Linking Products to a Cause or Affinity Group - Research Paper Example

?Review of “Linking products to a cause or affinity group: Does this really make them more attractive to consumers 0. Research Problem The key background or context under which the study of McKinnon and Harris (2006) was undertaken is that “cause-related marketing and affinity marketing” are becoming increasingly popular differentiation strategy given severe market competition and “consumer sophistication” (p. 135). As implicitly defined by Mekonnen and Harris (2006, p. 135), “cause-related “and “affinity marketing” associates the revenues of commercial organisations with the revenues of non-profit organisations by specifying the payments to the non-profit organisation that the commercial organisation will make as revenues of the latter increase. Mekonnen and Harris (2006) is an empirical investigation whether “cause-related” and “affinity marketing” enhances the product’s appeal and provides product differentiation (p. 135). In particular, the study investigated whether “linking a commercial organisation’s product with a non-profit organisation enhances the product’s appeal and provides differentiation from rival offers” (Mekonnen and Harris 2006, p. 135). Mekonnen and Harris (2006, p. 135) articulated that the purpose or aim of their study is to investigate the efficacy of the premise of “affinity marketing”. The research objectives were not explicitly stated but it can be surmised that, implicitly, the research objectives covered identification of values associated with the use of affinity cards and how they affect the likelihood of donation. 2.0. Literature Review Based on a review of literature, Mekonnen and Harris (2006, p. 136) claim that “there are significant gaps in understanding consumer attitudes” towards products being sold by “cause-related marketing” and “affinity marketing”. The lack of understanding on consumers attitudes also applies to the nature of consumers’ relationship with non-profit organisations (Mekonnen and Harris 2006, p. 136). According to Mekonnen and Harris (2006, p. 136), this is rooted to the “difficulty in obtaining access to consumers holding these products.” On the matter, the implicit claim is that their research is unique because they have “gained access to such consumers” (Mekonnen and Harris 2006, p. 136). Mekonnen and Harris (2006, p. 135) declared their work as a challenge to the assumption “that linking a product to a non-profit organisation enhances its appeal and provides a basis for differentiation.” For Mekonnen and Harris (2005, p. 135), “the efficacy of this premise depends on the type of cause or affinity group.” Unfortunately, however, the review of literature of Mekonnen and Harris (2006) does not amount to an illustration of the “significant gaps in understanding consumers’ attitude” in the literature. It is not clear from the review of literature what the various studies have made or the findings they have reached on the relationship between “cause-related market marketing” and “affinity marketing” on one hand and product sales on the other. Perhaps there was no empirical study done at all prior on cause-oriented and affinity marketing prior to their study but this is not clear from the review of literature. However, because of the failure to illustrate what empirical studies have done or to explicitly clarify at least that no study has been done, the review of literature fails to identify precisely or to justify precisely the significance of the Mekonnen and Harris (2006) endeavour in the overall literature on the subject. The review of literature narrated that cause-oriented marketing has been conventionally characterised as being focused on short-term campaign initiatives (Mekonnen and Harris 2006, p. 136). It also pointed out that the affinity marketing is a sub-set of cause-related marketing (Mekonnen and Harris 2006, p. 136). However, citing the work of Berger and others in 1999, Mekonnen and Harris (2006, p. 136) clarified that “affinity marketing can be viewed as effectively constituting long-term oriented cause-related marketing relationships targeted at a discrete group of consumers, rather than short-term mass-market oriented cause-related marketing programs, which have been the dominant form of cause-related marketing.” The review of literature of Mekonnen and Harris also clarified that “affinity marketing capitalizes on the goodwill that consumers have towards a group to which they belong as members or identify with as supporters, with products or services being targeted to members of the group using the endorsement of the group and its channels of communication” (2006, p. 136). For affinity marketing, the authors cited the example of MBNA whose financial services business markets a World Wide Fund for Nature (WWF) affinity credit card for WWF supporters through a leaflet mailed with the WWF’s magazine. Other examples were also provided in the review of literature. According to Mekonnen and Harris (2006, pp. 137-138), the type of benefits from affinity marketing can be classified into two dimensions. The first dimension pertains to the focus of benefit while the second dimension pertains to the type of benefit. Based on these two dimensions, four distinct products benefits can be identified (Mekonnen and Harris 2006, p. 137). The first dimension refers to whether the benefit outcome is focused on the group or the individual (Mekonnen and Harris 2006, p. 137). According to Mekonnen and Harris (2006, p. 137), the second dimension or the type of benefit can be functional or image benefits: functional benefits refer to products which are better while image benefits project an identity for the buyer of the product. 3.0. Paradigm and Design Cresswell (2003, p. 6) defined a paradigm as a set of assumptions on how knowledge will be learned during an inquiry. According to Cresswell (2003, p. 6), researchers stake a claim about what is knowledge (ontology), how we know it (epistemology), what values go into knowledge (axiology), how we write it (rhetoric), and the process for studying it (methodology). For Cresswell (2007, p. 5), a research design refers to “the entire process of research from conceptualizing a problem to writing research questions, and on to data collection, analysis, interpretation, and report writing.” Adopting the definition of Bogdan and Taylor in 1975, Cresswell (2007, p. 249) elaborated on what research design is, “I use this term to the entire process of research, from conceptualizing to writing the narrative, not simply the methods such as data collection, analysis, and report writing.” Many authors identify that the three fundamental categories of research design are the quantitative, qualitative, and mixed research designs. According to Marczyk et al. (2005, p. 20), “the ability of researchers to make valid causal inferences is determined by the type of research designs they use.” Mekonnen and Harris (2006) employed both qualitative and quantitative research methodology. This implies that Mekonnnen and Harris (2006) attempted to combine the ontology, epistemology, axiology, rhetoric, and methodologies associated with both quantitative and qualitative research strategies. The use of qualitative research methods for marketing is also discussed in Belk (2006). On the other hand, combining qualitative and quantitative research are discussed in Bergman (2008) and Cresswell (2003). 4.0. Methods As pointed out, the Mekonnen and Harris (2006) study employed both qualitative and quantitative methodologies. In the qualitative part of the research design, Mekonnen and Harris (2006) employed seven focus group (perhaps, more appropriately focus group discussion or focus group discussion/interviews) involving 26 participants (p. 140). The sampling is most likely convenience or purposive sampling and not random. It is hard to verify whether this is convenience or random because Mekonnen and Harris (2006) do not make a categorical admission or description of the sampling methodology. Mekonnen and Harris (2006, p. 140) pointed out that the participants to the focus groups “were recruited by telephone from a database of customers provided by the financial services organisation.” Convenience sampling, however, is an accepted sampling methodology for qualitative research, particularly for focus group discussion. However, some type of focus group discussions or group interview techniques make use of “key informants” or “key respondents” who are presumed, believed, or identified as among the most knowledgeable among the potential respondents. For example, a club officer can be presumed to have more knowledge of the organisation than an ordinary member does and, thus, they may be the more appropriate respondents than ordinary members for focus group discussions or interviews. This sampling procedure is oftentimes called purposive sampling or sampling that has a purpose or follows a set of criteria. It is important to point out that Mekonnen and Harris (2006) attempted to choose respondents for the focus groups who are in the better position to speak on behalf of many members. According to Mekonnen and Harris (2006, p. 140), “to ensure that the focus groups and interviews reflected the diversity of affinity relationships, the research included affinity groups from all of the five categories used by the credit financial services organisations, based on the nature of affinity group.” Mekonnen and Harris (2006, p. 140) identified the five sectoral categories as professional, membership, lifestyle, charity, and university. According to Mekonnen and Harris (2006, pp. 140), the focus group discussions and interviews were structured using a standard protocol to allow comparison across the groups. The discussion/interview topics included the “participant’s perception of affinity; their involvement and relationships with the affinity organisation and financial services organisations; their reasons for taking out their affinity credit card; their attitudes towards using and terminating their affinity credit card; and their views about credit cards in general” (Mekonnen and Harris 2006, p. 140). From each of the five categories, the Mekonnen and Harris (2006) study applied three selection criteria. The first selection criterion was that the university category should include old, new traditional and non-traditional universities (Mekonnen and Harris 2006, p. 140). The second selection criterion was that selections should be made from affinity schemes with APRs in which majority of the affinity schemes fell (Mekonnen and Harris 2006, p. 140). APR was not defined in Mekonnen and Harris (2006) but APR probably referred to the "annual percentage rate". The third criterion was that the affinity groups should have at least 1,000 members (Mekonnen and Harris 2006, p. 140). Thus, based on the foregoing qualifications by Mekonnen and Harris, the sampling procedure they followed is most likely purposive sampling. In analyzing qualitative data, Mekonnen and Harris used QSR N6 qualitative software “to facilitate the identification of recurring themes as well as contradictions across interviewees and sectors” (2006, p. 141). Meanwhile , in the quantitative part of their research design, Mekonnen and Harris (2006) implemented a survey, derived the descriptive statistics and conducted post hoc tests using Tukey HSD or “Tukey honestly significant difference” (Mekonnen and Harris 2006, p. 149). The quantitative research design component of Mekonnen and Harris (2006) is identified by Reid and Plank (2004, p. 6) as a “survey research design.” Based on the “six specific aspects of value identified through the focus groups” discussion and interviews, MacKennon and Harris (2006, p. 141) developed a questionnaire in which “the importance of each type of benefit was assessed using seven-point rating scales with verbal anchors of ‘not at all important’ to ‘very important’.” The respondents were also questioned on what credit cards they usually use. Prior to launching, however, the questionnaires were piloted or pre-tested among volunteers “to check for ambiguity, accuracy, and to monitor the length” or duration of questionnaire administration (Mekonnen and Harris 2006, p. 141). MacKennon and Harris (2006, p. 141) gathered “in-depth” comments form participants to revise the questionnaires prior to its full launched. A total of 5,000 questionnaires or 1,000 questionnaires for each affinity group category were emailed to select cardholders in which only 408 questionnaires returned for an overall response rate of only 8.16 per cent. Post hoc tests using Tukey was used to analyze whether “information use” was likely to affect the use of affinity cards (Mekonnen and Harris 2006, p. 149). In addition, Mekonnen and Harris (2006, p. 149) employed a GLM or a general linear model for tests of between-subjects effects on likelihood of donation information affecting use of affinity cards. In other words, the “General Linear Model” procedure was used to “test how likely it was that the information about the use of donation of the affinity groups would affect how cardholders used their affinity credit cards” (Mekonnen and Harris 2006, p. 149). It is important to note the limitation declared by Mekonnen and Harris (2006) on their work. The authors acknowledged that while the study covered a wide range of affinity categories, “all of the affinity credit cards were issued by one financial service organisation” (Mekonnen and Harris 2006, p. 135). Related to the use of mixed methods, it may be useful to review Table 2 on the difference between qualitative and quantitative methods: Table 1. Differences between quantitative and qualitative research Source: Hair et al. (2002, p. 212) The use of mixed methods is sometimes employed for triangulation purposes or for checking or validating a finding using several methods. However, Mekonnen and Harris (2006) employed the mixed method for a different purpose. In effect, the Mekonnen and Harris (2006) has two phases in which qualitative methods were used for the first phase and quantitative methods were used for the second phase. 5.0. Results and Presentation According to Mekonnen and Harris (2006, p. 136), their findings suggest that “cause-related” and “affinity marketing” affect or can increase product appeal but the efficacy of increasing product appeal “depends on the type of cause or affinity group in question.” Mekonnen and Harris (2006, p. 136) elaborated that that “linked products offer a range of individual and group benefits, but the value placed on these benefit categories was found to vary according to the type of affinity group.” The work of Mekonnen and Harris (2006) has weaknesses in data presentation. One such weakness, for example, is the failure to identify the vertical axis variable in their Figure 2 (p. 148). This is also true for their Figure 3 in Mekonnen and Harris (2006, p. 150). Nevertheless, although the vertical axis variable is not given, it is possible to know what the y-axis variables are by closely reading the texts of Mekonnen and Harris (2006). Mekonnen and Harris (2006, p. 135) pointed out that the practical implication of the findings of their work is that it highlights the need to identify the values of consumers and to tailor the affinity products “to the type of affinity organisation with which they are linked.” One of the key results of the Mekonnen and Harris (2006) study is in Table 2. Table 2. Affinity groups and importance of benefit on a 7-point scale Source: Table III, Mekonnen and Harris (2006, p. 147) The first column of Table 2 pertains to the affinity groups (overall is the aggregation of the charity, lifestyle, membership, professional, and university groups). Wallet, ego, donation, and promotion are four values. The figures on the cell represent the mean of respondents’ ranking on the importance of the value, with 1 representing “not all important” and 7 as “very important” (Mekonnen and Harris 2006, p. 147). The results from Table 2 are graphed in Figure 1. Figure 1. Affinity group and value rankings of key benefits Source: Figure 2, Mekonnen and Harris (2006, p. 148) On their own and without using Tukey or the GLM, both Table 1 and Figure 1 are already telling stories on what values are more important for each type of affinity groups. Since all n are higher than 30, we can even use the Z-statistics and interpret each numeral on Table 1 as the estimate of consumer ranking on the value. Of course, generalization may be inappropriate and the generalization may apply only to the population covered by the sample. Using Tukey HSD statistics, however, Mekonnen and Harris (2006, p. 149) interpreted their data to mean that affinity category significantly affected whether consumers would use their affinity credit card. The Tukey test and the rests related to making comparisons among treatment means is discussed in Kinnear and Gray (2008, pp. 265-271 and p. 301). Other discussions on the Tukey test are in Abdi and Williams (2010) and Crichton (1999, p. 304). On the other hand, Kinnear and Gray (2008, p. 254-256) covers the general linear model. Tabachnick and Fidell (2007, pp. 913-918) and Singh (2007, p. 149) also covered the general linear model. Unfortunately, the Mekonnen and Harris (2006) interpretation of their data does not fit very well with the intent of their research which is to find out if affinity produces product differentiation and increases the appeal of products to consumers. In this case, the product is the affinity card. If we want to find out whether the affinity cards boost sales then we have to compare the sales of affinity cards with non-affinity cards and find out whether the presence or existence of affinity cards boost the sale of credit cards or encourage purchases at volumes SIGNIFICANTLY larger than the credit cards without affinity or link to causes. The Tukey and general linear model procedure has the likelihood of donation to the affinity group as the dependent variable. Regardless of the significance of the statistics, the likelihood of donations to the affinity group may or may not increase credit card sales and sales of affinity cards. In other words, what is questionable here is the direct relevance of the findings to the research problem that the study is supposed to address. As the relevance of the research findings to the research objective is being raised, what is also questioned is the validity of the research findings. As Saunders et al. (2003, p. 101) put it, “validity is concerned whether the findings are really about what they appear to be about.” For Hair et al. (1995, p. 3), validity is the “extent to which a measure or a set of measures correctly represents the concept of study.” References Abdi, H. and Williams, L., 2010. Tukey’s honestly significant difference test. 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New York: Psychology Press. Marczyk, G., DeMatteo, D., and Festinger, D., 2005. Essentials of research design and methodology. New Jersey: John Wiley & Sons. Mekonnen, A. and Harris, F., 2006. Linking products to a cause or affinity groups: Does this really make them more attractive to consumers? European Journal of Marketing, 42 (1/2), 135-153. Reid, D. and Plank, R., 2004. Fundamentals of business marketing research. New York, London, & Oxford: Best Business Books. Saunders, M., Lewis, P. and Thornhill, A., 2003. Research methods for business students. Hanow, England: FT Prentice Hall. Singh, K., 2007. Quantitative social research methods. Los Angeles and London: Sage Publications. Tabachnick, B. and Fidell, L., 2007. 5th ed. Using multivariate statistics. Boston and London: Pearson. Read More