Journal of Distribution Science (유통과학연구)

Korea Distribution Science Association (한국유통과학회)
권호 표지
DOI QR코드

DOI QR Code

Effect of Influencer's Social Media Number of Followers on Purchase Intention in the Travel Industry of Vietnam: The Moderating Role of Package Tour Price

  • Thi Hoai DANG (Faculty of Political Theories, Thuongmai University) ;
  • Thi-Tuyet TRAN (Faculty of Hospitality and Tourism, Thuongmai University) ;
  • Cao Cuong HOANG (Institute of Business Administration, Thuongmai University)
  • Received : 2024.02.26
  • Accepted : 2024.04.05
  • Published : 2024.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose: Social media influencers (SMIs) have become significant sources of information influencing their followers' purchase intentions; few studies have been published on the effect of the number of followers and package tour prices on followers' purchase intention within the Vietnam travel industry utilizing naïve theories. This study examined the relationship between the number of followers and purchase intention and tested the moderating role of package tour price. Research Design, data and methodology: A 2 (number of followers: high vs. medium) × 2 (package tour price: high vs. low) between-subjects factorial design was used. 395 Vietnamese students (114 men, 281 women; Mage = 19.99, SDage = 1.25) from Thuongmai University participated in the study. ANOVA and PROCESS MARCO were used to test hypotheses. Results: Findings indicate that participants show a higher purchase intention for SMIs with a higher number of followers than those with a medium one. When the package tour price is high, participants with a medium number of followers show a greater purchase intention than those with a high one. Conclusion: This recommendsthat tourism managers collaborate with SMIs with a high number of followers when the package tour price is low and with SMIs with medium ones when the package tour price is high.

Keywords

1. Introduction

Social media influencer marketing has become a well-established strategy that significantly impacts consumer behavior. Social media influencers (SMIs) are defined as opinion leaders on digital social media platforms who effectively communicate with broad, unknown audiences (Gräve, 2017), and are regarded as trusted tastemakers in one or several niches (de Veirman et al., 2017). They significantly impact their followers’ attitudes and behaviors (Godey et al., 2016; Weismueller et al., 2020), often facilitated by word-of-mouth recommendations (Moldovan et al., 2017). This influence is derived from SMIs’ elevated status, social prestige, or expertise (Lin et al., 2018; Xiong et al., 2018). For example, roughly 50% of internet users follow SMIs’ accounts on social media and trust their suggestions, whereas approximately 40% make purchases after viewing influencers’ endorsed products on platforms such as Instagram or YouTube (Digital Marketing Institute, 2019; Gretzel, 2017). Thus, companies should pay special attention to SMIs astheir influence directly affectsfollowers’ (non)purchase decisions (García-de-Frutos & Estrella-Ramón, 2021).

Most existing literature has proved that different aspects of SMIs significantly impact purchase intention in different fields. More specifically, SMIs source characteristics, psychological factors, content attributes, and various other elements exerted a substantial impact on purchase intention (Sánchez-Fernández & Jiménez-Castillo, 2021; Vrontis et al., 2021) in beauty (Folkvord et al., 2020; Sokolova & Kefi, 2020), fashion (Gomes et al., 2022; Jansom & Pongsakornrungsilp, 2021; Sokolova & Kefi, 2020), tourism and events (Harb et al., 2019). This influence might originate from followers’ perception of high credibility (Al-Emadi & Ben Yahia, 2020; Sokolova & Kefi, 2020), familiarity and likeability (Torres et al., 2019; Trivedi & Sama, 2020), or the inclination to mimic (Ki & Kim, 2019). However, there is a lack of thorough understanding of “Which types of influencers are effective in which situations and for what purpose?” (Vrontis et al., 2021, p. 11). Consequently, we suggest that delving into a more systematic exploration of specific variables’ predictive value (Vrontis et al., 2021), mainly focusing on the number of followers, package tour price, and their interactions, can deepen our understanding of SMIs’ influence. By pursuing this avenue of research, we can significantly advance our knowledge of SMIs and provide practitioners with practical strategies for selecting suitable SMIs when implementing brand marketing on social media platforms.

Naïve theories are commonly understood as informal, common-sense explanations individuals employ daily to interpret their surroundings (Furnham, 1988). These explanations often deviate from formal, scientific interpretations of events and phenomena encountered in everyday experiences. They include two contradictory theories: the naïve theory of popularity and the naïve theory of exclusivity. The initial proposition posits that a product’s appeal escalates alongside its popularity (Cialdini & Goldstein, 2004; Deval et al., 2013), whereasthe subsequent one contends that exclusivity amplifies a product’s desirability (Steinhart et al., 2014). In this research, we apply the naïve theory of popularity to deduce the number of followers, which serves as an indicator of network size and popularity (Cialdini & Goldstein, 2004; Deval et al., 2013), and the naïve theory of exclusivity to signify package tour price (Deval et al., 2013). One perspective from earlier research suggests that a higher number of followers can result in broader dissemination of the commercial message (van Dijck, 2013) and enhance perceptions of popularity and likability (de Veirman et al., 2017). Consequently, there is a greater tendency for purchases compared with SMIs with a moderate number of followers (Jin & Phua, 2014). Alternatively, the pricing of (luxury) products is intrinsic to their exclusivity (Upshaw et al., 2017). Existing literature showed that (package tour) price substantially impacts customers’ purchase intention in the traditional marketing environment (Büyükdağ et al., 2020; Cheah et al., 2020; Hati et al., 2021).

However, to our understanding, the influence of the number of followers and the moderating impact of the package tour price on SMI advertising outcomes, including influencer likeability (de Veirman et al., 2017), brand attitude, and intention to purchase, have not been thoroughly explored. In particular, there needs to be more studies that concentrate on the moderating influence of package tour price on the number of followers and purchase intention. In addition, most related studies to SMIs have been conducted in different industries in the USA (Jang et al., 2021; Nafees et al., 2021; Rao Hill & Qesja, 2023), the Netherlands (Janssen et al., 2022), Spain (Sánchez-Fernández & Jiménez-Castillo, 2021), China (Wan et al., 2018), Australia (Kay et al., 2020), and India (Saima & Khan, 2021) while few studies have been conducted in the travel industry in Vietnam. Hence, to fill the gaps above, this paper seeks to investigate the impact of SMIs’ number of followers and the moderating effect of package tour prices on followers’ purchase intention within the travel industry in Vietnam.

This study makes two distinct contributions to the existing body of knowledge. Firstly, it emphasizes the importance of naïve theoriesin tourism research in assessing purchase intention in the SMI context. In other words, it suggests that SMIs with a substantial number of followers typically exert a more decisive influence on purchase intention than those with a moderate number of followers. Second, utilizing the logic of naïve theories, this study stands out as one of the initial attempts to explore the effects of the number of followers and the moderating impact of package tour price on purchase intention. Specifically, when the price of a package tour is elevated, SMIs with a moderate number of followers demonstrate greater efficacy in influencing customers’ purchase intention than those with a high number of followers.

2. Literature review

2.1. Naïve Theory in Social Media Influencer

Naïve theories, as defined by Furnham (1988), are informal, everyday explanations individuals use to comprehend their environment. These theories encompass two contradictory concepts: the naïve theory of popularity and the naïve theory of exclusivity. In one view, a product’s desirability is believed to rise with its popularity (Cialdini & Goldstein, 2004; Deval et al., 2013), whereas another perspective suggests that exclusivity enhances a product’s desirability (Steinhart et al., 2014). More specifically, consumers might perceive a product favorably if many others are interested (Deval et al., 2013). Conversely, a high level of interest from others might indicate less product distinctiveness (Lynn, 1992), leading customers to perceive the product as ordinary (Hui & Bateson, 1991; Machleit et al., 2000), thus decreasing purchase intention. To summarize, in certain circumstances, customers may be persuaded to purchase items that other customers have purchased, but in other cases, their attraction may be substantially stronger to unique goods given in limited editions.

While naïve theories can often lead to contradictory ideas, they were chosen for this study for three reasons. Firstly, naïve theories are frequently used by customers in their decision-making since these require little cognitive effort, intention, or conscious awareness in information processing (Deval et al., 2013). For instance, Metzger et al. (2010) showed that customers often employ cognitive heuristics to evaluate specific sources to limit their effort. Secondly, due to the intangible nature of travel products and the limited information available to customers, naïve beliefs regularly act as a reliance cue for decision-making (Deval et al., 2013). Accordingly, Deval et al. (2013) proved that when customers are provided with minimal information, such as price, they might presume that a product is of high or low quality by activating naïve beliefs, which influence purchase intention. Finally, there has been little research employing naïve theories to explain customers’ behavior in SMIs (Vrontis et al., 2021) in the travel industry.

Prior research has shown that customers’ purchasing intentionsin SMIs are significantly influenced by their naïve beliefs (Deval et al., 2013; Steinhart et al., 2014; Wan et al., 2018). For instance, Rao Hill and Qesja (2023) revealed that individuals exposed to micro SMIs showed a higher intention to acquire travel products than those exposed to macro SMIs after controlling for influencer authenticity. In contrast, Janssen et al. (2022) discovered that participants exposed to SMIs with substantial followers have stronger purchase intentions than those exposed to influencers with a medium number of followers after controlling for credibility and identity. Little attention has been paid to examining the influence of the number of followers and the moderating impact of a package tour price.

2.2. The Impact of the Number of Followers on Purchase Intention

The number of followers or friends on social media platforms reflects an influencer’s online popularity and can be used to forecast influencer marketing efficacy. The naïve hypothesis of popularity suggests that SMIs with more followers are more influential and popular than those with smaller followings (van Dijck, 2013). This is because consumers frequently try to minimize their cognitive effort by aligning with the preferences of the majority (friends or other followers) when evaluating the source credibility and the content of messages (Jin & Phua, 2014). Indeed, Metzger et al. (2010) have proved that customers often use cognitive heuristics and rely on others in their groups to evaluate sources of information. Hence, more followers seem to be more effective in influencer marketing.

While previous research has found that the number of followers influences purchase intention (Janssen et al., 2022; Jin & Phua, 2014; Weismueller et al., 2020), the results are conflicting. Studies have demonstrated, for instance, that SMIs who have more followers can increase the (commercial) message’s reach (van Dijck, 2013), make one appear more likable and popular (de Veirman et al., 2017), and increase the likelihood that a follower will make a purchase compared to SMIs with a medium number of followers (Jin & Phua, 2014; Janssen et al., 2022; Kay et al., 2020). Conversely, much research has revealed that people exposed to micro (medium) SMIs have a higher propensity to purchase travel products than those exposed to macro (high) SMIs (Rao Hill & Qesja, 2023). Furthermore, compared to a moderate number of followers, Westerman et al. (2012) showed that having too few or too many followers can negatively affect people’s perceptions of a social network. Therefore, more research on the relationship between follower count and purchase intention is essential. In this study, we propose hypothesis 1 as follows:

H1: Participants exposed to SMIs with a higher number of followers show a higher purchase intention than those exposed to SMIs with a medium number of followers.

2.3. The Moderating Effect of Package Tour Price

From the customer’s standpoint, price is the money they must forgo to obtain the good or service (Zeithaml, 1988). Price reflects the adage “you get what you pay for” and is used by many customers as a heuristic or signaling cue for quality when making decisions (Erickson & Johansson, 1985; Lien et al., 2015). The price of luxury products, according to the naïve theory of exclusivity, is part of their exclusivity (Upshaw et al., 2017). In other words, when a product’s price is high, customers typically believe it is exclusive (Deval et al., 2013), and only a few individuals possess it. As a result, high prices may indicate product distinctiveness (Lynn, 1992), induce customersto regard the product as exceptional (Hui & Bateson, 1991; Machleit et al., 2000), and influence their purchase intention.

Previous studies have demonstrated that price significantly affects customers’ intention to purchase (Büyükdağ et al., 2020; Cheah et al., 2020; Hati et al., 2021). For example, Suk et al. (2021) noted that a low price might give rise to an unfavorable view, such as cheap, which limits or negatively impacts consumers’ intention to buy a product or service. Conversely, a higher price makes customers perceive the product as more appealing, resulting in an increased purchase intention (Şener et al., 2019; Shirai, 2015). Given this, the authors of this study anticipate that when the price of a package tour is high, customers might regard it as exclusive (Deval et al., 2013), distinct (Lynn, 1992), extraordinary (Hui & Bateson, 1991; Machleit et al., 2000), and a good that only a few customers can possess. In contrast, if the package tour price is low, it may signal that the product is widespread and that many people can purchase it, influencing their purchase intention. Hence, we propose hypothesis 2 as follows:

H2: When the package tour price is low, participants exposed to SMIs with a high number of followers are shown higher purchase intention than those exposed to SMIs with a medium number of followers.

When the package tour price is high, participants exposed to SMIs with a medium number of followers are shown higher purchase intention than those exposed to SMIs with a high number of followers.

Thus, hypothesis 1 states that the number of followers influences purchase intention, whereas hypothesis 2 evaluates the moderating impact of the package tour price on the number of followers and purchase intention. The paper’s model is summarized in Figure 1.

OTGHB7_2024_v22n4_37_f0001.png 이미지

Figure 1: The proposed research model

It is hypothesized that participants exposed to SMIs with a higher number of followers show a higher purchase intention than those exposed to SMIs with a medium number of followers. When the package tour price is low, participants exposed to SMIs with a high number of followers are shown higher purchase intention than those exposed to SMIs with a medium number of followers.

3. Research Methods

3.1. Pilot Study

Two Pilot studies were carried out before the main experiment. Initially, five travel agency managers (3 men and two women; age M = 29.30, SD = 4.61) selected high and low price ranges for Danang package tours. Given the setting of our research within the travel industry, we focused on a 4-day, 3-night package tour (typical duration for domestic travelers), which included airline, hotel, meals, and entrance tickets for attractions. Danang was chosen because it is one of Vietnam’s most popular tourist destinations. Notably, all participants proposed a price range of 24,990,000 – 29,990,000 VND for the high end and 4,990,000 – 9,990,000 VND for the low end. Consequently, the main experiment included a high-priced package tour priced at 24,990,000 VND and a low-priced one priced at 4,990,000 ND.

Following that, we chose a mock-up influencer appearance. Previous research has demonstrated that the physical attractiveness of influencers affects customers’ purchase intention (Jansom & Pongsakornrungsilp, 2021; Lou & Kim, 2019). We selected a pool of six photos (3 women and three men) to minimize the potential impact of SMIs’ appearance. This study included 30 undergraduate students (Mage = 20.75, SDage = 1.86). These students rated the attractiveness of each photograph on a scale of 1 (describes very poorly) to 7 (describes very well). After analysis, one photo (a man) was selected because it had a middle-of-the-scale score of 4.00, ts < 1.03, ps > 0.05, indicating that the man was neither too attractive nor unattractive.

3.2. Main Experiment

Participants: Vietnamese students at Thuongmai University in Hanoi, Vietnam, participated in this study; they had to have a Facebook account and follow at least one SMI account. This ensured that they were aware of the SMIs’ content. For their engagement, the participants received a 10-point class attendance reward. In total, 421 students volunteered to participate in this study. However, because of incomplete responses, 26 respondents were excluded from the analysis, leaving a total sample size of 395 (114 men, 281 women; Mage = 19.99, SDage = 1.25).

Research tools and procedures: We selected Facebook as our primary social media site for three reasons. Firstly, according to Nguyen (2023), 95% of Vietnamese internet users are active Facebook users. Secondly, Facebook is a visually focused social media network that makes it simple for users to upload, share, and explore information (Bicen & Cavus, 2011). Lastly, many travel companies use Facebook to sell their products and services in Vietnam.

Participants were asked to complete two screening questions in Vietnamese. Namely, “Do you have a Facebook account?” and “Have you followed any SMIs’ Facebook accounts?”

Face-to-face interviews were used to collect the data betweenApril 4th and May 5th, 2023. This method aided the ability to convey critical information effectively, allowed more effective control over the response rate, and ensured a more accurate and reliable dataset.

Several sequential steps were involved in the data collection procedure. Initially, random numbers were used to allocate individuals to one of the four conditions. These four conditions were as follows: (1) the high price and follower condition, in which participants received a survey related to a travel destination with 21,000 followers and a (24,990,000 VND) high price; the high number of followers and the low price condition, in which participants received a survey with 21,000 followers and a (4,990,000) low price, the medium number of followers and the high price condition, in which participants received a survey with 2120 followers and a (24,990,000 VND) high price, and the medium number of followers and the low price condition, in which participants received a survey with 2,120 followers and a (4,990,000 VND) low price. When students arrived in the classrooms, they were seated at assigned tables and completed one of the four survey forms. Participants in each condition were provided with a brief explanation of the concept of SMIs based on Kim and Kim (2021). Next, they were shown six postings in the travel category as well as a mock-up of the SMI Facebook bio. Next, participants were requested to submit (1) personal data (e.g., age, gender, faculty, average daily Facebook usage hours, number of SMIs followed), (2) purchase intention, and (3) various SMI types and Facebook screenshots mock-ups.

Measure: We used a two-item scale modified from Pittman and Abell (2021), which had been based on Spears and Singh (2004) by replacing “product” with “package tour.” The two items were how likely participants would purchase a package tour and how interested participants would purchase a package tour. All items were assessed on a 7-point Likert scale, with 1 being “Not at all Likely/Interested” and 7 being “Very Likely/Interested”. The Alpha coefficient for the two items was .725, suggesting that these items were reliable. Similarly, participants responded to two questions adapted by de Veirman (2017) regarding having a high and medium number of followers. These included: (1) I found the influencer had a very small (= 1) versus very large (= 7) number of followers, and (2) I thought the influencer’s number of followers was smaller (= 1) versus larger (= 7) than the average influencer’s number of followers, and three questions about mock-up Facebook screenshots (1) I think there are Facebook posts like this in the real world, (2) I believe there are actual Facebook posts like this one, and (3) I think I have seen similar posts on Facebook before). Respondents rated these items on a 7-point Likert scale, ranging from 1 = strongly disagree and 7 = strongly agree. The Alpha coefficients for the high and medium number of followers and mock-up Facebook screenshots were .803 and .766, respectively, suggesting that these items were reliable.

Data analysis: The data was analyzed using ANOVA and macro process analysis in SPSS 24. ANOVA has been used in various studies in the context of SMIs (Rao Hill & Qesja, 2023; Steinhart et al., 2014; Wan et al., 2018), and the macro process is the appropriate approach for analyzing the moderating variable (Hayes, 2017) and, therefore they were used in this study. In this study, we examine the means of purchase intention between two groups in a high and medium number of followers and a high and low price using ANOVA. If the p-value is less than .05. This indicates that the purchase intention varies depending on the number of followers and price.

4. Results

4.1. Participant Profile

The participants’ demographics are presented in Table 1. 71.1% were female, with 26.6% being under 20. 17.7% of the students were from the hospitality-tourism faculty. Regarding Facebook usage, 59.7% reported spending 1 to 3 hours daily on the network. Additionally, 68.4% of them subscribed to 2 to 4 SMIs.

Table 1: Respondents’ profile

OTGHB7_2024_v22n4_37_t0001.png 이미지

4.2. Manipulation Check

Firstly, we examined the success of manipulating the medium and high number of followers using ANOVA. The findings revealed that participants differentiated the number of followers with Mmedium = 4.45 (SDmedium = 1.61) and Mhigh = 4.85 (SDhigh = 1.26), F = 6.807, p = .009. These results suggested that the number of followers had been successfully manipulated.

Secondly, we checked the success of manipulating the low and high prices of the package tour by using ANOVA. Results showed that participants distinguished the low and high price of the provided package tour with Mlow = 4.25 (SDlow = 1.46) and Mhigh = 4.76 (SDhigh = 1.36), F(1, 393) = 12.81, p < .001. These results proved that the package tour’s price manipulation was successful.

Finally, we examined the realism of the mock-up Facebook screenshots. The higher the score participants gave, the more realistic the mock-up Facebook screenshots appeared to them. The mean score was 5.21 (SD = 1.03), and there was no significant variation in perceptions across the four groups with F(2, 392) = .713, p > .05. Therefore, we consider that the realism of the screenshots was successfully manipulated.

4.3. Hypothesis Testing

Table 2 presents the mean and standard deviation of purchase intention. The mean purchase intention in high and medium numbers of followers was 4.39 (1.56) and 4.03 (1.37), respectively. The mean purchase intention in high and medium low package tour prices was 4.03 (1.39) and 4.33 (1.01).

Table 2: Mean of Variable for Purchase Intention

OTGHB7_2024_v22n4_37_t0002.png 이미지

Table 3 displays the findings of the correlation among variables. The correlation coefficients were ranging from .103 to .123. These values indicate positive relationships between the number of followers, package tour price, and purchase intention in the travel sector in Vietnam.

Table 3: Results of Correlation Among Variables

OTGHB7_2024_v22n4_37_t0003.png 이미지

Note: * p <.05; ** p < .001

The main and moderation analyses are presented in Table 4 and Figure 2. The number of followers has a statistically significant impact on purchase intention, β = -2.45, t(395)= -5.65, p < .001, and 95% confidence interval CI = [-3.30, -1.59] did not include zero. As a result, H1 is supported. Furthermore, the interaction between number of followers and package tour price was significant (β = 1.95, SE = .28, t(395) = 6.89, p < .001), and 95% confidence interval CI = [1.39, 2.51] did not include zero, indicating that package tour price moderated the association among the number of followers and purchase intention in the travel sector in Vietnam. At a low package tour price, the simple slope of the number of followers on purchase intention was significant (β = 1.45, SE = .21, t(395) = 6.90, p < .001), and 95% confidence interval CI = [1.04, 1.86] did not include zero, and at the high level of package tour price (β = -.50, SE = .19, t(395) = -2.64, p = .0086), and 95% confidence interval CI = [-.86, -.13] did not include zero. Thus, H2 is supported.

Table 4: The Main and Moderation Effect

OTGHB7_2024_v22n4_37_t0004.png 이미지

Note: ** p < .001

OTGHB7_2024_v22n4_37_f0002.png 이미지

Figure 2: The Estimated Means of Purchase Intention

5. Discussions

5.1. General Discussion

This research delved into how the number of followers influences followers’ purchase intention and the moderating effect of package tour price on followers’ purchase intention in the travel sector in Vietnam. We discovered that the number of followers statistically affects purchase intention using experimental design, ANOVA, and macro processesin data analysis. Moreover, package tour price moderated the association between the number of followers and purchase intention in the travel sector in Vietnam.

Similar to prior studies (van Dijck, 2013; Weismueller et al., 2020), our findings reinforce the positive impact of the number of followers on purchase intention. More specifically, customers tend to purchase package tours when they follow SMIs with a high number of followers compared to those with a medium number of followers (van Dijck, 2013; Weismueller et al., 2020). We argue that people follow the high number of followers by adopting the naïve beliefs of popularity in decision-making. One reason is that naïve beliefs often serve as a reliance cue for decision-making due to the nature of intangible package tours and the limited information available to customers (Deval et al., 2013). Customers are more likely to purchase when they follow SMIs with a high number of followers.

Our findings also suggest that package tour price moderates the relationship between the number of followers and purchase intention in the travel sector in Vietnam. While the moderating impact of package tour price on customer decision-making in SMI marketing has received less attention, the findings are consistent with previous research in other fields that argued that price, affirming that price exerts both direct and indirect effects on purchase decision-making (Cho & Sagynov, 2015; Lu et al., 2016). However, the results of this study indicate that when the package tour price is higher, its impact becomes more substantial as followers follow SMIs with a medium number of followers. This contradicts previous research, which discovered that SMIs with a high number of followers are likelier to purchase than SMIs with a medium number of followers (van Dijck, 2013; Weismueller et al., 2020). However, these results also reinforce specific findings indicating that customers who follow SMIs with a medium following are more inclined to purchase a product than those who follow SMIs with a high number of followers (Rao Hill & Qesja, 2023; Westerman et al., 2012). This can be explained by the moderating effect of package tour price. Specifically, when package tour price is high, fewer people can buy them, making package tours more desirable.

5.2. Theoretical Implications

This paper adds valuable insights to the body of knowledge regarding the influence of SMIs on purchase intention, particularly in the travel industry in Vietnam. First, the logic of naïve theories of popularity and exclusivity was supported by this study. Participants in the low-price condition tend to follow SMIs with a high number of followers, guided by the naïve theory of popularity. However, in the case of high price, their decision-making involvesthe naïve theory of exclusivity. Thus, naïve theories within the context of SMIs should be studied regularly as a subjective indicator if travel companies desire to know how customers react when SMIs post their travel experiences on social media.

Second, this study is one of the first to examine the moderating impact of package tour price between the number of followers and purchase intention. Existing research has examined how some variables, such as product type (Steinhart et al., 2014), product-influencer fit (Janssen et al., 2022), and disclosure (Kay et al., 2020), influence purchase intention. Our study, however, is the first to focus on the moderating impact of package tour price. When the price is high, customers who follow SMIs with a medium number of followers tend to purchase package tours than those who follow SMIs with a high number of followers. Thus, our study advances the literature on the moderating influence of package tour price on the purchase intention of those who follow a high or medium number of followers.

5.3. Practical Implications

Destination managers in Vietnam can benefit greatly from the practical consequences of our current study. The results indicate a positive correlation between followers’ purchasing intentions and the number of SMI followers. However, SMIs with a reasonable number of followers in SMIs have a higher impact when the price of a package tour is high. Consequently, destination managers should consider working with SMIs with a large following while promoting package tours at a reduced price point. On the other hand, destination managers in Vietnam should work with SMIs with a moderate number of followers for expensive package vacations.

6. Conclusion

This study aimed to investigate how purchasing intention in SMIs is influenced by the number of followers and the moderating effect of package tour price in the travel sector in Vietnam. The findings demonstrated that customers’ purchase intention was significantly influenced by their follower count and that the price of the package moderated the relationship between the number of followers and purchase intention. Specifically, participants were more inclined to buy package tours from SMIs with a large number of followers than from those with a medium number of followers. On the other hand, participants indicated a greater intention to purchase from SMIs with a medium number of followers compared to those with a high number of followers when the package tour price was high. One possible explanation is that when people follow SMIs with a high number of followers, they may unintentionally utilize the naïve theory of popularity. In contrast, when the tour price is high, they may rely on the naïve theory of exclusivity; therefore, their purchase intention varies.

The study has two significant contributions. Firstly, this study further validates the naïve theories in the field of tourism research by confirming the theories’ usefulness in explaining purchase intention in the SMI context in the travel sector in Vietnam. The findings reveal that when making decisions, people tend to follow the preferences of the high followers. Secondly, under the logic of naïve theories, the results of this study show that the number of followers positively affects purchase intention, and participants are willing to buy package toursfrom SMIs with a higher number of followers than those with a medium one. As a result, it is recommended that destination managers hire appropriate SMIs depending on the package tour price level.

This study has certain limitations despite these significant findings. Firstly, since the study data was collected from students aged 18 to 22 at a single university in Vietnam, the generalizability of our findings is limited. Future studies should also confirm the findings’ generalizability by incorporating a diverse range of individuals, including those from various colleges and age groups. Secondly, the sample contained a high proportion of female students. Thus, the generalizability of our findings may be limited. Hence, gender balance should be considered in future studies. Finally, the two items measuring purchase intention are asking “how likely” and “how interested” a participant was to purchase a tour package, which might be biased towards purchasing a tour package as opposed to not. Hence, future research could have evaluated whether any language used is leading (Loftus & Palmer, 1974) before conducting experiments.

References

  1. Al-Emadi, F. A., & Ben Yahia, I. (2020). Ordinary celebrities related criteria to harvest fame and influence on social media. Journal of Research in Interactive Marketing, 14(2), 195-213. https://doi.org/10.1108/JRIM-02-2018-0031
  2. Berger, J., & Heath, C. (2007). Where Consumers Diverge from Others: Identity Signaling and Product Domains. Journal of Consumer Research, 34(2), 121-134. https://doi.org/10.1086/519142
  3. Bicen, H., & Cavus, N. (2011). Social network sites usage habits of undergraduate students: Case study of Facebook. Procedia - Social and Behavioral Sciences, 28, 943-947. https://doi.org/10.1016/j.sbspro.2011.11.174
  4. Buyukdag, N., Soysal, A. N., & Ki̇tapci, O. (2020). The effect of specific discount pattern in terms of price promotions on perceived price attractiveness and purchase intention: An experimental research. Journal of Retailing and Consumer Services, 55, 102112. https://doi.org/10.1016/j.jretconser.2020.102112
  5. Cheah, J. H., Waller, D., Thaichon, P., Ting, H., & Lim, X. J. (2020). Price image and the sugrophobia effect on luxury retail purchase intention. Journal of Retailing and Consumer Services, 57, 102188. https://doi.org/10.1016/j.jretconser.2020.102188
  6. Cho, Y. C., & Sagynov, E. (2015). Exploring Factors That Affect Usefulness, Ease Of Use, Trust, And Purchase Intention In The Online Environment. International Journal of Management & Information Systems, 19(1), 21-36. https://doi.org/10.19030/ijmis.v19i1.9086
  7. Cialdini, R. B., & Goldstein, N. J. (2004). Social Influence: Compliance and Conformity. Annual Review of Psychology, 55(1), 591-621. https://doi.org/10.1146/annurev.psych.55.090902.142015
  8. de Veirman, M., Cauberghe, V., & Hudders, L. (2017). Marketing through Instagram influencers: The impact of number of followers and product divergence on brand attitude. International Journal of Advertising, 36(5), 798-828. https://doi.org/10.1080/02650487.2017.1348035
  9. Deval, H., Mantel, S. P., Kardes, F. R., & Posavac, S. S. (2013). How Naive Theories Drive Opposing Inferences from the Same Information. Journal of Consumer Research, 39(6), 1185-1201. https://doi.org/10.1086/668086
  10. Erickson, G. M., & Johansson, J. K. (1985). The Role of Price in Multi-Attribute Product Evaluations. Journal of Consumer Research, 12(2), 195-199. https://doi.org/10.1086/208508
  11. Folkvord, F., Roes, E., & Bevelander, K. (2020). Promoting healthy foods in the new digital era on Instagram: An experimental study on the effect of a popular real versus fictitious fit influencer on brand attitude and purchase intentions. BMC Public Health, 20(1), 1677. https://doi.org/10.1186/s12889-020-09779-y
  12. Garcia-de-Frutos, N., & Estrella-Ramon, A. (2021). You absolutely (don't) need this examining differences on customer engagement components for (anti)haul youtubers' videos. Journal of Research in Interactive Marketing, 15(1), 86-103. https://doi.org/10.1108/JRIM-11-2019-0181
  13. Godey, B., Manthiou, A., Pederzoli, D., Rokka, J., Aiello, G., Donvito, R., & Singh, R. (2016). Social media marketing efforts of luxury brands: Influence on brand equity and consumer behavior. Journal of Business Research, 69(12), 5833-5841. https://doi.org/10.1016/j.jbusres.2016.04.181
  14. Gomes, M. A., Marques, S., & Dias, A. (2022). The impact of digital influencers' characteristics on purchase intention of fashion products. Journal of Global Fashion Marketing, 13(3), 187-204. https://doi.org/10.1080/20932685.2022.2039263
  15. Grave, J. F. (2017). Exploring the Perception of Influencers vs. Traditional Celebrities: Are Social Media Stars a New Type of Endorser? Proceedings of the 8th International Conference on Social Media & Society, 1-5. https://doi.org/10.1145/3097286.3097322
  16. Harb, A. A., Fowler, D., Chang, H. J. (Julie), Blum, S. C., & Alakaleek, W. (2019). Social media as a marketing tool for events. Journal of Hospitality and Tourism Technology, 10(1), 28-44. https://doi.org/10.1108/JHTT-03-2017-0027
  17. Hati, S. R. H., Zulianti, I., Achyar, A., & Safira, A. (2021). Perceptions of nutritional value, sensory appeal, and price influencing customer intention to purchase frozen beef: Evidence from Indonesia. Meat Science, 172, 108306. https://doi.org/10.1016/j.meatsci.2020.108306
  18. Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford publications.
  19. Hui, M. K., & Bateson, J. E. G. (1991). Perceived Control and the Effects of Crowding and Consumer Choice on the Service Experience. Journal of Consumer Research, 18(2), 174-184. https://doi.org/10.1086/209250
  20. Jang, W. (Eric), Kim, J. (Jay), Kim, S., & Chun, J. W. (2021). The role of engagement in travel influencer marketing: The perspectives of dual process theory and the source credibility model. Current Issues in Tourism, 24(17), 2416-2420. https://doi.org/10.1080/13683500.2020.1845126
  21. Jansom, A., & Pongsakornrungsilp, S. (2021). How Instagram Influencers Affect the Value Perception of Thai Millennial Followers and Purchasing Intention of Luxury Fashion for Sustainable Marketing. Sustainability, 13(15), Article 15. https://doi.org/10.3390/su13158572
  22. Janssen, L., Schouten, A. P., & Croes, E. A. J. (2022). Influencer advertising on Instagram: Product-influencer fit and number of followers affect advertising outcomes and influencer evaluations via credibility and identification. International Journal of Advertising, 41(1), 101-127. https://doi.org/10.1080/02650487.2021.1994205
  23. Jin, S. A. A., & Phua, J. (2014). Following Celebrities' Tweets about Brands: The Impact of Twitter-Based Electronic Word-of-Mouth on Consumers' Source Credibility Perception, Buying Intention, and Social Identification with Celebrities. Journal of Advertising, 43(2), 181-195. https://doi.org/10.1080/00913367.2013.827606
  24. Kay, S., Mulcahy, R., & Parkinson, J. (2020). When less is more: The impact of macro and micro social media influencers' disclosure. Journal of Marketing Management, 36(3-4), 248-278. https://doi.org/10.1080/0267257X.2020.1718740
  25. Ki, C. W. 'Chloe,' & Kim, Y. K. (2019). The mechanism by which social media influencers persuade consumers: The role of consumers' desire to mimic. Psychology & Marketing, 36(10), 905-922. https://doi.org/10.1002/mar.21244
  26. Kim, D. Y., & Kim, H. Y. (2021). Influencer advertising on social media: The multiple inference model on influencer-product congruence and sponsorship disclosure. Journal of Business Research, 130, 405-415. https://doi.org/10.1016/j.jbusres.2020.02.020
  27. Lien, C. H., Wen, M. J., Huang, L. C., & Wu, K. L. (2015). Online hotel booking: The effects of brand image, price, trust and value on purchase intentions. Asia Pacific Management Review, 20(4), 210-218. https://doi.org/10.1016/j.apmrv.2015.03.005
  28. Lin, H. C., Bruning, P. F., & Swarna, H. (2018). Using online opinion leaders to promote the hedonic and utilitarian value of products and services. Business Horizons, 61(3), 431-442. https://doi.org/10.1016/j.bushor.2018.01.010
  29. Loftus, E. F., & Palmer, J. C. (1974). Reconstruction of automobile destruction: An example of the interaction between language and memory. Journal of Verbal Learning and Verbal Behavior, 13(5), 585-589. https://doi.org/10.1016/S0022-5371(74)80011-3
  30. Lou, C., & Kim, H. K. (2019). Fancying the New Rich and Famous? Explicating the Roles of Influencer Content, Credibility, and Parental Mediation in Adolescents' Parasocial Relationship, Materialism, and Purchase Intentions. Frontiersin Psychology, 10. Article 2567. https://www.frontiersin.org/articles/10.3389/fpsyg.2019.02567
  31. Lu, B., Fan, W., & Zhou, M. (2016). Social presence, trust, and social commerce purchase intention: An empirical research. Computers in Human Behavior, 56, 225-237. https://doi.org/10.1016/j.chb.2015.11.057
  32. Lynn, M. (1992). The Psychology of Unavailability: Explaining Scarcity and Cost Effects on Value. Basic and Applied Social Psychology, 13(1), 3-7. https://doi.org/10.1207/s15324834basp1301_2
  33. Machleit, K. A., Eroglu, S. A., & Mantel, S. P. (2000). Perceived Retail Crowding and Shopping Satisfaction: What Modifies This Relationship? Journal of Consumer Psychology, 9(1), 29-42. https://doi.org/10.1207/s15327663jcp0901_3
  34. Metzger, M. J., Flanagin, A. J., & Medders, R. B. (2010). Social and Heuristic Approaches to Credibility Evaluation Online. Journal of Communication, 60(3), 413-439. https://doi.org/10.1111/j.1460-2466.2010.01488.x
  35. Moldovan, S., Muller, E., Richter, Y., & Yom-Tov, E. (2017). Opinion leadership in small groups. International Journal of Research in Marketing, 34(2), 536-552. https://doi.org/10.1016/j.ijresmar.2016.11.004
  36. Nafees, L., Cook, C. M., Nikolov, A. N., & Stoddard, J. E. (2021). Can social media influencer (SMI) power influence consumer brand attitudes? The mediating role of perceived SMI credibility. Digital Business, 1(2), 100008. https://doi.org/10.1016/j.digbus.2021.100008
  37. Pittman, M., & Abell, A. (2021). More Trust in Fewer Followers: Diverging Effects of Popularity Metrics and Green Orientation Social Media Influencers. Journal of Interactive Marketing, 56(1), 70-82. https://doi.org/10.1016/j.intmar.2021.05.002
  38. Rao Hill, S., & Qesja, B. (2023). Social media influencer popularity and authenticity perception in the travel industry. The Service Industries Journal, 43(5-6), 289-311. https://doi.org/10.1080/02642069.2022.2149740
  39. Saima, & Khan, M. A. (2021). Effect of Social Media Influencer Marketing on Consumers' Purchase Intention and the Mediating Role of Credibility. Journal of Promotion Management, 27(4), 503-523. https://doi.org/10.1080/10496491.2020.1851847
  40. Sanchez-Fernandez, R., & Jimenez-Castillo, D. (2021). How social media influencers affect behavioural intentions towards recommended brands: The role of emotional attachment and information value. Journal of Marketing Management, 37(11-12), 1123-1147. https://doi.org/10.1080/0267257X.2020.1866648
  41. Sener, T., Biskin, F., & Kilinc, N. (2019). Sustainable dressing: Consumers' value perceptions towards slow fashion. Business Strategy and the Environment, 28(8), 1548-1557. https://doi.org/10.1002/bse.2330
  42. Shirai, M. (2015). Impact of "High Quality, Low Price" Appeal on Consumer Evaluations. Journal of Promotion Management, 21(6), 776-797. https://doi.org/10.1080/10496491.2015.1088922
  43. Sokolova, K., & Kefi, H. (2020). Instagram and YouTube bloggers promote it, why should I buy? How credibility and parasocial interaction influence purchase intentions. Journal of Retailing and Consumer Services, 53, 101742. https://doi.org/10.1016/j.jretconser.2019.01.011
  44. Spears, N., & Singh, S. N. (2004). Measuring Attitude toward the Brand and Purchase Intentions. Journal of Current Issues & Research in Advertising, 26(2), 53-66. https://doi.org/10.1080/10641734.2004.10505164
  45. Steinhart, Y., Kamins, M., Mazursky, D., & Noy, A. (2014). Effects of product type and contextual cues on eliciting naive theories of popularity and exclusivity. Journal of Consumer Psychology, 24(4), 472-483. https://doi.org/10.1016/j.jcps.2014.04.004
  46. Suk, M., Kim, M., & Kim, W. (2021). The moderating role of subjective norms and self-congruence in customer purchase intentions in the LCC market: Do not tell me I am cheap. Research in Transportation Business & Management, 41, 100595. https://doi.org/10.1016/j.rtbm.2020.100595
  47. Torres, P., Augusto, M., & Matos, M. (2019). Antecedents and outcomes of digital influencer endorsement: An exploratory study. Psychology & Marketing, 36(12), 1267-1276. https://doi.org/10.1002/mar.21274
  48. Trivedi, J., & Sama, R. (2020). The Effect of Influencer Marketing on Consumers' Brand Admiration and Online Purchase Intentions: An Emerging Market Perspective. Journal of Internet Commerce, 19(1), 103-124. https://doi.org/10.1080/15332861.2019.1700741
  49. Upshaw, D., Amyx, D., & Hardy, M. (2017). The Nature of Exclusivity. Journal of Marketing Development and Competitiveness, 11(2), 46-63.
  50. van Dijck, J. (2013). The Culture of Connectivity: A Critical History of Social Media. Oxford University Press.
  51. Vrontis, D., Makrides, A., Christofi, M., & Thrassou, A. (2021). Social media influencer marketing: A systematic review, integrative framework and future research agenda. International Journal of Consumer Studies, 45(4), 617-644. https://doi.org/10.1111/ijcs.12647
  52. Wan, X., Wang, T., & Wu, J. (2018). I'll Follow the Minority: The Effects of Sales Level on Purchase Intention of Self-expressive Products. Frontiers in Psychology, 9. https://www.frontiersin.org/articles/10.3389/fpsyg.2018.01135
  53. Weismueller, J., Harrigan, P., Wang, S., & Soutar, G. N. (2020). Influencer Endorsements: How Advertising Disclosure and Source Credibility Affect Consumer Purchase Intention on Social Media. Australasian Marketing Journal, 28(4), 160-170. https://doi.org/10.1016/j.ausmj.2020.03.002
  54. Westerman, D., Spence, P. R., & Van Der Heide, B. (2012). Asocial network as information: The effect of system generated reports of connectedness on credibility on Twitter. Computers in Human Behavior, 28(1), 199-206. https://doi.org/10.1016/j.chb.2011.09.001
  55. Xiong, Y., Cheng, Z., Liang, E., & Wu, Y. (2018). Accumulation mechanism of opinion leaders'social interaction ties in virtual communities: Empirical evidence from China. Computers in Human Behavior, 82, 81-93. https://doi.org/10.1016/j.chb.2018.01.005
  56. Zeithaml, V. A. (1988). Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence. Journal of Marketing, 52(3), 2-22. https://doi.org/10.1177/002224298805200302