Clothes and shoes were the top-purchased counterfeit product types. Global consumers resorted mainly to social media and e-retail platforms to purchase counterfeits, and among those purchasing counterfeits through social media, 68% did so via Facebook.

When differentiating counterfeit buyers and non-buyers, buyers were more likely to be male, younger, highly religious, frequent online shoppers, and from lower income households.

Despite consumers expressing they were mostly motivated by a deal or bargain (economic benefits motivation) to buy counterfeit products, enjoyment with counterfeit shopping and buying (hedonic motivation) positively predicted counterfeit purchase intentions and behaviors the strongest.

Attitudes and perceptions of what other people typically do positively contributed to counterfeit purchase intentions and behaviors. Consumers were more likely to have bought counterfeit goods (deceptively and non-deceptively) in the past and have intentions to do it in the future if:

• they had positive attitudes toward counterfeit purchasing, and
• they believed that their peers in the country, town, or the Internet (distal reference groups), as well as close friends and family (proximal reference groups) accepted this behavior and engaged in it.

If a consumer has high awareness of the risks of buying counterfeits online (threat severity) and believes that they can recognize counterfeit on e-retail platforms and avoid buying it (e-retail response efficacy) they are more likely to refrain from buying counterfeits compared to their counterparts with lower threat severity and e-retail response efficacy perceptions. On the other hand, greater feelings of vulnerability to the risks of counterfeit buying (threat susceptibility) and higher levels of self-efficacy, or confidence in one’s ability to protect themselves against counterfeit risks increased the likelihood of counterfeit purchase (both non-deceptive and deceptive).

Per our global segmentation analysis, on a global level, non-buyers, occasional buyers, and frequent buyers of counterfeit products share common demographic characteristics, yet they are vastly different in terms of what predicts their counterfeit purchase intentions, as well as non-deceptive and deceptive purchase.

Descriptive and predictive analytics showed significant differences in counterfeit purchase behavior and what predicts it. These insights are helpful for a more tailored approach to consumer education and interventions that aim at changing consumers’ counterfeit attitudes and purchase behaviors