The purpose of this paper is to understand the ethnocentric tendencies of Indian consumers towards foreign multi-brand retailers (FMBRs), and the influence that such ethnocentrism has on their attitudes towards, and future purchasing behaviour from, these international retailers.
The paper used a mall intercept method with a randomised data collection process to secure data from 119 organised retail shoppers in a major metropolitan Indian city. The analysis was carried out using analysis of covariance, bootstrapping mediation, multiple regression analysis, and Johnson’s relative weight analysis.
Two main results are as follows: concern for livelihoods of small retailers and a perception that earnings of foreign retailers are unjust are revealed as the most significant drivers of negative attitude towards FMBRs, and although high ethnocentric customers have a strong negative attitude towards FMBRs, they are open to the idea of making future purchases of goods that are not available with small retailers, from the foreign retail outlets.
The study sample is from a single metropolitan city, albeit one which serves as a miniature version of the Indian society. As a limitation, the results might not be generalisable to small, non-metropolitan Indian towns.
These results provide valuable input regarding the marketing strategy and sustainability of foreign retailers planning to launch operations in India. For example, FMBRs should position themselves as not being in competition with existing small retail shops by offering a different array of products. Further, younger and more educated Indians are the least ethnocentric towards FMBRs, thus making them an attractive target segment.
Extant research has studied consumer ethnocentrism of Indian consumers towards foreign brands and products, but not towards FMBRs. This paper attempts to fill that research gap.
Banerji, D. and Mishra, P. (2018), "An ethnocentric perspective of foreign multi-brand retail in India", International Journal of Retail & Distribution Management, Vol. 46 No. 3, pp. 230-246. https://doi.org/10.1108/IJRDM-11-2016-0204Download as .RIS
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As of 2017, out of the top 30 developing economies of the world, India is ranked the most attractive country in which to invest in organised retail (A.T. Kearney, 2017), up from its 2nd rank in 2016 (A.T. Kearney, 2016) and 15th rank in 2015 (A.T. Kearney, 2015). Though the emergence of modern trade in India is relatively new (Sengupta, 2008), “India’s huge market potential, fast growth, and improved ease of doing business” (A.T. Kearney, 2016, p. 3) have made the country an attractive avenue for investment in the organised retail space. However, the effect of the entry of foreign multi-brand retailers (FMBRs; e.g. Walmart, Carrefour, and Tesco) is vigorously debated in India today, mainly due to a perception that these retailers might have an adverse impact on the livelihoods of the multitude of shop owners of small shops (Tandon, 2016). It is also important to note that the “swadeshi” (“of one’s own country”) movement, which encouraged the consumption of indigenous products and boycott of imports, was a cornerstone of India’s freedom struggle and remains part of the Indian consciousness (Varman and Belk, 2009). Defined against India’s history of colonisation, the embedded notion of swadeshi “highlights the Indian people’s favor of self-sufficiency and suspicion of Western interests” (Fuchs and Glaab, 2010, p. 16). Thus, the swadeshi narrative, which came into existence at a particular historical juncture, continues to permeate the Indian consciousness even today (Chatterji, 2016; Varman and Belk, 2012), further legitimising the concerns of small Indian retailers. In essence, it is wrong to assume that FMBRs will necessarily enjoy easy success once they enter the emerging market. This paper explores the perception of Indian consumers towards FMBRs and the possible impacts such perceptions might have on future purchase behaviour with respect to these retailers once they launch operations in India.
Apprehensions that small store owners will not be able to live up to the competition from FMBRs have proliferated. For example, the current Indian Government policy allows FMBRs only up to 51 per cent ownership of their Indian arm (Mishra, 2017; Sarkar, 2017), while allowing up to 100 per cent ownership for single-brand retailers (Bailay and Anand, 2015). This differential approach reflects the position that FMBRs are potential threats to the livelihoods of small store owners, whereas single-brand retailers entering luxury and high technology segments do not pose any such danger. The government is hesitant to open the multi-brand retailing sector any further due to concerns that “small traders are not ready to face competition from established foreign companies” (Nair, 2016). On the other hand, small store owners not only offer unique services – such as credit purchase, home delivery for small buys, and sale of unpackaged items (e.g. sugar and flour) – but also maintain personal relationships with customers, which is a tough menu of services for modern retailers to match (Mukherjee, 2014; Pandey et al., 2015). Furthermore, it is expected that these FMBRs will bring certain benefits to the Indian market such as better prices for farmers’ produce, at times without any intermediaries, and savings from optimising the supply chain being passed to the end consumer (Patibandla, 2012; Sudhir, 2012). In sum, while a perceived dislike towards FMBRs among a section of the Indian population exists, some of it may be tenuous; a positive side to foreign retailers does exist.
Given this backdrop of contrary arguments, the objective of this research is to understand Indian consumers’ feelings and attitudes towards FMBRs which are expected to stock both domestic and international brands, and the effects of these feelings and attitudes on future buying intentions (FBIN). This inquiry proposes to make use of the concept of consumer ethnocentrism tendency (CET), which refers to consumers’ preference for domestic products and their prejudice against imports in the belief that buying foreign goods is potentially damaging to the national economy (Shimp and Sharma, 1987). Researchers have widely used the concept of CET as a reflection of the feelings of consumers expressed towards foreign manufacturers and brands; however, this exploration employs the concept in the context of FMBRs.
Research on ethnocentric tendencies of consumers towards foreign retailers is scarce (Carpenter et al., 2013). Even though extant research has tried to uncover the ethnocentric tendencies of Indian consumers towards foreign manufacturers and brands (Bawa, 2004; Deb and Chaudhuri, 2012, 2014; Kumar et al., 2009, 2011, 2013), however, there is no research to date that explores the ethnocentric tendencies of Indian consumers towards foreign retailers. This study measures and analyses the ethnocentric tendency of domestic consumers towards purchasing from FMBRs (consumer ethnocentrism towards foreign retailers (CETFR)), in line with pioneering research done in Australia (Zarkada-Fraser and Fraser, 2002), however, it goes a step further and also analyses Indian consumers’ negative attitude (NATT) towards FMBRs and FBIN from these foreign retailers. These constructs have not been studied together, which makes the conceptual model (Figure 1) novel. The present paper offers to increase our knowledge of Indian consumers’ attitudes towards FMBRs and their FBIN from these retailers. Once Indian consumers’ ethnocentric views and attitudes towards FMBRs – which are expected to stock both domestic and international brands – become apparent, the marketing teams of these retailers can use the information to develop appropriate strategies to placate consumers’ concerns.
The research contribution of this paper is threefold. First, the study measures ethnocentric views of Indian consumers specifically towards FMBRs, something not examined previously. While previous research has studied the relationship in an advanced economy (i.e. Australia), it is important to remember that the Indian setting is representative of the emerging economy context, for which “companies need to rethink strategies as past experience in home countries may not apply to emerging markets” (Tanusondjaja et al., 2015, p. 785). The investigation also identifies the most suitable target segments for FMBRs in the Indian market. Second, FBIN is considered, and the study provides a better understanding of the relationships between CETFR, NATT, and FBIN from an Indian consumer’s perspective. Third, the research attempts to arrive at an understanding of drivers of high NATT levels and to offer actionable managerial implications based on these drivers.
Theoretical development and hypotheses
This paper draws upon relevant literature examining the various determinants and outcomes of CET and formulates hypotheses that are empirically tested. The study considers the effect of two demographic variables – age and education level – on consumers’ CETFR levels, and the impact of CETFR on FBIN through NATT. Figure 1 presents the conceptual model as envisioned in the hypothesis development process.
Shimp and Sharma (1987) conceptualised CET as a nationalistic tendency to distinguish between products depending on whether or not they are manufactured in one’s country and to avoid buying foreign goods to support one’s domestic economy. Thus, ethnocentric consumers feel it is incorrect to purchase foreign goods due to the perception of losses to the local economy. Additionally, two properties of the CET construct are noteworthy: it is a general tendency as opposed to a particular attitude, that is, an outlook towards foreign products in general (Shimp and Sharma, 1987), and it has an affective aspect (Shankarmahesh, 2006), forming the basis for using the concept to understand feelings of Indian consumers towards foreign retailers. We do not expect CET towards foreign retailers to operate any differently than towards foreign manufacturers, since similar mental processes (e.g. nationalism) would be triggered in response to the concern for the loss of livelihood of domestic workers as well as small retailers. Our use of the concept is on similar lines as that of Zarkada-Fraser and Fraser (2002), who examined Australian consumers’ attitude towards foreign-owned supermarkets in relation to their ethnocentric levels and is, therefore, justified.
Demographic antecedents to CETFR
Extant research suggests that the age and education level of consumers are antecedents of CET (Josiassen et al., 2011; Meeusen et al., 2013). Empirical evidence favours the assertion that younger people – across different cultures – are usually more cosmopolitan (i.e. at ease with different cultures) in their outlook because they tend to accept global trends more readily, and, hence, are less ethnocentric (Cleveland et al., 2009; Javalgi et al., 2005). Research in an Indian setting has also found that younger Indians have a significantly higher acceptance of global cultural developments than older Indians (Gupta, 2013). While some studies find that younger Indian consumers have lower CET scores, Deb and Chaudhuri (2012), Kumar et al. (2011) and Upadhyay and Singh (2006) do not find support for that conclusion. The conflicting results, despite the sound theoretical rationale behind the position – that younger people have lesser ethnocentric tendencies – motivate the exploration of the relationship in the context of CETFR. This study posits:
Significant differences exist in CETFR levels across age groups; younger Indian consumers have significantly lower CETFR scores than older consumers.
Second, more highly educated people are seen as being less ethnocentric due to a wider exposure to perspectives and cultures outside their home country and a broader mental horizon (Meeusen et al., 2013). Research in Indian settings has uncovered conflicting results; while Deb and Chaudhuri (2012) and Kumar et al. (2011) find that more highly educated people have lower CET scores, Upadhyay and Singh (2006) do not support the association. Based on the theoretical logic of the relationship between education level and CET, the investigation postulates:
Significant differences exist in CETFR levels across education levels; more highly educated Indian consumers exhibit significantly lower CETFR scores than less educated consumers.
Direct consequence of CETFR: negative attitude towards foreign retailers (NATT)
Researchers have conceptualised attitude as “a relatively global and enduring evaluation of an object, issue, person, or action” (Hoyer et al., 2013, p. 128). In keeping with this conceptualisation that is widely used, our study also employs NATT as an overall negative evaluative appraisal of FMBRs.
Extant research in the Indian context shows contradictory results – while some research shows that high CET levels leads to negative attitudes towards foreign products (Deb and Chaudhuri, 2014; Kumar et al., 2011), another does not find any evidence of association between high CET levels and prejudice by Indian consumers towards the country of origin of foreign products (Deb and Chaudhuri, 2012).
Extending CET research to the realm of retailing companies, Zarkada-Fraser and Fraser (2002) investigate whether CET affects attitudes towards foreign supermarkets and find a significant negative relationship between the two. Thus, the paper puts forward the following proposition:
Indian consumers with higher CETFR levels show stronger NATT towards FMBRs.
According to the theory of planned behaviour, one’s attitudes, subjective norms, and perception of control come together to produce an intention, which is in turn influenced by actual control and determines one’s actual behaviour (Ajzen, 2012). Therefore, a sound theoretical basis exists for the link between attitudes and purchasing intention; this relationship also has empirical support (Halkias et al., 2016; Jiménez and San-Martin, 2016).
However, this relationship has been uncovered in extant CET research in a setting where consumers had access to the products or brands under question. On the other hand, this study intends to understand respondents’ feelings and purchase intentions towards FMBRs that are yet to appear in India. For this purpose, the authors bring in the construct of FBIN, defined as “a purchase probability of a customer actually willing to buy a product” (Wiedmann et al., 2011, p. 210), and suggest that consumers with high NATT levels will have a lower FBIN:
A significant negative association between NATT and FBIN from FMBRs exists.
Drivers of NATT
Concerns remain that FMBRs setting up shop in India will endanger the livelihoods of small store owners/traders; there were nationwide protests after approval of the proposal for foreign direct investment (FDI) in the retail sector by the Indian Government (Pasricha, 2011; PTI News Report, 2012). These concerns are not unfounded – reports in the US media also agree that “Walmart destroys retail jobs” (Worstall, 2013). Further concerns include that once FMBRs come in, they will stock their own or other international brands and Indian brands will lose out as a result (Guruswamy and Sharma, 2006). Against the backdrop of these concerns, the study will test the following hypotheses:
High concern for livelihoods of small shop owners increases NATT for Indian consumers.
Indian customers with high NATT will make a strong effort to buy from outlets that stock domestic brands.
Sampling and data collection
While doing a priori sample size calculations, we noted that the adequacy of a sample size depends on features of the data; for example, a sample size of between 100 and 200 observations is acceptable provided communalities are high, factors are well determined, and convergence to a proper solution is achieved (MacCallum et al., 1999). Further, de Winter et al. (2009) demonstrated that sample sizes in the above range are acceptable for a high level of loadings, a low number of factors, and a large number of variables (i.e. items). For example, for loadings greater than 0.4 (items of the CETSCALE have been consistently shown to have factor loadings more than 0.4, see Jin et al., 2015; Kumar et al., 2013), a single factor (CET is seen as an unidimensional construct), and six variables, a sample size of 102 is sufficient for factor recovery, whereas for 12 variables, a sample size of 64 suffices (de Winter et al., 2009, p. 154, Table II). Our sample size of 119 observations, therefore, meets the criteria for leading to a full factor recovery.
This study utilises the mall intercept method with face-to-face interviews – since it allows for identifying respondents who would be more representative of the shopper population than, say, college students (Shamim et al., 2016). We identified four organised retail stores (two Spencer’s, one More, and one Big Bazaar) in different parts of a major Indian metro city (Kolkata) and three possible time slots (10 a.m.-12 noon, 2-4 p.m. and 4-6 p.m.) for interviewing respondents. Further, the study employs a randomised data collection process to avoid respondent bias. The stores were coded from one to four and the time slots from five to seven; thus, 12 possible store-time combinations result; for example, the combination number 16 represents location number 1, that is, Spencer’s Garia, and time slot six, that is, from 2 to 4 p.m. For a particular day, the investigators selected a random number from the set of 12 possible combinations (for four stores×three time slots) and conducted surveys accordingly. Interviewers approached shoppers leaving stores after purchases and asked for help in conducting research. In the case of a family, the interviewer identified and interviewed the person making most of the shopping decisions. If the shopper was a student, the researchers thanked the person and did not ask any further questions. In return for their time, respondents received a token gift (a pen worth INR 80 equivalent to UK₤0.9 or US$1.2).
Further, we note that the CETFR questions are loaded in nature and could potentially prime respondents. In order to control for and check question order effects, we had three versions of our questionnaire a priori and any one was presented randomly to a respondent. In the first version, we asked the FBIN question first, while in version two we asked the NATT in the beginning and our third version started with the CETFR questions. We had 40 respondents each for the first and second versions of our questionnaire and 39 for the third version, bringing the total number of respondents to 119. These three groups did not show any statistically significant differences in: gender of respondents (F(2,116)=0.78, p=ns), age group (F(2,116)=2.23, p=ns), and highest education level (F(2,116)=0.77, p=ns). Therefore, the three groups of respondents were similar, at least on these three variables, to start off with. Further, these groups also did not vary significantly in terms of their average CETFR score (F(2,116)=2.14, p=ns), their NATT score (F(2,116)=1.21, p=ns) or FBIN score (F(2,116)=1.20, p=ns).Therefore, the design of this study is close to an experimental design due to randomisation of the data collection process (from the store selection, to timing of interviews, and the order of questions as well) and provides evidence to suggest that there was no impact of question order on the effects observed.
For this study, a total of 119 complete responses were collected (43 male and 76 female; n=119) over a period of 23 days from respondents across all age groups: 19-24 years (21 per cent); 25-34 (34 per cent), 35-44 (18 per cent), 45-54 (15 per cent), 55-64 (9 per cent) and 65 years and above (3 per cent; Table I). Respondents ranged across different levels of education: class 12 (7 per cent), graduate (69 per cent), postgraduate (18 per cent), and PhD (6 per cent).
This paper utilises the adapted version of Shimp and Sharma’s (1987) CETSCALE similar to the usage in a previous investigation in measuring consumer ethnocentrism towards foreign-owned stores (Zarkada-Fraser and Fraser, 2002). Some rewording of items was necessary to take into account the Indian retail context. The CETFR scale used has 11 items.
The CETSCALE has been used reliably in the Indian context (Deb and Chaudhuri, 2012, 2014), and in other many developing economies, for example, Chile and Hungary (Cleveland et al., 2009), China (Kan et al., 2014) and Poland (Huddleston et al., 2001). Hence, usage of the adapted scale for CETFR in our research in the Indian context builds on solid ground.
On a different note, the mall intercept study design put a severe time limitation on how long respondents were willing to spend answering survey questions and, hence, on the number of questions that could be asked. Therefore, the study looked at single-item measures to gauge attitude towards FMBRs and FBIN. Researchers do not usually recommend single-item measures because of: the susceptibility to random errors of measurement exist, for example, when one accidently ticks off the wrong option, the issue of simplifying complex constructs into a single question, and the complexity in measuring internal consistency (Sarstedt et al., 2016). However, extant literature permits researchers to use single-item measures in time-constrained situations and when the construct is doubly concrete, defined as those constructs that “have a simple, clear object (e.g. an ad or a brand) and a single and single-meaning attribute (e.g. liking)” (Bergkvist, 2015, p. 246). This study also adopts single-item measures for two constructs, NATT and FBIN, similar to the usage of single-item measures in extant research (Beck and Kenning, 2015; Eastlick and Lotz, 2011).
The final questionnaire used in this inquiry has 13 items (see Table II), apart from demographic questions regarding gender, age group, and highest education qualification; the respondents found this interview length to be acceptable.
Validation of scale adaptation (CETFR)
An initial examination of descriptive statistics of the 11 CETFR items revealed that three items (CE07, CE08, and CE09) had low correlations with the others and were dropped from further analysis. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy (for eight items) was meritorious at 0.88 (Hutcheson and Sofroniou, 1999); KMO values of all the individual eight items (diagonals of the anti-image correlation matrix) were greater than 0.82 (Field, 2013), and Bartlett’s test of sphericity was significant (χ2 (28)=408.45, p<0.001) which indicated suitability of the data for final factor solution.
Next, we choose to do an exploratory factor analysis (EFA) with maximum likelihood as the estimation method to examine the latent factor(s), that is, the source(s) of common variation inherent in the observed data (Henson and Roberts, 2006; Preacher and MacCallum, 2003). The remaining eight items meet the assumption of multivariate normality (skew <0.98 and kurtosis <1.45; Fabrigar et al., 1999, p. 283), implying that we can proceed with the maximum likelihood estimation method. The EFA results (maximum likelihood with direct oblimin rotation) clearly indicate a single-factor solution (hence no rotation could be done) that explains 51.95 per cent of the variance, and has an average factor loading of 0.66 across eight items (Table II). The χ2 is not significant (χ2 (20)=23.66, p=ns) suggesting that one cannot reject the null hypothesis that the model is a perfect fit for this data. Further, repeating the factor analysis with a PAF and direct oblimin rotation also leads to a single-factor solution. Taken together, the EFA results show evidence that the CETFR scale (with eight items) is unidimensional. Furthermore, the CETFR scale finally used for analysis (i.e. with eight items) shows an acceptable reliability with Cronbach’s α coefficient of 0.87, thus giving evidence of acceptable internal consistency.
It is necessary to check for common method variance (CMV) since interviewees provided responses for both the independent and the dependent variables. A Harman’s one-factor analysis yields three factors with eigenvalues greater than 1.0; the first factor explains only up to 44.69 per cent of the total variance. Harman’s test, therefore, indicates a poor fit for a single-factor model, suggesting that CMV is not an issue in this study. Even though some researchers have expressed concern that Harman’s test is insensitive (Podsakoff et al., 2003), recent advice recommends that the test “can detect biasing levels of CMV under conditions commonly found in survey-based marketing research” (Fuller et al., 2016, p. 3197). Therefore, CMV does not appear to affect this research.
An analysis of covariance (ANCOVA) (gender taken as a covariate) shows a statistically significant difference in CETFR scores across different age groups, F(5,112)=32.07, p<0.001, =0.59. Note that the sample sizes vary across the groups. When this occurs, the parametric bootstrap approach is recommended for a robust testing of differences of means (Xu et al., 2013; Zhang, 2015). Bootstrapped contrasts based on 10,000 samples reveal that compared with the CETFR score of the 15-24 years age group (MCETFR_15-24_years=15.12, 95% CI [13.94, 16.73]), the CETFR score of the 35-44 years age group is significantly higher (MCETFR_DIFFERENCE=−7.23, p<0.001, 95% CI [−11.34, −3.12]) as are the CETFR scores of the 45-54 years, the 55-64 years, and the 65+ age group. Therefore, the data support the assertion that older age groups have significantly higher CETFR scores, giving support to H1a (Figure 2).
A second ANCOVA analysis (with gender again as the covariate) helps us to test H1b; this test shows a significant difference in CETFR scores across different education levels, F(3,114)=8.75, p<0.001, =0.18. Bootstrapping indicates that compared with the CETFR score of the Class 12 group (MCETFR_Class 12=27.00, 95% CI [24.75, 28.80]), the CETFR score of the graduate group is significantly lower (MCETFR_DIFFERENCE=5.73, p<0.001, 95% CI [1.06, 10.39]) as are the CETFR scores of the postgraduate and the PhD group. This analysis validates the claim that the higher the education level, the lower the CETFR scores, thereby lending support to H1b (Figure 3).
Next, the study uses the SPSS PROCESS macro developed by Hayes (version 2.13.2) to conduct a serial mediation analysis to test H2 and H3 (Model 6: Hayes, 2013; see also Field, 2013). The SPSS PROCESS macro estimates bias-corrected bootstrap confidence intervals for coefficients based on a bootstrapping of 10,000 samples.
The first mediation analysis takes age group as the independent variable and FBIN as the dependent variable, with CETFR as the first mediator and NATT as the second (see Figure 4). Gender is a covariate. Age group is associated with increased CETFR, thus substantiating H1a (b=3.59, 95% CI [2.95, 4.23], t(116)=11.13, p<0.01), and CETFR is, in turn, related to increased NATT, giving support to H2 (b=0.11, 95% CI [0.09, 0.13], t(115)=11.14, p<0.01). However, the path from NATT to FBIN is not significant; thus, H3 is not supported in this serial mediation.
A second serial mediation analysis – the only change being that highest education group (edu_grp) is now the independent variable (see Figure 5) – shows that highest education level is associated with decreased CETFR, thus endorsing H1b (b=−4.39, 95% CI [−6.14, −2.64], t(116)=−4.97, p<0.01] and CETFR, in turn, is related to increased NATT, further substantiating H2 (b=0.13, 95% CI [0.12, 0.15], t(115)=16.66, p<0.01). However, the path from NATT to FBIN is significant; thus, H3 is not supported in this mediation analysis either.
The research then runs a step-wise multiple regression model with each of the eight CETFR items as the independent variables and NATT as the dependent variable, to understand which items are driving NATT, that is, to test H4 and H5. The final prediction model (step 6) of this step-wise regression has six items of the CETFR scale (Table III), is statistically significant F(6,112)=62.71, p<0.001, and explains approximately 77 per cent of the variance of NATT (R2=0.77, ΔR2=0.01, and adjusted R2=0.76).
The data meet the assumption of collinearity, with the highest value inflation factor (VIF) being less than 10 (VIF for item CE6 =3.39) and the lowest tolerance being more than 0.1 (tolerance for item CE6 =0.30) (Cohen et al., 2003). The regression residuals also agree with the requirement of independence (Durbin-Watson statistic =1.95). The normal probability plot and histogram of standardised residuals suggest that the data contain almost normally distributed errors while the scatterplot of standardised residuals indicate that the data agree with the notion of homogeneity of variance and linearity.
Further, to understand the relative importance of predictors, in Table IIIwe calculate Johnson’s relative weight index (ε, see Johnson, 2000), which is interpreted as the proportion of explained variance (R2) in the criterion variable associated with a predictor (Tonidandel and LeBreton, 2011, 2015). The advantage of ε is that it factors out the collinearity between predictor variables to produce an accurate estimate of the unique contribution of R2 that is coming from each predictor (Chao et al., 2008). Estimates of ε values give us robust assessments for the most important predictors – item CE4 explains 23 per cent, CE6 explains 20 per cent, and CE11 explains 19 per cent of R2 (Table III). The results indicate that item CE4 (“It is not right to purchase from foreign-owned big retail stores, as it pushes small shop owners out of business”) exerts the highest influence on NATT, and item CE11 (“I shop at retail stores that make a special effort to offer domestic brands”) has the third highest relative importance, supporting H4 and H5. It is also pertinent to mention that while item CE6 (“We should purchase products from Indian-owned big retail stores instead of letting foreign-owned big retail stores get rich off us”) was not hypothesised as having a significant impact on NATT, it shows the second highest influence after item CE4 (see item CE6 in Table III).
This paper is situated in the context of current debate about FDI in an emerging economy – India – in the organised retail sector and contributes to the understanding of Indian consumers vis-à-vis their perceptions towards FMBRs. The paper finds that younger consumers have lower CETFR levels as do consumers who have attained higher education levels, which compares favourably with extant CET research in India as well as other emerging economies (Cleveland et al., 2009; Deb and Chaudhuri, 2012; Hamelin et al., 2011).
However, the paper does not find support for the hypothesised negative relationship between NATT and FBIN. While surprising, extant research helps to explain this result. High consumer ethnocentrism is seen as influencing attitudes for products that are perceived as a threat to the domestic economy (Shankarmahesh, 2006). It is, therefore, likely that high CETFR respondents have a stronger NATT as far as those offerings perceived as a threat to the small stores/traders are concerned, while not exhibiting such negative attitude towards other offerings. In other words, purchasing a product from an FMBR that is not available at small stores (and hence does not put the small stores at risk) might be acceptable, thus explaining the lack of significant association between CETFR and NATT. In other words, while Indian consumers are not averse to the overall idea of buying from FMBRs in the future, they are likely to buy products that they do not see as being hurtful to the small stores.
Out of the factors driving NATT, the concern for small retailers’ livelihoods shows as the most prominent, as expected. The final hypothesis is also supported, that is, high NATT customers make an active effort to buy from outlets that stock domestic brands. Support for this hypothesis also bolsters the argument of perceived threat put forward earlier, that is, individuals will favour purchasing from FMBRs that do not threaten the small stores.
Another unexpected finding from this study is that Indian consumers may perceive the earnings of FMBRs as unjust (second highest influence of item CE6 on NATT; see Table III). This concern can be understood by looking at the track record of some foreign retailers who: consistently pay lower wages to their employees than the average retail worker earns, and aggravate the economic imbalance between different actors in the supply chain, that is, push prices paid to farmers and manufacturers down, thereby squeezing their margins (UNI Global Union, 2012). For example, a report by Consumers International estimates that only 4 per cent of the total price paid for a banana or a pineapple sold in a UK supermarket reached the worker based out of Costa Rica (Nicholson and Young, 2012). Thus, there are assessments in the Indian media based on incidences in other economies that the earnings of some of these international retailers come at a huge cost to many people (Swamy, 2011). Any FMBR planning to enter the Indian market has to ameliorate these apprehensions – including in actual practice as well –to have a sustainable business over a long term.
FMBRs looking to expand into India need to take cognisance of the fact that Indian consumers are concerned about the impact of these international retailers on the livelihoods of the small store owners. Hence, these retail companies should clearly position themselves as stocking a product range that is not available from the average small store owner, and hence, does not put the small owner’s business at risk. Better still, by offering certain services exclusively to small store owners, the FMBRs would project a more positive image.
Another hopeful prospect that emerges from this investigation is that Indian consumers do not shun shopping from FMBRs, despite being ethnocentric, since we do not find any significant path from NATT to FBIN. One attraction for shopping in these retail stores appears to be access to products or brands that are not perceived as a threat to the small stores. FMBRs should stock their in-house brands or other foreign brands so that they are not in direct competition with small stores. Stocking similar offerings as those of the small stores is likely to alienate Indian consumers. Further, offering services that are not available with the local small store, like several cashless payment options and unique experiences (e.g. dance parties; see Belanger, 2016) can also help FMBRs position themselves on a unique platform in Indian consumers’ minds.
Further, younger consumers, as well as those who are well educated, appear to be less ethnocentric towards FMBRs. Therefore, this research recommends that these categories of individuals be a major target segment for international retailers.
It is also important to take into account the perception of Indian consumers that the FMBRs’ earnings are unjust, as discussed earlier (the item CE6 has the second highest influence on NATT). These concerns have to be assuaged by FMBRs so that they are not perceived negatively by the Indian public. One of these steps, for example, could be to create awareness through the judicious use of integrated marketing communications about their proposed contribution to improving the lives of their suppliers, farmers, and other such communities. FMBRs can also highlight that with their advent, expansions and improvements are expected in food and non-food supply chains, thus generating employment in that sector and that, in turn, customers are likely to benefit from reduced prices.
Our research, therefore, offers specific, actionable insights for FMBRs in terms of their STP strategy for entry into the Indian market. Emerging markets may require specific segmentation and targeting strategies suited for them (Tanusondjaja et al., 2015); younger customers and those with high education levels appear to be the most promising segments for FMBRs that launch in India. Further, FMBRs will do good to position themselves as a completely different shopping destination than the local small store by stocking a new range of products as well as offering services that are not likely to be available with the small stores.
Limitations and future research
This study has some limitations, which, in turn, can lead to further research. First, the sample studied is from the metropolitan city of Kolkata, which is one of the largest metropolitan cities in India and serves as a microcosm of Indian society (Shaw, 2012). To better assess the applicability of these results across other Indian cities, researchers can elicit responses from other locations. By identifying cities whose residents have similar characteristics, the marketing teams of FMBRs will be able to strategize effectively.
Second, another interesting question that future researchers might want to look at is the choice of retail outlet based on the perceptions of value proposition, that is, the overall benefits minus the costs. The present research focusses on the aspect of choice of retail outlet in the backdrop of concerns that many Indians have about the survival of small retailers once FMBRs come in or, in other words, their ethnocentric feelings towards foreign retailers. We believe that the value-based adoption model (see Demangeot and Broderick, 2010; Kim et al., 2007) will provide a different, yet relevant lens to researchers to look at the same question of choice of retailer.
Third, since the construct of perceived threat is not included in the initial conceptual model, this study is unable to test the argument put forward in defence of lack of support for H3 (a significant negative association between NATT and FBIN from FMBRs exists). Future researchers may not only examine the argument empirically by including the construct of perceived threat as a moderator in their conceptual model but also include other potential antecedents, mediators, and moderators to have a clearer picture (see Shankarmahesh, 2006, for a detailed discussion).
Finally, even though the design of this study simulates an experimental design, an actual field experiment in a controlled environment where buying behaviour could be observed would provide more rigorous answers. This would, however, be possible, only after the FMBRs come in. Researchers might want to delve into this field experiment design in a future study.
In sum, this paper advances the understanding of ethnocentrism of Indian consumers towards FMBRs. An emerging economy such as India is impossible for foreign retailing companies to ignore, and with the gradual opening up of FDI in the retail sector, these companies need to reach out to the Indian consumer while launching their businesses. The results of this research can add to foreign retailing businesses’ awareness of, and alleviate potential risk factors through, proper planning for long-term business success.
Sample characteristics across age groups, highest education level, and gender
|Highest level of education|
|Age group||Class 12||Graduate||Postgraduate||PhD||Subtotal|
|65 years and older|
EFA Factor loadings and inter-item correlations based on a maximum likelihood estimation with oblimin rotationa for eight items for our ethnocentric tendency towards purchase from foreign retailers (CETFR) scale
|Item No.||Scale item||Extracted factor loadings||Item ra|
|CE4||It is not right to purchase from foreign-owned, big retail stores as it pushes small shop owners out of business||0.90||0.41|
|CE6||We should purchase products from Indian-owned, big retail stores (such as Big Bazaar, Pantaloons, Shoppers Stop, etc.) instead of letting foreign-owned big retail stores (like Walmart, Carrefour) get rich off us||0.89||0.39|
|CE3||Purchasing from foreign-owned, big retail stores is unIndian||0.72||0.33|
|CE11||I shop at retail stores that make a special effort to offer domestic brands||0.68||0.34|
|CE1||Indian people should always buy from Indian-owned, big retail stores (such as Big Bazaar, Spencer’s, Pantaloons) instead of foreign-owned, big retail stores (such as Walmart, Carrefour)||0.61||0.30|
|CE2||Only those products that are unavailable in India should be imported||0.57||0.27|
|CE5||A real Indian should always shop at Indian-owned, big retail stores||0.51||0.27|
|CE10||It is always best to shop in Indian-owned, big retail stores (such as Big Bazaar, Pantaloons, Shoppers Stop, etc.)||0.41||0.21|
Notes: n=119. Eigenvalue of the extracted factor – 4.16; per cent of variance explained – 51.95; Cronbach’s coefficient α – 0.87. a“Item r” refers to the inter-item correlation, which examines “the extent to which scores on one item are related to scores on all other items in a scale” (Piedmont, 2014, p. 3303). The figures presented are calculated employing all 11 items in the initial scale. Only one factor could be extracted (for eight items CE7, CE8, and CE9), and the solution converged in four iterations. Hence, the solution was not rotated. Item Nos CE7, CE8, and CE9 were dropped due to low correlation with other items
Source: Scaled items adapted from CETSCALE (Shimp and Sharma, 1987)
Linear regression predictors of negative attitude (NATT) towards foreign retailers
|Predictors (step 6)||b||95% CI for b||β||t||p||ε (Johnson’s relative weight)||95% CI for ε|
|Column (I)||Column (II)||Column (III)||Column (IV)||Column (V)||Column (VI)||Column (VII)||Column (VIII)|
|CE4||0.22||0.08, 0.33||0.28||3.46||0.00||0.18||0.14, 0.22|
|CE6||0.16||0.00, 0.31||0.18||2.16||0.03||0.16||0.12, 0.20|
|CE11||0.22||0.10, 0.31||0.23||3.89||0.00||0.15||0.10, 0.20|
|CE2||0.16||0.06, 0.25||0.16||2.99||0.00||0.10||0.06, 0.15|
|CE10||0.22||0.05, 0.37||0.13||2.74||0.01||0.06||0.02, 0.11|
|CE3||0.14||0.04, 0.25||0.15||2.43||0.02||0.12||0.09, 0.16|
Notes: R2=0.77 and ΔR2=0.01 for step 6 of stepwise regression. Degrees of freedom for each predictor’s t-value=112. The top three values of β and ε are in italicface. Columns I-II and IV-VI are from output of stepwise regression. Column III – based on 10,000 bootstrap samples. Columns VII-VIII – ε values (Johnson’s (2000) relative weights method) and 95% CI for ε are calculated with the help of RWA Web (Tonidandel and LeBreton, 2015). 95% CI values are based on 10,000 bootstrap samples
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The first author is grateful to Dr Krishanu Rakshit, in whose doctoral seminar class the idea for this paper germinated, and to Dr Saravana Jaikumar for his help on previous drafts of the manuscript. The authors are thankful to the editor and the two anonymous reviewers for their constructive comments and suggestions. The authors also thank Deborah Gray for copy editing an earlier version of the manuscript.