Journal of Internet Business

Issue 7- 2009

Identifying the Heavy Internet User Segment in Emerging Markets: The Evidence from Serbia and Ukraine William L. James, Ph.D. Professor of Marketing & International Business Hofstra University Alex Sharland, Ph.D. Associate Professor of Marketing & International Business University of South Alabama Goran Petkovic, Ph.D. Professor, Economics Faculty University of Belgrade Gladys Torres-Baumgarten, Ph.D. Associate Professor of International Business Ramapo College of New Jersey Tetyana Spivakovska Assistant Professor of Marketing National Technological University of Ukraine

About the Authors William L. James is Professor of Marketing and International Business at Hofstra University in Hempstead, New York.

He earned his Ph.D. in marketing from the Krannert School of

Management at Purdue University. His research has appeared in many marketing and management journals.

Alex Sharland is Associate Professor of Marketing and International Business at the University of South Alabama, Mobile, Alabama. He earned his Ph.D. in Marketing from The Florida State University.

Goran Petković is Professor in the Economic Faculty at the University of Belgrade teaching courses in retailing, marketing and commerce. He earned his Ph.D. from the University of Belgrade. Currently he is also serving as state secretary for tourism in the Serbian Economic Ministry. 1

Journal of Internet Business

Issue 7- 2009

Gladys Torres-Baumgarten is Associate Professor of Management and International Business at Ramapo College of New Jersey. She received her Ph.D. in international business from Rutgers University.

Tetyana Spivakovska is Assistant Professor of Marketing at National Technical University of Ukraine "Kiev Polytechnic Institute"; Kiev, Ukraine. Corresponding Author Mailing Address, Phone and E-mail

William L. James, Ph.D. Professor of Marketing & International Business Frank G. Zarb School of Business 222 Weller Hall Hofstra University Hempstead, NY 11549 USA Telephone: 516-463-5333 Email: [email protected] or [email protected]

2

Journal of Internet Business

Issue 7- 2009

Identifying the Heavy Internet User Segment in Emerging Markets: The Evidence from Serbia and Ukraine William L. James, Ph.D. Professor of Marketing & International Business Hofstra University Alex Sharland, Ph.D. Associate Professor of Marketing & International Business University of South Alabama Goran Petkovic, Ph.D. Professor, Economics Faculty University of Belgrade Gladys Torres-Baumgarten, Ph.D. Associate Professor of International Business Ramapo College of New Jersey Tetyana Spivakovska Assistant Professor of Marketing National Technological University of Ukraine Abstract Emerging markets represent an opportunity for many companies to build profit and market share to great effect.

However, to be successful, these companies need effective segmentation

strategies. This paper examines the extant literature and identifies what the current best practice appears to be. The study then suggests a more comprehensive segmentation combination and tests it using data from Serbia and Ukraine. The results indicate that the new combination of variables appear to offer a better method of segmenting emerging markets.

Key Words: Emerging Markets, Internet, Heavy Users, Psychographics, Serbia, Ukraine

3

Journal of Internet Business

Issue 7- 2009

Introduction Companies interested in developing successful marketing strategies in emerging markets would benefit from being aware of four conclusions that have emerged from the recent marketing and Internet literature. First, there is no doubt that emerging markets represent excellent areas of growth for both large and small companies (Subrahmanya 2007).

Second, the developing

literature in Internet usage indicates that greater Internet experience translates into: (i) a greater familiarity with online purchasing/consumption, and (ii) a greater likelihood of purchase intentions via the Internet (Aldrich, Forcht, and Pearson 1997). Third, the extant marketing strategy literature endorses the view that companies should seek out the heavy Internet-user segment because it represents an excellent target segment with great market potential (De Marez et al 2007). Finally, the emphasis on using demographic variables to identify heavy Internet users while prevalent in many strategies for developed markets, may not be nearly as effective in many developed markets as a combination of demographic and psychographic variables (Assael 2005). The central question in this study is whether emerging markets follow a development pattern similar to developed markets so that demographic variables are sufficient to identify heavy internet users, or whether the addition of psychographic variables improves the identification of heavy Internet users in emerging markets. Pursuing the logic of these conclusions, companies that seek to enter emerging markets must a) identify effective segmentation methodologies and b) recognize the heavy Internet user segment as the most likely to contain early adopters for Internet-based consumption. While this appears to be obvious, there are indications that market research is not delivering the information needed for effective and successful marketing strategies (Assael 2005). In particular, there is evidence that demographic variables are no longer effective means of segmenting Internet markets and identifying ‘heavy-user’ or ‘most-likely-to-buy’ segments (De Marez et al 2007; James et al 2008). The following study draws on these streams of research and strives to test the effectiveness of demographic and psychographic variables in emerging markets.

The

contribution of the current study is to demonstrate empirically the benefit available from including psychographic variables in the segmentation exercises. The paper is presented in three sections. The first identifies the literature on which the study is based and develops research propositions that form the basis of the quantitative analysis. 4

Journal of Internet Business

Issue 7- 2009

The second presents the results of the data analysis, and the final section discusses the conclusions, implications, and limitations of the research.

Literature Review and Study Propositions Market Potential – Emerging Markets For many companies, emerging markets represent the best opportunity for consistent growth in terms of revenue and profits (Economist 2008). In particular, in emerging markets there are excellent opportunities because of three favorable factors: a) In almost all emerging markets, most consumers have yet to be offered a full range of leading brands, making the market more open; b) In emerging markets, the local competitors are rarely in a position to compete in terms of access to resources; most notably money and expertise (Chrite 2002); c) Due to the above two factors, new and well organized foreign entrants can find a firstmover advantage that is not available to them in domestic or developed markets (Cui & Lui 2005). This conclusion is hardly a surprise to any marketing professionals, but as with many things, the devil is in the details. Market entry and successful marketing strategy often depend on many factors, one of which is an effective segmentation component (Kotler and Keller 2006, p.116).

Without good segmentation, strategy can be unfocused and ineffective (Ferrell &

Hartline 2007, p.122).

Emerging Markets - Strategic Segmentation The above three factors highlight the potential of emerging markets. Realizing market potential is critical for both new entrants and incumbents. Incumbents: In many emerging economies, local incumbent companies often do not have a strong resource base, and find it difficult to expand and take advantage of their ‘insider’ position (Healey 1994). Therefore, these companies must carefully screen market segments to make sure their resources are efficiently used. Segmentation represents an essential marketing strategy because the local companies cannot afford to make mistakes by spending resources on customer groups that do not provide good returns. 5

Journal of Internet Business

Issue 7- 2009

Outside Companies: ‘Foreign’ companies that are looking to successfully penetrate a new market are also focused on segmentation (Mahon and Vachani 1992). Treating a country as one mass market usually results in wasted resources (Schoenwald 2001). Larger companies often try to overwhelm the segments they enter by using all of their resources.

Smaller ‘foreign’

companies have to be more subtle and achieve many of the same goals without committing so much to the effort (Davis and Austerberry 1999). Based on the above, it is clear that segmentation represents one of the critical features for companies seeking a marketing strategy in an emerging economy. Therefore, all companies (large and small, foreign and domestic) seek to identify the variables that most effectively segment a market and predict meaningful market performance (Allred, Smith and Swinyard 2006). The next question is which segments are of particular interest to companies attempting to succeed in emerging markets, and how best to identify those segments? One that has attracted a lot of attention more recently is the heavy internet-user segment (De Marez et al 2007, James et al 2008). The following discussion identifies the segment and explains its value in terms of market entry and marketing strategy.

Emerging Markets – Internet-User Segment The literature on Internet usage and purchasing over the Internet in emerging markets is small but growing (Kuhlmeier and Knight 2005). Access to the Internet is a function of both personal assets (computer, and telephone access) and societal assets (telephone system, cable television, infrastructure development), and the rate of Internet usage and purchasing via the Internet is very different from those behaviors in developed economies (James et al 2008). The perceived risk of using the Internet for purchasing is higher in emerging markets because of concerns about information loss or theft (Miyazaki and Fernandez 2001). Furthermore, the inability to touch or feel the purchase can also be a reason for higher perceptions of risk in emerging markets (Tan 1999). These factors make early adopters so much more important. Companies need to segment internet usage and find the heavy user segments that will include the ‘early adopters’ and ‘early majority’ in market development (Fielding 2007). 6

Journal of Internet Business

Issue 7- 2009

1) The Internet user in an emerging market represents someone who is clearly an innovator, because the Internet represents something that is new and exciting to most who have never seen or used it before. 2) This person is someone who has greater interest in ‘outside/external’ things, and will probably be more prone to ‘try’ them. 3) The Internet user probably has more discretionary income than other members of that society/community by virtue of the fact that they have access to the devices needed to be ‘on’ the Internet. 4) In cultures with a modern orientation consumers are more receptive to innovation which suggests that opinion leaders, who are crucial to the rate of diffusion of new products, are likely to also be innovators (Schiffman and Kanak 2010, pp. 438-442). Past segmentation of internet users has focused on young (15-35) males, because in early internet usage studies this was the demographic group that represented the dominant early adopter/majority groups (Munnukka 2007). The dominant use of demographic variables is somewhat surprising because prior research in heavy user segmentation of consumer goods found that demographic variables only explained a modest amount of variance in the quantity of consumer goods purchased in the US (Frank et al 1972, pp. 72-73). More recent studies indicate that psychographic variables might increase explanatory power. For instance, consumer innovativeness has been linked to acceptance of online banking (Lassar et al. 2005, and AldasManzano et al. 2009) and Internet apparel shopping (Ha and Stoel 2004). These latter studies were in developed markets where the consumer acceptance of online purchase is widespread. As noted above, in emerging markets, this is not the case. However, it is probable that a more complex mix of variables (demographics and psychographics) will be necessary to effectively identify the heavy internet user in emerging markets (Assael 2005,James etal 2008).

The following empirical study seeks to verify this conclusion by testing the

effectiveness of demographic and psychographic variables in two emerging markets; Serbia and Ukraine.

7

Journal of Internet Business

Issue 7- 2009

Research Propositions The above review indicates that for companies to be more effective in developing marketing strategies in emerging markets, they must have effective methods of segmenting markets. By identifying people that are heavy internet-users, these companies have better prospects of achieving marketing success.

Furthermore, it appears that demographic variables can be

effective segmentation variables for a while, but when internet penetration rates are high, other variables have to be brought in.

These findings can provide the following research positions:

1) Demographic variables are effective predictors of heavy internet usage in emerging markets. This is the approach that at least in developed markets is no longer sufficient (Assael 2005). 2) A combination of demographic and psychographic variables is a more effective predictor of heavy internet usage in emerging economies than demographics alone. This is the approach that Assael (2005) suggests for developed markets and that James et al. (2008) suggest might also be appropriate in emerging markets.

Methodology Sample Background Serbia and Ukraine are both transitioning out of the command economies that dominated the region throughout much of the 20th century. While a study of international online marketing communications by Lynch and Beck (2001) suggested that there were few differences in Internet behaviors within the Central/Eastern European region. The study participants all belonged to the same multinational and corporate culture may have affected the findings. A recent study (James et al 2008) indicates that there are some Internet behavioral differences between Serbia and Ukraine. While both of these countries have relatively low Internet penetration rates, there are differences that might affect Internet behaviors. Both countries use languages very different from prevailing global and Internet languages. The Serbian language is from the South Slavic subgroup and written Serbian uses parallel Cyrillic and Latin alphabets. The Ukrainian language is from the East Slavic subgroup and written Ukrainian uses the Cyrillic alphabet. Each country’s major city, Kiev and Belgrade, is a major metropolitan area with a major university, diverse population and a long history as a 8

Journal of Internet Business

Issue 7- 2009

major cultural center. Ukraine was part of the USSR for a large part of the 20th Century while Serbia, as a part of the Socialist Federal Republic of Yugoslavia, was a nonaligned nation. The infrastructure of Ukraine is transitioning from the lengthy period as a part of the USSR while the Serbian infrastructure was negatively impacted by the civil strife involved in the breakup of the Socialist Federal Republic of Yugoslavia and the NATO military campaign of the 1990s. The most recent data from the CIA web site (www.cia.gov) estimates Internet usage in Ukraine and Serbia are estimated to be about 12% and 16% respectively. Since both anecdotal evidence and the latest data on the CIA web site (www.cia.gov) suggest users in these two countries are primarily urban, Ukraine 68% urban and Serbia 52% urban (World Factbook, www.cia.gov), the samples were gathered in two major urban areas, Kiev and Belgrade. Sample In the Ukrainian sample, collection took place in computer clubs (where you can play computer games, work with documents and print them). Two survey gatherers were assigned to each of the city’s 10 districts and independently surveyed 10 people at two different locations. Every third person was surveyed with the exception that if the percentage of women participating fell below 40%, the data collector would wait until the next possible participant was female. A total of 400 surveys were collected in Kiev, with 233 of the computer users also being Internet users. A similar procedure was employed in Belgrade. Eight data collectors interviewed Serbians in Belgrade: (seven achieved 50 complete interviews, while one was only able to complete 48 interviews). In Belgrade the sample of 398 computer users included 345 who were also Internet users. The computer club structure available in Kiev was not available so interviews were conducted at diverse locations throughout Belgrade including: Internet cafés; university computer labs; computer game places; and retail stores/company workplaces. These locations were chosen in order to provide geographic coverage similar to that provided by the Kiev computer clubs. Table 1shows the gender breakdown in the two samples. As we hoped both samples have almost equal numbers of males and females. Table 2 shows the age group distribution for each sample. The Serbian sample is somewhat younger with the modal category being those in their twenties. Table 3 shows the educational distribution of the respondents. The major difference is 9

Journal of Internet Business

Issue 7- 2009

that the modal category in the Serbian sample was some university education while the modal category in the Ukrainian sample was university graduate. Whether these differences represent population differences, differences in the population of computer users or sampling differences is unclear. The analytical model was structured to test for all possible country differences by using interaction terms. In the final model only two interactions out of nine were significant and neither involved a demographic variable. This suggests that sampling differences are not a significant issue in this data set. Totals in these tables do not add up to 798 due to a very small amount of missing data. Analytical Approach The analytical approach was binary logistic regression. The dependent variable consisted of two groups. These two groups were approximately the top and bottom quartiles by country based on the number of hours spent on the Internet in a typical week. In the Serbian sample the top quartile (n = 107) spent 10 or more hours per week on the Internet while in the Ukrainian sample the top quartile (n = 58) spent 18 or more hours per week on the Internet. In the Serbian sample the bottom quartile (n = 89) spent 2 or fewer hours per week on the Internet while in the Ukrainian sample the bottom quartile (n = 60) spent 3 or fewer hours per week on the Internet. The independent variables consisted of three types: demographic variables, psychographic variables and interaction terms. The demographic variables were gender, age, and education. The psychographic variables were: cognitive innovativeness (alpha = 0.74; Venkatraman and Price, 1990), sensory innovativeness (alpha = 0.70; Venkatraman and Price, 1990), opinion leadership (alpha = 0.84; Childers 1986 modification of King and Summers 1970), consumer ethnocentrism (alpha = 0.83; Cui and Adams, 2002), attitudes towards computers (alpha = 0.75; Jarvenpaa and Tractinsky, 1999), and attitudes towards web shopping risk (alpha = 0.72; Jarvenpaa and Tractinsky, 1999). In order to test for intercept differences country (Serbia = 0, Ukraine = 1) was included as an independent variable. In order to test for slope differences interactions of country and each of the demographic and psychographic variables were included as independent variables.

10

Journal of Internet Business

Issue 7- 2009

Results The first research proposition was tested by entering the demographic variables, their interactions with country and country as a block. The resulting equation was significant (Chisquare = 33.406, df = 19, p = 0.022) but only two independent variables had significant coefficients: gender (p = 0.001) and the interaction of gender and country (p = 0.0200). Model predictive ability, 136 cases correctly classified and 83 cases incorrectly classified

in the

analysis sample, was assessed using Press’(1972) Q statistic (Q = 12.08, df = 1, p < 0.001). Results for the holdout sample using Press’ Q statistic were not however significant with only 51 cases correctly classified and 36 cases incorrectly classified (Q = 0.047, df = 1, not significant). Psychographic variables and their interactions with country were then entered as a second block. The addition of this block of variables was significant (Chi-square = 99.078, df = 12, p = 0.001), indicating that the addition of the psychographic variables improves the predictive power of the binary logistic regression. Thus the second research proposition is supported. Table 4 presents the significant and marginally significant independent variables in the final binary logistic regression that includes both demographic and psychographic variables. Tables 5 and 6 assess how well the regression equation performs. In particular the classification results for both the analysis and holdout samples are assessed using the Q statistic from Press (1972). The largest group criterion, the most conservative criterion was used. Both the analysis sample and holdout sample classification rates are highly significant. Interpreting the results from Table 1 leads to the following conclusions: •

Females are more likely to be heavy users.

•

Cognitive Innovativeness is inversely related to degree of Internet usage.

•

Opinion Leadership is directly related to degree of Internet usage but less so for Ukrainians.

•

Consumer Ethnocentrism is inversely related to the degree of Internet usage for Serbians but for Ukrainians the relationship is direct.

•

Those under 30 years of age are more likely to be heavy users.

Conclusions The results presented above appear to provide support for both research propositions. That is: 11

Journal of Internet Business

Issue 7- 2009

1) Demographic variables are effective predictors of heavy internet usage in emerging markets; and 2) A combination of demographic and psychographic variables is the most effective predictor of heavy internet usage in emerging markets. The Munnukka (2007) study suggested that young males between 15 and 35 represent the most promising segment in emerging markets because of their heavy internet usage. The Assael (2005) study questioned whether demographic variables alone are sufficient, and the James et al (2008) study specifically recommends that psychographic variables in combination with demographic variables are more effective and accurate in segmenting emerging markets. The results of this study support the Assael and James et al studies.

Managerial Implications The literature review above indicates that emerging economies are places where companies can make profits. Furthermore, early adopters are an important market segment for companies entering such emerging markets because they tend to ‘lead’ others, resulting in greater likelihood of commercial success. In modern oriented cultures these early adopters are likely to include innovators who are also opinion leaders. Thus they can have a major impact on acceptance rates for new products and technologies. Heavy internet users in emerging economies have similar characteristics to early adopters, and can be used as a proxy group for early adopters. The key to a successful strategy is to accurately identify the heavy internet segment, and this study demonstrates that a combination of demographic and psychographic variables is a better methodology that just using demographic variables alone. This finding should better guide initial market research efforts of companies venturing into emerging economies. Instead of using only demographic data (possibly much of it from secondary sources), those companies would be better served, and achieve better results, if they use primary research and incorporate psychographic variables. It is possible that this “richer” research effort will take more time and cost more. However, the results are likely to be more accurate, resulting in a more effective entry to the market

12

Journal of Internet Business

Issue 7- 2009

Limitations and Extensions Almost all quantitative studies are sensitive to time location, and sample structure. This one is little different. A great deal of time and effort was invested into gathering the data appropriately in both Serbia and Ukraine. The primary reason for such effort was to guard against validity concerns. The study tried to control error in the development of the instrument and the data collection, so that the data analysis would be robust to concerns in this regard. The second question is whether the results of this study can be extrapolated to other emerging economies.

Serbia and Ukraine are located in ‘eastern’ Europe and share some

demographic elements in the population. The countries were both part of the former USSR. These similarities do not apply to all emerging markets, and it is possible that these demographic/historical factors drive the data results. A test of this effect would be to run a similar study in emerging markets that do not share these factors (non-Serb and non-former USSR).

References Aldas-Manzano, J., Lassala-Navarre, C., Ruiz-Mafe, C. and Sanz-Blas, S. (2009), “The role of consumer innovativeness and perceived risk in online banking usage”, International Journal of Bank Marketing, 27 (1), pp. 53-75. (Anonymous) (2008) “Racing Down the Pyramid: Pharmaceuticals”, The Economist, Vol. 389, Issue 8606, p. 76. Assael H. (2005), “A Demographic and Psychographic Profile of Heavy Internet Users and Users by Type of Internet Usage”, Journal of Advertising Research, Vol. 45, No. 1 (March), pp. 93-123.

Aldridge A., White A. and Forcht K. (1997) “Security considerations of doing business via the Internet: cautions to be considered”, Internet Research, Vol. 7, Issue 1, pp. 9-15. Allred C.R., Smith S.M. and Swinyard W.R. (2006) “E-shopping lovers and fearful conservatives: a market segmentation analysis”, International Journal of Retail & Distribution Management, Vol. 34, Iss. 4/5; pp. 308-333. Childers, T. L. (1986), “Assessment of the psychometric properties of an opinion leadership scale”, Journal of Marketing Research, 23 (2), pp. 184-188. Chrite, B. (2002), “Local Knowledge will Provide the Key”, Financial Times, August 26th, p 7.

13

Journal of Internet Business

Issue 7- 2009

Cui, C. C. and Adams, E.I. (2002), “National identity and NATID: An assessment in Yemen”, International Marketing Review, 19 (6), pp. 637-662. Cui, G. and Lui, H.K. (2005), “Order of Entry and Performance of Multinational Corporations in Emerging Markets: A Contingent Resource Perspective”, Journal of International Marketing, 13, No. 4 (Winter), pp. 28-56. Davis R. and Austerberry T. (1999), “Think small win big”, The McKinsey Quarterly, New York: 1999. pp. 28-37. De Marez L., Vyncke P., Berte K., Schuurman D., De Moor K. (2007) “Adopter Segments, adoption determinants and mobile marketing”, Journal of Targeting, Measurement and Analysis for Marketing, Vol. 16, No. 1, pp. 78-95. Ferrell O.C. and M. Hartline (2008) “Marketing Strategy”, 4th edition, Thomson South-Western: Fielding, Michael, “Explore New Territory”, Marketing News, March 2007, p.25 Ha, Y. and Stoel, L. (2004), “Internet Apparel Shopping Behaviors: The Influence of General Innovativeness”, International Journal of Retail & Distribution Management, 32 (8), pp. 377-385. Healey Nigel (1994) “The Transition Economies of Central and Eastern Europe”, Columbia Journal of World Business, Spring, Vol. 29, Issue 1, pp. 62-70. James W.L., Torres-Baumgarten G., Petkovic G., and Havrylenko T. (2008) “Exploring web language orientation in emerging markets: The case of Serbia and the Ukraine”, Journal of Targeting, Measurement and Analysis for Marketing, June 2008. Vol. 16, No. 3, pp. 189-202. Jarvenpaa, S. L. and Tractinsky N. (1999), “Consumer trust in an Internet store: A cross-cultural validation”, Journal of Computer Mediated Communication, 5 (2), (Accessed on-line July 31st 2009 at www.jcmc.indiana.com/). King, C. W. and Summers J.O. (1970), “Overlap of opinion leadership across consumer product categories”, Journal of Marketing Research, 7 (1), pp. 43-50.

Kotler P. and K. Keller (2006), “A Framework for Marketing Management”, Prentice Hall: Upper Saddle River, New Jersey, 3rd edition. Kuhlmeier D. and Knight G. (2005) “Antecedents to Internet-based purchasing: a multinational study”, International Marketing Review, Vol. 22, Issue 4, pp. 460-473.

14

Journal of Internet Business

Issue 7- 2009

Lassar, W.M., Manolis, C. and Lassar, S. (2005), “The relationship between consumer innovativeness, personal characteristics, and online banking adoption”, International Journal of Bank Marketing, 23 (2), pp. 176-199. Lynch, P. D. and Beck J.C. (2001), “Profiles of Internet buyers in 20 countries: Evidence for region-specific strategies”, Journal of International Business Studies, 32 (4), pp. 725748. Mahon J. and S. Vachani 1992 “Establishing a Beachhead in International Markets - A Direct or Indirect Approach”, Long Range Planning, London: Jun 1992. Vol. 25, Issue 3, pp. 6069. Miyazaki A. and Fernandez A. (2001) “Consumer perceptions of privacy and security risks for online shopping”, The Journal of Consumer Affairs, Summer 2001. Vol. 35, Issue 1, pp. 27-44. Munnukka Juha (2007), “Characteristics of Early Adopters in Mobile Communications Markets”, Market Intelligence Planning, Vol. 25, Issue 7, pp.719-731. Press, S. J. (1972). Applied Multivariate Analysis, Holt, Rinehart and Winston: New York. Schiffman, Leon G. and Leslie Lazar Kanuk (2010). Consumer Behavior, Prentice Hall: New Jersey, Upper Saddle River, 10th edition. Schoenwald M. (2001) “Psychographic segmentation: Used or abused?” Brandweek, Jan 22, 2001. Vol. 42, Issue 4, pp. 34-35. Subrahmanya M.H. Bala, (2007) “The process of technological innovations in small enterprises: the Indian way”, International Journal of Technology Management, Vol. 39, Iss. 3/4; pp. 396-411. Strauss, Judy, Adel El-Ansary and Raymond Frost (2006). E-Marketing, Pearson Prentice Hall: New Jersey, Upper Saddle River, 4th edition. Tan S.J. (1999) “Strategies for reducing consumers' risk aversion in Internet shopping”, The Journal of Consumer Marketing, Vol. 16, Issue 2, pp. 163-180. Venkatraman, M. P. and Price L.L. (1990), “Differentiating between cognitive and sensory innovativeness: Concepts, measurement, and implications”, Journal of Business Research, 20 (4), 293-315. World Factbook, www.cia.gov, accessed on December 13th 2009. Yakov Bart, Venkatesh Shankar, Fareena Sultan, Glen L Urban (2005) “Are the Drivers and Role of Online Trust the Same for All Web Sites and Consumers? A Large-Scale 15

Journal of Internet Business

Issue 7- 2009

Exploratory Empirical Study”, Journal of Marketing, Vol. 69, Issue 4 (October), pp. 133152.

16

Journal of Internet Business

Issue 7- 2009

Table 1 Respondent Gender by Country

Male Female

Serbia 208 189

Ukraine 208 188

Total 416 377

Serbia 83 208 36 35 34 2

Ukraine 70 130 63 57 55 21

Total 153 338 99 92 89 23

Table 2 Respondent Age by Country

Under 20 20-29 30-39 40-49 50-59 60+

Table 3 Respondent Highest Level of Education by Country Serbia Elementary School 13 Secondary School 60 Trade or Vocational 61 School Some University 193 University Graduate 67 Graduate/Professional 4 School

Ukraine 9 50 79

Total 22 110 140

95 163 0

288 230 4

17

Journal of Internet Business

Issue 7- 2009

Table 4 Significant Results of Binary Logistic Regression

Independent Variable

B

Significance Level Exp(B)

Gender

1.405

.034

4.077

Cognitive Innovativeness

-0.195 .030

0.823

Opinion Leadership

0.512

.001

1.668

Consumer Ethnocentrism

-0.227 .030

0.797

Attitude towards Web Shopping Risk

-0.339 .032

0.713

Consumer Ethnocentrism and Ukrainian 0.296 Opinion Leadership and Ukrainian

.020

1.345

-0.323 .010

0.724

Age Categories

0.064

Under 20 years old

3.321

.037

27.700

Between 20 and 29 years old

3.569

.019

35.479

18

Journal of Internet Business

Issue 7- 2009

Table 5 Model Fit Measures for Logistic Regression • • • •

Chi-square = 132.484, df = 31, p < .001 -2 Log Likelihood = 171.00 Cox and Snell R Square = .454 Nagelkerke R Square = .605

Table 6 Classification Rates for Analysis and Holdout Samples

Analysis Sample: • 182 cases correctly classified (83.1%) • 37 cases incorrectly classified (16.9%) • Press’ Q statistic = 89.54, df = 1, p < .001 Holdout Sample: • 64 cases correctly classified (73.6%) • 23 cases incorrectly classified (26.4%) • Press’ Q statistic = 9.22, df = 1, p < .01

19