Determinants Affecting Buying Decisions of Consumers for Counterfeit Products: An Exploratory Study in Raipur, India

Abstract

Purpose - The purpose of this paper is to elicit antecedents that influence the buying of counterfeit products in Raipur, an emerging capital city. Research Design, Data, and Methodology - 203 responses to the questionnaire were collected to come out with the results of the study. From the exploratory study, 20 variables were identified to have an influence on the buying of counterfeit products. Factor analysis was applied on the data collected and these items were grouped into four factors. Result - The findings suggest that safety implications have a significant impact on the buying of counterfeits in an emerging city - Raipur, India. Further studies that are specific to geographical locations could be carried out to validate the findings of this paper as the tastes and preferences of each of the markets are unique. Conclusions - For manufacturers, marketers and law enforcers it might be of great interest to learn that safety concerns are uppermost on the minds of people who deliberately or inadvertently consume counterfeits. If it can be conveyed effectively that consumption of counterfeits can cause more harm than increase in perceived value, it can be checked to a good extent.

1. Introduction

According to the cover story from a recent issue of Business Equity, one can pick any products from a well-known brand and discover that there is a counterfeit version available. The same article cited that in some cases, the appearance of the counterfeit version so closely matches the genuine article that even major retailers have unknowingly purchased counterfeits. Consumers buy counterfeit products to meet their esteem needs met, especially in cases when they are unable to buy the originals because of the price differences. Indeed, Business Bloomberg reported that patent pirates took their heaviest toll on U.S. software (annual loss, $9 billion), pharmaceuticals ($4 billion), movies ($1 billion), sound recordings ($2 billion), and books ($1 billion). Three of the four U.S. industries, music, movies, and software, are harmed by international counterfeits. The Business Software Alliance, an anti-counterfeiting consortium that represents such companies as Microsoft, Novell, and Lotus, has estimated software piracy rates for individual countries by comparing hardware sales and average software consumption patterns against payments for software. Some of their estimated software piracy rates include Indonesia (99%), Thailand (99%), Pakistan (99%), UAE (99%), Spain (88%), France (66%), and Britain (49%).

In general, consumers are more aware of counterfeit merchandise in the status apparel industry than in airline parts or pharmaceuticals. Counterfeiters have manufactured even relatively inexpensive products, such as shampoos, tea bags, cold drinks and other such items. While at one time counterfeit products were mostly confined to costly watches, designer apparel, movies, and DVDs, today counterfeiting is a major problem in such diverse product categories as pharmaceuticals, medical equipment, baby formula, electrical parts, automotive & aircraft parts and computer software. The South Asian markets are flooded with counterfeits. Another Bloomberg online article reported that as means to combat counterfeiters, many companies work on – 1. Improving the awareness levels of the consumers, 2. Conduct raids on counterfeiters, 3. Issuance of QR codes to verify the originality of the product in online environment.

Counterfeiting has almost become a way of life. In almost every city, there are places where fake counterfeiting goods are openly traded. In emerging cities, where the urban population tries hard to imitate their other metropolitan rich counterparts, the study gives a new insight into their buying behaviors. In this exploratory study, we attempt to understand the factors that influence people residing in newly formed states, particularly the capital city (which by its very own nature receives much attention for development, growth and investments), to purchase counterfeit goods, the type of goods they buy and their probable conditions for buying counterfeit products.

2. Literature Review

Counterfeiting has become a worldwide problem valued at more than $250 billion, accounting for almost 2% of world trade in 2009 (Interpol, 2009). Although some researchers classify counterfeiting as a victimless crime (Freedman, 1999), its impacts on the economy are multifaceted. Counterfeit markets may help stimulate demand for the product (Givon et al., 1995; Yoo & Lee, 2009) and provide symbolic benefits, such as providing consumers with social status from purported ownership at a lower quality and cost (Nia & Zaichkowsky, 2000; Yoo & Lee, 2009), but counterfeiting's adverse socioeconomic effects hinder innovation, employment, and trade (Olsen &Granzin, 1993).

For marketers, counterfeiting is a pertinent topic because of its negative ramifications for the performance of firms suffering the infringement (Bloch et al., 1993; Cordell et al., 1996; Grossman & Shapiro, 1988) and for consumers of premium brands, who may experience compromised exclusivity (Commuri, 2009). Counterfeiting can remove billions of dollars in business from legitimate manufacturers (Ang et al., 2001) and hurt economies by causing job losses or trade deficits (Tom et al., 1998). Counterfeiting also negatively affects governments through lost tax revenues, corruption (Vithlani, 2007), and funding criminal activities (Interpol, 2003). In particular, counterfeiting negatively affects new product development and brand building because counterfeiting infringes on intellectual property rights (Feinberg & Rousslang, 1990; Nill & Schultz, 1996). Counterfeiting results in sales losses and damaged brand reputations (Bloch et al., 1993; Green & Tasman, 2002) and can be detrimental to brand equity, which takes years to develop and is one of a company's most valuable assets (Green & Tasman, 2002).

Early research on counterfeiting focuses primarily on the role of counterfeiters or the supply side of counterfeiting (Bamossy & Scammon, 1985), but more recent studies focus on the demand side of counterfeiting (Ang et al., 2001; Nia & Zaichkowsky, 2000; Phau & Teah, 2009; Wilcox et al., 2009; Zaichkowsky, 2006). Demand for luxury brands drives the demand for counterfeits, and social goals underlie this purchase behavior (Ang et al., 2001; Nia & Zaichkowsky, 2000; Wilcox et al., 2009; Zaichkowsky, 2006). Counterfeit goods presented as real versions of products may deceive consumers (Zaichkowsky, 1995), however for prestige and luxury goods, consumers are often willing accomplices rather than victims of deception (Bloch et al., 1993). Consumers willing to buy counterfeits may acquire labels as “accomplices” or “sly shoppers” who purposefully purchase counterfeit goods to demonstrate their shrewdness (Tom et al., 1998). Tom et al. (1998) suggest that some counterfeit buyers believe that they are receiving comparable goods at cheaper prices than retail and do not think that purchasing counterfeits damages the economy at large. This research stream assumes that normally law-abiding citizens may simply see nothing wrong with purchasing counterfeits because they perceive prices of designer products to be unfair to consumers. Efforts to understand these behaviors examine the idea that consumers' moral beliefs play a major role on the demand side of counterfeiting (Shoham et al., 2008). Grounded in the theory of reasoned action, these researchers suggest that firms can discourage demand for illegitimate products and encourage purchases of their legitimate counterparts by highlighting the unethical implications of piracy.

3. Research Methodology

The research was conducted in two phases. First phase was exploratory in nature. It involved literature review and personal interactions with select academicians and practitioners to identify various attributes that influenced counterfeiting of products. This helped in designing a list of questions, which was discussed and reviewed by different faculty members and experts from the corporate world. A few minor changes in the language and modifications in the order of the arrangement of items were suggested. In the second phase, a structured questionnaire was prepared and was pre-tested on a small group of ten people, who were representative of the target population. A total of 203 respondents were obtained in total. Factor Analysis was performed on the data and the factors that emerged were ranked in order. This constituted the descriptive phase of the study.

3.1. Research Instrument

A list of attributes that influence buying of counterfeit products were identified from the literature and personal interactions with select practitioners, and academicians. The list of attributes was then scrutinized and reframed to make the questionnaire coherent with the study. The final questionnaire had twenty attributes related to buying of counterfeit products. Statements were structured to assess respondents’ level of agreement/disagreement about significance of individual attribute. Responses were to be recorded on a five-point Likert’s scale in which ‘1’ represented strong disagreement and ‘5’ represented strong agreement with the statement. Apart from twenty variables, it also contained questions about demographics of respondents, their likelihood of buying counterfeits, their perceptions on what are the most counterfeited goods and their awareness of locations where counterfeits are sold in their cities.

3.2. Sampling

The data collection phase involved administration of finalized research instrument to a specified number of respondents. For successfully applying factor analysis, number of cases had to be at least five times the number of variables. So the minimum sample required for this study was 100 (Bryant & Yarnold, 1995; Hair et al., 2006). Sample size for this study is 203. In this study, population was defined as all Indians who are consumers of goods. Convenience sampling method was used to collect data, through the mall intercept method.

4. Results

Demographic information collected with the help of the questionnaire was used to develop a profile of the respondents using simple numbers and percentages. Data relating to opinion about buying a counterfeit product was subjected to data reduction with the help of factor analysis.

In the opinion of the respondents, the goods that were most counterfeited were software, CDs and DVDs.

The counterfeit products that the respondents were most likely to buy turned out to be clothing and shoes. Software, CDs and DVDs; books and fashion accessories like bags, belts and sunglasses figured high on the list of things that people were likely to buy.

The respondents were inclined to buy a counterfeit product if it was close to the original (mean value of 3.611) and if the price of the counterfeit was considerably lower than the original (3.187).

There was a very high level of awareness about counterfeit products among respondents. All of the statements used to measure awareness level showed mean value greater than 3.00.

4.1. Checking Suitability of Data for Factor Analysis

Data was checked for suitability for factor analysis with the help of KMO test of sampling adequacy and Bartlett’s Test of Sphericity (Boyd et al., 2002; Malhotra, 2004). Correlation matrix for data set of respondents showed significant correlation between the majority of the variables indicating the existence of a fundamental structure. KMO Test of Sampling Adequacy value of 0.878 (Annexure IA) was higher than bench-mark value of 0.5. In Bartlett’s Test of Sphericity, calculated value of 1561.302 at 171 degrees of freedom and 5% level of significance (Annexure IA) was higher than the tabulated value. All these results indicated that it is a fit case for factor analysis.

4.2. Refining the Initial Output

Data was subjected to Principal Component Analysis (PCA) with Varimax rotation. It resulted in extraction of four factors explaining 57.948% of total variance associated with the problem. Factor output is presented in the form of Annexure I (B, C & D). However, Annexure I (D) shows that communality value for five variables (V17 – ‘Does not hurt the big companies since they make huge profits anyway’, V18 - ‘Gives me a sense of adventure and excitement’, V1-is acceptable’, V12- Should be legalized’ & V4 – Should be encouraged because they demonstrate initiative and ingenuity on the part of the counterfeiters) are very low. These variables have lowest factor loadings in the entire solution (0.378, 0.426, 0.428, 0.455 & 0.473 respectively). Hence, it was decided to refine the solution by sequential deletion of variables. V17 – ‘Does not hurt the big companies since they make huge profits anyway’) was deleted first as it had the lowest communality. Resulting output again reflected low values of communality for V18, V1, V12 & V4. Hence each one was sequentially removed from the problem leaving only fifteen variables. The variable ‘buying counterfeit - immoral and unethical’ showed a high level of cross loading between 2 factors and hence had to be dropped. Finally, only fourteen variables were remaining with a sample size of 203. This data set was again checked for suitability for factor analysis and gave following results after another round of Principal Component Analysis and Varimax rotation.

Value of KMO statistic is 0.830 whereas calculated value in Bartlett’s Test of Sphericity is 1005.604 with 91 degrees of freedom at 5% level of significance (Table-II). Again this proves to be a fit case for consideration under factor analysis.

[Table 1] Profile of Respondents

[Table 2] The Most Counterfeited Products According to the Respondents

Communalities for all fourteen variables are higher than 0.5 (Table III) indicating it to be an optimum solution and no need for further deletion of variables.

[Table 3] Most Likely Buying Counterfeited Products

[Table 4] Probable conditions for buying Counterfeit Products

[Table 5] Respondents’ Awareness about Counterfeit Products

[Table 7] Communality Values

This solution explains 65.356% of total variance associated with the problem (Table 8). Factor output consists of four factors based on fourteen variables (Table 9). Relevant factor loadings for each variable indicate that all the variables are adequately explained by derived factors.

[Table 8] Total Variance Explained for Fourteen Variable Problem​​​​​​​

[Table 9] Rotated Component Matrix for Fourteen Variable Problem

Extraction Method: Principal Component Analysis. / Rotation Method: Varimax with Kaiser Normalization. / Rotation converged in 7 iterations.​​​​​​​

[Table 10] Consolidated Factor Output & Allied Information

4.3. Labelling of Factors

Factor analysis condensed given statements to a set of four factors representing specific forces that constitute impulse buying behavior. These are presented in the table given below:

4.3.1. Factor 1

The most obvious factor emerging from this analysis explains 32.000% of total variance. It consists of five attributes (variables) namely buying a counterfeit product - offers as much value as the original, is as good as the original, gives as much satisfaction as the original, is a value for money proposition, demonstrates that one is a wise shopper. Each of these variables is strongly correlated with the extracted factor as is evident from factor loadings for each of these. Collectively, these variables bring out the utilitarian aspect of buying a counterfeit product. Hence it may suitably be labelled as ‘Utility Factor’. Though this factor attempts to explain 32.000% of total variance, it is not a dominant factor in the reasons for buying a counterfeit product by Indians (evident from the relatively lower average scores of 2.579 for this factor)

4.3.2. Factor 2

Second extracted factor explains 17.266% of total variance and comprises of four variables pertaining to buying behaviour. These include: buying a counterfeit product-hurts economy and increases unemployment. These variables are correlated with factor 2 as indicated by the factor loadings. Each of these attributes refers either to the economy or legal aspects. Hence this factor is labelled ‘Economic & Legal Factor’. Respondents have shown a high level of agreement for this factor, as is evident from high average scores of 3.366.

4.3.3. Factor 3

Third factor comprising two variables (attributes) explains 8.897% of variance. The attributes are: buying a counterfeit product endangers health and safety. Factor loadings of these two variables are significantly high on this factor ranging from 0.747 to 0.844. Collating all the variables, this factor may be labelled as ‘Safety’. This factor has the highestaverage score of 3.782 among all the factors and hence is the most significant one. All of its constituents are considered to be very significant as their average scoreslies in the range 3.72 to 3.83.

4.3.4. Factor 4

Factor number four has three variables (attributes) and explains 6.722% of total variance. Variables included in this factor are: buying a counterfeit product – helps fight exploitative big businesses, is socialistic, is the right thing to do because of exorbitant pricing of original products. These factors have been collectively labelled as ‘Socialistic’. This factor is not very potent as indicated by a low average score of 2.192 (the lowest among all four factors). Relative significance of factors is judged with the help of ranking of factors assessed by calculating average score per factor. This is arrived at by dividing the sum of average scores for all variables constituting a factor by number of variables. Results presented in Table VI indicate ‘Safety factor’ to be the most desirable factor (average score 3.782). Another factor that is significant in the buying of counterfeit products to respondents is ‘Economy and Legal Factor’ (average score 3.366). Factor labelled ‘Socialistic factor’ is a borderline case with average score of 2.990.The last factor ‘Utility Factor’ has average scores of much less than 3.0. i.e. 2.579 indicating that it is less significant.

[Table 6] KMO & Bartlett’s Test of Sphericity​​​​​​​

4.4. Validity and Reliability of Factor Output

Validity & reliability of factor output was checked statistically. Reliability was established by estimating Cronbach’s Alpha for each factor (Table VI). Alpha values for the factors were found to be respectively 0.826, 0.760, 0.742 & 0.668 which indicates that the output is reliable. Cronbach’s Alpha is defined as a measure of reliability that ranges from 0 to 1, with values of 0.6 to 0.7 regarded the lower limit of acceptability (Hair et al., 2010:92). Hair et al reflect that in exploratory research, values of 0.60 are acceptable. Convergent validity for a factor indicates that all variables constituting a single factor actually converge into it (i.e.) they share a high proportion of variance in common. Convergent validity was checked with the help of ‘Variance Extracted (VE)’. VE is calculated by adding squared factor loadings for all variables constituting a factor and then dividing it by number of variables.

Table 11 shows that Variance Extracted for each of the four factors is higher than 0.5.

[Table 11] Variance Extracted by Each Factor​​​​​​​\

5. Discussion

The results from the analysis of the research look interesting. From the ranking of the factors it is clearly visible that the respondents feel that the safety and health of the person is endangered by buying counterfeit products. The manifestation of this is damage to skin by using fake cosmetics, damage to eyes due to use of fake sunglasses and even physical cause of hurt to the self by the malfunction of a fake electric or electronic product.

5.1. Composition of Factors Influencing Buying of Counterfeit Products

A close look at the four factors influencing the impulse buying behaviour, clearly indicates that the buying of counterfeit products in India is guided by several not so apparent influences. The constituent factors that influence the buying of counterfeits in India include ‘Utility’, ‘Economy& Legal’, ‘Safety’and ‘Socialistic’ views. The widespread counterfeiting of goods cannot be checked unless the factors that encourage the buying of counterfeits are understood and addressed in an adequate way.

5.2. Differential Impact of Factors

Each of the constituent factors does not contribute equally to influence the impulse buying behavior. The research points out four factors for buying behavior of counterfeits, however all of them are not equally significant. Only two among the four factors (Safety and Economy and Legal) have an average score higher than three. This information would be valuable to manufacturers, law enforcers and other stake holders to devise measures to check counterfeiting and its consumption in India.

5.3. Relative Significance of Factors

Safety has turned out to be the foremost factor influencing the buying of counterfeit products in India. Though this factor turns out to be more significant, it tends to explain only 8.378% of the total variance in the buying behavior. The next significant factor that emerges is the Economy & Legal which explain 17.729% of the total variance. ‘Socialistic’ evolved as the next consequential factor that has an average score of 2.990. Unexpectedly, ‘Utility’ emerges as the least ranked factor although it explains 31.901% of the variances.

6. Conclusion

This research paper had twin objectives: identify factors that influence customers to purchase counterfeit goods in an emerging capital city - Raipur and identify the relative significance of these factors. For manufacturers, marketers and law enforcers it might be of great interest to learn that safety concerns are uppermost on the minds of people who deliberately or inadvertently consume counterfeits. If it can be conveyed effectively that consumption of counterfeits can cause more harm than increase in perceived value, it can be checked to a good extent. For example, if a message can be sent out that use of counterfeited cosmetics and creams can adversely and irreparably damage the skin, people would be dissuaded from buying counterfeits. Use of counterfeit sunglasses for instance, might actually end up harming the eyes as opposed to protecting it. Consumption of food and drinks very evidently can cause a direct and immediate harm. Advertisers can concentrate on this fear aspect and come out with warning advertisements which elaborate the harm caused by counterfeit products.