Keywords

User-generated Content, Brand Image, Buying Intentions, SOR Model.

Introduction

The wide spread of internet along with the extensive use social media platforms have transformed markets; nowadays power is shifting from businesses towards consumers. Today, consumers have much power and a greater influence. They are exposed to vast amount of data, products and services being buffered generally on the internet and more specifically on social media platforms. Thus, they are becoming more knowledgeable and informative in making buying decisions more than ever before (Bahtar et al., 2018).

Social media as web-based applications formed the bases for UGC, which refers to any content created and posted by users known as fans. It may include pictures, tweets, videos, blog posts and testimonials. It is a form of unpaid marketing where users promote the brand instead of the brand itself (Ngah et al., 2021).

With the constant peer feedback provided, social media is revolutionizing the purchasing process; it plays an important role in forming an attitude or a desire and willingness for a specific product, to making the buying decisions and even after the purchase is completed (Kunja & Acharyulu, 2018; Hossain et al., 2019b; Hawamleh et al., 2020). Any content generated by the user on social media is referred to as “electronic Word of Mouth” (eWOM) (Burgess et al., 2011; Ayeh et al., 2013). Nowadays and in light of the development of social media, consumer all around the world started to share their experiences with different brands (Al-Jabri & Sohail, 2012). Consumers are increasingly expressing their opinions and providing a feedback through a post, picture, video, or even a review of products as well as sharing it with their families and friends. This signifies the increasing importance of the content generated by users, which in fact represents an imperative source of information for companies and other users/customers at the same time.

Empirical evidence suggest that companies cannot ignore the impact of diverse forms of UGC on their reputation, brand image and the buying intentions of customers (Torlak et al., 2014; Confente et al., 2019; Oliveira & Casais, 2019; Mayrhofer et al., 2020). Although the relationship between UGC forms such as eWOM and consumer buying intentions is well established within the extent literature, the focus has been noticeably on new technologies and related industries (Torlak et al., 2014; Kudeshia & Kumar, 2017; Kunja & Acharyulu, 2018) and tourism (Marine-Roig et al., 2017; Narangajavana Kaosiri et al., 2019; Ray & Bala, 2021). Prior research illustrates a direct link between UGC and different marketing outcomes such as behavior (Cox, et al., 2009; Ye et al., 2011); service quality (Duan et al., 2013); and satisfaction (Narangajavana Kaosiri et al., 2019); and product development (Ho-Dac, 2020). However, the mechanism of the effect (e.g., mediation) remains insufficiently explored. Moreover, the majority of such studies have been carried out in Western cultures and developed economies.

The significance of UGC in driving customers’ responses may vary across nations as well as the divers’ types of products been sought. Typically, UGC posted on common Websites enjoys high levels of source credibility (Ma & Atkin, 2017) and is found to be more influential in affecting customer behavior compared to marketing generated content (Tsiakali, 2018). This becomes very significant when buying a risky and high involvement product such as cosmetics, in a highly social context such as Jordan. That is, the product may have a high potential of negative impact on users both mentally and physically if a wrong brand is chosen. The cosmetics sector is among the important sectors in Jordan in terms of size of trade. For instance, the industry reached 340 Million worth of imports in 2017 only (Roya, 2018). However, no research study has been devoted to examine the driving factors of the buying behavior and psychology of consumers in such vital sector. With this knowledge gap in mind, this study aims to examine the effect that a UGC has on consumer intentions to buy cosmetics in Jordan taking into consideration the mediating role of brand image on this effect.

Literature Review

Underpinning Theory: The S-O-R Model

The Mehrabian & Russel's (1974) model, also known as Stimulus-Organism-Response (S-O-R) theory was brought into the marketing literature to describe how customer responses (R) can be explained by internal emotional and cognitive states (O), which are initially triggered by external marketing stimuli (S). According to Chang et al. (2011) the term stimulus in the model is conceptualized as any factor that have a changing impact on individuals’ internal states. Organism comprises individual internal processes including perceptual, physiological feeling and thinking activities, intervening between the external stimuli and the final response of individuals (Bagozzi, 1986). Finally, response is the final outcome in the causality chain of the model, describing how people initially respond to a setting simply as approach or avoidance behaviors (Nusairat et al., 2020a).

Although the S-O-R model has been originated in the scope of environmental psychology, it has inspired much of marketing research over last decades (Donovan & Rossiter, 1982; Chebat & Michon, 2003; Kumar & Kim, 2014; Nusairat et al., 2017; Wu & Li, 2018; Konuk, 2019; Laato et al., 2020; Nusairat et al., 2020b) Aslam & de Luna, 2021). Such stream of literature describes stimulus (S) as any extraneous marketing-related factor, having a changing influence on customer internal states (O), which in turn drive customer response (R). In line with this, the present study introduces UGC as a stimulus, affecting what people think about brand (organism), which in turn fosters their behavioral intentions (response). Thus, based on Mehrabian & Russel's (1974) and Bagozzi (1986) model, which was stated that the Stimulus-Organism-Response (S-O-R) theory was brought into the marketing literature to describe how human behavior (R) can be explained by internal emotional states (O), which are initially triggered by external stimuli (S), the current study applies S-O-R to motivate the consumer buying intention alongside with inserting the brand image and improving it through user-generated content.

UGC and Consumer Buying Intention

As a type of social media content, User Generated Content (UGC) can be described as “media content created by members of the general public and includes any form of online content created, initiated, circulated and consumed by users” (Kim & Johnson, 2016). Prior research highlights the significance of UGC in predicting consumers' responses and in driving their decision making processes (Liu et al., 2011; Sin et al., 2012; Tsiakali, 2018; Khalil et al., 2020). Studies addressing UGC as a holistic concept have mainly focused on examining the particular characteristics of the content (Grover et al., 2019; Assaker, 2020; Daradkeh, 2021; Al-Adamat et al., 2020). In this extent, Owusu et al. (2016) for example examined how UGC features are linked to Web-purchase decision, and found that the web purchase decision can be affected mostly by relevance, credibility and up-to-dateness of the UGC information. Other studies investigated specific forms of UGC such as eWOM and measure its effect on the consumer buying and behavioral intentions (Torlak et al., 2014; Wang, 2015; Al-Gasawneh & Al-Adamat, 2020). Further, Torlak et al. (2014) in their study on the cell phone industry in turkey found that the eWOM has significant positive effect on consumer buying intention. Similarly, Wang (2015) studied the influence of eWOM on the tourists visit intention and revealed a significant impact of eWOM on tourist visit intention to select destinations around the globe. Accordingly, we hypothesize that:

H1 UGC has a positive effect on consumer intentions to buy cosmetic products.

UGC and Brand Image

Brand image is vital to consumers and firms alike. It is a cue, aiding consumers in their purchasing decisions by reducing pre-purchase consumer risk and post-purchase cognitive dissonance, associated with high involvement products (Keller, 2009). Brand image can also affect company-related aspects such as overall company's image and future, competitive advantage, prices of its brands and future marketing strategies (Brakus et al., 2009; Chen, 2010). Several empirical studies indicated that UGC has a significant impact on brand awareness (Elseidi & El-Baz, 2016; Tariq et al., 2017; Kala & Chaubey, 2018; Hossain et al., 2019a; Hossain et al., 2020; Boateng, 2021). In this venue, Torlak et al. (2014) reported a significant effect of eWOM on brand image and consumer buying intention. In the same vein, Elseidi & El-Baz (2016) claimed a significant positive impact of eWOM on brand image, consumer attitude and purchase intention. Moreover, Bambauer-Sachse & Mongold (2011) recommended eWOM as an important element in any online marketing strategy to enhance brand image and consumer buying intention. Therefore, it is hypothesized that:

H2 UGC has a positive effect on cosmetic product brand image.

The Mediating Role of Brand Image on Consumer Buying Intention

Brand image is defined as “the set of beliefs, ideas, and impression that a person holds regarding an object” (Kotler, 2001). It essentially evolves when consumers hold certain feelings, ideas or expectations towards a certain brand as they get to know and remember (Keller, 1993). Empirical evidence suggests that what images consumers form, is crucial to their behavioral intentions (Diamantopoulos et al., 2011; Jalilv & Samiei, 2012; Hutter et al., 2013; Yu et al., 2013; Kazmi & Mehmood, 2016). The findings of Yu & Chen (2013) for instance have asserted the significance of brand image in association with other perceptual states including country-of-origin image COI and self-congruity in fostering consumers buying intentions, with this effect become more powerful, when such states are consistent. Moreover, brand image have a critical role on the progress of consumer in buying decision making process. Consumers typically draw an impression based on product reviews, (Chakraborty & Biswal, 2020; Jahan et al., 2020), leading them to decide whether to proceed with further stages of the buying process or to terminate it (Hutter et al., 2013).

Brand image can also contribute to bridging an association between perceptual variables and behavioral intentions. In this instance, Diamantopoulos et al. (2011) investigated the role of COI and brand image in affecting consumer buying intention and found that COI impacts purchase intention indirectly through brand image. Similarly, brand image was also found to play a mediating role on the effect of country-of-origin image and buying intentions. More recently, Chakraborty & Biswal (2020) reported a mediating effect of brand image on the relationship between online hotel reviews and clients' hotel booking intentions. Accordingly, the following hypotheses are suggested:

H3 Brand image of cosmetic products positively affects consumer buying intentions.

H4 The relationship between UGC and consumer buying intentions is mediated by brand image.

Methodology

This research is a cross-sectional descriptive study in nature that aims to examine the relationships among a set of interrelated variables. It follows a deductive approach, whereby, a thorough analysis of relevant literature has led to the formulation of several research hypotheses, to be empirically tested.

As an internet-related subject, the most appropriate and convenient place to recruit participants is the internet. Therefore an online questionnaire survey was distributed to a convenient sample of cosmetic products buyers in Jordan over six weeks, from February to mid of March 2021. A total number of 361 questionnaires were returned, 45 of these included frivolous responses, thereby were discarded. As a result, 316 usable questionnaires were considered for data analysis. This meets the minimum sample size of 74 based on G-power calculations as recommended by Green's (1991) and Gefen et al. (2011) considering that our research conceptual model comprises two predicting variables.

The research questionnaire was divided into four parts as follows: the first section encompasses questions relating to personal and demographic information of the respondents such as; age group, gender, marital status and educational level. The second section consists of multi-item scales adapted from previously established measures exploring UGC based on (McKinney et al., 2002; and Negash et al., 2003; Owusu et al., 2016). UGC was particularly investigated in terms of six dimensions including UGC relevance (four items), UCG usefulness (four items), UCG indicativeness (four items), UCG credibility (four items), UCG ease-of-use (four items) and UCG up-to-dateness (four items). Brand image in the third section was explored using six items adapted from (Al-Dmour et al., 2013). The fourth section addressed buying intentions using five items borrowed from (Huy et al., 2019). All of the measurement items used was assessed on a 5-point Likert scale.

Modifications to the questionnaire items were made where needed to ensure that each item meets the study's unique objectives. External academics were also involved in assessing measures' validity. The measurements' internal consistency was also assessed using Cronbach's Alpha values, which were as follows: UCG relevance = 0.71, UCG usefulness = 0.79, UCG indicativeness = 0.72, UCG credibility = 0.89, UCG ease-of-use = 0.82, UCG up-to-dateness = 0.76, brand image=0.86 and buying intentions=0.77, suggesting that the measurements are sufficiently reliable. The main data analysis and hypothesis testing was then carried out using Smart PLS 3.0.

Results

In this study, there is one-second order construct named User- Generated Content and two first-order constructs named buying intention and brand image. The second-order construct (User- Generated Content) comprised of six first-order constructs as follows: UGC relevance, UGC usefulness, UGC indicativeness, UGC credibility, UGC ease-of-use and UGC up-to-dateness.

This will enrich the recognition of its conceptual and consensus elements. Likewise, the LOC was utilized in limiting the number of connections and also the number of hypotheses to be discussed in the structural model. According to Hair et al. (2019), such use facilitates the interpretation of the PLS route model. The two-stage method was used in this research. As a result, the repeated indicator technique was used in the first stage of this research. The LOC for first-order constructs were obtained here, and the weighting of the first-order variables was used in the calculation of the HOC contract's C.R and AVE in the second level. The measurement model was evaluated using convergent validity and discriminant validity in this analysis. The composite reliability, average variance extracts (AVE), and factor loading are all examined as part of the convergent validity evaluation. As a consequence, the results are shown in Figure 1 and Table 1. According to Hair et al. (2019) each item's loading was greater than 0.5, AVE figures were greater than 0.5, and CR figures were greater than 0.7.

Table 1 Measurement Model
First order Construct/LOC	Items	Factor loading	CR	AVE
user generated content relevance (UGC)	UGC1	0.889	0.934	0.780
	UGC2	0.880
	UGC3	0.884
	UGC4	0.870
user generated content usefulness (UGCU)	UGCU 1	0.906	0.961	0.860
	UGCU 2	0.919
	UGCU 3	0.946
	UGCU 4	0.939
user generated content indicativeness (UGCI)	UGCI 1	0.863	0.913	0.725
	UGCI 2	0.868
	UGCI 3	0.856
	UGCI 4	0. 818
user generated content credibility (UGCC)	UGCC 1	0.812	0.910	0.718
	UGCC 2	0.855
	UGCC 3	0.871
	UGCC 4	0.849
user generated content ease-of-use (UGCE)	UGCE 1	0.840	0.904	0.701
	UGCE 2	0.871
	UGCE 3	0.847
	UGCE 4	0.790
user generated content up-to-dateness (UGCUT)	UGCUT 1	0.847	0.909	0.714
	UGCUT 2	0.866
	UGCUT 3	0.869
	UGCUT 4	0.795
brand image (BI)	BI 1	0.744	0.884	0.561
	BI 2	0.791
	BI 3	0.751
	BI 4	0.675
	BI 5	0.752
	BI 6	0.776
buying intentions (BIN)	BIN 1	0.845	0.932	0.733
	BIN 2	0.857
	BIN 3	0.850
	BIN 4	0.875
	BIN 5	0.852
Second order constructs/HOC
user generated content (UGC)	UCGR	0.882	0.929	0.684
	UGCU	0.840
	UGCI	0.832
	UGCC	0.803
	UGCE	0.813
	UGCUT	0.790

HTMT tests the model's discriminant validity (Henseler et al., 2015), and the obtained HTMT build values in this analysis were less than 0.90, with values ranging from 0.065 to 0.881. Table 2 summarizes the findings. As a result, each latent construct calculation in this study was exclusively discriminant against each other, according to Henseler et al. (2015).

Table 2 Discriminant Validity (HTMT)
	BI	BIN	UGC	UGCC	UGCE	UGCI	UGCR	UGCU	UGCUT
BI
BIN	0.634
UGC	0.819	0.718
UGCC	0.630	0.669	0.862
UGCE	0.868	0.620	0.878	0.609
UGCI	0.688	0.648	0.881	0.673	0.639
UGCR	0.710	0.692	0.847	0.779	0.677	0.832
UGCU	0.064	0.065	0.394	0.099	0.078	0.080	0.240
UGCUT	0.754	0.551	0.851	0.582	0.821	0.607	0.611	0.086

The convergent validity and discriminant validity analysis findings for the measurement model demonstrate the appropriateness and accuracy of the measurement scale in the evaluation of the constructs and their relative objects in the CFA model. As a consequence, Table 1 and Table 2 show the research findings for convergent validity and discriminant validity, respectively.

The R2 values for BI and BIN are 0.580 and 0.479, respectively, as shown in Table 3. This means that 58.0 percent of BI variations are explained by the predictor (UGC), while 47.9% of BIN variations are explained by their predictors (UGC, BI). The R2 values are within the 0.19 cutoff value suggested in the work of (Chin, 1998). The Q2 value of 0.232 for BIN was significantly higher than zero, indicating that the model has predictive value, similar to the suggestions of (Chin, 2010). The model has a good degree of fit and has a lot of predictive power. The VIF values were also 2.692, 2.112, and 3.613, which were all less than 5 (Hair et al., 2019). UGC and BI had p-values of 0.000 and 0.011, respectively, for predicting BIN. The p-value of UGC for the BI predictive factor was 0.000, indicating that the probability of achieving prediction using absolute p-values is between 0.01 and 0.05. In addition, the path coefficient (S, B) values for (UGC>BIN), (UGC>BI), and (BI>BIN) were 0.630, 0.761, and 0.318, respectively, for (UGC>BIN), (UGC>BI), and (BI>BIN). This demonstrates that the relationships are positive, implying that hypotheses H1, H2, and H3 are right (Figure 2).

Table 3 Direct Effects of Structural Model
	Path	St, β	St. d	R2	Q2	F2	VIF	T-value	P-value	Decision
H1	UGC > BIN	0.630	0.122	0.479	0.232	0.051	2.692	5.163	0.000	Supported
H2	UGC> BI	0.761	0.138	0.580		0.057	2.112	5.514	0.000	Supported
H3	BI > BIN	0.318	0.088			0.042	3.613	2.473	0.011	Supported

Table 4 shows that the indirect effect of UGC on BIN by BI was positive and statistically relevant at the 0.05 level; =0.240, T-value=2.376, P-value = 0.002. Preacher and Hayes (2004) found that the indirect effect of Boot CI Bias Corrected did not straddle a 0 in the middle, indicating that a mediation effect was present (LL=0.055, UL=0.148). According to the findings, the mediation effect is statistically important. As a result, hypothesis H4 is verified.

Table 4 Indirect Effect of Structural Model
	PATH SHAPE	St. β	St. d	T values	2.50%	97.50%	p-values	Decision
H4	UGC > BI> BIN	0.240	0.101	2.376	0.0 55	0.148	0.002	Supported

Discussion

The main objective of this research was to investigate the influence of UGC on consumer intentions to buy cosmetic products through examining the mediating role of brand image in Jordan. PLS was utilized to examine the four study hypotheses proposed in this research. Results indicated that all hypotheses were supported. The first hypothesis (H1) relating UGC to consumer buying intentions was supported; UGC in cosmetics market was found to be a significant predictor of customer buying intentions. This conforms to the findings of previous empirical studies reported in the literature (Grover et al., 2019; Assaker, 2020; Daradkeh, 2021). Moreover, Al-Gasawneh & Al-Adamat (2020) claimed that eWOM significantly affect customer buying intentions. In the same vein, Wang (2015) pointed that customers show greater repurchase and revisit intentions for websites with eWOM. Moreover, within the extant literature many studies suggest that UGC can improve the level of brand image (Tariq et al., 2017; Kala & Chaubey, 2018; Boateng, 2021). This result supports the second hypothesis (H2). Such findings revealed that Jordanian customers affected by eWOM on brand image of cosmetic products. This result is in line with previous research findings; for instance, Elseidi & El-Baz (2016) proclaimed that eWOM is one of the best marketing strategies that could be implemented by firms in order to enhance their brand image. Other empirical studies reveal a significant influence between brand image and eWOM on consumer purchasing intention. Such findings imply that online communication among online communities can improve the purchasing decisions of cosmetic products especially when these brands are well known and easily memorable (Hutter et al., 2013; Yu et al., 2013; Kazmi & Mehmood, 2016). Therefore, such findings support hypothesis (H3). Finally, the mediating impact for brand image on customer buying intention was also supported. Thus, the hypothesis (H4) was supported. Such result supports the findings of Chakraborty & Biswal (2020) who reported a mediating role of brand image in the relationship between UGC and consumer buying intention.

Conclusions

The results of this research show the importance of UGC as a strong construct in forming online users' perceptions and driving their behavioral intentions toward utilizing such contents to support their purchasing decisions for cosmetic products. Moreover, UGC plays a vital role on brand image, suggesting that a good brand image push the customer to hold more favorable attitude towards this brand through creating a positive eWOM among peers. The research's contribution can be represented by a set of highlighted points. Theoretically, the research findings boost the existing literature on the influence of UGC on customer buying intentions developing countries such as Jordan. Moreover, on the contrary of prior studies, this research explores the UGC to recognize the impact of proposed elements on customer buying intentions, rather than concentrating on business generated content. Thus, the study's findings provide a comprehensive understanding about the area of UGC in general. Empirically, due to the limited empirical evidence on the impact of UGC on customer buying intentions towards high involvement personal products, this research provides valuable insights into how the UGC can be utilized to enhance brand image and to positively influence customer buying intentions. Finally, the current study provides a clearer understanding of the applicability of the traditional S-O-R model through testing the research model which addresses brand image as a mediating variable. This research suffered from some limitations especially sampling producers, which may not reflect an appropriate representation of cosmetic buyers in Jordan. However, such limitation could be avoided in the future by applying probability sampling technique to recruit respondents, so that generating more reliable and generalizable findings. Further research can be applied through considering the moderating role of demographic factors to understand how the cosmetic buyers' respond to UGC based on their demographics like age, gender and education.

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