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The impact of culture on National Spatial Data Clearinghouses

Joep Crompvoets and Harm Kossen
Wageningen UR, Centre for Geo-Information
P.O. Box 47, 6700 AA, Wageningen, The Netherlands
Tel. (+31) 317 474399 Fax. (+31) 317-474567
Email: Joep.Crompvoets@Staff.GIRS.WAU.nl, harm.kossen@student@girs.wau



Abstract
The main topic of this poster is to present an approach to analyse the impact of national culture on National Spatial Data Clearinghouses. The empirical methodology applied is based on the cultural dimensions as described by Hofstede (1980, 1997) and several clearinghouse characteristics. From this research, it can not be concluded that culture has a profound impact on the quality of implemented clearinghouses. However, dimension Masculinity/Femininity has a slight impact on the quality (visibility) of National Spatial clearinghouses.

Introduction
Designing and implementing a National Spatial Data Infrastructure has become a high priority for many nations. A Spatial Data Infrastructure (SDI) can be considered as a mechanism, which is able to reduce time, money and effort in assessing national spatial core datasets and to avoid unnecessary duplication in the harmonisation and standardisation of required datasets by promoting the sharing of available data. This is a national strategic implementation project because it could have potentially a very significant contributor to economic wealth in the emerging age of electronic commerce.

Implementing a NSDI requires political leaders to make key decisions on its structure and management. Their decisions often reflect the cultural values held by the country. One of the key components of a National SDI is the national spatial data clearinghouse (besides Standards, Institutional frameworks, Network architectures, Clearinghouses, Policy, Legislation and Human Resources). The subject of this poster is mainly focused on the impact of national culture on the quality of clearinghouses. Spatial data clearinghouses can be defined as a system of software and institutions to facilitate the discovery, evaluation, and downloading of digital spatial data, which usually consists of a number of servers on the Internet that contain information about available digital spatial data known as meta-data.

Implementations of clearinghouses on national level do not spread equally rapidly across countries, and this can not be attributed to wealth only. Countries react differently to the implementation of new technologies, for example GIS and clearinghouses, and cultural differences could be a base for this. It is believed that cultural conditions primarily determine the desirability to accept information technologies. The cultural desirability specifically relates to the functionality of GIS: communication and information sharing, strategic planning, operational planning and management, and monitoring and evaluation (Toorn en de Man, 2000). Clearinghouses will address communication and information sharing and here differences in culture could be especially obvious since it is these clearinghouses that will provide for a lot of openness and “visibility” not always desired by cultures. Although “non-technical” issues have been recognised as having influence on the implementation of new technologies, there is little literature that treats what can be done in advance to assess the social system of a country and its implications for the implementation of new technologies (Toorn en de Man, 2000).

The implementation of clearinghouse has only just begun (first implementation 1994), and therefor it will be interesting to see what the influence of culture is on this implementation. Although there have been a growing number of successes in clearinghouse implementations, there is also a considerable record of failures. In some cases, the reasons can be traced to under-estimation of the cultural factors in the organisations, which affect the implementation. With cultural differences in mind, it could be helpful in setting up action plans for the creation and improvement of new clearinghouses. For this purpose, it is important to observe differences in culture by describing them with operational variables (or dimensions) in culture.

Methodology
In order to analyse the impact of culture on clearinghouses, national culture dimensions are used. Hofstede (1980) has studied a large body of survey data, about the values of similar IBM employees in 50 different countries around the world. A statistical analysis of the answers on questions about the values of these employees revealed common problems, but with solutions differing from country to country, in the following areas:
  • Social inequality, including the relationship with authority;
  • The relationship between the individual and the group;
  • Concepts of masculinity and femininity;
  • Ways of dealing with uncertainty.
These four problem areas have been defined by the American sociologists Inkeles and Levinson 10 years before this study was done (Inkeles and Levinson, 1969). This prediction provides strong support for the theoretical importance of the empirical findings of this study. The four problem areas defined by Inkeles and Levinson and empirically found in the IBM data represent dimensions of culture. A dimension is an aspect of culture that can be measured relative to other cultures. Hofstede named those dimensions power distance, collectivism vs. individualism, femininity vs. masculinity and uncertainty avoidance (Hofstede, 1980). Together they form a four-dimensional model of differences among cultures. A score on each of the four dimensions characterises each country in this model. A fifth dimension has also been identified, namely long term orientation vs. short term orientation. This had not been discovered before because of a “Western” way of thinking by the researchers, but has been revealed by a following study on the IBM data of people’s values around the world using a questionnaire composed by “Eastern” minds (Hofstede, 1980).

In order to compare different national clearinghouses and to be able to use them in statistical analyses, they were evaluated by a set of clearinghouse characteristics: Number of datasets, Number of data themes, search mechanisms (like the use of Index maps, Keyword search, Spatial search, temporal search), access results (abstract, metadata, data) and other services like online mapping. Besides these culture and clearinghouse data, general country data like surface, number of inhabitants, population density, gross national product per capita, and demographic and human development data were collected.

After acquiring the needed data about national culture dimensions and clearinghouse characteristics of 50 countries, all the data were imported in SPSS 10.0 for Windows (SPSS Inc, 2000). In SPSS it is possible to analyse statistically data in all sorts of ways. For this research a factor analysis has been used in order to test the hypothesis that cultural variables are at the basis of underlying dimensions in the data matrix. Over these dimensions the dependant variables, with variables related to clearinghouse in particular, are spread. A factor analysis refers to a variety of statistical techniques that aim to reduce your dataset to a smaller number of hypothetical variables, or factors (Lewis-Beck, 1994).

Conclusions
From the factor analysis, can be concluded that wealth (expressed in GNP per capita) does not determine the quality of the clearinghouse. For example, countries like El Salvador, Nicaragua and Uruguay have established a nice clearinghouse for spatial data. Additionally, it can not be concluded that culture in general and most of the cultural dimensions in particular have a profound impact on the clearinghouse quality. However, a relation exists between Masculinity (as cultural dimension) and use of maps to search spatially. Masculine cultures are aggressive, winners win/losers lose, encourage visible success and focus on achievement and success. At this moment, the use of maps to search spatially can be considered as a direct visible success, because of its innovative and glossy character.

References
  • Hofstede G.,1980. Culture’s Consequences: International Differences in Work-related Values. Beverly Hills, California: Sage Publications.
  • Hofstede G., 1997. Cultures and Organizations: Software of the mind. London: McGraw-Hill.
  • Inkeles A, and D.J. Levinson, 1969. National character: the study of modal personality and sociocultural systems. In: The handbook of social Psychology, 2 nd edition, vol. 4, Lindsey and Aronson (eds), Reading MA: Addison-Wesley.
  • Lewis-Back M.S., 1994 Factor analysis and related techniques.-(International handbooks of quantitative applications in the social sciences; Vol. 5. Sage Publication, Ltd SPSS, 2000. SPSS for Windows 10.0. SPSS Inc. 1989-1999.
  • Toorn, W. van den, and E. de Man, 2000. Anticipating cultural factors of GDI. In: Geospatial data infrastructure. Concepts, cases and good practice (eds. R. Groot and J. McLaughlin). Oxford University Press.
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