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Article

What Makes GO-JEK Go in Indonesia? The Influences of Social Media Marketing Activities on Purchase Intention

by
Massoud Moslehpour
1,2,
Taufiq Ismail
1,3,
Bey Purba
1 and
Wing-Keung Wong
4,5,*
1
Department of Business Administration, College of Management, Asia University, Taichung 41354, Taiwan
2
Department of Management, California State University, San Bernardino, CA 92407, USA
3
Department of Management, Faculty of Economics and Business, Universitas Brawijaya, Malang 65145, Indonesia
4
Department of Finance, Fintech Center, and Big Data Research Center, Asia University, Taichung 41354, Taiwan
5
Department of Economics and Finance, The Hang Seng University of Hong Kong, Hong Kong
*
Author to whom correspondence should be addressed.
J. Theor. Appl. Electron. Commer. Res. 2022, 17(1), 89-103; https://0-doi-org.brum.beds.ac.uk/10.3390/jtaer17010005
Submission received: 25 August 2021 / Revised: 21 December 2021 / Accepted: 23 December 2021 / Published: 27 December 2021
(This article belongs to the Section Digital Marketing and the Connected Consumer)
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Abstract

:
This research examines the relationship between social media marketing activities and purchase intention mediated by trust and brand image to confirm the constructs with practical applicability, specifically in a growing online ride-hailing service company. This study employs a quantitative approach with a causal research design to test the proposed hypotheses to identify interrelationships between each pair of constructs. Data collection was performed through a survey of 350 respondents via an online questionnaire as the primary data source distributed to social media users in Indonesia who had experienced using GO-JEK services. In addition, EFA, CFA, SEM, and bootstrapping methods were run to analyze these research data. Social media marketing, trust, and brand image affect consumers’ purchase intention significantly. Among the five dimensions of social media marketing, the findings show that two dimensions—namely, entertainment and word of mouth, bring the most significant direct effect on purchase intention. Trust and brand image mediate the relationship between social media marketing and purchase intention. This study suggests practical directions for organizations. First, it reveals the social media dimensions that directly encourage purchase intention among consumers. Second, it explains that trust and brand image can amplify each variable’s influence on the purchase intention among consumers. GO-JEK is an example of the online ride-hailing industry that causes the generalizability issue in different business contexts. Based on our findings, there are some practical directions for GO-JEK. First, it reveals the social media marketing dimensions that directly encourage purchase intention among consumers to use GO-JEK. Second, it explains that trust and brand image can amplify the influence of each variable on consumers’ purchase intention. Very few studies investigated social media marketing’s role in a GO-JEK business model in the Indonesian context. This research delivers in-depth insights into the significant factors that affect Indonesian consumers to decide which product they intend to buy through the influence of social media activities.

1. Introduction

Technological developments have a strong impact on our lives, as technology is abundantly available and frequently promoted throughout our society [1]. Further, the positive benefits emerging from the use of technology appear in several sectors such as advancing healthcare, reforming business and manufacturing channels, improving access to information, adding new systems and educational methods, helping with daily housework, and providing easy access to transportation and logistics [2,3,4]. Technological development also touches the e-commerce sector that has grown in the past year. Recently, e-commerce shifted to mobile commerce (m-commerce) due to the development of internet technology and the emergence of mobile devices with rich features to such an extent that customers have changed the way they exchange financially through mobile transactions [5]. Conducting commercial transactions becomes easier and simpler since it can be accomplished using a mobile device in our hands, as internet technology and social media development encourage even faster growth of the m-commerce market.
Nylander [6] reported that Indonesia was one of the fastest-growing mobile-commerce markets globally. The rapid development of the m-commerce industry in Indonesia is due to its large population, growing middle-market segment, increasing penetration of smartphones, and the growing popularity of online shopping [6]. Among the gigantic tech industries in Indonesia, GO-JEK Indonesia is one of the largest local tech-company doing business in the m-commerce sector. GO-JEK valuation in 2020 reached USD 12.5 billion [7], contributing USD 7.1 billion to Indonesia’s economy in 2019 [8]. Large companies such as Google, Tencent, JD.com, Astra, Allianz, and Mitsubishi invested in Go-JEK Indonesia [9]. Therefore, this company is worth to be explored for its excellent performance in digital technology and social media advancement.
Social media is an effective tool for companies’ commercial interests and supports better business performance [10,11]. When making purchase decisions, consumers rely on their social media [12]. With the increasing number of consumers using social media and social media as a part of corporate strategy, social media facilitates the interaction between consumers and companies and also provides a platform for consumers to be connected and interact with other consumers [13]. Social media use also takes an expanding role as communication media from a customer perspective and as an option for corporate marketing communication strategy [12].
Social media can greatly benefit the organization [14,15,16]. With the growing m-commerce sector and the increasing number of social media users in Indonesia, GO-JEK can take advantage of this opportunity to strengthen its business. Through social media, companies reach consumers and deliver an effective marketing message to consumers. In particular, this study uses GO-JEK as an example of this growing online ride-hailing industry. Social media marketing provides GO-JEK with great benefits and influence in increasing the company’s popularity and income. The CEO of GO-JEK, Nadiem Makarim, revealed that social media is an effective tool for introducing GO-JEK and popularizing its brands to the wider community [17]. Therefore, GO-JEK can be a suitable example for other companies in the m-commerce sector that want to present their brands to the public through social media.
Therefore, this study examines the effect of social media marketing (SMM) activities on consumers’ purchase intention (PI) with trust (TRU) and brand image (BI) as the mediating variables. The dimensions of SMM activities used in this study consist of entertainment, interaction, trendiness, customization, and word of mouth. This study’s importance is to confirm the constructs’ theoretical and practical applicabilities, specifically in a growing online ride-hailing service company. Hence, this research is expected to enrich the theoretical perspective of the specific online service industry. The next section will discuss further the literature review foundation.

2. Literature Review and Hypotheses Development

This section presents a literature review to provide a firm theoretical foundation and other relevant descriptions of the constructs observed in this research by elaborating more on social media marketing activities, trust, brand image, and purchase intention to explain the interrelationship among those variables further.

2.1. Mobile Commerce and GO-JEK Indonesia

The development of mobile and internet technologies has led to the increasing penetration of mobile devices and their use in various areas of our lives [18]. Mobile phones are no longer just useful for communicating and have other meaningful purposes like a mobile transaction supported by internet access and feature advancement. The mobile commerce (m-commerce) sector is often treated as an extension of e-commerce [19,20] for its features of mobile purchasing, mobile banking, mobile payments, and mobile investment. Some large companies such as Uber, Starbucks, Square, Apple, and Facebook are examples of companies with businesses in mobile commerce.
GO-JEK in Indonesia is the largest local technology company in the m-commerce sector. Founded in 2010, GO-JEK started its business as a ride-hailing phone service company, similar to the business model by Uber, Lyft, and Grab. Currently, GO-JEK has evolved into an advanced technology company that provides online-based services. Previous studies use GO-JEK online transportation services as a case study related to service quality [21] and digital marketing implementation [22].

2.2. Purchase Intention

Purchase intention (PI) indicates the degree of consumer feeling how confident they are to buy a product or service [23]. PI is perceived as the key predictor of actual behavior [24] that raises a better opportunity to predict overt purchase behavior. Consumers will decide to buy the product after searching for information to buy the right product to meet their needs and desires [25]. In this research, PI is the pivotal construct to be investigated. Former research has demonstrated that an increase in PI reflects an increase in the chance of purchasing [26]. Some researchers explored PI in different industries using diverse theories such as the theory of planned behavior [24,27] and social presence [28]. However, this research will elaborate on social media marketing activities, trust, and brand image since those variables also strongly drive PI, as suggested by prior studies [29,30,31,32].

2.3. Social Media Marketing Activities

Social media marketing (SMM) activity has been an important part of business recently [33] that allows organizations to communicate with consumers easily and quickly [31]. Further, SMM activities provide cost efficiency to interact with customers as adopted by more and more companies and achieve wider acceptance among customers in various businesses [34]; therefore, SMM has achieved wide acceptance in e-commerce [32]. Social media’s role for a company is to build a close relationship and easy access between customers and companies [35]. Therefore, SMM is viewed as a new aspect of corporate marketing strategy that engages with marketing products, services, information, and ideas through online social media [36]. Creating appropriate and useful content for consumers allows the companies to predict their customers’ future buying behavior more precisely [34], increase the popularity of brand posts, attract new customers, build awareness, and increase sales and build loyalty.
Kim and Ko’s model [37] describes SMM activities on luxury brands using five dimensions: entertainment, interaction, trendiness, customization, and word of mouth (WOM). Although another SMM concept proposed by Sano [38] has different dimensions like applied interaction, trendiness, customization, and perceived risks, Kim and Ko’s model [37] is more widely used in former research. Therefore, this study assesses the SMM activities of GO-JEK Indonesia based on Kim and Ko’s five perspectives, including entertainment (ENT), interaction (INT), trendiness (TRD), customization (CUS), and word of mouth (WOM) as variables rather than dimensions, to deliver a different perspective in this research, by using second-order SEM.

2.4. Trust

The pivotal role of trust (TR) in influencing consumers’ decisions is highly perceived by marketing practitioners [39]. From a managerial perspective, online purchases typically depend on consumers’ trust in the provider [40]. Trust is one of the important issues when shopping online, especially buying and selling on social media, where there is great uncertainty due to a lack of face-to-face interaction among users [41]. Thus, trust is very important in brand communication with consumers. Trust is considered a pivotal determinant that directly influences users’ behavioral intentions to purchase online [30].

2.5. Brand Image

Previous studies explored the role of brand image (BI) on consumer behavior in the service sector include [30]; further, consumers tend to purchase products/services with well-established brand names [29]. Schmitt [42] stated that BI represents consumers’ views, perceptions, and attitudes about a particular brand. Therefore, companies should pay attention to the creation of their image. When consumers are dedicated to a brand, they will continue to follow its development and avoid competitors, benefiting the company [43]. According to Keller et al. [44], positive BI can be achieved by connecting strong, best, and unique connotations with brands in consumers’ minds. In addition, BI is recognized as a significant antecedent that directly influences price and online purchase intentions [29].
After elaborating on each of the variables involved in this research, the next session will further explain the hypotheses derived from the literature review. Finally, the hypothesis development session will describe both the direct effect of SMM, TRU, and BI on PI and the indirect effect of SMM through TRU and BI on PI.

2.6. Hypotheses

This study observes one independent variable (SMM), two mediating variables (TRU and BI), and one dependent variable (PI) in the analysis. Regarding the direct effects of social media marketing activities, trust, and brand image, Yimeng and Huifan [45] stated that SMM activities provide a significant and positive impact on consumer TRU in the retail banking industry. At the same time, similar finding in the hotel industry is also revealed [46]. Furthermore, according to Jung and Kim [47], SMM’s variable significantly affects consumers’ TRU, particularly word of mouth. Thus, it postulates the following hypothesis:
Hypothesis 1 (H1).
SMM activities significantly influence TRU.
Seo and Park [31] found that the company’s SMM had a significant effect on BI. They concluded that if the airline is actively marketing on social media, consumers will more likely remember or recognize the airline more accurately than its competitors. This result aligns with Perera and Perera’s study [48], in the hospitality sector, and Wang et al. [49] in a publishing company. In this paper, the second hypothesis is developed as follows:
Hypothesis 2 (H2).
SMM activities significantly influence BI.
Asokan and Saravanan [50] confirmed that TRU has a positive effect on PI behavior. Their research in India showed that when consumers trusted a brand, they bought the product. Other studies by Pothong and Sathitwiriyawong [51] in Thailand and Hsu et al. [52] in Taiwan also supported the findings. Therefore, it leads to the following hypothesis:
Hypothesis 3 (H3).
TRU significantly influences PI.
Yunus et al. [53] stated that the BI of a product is essential because it can enrich consumers’ knowledge about its product and influence purchase decisions. Furthermore, other studies by Shafiee et al. [54] in the Iran aviation industry and Lien et al. [30] in the Taiwan hotel industry also found the positive impact of BI on PI. Therefore, these studies led us to the following hypothesis:
Hypothesis 4 (H4).
BI significantly influences PI.
According to Gautam and Sharma [10], SMM activities have a significant positive influence on consumers’ PI for a luxury fashion brand. Another study by Spackman and Larsen [55] also supported this finding. The results revealed that when Facebook marketing was highly entertaining, interactive, and broadly shared by followers, it resulted in a better relationship between an organization and its customers. Therefore, the fifth hypothesis is supposed as follows:
Hypothesis 5 (H5).
SMM activities significantly influence PI.
Trust and brand image also have mediating effects on purchase intention.
In addition to the direct effect of TRU on PI, Khuong and Huong [56] found that TR is a good mediator between SMM and PI in the tourism industry. Dutta and Bhat [57] also found that TRU is a good mediator between SMM and PI in India. Thus, the sixth hypothesis states the following:
Hypothesis 6 (H6).
TRU mediates the relationship between SMM activities and PI.
Further, Yunus et al. (2016) found that BI partially mediated SMM activities and PI’s relationship. Tariq et al. [58] also concluded that BI is a good mediator between SMM activities and PI. Therefore, the last hypothesis is supposed as follows:
Hypothesis 7 (H7).
BI mediates the relationship between SMM activities and PI.
Examining the interrelationship among the constructs, including SMM activities, TRU, BI, and PI, the results of this study confirm the theoretical applicability and provide useful insights into some practical fields. In what follows, to justify the effectiveness of the interrelationship among SMM activities, TRU, BI, and PI theoretically, this research first discusses measurement, data, and methods utilized in this study to ensure several fit indices before analyzing the issue and revealing the findings.

3. Measurement, Data, and Method

3.1. Measurement and Scale

The questionnaire’s items were developed based on some related prior studies [37,59,60,61], with modification. The questionnaire consisted of two parts. The first part described the constructs’ instrument. First, 20 items from Kim and Ko [37] and Godey et al. [59] were used to measure SMM. Second, five items from Yahia et al. [60] and Hong and Cha [61] were used to measure TRU. Third, four items from Seo and Park [31] were used to measure BI. Lastly, four items were used to measure PI. Each item used a 5-point Likert Scale, ranging from strongly disagree to strongly agree. The second part described the demographic and general information of respondents.

3.2. Data

In this study, primary data were collected by distributing questionnaires online through Google Forms. Respondents are Indonesian social media users who have used GO-JEK services before. The distribution timeline of data collection took around four months. The internet-based surveys enable researchers to target a unique population, while respondents’ identities and privacy remain protected [62]. Hair et al. [63] stated that a model with less than 7 variables could use a minimum sample size of 300. This research took a total of 362 respondents participating in this research, though only 350 were usable responses.

3.3. Method

The analysis procedure followed some analytical tools such as descriptive statistical analysis, exploratory and confirmatory factor analysis, reliability analysis, structural equation modeling (SEM), and bootstrapping. Kaiser–Meyer–Olkin (KMO) was conducted to examine the data collection’s construct validity and feasibility before running the exploratory factor analysis. KMO value must be greater than 0.50, and the loading factor of each item must be greater than 0.50 to ensure that there are no items that correlate with each other [63].

4. Results and Findings

This section presents the main result of the research by describing the results of the data analysis that was carried out. Descriptive analysis, exploratory and confirmatory factor analysis, reliability, convergent and discriminant validity, structural equation modeling, and the bootstrapping method were used to prove the influence of mediating variables. Furthermore, this part also shows the findings for each hypothesis.

4.1. Demographic Characteristic

From 350 useable questionnaires obtained, most of the respondents (51.1%) were female. The ages ranged between 17 and 42 years old; further, 42.3% were between 22 and 26 years old. Meanwhile, a bachelor’s degree (60.9%) dominated the respondent’s educational background. More than half of the total respondents (53.1%) were employees. Regarding the types of social media used most frequently, most respondents (38.3%) used Facebook and YouTube more often than other social media.

4.2. Measurement Model and Fit Indices

Based on EFA results, four items were dropped (TRD3, TRD4, WOM2, and WOM3) due to insufficient factor loading (<0.50) to attain better reliability. By using SPSS 23, eight variables were extracted, comprising one independent variable (SMM with five dimensions: entertainment, interaction, trendiness, customization, and word of mouth), two mediating variables (TRU and BI), and one dependent variable (PI).
Table 1 shows the results of the CFA (Bradley, 2018; Strauss and Smith, 2009). From the obtained results, the proposed model had a good level of fit: χ2/df = 1.15, p-value = 0.02, goodness of fit index (GFI) = 0.93, comparative fit index (CFI) = 0.99, Tucker–Lewis index (TLI) = 0.98, and root-mean-squared error of approximation (RMSEA) = 0.02.
Table 2 presents the good results regarding discriminant validity, convergent validity, and composite reliability, with the value criteria for each parameter as follows: CR > 0.7, AVE > 0.5, MSV < AVE, and AVE > Max correlation [64,65].
In Table 3, some details about EFA, CFA, SEM, mean (M), standard deviation (SD), Cronbach’s alpha (α), CR, and AVE are presented. Further, based on all the reliability and validity tests performed, all instruments used in this study were reliable and valid.
ENT: ENT1. Content seems interesting; ENT2. Looking for information is exciting; ENT3. It is fun to collect information; ENT4. It is easy to kill time by looking for information;
INT: INT1. It enables information-sharing with others; INT2. Conversation or opinion exchange with others is possible; INT3. It is easy to provide my opinion; INT4. It is possible to achieve a two-way interaction;
TRD: TRD1. Information is the newest information; TRD2. Contents are up to date;
CUS: CUS1. It is possible to search for customized information; CUS2. It provides useful information feed for me; CUS3. Information can be looked at anytime, anywhere; CUS4. It is easy to search for the information I need;
WOM: WOM1. I would like to pass information through social media to my friends; WOM4. I would like to post popular things;
TRU: TRU1. GO-JEK is trustworthy; TRU2. GO-JEK is honest and truthful to me; TRU3. I trust information about GO-JEK on social media; TRU4. I believe that GO-JEK’s social media account is trustworthy; TRU5. I believe GO-JEK will keep its promises and commitments;
BI: BI1. GO-JEK is an excellent representative of the online service industry; BI2. GO-JEK is a customer-oriented company; BI3. GO-JEK is a leader in the online service industry; BI4. I have an impressive memory regarding GO-JEK;
PI: PI1. The probability that I would consider using GO-JEK’s service is high; PI2. If I were to use an online service, I would consider using GO-JEK’s service; PI3. The likelihood of my using a service from GO-JEK is high; PI4. My willingness to use GO-JEK’s service is high.

4.3. Structural Equation Modeling (SEM)

After obtaining an acceptable model from the analysis results, structural equations modeling (SEM), especially path analysis, was executed on the research variables. SEM is an extension of factor analysis and a methodology principally considered to test substantive theories from empirical data sets [66]. Meanwhile, the path analysis technique was used to determine the extent to which a model matches a dataset and test the interrelationship between a set of variables simultaneously. SEM was used to test hypotheses and the significance of the relationship between research variables. By using AMOS 21, the proposed model was analyzed, and the results of the SEM path analysis are illustrated in Figure 1.
Table 4 presents detailed results of the model fit indices of the proposed model in this study. χ2/df = 1.16, p-value = 0.01, GFI = 0.93, AGFI = 0.91, CFI = 0.99, and RMSEA = 0.02. Each GFI value exceeded the minimum criteria of acceptable values.
The standardized coefficient was used for a normalized value. As shown in Table 5, all hypotheses are accepted. SMM affected TRU, proven by the value = 0.50 ***, with p-value = 0.000. SMM was also proven to influence BI, as justified by the value = 0.30 **, with p-value = 0.002. TRU to PI (H3) showed the influencing relationship, with 0.20 *. The path from BI to PI showed a strong influence, with 0.23 ***. SMM was also found to positively affect PI = 0.32 **. Moreover, the analysis results showed that SMM’s significant dimensions in this study included customization, interaction, word of mouth, entertainment, and trendiness.
This study also investigated the specific influence of each dimension of SMM on TRU, BI, and PI. The analysis of each SMM dimension on PI without a mediating variable demonstrated that entertainment and word of mouth have a significant direct influence on PI, whereas interaction, trendiness, and customization are not significant.

4.4. Test of Mediating Variables

Mediating variables can be identified to explain the type and effect of the relationship between the independent and dependent variables to attain more accurate and useful research [67]. From bootstrapping, some important parameters included standard errors, and confidence intervals were accurately calculated. The bootstrap used in this study had 5000 bootstrap samples, percentile confidence intervals were set at 95% CI, and bias-corrected confidence intervals were set at 95% CI. The total, direct, and indirect effects could be calculated from bootstrapping to determine the mediating effect. If the mediating effect existed, the next step was to calculate the mediating variable effect size.
The results show that H6 and H7 had mediating effects. Further, Figure 2 shows the SEM results summary with TRU and BI as mediating variables. Each dimension of the SMM effect was further analyzed on PI, with TRU as a mediating variable. Based on the statistical analysis results, interaction and customization were two dimensions of SMM that had significant indirect influences on PI through TRU, while entertainment, trendiness, and word of mouth had no significant effect (Table 6).
Next, each dimension of the SMM effect on PI was also analyzed with BI as a mediating variable. Based on the results, customization was the only dimension with a significant indirect influence on PI through BI, while the rest were not significant (Table 6).
As shown in Table 7, both mediation hypotheses (H6 and H7) were supported; in addition, H6 and H7 were partial mediators because the effect of SMM on PI in the presence of TRU and BI as mediators was decreased in absolute size, making the ratio of direct effect (c’) smaller than the ratio of total effect (c) (Table 7).
The next section highlights, in more detail, the discussion, implications of this research, and some limitations and recommendations for further research.

5. Discussion

PI is one of the key predictors for actual purchase behavior, according to, for example, Peña-García et al. [24], in both theoretical and practical aspects. The theoretical side provides more information about the valid predictors that can enhance customers’ opportunity to purchase a particular product. In terms of practical aspects, GO-JEK is recommended to conduct its marketing segmentation based on consumers’ trust and develop distinct marketing strategies to boost brand image.
Among several PI predictors, this research focused on SMM (with five dimensions including ENT, INT, TRD, CUS, and WOM) for ride-hailing practice and the context of its additional services. SMM was adopted as one of the defining variables since SMM enables organizations to easily and quickly communicate with their customers [31], reduce costs for arranging interaction with customers [34], and gain greater popularity in the e-commerce context [32]. Other than SMM, TRU has become a pivotal element to encourage consumers’ decisions [39]. From a practical view, online purchase typically depends on trust [40], particularly in an online context [41]. Therefore, TRU was treated as the mediating variable that would be influenced by SMM and affects PI. Another mediating variable was BI that would predict PI and was influenced by SMM. BI is recognized as a significant antecedent that directly influences online PI [29]. Further, BI is also explored to prove the mediating role of SMM [53,58].
Furthermore, this study examined seven hypotheses using four variables, including one independent variable (SMM), which has five dimensions (ENT, INT, TRD, CUS, WOM), two mediating variables (TRU and BI), and one dependent variable (PI). To this end, we explored the role of SMM on TRU, BI, and PI among GO-JEK consumers in Indonesia and identified the roles of both TRU and BI on PI. Additionally, this research aimed to confirm the mediating role of TRU and BI in SMM and PI’s relationship.
We first found that SMM significantly influences TRU. This finding is consistent with most prior research’s findings [37,39,45,46,47,68,69]. Thus, the evidence infers that the company’s marketing activities on social media influence consumers’ trust in the company. This means if GO-JEK can appropriately execute a marketing strategy in social media, the consumer’s trust in GO-JEK will increase. With the trust of the company owned by the consumers, GO-JEK will be easier to maintain a better relationship with consumers. Our statistical analysis also shows that there are two dimensions of SMM significantly influencing TRU in this study—namely, INT and CUS. SMM through social media makes the company able to communicate directly and easily with consumers [70]. Effective communication has a direct and significant influence on customers’ trust [64]. Regarding customization, this is an essential element in the service industry that can be achieved through SMM [38]. Tailored service will provide customers more chances to become involved in the company’s service delivery, encouraging better trust among customers.
We also found that SMM significantly influences BI, indicating that its brand image will be more positive if the company can manage SMM well. This finding supports previous research findings [31,48,49,59]. The main goal of companies in their marketing is to have a good BI [31]. BI, as well as all the other factors that influence it, is important in the consumers’ minds. Social media is an efficient means of developing a positive BI.
Next, as Kim and Ko [37] suggest that TRU and PI are two correlated variables in luxury branded goods; this research supports their idea that TRU significantly influences PI. Our finding is consistent with the findings from former research [50,51,52,64]. Our findings infer that the greater the consumer’s trust in a brand, the greater the consumers’ intention to buy the product from it. TRU is an important key to building relationships between customers and the company, especially in online transactions [68]. TRU is important because online transactions’ complexity and diversity can lead to dishonest and unpredictable product providers’ behaviors. If there is a low consumer’s trust in the brand, the consumer PI will also be low, and vice versa.
We also found that SMM significantly influences PI, supporting the previous findings [10,37,55]. Thus, it can be concluded that consumer PI will be even greater when the SMM is well managed. Further, among the five dimensions of SMM, two dimensions significantly influence PI—namely, entertainment and word of mouth, while the other three do not. Focusing on these two dimensions enables a company to create a higher possibility to purchase among customers. The attractive and appealing contents of SMM are the necessity to encourage PI. In addition, the spread of positive word of mouth will trigger a higher intention to purchase among consumers.
Consistent with the findings from other researchers [30,53,54], this research also shows that BI has a significant direct effect on consumer PI, concluding that a better perception of the BI in the minds of consumers leads to higher consumer PI for the product. Before making a purchase decision, consumers first evaluate the BI of a company. Consumers may have either a poor perception or a good perception of a brand, influencing their purchase decisions. If the company does not have a good and strong BI, consumers will not buy their products, causing poor market share. Consumers will prefer to buy products that are considered to have good BI.
The last aspect is about TRU and BI’s mediating role in the relationship between SMM and PI as the dependent variable. Based on the results, it was found that the mediating effects of trust in the relationship between social media marketing and intention purchase exhibit significant results. This result is in line with previous studies [56,57]. Therefore, it can be stated that consumers might have high PI if the company puts a good amount of effort into SMM because the SMM activities used by the company can increase a sense of consumer TRU in the company. About the mediating role of BI, this research confirmed that BI mediates the role of SMM on PI, consistent with Yunus et al. [53] and Tariq et al. [58]. If the SMM content is significant, it creates better BI that will finally result in higher PI.

6. Conclusions, Limitation, and Recommendations

This research explored how SMM, TRU, and BI influence customer PI. SMM is one of the important aspects of marketing. We provided a theoretical explanation of the factors as the basic consideration for consumers to purchase a product influenced by social media marketing. Our findings showed that social media marketing, trust, and brand image significantly influenced purchase intention. Furthermore, to answer other research questions, in this research, we found that the dimensions of social media marketing with the top two most influential factors on consumer purchase intention are entertainment and word of mouth. Social media is a low-cost and easy tool to spread the word about a company’s product or brand. If it can be performed properly, social media will provide many benefits and conveniences for the company. The findings in this study provide further theoretical insight into the importance of SMM to encourage PI.
The findings also revealed that both trust and brand image have mediating roles. Further, TRU and BI’s mediation role in the relationship between SMM and PI was also confirmed. Thus, the proposed research question that trust and brand image make consumers’ intention to buy with the influence of social media marketing to be more positive was confirmed by our study.
For managerial aspects, the contribution of our paper included the following points: First, social media marketing dimensions consist of entertainment, interaction, trendiness, customization, and word of mouth, all of which affect consumers’ purchase intentions. In particular, two factors are more important and directly influence consumers’ purchase intention—namely, entertainment and word of mouth. According to this notion, in social media marketing, the companies must put more effort and attention into creating attractive content that can stimulate consumer interest, which will make consumers willing to spread positive information about the company. If GO-JEK could pay more attention to consumers’ complaints and suggestions through personal interactions, consumers would feel valued and feel that their needs have been well cared for, which creates consumers’ trust in the company. By making constant interactions with consumers, GO-JEK can better understand consumers’ needs and the company’s weaknesses, which will improve the company’s services.
Second, companies should treat social media marketing as an instrument to reach consumers and as a useful tool for creating trust and brand image. This study clearly shows that both trust and brand image are important to consumers. If the company wins consumers’ trust in the brand and has a good perception of the brand, consumers will be willing to buy the product. Thus, GO-JEK must initiate, create, and channel social media content according to the image the company wants to instill in the consumers’ minds. To support the argument, we found that customization of social media marketing significantly influences this study’s brand image. Therefore, GO-JEK must be able to build consumer trust to increase consumers’ purchase intentions.
The first limitation is our study is the generalization and respondents involved in this study. The respondents are all Indonesian customers that only focused on a single online service. The results obtained in the paper are useful for the online service sector but may not hold for other industries. Thus, further research could extend through analyzing the results for other countries, other industries, or other companies. Though the 350 respondents are theoretically sufficient, it will be better to obtain more sample size calculations in future research for attaining better consumer perceptions about the effects of social media marketing on purchase intention in different countries and industries. Suggestions for comparing consumers in different countries, industries, and companies could provide interesting and more in-depth results in the study.
Moreover, this study used only one independent variable (SMM), two mediating variables (TRU and BI), and one dependent variable (PI) in the analysis. Future studies could consider adding other variables that might influence purchase intention, including brand awareness and perceived risk as mediation variables and information and advertisement as other dimensions of social media marketing variables. In addition, this study used quantitative methods to investigate the influence of social media marketing on consumer purchase intentions. Future studies could also use qualitative methods or mixed-method in the analysis.

Author Contributions

Conceptualization, M.M. and B.P.; methodology, M.M. and W.-K.W.; validation, M.M., T.I. and W.-K.W.; formal analysis, B.P.; investigation, B.P.; writing—original draft preparation, M.M. and B.P.; writing—review and editing, M.M. and T.I.; visualization, T.I.; supervision, M.M. and W.-K.W.; project administration, T.I.; funding acquisition, M.M. and W.-K.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Acknowledgments

The authors are most grateful for unrelenting helpful comments and suggestions of the editor and the reviewers. The first author would like to acknowledge the continued support from Asia University and California State University San Bernardino.

Conflicts of Interest

The authors declare no conflict of interest.

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Jtaer 17 00005 g001 550
Figure 1. The result of SEM path analysis.
Figure 1. The result of SEM path analysis.
Jtaer 17 00005 g001
Jtaer 17 00005 g002 550
Figure 2. The SEM result with TRU and BI as mediation variables.
Figure 2. The SEM result with TRU and BI as mediation variables.
Jtaer 17 00005 g002
Table
Table 1. CFA goodness of test (N = 350).
Table 1. CFA goodness of test (N = 350).
GOF IndexAcceptable ValueCFA Model
χ2(Chi-square) 401.49
df(Degree of freedom) 349
χ2/df < 31.15
GFI > 0.900.93
Z > 0.900.99
TLI > 0.900.98
RMSEA ≤ 0.080.02
Table
Table 2. Tests of discriminant validity, convergent validity, and composite reliability.
Table 2. Tests of discriminant validity, convergent validity, and composite reliability.
CRAVEMSVMax rENTINTTRDCUSWOMTRUBIPI
ENT0.9120.7210.0610.2460.849
INT0.8300.5500.0930.3050.2460.742
TRD0.7540.6070.0300.1730.0360.0450.779
CUS0.8090.5150.0930.3050.1590.3050.1730.718
WOM0.7380.5960.0720.2690.0120.1810.0260.1670.772
TRU0.8430.5200.1600.4000.0910.2540.1370.2770.1650.721
BI0.8050.5090.1310.3620.0520.0890.1560.1810.0670.2090.714
PI0.8080.5130.1600.4000.2010.2080.1250.2000.2690.4000.3620.716
Note: CR > 0.7; AVE > 0.5; MSV < AVE; AVE > Max r, AVE is bold face diagonal.
Table
Table 3. Summary of reliability and validity.
Table 3. Summary of reliability and validity.
Var.ItemsEFA CFA SEM MSDαCRAVAVE (%)KMO
Min
Value 		>0.5>0.5>0.5 >0.6>0.7>0.5>50>0.5
ENTENT10.890.880.883.490.930.910.910.7278.990.85
ENT20.880.840.84
ENT30.900.870.87
ENT40.860.810.81
INTINT10.760.700.703.640.930.830.830.5566.100.80
INT20.780.720.72
INT30.820.800.80
INT40.820.740.74
TRDTRD10.880.840.843.231.090.750.750.6179.960.50
TRD20.890.720.71
CUSCUS10.820.750.763.350.920.810.810.5263.450.79
CUS20.730.670.66
CUS30.800.750.75
CUS40.760.690.69
WOMWOM10.850.920.933.490.980.700.740.6077.120.50
WOM40.870.590.59
TRUTRU10.800.830.833.750.840.840.840.5261.490.85
TRU20.760.690.70
TRU30.780.740.74
TRU40.750.660.66
TRU50.740.660.66
BIBI10.810.800.813.580.900.800.810.5162.980.79
BI20.780.720.72
BI30.750.650.65
BI40.770.670.67
PIPI10.750.740.743.750.860.810.810.5163.510.80
PI20.780.730.73
PI30.790.710.71
PI40.750.690.69
Total 0.83 68.550.81
Items details for each construct (questionnaire).
Table
Table 4. The result of SEM goodness-of-fit statistic.
Table 4. The result of SEM goodness-of-fit statistic.
GOF IndexAcceptable ValueSEM Model
χ2/df < 31.16
GFI > 0.900.93
AGFI > 0.900.91
CFI > 0.900.99
RMSEA ≤ 0.080.02
SRMR ≤ 0.080.05
Table
Table 5. Result of hypothesis testing using standardized estimates.
Table 5. Result of hypothesis testing using standardized estimates.
HypothesisPath β S.E.C.R. (T Statistics)p-ValueResult
H1SMM→TRU0.50 ***0.214.180.000Supported
H2SMM→BI0.30 **0.183.150.002Supported
H3TRU→PI0.20 *0.092.290.022Supported
H4BI→PI0.23 ***0.073.250.000Supported
H5SMM→PI0.32 **0.222.540.010Supported
* p < 0.05, ** p < 0.01, *** p < 0.001, NS = Not Significant.
Table
Table 6. Summary of mediation analysis results.
Table 6. Summary of mediation analysis results.
PathTotalDirectIndirectMediation
SMM→(TRU + BI)→PI0.49 **0.32 *0.17 *Supported (Partial Mediation)
HypothesisPathTotalDirectIndirectMediation
H6SMM→TRU→PI0.47 **0.36 *0.11 *Supported (Partial Mediation)
H7SMM→BI→PI0.48 **0.41 **0.07 *Supported (Partial Mediation)
* p < 0.05, ** p < 0.01, NS = Not Significant.
Table
Table 7. Summary of mediation analysis results (post hoc analysis).
Table 7. Summary of mediation analysis results (post hoc analysis).
HypothesisPathTotal (c)Direct (c’)Indirect (ab)Mediation
H6SMM→TRU→PI0.48 ***0.36 *0.12 *Supported
H7SMM→BI→PI0.48 ***0.41 **0.07 *Supported
* p < 0.05, ** p < 0.01, *** p < 0.001, NS = Not Significant.
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Moslehpour, M.; Ismail, T.; Purba, B.; Wong, W.-K. What Makes GO-JEK Go in Indonesia? The Influences of Social Media Marketing Activities on Purchase Intention. J. Theor. Appl. Electron. Commer. Res. 2022, 17, 89-103. https://0-doi-org.brum.beds.ac.uk/10.3390/jtaer17010005

AMA Style

Moslehpour M, Ismail T, Purba B, Wong W-K. What Makes GO-JEK Go in Indonesia? The Influences of Social Media Marketing Activities on Purchase Intention. Journal of Theoretical and Applied Electronic Commerce Research. 2022; 17(1):89-103. https://0-doi-org.brum.beds.ac.uk/10.3390/jtaer17010005

Chicago/Turabian Style

Moslehpour, Massoud, Taufiq Ismail, Bey Purba, and Wing-Keung Wong. 2022. "What Makes GO-JEK Go in Indonesia? The Influences of Social Media Marketing Activities on Purchase Intention" Journal of Theoretical and Applied Electronic Commerce Research 17, no. 1: 89-103. https://0-doi-org.brum.beds.ac.uk/10.3390/jtaer17010005

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