Beauty Is in the Eye of Beholder: Intracultural and Transcultural Heterogeneity of Individuals

Abstract

Most cross-cultural studies unintentionally give the impression that there is intracultural homogeneity. This research is among a few that suggests otherwise, that there is intracultural and transcultural individual heterogeneity in thinking and logic, or “Mindscape.” It is a replication and an expansion of the earlier study. It intends to verify the existence of intracultural heterogeneity in support of mindscape theory. In doing so, it employs a unique way of data collection and analysis. The analysis of data that were collected from six countries not only supports the earlier findings but also points to the existence of intracultural and transcultural heterogeneity of individuals.

Keywords

intracultural heterogeneity transcultural heterogeneity mindscape theory

Introduction

Well, look. Let’s suppose there are two kinds of geometry—we’ll limit it, for the sake of the example. Our kind, Euclidean, and another, which we’ll call x. X hasn’t much relationship to Euclidean. It’s based on different theorems. Two and two needn’t equal four in it; they could equal y₂, or they might not even equal.

—Lewis Padgett (1970)

The problems of stereotyping are well known. Discussions on these problems and the associated mistakes have been extensive (e.g., Bigler & Liben, 2007; Fiske, 2000; Katz & Braly, 1933; Lippmann, 1922; McCauley & Stitt, 1978; McCauley, Stitt, & Segal, 1980; Schneider, 2005). Many cross-cultural studies, however, have inadvertently ignored these problems and have considered intracultural attributes uniformly representing everyone similarly. Such a practice, or stereotyping, ignores that there are many variations and value differences among members of a culture (Osland & Bird, 2000).

In the discussion of national cultural or ethnic differences, it is not uncommon to stereotype. We talk about cultural differences in a macro sense, as if within each group there is uniformity in thought, logic, and values. In so doing, we overlook micro-level differences. Similarly, when creating a “diverse” group, representing as many variations as practical, it is common to construct a “heterogeneous” group by putting together one person from each category and ethnicity, as if each person is a true representative of a homogeneous collective. In effect, our effort is a disguised homogenization not heterogenization (Maruyama, 1995, p. 225).

Most research studies that emulated the research design of well-known social scientists, such as Hofstede (1980, 2001), have unintentionally contributed to this misunderstanding. Hofstede and later on House, Hanges, Javidan, Dorfman, and Gupta (2004) popularized the application of cultural dimensions in cross-cultural studies. From the list of more than 20 cultural attributes (Osland & Bird, 2000), these researchers popularized a few major ones such as individualism-collectivism, power distance, and so on. Following these pioneers, researcher who employed these attributes or dimensions assumed scores on them to represent national cultures. Therefore, they presumed that each country and the associated cultural characteristics of its people could be represented by scores on these dimensions. In this regard, they assumed, for example, that Asians were collectivist, ignoring those who might have more individualistic thoughts and behavior. Cross-cultural research and experience at the international level points out the inaccuracy of such an assumption. Beneath the apparent homogeneity in cultures, however, there are many variations among people based on their individual preference in values, beliefs, logic, and behavior. There might be individuals who could have similarities with others, in their own or other cultures, in thinking, belief, values, and logic. Past experience, upbringing, and education could differentiate people from one another, regardless of their affiliation with certain groups, countries, and cultures.

Research by Hofstede (1980) has been criticized for oversimplification and generalization (e.g., Kirkman, Lowe, & Gibson, 2006; McSweeney, 2002; Sivakumar & Nakata, 2001; Tung, 2008). Hofstede (2002) has responded to those criticisms, and particularly those by McSweeney (2002a), challenging the claims. Regardless of these criticisms and the response to them, Hofstede’s writings have inspired a large number of follow-up studies that could be considered the source of misconception of cultural homogeneity. Palich, Hom, and Griffeth (1995) pointed out the inappropriateness of assuming homogeneity of cultures by indicating that, in their research, only a very small percentage of variation in the person-to-person employee commitment could be explained by Hofstede’s cultural dimensions. Likewise, according to Kirkman et al. (2006), “The relatively low amount of variance explained by the cultural values in many studies underscores the existence of the many other forces besides culture determine the behavior and attitudes of individuals in societies” (p. 313). In all fairness to the pioneers of cultural research, such as Hofstede (1980) and House et al. (2004), who employed cultural dimensions, the original studies did not assume homogeneity in cultures. Hofstede, Bond, and Luk (1993), for example, emphasized the importance of using proper level of analysis in cross-cultural studies. Similarly, House et al. (2004) stressed that they “. . . were quite aware of the general ‘level of analysis’ problem that bedevils many cross-cultural research projects” (p. 99). Subsequent studies, however, used these dimensions in a manner that created the impression of cultural homogeneity. According to Brewer and Venaik (2012), even the pioneers themselves made the same mistake.

Regardless of the above criticisms, cross-cultural studies using cultural dimensions as a research design are very popular. However, not many of these studies have specifically addressed the issue of individual intracultural heterogeneity. People are different on important psychological attributes. The differences are both on a between-country and on a within-country basis (Brockner, 2005). Research studies have revealed that individual beliefs and values are the basis of employees’ reactions to various aspects of their work (e.g., Gefland, Erez, & Aycan, 2007; Tusi, Nifadkar, & Ou, 2007). In the same vein, Kirkman, Chen, Farh, Chen, and Lowe (2009) found that in China and the United States, the relationship between individual employees and managers were influenced by the power distance orientation of employees, themselves.

Taking into account the above discussion, the present research suggests that there is individual intracultural heterogeneity in thinking and logic, or “Mindscape.” Mindscape is explained shortly. This research is a replication and an expansion of an earlier study by Fatehi, Kedia, and Priestley (2015), who used data on mindscape from subjects in four different countries/cultures, and found the existence of transcultural mindscape types.

Replication makes important contributions to the advancement of knowledge. It is very essential to scientific inquiries. Without it, we may not be confident and be doubtful if an original study’s finding is valid or just spurious finding and a mere isolated coincidence (Popper, 2005). Although we acknowledge the value of replication, in practice, it is undervalued, and its importance overshadowed by our interest in novelty and creativity (Madden, Easley, & Dunn, 1995; Tsang & Kwan, 1999). “Replication is one of the most important tools for the verification of facts within the empirical sciences” (Schmidt, 2009, p. 90). “We can improve the present state of knowledge accumulation through encouraging more replication” (Tsang & Kwan, 1999, p. 771). A replication may not provide any new information; it could be just a confirmation, or refutation of the original study.

Although this study is a replication of a previous study, it is different in three main areas. First, it explains, in details, the basis of geometric patterns that were employed in the original study (and of course, in this one, too) in soliciting response from the subjects. Second, it expands data collection to six different geographic areas and countries/cultures. Third, it offers additional implications for theory and practice.

Mindscape Theory Explained

Magoroh Maruyama (1961, 1980, 1982, 1992c, 1993, 1994a, 1994b, 2007) proposed mindscape theory that elaborates on individual differences within and between cultures. Mindscape theory encompasses both epistemological heterogeneity (EH), and individual heterogeneity across cultures (IHAC).

Maruyama’s research and writings span more than four decades. From 1961 to 2007, he advanced the position that below the surface of homogeneous cultures, there are various types of mentality, logic, and values among individuals that he called “mindscapes.” Mindscape was defined as “. . . a structure of reasoning, cognition, perception, conceptualization, design, planning, and decision making that may vary from one individual, profession, culture, or social group to another” (Maruyama, 1980, p. 591). Based on the writing of Maruyama, Fatehi and Tate (2014) provided a brief discussion on the mindscape theory and the EH and IHAC as follows:

(a) Individual heterogeneity exists in each culture [EH]. Cultural members are diverse not only physically, but more importantly, psychologically. They possess different mentality and logic. They have different perceptual characteristics. There is no uniformity in their thinking and behavior. They formulate their own interpretation of cultural norms and expectations.

(b) Any individual type found in a culture can be found in other cultures, i.e. the individual types exist across cultures and they are not confined within a culture [IHAC]. There are individuals in each culture who have similarity in thinking and logic with some members of other cultures. Some Japanese, for example, may be more individualistic and may psychologically be similar to a typical [stereotyped] American.

(c) Cultural differences consist in the way [that] some type becomes dominant and suppresses, transforms, ignores or utilizes non-dominant types [EH and IHAC]. There are various mindscape types among members of all cultures. Some type, for various reasons, become dominant and others find it more convenient to go along with that position. Those who succumb to this domination use various strategies to disguise their differences. This gives the appearance of cultural homogeneity where there is actually heterogeneity. (pp. 270-271)

Maruyama (e.g., 1961, 1980, 1982, 1992c, 1993, 1994a, 1994b, 2007) introduced the concept of “Mindscape” as a method of discovering and observing heterogeneity in a seemingly homogeneous culture. Others (e.g., Caley & Sawada, 2000; Gammack, 2002; Maruyama, Zhankovski, & Fatehi, 1998) followed this line of thinking and research. To operationalize mindscape theory, a special survey of pictorial-geometric patterns was devised. As this survey was first used in Tokyo, Brussels and Budapest, it was named the “TOB Tests.” There are 42 geometric patterns in TOB Tests that are supposed to identify individual variations in cultures. These patterns are based on redundant and nonredundant complexity (Maruyama, 1981, 1992c), details of which will be discussed in the “Method” section. The lack of shapes and images, and the use of geometric patterns that can be made to be culture neutral, makes this type of survey very suitable for cross-cultural research. Geometric designs can be made not to have cultural significance; whereas, shapes and images may have certain cultural meanings. In preparing the TOB Test, respondents were asked to mention if any of those shapes have a meaning to them, then, those patterns that were deemed to have cultural meanings were discarded. Examples of shapes that had symbolic meanings were Red Cross, bird, dog, and windmill (Maruyama, 1995). The same survey was employed for this research.

According to the mindscape theory, in any culture there is heterogeneity of values, logic, and the ways of thinking among individuals. Intracultural differences exist as some types become dominant and extend their influence and control over the individuals of other types, who find it necessary to accept such domination. In each culture, the dominant type uses various tactics such as suppressing, transforming, or ignoring other types. This results in the appearance of homogeneity of cultures. Below the surface of homogeneity, however, there are all other types of individuals with various logics and mentalities. Notwithstanding the dominant national or cultural stereotype, all cultures include various mindscape types (Maruyama, 1995); however, four major types of H, I, S, and G are identified.

For personal benefits, convenience, and expedience, the nondominant individual logical types are transformed, hidden, or camouflaged in favor of the dominant type. Individuals for survival purposes and for the sake of fitting into the society develop various strategies. These strategies could include avoiding the mainstream dominant type by finding a niche, disguising one’s own type, and switching back and forth between one’s own and the dominant type in private and public life. In some cases, people may choose reversible repression of their own type, becoming a reformer, a rebel, or a troublemaker. In the worst cases, they drop out or emigrate (Maruyama, 1992a, 1992b, 1994b, 1995, 1999).

Below, the major characteristics of the four mindscape types are explained. We should mention that three of these types are identical to those that were independently identified by Harvey (1966). From extensive empirical research and statistical analysis, he deduced these types or systems, which he called various level of concreteness-abstractness. According to Harvey (1966), these systems evolve in individuals through experience and socialization process. Fatehi and Tate (2014) have offered a detailed discussion of these systems and their relations with Maruyama’s mindscape.

H stands for “hierarchy and homogeneity,” I stands for “isolationism, individualism, and independence,” S stands for “stabilizing,” and G stands for “generating.” The H-type predominates in cultures that emphasize order, procedure, and method. This type is classifying, competitive, zero-sum, sequential, and has the tendencies to honor hierarchy, rules, formalization, control, homogenization, intolerance of variety, functional interest in activities, competitive, zero-sum, and classifying. The I-type might be considered typical of “alternative” modes of behavior and idiosyncratic attitudes to work, especially among creative individuals and artists. The S-type might be considered typical of recreational and “partying” modes of behavior in which interactivity is primary. Characteristics of the G-type are heterogenization, pattern developing, spontaneity, poly-ocular vision, positive-sum, growth amplifying, and harmonizing. The G-type can be considered inspirational mode (Gammack, 2002). Venaik and Midgley (2015) considered H-type and I-type as more self-enhancement, and S-type and G-type as more self-transcendence.

Although there are many mindscape types, approximately two thirds of the population of most cultures consists of four logical types of H, I, S, and G. About one third of North Americans belong to H-type. I, S, and G types, and mixtures of them, make up another third. The rest belong to types that are different than these four types and their mixtures (Maruyama, 1995). Major characteristics of the four mindscape types are presented in Table 1.

Table 1.

Characteristics of the Four Major Mindscape Types.

Concept	Type
Concept	H-type	I-type	S-type	G-type
Use of logic	Homogenistic	Heterogenistic
Relationship with others	Hierarchical	Independent	Interactive
Preference for organizing	Classification	Random	Pattern-maintaining	Pattern-generating
General principle	Universalist	Individualistic	Mutualist	Mutualizing
Structure/order	Sequential	No order	Simultaneous	Simultaneous
Behavior	Competitive	Unique	Cooperative	Conge0rative
Negotiation/deals	Zero-sum	Negative-sum	Positive-sum
Event orientation	Extension	Caprice	Stability	Evolution
Perspective/view	One truth	Subjectivity	Poly-ocularity
Responsibility	Divided without overlapping	Each person chooses own	Overlapping among many persons
Ethics/belief	The stronger should dominate the weaker	Everybody should be self-sufficient	Mutual help to maintain harmony	Mutual help to generate new harmony
Decision	Majority rule, consensus, or by experts	Vote for own interest	Elimination of hardship on any individual	Find new ways to benefit all different individuals
Personal integrity	Adhere to absolute principles regardless of circumstances	Adhere to one’s own principle regardless of what others say	One must act contextually and situationally depending on circumstances. Those who stick to absolute principles or personal principles regardless of circumstances are immoral and immature

Source. Culled from the writings of Maruyama.

Note. Poly-ocularity: Viewing phenomenon from several perspectives. Homogenistic: Predominant use of one-logic. Heterogenistic: Using several logics. Universalist: Belief in a universal or general principle that applies to social situations. Mutualist: Opposite of individualistic (all for self). Sequential: One activity at a time, linear processing. Simultaneous: Multiple processes occur concurrently and can interact. Absorption: The result of deviation-counteractive forces, socialization, or institutionalization.

The Assumption of Intracultural Homogeneity

Most cross-cultural studies assume individual homogeneity within cultures. Many scholars, however, have criticized such an assumption (e.g., Bock, 1988; Fatehi et al., 2015, Fatehi & Tate, 2014; Kirkman et al., 2006; McSweeney, 2002a, 2002b, 2013; Steel & Taras, 2010; Tung & Verbeke, 2010; Venaik & Brewer, 2010; Venaik & Midgley, 2015; Williams & O’Reily, 1988). This is probably an unintended consequence of follow-ups of some well-known cross-cultural studies such as those by Hofstede (1980) and House et al. (2004).

More than three decades ago, Hofstede (1980) published Culture’s Consequences in which he reintroduced a few major cultural dimensions. This publication opened up a floodgate of research on the influence of culture on organizations. These cultural dimensions were a part of a larger number of dimensions that at different times and in various forms had been discussed by other scholars. The list of scholars who had addressed cultural attributes or dimensions begin chronologically with Parsons and Shills (1951), Kuhn and McPartland (1954), Hall (1959), Kluckhohn and Strodtbeck (1961), to Douglas (1970, 1982) and Thompson and Wildavsky (1986). We should mention that Trompenaars and Hampton-Turner (1993) offered their own dimensions a decade after Hofstede. The application of these dimensions enabled researchers to compare and contrast cultures.

Caprar, Devinney, Kirkman, and Caligiuri (2015) suggested that all the instruments that have been used to measure culture cover 26 dimensions. Osland and Bird (2000) produced a list of 22 dimensions with which cultural phenomenon could be discussed, and cautioned against stereotyping cultural phenomenon by using these dimensions. Many studies that have used research designs based on the major cultural dimensions that were popularized by Hofstede have ignored intracultural variations by stereotyping cultures. Stereotyping intracultural phenomenon ignores perceptual, behavioral, values, and belief variations among individuals within groups and cultures (e.g., Brockner, 2005; Gilovich, Keltner, & Nisbett, 2010; Tusi et al., 2007). The importance of intracultural variation has been acknowledged by many (e.g., Au, 2000; Au & Cheung, 2004; Bock, 1988; Feingold, 1994; Hofstede, 1980; Hofstede, Bond, & Luk, 1993; Wallace, 1970; Williams & O’Reily, 1988). These scholars have warned that attributing research finding of cultural level to the individual level is wrong. Acknowledging such an oversight, some have suggested improvements by drawing from other social science fields such as anthropology, sociology, and experimental psychology (Peterson & Barreto, 2014).

Confusion about the two levels of analysis probably stems from the well-known discussion on General Systems Theory that “each level includes all those below it” (Boulding, 1956, p. 207). But this is more applicable at a macro level and is not appropriate for a micro-level analysis. The aggregate of individuals’ characteristics, which are similar or dissimilar, is not applicable to the society level. This is commonly known as “ecological fallacy” error. The problem of this ecological fallacy in cross-cultural studies have been noticed by many researchers (e.g., Brewer & Venaik, 2012; Gefland et al. 2007; McSweeney, 2002a, 2002b, 2013) Even though the original researchers, such as Hofstede (1980), Hofstede et al. (1993), and House and Hanges (2004), of the GLOBE project, cautioned against such an assumption, in most cases, their advice was not heeded by those who followed. Hofstede et al. (1993), for example, mentioned that the evidence obtained at one level of analysis should not be applied to another level. According to Brewer and Venaik (2012), however, it appears that not only other researchers have made the “ecological fallacy” error, but the same mistake has also been committed by the original authors themselves.

The implicit assumption of intracultural homogeneity has been, and still is, the dominant research mode in cross-cultural studies. Most studies advocate the concept that the programming of the minds in the society takes place uniformly for all. This programming of the mind, therefore, could differentiate one group of people from others regardless of variations within each group. By ignoring individual differences among people, uniformity is assumed within each group in logic, mentality, and behavior. Not surprisingly, many cross-cultural studies find differences between cultures. Of course, there are indications pointing out that cultures are different. In this way, the aggregate data suggest variation among culture but leaves out individual difference within cultures as well.

We know intuitively that there are differences among individuals regardless of the culture to which they belong. The differences are due to variations in psychological attributes (Brockner, 2005) among other things. There could be people who have similarities with some and differences with other individuals. This variation in individual behavior and preferences within cultures has been found by some studies (e.g., Katz, 2005; Thompson & Phua, 2005). The mistake of assuming homogeneity within cultures was highlighted by Palich et al. (1995) who found that cultural dimensions could explain only a very small percentage of variance in employee commitment to the organization. Similarly, Steel and Taras (2010) noted that an intracountry basis was the source of up to 90% of differences in cultural values.

Gerhart and Fang (2005) found that only a very small sliver of individuals’ value differences could be explained by the Hofstede’s cultural dimensions. Similarly, Oyserman, Coon, and Kemmelmeier (2002) found that only a fraction of the variance in individualism-collectivism dimension could be attributed to the country culture. Inkeles and Levinson (1954/1969), who are credited as predecessor to the research by Hofstede on cultural dimensions (Minkov & Hofstede, 2011), asserted that

. . . there are actually a great variety of individual personality characteristics and patternings [sic] in any society; a modal personality structure is, then, one that appears with considerable frequency. There may, of course, be several modes in any distribution of variants. (p. 424)

The above discussion points out that probably the individual differences should have been the focus of inquiry, instead of the assumption of similarities. This point was emphasized by an executive who worked and lived in several foreign countries. According to her (Osland & Bird, 2000),

Because a team member comes from a country where a particular orientation exists does not mean that she will necessarily embody that orientation. Cross cultural tools are not flawlessly predictive, so be prepared for individual surprises and contradictions. (p. 77)

Maruyama’s research and writings, in more than 40 years, have dealt with these types of surprises and contradiction by suggesting that there might be various mindscape types among people. He also devised a unique survey and specific statistical analysis to tease out these variations. Maruyama’s studies have proved to be a useful tool that is applied to diverse disciplines, such as management (Noe, Alroe, & Langvad, 2005), multifunctional farming, rural development (Noe et al., 2005, Noe, Alroe, & Langvad, 2008), and psychology (Hentschel & Sumbadze, 2002; Yolles & Fink, 2009) among others. Research by Fatehi et al. (2015) using data from four countries seemed to support the mindscape theory. The present research expands that investigation to new geographic areas and six new countries/cultures. It is a replication and an expansion on the previous study, and intends to test the existence of intracultural heterogeneity that may support mindscape theory. To test the following hypothesis, it expands the sample size to 217 respondents in six countries.

Hypothesis 1: There is intracultural individual heterogeneity, or mindscape, in the six countries under investigation.

Because mindscape theory proposes that cultural heterogeneity is not confined to any particular culture and are transcultural, we test a second and related hypothesis, too.

Hypothesis 2: These individual heterogeneity or mindscape types are transcultural.

Method

To operationalize the mindscape theory that suggests the existence of heterogeneity within and between cultures, we use a unique pictorial survey method. In most cross-cultural studies, the customary practice is to translate these surveys into other languages for international use. It is well known that translations do not do justice to the originals (Ervin & Bower, 1952; Sperber, 2004). Some terms do not have equivalents in other languages, and therefore cannot be translated. Even when terms could be translated, the original meanings are often very difficult to convey.

This research uses a nontraditional survey method that was developed by Maruyama and used by other scholars (e.g., Fatehi et al., 2015; Gammack, 2002; Hentschel & Sumbadze, 2002). It is a 42-item pictorial-geometric survey that is supposed to be culture neutral. Earlier, briefly it was mentioned that both redundant and nonredundant complexity were the basis for the construction of these patterns. Fatehi et al. (2015) only briefly referred to the nonredundant complexity aspects, leaving out redundant complexity features. The following is a full description of the basis for constructing these patterns.

These patterns were devised based on the understanding that there are cultural, sociohistorical, and individual differences in preference in esthetics. In this regard, extreme differences exist between the Eastern and the Western cultures. In the West, redundant complexity (various forms of symmetry, such as diagonal, vertical, horizontal, reversal, rotational, and isomorphic and repetition) is more prevalent in consideration of esthetics, whereas the opposite is common in the East. Traditional European architectures, for example, were designed with the concept of repetition, similarity, opposition, and tension between mass and space, and various forms of symmetries. Traditional Japanese floral composition and architecture, on the contrary, avoids repetition and regular geometric forms, and believes in convertibility and the interweaving of masses and spaces, in effect reliance is on nonredundant complexity (the absence of repetition, isomorphic, and symmetry). The Japanese consider harmony among dissimilar features enhancing the individuality of each unit (Maruyama, 1992b). In Figure 1, symmetry and repetition (redundant complexity) and the lack of them (nonredundant complexity) are recognizable in various patterns. Patterns 6, 17, and 20, for example, are vertical and horizontal symmetries. Diagonal symmetry is noticeable in Patterns 1, 6, 12, 17, 18, 20, 23, 27, and 37. Patterns 6, 10, 17, 20, and 34 are rotational symmetry of 90 degrees. Rotational symmetry of 180 degrees is present in Patterns 6, 10, 17, 20, 27, 34, and 37. Repetition is recognizable in patterns of 15, 23, 27, and 29. Patterns of 27 and 37 are black/white reversal symmetries. Nonredundant complexity is noticeable in Patterns 11, 13, 16, 19, 22, 24, 25, and 32. These patterns include various forms of preference for esthetic.

Figure 1.

TOB test patterns.

In our survey, respondents were asked to rate each of the 42 shapes on a scale of beauty- ugly, ranging from 1 to 7, with 1 being ugly and 7 beautiful. By using these geometric patterns, the usual problems of other surveys, as discussed earlier, are largely alleviated.

Data Analysis

The participants in this survey came from the six countries of France, Jordan, Kazakhstan, Peru, South Korea, and the United States, representing a wide geographical diversity. Such a geographical diversity (Europe, Middle East, Central Asia, South America, Far East, and North America) serves the assumption of mindscape theory that mindscape types are prevalent in all cultures and are not limited to a particular country or region. Also, as Meyer, van Witteloostuijn, and Beugelsdijk (2017) mentioned, “In international business, where the validity of theory across geographic context is a key theme, offering evidence from two or more countries would often be a valuable contribution” (p. 541).

Mindscape data were collected from 220 undergraduate students. Age and gender variations were prominent features of the sample (Table 2). Age variation was from 17 to 35 for both males and females. This indicates that traditional concern with student sample that could have uniform characteristics is not present in this sample. The research questionnaire was in simple English. See Figure 1 for a sample of pictorial-geometric patterns representing “Mindscapes.” All participants were proficient in English, even though proficiency in English was not critical for this type of survey. Only familiarity with the English language was required. To mitigate order bias (Paulhus, 1991), or bias associate with a common measurement (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003), the questionnaire pages were randomly stapled together. For this purpose, pages were “numbered” using symbols such as *, %, #, ^, $, and @. Afterward, the completed questionnaire pages were collated and stapled using a uniform order.

Table 2.

Demographic Distribution by Age and Gender.

	17-24	25-29	30-34	>35	Age N/A	Male	Female	Gender N/A	Total
France	28	1	0	0	4	17	13	3	33
Jordan	7	19	11	5	2	20	23	1	44
Kazakhstan	25	2	2	0	0	10	19	0	29
Peru	24	5	1	0	3	15	16	2	33
South Korea	39	4	1	2	4	11	34	5	50
United States	19	3	6	3	0	12	19	0	31
Total	142	34	21	10	13	85	124	11	220

A total of 220 subjects completed the questionnaires: 33 in France, 44 in Jordan, 29 in Kazakhstan, 33 in Peru, 50 in South Korea, and 31 in the United States. Table 2 depicts simple statistics of the sample. Similar to the study by Fatehi et al. (2015), heterogram analysis (factor analysis combined with cluster analysis) was performed on the completed questionnaires as follows: (a) The raw data on all subjects for the six countries were used as an input, and a correlation matrix was constructed using responses on an ugly-beauty scale; (b) from the correlation matrix, a factor analysis of the items was performed; and (c) the result of the previous step was cluster analyzed. For a detailed discussion on this method of data analysis, refer to Fatehi et al. (2015).

Results

Heterogram analysis involves a combination of factor analysis and cluster analysis. Therefore, first, exploratory factor analysis was used on the individual responses to ascertain if representations of mindscape types were present among the 42 geometric patterns. With the use of an orthogonal rotation, three meaningful factors were found. These factors had large, positive, eigenvalues of 8.41, 3.48, and 1.98. After the oblique “promax” rotation, 67% of the total variance among the responses was accounted for by these three factors. The three factor solution indicated that each individual geometric pattern was uniquely attributed to one of the three factors, with all patterns loading with values at or above 0.40 (two patterns loaded at 0.39). Table 3 includes the factor loadings. Bold fonts show the final loading for each pattern. A total of 19 patterns loaded on Factor 1, 15 patterns loaded on Factor 2, and eight patterns loaded on Factor 3.

Table 3.

Factor Loadings for the Geometric Patterns of Mindscapes.

Pattern	Factor 1	Factor 2	Factor 3
B1	12	48	11
B2	20	−12	46
B3	−8	14	75
B4	−23	47	31
B5	9	42	8
B6	6	−8	66
B7	40	−25	17
B8	11	25	45
B9	28	−15	46
B10	−21	42	27
B11	44	−7	28
B12	11	7	63
B13	28	43	5
B14	53	−8	21
B15	−17	65	12
B16	46	22	4
B17	−8	48	34
B18	57	−10	22
B19	42	32	−2
B20	20	41	−3
B21	61	−29	−1
B22	47	19	−4
B23	78	−14	−19
B24	4	66	−25
B25	49	26	−4
B26	16	64	−23
B27	46	6	17
B28	47	9	9
B29	19	60	−6
B30	47	2	1
B31	49	9	14
B32	3	58	12
B33	−14	66	−5
B34	49	−9	38
B35	27	42	17
B36	51	31	−1
B37	47	3	6
B38	39	32	−3
B39	22	21	39
B40	29	15	43
B41	−10	55	5
B42	40	21	18

The above described factor pattern is consistent with Maruyama’s (1995) assertion about IHAC. A good indication of this is the distribution of patterns across factors.

We continued with the heterogram analysis as it was outlined by Maruyama (1995, 1999). The second step was clustering the grouping of the patterns across the rotated factors. The three factors represented the measures of similarity upon which the respondents were clustered.¹ As the three factors were extracted simultaneously, they were standardized during the extraction. Therefore, no additional standardization of data in advance of clustering was necessary.

The available methods for observation grouping include Ward’s, Centroid, Complete, Density, Median, and Single. From these methods, the Ward’s method was selected. This method performs particularly well when there are no outliers in the data, and tends to generate more easily interpretable, similarly sized clusters (Blashfield, 1976; Morey, Blashfield, & Skinner, 1983; Punj & Stewart, 1983). A three-cluster solution was generated using a hierarchical clustering approach. In cluster analysis, the selection of the final number of clusters depends on the interpretation of the results. In the present analysis, the dendrogram (tree diagram) showed a significant jump in the fusion after the three-cluster solution. This observation was consistent with inflections of the assessment of primary metrics for the pseudo-t² (14.2) and the pseudo-F statistic (5.4). It indicated the presence of a three-cluster solution. Table 4 depicts the distribution of responses for this solution.

Table 4.

Frequency Distribution for Clusters.

Cluster	Frequency	Percentage	Root Mean Square SD	Nearest cluster	Distance between cluster centroids
1	44	28.39	0.7698	3	1.8834
2	62	40.00	0.8283	3	1.5998
3	49	31.61	0.6495	2	1.5998

The evaluation of the distribution of the clusters by country reveals cultural heterogeneity. If the cultural homogeneity was present, we would expect to see clusters—which were generated from rotated factors—closely aligned with individual countries. As can be seen in Table 5, this is not the case. In each cluster, the number of observations is well distributed across the individual countries. Furthermore, indication came from a chi-square analysis indicating that cluster membership is not related to the country of the respondent (χ² = 14.62, p = .1465).

Table 5.

Distributions for Clusters by Country (Country by Cluster).

Cluster	France	Jordan	Kazakhstan	Peru	South Korea	United States	Total
1	7 15.91% (35.00%)	14 31.82% (40.00%)	4 9.09% (19.05%)	2 4.55% (13.33%)	12 27.27% (29.27%)	5 11.36% (21.74%)	44 100%
2	6 9.68% (30.00%)	14 22.58% (40.00%)	14 22.58% (66.67%)	7 11.29% (46.67%)	15 24.19% (36.59%)	6 9.68% (26.09%)	62 100%
3	7 14.29% (35.00%)	7 14.29% (20.00%)	3 6.12% (14.29%)	6 12.24% (40.00%)	14 28.57% (34.15%)	12 24.49% (52.17%)	49 100%
Total	20 (100%)	35 (100%)	21 (100%)	15 (100%)	41 (100%)	23 (100%)	155

Applying factor analysis procedure to the 42 geometric patterns generated three dominant types. This supports the hypotheses and indicates the presence of the mindscape types. We should mention here that Maruyama sometimes had used three and some other times five types. Finally, he settled on four mindscape types (Maruyama, 1995). These factors were then used to cluster the individual respondents against the factors or mindscape types, where cluster membership indicates similarity. For details on the procedure on data analysis, see Fatehi et al. (2015). No consistency was observed in mindscape types by country. The mindscape types were transcultural. This research provided evidence that indeed there are different mindscape types within cultures and these types are transcultural.

Conclusion and Implications

This study was a replication and an extension of an earlier study by Fatehi et al. (2015). It supported the hypothesis and confirmed not only the existence of intracultural heterogeneity but also corroborated the assertion by the mindscape theory about transcultural heterogeneity. This study expanded the test of the mindscape theory to six countries and diverse geographic areas. The six-country data format in a diverse geographic representation provided a stronger confirmation of the theory. It is in agreement with all of the conclusions of the previous study and the implications as follows:

The mindscape theory may identify the sources of inconsistencies in cross-cultural studies.

This has an impact on various managerial practices such as motivation and conflict resolution.

Institutions should leave sufficient room for nonconformity and dissent through policies that support the nondominant mindscape types.

The present labor-management relations that is based on the H-type mindscape could be better off changing to other types.

The H-type mindscape that equates economic prosperity and growth with private business and advocates a smaller noninvolved government is not consistent with other mindscape types that do not preclude an efficient government involvement, and

Dealing with and benefiting from the diversity that could result from the continued migration and cross-border marriages is more in line with the acceptance of transcultural heterogeneity of the mindscape types.

It is a usual practice to tailor management development programs to a predetermined and preferred style. In Theory X/Theory Y program, for example, it is recommended that participants learn to act as Theory Y. Similarly, in Blake and Mouton Managerial Grid, it is proposed that a Team Leader is the best manager, and other styles, that is country club management, impoverished management, and dampened pendulum, are not. Therefore, specific programs are tailored to fit the preferred style and position. In both cases, it is assumed that each identified group, and all the individuals within the group, has the same mindscape type. Such an assumption, we suggest, is not a proper position. The existence of intracultural and transcultural types needs to be acknowledged, and appropriate management development programs should be devised.

At the beginning of this article, we opened up our discussion with a reference to a common practice for forming a diverse group through stereotyping. We will end it with a reminder that in the formation of groups such as task groups, project groups, or focus groups, we could benefit by acknowledging the existence of various mindscape types, and the utilization of them in our undertakings. Including these types in group formation could be more fruitful than the usual practice of creating diversity by the selection of one person from each ethnicity or demographic category, for so doing, however, we lack the understanding of how to identify these various mindscape types. Geometric, culture-neutral patterns that we used in this research identify the existence of various mindscape types intraculturally and transculturally. These patterns, however, so far, have not been associated with specific mindscape types. Such an identification and association is the task of future research. In addition, TOB Tests were used in Tokyo, Brussels, and Budapest. Originally, in preparing the TOB Tests, based on respondents’ opinion, patterns that were deemed to have cultural meanings were discarded. The same survey was used in this study. Therefore, it is possible that certain items that were possibly culturally meaningful for the respondents contaminated the finding. Also, asking respondents to rate the geometric shapes from “ugly” to “beautiful,” their responses might not be cross-culturally equivalent. This problem could possibly be mitigated by using ranking instead of Likert-type style scale (Harzing, 2006).

Footnotes

Acknowledgements

This study was supported by a research leave by Kennesaw State University.

Authors’ Note

An earlier version of this article was presented at the 2016 Academy of International Business Conference, June 27 to 30, New Orleans, Louisiana.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Kamal Fatehi is a professor of international management and former chair of management and entrepreneurship department at Coles College of Business, Kennesaw State University. He has taught at Louisiana State University, Western Illinois University, Eastern Illinois University, Wichita State University, and Texas A&M International University, where he was the interim dean of College of Business. He is a Fulbright senior scholar and Fulbright senior specialist. He has a large number of publications, and two international management textbooks published by Prentice Hall and SAGE.

Jennifer L. Priestley is the associate dean of The Graduate College and the director of the Analytics and Data Science Institute at Kennesaw State University. In 2012, the SAS Institute recognized her as the 2012 Distinguished Statistics Professor of the Year. She served as the 2012 and 2015 cochair of the National Analytics Conference. Datanami recognized her as one of the top 12 “Data Scientists to Watch in 2016.” She has authored dozens of articles on binary classification, risk modeling, sampling, statistical methodologies for problem solving, and applications of big data analytics, as well as several textbook manuals for Excel, SAS, JMP, and Minitab. Prior to receiving a PhD in statistics, she worked in the financial services industry for 11 years that included VP positions in European Union, London, Irish, and Scottish banks, and in the United States, MasterCard International, AT&T Universal Card, and Andersen Consulting.

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