What impact does the use of sports celebrity endorsers have on the average person?

• PDFView PDF

Celebrity endorsements are a well-established marketing strategy used since the late nineteenth century [Erdogan 1999]. While the strategy was first applied in traditional brand or product marketing [Erdogan 1999], it has spread to any form of marketing communication, including political marketing [Chou 2014, 2015], health communication, and the marketing of non-government organizations [NGOs; *Jackson 2008; *Wheeler 2009; *Young and Miller 2015]. Current estimates indicate every fourth to fifth advertisement incorporates this strategy, though this varies across countries [USA: 19–25%, Elberse and Verleun 2012; Stephens and Rice 1998; UK: 21%, Pringle and Binet 2005; India: 24%, Crutchfield 2010; Japan: 70%, Kilburn 1998; Taiwan: 45%, Crutchfield 2010]. In addition, longitudinal analyses show a steady increase over the past years [Erdogan 1999; Pringle and Binet 2005].

Hence, many studies have been conducted to test whether consumer attitudes and behavior are changed by celebrity endorsements. So far, results have been summarized in three narrative [Bergkvist and Zhou 2016; Erdogan 1999; Kaikati 1987] and one quantitative review [Amos et al. 2008; see the Appendix for a summary of the review]. The quantitative review of Amos and colleagues focused on source effects of celebrity endorsers. In short, it asked which source variables [e.g., expertise, attractiveness] exert which influence on advertising effectiveness. However, it did not test whether the obtained effect sizes were significant, but solely tested whether they were significantly different from each other. Hence, up until now, there is no meta-analytic knowledge about whether celebrity endorsements actually influence consumers’ responses, including the size of their influence. In addition, there is no knowledge about whether effects differ in terms of specific outcomes [e.g., cognitive, affective, behavioral]. The reason is that Amos et al. [2008] applied only a combined measure of advertising effectiveness. Furthermore, frequently claimed propositions like the match-up hypothesis or the proposition of stronger effects in the case of unfamiliar brands have never been tested on a meta-analytic level. This seems particularly pressing considering the fact that practitioners frequently refer to such claims [Erdogan 1999]. Besides, there are conflicting results of individual studies, for instance, when it comes to the endorser’s sex or endorsement repetition [Bergkvist and Zhou 2016; Erdogan 1999]. Last but not least, numerous studies have been conducted since the last quantitative review in 2004 [Bergkvist and Zhou 2016].

The present meta-analysis seeks to address these shortcomings by integrating research published through April 2016, by reporting average effect sizes according to various advertising outcomes including respective confidence intervals, and by performing moderator analyses testing the impact of various endorser and endorsed object variables. In terms of methodological advancement, we apply multilevel modeling accounting for the dependence of multiple effect sizes and we estimate publication bias, both important issues in meta-analysis [Borenstein et al. 2009]. Finally, we provide practitioners with empirically derived implications for how to choose the right celebrity and offer researchers an agenda for future research.

Conceptual framework

Following McCracken [1989], celebrity endorsements are understood as a marketing technique in which an individual enjoying public recognition “uses this recognition on behalf of a consumer good by appearing with it in an advertisement” [p. 310]. The effects of endorsements can well be explained within the advertising effectiveness model provided by Ladvidge and Steiner [Lavidge and Steiner 1961]. Studies have mostly investigated celebrity endorsements according to one or more of the model’s advertising functions [Bergkvist and Zhou 2016; Erdogan 1999; Kaikati 1987]. Furthermore, the model has revealed itself as fruitful in a similar meta-analysis [Grewal et al. 1997]. It enables a systematic organization of the analyzed dependent variables and moderators, and specifies their relationships [see Fig. 1]. According to the model, advertising serves to influence three basic psychological dimensions: the cognitive, the affective, and the conative. “Advertising’s cognitive function provides information and facts for the purpose of making consumers aware and knowledgeable about the sponsored brand. Advertising’s affective function creates liking and preference for the sponsored brand – preference presumably refers to more favorable attitudes. Advertising’s affective function, therefore, is to persuade. Finally, advertising’s conative function is to stimulate desire and cause consumers to buy the sponsored brand” [Grewal et al. 1997, p. 2]. Important to note, we do not suggest that these outcomes necessarily take place in a particular sequence [i.e., cognition = > affect = > behavior]. Following more recent advancements in the conceptualization of advertising effects, we propose that each of the outcomes may be independently influenced by celebrity endorsements. In addition, all outcomes are assumed to be interrelated. They possibly influence or interact with each other [Vakratsas and Ambler 1999]. This is indicated by the double-headed arrows in Fig. 1.

Fig. 1

Celebrity endorsement effectiveness model adapted from Grewal et al. [1997]

Full size image

Cognitive effects

Cognitive effects include awareness and knowledge about an endorsed object. Establishing awareness starts from creating attention and interest [Lavidge and Steiner 1961]. Directing one’s attention involves controlled as well as automatic processes [Kahneman 1973]. Both processes can be influenced by celebrity endorsements. First, people who are interested in a particular celebrity are assumed to purposefully direct their attention to this celebrity’s ad [*Wei and Lu 2013]. Second, people’s attention is automatically directed. Humans tend to give preferential treatment to stimuli that are related to their goals [Lang 2000]. In addition, celebrities are well-known, resulting in more accessible representations in memory [Erfgen et al. 2015]. This should foster automatic attention, too [Bargh and Pratto 1986].

Once a celebrity endorsement grabs their attention, consumers are assumed to become more interested in the advertised object as compared with a non-endorsed or other-endorsed object. This due to the fact that celebrities possess inherent news value caused by their celebrity status [Corbett and Mori 1999]. Since celebrities are generally liked, consumers also tend to be more motivated to assess what kind of object a celebrity is endorsing. As a result, object recall and recognition is assumed to be enhanced due to greater message elaboration [*Petty et al. 1983].

In terms of knowledge, celebrity endorsements are assumed to influence the meaning of the endorsed object [*Miller and Allen 2012] as well as perceptions about its price, its taste level, the risk of buying it, or the perceived information value of the endorsement [*Biswas et al. 2006; *Dean and Biswas 2001; *Freiden 1982; *Friedman et al. 1976; *Young and Miller 2015]. Based on the mechanism behind these effects, consumers are assumed to conclude that an object has a specific attribute when they perceive this object as paired with a celebrity known for this attribute [e.g., premium price with a high-class celebrity; *Miller and Allen 2012]. The process can be conceptualized as propositional learning [De Houwer 2009]. Consumers have experienced in their past that people frequently present themselves with objects they share similarities with [Elliot and Wattanasuwan 1998]. “Once a relation between two events has been discovered in the past, it is likely that this knowledge is used to generate propositions about similar events in the present” [De Houwer 2009, p. 8]. As a result, celebrity attributes created through celebrities’ role in society transfer to associated objects [McCracken 1989]. In conclusion, it is proposed that celebrity endorsements influence consumers’ cognitions including attention and interest, awareness, as well as perceptions.

H1: As compared with non-celebrity endorsements or no endorsements, celebrity endorsements evoke greater attention, interest, and awareness as well as perceptions more in line with the respective endorser.

Affective effects

Affective effects pertain to attitudes toward the ad and attitudes toward the advertised object. This influence may best be explained with regard to balance theory [Heider 1946, 1958; see also Mowen and Brown 1981]. The theory explains a person’s desire to maintain consistency among a triad of linked cognitions. It follows that people generally strive for a consistent organization of their cognitive structures, experiencing this state as most tension-free. In the case of celebrity endorsements, the cognitive triad consists of the consumer, the celebrity, and the endorsed object or the endorsed ad, respectively. A consistent state is achieved if the consumer perceives the celebrity and the endorsed object/ad as equally valenced [i.e., as both positive or both negative] because celebrities endorsing an object are usually seen as positively related to that object or the respective ad [Erdogan 1999]. Starting from the premise that researchers and practitioners usually employ likeable celebrities, it can be hypothesized that celebrity endorsements positively impact consumers’ attitudes toward the ad and attitudes toward the endorsed object. Only then are consumers’ attitudes and their liking for the respective celebrity of the same valence [i.e., both positive; Heider 1946, 1958]. Although there may be similar effects for likeable non-celebrity endorsers, these are assumed to be notably weaker. This is due to the fact that consumers are familiar with celebrities by definition. As a result, relationships with celebrities are more affectional as compared with unknown non-celebrity endorsers [Dibble et al. 2016].

H2: As compared with non-celebrity endorsements or no endorsements, celebrity endorsements evoke more positive attitudes toward the ad and the endorsed object.

Behavioral effects

Behavioral effects include purchasing or using an object [e.g., *Freiden 1982; *Kamins 1989; *Kamins and Gupta 1994; *Roozen and Claeys 2010; *Siemens et al. 2008], sharing object information, volunteering, supporting a charitable cause, or voting for a political candidate [Myrick and Evans 2014; *Pease and Brewer 2008; *Wei and Lu 2013; *Wheeler 2009]. Such effects are frequently explained with regard to the theory of planned behavior [Ajzen 1991]. According to the theory, behavior is strongly determined by behavioral intentions. These are, in turn, influenced by consumers’ attitudes, the perceived subjective norm, and the perceived behavioral control. As long as consumers are able to exert the respective behavior [behavioral control], and as long as consumers do not feel social pressure to avoid the behavior [subjective norm], attitudes largely predict behavioral intentions. The assumptions have been supported by various meta-analyses [Armitage and Conner 2001; Kim and Hunter 1993]. Accordingly, the more positive attitudes assumed in H2 should lead to stronger behavioral intentions and respective behavior. Corresponding effects were, for instance, found by Fleck et al. [2012] and Mishra and Mishra [2014]. We consequently hypothesize:

H3: As compared with non-celebrity endorsements or no endorsements, celebrity endorsements evoke stronger behavioral intentions and behavior.

Moderators

Studies investigating the applied advertising effectiveness framework have consistently found that people respond differently to advertisements depending on characteristics of the ad, the advertised object, and individual characteristics [Vakratsas and Ambler 1999]. This is frequently intended by the advertiser, tailoring advertisements to specific consumers and their needs [Lavidge and Steiner 1961]. In line with this reasoning, we included various moderators within our framework accounting for the fact that consumers do not respond uniformly to advertising [cf. Figure 1; Lavidge and Steiner 1961]. Following Grewal et al. [1997], our analysis of moderators is limited to those that [1] are theoretically relevant, [2] provide a sufficient number of effect sizes, [3] show sufficient variance to test the moderation, and [4] are important to advertisers. In terms of number of effect sizes, Higgins and Green [2011] suggest considering moderator analysis only if there are ten or more studies incorporating the moderators. Seven moderators met the criteria: endorser sex, endorser type, endorser match, endorsement explicitness, endorsement frequency, familiarity of the endorsed object, and endorsement type of the comparison group.

Endorser sex

Though endorser sex has generally been viewed as influential [e.g., Erdogan 1999; McCracken 1989], hardly any study explicitly addressed this variable in empirical research. Most studies have investigated either female or male endorsers [for the only exception, see Freiden 1984]. “The dearth of research on endorser gender effects is somewhat surprising as persuasion research shows that men and women respond differently to male and female communicators” [Bergkvist and Zhou 2016, p. 11]. Hence, meta-analysis seems especially valuable [Lipsey and Wilson 2001]. Assumptions about possible effects may be derived from studies on non-celebrity spokespersons. According to Kenton [1989], the credibility and persuasiveness of a spokesperson depends on four dimensions: goodwill and fairness [e.g., unselfishness], prestige [e.g., power, status], expertise [e.g., competence], and self-presentation [e.g., confidence]. Research has revealed women to be higher ranking on goodwill and fairness, whereas men outperform women on the remaining dimensions [Kenton 1989]. As a result, male spokespersons were frequently more persuasive than female ones [e.g., Cabalero et al. 1989; Whittaker 1965]. Transferring this to the present context, consumers may perceive male celebrity endorsers as more credible due to higher levels of expertise and prestige [Cabalero et al. 1989]. As a result, male celebrities are assumed to evoke stronger endorsements effects when compared to female ones.

H4: Male celebrity endorsers evoke stronger endorsement effects when compared to female ones.

Endorser type

No study has explicitly investigated different types of celebrity endorsers. Instead, studies have typically focused on only one type. For instance, studies have explored actors, models, musicians, athletes, or TV hosts [e.g., *Dean and Biswas 2001; *Frizzell 2011; *Pease and Brewer 2008; *Wheeler 2009; *Wei and Lu 2013]. By joining the results of several studies, meta-analysis can provide information whether certain endorser types perform better than others do [Lipsey and Wilson 2001].

Starting from the premise that endorsement effects depend on the strength of the relationship a consumer shares with a celebrity [McCracken 1989], research on parasocial interaction can provide insights. Specifically, studies have revealed that people tend to develop relationships with celebrities, merely known from the media, just as they would do with real life persons [Dibble et al. 2016]: Upon encountering a celebrity on television, radio, or the Internet, consumers may parasocially interact with the celebrity, storing this experience in a relationship schema [Klimmt et al. 2006]. The more frequently a celebrity is encountered and the more intense each interaction experience is, the more likely a strong consumer–celebrity relationship is formed [Klimmt et al. 2006]. Looking at different kinds of celebrities, consumers are particularly likely to form a strong relationship with actors. First, consumers are audiovisually exposed to actors, creating a particularly rich experience, and second, experience is usually based upon multiple encounters over a longer period: “Over time, viewers become familiar with characters and performers on continuing series and often feel as though they know these individuals as well as they know their friends and neighbors. The importance of characters to viewers frequently extends beyond the viewing situation to include the sense of having personal relationships with the characters, deep concern about what happens in their ‘lives,’ and/or a desire to become like them in significant ways” [Hoffner and Buchanan 2005, p. 326].

According to McCracken [1989], this exact type of relationship causes consumers to accept celebrities’ influence more readily. The following hypothesis is proposed:

H5: Actors elicit stronger celebrity endorsement effects when compared to other types of celebrities such as models, musicians, athletes, or TV hosts.

Endorser match

Several studies have investigated the so-called product match-up hypothesis that assumes the effectiveness of celebrity endorsements is partially dependent on the degree of perceived fit between an endorsed object and the respective celebrity [Erdogan 1999]. A good match may be an attractive model presenting cosmetics, whereas a bad match may be an athlete trying to sell a guitar. The process underlying the product match-up hypothesis can be explained with regard to Social Adaptation Theory [Kahle and Homer 1985; Kamins 1990] or Schema Theory [Lynch and Schuler 1994]. Social Adaptation Theory assumes that people use information sources as long as they facilitate adaptation to their environment. If a match exists between a spokesperson and a product on some relevant attribute, the spokesperson becomes an information source of adaptive significance on which people may rely [Kamins 1990]. Schema Theory posits that attributes of celebrities can be integrated more easily with existing product schemas if the celebrity schemas match the product schemas [Lynch and Schuler 1994]. Both theories assume enhanced effects in the case of congruence. Accordingly, several studies have supported the product match-up hypothesis [Erdogan 1999]. We thus hypothesize larger effects for object–endorser congruence compared to incongruence.

H6: Congruent celebrity endorsers evoke stronger endorsements effects when compared to incongruent ones.

Endorsement explicitness

Explicitness can broadly be categorized into two modes: implicit and explicit endorsements. Whereas implicit endorsements refer to situations where celebrities simply use an object or merely appear jointly without overtly announcing their support [“I use this object”; *Miller and Allen 2012], explicit endorsements refer to situations where celebrities overtly express their support for an object [“I endorse this object”; *Miller and Allen 2012]. To the best of our knowledge, no study has ever compared both modes; instead, they have researched either implicit or explicit endorsements. Though effects have been found with both modes, one mode may be more effective than the other [implicit: e.g., *Miller and Allen 2012; explicit: *Dean and Biswas 2001; *Friedman and Friedman 1979]. According to Russell and Stern [2006], consumers infer the celebrity–object association to be of greater strength if celebrities explicitly express their support, signaling commitment and reliability. In addition, consumers may not even realize that an object is endorsed if the endorsement is too subtle. We consequently propose that explicit endorsements are more effective that implicit ones.

H7: Explicit endorsements evoke stronger effects than implicit ones.

Endorsement frequency

Celebrities may also vary in their endorsement frequency. Consumers are highly likely to encounter celebrity endorsements multiple times via various media channels, including TV, billboards, print advertising, radio, and the Internet. Research on classical conditioning suggests that effects may occur as early as a single pairing of a celebrity with an endorsed object [e.g., Ambroise et al. 2014; Gorn 1982]. However, other research suggests that effects tend to be greater the greater the number of pairings. For instance, Stuart et al. [1987] increased the number of pairings from one to three, to ten, and eventually to twenty, revealing a steady increase in effectiveness. Although these results do not directly refer to celebrity endorsements, similar effects can be assumed because celebrity endorsements are often seen as a certain type of classical conditioning [e.g., *Chen et al. 2012]. The following hypothesis is proposed:

H8: Celebrity endorsement effects increase with increased endorsement exposure.

Familiarity of the endorsed object

Next to the celebrity, the endorsed object itself may impact endorsement effectiveness [*Friedman and Friedman 1979]. For instance, researchers assume stronger effects, with decreasing familiarity with an endorsed object [*Miller and Allen 2012]. Object familiarity can be understood as the number of object-related experiences accumulated by a consumer [Alba and Hutchinson 1987]. These experiences can be obtained directly and indirectly, such as through celebrity endorsements [Kent and Allen 1994]. The more familiar a person is with an object, the more comprehensive his or her knowledge structures can become [Keller 2012]. Given that consumers already possess a rich network of associations representing an object, attitudes, and behavior appear more difficult to change [Cacioppo et al. 1992]. Accordingly, Ambroise et al. [2014] reported stronger celebrity endorsement effects with unfamiliar compared to familiar brands. Similarly, Shimp et al. [1991] showed the likelihood of conditioning effects for unknown or moderately known objects, but not for well-known ones. We consequently propose stronger celebrity endorsement effects for unfamiliar objects when compared to familiar ones.

H9: Celebrity endorsement effects are stronger for unfamiliar objects when compared to familiar ones.

Endorsement type of the comparison group

Investigating the effectiveness of celebrity endorsements through experiments, researchers have chosen various control groups. Frequently, celebrities are compared with a non-endorsed condition [e.g., *Martín-Santana and Beerli-Palacio 2013], an expert [e.g., Biswas et al. 2006], or an ordinary consumer [e.g., *Dong 2015]. Less frequently, celebrities are compared with an unknown model or athlete [e.g., *Roozen and Claeys 2010], an employee of the selling company [*Maronick 2005], a quality seal or an award [*Dean and Biswas 2001], or an endorser brand from the same product category [*Sengupta et al. 1997]. Studies typically apply one or two of these comparison groups. Thus, they enable assertions about whether celebrity endorsements outperform a single kind of endorsement or no endorsement. By contrast, meta-analysis enables comparisons across all types of endorsements simultaneously. Therefore, we can see whether celebrity endorsements outperform any other kind of endorsement. We can also test whether specific differences in performance [e.g., celebrity vs. expert] are significantly different from other performance differences [e.g., celebrity vs. ordinary consumer]. Marketing managers can thus gain valuable knowledge when deciding on celebrity endorsers, any other kind of endorsement, or no endorsement at all. We consequently ask:

RQ1: Do celebrity endorsements differ in their effectiveness depending on the control group applied?

A concise summary of the existing knowledge on celebrity endorsement effects can be found in Table 1. Looking at the main results, celebrity endorsements are shown to affect cognitive, affective, and conative outcomes. Furthermore, most studies have looked at endorsements of for-profit causes. Results frequently appear to be mixed. In addition, some studies show no effects at all. This meta-analysis will shed light on these mixed results by calculating an overall effect. In addition, mixed results can be clarified by adding potential moderators to the analysis. Furthermore, the meta-analysis will close gaps in the literature by investigating between-study differences that cannot be explored with single studies [e.g., endorser sex, endorser type, endorsement explicitness]. Looking at the investigated outcomes, most studies have investigated affective reactions followed by cognitive and conative ones. Meta-analysis will provide insights about whether there are any differences in terms of effectiveness per outcome type.

Table 1 Summary of research on the effectiveness of celebrity endorsements

Full size table

Methods

Study retrieval

Literature search

Studies were collected from three major databases [Business Source Premier, PsychINFO, Communication and Mass Media Complete]. The search included all peer-reviewed articles written in English and published through April 2016. The databases were examined using the term celebrit* in combination with endors*, spokes*, or advert* in any available search field. The search resulted in 1025 articles. About 300 of them were quantitative studies, including content analyses, surveys, and experimental studies.

Inclusion criteria

These quantitative studies were narrowed down based on the impact of celebrity endorsements on endorsed objects. Three criteria had to be met. First, only experimental studies were included because only they enable causal assertions [Shaughnessy and Zechmeister 1997]. The studies had to compare an experimental group to a control group. While the experimental group had to feature a celebrity endorsing an object, the control group had to include the same object, either non-endorsed or endorsed by a non-celebrity spokesperson. Studies that compared various types of celebrity endorsements but did not feature a non-celebrity control group were excluded [e.g., Ambroise et al. 2014; Kamins 1990]. Second, the celebrities had to be actually existing celebrities, thus excluding studies that investigated the impact of fictitious and imagined celebrities, as their validity is arguably limited. Third, the studies also had to report effect measures related to the endorsed object, excluding studies that solely reported measures related to the endorser [e.g., Cho 2010] or the general acceptance of celebrity endorsements [e.g., Becker 2013]. In addition, a measure was considered only if it was possible to obtain at least two effect sizes. Otherwise, the meta-analyzed effect size would equal the sole obtained effect size, rendering meta-analysis useless. This resulted in 15 eligible measures: attention to and interest in an ad, awareness of an endorsed object [recognition and recall], attitude toward an ad, attitude toward the endorsed object, perceived credibility of the ad and advertiser, meaning transfer [in the sense of transferring a celebrity’s meaning to a brand], evoked feelings, estimated price of a product, taste of a product, estimated information value of an ad, planning to inform oneself more about an endorsed object, perceived increase of knowledge, perceived risk when buying or using a product, brand choice, and behavioral intentions [intention to purchase or use an object, intention to volunteer, intention to support a charitable cause by spending time or money, and intention to share an endorsed object online; cf. Motyka et al. 2014]. No limitations were placed regarding the endorsed object encompassing any kind of object, such as product, brand, organization, behavior, or charitable cause.

Results

Based on these criteria, 44 manuscripts remained [the majority of the 300 quantitative studies were content analyses, surveys, or experimental studies comparing celebrities with celebrities]. Eight of these [Chou 2014; Fireworker and Friedman 1977; Freiden 1984; Jain et al. 2011; Ross et al. 1984; Sanbonmatsu and Kardes 1988; Veer et al. 2010] had to be excluded, as they lacked appropriate statistical information to calculate effect sizes with the formulas suggested by Lipsey and Wilson [2001]. Beforehand, all authors had been contacted and asked to provide missing statistical information if possible. According to Eisend [2009], about 18% exclusion is not uncommon in meta-analysis, and it matches other meta-analyses in marketing [Brown and Stayman 1992; Szymanski et al. 1995; Tellis 1988]. The remaining 36 manuscripts yielded 46 independent studies, coming to 10,357 participants.

Meta-analytic procedures

Effect size calculation

The standardized mean difference [d] was used as the effect size estimate according to the formulas provided by Lipsey and Wilson [2001]. All available statistical information was incorporated [e.g., means, standard deviations, t- and F-statistics, and frequencies]. Since this effect size estimate has been shown to be upwardly biased when calculated from small sample sizes [Lipsey and Wilson 2001], all estimates were corrected for sample size bias [Hedges 1981]. Positive d-values indicated a stronger effect of a celebrity endorsement compared to a non-endorsed or non-celebrity endorsed message, whereas negative d-values indicated a stronger effect of the non-endorsed or non-celebrity endorsed message. In total, 367 effect sizes were obtained. The ratio of effect sizes [367] to the number of studies [46] is the rule rather than the exception when analyzing various dependent variables [Eisend 2006, 2009; Szymanski et al. 1995].

Effect size integration and meta-analysis

Estimates were based on random-effects models. Fixed-effects models assume that all studies included in the meta-analysis are practically identical, having the same true effect size. In contrast, random-effects models assume differing true effect sizes varying, for instance, because of different participants or treatments. Specifically, true effect sizes are assumed to be distributed around some mean whereby the studies included in the analysis are assumed to represent a random sample [Borenstein et al. 2009]. This model was much more realistic, as participants and study settings certainly differed across studies. In addition, results may be generalized because the investigated studies are treated as a random subset of a larger study population [Hedges and Vevea 1998]. Several studies reported results that enabled obtaining more than one effect size per dependent variable. Performing a meta-analysis on these studies would violate the assumption of independence of effect sizes and assign more weight to the studies producing more than one effect size. Previous studies mostly ignored these problems, aggregated effect sizes into a single effect size [or chose only one effect size per study], or performed the so-called shifting the unit of analysis approach [Cheung 2014]. This approach averages effect sizes within differing units depending on the current research question [e.g., study as a unit or study characteristics, such as gender of participants as a unit].

While ignoring these problems is clearly not satisfactory, the latter two approaches are rather broadly accepted [Borenstein et al. 2009; Cooper 2010]. However, aggregating effect sizes or choosing only one effect size per study may strongly reduce the number of effect sizes, thus lowering the power of statistical tests. In addition, statistical information is lost, resulting in less precise estimates. The same deficits apply to shifting the unit of analysis [Cheung 2014]. Researchers recently suggested treating meta-analysis as a multilevel model to address these drawbacks [e.g., Cheung 2014; Field 2015; Konstantopoulos 2011]. The basic idea nests the effect sizes [first level] within the studies [second level; Konstantopoulos 2011]. The resulting model then looks like Eqs. [1] and [2]:

$$ {\gamma}_i={\lambda}_i+{e}_i\left[ first\ level\ or\ within- study\ model\right] $$

[1]

$$ {\lambda}_i={\beta}_0+{u}_i\left[ second\ level\ or\ between- study\ model\right]. $$

[2]

“Effect sizes [γ] in the ith study are predicted from the ‘true’ effect size for that study [λi] and some error [ei] [note that the variance of ei is the sampling variance of that study]. The true effect size for a study is made up of the average population effect [β0] – which is the thing we usually want to estimate in meta-analysis – and some between-study error [ui] [note that the variance of this between-study error is the heterogeneity of effect sizes across studies, which in traditional meta-analysis is denoted as τ2]” [Field 2015, p. 18].

Writing the model in a single-level notation results in Eq. [3]:

$$ {\gamma}_i={\beta}_0+{u}_i+{e}_i. $$

[3]

In this equation, it becomes evident that the variance of an observed effect size [γi] is decomposed into a sampling variance component [ei] and the between-study error or random effect [ui], as in traditional meta-analysis. However, since ui denotes a study-specific random effect of an ith study, the same random effect can be assigned to effect sizes stemming from the same study while effect sizes stemming from different studies receive different random effects [Konstantopoulos 2011; Viechtbauer 2015]. Consequently, all effect sizes can be taken into account without aggregation and loss of information. The dependence or independence of the effect sizes is explicitly modeled by assigning the correct random effect. Furthermore, a third level may be introduced when estimating an overall effect size composed of effect sizes [first level] nested within different types of effect sizes, that is, dependent variables [second level], which are, in turn, nested within different studies [third level]. The variance would then be decomposed into sampling variance, between-type of effect size variability, and between-study variability. This analysis is necessary for testing whether it makes sense to analyze different types of effect sizes separately [between-type of effect size variability] and whether it makes sense to analyze our moderators at all [between-study variability; Konstantopoulos 2011].

Following these recommendations, all analyses were carried out using the rma.mv[] function of the R metafor package [Viechtbauer 2010]. A maximum likelihood estimator, the typical method to estimate multilevel models, was applied [see Konstantopoulos 2011; van den Noortgate et al. 2014]. Average effect sizes were estimated taking the random-effects perspective, and moderator analyses were performed applying the mixed-effects models [meta-regression]. As the studies showed considerable variance in sample size and some studies produced multiple effect size estimates, effect sizes were weighted by sample size and the number of effect sizes per study. Specifically, effects sizes were weighted by the ratio of their study’s sample size to the number of effect sizes measuring the same dependent variable within the study [Eisend 2009]. As a result, studies reporting only one effect size received the same weight as studies reporting multiple effect sizes if their sample size was equal.

Moderators

The moderators can be grouped as endorser variables, endorsed object variables, and endorsement type of the comparison group. The variables were coded by two independent coders based on the information available in the manuscripts and complemented by the English Wikipedia pages of celebrities where necessary. Agreement was perfect except for endorser match, which yielded an acceptable Krippendorff’s alpha of .74 [Krippendorff 2004]. Discrepancies were resolved by discussion after a review of the article.

Endorser variables

The endorser’s sex was coded as female [0] or male [1], according to the description of the study authors. Typical descriptions were male/female, Mr./Mrs., or he/she. If the manuscript provided no gender information, the endorser’s English Wikipedia page was consulted. The endorser type was coded as actor [0], model [1], athlete [2], musician [3], or TV host [4], according to the description of the authors. The authors typically described their celebrities by using one of the aforementioned professions. If the manuscript provided no related information, the endorser’s English Wikipedia page was consulted. If the Wikipedia page presented various professions, the first was chosen.

The endorser match was coded as incongruent [0] or congruent [1], according to the description of the authors. Frequently used descriptions were not matching/matching, not fitting/fitting, or being incongruent/congruent to an endorsed object. Furthermore, some studies reported pretests explicitly testing the congruence of the endorser and endorsed object. The moderator was then coded accordingly. If the authors used existing advertising, the endorser was coded as congruent because advertisers usually put considerable time and effort into finding a matching endorser [Erdogan 1999]. The endorsement explicitness was coded as implicit [0] or explicit [1] according to the description of the authors. Following *Miller and Allen [2012], the endorsement was coded as implicit when the endorser and the endorsed object appeared merely as paired without the endorser explicitly announcing his or her endorsement [e.g., classical conditioning procedure or ad merely displaying object and celebrity]. An explicit endorsement was coded if the endorser’s support for an object could be explicitly read or heard by the study participants [e.g., “I think XY is …” or “I love XY”]. In addition, signatures were coded as explicit endorsements.

The endorsement frequency was coded continuously starting from one [1] and stretching—theoretically—infinitely, though the maximum number of endorsements was 10.

Endorsed object variables

The familiarity of the endorsed object was coded as unfamiliar [0] or familiar [1] according to the description of the authors. Familiarity refers to whether the endorsed object was known by the participants. Typical descriptions in the articles were unknown/known, fictitious, or having a strong reputation. Furthermore, some studies reported pretests assessing object familiarity. The moderator was then coded accordingly.

Endorsement type of the comparison group

We coded whether the comparison group perceived the object as non-endorsed [0; appearing without any support] or as endorsed by a non-celebrity spokesperson or organization. The endorsed categories were expert [1], an employee of the selling company [2], an ordinary consumer [3], an unknown model or athlete [4], a quality seal or award [5], a government employeeFootnote 1 [6], or an endorser brand from the same product category [7]. The authors typically described the comparison groups by using one of the aforementioned category names.

Results

Overall analysis

Testing first whether it makes sense to analyze different types of effect sizes separately and whether it makes sense to analyze the moderators at all, we calculated the three-level model [Konstantopoulos 2011]. Effect sizes [first level] were nested within different types of effect sizes [second level], which were, in turn, nested within different studies [third level]. We observed no significant overall effect of the celebrity endorsements on participants’ responses [d = .04, 95% CI [−.09, .17], ns]. However, highly significant heterogeneity was found among effect sizes [Q [366] = 1095.77, p