17% of consumers in UK buy products that risk their health and safety

Product counterfeiting is a huge, global industry. The OECD and EUIPO (2021) report valued the international trade in counterfeit foods in 2019 at $464 billion, over 70% of which originates in China/Hong Kong, whilst Turkey’s share has tripled to 12%. The economic, societal and personal impacts of the trade are well documented: loss of economic output, loss of tax revenue, loss of legitimate jobs, underpaid workers and labour abuses, and dangerous products that cause injury and death (Chaudhry, 2022; Wilson, 2022). Yet, the grim reality is that ordinary people are complicit in the trade by creating the demand for counterfeit products. This demand has been enabled by the advent of e-commerce on the Internet, connecting consumers with suppliers on the other side of the world. ‘Social commerce’ has further facilitated the trade, that is, commerce that takes place via social media platforms (Zhang & Benyoucef, 2016). The number of companies offering influencer marketing services has substantially increased in just the last few years. It grew by 26% in 2021 alone to reach 18,900 firms worldwide (Influencer Marketing Hub, 2022).

35% of males aged 16 to 60 are knowing buyers of counterfeits

The use of social media (SM) influencers as a marketing tool has inevitably led to some attaching themselves to the trade in counterfeits. The emergence of deviant SMinfluencers, who promote and endorse counterfeit goods, is attracting attention (Chaudhry, 2022). Amazon launched a rare lawsuit in 2020 against two influencers, accusing them of promoting counterfeit products listed on Amazon’s platform (Palmer, 2021). The influencers reached a financial settlement with Amazon in 2021 and were barred from linking to, marketing or selling on the platform. A pilot study commissioned by the IPO surveyed 1,000 females aged 16 to 60, active on social media and resident in the UK to provide insight into the impact of SM influencers on consumer decisions to purchase counterfeit goods (Shepherd et al., 2021). The survey found that 10% are prompted by SM influencers to buy counterfeits, and a further 3% use the recommendations of the influencers in their searches for counterfeits. The research deliberately focused on females because industry reports indicate that influencer marketing is highly gendered (Influencer Marketing Hub, 2021; The Week, 2021), and a study by Klear (2019) found that 84% of influencers who create sponsored posts are female. We therefore expected deviant SM influencers to have a higher impact on females. The present research replicates this study in the UK but targeting the equivalent male population.

Research design

The research design involved an anonymous online survey of 1,000 male participants based in the UK, aged 16 to 60 and who use social media at least once per week. The survey targeted the male population in order to supplement the previous study that was limited to females. The results cannot therefore be generalised beyond the limits of the sample frame. Furthermore, as the self-report survey inquired into deviant purchasing behaviour, the level of counterfeit purchasing may be underestimated due to social desirability bias (Jann et al., 2019). The survey was administered through the Qualtrics online system and drew on the Qualtrics panel using the representative quotas for age and regional distribution in the Appendix. The data was collected during August 2022. The questionnaire replicated the previous questionnaire except for minor adjustments to accommodate male respondents, for example, replacing ‘Chanel’ with ‘Nike’. To quantify the level of influence of social media personalities, the questionnaire asked respondents whether they had purchased counterfeit goods in the prior year as a result of influencer endorsements. The survey used the following definition of counterfeit to guide the respondents:

17% buy products that risk their health and safety

Counterfeits are items that look identical to a genuine product with or without the official branding/logo, but are not made by the brand and may be of lower quality, for example, sneakers of an identical design to Nike Sneakers with or without the Nike logo.

The majority of the questions were multiple choice, single answer questions set out on four-point scales, for example: not important at all, somewhat unimportant, somewhat important, very important plus ‘don’t know’ where appropriate. This approach allowed the responses to be categorised into two groups for analytical purposes, negative responses, and positive responses. The findings set out in this report use this binary classification. The analysis is based on simple descriptive statistics, tabulated summaries and charts to identify trends. The report should be read in conjunction with the previous report that focused solely on female consumers (Shepherd et al., 2021).

Counterfeit purchasing

This section of the report addresses the main aim of the research: the extent to which SM influencers are successful in influencing male consumers’ decisions to purchase counterfeit goods.

Key findings

• twice as many male as female participants purchase counterfeit goods: 35% of males knowingly purchased a counterfeit in the year prior to the survey
  compared to 17% of female participants in the previous research

• 60% of male participants who have knowingly purchased a counterfeit are aged 16 to 33, generating 61% of demand

• 36% of knowing buyers are habitual buyers, generating 67% of the demand.

• 17% buy products that risk their health and safety

• sports and sportswear, clothing, accessories, jewellery and watches are the most popular product categories

• 31% of male participants are influenced by social media endorsements.

• 7% proactively search for counterfeit items, using the SM posts to assist in their searches

• 24% are prompted by SM endorsements to buy counterfeits

• 18% are knowing responders who are aware the products are counterfeit

• 6% are deceived responders who are unaware the products are counterfeit

3.1 Knowing purchasers of counterfeits

In order to set the context for assessing the impact of SM influencers, the respondents were asked how many counterfeit products they had intentionally purchased in the prior year. Overall, 35% reported that they had knowingly purchased counterfeits (Table 1). This is double the percentage of females (17%) in the previous survey (Shepherd et al., 2021).

The age-related prevalence of counterfeit purchasing in Figure 1 illustrates four notable features. Firstly, as with females, the prevalence of male purchasers of counterfeits reduces with age: 60% of the male knowing buyers are in the 16-33 age group. These younger males are twice as likely (53%) to knowingly make illicit purchases than those aged over 34 (23%). Secondly, prevalence is higher for males in all age groups. Thirdly, compared to females, the steep decline in the prevalence of deviant male purchasing is delayed by 5 to 10 years. Finally, the gender difference is mainly driven by repeat (2-5/year) or habitual (6+/year) purchasing. Thus, males sustain the highest level of deviant purchasing into their 30s and remains relatively high at least until they reach 60.

Table 1: Intentional counterfeit purchasers

Figure 1: Age distribution of intentional counterfeit purchasing

A rough estimate of purchasing demand is obtained using the frequency category mid-points in Table 1, for example 3.5 is the mid-point of the ‘2 to 5’. The calculations are set out in Table 2, which shows that:

• 60% of knowing buyers are aged 16 to 33, generating 61% of demand
• 36% of knowing buyers are habitual buyers, generating 67% of the demand

Table 2: Counterfeit demand matrix

3.2 Types of counterfeit products

The respondents indicated the types of counterfeits they intentionally purchased in the prior year (Table 3). The total adds up to more than 35% because 42% of the counterfeit buyers had purchased products from multiple groups. The top three product categories for males are sports and sportswear (14%), clothing and accessories (13%), jewellery and watches (10%). In comparison, the top three categories for females are heavily skewed to clothing and accessories (10%), jewellery and watches (5%), beauty and hygiene (5%).

The data indicates a high demand for counterfeits that place consumers at risk for their health and safety. Overall, 17% of male consumers purchased fakes in at least one of the higher risk categories: beauty/grooming/hygiene, electrical products or electronics, toys, or alcohol. This is double the number of females (8%) who purchased counterfeits in these product categories. The implication of these findings is that a substantial minority of consumers are either unaware of the risks or are content to take the risks.

• 60% of knowing buyers are aged 16 to 33, generating 61% of demand
• 36% of knowing buyers are habitual buyers, generating 67% of the demand
• Fashion, accessories, jewellery and cosmetic products are the most popular product categories
  

  31% of UK male participants aged 16 to 60 are influenced by SM endorsements in their purchases of counterfeits

Impact of social media influencers

Table 3: Counterfeit product groups intentionally purchased

3.3 SM influenced purchasers

Overall, 31% of male respondents reported that they had purchased counterfeits in the prior year, either deliberately or by mistake, following SM influencer endorsement. Table 4 sets out the number, percentage and age group of male buyers. It is also categorised into the number of products they reported buying in the prior year. It shows that about 10% of males are very frequent buyers, purchasing six or more endorsed products in a year. The implication is that the purchasing interactions with social media influencers becomes a habitual routine activity for about one-third of buyers.

Table 4: SM endorsed counterfeit purchasers

The overall split between knowing and unknowing purchasers is (Figure 2):

• knowing purchasers: males 25%, females 10%
• unknowing purchasers: males 6%, females 4%

The age distribution of deviant purchasing is notably similar to Figure 1, highest in the 16-24y group and, in contrast to females, males’ purchasing is substantially sustained into their 30s. The male 16-33y group is over twice as likely (49%) than older consumers (19%) to buy endorsed counterfeits, and it accounts for 62% of all male purchasers.

Figure 2: Age distribution of SM endorsed counterfeit purchasing

3.4 Pathway to the SM endorsement

In order to provide insight into the purchasing pathway, the respondents were asked how they encountered the endorsements broadcast by the SM influencers (Table 5). The results indicate that buyers mainly encounter the endorsements by chance.

Table 5: Endorsement pathway

Table 6 sets out the data as a matrix of four categories in two dimensions: consumer (knowing / deceived), SM influencer (assist / prompt). It highlights three types of consumers and the associated interactions with the SM influencers: counterfeit hunters, knowing responders, and deceived responders. The hunters in the sample (7%) set out with the intention of buying counterfeit goods and encounter SMendorsements during their online searches. An endorsement has no effect on the hunter’s pre-existing intention to buy counterfeits, but the SM posting is a facilitating step on the consumer journey, and it may affect which counterfeit product the buyer selects.

On the other hand, SM influencers are key catalysts in creating the intention to buy counterfeits amongst the responder consumers. Influencer endorsements were successful in prompting 24% of the sample to purchase counterfeits. Again, the younger generation of participants is far more susceptible to the influencers’ guile, inducing over one-third of males (38%) to respond positively compared to 15% of the over 33y group (Table 5). Three-quarters of responders are knowing responders (18.5%) who realise the products are counterfeit. The deceived responders (5.6%) were unaware at the time of purchase that the products are counterfeit.

Table 6: Influencer-consumer matrix

Factors influencing purchasing decisions

The results hitherto clearly show a correlation with age. This section of the report explores additional determinants. It is organised into three themes: the role of trusted others, attitudes to counterfeits, and risk perception.

Key findings

• four key factors influence counterfeit purchasing decisions: trusted others including complicit influencers, rationalisations, risk blindness and risk appetite
• widespread definitional confusion supports the rationalisations
• males are more susceptible to these influences than females
• younger males are more susceptible to these influences than older males
• Overall, 23% of males are more likely to buy counterfeits when they are endorsed by influencers
• overall, about 40% approve rationalisations that justify buying counterfeits.
• 23% believe counterfeits are not a health and safety threat
• 19% believe counterfeits do not harm businesses and jobs

4.1 Trusted others

Importance of trusted others

In order to understand the influence of trusted others, the respondents indicated the importance of family, friends and colleagues on their legitimate purchasing decisions. They also recorded the extent to which social media content helped inform their decisions. This variable represents the broad influence of social media including online networks and SM influencers.

The results indicate that family has the most social influence across all age groups (important to 71% overall) followed by friends (68% overall). Although the respondents report that social media is the least influential, it still has a substantial impact in helping male participants with their purchasing decisions (36% overall). However, the most important result is that participants from the younger age groups are more susceptible than those from the older groups to the influence of others (Figure 3), particularly in relation to social media. For females, the influence of social media is highest in the 16-24y group (Shepherd et al., 2021) and then declines, whereas it peaks later in the 25-33y group for males at 53%. The elevated importance of social media in making purchasing decisions in this age group partly explains their high level of counterfeit purchasing.

Figure 3: Importance of trusted others

Trust in social media influencers

The trust relationship between consumers and social media influencers is quantified in Figure 4 using trust and intention dimensions:

Trust dimensions

Verification – belief that SM influencers must have tried the endorsed products

Safety – belief that endorsed products must be safe

Intention dimensions

Genuine products – more likely to buy genuine products because of the endorsement

Counterfeit – more likely to buy counterfeit products because of the endorsement

The correlation between trust and intention illustrates the influence of SMpersonalities: higher trust levels leads to an increased likelihood of purchasing both genuine and counterfeit products. Overall, 41% of male participants are more likely to buy genuine products that are endorsed by SM influencers, and 23% are more likely to buy endorsed counterfeits. The correlation of the age profile with purchasing habits (Figure 1 and Figure 2) and the importance of social media (Figure 3) underscores the power of SM influencers, especially amongst younger males, and partly explains why males sustain their level of counterfeit purchasing into their 30s. The 16-33y group is the most likely to buy counterfeits (35%) compared to 6% of the 52-60y group. The most concerning impact of SM influencers is their role in lowering perceptions of safety risk. It is a contributory factor in the purchasing of higher risk products: 48% of the 16-33y group believe the influencer endorsements mean that the products are safe.

Figure 4: Trust in SM influencers

4.2 Attitudes to counterfeits

Rationalisations: acceptability of buying counterfeits

The previous IPO report identified the role of rationalisations in lubricating the pathway to purchasing counterfeit goods (Shepherd et al., 2021). They are the excuses individuals construct to justify their irrational or deviant behaviour to themselves and to others (Shepherd & Button, 2018). In the present research, the respondents rationalise that purchasing counterfeits is acceptable when it concerns luxury products and when high prices and quality are irrelevant (Figure 5). Using a denial of victim rationalisation identified by Sykes and Matza (1957), 42% of males believe the trade in counterfeits is the manufacturers’ fault for overpricing high brand products. The age distribution for these rationalisations again follows the previous profiles with the majority (58%) of the 16-33y group approving the rationalisations compared with about 20% of the 52-60y group.

Figure 5: Acceptability of buying counterfeits

Rationalisations: definition confusion

Denial of crime is an important rationalisation where a person does not acknowledge that a proscribed behaviour is a crime or involves wrongful intent (Benson, 1985). A confused understanding of what constitutes illegal behaviour feeds into this rationalisation, making it easy for individuals to justify that their intended actions are not wrong. The term ‘counterfeit’ is not defined in statutes, nor is there consensus as to its definition in the literature. The survey sought insight into the respondents’ understanding of the term. The questionnaire asked respondents what the word ‘counterfeit’ meant to them.^{[footnote 1]}They were presented with a range of options in three categories:

Clearly counterfeit: products that copy the design of a legitimate brand and falsely carry its trademark

Possibly counterfeit: products that copy the design of a legitimate brand without its trademark

Legitimate: products which are designer inspired but are clearly different and without the designer trademark / products which are totally different in all aspects to another brand

For each of the definitions selected by respondents, Table 7 sets out the percentage who purchased endorsed counterfeits in the previous year.^{[footnote 2]} It demonstrates that consumers’ purchasing behaviour is closely correlated with their understanding of the term ‘counterfeit’. 35% of respondents selected the ‘clearly counterfeit’ definition and just 1 in 10 (10%) purchased counterfeits. The 40% who selected the ‘legitimate’ definition are the most prolific buyers (53%). The implication is that definitional confusion about what constitutes a counterfeit feeds rationalisations and leads to increased deviant purchasing.

Table 7: Purchasing prevalence for selected definition

Analysis of the data by age follows the familiar descending profile (Figure 6). It reveals the section of the male population most likely to purchase endorsed counterfeits: the 51% of young males aged 16-24y who equate legitimate products with the word ‘counterfeit’. Nearly three-quarters (71%) of this confused group acknowledged purchasing endorsed counterfeits in the prior year. Maturity brings a clearer understanding of what ‘counterfeit’ means and a commensurate decline in deviant purchasing.

Figure 6: Age distribution of purchasing prevalence for selected definition

Importance of product factors

The survey gathered data on the respondents’ attitudes towards a range of products factors, all of which are indicators of customer expectations regarding quality, safety and customer experience. Legitimate companies regard these factors as essential elements in sustaining their businesses. However, a significant minority of males dismiss these factors as unimportant. The age distribution in Figure 7 inverts the results to focus on this dismissive minority: it plots the percentage in each age group who reported the factors as unimportant. The close correlation with attitudes to counterfeits and purchasing habits across the age profile partly explains why a minority are prepared to purchase counterfeits. Their dismissive attitude to key factors, such as quality, safety and market reputation, means that it is economically rational for them to purchase low cost counterfeits. It is therefore not surprising that the most striking differentiation across the age groups is the indifference to fake goods in the younger generation: 28% of the 16-24y group are unconcerned if intended purchases are counterfeit, nearly four times higher than the 8% in the 52-60y group.

Figure 7: (Un)importance of product factors

4.3 Perception of risk

Understanding consequences of buying counterfeits

The research sought to quantify the respondents’ understanding of the socio-economic and safety threats associated with counterfeit products. The respondents indicated the strength of their agreement with the following statements:

• buying counterfeits harms businesses and jobs
• buying counterfeits poses a threat to health and safety

Figure 8 inverts the results to plot the percentage in each age group who do not agree with the statements. A large minority of respondents are dismissive of these risks. Overall, 19% do not see counterfeits as a threat to businesses and jobs, and 23% do not perceive the health and safety risks. The chart again shows that ambivalence in the younger groups is significantly higher than in the older generations.

Figure 8: Perception of counterfeit harm risks

Risk appetite

Respondents’ willingness to take risks was measured using the 11 point self-perception scale recommended by Dohmen et al. (2011) as the most effective measure of general risk attitudes. Respondents were asked to rate their willingness from 0 to 10, where 0 means ‘I am not at all willing to take risks’ and 10 means ‘I am very willing to take risks’. This is an un-calibrated, arbitrary scale, however it has internal validity in assessing the differences between sample groups. The results were categorised into three groups for the analysis:

Risk averse – responses 0 to 3: males 21%, females 29%

Risk neutral – responses 4 to 6: males 39%, females 40%

Risk taker – responses 7 to 10: males, 40%, females 31%

The risk neutral group is close to 40% for both females and males across all ages. However, a significantly higher portion of males are risk takers (40%) compared to females (31%). The age profile indicates that risk appetite is a significant factor in purchasing behaviour (Figure 9). That the highest level of risk appetite is within the male 25-33y group (63% risk takers) suggests it is a significant factor in sustaining high levels of deviant purchasing into their 30s.

Figure 9: Risk appetite of respondents

Figure 10: Influences on counterfeit purchasing decisions

Conclusions

This study sought to quantify the impact of social media influencers on the intentions of male adults to purchase counterfeit products. The target population was UK males, aged 16 to 60 who regularly use social media. The study replicated a recent IPO project which focused on females (Shepherd et al., 2021). The present study found that over twice as many males (24%) as females (10%) are prompted to purchase counterfeit goods by SM influencers. The top three product categories are sports and sportswear, clothing and accessories, and jewellery and watches. As with females, age is a strong determinant for males with younger males six times more likely than those in their 50s to be induced by SM influencers into buying counterfeits.

As the female-only survey found, four factors influence consumers’ decision to purchase counterfeit product: the influence of trusted others including SM influencers, perceptions of the risks associated with counterfeits, risk appetite, and rationalisations which justify the deviant purchasing. Like the purchasing habits, all four factors are age and gender related. Young males place greater trust in SM influencers, are more blind to the risks, have a higher risk appetite, and more readily construct rationalisations to justify their deviant purchases. Consequently, they buy the most counterfeits.

The analysis of the consumers’ understanding of the term ‘counterfeit’ offers a significant insight into the demand for counterfeits. Widespread misunderstanding of what ‘counterfeit’ means feeds into the rationalisations to normalise the buying habits. The analysis presents empirical evidence of the impact of this definitional confusion: half of males aged 16 to 24 are completely confused about what counterfeit means, and 71% of this group purchase counterfeits. SM influencers effectively exploit this confusion to promote the illegal goods. This implies that an effective counter-narrative industry and regulators need to construct a clearer more coherent definition and meaning of ‘counterfeit’.