Habit As a Key Predictor of Internet Email Behavior

ABSTRACT - Habit is an important component of consumer behavior and is often used interchangbly with the effect of past behavior. One of the simplest models that provides a good representation of consumer behavior for repetitive choices is the Negative Binomial Distribution (NBD) model. This exploratory study investigated the ability of the NBD model to explain reported Internet email behavior. It found that the model was able to provide a good fit to the online behavioral data. Moreover, the study found no differences in demographics, psychographics, Internet use and perceptions between light and heavy email user groups. The implications of these findings to marketing and Internet policies were discussed.

Citation:

Desmond Lam and Dick Mizerski (2005) ,"Habit As a Key Predictor of Internet Email Behavior", in AP - Asia Pacific Advances in Consumer Research Volume 6, eds. Yong-Uon Ha and Youjae Yi, Duluth, MN : Association for Consumer Research, Pages: 13-20.

Asia Pacific Advances in Consumer Research Volume 6, 2005      Pages 13-20

HABIT AS A KEY PREDICTOR OF INTERNET EMAIL BEHAVIOR

Desmond Lam, University of Western Australia, Australia

Dick Mizerski, University of Western Australia, Australia

ABSTRACT -

Habit is an important component of consumer behavior and is often used interchangbly with the effect of past behavior. One of the simplest models that provides a good representation of consumer behavior for repetitive choices is the Negative Binomial Distribution (NBD) model. This exploratory study investigated the ability of the NBD model to explain reported Internet email behavior. It found that the model was able to provide a good fit to the online behavioral data. Moreover, the study found no differences in demographics, psychographics, Internet use and perceptions between light and heavy email user groups. The implications of these findings to marketing and Internet policies were discussed.

INTRODUCTION

The Internet is probably the fastest growing medium in the human history. It offers many benefits to the modern community and may be the most widely-used medium for communication. According to Nua Internet survey, there are now more than 600 million Internet users worldwide (Nua.com, 2003). The figure has been growing at an exponential rate since the last decade. This rapid adoption of the Internet has greatly increased the amount and diversity of information online (Levene and Poulovassilis, 2001). According to eMarketer, the worldwide ecommerce revenues are expected to total US$ 2.7 trillion by 2004 (Nua.com 2003). While about 70 percent of companies in U.S. have experimented with purchasing online, only less than 10 percent of their total spending is currently being channeled via the Internet. Thus, it seems that faced with the explosive growth of the Internet and e-commerce, marketers and policymakers are still investigating the optimal ways to use and regulate this relatively new business and communication medium (Sheehan and Hoy 2000). To date, the majority of research on consumers’ behavior on the Internet have been from a psychological point of view, focusing on cognitive factors such as attitudes toward websites (Balabanis and Vassileiou 1999) and toward Internet advertising (Kwak, Fox, and Zinkhan 2002), shopping experiences and search (Ward and Lee 2000), influences of online information (Chatterjee 2001), website brand loyalty (Holland and Baker 2001), trust and reputation (Xu and Yadav 2003), and perceptions toward email (Gefen and Straub 1997; Gattiker, Pedersen and Perlusz 2002; Marinova, Murphy and Massey 2002). However, there is no reported empirical-based research on the effect of habitual responses on users’ Internet behavior. This study will use a stochastic paradigm to explain online consumer behavior.

HABITUAL BEHAVIOR AND THE NBD MODEL

While habitual behavior has been studied for well over eighty years (i.e. Watson 1919, and Allport 1924) and is viewed as an important component of consumer behavior, the effect of habits is often overlooked in marketing. Triandis (1977) defines habitual behavior as equivalent to past behavior due to its repetitive nature. He included habit in his attitude-behavior model as a joint predictor of intention to future behavior. Allport (1985) also see habit as a basic driver of behavior, which can be defined by "choose what one chose last time" heuristic (Bettman, Johnson, and Payne 1991). Many recent researchers have now used habit interchangeably with past behavior and have recognized the importance of habits in humans’ everyday lives (East 1997; Ouellette and Wood 1998).

Habitual behaviors are now thought to be an important component of consumer brand loyalty (Olsen 1995; East 1997). When one considers consumers in aggregate, one will find that many consumer markets are relatively stable and appear to follow some simple empirical marketing laws (Ehrenberg 1971). This means it is common to find regularity in the behavior of consumers (East 1997) in their frequency of patronage and purchasing (Ehrenberg 1972, 1988). Consumers are said to have habits when they repeatedly produce the same behavior under similar contexts (East 1997). According to Ehrenberg (1988), consumers’ tendencies to produce such repeated behaviors can be modeled as a stochastic process without referring to any cognitive components. For example, Kanvil and Umeb (2000)’s study on consumers’ motivation to smoke cigarettes found that past behavior explained most (70%) of the variation, and that health cognitions explained only a small proportion (3%) of that variation. In another example, Barwise and Ehrenberg (1988), in their study of television viewing, found the daily pattern of television viewing across large populations very steady over different days. According to them, there was a tendency for people who view television at one period to also view at the next period (Ehrenberg 1971).

Models that analyze patterns of human behavior are commonly known as stochastic preference models (Morrison and Schmittlein 1988; Wagner and Taudes 1987). These models are often very accurate in describing past usage and predicting future responses simply based on observed or reported behavior. One of the simplest and most widely-reported stochastic models that provides a reasonable representation of observed consumer buying patterns is the Negative Binominal Distribution (NBD) model. The NBD model, first examined by Greenwood and Yule (1920) in terms of the incidence of recurring diseases and accidents, was introduced to marketing by Andrew Ehrenberg in 1959. It is a simple mathematical model used to predict repeat purchases using information on the penetration, purchase frequency, and period (East 1997). The NBD model is also the basis of the Dirichlet model, which is used to predict brand shares in a product category (Uncles and Ehenberg 1990). Both the NBD and the Dirichlet models have been applied to a wide range of goods and services (Uncles, Ehrenberg and Hammond 1995), and have demonstrated the existence of stable buying propensities of frequently-purchased products in stationary markets (Morrison and Schmittlein 1988). Despite the ability to explain and predict market phenomenon, the "plain-vanilla" NBD model does not require the inputs of any marketing (e.g. pricing or promotion expenditures) or attitudinal (e.g. perceptions, beliefs or intentions) variables. It provides a probability density function that is based solely on the penetration of the market (i.e. percent of the population purchasing), the average frequency of those that purchase, and the time period over which the purchase is reported.

Although each individual in a market may use a cognitive basis for their decision, the population of individuals can often be accurately described with a stochastic model using only past behavior (Ehrenberg 1972). The patterns of behavior predicted or derived by an NBD model can be used to gauge the impact of marketing activities by providing a baseline for buyers and their usage. This baseline forms the "benchmark" for comparing with data about that behavior under conditions of these marketing activities. In a sense, the NBD reflects the influence of past, hence habitual, repetitive behavior in a market. As noted earlier, past behavior or habit is seen as the major cause of future behavior (Ouellette and Wood 1998).

Like many consumer products and media use, Internet usage may have reached a relatively high frequency of repetitious behavior (Said and Mizerski 2002) since its mass adoption in the early 1990s. The markets for frequently-purchased products may reach maturity within a relatively short time frame. For example, a study by Mizerski and Mizerski (2001) found the U.S. Florida’s lotto market had reached maturity within the first three years of its introduction and a stochastic pattern of play was established within the first six months of Lotto introduction. Hence, one may also expect consumers’ Internet email usage to have reached a relatively stationary condition such that it may follow the same NBD pattern of behavior. This behavior, on an aggregate level, may reflect an underlying stochastic pattern often observed in many consumer and industrial markets involving frequent purchases of multiple brands (Ehrenberg 1972; East 1997). A strong habitual response may provide a better empirical explanation of Internet email behavior than demographics and/or the other more cognitive paradigms (Ehrenberg 1959; Ehrenberg et al. 1994; East 1997). Hence, this study will investigate whether the NBD fits reported email behavior and provide an alternative explanation of Internet behavior.

HYPOTHESIS

The objective of this study is to determine if there is evidence to support that Internet email behavior follows a habitual pattern and, hence, conforms to the NBD model. As mentioned earlier, the NBD model had been used in numerous occasions and, in most cases, the NBD model fitted the data very well and was able to forecast future behavioral patterns with high accuracy (Ehrenberg 1971; East 1997). If an NBD is applied to Internet email behavioral data, then one will expect no significant differences between the proportion of users derived from the NBD model and the reported or observed data. Hence,

H1: There will be no significant difference between the reported proportion of email use and the expected NBD.

Moreover, one closely associated theme to the NBD patterns is that there are usually few or no demographic differences between the light and heavy users. For example, Barwise and Ehrenberg (1988) found that there was very little variation in the way different subgroups of the population allocate their viewing across the different program categories. The composition of the audience for most programs was similar in demographics or television-usage terms and these programs were positively liked by nearly all of their viewers except a few. Such observations of indifferences were in-line with those reported by other behavioral researchers such as Hammond et al. (1996).One will find that in any stochastic or habit-driven processes where the effects of past behaviors are strong and predominated over other factors such as demographics. Hence,

H2: There will be no significant differences between light and heavy email users in their reported age, gender, and number of years on the Internet.

Internet users’ perception towards online privacy may potentially affect their behavior on the Internet (Korgaonkar and Wolin 1999). About 69% of consumers in a recent survey did not use the Internet for commercial purposes because they were afraid that their personal information would not be kept private (NFO Interactive 1999). Moreover, some researchers had found that Internet users could differ in their Internet use according to their locus of control requirement (Hoffman, Novak and Schlosser 2003). While no research has been conducted that studied the differences between light and heavy email users in terms of their reported general Internet perception toward privacy and general usage pattern, one expects these factors to have no significant effects on email usage behavior in an NBD-or habit-driven environment. Thus,

H3: There will be no significant differences between the light and heavy email users in their reported Internet use and perceptions toward privacy.

Internet users’ cultural and personal values may also affect their behavior on the Internet. For example, Chau et al. (2002) found significant behavioral differences between Internet users from Hong Kong and U.S.A. Similarly, Hoffman, Novak and Schlosser (2003) found that consumers on the Internet behaved differently according to their locus of control. However, in a habit-driven environment, the effect of past behavior is expected to predominant. If the Internet email environment is in fact driven by habits, the effects of cultural and personality values are expected to conform to the NBD-based expectations. Hence,

H4: There will be no significant differences between light and heavy email users in term of their cultural dimensions and locus of control.

METHODOLOGY

The data used for this study (n=122) was collected from a convenience sample of undergraduates. A survey was used to measure their students’ perceptions and reported behavior when using the Internet. The respondents were business undergraduates at an Australian university. The age of the respondents ranged from 18 to 31 years old, with a median age of 21 years old. Approximately 62% of the respondents were female.

Each respondent was given a questionnaire with items on their cultural values, external locus of control, Internet perceptions, and their email behaviors. In order to obtain data on Internet email usage behavior, the respondents were asked to state the number of emails they have forwarded and sent in the last 24 hours. These two items formed the dependent variables in this study. Information on demographics was also collected, which included age, gender, number of years on the Internet, and number of years in Australia. The respondents also provided information on their cultural values, personal values in terms of external locus of control, and Internet perceptions and usage. These items were measured on a 1 (strongly disagree) to 5 (strongly agree) scale. The items on Internet privacy and usage were adapted from ninth’s GVU’s WWW survey (1998).

The items on cultural values were adapted from Dorfman and Howell (1988), which were based on Hofstede (1966)’s four main cultural dimensions, namely, Individualism, Uncertainty Avoidance, Masculinity, and Power Distance. The items on locus of control were selectively adopted from Levenson (1974)’s original scale and measured the extent of external locus of control. The locus of control construct is one of the most widely-studied personality concepts (Matsumoto 2000). People can differ in term of how much control they believe they have over their behavior and their environment. In locus of control concept, outcomes are seen either as dependent on one’s own actions or determined by fate, chance or powerful others (Rotter 1966).

A series of factor analysis and subsequent reliability tests were performed on these items with the results shown in table 1 below. The obtained alphas ranged from 0.540 to 0.862. According to Nunnally (1967), reliabilities in the range of 0.5 to 0.6 are satisfactory in the early stages of research. Hence, the obtained coefficients were deemed sufficient given the exploratory nature of this study. The variables were obtained by taking simple averages of the constituted items.

ANALYSIS AND FINDINGS

The NBD analysis was conducted using software obtained from Wright (1999). Three variables are required in each NBD analysis, namely, the penetration, the frequency of use and the period of use. The penetration figures for the reported observed and the NBD expected Internet email use were obtained, along with the average number of emails forwarded and sent. For the case of Internet email forwarding, the penetration was 34.4% (out of 122 respondents) with an average frequency of 6.45 emails. Internet email sending had a much higher penetration of 76.9% (out of 122 respondents) and an average frequency of 5.85 emails. The fit of the each NBD model was tested with a regression analysis, comparing the observed distributions to the NBD-derived theoretical distributions (Morrison & Schmittlein 1988). The correlations were relatively high for both cases of Internet email forwarding and Internet email sending; R=0.695, p<.001 for email forwarded and R=0.895, p<.01 for email sent (see figure 1). The results showed that there were some deviations between the observed and NBD-derived distributions. These deviations may be largely the result of respondents simplifying their reporting by rounding up their answers, which is an apparent weakness of self-reporting survey on usage (Nisbett and Wilson 1977). Nonetheless, the results support the existence of potentially strong habitual effect of Internet email use.

The respondents were then median split into light and heavy users in terms of both Internet email forwarding (median=3 emails) and sending (median=4 emails). Chi-square statistics were used to test for the goodness-of-fit between the observed and theoretical/NBD-derived distributions. Table 2 summarizes the results of the tests. No significant differences were found in both cases (p>.05). The finding, hence, supports first hypothesis that there is a strong habitual element in Internet email use.

Given the close approximations of the NBD to reported email user distributions, one may suspect that the demographics, cultural and personality background of the light and heavy user groups to have strong effects on the results. Table 4 and 5 show the results of comparisons between the light and heavy user groups in terms of these variables using MANOVA. Prior to that, a multiple analysis of covariance (Table 3) was performed with the demographic variables age, number of years of online experience, and number of years in Australia treated as potential covariates and tested for potential effects over other variables. No significant effects were found (p>.05).

Given the insignificant effects of these possible covariates, a final MANOVA was conducted including all the variables so as to compare the differences between light and heavy user groups in both cases of Internet email forwarding and sending. The results of the MANOVA are shown in Table 4 and 5 below. A chi-square test was also performed to test for the gender differences between the groups in both cases. The results showed no significant differences in either cases (p<.05). For the case of email forwarded, 41% of light users were male as opposed to 35% among the heavy users (X²⁼0.155, df=1, p=.694); for the case of email sent, 40.4% of light users were male as opposed to 42% among the heavy users (X²⁼0.011,df=1,p=.916).

Interestingly, the comparison between the light and heavy user groups in both cases did not yield any significant differences (p>.05) in demographics (hypothesis 2), and cultural and personal values (hypothesis 4). However, Internet usage appeared to be different between light and heavy groups of email senders. Naturally, one would expect little differences between the groups because of the clear evidence of stochastic pattern. The lack of significant differences on all but one of these independent variables supports the second (hypothesis 2) and fourth (hypothesis 4) hypotheses. While the third hypothesis (hypothesis 3) was supported in the case of Internet email forwarding, it was not so in the case of Internet email sending. Nevertheless, the overall findings had provided sound evidence for a stochastic or habitual pattern in Internet email use.

DISCUSSION AND CONCLUSION

The results have shown that the NBD model fit the reported Internet email use. Moreover, except for a single case, there were no significant differences between the light and heavy users in terms of their demographics, cultural values, personal values, Internet usage and perceptions. In essence, the two user groups in each case appear to be very similar except for the number of emails that they forwarded or sent. These findings reinforce the proposition that online behavior such as forwarding and sending emails appears to reflect a stochastic process much like product purchases in matured markets (Ehrenberg 1959), television viewing (Goodhardt et al. 1975; 1987) and gambling behavior (Mizerski, Mizerski and Miller 2000).

While the Internet is a relatively new medium, past studies have shown that rapidly-evolving industries may show sign of maturity quickly within a few years of introduction (Mizerski and Mizerski 2001). The fact that online habitual effects were detected now meant that one would likely to see even stronger habitual effects on the Internet in future. That there is an NBD pattern of reported online behavior has important implications to marketers and internet policy makers. The findings suggest that habit or past behavior is a strong driver of Internet email usage behavior. An earlier study by Jolly (2003) on the relative strength of habit and cognitive-based intentions in online gambling had shown that the habit construct had greater explanatory and predictive power then cognitions. A strong habitual response suggests that changing the patterns of Internet email use may be very difficult.

For government policy makers or corporations alike who want to encourage the adoption of the Internet email system as the major communication tool so as to achieve a paperless environment, these findings have several implications. To encourage a change in online habits, they will need to implement strategies to initiate the habit of using emails as a main source of communication. It is understandable that in a habitual environment, any cognitive-based strategies will not likely to be effective. This is based on extensive research on promotion efforts with NBD-type markets such as the consumer packages good (c.f. Ehrenberg et al. 1994) where few examples of success can be found. The success of traditional advertising or communication methods, by working through the buyers’ cognitive structure, has had little support for long-term brand building in NBD-type markets (Barwise & Ehrenberg 1988). The usual generalization in NBD markets is that one should increase penetration of the market and frequency of purchase will follow (East 1997). In order to promote email usage, regulators may have to provide incentives strong enough to switch behavior such as encourage quicker tax return if annual tax filing are done through the Internet email system or make departmental queries significantly faster through emails. Based on reinforcement theory, the purpose is to encourage email usage through incentives, stimulating a new habit or switching from an old one for a given period of time such that when incentives are retracted, new behavior still follows.

Very often, the desire of corporations as well as government Internet public policy makers is to stop the forwarding of nuisance emails on a massive scale such as spamming and deadly Internet viruses. The current findings will help them to implement more effective policies. Generally, more heavy-handed behavioral-based methods such as fines will be needed to discourage or prevent the habits of forwarding such emails. Online advertising or warning messages are unlikely to have any significant effects. The discovery of a strong habitual behavior in email use would provide support for tougher legislations and more governmental control over the Internet as opposed to the self-regulatory frameworks adopted by many western countries such as Australia and USA. This is much similar to the governmental control on compulsive gambling and alcohol drinking.

Despite the conclusions, the results that were based on a relatively small sample size should also be read with caution. Extrapolation of the results must be made cautiously given that the current exploratory study was conducted on a single country and only represented by a sample of higher-education student population. While the NBD model was able to explain the proportion of email activities, these results were based on self-reports. These types of data have a tendency to produce "lazy survey responses" (Nisbett and Wilson 1977). Future research will attempt to measure "real-time" behavior of an enlarged number of participants in order to enhance internal and external validity. Furthermore, the support of the NBD does not rule out other explanations and theoretical paradigms (cf. East 1997) of Internet behavior. Situational or contextual effects and other personality constructs have not been examined in this study. Moreover, Internet users’ behaviors may change depending on the types of product or corporate emails that they received. Research into these areas will likely to provide further insights to current study. If Internet users’ email usage follows an NBD pattern of behavior, one may also suspect these users’ online purchasing behavior to conform to the stochastic theory. Since purchases of frequently-bought products in matured markes have shown to follow the NBD pattern (Ehrenberg 1959), one may think that such patterns of purchasing will be found on the Internet too. This will be an interesting area for future research.

TABLE 1

VARIABLES ALONG WITH THEIR FACTOR AND RELIABILITY TESTS RESULTS

FIGURE 1

NBD-DERIVED RESULTS VERSUS OBSERVED DATA FOR FORWARDING AND SENDING EMAILS

TABLE 2

STATISTICAL TEST OF SIGNIFICANCE BETWEEN OBSERVED AND NBD-DERIVED RESULTS

TABLE 3

EFFECTS OF COVARIATES

TABLE 4

MANOVA RESULTS FOR THE CASE OF FORWARDING INTERNET EMAIL

TABLE 5

MANOVA RESULTS FOR THE CASE OF SENDING INTERNET EMAIL

REFERENCES

Allport, F. H. (1924), Social Psychology, Boston: Houghton Mifflin.

Allport, Gordon (1985), "The Historical Background of Social Psychology," in The Handbook of Social Psychology , Vol. 1, eds. G. Lindzey and E. Aronson, New York: Random House, 1-46.

Balabanis, George and Stefanos Vassileiou (1999), "Some Attitudinal Predictors of Home-Shopping through the Internet," Journal of Marketing Management, 15, 361-385.

Barwise, P. and A.S.C. Ehrenberg (1988), Television and Its Audience, London: Sage Publications.

Bettman, James R., Eric J. Johnson, and John W. Payne (1991), "Consumer Decision Making," in Handbook of Consumer Behavior, eds. Thomas S. Robertson and Harold H. Kassarjian, Englewood Cliffs (NF), 50-84.

Chatterjee, Patrali (2001), "Online Reviews: Do Consumers Use Them?," Advances in Consumer Research, 28, 129-133.

Chau, Y.K., M. Cole, A. P. Massey, M. Montoya-Weiss, and R. M. O’Keefe (2002), "Cultural Differences in the Online Behavior of Consumers", Communications of the ACM, 45(Oct), 138-143.

East, R. (1997), Consumer Behaviour; Advances and Applications in Marketing, London: Prentice Hall Europe.

Ehrenberg, A., K. Hammond, and G. Goodhardt (1994), "The After-Effects of Price Related Consumer Promotions," Journal of Advertising Research, 34(4), 11-21.

Ehrenberg, A.S.C. (1959), "The pattern of consumer purchases," Applied Statistics, 8(1), 26-41.

Ehrenberg, A.S.C. (1971), "Regularities of Behaviour," in Consumer Behaviour, eds. A.S.C. Ehrenberg and F.G. Pyatt, U.K: Penguin Books, 13-27.

Ehrenberg, A.S.C. (1972), Repeat-Buying: Theory and Applications, London: North-Holland Publishing.

Ehrenberg, A.S.C. (1987), The Television Audience: Patterns of Viewing, An Update, England: Gower.

Gattiker, Urs E., Pia Ostergaard Pedersen, and Stefano Perlusz (2002), "Relationship Marketing: an Empirical Investigation about Users’ Acceptance of Free Email/SMS Content," EICAR Conference Best Paper Proceedings, 152-193.

Gefen, David and Detmar W. Straub (1997), "Gender Differences in the Perception and Use of E-Mail: an Extension to the Technology Acceptance Model," MIS Quarterly, December, 389-400.

Goodhardt, G.J., A.S.C. Ehrenberg, and M.A. Collins (1975), The Television Audience: Patterns of Viewing, England: Saxon House.

Greenwood, M. and G. Yule (1920), "An Inquiry into the Nature of Frequency-Distributions Representative of Multiple Happenings with Particular Reference to the Occurrence of Multiple Attacks of Disease or of Reported Accidents," Journal of the Royal Statistical Society, 83, 255-279.

GVU’s Ninth WWW User Surveys (1998), http://www.gvu.gatech.edu/user_surveys/.

Hammond, K., A.S.C. Ehrenberg, and G J. Goodhardt (1996), "Market segmentation for competitive brands," European Journal of Marketing, 30(12), 39-49.

Hoffman, Donna L. and Thomas P. Novak (1996), "Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations," Journal of Marketing, 60(July), 50-68.

Hoffman, Donna L., Thomas P. Novak, and Ann E. Schlosser (2003), "Locus of Control, Web Use, and Consumer Attitudes toward Internet Regulation," Journal of Public Policy and Marketing, Vol. 22(1), 41-57.

Holland, Jonna, Stacey Menzel Baker (2001), "Customer Participation in Creating Site Brand Loyalty, " Journal of Interactive Marketing, 15(4), 34-45.

Jolley, D.W. (2003), "The effects of habit and satisfaction on customer profitability: an application to online gambling," unpublished dissertation, University of Western Australia.

Kanvil, Nashiba and Kanayo F. Umeh (2000), "Lung Cancer and Cigarette Use: Cognitive factors, Protection Motivation and Past Behaviour, British Journal of Health Psychology, 5, 235-248.

Korgaonkar, Pradeep K. and Lori D. Wolin (1999), "A Multivariate Analysis of Web Usage," Journal of Advertising Research, 39(March/April), 53-68.

Kwak, Hyokjin, Richard J. Fox, and George M. Zinkhan (2002), "What Products Can Be Successfully Promoted and Sold via the Internet?," Journal of Advertising Research, 42(1), 23-38.

Levene, Mark and Poulovassilis, Alexandra (2001), "Web Dynamics," http://www.nagivationzone.com/library/papers/web.dynamics.pdf.

Levenson, Hanna (1974), "Activism and Powerful Others: Distinctions Within the Concept of Internal-External Control," Journal of Personality Assessment, 38, 377-383.

Marinova, Ana, Jamie Murphy, and Brian L. Massey (2002), "Permission E-mail Marketing as a Means of Targeted Promotion," Cornell Hotel and Restaurant Administration Quarterly, February, 61-69.

Matsumoto, David (2000), Culture and Psychology, 2^nd Edition, USA: Wadsworth.

Mizerski, D. and K. Mizerski (2001), "The effect and implications for a stochastic pattern of lotto game play," International Gambling Studies, September(1), 135-153.

Mizerski, D., K. Mizerski, and R. Miller (2000), "A new paradigm to evaluate pathological gambling behaviour," Proceedings of the National Association of Gambling Studies National Conference, Mildura, Australia: NAGS,.

Morrison, D. G. and D. C. Schmittlein (1988), "Generalizing the NBD model for consumer purchases: what are the implications and is it worth the effort?," Journal of Business and Economic Statistics, 6(2), 145-159.

NFO Interactive (1999), "Promise Not to Tell and I’ll Gladly Shop Online," http://sellitontheweb.com/ezine/news0298.shtml.

Nisbett, Richard E. and Timonthy DeCamp Wilson (1977), "Telling More Than We Can Know: Verbal Reports on Mental Processes," Psychological Review, 84(3), 231-259.

Nua.com (2003), http://www.nua.com/surveys.

Nunnally, Jum C. (1967), Psychometric Theory, New York: McGraw-Hill.

Olsen, Barbara (1995), "Brand Loyalty and Consumption Patterns," in Contemporary Marketing and Consumer Behavior: An Anthropological Sourcebook, ed. John F. Sherry Jr., Thousand Oaks (CA): Sage Publications, 245-281.

Ouellette, J. and W. Wood (1998), "Habit and Intention in Everyday Life: The Multiple Processes By Which Past Behaviour Predicts Future Behaviour," Psychological Bulletin, 124(1), 54-74.

Rotter, Julian B. (1966), "Generalized Expectancies for Internet versus External Control of Reinforcement," Psychological Monographs: General and Applied, 80, 1-28.

Said, L.R. and D. Mizerski (2002), "Applying the NBD to predict the online game play of youth and young adults," in Proceedings of the Marketing and Public Policy Conference, eds. S. Burton, J.C. Andrews, M.B. Mazis, AMA, Washington DC.

Sheehan, Kim Bartel and Mariea Grubbs Hoy (2000), "Dimensions of Privacy Concern Among Online Consumers," Journal of Public Policy & Marketing, 19(1), 62-73.

Triandis, H.C. (1977), Interpersonal Behavior, Monterey, CA: Brooks/Cole.

Uncle, M. and A.S.C. Ehrenberg (1990), "The Buying of Packaged Goods at US Retail Chains," Journal of Retailing, 66(3), 278-296.

Uncles, M., A.S.C. Ehrenberg, and K. Hammond (1995), "Patterns of Buyer Behavior: Regularities, Models, and Extensions," Marketing Science, 14(3), 71-78.

Wagner, U. and A. Taudes (1986), "A Multivariate Model of Brand Choice and Purchase Incidence," Marketing Science, 5, 219-244.

Ward, Michard R. and Michael J. Lee (2000), "Internet Shopping, Consumer Search and Product Branding," Journal of Product & Brand Management, 9(1), 6-20.

Watson, J. B. (1919), Psychology from a Standpoint of a Behaviorist, New York: J. B. Lippincott.

Wright, M. (1999), NBDNORMS Version 0.1 (Beta): Software for Calculating NBD and Mixed Poisson Norms of Buyer Behaviour, New Zealand.

Xu, Bo and Surya Yadav (2003), "Effects of Online Reputation Service in Electronic Markets: A Trust-Based Empirical Study," Ninth Americas Conference in Information Systems, 404-409.

----------------------------------------