class 2 was compared with class 3; and class 1 was compared with class 3. Since the three samples were independent samples, the last term of the equation involving the covariance was omitted. The variance was calculated as the squared standard error. Only the significant z-scores are reported now.


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Figure 1. Means of decisions in market research, personnel, and advertising over 23 months of CHOCO FINE for the three groups of students.


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Table 2. Means and standard errors of the intercepts and slopes for each sample.

Mean S.E.

Class 1: German business students

I_Personnel 109.37 4.28

S_Personnel 0.48 0.57

I_Advertising 113.25 123.17

S_Advertising 19.01 15.70

I_Market Research 137.40 27.94

S_Market Research -‐4.12 1.71

Class 2: U.S. business students







Class 3: U.S. psychology students







Calculating the z-scores of the differences between class 1 and class 2 (German business and U.S. business students), the only significant differences were found in expenses for market research. Intercept as well as slopes differed significantly between the two groups (z for I_Market Research = 2.27, p = .023; z for S_Market Research = 2.87, p = .004; all two-tailed). German business students had a lower intercept and a lower slope indicating less spending for market research and less flexible decisions. Comparing the z-scores of class 1 and 3, the German business students and the U.S. psychology students, only z for I_Market Research = 1.99, p = .047 was significant, showing again that German business students spent less for market research.


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Comparing the two U.S. samples, classes 2 and 3, the U.S. business students had a marginally significant higher slope than the U.S. psychology students, showing more change and flexibility in the U.S. business students’ market research spending, z for S_Market Research = 1.72, p = .086. Results partly confirmed the cross-cultural hypothesis. Whereas no or only minimal differences in intercepts and slopes were found between the two U.S. samples, the German sample differed from both U.S. samples in market research expenses (not in Advertising and not in Personnel). The German sample had the lowest slopes and the lowest intercept in market research expenses. Hypothesis 4 was an alternative hypothesis and based on research on experts and novices and the deliberate-practice theory. It stated that German and U.S. business students will show more flexible decision making than U.S. psychology students. To test this hypothesis, we refer back to Table 2. As mentioned previously, U.S. business and psychology students did not differ significantly in any single slope or intercept (only minimally in the slope for market research). German business students differed from both U.S. samples in expenses for market research. Thus the results did not support the deliberate-practice theory.

DISCUSSION

The goal of this study was to investigate dynamic decision making behavior in a business simulation. Postulating cultural differences and taking a process perspective, we argued that different cultural groups will follow different behavioral decision-making patterns of change and stability over time (Strohschneider, 2001). We focused on the possible influence of uncertainty avoidance (UA; Hofstede, 2001, p. 161) and intolerance of ambiguity (ITA; Venkatraman, Aloysius, & Davis, 2006) on the stability versus flexibility and the decision intensities by investigating slopes and intercepts of decision making in personnel, advertising, and marketing research. Hypothesis 1 predicted that German students will show higher uncertainty avoidance and intolerance of ambiguity scores than U.S. students. The three groups, German business students, U.S. business students, and U.S. psychology students did not differ in uncertainty avoidance, but in intolerance of ambiguity. One possible reason for the non-significant results regarding UA could be the low reliability of the uncertainty scenario measurement. Contrary to the hypothesis, German students had significantly lower ITA scores compared to both US samples, which did not differ significantly from each other. One possible explanation for this finding could be the time of the study. This study was conducted in 2010 during the worldwide recession. Whereas the United States was heavily affected, Germany was relatively unaffected and their economy was booming. Thus it is possible that the economic and political situation in the United States increased students’ intolerance of ambiguity. Hypothesis 2 predicted that overall high UA and ITA will predict cautious and stable decision making. Low UA and ITA will predict intensive and flexible


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decision making. Overall, and for all samples, UA and ITA never predicted any slope or intercept in advertising, market research, and personnel decisions. Thus, decision making stability and change were not affected by these cultural values. One possible reason for these non-significant effects could be the data set that was employed. We focused our analyses on all decisions over the 24 month period. It could be that these cultural values only affect decision making primarily at the beginning of the novel situation. Once participants familiarize themselves with the task, these values could lose their overall impact. In fact, the variability of the decision curves in advertising and market research actually suggest more flexibility towards the first half of the simulation. Therefore, we ran the same Mplus growth mixture modeling for the first 12 months only. Results showed that ITA significantly influenced the slope of personnel decisions (Mean for S_PERS ON ITA = -1.82, S.E. = 0.46, p < .001), but not the other 5 slopes and intercepts. Unfortunately, results of the current study could not clarify the contradicting results discussed in the introduction section on UA and flexible and stable decisions (e.g., Ayoun & Moreo, 2008; Brinckman, Grichnik, & Kapsa, 2010; Geletkanycz, 1997). Hypothesis 3 predicted that German students will show more caution and stable decision making compared to both U.S. samples. The two U.S. samples did not differ significantly in any slope or intercept (only marginally in their flexibility in market research decisions). The German sample differed from both U.S. samples in market research expenses (not in Advertising and not in Personnel). The German sample had the lowest slopes (indicating little change and flexibility) and the lowest intercept (indicating lowest expenses and little decision intensity) in market research expenses. In line with the hypothesis, at least in market research, German students were more cautious and stable. It is interesting that the cross-cultural differences refer to information collection in market research and therefore refer back to ambiguity of the situation (Venkatraman, Aloysius, & Davis, 2006). Hypothesis 4 was an alternative hypothesis and based on the deliberate-practice theory. Specifically, it predicted that experts in the business field, German and U.S. business students, would significantly outperform the U.S. psychology students due to their expertise. In other words, it was postulated that German and U.S. business students will show more flexible decision making than U.S. psychology students. Data did not confirm this hypothesis. U.S. business and psychology students did not differ significantly in their flexibility and decision intensities.

CONCLUSION LIMITATIONS One limitation of the current study was the low reliability of the uncertainty avoidance scenario measure. This could be due to the low number of items that were on the scale: three. A second limitation of this study could be related to the use of a business simulation. Some researchers voice criticism over the use of


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business simulations in research or training. They argue that simulations are either too simplistic or they are too complex. If they are too simplistic, they do not simulate important aspects of the real-world domain. If they are too complex, the linkage between a participant’s decisions and the outcome, i.e., between cause and effect, are obscured (Cannon, 1995). However, the current authors believe that, even in real life, the linkage between a manager’s decision and the outcome is not always linear and direct. Many factors can influence the outcome of a decision. MANAGERIAL IMPLICATIONS AND DIRECTIONS FOR FUTURE RESEARCH The first implication of this study is that it contributed to the growing literature on business decision making across cultures and showed cross-cultural differences in decision-making behavior. Although no cultural differences were found regarding advertising and personnel decisions, German students were less flexible and conducted less market research compared to the U.S. business and U.S. psychology students. The postulated values of uncertainty avoidance and intolerance of ambiguity, however, could not explain differences in decision-making stability and flexibility adequately. Future research could investigate several cultural-level variables to predict decision making. Second, the simulation methodology utilized in this research allowed us to use a unique data set. Many studies on cultural values have focused on self-report questionnaires (Baumeister, Vohs, & Funder, 2007). These questions provide a snapshot view, and don’t take subsequent decisions into account. The CHOCO FINE simulation records numerous decisions over a given time period. Findings from this data are much more generalizeable to the managerial environment than a questionnaire. Future research utilizing this and other simulations should provide researchers with a more realistic tool to test these variable relationships. Ultimately, results could be useful for managers of international corporations in their international activities. An awareness of cultural differences in decision making aids all types of planning in multinationals while facilitating an understanding of multi-domestic environmental concerns. The intolerance of ambiguity or avoidance of uncertainty speaks directly to how much risk an international organization and its management is willing to take on. Bringing their strategic, tactical, and operational plans in line with the cultural boundaries of their operations is a challenge that all multinational corporations must face. This study aids in providing some clarification as to the role culture plays in major planning and decision making.

Notes This research was supported by a Humboldt Fellowship for Experienced Researchers to the first author. We are thankful to Ulrike Seifert and Christian Zeeh for their help during data collection and analysis. We also would like to especially thank Cameron McIntosh for his help with the statistical analyses in MPlus.


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REFERENCES

Ayoun, B. M., & Moreo, P. J. (2008). The influence of the cultural dimension of uncertainty avoidance on business strategy development: A cross-national study of hotel managers. International Journal of Hospitality Management, 27, 65-75.

Baumeister, R. F., Vohs, K. D., & Funder, D. C. (2007). Psychology as the science of self-reports and finger movements: Whatever happened to actual behavior? Perspectives on Psychological Science, 2, 396-403.

BIBB (2003). 'Strategic flexibility' - a vital tool for today's specialists and managers. Retrieved on October 20, 2011 from http://www.bibb.de/en/8494.htm

Brehmer, B., & Dörner, D. (1993). Experiments with computer-simulated microworlds: Escaping both the narrow straits of the laboratory and the deep blue sea of the field study. Computers in Human Behavior, 9, 171-184.

Brinckmann, J., Grichnik, D., & Kapsa, D. (2010). Should entrepreneurs plan or just storm the castle? A meta-analysis on contextual factors impacting the business planning –performance relationship in small firms. Journal of Business Venturing, 25, 24-40.

Brislin, R. W. (1970). Back translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1, 185–216.

Budner, J. (1962). Tolerance of ambiguity as a personality variable. Journal of Personality, 30, 29-40.

Cannon, H. M. (1995). Dealing with the complexity paradox in business simulation games. Developments in Business Simulation & Experiential Exercises, 22, 96-102.

Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55-81.

Dickinson, J. R., Gentry, J. W., & Burns, A. C. (2004). A seminal inventory of basic research using business simulation games. Developments in Business Simulation and Experiential Learning, 31, 345-351.

Dörner, D. (1993, 2000). SchokoFin. Computer simulation [Choco Fine. Computer simulation]. Otto-Friedrich Universität Bamberg,Germany.

Dollinger, M., Saxton, T., & Golden, P. (1995). Intolerance of ambiguity and the decision to form an alliance. Psychological Reports, 77, 1197-1198.

Einhorn, H. J., & Hogarth, R. M. (1986). Decision making under ambiguity. Journal of Business, 59, 225–250.


28

Ericsson, K. A., Charness, N., Feltovich, P. J., & Hoffman, R. R. (Eds.) (2006). The Cambridge handbook of expertise and expert performance. New York, NY: Cambridge University Press.

Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100, 363-406.

Faria, A. J., & Nulsen, R. (1996). Business simulation games: Current usage levels. A ten-year update. In A. L. Patz & J. K. Butler (Eds.), Developments in business simulation and experiential exercises (pp. 22-28). Madison, WI: Omnipress.

Fox, C. R., & Weber, M. (2002). Ambiguity aversion, comparative ignorance, and decision context. Organizational Behavior and Human Decision Processes, 88, 476–498.

Furnham, A., & Ribchester, T. (1995). Tolerance of ambiguity: A review of the concept, its measurement and applications. Current Psychology, 14, 179-199.

Geletkanycz, M. A. (1997). The salience of 'Culture's Consequences': The effects of cultural values on top executive commitment to the status quo. Strategic Management Journal, 18, 615-634.

Gupta, P. P., & Fogarty, T. J. (1993). Governmental auditors and their tolerance for ambiguity: An examination of the effects of a psychological variable. Government Accountants Journal, 42, 25-35.

Güss, C. D. (2011). Fire and ice: Testing a model on cultural values and complex problem solving/dynamic decision making. Journal of Cross-Cultural Psychology, 42, 1279–1298.

Güss, C. D., Tuason, M. T., & Gerhard, C. (2010). Cross-national comparisons of complex problem-solving strategies in two microworlds. Cognitive Science, 34, 489–520.

Güss, C. D., & Fadil, P. (in preparation). Strategic differences between experts and novices in a business simulation.

Hofstede, G. (1980). Culture's consequences: International differences in work related values. Beverly Hills, CA: Sage.

Hofstede, G. (2001). Culture’s consequences: Comparing values, behaviors, institutions, and organizations across nations. Beverly Hills, CA: Sage.

House, R., Hanges, P., Javidan, M., Dorfman, P., & Gupta, V. (Eds.) (2004). Culture, leadership, and organizations: The GLOBE study of 62 societies. Thousand Oaks, CA: Sage.

Johnson, T. P., Shavitt, S., & Holbrook, A. L. (2011 ). Survey response styles across cultures. In D. Matsumoto & F. J. R. van de Vijver (Eds.), Cross-


29

cultural research methods in psychology (pp. 130-175). Cambridge: Cambridge University Press.

Jung, T, & Wickrama, K.A.S. (2008). An Introduction to Latent Class Growth Analysis and Growth Mixture Modeling. Social and Personality Psychology Compass, 2, 302–317.

Keys, B., & Wolfe, J. (1990). The role of management games and simulations in education and research. Journal of Management, 16, 307-336.

König, C., Steinmetz, H., Frese, M., Rauch, A., & Zhong-Ming Wang, Z.-M. (2007). Scenario-based scales measuring cultural orientations of business owners. Journal of Evolutionary Economics, 17, 211–239.

Lane, D. C. (1995). On a resurgence of management simulations and games. The Journal of the Operational Research Society, 46, 604-625.

Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). New York, NY: John Wiley & Sons.

Muthén, B. O. (2004). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In D. Kaplan (Ed.), Handbook of quantitative methodology for the social sciences (pp. 345-368). Newbury Park, CA: Sage Publications.

Muthén, B. (2008). Latent variable hybrids: Overview of old and new models. In Hancock, G. R., & Samuelsen, K. M. (Eds.), Advances in latent variable mixture models (pp. 1-24). Charlotte, NC: Information Age Publishing, Inc.

Muthén, L. K., & Muthén, B. O. (2006). Mplus 5. Computer program. http://www.statmodel.com/index.shtml

Proctor, R. W., Nof, S. Y., Yih, Y., Balasubramanian, P., Busemeyer, J. R., Carayon, P., … Salvendy, G. (2011). Understanding and improving cross-cultural decision making in design and use of digital media: A research agenda. International Journal of Human-Computer Interaction, 27, 151-190.

Ralston, D. A., Gustafson, D. J., Cheung, F. M., & Terpstra, R. H. (1993). Differences in managerial values: A study of U.S., Hong Kong and PRC Managers. Journal of International Business Studies, 24, 249-275.

Ram, N., & Grimm, K. J. (2009). Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups. International Journal of Behavioral Development, 33, 565–576.

Schere, J. (1982). Tolerance of ambiguity as a discriminating variable between entrepreneurs and managers. Proceedings of the Academy of Management, 45, 404-407.


30

Sireci, S. G. (2011). Evaluating test and survey items for bias across languages and cultures. In D. Matsumoto & F. J. R. van de Vijver (Eds.), Cross-cultural research methods in psychology (pp. 216-243). Cambridge: Cambridge University Press.

Stratx (2011). Markstrat. Retrieved on November 20, 2011 from http://www.stratxsimulations.com/markstrat_online_home.aspx

Strohschneider, S. (2001). Kultur – Denken – Strategie: Eine indische Suite [Culture- Thinking- Strategy: An Indian Suite]. Bern: Huber.

Sully de Luque, M., & Javidan, M. (2004). Uncertainty avoidance. In R. J. House, P. J. Hanges, M. Javidan, P. Dorfman, & V. Gupta (Eds.), Culture, leadership, and organizations: The GLOBE study of 62 societies (pp. 602-652). Thousand Oaks, CA: Sage.

Thornton, G. C., III, & Cleveland, J. N. (1990). Developing managerial talent through simulation. American Psychologist, 45, 190-199.

Triandis, H. C. (1995). Individualism and collectivism. Boulder, CO: Westview.

Tuellera, S., & Lubke, G. (2010). Evaluation of structural equation mixture models: Parameter estimates and correct class assignment. Structural Equation Modeling, 17, 165-192.

Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453-458.

van Oudenhoven, J. P. V., Mechelse, L., & de Dreu, C. K. W. (1998). Managerial conflict management in five European countries: The importance of power distance, uncertainty avoidance, and masculinity. Applied Psychology: An International Review, 47, 439-455.

Venkatraman, S., Aloysius, J. A., & Davis, F. D. (2006). Multiple prospect framing and decision behavior: The meditational roles of perceived riskiness and perceived ambiguity. Organizational Behavior and Human Decision Processes, 101, 59–73.

Wolfe, J., & Roberts, R. C. (1986). The external validity of a business management games: A five-year longitudinal study. Simulation & Gaming, 17, 44-59.

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C. Dominik Güss Paul Fadil Stefan Strohschneider · International Business: Research, Teaching and Practice 2012 6(2) 13 Dynamic decision making (DDM) can follow various strategic