Full model simulation Following the analysis for the fashion items, we use the parameter range of Table 2 to explore the monetary consequences of the introduction of a knockoff. The ranges were taken separately for each of the two product groups. To obtain a range of values of and parameters, α β we draw for members of each group a random value in the range of the group mean, plus or minus 20% (i.e., if handbag average is .76, the range would be between .61 and .91). We took a α random draw of 10,000 parameter sets for each product group, and compared the NPV obtained at the end of the growth horizon. The time horizon used for the simulation was 132 months (periods), the maximum number of periods in our dataset. In the simulated growth parameters, we used the variability of the distribution (σ/μ) rather than the standard deviation (σ), as varying and orthogonally does μ σ not take into consideration their correlation, nor the relative effect of on (Snedecor and σ μ Cochran, 1989). Due to the possible nonlinear effects of the diffusion and threshold parameters on the NPV ratio, we used a lognormal configuration when drawing the patterns (Goldenberg et al. 2007). Since the external influence parameter (p) is significantly correlated with in our μ sample (for both handbags and apparel, with correlations of .893 and .761 respectively), we controlled for that in our pattern draws (the correlations between all the other parameters were not significant). To translate the temporal adoptions into NPV, we assumed an annual discount rate of 10%, and assumed one unit of revenue for each item upon adoption. on NPV Results: Knockoffs’ Effects Table 3 presents the results of the three main effects substitution, acceleration, and – uniqueness as well their combined effect on the NPV of the original in percentage of change. –
Full model simulation Following the analysis for the fashion items, we use the parameter range of Table 2 to explore the monetary consequences of the introduction of a knockoff. The ranges were taken separately for each of the two product groups. To obtain a range of values of α and β parameters, we draw for members of each group a random value in the range of the group mean, plus or minus 20% (i.e., if handbag average α is .76, the range would be between .61 and .91). We took a random draw of 10,000 parameter sets for each product group, and compared the NPV obtained at the end of the growth horizon. The time horizon used for the simulation was 132 months (periods), the maximum number of periods in our dataset. In the simulated growth parameters, we used the variability of the distribution (σ/μ) rather than the standard deviation (σ), as varying μ and σ orthogonally does not take into consideration their correlation, nor the relative effect of σ on μ (Snedecor and Cochran, 1989). Due to the possible nonlinear effects of the diffusion and threshold parameters on the NPV ratio, we used a lognormal configuration when drawing the patterns (Goldenberg et al. 2007). Since the external influence parameter (p) is significantly correlated with μ in our sample (for both handbags and apparel, with correlations of .893 and .761 respectively), we controlled for that in our pattern draws (the correlations between all the other parameters were not significant). To translate the temporal adoptions into NPV, we assumed an annual discount rate of 10%, and assumed one unit of revenue for each item upon adoption. Results: Knockoffs’ Effects on NPV Table 3 presents the results of the three main effects – substitution, acceleration, and uniqueness – as well their combined effect on the NPV of the original in percentage of change.
FashionPiracy-2.docx