Int. J. Internet Marketing and Advertising, Vol. 10, Nos. 1/2, 2016 Consumer social orientation-based personality and social media use: an exploration among young US consumers Ainsworth Anthony Bailey* Department of Marketing & International Business, College of Business & Innovation, University of Toledo, 2801 Bancroft St, Toledo, OH 43606, USA Email: ainsworth.bailey@utoledo.edu *Corresponding author Mohammed Slim Ben Mimoun Department of Marketing, SKEMA-Université Lille Nord de France, Avenue Willy Brandt EURALILLE 59777, France Email: mohamedslim.benmimoun@skema.edu Abstract: This paper reports on a study that developed and tested a model of the link between certain social orientation-based personality traits and consumers’ attitude toward online social networking and attitude toward marketers’ social networking sites. The study also explored the subsequent influence of these attitudes on likelihood of recommending marketers’ social networking sites. The study finds that interdependent self-construal directly and indirectly impacts the attitudinal and intention variables, while social sharing disposition and susceptibility to social networking influence vary in their direct and indirect impact. The study results also reaffirm the attitude– behavioural intentions link, as well as the dual mediation hypothesis, in online social networking attitudes. Keywords: attitudes; self-construal; social media; social networking; social sharing disposition; susceptibility to social networking influence. Reference to this paper should be made as follows: Bailey, A.A. and Ben Mimoun, M.S. (2016) ‘Consumer social orientation-based personality and social media use: an exploration among young US consumers’, Int. J. Internet Marketing and Advertising, Vol. 10. Nos. 1/2, pp.1–27. Biographical notes: Ainsworth Anthony Bailey (PhD, University of Iowa) is an Associate Professor of Marketing in the College of Business & Innovation, University of Toledo, Ohio, USA. In his research, he currently focuses on the use of celebrities as endorsers in advertising, social media and their effects on consumers’ behaviour, and the use of technology in retailing. His research has appeared or is forthcoming in various marketing-related academic journals: Journal of Advertising, Psychology and Marketing, Journal of Marketing Communications, Journal of Interactive Advertising, International Journal of Retail and Distribution Management, and Journal of Retailing and Consumer Services, as well as in various national and international conference proceedings. Copyright © 2016 Inderscience Enterprises Ltd. 1 2 A.A. Bailey and M.S. Ben Mimoun Mohammed Slim Ben Mimoun is an Associate Professor of Marketing at SKEMA Business School, University of Lille and a member of the MERCUR research centre. He is also an Associate Professor of Marketing at University of Sousse, Tunisia. He holds a PhD in Marketing from IAE of Lille, University of Lille, and a master degree in Marketing from the ISG of Tunis, University of Tunis. He also graduated for the Habilitation Universitaire in 2014 at University of Sousse. His current research examines online consumer behaviour, embodied virtual agents, human–computer interaction issues, social networks, adoption of new technologies, smart retailing, and shopper marketing. He has published in international peer-reviewed academic journals (Information & Management, Journal of Retailing and Consumer Services) and in international and national academic conferences proceedings (Advances in Consumer Research, Association Française de Marketing, EMAC, Academy of Marketing, American Marketing Association, Academy of Marketing Science). BOSTON, MA: Social media platforms play an increasingly important role in directing online consumer traffic, as new research shows people now rely less on homepages and search engines than ever before. According to content discovery specialist Shareaholic, based on data collected from more than 300,000 websites reaching a global audience of more than 400m unique monthly visitors, the top eight social networks – Facebook, Pinterest, Twitter, StumbleUpon, Reddit, Google Plus, LinkedIn, and YouTube – drove 31.24% of overall traffic to sites in December 2014, up from 22.71% a year earlier (WARC, 2015). 1 Introduction A number of major companies have been turning to social media as a way to build greater connections and foster interactions with their customers, in light of the kind of information contained in the above extract. At the same time, many observers have noted that, owing to social media, marketing communication is no longer a case where information flow is dominated by the marketer. Consumers are increasingly turning to social media for marketing information and using the same social media to reach marketers and other consumers with information (Naylor et al., 2012; Gensler et al., 2013). Some are becoming missionaries for brands by forming brand fan clubs on social networking sites such as Facebook and, at the same time, others are using these same platforms as a way to provide feedback, positive and negative, to marketers and their brands (e.g., Winer, 2009; Kozinets et al., 2010; Roberts, 2010; Gironda and Korgaonkar, 2014). Given the nature of social media, a number of brands have used them in efforts to foster higher levels of engagement with their current and potential customers and create value for them (Kim and Ko, 2012; Labrecque, 2014; Trainor et al., 2014). However, we argue that owing to social orientation, these consumers may have different attitudes toward and perceptions of the use of social media by brands, as some other researchers have found in the case of cultural orientation (Goodrich and de Mooij, 2014). Consumers use social media to interact with other consumers, and many may be motivated to do so based on personality factors. While there is a flurry of brands using social media to engage consumers, and while there has been increased use of social media by consumers, there still remains a need to understand the factors that motivate consumers in their use of social media (Hill and Consumer social orientation-based personality and social media use 3 Moran, 2011). To this end, this paper reports on a study that developed and tested a model of the link between certain social orientation-based personality traits (social sharing disposition, interdependent self-construal, and susceptibility to social networking influence) and their impact on consumers’ attitude toward social networking (ATSN) and attitude toward marketers’ social networking sites (ATMS). The study also explored the subsequent influence of these attitudes on likelihood of recommending marketers’ social networking sites (RECMEND). This research drew on a number of research streams: social sharing (Singh-Manoux and Finkenauer, 2001); social influence, in particular consumer susceptibility to interpersonal influence (Bearden et al., 1989); and the literature on self-construal (Singelis, 1994) in an effort to understand social orientation factors impacting consumers’ attitudes and intentions, as they relate to social media use. The paper makes a number of contributions. For example, despite the increased use of social networking sites by brands, there still remain various uncertainties surrounding the efficient use of these new media and individual difference factors that could influence consumers in their use of these media. The study focuses on social orientation variables that might influence consumers in their use of social media, given the nature of the media (Vallaster and von Wallpach, 2013). The study’s findings also reinforce the links between attitudes (attitude toward social networking and attitude toward marketers’ social networking sites) and intentions (in this case, recommendation likelihood) that have been established in prior research in other domains other than social media. The paper is organised as follows: First, there is a brief review of the existing literature on consumer use of social media. The next section looks at the variables of interest and develops the model and the related hypotheses to be tested. This is followed by a discussion of the study that was undertaken to test the hypotheses, with information provided on the participants, data collection, measures used in the study, and testing of the model and hypotheses. The discussion of the results of the study follows, as does a discussion of the implications from a research and managerial perspective. The paper concludes with a discussion of the limitations of this work and some possible future research avenues. 2 Consumer social media use Growth of the most popular social media platforms underscores the significance of digital media (Patterson, 2012). During 2009, Facebook had 350 million per month active users worldwide, according to Inside Facebook (December 2009); in September 2012, it had reached 1 billion users (Vance, 2012) and in 2013, the figure was 1.15 billion (Bernstein, 2013). During 2007, Twitter’s first full year in operation, the company averaged 5,000 tweets per day. In February 2010, Twitter indicated that 50 million tweets were being sent on a daily basis (Robles, 2010). In June 2012, that number had reached 400 million (D’Orazio, 2012). The Pew Research Internet Project reported that, at the start of 2014, 74% of online adults were using social networking sites. Pew Research makes a distinction between creators (those online consumers who post pictures or videos they themselves have created; some 46% of online internet users) and curators (those online consumers who re-post or share images they have found online; 41% of online internet users) (Pew Research Center, 2014). As more and more consumers and brands have been using social media, there has been an attendant increase in academic research on social media use. The topics covered in this emerging research stream have been varied. For example, Gironda and 4 A.A. Bailey and M.S. Ben Mimoun Korgaonkar (2014) conducted enquiry into the factors that motivate consumers to use social networking sites (SNS) for three different kinds of activities: general SNS usage; the joining of a business’s SNS page; and clicking on an advertisement on a SNS. They found that among the factors that impacted use of SNS were attitude, compatibility, relative advantage, complexity, normative influences, and self-efficacy. The factors varied depending on the specific activity involved. Muk (2013) investigated the reasons that millenials liked brands on social media. He studied a sample of consumers in the southern USA, using an extended technology acceptance model (TAM) as the theoretical underpinning for the study. Among the factors explored were perceived ease of use and perceived usefulness, as well as the interrelationships among consumers’ attitudes, social influence, and intentions. He found that the attitudes toward brand pages and social influence significantly predict these consumers’ intentions to become fans of brand pages. In addition, these consumers’ attitudes toward social media are affected by perceived ease of use and perceived usefulness of the social media, as well as social influence from peers. Goodrich and de Mooij (2014) assessed the extent to which social media are ‘social’ by comparing the use of social media and other information sources by consumers in some 50 countries. The cultural dimensions of individualism and collectivism were used in this investigation. These researchers found that cultural factors do, in fact, impact social media use, with consumers in collectivist cultures relying more on social media than consumers in individualistic cultures. Similarly, Minton et al. (2012), in their study of social media use in the USA, Germany and Korea, found that social media involvement was highest in Korea, a collectivist country. Taylor et al. (2011) explored consumers’ attitudes towards advertising on social media sites and found that perception of exploitation of the social media by marketers negatively affected consumer attitudes. They found also that the most favourable responses were to advertising with entertainment or information value. Some researchers, however, have pointed out some of the negative aspects of social media use. For example, Tuten and Angermeier (2013) argue that among the negative utilities to consumers are security breaches, loss of privacy, and loss of control over online content. In the case of marketers and brands, negative utilities include spread of negative press and negative electronic word-of-mouth, as well as new threats of legal liability. Chen et al. (2012) also found that online reviews by third parties, negative and positive, influenced stock returns in the direction of the valence of these reviews. 3 Conceptual model and hypotheses In this section, focus is on the development of the conceptual model of social media use, as well as hypotheses that flow from it. The conceptual model that formed the basis for the study reported in this paper is depicted in Figure 1. The ensuing discussion expands on the model. 3.1 Social sharing disposition According to Singh-Manoux and Finkenauer (2001), social sharing is “the process during which a person, having experienced an emotion, recounts this experience to his or her social environment” (p.647). This process, according to these researchers, involves at least three salient things: an emotional experience; information transmission; a Consumer social orientation-based personality and social media use 5 sociocultural environment (p.647). Much of the research on social sharing has been done in the context of emotions and has focused on daily emotional experiences and dreams (see, for example, Singh-Manoux and Finkenauer, 2001; Curci and Rimé, 2008). This stream of research on social sharing has also established that, among other things, social sharing helps in the formation of bonds with others (Curci and Rimé, 2008) and the pattern differs based on cultural orientation, individualism versus collectivism (Singh-Manoux and Finkenauer, 2001). Figure 1 Notes: Conceptual model Model indices: CFI = 0.937; TLI = 0.931; RMSEA = 0.058; SRMR = 0.078; Chi-square = 1083.89, df = 608, p = 0.000. The study reported in this paper draws on this view of social sharing and posits the idea of social sharing disposition (SSD). In this study, SSD is viewed in the context of social networking sites. It is viewed as the degree to which consumers are motivated to pass along information to others or a group through social networking sites. The group can be a family, a friendship group, a work group, or an online community. It may be considered a personality trait, and, as in the cases of other personality traits, consumers are characterised by varying degrees of this trait, with social sharing disposition ranging on a continuum from low to high. That is, there are some consumers who are highly motivated to pass along information to others and other consumers who harbour no such desires. Obviously, information sharing via online social networks would be an important activity in which brands would want their consumers to engage, particularly when the information is positive. At the same time, if the information is negative, brands would probably prefer that consumers limit the amount of social sharing in which they engage. Various studies have underscored the impact of this kind of social sharing and, thus, the need for brands using social networking to understand the nature of social sharing and its impact on attitudes and intentions in a social media context. In a study on the social uses of advertising, Mitchell et al. (2007) asked a sample of young males to record their uses of advertising in a two-week period. They viewed social uses of advertising as being strongly related to word-of-mouth communication and as a factor that could play a role in extending the lifespan of an ad. They expressed the belief that: “the social uses of advertising are greater when they are consumed, and resonate not just within a certain societal group or society at large, but when you have true 6 A.A. Bailey and M.S. Ben Mimoun within-group effects such as within friendship or work groups who come to own their interpretation of the ad” (p.205). These researchers reported that the participants in their study indicated frequency use of advertising information such as taglines, and “they mainly use ads with people of the same age who are ‘on the same wavelength’ and ‘know what you’re on about’” (p.209). Dhar and Chang (2009) conducted a study in which they assessed the impact of online user-generated content on consumers. In particular, they tracked how online music sales were impacted by blogging and social networking. They found that online chatter was a good predictor of music sales. In a study that focused on antecedent factors that facilitated mobile marketing acceptance in an established market (USA) and an emerging market (Pakistan), researcher Okazaki (2009) found evidence that social enhancement was among the gratification factors that influenced consumer desire to engage in electronic word-of-mouth, which in turn impacted consumer online social intentions. Yaylı and Bayram (2012) conducted a study of consumer use of online product reviews and found that these influenced consumer purchasing behaviour and product choice. Research by Marketing Charts (2013) has also shown that a majority of digital marketers believe that social sharing, for example having social sharing icons on a Web page, was more effective than other factors in increasing conversion rates. The research company found that it was more effective than other elements such as navigation, page copy, offers or promotions, and photos or images. Recently, Blazevic et al. (2014) conducted research in which they developed and validated a scale that they labelled general online social interaction propensity, GOSIP. They defined this construct as “a trait-based individual difference in the predisposition to enter into online discussions” (p.89). They further argued that the definition “presumes that consumers are active participants in the online marketplace, and that attention should be paid to their interaction preferences” (p.89). GOSIP, according to these authors, helps to explain consumer engagement behaviours with brands, as well as interactive behaviour with other consumers. They established the nomological and predictive validities of the scale, which indicated that, in fact, this individual difference factor explains consumer differences in online interactions and engagement. The study reported here views social sharing disposition in a similar light. The studies cited above all underscore that social sharing in a marketing communication context is important; however, this issue has not been explored in the context of usage of social networking sites. In this study, focus is on the link between this variable, among others, and consumers’ attitudes. A variable of interest to marketers who use social networking sites should be the attitude of consumers toward the activity of social networking, in general, and marketers’ social networking sites (SNS), in particular. Attitude toward social networking is seen as a general favourable or unfavourable disposition toward the activity of social networking. In the same vein, attitude toward marketers’ social networking sites is seen as a general favourable or unfavourable disposition toward social networking sites operated by marketers. The expectation is that social sharing disposition will affect attitudes toward online social networking, given that social sharing allows for social connections with other consumers. This leads to the following hypotheses: H1a. Social sharing disposition will significantly and positively impact attitude toward online social networking. H1b. Social sharing disposition will significantly and positively impact attitude toward marketers’ social networking sites. Consumer social orientation-based personality and social media use 7 3.2 Social influence: susceptibility to social networking influence An important aspect of the consumer decision-making process is social influence. Individuals’ decisions are affected by other people in various contexts. Kelman (1961) indicates that the extent to which individuals are influenced by others is a function of their willingness to accept the mandates of the group. Bearden et al. (1989) proposed the concept of consumer susceptibility to interpersonal influence (CSII) as a general trait that varies across individuals. Contending that there are differences among consumers in their responses to interpersonal influence, these authors suggested also that CSII is related to other individual difference factors such as self-esteem and self-confidence. It consists of a normative and informational dimension, which relate to the extent to which consumers seek to live up to others’ expectations, or observe others or see information from others when they have to make decisions. There have been prior studies that have examined the relationship between CSII and various consumer-related constructs and behaviours. For example, Park and Lee (2009) conducted a study in which they assessed US and Korean consumers’ response to online reviews. In particular, they sought to determine the impact that CSII had on consumers’ perceptions of the usefulness of online reviews. They predicted, and found support, that there was a positive relationship between CSII and perceived usefulness of online reviews. Hoffmann and Broekhuizen (2009) conducted studies in an investment context to assess the impact of CSII on consumers’ investment decisions. They found that CSII did impact investment behaviour; in particular, the information and opinions of others had a consistent impact on investment behaviour. Clark and Goldsmith (2006) found a negative link between susceptibility to interpersonal influence and consumer innovativeness. In essence, consumers who were more susceptible to interpersonal influence were less innovative than consumers who were less susceptible to interpersonal influence. This study draws on the idea of consumer susceptibility to interpersonal influence proposed by Bearden et al. (1989) to propose the concept of susceptibility to social networking influence (SSNI). This construct, in the domain of social media, refers to the extent to which social media users may be influenced by other social media users. The concept of susceptibility to social networking influence relates specifically to the social media world, where consumers may know or not know the other social media users with whom they interact. This concept suggests that social media users differ in the extent to which they are likely to adhere to the wishes and dictates of others or are influenced by the information from others that they may encounter in social networking. SSNI relates to the extent to which a person is affected by the opinions expressed via social networking sites. In this case, the social networking site can be viewed as an information source, in the same way one could view advertising, celebrity endorsers, salespersons, a brand’s website, and other consumers as information sources. The SSNI construct addresses, for example, such questions as: What is the extent to which a person is impacted by messages delivered via, for example, Twitter or Facebook? To what extent are a person’s views or decisions swayed by information on a brand’s Facebook page? Prior research has established that there are various antecedents to engagement in social media use, such as electronic word-of-mouth, and among these antecedents are individual differences (Chu and Kim, 2011). Chu and Kim (2011) found significant impact of normative influence on consumer engagement in eWOM as well as partial 8 A.A. Bailey and M.S. Ben Mimoun significance of informational influence on eWOM, which indicates the impact of different types of social influence on online engagement. Chatterjee (2011) also found an impact of influencers, as compared to marketers, on consumers’ online recommendations and referrals. Naylor et al. (2012) conducted a study in which they explored brands’ decision to hide or reveal the demographic characteristics of their online supporters. These researchers found that even when consumers only passively experience the presence of these supporters or even when the supporters’ presence can be viewed as ‘mere virtual presence’, the supporters’ demographic characteristics had the potential to influence target consumers’ brand evaluations and purchase intentions. Susceptibility to social networking influence will also have an impact on consumers’ attitudes and intentions, consistent with the findings related to consumer susceptibility to interpersonal influence. In addition, recent research by Ogonowski et al. (2014) found that online social presence had a positive impact on initial trust of a website and also influenced enjoyment of the website and perceived usefulness of the website. This leads to the following hypotheses. H2a. Susceptibility to social networking influence will significantly and positively impact attitude toward online social networking. H2b. Susceptibility to social networking influence will significantly and positively impact attitude toward marketers’ social networking sites. 3.3 Interdependent self-construal The construct of interdependent self-construal was developed by Markus and Kitayama (1991), who argued that people hold different construals of themselves and others. For example, in discussing differences between the American culture and the Japanese culture, they posited that American consumers tend to focus on, and assert, the self. On the other hand, Japanese consumers emphasise getting along with others and focus on harmonious interdependence with them. Markus and Kitayama (1991) referred to the kind of self-construal that characterised the USA as independent self-construal and that which characterised Japan as interdependent self-construal. Singelis (1994) subsequently developed a scale to measure independent and interdependent self-construal. Singelis also suggested that a culture can contain people who exhibit both types of self-construals, even if one type of self-construal predominates in the culture. In collectivist cultures, where interdependent self-construal dominates, the key cultural tenets have been said to be the maintenance of harmonious relationships and cohesion among group members; respect for hierarchy; and the preservation of face within the group (Triandis et al., 1990; Koh et al., 2010). Extant research has also established that self-construal impacts how consumers respond to different marketing stimuli (Kramer et al., 2007; Hui et al., 2011; Sung and Choi, 2011). Kramer et al. (2007) explored product personalisation and found that consumers characterised by interdependent or collectivistic self-construals preferred product recommendations that were personalised to the collective preferences of relevant in-groups. They found too that this was especially so for goods subject to public scrutiny. Hui et al. (2011) looked at how prior relationship influenced consumer response to service failure, in light of the nature of consumers’ self-construal, independent or interdependent. They found that prior relationship had a greater effect on consumer response among consumers of interdependent selfconstrual. Consumer social orientation-based personality and social media use 9 Kim et al. (2011) indicate that individuals in different cultural contexts utilise social networks with different motives, reflecting their prevalent cultural values. They investigated the use of social networks by American and Korean college students and found that for Korean students (interdependent self-construal), their focus was more on using social networks to obtain social support from existing social relationships; on the other hand, American students placed more focus on the entertainment role of social networks. Vaidyanathan et al. (2013) conducted research on cause-related marketing and found that interdependent self-construal had an impact on consumers’ willingness to pay higher prices for a product used to support a pro-social cause. Muk et al. (2014) conducted a study related to the intentions of consumers from the USA (independent selfconstrual) and Korea (interdependent self-construal) to become fans of brand pages. The aim was to determine whether cultural differences impacted intentions. They found that interdependent self-construal had a stronger impact on attitudes and intentions than did independent self-construal. The study reported in this paper was undertaken to assess the impact of social orientation-based personality differences on attitudes in the domain of social media use. However, the focus was a within-culture focus, that is, within the USA. It was motivated by the need to assess the impact of interdependent self-construal on attitudes and behavioural intentions related to social media. Some researchers have found that there are intra-culture differences that parallel those found across cultures (Triandis, 1988; 1989). Therefore, in this study, we looked at consumers from one culture, the US, and applied the factor of interdependent self-construal (Markus and Kitayama, 1991; Singelis, 1994) as an individual personality variable (Singelis, 1994). As indicated above, studies from the stream of research on self-construal indicate that consumers characterised by interdependent self-construal, or from interdependent cultures, are driven by social motives such as relating well to others and focusing on relationships with others. The expectation is that this will be replicated in the context of online social networking. Online social networking allows consumers to connect with each other. These connections are important to consumers characterised by interdependent self-construal. Therefore, the presumption is that interdependent selfconstrual will have an impact on consumers’ attitudes toward online social networking, and, by extension, it should also have an impact on attitudes toward marketers’ networking sites. This leads to the following hypotheses: H3a. Interdependent self-construal will significantly and positively impact attitude toward online social networking. H3b. Interdependent self-construal will significantly and positively impact attitude toward marketers’ social networking sites. 3.4 Attitudes and social media use There has been a long stream of research on attitude and the link between attitudes and subsequent consumer intentions and behaviours (Fazio et al., 1989; Glasman and Albarracíin, 2006). In the case of the internet context, research has also shown a link between consumers’ attitudes to the web/internet and their subsequent behaviours and intentions (see, for example, Müller et al., 2008) as well as the effectiveness of brand websites in building brands (Ha and Chan-Olmsted, 2004). In addition, Pelling and White 10 A.A. Bailey and M.S. Ben Mimoun (2009) found a partial link between attitudes and young people’s intentions to engage in high-level social networking. Hence, brands should be interested in the attitudes explored in this paper. Consumers’ attitude toward an attitude object will impact their attitude toward other objects associated with the initial attitude object. For example, if consumers have a positive attitude to an object, this positive attitude will transfer to other objects linked to the initial attitude object. Findings from the stream of research on attitudes attest to this. For example, it has been established that a favourable attitude toward an ad leads to a favourable attitude toward the brand in the ad (MacKenzie et al., 1986; Homer, 1990); attitude toward an advertiser influences attitude toward the ad by that advertiser (Kim et al., 2012); and attitude toward mobile advertising is an antecedent to attitude toward mobile phone ads (Drossos et al., 2013). For that reason, the expectation is that there will be a positive relationship between attitude toward online social networking and attitude toward marketers’ social networking sites. H4. Attitude toward online social networking will significantly and positively impact attitude toward marketers’ social networking sites. 3.5 Recommendation likelihood Marketing researchers have established that consumers serve as recommendation agents for various things, including products, brands, and retailers (Chatterjee, 2011). This study focuses on recommendation likelihood in the context of social networking sites. Prior research has shown that, among other things, consumers are likely to look to a number of sources for recommendations when they have to make consumption decisions (Kohler et al., 2011) and that consumers are impacted by the recommendations of other consumers (Lee et al., 2008). In addition, research has shown that consumers engage in making recommendations to other consumers (Ryu and Feick, 2007) and that consumers engage in different kinds of activities such as ‘consuming’, ‘contributing’, and ‘creating’ (Muntinga et al., 2011). Hence it would be important for brands using social media to understand the extent to which social networking site users are likely to engage in making recommendations about social networking sites to other users. Above, research from the stream on consumer attitudes that established the links among attitude objects was cited. Prior research has also established a link between attitudes and intentions as well as between attitudes and actual behaviour (see, for example, recent work by Oberecker and Diamantopoulos, 2011, on country affinity and behavioural intentions; Shih et al., 2013 on affective attitudes and e-word-of-mouth intentions). This stream of research has established also that attitudes toward an object influence behaviours in which consumers might engage toward the object; that is, there is an attitude–behaviour link. For example, a favourable attitude toward an ad positively impacts brand choice (Biehal et al., 1992). As reported earlier, Muk (2013) found that the attitudes toward brand pages significantly predicted consumers’ intentions to become fans of brand pages. Recently, Park and Kim (2014) found that experiential and functional benefits of a brand’s social networking site led consumers to spread good words about a brand’s social networking site. Logan and Bright (2014) also found, in their study of daily-deal sites, that consumers’ attitudes towards the use of daily-deal sites had a positive influence on their intentions to use daily-deal sites. This leads to the following predictions regarding attitudes and recommendation likelihood in a social networking context. Consumer social orientation-based personality and social media use 11 H5a. Attitude toward online social networking will significantly and positively impact the likelihood of recommending marketers’ social networking sites. H5b. Attitude toward marketers’ social networking sites will significantly and positively impact the likelihood of recommending marketers’ social networking sites. 3.6 Mediation Prior research has shown that attitudes tend to mediate the relationships between various attitudinal antecedents and behaviour. MacKenzie et al. (1986), in an investigation of the role of attitude toward the ad in impacting advertising effectiveness, found that ad attitude influenced brand attitude both directly and indirectly, and this occurred through ad attitude’s effect on brand cognitions. Similarly, Homer (1990) also found evidence of this mediation role of attitude toward the ad. She conducted research that reinforced the findings of MacKenzie et al. (1986) regarding the dual mediation hypothesis that contends that attitude toward the ad influences brand attitudes both directly and indirectly through brand cognitions. Brown and Stayman (1992) also conducted a meta-analysis that revealed that there was an indirect influence of ad attitudes on brand attitudes through brand cognitions. Recently, Liang et al. (2013) conducted a study in which they investigated the impact of three antecedents (adoption of electronic communication technology; consumer dis/satisfaction with travel consumption experience; and subjective norm) on consumers’ (travellers’) attitude toward eWOM communication and intention to use eWOM communication media. Their study found that overall attitude toward eWOM communication partially mediated the relationships between two antecedents (adoption of electronic communication technology and subjective norm) and intention to use eWOM communication; it fully mediated the relationship between consumers’ dis/satisfaction with their travel consumption experience and their (travellers’) intention to use eWOM communication media. In light of prior research that has found mediational roles of different attitudinal constructs on intentions and behaviours (e.g. MacKenzie et al., 1986; Homer, 1990; Liang et al., 2013), the expectation is that there will be a mediating role of the attitudinal variables in the social orientation–recommendation behaviour relationship. In particular, attitude toward online social networking is expected to mediate the impact of the social orientation-based personality variables – social sharing disposition, self-construal, and susceptibility to social networking influence – on attitude toward marketers social networking sites and on recommendation likelihood. Essentially, the consumer’s attitude toward online social networking will be positively or negatively impacted by each of the antecedents, and this attitude will, in turn, impact recommendation likelihood. Attitude toward marketers’ social networking sites is also expected to mediate the impact of attitude toward online social networking on recommendation likelihood for a similar reasoning: the consumer forms positive attitude toward marketers’ social networking sites as a result of his or her attitude toward online social networking, and this attitude carries over to recommendation likelihood. These hypothesised paths are provided in Table 3 (H6a–H9) and put forward below. H6a: Attitude toward online social networking mediates the relationship between social sharing disposition and the likelihood of recommending marketers’ social networking sites. 12 A.A. Bailey and M.S. Ben Mimoun H6b: Attitude toward marketers’ social networking sites mediates the relationship between social sharing disposition and the likelihood of recommending marketers’ social networking sites. H7a: Attitude toward online social networking mediates the relationship between susceptibility to social networking influence and the likelihood of recommending marketers’ social networking sites. H7b: Attitude toward marketers’ social networking sites mediates the relationship between susceptibility to social networking influence and the likelihood of recommending marketers’ social networking sites. H8a: Attitude toward online social networking mediates the relationship between interdependent self-construal and the likelihood of recommending marketers’ social networking sites. H8b: Attitude toward marketers’ social networking sites mediates the relationship between interdependent self-construal and the likelihood of recommending marketers’ social networking sites. H9: Attitude toward marketers’ social networking sites mediates the relationship between attitude toward online social networking and the likelihood of recommending marketers’ social networking sites. 4 Method 4.1 Participants Data were collected in an online survey of students enrolled in business classes at a Midwestern university. This audience was used since prior research has established that, in the USA, members of this audience are heavy users of social media (e.g., Hampton et al., 2011; Muk, 2013). Participants accessed the questionnaire online through Survey Monkey. After eliminating five questionnaires for incompleteness, there were 236 useable responses from participants, with female (male) participants accounting for 46% (54%) of the sample. The majority (75%) fell into the age range 18–24 years. Though most participants (62%) taking part in the study were undergraduate students, college graduates (26%) and graduate students (4%) also took part. They were offered extra course credit for their participation. Measures Scales for four of the measures used in the study (interdependent self-construal; two attitude scales; recommendation likelihood) were derived from existing literature. Items for two scales (social sharing disposition and susceptibility to online networking influence) were generated by one of the authors for use in this study. Interdependent selfconstrual was measured using the scale developed by Singelis (1994). Attitude toward social networking (ATSN) and attitude toward marketers’ social networking sites (ATMS) were both measured using six-item 5-point scales, and the likelihood that respondents would recommend social networking sites to other consumers was assessed by a 3-item 5-point scale (see Lee and Aaker, 2004; Sung and Choi, 2011). Consumer social orientation-based personality and social media use 13 The measure for susceptibility to social networking influence (SSNI) was based on eight items contained in Table 1. Each item was measured on a 5-point scale anchored by 1 = Strongly disagree and 5 = Strongly agree. These 8 items were summed to form a scale, SSNI scale, with higher scores indicating a high degree of susceptibility to social networking influence and lower scores indicating lower levels of susceptibility to social networking influence. Given that this construct is new, initial reliability assessments were undertaken, based on two studies. EFA using maximum likelihood extraction and varimax rotation was conducted based on data from this study. This resulted in a onefactor solution that explained 58.80% of the variance. Factor loadings ranged from a low of 0.633 to a high of 0.894. Cronbach alpha for the 8-item scale in this study was 0.917. Corrected item-to-total correlations ranged from a low of 0.612 to a high of 0.835. Scale mean was 21.86, with a standard deviation of 7.21. A second study involving 160 students was conducted for further assessment of the scale’s validity, using CFA. Cronbach alpha for the scale was 0.923. Based on the CFA, the model fit was good: CFI = 0.984; TLI = 0.973; SRMR = 0.032; and RMSEA = 0.073. The item loadings were all significant at p < 0.000. AVE was 0.598, and composite reliability (CR) was 0.965. The results from the two studies confirm the scale’s reliability and validity. Table 1 Properties of scales used in the study Scale/items Factor loadings α AVE CR 0.321 0.941 Scale: Interdependent Self-Construal (Singelis 1994; ISC) 1. I have respect for the authority figures with whom I interact. 0.640*** 2. It is important for me to maintain harmony with my group. 0.736*** 3. My happiness depends on the happiness of those around me. 0.435*** 4. I would offer my seat on a bus to my professor. 0.580*** 5. I respect people who are modest about themselves. 0.691*** 6. I will sacrifice my self-interest for the benefit of the group I am in. 0.624*** 7. I often have the feeling that my relationships with others are more important than my own accomplishments. 0.447*** 8. I should take into account my parents’ advice when making education/career plans. 0.586*** 9. It is important to me to respect decisions made by the group. 0.745*** 10. I will stay in a group if they need me, even when I am not happy with the group. 0.483*** 11. If my brother or sister fails, I feel responsible. 0.376*** 12. Even when I strongly disagree with group members, I avoid an argument. 0.335*** 0.85 A.A. Bailey and M.S. Ben Mimoun 14 Table 1 Properties of scales used in the study (continued) Scale/items Factor loadings α AVE CR Scale: Susceptibility to social networking influence scale (SSNI Scale) 1. I find the information on SN sites to be credible. 0.633*** 2. Social networking websites influence my daily life. 0.756*** 3. I am usually swayed by information I see on SN sites. 0.894*** 4. I usually act on information I receive from others on SN sites. 0.871*** 5. My behaviour has been affected before by SN. 0.641*** 6. I find it difficult to resist the influence of SN sites. 0.785*** 7. My buying decisions are likely to be influenced by information on marketers’ SN sites. 0.752*** 8. My opinions about brands are likely to be influenced by information on marketers’ SN sites. 0.762*** 0.92 0.573 0.961 0.93 0.724 0.967 Scale: SNS Social sharing disposition (SSD)† 1. I am the kind of person who likes to share information with others on SNS. 0.826*** 2. I like SNS because they can be used to share information with others. 0.888*** 3. I frequently share information with others on SNS. 0.866*** 4. There is a strong probability that I will share information with others through SNS. 0.828*** 5. Social networking is good because it allows me to share a lot of information with others. 0.845*** Scale: Attitude toward social networking (ATSN): In general, how would you classify your attitude toward the activity of online social networking? Negative/Positive 0.927*** Unfavourable/Favourable 0.918*** Poor/Excellent 0.899*** Disagreeable/Agreeable 0.914*** Unpleasant/Pleasant 0.894*** Bad/Good 0.892*** 0.97 0.824 0.982 Scale: Attitude toward marketers’ social networking sites: In general, how would you classify your attitude toward marketers’ social networking websites? Negative/Positive 0.909*** Unfavourable/Favourable 0.917*** Poor/Excellent 0.885*** Disagreeable/Agreeable 0.899*** Unpleasant/Pleasant 0.873*** Bad/Good 0.908*** 0.96 0.807 0.981 Consumer social orientation-based personality and social media use 15 Properties of scales used in the study (continued) Table 1 Scale/items Factor loadings α AVE CR Scale: Recommendation likelihood As a consumer, how likely is it that you would recommend a brand’s social networking website to another consumer? 1. Very unlikely-Very likely 0.884*** 2. Impossible-Possible 0.876*** 3. Improbable-Probable 0.954*** Notes: 0.93 0.820 0.966 † (Measured on 5-point Likert scales anchored by “Strongly disagree”= 1 and “Strongly agree”= 5). ***p < 0.001, **p<0.01, *p < 0.05. Five items were used to measure social sharing disposition. All were measured on 5-point scales (1 = Strongly disagree; 5 = Strongly agree), and the items were summed to determine the social sharing disposition of the respondents. Higher scores indicate higher social sharing disposition. Since this was also a new construct, we assessed the items using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Based on data from this study, coefficient alpha for the 5 items was 0.929. Corrected item-tototal correlations ranged from a low of 0.792 to a high of 0.844. As a result, the 5 items were retained for further analysis. Exploratory factor analysis (EFA) using maximum likelihood extraction and varimax rotation resulted in all the items loading on one factor; this factor explained 77.91% of the variance. Factor loadings ranged from a low of 0.826 to a high of 0.888. The scale’s mean was 16.22, with a standard deviation of 4.86. In order to further assess the validity of the scale, a second study was done to collect data for a CFA. A total of 152 useable responses were obtained. Cronbach alpha for the scale based on this second administration was 0.926. Based on the CFA, the model fit was good: CFI = 0.995; TLI = 0.989; SRMR = 0.019; and RMSEA = 0.067. The item loadings were all significant at p < 0.000. AVE was 0.698, and CR was 0.963. The results supported the reliability and validity of the SSD scale. The above initial scale development stages for SSD and SSNI established that these constructs can be measured with the items used. The results section contains information on reliability and validity assessment of the scales, and Table 1 contains additional information on the psychometric properties of the scales used in the study. 5 Results 5.1 Reliability and validity Before testing the hypotheses, the items for all the scales in the study were subjected to exploratory factor analysis, using principal components analysis and varimax rotation. A 6-factor solution fitted the data well, and all the items loaded on their appropriate scales. Following this, confirmatory factor analysis (CFA), using Mplus v7, was also used to assess the properties of the scales. The 6-factor, 40-item measurement model fit the data well: CFI = 0.929; TLI = 0.923; RMSEA = 0.057; Chi-square = 1274.45, p =0.000 (see Browne and Cudeck, 1993). Convergent validity of the constructs was also established. Items loaded on the appropriate factors and factor loadings were all significant at p-value < 0.001. 16 A.A. Bailey and M.S. Ben Mimoun To examine the uniqueness of each measure, discriminant validity was determined. Fornell and Larcker (1981) indicate that discriminant validity can be established through the average variance extracted (AVE) method by comparing AVE values with squared correlation values (shared variance) for constructs in a model. If the AVE for a construct is greater than its squared correlation values (shared variance) with any other construct in the model, or if the AVE of construct A and the AVE of construct B are both greater than the squared correlation of A and B, then there is discriminant validity. As evident from Table 2, the AVE for each of the constructs exceeds the squared correlation value (shared variance) of the construct with the other constructs in the model. While the AVE for interdependent self-construal did not reach the 0.50 threshold recommended by Hair et al. (2010), this scale has been in use for a while, and its Cronbach alpha (0.85) and composite reliability (0.941) obtained in this study were very good. Dropping items with low factor loadings did not have a significant impact on the results. In addition, the convergent validity and discriminant validity of the variables were established. Table 1 has information on the factor loadings, Cronbach alphas, and AVEs for all the scales in the study. Table 2 provides correlation information. Descriptive statistics and correlations for scales used in model Table 2 ISC Mean SD ISC 42.81 7.18 0.321* SSNI SSD SSNI 21.86 7.21 0.150 0.573 SSD 16.22 4.86 0.196 0.029 0.724 ATSN ATMS ATSN 22.17 5.27 0.277 0.138 0.549 0.824 ATMS 20.67 5.56 0.145 0.072 0.288 0.524 0.807 RECMEND 9.39 3.27 0.135 0.067 0.267 0.486 0.591 Notes: RECMEND 0.820 *Numbers on the diagonal represent the AVEs for each factor. ISC: Interdependent self-construal; SSNI: Susceptibility to social networking influence; SSD: Social sharing disposition; ATSN: Attitude toward online social networking; ATMS: Attitude toward marketers’ social networking sites; RECMEND: Likelihood of recommending marketers’ social networking sites. 5.2 Test of structural relationships Following the establishment of reliability and validity, structural equation modelling using Mplus v.7.0 (Muthén and Muthén, 1998–2014) was used to assess the predicted structural relationships. This is consistent with the recommendation by Anderson and Gerbing (1988) regarding the two-stage process in model testing. Model indices indicate that, overall, the model was a satisfactory fit: CFI = 0.937; TLI = 0.931; RMSEA = 0.058; SRMR = 0.078; Chi-square = 1083.89, df = 608, p = 0.000. The outcomes for the hypotheses testing are summarised in Table 3. H1a predicted that social sharing disposition should be significantly and positively related to attitude toward online social networking. The coefficient was positive and significant, thus lending to support for H1a. In the case of H1b, which predicted a significant and positive relationship between, social sharing disposition and attitude toward marketers’ social networking sites, the relationship was not significant; so H1b was not supported. Susceptibility to social influence was not significantly related to attitude toward online social networking, as theorised in H2a; however, it was Consumer social orientation-based personality and social media use 17 significantly and positively related to attitude toward marketers’ social networking sites, as stated in H2b. Support was found for H3a and H3b, which predicted a significant and positive relationship between interdependent self-construal and attitude toward online social networking and attitude toward marketers’ social networking sites, respectively. Table 3 Hypotheses and outcomes Hypothesis*: Estimated paths Beta p-value Outcome H1a: SSD  ATSN 0.515 0.000 Supported H1b: SSD  ATMS 0.009 0.920 Not supported H2a: SSNI  ATSN 0.071 0.380 Not supported H2b: SSNI  ATMS 0.245 0.002 Supported H3a: ISC ATSN 0.173 0.003 Supported H3b: ISC  ATMS 0.201 0.001 Supported H4: ATSN  ATMS 0.367 0.000 Supported H5a: ATSN  RECMEND 0.240 0.000 Supported H5b: ATMS  RECMEND 0.476 0.000 Supported H6a: SSD  ATSN  RECMEND 0.124 0.001 Supported H6b: SSD  ATMS  RECMEND 0.004 0.920 Not supported H7a: SSNI  ATSN  RECMEND 0.017 0.393 Not supported H7b: SSNI  ATMS  RECMEND 0.117 0.005 Supported H8a: ISC  ATSN  RECMEND 0.042 0.020 Supported H8b: ISC  ATMS  RECMEND 0.096 0.002 Supported H9: ATSN  ATMS  RECMEND 0.175 0.000 Supported Notes: *Hypotheses H1a-H5b relate to direct relationships; Hypotheses H6a-H9 relate to mediated relationships. ATMS: Attitude toward marketers’ social networking sites; ATSN: Attitude toward online social networking; RECMEND: Likelihood of recommending marketers’ social networking sites; ISC: Interdependent self-construal; SSD: Social sharing disposition; SSNI: Susceptibility to social networking influence. The model contained predictions regarding the attitude variables. Support was found for the hypothesised links between the attitudinal variables in the model As predicted in H4, attitude toward online social networking sites had a positive and significant impact on attitude toward marketers’ social networking sites. Attitude toward online social networking also had a significant and positive impact on the likelihood of recommending marketers’ social networking sites, supporting H5a; and attitude toward marketers’ social networking sites had a significant and positive impact on the likelihood of recommending marketers’ social networking sites, supporting H5b. In the case of the hypotheses regarding the mediational roles of the attitude variables all the hypotheses with the exception of H6b (Social sharing disposition  Attitude toward marketers’ social networking sites  Likelihood of recommending marketers’ social networking sites) and H7a (Susceptibility to social networking influence  Attitude toward online social networking  Likelihood of recommending marketers’ social networking sites) were supported. A.A. Bailey and M.S. Ben Mimoun 18 6 Alternative model testing The proposed model in the study (Figure 1) predicts various direct links among the variables in the study. An alternative model, Figure 2, forms the basis for a test of model fit, with the aim of comparing the results from this alternative model with the results from the test of the proposed model. The alternative model proposes a plausible link between interdependent self-construal and the two other social orientation-based personality factors: social sharing disposition and susceptibility to social networking influence. Interdependent self-construal is characterised by social motives and relationships with others, leading to the likelihood that consumers for whom interdependent selfconstrual is dominant would have a higher propensity to engage in social sharing and higher susceptibility to social networking influence. These expectations lead to the links in the competing model. The proposed model is nested in the alternative model. Figure 2 Competing model Social sharing disposition (SSD) Attitude toward online social networking (ATSN) Recommendation likelihood (RECMEND) Interdependent self-construal (ISC) Susceptibility to SN influence (SSNI) Notes: Attitude toward marketers’ social networking sites (ATMS) Model indices: CFI = 0.922; TLI = 0.915; RMSEA = 0.064; SRMR = 0.155; Chi-square = 1198.40, df = 609, p = 0.000. The alternative model has fairly good model indices: CFI = 0.922; TLI = 0.915; RMSEA = 0.064; SRMR = 0.155; Chi-square = 1198.40, df = 609, p = 0.000. The primary distinction between these indices and the indices for the proposed model is SRMR (0.078 = proposed model; 0.155 = alternative model). Given the relatively good fit of the alternative model, we followed recommendations to conduct a Chi-square difference test (Anderson and Gerbing, 1992) to ascertain whether one model performed better than the other. The significant Chi-square difference test (∆χ2 = 114.51, ∆df = 1, p < 0.001) reveals that the proposed model is superior to the competing model; hence, the links between interdependent self-construal and the two other social orientation-based personality factors does not improve the model. 7 Discussion This study represented an effort to assess the impact of certain social orientation-based personality factors on consumers’ social media attitudes and intentions. The aim of the Consumer social orientation-based personality and social media use 19 study was to contribute to the understanding of consumers’ engagement with social networking and to assess how these social orientation-based personality variables could impact how consumers view and respond to social networking. The view is that such an assessment could provide useful information to the many brands that are involved in using social media to engage their customers. The overall results from this study highlight the importance of personality factors in affecting consumer response to social networking. The results clearly indicate a role for the constructs of social sharing disposition and susceptibility to social networking influence in the assessment of consumer use of social media. Interestingly, while susceptibility to social networking influence is not significantly related to attitude toward online social networking, it is significantly and positively related to attitudes toward marketers’ social networking sites. The study also indicates that there are effects linked to social media use based on interdependent self-construal, as it clearly impacts attitudes and intentions, both directly and indirectly. The study results also lend support for a dual mediation hypothesis, previously found in the realm of attitude toward the ad and brand attitude. These results show, for example, that attitude toward online social networking directly affects the likelihood of recommending marketers’ social networking sites, and it indirectly affects recommendation likelihood through its effects on attitudes toward marketers’ social networking sites. Of note in the results is the lack of support for four of the hypothesised paths in the model; two were direct paths and two were indirect. In the case of H1b, the prediction was that social sharing disposition would significantly and positively impact attitude toward marketers’ social networking sites (SSD  ATMS). H2a had predicted that susceptibility to social networking influence would significantly and positively impact attitude toward online social networking (SSNI  ATSN). H6b and H7a were based on expected mediations. H6b contended that attitude toward marketers’ social networking sites mediates the relationship between social sharing disposition and the likelihood of recommending marketers’ social networking sites (SSD  ATMS  RECMEND); while H7a argued that attitude toward online social networking mediated the relationship between susceptibility to social networking influence and the likelihood of recommending marketers’ social networking sites (SSNI  ATSN  RECMEND). When taken together with the results for the supported hypotheses, the lack of support for H1b, H2a, H6b, and H7a suggest that while both social sharing disposition and susceptibility to social networking influence play a role in impacting social media attitudes and intentions, they do so through different routes. The two variables also operate differently from interdependent self-construal. Interdependent self-construal directly impacts both attitude toward online social networking and attitude toward marketers’ social networking sites, and it indirectly impacts the likelihood of recommending marketers’ social networking sites indirectly through both types of attitudes. It is possible that someone high in social sharing disposition may see social networking in general as a way to engage with others and not see marketers’ social networking sites as necessary for this engagement to take place. This could possibly explain the lack of support for H1b and H6b. In the case of susceptibility to social networking influence, someone who is high in susceptibility to social networking influence may see marketers’ social networking sites as more concrete forums for 20 A.A. Bailey and M.S. Ben Mimoun persuasive information, as against just social networking sites in general. Hence these consumers would display more favourable attitudes to these specific sites, given the possibility that they provide more information. This could explain the lack of an impact of this variable on attitude toward a general measure of attitude toward social networking. 7.1 Research implications This paper reported on research that invoked various social orientation-based personality factors and assessed their inter-relationships and their relationships with consumers’ attitudes, particularly their attitudes toward online social networking and marketers’ social networking sites. The research reported here establishes that these personality factors are antecedents to attitudes and intentions. These factors can shed light on consumer use of social media, particularly social networking sites; whether consumers may be influenced by marketers; and whether they could influence other consumers via social media. The study’s results point to the important role of a hitherto uninvestigated construct, susceptibility to social networking influence. The findings regarding this construct not only show its importance in the social media domain but also support findings related to the role of consumer susceptibility to interpersonal influence, the construct on which it was based. Not much research has explored online social sharing and its role in social media use. This research shows that social sharing disposition, which would be important to brands that want consumers to disseminate information, plays a role in influencing consumers’ attitudes and intentions when it comes to social networking. The study provides evidence that the constructs of susceptibility to social networking influence and social sharing disposition are robust and should be taken into account in future investigations involving consumer response to social networking and social media use. The study looked specifically at attitudes and intentions, with the intention studied here being likelihood of recommending marketers’ social networking sites. The findings support previous research about attitudinal links that has been conducted in other domains. In the case of attitudes, the results show that in the case of social media use, attitudes predict behaviour and intentions. This supports recent similar findings by Gironda and Korgaonkar (2014) regarding the links among attitudes toward social networking sites (SNS) activity, intention to engage in SNS activity, and subsequent SNS activity. The findings related to attitudes are also consistent with findings by Muk (2013), who found that toward brand pages and social influence significantly predicted consumers’ intentions to become fans of these brand pages. To enhance the stream of research in this domain, subsequent research can explore what are some other individual difference factors that could impact social media attitudes and intentions. In addition, it would be a worthwhile undertaking to determine possible antecedents to the social orientation-based personality factors that were investigated, as well as other behavioural outcomes in which consumers might engage. For example, factors such as social connectedness, need to belong, and introversion/extroversion could be among other individual difference factors worthy of investigation. These research endeavours can only enhance the body of literature on social media use. Consumer social orientation-based personality and social media use 21 7.2 Managerial implications Though there has been an increased use of social media by both brands and consumers from all over, theorising and research on how personality factors might impact use of social media have not kept pace with this flourish of activities. The major thrust of this paper is that such research is necessary if marketers are to fully understand the diverse factors that may play a role in influencing consumers’ response to social networking and engagement in social media use. Marketing communications managers usually have different communication objectives for their brands and their use of social networking sites could be dictated by the objectives that they are seeking to attain. If the objective is, for example, to build community, then they have to take into account the impact of the social orientation-based personality traits investigated in this study. Consumers can be segmented and targeted with online social networking efforts on the basis of these personality traits, as the study results indicate that they differentially influence consumers’ attitudes in an online social networking domain. Marketing communications managers should also make an effort to tap into the personality variables as a basis for segmenting and targeting those consumers who will engage in eWOM for the brand (as generated in recommendation likelihood). Social media activities generate eWOM, and it has been shown that eWOM affects purchasing decisions (Yaylı and Bayram, 2012). Hence, effort should be made to use personality profile to reach consumers that will generate eWom on behalf of brands. For example, the results indicate more favourable attitudes towards marketers’ social networking sites among consumers who are more susceptible to social networking influence. These consumers are more likely to recommend marketers’ social networking sites. The same is true for consumers with high levels of interdependence self-construal. The results indicate that they are more likely to have favourable attitudes towards marketers’ social networking sites and are more likely to recommend these sites. Therefore, targeting consumers on the basis of their level of susceptibility to social networking influence or interdependent self-construal could be a way to generate eWOM, by way of recommendation of marketers’ social networking sites. The mediational routes involving these variables and the support of the hypotheses involving these mediational routes suggest that the way to favourable eWOM is through marketers’ social networking sites. In the broader context of social media as a marketing platform, companies should also give consideration to the cultural context in which they are using social networking sites to engage consumers. Brands could endeavour to target a culture or different groups within a culture based on their level of interdependent self-construal. This highlights the importance that the brands would have to attach to conducting marketing research in an effort to determine which consumers fall into what group. 8 Limitations and future research The research reported here does have some limitations that should be taken into account in conducting future research. For example, the sample was a predominantly college 22 A.A. Bailey and M.S. Ben Mimoun student sample. Though much research indicates that this group is at the forefront of the usage of social media, care should be taken not to assume that these findings will be the same across all age groups in the USA. Therefore, follow-up studies can look at other groups such as teens or senior consumers. Further studies could also look at the extent to which gender might be a factor in influencing attitudes and intentions in online social networking. The moderating roles of other demographic factors could also be the basis for exploration. For example, in the USA, there is an increasingly diverse population, as a result of immigration. Therefore, research into demographic factors within a culture, for example, national origin, that impact the attitudes and intentions in our study would add to our understanding of consumer response to social networking. Researchers could seek to study other outcome variables other than recommendation likelihood that may be impacted by the personality variables in this study. For example, consumers engage in different kinds of activities on social networks: transmission of negative and positive information about brands, commenting on brands, creating and disseminating content about brands, among other things. These are actual behaviours in which consumers indulge and which can serve as key indices of their levels of engagement with brands. Are those behaviours impacted by these and other personality variables? Additional questions also arise: Are there other individual difference factors that influence consumer response to social media? Are there cultural factors that influence attitudes toward online social networking and marketers’ social networking sites? Do consumers across different cultural divides view social networking sites as brands and develop different attitudes to each one, as they do with brands, in general, thus influencing how they use them? What are the implications for brands, especially global brands? Do consumers transfer their attitudes toward a brand’s social networking activities to the brands themselves? These questions remain unanswered. Equally, cultures that differ in their consumers’ general levels of interdependent and independent self-construal that have not been studied before could form the basis for future studies. For example, this stream of research could benefit from investigations of consumers in the Middle East, Latin America and the Caribbean, and countries in Eastern Europe, which typically are cultures where interdependent self-construal tend to be more dominant than independent self-construal. Ultimately, these studies can only lead to better understanding of the varied factors that influence consumer response to online social networking. Two of the constructs in this study were developed for this study. They exhibited acceptable face validity, as well as convergent and discriminant validities. Their reliabilities were also good. Future research aimed at establishing additional reliability of these measures could be conducted. Notwithstanding these limitations and future research avenues, the study reported in this paper has provided research and managerial insights related to certain social orientation-based personality factors that explain consumers’ attitudes and intentions when it comes to online social networking. It adds to other studies that have looked at the impact of different variables on social media use and consumer response to social media. It investigated constructs that, hitherto, had not been explored in this stream of research. Consumer social orientation-based personality and social media use 23 References Anderson, J.C. and Gerbing, D.W. (1988) ‘Structural equation modeling in practice: a review and recommended two-step approach’, Psychological Bulletin, Vol. 3, No. 3, pp.411–423. Anderson, J.C. and Gerbing, D.W. (1992) ‘Assumptions and comparative strengths of the two-step approach’, Sociological Methods & Research, Vol. 20, No. 3, pp.321–333. Bearden, W.O., Netemeyer, R.G. and Teel, J.E. (1989) ‘Measurement of consumer susceptibility to interpersonal influence’, Journal of Consumer Research, Vol. 15, No. 4, pp.473–481. Bernstein, J. (2013) Social media in 2013: By the numbers. Available online at: http://www. socialmediatoday.com/content/social-media-2013-numbers (accessed on 8 February 2015). Biehal, G., Stephens, D. and Curlo, E. (1992) ‘Attitude toward the ad and brand choice’, Journal of Advertising, Vol. 21, No. 3, pp.19–36. Blazevic, V., Wiertz, C., Cotte, J., de Ruyter, K. and Keeling, D.I. (2014) ‘GOSIP in cyberspace: conceptualization and scale development for general online social interaction propensity’, Journal of Interactive Marketing, Vol. 28, No. 2, pp.87–100. Brown, S. and Stayman, D. (1992) ‘Antecedents and consequences of attitude toward the ad: a meta-analysis’, Journal of Consumer Research, Vol. 19, No. 1, pp.34–51. Browne, M.W. and Cudeck, R. (1993) ‘Alternative ways of assessing model fit’, in Bollen, K.A. and Long, J.S. (Eds): Testing Structural Equation Models, Sage, Newbury Park, CA, pp.136–162. Chatterjee, P. (2011) ‘Drivers of new product recommending and referral behavior on social network sites’, International Journal of Advertising, Vol. 30, No. 1, pp.77–101. Chen, Y., Liu, Y. and Zhang, J. (2012) ‘When do third-party product reviews affect firm value and what can firms do? The case of media critics and professional movie reviews’, Journal of Marketing, Vol. 76, No. 2, pp.116–134. Chu, S-C. and Kim, Y. (2011) ‘Determinants of consumer engagement in electronic word-ofmouth (eWOM) in social networking sites’, International Journal of Advertising, Vol. 30, No. 1, pp.47–75. Clark, R.A. and Goldsmith, R.E. (2006) ‘Interpersonal influence and consumer innovativeness’, International Journal of Consumer Studies, Vol. 30, No. 1, pp.34–43. Curci, A. and Rimé, B. (2008) ‘Dreams, emotions, and social sharing of dreams’, Cognition & Emotion, Vol. 22, No. 1, pp.155–167. Dhar, V. and Chang, E.A. (2009) ‘Does chatter matter? The impact of user-generated content on music sales’, Journal of Interactive Marketing, Vol. 23, No. 4, pp.300–307. D’Orazio, D. (2012) Twitter breaks 400 million tweet-per-day barrier, sees increasing mobile revenue. Available online at: http://www.theverge.com/2012/6/6/3069424/twitter-400-milliontotal-daily-tweets (accessed on 8 February 2015). Drossos, D.A., Giaglis, G.M., Vlachos, P.A., Zamani, E.D. and Lekakos, G. (2013) ‘Consumer responses to SMS advertising: Antecedents and consequences’, International Journal of Electronic Commerce, Vol. 18, No. 1, pp.105–136. Fazio, R.H., Powell, M.C. and Williams, C.J. (1989) ‘The role of attitude accessibility in the attitude-to-behavior process’, Journal of Consumer Research, Vol. 16, No. 3, pp.280–288. Fornell, C. and Larcker, D.F. (1981) ‘Structural equation models with unobservable variables and measurement error: algebra and statistics’, Journal of Marketing Research, Vol. 18, No. 1, pp.382–388. Gensler, S., Völcknerb, F., Liu-Thompkins, Y. and Wiertz, C. (2013) ‘Managing brands in the social media environment,’ Journal of Interactive Marketing, Vol. 27, No. 4, pp.242–256. Gironda, J.T. and Korgaonkar, P.K. (2014) ‘Understanding consumers’ social networking site usage’, Journal of Marketing Management, Vol. 30, Nos. 5/6, pp.571–605. 24 A.A. Bailey and M.S. Ben Mimoun Glasman, L.R. and Albarracíin, D. (2006) ‘Forming attitudes that predict future behavior: a meta-analysis of the attitude-behavior relation’, Psychological Bulletin, Vol. 132, No. 5, pp.778–822. Goodrich, K. and de Mooij, M. (2014) ‘How “social” are social media? A cross-cultural comparison of online and offline purchase decision influences’, Journal of Marketing Communications, Vol. 20, Nos. 1/2, pp.103–116. Ha, L. and Chan-Olmsted, S.M. (2004) ‘Cross-media use in electronic media: the role of cable television web sites in cable television network branding and viewership’, Journal of Broadcasting & Electronic Media, Vol. 48, No. 4, pp.620–645. Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2010) Multivariate Data Analysis, 7th ed., Prentice Hall, Upper Saddle River, New Jersey. Hampton, K.N., Sessions, L.F., Rainie, L. and Purcell, K. (2011) Social networking sites and our lives, Pew Internet & American Life Project, Washington, DC. Hill, R. and Moran, N. (2011) ‘Social marketing meets interactive media’, International Journal of Advertising, Vol. 30, No. 5, pp.815–838. Hoffmann, A.O.I. and Broekhuizen, T.L.J. (2009) ‘Susceptibility to and impact of interpersonal influence in an investment context’, Journal of the Academy of Marketing Science, Vol. 37, No. 4, pp.488–503. Homer, P.M. (1990) ‘The mediating role of attitude toward the ad: Some additional evidence’, Journal of Marketing Research, Vol. 27, No. 1, pp.78–86. Hui, M.K., Ho, C.K.Y. and Wan, L.C. (2011) ‘Prior relationships and consumer responses to service failures: a cross-cultural study’, Journal of International Marketing, Vol. 19, No. 1, pp.59–81. Kelman, H.C. (1961) ‘Processes of opinion change’, Public Opinion Quarterly, Vol. 25, Spring, pp.57–78. Kim, A.J. and Ko, E. (2012) ‘Do social media marketing activities enhance customer equity? An empirical study of luxury fashion brand’, Journal of Business Research, Vol. 65, No. 10, pp.1480–1486. Kim, J., Baek, Y. and Choi, Y.H. (2012) ‘The structural effects of metaphor-elicited cognitive and affective elaboration levels on attitude toward the ad’, Journal of Advertising, Vol. 41, No. 2, pp.77–96. Kim, Y., Sohn, D. and Choi, S.M. (2011) ‘Cultural difference in motivations for using social network sites: a comparative study of American and Korean college students’, Computers in Human Behavior, Vol. 27, No. 1, pp.365–372. Koh, N.S., Hu, N. and Clemons, E.K. (2010) ‘Do online reviews reflect a product’s true perceived quality? An investigation of online movie reviews across cultures’, Electronic Commerce Research and Applications, Vol. 9, pp.374–385. Kohler, C.F., Breugelmans, E. and Dellaert, B.G.C. (2011) ‘Consumer acceptance of recommendations by interactive decision aids: the joint role of temporal distance and concrete versus abstract communications’, Journal of Management Information Systems, Vol. 27, No. 4, pp.231–260. Kozinets, R.V., de Valck, K., Wojnicki, A.C. and Wilner, S.J.S. (2010) ‘Networked narratives: understanding word-of-mouth marketing in online communities’, Journal of Marketing, Vol. 74, No. 2, pp.71–89. Kramer, T., Spolter-Weisfeld, S. and Thakkar, M. (2007) ‘The effect of cultural orientation on consumer responses to personalization’, Marketing Science, Vol. 26, No. 2, pp.246–258. Labrecque, L.I. (2014) ‘Fostering consumer–brand relationships in social media environments: the role of parasocial interaction’, Journal of Interactive Marketing, Vol. 28, No. 2, pp.134–148. Lee, A.Y. and Aaker, J. (2004) ‘Bringing the frame into focus: the influence of regulatory fit on processing fluency and persuasion’, Journal of Personality and Social Psychology, Vol. 86, No. 2, pp.205–218. Consumer social orientation-based personality and social media use 25 Lee, J., Park, D-H. and Han, I. (2008) ‘The effect of negative online consumer reviews on product attitude: an information processing view’, Electronic Commerce Research & Applications, Vol. 7, No. 3, pp.341–352. Liang, S., Ekinci, Y., Occhiocupo, N. and Whyatt, G. (2013) ‘Antecedents of travellers’ electronic word-of-mouth communication’, Journal of Marketing Management, Vol. 29, Nos. 5/6, pp.584–606. Logan, K. and Bright, L. (2014) ‘Deal me in! Assessing consumer response to daily-deal sites’, International Journal of Internet Marketing and Advertising, Vol. 8, No. 3, pp.161–180. MacKenzie, S.B., Lutz, R.J. and Belch, G.E. (1986) ‘The role of attitude toward the ad as a mediator of advertising effectiveness: a test of competing explanations’, Journal of Marketing Research, Vol. 23, No. 2, pp.130–143. Marketing Charts (2013) Digital marketers say social sharing is highly effective for boosting conversion rates. Available online at: http://www.marketingcharts.com/online/digitalmarketers-say-social-sharing-is-highly-effective-for-boosting-conversion-rates-29205/ (accessed on 8 February 2015). Markus, H.R. and Kitayama, S. (1991) ‘Culture and the self: Implications for cognition, emotion, and motivation’, Psychological Review, Vol. 98, No. 2, pp.224–253. Minton, E., Lee, C., Orth, U., Kim, C-H. and Kahle, L. (2012) ‘Sustainable marketing and social media’, Journal of Advertising, Vol. 41, No. 4, pp.69–84. Mitchell, V-W., Macklin, J. and Paxman, J. (2007) ‘Social uses of advertising: an example of young male adults’, International Journal of Advertising, Vol. 26, No. 2, pp.199–222. Muk, A. (2013) ‘What factors influence millennials to like brand pages?’ Journal of Marketing Analytics, Vol. 1, No. 3, pp.127–137. Muk, A., Chung, C. and Kim, J. (2014) ‘A cross-national study of the influence of individualism and collectivism on liking brand pages’, Journal of International Consumer Marketing, Vol. 26, No. 2, pp.122–137. Müller, B., Florès, L., Agrebi, M., and Chandon, J. (2008) ‘The branding impact of brand websites: do newsletters and consumer magazines have a moderating role?’ Journal of Advertising Research, Vol. 48, No. 3, pp.465–472. Muntinga, D.G., Moorman, M. and Smit, E.G. (2011) ‘Introducing COBRAs: exploring motivations for brand-related social media use’, International Journal of Advertising, Vol. 30, No. 1, pp.13–46. Muthén, L.K. and Muthén, B.O. (1998–2014) Mplus User’s Guide, 7th ed., Muthén & Muthén, Los Angeles, CA. Naylor, R.W., Lamberton, C.P. and West, P.M. (2012) ‘Beyond the “like” button: the impact of mere virtual presence on brand evaluations and purchase intentions in social media settings’, Journal of Marketing, Vol. 76, No. 6, pp.105–120. Oberecker, E.M. and Diamantopoulos, A. (2011) ‘Consumers’ emotional bonds with foreign countries: does consumer affinity affect behavioral intentions?’ Journal of International Marketing, Vol. 19, No. 2, pp.45–72. Ogonowski, A., Montandon, A., Botha, E. and Reyneke, M. (2014) ‘Should new online stores invest in social presence elements? The effect of social presence on initial trust formation’, Journal of Retailing and Consumer Services, Vol. 21, No. 4, pp.482–491. Okazaki, S. (2009) ‘Social influence model and electronic word of mouth’, International Journal of Advertising, Vol. 28, No. 3, pp.439–472. Park, C. and Lee, T.M. (2009) ‘Antecedents of online reviews’ usage and purchase influence: an empirical comparison of U.S. and Korean consumers’, Journal of Interactive Marketing, Vol. 23, No. 4, pp.332–340. Park, H. and Kim, Y-K. (2014) ‘The role of social network websites in the consumer–brand relationship’, Journal of Retailing and Consumer Services, Vol. 21, No. 4, pp.460–467. 26 A.A. Bailey and M.S. Ben Mimoun Patterson, A. (2012) ‘Social-networkers of the world, unite and take over: a meta-introspective perspective on the Facebook brand’, Journal of Business Research, Vol. 65, No. 4, pp.527–534. Pelling, E.L. and White, K.M. (2009) ‘The theory of planned behavior applied to young people’s use of social networking Web sites’, CyberPsychology & Behavior, Vol. 12, No. 6, pp.755–759. Pew Research Center (2014) Social networking fact sheet. Available online at: http://www. pewinternet.org/fact-sheets/social-networking-fact-sheet/ (accessed on 8 February 2015). Roberts, J. (2010) ‘Mobilise the people to shape your brand’, Marketing Week, Vol. 33, No. 6, pp.16–20. Robles, P. (2010) Twitter’s 50m daily tweets and what they mean for brand marketers. Available online at: http://econsultancy.com/blog/5466-twitter-s-50m-daily-tweets-and-what-they-meanfogr-brand-marketers (accessed on 8 February 2015). Ryu, G. and Feick, L. (2007) ‘A penny for your thoughts: referral reward programs and referral likelihood’, Journal of Marketing, Vol. 71, No. 1, pp.84–94. Shih, H., Lai, K. and Cheng, T. (2013) ‘Informational and relational influences on electronic word of mouth: an empirical study of an online consumer discussion forum’, International Journal of Electronic Commerce, Vol. 17, No. 4, pp.137–166. Singelis, T.M. (1994) ‘The measurement of independent and interdependent self-construals’, Personality and Social Psychology Bulletin, Vol. 20, pp.580–591. Singelis, T.M., Triandis, H.C., Bhawuk, D.P.S. and Gelfand, M.J. (1995) ‘Horizontal and vertical dimensions of individualism and collectivism: a theoretical and measurement refinement’, Journal of Cross-Cultural Research, Vol. 29, No. 3, pp.240–275. Singh-Manoux, A. and Finkenauer, C. (2001) ‘Cultural variations in social sharing of emotions: an intercultural perspective on a universal phenomenon’, Journal of Cross-Cultural Psychology, Vol. 32, No. 6, pp.647–661. Sung, Y. and Choi, S.M. (2011) ‘Increasing power and preventing pain: the moderating role of self-construal in advertising message framing’, Journal of Advertising, Vol. 40, No. 1, pp.71–85. Taylor, D.G., Lewin, J.E. and Strutton, D. (2011) ‘Friends, fans, and followers: do ads work on social networks?’ Journal of Advertising Research, Vol. 51, No. 1, pp.258–275. Trainor, K.J., Andzulis, J., Rapp, A. and Agnihotri, R. (2014) ‘Social media technology usage and customer relationship performance: a capabilities-based examination of social CRM’, Journal of Business Research, Vol. 67, No. 6, pp.1201–1208. Triandis, H.C. (1988) ‘Collectivism and individualism: a reconceptualization of a basic concept in cross-cultural psychology’, in Bagley, C. and Verma, G. (Eds): Personality, Cognition, and Values: Cross-cultural Perspectives of Childhood and Adolescence, McMillan, London, England, pp.60–95. Triandis, H.C. (1989) ‘The self and social behavior in differing cultural contexts’, Psychological Review, Vol. 96, No. 3, pp.506–520. Triandis, H.C., McCusker, C. and Hui, C.H. (1990) ‘Multimethod probes of individualism and collectivism’, Journal of Personality & Social Psychology, Vol. 59, No. 5, pp.1006–1020. Tuten, T. and Angermeier, W. (2013) ‘Before and beyond the social moment of engagement: perspectives on the negative utilities of social media marketing’, Gestion 2000, Vol. 30, No. 3, pp.69–76. Vaidyanathan, R., Aggarwal, P. and Kozłowski, W. (2013) ‘Interdependent self-construal in collectivist cultures: effects on compliance in a cause-related marketing context’, Journal of Marketing Communications, Vol. 19, No. 1, pp.44–57. Vallaster, C. and von Wallpach, S. (2013) ‘An online discursive inquiry into the social dynamics of multi-stakeholder brand meaning co-creation’, Journal of Business Research, Vol. 66, No. 9, pp.1505–1515. Consumer social orientation-based personality and social media use 27 Vance, A. (2012) ‘Facebook: the making of 1 billion users’, Business Week. Available online at: http://www.businessweek.com/articles/2012-10-04/facebook-the-making-of-1-billion-users (accessed on 8 February 2015). WARC (2015) Social drives more web traffic. Available online at: http://www.warc.com/ LatestNews/News/EmailNews.news?ID=34223&Origin=WARCNewsEmail&CID=N34223& PUB=Warc_News&utm_source=WarcNews&utm_medium=email&utm_campaign=WarcNe ws20150128 (accessed on 8 February 2015). Winer, R.S. (2009) ‘New communications approaches in marketing: Issues and research directions’, Journal of Interactive Marketing, Vol. 23, No. 2, pp.108–117. Yaylı, A. and Bayram, M. (2012) ‘e-WOM: the effects of online consumer reviews on purchasing decisions’, International Journal of Internet Marketing and Advertising, Vol. 7, No. 1, pp.51–64.