Consumers’ Identification
and Beyond: Attraction,
Reverence, and Escapism
in the Evaluation of Films
Michela Addis
University of Rome 3, Italy, and SDA Bocconi School of Management
Morris B. Holbrook
Columbia University
ABSTRACT
Secondary real-world data on evaluations by the general public of
the 440 movies ever nominated for the Best Picture Academy Award
are used to explore the role of female/male consumers’ identification
with the leading actress/actor in determining judgments of motion
picture excellence. Beyond identification, age- and gender-based similarities with other movie components—namely, the leading oppositegender star, the director, and the setting—underlie other potential
psychological mechanisms relevant to explaining the evaluations of
films. Contrary to expectations, the findings indicate that identification with the same-gender, same-age leading star plays no significant role. Conversely, younger opposite-gender leading stars, older
directors, and unfamiliar temporal settings contribute to favorable
evaluations—thereby supporting the hypotheses of romantic attraction as a source of star power; reverence toward more mature directors; and an eagerness to escape from ordinary life, respectively.
© 2010 Wiley Periodicals, Inc.
Movie celebrities are generally considered one of the most relevant antecedents of
motion picture success. Actors and actresses as well as directors strongly contribute
Psychology & Marketing, Vol. 27(9): 821–845 (September 2010)
View this article online at wileyonlinelibrary.com
© 2010 Wiley Periodicals, Inc. DOI: 10.1002/mar.20359
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to the success of films, both at the box office and in video rentals (Elberse, 2007;
Elberse & Eliashberg, 2003; Ravid, 1999; Simonton, 2009; Wallace, Seigerman, &
Holbrook, 1993). Indeed, “big name” participants attract huge audiences by virtue
of their roles in signaling movie quality to the market (Albert, 1998; HennigThurau, Houston, & Walsh, 2006; Hennig-Thurau, Walsh, & Wruck, 2001). Hence,
movie stars encourage a special kind of “human branding” (Thomson, 2006), able to
attenuate the consequences of negative evaluations by film critics (Levin, Levin, &
Heath, 1997). Their appeal to large audiences is rewarded by privileged cachet in
the motion picture industry as well as in the advertising industry, where companies spend billions of dollars on celebrity endorsements (Lennon, 2006). In such cases,
the audience’s familiarity with the celebrities increases their advertising efficacy
(Agrawal & Kamakura, 1995).
At the basis of a star’s appeal is the emotional bond—varying in intensity—that
the consumer forms with a given celebrity (Escamilla, Cradock, & Kawachi, 2000;
Johnson, 2005; Levy, 1989; Till, Stanley, & Priluck, 2008). Such attachments inspire
the “individual’s pattern of thought and behavior” (Cressey, 1938, p. 517), and
in the strongest cases they can encourage people to participate imaginatively in
a movie and to imitate a celebrity’s behavior in their real lives. In general, personal
values might be shaped by those of the protagonists in films (Beckwith, 2009).
For example, adolescents tend to adopt the smoking habits of their favorite movie
stars (Distefan, Pierce, & Gilpin, 2004; Pechmann & Shih, 1999).
This kind of emulation is generally regarded as evidence for an identification
theory, in which the perceived similarities between consumers’ self-concepts
and various stars affects audience preferences (Gutman, 1973; Johnson, 2005).
Indeed, similarities between consumers’ self-concepts and brands can refer to
a multiplicity of drivers (Friedmann, 1986; Onkvisit & Shaw, 1987), as a very general concept. But, in the motion-picture industry, the degree of match between
the cinematic offering and a consumer’s self-concept may depend only partly
on the stars who appear on screen. Thus, motion pictures—like advertisements
(Wang & Mowen, 1997)—embed many personally significant aspects of potential similarity. Specifically—though, with films, identification has been investigated only with regard to the presence of leading stars (Beckwith, 2009;
Shachar & Emerson, 2000; Williams & Qualls, 1989)—the range of variables
on which the degree of self-match depends has been extended, in the area of
advertising, to include the celebrities’ appearances (Bjerke & Polegato, 2006); the
consumers’ personality traits such as their levels of introversion/extroversion
(Hong & Zinkhan, 1995), femininity/masculinity (Chang, 2005a), or individualist/
collectivist values (Chang, 2005b); and other variables related to the situation
portrayed in the ad (Greeno & Sommers, 1977; Onkvisit & Shaw, 1987). In short,
due to the high complexity and hedonic value of motion pictures, interactions
between viewers and films emerge as a relevant but inadequately explored concept (Eliashberg & Sawhney, 1994; Holbrook & Schindler, 1994). A deeper investigation of several film components beyond the same-gender leading star could
reveal a complex set of psychological mechanisms driving consumers’ evaluations,
over and above the traditional role of identification.
Obviously, because the motion picture experience entails an essential unity
(Cressey, 1938), the present study does not aim at identifying the net contribution of each mechanism in shaping individuals’ attitudes toward films. Instead,
it adopts a more general perspective on the psychological mechanisms of identification by taking into account not only the same-gender star, but also the
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opposite-gender star, the director, and the setting. Indeed, these drivers are here
analyzed together, as when watching a motion picture, consistent with findings
that motion picture success is driven not by a single star but, rather, by the
whole team of cinematic participants (Meiseberg, Ehrmann, & Dormann, 2008).
Further, a new dependent variable is adopted (continuous measures of consumers’ attitudes toward the cultural offerings of interest), rather than focusing only on a less sensitive behavioral variable (dichotomous choices to watch
a film or not). Indeed, consistent with the objective of looking at differences in
consumers’ evaluations of motion pictures and their antecedents (Krider, 2006),
audience ratings appear to offer one of the most promising but still understudied variables of interest (Chang & Ki, 2005; Plucker et al., 2009).
Motion pictures serve as the key focus in light of four major considerations.
First, rich data for the motion picture industry are available, allowing the examination of the relevant issues in the context of non-experimental, real-world
observations. Further, analyzing secondary data that are publicly available but
neglected may lead to new knowledge, even if many data limitations are inherent (as discussed later). Second, several studies have already focused on ratings, purchases, and consumption of movies, developing useful background
knowledge. Third, in the film industry, consumers engage in intense consumption experiences in ways that entail strongly cathected cognitive and emotional
responses (Cohen, 2001). Hence, a deeper knowledge of these processes—
especially those that involve the consumer’s identification—should contribute
to the refinement of marketing strategies for new offerings. Fourth and finally,
because motion pictures are long recognized as an influential and effective
medium of information (Luchins & Luchins, 1984), understanding their attendees’ sensitivities appears to be a crucial goal for any society.
In this spirit, the present study analyzes real-world data on several different
motion picture components that together create the overall audience responses
that—by virtue of their favorability or unfavorability—spell cinematic success
or failure (Elberse, 2007; Eliashberg & Sawhney, 1994; Ferguson, 2009;
Holbrook & Schindler, 1994). Toward this end, IMDb.com provides public, broad,
and rich data useful in investigating some of the psychological mechanisms that
influence movie evaluations by consumers.
Accordingly, the present study examines the matches between two commonly studied facets of consumers’ selves—namely, age and gender—and those
related to the various film components to explain their effects on the consumers’ evaluative judgments of motion pictures. As further discussed in what
follows, ideally, other more psychological matches should be included in the
analysis. But, realistically, limitations in the available data restrict the analysis to the more traditional demographic matches commonly adopted in motion
picture research.
In accord with these various objectives, the present study pursues the following structure. First, four hypotheses are developed to explore the role played
by four different drivers—same-gender star, opposite-gender star, director, and
setting—in influencing movie evaluations in accord with the principles of four
psychological mechanisms: identification, romantic attraction, reverence toward
directors, and escapism, respectively. Next, the empirical analysis addresses
these hypotheses. Finally, relevant conclusions are drawn concerning
the research findings, managerial implications, limitations, and directions for
future research.
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THE PSYCHOLOGICAL MECHANISMS AT THE BASIS OF
FILM EVALUATIONS
Motion pictures have long been studied as a source of imaginative and emotional states that derive from their ability to influence imitation, thinking, and
daydreaming (Cressey, 1938; Green & Brock, 2000). These processes, like other consumer attitudes or behaviors, involve people’s self-concepts (Grubb & Grathwohl,
1967; Reed, 2002). Specifically, consumer research has long recognized that individuals tend to buy products and brands consistent with their self-perceptions
(Grubb & Hupp, 1968; Folkes & Kiesler, 1991; Grubb & Stern, 1971; Reingen
et al., 1984; Ross, 1971; Sirgy, 1982). Such a link has already been found with
regard to television-viewing choices, which are also driven by identification with
the main characters (Shachar & Emerson, 2000). Similarly, psychological
researchers agree that narratives—including movies—are more persuasive when
audience members identify with characters (Green, 2007; Green, Brock, & Kaufman,
2004). Identification—that is, “the most powerful form of connection with characters” (Green, 2007, p. 101)—results from individuals’ transportation into
the narrative worlds in ways that emerge when they are engaged cognitively and
emotionally so as to get lost in the narratives (Green, 2005; Green & Brock,
2000). Even if identification is not entirely known or measured, similarity and
homogeneity are relevant preconditions (Bandura, 1986; Dal Cin et al., 2007;
Green, 2004).
In line with previous research, the present study assumes that identification
might contribute significantly to audience evaluations of motion pictures. But,
unlike earlier work (Shachar & Emerson, 2000; Slater & Rouner, 2002), the
analysis is not limited to the degree of similarity with the protagonists. Rather,
the perspective is expanded by investigating four cinematic informational cues—
leading actor, leading actress, director, and setting—so as to capture their impacts
on viewers’ evaluations. In this way, understanding the psychological processes
that drive consumers’ evaluations of films should provide researchers and managers with useful insights.
The Identification Mechanism
Despite the wide range of possible drivers of fit between a film and the consumer’s self, this phenomenon has usually been investigated by means of movie
stars as the main vehicle of identification. The latter has been defined as the psychological process through which an individual “consciously or unconsciously
recognizes him/herself in, or wishes to be, another individual so that he/she
becomes involved in that individual and vicariously participates in his/her activities, feelings, and thoughts” (Feilitzen & Linné, 1975, p. 52), losing his/her selfawareness (Cohen, 2001), and sharing his/her existence (Green, Brock, &
Kaufman, 2004). This construct plays a relevant role in explaining individuals’
social relationships (Brewer & Gardner, 1996; Byrne, 1961; Byrne & Griffit,
1973; Hogg & Abrams, 1988; McPherson, Smith-Lovin, & Cook, 2001; Perkins,
Thomas, & Taylor, 2000; Reed, 2002; Snyder & Cantor, 1998; Tajfel, 1981, 1982)
insofar as the perceived similarity increases interpersonal attraction (Tesser,
1971) via the audience member’s favorable attitude toward some character or
celebrity (Hirschman, 1983). This tendency appears especially strong for those
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viewers who seek self-development in motion pictures or who interpret films
as a source of models for their emotions (Tesser, Millar, & Wu, 1988).
In order to identify with others, individuals should perceive their counterparts as similar to themselves with regard to attributes such as beliefs, values,
status, age, occupation, gender, race, or education (Lazarsfeld & Merton, 1954).
Specifically, homophily refers to both (1) the objectively observable level of similarity and (2) the subjectively experienced similarity between the members of
a dyad (Rogers & Bhowmik, 1971). While the former indicates matches or mismatches on specific variables measured for both individuals, the latter reflects
the degree to which they perceive themselves as similar. In both cases, homophily
involves the sharing of a common code, where that code fosters an effective flow
of communication (Gilly et al., 1998; Price & Feick, 1984; Johnson Brown &
Reingen, 1987; Prisbell & Andersen, 1980). All this therefore suggests that consumers’ resemblances to movie stars in terms of gender and age produce strong
emotional ties to the relevant media characters and drive consumers’ evaluations
of films (Cohen, 2001; Eyal & Rubin, 2003; Hoffner & Cantor, 1991; Schneider
et al., 2004; Shachar & Emerson, 2000; Turner, 1993).
H1:
The Identification Mechanism: Consumers favor motion pictures in which
same-gender stars closely match their own age characteristics.
Romantic Attraction
Though the identification mechanism provides the most often explored explanation for consumers’ evaluations of media characters and famous stars, it is not
the only psychological mechanism deserving consideration. In their analysis of
age, gender, and attitude toward the past as predictors of consumers’ preferences toward photographic representations of movie stars, Holbrook and
Schindler (1994) proposed an alternative perspective when they unexpectedly
found that consumers tend to orient their preferences toward opposite-gender
stars—female respondents toward male stars and male respondents toward
female stars. They conclude that stars are vehicles for a sex-based emotional load
in which opposites attract. Similar findings in favor of heterophily have been
reported by Feilitzen and Linné (1975), who conclude that the general rule of
similarity-based identification has some exceptions—as when girls identify with
male characters. Further, the source-attractiveness model has long recognized
the celebrities’ attractiveness, along with similarity and familiarity, as one driver
of communication effectiveness (McGuire, 1985).
Building on these results, one could propose that star power emerges also
with regard to the opposite-gender audience. That is, for the majority of heterosexual viewers, stars may attract opposite-gender individuals because of an
emotional romantic appeal that depends on their physical appearance.
Further—because perceived beauty is negatively correlated with age when the
judge and target are of opposite genders (Henss, 1991), leading to the belief that
“what is beautiful and younger is better” (Perlini, Bertolissi, & Lind, 1999,
p. 352)—one would expect consumers to favor younger opposite-gender stars.
H2:
Romantic Attraction: Consumers favor motion pictures that feature
younger opposite-gender stars.
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Reverence Toward Directors’ Expertise
Though usually neglected in the stream of research on films, an additional psychological driver of audience evaluations might involve the role of directors.
Indeed, advertising research generally accepts that the degree of fit between
the ad and the self can refer to any feature(s) of the consumer’s self-concept
evoked by the message (Bjerke & Polegato, 2006; Chang, 2005b; Hong & Zinkhan,
1995). Because self-schema sets are “a collection of related verbal, symbolic, and
visual schemas” that are “relevant to the self” (Brock, Brannon, & Bridgwater,
1990, p. 287), any contents of the message can match the self, stimulate viewers’ memories about their personal lives, and create connections with their direct
personal experiences (Baumgartner, Sujan, & Bettman, 1992; Krugman, 1967).
Similarly to ads, motion pictures potentially offer an abundance of possible congruency dimensions, which are deeply influenced by the director who sets the
particular perspective that guides the offering seen by viewers. Hence, the director’s approach to portraying cinematic situations is fundamental to shaping
consumers’ evaluations of a film (Cohen, 2001), even if consumers are not consciously aware of this directorial role.
Further, by extrapolating the literature on leadership and regarding directors
as the leaders of motion picture creation, their age and experience matters
greatly in coordinating the components of a film in ways that work together.
Psychological studies agree that group members are more willing to follow individuals who are male—generally considered to be more powerful, more active,
more instrumental, and more insightful (Morrison, Greene, & Tischler, 1985)—
and who have previously demonstrated their task abilities (Hollander, 1985).
Specifically, a leader’s level of expertise in specialized aspects of the arts and other
fields is positively correlated with age (Caldwell & Wellman, 1926; Van Vugt,
2006). Film direction appears to be no exception to this rule. Consequently, consumers should appreciate and respect the cinematic leadership of experienced
directors, even if they are not aware of the directors’ ages. In contrast to actors
and actresses, directors are usually concealed from viewers’ eyes, but their influence still appears indirectly via their choices of acting styles, shooting techniques, mise-en-scène, and so forth.
H3:
Reverence Toward Directors’ Expertise: Consumers favor motion pictures
made by older directors for whose styles they feel reverence.
Eagerness to Escape Everyday Reality
Finally, the consumer’s favorability toward a motion picture should reflect its temporal setting. According to Hirschman (1983), consumers may undertake hedonic activities in order to escape from their everyday reality. Indeed, escapism is
considered one prominent explanation of television exposure (Kubey, 1986) and
movie attendance (Katz, Haas, & Gurevitch, 1973), leading audience members to
appreciate unrealistic films (Tesser, Millar, & Wu, 1988). Alienating experiences—
such as those encountered at work or other unpleasant life experiences—put people in need of escaping. Thanks to involvement in engaging activities, individuals
escape by becoming completely absorbed and transported as they enter into a state
of full psychological immersion (Green, 2004; Mathwick, Malhotra, & Rigdon,
2001; Mathwick & Rigdon, 2004). Elaborating on such results and assuming that
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movies take place in specific time-related settings (Pollio et al., 2003), viewers
show disfavor toward motion pictures that remind them of ordinary everyday
experiences. Rather, they prefer to watch stars acting in unknown environments
so that unfamiliarity with the setting serves as a proxy for escapism.
H4:
Eagerness to Escape Everyday Reality: Consumers favor motion pictures
set in unfamiliar times and via which they can escape from their everyday routines.
METHOD
Sample
To test the aforementioned hypotheses, the empirical study relies on secondary
sources that provide a large real-world database suitable for cross-sectional
analysis across films. In order to control the intrinsic quality of the motion pictures included, the Academy Award nominations for Best Picture—generally
regarded as reflecting the industry’s quality standards for any given year—
served to identify a sample of Oscar-worthy films for investigation. Any other
variable to select the sample—such as (say) box-office performance or video
rentals—could be misleading insofar as commercial appeal and artistic excellence are uncorrelated aspects of motion picture success (Holbrook & Addis,
2008). Hence, to control for quality, the sample includes all movies ever nominated for the Best Picture award—that is, 440 movies nominated from 1927/28
(the first year of the Oscars) to 2003 (the 76th annual ceremony). During the earliest years, ten films were nominated; after that, five movies per year are usually listed in the official documents.
With this sample of films, the methodological barriers to addressing the
hypotheses posed earlier are quite challenging. Unfortunately, it is not possible
simply to expose thousands of audience members to the 440 motion pictures of
interest—requiring millions of hours of viewer time—in order to collect evaluations from an adequately large group of individuals with different genders and ages.
Nonetheless, proxy measures at the subaggregate or segment-specific level are
available from the Internet Movie Database at www.imdb.com. Even if such
data do not allow specific interpretations, they have two important benefits:
(1) They provide a large sample of films; and (2) they are public and easily available. Indeed, this source has frequently been used in previous motion picture
studies and represents a demonstrably valid proxy for similar ratings in an offline world—as demonstrated by Dellarocas, Awad, and Zhang (2004), who found
a strongly supportive correlation of r ⫽ 0.84 between the IMDb ratings and ratings by a nationally representative sample of 1970 respondents. However, despite
such reassurances, subaggregate or segment-specific IMDb data have not previously been used in a manner comparable to the present study.
Specifically, on the IMDb Web site, registered audience members express
their evaluations of films on a 10-point scale ranging from 1 (“awful”) to 10
(“excellent”). In general, the titles in the sample have received a favorable average rating of 7.54 from consumers, with the range delimited by 5.5 for Alibi
(1929) and 9.1 for The Lord of the Rings: The Return of the King (2003). At the
same time, each movie has been evaluated by an average of 11,475 viewers,
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Table 1. Number of Titles Evaluated by Each Age–Gender Group.
Age–Gender Group
Males under 17 years old
Females under 17 years old
Males between 18 and 29 years old
Females between 18 and 29 years old
Males between 30 and 44 years old
Females between 30 and 44 years old
Males over 45 years old
Females over 45 years old
Number of Evaluated Films
408
349
438
431
437
427
437
435
with the minimum being The Patriot (1928, 5 consumers) and the maximum
being The Lord of the Rings: The Return of the King (2003, 137,506 votes). The
professional version of IMDb.com provides extra information on the birthdays
of actors, actresses, and directors as well as on the breakdown of consumer evaluations by age and gender. Thus, the movie ratings represent evaluations by
eight different groups of people—namely, two genders (men and women) in four
age groups (under 17, between 18 and 29, between 30 and 44, and over 45 years
old). Ideally, this approach would produce 3520 mean evaluations (440 movies
by 8 groups of people, averaged across group members). But some groups did not
evaluate every film in the set. Hence, the analysis is based on a slightly reduced
sample of 3,362 group-level evaluations. Overall, the number of responses
includes 3,356,647 consumer evaluations of the Oscar-nominated motion pictures (2,821,754 by men, 534,893 by women). Table 1 presents the number of titles
evaluated by each age–gender group.
Variables
Dependent Variable: Standardized Ratings. To remove any scaleresponse biases in the ratings by the eight different age-and-gender groups,
these ratings were standardized across all movies for each group separately
(M ⫽ 0.0, SD ⫽ 1.0).
Control Variables and Independent Variables. Table 2 presents
descriptive statistics for the non-dummy control variables discussed here. Further, the relevant information about each movie on several control variables
and independent variables can be grouped into three categories.
(1) Control Variables: Movie Features. Many variables besides gender and
age are likely to affect evaluative ratings (Cohen, 2001; Holbrook, 1999), representing possible sources of bias that should be controlled for. Hence, the first
group of control variables includes any traditional feature of a film that relates
either to its general aspects or to its time dimensions, as follows.
Genres. Each movie is represented by 18 zero-one dummy variables indicating its inclusion in any of 18 IMDb genre categories—Action, Adventure,
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Table 2. Descriptive Statistics for the Non-Dummy Control Variables (N ⴝ 3520).
Variable
Mean
Extra awards
0.0000000
Length
124.53
Estimated budget 25,490,761.9039
Age of the movie
37.7160
Main actor’s age
40.1727
Main actress’s age
32.6874
Director’s age
45.6932
SD
Minimum
22.97858103 ⫺85.37190
27.983
60
27,626,045.61114 313,649.03
22.15868
0.07
9.78018
9.00
9.73418
10.00
8.71820
26.00
Maximum
142.67028
238
264,440,000.00
72.02
76.00
80.00
79.00
Animation, Comedy, Crime, Drama, Family, Fantasy, Film Noir, Horror, Music,
Musical, Mystery, Romance, Science Fiction, Thriller, War, and Western.
Length. The duration of each film is measured in minutes of running
time.
Color. A zero-one dummy variable indicates black-and-white (0) versus
color (1).
Voice. The age of some movies also required a dummy variable coded 0
for silent films and 1 for “talkies” that feature spoken dialogue.
Setting. Another dummy variable is coded zero-one to capture the
absence/presence of a specific setting.
Winner. Another dummy variable equals 1 if the motion picture received
the Academy Award for the Best Picture, 0 otherwise. (Recall that all films
in the sample were nominated for the Best Picture award.)
Extra Awards. Because the number of possible awards for a movie has
increased over time as the Oscar ceremony has become more and more
elaborate, the number of awards that were given to the movies in the
database has been summed for each year and used to adjust the data by
regressing the number of awards for each movie on the overall number of
awards that year and viewing the residuals of this regression as the “extra”
awards that each movie received (above or below what would have been
expected based on the total number of awards obtained by all movies during that year).
Estimated Budget. The production budget is known for only 233 of the
440 films (expressed in the money value of the specific year of release). In
order to work with these data, the budgets of foreign movies were first
transformed into equivalent American dollars. Second, these figures were
adjusted for inflation (using the EH.net Web site to convert all budgets to
2003 dollars). Third, budgets for the remaining 207 movies for which this
piece of information was missing were estimated by using the best possible set of predictors among the features of the movie (length, setting, voice,
color, genre, age of the movie, and the logarithm of the age of the setting).
A stepwise regression produced a final model with R2 ⫽ 0.604 (p ⬍ 0.001).
Because the distribution for this variable was strongly skewed—with very
small numbers of very high values—natural logarithms were computed to
obtain LnEstBudget. Further, it is likely that this variable has a potential non-monotonic effect on the DV, insofar as consumers do not favor
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films reflecting very low or very high budgets. To capture this potential
effect, LnEstBudget was first standardized and then squared, with both
the linear and quadratic terms included in the model.
(2) Control Variables: Time-Related Dimensions of the Movie. The time-related
aspects of motion pictures include a number of variables, also available from
IMDb, for which it is also necessary to control in order to isolate the effects of
the key matching factors.
Age of the Movie. This variable answers the question, “How old was the
movie when it was rated?” and captures any consumer attitudes toward
films released during a specific time period, such as the “foof” (fan of old
films) factor. It is computed as the difference between the year of rating
and the year of release. While the year of release is easily determined,
precise information on the year of rating is not provided by IMDb and,
therefore, required estimation. Toward this end, the proportion between
the number of people that rated each movie on two different days a month
apart was computed, and this proportion was used to estimate the rating
year, on the assumption that movies with higher/lower increases in the
number of evaluations in one month are the more/less recently evaluated
movies. To check for a potential non-monotonic effect of the age of the
movie on the DV, the standardized original variable and its quadratic
term were both included in the analysis.
Main Actor’s Age at Release. As an indication of the main actor starring in each motion picture, Maltin (2001) lists the cast members according to their relevance in the film. IMDb.com provides each male star’s
date of birth. Subtracting his birth date from the film’s release date gives
his age when he starred in that film. In order to test for a potential inverted
U-shaped relationship, the standardized value of this variable and its
squared standardized value were both included. Since it can be assumed
that the main actor’s expertise increases with age, controlling for the latter indirectly captures the main actor’s expertise, in the absence of other
more direct measures.
Main Actress’s Age at Release. A similar procedure produced the standardized age and age-squared for the main actress in each film.
Director’s Age at Release. Again, the same procedure produced the
standardized age and age-squared for the director of each film at the time
of its release. Controlling for this variable also provides an indirect control for the director’s expertise, which could lead to a higher-quality film.
Consequently, any other effects captured by the match between viewer
and director (but also main actor and actress) are not confounded with
the audience’s appreciation of expertise.
(3) Independent Variables: Self-Related Variables. The present study focuses
on similarities/differences in age- and/or gender-group membership (after categorizing the relevant celebrities into the same age-based customer-classification
scheme described earlier). Expanding upon Gilly et al. (1998) and Shachar and
Emerson (2000), relationships between the DV and various IVs based on the
comparative ages and genders of viewers and movie celebrities (actors, actresses,
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and directors) are examined, as well as the familiarity of the temporal setting for
each film. However, unlike earlier research, the present study focuses not on the
age differences as absolute values but, rather, on the signed values of these differences, which capture whether the relevant celebrity is in the same age category, is younger, or is older. Hence, for each celebrity, two dummy variables have
been created (with the third redundant dummy variable omitted), as follows.
Same-Gender Star. The first two zero-one dummies represent the samegendered stars for the various motion pictures in the data set: (1) SameGender Same-Age Star captures similarity in both gender and age (1 if the
on-screen same-gender star and the consumer are in the same age category, 0 otherwise); and (2) Same-Gender Younger Star captures similarity
in gender but a difference in age (1 if the on-screen same-gender star is
younger than the consumer, 0 otherwise). Recall that, according to H1,
Same-Gender Same-Age Star is expected to have a positive significant
effect on consumers’ evaluations. Indeed, according to identification theory, the age match between the star on the screen and the viewer is the
focus of analysis, regardless how old the star might be in real life at
the time of the film’s evaluation.
Opposite-Gender Star. The procedure applied for the same-gender star
has also been followed for the opposite-gender star to create two zero-one
dummy variables: (1) Opposite-Gender Same-Age Star (1 if the oppositegender star and consumer are in the same age category, 0 otherwise) and
(2) Opposite-Gender Younger Star (1 if the opposite-gender star is younger
than the consumer, 0 otherwise). According to H2, due to romantic attraction, Opposite-Gender Younger Star is expected to have a positive significant effect on consumers’ evaluations.
Age-Match Director. The age match between consumer and director
generates two more dummies similar to those just described except that,
because H3 proposes a feeling of reverence toward older directors, the
analysis focuses on (1) Same-Age Director (1 if the director and consumer
belong to the same age category, 0 otherwise) and (2) Older Director (1 if
the director is older than the consumer, 0 otherwise). According to H3,
due to reverence, Older Director is expected to have a positive significant
effect on consumers’ evaluations.
Experienced Setting. To compute a measure for the experienced setting, the epoch in which a movie is set, as given by IMDb and other sources
(Maltin, 2001) represents the starting point. Experienced Setting operationalizes the familiarity of the setting and equals 0 if consumers were not
alive (2,654 cases, 2,297,290 votes) or 1 if they were alive (708 cases,
1,059,357 votes) at the time of the film’s setting. (For the eight movies
whose settings are non-specific or absent, Experienced Setting is coded
as equal to 0.) As stated by H4, due to escapism, Experienced Setting is
expected to have a significant negative effect on consumers’ evaluations.
Analyses
Because each observation represents the (standardized) evaluation of a movie by
a specific group of people (defined by gender and age), the fact that each rating
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is based on a group of different size must be taken into account. Accordingly,
weighted ordinary least squares (WOLS) has been adopted, with the numbers of
votes for each movie serving as the frequency weights. With the available data,
this approach is analogous to running an unweighted regression on a similar
data set that has the correct number of replications for each observation
(Winship & Radbill, 1994). In sum, WOLS takes into account the different levels of interest that each motion picture generates to stimulate Web users to give
their evaluations.
RESULTS
Regressing consumers’ standardized evaluative ratings on the whole set of control variables and independent variables just described produced a strong degree
of explained variance [R2 ⫽ 0.485, F(41,3320) ⫽ 76.294, p ⬍ 0.001]. The relevant
standardized beta coefficients, t-values, p-values, and VIFs appear in Table 3.
Movie Features
As shown in Table 3, consumers respond favorably to five genres—namely, action
(bAction ⫽ 0.045, t ⫽ 2.122, p ⫽ 0.03); crime (bCrime ⫽ 0.132, t ⫽ 8.168, p ⬍ 0.001);
family-oriented genre (bFamily ⫽ 0.039, t ⫽ 2.035, p ⫽ 0.04); music (bMusic ⫽ 0.056,
t ⫽ 4.031, p ⬍ 0.001); and war (bWar ⫽ 0.050, t ⫽ 3.108, p ⫽ 0.002). Consumers
also strongly favor motion pictures that received attention in the form of extra
awards from industry members (bExtraAwards ⫽ 0.136, t ⫽ 8.424, p ⬍ 0.001) and,
specifically, films that actually won the Academy Award for Best Picture
(bWinner ⫽ 0.102, t ⫽ 6.445, p ⬍ 0.001). Further, consumers also react positively
to longer films (bLength ⫽ 0.167, t ⫽ 7.000, p ⬍ 0.001) and to the (estimated) production budget according to an inverted U-shaped effect (bZLnEstBudget ⫽ 0.027,
t ⫽ 1.168, n.s.; bZLnEstBudgetSquared ⫽ ⫺0.265, t ⫽ ⫺14.803, p ⬍ 0.001). Differentiating the favorability equation with respect to ZLnEstBudget, setting the derivative equal to zero, and solving for its optimal value shows that the peak response
occurs at an estimated production budget of $16,251,325 in 2003 dollars—for
example, Jezebel (1938) and A Place in the Sun (1951).
Conversely, consumers’ evaluations respond negatively to ten genres—namely,
comedy (bComedy ⫽ ⫺0.040, t ⫽ ⫺2.577, p ⫽ 0.01); drama (bDrama ⫽ ⫺0.134,
t ⫽ ⫺6.440, p ⬍ 0.001); fantasy (bFantasy ⫽ ⫺0.088, t ⫽ ⫺2.999, p ⫽ 0.003);
horror (bHorror ⫽ ⫺0.037, t ⫽ ⫺2.306, p ⫽ 0.02); musical (bMusical ⫽ ⫺0.116,
t ⫽ ⫺7.046, p ⬍ 0.001); mystery (bMystery ⫽ ⫺0.032, t ⫽ ⫺2.017, p ⫽ 0.04); romance
(bRomance ⫽ ⫺0.166, t ⫽ ⫺10.776, p ⬍ 0.001); science fiction (bScienceFiction ⫽ ⫺0.057,
t ⫽ ⫺3.222, p ⬍ 0.001); thriller (bThriller ⫽ ⫺0.044, t ⫽ ⫺2.511, p ⫽ 0.012); and
western (bWestern ⫽ ⫺0.078, t ⫽ ⫺5.771, p ⬍ 0.001). Besides genres, consumers
respond negatively to color cinematography (bColor ⫽ ⫺0.201, t ⫽ ⫺8.076,
p ⬍ 0.001) and to motion pictures provided with a temporal setting in a specific
time period (bSetting ⫽ ⫺0.339, t ⫽ ⫺10.695, p ⬍ 0.001).
Time-Related Dimensions of the Movie
Among the time-related movie dimensions, the age of the film itself exerts no significant effect (bZAgeMovie ⫽ ⫺0.039, t ⫽ ⫺1.060, NS; bZAgeMovieSquared ⫽ ⫺0.051,
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Table 3. WOLS Regressions of Standardized Evaluations on the Control Variables
and Independent Variables (Standardized Beta Coefficients, t-Values,
p-Values, and VIFs).
Independent Variables
Movie Features
Genres
Action
Adventure
Animation
Comedy
Crime
Drama
Family
Fantasy
Film noir
Horror
Music
Musical
Mystery
Romance
Science fiction
Thriller
War
Western
Length
Color
Voice
Setting
Winner
Extra awards
LnEstimatedBudget
Z scores
Squared Z scores
Standardized
Beta Coefficients
t-Values
p-Values
VIFs
0.045
0.015
⫺0.007
⫺0.040
0.132
⫺0.134
0.039
⫺0.088
⫺0.020
⫺0.037
0.056
⫺0.116
⫺0.032
⫺0.166
⫺0.057
⫺0.044
0.050
⫺0.078
0.167
⫺0.201
0.012
⫺0.339
0.102
0.136
2.122
0.672
⫺0.440
⫺2.577
8.168
⫺6.440
2.035
⫺2.999
⫺1.455
⫺2.306
4.031
⫺7.046
⫺2.017
⫺10.776
⫺3.222
⫺2.511
3.108
⫺5.771
7.000
⫺8.076
0.956
⫺10.695
6.445
8.424
0.034
n.s.
n.s.
0.010
0.000
0.000
0.042
0.003
n.s.
0.021
0.000
0.000
0.044
0.000
0.001
0.012
0.002
0.000
0.000
0.000
n.s.
0.000
0.000
0.000
2.924
3.374
1.448
1.546
1.675
2.802
2.413
5.580
1.165
1.647
1.256
1.762
1.640
1.538
2.020
2.008
1.685
1.187
3.671
3.993
1.015
6.463
1.624
1.692
0.027
⫺0.265
1.168
⫺14.803
n.s.
0.000
3.455
2.061
⫺1.060
⫺1.821
n.s.
n.s.
8.637
4.971
6.291
⫺4.222
0.000
0.000
2.220
1.733
4.948
⫺7.911
0.000
0.000
2.270
1.473
⫺14.718
9.246
0.000
0.000
2.328
1.361
⫺0.254
1.555
n.s.
n.s.
1.806
3.017
Time-Related Dimensions of the Movie
Age of the movie:
Z scores
⫺0.039
Squared Z scores
⫺0.051
Main actor’s age at release:
Z scores
0.117
Squared Z scores
⫺0.069
Main actress’s age at release:
Z scores
0.093
Squared Z scores
⫺0.120
Director’s age at release:
Z scores
⫺0.280
Squared Z scores
0.134
Self-Related Variables
Same-gender same-age star
Same-gender younger star
⫺0.280
0.134
(Continued)
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Table 3. (Continued)
Independent Variables
Opp-gender same-age star
Opp-gender younger star
Same-age director
Older director
Experienced setting
Standardized
Beta Coefficients
t-Values
p-Values
VIFs
0.069
0.106
0.065
0.084
⫺0.117
4.155
4.616
2.441
2.199
⫺6.694
0.000
0.000
0.015
0.028
0.000
1.799
3.396
4.547
9.414
1.986
F(41,3320) ⫽ 76.294
F
R-squared
0.000
0.485
Note: NS ⫽ not significant.
t ⫽ ⫺1.821, n.s.), indicating consumers’ apparent indifference toward this variable. Instead, consumers appear quite sensitive to the age of the main stars on
the screen (bZMainActorAge ⫽ 0.117, t ⫽ 6.291, p ⬍ 0.001; bZMainActorAgeSquared ⫽ ⫺0.069,
t ⫽ ⫺4.222, p ⬍ 0.001; bZMainActressAge ⫽ 0.093, t ⫽ 4.948, p ⬍ 0.001;
bZMainActressAgeSquared ⫽ ⫺0.120, t ⫽ ⫺7.911; p ⬍ 0.001). The relative peaks in favorability occur at on-screen ages of 52.66 years for the main actor and 40.36 years
for the main actress, indicating a general preference for middle-aged male and
mature female stars.
Further, a U-shaped relationship links the Director’s Age at Release to consumer
evaluations (bZDirectorAge ⫽ ⫺0.280, t ⫽ ⫺14.718, p ⬍ 0.001; bZDirectorAgeSquared ⫽ 0.134,
t ⫽ 9.246, p ⬍ 0.001), with minimum favorability for movies made by 59.9-yearold directors. Consequently, it appears that consumers are attracted either by
more youthful or by more venerable cinematic styles and points of view.
Hypothesis Tests
The self-related variables specifically test the four key hypotheses developed earlier. H1 proposes that Same-Gender Same-Age Star has a significantly positive beta.
However, neither Same-Gender Same-Age Star (bSame-GenderSame-AgeStar ⫽ ⫺0.004,
t ⫽ ⫺0.254, n.s.) nor Same-Gender Younger Star (bSameGenderYoungerStar ⫽ 0.034,
t ⫽ 1.555, n.s.) plays a significant role in driving consumers’ evaluations. The
identification mechanism based on age and gender similarities is thereby disconfirmed.
Instead, the results do support the remaining three hypotheses. With regard
to H2, the data show a significant positive effect of Opposite-Gender Younger Star
(bOppGenderYoungerStar ⫽ 0.106, t ⫽ 4.616, p ⬍ 0.001), but also a weaker significant
positive effect of Opposite-Gender Same-Age Star (bOppGenderSameAgeStar ⫽ 0.069,
t ⫽ 4.155, p ⬍ 0.001), indicating a romantic attraction to opposite-gender stars
of younger or similar (rather than older) ages.
H3 also finds support in the analysis, with significantly positive betas for
Same-Age Director (bSame-AgeDirector ⫽ 0.065, t ⫽ 2.441, p ⫽ 0.02) and Older Director (bOlderDirector ⫽ 0.084, t ⫽ 2.199, p ⫽ 0.03).
Finally, in support of H4 regarding the role of escapism, Experienced Setting
shows the expected negative beta (bExperiencedSetting ⫽ ⫺0.117, t ⫽ ⫺6.694, p ⬍ 0.001).
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Further analyses devoted to testing the equivalence of the two pairs of regression coefficients for Opposite-Gender Star and Director show a significantly better fit for the full regression equation than for one assuming equivalent
coefficients for the two drivers [⌬R2 ⫽ 0.001, ⌬F(2,3322) ⫽ 3.619, p ⬍ 0.03].
DISCUSSION
Subject to certain limitations and caveats, this study contributes to the understanding of audience attitudes toward films—a necessary step in explaining and
predicting audience behavior (Austin, 1982)—with specific regard to the processes
through which movies stimulate individuals. By recognizing that films generate
unified experiences (Cressey, 1938), the findings appear to support the anticipated role of a psychological match between the movie and the self, via an expansion of the possible drivers considered. Further, when combined, all drivers
contribute significantly, with the sole exception of the driver most frequently studied in the past—namely, according to the conventional wisdom, the same-gender
same-age variable on which identification is based. Indeed, results regarding the
failure of the same-gender variables to contribute to the explanation of consumers’
evaluations cast doubts on the importance of any role played by the mechanism
of identification based on objective chronology- and gender-based similarity.
This lack of support for H1 might also be due to a fantasizing process that is
not taken into account here. Specifically, because consumers might appreciate
movies as fantasizing and escapist opportunities (Hirschman & Holbrook, 1982),
they might appreciate more what they perceive as similar to their ideal selves—what
they would like to be—rather than their actual selves (Dolich, 1969; Grubb & Hupp,
1968; Gutman, 1973; Hamm & Cundiff, 1969; Landon, 1974; Ross, 1971). And if
the ideal self differs from the actual self in age, viewers might identify with
older or younger stars and characters, thereby disconfirming H1. Specifically, not
only the subjective perception of time (i.e., cognitive age) could greatly affect
consumers’ reactions to films, but also some young/old consumers could prefer
motion pictures starring older/younger same-gender leading participants, as a
consequence of their future/past ideal selves. However, because a secondary
data analysis cannot resolve whether consumers adopt the actual or ideal self,
this issue remains undecided at present.
Rather, when analyzing both same-gender and opposite-gender stars together
as vehicles for identification and attraction, respectively (with the needed caution), the results support the intuition (H2)—based on the cinema’s obsession
with attractive young stars (Walsh, 1989)—that on-screen stars appeal to consumers by virtue of their romantic attractiveness (Holbrook & Schindler, 1994),
thereby disconfirming more common expectations (H1), according to which viewers tend to identify with same-gender stars (Maccoby & Wilson, 1957). Star
power does play a relevant role in driving consumers’ evaluations—as previous
studies have found, albeit with mixed results (Desai & Basuroy, 2005; Elberse,
2007; Ravid, 1999; Wallace, Seigerman, & Holbrook, 1993)—but this role seems
to depend more on attraction than on identification, at least with regard to
actual age and gender. Indeed, stars’ emotional impacts on audiences and their
consequently high salaries (Simonton, 2009) appear to derive from their power
as romantic and sexually attractive models. Hence, star power—which is a central concept deserving researchers’ attention (Eliashberg, Elberse, & Leenders,
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2006)—should be interpreted as romantic appeal, thereby capturing the romantic attraction of older viewers to younger opposite-gender stars.
Beyond stars on the screen, the present study also supports the inclusion of
directors and the temporal setting among the drivers affecting consumers’ evaluations of movies, showing significant effects for both.
Specifically, the role of the director emerges as a relevant factor in psychological processes behind viewers’ evaluations of films, with positive contributions from directors of both similar and older ages. This finding suggests that
a director’s style and value system appeal to consumers either if they share his
perspective on life or if they respect his comparatively seasoned point of view.
[Note that, with regard to the director, it is not possible to investigate the role
of gender similarity, due to the general absence of female directors—the only
exceptions at the time of the study being Jane Campion’s The Piano (1993) and
Sofia Coppola’s Lost in Translation (2003).]
The last driver of movie evaluations related to consumers’ self-related
processes refers to the temporal setting. Here, the findings reveal that consumers favor movies set at times when they were not alive. Thus, contrary to the
previous literature on symbolicity as the link between a film and positively perceived cognitive categories (Hennig-Thurau, Walsh, & Wruck, 2001), consumers
actually appear to prefer unfamiliar settings found in escapist or fantasy-evoking
motion pictures representing eras that they have never actually experienced. This
result is also consistent with the large significant negative beta registered for
the presence of a specific setting in the motion picture. Further, one might view
this result as new evidence for consumers’ satiation with already-known themes,
as Sood and Drèze (2006) have found with regard to sequels. But additional
research is needed on this topic, which might also contribute to a better understanding of individuals’ transportation into personal and autobiographical narratives (Green, 2005).
Apart from settings, consistent with previous studies focused on drivers of
viewer behaviors, other objective characteristics of motion pictures influence
evaluations by consumers. However, in general, the noticeably mixed results
obtained by previous studies with regard to the most traditional features did not
allow the formulation of precise hypotheses. The negative effect of drama and
the positive effect of family movies and running time seem to be consistent with
earlier studies on box-office success (Chang & Ki, 2005) or popular appeal
(Holbrook, 1999). Also, the unusually long period of time investigated here
reveals that consumers react favorably to black-and-white films, likely as an
appraisal of inherent film features common in the relevant years of production.
Unfortunately, the data do not indicate which movie characteristic related to the
black-and-white era (e.g., the past character of the story line, the development
of certain themes that were common in those years, or other aspects of past productions) has the real impact on consumers’ evaluations; in any of these cases,
black-and-white serves as the mediating variable. Moreover, the results suggest that viewers’ evaluations are associated with awards (extra awards as well
as the Best Picture award). Further, findings about the non-monotonic relationship of consumers’ evaluations with the logarithms of estimated budgets
indicate that, in order to achieve maximally favorable impressions, filmmakers
should incur production costs that are not too high and not too low but, in the
immortal words of Goldilocks, “just right.” Films like Master and Commander
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(2003), Titanic (1997), and Cleopatra (1963)—whose adjusted estimated
budgets in 2003 dollars were $150,000,000, $230,000,000, and $264,440,000,
respectively—register low levels of appraisal (though it could be argued that
they might have reached even lower levels without such big budgets). In the
film industry, sky-high production budgets usually reflect impressive special
effects or the hiring of highly bankable superstars. The present data set does not
permit a more focused analysis that splits the budget into these or other subcomponents so that a definitive causal inference remains beyond our scope.
Indeed, one might expect that investments in star power and in special effects
produce different audience responses. Only further research can address this topic
adequately.
Finally, the temporal dimensions of motion pictures emerge as relevant. Specifically, even if the age of the movie itself is not significant, three non-monotonic
relationships link the ages of cinematic participants to viewers’ evaluations.
Interestingly, the peaks for the main actor’s age (53 years) and the main actress’s
age (40 years) differ by about 13 years. According to viewers’ preferences,
actresses should be noticeably younger than actors. Think of Humphrey Bogart
and Audrey Hepburn, Fred Astaire and Leslie Caron, Cary Grant and Grace
Kelly, Michael Douglas and Catherine Zeta-Jones, or Harrison Ford and Annette
Bening—to mention just a few obvious examples. Indeed, about 83% of the
actresses starring in a film nominated for Best Picture are under 40 years old,
while only 51% of leading actors are under that age. This difference could be
explained by at least three main considerations. First, it could be that women
show better acting abilities than men, so that they get a nomination earlier in
their life cycle. Second, it could be that a more competitive struggle for actors
than for actresses results in a postponed recognition of male stars’ acting skills.
Third, it could be that—more than actors—actresses are victims of the social
stereotype that regards youthfulness as an aspect of beauty or sex appeal. Even
if there is no conclusive evidence that favors any one of these three explanations
over the others, the third appears most reasonable (if most undesirable) and
supports previous comments in the literature (Holbrook & Schindler, 1994).
The relationship that links directors’ ages and viewers’ evaluations is
U-shaped, indicating that consumers favor films made by very young or very old
directors. This finding contradicts the actual practice common in the industry.
Indeed, less than 5% of nominated directors are older than 60 years, and only
25% are younger than 39 years. Hence, the remaining 70% of the nominated
films were made by directors between 40 and 60 years old, to whom viewers
react least favorably, even if unconsciously and without knowing how old the
directors are. However, these findings suggest that the film industry fails to
appreciate both younger and older directors, resulting in a gap between the
beliefs of industry members (as expressed by the Oscar nominations) and
the preferences of ordinary consumers (as revealed by the IMDb ratings). This
issue deserves attention in future research.
From the managerial point of view, our findings suggest that the recipe for a
favorably evaluated motion picture varies from one customer target to the next.
Hence—in films, as elsewhere—one size does not fit all. In order to become
one of the most appreciated films, a motion picture should conform to one or
more of the crime, music, war, action, or family genres; it should win the Best
Picture award, along with a lot of additional awards; it should be as long as
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possible and shot in black and white rather than color; it should take place in an
unfamiliar or unrecognizable temporal setting; and it should reflect a $16 million
production budget in 2003 dollars. Further, its participants should be chosen
with great care. The actor should be about 53 years old, the actress almost 40
years old, and the director as far from the age of 59 years as possible. Such a case
appeared in The Fugitive (1993), where Harrison Ford was 51, Sela Ward was
37, and the director Andrew Davis was 46 years old. Even better examples were
The Piano (1993) and Julia (1977). The Piano was made by Jane Campion, who
at the time was 39 years old, and starred Harvey Keitel and Holly Hunter,
who were 54 and 35 years old, respectively. On the other hand, Julia was made
by Fred Zinnemann, who was 70 years old, and starred Jason Robards and Jane
Fonda at 55 and 40 years old. Further, results for the opposite-gender star and
film setting indicate that Julia should be highly appreciated by men over
45 years old, but not so old that they were alive at the time of the film setting
(the 1930s).
Overall, our findings also suggest some important managerial implications
for other businesses that use celebrities in their product offerings or promotional communications. Indeed, it appears that the advertising industry should
adjust the features of its communications (leading actor, leading actress, director, setting) to the age- and gender-related characteristics of the relevant target
audiences. Catering to these drivers of preferences should help to create stronger
emotional bonds with the customer targets (though no clear implications for
product sales are available at present). For example, assuming a female target
audience, the image of Nicole Kidman as the expensive worldwide celebrity
endorser for Chanel No. 5 was replaced by a younger actor, Audrey Tatou, who
might better be able to attract the relevant customers romantically. Endorsements benefit not so much from the top stars as from the right ones.
Generally, marketing policies should move from a focus on genres to a focus
on audience demographics. This shift is already a reality at Fox Filmed Entertainment, which in 2006 announced the formal launch of the Fox Faith Home
Entertainment Division, following the launch of Fox Atomic, substituting new
knowledge on the audience’s demographics for the old traditional genre lines of
motion pictures as the internal organizational criteria. Audience demographics
should also drive the producers’ choices in promotional strategy. Movie trailers
and posters should be focused on scenes starring the opposite-gender leading
star—that is, the leading actor if the film is targeted to a female audience or the
leading actress if the movie appeals primarily to men, even if this practice might
lead to overemphasizing their respective roles.
Beyond promotion, audience demographics should also influence the merchandising strategies by which film producers aim to expand profits. Further,
understanding the sources for star power should redirect investments in product placements and on-camera plugs by leveraging the role of stars as romantic objects.
FUTURE RESEARCH
The psychological mechanisms underlying consumers’ evaluations of films appear
not only as complex, but also as heterogeneous. Including all of them is beyond
the scope of the present study, but further research could interestingly extend
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the present approach by including other aspects of identification, attraction,
reverence, and escapism. Specifically, the identification process might be broadened by including matches between viewers and stars on race and ethnicity,
which studies on advertising have found to be relevant (Ryu, Park, & Feick,
2006; Simpson et al., 2000), as well as the perceived ties to the culture of origin
via ethnic identification (Deshpandé, Hoyer, & Donthu, 1986; Donthu &
Cherian, 1992; Glaser, 1958; Qualls & Moore, 1990; Whittler, 1989; Xu et al.,
2004). At the same time, the assessment of romantic attraction might benefit from
the inclusion of consumers’ sexual interpretations of themselves and the level
of a star’s perceived beauty and appeal. Indeed, the latter depends on a range
of universal cues, among which youth is simply one of several (Ford & Beach,
1951), and on some additional variables such as general social and economic
conditions: The Environmental Security Hypothesis states that, as environmental threats increase, more mature facial features are preferred to less mature
(Pettijohn, 2003; Pettijohn & Tesser, 1999, 2003, 2005). Because imdb.com started
to collect data in 1994, the data set could reflect the period of worldwide instability that began, after a period of general wealth, with the social and economic
crises of 2000–2001. Hence, the inclusion of this variable—along with other
potential drivers of connections with stars, such as types of facial features—
might contribute to explaining audience evaluations of mature/youthful stars.
Similarly, films can support consumers in their willingness to escape even toward
contemporary (but not ordinary) experiences, making familiarity a broader concept than past experiences. Future research should also look for differences in
responses between men and women, a goal for which the present data are not
reliable due to a huge difference in sample sizes for men and women—almost
six men for every woman—as well as any potential differences in responses
among age groups, which again are not addressable here.
Further, including other potentially interesting variables may help to uncover
new psychological mechanisms. For example, as already found with regard to the
individual’s transportation into a narrative (Green, 2004), consumers’ familiarity with geographic locales portrayed or with aspects of plotlines could play
a role, as could demography-related preferences for specific genres, themes, costumes, or other cinematic components. Future research should also test the
emerging psychological processes with more directed and fine-tuned observations
that provide a more powerful and precise analysis (Farebrother, 1979; Garrett,
2003), sacrificed here for the benefit of examining large-scale, real-world data.
Indeed, the age-related phenomena would benefit from gathering precise data
on the ages of consumers, abandoning the use of categorical matches. Similarly,
viewers’ actual knowledge of participants’ ages should be included since this
ignorance might mitigate the resulting importance of this driver, as well as personal motivations of movie viewers and their personal attitudes toward film
narratives. Since films perform different functions for different people (Tesser,
Millar, & Wu, 1988), knowing the types of moviegoers and each respondent’s
transportation proneness would help toward refining the results. At the same
time, more direct measures of stars’ and directors’ expertise than simply
controlling for their ages could help to refine the present findings. Further, information on consumers’ viewing experiences—for example, whether they watched
the film in a posh movie theater, at a dilapidated drive-in, on an old-fashioned
television set, or via a new-fangled flat-screen satellite-fed high-definition homeentertainment system—should be important (Tesser & Shaffer, 1990), especially
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since older motion pictures now appear mostly on television or via other forms
of home entertainment.
Moreover, the identified psychological mechanisms might develop important
multiplicative interactions of relevance to the overall combinations of mutually
reinforcing or cancelling determinants (Kian, Rosen, & Tesser, 1973), which can
be examined only through a multiplicative approach. Indeed, by adopting an
essentially correlational WOLS-based analysis, the present study follows
an inherently additive approach, neglecting any synergies among the various independent variables. Further, future studies should also permit ensuring the
independent nature of the observations, an important assumption that cannot
be guaranteed here because some consumers might have expressed their ratings
more than once on the IMDb Web site.
A better understanding of these psychological mechanisms toward motion pictures and their protagonists could be useful in guiding the production and promotion of offerings, not only in the film industry but also in other cultural industries,
especially television and advertising. Learning better to address individual differences may increase the market performances of these cultural offerings. Beyond
this, researchers may hope to gain an enhanced understanding of consumers’ aesthetic evaluations and of the satisfactions enjoyed in their cultural experiences.
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Correspondence regarding this article should be sent to: Michela Addis, Associate Professor, Faculty of Economics, University of Rome III, Via Silvio D’Amico 77, Rome, 00145,
Italy ([email protected]).
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