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Climate and work values: a comparison of cold, warm, and hot regions in China.


* A comparison among Chinese employees in three different climatic areas revealed different scores on five work values of Hofstede's model.

* With regard to power distance, temperate regions showed greater scores than colder-than-temperate and hotter-than-temperate regions. In terms of masculinity, the results showed that temperate regions have greater scores than colder-than temperate and hotter-than-temperate regions. Regarding collectivism, hotter-than-temperate regions also showed a higher collectivism score than did colder-than-temperate regions. With regard to uncertainty avoidance, temperate regions showed greater scores than colder-than-temperate and hotter-than-temperate regions, even though colder-than-temperate and hotter-than-temperate regions did not present any significant differences.

* The results from this article show that China should not be considered as a homogeneous market and suggest that multinational corporations (MNCs) need to take differentiated management strategies across regions in China.

Keywords: Climate. Work values. China


This study examines work value differences of cold, warm, and hot regions in China. As a major emerging market, China has attracted many multinational corporations (MNCs). However, MNCs' misconception of China as being a large emerging market with single homogeneous population often leads to difficulties in developing effective management strategies in China (Cui and Liu 2000). Geographically, China is a very vast country with different climatic conditions across regions. Many studies were conducted on climate and other human behaviours as cross-national studies. However, few studies have attempted to examine subcultural differences in terms of climate in China.

Given that research on regional differences rather than cross-national studies breaks down a generalised description of a country into more meaningful subunits, it is practically and theoretically an interesting approach (Huo and Randall 1991). Previous research provides large pictures of the relationship between climate and cultural values: this research on subcultural differences is equally useful in that it helps one to understand specific cultural differences, especially if the country is heterogeneous between the regions (Huo and Randall 1991) and if that society is large and complex like China (Goodman 1992; Robertson 1993).

From this perspective, this study attempted to extend the existing literature in various ways. First, this study conducts exploratory data analysis of subcultural value differences between employees living in different climatic regions. As Fukuda and Wheeler (1988) lamented, China was considered as having only a single, simplistic culture by sharing a cultural value system, even though many subcultural differences exist within China.

Second, few studies have attempted to focus on climatic factors that influence cultural differences across regions. Although previous studies (Ronen and Shenkar 1985: Triandis 1989; Gomez-Mejia and Palich 1997; Hofstede 2001; Lenartowicz et al. 2003: Den Hartog 2004; Emrich et al. 2004; Carl et al. 2004; Sully et al. 2004; Huo and Randall 1991) have suggested the existence of cultural differences across regions in terms of dogma, economic exchange, economic development, religion, languages, and ethnic heterogeneity and political turmoil, few have examined how climate influences work values. As Vliert et al. (1999) argues, temperature is an important factor that can explain cultural differences across countries. Van de Vliert et al. (1999) used cross-national data sets to examine the association between ambient temperature and masculinity in 53 countries and chose average daytime temperature of the country's capital city as the indicator of ambient temperature (Garver et al. 1990). However, as he admitted, if the capital of the city is eccentrically located like China, it is possible that the within-country variance in ambient temperature is significant compared to the variance between countries. The ambient temperature is also likely to be an important factor to show regional differences as well, given that temperature difference between the north and south parts of China is greater than that between countries. Specifically, the annual average temperature of Harbin and Sanyya are about 3.44[degrees]C and 26.98[degrees]C, respectively, and the temperature difference between two regions is about 23.54[degrees]C. The difference between two regions within China is greater than that between Finland and Jamaica, which is 22.8[degrees]C and is associated with a 42-point difference in terms of masculinity.

Third, this article includes five cultural dimensions of Hofstede rather than one or two cultural dimensions. The study by Vliert et al. (1999), an influential study, mainly focuses on the relationship between temperature and masculinity. Few studies attempted to examine regional value differences in terms of five dimensions (Hofstede 1990; Ralston et al. 1999). Following Hofstede's (1980) argument that the VSM is the proper tool to investigate regional differences, this paper compares regional work values in several regions in China. The extension of previous research to a better understanding of the relationship between climate and work values in China would provide more appropriate Chinese management strategies for multinational corporation managers.

Literature Review and Theoretical Framework

Climate-Culture Research

A review of previous research reveals that some research streams can be summarised as follows. First, most research conducted to date has explored the relationship between climate and values at the national level. For example, Vliert et al. (1999) examined the association between ambient temperature and masculinity in 53 countries using cross-national data. They measured the average daytime temperature of the country's capital city as the indicator of ambient temperature (Garver et al. 1990). However, as they admitted, if the capital of city is eccentrically located like China, it is possible that the within-country variance in ambient temperature is significant compared to the between-country variance. Specifically, the annual average temperature differences of Harbin and Sanyya within China is greater than that between Finland and Jamaica, and the latter two countries show great work-value differences in terms of masculinity.

Second, numerous dimensions can be used as a proxy variables for climate such as temperature (Vliert et al. 1999; McClelland 1961; Huntington 1915; Persinger 1980; Ganjavi et al. 1985), latitude (Huntington 1915; Hofstede 1980), humidity (Goldstein 1972; Howarth and Hoffman 1984), sunlight (Howarth and Hoffman 1984; Auliciems 1972; Thorson and Kasworm 1984), storm (Huntington 1945; Persinger 1980), and rain (Thorson and Kasworm 1984; DeFronzo 1984). in addition, Gupta and Javidan (2004) organized climate into categories that included tropical humid and monsoon, tropical wet and dry climate, tropical and subtropical desert, subtropical humid climate, subtropical wet and dry climate, marine west coast, and continental.

Third, previous research has shown an effect of climate on human beings' behaviour. However, few attempt to explain how climate affects work values. This study focused on the work values of employees in an effort to provide guidance for MNCs (multinational corporations). For example, Persinger (1980) and Tromp (1963) showed that temperature has a substantial effect on the human physiology, including the adrenal system, central nervous system, and particular organs. Several studies have shown that climate has a positive effect on overall mood (Howarth and Hoffman 1984), environmental pleasantness (Auliciems 1972), and optimism (Howarth and Hoffman 1984), happiness, depression, marital conflict, alcoholism, and suicide (Thorson and Kasworm 1984). In sum, few have attempted to investigate how climate impacts work values within China, despite China's being considered a large emerging market for MNCs. Given that few researchers except GLOBE team and Hofstede (2001) have provided some logic to develop the relationship between climate and work values, we tried to find some logic for the hypotheses based the suggestions of Hofstede and GLOBE. Hofstede(1980) investigated 116,000 IBM employees' work values in 40 countries and reported systematic cultural differences across countries in terms of power distance, individualism, uncertainty avoidance, masculinity, and long-term orientation.

GLOBE expanded Hofstede's five cultural dimensions to nine. It maintained Hofstede's labels Power Distance and Uncertainty Avoidance and split individualism into Institutional Collectivism and In-group Collectivism. Masculinity-femininity divided into Assertiveness and Gender Egalitarianism. Long-term orientation became Future Orientation.

Regional Differences in China

The popular misconception of China is that China is a single, homogenous market. In reality, China has large geographic areas with significant regional disparities and multicultural groups (Cui and Liu 2000). Multinational firms investing in China will face rather different environments according to the location they select. However, previous studies did not show any consistencies in the way in which regions should be classified in China. The number of regions that compose China varies from three to nine, as shown in Table 2. Before 1996, the Chinese government mainly paid attention to the classification of the regions to develop a governmental development program. With the founding of New China in 1949, geographers and economists have suggested many Chinese regional division programs to meet regional policy development goals. Since 1949, China has been divided into six administrative regions. In 1958, the State Planning Commission established seven economic cooperation areas to take full advantage of regional labour and resources, In the 1980s, scholars divided China into three zones: Eastern, central and western (Table 1).

In the seventh five-year plan, the Chinese government formally proposed a theory of the country's having three economic zones. From the seventh five-year plan to the ninth five-year plan, the three-zone theory was widely used in various fields. However, the classification of three regions appears to be too simple to develop regional policies in-depth. In 1996, as part of the "Ninth Five-Year Plan", the Chinese government proposed splitting the country into seven major economic zones. At the same time, many scholars also supported the suggestions of the government. During the "Eleventh Five-Year" period, they suggested dividing China into eight integrated economic zones to meet regional research needs and adapt to the regional policy goals. On the other hand, some researchers have tried to classify China into several regions to find regional differences. Ralston et al. (1996) compared work values across six regions in China: North-East, North-West, East-Central, Central-South, South-West, and North-West. In the process of developing these comparisons, they identify six regional clusters based on the infrastructure characteristics of the regions such as location characteristics, industrialisation characteristics, and education characteristics. In addition, Cui and Lia (2000) segmented China into seven regional markets based on economic development and consumer purchasing power: South China, East China, North China, Central China, South-West, North-East, and North-West.

In sum, previous research has classified China into from three to nine regions based on many factors, such as economic development and geographical adjacent. Few have attempted to divide China into regions based on climate. This article tries to classify China into three regions based on climate to examine differences in work values across regions.

The Relation of Climate to Work Values in China

After an extensive review of regional classification and cross-national research on the relationship between climate and culture, we developed a theoretical framework to examine how climate affects work values in the Chinese context. We classified China into three regions in terms of temperature because the objective of this article is to examine the relation between climate and work values across regions in China. Previous research has erred by measuring climatic temperature on Celsius or Fahrenheit scales with an arbitrary zero point of reference and has found no relation between climate and other human behaviours (Van de Vliert et al. 1999). A temperature at which human beings, a warm-blooded species, feel comfortable will be a more appropriate point of reference. Both colder-than-temperate and hotter-than-temperate climates require more cultural adaptation on the part of human inhabitants. Van de Vliert (2007) suggested the adoption of 22[degrees]C (~72[degrees]F) as a point of reference for temperature climate. On the other hand, several early studies have shown that with moderate temperatures of 16-21[degrees]C (60-70[degrees]F), people are most active and have high energy levels (Huntington 1915). McClelland (1961) demonstrated that cultures with a mean annual temperature from 40[degrees]F to 60[degrees]F have a higher need for achievement than do those in warmer or colder climates. Although Van de Vliert (2007) took a higher temperature point of reference, he used the average daytime temperature, which is usually higher than the annual average temperature. For example, his data showed that Beijing in China has an average daytime temperature of 18.42[degrees]C and an annual average temperature of 11.38[degrees]C, showing a temperature difference of 7[degrees]C between two measures. We integrated primarily the ideas of Van de Vliert, McClelland, and Huntington to choose a temperature point of reference to develop a regional comparison of China.

The temperate regions have temperatures ranging from 10[degrees]C to 20[degrees]C, and colder-than-temperate and hotter-than-temperate regions are below 10[degrees]C and over 20[degrees]C, respectively. Based on these criteria, China was divided into three principal regions: The Colder-than-temperate Region (G1), the Temperate Region (G2), and the Hotter-than-temperate Regions (G3), as shown in Fig. 1. Each region includes the following areas.

Colder-than-temperate (G1): Heilongjiang Province, Jilin Province, Liaoning Province, Neimenggu Province, Xinjiang Province, North Gansu Area, North Hebei Area.


Temperate regions (G2): Beijing, South Hebei Area, Shanxi Province, Ningxia Province, Qinghai Province, Shandong Province, Jiangsu Province, Anhui Province, Shanxi Province, Henan Province, Hubei Province, Chongqing Province, Sichuan Province, Xizang Province, South Gansu Area, and Hunan Province.

Hotter-than-temperate regions (G3): Hainan Province, Guangdong Province, and Fujian Province.

This article mainly focuses on work values of employees; we thus chose some cities and regions of eastern areas from three representative regions, given that it is difficult to acquire employee samples from western areas due to the lack of MNCs. Yanji was selected as representative of the colder-than-temperate region, Shanghai and Wuhan were selected for the temperate region, and Shenzhen and Hainandao were selected for the hotter-than-temperate region. Table 2 shows the characteristics of five cities.


There was a strong debate on Hofstede and GLOBE in JIBS recently (e.g. Hofstede's article 'What did GLOBE really measure' and responses). However, as Hofstede mentioned, GLOBE not only adopted the dimensions paradigm, they also started from Hofstede five cultural dimensions even though there are differences between GLOBE and Hofstede. First and foremost, dependent variables in the current study are work values measured by Hofstede. We used 1997 Hofstede Value Survey Module to calculate the scores of five work value dimensions. Therefore, we followed Hofstede's (2001) arguments and developed our hypotheses. However, Hofstede (1980) did not provide any arguments about the relationship between climate and long-term orientation. We had to try to find some logics for the hypothesis on the relationship between climate and long-term orientation.

The reason that we used Hofstede and GLOBE model was not because these two research models measured same cultural dimensions but because GLOBE model provides some logic to develop our hypotheses given that GLOBE reiterated Hofstede model and extend to nine cultural dimensions and tested empirically the relationship between work values and climatic clusters. In sum, we assume that it is possible to adopt some logics from the results of the relationship between climatic cluster and work values of GLOBE, although GLOBE and Hofstede cultural values are not exactly same things.

However, it should be noted that Hofstede and GLOBE measured very different things using the same names. Hofstede (2010) argued that GLOBE's questions were formulated so differently from those of Hofstede. He also pointed out that his uncertainty avoidance is actually strongly negatively correlated with GLOBE's uncertainty avoidance as is and weakly positively with GLOBE's uncertainty avoidance "should be".

Climate and Power Distance

Power distance is the extent to which people believe that power is distributed unequally and accept an unequal distribution of power as the proper way for social systems to be organised (Hofstede 1980). In organisations, power distance influences the amount of formal hierarchy, the degree of centralisation, and the amount of participation in decision-making. Companies in a high-power-distance region tend to be more centralised and have less employee participation in decision-making (Newman and Nollen 1996). Ronen and Shenkar (1985), Den Hartog (2004), and Emrich et al. (2004) argued that a warm climate favours high power distance. Parker (1995) suggested that climate has an influence on cultural values through labour productivity and health. The proliferation of insects and parasites in tropical regions results in the faster transmission of diseases (Landes 1998). On the other hand, insect and parasite populations do not proliferate in cold regions, and people in colder regions are more likely to enjoy healthier lives. Therefore, it can be expected that an equivalent amount of physical effort is more productive in temperate regions than it is in tropical regions, where there was a greater need for a higher amount of effort for a given level of performance (Parker 1995; Gupta and Hanges 2004). In conditions where labour productivity is low, people may be more willing to control labour for its effective organisation, leading societies to show higher levels of power distance.

Hofstede (2001) demonstrated that geographical latitude has a negative relationship with the power distance of societies. He suggested that people in cold areas have a greater demand for developing technology to survive against the harsh natural environment. As a result, societies in harsh areas need education for those of lower socioeconomic status to generate more social mobility and the development of a middle class. But in warmer climates, there was less of a need for technology, or of education for those of lower socioeconomic status, and agriculture provided sufficient means for survival due to the warm climate. As a result, there was less social mobility and greater concentration of wealth and political power in a few hands, and authority therefore was not questioned. Contrary to Hofstede's thesis, Gupta and Javidan (2004) showed that climate does not have any overwhelming impact on power distance values. According to Parker (1995), Landes (1998), and Gupta and Hanges (2004), people in a hot and tropical climate will show high power distance. According to Hofstede (2001), people in warm areas have high power distance. Despite these previous inconsistent results, we developed the following hypothesis based on Hofstede's thesis.

Hypothesis 1: Temperate regions will have greater scores of power distance than hotter-than-temperate and colder-than-temperate regions.

Climate and Masculinity

Although Hofstede (2001) used geographical latitude as an indicator of climate and found no correlation between the masculinity index and the geographical latitude of a country, he suggested the possibility of a relationship between geographical latitude and masculinity. Other studies have suggested that a warm climate favours a masculine culture (Carl et al. 2004; Den Hartog 2004; Emrich et al. 2004; Ronen and Shenkar 1985). As a consequence, geographical latitude would have a negative correlation with the masculinity index. As Hofstede (2001) suggests that societies in areas of cold temperature are characterised as having low masculinity, individuals from high-latitude countries with cold weather should exhibit substantial femininity to increase their survival rates by maintaining positive cooperation between men and women (Hofstede 2001). Previous research has suggested that climate may be an interesting variable to study the relationship between individual temperature and aggression, which is closely related to masculinity (Van de Vliert et al. 1999). Schwartz (1968) argued a curvilinear association between the mean annual temperature and the frequency of coups and terrorism. In a similar study, Van de Vliert et al. (1999) examined the association between ambient temperature, masculinity, and political violence. The study demonstrated that countries with moderate climates suffer more from domestic political violence than colder countries and suffer slightly more than hotter countries. Paternal investment theory can explain why culturally more masculine societies evolved in warmer climates (Van de Vliert et al. 1999). Based on these arguments, we set following hypothesis.

Hypothesis 2: Temperate regions will have greater masculinity scores than colder-than-temperate and hotter-than-temperate regions.

Climate and Individualism

The temperate climates facilitated more technology advances than in tropical climate. Advanced technologies, such as the heavy plow and high-productivity crops, may be related to in-group collectivism (Sachs 2001; Gupta and Hanges 2004). The need to rely on other group members in their societies will be reduced, thereby encouraging tendencies towards having more independent values. Hausman (2001) showed that tropical, landlocked regions have several developmental liabilities and need to have high transportation levels to transport goods between nations. People in tropical and landlocked nations have more interest in-group to maintain good relations with their in group rather than to try to exchange with other countries.

Hofstede (2001) suggested that geographical latitude may be a determinant of individualism. He argued that people in countries with moderate and cold climates depend more on their ability to fend for themselves for survival. This leads to the greater education level of children, making them to be independent of more powerful people and making them favour a degree of individualism. In sum, previous researchers have not d consistent results. Sachs (2001) and Gupta and Hanges (2004) suggested that people in temperate regions will show the highest degree of collectivism, while Hofstede (2001) expected hot regions to have high collectivism. The present study follows Hofstede (2001) and Hausman (2001) and develops the following hypothesis.

Hypothesis 3: Hotter-than-temperate regions will have greater collectivism scores than colder-than-temperate and temperate regions.

Climate and Uncertainty Avoidance

Hofstede (2001) showed that there is no significant relation between climate and uncertainty avoidance. On the other hand, Luque and Javidan (2004) reported that 54% of the variation in the uncertainty avoidance value of GLOBE scales was explained by climate difference. They showed that climatic differences have a strong effect on societal uncertainty avoidance values. Uncertainty avoidance values are distinctively strong in tropical wet and dry climates. Based on these arguments, the following hypothesis was developed.

Hypothesis 4: Hotter-than-temperate regions will have greater uncertainty avoidance scores than temperate and colder-than-temperate regions.

Climate and Long-Term Orientation

The findings from prior research (Ashkanasy et al. 2004) have shown that nontropical societies tend to favor a lower future orientation due to a higher perceived opportunity cost of resources. People in maritime and continental climates tend to have lower future orientation scores and higher aspirations for consumption. The results may reflect respondents' desire to enjoy the present and stronger planning orientation is considered valuable in tropical wet and dry climates, in similar vein, people in the tropics have greater total resources and more diversity in resources, leading the societies to have higher total opportunities. Therefore, it can be assumed that future orientation becomes less costly and more beneficial in tropical societies (Molles 2001). We set forth following hypothesis based on these discussions.

Hypothesis 5: Hotter-than-temperate regions will have greater long-term orientation scores than temperate and colder-than-temperate regions.


Data Collection

Data were collected from five regions from three climatic clusters. A total of 300 responses were collected. We omitted 90 respondents who are not from the regions where they are currently working to ensure that the climate has an influence on the work values of employees. Finally, a total of 210 samples were entered for final analysis: 90 respondents were from G1, 60 were from G2, and 60 were from G3 regions. In region 1, 46% of respondents were male and 54 were female, in region 2, 37% of respondents were male, and 63% were female, and in region 3, 48% of respondents were male, and 52% were female. In the three regions, most of the respondents were between the ages of 20 and 30. More than half of the respondents had a bachelor's degree (50%, 53%, 53% in G1, G2, and G3, respectively). A number of industries are represented among the employees. Most employees work for manufacturers (32% in region 1, 22% in region 2, and 15% in region 3, respectively) while others worked in construction, financial services, and retailing.


The temperature data of 67 regions was derived from the China Guide Book (2003). Then, the 67 regions were classified into three regions: G1 (colder-than-temperate) includes 20 regions, 41 regions were G2 (temperate region), and 6 regions composed G3 (hotter-than-temperate) (see Table 5). As a next step, representative regions were selected from G l, G2, and G3. Yanji was chosen to represent the G I group, Shanghai and Wuhan were chosen for the G2 group, and Shenzhen and Hainandao were chosen for the G3 group. Five regions, Yanji, Shanghai, Wuhan, Shenzhen, and Hainandao, were used to measure the work values of employees.

We used the 1997 version of Hofstede's Value Survey Module to measure work values. The questionnaire, originally in English, was translated into Chinese and back-translated to check for accuracy (Brislin 1970; Serkarn 1983). The discrepancies in the translation were adjusted by the translators.

Dependent Variable: Hofstede's Cultural Dimensions

Five work values developed by Hofstede were used as dependent variables. This paper examined cultural differences between three regions using the VSM in terms of five work value dimensions: Power distance (PDI), uncertainty avoidance (UAI), individualism (IDV), masculinity (MAS), and long-term orientation (LTO). Hofstede's VSM 97 version is as follows.

Calculation of power distance index: --35 m (N3) + 35m (N6) + 25m (N14) - 20m (N17) - 20

Following Hofstede's (1994) method, the power distance index (PDI) was calculated on the basis of the two regions' mean scores for these four conditions:

1. Manager performance (N3). Having a good working relationship with one's direct superior;

2. Importance of consultation (N6). Being consulted by one's direct superior in his/her decisions;

3. Subordinates are afraid (M14). In the respondent's experience, subordinates are afraid to express disagreement to their superiors;

4. Employees having two bosses (N17). Having an organisational structure in which certain subordinates have two bosses, which should be avoided at all costs.

Calculation of individualism: - 50 m (Nl) + 30 m (N2) + 20 m (N4) - 25 m (N8) + 130

According to Hofstede (1980), individualism stands for a society in which the ties between individuals are loose. Individualism (IDV) was calculated based on the following four conditions:

1. Personal time (N1). Having sufficient time for one's personal or family life;

2. Physical workplace conditions (N2). Having good physical working conditions (good ventilation and lighting, adequate work space, etc.);

3. Employment security (N4). Job security;

4. Variety (N8). Enjoying an element of variety and adventure in one's job.

Calculation of the masculinity: 60 m (N5) - 20 m (N7) + 20 m (N15) - 70 m (N20) + 100

Masculinity describes divisions in gender roles. The masculinity (MAS) index is based on the following conditions:

1. Cooperation (N5). Working with people who cooperate well with one another;

2. Advancement (N7). Opportunities for advancement to higher-level jobs;

3. Trust (N15). The belief that most people can be trusted;

4. Failure and responsibility (N20). The belief that when people have failed in life, it is often their own fault.

Calculation of the uncertainty avoidance: 25 m (N 13) + 20 m (N 16) - 50 m (N 18) - 15 m (N19) + 120

Hofstede (1980) defines uncertainty avoidance as the extent to which the members of organisations within a society feel threatened by uncertain, unknown situations. The uncertainty avoidance index (UAI) can be calculated based on the following conditions:

1. Stress (N13). Often feeling nervous or tense at work.

2. Good manager (N16). The belief that one can be a good manager without having precise answers to most questions that subordinates may ask about their work.

3. Competition (N18). The belief that competition between employees usually does more harm than good.

4. Rule orientation (N19). The belief that a company or organisation's rules should not be broken, not even when the employee thinks it is in the company's best interests.

Calculation of the long-term orientation. + 45 m (Ng) - 30 m (N10) - 35 m (N11) + 15 m (N12) + 67

Long-term Orientation is characteristic of society that fosters virtues, in particular perseverance and thrift, which are oriented toward the future. The Long-term Orientation Index (LTO) is composed of following four questions.

1. Personal steadiness and stability (N9).

2. Thrift (N10).

3. Persistence (perseverance) (N11).

4. Respect for tradition (N12).

Independent Variable: Temperature

Although numerous dimensions, such as temperature, latitude, humidity, sunlight, storm, and rain, can be used as proxy variables for climate, this study used the annual average temperature as a proxy of climate, given that most studies (Van de Vliert et al. 1999; McClelland 1961; Huntington 1915; Persinger 1980; Ganjavi et al. 1985) used temperature to investigate the relationship between climate and culture. Three temperature regions in this article were classified as temperate regions (G2), colder-than-temperate regions (G1) and hotter-than-temperate regions (G3).

Control Variable: Economic Development, Population, Ethnicity, Latitude, and Department

Previous studies (Ronen and Shenkar 1985; Triandis 1989; Gomez-Mejia and Palich 1997; Hofstede 2001; Lenartowicz et al. 2003; Den Hartog 2004; Emrich et al. 2004; Carl et al. 2004; Sully de Luque and Jayidan 2004; Huo and Randall 1991) have suggested many determinants of cultural differences across country in terms of economic exchange, income, economic development, religion, languages, and ethnic heterogeneity and political turmoil. Among these factors, we then selected the appropriate variables to explain cultural differences between regions in China.

Income and economic development are selected as one factor due to conceptual similarity. In addition, political turmoil such as the Cultural revolution was not included in this study because of the difficulty in measuring political turmoil in China which might influence all Chinese regions and not specific areas.

Previous studies suggested that religion and language are related to uncertainty avoidance (Weber 1958; Hofstede 2001; Ronen and Shenkar 1985; Gomez-Mejia and Palich 1997; Carl et al. 2004; Emrich et al. 2004), Sully de Luque and Javidan (2004). Specifically, Catholicism has high uncertainty avoidance whereas Protestantism, Buddhism and Islam have low uncertainty avoidance. In terms of language, countries that use two second-person singular pronouns such as Arabic, German, and Spanish have higher uncertainty avoidance scores. However, it is not possible to use religion and language to find regional cultural differences within China because the country has its own Chinese language. Religion was permitted by Chinese law but did not strongly influence the Chinese because religious activities are still being controlled by the Chinese government. Moreover, it is not likely to expect that religions in China influenced specific regions, thereby enabling regional differences in work value to take place.

Therefore, considering the Chinese context, language and religion in this study are not likely to be proper predictors of cultural differences between regions in China. Finally, as control variables of regional cultural differences in China, this study includes economic development beside climate. In addition, population was included given that Hofstede (1980) argued that population may have positive relation to power distance cultural dimension. Finally, we also included ethnicity, latitude, and department (General, RandD, and Marketing) as control variables. We ran MANCOVAs for each of the five dependent variables using five control variables as covariates.

Moreover, as you suggested that other groupings might also generate regional differences, two cities in same climate regions were compared to check possible city effect. Shanghai and Wuhan were selected for the temperate region, and Shenzhen and Hainandao were selected for the hotter-than-temperate region. Table 3 shows the characteristics of five cities. We performed a t-test to compare work values between Shanghai and Wuhan in the same temperate zone. In addition, Shenzhen and Hainandao as two cities in the hotter-than-temperate regions were compared. A comparison of two cities in the same climate zones did not show any significant differences in terms of five cultural dimensions. Only individualism between Shanghai and Wuhan showed a significant difference. These results are consistent with those from MANCOVA and ANOVA analysis. One possible explanation for these results that GDP differences between Shanghai and Wuhan generated work value differences given that GNP are most strongly correlated with individualism among five cultural dimensions (Hofstede 1991).


We ran Duncan multiple comparison tests to identify the cultural differences between groups for each of these measures. Table 4 presents findings of the multiple comparison tests. A comparison of work values among Chinese employees in three areas revealed different scores in PDI, UAI, IDV, MAS, and LTO value dimensions.

With regard to power distance, temperate regions showed greater scores than colder-than-temperate and hotter-than-temperate regions. The findings from this article support Hofstede's thesis that there was less social mobility and greater concentration of wealth and political power in a few hands in the temperate region, thereby showing a high power distance. Therefore, hypothesis 1 was supported. The findings from this article also support those of Den Hartog (2004) and Emrich et al. (2004) and show that employees in temperate regions like Shanghai and Wuhan have a high power distance.

In terms of masculinity, as expected by hypothesis 2, the results showed that temperate regions have greater scores than hotter-than-temperate regions, although there are no significant differences between temperate and colder-than-temperate regions.

Regarding hypothesis 3, the results showed that temperate regions showed greater scores of collectivism than colder-than-temperate regions, partially supporting hypothesis 3. The results from this study support those of Gupta and Hanges's study (2004). Temperate regions like Shanghai and Wuhan in China have advanced technology and high-productivity crops, which may be relevant for collectivism in these areas.

With regard to UAI, temperate regions showed greater scores than colder-than-temperate and hotter-than-temperate regions, even though colder-than-temperate and hotter-than-temperate regions did not exhibit any significant differences. One possible explanation for this result is that violent events occurred more frequently in warm regions than in cold and hot countries (Van de Vliert et al. 1999). Schwartz (1968) also found that temperate countries tend to suffer more from violent political action against and by their government. It can be assumed that political violence in warm regions like Wuhan in China might increase the uncertainty avoidance level (Huo and Randall 1991).

According to hypothesis 5, it was expected that hotter-than-temperate regions will have greater long-term orientation scores than temperate and colder-than-temperate regions. The results show that the score of long-term orientation in hot climate was higher than that in cold climate, thus partially supporting hypothesis 5.

Discussion and Conclusion

This paper examined differences in subcultural work values that may be influenced by climate differences. The findings from the current study show that there are cultural differences among regions at different temperature levels within China along the cultural dimensions of individualism, power distance, masculinity, uncertainty avoidance, and long term orientation. Previous research has investigated cross-national differences in values and human behaviour, as evidenced when studying internally homogeneous countries. However, when researchers examine a culturally heterogeneous country such as China, these results can only provide a very limited understanding of cultural differences across regions within China. Cultural differences among regions in China should be specifically explored, given that the variance of scores within China is not always smaller than that between China and other countries.

The findings of this study raise some interesting questions. Does China have enough differences across regions in terms of temperature to create differences in work values? Therefore, it will be interesting to find some countries like China that show temperature differences of approximately 23.54[degrees]C within them and whether there exist cultural differences across regions. It will be possible to include Algeria, Australia, Canada, India, and the United States in studies of the relationship between climate and work values and compare the results from these countries, given that the capital city is eccentrically located and within-country regional temperature differences are great.

The second question concerns the reference temperature used to classify regions. This article classified China into three regions based on annual average temperature. Classical scholars such as Hippocrates, Montesquieu, and Quetelet failed to find any relations of climate with culture because they measured climatic temperature on Celsius or Fahrenheit scales with an arbitrary zero point of reference. Given that humans are a warm-blooded species, colder-than-temperate and hotter-than-temperate regions require more cultural adaptation for humans to survive in the long run. However, which temperature to adopt as a point of reference is an important issue. This study used 10-20[degrees]C as a point of reference for temperate regions. It should be further analysed in future studies. The limitation of this study is that only 5 cities among 67 regions were included, so the findings from this research should be cautiously interpreted.

Third, this paper used various differences in work values such as individualism, power distance, masculinity, uncertainty avoidance, and long term orientation which may be influenced by climate differences. Few studies have attempted to examine differences in regional values in terms of five work value dimensions. Vliert et al. (1999) mainly focus on masculinity and other human behaviours such as happiness, motives for volunteer work, motives for paid work, and leadership style. However, it will be an interesting research topic if it can be extended to other value research models such as GLOBE and Schwartz.

Fourth, it will be interesting to examine whether the relation between climate and work values is linear or curvilinear, Vliert et al. (1999) showed a curvilinear relation between climate and masculinity based on paternal investment theory to explain why culturally more masculine societies evolved in warmer climates. The current study implies that the relationship between climate and values could be very complex. For example, the results showed that work values such as power distance, masculinity, and uncertainty avoidance have a curvilinear relation with climate. On the other hand, individualism showed a linear relation with climate. The theoretical logic driving a curvilinear relationship needs to be further analysed and developed.

Our findings carry some important managerial implications for MNCs in China. Given that work values of Chinese employees were revealed to differ between climatic regions, MNCs should develop differentiated management strategies tailored to different regions in China. If MNCs have many subsidiaries in many provinces in China, it will be possible to develop differentiated motivational strategies for employees across regions. For example, PDI values are higher in warm climates than in the colder region 1. Employees in warm regions such as Shanghai and Wuhan are willing to accept uneven distribution of power and wealth and prefer to have autocratic or paternalistic relations. Subordinates acknowledge the power of others based on their formal, hierarchical positions. Finally, employees in hot and cold areas like Hainandao and Shenzhen are less likely to accept power relations and prefer more consultative or democratic leaders (Figs. 2 and 4).




MAS values are higher in warm climates than in the colder region 1. Employees in warm regions such as Shanghai and Wuhan might prefer merit-based rewards to a higher degree than employees in hot areas like Hainandao would because the use of merit-based rewards is relevant to the masculine cultural dimension (Newman and Nollen 1996).

Workers in the temperate region show strong uncertainty avoidance. It can be assumed that employees in Shanghai and Wuhan relatively prefer explicit rules and formally structured activities and tend to remain longer with their present employers. The colder-than-temperate region shows weak uncertainty avoidance, and employees in Yanji prefer implicit or flexible rules or guidelines and informal activities. Employees tend to change employers more frequently.

On the other hand, individualism has a linear relation with climate, as shown in Fig. 3. The colder-than-temperate region favours individualism the most, followed by the temperate region, with the lowest IDV value being found in the hotter-than-temperate region. It means that employees in Yanji are expected to develop and display their individual personalities and to choose their own affiliations.

Table 5: Regions and average annual temperature in China

Regions 1 2 3 4 5 6 7

Dalian -5.1 -3.4 2.2 9.3 15.4 19.9 23.5
Shenyang -12.2 -8.1 -0.1 9.0 16.2 21.7 24.8
Dandong -9.0 -6.0 1.1 8.5 15.0 19.6 23.4
Changchun -16.8 -12.7 -3.6 6.8 14.5 20.0 23.3
Jilin -16.4 -6.2 4.5 11.2 15.8 19.0 21.4
Yanji -18.0 -15.0 -4.5 6.0 14.4 20.0 22.8
Changbaishan -14.1 -10.5 -1.8 6.6 13.8 17.7 21.8
Haerbin -19.7 -15.6 -5.4 5.9 14.1 19.8 23.2
Qiqihaer -19.6 -15.8 -5.4 5.1 13.7 19.9 22.8
Man -1.3 2.1 8.0 14.0 19.1 26.0 26.7
Yinchuan -9.0 -4.7 3.0 10.7 16.9 21.3 23.5
Lanzhou -6.7 -1.8 5.6 11.9 16.9 20.6 22.4
Jiuquan -9.2 -4.9 2.5 9.8 16.2 20.2 22.5
Jiayuguan -10.8 -2.4 1.6 12.5 15.1 21.7 22.6
Dunhuang -8.2 -2.8 5.4 12.7 18.7 23.2 25.5
Wulumuqi -15.6 -11.8 -1.8 9.7 16.8 22.7 24.3
Tulufan -9.2 -1.4 9.5 18.9 25.7 31.2 33.0
Kashi -6.6 -1.2 7.6 15.6 20.1 23.6 25.9
Chengdu 5.6 7.6 12.1 17.0 21.5 23.7 25.8
Leshan 7.0 9.7 12.4 22.4 22.2 24.1 26.8
Emeishan 6.6 9.1 11.5 21.7 21.5 23.8 26.3
Chongqing 7.0 9.0 14.0 19.0 22.0 25.0 29.0
Guiyang 5.2 6.5 11.8 16.6 19.9 22.3 24.4
Kunming 8.5 10.1 13.7 17.1 19.0 19.6 19.9
Dali 8.5 10.1 13.7 17.1 19.0 19.6 19.9
Jinghong 14.4 16.3 19.7 22.5 25.1 24.9 23.6
Xining -8.1 -4.2 2.2 8.0 12.4 15.4 17.7
Lasa -1.5 1.2 4.9 8.5 12.9 15.9 15.4
Rikaze -1.5 0.5 3.3 7.4 12.8 16.7 14.2
Beijing -4.4 -2.1 4.7 13.0 18.9 23.6 25.6
Tianjin -4.0 -1.6 5.0 13.2 20.0 24.1 26.4
Chengde -9.3 -5.8 2.0 11.3 18.4 22.2 24.4
Qinhuangdao -6.4 0.0 5.3 12.2 17.6 20.8 25.0
Qingdao -1.6 0.2 4.7 10.4 16.1 20.4 24.1
Yantai -1.7 -0.7 4.5 11.5 17.7 22.1 25.2
Jinan -1.1 1.7 8.1 15.8 22.3 26.8 28.0
Taishan -6 -3 9 10 15 20 22
Qufu -1.4 1.1 7.6 15.2 21.8 26.3 27.4
Kaifeng -0.1 4.9 8.7 16.9 19.3 25.5 28.0
Zhengzhou -0.2 2.6 8.0 15.1 21.2 26.4 27.3
Luoyang -0.5 2.2 7.8 14.8 21.0 25.9 27.1
Datong -10.2 -8.3 -1.0 7.3 15.8 20.4 21.1
Taiyuan -6.6 -3.1 3.7 11.4 17.7 21.7 23.5
Wutaishan -14.4 -8.4 -4.2 6.2 7.2 12.9 15.4
Huhehaote -12.7 -8.2 0.2 8.2 15.1 20.2 22.5
Shanghai 3.5 4.6 8.3 14 18.8 22.3 27.8
Suzhou 3.4 14.6 8.9 14.8 19.5 24.0 28.5
Wuxi 3.3 6.9 9.2 18.0 20.9 24.4 29.9
Zhenjiang 2.8 6.7 9.1 17.9 20.7 24.3 29.7
Yangzhou 2.8 6.7 9.1 17.9 20.7 24.3 29.7
Nanjing 2.0 3.8 8.4 14.8 19.9 24.5 28.0
Hangzhou 3.8 5.1 9.3 15.4 20.2 24.3 28.6
Shaoxing 4.6 8.4 10.3 19.2 21.6 24.4 29.6
Ningbo 5.2 8.6 10.6 19.2 21.5 24.4 29.6
Huangshan 4.0 6.1 10.4 16.2 20.8 24.3 28.2
Hefei 2.1 4.2 9.2 15.5 20.6 25.0 28.3
Wuhan 3.5 5.6 10.4 16.3 21.7 25.8 28.9
Guangzhou 13.7 14.8 17.7 21.9 25.6 27.2 28.3
Shenzhen 14.1 15.0 18.4 22.2 25.3 27.3 28.2
Nanching 4.2 5.9 10.6 16.5 21.8 25.7 29.5
Jingdezhen 4.7 9.2 11.0 20.6 23.4 25.1 28.6
Changshan 4.7 6.3 11.3 17.3 22.2 25.9 29.5
Yueyang 3.4 8.9 10.2 20.7 22.3 25.4 29.4
Fuzhou 10.8 10.8 13.6 18.1 22.4 25.5 28.7
Xiamen 13.5 13.3 15.4 19.3 23.5 26.5 28.7
Guilin 7.9 9.5 13.5 18.6 23.4 26.2 28.4
Haikou 17.2 18.6 21.4 25.0 27.5 28.0 28.4
Sanyya 23.7 23.9 26.4 28.1 29.0 30.0 29.4

Regions 8 9 10 11 12

Dalian 24.4 20.0 13.7 5.6 -1.5
Shenyang 23.6 17.2 9.4 -0.1 -8.9
Dandong 23.8 18.3 11.1 2.6 -5.5
Changchun 21.7 15.2 6.9 -3.8 -13.2
Jilin 20.0 16.2 9.2 -6.1 -10.1
Yanji 21.7 14.8 6.5 -5.0 -14.0
Changbaishan 21.2 15.1 7.0 -2.7 -11.4
Haerbin 21.4 14.6 5.8 -5.9 -16.9
Qiqihaer 21.0 14.0 4.7 -7.7 -16.4
Man 25.4 19.4 13.6 6.6 0.7
Yinchuan 21.8 16.1 9.1 1.0 -6.7
Lanzhou 21.2 16.2 9.7 1.8 5.1
Jiuquan 21.1 15.1 7.7 -1.0 -7.0
Jiayuguan 21.0 16.7 8.6 2.5 -4.4
Dunhuang 24.1 17.8 9.4 0.7 -6.5
Wulumuqi 22.7 16.8 7.5 -3.2 -11.8
Tulufan 30.7 23.5 12.5 1.8 -7.0
Kashi 24.7 19.9 12.5 3.7 -3.7
Chengdu 25.1 21.4 16.4 12.0 7.3
Leshan 25.2 23.4 19.3 15.9 9.4
Emeishan 25.1 23.0 18.5 15.5 9.2
Chongqing 30.0 26.0 21.0 15.0 10.0
Guiyang 23.8 20.8 16.1 11.7 7.3
Kunming 19.4 17.8 15.1 11.7 8.8
Dali 19.4 17.8 15.1 11.7 8.8
Jinghong 24.4 24.1 22.7 19.0 16.1
Xining 16.9 12.5 7.0 -0.6 -6.7
Lasa 14.5 13.1 8.6 2.8 -1.3
Rikaze 13.3 12.9 9.1 2.1 -3.3
Beijing 24.0 19.1 12.2 4.3 -2.3
Tianjin 25.5 20.8 13.6 5.2 -1.6
Chengde 22.8 17.2 10.0 0.6 -7.4
Qinhuangdao 24.0 21.3 14.4 2.5 -1.5
Qingdao 25.5 21.2 15.4 8.2 1.4
Yantai 25.2 21.5 15.5 8.4 1.7
Jinan 26.4 22.0 15.8 8.1 1.4
Taishan 21 16 10 3 -4
Qufu 26.2 21.7 15.8 7.9 1.1
Kaifeng 26.1 23.5 17.3 9.9 3.6
Zhengzhou 26.0 21.1 15.1 7.9 1.8
Luoyang 25.7 20.6 14.9 7.5 1.5
Datong 19.8 16.0 8.0 -1.8 -5.4
Taiyuan 21.8 16.1 9.9 2.1 -4.9
Wutaishan 13.8 12.2 4.0 -1.6 -8.4
Huhehaote 20.7 14.6 6.9 -2.5 -11.0
Shanghai 27.7 23.6 18 12.3 6.2
Suzhou 28.0 23.4 17.9 12.1 6.0
Wuxi 29.7 24.0 19.7 14.9 7.6
Zhenjiang 29.1 23.8 18.3 14.1 6.8
Yangzhou 29.1 23.8 18.3 14.1 6.8
Nanjing 27.8 22.7 16.9 10.5 4.4
Hangzhou 28.0 23.3 17.7 12.1 8.9
Shaoxing 29.5 23.6 20.3 15.6 9.1
Ningbo 29.5 23.8 16.2 16.1 9.7
Huangshan 27.8 23.5 17.5 11.6 6.0
Hefei 28.1 22.9 17.0 10.6 4.6
Wuhan 28.6 23.8 18.0 11.8 5.9
Guangzhou 28.1 27.1 23.9 19.6 15.7
Shenzhen 27.8 26.6 23.7 19.7 15.9
Nanching 19.1 24.6 18.5 12.4 6.8
Jingdezhen 29.2 24.7 21.1 15.9 9.5
Changshan 29.2 24.7 18.8 12.8 7.2
Yueyang 29.8 24.8 20.5 16.1 9.0
Fuzhou 28.4 26.1 21.9 18.0 13.5
Xiamen 28.6 27.6 24.0 20.1 16.1
Guilin 27.9 25.9 21.1 15.3 10.3
Haikou 27.7 26.9 24.7 21.8 18.7
Sanyya 28.9 28.1 27.3 26.2 22.8

Regions Average annual Group

Dalian 10.33 G2
Shenyang 7.71 G1
Dandong 8.57 G1
Changchun 4.86 G1
Jilin 6.54 G1
Yanji 5.79 G1
Changbaishan 5.22 G1
Haerbin 3.44 G1
Qiqihaer 3.02 G1
Man 13.36 G2
Yinchuan 8.58 G1
Lanzhou 10.24 G2
Jiuquan 7.75 G1
Jiayuguan 8.72 G1
Dunhuang 10.00 G2
Wulumuqi 6.36 G1
Tulufan 14.10 G2
Kashi 11.84 G2
Chengdu 16.29 G2
Leshan 18.15 G2
Emeishan 17.65 G2
Chongqing 18.92 G2
Guiyang 15.53 G2
Kunming 15.06 G2
Dali 15.06 G2
Jinghong 21.07 G3
Xining 6.04 G1
Lasa 7.92 G1
Rikaze 7.29 G1
Beijing 11.38 G2
Tianjin 12.22 G2
Chengde 8.87 G1
Qinhuangdao 11.27 G2
Qingdao 12.17 G2
Yantai 12.57 G2
Jinan 14.61 G2
Taishan 9.42 G1
Qufu 14.23 G2
Kaifeng 15.30 G2
Zhengzhou 14.36 G2
Luoyang 14.04 G2
Datong 6.81 G1
Taiyuan 9.44 G1
Wutaishan 2.89 G1
Huhehaote 6.17 G1
Shanghai 15.59 G2
Suzhou 16.76 G2
Wuxi 17.38 G2
Zhenjiang 16.94 G2
Yangzhou 16.94 G2
Nanjing 15.31 G2
Hangzhou 16.39 G2
Shaoxing 18.02 G2
Ningbo 17.87 G2
Huangshan 16.37 62
Hefei 15.68 G2
Wuhan 16.69 G2
Guangzhou 21.97 G3
Shenzhen 22.02 G3
Nanching 16.30 G2
Jingdezhen 18.58 G2
Changshan 17.49 G2
Yueyang 18.38 G2
Fuzhou 19.82 G2
Xiamen 21.38 G3
Guilin 19.00 G2
Haikou 23.82 G3
Sanyya 26.98 G3

G1 Region: Colder-than-Temperate (below 10[degrees]C)

G2 Region: Temperate (10-20[degrees]C)

G3 Region: Hotter-than-Temperate (over 20[degrees]C)

Received: 07.09.2010 / Revised: 27.02.2011 / Accepted: 28.02.2011 / Published online: 14.01.2012


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Asoc. Prof. J.-W. Kwon ([mail])

Department of International Trade and Business, College of Business Administration, Kangwon National University, Kangwon, Korea e-mail:

Researcher C. Shan

Cross-Cultural Management Research Centre, Kangwon National University, Kangwon, Korea

DOI 10.1007/s11575-011-0120-1
Table 1: Examples of regional classification

 Researcher Year Region

Six-integrated- Government 1949-1954 North-East Area; the
economic-areas Lower and Middle
 Reaches of the Huang
 River Area, the
 Middle and Lower
 Reaches of
 Changjiang River
 Area; South-East
 Seaside; South-West
 Area, North-West

Seven economic Government 1958 North-East Area;
cooperate areas South-West Area;
 North-West Area,
 Northern Mid-China;
 Eastern Mid-China;
 Mid-China; Southern

Three zones Government 1986 East Zone; Middle
Seven Government 1996 Zone; West Zone
economic-areas Bohai Economic Zone
 Centre; North-East
 Economic Zone; the
 Changjiang River
 Delta and Sea side
 Area; South-East
 Seaside Economic
 Area; Great
 South-West Economic
 Area; North-West
 Economic Area; Five
 Large Province Area

Six cities Ralston et al. 1996 Northeast, North
 Central, East
 Central, Central
 South, Southwest,

Seven regions Cui and Liu 2000 South China, East
 China, North China,
 Central China,
 Northeast, Northwest

Eight areas Government 2002 North-East Area;
 North Seaside Area;
 East Seaside Area;
 South Seaside; the
 Middle Reaches of
 the Huang River
 Area; the Middle
 Reaches of
 Changjiang River
 Area; South-West
 Area; Big North-West

Nine China City 2004 North-East Area;
economic-areas Competitiveness North Seaside; North
 Report Land; East Seaside;
 East Land; Middle
 Land; South Seaside;
 West Land;
 South-West Land

Nine large-city- China City 2004 Shen-DaArea;
economic-circles Competitiveness Jing-Jin-Ji-Area;
 Big-Shanghai Area;
 Pearl River Delta;

"7+ 1" Report Area; Xiang-E-Gan
Economic-areas Mr. Jincheng 2005 Area; Cheng-Yu Area
 Xiao Pearl River Delta
 Area; the Changjiang
 River Delta Area;
 Bohaiwan Area;
 North-East Area;
 Central Plains Area;
 South-West Area;
 Shan-Gan Ning-Qing
 Area: Western Area

Table 2: The summary of the geographic and economic
characteristics of the six cities

Regions ([degrees]C) Latitude

G1 Yanji 5.79 39[degrees]01'-39[degrees]04'

G2 Shanghai 15.59 30[degrees]41'-31[degrees]53'
 Wuhan 16.69 29[degrees]58'-31[degrees]22'

G3 Shenzhen 22.02 22[degrees]-23[degrees]
 Hainandao 26.98 18[degrees]10'-20[degrees]10'

Regions Population GDP (million/RMB)

G1 Yanji 2,118,087 170

G2 Shanghai 9,495,701 14,900
 Wuhan 4,488,892 4500

G3 Shenzhen 8,000,000 8245
 Hainandao 8,647,300 1229

G1 Colder-than-temperate region, G2 Temperate region.
G3 Hotter-than-temperate region

Table 3: Cultural differences across regions: results of MANCOVA

Variables PDI IDV MAS

 F F F

Temperature 10.51 ** 1.11 2.37 ([dagger])
GDP 1.84 0.09 0.18
Ethnicity 0.00 3.00 ([dagger]) 0.48
Population 1.00 2.38 0.43
Department 0.11 0.97 0.01
Latitude 7.15 ** 0.52 2.63

Variables UAI LTO

 F F

Temperature 7.24 ** 4.48 *
GDP 1.71 1.67
Ethnicity 1.96 0.01
Population 0.42 1.29
Department 0.22 0.59
Latitude 0.59 0.00

* p <0.05; ** p <0.01; ([dagger]) P<0.01

Table 4: Climate and cultural differences across regions

 Cold Warm Hot Expected
 (G1) (G2) (G3) F Results (Duncan)

1. PDI 31.61 56.35 26.66 4.91 ** G1<G2=G3 G1=G3<G2
2. IDV 91.61 74.23 71.02 4.26 * G2<G3=G1 G1=G2=G3
3. MAS 90.80 98.13 51.53 2.84 + G1=G3<G2 G3<G1=G2
4. UAI 91.69 128.30 106.66 7.99 ** G1<G2=G3 G1=G3<G2
5. LTO 5.95 57.00 55.20 5.94 ** G1=G2<G3 G1<G2=G3

PDI Power distance, IDV Individualism. MAS Masculinity, UAI
Uncertainty avoidance Value survey module (1994): Formulas for
index calculation

(1) IDV=-50 m (01)+30 m (02)+20 m (04)-25 m (08)+ 130

(2) MAS=60 m (05)-20 m (07)+20 m (15)-70 m (20)+ 100

(3) PDI=-35 m (03)+35 m (06)+25 m (14)-20 m (17)-20

(4) UAI=25 m (13)+20 m (16)-50 m (18)-15 m (19)+ 120

(5) LTO=45 m (N9)-30 m (N10)-35 m (N11)+ 15 m (N12)+67

[??] All content questions are scored on five-point scales in
which m(03) is the mean score for question 03, etc.

* p<0.05; ** p<0.01; ++ p<0.01
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Author:Kwon, Jong-Wook; Shan, Chuanxuan
Publication:Management International Review
Geographic Code:9CHIN
Date:Jul 1, 2012
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