MỤC LỤC
Despite many efforts to enforce intellectual laws as well as anti- counterfeiting activities, counterfeiting is still an international problem in various regions—. To make it worse, many luxury brand consumers intentionally buy counterfeits as an inexpensive alternative to real products, despite the risks they may face, as well as the efforts brands have made to distinguish their products from counterfeits (Perez, Castaủo, & Quintanilla, 2010).
Therefore, it is essential to investigate counterfeit consumers‘ motivations, in order to minimize the threats of luxury brand counterfeits. Hence, it is worth studying what are the effects of these two important concepts in consumer behavior study in our model of counterfeit purchase intention.
Research objectives
Scope of the study
Research significance
Measurement and hypothesis tests are run and presented in details, using Statistical Package for Social Science (SPSS) 16.0 and AMOS 20.0 of International Business Machines Corporation (IBM). Limitations of this paper are also clearly stated to suggest further researches in the future.
On the contrary, non-deceptive counterfeits are products that can be distinguished from the real products easily by the buyers—based on various types of differences, for example in product appearances, in trademarks, or in distribution channels—and consumers purchase them intentionally. Firstly, Model A was used to test the effect of value-expressive function on counterfeit purchase intention (H1), as well as the moderation effect of product involvement on the relationship between value-expressive function and counterfeit purchase intention (H3) using hierarchical multiple regression. Secondly, Model B was used to test the effect social-adjustive function on counterfeit purchase intention (H2), as well as the moderation effect of product involvement on the relationship between social-adjustive function and counterfeit purchase intention (H4) using hierarchical multiple regression.
Respondent A and Respondent E mistakenly interpreted PK3 as ―need to gather very little information‖ due to. (the item turned to be ―If I had to purchase fashion products today, I would need to gather very little information in order to make decision.‖). However, Respondent C suggested that the question should be in more details—it should state clearly that irrelevant/relevant to something or anything.
In summary, the items were not changed after the pilot study, and were used in the main survey.
Data collection result and demographics
Specifically, in the scale measuring product knowledge, item PK3 was suggested to be eliminated to improve the Cronbach‘s alpha score from .817 to .852. In the construct counterfeit purchase intention, item CP3 was suggested to be eliminated to improve the Cronbach‘s alpha score from .896 to .952. All Cronbach‘s alphas were above .7—with the lowest Cronbach‘s alpha to be that of the social-adjustive function scale, .835, passed the criteria.
The Kaiser-Meyer-Olkin measure of sampling adequacy score was .868, higher than the .6 criterion, and Barlett‘s Test of sphericity was strongly significant (p =.000), which means that exploratory factor analysis was valid. Composite reliability and average variance extracted scores were also calculated based on the standardized regression weighted scores of the modified model. All composite reliability score were above .7, and all average variance extracted scores were above .5, passing the criteria set up earlier in Chapter 3.
All square roots of the average variance extracted of each construct exceeded the construct‘s correlations with other constructs, therefore passed the divergent reliability test. As clearly demonstrated in the table above, all correlations passed this criterion (with the highest to be the correlation between value- expressive function and social-adjustive function with the score of .496), implying there.
Model A2 and A3 showed that there was significant R-square change (an increase of .024, significant at the .05 level). Furthermore, the beta coefficient of moderation predictor (Value-expressive function x Counterfeit purchase intention) was also significant. The moderation effect of product involvement on the relationship between value-expressive function and counterfeit purchase intention is demonstrated in Figure 4.2.
Model B was used to test the impact of social-adjustive function on counterfeit purchase intention (H2), as well as the moderation effect of product involvement on this relationship (H4). Furthermore, the beta coefficient of moderation predictor (social-adjustive function x Product involvement) was also insignificant. Model C was used to test the moderation effect of product knowledge on the relationship between social-adjustive function and counterfeit purchase intention (H5).
Model C1 and C2 implied that there was significant moderation effect of product knowledge on the relationship between social-adjustive function and counterfeit purchase intention. The moderation effect of product knowledge on the relationship between social- adjustive function and counterfeit purchase intention is demonstrated in Figure 4.3.
Perez et al (2010) also found that consumers sometimes buy counterfeits to prove that they could outsmart other people, which means that brands focusing on helping customers express the value of ―I am smarter than others‖ might have to face unintended backfire. Wilcox et al (2009) found that the effects of social-adjustive function on counterfeit purchase intention are stronger than those of value-adjustive function, which makes the result of H2 more understandable. Not surprisingly, the moderation effect of product involvement on the relationship between value-expressive function and counterfeit purchase intention was significantly negative (βvalue-expressive function x product involvement = -.519; p = .029; H3 was supported).
This strengthened the arguments presented in Chapter 2 that high product involvement made customers think about the risks as well as the self-deceiving feeling more carefully and thoroughly, devaluing any benefits they might gain from buying counterfeits. On the other hand, the moderation effect of product involvement on the relationship between social-adjustive function and counterfeit purchase intention was not significant (βsocial-adjustive function x product involvement = -.299; p = .268; H4 was not supported), although the impact of the interaction was negative as proposed. This might due to the fact that the measurement of product involvement was simplified into high- and low-involvement by applying median-split only—following the common approach proposed by Zaichkowsky (1994), whereas McQuarrie and Munson (1992) argued that there might be significant differences among different kinds of high involvement and this construct should be measured in details.
For example, there might be differences between a product that is important but not interesting and a product that is interesting but not important, but with median-split, these two different kinds of product might be categorized into the same group. Meanwhile, the result (βsocial-adjustive function x product knowledge = -.601; p = .042) indicated that product knowledge significantly affected the relationship between social-adjustive function and counterfeit purchase intention (H5 was supported).
Therefore, the moderation effect might have been significant if detail categorize methods had been employed. This supported the arguments in Chapter 2 that high product knowledge made consumers more aware of the risks they might face and lowered their intention to buy counterfeits. Contrary to our hypothesis, value-expressive function was found to have a positive effect on counterfeit purchase intention, though the effect was not significant.
However, the instability of the impact of value-expressive function on counterfeit purchase intention was stated earlier in other works. The fact that H4 was not supported might due to the over-simplification of the median-split method.
Overview
It is worth noting that self-expressive value does not decrease but increases the intention to buy counterfeits, contrary to the propositions of the previous studies, though the impact is found to be insignificant in this paper. Furthermore, H3 implies that products that are marketed as a self-expressive solution should also focus on increasing the involvement level. In other words, products that help customers to ―be your true self‖ should be demonstrated as important and interesting articles to lower customers‘ intention to buy counterfeits.
On the other hand, it can be inferred from the result of H4 that marketing campaigns focusing on increasing the involvement level are not significantly effective to eliminate or lower demand-side counterfeit risks if the products are presented as a social-fit-in solution. Instead, social-fit-in brands should consider informative campaigns, based on the fact that H5 is supported. In other words, marketing campaigns of those brands should educate the customers to increase their knowledge about the product as well as the product category to lower their counterfeit purchase intention.
In summary, in order to reduce the risks of counterfeits, luxury brands should consider these two marketing strategies: (1) value-expressive function based combines with product-involvement-focused marketing strategy, and (2) social-adjustive function based combines with product-knowledge-focused marketing strategy.
Secondly, the relationship between value-expressive function and counterfeit purchase intention is positive, opposite to the propositions of previous studies—though the effect is not significant enough. Thirdly, this paper only focuses on fashion-related luxury products, and does not cover other important luxury product categories such as smartphone, furniture or car. Finally, as founded, product involvement has significant moderation effect on the relationship between value-expressive function and counterfeit purchase intention.
In other words, there would be differences between two different high-involvement product groups—for example between a product which has high importance and low interest level and a product which has low importance but high interest level. That is the reason why it is expected that different groups of high-involvement products might also have different impacts. Therefore, researches investigating the differences among different groups of product involvement products—specifically among high-interest-low-importance, low-interest-high-importance and high-interest-high-importance products, as well as in.
It is interesting to know if consumers from other cultures react differently, or if different kinds of high-involvement products produce different results or not. The role of brand image, product involvement, and knowledge in explaining consumer purchase behaviour of counterfeits: Direct and indirect effects. Luxury consumption in the trade-off between genuine and counterfeit goods: What are the consumers‘ underlying motives and value-based drivers?.
PK4: I feel very confident about my ability to tell the difference in quality among different brands of fashion products.