Research and analysis

The impact of complicit social media influencers on counterfeit purchasing among male consumers in the UK

Published 22 February 2024

Research commissioned by the Intellectual Property Office and carried out by Dr David Shepherd, Kate Whitman, Professor Mark Button from the University of Portsmouth, and Dr Jeremy Wilson from Michigan State University.

Dr David Shepherd undertakes qualitative and quantitative research within the field of economic crime covering offenders, victims, online behaviour, organisations, prevention and enforcement. David has published widely on these topics with 30 research based publications in academic articles, books and technical reports. He has worked on multiple research programmes funded by the EPSRC, the Home Office, FCDO, NGOs and private sector companies.

Kate Whitman is a PhD student at the Economics & Finance faculty of the University of Portsmouth with a behavioural science background. Her research examines the impact of persuasive messages on observed digital piracy behaviour. She previously worked in data analytics and insights at the anti-piracy department of the BPI, the insights department of the IFPI and the behavioural insights team at Canvas8. She has an MSc in Behavioural, Decision Making Science, an MA in Audio Technology and has lectured two marketing modules for The London College of Music & Media. Kate also spent 10 years running a music marketing and social media business which had clients/projects such as TV channels, headphone brands, festivals and major and indie record labels.

Professor Mark Button is founder and Director of the Centre for Counter Fraud Studies at the School of Criminology and Criminal Justice, University of Portsmouth. Mark specialises in economic crime, cyber-fraud, security and private policing. He has published extensively on these topics. He has undertaken a wide range of research funded by EPSRC, the Home Office, ACPO, DCMS, FCDO, NGOs and private sector companies. Mark acted as a consultant for the United Nations Offices on Drugs and Crime on developing international standards for Civilian Private Security Services, and the United Nations and European Union on enhancing civilian oversight of the Turkish private security industry.

Jeremy M. Wilson, Ph.D., is a Professor of Criminal Justice at Michigan State University where he founded and directed for ten years the Center for Anti-Counterfeiting and Product Protection. As a scholar, educator, technical assistance provider, advisor, and consultant, he has spent decades working closely with industry, law enforcement, government, and other institutions to bring science to the development, implementation, and evaluation of strategies to bolster brand protection, protect intellectual property, and promote public safety. His partnerships and impact have resulted in over 160 publications, more than $14Million in sponsored projects, and honors from industry, law enforcement and academia.

Acknowledgements

The researchers would like to acknowledge the invaluable assistance of the IPO team including Pauline Beck, David Humphries, Sam Brand and Connor Morgan-Matthews.

Executive Summary

Intellectual property rights underpin the innovation that drives the free-market economy and enhances the welfare of the public. They are crucial in providing rights owners with the protections they need to invest in creative ideas and the development of their brands. Intellectual property rights also provide important signals to consumers about the source, quality and safety of a particular product, for example via trademarks, logos and slogans. The illicit trade in counterfeit goods directly harms the market, hinders development and undermines public welfare. Globalisation and the digital economy have provided criminal enterprises access to markets across the world. The international trade in counterfeit goods in 2019 was worth $464 billion (OECD & EUIPO, 2021).

An important recent trend is the increased role of social media influencers in facilitating the trade. Influencers are regarded as trusted opinion leaders in their online communities, so their views matter to followers. Rights holders leverage this trust by offering products to influencers who can help promote them. However, some complicit influencers promote the illicit wares whilst reassuring potentially susceptible followers that buying counterfeits is both rational and acceptable. Previous research commissioned by the Intellectual Property Office (IPO) focused on female consumers aged 16 to 60 in the UK; it found that endorsements by social media influencers prompted 10% of this population group to purchase counterfeit goods (Shepherd et al., 2021).

The present study replicates this research but focuses on male consumers. It finds that male consumers are over twice as susceptible to the social media influencer problem as female consumers: 24% of UK male consumers aged 16 to 60 purchase counterfeits endorsed by social media influencers. The previous research identified four factors which increase the likelihood of counterfeit purchasing: trusted others including complicit influencers, rationalisations, risk blindness and risk appetite. These factors are more prevalent amongst the male population thus emphasising their causal contribution to the demand for counterfeits. The analysis further reveals that widespread confusion over the term ‘counterfeit’ has a significant role in purchasing decisions and fuelling demand.

This research points to several important policy implications for addressing the detrimental effects of complicit influencers. These primary concern

• aiming interventions at young, habitual consumers of counterfeits
• education approaches focused on the influence of trusted others, risk blindness, risk appetite and rationalisations
• developing and communicating a coherent definition of “counterfeit” and associating negative consequences with it
• educating the younger age group with information about product-specific risks
• rallying stakeholders to disrupt the online pathways that influence the purchase of counterfeit products
• encouraging brand owners to proactively assess the risks SM influencers pose to their products and customers, and initiate strategies to mitigate them

The report concludes with a brief discussion about opportunities for future research.

Key Findings

Knowing buyers

• 35% of male consumers aged 16 to 60 are knowing buyers of counterfeits
• 17% buy products that risk their health and safety
• 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

Impact of social media influencers

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

• 7% are counterfeit hunters who use the SM postings 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

Factors influencing purchasing decisions

• four key factors influence counterfeit purchasing decisions: trusted others including complicit influencers, rationalisations, risk blindness and risk appetite
• widespread definition confusion over the meaning of ‘counterfeit’ is a type of rationalisation that fuels demand
• younger generations are much more susceptible to the influence of SM personalities, are more likely to construct rationalisations to justify illicit purchases, are the most confused about the meaning of ‘counterfeit’, have a lower risk perception and have a higher risk appetite

Introduction

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).

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 SM influencers, 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 female consumers 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 female consumers 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 influencers. We therefore expected deviant SM influencers to have a higher impact on female consumers. 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 female consumers. 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:

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 male participants 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 female respondents (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 female purchasers, 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 male buyers are twice as likely (53%) to knowingly make illicit purchases than those aged over 34 (23%). Secondly, prevalence is higher for male buyers in all age groups. Thirdly, compared to female buyers, 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, male consumers 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

Number of products	Number of respondents	All (n=1,000)	16-33 (n=394)	34-60 (n=606)
None	652	65.2%	47.0%	77.1%
1	77	7.7%	10.7%	5.8%
2 to 5	146	14.6%	22.6%	9.4%
6 to 9	96	9.6%	15.2%	5.9%
10 to 19	22	2.2%	3.6%	1.3%
20 or more	7	0.7%	1.0%	0.5%
Total counterfeit purchasers	348	34.8%	53.0%	22.9%

Figure 1: Age distribution of intentional counterfeit purchasing

Age range	16-24		25-33		34-42		43-51		52-60
Gender	Male	Female	Male	Female	Male	Female	Male	Female	Male	Female
1/year	11%	17%	10%	7%	7%	8%	5%	0%	5%	3%
2-5/year	23%	14%	22%	10%	14%	8%	9%	3%	5%	1%
6+/year	21%	9%	19%	6%	11%	2%	7%	1%	5%	1%
Total	56%	41%	51%	23%	32%	17%	21%	5%	15%	5%

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

Age range	16-33	34-60	Total
Habitual buyer	22% buyers	14% buyers	36% buyers
	41% demand	25% demand	67% demand
Occasional buyer	38% buyers	26% buyers	64% buyers
	20% demand	13% demand	33% demand
Total buyers	60% buyers	40% buyers	100% buyers
	61% demand	28% demand	100% demand

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 male purchasers are sports and sportswear (14%), clothing and accessories (13%), jewellery and watches (10%). In comparison, the top three categories for female purchasers 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 female consumers (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.

Table 3: Counterfeit product groups intentionally purchased

Product group	16-33 (n=394)	34-60 (n=606)	All (n=1,000)
Sports and sportswear goods	20.1%	10.2%	14.1%
Clothing and accessories	19.3%	8.4%	12.7%
Jewellery and watches	17.0%	5.6%	10.1%
Electrical products	11.9%	4.5%	7.4%
Electronics, computers, phones	10.4%	3.3%	6.1%
Beauty/grooming/hygiene products	6.9%	2.6%	4.3%
Alcohol	5.6%	2.5%	3.7%
Toys	4.6%	3.0%	3.6%
Other	0.3%	0.2%	0.2%

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 male consumers 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

Number of products	Number of respondents	All (n=1,000)	16-33 (n=394)	34-60 (n=606)
None	689	68.9%	50.8%	80.7%
1	84	8.4%	13.7%	5.0%
2 to 5	130	13.0%	20.6%	8.1%
6 to 9	73	7.3%	11.7%	4.5%
10 to 19	18	1.8%	2.3%	1.5%
20 or more	6	0.6%	1.0%	0.3%
Total of counterfeit purchasers	311	31.1%	49.2%	19.3%

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 female consumers, male consumers’ 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

Age range	16-24		25-33		34-42		43-51		52-60
Gender	Male	Female	Male	Female	Male	Female	Male	Female	Male	Female
Knowingly	41%	26%	40%	13%	21%	7%	17%	2%	6%	2%
Unknowingly	10%	10%	7%	4%	8%	4%	1%	0%	2%	1%
Total	51%	36%	47%	18%	30%	11%	18%	2%	8%	3%

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

Pathway	16-33	34-60	All
Prompted by endorsements
Searched for legitimate brand and the endorsement came up	19.3%	7.1%	11.9%
Follow the influencer who posted the endorsement	7.4%	3.3%	4.9%
Newsfeed message from a friend/family interaction with the endorsement	7.9%	3.5%	5.2%
Appeared as a sponsored ad on the social media platform	3.0%	1.0%	1.8%
Other	0.8%	0.2%	0.4%
Total	38.3%	15.0%	24.2%
Counterfeit hunters – planned purchasers
Searched for fakes and the endorsement came up	10.9%	4.3%	6.9%
Total	49.2%	19.3%	31.1%

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 SM endorsements 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 male participants (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

SM influencer role	Knowing consumer	Deceived consumer	Total
Assist – hunters’ planned purchases	6.9%	-	6.9%
Prompt – responders’ opportunistic purchases	18.5%	5.6%	24.2%
Total	25.4%	5.6%	31.1%

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
• male consumers are more susceptible to these influences than female consumers
• younger male consumers are more susceptible to these influences than older male consumers
• Overall, 23% of male consumers 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 female participants, 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 male participants 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

Age range	16-24	25-33	34-42	43-51	52-60
Opinion of family	67%	77%	73%	70%	67%
Opinion of friends	72%	79%	69%	69%	51%
Opinion of colleagues	55%	64%	51%	49%	30%
Influence of social media	39%	53%	40%	31%	16%

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 SM personalities: 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 male participants, and partly explains why male consumers 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

Age range	16-24	25-33	34-42	43-51	52-60
Social media influencers must have tried the product	52%	53%	42%	31%	27%
Influencer endorsements mean the products are safe	45%	52%	43%	27%	20%
More likely to purchase genuine product	43%	45%	46%	42%	30%
More likely to purchase counterfeit product	34%	36%	23%	14%	6%

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 male consumers 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

Age range	16-24	25-33	34-42	43-51	52-60
When price of genuine product is high	59%	58%	41%	29%	23%
When quality does not matter	60%	57%	38%	27%	21%
When it concerns a luxury product	53%	57%	39%	27%	16%

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

Nominated definition	Clearly counterfeit	Possibly counterfeit	Legitimate	Total/overall
% Nominated definition	35%	25%	40%	100%
% Purchased endorsed counterfeits	10%	25%	53%	31%

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 male consumers 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

Age range	16-24y	25-33y	34-42y	43-51y	52-60y
Select clearly counterfeit	25%	16%	9%	7%	3%
Select possibly counterfeit	38%	39%	23%	18%	5%
Select legitimate	71%	64%	50%	32%	23%

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 male participants 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

Age range	16-24	25-33	34-42	43-51	52-60
Reputation of brand	32%	22%	17%	19%	16%
Reputation of store	27%	22%	14%	12%	15%
Safety of product	23%	17%	11%	9%	5%
Price	19%	8%	11%	6%	6%
Quality of product	18%	16%	11%	6%	6%
Whether product genuine or counterfeit	28%	20%	13%	9%	8%

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

Age range	16-24	25-33	34-42	43-51	52-60
Buying counterfeits does not harm businesses and jobs	34%	24%	16%	13%	11%
Buying counterfeits does not pose a threat to health and safety	40%	26%	23%	15%	14%

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 female and male consumers across all ages. However, a significantly higher portion of male consumers are risk takers (40%) compared to female consumers (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

Age range	16-24	25-33	34-42	43-51	52-60
Risk averse - female	12%	23%	31%	37%	39%
Risk taker - female	51%	38%	29%	21%	19%
Risk averse - male	8%	6%	9%	16%	30%
Risk taker - male	50%	63%	50%	44%	33%

Discussion of findings

This study supports the previous research into the impact of SM influencers on female consumers (Shepherd et al., 2021; Wielki, 2020) in finding that SM influencers have a profound impact on the purchasing intentions of some consumers. The broadcast endorsements of trusted SM influencers stimulate demand for both legitimate and counterfeit products. The previous IPO report focused on female consumers based on an assumed higher proclivity for purchasing counterfeits endorsed by SM influencers (Shepherd et al., 2021). The basis of this assumption was that the SM influencer market is highly gendered towards female consumers (Klear, 2019). However, this assumption has proved false. The current research finds that 24% of male group aged 16 to 60 are prompted by SM influencers to purchase counterfeits, over double the 10% for female group.

The age profile for male participants follows the same pattern as female participants, but with an important difference. Male consumers sustain a high level of counterfeit purchasing into their 30s. As a consequence, the decline in the attraction of counterfeits is delayed by 5 to 10 years compared to female consumers. SM influencers are 6 times more successful in prompting half of younger male consumers (49% of 16-33y) to purchase counterfeits compared with 8% of the 52-60y group. This leads to a heavily skewed demand profile whereby 22% of knowing buyers are young, habitual buyers who generate 41% of the demand for counterfeits.

The previous research into female consumers identified four important psychological influences on purchasing decisions: trusted others (including SM influencers), rationalisations, risk blindness, and risk appetite (Figure 10). The present research supports these findings. Indeed, the increased prevalence of these factors in male consumers correlates with their increased propensity to purchase counterfeit products. In making their purchasing decisions, male consumers place more importance in the opinion of others, notably SM influencers; they are more likely to rationalise the purchases; they are more dismissive of product factors that legitimate companies view as essential to their businesses, especially safety and quality; and they have a greater appetite for risk. These four factors help to explain their higher level of demand for counterfeits compare to female consumers.

The present research further suggests that the lack of clarity and confusion around the term ‘counterfeit’ has a significant influence on purchasing behaviour. Increased confusion and misunderstanding is associated with substantially increased counterfeit purchasing. Lack of clarity about what behaviour crosses the wrongdoing threshold has two cognitive impacts. Firstly, some people do not recognise the behaviour as wrong at all. Secondly, others manipulate the definition of deviant behaviour to suit their objectives even though they know the behaviour is wrong. Drawing on the ‘denial of crime’ rationalisation identified by Benson (1985), this ‘definition confusion’ is added to the list of rationalisations in Figure 10.

The definitional uncertainty is readily exploited by SM influencers to overcome the residual reluctance of some consumers, especially young consumers. The influencers claim in YouTube videos that products are identical to genuine items, but are not counterfeits. They frequently use alternative terms like ‘rep’ (replicate) or ‘dupe’ (duplicate) (Chaudhry, 2022). These slang words are used to divert consumers’ perceptions away from the wrongness of counterfeits and provide them with ready-made, shared rationalisations for their conduct. The impact is most profound on young consumers: 51% of male consumers aged 16-24y equate legitimate products with ‘counterfeit’, and 71% of them purchase endorsed counterfeits. The empirical evidence indicates that industry and law enforcement need to work on developing a clear and common definition of ‘counterfeit’, and work much harder on communicating the deviant meaning of the word.

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 male consumers, aged 16 to 60 who regularly use social media. The study replicated a recent IPO project which focused on female consumers (Shepherd et al., 2021). The present study found that over twice as many males (24%) as females participants (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 female consumers, age is a strong determinant for male consumers with younger male consumers 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 male consumers 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 male consumers 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’.

Recommendations

Five policy implications arise from the research.

1. Policies aimed at reducing the demand for counterfeit products should prioritise younger, habitual consumers of counterfeits.

2. Educational approaches should focus on the influence of trusted others, risk blindness, risk appetite and rationalisations.

3. Industry and regulators need to develop and broadly communicate a coherent definition of ‘counterfeit’, clearly associating it with immoral wrong, and investing its meaning with real risks and harms.

4. Educating the younger age group about the safety risks of counterfeits should be a high priority, particularly in relation to specific product groups such as beauty/grooming/hygiene, electrical and alcohol.

5. Regulators and brand owners should engage with social media platforms, online marketplaces, and especially the influencer marketing industry to tackle deviant SM influencers and disrupt the purchasing pathways. This would also identify any gaps in the regulatory framework that may need to be addressed in order to make the companies accountable for facilitating SM influencers who advertise counterfeits.

6. As part of an ongoing risk assessment strategy, brand owners should canvas SM platforms and online marketplaces to identify complicit SM influencers and institute strategies to mitigate the risks they pose.

It is recommended that future research should be directed towards disrupting the social influences that culminate in deviant purchasing. The research should involve the key actors: consumers, influencers, brand owners, online marketplaces, social media companies, and the influencer marketing industry.

1. Qualitative consumer research would provide deeper insights into consumers’ attitudes and behaviours.

2. Research into the motivations, attitudes and perceptions of deviant SM influencers. It should address the techniques they use to assist consumers in rationalising their wrongdoing, and identify the methods they use to monetise their activities.

3. Research into the market’s awareness of deviant SM influencers and the market’s policies, if any, for dealing with the problem. This research should encompass the four key elements of the market: brand owners, online marketplaces, social media companies and the emerging influencer marketing industry.

4. Behavioural science research into effective methods for countering the influence of deviant SM influencers, considering the various options available to SM platforms, online marketplaces, brand owners, regulators and law enforcement.

References

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Chaudhry, P. E. (2022). Dupe influencers exploiting social media to peddle luxury fakes. Business Horizons.

Dohmen, T., Falk, A., Huffman, D., Sunde, U., Schupp, J., & Wagner, G. G. (2011). Individual risk attitudes: Measurement, determinants, and behavioral consequences. Journal of the European Economic Association, 9(3), 522-550.

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Appendix

Age (16-60)	16-24y	25-33y	34-42y	43-51y	52-60y	All
n	194	200	214	216	176	1,000
%	19%	20%	21%	22%	18%

Table for Sample regional quota

Region	Respondents
East Midlands	7%
East of England	9%
London	13%
North East	4%
North West	11%
Northern Ireland	3%
Scotland	8%
South East	14%
South West	9%
Wales	5%
West Midlands	9%
Yorkshire and Humber	8%
Total	100%

1. This question came before the definition presented to respondents for the questions relating to attitudes to counterfeits and purchasing habits. ↩

2. Based on the definition presented in the questionnaire (see the ‘Research Design’ section). ↩