Marketing and consumption of art products: the movie industry

Elif Ulker-Demirel (School of Applied Sciences, Trakya University, Edirne, Turkey)
Ayse Akyol (Faculty of Economics and Administrative Sciences, Trakya University, Edirne, Turkey)
Gülhayat Gölbasi Simsek (Faculty of Art and Sciences, Yildiz Technical University, Istanbul, Turkey)

Arts and the Market

ISSN: 2056-4945

Publication date: 8 May 2018

Abstract

Purpose

The purpose of this paper is to investigate the impact of the importance, assigned by audiences, of factors such as people, movie features, script, price, promotion, and distribution channels (defined as a movie marketing mix) on the audience’s buying intentions, as well as the impact of their buying intentions on word of mouth (WOM). In addition, the intention is to explore the relationship between the preference and frequency of people’s cultural event attendance with their buying intention and the relationship between people with extroverted personalities and WOM.

Design/methodology/approach

The data were collected from 904 valid surveys conducted in Beyoglu, one of the important centres for the culture and art life of the Istanbul.

Findings

The results show that promotion, actor or actress, and diversity of distribution channels have a positive effect on people’s purchase intention. In addition, the frequency of attendance to cultural events can be determinative of the audience and helpful for industry professionals.

Originality/value

Although there have been a number of studies that examine the simple relationships among some of these variables (movie marketing mix, attendance, purchase intention, WOM, extraversion), there is still a gap in the literature with regard to these variables in an integrated framework. Considering these variables in the same model and analysing the effects of each dimension individually provides a better explanation of consumer purchase intention and post-purchase behaviour in the movie industry. This study extends the previous research by incorporating the concept of movie marketing and consumption by improving the scale with data collected in Istanbul, Turkey.

Keywords

Citation

Ulker-Demirel, E., Akyol, A. and Simsek, G. (2018), "Marketing and consumption of art products: the movie industry", Arts and the Market, Vol. 8 No. 1, pp. 80-98. https://doi.org/10.1108/AAM-06-2017-0011

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Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


Introduction

Over the past two decades, art organisations have shown a growing interest in the marketing concept. Because of the increased competition in the entertainment industry, the adoption of marketing strategies has taken a crucial role in reaching art consumers for industry professionals.

Marketing is a strategical process, the starting point of which is investigating and understanding consumer wants and needs (Askegaard, 1999). However, in terms of art marketing, this consumer-centric approach is still argued because of the varying nature of art as a product. A product represents a bundle of tangible and intangible benefits – including brand values purchased by the consumer (Chong, 2008). However, artistic products are rich in cultural meaning (Colbert and St James, 2014) and do not exist to fulfil a market need. The raison d’être of art is independent of the market, which makes it a particular marketing challenge (Colbert, 2003).

Art marketing is the art of reaching market segments that are likely to be interested in a product while adjusting the product to the commercial variables – price, place, and promotion. Such marketing is designed to put the product in contact with a sufficient number of consumers and reach the objectives consistent with the mission of the relevant cultural organisation (Colbert et al., 2001).

A part of creative industries, cinema has existed in our lives for more than a century. Cinema gathers from the dynamics of life and is created as a form of art that reflects these dynamisms (Silvia and Berg, 2011). Today, cinema has become a large industry and economic structure that feeds the many subsectors that have developed throughout the history of movie with a social and technological structure and that exists in the context of different forms of daily life (Monaco, 2013).

Our research uses a holistic approach to analyse the effects of the factors (e.g. people, script, features, price, promotion, and distribution, defined in this paper as the “movie marketing mix”) that could be important in terms of their relationship with audiences and audiences’ purchase intentions and word of mouth (WOM). Therefore, the paper aims to add a new dimension to the literature in terms of the integration of attendance, WOM, and the extraversion given that the existing studies are generally limited to measuring relationships between separate marketing mix elements with purchase intentions and attendance.

In addition, based on Cronin et al. (2000) suggestion in their study that partial examinations of the simple bivariate links among any of the factors and purchase intention and other constructs may mask or overstate their true relationships due to omitted variable bias, and in order for a more pragmatic picture of the underlying relationships among the variables to emerge, the present study takes a holistic approach and examines the relationships among these variables, specifically, the movie marketing mix, purchase intention, WOM, attendance, and extraversion. Although there have been many studies that examine the simple linear relationships of these constructs in various combinations, there is still to be a resolved gap in our understanding about the direct and indirect impact of these constructs in an integrated framework. Accordingly, this study aims to address this identified gap.

The movie as a product

This study proposes a movie marketing mix based on the traditional marketing mix (4P), which covers product, price, promotion, and place (distribution). At this point, stars and directors as people, script, and movie features will be considered as the elements of product.

People

According to Albert (1998), in the movie industry, a star is often the key ingredient to getting a movie made, not only because they have box office appeal but also because they represent a known part of a consumer choice mechanism. There is extensive research on the effects of movie stars on the box office success of movies in the literature (Elberse, 2007; Albert, 1998; De Vany and Walls, 1999). This study aims to explain the audience aspect of this issue to understand the influencing factors of movie preferences. Basically, we seek to answer the following question: is the star in the movie or its director important in audiences’ movie preferences?

According to the brand theories, directors can be considered to be ingredient in branding as in the case of superstars. That is, as with superstars who contribute to the box office success by attracting a persona-based audience, renowned directors are assumed to have a similar attracting power (Chang and Ki, 2005).

Features

According to literature, the knowledge about the country of origin stimulates consumers’ interest (Gazley et al., 2011; Hong and Wyer, 1989). In this study, the role of the country of origin on movie audiences’ choices and the foreign or domestic movie choices of audiences were determined separately.

Script

The script can be determinative in terms of audiences’ choices. Kerrigan (2010) emphasised the importance of script quality and the link between the genre and story as well as other elements of the movie marketing mix. In this study, we predict that the script of a movie, adapted from popular novels or based on real stories, may affect the audiences’ decisions. Thus, we predict that the artist or director, script, country of origin, and whether the movie is domestic or foreign, may be important factors in terms of audiences’ movie choices and may affect their purchase intention. Therefore, the following hypotheses are formulated:

H1.

The importance assigned by audiences on the factor of the people in a movie has an impact on their purchase intention.

H2.

The importance assigned by audiences on the features of a movie has an impact on their purchase intention.

H3.

The importance assigned by audiences on the factor of the script has an impact on their purchase intention.

Price

In general, in any given movie theatre, tickets are priced uniformly, regardless of the movie’s variable popularity, the day of the week, and the time of the year (Einav and Orbach, 2001). However, there are some exceptions such as reduced ticket prices for different time schedules (such as morning sessions and bargain matinees) and boosting sales for the days with low demand. Furthermore, some companies offer a discount to their customers or specific consumers on ticket prices. Thus, movie audiences can place importance on ticket prices, and the movie theatres’ different ticket-pricing strategies may be a determinative factor in terms of their decisions. Therefore:

H4.

The importance assigned by audiences on the factor of price has an impact on their purchase intention.

Promotion

After the completion of a movie, promotion activities are carried out to reach the audiences. At this point, promotional tools such as trailers, movie posters may directly affect people’s decisions.

Critics

There are numerous studies that have focussed on the impact of critics’ reviews on box office success (Koschat, 2012; Wanderer, 2015; Basuroy et al., 2003; Eliashberg and Shungan, 1997). There are two possible perspectives on the role of critics as “influencers” and “predictors”. First, critics could be opinion leaders who influence their audience and, consequently, the commercial box office performance of motion pictures. Second, critics could be predictors of their respective audiences. The influencer perspective implies that movie reviews directly affect the consumer’s decision-making process. The predictor perspective suggests that reviews only predict consumers’ decisions (Azuela-Flores et al., 2012; Basuroy et al., 2003; Eliashberg and Shungan, 1997).

Trailers

Trailers provide unique and specific rhetorical structures that fold visual and auditory evidence of the movie production industry’s assessment of its actual audience (as well as its desires for a potential audience) into a one- to-three-minute cinematic experience (Kerran, 2004).

Movie poster

The poster may have more visibility than any other pieces of artwork in promoting the movie. Therefore, it must convey a succinct and compelling message. This will be the piece most likely picked up by the press for initial coverage, as well as the enduring image at the box office (Ulin, 2010).

Social media

Social media can reach different audience groups to create interest and thus reach social circles of audiences through pages sharing the content of the movie, including promotions, trailers, and screening dates with places. Thus:

H5.

The importance assigned by audiences on the factor of movie promotion has an impact on their purchase intention.

Distribution

After releasing a movie in the theatres, it is followed by a release in DVD/Blu-ray format (home video) to retail markets. After that, it is displayed on satellite or cable channels and through internet options such as video-on-demand (VOD) or Pay TV and finally, it is made available for television. With these distribution options, which are referred to as sequential distribution, consumers have an opportunity to consume a product repeatedly (e.g. rent a movie they saw and liked in the theatre) (Hennig-Thurau et al., 2006). Today, after the theatrical release, it has become easy to gain access to movies due to the internet options for audiences. These changes in timing and order of movie distribution channels require the implementation of different strategies in terms of studios and theatre owners (Hennig-Thurau et al., 2007). They also provide different movie viewing options to audiences. However, such changes could affect audiences’ preferences for purchase.

Movie theatres

The distributor chooses a release pattern – the number and location of theatres where a movie is licensed to play or is “booked” – based on a priority appraisal of demand. The size of the initial release determines the number of copies that are required for the distribution to each theatre. Beyond that initial supply, the number of viewers who can see the movie can increase the capacity of the theatres (De Vany, 2004).

Home video

Unlike traditional distribution activities, income from DVD rentals and sales can even compete with the US box office revenues. In addition to that, in terms of independent producers and distributors, DVDs are considered to be a different source of income.

Television

Television is a different area for movie distribution. It is a time-based medium and focusses on time slots. Programming is primarily driven by ratings, and the product is developed to cater to the audience (Ulin, 2010).

Due to the development of internet technologies, producers have the ability to directly reach audiences through different mediums. Today, broadband internet has allowed audiences to enjoy movies conveniently at home through different channels such as VOD, pay-per-view (PPV) (Nam et al., 2015). Thus:

H6.

The importance assigned by audiences to the factor of distribution has an impact on their purchase intention.

Attendance

In our study, we were interested in the following question: are marketing activities important for a particular movie in terms of audiences that frequently attend cultural activities? Thus, we examine the relationship between the frequency of attendance and movie marketing practices. We argue that attendance could moderate the impact of purchase intention on movie marketing mix elements. Therefore:

H7.

The frequency of attendance has an impact on people’s purchase intention.

H8.

The frequency of attendance has a moderating effect on the relationship between the importance assigned by audiences to the factor of people in a movie and their purchase intention.

H9.

The frequency of attendance has a moderating effect on the relationship between the importance assigned by audiences to the features of a movie and their purchase intention.

H10.

The frequency of attendance has a moderating effect on the relationship between the importance assigned by audiences to the script and their purchase intention.

H11.

The frequency of attendance has a moderating effect on the relationship between the importance assigned by audiences to the factor of price and their purchase intention.

H12.

The frequency of attendance has a moderating effect on the relationship between the importance assigned by audiences to the factor of promotion of a movie and their purchase intention.

H13.

The frequency of attendance has a moderating effect on the relationship between the importance assigned by audiences to the factor of distribution of a movie and their purchase intention.

Purchase intention, WOM, and personality

A consumer’s perceived benefit and value determines purchase intention (Wang and Tsai, 2014). Intentions are assumed to capture the motivational factors that influence a behaviour; they are indications of how much people are willing to try and how much of an effort they plan to exert to perform the behaviour (Azjen, 1991).

WOM has been examined in a wide range of studies in the marketing literature. It is an informal positive or negative communication between consumers about the objectively or subjectively perceived characteristics of products, brands, or services (Buttle, 1998; Hausmann and Poellman, 2016). From a movie industry perspective, whereas marketing plays an important role in a movie’s opening weekend, consumer WOM has been frequently cited as the most important factor that determines the long-term success of motion pictures and other experience goods. However, until recently, the reliable measurement of consumer WOM has remained elusive. The situation is changing due to the emergence of internet-mediated communities in which consumers exchange their experiences about products and services (Dellarocas et al., 2007). The most likely, and most effective, alternative quality signals that a potential viewer is exposed to the sentiments of other viewers who have already seen a motion picture. These sentiments are largely communicated informally, through personal WOM. Increasingly, online WOM on fan websites, such as www.boxofficemojo.com, contribute to the communication of consumer sentiments (Koschat, 2012).

The relationships between the people’s attendance of the cultural activities and personality traits have been examined in a broad range of disciplines. In this study, we consider the extraversion personality to be one of the big-five personality factors structure (John and Srivastava, 1999). We planned to investigate the impact of extraversion personality on WOM. Thus, we hypothesise the following:

H14.

Purchase intention has an impact on WOM.

H15.

The personality trait of extraversion has an impact on WOM.

Methodology

The data were analysed and interpreted through SPSS 15 and Lisrel 8.80. This process was followed by two-step structural equation modelling (SEM). First, the measurement model was tested. By testing the measurement model, we aimed to determine the links between the indicator and latent variables, as well as the measurement properties of the model, i.e., reliability and validity (Jöreskog and Sörbom, 1996). Second, SEM was used to test the proposed hypothesis relationships. In addition to this, principal component analysis (PCA) was used for “attendance” items for dimension reduction. The proposed model for this study was developed based on the above literature review and is shown in Figure 1.

As a continuation of the previous studies on the relationship between movies and marketing, our proposed model examined the variables with an integrated framework. Unlike examining simple relationships among each variable in terms of audiences’ purchase intentions and WOM, the aim of the study was to explain these relationships with an integrated concept. It is aimed to examine the effect of the movie marketing mix (4P) elements on the purchase intentions of audiences and, therefore, the indirect effects on their sharing behaviour with regard to these elements. In addition, it is aimed to examine the direct effects of the frequency of attendance of audiences to cultural activities on their purchase intentions, and this variable is included in the model as a moderating construct between 4P elements and purchase intention. In addition, extraversion was included in the model. This was done to examine the effects of having an extroverted personality on sharing behaviour. The proposed model is presented in Figure 1.

This study was conducted in Istanbul, Beyoğlu, and completed in March 2014. The sample comprises movie theatres that are located in the Beyoğlu, which is still the liveliest and oldest district of culture and art and is where cinema culture was born in Istanbul since the late Ottoman Empire. It is an intensive district where many people of different demographics and cultural backgrounds spend their time, especially on weekends. The study aimed to connect with audiences who have a good deal of knowledge about cinema. Thus, the respondents were selected based on a judgment sampling for the purpose of this study. With this purpose, the study focussed on specific individuals who can provide meaningful data with which to understand audiences’ behaviours. This is consistent with the logic of judgment sampling that involves the choice of individuals who have possess requisite information (Sekaran, 2003).

The survey was conducted among 925 people at seven movie theatres that are located in Beyoğlu, and 904 usable questionnaires were obtained. These movie theatres are Beyoğlu Atlas, Beyoğlu Beyoğlu, Beyoğlu CineMajestic, Beyoğlu Pera, Beyoğlu Fitaş, Beyoğlu Yesilcam, and Cinema Pink Taksim Demirören. The sample comprised females (52.1 per cent) and males (47.9 per cent). The ages of the respondents were 18-25 (28.7 per cent), 26-35 (43.4 per cent), 36-45 (16.6 per cent), 46-55 (8.2 per cent), 56 and older (3.1 per cent). The educational status of the respondents was primary school (4.5 per cent), high school (21.5 per cent), university (50.7 per cent), master (15.8 per cent), and PhD (7.5 per cent).

A survey approach was used to collect the data to test the proposed model. The survey was based on multiple-item measurement scales taken from the current research (Chang and Ki, 2005; Finsterwalder et al., 2012; Gazley, et al., 2011; John and Srivastava, 1999; McManus and Furnham, 2006) and covered six sections: movie marketing mix, purchase intention, personality traits, attendance, WOM, and demographics. In measuring attendance, we drew on McManus and Furnham (2006); in measuring personality traits, we relied on the big-five personality traits by John and Srivastava (1999); to measure the movie marketing mix, the items were adapted from numerous authors, including Finsterwalder et al. (2012), Gazley et al. (2011), and Chang and Ki (2005). The items were scored on a seven-point Likert scale ranging from “strongly disagree” (1) to “strongly agree” (7). Furthermore, PCA was used to obtain a single index for attendance.

To validate our measurement, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used together. For the first step, EFA was conducted for all the items to identify multiple factor structure, item loadings, and item cross-loadings. To confirm the multiple factor structure, CFA was applied. Then, SEM was utilised to test the hypotheses. CFA and SEM were assessed by using the maximum likelihood estimation.

Results

Measurement model

In this study, nine factors conceptually meaningful were extracted by using principal components factoring with varimax rotation. The value of the Kaiser-Meyer-Olkin measure of sampling adequacy is 0.821 (should be larger than 0.5) indicating that the factor analysis is appropriate (Hair et al., 2009). Bartlett’s test of sphericity is 9,547,722 (p=0.000). A ratio of 67.502 per cent of the total variance is accounted for with these nine factors. The first factor, the price factor, is 12.071 per cent of the total variance, the distribution factor is 8.448 per cent of the total variance, the intention factor is 8.073 per cent of the total variance, the promotion factor is 7.970 per cent of the total variance, the extraversion factor is 7.872 per cent of the total variance, the WOM factor is 7.626 per cent of the total variance, the script factor is 5.633 per cent of the total variance, the movie features is 5.142 per cent of the total variance, and finally the people factor explains the 4.667 per cent of the total variance. The varimax-rotated factor loadings and cross-loadings are given in Table I.

Loadings in excess of 0.71 are considered excellent, 0.63 very good, 0.55 good, 0.45 fair, and 0.32 poor (Tabachnick and Fidell, 2013). As shown in Table I, most of the item loadings are excellent, four of them are very good (DIST1, DIST2, DIST4, and PROM1) and, finally, one of them is good (DIST5). Therefore, all the factor loadings are very high and the cross-loadings are all low, indicating validity in terms of an EFA perspective.

Following the EFA, CFA was conducted to confirm the measurement model with the nine factors. The measurement model fit indicates the results showed a good fit to the data according to Hu and Bentler’s two-index strategy, which suggested to standardised root mean square residual (SRMR) and non-normed fit index (NNFI) (TLI), RMSEA or the comparative fit index (CFI) (Hooper et al., 2008; Hu and Bentler, 1999; Fan and Sivo, 2005). According to this strategy, measurement model fit was acceptable using the SRMR=0.039 (<0.05) as well as an additional fit index such as the RMSEA=0.03 (<0.05). The other fit indices were found at acceptable levels such as χ 2 =659.98, df= 314, p-value= 0.0, a goodness of fit index (GFI)= 0.93, an adjusted goodness of fit index (AGFI)= 0.91, a normed fit index (NFI)= 0.91, NNFI= 0.90, and CFI= 0.94.

Unstandardised loadings (i.e. with unit variance scaling of the factors), standardised loadings (i.e. with unit variance scaling of the indicator variables as well as the factors) from the latent variables to the indicator variables, t-values, and R2 values were examined regarding the measurement model, and the results are presented in Table II. This table shows that all of the path coefficients are significant (p<0.01). Therefore, the model demonstrated convergent validity. Furthermore, the coefficient of determination (R2) indicates a measure of the variability of each item explained by the latent variable. When the R2 values are examined, the minimum value is 0.23 and the maximum value is 0.76. Item DIST5 was removed from the model because the R2 value was less than 0.20. This may be due to the possibility of confusion about illegal downloading behaviour. The discriminant validity of the factors was evaluated using the strategy of Fornell and Larcker (1981). This strategy recommends comparing the average variance extracted (AVE) with the variance shared between the construct and other constructs in the model (Molina et al., 2009). As shown in Table III, the square roots of the AVE values are presented on the diagonal. The Pearson’s correlations of the aggregated scores of the items are presented above the diagonal, and the latent variable correlations obtained from the measurement model are presented below the diagonal. These values are greater than the correlations between the constructs and are indicative of discriminant validity among the constructs. As summarised in Table III, the square roots of the AVEs are greater than the correlations between the constructs, which indicates discriminant validity for each construct. The results suggested a strong discriminant validity of measurements.

To examine reliability, Cronbach’s α coefficients of each construct are represented in Table IV, except for people and features, all above the minimum required value of 0.70 for scale acceptability consistency (Cronbach, 1951). The composite reliability values should be over 0.7 (Hair et al., 2009). As shown in Table IV, the composite reliabilities all exceed 0.70 except for two constructs (people and features). This indicates an acceptable reliability of the measured constructs. The reliability values for the “people” factor were found to be lower. It is preferred that this factor is used in the study because it is an important factor of the proposed model. Consequently, the model is valid (according to the nine-factor structure); the results are consistent and reliable.

Another variable used in this model is attendance. This variable is adopted to examine the relationship between the frequency of attendance to cultural activities and marketing practices. PCA was applied to 16 “attendance” items (shown in Table V). For the purposes of the study, these items were taken and adapted from the research of McManus and Furnham (2006). The first component explained 32.21 per cent of the total variance with an eigenvalue of 5.154. The higher values of this component score indicate the higher attendance. The component score of the first component was used as a single indicator latent variable, called attendance. As shown in the Table V, higher participation in cultural activities such as “go to classical or modern ballet/dance” and “go to the theatres (plays/musicals)”, causes a higher level of attendance score.

Structural model

The proposed hypotheses were tested through SEM. The structural model was tested, and it was found that the model fit was acceptable according to Hu and Bentler’s two-index strategy (Fan and Sivo, 2005; Hooper et al., 2008; Hu and Bentler, 1999). According to this strategy, the structural model fit was acceptable using SRMR=0.03, as well as an additional fit index such as the RMSEA=0.03. The other fit indices were found to be at acceptable levels such as χ 2 =939.09, df=461 and p-value =0.0, CFI=0.93, AGFI=0.90, NNFI=0.91 and finally, NFI=0.90. The hypothesis testing results are given in Table VI.

H1 and H5-H7, which support that the importance assigned by audiences to the factors of people, promotion, place, and attendance positively affect purchase intention, have a positive impact on audiences’ purchase intention. As in H7, the frequency of attendance has a positive impact on purchase intention. The results of H3 (−2.06) suggest that scripts based on a novel or true story have a negative impact on purchase intention.

By contrast, H2 (−0.46), which predicts that the importance assigned by audiences to the features of a movie such as domestic or foreign and country of origin, and H4 (−0.77), which predicts that theatres’ ticket-pricing strategies have an impact on people’s purchase intentions, were not supported.

The moderating effects of the attendance on the impact of six hypothesised movie marketing mix items were examined. H12 was supported as the moderating effect of the attendance on the impact of promotion on purchase intention. The results of H12 show that the promotion impact on purchase intention is important for people who rarely attend cultural events and, conversely, promotion impact on purchase intention is not important for people who frequently attend cultural events. However, H8-H11, and H13 were not supported.

It is found that the importance assigned by audiences to the factor of people in a movie has an impact on their purchase intentions (H1). However, level of attendance in cultural activities did not change the influence of the importance assigned by audiences to the factor of people (e.g. actors and actresses and directors in movies) and did not become more important in the audience’s decisions (H8). Similarly, the importance assigned by audiences to features such as country of origin, domesticity or foreignness of a movie has no impact on their purchase intentions (H2). However, the level of attendance in cultural activities did not change the dimension of this relationship (H9). When H3 is examined, it is found that the importance assigned by the audiences to the script has a negative impact on their purchase intentions. Audiences tend to prefer to watch movies with an authentic script instead of movies that are based on a real story or a novel. At this point, the level of attendance of audiences in cultural activities did not affect this relationship (H10).

Our results show that the importance assigned by audiences to the price has no impact on their purchase intentions (H4). It is seen that ticket prices or other pricing options (e.g. bargain matinees) have no importance in terms of audiences about the movie they want to watch, and they tend to buy regardless of the ticket price. However, the level of attendance of people in cultural activities did not make them sensitive to price. They tend to prefer to watch the movies that they want to (H11). Our results present that distribution was important in terms of audiences and has an impact on their purchase decisions (H6). It could be indicated that the variety of distribution channels, the screening in many movie theatres, DVD, and PPV options are important for audiences and could affect their purchase intentions. However, the level of attendance did not affect the dimension of this relationship (H13).

The results of H14 suggest that people who prefer to watch a particular movie have the intention to share their opinions about the movie. Therefore, purchase intention has a positive impact on WOM, and H14 was also supported. H15, which predicts that extrovert, social people prefer to share their opinions and comments about the movie they preferred to watch, was supported.

Finally, the squared multiple correlation (SMC) values are shown in Table VI. As shown in Table VI, 17 per cent of the variance of buying intention was explained by people, features, script, price, promotion, distribution, and attendance factors, while 19 per cent of the variance of WOM was explained by buying intention and extraversion factors.

Conclusions

The findings show that actors and directors have an impact on audiences’ preferences. Actors and directors can be determinative in terms of movie expectations of audiences. Stars have an impact on revenue, primarily due to their ability to generate buzz and drive audiences to the theatres, especially during the opening week (Karniouchina, 2011). Similarly, Finsterwalder et al.’s (2012) study concludes that well-known directors have a strong positive influence on the expectations of the quality of a movie. Our study revealed that script as a product has a negative impact on purchase intention. This result showed that audiences prefer to watch movies with authentic scripts. A movie script that is based on a novel or a real story may have a negative impact on audiences’ movie preferences. The findings of this study showed that promotion activities can be important for audiences’ decisions regarding the movie they want to watch. Reviews by critics and the review ratings of audiences, comments on social media, and suggestions from friends create both an image and awareness of audiences’ interest in a movie.

One of the main factors to be taken into account by audiences is the variety of distribution channels. Audiences prefer to watch a movie when they want and on whatever distribution channel they prefer to use. This could be due to shifting preferences towards the home viewing of movies. Options such as VOD and PPV, which offer audiences the ability to time-shift the movies they prefer to watch, make the viewing experience easier and more comfortable for audiences. This flexibility has caused people to develop a tendency towards home viewing in contrast to movie theatres. However, when we consider the level of attendance at cultural activities, it does not affect the dimension of the relationship between the variety of distribution channels and purchase intention in terms of people who rarely attend cultural activities (for economic or other reasons). Promotion or WOM could be a supporting factor for their attendance at this point. Similarly, for people with frequent attendance to cultural activities, screening in many movie theatres, and different movie-watching options did not change the effect of their purchase intentions. It could be interpreted that they are people who follow cultural activities; therefore, they have an idea of where they can find the movie they prefer to watch and which distribution channel they prefer. Thus, this situation does not change the effect of their purchase intentions.

However, the variety of distribution channels, the accessibility through various distribution channels of a movie and reviews by movie critics, audience reviews, and comments on social media as promotional tools all can be expressed as factors that shape and influence audiences’ purchase intention. This research found that ticket prices and theatres’ pricing strategies have no impact on audiences’ preferences and purchase intention. Therefore, their decision regarding which movie they want to watch is not shaped by ticket prices and theatres’ pricing strategies. Similarly, Reddy et al. (1998) found that ticket prices do not have a significant relationship with attendance. In addition, the level of attendance of people in cultural activities did not affect the dimension of the relationship between ticket prices and purchase intention. It should be noted that for the people who frequently attend cultural activities, ticket prices or other pricing options did not become important for them. It can be interpreted that these audiences’ intentions to purchase were not affected by price options or discounts. Additionally, whether a movie is domestic or foreign, and its country of origin, have no significant influence on audience preferences. This could be due to cultural differences. Differences in aesthetic tastes, social and cultural values, language, and other factors may lead to different judgements of whether an individual prefers a certain a movie better than another (Lee, 2006).

This analysis shows that people who prefer to watch a particular movie have the intention to share their opinions about the movie. WOM plays an important role in the movie industry, and it provides important information that helps audiences decide whether to see a movie (Ladhari, 2007b). For experience goods such as movies, before watching a movie, audiences tend to rely on their decision according to psychological inputs (e.g. expectations), and informational inputs (e.g. WOM) (Neelamegham and Jain, 1999). Especially during pre-release and the opening week of a movie, WOM activities are high, and this creates relatively high expectations for audiences (Mohr, 2007). These activities can shape audiences’ thoughts and affect their intentions to watch a movie. They can share their opinions with friends in person or on the web about a movie they intend to watch. Thereby, they become a part of the WOM that is created around the pre-release of the movie. According to Ladhari’s (2007a, b) study, these expectations, referred to as affective expectations, also have an effect on audiences’ satisfaction and intention to share their experiences (WOM).

Finally, the findings show that extraverted people who prefer interpersonal interaction tend to share their comments about the movies they prefer to watch. Active interpersonal communication about movies can be expected to exist because they are entertainment goods, and this communication may influence audiences (Liu, 2006). Similarly, Mooradian and Swan (2006) stated that the people characterised by higher extraversion are more likely to rely on interpersonal sources of information, that is, on word of mouth, which is an important source of information for product information.

Managerial implications

Social media and reviews on websites are an effective way to reach audiences especially those who are hesitant about which movie they prefer to watch. At this point, social media tools have become a key factor in reaching audiences. It is important to develop a professional social media strategy to manage the information flow, to create awareness about the movie, and to keep this information flow updated. Similarly, generating successful “buzz” can help drive consumer interest and draw audiences to theatres (Mohr, 2007). The buzz-generating approach is perceived to be suitable, especially for blockbuster movies, to promote commercial success by encouraging enhanced market performance (Holbrook and Addis, 2008).

The results show that it is important to reach audiences through different distribution channels. In terms of audiences, there are some components that are taken into consideration about the movies, such as screenings in several movie theatres, premium channels, and PPV options. In addition to that, movie theatres’ session times can be difficult to adjust in terms of audiences. Therefore, producers and distributors should consider this issue because of the shifting preferences towards the home viewing of movies.

Movie industry professionals should consider the frequency of attendance to cultural events, which can be determinative for audiences. It is also important to consider people who rarely go to the cinema. It can be effective to pull them into movie theatres and to create a cinema experience for them. It is necessary to detect potential audiences, to develop marketing strategies according to their preferences and tastes and, finally, to manage marketing messages to the potential audiences. It is important to detect the right audience segments and understand them to more effectively create and build relationships. At this point, marketers must meet potential audience demands (Swanson and Davis, 2006). Furthermore, a strategic approach should be taken to balance to the two audience groups, which are frequent attendees and rare attendees to provide a sustainable audience and long-term relationship with them for the future (Hayes and Slater, 2002).

Limitations and future research directions

Some limitations should be considered in this study. First, the sample comprises movie theatres in the Beyoğlu region in Istanbul and reflects not only the demographic and cultural diversity of Istanbul but also many movie theatres in this region. Therefore, future studies could embrace wider population samples. In addition, this research was conducted in Turkey; consequently, the results reflect the Turkish population. As a result, the generalisability of our findings may be limited.

This research is concentrated on the movie industry. Future studies could incorporate other branches of the arts. Moreover, the data used in this study were obtained during March 2014. For future research, a longer duration of data may extend the scope of the research. In the additional studies, the scales created in this study could be improved, and implications could be compared. In addition, examination of the indirect effect of WOM upon other elements of the model (e.g. purchase intention, marketing mix elements) can be considered by future studies. The reliability values for the “people” factor were found to be lower. This factor was used in the study because it directly affects audiences, and thus it is an important factor in the proposed model. In the future studies, extending the number of questions could increase the reliability values. Finally, the SMC values were relatively low. Cultural consumption may be related to many other variables in addition to the marketing concept. Therefore, these relatively low values related to marketing can be considered to be appropriate for the study.

This study contributes to the existing research by analysing the relationship between the marketing mix concept and movies, as well as movie consumption in terms of audiences’ choices, attendance frequency, and personality traits. These results can be determinative in terms of industry professionals and marketers to understand the factors that affect movie audiences’ preferences, their decision process and the marketing tools they use, as well as the factors that shape audiences’ purchase intentions.

Figures

Proposed model

Figure 1

Proposed model

Varimax-rotated factor loadings and cross-loadings

Factors ıtems Factors
People Script Features Price Promotion Distribution Extraversion Intention WOM
People (PEOP)
PEOP1: actor/actress in the film is important in my film preferences 0.738 0.014 0.049 0.038 0.200 0.116 0.080 0.011 0.091
PEOP2: director of the film is important in my film preferences 0.830 0.100 0.084 0.076 0.002 0.008 −0.011 0.101 −0.001
Script (SCRPT)
SCRPT: film based on a novel is important in my film preferences 0.103 0.858 0.156 0.108 0.091 0.040 0.026 −0.025 −0.028
SCRPT2: film based on a real story is important in my film preferences 0.021 0.846 0.090 0.146 0.112 0.083 0.070 −0.014 −0.002
Features (FEAT)
FEAT1: being domestic or foreign of a film is important in my film preferences 0.026 0.134 0.808 0.032 0.084 0.067 0.030 −0.060 0.069
FEAT2: film’s country of origin is important in my film preferences 0.113 0.096 0.837 0.112 0.064 −0.009 0.051 0.027 0.031
Price (PRC)
PRC1: bargain matinee is important in my film preferences −0.013 0.034 0.033 0.810 0.089 0.119 0.006 0.006 0.048
PRC2: reduced prices in morning sessions are important in my film preferences 0.105 0.060 0.034 0.801 0.019 0.082 −0.026 0.012 −0.019
PRC3: ticket promotion campaigns of GSM companies (Turkcell, Vodafone, etc.) are important in my film preferences 0.019 −0.040 0.029 0.809 0.165 0.222 0.072 0.052 0.047
PRC4: ticket campaigns of deal websites are important in my film preferences −0.001 0.092 0.052 0.813 0.123 0.218 0.092 0.018 −0.019
PRC5: club membership options of movie theatres are important in my film preferences 0.041 0.177 0.051 0.775 0.079 0.179 0.076 0.026 0.000
Promotion (PROM)
PROM1: critic reviews are important in my film preferences 0.093 0.105 0.074 0.161 0.621 0.003 0.070 0.077 −0.029
PROM2: audience reviews are important in my film preferences 0.133 0.065 −0.024 0.125 0.804 0.116 0.029 0.041 0.069
PROM3: my friends’ suggestions are important in my film preferences 0.001 0.059 0.044 0.020 0.727 0.073 0.002 0.073 0.136
PROM4: reviews in social media are important in my film preferences −0.009 −0.025 0.081 0.094 0.711 0.242 0.138 0.089 0.071
Distribution (DIST)
DIST1: variety of distribution channels are important in my film preferences 0.052 0.106 0.003 0.130 0.135 0.682 0.042 0.066 0.000
DIST2: screening of a film in many movie theatres is important in my film preferences 0.061 −0.041 −0.108 0.168 0.131 0.662 0.068 0.119 0.141
DIST3: accessibility to DVD/Blu-ray options of a film is important in my film preferences 0.054 0.019 0.057 0.161 0.059 0.753 0.024 0.036 0.019
DIST4: accessibility to a film by pay-per-view, cable channels, etc. is important in my film preferences 0.088 0.188 −0.019 0.283 −0.002 0.601 0.143 −0.086 −0.109
DIST5: accessibility to the movies via internet (download, movie watch channels) is important in my film preferences −0.121 −0.100 0.192 0.110 0.120 0.569 −0.053 0.246 0.071
Extraversion (EXTR)
EXTR1: I see myself a person who is talkative 0.033 0.028 0.009 0.026 0.046 0.046 0.866 0.096 0.105
EXTR2: I see myself a person who generates a lot of enthusiasm 0.041 0.090 0.015 0.099 0.044 0.059 0.830 0.047 0.052
EXTR3: I see myself a person who is outgoing, sociable 0.002 −0.015 0.062 0.038 0.120 0.066 0.841 0.072 0.092
Purchase Intention (INT)
INT1: I intend to watch such a film 0.103 −0.016 −0.005 0.007 0.091 0.103 0.051 0.808 0.140
INT2: I plan to watch such a film 0.056 0.012 −0.025 0.054 0.092 0.090 0.096 0.868 0.143
INT3: I want to watch such a film −0.025 −0.033 −0.009 0.018 0.088 0.092 0.083 0.848 0.165
Word of Mouth (WOM)
WOM1: I say positive things to other people about that film 0.064 −0.038 0.017 −0.010 0.113 0.029 0.103 0.150 0.818
WOM2: I would recommend that film to anyone who asks about my opinion 0.057 −0.061 0.126 −0.007 0.041 0.040 0.035 0.102 0.872
WOM3: I would refer to my friends about that film −0.024 0.067 −0.027 0.055 0.081 0.036 0.119 0.195 0.778

Measurement model estimates, t and R2 values

Factors Unstandardised loading Standardised loading t-value R 2
People (PEOP)
PEOP1 1.08 0.70 10.44 0.47
PEOP2 0.82 0.48 9.36 0.23
Script (SCRPT)
SCRPT1 1.56 0.78 21.34 0.61
SCRPT2 1.51 0.76 20.28 0.58
Features (FEAT)
FEAT1 1.32 0.66 13.37 0.44
FEAT2 1.40 0.70 14.54 0.49
Price (PRC)
PRC1 1.59 0.74 27.90 0.55
PRC2 1.51 0.70 26.27 0.49
PRC3 1.82 0.83 37.54 0.69
PRC4 1.34 0.85 43.01 0.72
PRC5 1.65 0.79 33.56 0.62
Promotion (PROM)
PROM1 0.98 0.54 14.91 0.29
PROM2 1.35 0.77 23.88 0.60
PROM3 0.93 0.59 16.40 0.35
PROM4 1.17 0.68 20.30 0.47
Distribution (DIST)
DIST1 1.16 0.61 16.80 0.37
DIST2 1.11 0.64 16.92 0.41
DIST3 1.28 0.67 20.29 0.45
DIST4 1.18 0.57 16.47 0.32
Extraversion (EXTR)
EXTR1 1.30 0.83 26.50 0.69
EXTR2 1.16 0.75 22.12 0.56
EXTR3 1.19 0.78 23.01 0.61
Intention (INT)
INT1 0.97 0.73 17.35 0.54
INT2 1.06 0.87 20.27 0.76
INT3 0.88 0.81 15.27 0.66
WOM (WOM)
WOM1 1.08 0.78 20.85 0.61
WOM2 0.99 0.83 19.34 0.68
WOM3 0.97 0.69 16.92 0.48

Comparison of square roots of AVE’s and correlations to assess discriminant validity

Price Distribution Promotion Script Features People Intention WOM Extraversion
Price 0.79 0.499 0.287 0.251 0.150 0.166 0.087 0.057 0.134
Distribution 0.56 0.60 0.334 0.180 0.116 0.164 0.241 0.135 0.170
Promotion 0.35 0.43 0.66 0.200 0.166 0.217 0.207 0.217 0.181
Script 0.31 0.22 0.27 0.78 0.260 0.192 −0.16 −0.37 0.104
Features 0.21 0.15 0.24 0.41 0.68 0.179 0.065 0.100 0.112
People 0.21 0.27 0.35 0.28 0.30 0.60 0.185 0.138 0.128
Intention 0.12 0.29 0.28 −0.01 0.01 0.20 0.81 0.398 0.202
WOM 0.06 0.14 0.25 −0.03 0.17 0.20 0.40 0.77 0.220
Extraversion 0.17 0.21 0.24 0.15 0.13 0.18 0.24 0.25 0.79

Notes: Square root of average variance extracted (AVE) is shown on the diagonal and in italic; correlation coefficients are shown on the off diagonal; all correlations are significant at the 0.01 level

Evaluation of the reliability of the constructs

Construct Number of items Cronbach’s α Composite reliability
People 2 0.493 0.496
Features 2 0.632 0.663
Script 2 0.797 0.797
Price 5 0.888 0.888
Promotion 4 0.663 0.745
Distribution 4 0.709 0.730
Intention 3 0.841 0.884
Extraversion 3 0.830 0.831
WOM 3 0.804 0.805

The principle component loadings on the first component

Attendance Items Loadıng
ATT1 Listen to popular music 0.143
ATT2 Listen to classical music 0.440
ATT3 Go to pop concerts/discos 0.481
ATT4 Go to classical music concerts/opera 0.679
ATT5 Play a musical instrument 0.484
ATT6 Go to museums or art galleries 0.681
ATT7 Go to the cinema 0.474
ATT8 Act or otherwise take part in theatre 0.504
ATT9 Read about art in newspapers, magazines or books 0.529
ATT10 Draw or paint 0.554
ATT11 Read a novel 0.629
ATT12 Read non-fiction books 0.597
ATT13 Read poetry 0.613
ATT14 Go to the theatres (plays/musicals, etc.) 0.699
ATT15 Go to classical or modern ballet/dance 0.706
ATT16 Go dancing (any form) 0.608

Hypothesis of the structural model, unstandardised and standardised path coefficients, hypothesis testing results and SMC values

Hypothesis Unstandardised path coefficient Standardised path coefficient SE t-value Result SMC value
H1: people→purchase ıntention 0.14 0.13 0.07 2.14 Supported 0.17
H2: features→purchase ıntention −0.03 −0.02 0.06 −0.46 Unsupported
H3: script→purchase ıntention −0.12 −0.11 0.06 −2.06 Supported
H4: price→purchase ıntention −0.04 −0.04 0.06 −0.77 Unsupported
H5: promotion→purchase ıntention 0.23 0.21 0.06 3.54 Supported
H6: distribution→purchase ıntention 0.21 0.19 0.07 2.96 Supported
H7: attendance→purchase ıntention 0.06 0.12 0.02 3.26 Supported
H8: attendance×people→purchase ıntention 0.01 0.03 0.02 0.61 Unsupported
H9: attendance×features→purchase ıntention 0.02 0.05 0.02 1.30 Unsupported
H10: attendance×script→purchase ıntention 0.02 0.06 0.01 1.67 Unsupported
H11: attendance×price→purchase ıntention −0.02 −0.06 0.02 −1.14 Unsupported
H12: attendance×promotion→purchase ıntention −0.05 −0.10 0.02 −2.18 Supported
H13: attendance×distribution→purchase ıntention −0.03 −0.07 0.02 −1.47 Unsupported
H14: intention→WOM 0.38 0.38 0.05 7.51 Supported 0.19
H15: extraversion→WOM 0.20 0.18 0.05 4.05 Supported

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Further reading

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Nelmes, J. (2012), Introduction to Film Studies, Routledge, New York, NY.

Acknowledgements

This research was supported in part by grants from the Trakya University Scientific Research Projects Unit by No. 2013/103.

Corresponding author

Elif Ulker-Demirel is the corresponding author and can be contacted at: elifulker@trakya.edu.tr

About the authors

Elif Ulker-Demirel (PhD, Business Administration, Trakya University) is an Assistant Professor at Trakya University, Edirne, Turkey. Her research interests include cultural and art industries, consumer behaviour.

Ayse Akyol (PhD, Marketing, University of Portsmouth) is a Professor at Trakya University, Edirne, Turkey. Her research interests include international marketing, marketing research, contemporary issues in marketing.

Gülhayat Gölbasi Simsek (PhD, Statistics, Marmara University) is a Professor at Yıldız Technical University, Istanbul, Turkey. Her research interests include multivariate data analysis, statistical modelling and simulation.