Original Research

Examining the usage of Instagram as a source of information for young consumers when determining tourist destinations

Christina I.V. Gumpo, Tinashe Chuchu, Eugine T. Maziriri, Nkosivile W. Madinga

Received: 31 July 2019; Accepted: 19 Nov. 2019; Published: 24 Feb. 2020

Copyright: © 2020. The Author(s). Licensee: AOSIS.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background: Online social networking websites have revolutionised communication in the postmodern era of today. Specifically, Instagram has witnessed a phenomenal growth, and the site is particularly popular with young consumers in South Africa. Instagram is seen by this cohort as a source of information to determine their tourism destinations.

Objectives: This research examined how perceived usefulness of Instagram, perceived ease of use of Instagram and perceived credibility of Instagram influence attitude towards the use of Instagram, intent on using Instagram and actual use of Instagram to identify tourist destinations amongst young consumers.

Method: A survey questionnaire was administered to 349 young student consumers who were based in the Johannesburg Metropolitan area of the Gauteng province, South Africa. A structural equation modelling procedure was utilised in testing the proposed relationships.

Results: The results indicate that all the hypotheses suggested have been positive and significant. It is worth noting that there was the strongest connection between attitude towards Instagram for identifying travel destinations and intention to use Instagram for identifying travel destinations.

Conclusion: Tourism organisation marketers may be able to devote time in researching the practicality of using Instagram to increase awareness of tourist locations.

Keywords: social media; Instagram; technology acceptance model; generation Y; digital marketing.

Introduction

Social media has become increasingly common as a means of communication in the postmodern era of today. With the increasing popularity of traveller-generated websites, Yoo and Gretzel (2010) have placed the dominant importance of social media in the traveller scheduling phase. Instagram is one of the social media platforms most frequented in promotional activity through Internet in recent years (Wicaksono & Rumyeni 2017). A research by Lyu and Wang (2015) found that the percentage of people using traditional data centres (travel agents and tour operators) to obtain travel data has fallen by 27.6% in Korea because of the introduction of fresh Internet technologies (e.g. social media). They observed that travellers now prefer social media to traditional data sources (Lyu & Wang 2015). According to Fatanti and Suyadnya (2015), the effect of social media in the tourism industry has been significantly increased to encourage tourists’ willingness to consider social media data as their travel reference. Fatanti and Suyadnya (2015) contend through this phenomenon that Instagram challenges the creativity of tourism players in promoting tourism destinations around the globe. Parsons (2017) also discovered that Instagram affects the younger generation and their travel decisions. Despite this noted interest in social media and Instagram research, there are some gaps in the existing empirical literature relating to the use of Instagram; hence, there is a need for further scholarly introspection. Most international studies have been conducted in countries like, inter alia, Taiwan, Kuwait, the United Arab Emirates, the United States, Scotland and Indonesia. For instance, Huang and Su (2018) identified Instagram usage motives and subjects of concern amongst Taiwanese university learners (Huang & Su 2018).

Al-Kandari, Al-Hunaiyan and Al-Hajri (2016) examined the impact of culture on Kuwaiti higher education students’ use of Instagram, while Wally and Koshy (2014) evaluated the use of Instagram as a marketing tool by female entrepreneurs from Emirati. Trifiro (2018) performed a survey focused on the use of Instagram and its impact on well-being and self-esteem amongst undergraduate learners from schools and universities across the United States, while Mahoney et al. (2016) performed an assessment of Instagram use by the Scottish Electorate and Fatanti and Suyadnya (2015) determined how Instagram creates tourism destination brand in Indonesia. Against the aforementioned background, little is known about the same in the developing parts of the world. In the South African context, prior research on Instagram was conducted in a variety of contexts. For example, Kleintjes (2017) focused on how conspicuous consumption on Instagram contributes to brand value development in South Africa. Another study was conducted by Torr (2018) who examined South African female adolescents’ attitudes and behaviour towards posting food pictures on Instagram.

Hene (2015) investigated the psychological impact of Instagram on the self-esteem of young South African adolescents, while Becker (2016) reviewed the extent to which Instagram could be considered as a prospective alternative platform for visual culture in South Africa.

Inferring from the aforementioned studies, there is scant evidence of studies focusing on how perceived usefulness (PU) of Instagram, perceived ease of use (PEOU) of Instagram and perceived credibility (PC) of Instagram influence attitude towards Instagram, intention towards the use of Instagram and the actual use (AU) of Instagram amongst generation Y students in South Africa. Within the context of this research, young consumers refer to individuals in their 20s and 30s. It will be naive and unwise to believe a priori that in developing nations such as South Africa, findings from the developed world can be implemented pro rata. This lacuna is therefore subject to confirmation and also deserves to be addressed on its own. In terms of the conceptual model proposed in this study, it can be noted that it is one of a kind. This research thus adds to the literature by testing the suggested model in a developing country context. The technology acceptance model (TAM) has evolved over the years and significant progress has been made in predicting user acceptance of technology (Davis 1989; Venkatesh & Davis 2000). It is based on the theory of reasoned action (TRA), which posits that causal connections can be made between beliefs, intentions and behaviours (Fishbein & Ajzen 1975). Perceived usefulness and the PEOU are assertions that were proposed by the TAM (Davis 1989).

This article is organised as follows: firstly, the study context is provided, and the literature is subsequently provided, followed by a conceptual model and creation of hypotheses. The research design and methodology are then presented followed by the study findings and discussions. Finally, the article concludes with implications, limitations and future research directions.

Motivation and focus of research

Because Instagram is a social networking site, it would be appropriate to use the TAM as the framework for this study. This is because the TAM has become widely popularised (Lee, Kozar & Larsen 2003; Rauniar et al. 2014; Van den Hooff, Groot & De Jonge 2005) as an objective approach to predict acceptance and usage of new technologies in various fields. It is believed that Instagram worth is estimated to be more than $100 billion (Bloomberg 2018). In addition, Bloomberg (2018) stated that the photo-sharing platform would exceed 1 billion monthly active users by 2019. Instagram is widely considered to be a perfectly built app for the mobile experience and has risen to become one of the most dominant mobile apps (Forbes 2017). As of 2017, Instagram was the social networking choice of about $700 million monthly active users (Forbes 2017). In addition, Instagram is attracting new users more rapidly than Facebook’s main site and has potential to exceed 2 billion users by 2023 (Bloomberg 2018). Instagram’s audience is young, making it more attractive to advertisers (Forbes 2017). The TAM by Davis (1989) is the most suitable framework for explaining the attitude, use intention and AU of a technology-based system (Dimitriadis & Kyrezis 2011), therefore making it appropriate and relevant for this study.

Contextualisation of the study

Instagram

Instagram is an application that allows users to share pictures of their lives with others and can be the source of an electronic mouth word (eWOM) (Vidyanata, Sunaryo & Hadiwidjojo 2018). A research by Jang et al. (2015) found that 91% of travellers choose to share their travel experiences through pictures, and more than 50 million travel pictures have been shared on Instagram since it was introduced in 2010. Instagram is one of the most common and preferred social media platforms and young consumers’ platform for expressing and sharing experiences (Anuar & Ihsanuddin 2016). Barton et al. (2013) believe that young consumers and today’s social media are inseparable. They like sharing and posting photos on Instagram, particularly as they travel (Barton et al. 2013). Despite its popularity amongst young consumers, some questions about Instagram remain unanswered. Amongst them are the perceived factors that influence attitudes towards using Instagram, the intention to use Instagram and the AU of Instagram to determine destinations for tourism amongst young consumers. This warrants this study’s need. An example of an Instagram platform is shown in Figure 1.

FIGURE 1: An Instagram homepage platform.

The rationale and significance of choosing young consumers

Bolton et al. (2013) argued that young consumers are technology-savvy and a visually advanced generation, making it simple for them to adapt to the use of tourism technology as they are born along with the introduction of technology. During their travel planning, the young consumers use countless Internet data sources (Schiopu et al. 2016). According to Starcevic and Konjikušić (2018), when equated to other generations, they are the most engaged in social media, where they find motivation for travelling in most cases. They browse the content of other users, such as Instagram pictures (Starcevic & Konjikušić 2018). The extent to which a destination is ‘Instagrammable’ is considered to be one of the greatest concerns for young consumers (Le 2018). Instead of uploading pictures of lovely beaches or ocean sunsets on Instagram as an outlet for virtual memories to look back on, users of Instagram mostly post their holiday snaps to incite envy amongst friends and followers (Le 2018).

Theoretical models grounding the study

Diverse models exist for seeking to predict and explain human behaviour. This study is grounded in the TAM and the information adoption model (IAM), as they relate to the suggested constructs and interactions between the constructs. The following sections discuss the TAM and the IAM models.

The technology acceptance model

This model was initially proposed by Davis in 1989 and it has grown to be an extensively used model to explain user acceptance of new technologies (Matikiti, Mpinganjira & Roberts-Lombard 2018). Technology acceptance model was created from the TRA (Fishbein & Ajzen 1975), and it offers a foundation for tracking how internal factors affect new systems. This model represents the fact that the real use of a fresh technology depends on the attitude of the user towards the technology, the PEOU of the technology and the perceived advantages that can be derived from its use (see Figure 1). Accordingly, behaviour is anticipated using the two variables of PEOU and PU (Fishbein & Ajzen 1975). From this model, it is deduced that a person will be willing to embrace it whenever he or she perceives that using a certain technology will help improve efficiency or deliver some advantages. The model has been shown to be accurate in describing acceptance behaviour in certain fields of information systems (Matikiti et al. 2018). Moreover, Dixit and Prakash (2018) found that TAM can be used as a model for social networking behaviour (see Figure 2).

FIGURE 2: Technology acceptance model.

Information adoption model

This model was developed by Sussman and Siegal (2003) to better understand how to accept information about specific feelings, actions or technology in individual forms (Filieri & McLeay 2013). The theory practices the argument quality, the source credibility and the information usefulness as a mediator (Abang-Othman, Abdul-Gani & Ahmad 2017). Therefore, information technology and information systems are now developing at a phenomenal pace and becoming crucial components in people’s lives, and with the advent of online platforms, people are starting to exchange different kinds of information on the web and, meanwhile, are looking for basic and important online information (Abang-Othman et al. 2017). From the aforementioned explanations, it can be deduced that the issue of consumer information adoption is one of the most popular topics in consumer behaviour research. Abang-Othman et al. (2017) describe that the choice of hotels involves a large search for information. Subsequently, to understand complex decisions, IAM is critical. Customers carefully select from a few sources of information in view of their hotel plans or their information requirement (Mayr & Zins 2009; Vogt & Fesenmaier 1998), thereby generating an impression that customers choose specific sources of information and their accuracy differently. The summation of the reliability of the claim and the validity of the source is calculated in this study as PC. Information adoption model can clarify how people adopt information and change their intentions and behaviours within the communication platforms that have been interfered with (Abang-Othman et al. 2017). Previous studies have tried to find how IAM helps in better understanding how individuals expect to accept information on particular ideas, attitudes and technologies (Filieri & McLeay 2013; Sussman & Siegal 2003). Nevertheless, IAM can extend to inspect the adoption of advice (Sussman & Siegal 2003). The IAM is shown in Figure 3.

FIGURE 3: Information adoption model.

Empirical literature

This section discusses the different research variables undertaken as part of this study.

Perceived usefulness

Rauniar et al. (2014) defined PU as the extent to which the social media user believes that using a specific social media site helps meet the individual’s related goal-driven needs. In addition, Nkoyi, Tait and Van der Walt (2019) agreed that PU is the degree to which a person thinks that using a particular scheme will improve his or her job performance; as such, PU is the subjective probability of the user that using a particular technology will boost his or her job performance. In addition, it can be observed that PU is the conviction that visitors would enhance their event experience by using the request for an event or conference (Nkoyi et al. 2019).

Perceived ease of use

Davis (1989), the pioneer of the TAM, describes PEOU as the degree to which a person thinks he or she will be free from mental effort to use a specific system. According to Chinyamurindi and Louw (2010), it is the degree to which an individual thinks that using the scheme would be effortless. Maduku and Mpinganjira (2012) stress that it will be more probable to adopt a system requiring less technical abilities and operational attempts. In addition, Rauniar et al. (2014) describe PEOU as the degree of effort-free social media site. In fact, PEOU in this context is the evaluation of generation Y students that it is simple to use and requires little effort when looking for tourism destinations on Instagram.

Perceived credibility

Perceived credibility is variable is not included in the TAM, but has been included for the purpose of the study. This variable was added to the TAM as an external variable. Credibility is described as the perception held by people as to whether or not the advertiser is able to produce the data supplied in a particular media car advertisement (Grove, Ehlers & Jordaan 2011). According to Wang et al. (2003), PC is the extent to which a consumer thinks that system use will not pose any safety or privacy threats. The notion of credibility is strongly associated with the notion of trust that has to do with individual trust or faith (Flanagin & Metzger 2008). In the context of this research, the term credibility will be referred to as the perception of the credibility of the information received rather than the direct evaluation of the practical quality of the information.

Attitude towards use

Attitudes can be defined as the general evaluation of a person performing a specific behaviour (Çelik & Yilmaz 2011). An attitude is the enduring favourable or unfavourable assessments, emotional emotions and tendencies of action towards a certain behaviour of a person (Roberts-Lombard & Parumasur 2017). If the behavioural attitude is positive, the willingness of the individual to execute that specific behaviour rises (Ajzen 1991). Based on the above explanations, it can be inferred that if a person has a favourable attitude towards performing behaviour, he or she will be more likely to conduct a particular behaviour. The researchers are therefore seeking to determine attitudes towards using Instagram for the purpose of this research.

Intention to use

Intention refers to decision-making that proposes a reason to partake in an event or when a consumer plans to buy a specific product in a specific condition (Mirabi, Akbariyeh & Tahmasebifard 2015). It can also be seen as the instant determinant of behaviour and the precursor of future buying choices (Ajzen 1991). Moreover, Shanmugam, Savarimuthu and Wen (2014) explain that intention to use is defined as a measure of a person’s likelihood of adopting an application. In this research, the intention to use is seen as the ongoing intention to search for and acquire data about tourist locations using Instagram.

Actual use

Rauniar et al. (2014) define real usage in terms of the user’s social media frequency. Matikiti et al. (2018) argue that TAM reflects the fact that the AU of a new technology depends on the attitude of the user towards that technology, the PEOU of that technology and the perceived benefits that can be derived from its use. Asiimwe and Grönlund (2015) explain that behavioural intention (BI) results in the real use of a system. This opinion coincides with Wentzel’s (2012) views, who explains that BI to use directly determines the AU of a system according to the TAM. The model for this research therefore indicates that the AU of an individual is directly affected by the intention to use Instagram.

Conceptual model and hypothesis formulation

Figure 4 illustrates the conceptual model reflecting the distinct paths and connections between the constructs under investigation. The subsequent sections will then provide the formulation of the hypotheses for the present research.

FIGURE 4: Conceptual framework.

Perceived usefulness, perceived ease of use, perceived credibility of Instagram and attitudes towards the use of Instagram

In development of the hypotheses it was imperative to consult prior literature on the application of the TAM where social media networks were concerned. Zhao, Chen and Wang (2016) investigated social media antecedents of loyalty (continuance usage and willingness to pay). Dhume et al. (2012) established that attitude to use social media mediated the relationship between subjective norms, PU and PEOU, and the intention to use social media. This then provided support for adapting the TAM for the research in question, and the proposed hypotheses, however, are deficiencies in the literature that has determined the nexus between PU, PEOU, PC of Instagram and attitudes towards the use of Instagram. However, there are closely related studies that have found similar associations amongst these variables. Baby and Kannammal (2019) posited that intention and actual system use are directly associated. For instance, Herrero and Martin (2012) discovered that the PU of new technology has an influence on tourists’ attitudes. As for social networking sites, Dixit and Prakash (2018) suggested that the external variables of the TAM impact both PU and PEOU of social networking sites. Furthermore, Dixit and Prakash (2018) posited that attitude towards social networking sites directly influences BI to use these sites and ultimately leads to their actual adoption. In addition, Maduku (2016) found that PEOU has a significant, positive effect on attitude towards Internet banking use. Nedra, Hadhri and Mezrani (2019) postulated that Instagram’s PEOU directly influences attitude towards the use of Instagram. Furthermore, PU of Instagram is positively related to attitude towards the use of Instagram (Nedra et al. 2019). Moreover, Mir and Zaheer (2012) learnt that PC has a positive effect on consumer attitudes towards user-generated content (UGC). Against this background, the following hypotheses were formulated:

H1: Perceived usefulness of Instagram has a positive and significant impact on attitudes towards the use of Instagram amongst generation Y students at a selected university in South Africa.

H2: Perceived ease of use of Instagram has a positive and significant impact on attitudes towards the use of Instagram amongst generation Y students at a selected university in South Africa.

H3: Perceived credibility of Instagram has a positive impact on attitudes towards the use of Instagram amongst generation Y students at a selected university in South Africa.

Attitudes towards the use of Instagram and intention towards the use of Instagram

As far as the TAM is concerned, PEOU directly affects BI and AU of a system (Baby & Kannammal 2019). It should be noted that BI and AU are affected by PEOU separately and independently (Baby & Kannammal 2019). Ng, Shroff and Lim (2013) used TAM to explain why users accept or reject information technology, and they found that attitude towards usage evidenced a direct relationship with BI to use. Swart, Sotiriadis and Engelbrecht (2019) argued that influence along with attitude is an important factor to consider in determining tourists’ BI. Moreover, Suki and Suki (2011) reinforced that attitudes have a strong, positive direct influence on intention to use mobile devices or services. Attitude towards the use of Instagram influences intention to use Instagram (Nedra et al. 2019). In addition, Nedra et al. (2019) suggested that perceived pleasure from Instagram is associated with the intention to use Instagram. The present study therefore proposes the following hypothesis:

H4: Attitudes towards the use of Instagram has a positive and significant impact on intention towards the use of Instagram amongst generation Y students at a selected university in South Africa.

Intention towards the use of Instagram and actual use of Instagram

It is vital to elucidate on the relationship that exists between intention towards use and actual usage. Previous studies have shown that there is a connection between these two variables; for instance, Cheung and Vogel (2013) found that the intention to use technology will lead to consumers’ actual adoption or use of that technology. Moreover, Yi and Hwang (2003) found a direct and significant relationship between BI and actual usage of the web-based environment in their study. The perceived use of social networks and PU is considered to directly lead to intention to pay for social networking (Sin, Nor & Al-Agaga 2012). The following hypothesis was subjected to testing in this study:

H5: Intention towards the use of Instagram has a positive and significant impact on actual use of Instagram amongst generation Y students at a selected university in South Africa.

Research methodology

This study was quantitative in nature and the design allowed for soliciting information relating to PU of Instagram for identifying travel destinations, PEOU of Instagram for identifying travel destinations, PC of Instagram for identifying travel destinations, attitude towards the use of Instagram for identifying travel destinations, intention towards the use of Instagram for identifying travel destinations and the AU of Instagram for identifying travel destinations. In addition, the approach enables one to examine the causal relationships with the constructs used in the study.

Sample and data collection

This study made use of young Instagram users in their 20s and 30s from a large university in Johannesburg, South Africa. The research focused primarily on Instagram, as some youth are influenced by Instagram to determine their vacation decisions (Le 2018). Participants included in the sample had to be active, registered students at the time of data collection. A main identifier of this criterion was the student card which held each student’s name and the year of registration. With regard to the sampling frame, a list of registered students was used as a sampling frame within the university database. Thus, this study used a simple random sampling technique because each element of the population had an equal and known chance of being selected as part of the sample (Weideman 2014), for example, where every name on the list of students registered in the university database had an equal chance of being selected. Raosoft calculator was used to calculate the sample size (Raosoft Inc. 2004). The calculation regarded the complete student population enrolment of approximately 33 346, a 5% margin of error, 90% interval of confidence and the suggested 50% allocation, and returned a minimum sample size of 377 participants. Of the 377 questionnaires distributed, 349 questionnaires returned were usable, resulting in a response rate of 92.5% in terms of usability of the measures.

Measuring instrument

Research scales were operationalised based on extant literature. Proper modifications were made to fit the current research context. Perceived usefulness, PEOU, attitudes towards the use BI and AU were measured through questions adapted from Davis (1989). Moreover, questions on PC were measured from items adapted from Adesina and Ayo (2010). All measurement items were measured on a five-point Likert scale, and the scale indicators were affixed to a strongly disagree (1) to strongly agree (5) Likert-scale continuum.

Data analysis

The gathered data were recorded on a Microsoft Excel spreadsheet after screening returned questionnaires. The data were analysed using descriptive statistics, Cronbach’s alpha values and correlations, and the Statistical Package for Social Sciences (SPSS version 25.0). To test the psychometric properties of the measurement scales and hypotheses, the Analysis of Moment Structures (AMOS version 25.0) statistical software was utilised.

Respondent profile

Table 1 shows the depiction of the participants. The respondents were requested to report their demographic data, including gender, age, economic status based on respondents’ monthly allowance and ethnicity. The respondents were mainly men (54.4%). The majority of the respondents indicated that they were between 18 and 22 years old (79.9%). In addition, most of the respondents reported that they have an average economic status (57.6%). In terms of ethnicity, the majority of the respondents revealed that there were black people (62.8%).

Research results

This section focuses on the results of confirmatory factor analysis (CFA), hypothesis tests performed through structural equation modelling (SEM) and discussions. A CFA is a unique type of factor analysis used to assess whether a construct’s measurements are compatible with that of construct’s nature. The SEM method was used to evaluate interactions between variables that are latent (unobservable) such as dependent and independent constructs (Bagozzi & Yi 2012).

Psychometric properties of measurement scales

The assessment of the measurement scales’ psychometric characteristics was performed through a CFA to determine the construct’s reliability, validity and model fit. Table 2 shows the outcomes of the CFA assessment.

According to Nunnally (1978), the reliability of a measure is supported if Cronbach’s alpha is 0.7 or higher. The results provided in Table 2 show that the Cronbach’s alpha values for each research variable were as follows: PU = 0.821, PEOU = 0.773, PC = 0.725, ATT = 0.826, BI = 0.886 and AU =0.901. Cronbach’s alpha scores indicate that each construct exhibits strong internal reliability (Lee 2009). Because the Cronbach’s alpha values of the constructs exceeded the recommended 0.70, this shows that all the variables were reasonably reliable.

Table 2 shows the loading of each item on their construct. The lowest value for each respective item loading for the research constructs is 0.501; all the individual item loadings exceed the recommended value of 0.5 (Anderson & Gerbing 1988). This indicates that all the measurement instruments are acceptable and reliable because all the individual items converged well and with more than 50% of each item’s variance shared with its respective construct (Fraering & Minor 2006).

Composite reliability (CR) and average variance extracted (AVE) for each construct were also computed and assessed to determine if they met the required thresholds of reliability and validity. As per the results shown in Table 2, the lowest CR value 0.721 is well above the recommended value of 0.6 (Hulland 1999), while the lowest obtained AVE value 0.416 is above the recommended value of 0.4 (Anderson & Gerbing 1988). This indicates that convergent validity was achieved, further confirming excellent internal consistency and reliability of the measurement instruments used. By and large, these results provided evidence for acceptable levels of research scale reliability (Chinomona & Chinomona 2013). According to Field (2013), discriminant validity refers to items measuring different concepts. Table 3 shows the results of the discriminant validity analysis. As shown in Table 3, all the correlation coefficients of this study fell below 0.70, thereby confirming the theoretical uniqueness of each variable in this research (Field 2013).

Model fit analysis

According to Anderson and Gerbing (1988), model fit analysis is a process that assesses how well the model represents the data. In this study, model fit was tested by using the following indices: chi-square/degrees of freedom, comparative fit index (CFI), incremental fit index (IFI), Tucker–Lewis index (TLI), normative fit index (NFI), goodness of fit (GFI) and root mean square error of approximation (RMSEA). The acceptable thresholds should be equal to or greater than 0.90 for CFI, IFI, RFI, NFI, GFI and adjusted goodness of fit index (AGFI). For chi-square/degrees of freedom, a ratio of 3:1 or less is recommended and the RMSEA value should be equal to or less than 0.08 (Lysons & Farrington 2012). The general model fit indices for both the CFA and SEM models are shown in Table 4.

Figure 5 shows the structural model.

FIGURE 5: Structural model.

Outcome of hypotheses testing

In this research, path coefficient values and p-values for the structural model were used to determine the testing of the hypotheses. The structural model test outcome showed that all the five hypotheses (H1–H5) were supported by the study data (see Table 5). The first hypothesis, PU of Instagram, directly influences attitudes towards Instagram. This relationship had an estimate of 0.479 and a p < 0.01 level of significance. The second hypothesis, PEOU, directly and positively affects consumer attitudes towards the use of Instagram. This relationship had an estimate of 0.678 and a p < 0.01 level of significance. The first two hypothesis outcomes suggested that the more consumers perceived Instagram as useful and easy to use, the more positive their attitudes towards Instagram would become. The third hypothesis, the PC of content on Instagram, impacts consumer attitudes towards its use. The outcome confirmed this hypothesis, suggesting that perceptions towards Instagram’s credibility indeed mattered to consumers, as this was evident through a positive estimate of 0.458 and a p < 0.01 level of significance. The fourth hypothesis impacts consumer attitudes towards Instagram and intention to use Instagram. This was observed to be the strongest relationship, with an estimate of 0.969 and a p < 0.01 level of significance. Lastly, the fifth hypothesis, intention to use Instagram, was positively associated with the AU of the application. This assumption was supported by the outcome as it was revealed that the relationship had a positive estimate of 0.917 and a p < 0.01 level of significance. This possibly suggested that young consumers who intended to use Instagram actually ended up using it.

Ethical consideration

An ethical clearance certificate (protocol number: CBUSE/1243) to conduct the study was obtained from the University of the Witwatersrand Research Ethics Committee. In addition, this research study acted in accordance with the ethical standards of academic research, which, amongst other things, include protecting the identities and interest of respondents and assuring confidentiality of information provided by the participants. Respondents provided informed consent for this research and were informed beforehand about the reason and the nature of the investigation to ensure that participants were not misled. Despite all the above-mentioned precautions, it was made clear to the participants that the research was only for academic research purpose and their participation in it was absolutely voluntary. No one was forced to participate in the study.

Discussion of results

Based on the findings it was established that PU of Instagram and its PEOU directly influence consumer attitudes towards Instagram. This suggested that the more Instagram users appreciated the application, the more inclined they would be towards its use. This finding is in line with Dixit and Prakash (2018) who stated that attitudes towards the use of a social networking site were indeed directly and positively impacted by both the perceived use and perceived ease of using that site. The strongest relationship attitudes and intention, which produced an estimate of 0.969 and p < 0.01 level of significance, suggested that if consumers viewed Instagram positively, their intention to use the application would increase exponentially. This finding was in line with Rauniar et al.’s (2014) finding who established that intention to use a social networking site (in their case Facebook) and AU were the most closely associated constructs of the TAM. With an estimate of 0.5, the strongest relationship was found between intention and actual usage of a social networking site in Rauniar et al.’s (2014) study. The statistical analysis exposed that PU of Instagram has a positive impact on attitudes towards the use of Instagram. The results obtained in this study are in line with the findings of Van De Bogart and Wichadee (2015) who found that students’ PU of LINE positively influenced their attitude towards usage. In addition, the findings of this research also confirmed that there is a positive association between perceived ease of use of Instagram and attitudes towards the use of Instagram. This is congruent with Mohd Suki and Mohd Suki’s (2011) assertion that consumers who find the usage of technology to be effortless (both mentally and physically) are likely to have a greater propensity to rely heavily on the usage of m-commerce transactions rather than traditional brick-and-mortar purchases.

Furthermore, the statistical analysis showed that PC of Instagram has a positive impact on attitudes towards the use of Instagram. These findings are in accordance with Mir and Zaheer (2012) who proved that PC of UGC on social media sites affects the attitude towards product-related UGC positively. In addition, the statistical analysis exposed that attitudes towards the use of Instagram have a positive impact on intention towards the use of Instagram. These results agree with the works of Kanchanatanee, Suwanno and Jarernvongrayab (2014) who found that attitude towards using E-marketing needs to be a significant determinant of small- and medium-sized business (SMB) owners’ intention to use E-marketing. Moreover, it was found that intention towards the use of Instagram has a positive impact on the AU of Instagram. These findings reinforce the results obtained by Wu, Tao and Yang (2008), who stated that BI of third-generation (3G) mobile telecommunication services has positive influence on the AU behaviour.

Implications

From the results of this research, numerous implications arose. These include theoretical, practical and political implications. Firstly, it has been established theoretically that PU of Instagram, PC of using Instagram and perceived ease of using Instagram for identifying travel destinations positively affect young consumers’ attitudes. This offers practical implications for tourism organisations, such as Destination Marketing Organisations (DMOs), to concentrate more on making their Instagram accounts in terms of content very comprehensive to impact the decision-making process for young travellers. In terms of policy, governments are urged to implement laws that enable social media platforms to be secure and reliable sources of tourism data. This implies that safety measures should be implemented by businesses that post and handle instagram accounts to safeguard social media users. Governments could, for example, force advertisers to take quality control measures to ensure that the information they post on instagram is not fabricated and is verifiable. This could be accomplished by making these companies to include website connections for each Instagram image, video or text they post on the platform.

Recommendations and contributions of the study

This research found that the strongest connection was that of Instagram’s BI and AU of Instagram for tourism purposes. It could therefore be suggested that digital marketers direct more resources towards understanding the behaviours of young consumers to provide them with the correct Instagram content and data. The results also revealed that PEOU credibility and PU affect attitude. The recommendation that tourism marketers are encouraged to portray their content in a trustworthy manner in the most reader-friendly way for users as this was seen to influence attitudes of Instagram users when it came to consuming destination marketing content. Furthermore, digital marketers should attempt to comprehend the attitudes of young Instagram users to examine what they post and share with their classmates or friends. This would educate digital marketers about how these attitudes affect the BIs of clients towards Instagram as an instrument for tourism marketing.

Conclusion, limitations and suggestions for future research

The objective of this research was to explore the use of Instagram as a source of data for destinations. Therefore, it can be observed that this study closes current research gaps and deepens the knowledge of TAM variables (PEOU, PU, PC, attitudes, BI and AU) that could be used to explore Instagram’s efficiency as a tourism data resource used to identify tourism destinations within the generation Y cohort. Theoretically, further research could investigate potential hypotheses that were not tested in this study, for example, a direct relationship between PU and intention to use Instagram as well as a direct relationship between PC and AU of Instagram. These two tests could help explain the extent to which actual users of Instagram trust the platform as in information sources without the influence of mediators as in this study. While this article creates significant additions to academic and corporate practice, there are limitations to this research. The study focused only on Instagram; thus, future research is anticipated to use other social media platforms. The study only focused on young Instagram users in their 20s and 30s from one location which limited the generalisability of findings as they do not talk about the entire population of prospective Instagram clients. Consequently, it would be essential to replicate the research with a more diverse sample who have smartphones to access Instagram. Moreover, because this study used a quantitative approach, future studies could also use a mixed-method approach so that in-depth views of young Instagram users can be captured.

Acknowledgements

The authors would like to thank the editor and all the anonymous reviewers for their invaluable comments and feedback. The authors are also thankful to the students who responded to this study.

The authors pronounce that they have no money-related or individual relationship that may have affected them improperly in composing this article.

C.I.V.G. was responsible for the primary research and write-up of the article. T.C. was responsible for data analysis, data interpretation and manuscript compilation. E.T.M. performed data interpretation and manuscript review. N.W.M. performed the literature review and manuscript review.

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Data sharing is not applicable to this article as no new data were created or analysed in this study.

The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.

References