The impact of cultural differences on te

This article appeared in a journal published by Elsevier The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited In most cases authors are permitted to post their version of the article (e.g in Word or Tex form) to their personal website or institutional repository Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit: http://www.elsevier.com/copyright Author's personal copy Journal of World Business 48 (2013) 20–29 Contents lists available at SciVerse ScienceDirect Journal of World Business journal homepage: www.elsevier.com/locate/jwb The impact of cultural differences on technology adoption Sang-Gun Lee a, Silvana Trimi b,*, Changsoo Kim c a Department of Business Administration, Sogang Business School, Sogang University, Seoul, South Korea Department of Management, College of Business Administration, University of Nebraska, Lincoln, NE 68588-0491, USA c Abiz Management Research Institute, College of Business Administration, Ajou University, Suwon, South Korea b A R T I C L E I N F O A B S T R A C T Keywords: Cross-cultural research Cultural dimensions Diffusion models National culture Technology adoption This study examines the impact of Type I and Type II cultural differences on mobile phone adoption patterns We use Hofstede’s cultural dimensions to examine cultural differences of two countries (Type I: the U.S.; Type II: S Korea) and employ the Bass diffusion model to delineate innovation and imitation effects on mobile phone adoption The results show that in Type I culture innovation factor has a significantly higher level of effect on adoption than it does in Type II culture; and in Type II culture imitation factor has a higher degree of effect on adoption than it does in Type I culture These findings imply that in individualistic cultures, people tend to seek information on their own from direct and formal sources, whereas in collectivistic cultures, people rely more on subjective evaluation of an innovation, conveyed from other-like-minded individuals who already have adopted the innovation ß 2012 Elsevier Inc All rights reserved Introduction During the last two decades, Information and Communication Technology (ICT) has seen dramatic advances and diffusion Many ICT products or services have become necessities of everyday life The effectiveness and efficiency of ICT deployment and use are influenced by national (Al-Ghatani, 2003; Erumban & de Jong, 2006; Straub, 1994; Taras, Steel, & Kirkman, 2011, 2012) and organizational cultures (Cao & Everard, 2008; Lim, Yeow, & Yuen, 2010; Schiller & Cui, 2010) National cultures even play significant roles in the development of national information infrastructure (Apfelthaler, Muller, & Rehder, 2002; Dimitratos, Petrou, Plakoyiannaki, & Johnson, 2011; Garfield & Watson, 1998; Ralston, Hallinger, Egri, & Naothinsuhk, 2005) Cultures at the national level exert a subtle, yet powerful, influence on people and organizations (Leidner & Kayworth, 2006) Systems quality and culture significantly affect trust in the ICT artifact and therefore in their adoption (Vance, Elie-dit-cosaque, & Straub, 2008) Prior research in the effect of culture on ICT diffusion, as shown in Tables and 2, mostly used survey research, case studies or field investigation involving limited numbers of subjects To truly capture the impact of national culture on technology adoption, a study should include the entire population of a country In this study, we examined the impact of national culture on mobile phone adoption by including the entire population of mobile * Corresponding author Tel.: +82 705 7987; fax: +82 705 8519 E-mail addresses: slee1028@sogang.ac.kr (S.-G Lee), strimi@unlnotes.unl.edu (S Trimi), changsookim321@gmail.com (C Kim) 1090-9516/$ – see front matter ß 2012 Elsevier Inc All rights reserved http://dx.doi.org/10.1016/j.jwb.2012.06.003 phone subscribers of each country studied We also use and adopt the most appropriate research models in this study: the Bass model for the product diffusion/adoption, and the Hofstede’s model for the national cultural dimensions 1.1 The Bass model The Bass model (1969, 2004) has been employed by numerous studies to analyze sales and the diffusion process of a product In this study, the Bass model is used for the verified official timeseries data (from 1985 to 2008) of the number of all mobile phone adopters (see Table 3) The Bass model includes the innovation effect, which comes from the adopter’s self-perception and the product’s utility; and the imitation effect, which results in from interactions between early adopters and potential adopters of a product In the cumulative curve of adoption, the innovation effect shows a convex shape, while the imitation effect has a concave curve, as shown in Fig 1.2 The Hofstede model Hofstede’s (1991) cultural dimensions model classifies national cultures into four types Griffith, Hu, and Ryans (2000), based on the Hofstede’s dimensions, suggested two extreme cultural types for study: Type I (individualistic, weak uncertainty avoidance, and low long-term orientation) and Type II (collectivistic, strong uncertainty avoidance, and high long-term orientation) For our research, these two extreme contrasting culture types are chosen to investigate the effect of national cultures on the adoption of mobile phones We selected two countries, one for each of the two Author's personal copy S.-G Lee et al / Journal of World Business 48 (2013) 20–29 21 Table Hofstede’s cultural values Cultural value Definition Individualism/collectivism (IC) Degree to which the individual emphasizes his/her own needs as opposed to the group needs and prefer to act as an individual rather than as a member of a group Degree to which large differentials of power and inequality are accepted as normal by the individual Power distance will condition the extent to which the employee accepts that his/her superiors have more power Uncertainty avoidance is the level of risk accepted by the individual, which can be gleaned by his/her emphasis on rule obedience, ritual behavior and labor mobility This dimension examines the extent to which one feels threatened by ambiguous situations The degree to which gender inequalities are espoused by an individual Individuals who espouse masculine values emphasize work goals such as earnings, advancement, competitiveness, performance and assertiveness On the other hand, individuals who espouse feminine values tend to emphasize personal goals such as a friendly atmosphere, comfortable work environment, quality of life and warm personal relationships The degree to which society does or does not embrace long-term devotion to traditional values Power distance (PD) Uncertainty avoidance (UA) Gender role orientation (MF) Long-term orientation (LTO) Table Culture, ICT adoption and diffusion at the national level Researcher Independent variables Dependant variables Methodology and measure of national culture Published journal Straub (1994) Perceived usefulness, ease of use Media use (and fax), national culture (UA) Information Systems Research Straub, Keil, and Brenner (1997) Perceived usefulness, ease of use Galliers et al (1998) National culture Information systems use, national culture (IC, UA, PD, MF) Rate of technology adoption Multi-method study (field interviews, survey, policy capturing) comparing U.S and Japanese respondents, Hofstede’s cultural indices Survey of airline employees from U.S., Japan and Switzerland, Hofstede’s culture indices Garfield and Watson (1998) Griffith (1998) National culture (UA, PD) Jarvanpaa and Leidner (1998) Resource-based competencies Hasan and Ditsa (1999) National culture (UA, PD, IC, MF) Al-Ghatani (2003) Perceived attributes of technology Rate of technology adoption, national culture Thatcher et al (2003) Personal innovativeness with information technology Chui and Kwok (2008) National culture (UA, IC, PD, MF), qualitative and quantitative work overload National culture dimensions Linghui and Koveos (2008) Fischer and Mansell (2009) GDP, national culture (UA, MF) National culture (IC, PD), economic variables National culture (PD) Structure of national information Infrastructure Satisfaction with Group Support Systems (GSS) Information services industry diffusion national culture (IC, UA) Technology transfer outcome Life insurance consumption National culture (IC, LTO, PD) Types of organizational commitment Information and Management Single site case study, culture not explicitly measured Descriptive case study of government archives across countries, Hofstede’s cultural indices Laboratory experiment comparing U.S and Bulgarian student GSS teams, Hofstede’s culture indices Single site case study (semi-structured interviews) of Mexican firm, Hofstede’s culture indices Interpretive field study of 10 organizations in Middle East, Africa and Australia, Hofstede’s culture indices Survey of 1200 Saudi managers and government officials, culture not explicitly measured Survey of U.S college students, cultural indices by Hofstede Information Technology for Development Journal of Strategic Information Systems Interacting with Computers Survey research with data from 1976 to 2001 across 41 countries, cultural indices by Hofstede Survey research, cultural indices by Hofstede Journal of International Business Studies Survey research, cultural indices by Hofstede Information Systems Research Journal of Global Information Management Information Technology for Development Journal of Computer Information Systems Journal of International Business Studies Journal of International Business Studies Adapted from Leidner and Kayworth (2006) Table The Numbers of mobile phone subscribers in the U.S and South Korea Year U.S S Korea Year U.S S Korea 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 340,213 681,825 1,230,855 2,069,441 3,508,944 5,283,055 7,557,148 11,032,753 16,009,461 24,134,421 33,758,661 44,042,992 4685 7093 10,265 20,353 39,718 80,005 166,198 271,868 471,784 960,258 1,641,293 3,180,989 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 55,312,293 69,209,321 86,047,003 109,478,031 128,500,000 141,800,000 160,637,000 184,819,000 213,000,000 248,180,000 263,000,000 270,500,000 6,828,169 13,982,919 23,442,724 26,816,398 29,045,596 32,342,493 33,591,758 36,586,052 38,342,323 40,197,115 43,497,541 45,606,984 U.S data from ITU World Telecommunication; Korean data from the Korean Communications Commission Author's personal copy S.-G Lee et al / Journal of World Business 48 (2013) 20–29 22 ance and gender role orientation (Hofstede & Hofstede, 2005) The long-term orientation was later added as a fifth dimension (Hofstede & Bond, 1988) Table summarizes Hofstede’s cultural values and their definitions Hofstede’s cultural dimensions have been employed by many IS studies These studies suggest that national culture has significant relationships with the structure of national information infrastructure, the rate of technology adoption, technology transfer, and personal innovativeness (Galliers, Madon, & Rashid, 1998; Garfield & Watson, 1998; Hasan & Ditsa, 1999; Thatcher, Srite, Stepina, & Liu, 2003) A large number of subjects are required to measure a nation’s cultural characteristics for an unbiased study As shown in Table 2, most of these studies used specific types of subjects, such as college students, airline employees and government officials Fig Innovation and imitation effects on technology adoption Adapted from Mahajan, Muller, and Wind (2000) cultural types: the U.S as a representative of Type I culture and South Korea for Type II culture In this research, we hypothesize and test whether in Type I culture (i.e., the U.S.) the innovation effect is higher and imitation effect lower on adopting ICT than in Type II culture The rationale is that the Type I characteristics of strong individualism, weak uncertainty avoidance, and low longterm orientation will reinforce personal and product innovativeness and will moderate the imitation effect in technology adoption Conversely, in Type II culture (i.e., South Korea) imitation will have a higher degree of effect and innovation a lower effect in ICT adoption than countries of Type I culture because collectivism, strong uncertainty avoidance, and high long-term orientation intensify the imitation behavior of adopters and moderate personal innovativeness In sum, our research questions are as follows: Q1 Is there a significant pattern in ICT adoption that can be explained by the diffusion model? Q2 If so, the cultural differences have any effect in the diffusion pattern? The paper is organized as follows: In the second section, we summarize Hofstede’s cultural dimensions and relevant prior research on the impact of cultural values on the diffusion of IT; The third section presents development of hypotheses based on relevant prior studies on cultural dimensions and technology adoption; The fourth section presents the study’s research methodology and data collection procedure; In the fifth section, the results of our analysis will be provided; The sixth section discusses the finding of the study; and finally the conclusion section includes implications, contributions, and limitations of the study Literature review 2.1 Culture and cultural dimensions Culture is conceptualized as shared symbols, norms and values in a social collectivity such as a country The most popular cultural theory that has been adopted in information systems (IS) research is Hofstede’s model Hofstede (1980) defined culture as ‘‘the collective programming of the mind which distinguishes the members of one human group from another.’’ Hofstede also developed an index model and proposed four widely cited dimensions of national culture: individualism/collectivism, power distance, uncertainty avoid- 2.2 Culture types based on cultural dimensions Griffith et al (2000) studied Type I and Type II cultures, according to three of the five aforementioned dimensions: individualism, power distance, and uncertainty avoidance Type I culture (e.g., the United States and Canada) has ‘‘individualisticsmall power distance-weak uncertainty avoidance’’ characteristics Type II culture (e.g., Chile and Mexico) involves ‘‘collectivistic– large power distance–strong uncertainty avoidance’’ characteristics This classification of cultural types has been examined and used by many studies Huff and Kelly (2003) investigated whether a firm’s national culture has an impact on its internal and external trust propensities Consistent with Griffith et al.’s (2000) study, the results showed that managers in Type I culture, the United States, managers demonstrated a higher level of external trust than did managers in Type II culture (Asia – China, Korea, Taiwan, etc.) Kim (2008) used the concept of cultural types to identify selfperception-based versus transference-based trust determinants in computer-mediated transactions In his study, the mean values of the self-perception-based trust determinants of the U.S sample (i.e., Type I) were higher than those of the Korean sample (i.e., Type II), whereas all the mean values of the transference-based trust determinants of the Type II culture were higher than those of Type I culture This result shows that self-perception-based determinants are more likely related to Type I culture than to Type II culture and that transference-based trust determinants are less likely related to Type I culture than Type II culture Consequently, Type I culture is a society that gives great consideration to individual perception while Type II culture accounts for a society that gives much weight to social perception 2.3 The diffusion of innovation and culture types The adoption and diffusion of new ideas or new products by a social system were thoroughly discussed by Rogers (1983, 1995, 2003) The Diffusion of Innovation Theory (DIT) suggests that the patterns of IT acceptance (termed adoption in this context) within a network of users are shaped via a process of communication and social influence, where later adopters are informed of the availability and utility of new IT innovations by earlier adopters (Rogers, 1995) The pattern of the cumulative adoption frequency of innovation over time forms an S-shaped curve This curve explains the behavior of adopters and is referred to as the diffusion model Although there are many variant types of diffusion models, the Bass model is perhaps the most popular Based on the timing of adoption, Bass classified the adopters as follows: (1) Innovators; (2) Early adopters; (3) Early majority; (4) Late majority; and (5) Laggards (Fig 2) Bass defined innovators as individuals who early Author's personal copy S.-G Lee et al / Journal of World Business 48 (2013) 20–29 23 the rate of diffusion at time t The S-shaped cumulative adoption pattern as well as the non-cumulative adoption pattern is shown in Fig Eq (1) is the sum of external (innovation) Eq (2) and internal (imitation) Eq (3) The external or Coleman’s (1966) model, Eq (2), assumes that the diffusion rate at time t is based on such innovation factors as usefulness and easy-of-use of the technology and therefore depends only on the number of potential adopters in the social system at time t (Fig 4) This assumption means that only limited communication exists between early adopters and potential adopters and that early adopters not significantly affect the decisions of potential adopters (Coleman, 1966) dNtị ẳ pẵm À Nðtފ dt Fig Adopter categories and the diffusion curve Adapted from Rogers (1995) on elect to adopt an innovation independently of the decisions of others in a social system While imitators are defined as the adopters whose timing of adoption is influenced by the pressure of their social systems; the pressure increases for later adopters as the number of previous adopters increases Bass (1969) developed a growth model to determine the timing of the initial purchases of new products The effects of innovation and imitation are suggested in his model The Bass model can be stated as: dNðtÞ ẳ ẵ p ỵ qNtị ỵ ẵm Ntị dt (1) where N(t) is the cumulative number of adopters at time t, m is the number of potential adopters of the innovation, p is a non-negative constant (usually as the coefficient of external influence or innovation), q is a non-negative constant (the coefficient of imitation), and dN(t)/dt is the first derivative of N(t) representing (2) The internal model, Eq (3), also referred to as the pure imitation diffusion model, posits that diffusion occurs through social contacts The primary function of communication among individuals is through the interaction between early adopters and future adopters via such imitation factors as subjective norm and word of mouth This model is highly useful when investigating the impact of early adopters’ experiences to determine late adopters behavior dNtị ẳ qNtịẵm Ntị dt (3) The innovation effect p (Eq (2)) comes from individual perception; while, imitation effect q (Eq (3)) comes from the social effects, influenced by cultural factors As we mentioned earlier, Type I culture describes a society that gives much weight to individual perception; in an individualistic cultural type, individuals look after their self-interests (Hofstede, 1980; Griffith et al., 2000) Type II culture describes a society that gives much consideration to social perception Members of a collectivist cultural type tend to share similar opinions and beliefs, working toward a feeling of harmonious interdependence (Griffith et al., 2000) Hence, high individualism (ID) reflects p value of the external model that assumes no communication; while, high collectivism Fig The Bass/Mansfield model (1969, 1961) Fig The Coleman model (1966) Author's personal copy 24 S.-G Lee et al / Journal of World Business 48 (2013) 20–29 Fig Cultural dimensions, culture types, and diffusion factors (CO) reflects q value of the internal model that assumes that diffusion is based on the enhanced interaction between early adopters and future adopters Additionally, uncertainty avoidance (UA) is also related to diffusion factors Cultures of weak UA type accept higher levels of risk and not attempt to control uncertainty Individuals are socialized to accept it (Hofstede, 1980; Inkeles & Levinson, 1969) Alternatively, cultures of strong UA type attempt to formulate ways of controlling future events, thus reducing uncertainty and risk (Hofstede, 1980; Inkeles & Levinson, 1969) Until risk acceptance has disappeared, members of a strong UA cultural society hesitate to accept a new technology This is similar to the Sshaped diffusion process According to theory of diffusion/ adoption, adopters hesitate to adopt technology for the period from the start point to the critical mass point (16% adoption) However, after superior performance of the technology has been confirmed by early adopters, the adoption rate increases rapidly Thus, we can firmly state that the imitation effect in a Type II culture is greater than in a Type I culture Finally, long-term orientation (LTO) also enhances the imitation effect A society that has a high LTO score emphasizes values such as persistence, building relationships, thrift, loyalty and trustworthiness Meanwhile, a society that has low LTO emphasizes values such as personal steadiness and stability China, Japan and South Korea represent countries that have high LTO scores In high-LTO cultures, traditions and commitments become impediments to change; however, once a change is socially accepted, the speed of change is extremely fast Consequently, a high LTO culture is related to a low innovation effect but a high imitation effect of diffusion We omitted power distance (PD) dimension because mobile phone is not a product that is purchased based on top-down decisions We summarize the relations of cultural dimensions, type of cultures, and factors of diffusion in Fig 5: in Type I culture innovation has a greater effect on adoption than it does in a Type II culture; in a Type II culture however, imitation has a greater effect on adoption than it does in a Type I culture 3.1 Types I culture (the U.S.) versus Type II culture (South Korea) The U.S and South Korea are with contrasting cultural dimensions: Korea’s Hofstede (1980) scores are nearly opposite to those of the United States across all five cultural dimensions The United States has higher individualism (ID: score = 91) and lower power distance (PD: score = 40), UA (score = 46) and LTO (score = 29) By contrast, South Korea has lower ID (score = 18) than those of the U.S and the world average, while it has higher PD (score = 60), UA (score = 85) and LTO (score = 75) Until 1997, the percentage of mobile phone adoption in the U.S was higher than that in South Korea (Fig 6) However, there has been an explosion in mobile phone adoption in South Korea since 1996 leading to a mobile phone adoption rate much greater than that of the U.S since 1998 We believe this rapid adoption rate demonstrates a strong imitation effect reflecting cultural characteristics of South Korea To validate our belief, we first examined whether or not there is a significant pattern of mobile phone adoption in both countries To this, our fitted model should reject the White-noise model The null hypothesis of this test is the White-noise model If the White-noise null hypothesis is rejected, we can make the assertion that there are innovation and imitation effects If the fitted model (the Bass model) rejects the White-noise model, then we could also suggest that our proposed Bass model can explain the cultural aspects of technology adoption in various nations The Bass model consists of the innovation (external) effect and the imitation (internal) Development of hypotheses In this section, we present the research concept used to analyze the adoption of mobile phones in two countries, the U.S and Korea We chose these two countries as our study samples because they have similarities in the maturity of their ICT adoption and e-commerce, but each represents a very distinct culture type Fig Mobile phone adoption in the U.S and South Korea (1985–2008) Author's personal copy S.-G Lee et al / Journal of World Business 48 (2013) 20–29 Innovation Factors Usefulness 25 Individualism (91) Ease of Use Adoption Imitation Factors Subjective Norm Word of Mouth Weak Uncertainty Avoidance (46 ) Short-term Orientation (29) Type I: Individualism – Weak Uncertainty Avoidance – Short Term Orientation Fig Adoption process of Type I (the U.S.) culture effect It would be difficult to explain the different adoption patterns in the two countries via the effects of innovation and imitation unless the alternative model (the Bass nonlinear estimation model) is accepted Hence, our first hypothesis is as follows H1 The adoption rate of mobile phone is significantly related to types of cultures To further investigate the effect of culture, we developed two other hypotheses We suggest two types of diffusion of mobile phone adoption based on the types of culture In a Type I culture (the U.S.), cultural values such as individualism, weak uncertainty avoidance, and short-term orientation could enhance the innovation effect and moderate the imitation effect in technology adoption (Fig 7) In a Type II culture (South Korea) cultural values such as collectivism, strong uncertainty avoidance, and long-term orientation could enhance the imitation effect and moderate the innovation effect of adoption (Fig 8) The numbers in parentheses are Hofstede’s scores Hence, the following hypotheses are developed Innovation Factors Usefulness H2a The innovation effect on the mobile phone adoption in the U.S is greater than in S Korea H2b The imitation effect on the mobile phone adoption is greater in S Korea than in the U.S Research method and data collection 4.1 Non-linear Bass diffusion model The Bass model is one of several types of diffusion models The linear approach of the Bass diffusion model has certain econometric limitations, such as multicollinearity and nonavailability of standard errors for the crucial parameters – p (coefficient of external influence), q (coefficient of internal influence), and m (number of eventual adopters) (Mahajan, Muller, & Bass, 1990) To overcome such limitations, we adopted the nonlinear least squares (NLS) approach (Venkatraman, Loh, & Koh, 1994) To assess innovation and imitation influences over time, this study utilized a time-series analysis using SAS software We set constant m as the approximate real populations (potential Collectivism (low individualism) (18) Ease of Use Adoption Imitation Factors Subjective Norm Word of Mouth Strong Uncertainty Avoidance (85) Long-term Orientation (75) Type II: Collectivism – Strong Uncertainty Avoidance – Long Term Orientation Fig Adoption process of Type II (South Korea) culture Author's personal copy 26 S.-G Lee et al / Journal of World Business 48 (2013) 20–29 adopters) of the U.S and South Korea, based on Bass’ theory (1969, 2004) The gradient descent parameter estimation method was used In the hypothesis test, the null hypothesis assumes that the adoption pattern can be modeled as a White-noise or as a random walk process, i.e., the differences in the noncumulative adoption time-series appear to be random That is, xtị ẳ xt 1ị ỵ etị (4) where x(t) is the number of adopters at time t, and the residuals e(t) have a zero mean On the other hand, our alternative hypothesis is the Bass model as shown in Eq (1) Using the derivation of Mahajan, Sharma, and Bettis (1988), the regression analogue is stated as: xtị ẳ b1 xt 1ị ỵ b2 N t 1ị þ eðtÞ Country U.S mobile phone subscribers (1985–2008) S Korea mobile phone subscribers (1985–2008) Model Parameter estimation p (innovation effect) q (imitation effect) Model fitness F-value R2 White-noise testing Null value Test statistic Bass/Mansfield Bass/Mansfield 0.00119 0.3337 0.000631 0.5328 56.95*** 0.7621 20.29*** 0.8999 a=0 a=0 t = 2.65** t = 2.86** ** *** p < 0.05 p < 0.01 (5) where b1 ẳ ỵ q p, b1 > 1, b2 ¼ Àq=m, b2 < 0, and N à t 1ị ẳ N2 t 1ị N2 ðt À 2Þ This linear model has some weaknesses as stated above Venkatraman et al (1994) and Srinivasan and Mason (1986) found that the nonlinear least squares (NLS) estimation procedure actually performs better and generates a more significant estimated value than its ordinary least squares (OLS) and maximum likelihood estimation (MLE) counterparts Therefore, in our study we chose the NLS estimation procedure 4.2 Nonlinear estimation of the Bass diffusion model We applied the nonlinear estimate procedure used by Venkatraman et al (1994) We employed the following functional form which is also our alternative hypothesis for the first test:   exp p ỵ qịtị exp p ỵ qịt 1ịị xtị ẳ m ỵ q= pị exp p ỵ qịtị ỵ q= pị exp p ỵ qịt 1ịị (6) 4.3 White-noise test The White-noise test was used to determine whether or not there are innovation or imitation effects in the technology adoption process If the null hypothesis is rejected, then we can say that there are innovation or imitation effects Since we use a nonlinear model as the alternative hypothesis, which is nonnested, the use of the F-test is econometrically inappropriate (Venkatraman et al., 1994) Therefore, we employ the J-test of Davidson and MacKinnon (1981) We performed the following regression: ỵ etị xtị ẳ aị f tị ỵ agtị Table NLS specifications for comparing the U.S with S Korea countries This data was adopted in the nonlinear estimation model for Hypotheses and Results 5.1 Statistical results This first test conducted was to ascertain whether or not the out-fitted models are random walk processes Table summarizes the results of the hypotheses tests All models (U.S and S Korea) rejected the White-noise model (p < 0.05), indicating that there were innovation and imitation effects The F-values for model fitness were also highly significant (p < 0.001) The fitted models had high R2 values ranging from 0.7621 to 0.8999 All results supported our first hypothesis Figs and 10 describe the fitness of our suggested Bass models The Bass fitted model for the U.S has a more similar graph pattern (Fig 9), with a greater R2 value (0.8999) than that of South Korea (0.7621) However, the estimated graph for South Korea by the Bass model has an S-shape (Fig 10) This means that the mobile phone adoption pattern of South Korea could also be well explained by DIT although the R2 (0.7621) of that model was smaller than that of the U.S model For Hypothesis 2, the innovation effect value p (0.000631) of the U.S mobile phone market was greater than that of South Korea (0.00119) Moreover, the imitation effect value q (0.5328) of South Korea was greater than that of the U.S (0.3337) These results support our prediction In sum, since the Bass model rejected the White-noise model, we could conclude that coefficients p and q of each model could represent the characteristics of two distinctive cultures (7) where f(t) = x(t À 1) + e(t) is the null White-noise model, g(t) is the predicted value under an appropriate alternative model based on the maximum likelihood estimation, a is a constant, and e(t) is a random error that is normally and independently distributed, with the mean value of zero and a constant variance The econometric properties of estimation and inference using the above equation enable us to test the alternative hypothesis by applying the conventional asymptotic t-test for the null hypothesis, and with a = 4.4 Data collection The verified official data of the ITU World Telecommunication, and the Korean Communications Commission were used in the time series study Table shows the verified official data of the two Fig Comparison between the actual mobile phone adoption and the Bass fitted model for the U.S Author's personal copy S.-G Lee et al / Journal of World Business 48 (2013) 20–29 Fig 10 Comparison between the actual mobile phone adoption and the Bass fitted model for S Korea Discussion This study adopted a longitudinal perspective to the study of mobile phone adoption in two coutries with contrasting cultures, the U.S and South Korea Through the hypotheses testing, we found that innovation and imitation effects are important determinants for each country’s mobile phone diffusion Specifically, the innovation value p of a Type I culture (i.e., the U.S.) is greater than that of a Type II culture (South Korea) and the imitation value q of a Type II culture is greater than that of a Type I culture 6.1 Higher innovation effect in the U.S The external model assumes no communication between early adopters and late adopters (Bass, 1969) The innovation effect value p reflects technology acceptance that is formed by selfperception and self-interest through individuals’ direct experience about usefulness and ease of use If an individual is truly impressed by the superior performance of a new innovative technology, then this individual would adopt that technology People in individualistic cultures (i.e., Type I) are more likely to seek information on their own from direct and formal sources; they view themselves as independent decision makers and are somewhat separated from the social context (Kim, 2008) These individualistic characteristics may increase the innovation effect on ICT adoption in Type I culture As shown in Figs 6, and 10, the percentage of mobile phone adoption in the initial stage (from 1985 to 1996) was much greater in the U.S than in South Korea; however, during 1997–2000, the mobile phone adoption increase in the U.S was much slower than in South Korea These numbers show that in Type I culture, with low uncertainty avoidance and short-term orientation, the innovation effect in technology adoption is higher 27 individual ‘I’ In East Asia, individual identity is based on the social network to which one belongs (Hofstede, 1980) Koreans regard the sense of belongingness as one of their central cultural values because of their Confucian roots (Lee, 2003) In South Korea, the initial adoption rate of mobile phone was very low However, after 1997, when the social perception of mobile phones was developed rapidly, the adoption rate has increased dramatically (Figs and 10) This adoption pattern shows the collectivistic behavior of South Koreans The East Asian Confucian values are also closely related to the higher imitation effect of mobile phone adoption One of the main differences between short-term and long-term orientation societies is the willingness to subordinate oneself for a common purpose (Hofstede, 1980) The South Korean government encouraged its people to subscribe to mobile phone services by providing subsidies (now, mobile service providers KT, SKT and LGU are giving subsidies) Keeping pace with governmental policy, people in South Korea are increasingly eager to subscribe to mobile phone services Thus, not simply cheap costs, but the Confucian value of common purpose going back into history might have contributed to the higher rate of mobile phone adoption and IT development in South Korea Conclusion 7.1 Research contributions This research provides several theoretical contributions These contributions should create a new fertile ground for future research about the cultural impact on ICT adoption (1) The sample: until now, IT adoption studies related to culture have mainly been done based on small samples of population To analyze the effects of national culture on the mobile phone adoption patterns, this study used the entire population of mobile phone subscribers of two representative countries of Type I and Type II culture, thus providing a more objective and accurate measurement of cultural effects on ICT adoption (2) Methodology: This study is one of the few IS studies (according to our knowledge) that employ a mathematical model for its research (3) Time series data: This research provides a complete view of the entire mobile phone adoption process, allowing us to observe the variances in the adoption factors throughout the adoption phases (early, development, maturity) We delineated the adoption cycle based on cultural types as shown in Fig 11 In the initial period, the adoption rate in an individualistic culture (Type I) is greater than that of a collectivistic culture society (Type II); during the development period, a Type II culture society has a greater rate of adoption; and in the maturity stage, the slope of the adoption curve for Type II culture decreases, while that of Type I culture is maintained or slightly declined 6.2 Higher imitation effect in South Korea 7.2 Managerial relevance The internal model assumes that the technology adoption rate is determined by the interaction between early adopters and future adopters The imitation effect value q reflects social influences such as subjective norm (SN) and word of mouth Specifically, for the inexperienced users in Type II cultures, the effect of SN on perceptions and behavior is likely to be greater (Karahanna & Straub, 1999; Venkatesh & Davis, 2000) During the diffusion process, most people not evaluate an innovation on the basis of self-assessment but rather on a subjective evaluation conveyed from other-like-minded individuals who have already adopted the innovation (Rogers, 2003) People in the collectivistic cultures (i.e., Type II) place a great importance on ‘we’ rather than on the Today, organizations operate and compete in the networked global market Even a small turbulence in one corner of the world may cause huge and unpredictable changes in other places ICT has been widely adopted to manage dynamic global forces while considering the diversity in cultures and markets This study has some practical insights that are relevant to management strategies for ICT adoption, based on national culture and adoption stages For example, to penetrate in a Type I culture country (the U.S., Australia, Canada, Netherlands, etc.) with a new technology, organizations should focus on specific innovative features of the technology or product, such as perceived usefulness, perceived Author's personal copy S.-G Lee et al / Journal of World Business 48 (2013) 20–29 28 Fig 11 Mobile phone adoption patterns of Type I and Type II cultures ease of use, job relevance, output quality, and the like On the other hand, in Type II cultural countries (Korea, Brazil, Japan, Thailand, etc.), introduction of a new technology or product requires strategies that could influence the social determinants because people in a Type II culture depend on other like-mind peers’ evaluation Therefore, in these collectivistic countries, developing, participating, and closely following word-of-mouth tools such as bulletin boards, blogs, or social networks are of the utmost importance because they build social and emotional cues for adoptions In the past, product-focused innovation and strategies worked to expand the customer base Today’s sophisticated and wellinformed customers want the total ecosystem of an ICT (e.g., a smart phone) Organizations must devise value-added service innovations to meet customers’ needs Such service innovations surrounding a technological product represent tacit knowledge that would be difficult to imitate by competitors (Chesbrough, 2011) Management strategies that combine service innovations with cultural uniqueness of customers are essential for effective technology adoption 7.3 Limitations and future research needs The limitations of this study are as follows; first, we used data from only two countries, one representing Type I and the other for Type II culture To generalize the findings of our study, time-series data of many other countries would be needed Second, mobile phone adoption may not be the best representative of ICTs The findings of the study would be more robust and provide a clearer picture (perhaps, a different one) if we use other ICT devices and then compare the results Third, our study used Hofstede’s scores, reported in his 1980 work, as indices of cultural values of each country, rather than directly measuring each consumer’s cultural values today This could overlook the possibility of individual differences, as well as the fast changing cultural values of nations due to globalization and technological advances Hofstede’s cultural paradigm has also been criticized by some researchers (e.g., McSweeney, 2002) Lastly, other factors for adoption, such as a country’s specific situation, government policies, market competition, and economic development trend could affect the 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