Role of Advertising In the Development of Brand Image With Reference to Insurance Industry

Brand identity and brand image are related concepts. They are essential ingredients of strong brands. In order to build and maintain brand loyalty, it is imperative that these two be in harmony. Value for the firm as well as the consumer can be created only when the consumer understands and appreciates the brand message. The brand also has to be perceived to be addressing consumer needs better than the competition. In an over-communicated marketing environment it is very easy for brand identity (created by the company) and brand image (created by consumer perceptions) to be out of sync. When this happens, consumers will move on. They live in a world of multiple options — there are countless other competitive brands waiting to entice them with their own alluring messages. From a communications perspective it is logical to believe that a strong link between brand identity and brand image will lead to enhanced brand loyalty. Congruency between image and identity implies that the consumer has great understanding of (and agreement with) the brand message and is, therefore, likely to be loyal to the brand. Traditional notions of increasing sales through aggressive persuasion and generating repeat purchases through stimulus–response approaches such as classical and instrumental conditioning are doomed to failure. In addition, mass media will be less effective. Connections between the brand and consumer have to be established through dialogue and more customized interactions that relate consumer needs, motivations and dreams to the core benefits provided by the brand. Areas crucial for brand identity–brand image congruence are highlighted. The study will result in to a two standardize measures to evaluate advertising message and the brand image. The study will result to in two regression equations reflecting the casual relationship between each of the two independent variables taken separately with the dependent variable. These equations can be used to evaluate how much the independent variable effects the dependent variable i.e. Brand image. Casual relationships would be evaluated between the independent variable and the dependent variable (brand image) through multiple regression equation. Interaction between independent variable would also be worked out and its effect would be evaluated. The gaps in knowledge regarding brand image development will be brought out clearly after comprehensive review of literature. This will indicate areas for further research. The relationship developed between independent and dependent variables would become the basis for deeper analysis of the effect of this variable on dependent variable in various conditions.

Many marketing and advertising studies have examined ways of advertising execution. Among these approaches, message framing has emerged as one of the most significant models of media effects in the past decade (Price and Tewksbury, 1997). Researchers pay attention to how advertising messages are presented to consumers because the way information is framed or executed may significantly influence consumers' judgment and decisions about products (for a review of framing research, see Levin, Schneider, and Gaeth, 1998). For example, a message could accentuate potential gain to consumers resulting from product use (i.e., a positively-framed message). Alternatively, a message could emphasize possible loss if the product is not chosen (i.e., a negatively-framed message). The issues in framing research on choice behaviors (e.g., Rothman and Salovey, 1997; Smith, 1996). In this article, perceived risk associated with product use and individual differences in mood are proposed as potential moderators on framing effects. Healthcare contexts are emphasized because of the market value {Business Wire, 2005) and the importance for public policy and communication (Moorman and Matulich, 1993). How should advertisers frame messages that are intended to promote healthcare products of varying degrees of perceived risk? Should they emphasize potential gains resulting from using the product or the negative consequences of not buying it? Support for the belief that advertising effectiveness is moderated by the program environment has been reported in the marketing literature (cf. Kennedy, 1971; Wright, 1974, 1981; Yuspeh, 1979). For example, Kennedy (1971) found that advertising presented in the context of a situation comedy induced greater recall than when the same messages were shown in the context of a suspense thriller. Yuspeh (1979) observed that the recall of advertising was affected by the television programs in which they were placed and that this outcome occurred for different types of programs. These findings establish the program environment as a factor that can affect the audience's responses to advertising placed within the program. But the findings are not informative about when or why programs produce particular advertising effects. The idea that it is desirable to develop an advertising message that clearly distinguishes a product from its competitors has been advocated by American advertising practitioners for many years. Throughout the history of modern advertising, industry leaders have recommended that the advertising message be used as a way to differentiate the advertised brand from competitive brands (Stewart and Furse 1986, pp. 11-12; Aaker, Batra, and Myers 1992, pp. 370-405). For example, Borden (1942), an early pioneer in the field of advertising research, was a proponent of brand differentiation through effective advertising. David Ogilvy (1963) shared the belief that advertising should be used to clearly distinguish a brand from its competitors by creating a unique image. More recently, Ries and Trout (1972, 1981) have popularized the idea of using advertising to stress a unique product benefit, thus distinguishing the brand from its competitors and increasing advertising effectiveness. It is well Known that both the nature of the advertising message and the amount of information attached to it vary with the medium used (Tirole, 1988). It is generally the case that advertising in electronic media (i.e. television (TV) and radio) is characterized by rather low informational content. On the other hand, newspapers and magazines are utilized for advertising campaigns conveying significant amounts of measuring a person's involvement in advertising message content is becoming an increasingly important issue in experimental research today. The Attitude-Toward-the-Ad Model (Lutz 1985; Mitchell and Olson 1981; Shimp 1981) A soimd manipulation of involvement will help to enhance internal validity (i.e., the ability to draw cause and effect inferences) and rule out confounding extraneous variable explanations (Carlsmith, Ellsworth, and Aronson 1976; Cook and Campbell 1979). However, manipulations of involvement in advertising message content vary greatly, including instructions for the memorization of ad content, expectations of purchase decisions, implications of purchases influenced by brand differences, expectations of local product availability, and distraction to reduce involvement (Laczniak, Muehling, and Grossbart 1989; Leigh and Menon 1987; Park and Young 1986;). To be successful, however, researchers must manipulate differing levels of involvement in ad content, while holding all other factors constant (Andrews 1988; Apsler and Sears 1968). Brand image attributes are those pieces of information that are linked to the brand in consumer memory and thus make up part of the brand's image (Keller 1993). These attributes may take the form of anything that is experienced in the same context as the brand, and can come from a variety of sources, including consumer experiences with the brand, marketing communications or word of mouth (Krishnan 1996). Particular types include descriptive information (e.g. has four doors), benefits (e.g. low in fat) and usage situations (e.g. can eat when I am walking) (Joyce 1963; Holden &c Lutz 1992). Anything that is encountered with the brand may, if sufficiently processed, become linked to the brand name in memory and thus become part of that brand's image. Given the wide variation in substance, many researchers have attempted to classify these attributes into useful categories. Establishing expected empirical patterns is of importance, since it allows future researchers to employ prior knowledge in an aid to understanding what to expect when measuring the perceptions that consumers hold. Given that we know very little about the negative perceptions about brands that people hold and how these negative perceptions influence decision making, this application of prior knowledge is an important foundation step.

1. To develop and standardize the measures to evaluate advertising message, and brand image. 2. To develop relationship between individual independent variable and dependent variable.

Sampling Techniques: Purposive sampling techniques were used to select sample elements. Sample Size: Thirty elements were selected from each emotion for each company, it means six emotions were taken for each company that makes total sample size for each company equals to one hundred and eighty and there are three companies to be compared that makes total sample equals to five hundred forty.

The causal relationship between the individual independent variable & the dependent variable was evaluated using simple regression. Partial least square method was used to develop a simple model indicating the relationship of independent & dependent variables.

Simple linear regression analysis was applied to investigate the relationship between advertising message as independent variable and brand image as dependent variable. The relationships between both the variables (dependent and independent) were established by using Regression analysis with the help of SPSS-16.0 software.

The cause and effect relationship between the advertising message and brand image is significant as indicated by beta value of 0.689 and t value of 22.033 significant at 0 level of significance. R2

indicates the percentage error of the dependent variable explained by the independent variable. Coefficient r2 value is 0.474 indicating that change of 100% in advertising message will make 47.4% changes in the brand image. Thus advertising message is a strong predictor of brand image. The regression equation on the basis of linear regression can be framed as given below:

Throughout the history of modern advertising, industry leaders have recommended that the advertising message be used as a way to differentiate the advertised brand from competitive brands (Stewart and Furse 1986; Aaker, Batra, and Myers 1992). For example, Borden (1942), an early pioneer in the field of advertising research, was a proponent of brand differentiation through effective advertising. David Ogilvy (1963) shared the belief that advertising should be used to clearly distinguish a brand from its competitors by creating a unique image. Rosser Reeves (1966), another early Before applying regression analysis the relationship between the two variables was checked through curve fitting. The results of curve fitting analysis displayed in table 4.15 above show that the R, R2, F and Beta values of Linear curve are among the highest for all the curves. Some of the other curves have higher values on some parameters and low on others; whereas linear curve has consistently high values. Therefore, linear regression was applied to evaluate the relationship between the independent variable (advertising message) and the dependent variable the (brand image) of LIC brand.

Y= a + bx +e Y= 44.156 + .636 x + e X= independent variable (advertising message) Y= dependent variable (Brand image) E = error term The cause and effect relationship between the advertising message and brand image is significant as indicated by beta value of 0.674 and t value of 12.182 significant at 0 level of significance. R2 indicates the percentage error of the dependent variable explained image. Thus advertising message is a strong predictor of brand image.

Y= a + bx +e Y= 47.261 + 0.625 x + e X= independent variable (advertising message) Y= dependent variable (Brand image) E = error term

The cause and effect relationship between the advertising message and brand image is significant as indicated by beta value of 0.679 and t value of 12.341 significant at 0 level of significance. R2

indicates the percentage error of the dependent variable explained by the independent variable. Coefficient r2 value is 0.461, indicating that change of 100% in advertising message will make 46.1% changes in the brand image. Thus advertising message is a strong predictor of brand image.

The cause and effect relationship between the advertising message and brand image is significant as indicated by beta value of 0.633 and t value of 10.905 significant at 0 level of significance. R2 indicates the percentage error of the dependent variable explained by the independent variable. Coefficient r2 value is 0.401, indicating that change of 100% in advertising message will make 40.1% changes in the brand image. Thus advertising message is a strong predictor of brand image. Y= a + bx +e Y= 47.261 + 0.625 x + e X= independent variable (advertising message) Y= dependent variable (Brand image) E = error term

The objective was to find out the relationship between advertising message as independent variable and brand image as dependent variable using simple linear regression. Before applying regression analysis the relationship between the two variables advertising message and brand image was checked through curve fitting for all the three insurance companies separately. values on some parameters and low on others, whereas linear curve has consistently high values. Therefore, linear regression was applied to evaluate the relationship between the independent variables (ad message) and the dependent variables the (brand image) of all the three brands included in the study. The relationship was found to be high for all the three Insurance brands.

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