Online Appendix to: Online Gambling Behavior: The Impacts of. Cumulative Outcomes, Recent Outcomes, and Prior Use

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1 Online Appendix to: Online Gambling Behavior: The Impacts of Cumulative Outcomes, Recent Outcomes, and Prior Use Appendix 1. Literature Summary Table 1 summarizes past studies related to gambling behavior in terms of several criteria including theories, methodologies, and findings. Each of these prior studies sheds light on a certain aspect of our examination into online gambling, but none of them cover the same spectrum of the present study. In particular, although early gambling studies paid attention to the amount of the bet as a focal behavior (as with the present study), they were conducted in experimental, but not real-life, settings (Tversky and Kahneman 1981, Thanler and Johnson 1990). Meanwhile, whereas recent gambling studies were conducted in field settings, they examined other behaviors such as the number of casino visits, the number of bets placed, or frequency of purchase (Oh and Hsu 2001, Croson and Sundali 2005, Lam and Mizerski 2009). It should also be noted that these experimental and field studies rarely take into account cumulative wins and losses because of the limitations in tracking individual-level data in the contexts of offline gambling. An exception is a casino gambling study by Narayanan and Manchanda (2012), which differentiates between recent and cumulative outcomes. Yet their study focused on within-trip analysis instead of tracing individuals behavior over a long period of time across trips. 1

2 Study Tversky and Kahneman (1981) Methodology One-stage experiments Table 1. Summary of Past Studies Related to Gambling Behavior Context / Sample Sample Theory Controlled variables Findings Setting frame size Lab Students Prospect theory na People are risk averse in the gain frame but risk seeking in the loss frame. Thaler and Johnson (1990) Two-stage experiments Lab Students House money effect; Breakeven effect na People are risk averse after prior losses but risk seeking after prior gains. Even after prior losses people become risk seeking when there is a chance for break-even. Oh and Hsu (2001) Longitudinal; Survey Two land-based casinos of Iowa Gamblers in Iowa 485 Theory of planned behavior Past behavior; Attitude; Subjective norm; Perceived behavioral control; Intention; Future behavior The number of casino visits for gambling in the past has a positive relationship with the number of casino visits for gambling in the future. Croson and Sundali (2005) Longitudinal; Videotapes Roulette Gamblers in a casino 139 Hot hand The number of bets placed on the previous spin; The number of bets placed on their first trial Winning a bet increases the number of bets placed in the next period. Lam and Mizerski (2009) Longitudinal; Survey Lotto; Gaming machines; Horse racing; Bingo Gamblers in Australia 3498 NBD- Dirichlet model Aggregate penetration; Average frequency of purchase in the population Gambling behavior is primarily dependent on past behavior. Narayanan and Manchanda (2012) Longitudinal; Archival Casino Gamblers with loyalty cards in a casino 2000 Gambler s fallacy Last bet; Last win; Last loss; Cumulative win, Cumulative loss The amount of the bet is negatively associated with last bet and positively associated with last loss. 2

3 Appendix 2. Definitions and Explanations of Variables in Table 1 The variables and symbols in Table 1 in the main paper are defined as follows: t = 9, 10,, T (maximum 35) identifies the week since registration. i = 1, 2,, I identifies the individual user. j = 0, 1,, 4 identifies different countries (i.e., 0 as Germany, 1 as Greece, 2 as Poland, 3 as Spain, and 4 as Turkey). ln( STAKES country j. ln( STAKES country j. CUMUGAIN j, i, t ) aggregate amount of monies in Euros wagered during week t by subject i from ) j, i, t 1 j, i, t 1 aggregate amount of monies in Euros wagered during week t - 1 by subject i from cumulative net gain in 1,000 Euros, if monies won exceed monies wagered until the start of week t (0 otherwise), for subject i from country j. CUMULOSS j, i, t 1 cumulative net loss in 1,000 Euros, if monies wagered exceed monies won until the start of week t (0 otherwise), for subject i from country j. GAIN immediate gain in 1,000 Euros, if monies won exceed monies wagered during week t - 1 j, i, t 1 (0 otherwise), for subject i from country j. LOSS immediate loss in 1,000 Euros, if monies wagered exceed monies won during week t - 1 j, i, t 1 (0 otherwise), for subject i from country j. RU j, i, t 1 level of Regular Use: operationalized as the number of active gambling weeks during the past 6 weeks before the end of week t - 1 by subject i from country j. EU j, i, t 1 level of Extended Use: operationalized as the number of different gambling service types that subject i from country j participated in during the week before the start of week t - 1. FreqUse j, i Frequent User dummy variable: equal to 1 if subject i from country j placed a relatively large number of bets during initial period, 0 otherwise. OccaUse j, i Occasional User dummy variable: equal to 1 if subject i from country j placed a relatively small number of bets during initial period, 0 otherwise. ln ( AGE ) logarithm of the age of subject i from country j upon registration of account. j, i GENDER j, i dummy variable equal to 1 if subject i from country j is a male, 0 otherwise. GREECE dummy variable equal to 1 if subject i is from Greece, 0 otherwise. j POLAND dummy variable equal to 1 if subject i is from Poland, 0 otherwise. j SPAIN dummy variable equal to 1 if subject i is from Spain, 0 otherwise. j TURKEY dummy variable equal to 1 if subject i is from Turkey, 0 otherwise. j ε within subject error term in week t by subject i from country j. j, i, t ε within subject error term in week t - 1 by subject i from country j. j, i, t 1 φ error term, which is distributed iid N(0, σ 2 ). j, i, t ρ correlation between current and lagged within-subject errors. υ error term, which is distributed iid N(0, τ j, 1 ). ji,,0 3

4 υ error term, which is distributed iid N(0, τ j, 2 ). j, i,1 ω error term, which is distributed iid N(0, τ 1 ). j, i,0 ω error term, which is distributed iid N(0, τ 2 ). j, i,1 4

5 Appendix 3. The Integrated Model Estimation results for the integrated model are presented in Model 8 of Table 4 in the main paper. The INTERCEPT (δ 0,0,0 ) indicates the baseline stakes for moderate gamblers from Germany. Because the Centering Within Cluster method is used to center the Level 1 variables, the coefficients on Level 2 and Level 3 can be understood as the deviation from the average baseline level of gambling. Thus, the dummy variables (GREECE, POLAND, SPAIN, TURKEY, FreqUse, OccaUse, and Gender) represent the differences in baseline stakes among gamblers from these groups. At the country level, the results show that baseline stakes are significant for each country group and vary across the five countries (except that Germany and Turkey are at the same level, δ 4,0,0 = 0.014, p >.10). For example, as the intercept group, the Germans ln(stakes j, i, t) is larger only than that of the Poles (0.723 for the Germans and = for the Poles), with the Turks and Germans at the same level, after the Spanish ( = 1.029) as the second largest group, and the Greeks as the largest ( = 1.484). At the gambler level, the coefficients of FreqUse (π 1,0 = 1.102) and OccaUse (π 2,0 = ) mean that compared with moderate gamblers, frequent gamblers on average have a 110.2% higher level of baseline gambling, but occasional gamblers have a 29.6% lower level of baseline gambling. The coefficient of age (π 3,0 = 0.636) represents a 0.636% increase in the average amount of gambling from the baseline level because of a 1% increase in age. Finally, the coefficient of gender (π 4.0 = 0.163) indicates that on average, male gamblers wager 16.3% more than female gamblers. 5

6 Appendix 4. Additional Analyses 4.1. Alternative Time Window Choices in Measuring Key Research Variables Some of our key research variables, including online gambling, regular use, and extended use, involve the choice of an appropriate time window in which to measure them. In this subsection we provide additional explanations of our choices and of the robustness of our results with respect to choices of alternative time windows. Online gambling. Online gambling is measured by the aggregated amount of money wagered during a week. Although aggregating stakes in a weekly fashion is reasonable theoretically, our results could be sensitive to alternative time window choices, such as aggregating in a half-week (4 days) or two-week (14 days) time window. Therefore, we reanalyzed the integrated model by using either a half-week or a two-week time window. The results of research hypotheses from these alternative analyses are consistent with our original results that were based on a one-week time window. Regular use. Regular use is measured as the number of active gambling weeks during the past six weeks. Based on the discussion in 2.3, regular use would presumably require a longer time to observe, because the consistency of a gambler s behavior is not immediately clear by observing it for only one week. Although a six-week time window is theoretically sound, an alternative choice of a time window could alter our results substantially. Thus, we checked if a smaller (e.g., four weeks) or larger (e.g., eight weeks) window affected our results. We found that a large proportion (over 35%) of our sample gambled for four of the past four weeks; thus, using four weeks as the time window would fail to capture the variation in the level of regular use among these observations. In this case, regular use based on a four-week window is not adequate. Meanwhile, we reanalyzed the model using an eight-week time window and found consistent results. Extended use. Extended use in our context is conceptualized as the number of gambling service types in the bwin system recently adopted by a user. This should be quite clear by observing gambling over one week. We also reanalyzed our model using five alternative time windows (i.e. 2, 3, 4, 5, and 6 weeks) for extended use and obtained results consistent with the original version Aggregation Type of Weekly Online Gambling 6

7 In this subsection, we further explain how we aggregated online gambling in a week and whether our aggregation method is robust. Online gambling was measured as the aggregated total amount of stakes bet within a certain week. We considered using total weekly bets to represent online gambling primarily because this approach is consistent with the most common practice in prior IS research. Specifically, most prior studies of IT use have relied on some form of the aggregated total to measure system use. For example in Venkatesh et al. (2008, p. 409), in measuring the duration of system use, the authors asked On average, how many hours do you use the system each week? This instrument essentially measures the total number of hours of system use in a typical week. Also in Adams et al. (1992, p. 229), system use is measured by the number of messages they had sent and received on the previous working day, as well as the number they sent and received on a typical day. They, too, essentially used the total number of messages to represent system use. Kraut et al. (1999, p. 293) adopted the weekly total method in measuring various aspects of Internet use. Such a practice is also common in other social science fields, such as sociology, political science, consumer behavior, marketing, etc. In contrast, using the highest or lowest value in measuring system use is much less common in the prior literature. We tried to further reveal the within-week consistency and variation in our online gambling data. Within-week inconsistency in gambling, if any existed, would primarily be in a form in which some people place heavy bets on a single day of a week and others place small bets almost daily during a week. To show that the within-week consistency of gambling is relatively high, we used Cranbach s α. If we classify each of the seven days of a week into seven different variables, they can be seen as seven items for a construct in the additive form, which is the total gambling. Then a large α (above 0.8) would indicate that most people in our sample gamble consistently across the seven days of a week. If this is true, then the scenario mentioned earlier would have been largely false: that many people tend to place heavy bets on only one day of a week while many others tend to place smaller bets almost daily during a week. We calculated Cranbach s α in the way described above. For the entire sample, Cranbach s α is very high (0.93), which, at least to some extent, implies that over the period we observed, the gambling usage pattern across seven days of a week is very consistent for most of the gamblers in our sample. 7

8 To evaluate the potential impact of the within-week variation on our results, we reanalyzed our model using a subsample that constitutes 90% of our original sample. This subsample was selected based on the 90 th percentile of the coefficient of variation (Harrison and Klein 2007, also referred to as relative standard deviation ) as a measure of within-week variation in the daily stakes during a certain week. Therefore, a significant proportion (10%) of the original sample, those gambling activities that vary most dramatically within a week, were excluded from the subsample. The results based on this subsample were consistent with our original results, indicating that the loss of a medium amount of within-week variation because daily gambling was aggregated into weekly measures should not damage our results significantly Occasional-Moderate-Frequent Users Re-grouping Our integrated model used a grouping method that partitions the entire sample into occasional-moderatefrequent groups as percentages. Because of the controversy in the relevant literature about grouping methods, it is reasonable to assume a 10% interval for the occasional-user group. Moreover, defining frequent users as a 7% category seems too low; therefore, we allowed it to expand to as much as 20%. Altogether, we tried nine additional models with the occasional-moderate-frequent users groupings as follows: (1) With the occasional-user group comprising 30%: a ; b ; c (2) With the occasional-user group comprising 40%: a ; b ; c (3) With the occasional-user group comprising 50%: a ; b ; c The results of the model comparisons were reviewed, but there were essentially no differences between the integrated model and these nine Addition of Discrete Variable Week t In addition, we compared an alternative model that differs from the integrated model only in its inclusion of a discrete variable Week t in its Level 1 Equation (1). Although a discrete variable or a series of dummy 8

9 variables are frequently included for the time effect in a panel-data model, we did not originally resort to this because no effect of this variable is hypothesized in our specific context. However, for the sake of model diagnostics, we examined its effect on the entire model by including it in this alternative model. The effect of this time variable is present in the results; nevertheless, not a single estimated coefficient diverges significantly from the original estimation. References Adams, D.A., R.R. Nelson, and P.A. Todd (1992), Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication, MIS Quarterly, 16, 2, pp Croson, R. and J. Sundali (2005), The Gambler s Fallacy and the Hot Hand: Empirical Data from Casinos, The Journal of Risk and Uncertainty, 30, 3, pp Harrison, D. and K. Klein (2007), What s the Difference? Diversity Constructs as Separation, Variety, or Disparity in Organizations, The Academy of Management Review, 32, 4, pp Kraut, R., T. Mukhopadhyay, J. Szczypula, S. Kiesler, and B. Scherlis (1999), Information and Communication: Alternative Uses of the Internet in Households, Information Systems Research, 10, 4, pp Lam, D. and R. Mizerski (2009), An Investigation into Gambling Purchases Using the NBD and NBD- Dirichlet Models, Marketing Letters, 20, 3, pp Narayanan, S. and P. Manchanda (2012), An Empirical Analysis of Individual Level Casino Gambling Behavior, Quantitative Marketing and Economics, 10, 1, pp Oh, H. and C. Hsu (2001), Volitional Degrees of Gambling Behaviors, Annals of Tourism Research, 28, 3, pp Thaler, R.H. and E.J. Johnson (1990), Gambling with the House Money and Trying to Break Even: The Effects of Prior Outcomes on Risky Choice, Management Science, 36, 6, pp Tversky, A. and D. Kahneman. (1981), The Framing of Decisions and the Psychology of Choices, Science, 211, 4481, pp Venkatesh, V., S.A. Brown, L.M. Maruping, and H. Bala (2008), Predicting Different Conceptualizations of System Use: The Competing Roles of Behavioral Intention, Facilitating Conditions, and Behavioral Expectation, MIS Quarterly, 32, 3, pp