National Culture and Information Technology Product Adoption



National Culture and Information Technology Product Adoption

Kallol Bagchi Paul Hart Mark F. Peterson

University of Texas Florida Atlantic Florida Atlantic

at El Paso University University

kbagchi@utep.edu hart@fau.edu mpeterso@fau.edu

Abstract

National culture is likely to play a role in Information Technology (IT) adoption. Data from a large scale study of national culture are used to predict the adoption of six information technologies -- PC, Telephone, Cell Phone, Fax, the Internet, and Pager — over a ten year period in thirty one nations. The results show that even after controlling for national economic and social differences, national cultural dimensions significantly predict most IT product adoptions.

KEYWORDS

Information Technology, IT Product, Adoption, National Culture

Introduction

The worldwide growth of information technology (IT) is phenomenal, but growth has not occurred at the same pace in all nations. Worldwide, IT spending grew by more than 10 percent annually during much of the past decade, which outpaced overall global economic growth (Microsoft, 2002). Since the rate of IT growth in different countries varies from two to five times the national rate of overall economic growth, other factors besides economic growth are likely to explain national differences in IT investment. We propose that national culture characteristics are among the other factors that explain differences in IT growth.

Culture has been considered something that affects the reason for and rate of IT adoption (Harvey, 1994; Krumholtz et al., 2000). Robey and Rodriguez-Diaz (1989) suggest that culture can impede IT implementation efforts because of differences in the way ITs are interpreted and given meaning. For example, if ITs are typically introduced by young people with recent technical education, then cultural values may support a particularly strong reluctance to avoid ITs by senior managers in those societies that link status to age. Watson and colleagues (1994) described ways in which culture shapes the adoption of technology while studying the cross-national adoption of group support systems. Culturally compatible features of a technology will be appropriated and the remaining features of the technology will be reshaped or ignored. For example, culture plays a significant role in decision making (Peterson, Miranda, Smith & Haskell, 2003) and the type of technologies used to support decision making (Quaddus and Tung, 2002). While these studies consider cultural differences in the use of technologies, other studies suggest that culture is one among a number of national-level factors associated with the rate of IT adoption. The growth of computerization has been found to differ among nations and cultural regions. In recent times, computerization has grown rapidly within some Asian countries, known as Asian Tigers, but much more slowly in most African countries. Economic factors alone do not fully explain the disparity in such growth patterns. Similarly, the adoption of mobile telephones has been uneven across even industrialized nations, and this growth is arguably not only due to economic factors. One report (Intecom, 2000) noted that 75 percent of the people in Finland and Iceland used mobile telephones compared to about 40 percent in the U.S., although the U.S. economy was stronger than the Finnish economy. (The GDP per capita of the U.S. and Finland in 1990 were $18,399 and $14,216 respectively). Goodman and colleagues (1991) stated that "there are important historical, social, cultural and economic reasons for computing and telecommunication disparities (among nations) and, for better or worse, these differences make the world a more complicated and interesting place" (p. 19).

A few empirical studies have investigated the relationship between national culture and IT adoption (Straub, 1994, Straub et al., 1997). For example, Straub and colleagues (1997) found that the technology adoption model (TAM) could not predict technology use across all cultures. The TAM identifies the factors that are responsible for acceptance and use of technology in an organization such as the perceived ease of use and overall usefulness of a technology. The authors conjectured with reservations that national culture played a role. They conclude their study by suggesting: “It is not possible to say with certainty that a link between cultural factors and technology has been empirically established”. A major difficulty in establishing a link between culture and technology adoption is that factors other than culture may explain national differences in technology use.

In order to link culture to technology, one needs to control other national variables such as economic, social, and institutional indicators. National variables such as the main economic indicator GNP (or GDP) per capita are important (Barro, 1997). To quote Hofstede (2001, p. 68), “If hard variables (economic, biological, technological) predict a country variable better, cultural indexes are redundant.” He encourages researchers to use GNP per capita as an additional control variable to examine the unique effects of culture. This paper attempts to use GDP per capita as a control variable. Controlling for GDP and other societal indicators is a conservative approach to assessing the effects of culture since there is a strong relationship between culture and economic development as well as between culture and social and political systems (Inglehart and Baker, 2000; Granato et al., 1996). Inglehart (2000) pointed out the difficulty of proving the direction of causality. However, his empirical cross-sectional study provides evidence that a strong positive relationship exists between culture and national economy. In addition to controlling GDP, this paper introduces two more national indicators of economic and cultural heterogeneity other than cultural dimensions.

Studies linking culture to IT adoption to date have largely been small in scale or limited to a single organization. Most of these studies have tried to compare and contrast IT adoption in two or three countries and have attributed the adoption differences to culture (Martinsons and Davison, 2003). Despite their limitations, these studies show the importance of more carefully considering the role of culture in IT adoptions. Research is now needed to take a broader look at national culture and trends in IT adoption at a national level across the most frequently used ITs in an expanded set of nations. In order to progress in this way, the nature of IT adoption needs to be understood and the factors underlying adoption, and the culture among them, need to be identified from prior research (Rogers, 1981). The present paper, therefore, empirically assesses the impact of national culture on IT adoption in combination with other variables that have been shown to affect adoption. Our analysis will begin by providing a view of national culture, then we will develop hypotheses based on a more thorough consideration of the research introduced above about a culture’s implications for IT adoption. The hypotheses are tested using data based on culture scores. The analysis of culture effects control for three potential confounds as detailed below. These confounds are variables suggested by research noting the link of some cultural variables to GDP (e.g., Hofstede, 2001) and other research noting that research compiled about mean scores for culture and overall GDP scores need to also consider issues of within-nation heterogeneity (Au, 1999). These variables are used to predict the adoption of the most widely used IT products developed since the beginning of the 20th Century in most nations -- Personal Computers (PCs), Telephones, Cell Phones, Fax, the Internet, and Pagers.

National Culture

Culture is defined as “the integrated pattern of behavior that includes thought, speech, action, and artifacts, and it depends on man’s capacity for learning and transmitting knowledge to succeeding generations” (Adler, 1997). National culture, therefore, reflects national patterns in the core values and beliefs of individuals, which are formed during childhood and reinforced throughout life (Shore & Vankatachalam, 1996). Once set in place, culture has its own independent effect on a nation’s response to world events including new technologies. Almost every nation has an identity and common cultural experiences with a particular history, language, and set of religious traditions. These experiences shape the way all members of the nation view themselves and their world, even when individuals vary in their personal attitudes toward these common cultural experiences. In the area of information technology, for example, having experience with omnipresent telephones, PCs, and cell phones is an inevitable cultural fact in the United States that has pervasive social implications even though individuals vary in their personal attitudes and use of these technologies. Despite its significance, scholars have often overlooked the importance of culture (Boyacigiller & Adler, 1991).

The following discussion primarily focuses on the contributions to culture analysis derived from information about national culture dimensions provided by Hofstede (2001). His work focuses on societal values as reflected in configurations of items analyzed at the level of nations that are comprised of national average value statements provided by individuals. We will next introduce Hofstede’s view of culture, then we will summarize the critiques and alternatives to his model and indicate why it remains preferable to other available alternatives.

Hofstede's Work

According to Hofstede, culture is “the collective programming of the mind that distinguishes the members of one group or category of people from another” (p. 9, Hofstede 2001). Hofstede developed an empirically-based typology of cultural attributes by analyzing data obtained from surveys conducted among individuals in 53 nations in 1968 and 1972. Since all 116,000 respondents were employees of the same firm, IBM, Hofstede was able to hold constant the influence of industry and corporate culture. Based on the data obtained, he classified countries along four dimensions: power distance, uncertainty avoidance, individualism/collectivism, and masculinity/femininity. Hofstede rated each of the 53 countries in his study by these cultural dimensions (Hofstede, 2001). For example, compared to other cultures, the U.S. is very high in individualism, somewhat below average in power distance, about average in uncertainty avoidance, and somewhat above average in masculinity.

The ways in which Hofstede constructed these dimensions and the scores he provides for them have been criticized on a number of grounds. However, he and others have shown continuing relationships between these dimensions and many nation-level socio-economic, cultural, and business indicators. Hofstede’s work is still widely used and has been cited in more than 1,500 scholarly works in various fields (Hofstede, 2001). Sondergaard (1994) noted that Hofstede’s research has provided a theoretical paradigm and data that is used in other studies. A number of alternative approaches to analyzing cultural values have developed since Hofstede first presented his research, but these alternatives are less satisfactory for our present analysis. Schwartz (1994) provides scores for 23 nations and ethnic groups based on the values expressed by teachers. The limited number of nations for which he has provided this data restricts its utility for the present research. Trompenaars and Hampden-Turner (1998) also provide culture values data for several dozen nations. However, the data is based on individual items rather than indices, and the researchers confound nation with industry, organization, and occupation specialty, and the items appear to mainly reflect various forms or aspects of the single dimension of individualism-collectivism (Hofstede, 2001; Smith, Dugan & Trompenaars, 1996). Inglehart (1998) also provides values data for 43 nations and ethnic groups. However, his work value items have limited distributions (mainly the two-categories “mentioned” and “not mentioned”), and multiple-item dimensions only have been developed from his project for a limited number of politically relevant constructs. Given the limitations of the other options available and the evidence of continued validity in applications of Hofstede’s measures (Hofstede, 2001; Smith, Peterson & Schwartz, 2002), we decided to focus on Hofstede’s scores for culture dimensions. Separate analyses using indices from several other culture projects applied to a smaller number of nations are available from the authors.

Culture and IT adoption

Studies of culture and IT adoption can be divided into two parts (Robey and Rodriguez-Diaz, 1989): (1) the effect of national culture on IT (Ein-Dor, Segev, and Orvad, 1993; Straub, 1994); and (2) the effect of organizational culture on IT (Burkhardt, 1994; Cooper, 1994; Robey, Gupta, and Rodriguez-Diaz, 1992). This paper deals with only national culture. Ein-Dor et al. (1993), Palvia (1998), and Gallupe and Tan (1999) have suggested that national culture is an important factor in global information management. As noted briefly above, several empirical works have studied the influence of culture on IT adoption. Straub (1994), in a trend-setting study, discussed the impact of culture on telephone, e-mail, and fax use in the U.S and Japan. His observation was that telephone use was very similar in both nations. However, fax and e-mail use varied distinctly, with Japan being a heavy user of fax and the U.S. making more use of e-mails. Straub ascribed these differences to several things including a combination of Hofstede’s cultural dimension of uncertainty avoidance and the pictorial characteristic of the Japanese written language. The high level of uncertainty and ambiguity avoidance in Japanese culture combined with the ambiguity that can arise when representing Japanese characters by the letters used in e-mail communication prevented extensive use of e-mail. Fax, on the other hand, allowed Japanese characters to be used, thus reducing uncertainty and ambiguity. Hill et al. (1998) and Straub et al., (2001) investigated the impact of beliefs and values about IT transfer in Arab countries and found that Arab cultural beliefs are a strong predictor of resistance to IT adoption. Hasan and Ditsa (1999) studied IT adoption in West Africa, the Middle East, and Australia and observed that culture was an important factor in differences in the impact of IT in those countries. Phan and Oddou (2002) similarly tried to explain IT adoption in Vietnam in terms of culture. Png et al. (2001) observed that nations with high uncertainty avoidance would be less likely to adopt technologies such as frame-relay.

The Technology Adoption Model (TAM) (Davis, 1989) provides an important theoretical explanation of IT adoption. Several authors have investigated the impact of culture on TAM. TAM postulates that beliefs such as perceived ease of use and perceived usefulness of the technology strongly influence attitudes toward a new technology. These attitudes in turn influence intentions, and ultimately, one’s behavior toward using IT. Many empirical studies about TAM have shown that it has reasonable explanatory power explaining about 40% of the variance in IT use (Taylor and Todd, 1995; Davis, 1993). Straub, Keil & Brenner (1997) tested TAM across three nations: Japan, Switzerland, and the U.S. Rose and Straub (1998) applied TAM to the Arabic World. The fit of TAM in Japan was insignificant whereas it was found to be a good explanation for IT use in Arab nations and in Switzerland. These differences in fit have been ascribed to the influence of national culture. Mao and Palvia (2002) also studied the impact of culture on TAM in China and observed that normative beliefs and subjective norms are influenced by some national cultural characteristics.

In general, these and other studies of culture and IT adoption suggest the relevance of Hofstede’s work for IT adoption (Ford et al., 2003; Harvey, 1997; Kambayashi, 2002; Krumbholz et al., 2000; Mejias et al., 1997; Straub, 1994; Watson, Teck and Raman, 1994). These studies have examined the implications of national culture for specific technologies such as GDSS, E-mail, and Fax, however the analyses have typically been restricted to two or three nations in any one study.

[pic]

Figure 1. Antecedents in IT adoption.

A simplified national-level model of IT adoption that emphasizes the variables included in the present study is shown in Figure 1. We propose that three types of indicators - cultural, institutional, and economic - contribute to IT adoption. Cultural indicators could be Hofstede’s, Inglehart’s, or other suitable dimensions. Institutional indicators measure a quality of some institution category such as regulatory burden, corruption level, or legal system characteristics. Economic indicators include variables like GDP per capita, inflation, and income inequality. Technology transfer literature also shows that speed of a technology adoption in a nation depends on income per capita (economic), human capital (economic/institutional), openness (institutional) , type of government (institutional), among other things (Caselli and Coleman, 2001; Comin and Hobjins, 2004; Lee, 2000). This study focuses primarily on the importance of national culture and, in particular, its influence on IT adoption while controlling some other institutional indicators that prior research suggests are most necessary to control in order to identify the effects of culture. The present national level analysis predicting IT adoption from Hofstede’s culture dimensions fits within Kwon and Zmud’s macro-level category of IT adoption studies (Kwon and Zmud, 1987; Bagchi, 2001). In this sort of research, the adoption and diffusion of an IT depend on factors related to internal and external environments of an organization including individual, structural, technological, task-related, and external environmental factors (Kwon and Zmud, 1987). Factors such as job tenure, cosmopolitanism, education, and role involvement are examples of individual factors. Examples of structural factors include specialization, centralization, formalization, and informal network. The technological factors are compatibility, relative advantage, and complexity (Rogers, 1983). Task-related factors are task uncertainty, autonomy, responsibility, variety, identity, and feedback. Environmental factors include heterogeneity, uncertainty, competition, concentration/ dispersion, and inter-organizational dependence.

[pic]

Figure 2. The extended version of Kwon and Zmud’s framework.

Kwon and Zmud (1987) argued that an IT is a formal, deliberately planned technological innovation that is introduced into an organization in response to the organization’s perceived needs. These needs span a broad range of individual and organizational motives. However, organizations are not homogenous throughout their global operations and do not exist in isolation. An organization may have different branches in many nations and workers from different nations. Even an organization with a single-nation focus has a destiny dependent at least partly on its nation's particular characteristics. Therefore, national-level characteristics can form a distinct set of separate motives and can play a significant role in the adoption and diffusion of an IT. Figure 2 shows the extended version of this framework that includes the national level variables that we will have in mind in developing hypotheses.

HypothesES: cultural dimensions, NATIONAL WEALTH, AND IT ADOPTION

We propose that each of Hofstede’s dimensions will predict national cultural differences in IT adoption. Nations differ greatly in each of the four dimensions suggested by Hofstede (2001). Next, we describe these four dimensions and provide rationales for their inclusion in the study. When making predictions about the likely implications culture has for technology use, it is important to avoid an overly deterministic view of either technology or culture. That is, while communication technologies impose some use limitations (e.g., the operation of cell phones depends on having a strong enough signal in a given locale), there is considerable cultural flexibility in how technologies are used. Similarly, while cultures can promote, resist, or shape technology use, they do not wholly determine technology use. Nickles (2003), for example, provides a persuasive historical analysis that suggests that characteristics of the telegraph such as its cost and speed tended to centralize and bureaucratize world diplomacy. However, national cultures predisposed to bureaucracy like that of 19th century Germany were more prone to accept this bureaucratic tendency while others like 19th century France found ways to avoid its constraints. In applying culture to organizations, Hofstede (2001) is careful to distinguish cultural values from organizational practices. The same practice can be used within many cultures. From Hofstede’s perspective the use of technology is much the same as the use of any social practice. While we will predict ways in which culture is likely to influence technology adoption, it is important to recognize that the predictions we make are based on the balance of evidence about present culture and technology. In many instances, however, the current literature provides reasons to expect positive and negative relationships between a culture dimension and an IT adoption. In such ambiguous cases we propose the most likely relationships based on the existing literature, but in so doing we recognize limitations in the literature that allow possible relationships opposite from those that we hypothesize.

In forming hypotheses, we will be relying on certain theoretically significant characteristics common to the ITs that have been most broadly adopted since the early 20th century that distinguish them as a set from prior communication media. One characteristic they share is that they are substantially higher in convenience and lower in time needed to engage in an interaction than are face-to-face conversations. Current communication ITs are also much quicker than surface mail and much less expensive than telegraphs, the two earlier communication technologies that they replaced to varying degrees (Nickles, 2003). Beginning with the telephone these sorts of advantages made communication technologies readily accessible to a large number of people. In so doing, these characteristics have provided an opportunity for organizations to democratize and decentralize. Nevertheless, a society with a predilection for central control can continue to screen, censor, or otherwise restrict access to the people in order to preserve the legitimacy of hierarchy and centralization.

Individualism and Collectivism (IC). Individualism refers to a loosely coupled social network where people take care of themselves and move readily from one social group to another. In contrast, collectivism refers to a tightly coupled social network where long lasting patterns of interdependence are recognized and a strong sense of group identity is present. People in collectivist societies tend to be linked to fewer, stronger, and longer lasting groups whose functions overlap between work, family, and friends. Not only do the major types of groups such as work, family, and friend to which individuals are linked tend to be more distinct from one another in individualistic societies, but the strength of membership in each also changes more easily (Triandis, 1995). ITs have provided a means for the complex, changing patterns of interdependence in individualistic societies to be managed. IT is commonly used to promote the strengths and overcome the limitations of these characteristics of individualistic societies. It does so by allowing people to work more independently from one another in the sense that they have an increased option to maintain greater physical distance and schedule their activities to meet the needs of the various groups to which they belong without concern for the location of others. ITs can certainly be used in collectivist societies as well to promote continuing contact between members of the more comprehensive, stable groups that characterize collectivism. Modern systems such as Group Support Systems (GSS) are better suited for adoption in collectivistic societies (Hasan and Ditsa, 1999). However, the literature noted above that compares IT use in individualistic and collectivistic societies suggests that the opportunity for face-to-face interaction typically will be greater and the necessity of ITs for maintaining relationships typically will be lower in collectivistic than in individualistic societies given typical differences in the way groups function and how interdependence is handled in each. So, IT adoption in individualistic nations is likely to occur more quickly than in collectivistic nations.

Table 1. Characteristics of low and high individualistic societies.

|Low Individualism* |High Individualism* |

|Example nations: Uruguay, Mexico, Pakistan, Peru, Philippines |Example nations: Australia, Canada, Denmark, Finland, France, |

| |India, U.S., U.K. |

|Imposes a limit on possibilities of transferring technologies |Adopts technologies developed in Western societies |

|Group decisions are better; in-group mentality |Individual decisions are better; groups matter less |

|Less economic development, less modern industry |More economic development, more modern industry |

|Use fewer home computers or telephone answering machines; social |More likely to own a home computer or telephone answering |

|network is main source of information |machine; media is main source of information |

|Science and technology treated as mystery; public uninformed |Science and technology treated as matter of fact; public informed|

|about technological facts |about technological facts |

* For this study, an IC index value is considered high if it is above the mean value of all nations (mean=51); otherwise, it is considered low. Source Hofstede (2001).

In addition, nations with high individualism also are more inclined to adopt technologies developed in Western societies (such as IT products) and tend to have more home computers or telephones (Table 1) than do less individualistic nations. It is also reasonable to assume that people in nations with high individualism have more purchasing power and are more aware of technological developments. This may help in high technology adoption (Table 1). The first hypothesis can now be derived.

Hypothesis 1: Information technology adoption is greater in nations with high individualism.

Power Distance (PD). This measure refers to the extent to which a society accepts unequal power distribution between individuals, institutions, and firms. A large-power distance society means that people in that culture more readily accept wider differences in power compared to small-power difference cultures (Table 2). For example, in some large-power distance cultures, management decisions will mostly be centralized and hierarchical. In small-power distance cultures, management decision will be decentralized and more participative. Since technology access can be considered a symbol of power and used to maintain centralized control, it might be expected to be in high demand in high-power distance societies. This was very much the case with the telegraph (Nickles, 2003). However, IT innovations since the telegraph tend to first rest in the hands of recently educated, junior, lower level employees rather than senior managers. Consequently, we expect that small power distance societies will find it more generally desirable and easier to implement such technologies than will large power distance societies.

Table 2. Characteristics of low and high power distance societies.

|High PD* |Low PD* |

|Example nations: Brazil, France, India, Uruguay, Mexico, Peru, |Example nations: Austria, Australia, Canada, Denmark, Finland, |

|Philippines, Venezuela |U.S., U.K. |

|Most should be dependent |All should be interdependent |

|Centralized decision structure |Decentralized decision structure |

|Autocratic decision-making by managers |Decision-making shared by managers and workers |

|Less need for technology |More need for technology |

|More static society |Technological momentum of change |

*:For this study, a PD index value is considered high if it is above the mean value of all nations (mean=39); otherwise, it is considered low. Source Hofstede (2001).

In addition, since nations with low power distance values have more innovative technologies and have social structures more likely to support the technological momentum of change (Table 2), we expect that low PD societies will more readily adopt information technologies.

Hypothesis 2: Information technology adoption is greater in nations with low power distance.

Uncertainty Avoidance (UA). This cultural attribute reflects the extent to which a society senses and tries to avoid threats from uncertain and ambiguous situations. Firms in high-uncertainty nations may have more rigid rules and exhibit less tolerance for new and uncommon ideas and behaviors (Table 3). This dimension is related to need for security, dependence on experts, and information application. The societal norm in countries with low UA scores includes a tolerance for uncertainty (Hofstede, 2001). There are some reasons for predicting that high uncertainty avoidance will be associated with high IT adoption and others for predicting the reverse relationship. Computers and personal computers, in particular, can routinize jobs, thus reducing uncertainty in a way that fits well with societies high in uncertainty avoidance. Fax machines and e-mail can provide documentation in a way that also suits high uncertainty avoidance. Telecom products such as telephones, fax machines, and cell phones can reduce uncertainties by promoting communication to resolve questions quickly, although they do so in a way that does not leave a paper record (Straub, 1994). However, any IT adoption - especially adoption of very new technologies - requires adopters to accept the sorts of uncertainty associated with any sort of change (Png et al., 2001). IT in general is risky and has flourished in low UA nations (Hasan and Ditsa, 1999). For our present analysis focusing on the speed of new technologies adoption, the uncertainty associated with innovation is a particularly important consideration. Since low uncertainty avoiding societies tend to have a high rate of innovation and are more trustful and can accept the uncertainties associated with trying a new technology (Table 3), we postulate that low UA societies will adopt more information technologies.

Table 3. Characteristics of low and high uncertainty avoiding societies.

|High UA Index* |Low UA Index* |

|Example nations: Austria, Brazil, France, Japan, Uruguay, Mexico,|Example nations: Australia, Canada, Denmark, Finland, India, |

|Peru, Venezuela |Philippines, U.S., U.K. |

|Less interpersonal trust |Most people can be trusted |

|Only known risks are taken |Willingness to unknown risks |

|More written rules |Fewer written rules |

|Less use of Internet and teletext |More use of Internet and teletext |

|Low rates of innovations; innovations resisted |High rates of innovation; innovations welcomed |

* For this study, an UA index value is considered high if it is above the mean value of all nations (mean=64); otherwise, it is considered low. Source Hofstede (2001).

Hypothesis 3: Information technology adoption is lesser in nations with high uncertainty avoidance.

Masculinity and Femininity (MF). Masculine cultures are more assertive and value achievement and materialism. To the extent that a culture is feminine, the values of human relationships and concern for others are high. Assertiveness, performance, success, and competition are key factors in a masculine culture; quality of life, service, and care for the weak are the hallmarks of a feminine culture (Table 4). A high MF value means that a country is higher on the masculine polarity and a low MF value means that a country is higher on the feminine polarity. Like uncertainty avoidance, there are some reasons for expecting greater IT adoption in masculine societies and other reasons for expecting greater IT adoption in feminine societies. ITs promote more cooperation at work and a better quality of life at home, and these values are espoused in nations with a low masculinity index. To the extent that IT adoption is for personal rather than work use, we anticipate that it will be greater in feminine than in masculine cultures. On the other hand, business IT adoptions are typically driven by interests in being efficient and competitive, so masculine societies may adopt ITs more quickly than feminine societies. An important question in determining whether business IT adoption will be more extensive in masculine or feminine societies is how readily most businesses recognize that the advantages ITs have for promoting the social side of business collaboration outweigh the obvious costs of ITs. Our sense is that IT costs will weigh more heavily in the minds of business adopters in masculine societies, whereas the advantages that ITs have for promoting cooperation will carry more weight in feminine societies. As Hasan and Ditsa note, feminine cultures feel more comfortable with modern user-friendly ITs whereas masculine societies may adopt an IT for its own sake (Hasan and Ditsa, 1999). On balance, we expect that the quality of life implications that ITs have for personal use and the cooperative basis that ITs have for promoting business use will mean that societies scoring toward the feminine pole of the masculine/feminine dimension will adopt more information technologies than will those toward the masculine pole.

Hypothesis 4: Information technology adoption is greater in culturally feminine rather than masculine nations.

Table 4. Characteristics of low and high masculine societies.

|High MF* |Low MF* |

|Example nations: Austria, Australia, Canada, India, Japan, |Example nations: Denmark, Finland, France, Uruguay, Peru, Spain, |

|Mexico, Phillippines, Venezuela, U.S., U.K. |South Korea, Sweden |

|Live in order to work |Work in order to live |

|Purchase for showing off |Purchase for use |

|Smaller share of women in professional and technical jobs |Larger share of women in professional and technical jobs |

|High percentage of poor and illiterate |Low percentage of poor and illiterate |

|Only women are supposed to be concerned with quality of life |All are concerned with quality of life |

*:For this study, a MF index value is considered high if it is above the mean value of all nations (mean=39); otherwise, it is considered low. Source Hofstede (2001).

In addition to cultural variables, other societal characteristics may affect IT adoption. First, since wealthy nations can adopt an IT faster than poorer nations, economic indicators such as GDP per capita should affect IT adoption. A nation’s cultural heterogeneity may also influence IT adoption. The well being of each individual in a nation may require people to sort themselves into ethnic subgroups made up of culturally compatible people who are mutually supportive, even if economic considerations make it in their best interest to be part of a nation comprised of several cultural groups (Petersen, 1997; Masters and McMillan, 1999). Obvious examples include Canada and Switzerland. The proportion of people who are outside the dominant group of a nation may have a culture of their own, which could be different from the dominant group’s culture. The IT adoption of a nation may be influenced by its heterogeneity (captured by the ethno-linguistic fractionalization (ELF) index), as different groups may take different approaches to IT adoption. Also, ethnic heterogeneity has been shown to impact the level and rate of national economic growth (Easterly and Levine, 1997; Rodrik, 1998; Mauro, 1995). Thus, ethnic heterogeneity is an important cultural consideration.

Another important parameter to consider is the Gini index (or income inequality index) of a nation. Income inequality existed before many IT adoptions. According to some researchers, for the U.S., most of the growth in inequality happened between 1979 and 1984 (Card et al., 2002). The less wealthy people and smaller businesses in many nations with large inequality may face serious obstacles buying and adopting ITs. That is, a modest GDP especially limits the opportunity for many people to adopt ITs if income is concentrated in the hands of a relatively small group of citizens.

In summary, we need to control overall economic well being as well as economic and ethnic heterogeneity. We postulate that national culture will have significant effects on IT adoption, even after controlling the economic and heterogeneity factors of a nation.

Hypothesis 5: Controlling for economic and heterogeneity indicators, culture will play a significant role in IT adoptions.

Information technologies investigated

The six information technologies studied include the most extensively adopted communication technologies of the past 100 years -- personal computers (PCs), telephones, cellular phones, pagers, fax machines, the Internet. The data on various technologies were collected from multiple sources and obtained after a comprehensive effort to ensure that all possible secondary data sources were examined. The data obtained covers all of the six products for the relevant time period (1989-1998). Among these technologies, the telephone has been around for more than 100 years in some nations, whereas some technologies such as the Internet are comparatively new (de Sola Pool, 1981). Because adoption patterns repeat themselves over time for new technologies, we expect that the results based on the data available are robust and generalizable to other information technologies.

Table 5 provides an example of adoption data for a few of the technologies targeted in this investigation, GDP per capita, four Hofstede cultural dimension values, and other predictors for five nations -- the U.S., India, Germany, France, and Brazil. The IT data are in units per 1,000 with Internet measured by the number of Internet hosts per 1,000 in 1995. GDP is measured in GDP per capita (constant 1995 US$). The data shows considerable variability even for only five nations. Most of these nations were European or North American or from the Organization for Economic Cooperation and Development (OECD) group with a few third-world nations. This combination of nations resulted from data availability. These nations are:

Australia, Austria, Brazil, Canada, Chile, Denmark, Finland, France, Germany, India, Ireland, Italy, Japan, South Korea, Mexico, Netherlands, New Zealand, Norway, Pakistan, Peru, Philippines, Portugal, South Africa, Spain, Sweden, Switzerland, Turkey, the U.K., Uruguay, the U.S., and Venezuela.

Table 5. Sample data for five nations.

|Nations/ |PC* |Tele |

|1995 | |phone* |

|Cultural Dimensions |Hofstede (2001) |Four cultural dimensions: UA, PD, IC and MF. |

|Economic Condition |The World Bank database |Base GDP per capita per nation. |

|(GDP) | |Measured: 1989, 1992, 1995, and 1998. |

|ELF index (ELF) |Mauro (1995) |ELF quantifies ethno-linguistic fractionalization|

| | |in countries and represents the probability that |

| | |two individuals drawn randomly will be from |

| | |different groups. |

|Gini Index |World Bank |The level of income inequality of a nation. |

|(INEQ) | | |

|IT adoption data (IT)|The World Bank database; The ITU database |The variable value is the IT adopted per 1,000 |

| | |population. |

| | |IT denotes PC, the Internet, cell phone, |

| | |telephone, fax machine, and pager. |

| | |Measured: 1989, 1992, 1995, and 1998. |

Details about the derivation and nature of the measures of national cultural dimensions we use are provided in Hofstede (2001). Two measures are used to represent cultural and economic heterogeneity in the nations studied. Ethnic heterogeneity in a nation’s population is captured by the ELF index. The ELF index quantifies the heterogeneity of countries based on the number of the nation’s ethnic and linguistic groups. It represents the probability that two individuals drawn randomly from a given nation will be from different groups such that a low value indicates less fragmentation. Although this index was made available to English speaking audiences by Mauro (1995) from where we obtained national scores, the original data source is the department of Geodesy and Cartography in the USSR that gathered the data (Atlas Narodov Mira, (Moscow, 1964)). Similarly, the Gini index captures the level of income inequality in a given nation such that a low value of Gini index denotes more income inequality. It is expected that IT adoption will be negatively correlated with both the Gini index and the ELF index. The Gini Index data and GDP data are from the World Bank.

The criterion data representing the adoption of ITs are collected from World Bank and ITU databases. Data for six ITs were collected from 1989 to 1998 for four years (1989, 1992, 1995, 1998). Due to limitations in data availability, 1989 was excluded in analyses for the Internet, and 1998 was excluded for analyses about the fax machine. The subset of nations that includes all indicators ranges from 29 to 31 nations for each IT, hence regressions testing the predicted relationships are based on sample sizes from 29 to 31 nations.

The model of our study can be expressed as:

ITadopi, = (1 + (1 ELFi +(2 GDPi +(3 INEQi + (4-7CULji + (i …(1)

where

ITadop i, = Average no. of IT product installation in nation i during period 1989-

98 (per 1,000)

CUL ji, = Cultural indicator j of nation i (Hofstede Index: j=PD, UA, IC, MF)

GDP i, = Average of GDP per capita for nation i during period 1989-98 (per 1,000)

INEQi = Average Gini Index for nation i

ELFi = the ELF variable for nation i

(1 = intercept term

(11…1m = the coefficient for hypothesized variables

(i = error term

Data were smoothed by averaging all data for each nation from the four time periods (1989, 1992, 1995, 1998) that were studied. Ordinary least square (OLS) multiple regressions in stages was then used for each IT.

Results

Results are presented below in three stages. We first discuss the metric characteristics of the data, then zero-order correlations among the variables, and finally the results of multiple regressions. The results are shown in Tables 7-10.

Although the number of nations studied here is large for comparative research, it is still small enough that we took special care to determine whether the characteristics of the data made regression in an appropriate analysis method. The structure of the data is found to be appropriate for multiple regressions. The tests described in Table 7 indicate that the residuals of the IT regression models are normally distributed. The data for each IT were also tested for heteroscedasticity using White’s test. The results showed little heteroscedasticity. Serial correlation problems were generally absent with Durbin Watson statistics above or near 2.0 except in the case of pagers. Consequently, the regression for pagers was done using the lag of the dependent variable in the regression. Multicollinearity among the independent variables was negligible (VIF values for the 5 independent variables ranged from 1.6 to 3.2, much less than 10, a frequently suggested cut-off number for multicollinearity). Thus the regression analyses were largely free from statistical problems (Greene, 2000).

Table 7 shows Pearson’s correlations between the predictor variables and adoption rates for the technologies studied. The relationships between the control variables, particularly GDP and income inequality, and the technology adoption measures are strong and statistically significant. The positive relationships of GDP and income inequality (in which a high value indicates less inequality) with technology adoption are consistent with the argument that economic factors affect adoption of technologies and that they need to be controlled before assessing the effects of culture. The ELF index has the expected significant relationship with IT adoption for only three technologies: cell phone, telephone, and fax machine.

The correlations of the culture dimensions with the technology adoption variables are in the directions predicted in the hypotheses (H1-H5) and most are significant. For PC, Internet, telephone, and pager adoption, the highest correlation is with the cultural index individualism-collectivism.

The regressions were done in two steps. In the first step, we entered GDP, ELF, and INEQ as control variables. The culture dimensions were added as a second step. The incremental variance explained by these culture dimensions is indicated in Table 8. When controlling for ELF, INEQ, and GDP, the variance explained by cultural variables was still significant for all six ITs. It appears that culture has a significant relationship to technology adoption that in many cases match the hypothesized relationship. The contributions of Hofstede’s cultural dimensions to R2 values varying from .1% to 20% are statistically significant in foremost ITs. In the case of Internet adoption[1], the overall R2 was low although marginally significant at the p ................
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