Keywords

1 Introduction

Instagram is a social network that is based on the exchange of images and short clips between users. In fact, this platform is defined as a quick and specific way of sharing life events with friends through a series of images [1]. Instagram was launched on October 6, 2010. In its first year, it reached 12 million users and was purchased by Facebook on April 9, 2012 for 1 billion dollars [2]. Currently, Instagram is the fourth largest social network in active users, with 800 million users per month, being surpassed by Facebook (2.167 million), YouTube (1.500 million) and WhatsApp (1.300 million). However, it presents the largest growth and has doubled its number of users in only two years.

Among the features and functions of Instagram, one of the most used and eye-catching is Instagram Stories, which falls within the “ephemeral” category. Ephemeral messaging is a characteristic that allows shared posts or content to disappear automatically within a certain time [3], which in the case of Instagram Stories is 24 h.

Such is the relevance reached by Instagram Stories that more than 250 million active users use this tool daily. This has led major brands to focus their advertisement on this tool, paying for having stories linked to the offer of a product or service appearing in the stories of their followers. Furthermore, more than 80% of Instagram users follows at least one company or business profile, which makes Instagram an attractive platform for major brands.

In this case, it is relevant to address generation Z, which is the generation most exposed to technology, even since birth. Some researchers have found that people from this generation prefer communication via ephemeral messaging [3].

This study analyzes several factors that may affect the intention to use Instagram Stories using structural equation modelling (SEM), in addition to a conceptual basis from the technology acceptance model (TAM). Exogenous variables external to TAM were used, namely perceived enjoyment and social presence, which were described by Coa and Setiawan in the study titled “Analyzing Factors Influencing Behavior Intention to Use Snapchat and Instagram Stories”. Additionally, two variables were added related to narcissism from the perspective of envy were derived from watching an Instagram story. These two variables are benign envy and malicious envy [4].

Regarding the above, it must be noted that the visual content shared on Instagram can reduce people’s well-being, especially because it may facilitate social comparison and cause negative emotions like envy [4]. Based on this point of view, the variables benign envy and malicious envy are incorporated into the model as a way to explore their effect on the intention to use Instagram stories and on the other variables from TAM.

To analyze the user behavior in terms of adoption of innovative technologies, scientific literature has created a number of behavior theories and intention models over the last four decades [5]. The development of these theories emerges from the technology acceptance model (TAM), which has been considered the most robust, parsimonious and influencing on innovation acceptance behavior [6, 7]. TAM departs from the theory of reasoned action (TRA) [8], which was designed to explain virtual behavior, and adapts it to model the user acceptance of information systems [6]. This model has been modified several times and one of its most used variations integrates perceived enjoyment and social presence as variables in studies that explain user behavior toward information technologies.

In this sense, the application of this model to social networks is relevant, as they are one of the most effective means of communication nowadays. In this particular case, Instagram is the popular application or social network, whose Stories tool enjoys considerable use since ephemeral content is one of the most effective tools currently. Thus, it is necessary to study the variables that affect the intention to use this tool in order to produce knowledge useful for future enterprises, as well as new alternatives to direct advertisement in this medium, contributing with ideas for the marketing of interested companies.

2 Literature Review

2.1 Perceived Enjoyment

Perceived enjoyment (PE) is defined as a subjective psychological experience that indicates how pleasant a specific experience is. PE has an exploratory nature in information technologies and computer-mediated environments fields. High enjoyment can lead to the adoption of a technology, even if such a technology does not imply an increase in the productivity of the person adopting it. Some studies often use perceived enjoyment as a variable because entertainment, which is related to enjoyment, is an aspect that plays a key role in the acceptance of a technology. Many systems or technologies more oriented to pleasure than productivity have been designed (hedonic information systems), and social networks are one of them. Therefore, the use of perceived enjoyment could be more suitable for analyzing user acceptance of social networks [3].

In the current context, we consider social networks from both a utilitarian and a hedonic perspective. The original TAM model, its related models and the unified theory of acceptance and use of technology (UTAUT) were only developed and validated in the context of utilitarian systems within a professional setting [9]. Although current information technologies reflected in social networks are mostly employed for leisure and fun, the literature shows that perceived enjoyment is positively related to the constant intention of using social networks [9, 10]. In a study, attitude toward using is a mediating factor between perceived enjoyment and intention to use [3].

2.2 Social Presence

Social presence (SP) is defined as a means that allows a user to feel that everyone is psychologically present. Social presence occurs if there is an interaction between user and technology that makes the user feel the presence of others as well as human warmth [3], that is to say, a feeling closeness with someone who is physically far. A technology can transmit human warmth when it is capable of delivering communication, socialization and sensitivity of human feelings.

The achievement of the above can be enhanced through emoji, images, and videos, as well as effects used today that mostly belong to Instagram. In other words, adding visual content enrichens the message and thereby social presence is increased.

2.3 Benign Envy

Benign envy, a non-malicious form of envy, has the purpose of improving one’s situation. When comparing benign and malicious envy, they are different in terms of feelings, thoughts, action tendencies, motivational objectives and motivational experiences [11]. Benign envy promotes a strength that encourages people to work harder to achieve what others have done [12]. It is a positive and motivating force experienced by people who admire others [9, 13]. In the current context, benign envy is closer to a comparison with famous people, for example, athletes, actors, music artists, etc. This comparison leads us to follow their trends, clothes and articles, as well as gestures, websites they visit and places they go to, to narrow the gap that separates us from them. In this way, people adopt the superiority of the person they envy, since it is not in their hands to perform actions to make these people lose their superiority and therefore the only solution is to become closer to them.

2.4 Malicious Envy

Malicious envy is simply called “envy” [14] and is a destructive form of envy that aims at overriding someone. It describes a situation in which a person makes an upward comparison with advantaged people and experiences a feeling of inferiority. This could generate malicious thoughts about others failing or even make people hurt the person they envy [9].

Malicious envy is associated with a series of complex negative feelings of injustice, deprivation, frustration and depression [9]. In this sense, referring to the Chilean context, malicious envy is an upward comparison with a close person, which consists of trying to make that person equal to myself by eliminating their superiority. To this end, people would use all the resources available to eradicate this superiority, such as malicious comments or actions that are morally reprehensible.

2.5 Technology Acceptance Model

The first technology acceptance theory, developed 1989, was termed TAM (Technology Acceptance Model). This theory is an adaptation of the Theory of Reasoned Action (TRA) for information technologies [15] that is used to represent how users accept and use a new technological tool.

TAM postulates two main factors relevant to technology acceptance, namely perceived usefulness and perceived ease of use. The model contains four variables, which are explained below (see Fig. 1):

Fig. 1.
figure 1

Technology acceptance model [6].

Full size image

Perceived Usefulness (U): extent to which one person believes that using a specific system will improve his work performance.

Behavioral Intention (BI): extent to which one person has made conscious plans for developing, or not, some future behavior.

Perceived ease of use (E): extent to which future users believe that using a particular system is effortless.

Attitude toward using (A): Positive or negative feeling related to a behavior.

3 Methodology

This study is divided into two stages: exploratory and conclusive. The exploratory stage comprises a review on the literature about Instagram, Instagram Stories, Snapchat and social networks in general. This will be linked to the models currently known for determining the factors influencing the intention to use a technology (TAM, UTAUT and derived model) in order to find out the main variables involved in the intention to use Instagram Stories. A questionnaire was created based on this review. This quantitative tool contained 29 observable variables aimed at defining the eight latent variables. A 5-point Likert scale was used, ranging from (1) totally disagree and (5) totally agree. The model used is an extension of TAM [3], to which, as mentioned above, the social presence and perceived enjoyment variables are added. Now, since this last variable is extremely important for technology acceptance models, especially for the intention to use social networks, two more variables related to perceived enjoyment are added [9]. These variables are benign envy and malicious envy.

The final model for analysis is presented in Fig. 2 and is based on the following research hypotheses.

Fig. 2.
figure 2

Instagram stories proposed model

Full size image

  • H1: High social presence will result in high perceived enjoyment.

  • H2: High social presence will result in high perceived usefulness.

  • H3: High ease of use will result in high perceived usefulness.

  • H4: High ease of use will result in a favorable attitude toward using.

  • H5: High perceived usefulness will result in a favorable attitude toward using.

  • H6: High perceived enjoyment will result in a favorable attitude toward using.

  • H7: A favorable attitude toward using will result in high intention to use.

  • H8: High perceived usefulness will result in high intention to use.

  • H9: High benign envy will result in high perceived enjoyment.

  • H10: High malicious envy will result in low perceived enjoyment.

The analysis conducted is quantitative and has a confirmatory approach. The non-probabilistic method called sampling by convenience was used. In other words, only Instagram users responded the questionnaire, which also implies that they use Instagram Stories. The survey was conducted online through the SurveyMonkey platform. Four hundred and one questionnaires were completed. The majority of respondents were university students across Chile, mainly from the Metropolitan and Valparaíso regions, because the survey was disseminated in a university context. Answers were analyzed through structural equation modelling (SEM) using SPSS AMOS, while construct reliability was assessed with IBM SPSS Statistics.

4 Results

4.1 Construct Reliability

Cronbach’s alpha was used to assess construct reliability (see Table 1).

Table 1. Cronbach’s alpha (Source: Created by the authors).
Full size table

The limit value of this indicator is .6. This is because the constructs have less than 10 items. In addition, all constructs exhibit a Cronbach’s alpha higher than that limit.

4.2 Model Adjustment

Table 2 shows the goodness of fit indicators for the models described. For the Cmin/DF ratio, values between 1 and 3 indicate a good fit and values from 3 to 5 point an acceptable fit that needs to be confirmed by other indicators. In this case, it is clearly observed that in the original model (initial), the Cmin/DF value was between 3 and 5. Then, if the model without PS3 falls within the range between 1 and 3, it has a good adjustment. Nevertheless, the other indicators also need to be revised. First, the Cmin probability level does not change significantly and remains below .05; therefore, the model is acceptable according to these criteria. Additionally, GFI statistics improved from .832 to .851, approaching .9. RMSEA also showed a slightly improve, going from .071 to .069. The above indicates an acceptable absolute fit. NFI also improved notoriously from .804 to .820, while CFI reached a value very close to .9, increasing from .858 to .874, which is an acceptable incremental fit. Finally, the parsimony fit of PNFI also slightly improved, with values increasing from .726 to .738.

Table 2. Goodness-of-fit statistics (Source: Created by the authors).
Full size table

From this section, we can conclude that deleting PS3 improves the model in such a way that this presents an acceptable adjustment. Thus, the following step is to analyze the model without PS3 as our “main model”.

4.3 Hypotheses and Regression Estimators of the Model Without PS3

The Table 3 presents the regression estimators and significance of each relationship:

Table 3. Regression estimators and significance of each latent variable in the model (Source: Created by the authors).
Full size table

When analyzing the P-Value, it must be noted that both attitude toward using (AU) and perceived utility (PU) are significant for predicting the behavioral intention to use (IU) of Instagram Stories, as their P-value is lower than .05. Likewise, both social presence (SP) and ease of use (EU) are significant to predict perceived usefulness (PU). Additionally, social presence (SP) and benign envy (BE) significantly predict perceived enjoyment (PE), as opposed to malicious envy (ME), which is not significant to predict perceived enjoyment as shown by a P-value of .256 > .05. It is noteworthy that perceived enjoyment (PE) and ease of use (EU) predict attitude toward using (AU) in a significant way, whereas perceived usefulness (PU) is not significant in this relationship, as its P-value is .940 > .05.

These results show that exogenous latent variables are the variables that best explain endogenous latent variables, that is, with an acceptable significance. Thus, hypotheses H1, H2, H3, H4, H6, H7, H8 and H9 are accepted. On the contrary, hypotheses H5 and H10 are rejected.

It must be noted that the R2 value of behavioral intention to use (IU) is .499, that is to say, the predictors (exogenous latent variables) of IU (endogenous latent variable) are estimated to explain 49.9% of its variance. In other words, the error variance of IU is approximately 5.1% of IU variance. In addition, the predictors of perceived enjoyment (PE) explain 58.3% of its variance. Predictors of attitude toward using (AU) explain 79.2% of variance. Finally, predictors of perceived usefulness (PU) explain 86.8% of variance.

5 Conclusions

The main objective of this study was to know the factors that influence the intention to use the tool Instagram Stories. The most influencing factor was attitude to use (.539), which means that the more positively the use of Instagram stories is perceived, the more the intention to use it. Perceived usefulness, another factor influencing intention to use in this study, presented a weaker relationship with the construct (.228), in other words, the fact that people feel that Instagram Stories improves their work performance also influences intention to use, but in a more subtle way when compared with the attitude toward using it. From a psychological perspective, a social network needs to generate a positive feeling of be useful for work, so workers do not feel a guilty conscience for spending time on the phone. In addition, psychologists and psychiatrists are often interviewed on TV about the problems that an addiction to social networks may cause, such as on the ego of when working on a profile showing a happy life that may be not true in real life. In summary, social networks need to be perceived as something that brings positive emotions, harmless to mental health and useful for work. The last point can be exemplified by the fact that many people offer products or services through their Instagram profile, particularly by means of stories. In this way, they are giving use to their account and often this mechanism is so successful that it becomes their occupation. This is one of the modern uses of Instagram and features like Stories.

Other latent variables worth analyzing, like perceived enjoyment, should be mentioned. However, in this context, social presence (.743) is the most influencing factor for intention to use. This indicates that feeling human presence on Instagram Stories is strongly related with possible enjoyment while using this application. Since family and friends are usually followers on this social network, feeling their presence and keeping up with their lives is always pleasant, especially if they are physically apart. Therefore, calling upon the emotions of Instagram Stories users is also a good strategy for increasing the daily enjoyment of the application. Benign envy also slightly influences perceived enjoyment (.167), because this type of envy is usually directed to famous people or to people who, at least, are perceived as famous. From this perspective, users try to reach an image of this person by their own means. The above is often observed in ad campaigns, in which a famous person wears or states that use a product to make their followers automatically want to purchase it. The model in this study indicates that this type of envy or upward relationship increases the enjoyment of users when using Instagram Stories, as watching the stories of someone perceived as superior and, in turn, the items or services they use or consume, may lead users to adopt some aspects of their lifestyle. Nevertheless, the influence of malicious envy on perceived enjoyment is close to zero (.052) and thus it has no significant effect on perceived enjoyment, which is proved by the rejection of the hypothesis that both types of envy are related through a p-value equal to .256 > .05. This is in disagreement with the assumptions made prior to this study, since this type of envy generates frustration and hatred, and this is negatively related to perceived enjoyment, that is, an increase in malicious envy reduces perceived enjoyment. As mentioned above, people tend to answer questions about malicious envy incorrectly on purpose, in other words, if they have this feeling, they will hide it even if the questionnaire is anonymous. This is related to a trend to respond to sentences on this topic assertively, because accepting envy is not good in the eyes of society in general. Therefore, people would conceal or deny this feeling, but it is clear that malicious envy exist, and the literature already confirms this.

In another vein, perceived usefulness also operates as a latent variable and social presence is the most influencing factor in this relationship (.859). In other words, feeling the presence of other close people would improve performance at work. This is associated with the psychological fact that feeling the presence of close people supports work performance. Ease of use also affects perceived usefulness, albeit to a lesser extent tan social presence (.359). This is explained by the fact that if an application is easy to use, users will not waste time learning how to use it and can devote time to work.

Finally, the last latent variable of the model is attitude toward using, which is mostly influenced by perceived enjoyment (.849). The amusement we feel when using Instagram Stories strongly influences attitude toward using, that is to say, it brings positive feelings toward the use of the application. Moreover, ease of use also affects this variable less than perceived enjoyment (.280), which entails that the easier to use an application like Instagram Stories, the more positive the feelings toward its use, as comfort increases. The role of perceived usefulness as a predictor of attitude toward using was also analyzed, yielding a .006 coefficient. Since the coefficient is very close to zero, we presume that there is no relationship between the two variables, which is also proved by a p-value equal to .940 > .05; therefore, this hypothesis is rejected. This may be explained by the fact that respondents use Instagram Stories for leisure more than usefulness. In this sense, we may see that most people do not see Instagram as a source of income, which has been gradually changing due to the success of several enterprises that share their profile in this social network and all the factors this involves.

From the above, a chain reaction may be assumed regarding Instagram Stories. A high social presence clearly increases perceived enjoyment, which notoriously influences the attitude toward using this application, and this is the most relevant factor in the intention to use this tool. Thus, social presence is a key factor in the model, as it strongly affects the variables with which it is related.