Sustainable Moviegoer Intention to Attend Cinemas Based on the Theory of Planned Behavior

Abstract

The present research aims to determine which factors of the theory of planned behavior most influence the intention to watch Mexican movies, and, at the same time, to measure the impact of eWOM and the level of audience involvement in the intention. For this purpose, an online questionnaire was applied through social networks, obtaining a sample of 334 Mexican people over 18 years old. The data were analyzed using a partial least squares structural equation model (PLS-SEM). The results confirmed that the variables that explained the intention to watch Mexican movies were attitude, perceived purchase control, and involvement, with the latter being the attitude variable the one that contributed the most to intention. The present research contributes to the literature on movie consumption in Mexico with an empirical perspective from the marketing field.

1. Introduction

In Mexico, the footprint left by the cinema is undeniable, as it constitutes a window for the study of societies and sustainable business. Currently, the world of cinema includes topics on the environment and the relationship between man and the planet [1], namely sustainability, and sustainable development in the film industry is conditioned by a variety of cultural promotion channels. These situations show the interest in working under a sustainable approach, evidencing actions that cinema has influenced from different perspectives, styles, and genres throughout its history [2],

These actions present the world we inhabit in images that surprise and amaze, but also consider more conceptual pieces that invite critical reflection on the intervention and life of man on the planet. In the report of the European Commission [3], it is stated that cinema is seen as an entertainment product and, at the same time, as a cultural product, just like television, ballet, museums, and concerts, which have been maintained over time. According to the Culture Satellite Account of the National Institute of Statistics, Geography and Informatics (2020) [4], the cultural sector in Mexico represents 3.1% of the gross domestic product (724,453 million pesos). Of this total, the audiovisual industry—cinema, internet, radio, and television—represents 36.9%, with cinema being one of the most important factors. In addition, film production in Mexico has boosted the country’s financial and economic growth and development, through the dissemination of Mexican culture around the world; for example, through the impact on the tourism industry because of the landscapes shown in films or on mining due to the demand for the mineral compounds needed to produce films, such as silver compounds [5,6],

Previous literature has investigated numerous predictive indicators to measure movie viewing intention, with unsatisfactory results [7,8]. Despite the fact that movies can strongly influence individuals and society, both economically and psychologically, empirical evidence on the social and economic impact of movies is scarce [9,10,11]. Likewise, the authors of [12,13] describe that research on movie ticket purchase intention is scarce and, for this, the study based on the theory of planned behavior (TCP) is essential [14,15]. Therefore, the objective of this study is to determine which factors of the theory of planned behavior most influence the intention to see Mexican movies and, likewise, to measure the impact of electronic eWOM and the level of audience involvement in intention [16,17,18]. This study seeks to contribute, first, to the literature on film consumption in Mexico with an empirical perspective from the field of marketing, and also to provide a basis for Mexican filmmakers to continue their work in this cultural field. The rest of this article is organized as follows: the first section develops the literature on the study variables to support the hypotheses. In the second section, the methodology applied is described, followed by the results obtained and, finally, conclusions, discussions, limitations, and future lines of research are established.

2. Background

2.1. Sustainable Purchase Intention and Cinema

The concept of responsible consumption is complex and is characterized by a great diversity of practices. The first definition was coined by Fisk (1973) [19], who referred to responsible consumption as the rational and efficient use of resources with respect to the global human population. Sustainable consumption, which is at the center of economic, social, ecological, and ethical debates, is increasingly being challenged by anti-consumption movements [20]. The idea of sustainable consumption has received a great deal of attention [21]. International policy organizations (e.g., Organization for Economic Cooperation and Development, the United Nations, and Commission for Sustainable Development) and international research programs (e.g., in the United States, Norway, and the Netherlands) are of the view that current consumption patterns and levels are not ecologically sustainable [22,23]. This is evident in nascent efforts not only to promote sustainable consumption, but also to understand how sustainability changes can be undertaken.

According to [24], cinema, like any other cultural industry, consists of the design, production, and commercialization of manufactures destined to the transmission of symbolic contents, whose activities are conditioned by the systems of the economic, cultural, and political conditions of each country, and interrelated with the industries dedicated to culture, social communications, and entertainment. For its part, marketing plays an important role in the opening weekend of a movie, so the consumer’s perceived value and benefits of watching a movie at the cinema will determine their purchase intention [9,25].

Therefore, making significant progress towards sustainability requires radical solutions that go beyond the development of new goods or services, and the substitution of products in the market to establish a holistic approach that promotes and encourages the acceptance and adoption of sustainable practices [26,27]. Because of the above, the perspectives of consumer behavior in companies with sustainable viewers propose that this type of company must adopt innovative strategies in line with the challenging demands of the environment [28]; therefore, traditional organizations yield their space to the incorporation of mechanisms that provide answers to the needs of the more sustainable business world, in order to become intelligent, sustainable, proactive, dynamic, creative, and decentralized organizations, where this type of human behavior is the fundamental piece for the achievement of organizational objectives.

Through marketing strategies, this encourages organizations to manage strategies with platforms for sustainability, and there is a connection between sustainability and eWOM [19], and actions that are aligned with the challenges that the environment demands. However, the sustainable development of the film industry remains a great challenge, and resources must be used in a better, more reasonable, and planned way. To this end, Dudukovic et al. [20] used spatial decision support system techniques based on geographic information systems to help achieve the proper location of newly planned industries

The software tool is of great use for developing countries, where sustainable industrialization is still in its premature stages. Sotoudeh et al. [21] describe the importance of technologies in sustainable development. For example, some strategies that support cleaner production and the European Union’s Environmental Technologies Action Plan (ETAP).

Regarding purchase intention, the authors of [22] mention that intention predicts future behaviors of consumer behavior, and, in turn, the consumer, when presenting demands, will seek relevant information considering personal experiences and external environments to satisfy them. It usually refers to an individual’s willingness to perform a specific act, which is perceived as the premise for direct action [29,30]. Therefore, understanding people’s intentions and the reasons they are willing to use a product or service is critical for marketers and marketing stakeholders [25,26]. In addition, intentions capture the motivational factors that influence a behavior, in other words, they indicate how much people are willing to try and how much effort they plan to make to perform a certain behavior [9]. Indeed, consumer behavior is determined by consumer intention [18]. In such a sense, the viewer’s intention to attend a cinema will be influenced by their attitudes, subjective norms, and perceived behavioral control.

E-commerce is a reality today. Companies with a physical presence have realized the opportunities offered by e-commerce and have incorporated this channel into their traditional channels [31]. On the other hand, companies have been created that try to exploit this sales channel exclusively. The theory of planned behavior (TPB), proposed by some authors, seeks to predict a given behavior by taking into account both internal and external factors that enable, contextualize, and constrain the action [31,32,33,34]. According to [32], social science researchers recommend using TPB when studying consumer behavior [35], which assumes that intention is considered as a precursor to and the best predictor of behavior. However, this model postulates that the intention to perform a given behavior is the most immediate antecedent and the best predictor of the actual behavior performance [36].

According to this model, an individual’s behavior is explained in terms of his or her intention [34]. The latter, in turn, is conditioned by three factors:

(a)

the positive or negative attitude towards a given action (attitudes).

(b)

the perceived approval or disapproval of the behavior in question by the people who make up the individual’s environment (subjective norm).

(c)

the perception about the ability to influence the final outcome based on the tangible and intangible resources required to carry out the action (perception of control).

These three factors are interrelated. The relative influence of each on the intention to act, and on the action itself, varies depending on the type of behavior being studied and on the context-specific situations [22,31].

The TPB model has been used and validated in other studies to analyze an individual’s behavioral intention [22,37,38,39,40,41,42]. According to Ajzen (2011) [43], adding new variables to the theory of planned behavior model should improve the prediction of intention and behavior, as there is a gap between intention–behavior, and the model by itself is not capable of predicting behaviors that are not driven by intentions such as habits and impulsive purchases [44]. For this reason, several studies have tried to improve the predictive power of the model by adding new variables [32,36,42,43,45].

Within the field of filmmaking, TPB has been applied in various research linked to how purchase and consumption decisions are made for various products and services, and thus test its relevance to different populations, behaviors, and product categories [31]. For example, this model has been used to study green product consumption intention [46], online service adoption [47], and financial institution choice patterns. On the other hand, the initial constructs of the model have been extended in order to better explain certain specific phenomena, such as the impact of e-WOM on tourist destination selection. Similarly, the authors of [30] included the original hedonism model as an additional factor in order to explain young people’s loyalty to cellphones.

2.2. Attitude

Attitude refers to people’s opinion or perception towards something; in this study, it refers to people who simply have a favorable attitude towards attending the cinema to watch Mexican movies [26]. Previous studies have shown a strong relationship between attitude and intention in the contexts of digital platform use and in the purchase of organic products [27,28,29], green products [30], and luxury goods [31]. Likewise, attitude in an art product context (cinema and television) has been shown to play a key role in explaining the purchase of such products [32]. With reference to movie attitude, the authors of [32] mention that this has a direct and significant influence on the intention to watch movies. Based on the above, the following hypothesis emerges:

Hypothesis 1 (H1). 

Attitude positively affects the intention to watch Mexican movies.

2.3. Subjective Norms

Schiffman and the authors of [33] define subjective norms as the feelings perceived by individuals regarding what other important people, such as family, friends, or classmates, would think of the action they perform, i.e., whether they would rate it as favorable or unfavorable, which represents the motivation to please the expectations of these people through their behavior. The authors of [34,48] define these social expectations as normative beliefs, which refer to the subjective expectation or probability that given a group referent, be it friends, family, partner, or coworkers, they would approve or disapprove of this behavior. Therefore, in the context of movies, the higher the evaluation of perceived subjective norms (significant others have a positive or negative opinion towards the behavior), the higher the intention to watch movies [18,32]. To exemplify the above, the authors of [18] found that friends were the social figures that influenced participants’ intention to buy movie tickets the most, as young participants spend more time with friends and people of the same age group and, therefore, they considered the influence of friends to be reliable. Similarly, the authors of [48] deduced that the social environment in which young people share—family, friends, and school or workmates—can influence their buying behavior. Because of the above considerations, the following hypothesis is proposed:

Hypothesis 2 (H2). 

Subjective norms positively affect the intention to watch Mexican movies.

2.4. Perceived Behavioral Control

As for perceived behavioral control, the authors of [49] describe it as people’s perception of the ease or difficulty of performing the behavior of interest, as well as the resources and opportunities available for behavioral achievement. Likewise, perceived behavioral control refers to the control that the consumer has over his or her purchase decisions, in other words, whether the individual has the capacity or sufficient resources—money, time, and skills—to perform a specific behavior [50,51]. According to [37], the perceived behavioral control variable can be added to the CTP model to increase the power to predict whether an individual will perform a given behavior or not, especially in complex behaviors.

For their part, the authors of [18] mention that perceived behavioral control is formed by control beliefs, i.e., the various factors that support an action/behavior, and control power, i.e., one’s strength related to the factors that support the behavior. With reference to cinema, perceived purchase control reflects the viewer’s willingness to watch a given movie, if he/she possesses the purchasing power, time, and money to spend it on movie attendance [39,40]. Based on the above, the following hypothesis is proposed:

Hypothesis 3 (H3). 

Perceived behavioral control positively affects the intention to watch Mexican movies.

2.5. eWOM (Electronic Word of Mouth)

Word-of-mouth has been studied in marketing-focused literature, providing informal positive or negative communication between consumers about objectively or subjectively perceived characteristics of products, brands, and services [52]. According to [53], word of mouth is based on trust and on the fact that the sender and the recipient of the recommendations come from the same social environment. As for the context of electronic word-of-mouth (eWOM), it is described as any positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the internet in various environments (email, social networks such as Facebook, and newsgroups) [54]. However, from a film perspective, word-of-mouth has been cited as the most important factor determining the long-term success of films and other experiential goods [9]. In addition, electronic word-of-mouth about a film begins to build prior to its release, usually on websites such as the Hollywood Stock Exchange. Other times, the preliminary version of electronic word-of-mouth presented on websites, blogs, and social media channels reflects the extent to which potential consumers know and show interest about a specific movie before its theatrical release, decreasing the risk of box office failure [11]. Based on the above, the following hypothesis is proposed:

Hypothesis 4 (H4). 

eWOM positively affects the intention to watch Mexican movies.

2.6. Involvement

According to [32], involvement is seen as an enduring state of emotional attachment that is intrinsically motivated by the congruence between the product and the self-image, or by the pleasure obtained from thoughts related to the product and its use. The authors of [21] mention that involvement consists of individual concerns about an activity, so it is used to understand personal consumption behaviors and decision processes.

Previous studies have shown that involvement has a positive impact on purchase intention, i.e., consumers who are highly involved in movies can easily distinguish movies with high quality content [55]. Likewise, audience engagement affects other consumers in a variety of ways, such as manifesting pleasure or liking when watching a movie, satisfaction, and post-viewing behavioral intentions [56]. Audience involvement may affect intentions to visit places that are seen in movies, given that movie-induced travel is highly dependent on the level of audience involvement. Based on the above, the following hypothesis is proposed:

Hypothesis 5 (H5). 

Consumer involvement in movies positively influences the intention to watch Mexican movies.

Figure 1 shows the proposed study model.

3. Materials and Methods

The partial least squares structural equation model (PLS-SEM) was chosen for the data analysis. PLS-SEM has received appreciation from marketing scholars in interdisciplinary research, and PLS-SEM is the most suitable choice to predict the consumer purchase intention [57]. The PLS-SEM model is also used when there is little previous evidence regarding the relation between the included variables [58]. The first step of the PLS-SEM model was to measure the reliability for each of the reflective constructs of the model by means of an internal reliability and composite reliability analysis. Next, the structural model was evaluated by determining the strength and direction (positive or negative) of the causal relationships established between the constructs by evaluating the path coefficients, using bootstrapping with 5000 subsamples and a significance level of 0.05 [59]. Next, the predictive power of the model was evaluated through the adjusted R2, for which, according to [58], values of 0.25, 0.50, and 0.75 are considered weak, moderate, and important, respectively. Subsequently, the effect size f2 was identified, which according to Cohen (1992) values of 0.02, 0.15, and 0.35 are considered small, moderate, and large effects, respectively. Finally, the predictive relevance was evaluated by means of the Q2 indicator, which should be positive; in addition, the Q2 effect of each construct was examined, and values of 0.02, 0.15, and 0.35 indicate low, medium, and high predictive relevance, respectively [59].

The type of sampling employed was non-probabilistic, applying the convenience [44]. As it allows for easy access, geographic proximity, time availability, and willingness to participate [60], the snowball technique, with the purpose of facilitating access to the hidden population, expands the sample size and scope of the study. In addition to reducing cost and time, it invites users to participate voluntarily and share the survey with their Facebook contacts [61,62]. In addition, the use of this type of sampling allows for obtaining data that cannot be obtained by random sampling and, especially, when the population is very large, as in the present study [48]. Additionally, the snowball technique was employed has been employed by other stakeholders to support in the distribution of the instrument [63].

Sample and Measurement Instrument

The sample obtained was of 334 people over 18 years of age, most of whom reside in northeastern Mexico, of both genders, and who also go to the conventional cinema. However, in the present study, PLS SEM was used to analyze the data, as it can be applied to small samples as it performs estimates of the model structures and not of all the parameters at the same time [64]. To determine the minimum sample size for the PLS SEM, the statistical power, the level of significance, the size effect, and the number of predictors were specified [61]. A statistical power of 80% was specified with a significance level of 5%, as these are suitable for studies focused on consumer behavior [65]. In addition, five predictors were considered according to the model (attitudes, subjective norms, perceived purchase control, eWOM, and involvement), so the required size was 92 observations, meeting the minimum requirement [66]. As a method of data collection, an online survey was applied through Google Forms. The advantage of conducting online surveys is the low cost, it allows for feedback in a faster way, it has a better coverage, it is done in less time, and allows the researcher to contact the sample group faster [66]. The survey was sent through Facebook and WhatsApp, as according to [61], “Facebook” users are more likely to disclose data and share information; in addition, it has been used as a tool for research in many fields of knowledge. Furthermore, online surveys or web surveys are strategies used for obtaining primary data that can be conducted using email address records, user panels on the Internet, and for the dissemination of the research link through websites or social networks [67,68].

However, it is worth mentioning that because of the COVID-19 contingency, which caused the elimination of physical contact with other people and a greater awareness of the presence of germs in public areas, leading people to question the need to meet for personal or business reasons, the use of online platforms such as Zoom, WebEx, Skype, FaceTime, and Loom [69] was considered to apply the measurement instrument. According to [68], online surveys have been used during this contingency because of their low cost of implementation and the ease of handling the information by respondents and researchers.

Previously, data analysis was performed in IBM SPSS to analyze the demographic characteristics of the sample, which can be seen in

Table 1. Demographic profile of survey respondents. Source: own elaboration.

Variables	Domain	Frequency	Percentages
Sex	Women	222	66.5
	Men	112	33.5
Civil Status	Single	233	69.8
	Married	61	18.3
	Divorced	2	0.6
	Unmarried	36	10.8
	Widowed	2	0.6
Educational Level	Bachelor’s degree	212	63.5
	Graduate degree	53	15.9
	High School	64	19.2
	High School	4	1.2
Occupation	Homemaker	13	3.9
	Student	74	22.2
	Private Employee	78	23.4
	Public employee	59	17.7
	Laborer	4	1.2
	Merchant	13	3.9
	Retired	3	0.9
	Unemployed	10	3.0
	Various trades	12	3.6
Income	From 0 to 3080 monthly	94	28.1
	From 3080 to 15,429	175	52.4
	More than 15,429	65	19.5
State of residence	Tamaulipas
	Nuevo León
	Coahuila
	Distrito Federal

. Of the 334 responses, 66.5% were female and 33.5% were male. In addition, 69.8% were single, 18.3% were married, 10.8% were cohabiting, and 0.6% were divorced. Furthermore, 63.5% had a higher level of education, 15.9% had a graduate degree, 19.2% had a high school education, and the remaining 1.2% had a high school education. Regarding their occupation, 23.4% were employees of private companies, 17.7% were retired, 22.2% were students, 3% were unemployed, 3.9% were housewives, 3.9% were merchants, and the rest worked in public companies or other trades. Likewise, 52.4% earned between 3080 and 15,429 pesos, 28.1% earned 3080 pesos on average, and 19.5% earned more than 15,429 pesos per month. In terms of age, 35.17% were between 17 and 27 years old, 39.53% were in an age range of 28 and 38 years, 17.73% were between 39 and 49 years old, 6.10% were between 50 and 60 years old, and 1.45% were between 61 and 71 years old.

Regarding the design of the instrument, the first part of the survey presents a filter question where respondents are asked to indicate which movies they regularly attend, as well as a list of Mexican films in order to identify how related respondents are to the product [70,71]. The lists of films were taken from statistical data available in the annual reports of the National Chamber of the Film Industry from 2012 to date, as well as from the Statistical Yearbook of the Mexican Institute of Cinematography from 2010 to 2019. Some of the films with more viewers of each year since 2010 were chosen, which had had over one million attendees in theaters; in addition, those found in the National Survey of Consumption and Perception of Mexican Cinema [72] were taken into account, as well as journalistic notes from specialized media such as Cinepremiere (2011) and Forbes (2013) [73]. The above established a sample that could be considered somewhat representative of the Mexican population.

The second part corresponds to the items related to the [36] (Table 2) theory of planned behavior model, namely: attitude towards the stimulus product, social norms, perceived level of purchase control, and intention to see Mexican films in the cinema, as well as the variables of involvement and electronic word-of-mouth (eWOM). A five-point Likert scale was applied to all items, where 1 is “strongly disagree” and 5 is “strongly agree”. Regarding the measurement of the attitude, social norms, and perceived purchase control variables, they were based on the items used by [39,58,59,60,61,62,63,64].

Regarding the purchase intention variable, this was based on the items proposed by [74,75]. As for the EWOM and involvement variables, they were based on the items used by [76]. In the third part, which refers to consumption habits and preferences, reagents were adapted from the survey conducted by [77,78] on cinema consumption habits, as well as from the National Survey of Consumption and Perception of Mexican Cinema [79,80,81,82]. Finally, demographic data were requested from the participants concerning their place of residence, age, gender, occupation, degree of studies, marital status, and income. Data collection took place between 15 January and 5 March 2021.

Table 2. Study measurements.AC: Attitude; NS: Subjective norm; CCP: Perceived behavioral control; INT: Intention; INVOL: Involvement.

Items	Measurement Items	Adopted From
Attitude
AC1	I think that watching a Mexican movie at the cinema is a good idea.	[36,53]
AC2	I think that watching a Mexican movie at the cinema is funny.
AC3	I like the idea of watching a Mexican movie at the cinema.	[83]
AC4	Watching a Mexican movie at the cinema is a pleasant experience
Subjective norms
NS1	When I go to the cinema, I usually watch a Mexican movie if my friends watched it.	[11]
NS2	When I go to the cinema, I usually watch a Mexican movie if my family watched it.
NS3	When I go to the cinema, I usually watch a Mexican movie after seeing the reviews on social media.
NS4	When I go to the cinema, I usually watch a Mexican movie because some influencer, actor, or actress recommended it.
Perceived Behavior Control
CCP1	I can easily identify which movie is Mexican when I go to the cinema.	[11,83]
CCP2	Watching a Mexican movie is entirely up to me.
CCP3	If the Mexican movie that I want to watch is not in a cinema, I would go to another cinema to see it.	[11]
CCP4	It is likely that I would pay to see a Mexican movie in the cinema.
Purchase intention
INT1	If I have a choice between watching a Mexican movie in the cinema or online, I will choose in the cinema.	[84]
INT2	If I can choose between a Mexican and a foreign film, I will choose the Mexican film.
INT3	If I went to the cinema today, I would choose to watch a Mexican movie.	[85]
INT4	I plan to watch Mexican movies in the cinema in the future.	[46]
eWOM
EWOM1	To make sure before watching a Mexican movie in the cinema, I often read the reviews of other people who have previously seen it.	[82,86,87]
EWOM2	To choose a Mexican movie when I go to the cinema, I usually read the opinions of other people on social networks.
EWOM3	If I do not read other people’s reviews about a Mexican movie before watching it at the cinema, I am worried about my decision.	[86]
EWOM4	When I go to the cinema to watch a Mexican movie, I often read other people’s reviews to find out the good impressions.
Involvement
INVOL1	Mexican movies are interesting.	[11]
INVOL2	When I watch a Mexican movie, I feel myself as being part of the story.
INVOL3	I feel comfortable watching Mexican movies and as if the characters are my friends.
INVOL4	I read what is published on the internet or media about Mexican movies.
INVOL5	When I watch a Mexican movie, I encourage my friends to see it.
INVOL6	When I watch a Mexican movie, I talk about the story or the characters with friends or family.

Table 2. Study measurements.AC: Attitude; NS: Subjective norm; CCP: Perceived behavioral control; INT: Intention; INVOL: Involvement.

Items	Measurement Items	Adopted From
Attitude
AC1	I think that watching a Mexican movie at the cinema is a good idea.	[36,53]
AC2	I think that watching a Mexican movie at the cinema is funny.
AC3	I like the idea of watching a Mexican movie at the cinema.	[83]
AC4	Watching a Mexican movie at the cinema is a pleasant experience
Subjective norms
NS1	When I go to the cinema, I usually watch a Mexican movie if my friends watched it.	[11]
NS2	When I go to the cinema, I usually watch a Mexican movie if my family watched it.
NS3	When I go to the cinema, I usually watch a Mexican movie after seeing the reviews on social media.
NS4	When I go to the cinema, I usually watch a Mexican movie because some influencer, actor, or actress recommended it.
Perceived Behavior Control
CCP1	I can easily identify which movie is Mexican when I go to the cinema.	[11,83]
CCP2	Watching a Mexican movie is entirely up to me.
CCP3	If the Mexican movie that I want to watch is not in a cinema, I would go to another cinema to see it.	[11]
CCP4	It is likely that I would pay to see a Mexican movie in the cinema.
Purchase intention
INT1	If I have a choice between watching a Mexican movie in the cinema or online, I will choose in the cinema.	[84]
INT2	If I can choose between a Mexican and a foreign film, I will choose the Mexican film.
INT3	If I went to the cinema today, I would choose to watch a Mexican movie.	[85]
INT4	I plan to watch Mexican movies in the cinema in the future.	[46]
eWOM
EWOM1	To make sure before watching a Mexican movie in the cinema, I often read the reviews of other people who have previously seen it.	[82,86,87]
EWOM2	To choose a Mexican movie when I go to the cinema, I usually read the opinions of other people on social networks.
EWOM3	If I do not read other people’s reviews about a Mexican movie before watching it at the cinema, I am worried about my decision.	[86]
EWOM4	When I go to the cinema to watch a Mexican movie, I often read other people’s reviews to find out the good impressions.
Involvement
INVOL1	Mexican movies are interesting.	[11]
INVOL2	When I watch a Mexican movie, I feel myself as being part of the story.
INVOL3	I feel comfortable watching Mexican movies and as if the characters are my friends.
INVOL4	I read what is published on the internet or media about Mexican movies.
INVOL5	When I watch a Mexican movie, I encourage my friends to see it.
INVOL6	When I watch a Mexican movie, I talk about the story or the characters with friends or family.

4. Results

4.1. Model Assessment

First, confirmatory analysis was performed to assess the goodness-of-fit of the saturated model [88].

Table 3. Valuation of the model. Source: own elaboration.

Saturated Model	Value	95%	99%
SRMR	0.06	0.040	0.042
d_ULS	1.766	0.377	0.417
d_G	0.796	0.309	0.326

Note: Analysis was performed with a full bootstrapping test with 5000 subsamples, and a one-tailed test with a significance level of 5%.

shows the results of the analysis, where the SRMR value was below the recommended threshold value of 0.080, and dULS and dG should be less than the 95% quantile of the bootstrap discrepancies [89]. The data obtained indicate a good model fit [46], so we proceeded to perform the assessment of the measurement model.

4.2. Internal Consistency and Reliability

For continuity, the analyses of the internal reliability of the data and composite reliability were performed, confirming the robustness of the data.

Table 4. Model measurement results. Source: own elaboration. Items, Factor Loadings, Composite Reliability (FC), Average Variance Extracted (AVE), Heterotrait–Monotrait ratio (HTMT).

	Convergent Validity			Internal Consistency		Discriminant Validity
Construct	Item	Loads >0.70	AVE >0.50	FC 0.60–0.90	Alpha 0.60–0.090	HTMT
Attitude	ACT1	0.937
	ACT2	0.929	0.868	0.963	0.949	Yes
	ACT3	0.934
	ACT4	0.926
Subjective norms	NS1	0.809
	NS2	0.839	0.637	0.875	0.811	Yes
	NS3	0.827
	NS4	0.711
Perceived behavioral control	CCP1	0.313
	CCP2	0.862	0.571	0.828	0.724	Yes
	CCP3	0.784
	CCP4	0.910
Involvement	INVOL1	0.846
	INVOL2	0.804	0.632	0.911	0.882	Yes
	INVOL3	0.752
	INVOL4	0.692
	INVOL5	0.856
	INVOL6	0.807
EWOM	EWOM1	0.909
	EWOM2	0.903	0.723	0.911	0.876	Yes
	EWOM3	0.676
	EWOM4	0.890
Purchase intention	INTEN1	0.760
	INTEN2	0.823	0.708	0.926	0.896	Yes
	INTEN3	0.877
	INTEN4	0.869
	INTEN5	0.873

shows that the composite reliability values met the recommended threshold of 0.70 [84,85], except for items CCP1, INVOL4, and EWOM3, which were eliminated, and the composite reliability analysis was performed again (

Table 5. Model measurement adjustment. Source: own elaboration. Items, Factor Loadings, Composite Reliability (FC), Average Variance Extracted (AVE), Heterotrait–Monotrait ratio (HTMT).

Construct	Item	Loads >0.70	AVE >0.50	FC 0.60–0.90	Alpha 0.60–0.090	HTMT
Attitude	ACT1	0.937
	ACT2	0.929	0.868	0.963	0.949	Yes
	ACT3	0.934
	ACT4	0.926
Subjective norms	NS1	0.800
	NS2	0.831	0.717	0.884	0.813	Yes
	NS3	0.836
	NS4	0.714
Perceived behavioral control	CCP2	0.853
	CCP3	0.794	0.734	0.892	0.819	Yes
	CCP4	0.920
Involvement	INVOL1	0.859
	INVOL2	0.804	0.676	0.912	0.880	Yes
	INVOL3	0.757
	INVOL5	0.862
	INVOL6	0.824
EWOM	EWOM1	0.926
	EWOM2	0.906	0.821	0.932	0.893	Yes
	EWOM4	0.885
Purchase intention	INTEN4	0.957	0.921	0.959	0.914	Yes
	INTEN5	0.962

).

Likewise, the Cronbach’s alpha values were between 0.724 and 0.949, confirming the value recommended by Hair Jr. et al. (2019). Regarding convergent validity, this is given when all the items of the construct present loadings higher than 0.70, a communality of the items, and the average extracted variance of the construct is higher than 0.50 [36].

4.3. Convergent Validity

Regarding discriminant validity, three criteria were considered [86]. The first criterion is the Fornell–Larcker criterion, which requires that the square root of AVE (

Table 6. Fornell and Larcker criterion. Source: own elaboration. AC: Attitude; NS: Subjective norm; CCP: Perceived behavioral control; INT: Intention.

	Act	CCP	INT	Involvement	Subjective Norms	eWOM
Act	0.932
CCP	0.789	0.857
INT	0.792	0.825	0.842
Involvement	0.752	0.723	0.804	0.822
Subjective norms	0.494	0.435	0.476	0.552	0.798
eWOM	0.155	0.077	0.171	0.322	0.400	0.905

, in bold) for each latent variable is greater than the correlation with any other latent variable, meaning that a construct must share more variance with its measures than it shares with another construct in a given model [87].

The second criterion is that the loading of each indicator is expected to be greater than all its cross-loadings, and

Table 7. Cross-loads analysis. Source: own elaboration. ACT: Attitude; NS: Subjective norm; CCP: Perceived behavioral control; INT: Intention.

	ACT	CCP	INT	Involvement	NS	eWOM
ACT1	0.937
ACT2	0.929
ACT3	0.934
ACT4	0.926
CCP2	0.668	0.860
CCP3	0.532	0.794
CCP4	0.803	0.913
EWOM1						0.930
EWOM2						0.907
EWOM4						0.878
INT1			0.760
INT2			0.823
INT3			0.878
INT4			0.868
INT5			0.873
INVOL1				0.857
INVOL2				0.812
INVOL3				0.764
INVOL5				0.858
INVOL6				0.817
NS1					0.809
NS2					0.839
NS3					0.827
NS4					0.711

shows that each construct has loadings with higher values than its cross-loadings. Finally, the third criterion is the Hetrotrait–Monotrait ratio analysis (HTMT), which is applied as a result of the criticisms that have arisen regarding the Fornell–Larcker test [90].

Likewise,

Table 8. Criteria for Heterotrait–Monotrait ratio (HTMT). Source: own elaboration. ACT: Attitude; NS: Subjective norm; CCP: Perceived behavioral control; INT: Intention; INVOL: Involvement.

	Act	CCP	INT	Involvement	NS	eWOM
Act
CCP	0.882
INT	0.801	0.842
Involvement	0.816	0.837	0.802
NS	0.542	0.508	0.449	0.643
eWOM	0.160	0.101	0.195	0.354	0.474

shows that there was discriminant validity, as the values obtained were less than 0.90, except for the intention variable, so items INTEN1, INTEN2, and INTEN3 were eliminated in order to obtain a good discriminant validity [91].

4.4. Path Analysis

Subsequently, the relationship between the constructs was determined by applying the PLS bootstrapping algorithm with a complete result, with a subsample of 5000, and a one-tailed t-test, with a significance level of 0.05%. Figure 2 shows the results of the structural model with the path coefficients.

Regarding the above considerations,

Table 9. Hypothesis test results. Source: own elaboration. ACT: Attitude; NS: Subjective norm; CCP: Perceived behavioral control; INT: Intention; INVOL: Involvement.

Hypothesis	Relation	Path Coefficient	Value t	Value p	Construct Size Effect f2	Accepted/ Rejected
H1	ACT → INT	0.315 ***	4.381	0.000	0.086	Accepted
H2	NS → INT	−0.061 ns	1.481	0.069	0.007	Rejected
H3	CCP → INT	0.299 ***	4.168	0.000	0.083	Accepted
H4	INVOL → INT	0.293 ***	4.480	0.000	0.080	Accepted
H5	eWOM → INT	0.042 ns	1.049	0.147	0.004	Rejected

Note: Bootstrapping was performed on 5000 subsamples, full, one-tailed t-value; 1.645 (p < 0.05 *), 2.33 (p < 0.01 **), and 3.092 (p < 0.001 ***), ns: not significant.

shows the results of the hypothesis testing, where it was observed that attitude is the variable that most influences the intention to watch Mexican movies (β = 0.315, p < 0.001), followed by perceived behavioral control (β = 0.299, p < 0.001) and consumer involvement (β = 0.293, p < 0.001). In the case of the variables of subjective norms and eWOM, the p-value was greater than the estimated, so hypotheses 2 and 5 were rejected. Figure 3 shows the path coefficients and p-values.

The predictive power of intention, attitude, perceived purchase control, and involvement on the intention to watch Mexican movies was moderate (adjusted R2 of 0.647), and the construct size effect of attitude, involvement, and perceived behavioral control was small (f2 of 0.086, 0.080, and 0.083, respectively). On the other hand, the model presented a predictive relevance, as Q2 was greater than zero (0.590), which was obtained using the blindfolding algorithm. Therefore, attitude, involvement, and perceived purchase control directly and positively influenced the intention to see Mexican movies in cinemas.

5. Discussion

As previously mentioned, this study aims to test an extended framework of the theory of planned behavior in order to explain the intention to watch Mexican movies. The findings highlight that the attitude of the audience influences the intention to watch Mexican movies. This result is consistent with the past studies that documented the impact of attitude in intention, which considered attitude to be a major influence on consumer intention behavior [32,92]. Mexican consumers feel that going to the movie theater is a pleasant experience, as since they consider themselves a part of it and end enjoy the screen size, audio, seating arrangement, availability of food and beverages, and air conditioning, among other aspects.

The results also show that perceived behavioral control significantly affects purchase intention to watch Mexican movies. It provides the most considerable influence on the intention to watch Mexican movies at the cinema compared with other variables [18]. Furthermore, if the movie is available in theaters, consumers will not see it on digital platforms. According to El Universal (a Mexican newspaper founded in 1916, which is one of the most widely circulated newspapers in Mexico. Retrieved from: https://es.wikipedia.org/wiki/El_Universal_(M%C3%A9xico), accessed on 12 May 2021), Mexicans are more willing to return to movie theaters despite COVID 19, so price promotions can improve profitability and loyalty.

In addition, audience involvement with the intention to watch Mexican movies was found to be significant. Similarly, the authors of [43,93] found that when viewers were interested in the characters of a program, they felt involved in the plot of the story, so if the audience finds that the programs relate to their own experience, they are likely to exhibit a higher level of involvement and increased intention to watch the program in the future.

Regarding, eWOM, the results differ with the studies conducted by [41,91], who enhanced the importance of the use of recommendations on digital platforms and social networks, as these tools are more efficient compared with traditional ones, such as posters, newsletters, and flyers. In this case, the advertisements of the movies on TV or traditional WOM (word of mouth) could be more efficient to encourage Mexicans to watch a movie in cinemas that on digital platforms. Finally, social norms were found no to be significant. It can be interpreted that these audiences’ intentions to watch Mexican movies were not affected by comments from friends or family. This differs from [42,45,94], who determined that subjective norms exert a positive effect on purchase intention.

6. Conclusions

The results of the study show that attitude, perceived behavioral control, and the involvement of the audience in Mexican movies positively affect the intention to see them in cinemas. This reveals that audiences in northeast Mexico have a positive attitude towards Mexican movies, as it offers them a fun time, as expressed by [41,95,96]. Moreover, perceived behavioral control showed that the audience will show a greater intention to attend the cinema if they have the money, time, and availability to watch a Mexican movie. Furthermore, it was observed that the audience is strongly involved in the stories, characters, social issues, and events when they watch a Mexican film, as it reflects their own beliefs, and, as a result, increases their intention to go to the cinema, as [26,97,98] have expressed. It can be said that the attitude variable has an influences of 23.5%, followed by perceived purchase control at 22%, and audience involvement at 21.3% on the intention to see Mexican movies at the cinema.

However, the results showed that the influence of friends, families, coworkers, or people important to the audience were not a determining factor in showing intention to watch Mexican movies in the cinema. Similarly, opinions about the Mexican movies on social networks or certain digital platforms did not influence the intention. The results show that TPB is a useful framework to understand the investigated behavior and especially the intention to watch Mexican movies in cinemas. This is why this study is a ground-breaking study on the film field.

6.1. Theorical Contribution

The present research makes evident the contribution made by academics and researchers, as well as organizations, who bet on the application of TPB [99,100]; the theory of planned behavior has added the perceived behavioral control also known as the locus of control (as a possible equivalent of self-efficacy), which refers to a person’s perceptions about the presence or absence of required resources and opportunities, which leads the person to an evaluation of the situation regarding how likely it is that with their resources, they can perform the behavior, this is in addition to the external factors for which the subject does not have absolute control [101,102]. In addition, the theory of planned behavior has been shown to be superior to the theory of reasoned action in predicting behavior.

Studies on consumer purchase intentions have been some of the most significant and interesting academic approaches for this community; however, the complex decision-making process in terms of intentions to purchase welfare products or services has not been well investigated, leaving interesting expectations for researchers. The results of the present study aligned with previous studies that supported the applicability of the TPB model [102,103,104]. All constructs, including attitude, subjective norms, and perceived behavioral control, were found to contribute significantly to the viewer’s sustainable intention to attend cinemas based on the theory of planned behavior.

6.2. Managerial Implications

Our results have several important implications. The present study is innovative and contributes to the planned behavior theory model by adding two variables that enrich its predictive value. In addition, the study of the behavior of consumers within cinemas in Mexico is relatively new, and there are few works related it [100,103]. This study indicates that distributors and media must work together to improve their messages on social networks and digital platforms in order to attract more audiences to cinemas, for example, encourage their audience to leave a comment on a cinema’s Facebook wall, but also to spread the message on their personal networks. In addition, cinemas should offer a value proposition so that movie theaters can continue to play an important role in being the primary outlet for watching at least some types of filmed entertainment—it is important to point out that Mexicans like going to movies with friends, family, and children as a way to spend time with them out of the house and to have the experience of watching a movie in a big screen, with other people in the screening room, and to eat popcorn and beverages at the same time and place. To increase the audience’s attention, eye-catching titles and posters should be presented. Furthermore, it is important that producers and distributors consider deals for Mexican movie screenings in several cinemas, as movie theatres’ session times and ticket costs can be difficult to adjust in terms of audiences [104]. Moreover, the movie theater industry should adopt digital screening technology and the use of big data to better know the audience and be in touch with them.

6.3. Limitations and Future Research

The present study has some limitations. First, the study was conducted in the northeast of Mexico, so it cannot be replicated in other parts of Mexico or other countries. Second, the study focused on measuring the intention to watch Mexican movies, so future studies should measure a different movie genre. Third, the sample size was small, and the majority were young people between 20 and 30 years old. Therefore, the sample cannot be further expanded, and comparative studies between youth, young adults, adults, and older adults need to be carried out. Fourth, the model study was based on TPB, so it would be useful to add other variables that could improve the predictable power, such as movie experience, environmental sentiment, flow, entertainment, and trust.

Finally, as mentioned above, this study is a ground-breaking in the film field, so future research could use it as a basis for studies based on purchase intention focused on art products.