ISSN : (Online)
DOI : https://doi.org/10.14377/JAPR.2014.9.30.45
Do Brands Talk Differently? : An Examination of Product Category Involvement of Elaboration Likelihood Model in Facebook
Social media has substantially changed communication between many organizations, communities, and individuals. The emergence of social media has made it possible for individuals to communicate with nearly limitless others about a vast number of subjects (Mangold & Faulds, 2009). The population of online users engaging in such communication is substantial. As of May 2013, the Pew Research Center (2013) found that 72% of online adults used social media. Further, in the United States, four out of top 10 most frequently visited websites (Facebook (#2), YouTube (#3), LinkedIn (#6), and Twitter (#8)) involve social media. The impact of social media on organizations and individuals has generated enormous scholarly interest as well. The field of communication, for example, is replete with studies exploring the relationships between social capital, interpersonal communication, advertising, public relations, and social media. In many fields, in fact, the dynamic mechanisms involved in the use of social media are among the most popular subjects of inquiry for researchers these days.
Marketing and advertising professionals also have a stake in this knowledge, since their businesses increasingly rely on social media to communicate with consumers. Social media tools have the potential to increase essential brand communication, thus enhancing the familiarity of existing or potential consumers with the brand itself, and even more importantly, imbuing brand loyalty to the product (boyd & Ellison, 2007; Sung, Kim, Kwon, & Moon, 2010; Trusov, Bucklin, & Pauwels, 2009). Yet, social media also presents an enormous challenge for companies with regard to customer engagement or satisfaction that are related to brand image. Because customers no longer want to be talked at, they now expect companies to listen and respond to them (Kietzmann, Herkens, McCarthy, & Silvestre, 2011).
The present research examines whether brand communication in a real situation has different set of strategies by product categories or not. Using involvement from Elaboration Likelihood Model (Petty & Cacioppo, 1981), this study explores the relationships between various marketing communication strategies and product categories in an online setting. Results derived from brands’ messages in social media (e.g., Facebook) will enable the researchers to propose valuable implications for both academic and practitioners.
Elaboration Likelihood Model
The Elaboration Likelihood Model (ELM) has been used to explain how people’s attitudes are formed and changed in response to persuasive messages (Petty & Cacioppo, 1981). The explanatory power of the ELM comes from its divergence from previous “variable-oriented” approaches that focus on the salience of a particular source, message, or receiver variable. Instead, the ELM embraces a more process-oriented approach (Perloff, 1993) and identifies “two relatively distinct routes to persuasion” (Petty & Cacioppo, 1986, p. 3) each of which differentially influences the effectiveness of persuasive communication.
The ELM distinguishes between two routes to persuasion, the central route and the peripheral route, each distinguished by the extent to which a person “thinks about the issue-relevant arguments contained in a message” (Petty & Cacioppo, 1981, p. 128). When central route processing yields attitude changes, it is based on deliberate evaluation of the arguments contained within a message. By contrast, persuasion that occurs through the peripheral route is associated with less thoughtful processing, such as a reliance on affective cues or heuristics that are unrelated to the actual merits of the message. The assumption of the ELM is that individuals are surrounded by so many different persuasive messages that it is not adaptive, if not impossible, to carefully evaluate the merits of all encounters with persuasive messages (Petty & Cacioppo, 1981). Thus, people are likely to exert considerable elaborative effort in some situations and much less in others depending on their level of motivation and ability.
Central route processing, then, is characterized by considerable cognitive processing or elaboration. People tend to process information with relatively high motivation and involvement (Petty & Cacioppo, 1981, 1986; Petty & Wegener, 1999). In order to determine its merit, thought processing via the central route engage in more deliberate consideration of the ideas in a message or issue (Stephenson, Benoit, & Tschida, 2001). They carefully evaluate the arguments of the message and scrutinize information according to their present knowledge. An elaboration is facilitated when the central features of the issue or message are straightforward and complete. Thus, beyond simply understanding the arguments in the message, elaboration involves generating many of one’s own thoughts in response to the issuerelevant information to which one is exposed (Petty & Wegener, 1999).
On the other hand, the peripheral route to persuasion is successful when the receiver has low involvement or low motivation, and therefore does not thoroughly process persuasive information (Petty & Cacioppo, 1981). In such cases of low elaboration a person still may be persuaded, but by factors irrelevant to the actual content of the message (Moore, 2001). In the real world situation, the people’s life is complex and it often prohibit them from paying close attention to the details of every message. Therefore, persuasive techniques designed to stimulate this kind of peripheral route processing are widely used in marketing, advertisements, political campaigns, and public relations.
The Role of Product Category Involvement in the Consumer Decision-Making Process
Consumers may or may not spend a lot of time and cognitive effort on decisions about the products they purchase. This thought-processing gap often corresponds to the type of product being considered. Simply put, some product categories have been found to stimulate greater involvement and cognitive elaboration than others (Keller, 1993; Radder & Huang, 2008). Companies generally seek to develop marketing strategies that compel consumers to identify their brand products so as to build brand awareness and encourage brand preference (Keller, 2003). Because the consumer’s level of involvement during message processing is pivotal in this process, marketers must highlight the personal relevance of a product based on the values and interests of the audience. In this way, they are more likely to stimulate central route processing as well as more enduring brand loyalty (Petty, Cacioppo, & Schumann, 1983; Petty, Cacioppo, Strathman, & Priester, 2005; Zaichkowsky, 1994).
In addition, research on categorization indicates that when similar objects are grouped together, information-processing efficiency and cognitive stability are enhanced (Batra & Ahtola, 1991). When a variety of objective product attributes are presented, for instance, those with similar specifications are more likely to be categorized in a subgroup. Two product categories adopted by Batra and Ahtola (1991) are the presence of hedonic or utilitarian elements. The former concerns affective gratification of non-essential “wants”; the lat-ter is about the fulfillment of instrumental needs. Therefore, in relation to the ELM, the hedonic elements can be processed via peripheral route with low involvement, while the utilitarian elements can be delivered via central route with high involvement. In this study, the extent to which brand communication differs according to these two product categories was explored.
Types of Advertising Appeal and Product Category Involvement
In the advertising field, two major approaches that draw the attention of consumers are informational versus emotional appeals (Johar & Sirgy, 1991). Informational appeals inform consumers of key benefits about the product, while emotional appeals are aimed at evoking feelings in consumers (Dens & Pelsmacker, 2010). In Katz’s (1960) functional theory of attitudes these two types of appeals were called value-expressive appeals and utilitarian product appeals, respectively. Katz (1960) further identified four functions of an object that may serve for a person: 1) ego-defensive, 2) knowledge, 3) utilitarian, and 4) value-expressive; he argued that one’s attitude toward an object is determined by the function it serves for that individual. Thus, according to Johar and Sirgy (1981), a car may be experienced as a utilitarian object if consumers primarily are concerned with its attributions or functions, but as a value-expressive object if the decision- making process revolves around its perceived quality. This question about the relationship between product categories and communication is in line with findings of a recent study by Gnepa (2012) that identified significant differences in advertising message strategies of two different print advertisements in Time magazine, one for a high-involvement product (e.g., automobiles) and the other for a low-involvement product (e.g., soft drinks). In a related study of print advertising in magazines, Lee, Chung and Taylor (2011) found that brands in the high involvement product category (e.g., financial services) provided more information about the product in their advertisements than brands in other categories. More specifically, they found that more detailed and extensive advertising message strategies were applied by brands in high-involvement than low-involvement product categories.
RQ 1. How do advertising appeals in brand’s social media differ in high- and low- involvement product categories?
Although people may carefully attend to and elaborate on the content of a persuasive message, they also process the message quite superficially, attending only to cues peripheral to its content, such as mood, length or source of the message. The Heuristic-Systematic Model (HSM) developed by Chaiken (1980, 1987) is a widely known communication model that attempts to characterize these two modes of processing-heuristic and systematic-and to specify the conditions that govern each. A primary assumption of the HSM is the sufficiency principle, which asserts that individuals have limited cognitive resources for information processing and so tend to minimize their intake and processing of messages accordingly (Chaiken, 1980). In this sense, the main difference between ELM and this model is that HSM is not a one-to-one fashion with the types of informational cues (message content vs. other cues), as suggested by some researchers (Kruglanski & Thompson, 1999). Specifically, people can scrutinize peripheral cues to the message content, or they can process the message content heuristically.
Brown, Homer, and Inman (1998) also suggest high involvement product category causes consumers to exert the cognitive effort required to evaluate issue-relevant arguments about performance, main features, or function while low product category involvement engenders more affective responses to object features influencing mood, such as celebrity endorsements, music, and so on.
RQ 2. How do issue-relevant message arguments in brand’s social me-dia differ in high- and low-involvement product categories?
Taylor’s Six-Segment Message Strategy Wheel
Taylor (1999) identified a dichotomy in advertising message strategies. In his comprehensive model, advertising appeals are divided into six types, three categorized as information/transmission (ration, acute needs, and routine) and three as transformation/ritual (ego, social, and sensory). According to Taylor’s model (1999), ration type appeals of the Information/ transmission strategies are based on the consumer’s need for information, and apply to such product categories as computers and cars. In this case, the role of advertising is to inform and persuade based on logic (Golan & Zaidner, 2008; Taylor, 1999). Appeals in the acute need segment respond to the consumer’s need to make a purchase decision within a limited time. Such appeals would seek to stimulate brand familiarity and recognition for product categories such as batteries or tires. Alternately, appeals in the routine segment focus on the consumer’s ordinary buying habits for everyday product categories like coffee, cereal, or household products instead of purchases motivating large amounts of deliberation. On the other hand, advertising strategies that fall into the ego segment of Taylor’s transformation/ritual appeals are designed to evoke emotional and personal responses. This appeal is to match product characteristics to ego-enhancing products like those in the luxury goods or apparel product categories. Transformational advertising appeals in the social segment involve engagement with other people or social agreement through product consumption; such strategies introduce the recipient of the product into the decision, as with jewelry, greeting card, and holiday gift products, for example. Finally, sensory transformative appeals seek to associate particular product categories (e.g., food or beverage) with satisfaction of any of the five senses of smell, touch, hearing, taste, or sight.
RQ 3. How do transformation/ritual and information/transmission ap-peal strategies in brand’s social media differ in high- and low- involvement product categories?
In brand’s microblogging situation, an action is a certain response to a brand or object, which is a self-contained recipient of the action (Jansen, Zhang, Sobel, & Chowdury, 2009; Zhang & Jansen, 2008). The action-object pair approach to the study of social media messages is used to analyze interaction between user and the brand. In a similar situation, Jansen and olleagues (2009) applied brand tweets as electronic word of mouth (eWOM) for measuring action codes and found practical implications that microblogging plays a significant role for the success of marketing advertising. It is because the action-object approach can help marketers by offering the brand equity (e.g., brand identity or brand image) for interactions concerning the user and delivering. In order to identify the user and communicate well with them, the brands provide the related information, offer feedbacks, announcements, and comments to each user and those actions are considered as the interaction.
In this study, researchers followed Zhang and Jansen (2008)’s concept of action codes in the analysis of social media advertising and marketing messages. Transactions included such actions as brand announcements of upcoming products and instances of users answering questions, doing simple chitchatting, critiquing, providing ideas to improve products, and complimenting the brand, etc. The action codes used in this study are listed in Table 1.
Table 1 . Action Codes and Definition
RQ 4. How do action codes in brand’s social media differ in high- and low- involvement product categories?
Social media is increasingly used as a marketing communication tool for distributing information about brands and companies. By using multiple methods like banners, pop-ups, and corporate websites, marketers have expanded their online platforms, enabling them to offer unlimited messages and increase consumer awareness for their brands (Kwon & Sung, 2011). Previous studies have addressed virtual brand communication in Facebook and Twitter to examine social and psychological motives such as interpersonal utility, entertainment seeking, and information seeking among the members of brand communities (Choi, Rifon, Trimble, & Reece, 2006; Kwon & Sung, 2011; Sung et al., 2010;). Resnik and Stern (1977) used 14 information types of product characteristics, including price, quality, performance, components, availability, and special offers to potential customers. Choi and colleagues (2006) studied the current advertising information environment and identified five additional types of information: toll-free number, mail-in address, website address, disclaimer, and brand name. By examining their customer’s reactions or feedbacks of various types of brand’s product information, marketers can set up the strategy to have impact on consumers’ perceptions of their brands and eventually maximize their sales. The information types examined in the social media sampled for this study are listed in Table 2.
Table 2 . Information Types
This study, then, examined the following research question:
RQ 5. How do information types in brand’s social media differ in high- and low- involvement product categories?
Verbal and Non-verbal Cues
Attributing human characteristics to nonhuman entities is a pervasive tendency among most people (Caporael & Heyes, 1997). In both communication and marketing research, the psychological approach of imbuing a brand with personality is known as creating “brand personality” (Aaker, 1997). Moon (2000) found that when people see a technology with characteristics associated with human behavior they are likely to respond by making social attributions that then lead their interpersonal behavior.
Marketers often seek to make use of these anthropomorphic tendencies in order to create a brand presence in social media (Aggarwal & McGill, 2007). Therefore, brands in recent days may seek to generate a brand personality, for example, by using social media to open a dialogue, provide quick feedbacks, chitchat like a close friend, and so on. By engaging in brand-consumer interactions using social media, a brand can facilitate ongoing communication between itself and consumers, as well as among consumers. Pollach (2005) suggested that verbal cues in the imperative form, such as asking for relationship (e.g., follow, stay tuned, or become a fan on other media), asking for feedbacks (e.g., ‘let us know what you think,’ ‘email us,’ or ‘send us message’), redirecting to other media (e.g., ‘check out the links,’ or new ads) better involve readers in a discourse. Identification of such human characteristic variables, then, may imbue brands with personality (Aaker, 1997) and that indicates extent to brands are successful in engaging consumers in conversations.
Marketers often seek to make use of these anthropomorphic tendencies in order to create a brand presence in social media (Aggarwal & McGill, 2007). Therefore, brands in recent days may seek to generate a brand personality, for example, by using social media to open a dialogue, provide quick feedbacks, chitchat like a close friend, and so on. By engaging in brand-consumer interactions using social media, a brand can facilitate ongoing communication between itself and consumers, as well as among consumers. Pollach (2005) suggested that verbal cues in the imperative form, such as asking for relationship (e.g., follow, stay tuned, or become a fan on other media), asking for feedbacks (e.g., ‘let us know what you think,’ ‘email us,’ or ‘send us message’), redirecting to other media (e.g., ‘check out the links,’ or new ads) better involve readers in a discourse. Identification of such human characteristic variables, then, may imbue brands with personality (Aaker, 1997) and that indicates extent to brands are successful in engaging consumers in conversations. face-to-face communication (Brown, Broderick, & Lee, 2007; Kwon & Sung, 2011). Pena and Hancock (2006) sought to overcome this impersonal feature by socio-emotional features (e.g., user actions, emotions, and moods). For example, emoticons are symbols formed with keyboard characters resembling facial expressions, abbreviations, repeated punctuation, and intentional misspellings (Hancock, 2004; Walther & D’Addario, 2001). With such nonverbal cues, brands can communicate emotions and verbal subtleties to consumers (Nastri, Pena, & Hancock, 2006).
This study examines the extent to which Facebook brand pages in different product categories sought to convey personalities and human characteristics through verbal and non-verbal cues (see Tables 3 and 4), leading to the research questions:
RQ 6. How do verbal cues differ in high- and low- involvement prod-uct categories?
RQ 7. How do nonverbal cues differ in high- and low- involvement product categories?
Table 3 . Verbal Cues
Table 4 . Nonverbal Cues
The current study employed various dimensions of coding items that were identified in previous research. After collecting data derived from Facebook pages for selected brands, the researchers conducted content analysis to examine the communication strategies that each brand’s official Facebook page used. Content analysis was deemed to be an appropriate method for this purpose as it provided a procedure for classifying qualitative information as well as yielding data amenable to quantitative manipulation (Krippendorff, 2012). The unit of analysis was each individual posting on each brand’s official Facebook page from the period of July 1, 2013 and August 31, 2013.
To ensure the representativeness of the sample, researchers collected a list of the 2012 top 100 brands from the InterBrand website (www. interbrand.com) and divided them into eighteen of the product categories (i.e., alcohol, apparel, automotive, electronics, home furnishings, media, res-taurants, etc.) identified by InterBrand. To ensure and classify each product category according to high and low involvement criteria, fifty-six students were asked to evaluate all product categories using the seven-point Likert- scaled question items from Zaichkowsky (1994)’s Personal Involvement Inventory (e.g., “To me, a(n) XXX product category is unimportant/important, boring/interesting, irrelevant/relevant, unexciting/exciting, etc.). Based on respondents’ involvement toward each product category, and availability of Facebook pages, a total of six product categories (top 3 high-involvement categories and top 3 low-involvement categories) were selected and brands were classified. The results from paired sample T-test showed that automotive (M = 5.26, SD = 1.29), apparel (M = 5.37, SD = 1.45), and electronics (M = 5.69, SD = 1.21) were considered high-involvement product categories (Mhigh = 5.44, SDhigh = .61), while alcohol (M = 4.27, SD = 1.57), financial services (M = 4.62, SD = 1.44), and luxury (M = 4.08, SD = 1.68) were considered low-involvement product categories (Mlow = 4.32, SDlow = .79) and these two different product categories are statistically different each oth-er (t = 10.97, p < .001).
For each category, the researchers chose three of the top ranked-brands on the InterBrand website (total of 18 brands) and collected individual postings during two-month period (From July 2013 to August 2013). Thus, the final content analysis included a total of 430 discrete postings from Facebook brand pages. The specific brand names in each product category and page links are presented in Table 5.
Table 5 . High-Involvement and Low-Involvement Facebook Accounts by Brand
Coding Framework and Procedure
The data coding was based on a classification followed by each variable. Two coders in the United States did the coding. Both received training for each concept for coding. They first analyzed each brand posting (unit of analysis), based on a set of simple variables such as coding IDs, dates, the number of likes, etc. and explored whether it has presence or absence of concepts for each unit. In order to check intercoder reliability, the researchers chose 43 tweets (10% of the total) and applied intercoder reliability calculation by using Krippendorff’s alpha (Krippendorff, 2012) and Cohen’s Kappa (Cohen, 1968). Both ranged from 76% to 96% with an average of 87%. Reliability scores were 80% or higher in most categories.
Significance of product category comparisons between high and low involvement was determined by chi-square test (χ2) with Yate’s correction when appropriate using the commercially available software program SPSS® version 22. Pearson’s chi-square was applied to all contingency tables, and the test for independence was conducted to evaluate differences between high- involvement group and low-involvement group. The level of significance was adjusted for multiple comparisons using the Bonferroni adjustment to account for spurious significant differences.
RQ1: Advertising appeals. A 2 x 2 chi-square test indicated that a statistically significant relationship existed between product category involvement and advertising appeals (χ2 = 30.08, p < .001). Informational messages were much more prevalent in the high-involvement product category than in the low-involvement product category (high = 43.1 %, low = 18. 5%). Emotional messages were more prevalent in the low-involvement product category (high = 56.9 %, low = 81.5 %). Overall, however, regardless of the product category, emotional advertising appeals outperformed informational appeals on social media brand pages (see Table 6).
Table 6 . Message Strategy in High-avd-Low Involvement Product Categories
RQ2: Issue relevance. A 2 x 2 chi-square test indicated that the relationship between product category involvement and level of issue relevance was also significant (χ2 = 24.17, p < .001). Both high- and low-involvement product categories delivered messages focused on low-issue relevant items such as mood, celebrity endorsement, and aesthetic elements rather than on high-relevant items such as main function, price, release date, and so on. Moreover, messages in low-involvement products concentrated more on mood and aesthetic messages (85.9 %) as compared to messages about high involvement products (65.3 %). Results are depicted in Table 7.
Table 7 . Issue Relevance in High-and Low-Involvement Product Categories
RQ3: Message strategies. A statistically significant relationship was found between product category involvement and the message strategy (χ2 = 50.72, p < .001). Among the six message strategies, the Facebook brand pages for products in the high-involvement category showed more rational messages than for those in the low-involvement category (high = 19.6 %, low = 2.9 %). Though the low-involvement product category reported higher frequency in the sensory segment (high = 31.1 %, low = 49.8 %), the social segment, one of the affective advertising appeals, was seen more in the low-involvement product category (high = 6.2 %, low = 17.1 %). Results are provided in Table 8.
Table 8 . Specific Message Strategy in High-and Low-Involvement Product Categories
RQ4: Action codes. A 2 x 5 chi-square test found a statistically significant relationship between product category involvement and action codes (χ2 =75.72, p < .001). Among the action codes, comments regarding the objects were the most featured in both product categories, but messages in low involvement product featured almost double than posts in the high involvement products (high = 42.7 %, low = 82.0% ). Announcements about upcoming objects or events were reported more in the high involvement than low involvement category (high = 40.9 %, low = 16.1 %). These results are depicted in Table 9.
Table 9 . Action Codes in High-and Low-Involvement Product Categories
RQ5: Information types. A significant association existed between product category involvement and information type (χ2 = 33.88, p < .001). Messages in high-involvement products were about product-related postings (e.g. price, promotion, availability, or special offers), whereas low-involvement product category postings addressed source-related messages more, which focuses on company-sponsored search, celebrities, or media. More results are reported in Table 10.
Table 10 . Information Types in High-and Low -Involvement Product Categories
RQ6: Verbal cues. A 2 x 5 chi-square test indicated the presence of a significant relationship between product category involvement and verbal cues. Both high- and low-involvement products’ five verbal cues we examined depicted redirecting to other media such as YouTube, Twitter, or Instagram to deliver same messages via other vehicles. Though high-involvement products relatively delivered more messages asking for feedback or asking for relationship with others compared to low-involvement products, they were not significantly different each other. More than half (51.7 %) of the messages in low-involvement product categories did not use any verbal cues in their posts, a lot more than high-involvement product categories (28.9 %).
Table 11 . Verbal Cues in High-and Low-Involvement Product Categories
RQ7: Non-verbal cues. A significant relationship exists between product category involvement and non-verbal cues. Messages for high-involvement products used significantly more repeated punctuation marks (high = 8.4 %, low = 2.0 %) and emoticons (high = 7.1 %, low = 0.0 %) than for low involvement products. However, most postings on both high and low involvement product pages did not contain such nonverbal cues (high = 84.0 %, low = 95.6 %).
Table 12 . Nonverbal Cues in High-and Low-Involvement Product Categories
The most frequently used terms regarding each product category are shown in Figure 1 and Figure 2. Both figures elaborate the representative terms related to brands in the given data set. Some terms appear to be significantly bigger than other terms, which reflects the frequencies of the terms in the postings. ‘Photos’ appears to be big in both high- and low- involvement prod-ucts due to the characteristics of Facebook. Figure 1 shows ‘Look’, ‘Watch’, or ‘Check’ for redirecting posting to other media such as YouTube or Twitter in high-involvement products. Also, ‘Today’, ‘New’, ‘Collection’, or ‘Summer’ indicate announcement among action codes in high-involvement. On the other hand, Figure 2 describes source or products (e.g. ‘Budweiser’, ‘Corona’, ‘Amex’, or ‘Louis Vuitton’) among information types in low-involvement. Sensor or social-related terms (e.g. ‘Thanks’, ‘Share’, ‘Great’, ‘Community’, ’Involved’, ‘Party’, ‘Connect’, or ‘People’) were also found. Redirecting-related terms such as ‘Check’, ‘Watch’, or ‘Look’ were also shown.
Figure 1 . Word cloud of frequent terms for high involvement categories
Figure 2 . Word cloud of frequent terms for low involvement categories
As social media becomes a more popular tool in the marketing and advertising arena, business should put more effort to explore and understand the dynamics behind the use of social media in the marketing communications of global brands. The lack of empirical studies on this topic led to this investigation of the nature of brand communication through social media, particularly when taking into account different levels of consumer involvement associated with product category.
Both theoretical and practical implications emerged from the results. A variety of theories related to advertising appeals, strategies, and other variables were integrated in the analysis of brands’ Facebook pages. Informational appeals score especially well in high-involvement situations, whereas emotional appeals performed better in low-involvement situations. This is in line with findings of previous research that an informational appeal is effective when consumer involvement is high (Dens & De Pelsmacker, 2010; Erevelles, 1998). In contrast, in a low product category involvement situation, it induces ‘peripheral route’ to persuasion in which consumers evaluate products more superficially (Coulter, 2005). In this study, all the low-involvement product categories such as alcohol, financial services, and luxury used a considerable amount of emotional appeals in their messages.
In regard to the variable of issue-relevance, low issue-relevant messages were more frequent in the low-involvement product category and high is-sue-relevant messages were more frequent in the high-involvement product category on Facebook brand pages. Interestingly, however, both high- and low-involvement products did not specify their products, but kept expressing mood-related (“It’s going to be a great day”), aesthetic messages (“Looks delicious”), suggesting that one of the purposes in brand communication via social media such as Facebook or Twitter is to get exposed to potential consumers or become familiar with them on a daily basis (Lee & Lee, 2013). Talking directly about the function or availability of a brand’s prod-ucts, then, is not necessarily their goal.
The analysis of the six message strategies identified by Taylor (1999) revealed that the Facebook brand pages for products in the high-involvement product category (such as automotive) tended to contain messages using a rational strategy and those in the low-involvement product category (such as alcohol or luxury) tended to use sensory strategies. Nevertheless, the pages of some high-involvement products such as electronics (Nokia, Canon, or Sony) and apparel (H&M, ZARA, or Ralph Lauren) also contained messages using a sensory strategy. Further, the messages for high-involvement products like automobiles (Ford, Honda, or Toyota) used ego-related messages to communicate with consumers. Conversely, luxury products (Louis Vuitton, Gucci, or Hermes) were also found to use the routine message strategy in order to serve a simple reminder without telling details.
By the same token, the types of action codes on the Facebook pages of brands in high- and low-involvement product categories were mixed. For instance, apparel (high), electronic (high), and luxury (low) brands used a large amount of announcement (e.g., upcoming events or brand-new items) on their Facebook pages, while alcohol (low), automotive (high), and financial services (low) brands expressed mostly neutral comments regarding objects.
It was interesting to find that product-related information (e.g., components, functions, and special offers) was frequently seen in Facebook messages for both low-involvement products (luxury) and high-involvement products (apparel or electronics). Such messages were more likely to announce a new season’s items or publicize a product’s new features. On the other hand, Facebook pages for alcohol (low) and financial services (low) posted mostly source-related information (e.g., independent research, news articles, etc.) because those product categories have relatively more resources with which to inform consumers.
Lastly, in regard to verbal cues, Facebook messages for high-involvement products asked for feedback and directed consumers to their websites in order to sell their products. Regardless of the product category, however, a common strategy was redirecting consumers to other media through links to YouTube, Twitter, or Instagram. Because Facebook is optimized by linking to YouTube or posting pictures, this strategy allows consumers to engage fully with brands through their five senses. On the other hand, there was limited non-verbal use on brand Facebook pages in both product categories. The official nature of brands’ Facebook may account for the use of more official words there. These findings contradict, to some extent, the findings of a recent study that brand communication on Twitter tended to employ emoticons and repeated punctuation in order to build virtual relationships with their consumers (Lee & Lee, 2013). In a more practical vein, our results indicated that, when visiting brands’ social media sites, consumers generally expect conversations rather than simple marketing promotions (Millward Brown, 2010). Marketers should take note that they can use social media to interact with their potential consumers and convey brand personality and brand characteristics.
This exploratory study has both academic and practical implications for brand communication in social media. However, some limitations of the study point to research opportunities for the future. First, the use of the high- and low-involvement variable described in Petty and Cacioppo’s Elaboration Likelihood Model may not be the most important influence in decision-making, or may be influenced by socio-economic variables such as income or education. Therefore, future studies could generalize individual’s personal involvement in a more clear way to each product category and divide those results into high-and low-involvement product. Second, though selected product categories were classified as high and low based on previous studies (Golan & Zaidner, 2008) and confirmed with college student’s level of involvement by using personal involvement inventory measure (Zaichkowsky, 1943), some product categories contained mixed characteristics in terms of involvement (e.g., apparel or luxury). Thus, future studies could justify the generalizability of selected brands or product categories. Hence, future studies should expand to other product categories (e.g., beverages, sporting goods, FMCG, media, restaurants, etc.) to obtain more diverse data and increase the generalizability of the findings. Future studies implementing these suggestions will contribute to our knowledge about the use of brand communication in social media.
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