Sage Journals: Discover world-class research

Abstract

This study investigates the mediating effect of brand experience on the relationship between customer motivation and engagement behavior and conceptualizes customer motivation from the multiple dimensions of information seeking, entertainment, and social interaction. Based on 565 valid questionnaires, it analyzes the impact of customer motivation on brand experience and customer engagement behavior using SPSS and AMOS. First, customer motivation with information seeking, entertainment, and social interaction significantly impacts customer brand experience. Second, customer brand experience significantly impacts customer engagement behavior of reuse, feedback, and WOM intentions. Finally, this study explores the mediating role of customer brand experience between customer motivation and customer engagement behavior and its impact on social media. It provides a reference for social media literature research. These findings will provide insights on motivating customers to participate in social media.

Keywords

U&G theory social media customer motivation brand experience customer engagement behavior

Introduction

Digital, mobile, and social media have become an indispensable part of people’s daily lives worldwide. Over 4.5 billion people use the Internet, and over 3.8 billion use social media (Global Web Index, 2020). With the rapid development of communication technology and the rapid popularization of intelligent devices, social media has changed the lives of modern people (Andangsari et al., 2013). Social media provides a highly interactive platform for people to share, co-create, discuss, and modify original content (Kaplan & Haenlein, 2010). Social media allows users to connect through information sharing on the home page, an important channel for people to create, share, and exchange information through online communities and interpersonal networks (Evans & Krauthammer, 2011; Joo et al., 2020). Weibo, one of the most influential social media platforms in China, was considered the research brand. In contrast, brand experience mediates the relationship between customer motivation and engagement behavior.

Research on the antecedents and outcomes of social media participation has opened up avenues over the past few years. Liu and Bakici studied the concept of customer motivation to use social media. They proposed three dimensions: information sharing, entertainment, and social interaction (Liu & Bakici, 2019). Information sharing, entertainment, and social motivation strongly predict customer experience (Leftheriotis & Giannakos, 2014; Liu & Bakici, 2019). The concepts of experience and emotion have been widely recognized in brand-related research. Brand experience includes sensory, cognitive, emotional, and behavioral experiences (Brakus et al., 2009). Brand experience positively impacts brand trust, loyalty, and commitment (Khan, Hollebeek et al., 2018; Khan & Rahman, 2017). Some studies have confirmed the mediating role of brand experience between brand value and customer engagement behavior (Carlson et al., 2019). However, the mediating role of brand experience in the context of social media remains unclear. Consistent with previous research on customer motivation using a multidimensional method, this study can theoretically infer the brand experience evaluation reflecting the use of judgment in the context of social media. Therefore, this study considers brand experience as the mediating variable.

In recent years, customer engagement behavior (CEB) has attracted extensive attention in literature of marketing. Enterprises introduce strategies to encourage customers to participate in some voluntary activities, independent, and brand beneficial behaviors, including purchasing and beyond purchasing behaviors (Kumar et al., 2010; Pansari & Kumar, 2017). Interest in CEB has peaked with the convergence of social and mobile media technologies creating value for brands and customers (Hollebeek & Macky, 2019). CEB is a multidimensional concept comprising cognitive, emotional, behavioral, and social dimensions (Islam et al., 2019). Carlson et al. (2019) studied CEB from four dimensions: purchase behavior, feedback behavior, influencer behavior, and brand page persistence intention (Carlson et al., 2019). Additionally, consumer behavior on social media can also be used as recognition or word-of-mouth (WOM), affecting their purchase intention (Wang et al., 2012). This study examines CEB from three dimensions: reuse, feedback, and WOM intentions based on social media characteristics. Furthermore, although the study theoretically discusses the concept of CEB in the context of social media technology (Hollebeek & Macky, 2019; Khan, Chelliah, et al., 2019), there is no empirical evidence to prove the components of CEB and its impact on consumer behavior. Therefore, it is necessary to make more theoretical and practical elaboration on the specific driving factors of CEB in the emerging social media platform market.

This study explores the impact of consumer motivation on consumer brand experience (in Weibo) in China. It identifies the relationship between customer brand experience and CEB. Furthermore, this research determines the mediating effect of customer brand experience on the relationship between customer motivation and CEB. Weibo, one of China’s leading social media platforms, was selected for this study. The Uses and Gratifications (U&G) theory explains the psychological and social antecedents of media consumption and the subsequent attitude and behavioral effects (Carlson et al., 2019; Hollebeek & Macky, 2019). Based on the U&G theory, this study discusses the impact of customer motivation on CEB and the mediating role of brand experience in the relationship between customer motivation and CEB. Through a survey of 565 customers with Weibo experience, this study mainly discusses the following points. The first is to explore the motivation to have a positive impact on customer brand experience. The second is to verify whether brand experience positively impacts CEB. The following sections discuss the research content of this study and explain the research objectives.

This study contributes to the existing literature on CEBs in several areas. First, customer motivations to use social media are clarified, and the research is carried out from three dimensions: information seeking, entertainment, and social interaction motivation. Second, by examining the mediating role of customer brand experience, this study links customer motivation with their experience using social media. Thus, it expands the literature on CEB. Third, it also helps practitioners motivate customers to use social media based on their social media experience. This paper presents a literature review on CEB, introduces the theoretical basis and development hypothesis, proposes methods and data analysis, and discusses the research results and enlightenment.

Literature Review and Hypotheses Development

Customer Brand Experience

The concept of experience in consumer research, namely experience marketing, and consumption experience (Holbrook & Hirschman, 1982), makes people realize the critical role of brand experience in strengthening the relationship between consumers and brands (Brakus et al., 2009; Chang & Chieng, 2006). Studies interpret the brand experience as a combination of consumption, shopping, product, and service experience generated by the interaction between consumers and brands (Brakus et al., 2009; Chattopadhyay & Laborie, 2005). Brand experience is related to brand personality, participation, attitude, and attachment but differs from the structures of these concepts. An evaluation of the brand from the consumers’ perspective is more comprehensive (Brakus et al., 2009). Brand experience refers to consumers’ perception of the moment they act with the brand (Alloza, 2008).

Currently, with the popularity and application of social media platforms in enterprise marketing, social media brands have been widely studied by scholars. Social media brand experience refers to the overall brand experience that customers obtain from social media. This concept includes customers’ search for information through social media, their feelings, and impressions of social interaction. Thus, brand experience comes from customers’ psychological perceptions and social interactions on social media (Wang et al., 2019). With the popularity of the Internet, scholars have focused on customer experiences in the network environment. Scholars have proposed and validated new concepts about the Internet, including online experience (Novak et al., 2000), online shopping, retail experience (Noble et al., 2005), and online community experience (Nambisan & Watt, 2011).

According to a study by Smith (2016), over 70% of Internet customers are active social media customers. As a leading social media platform, Weibo is similar to Twitter and Facebook. Its most remarkable characteristics are its speed, sound effects, and broad coverage. Customers can find information in a short time. Entertainment is also one of the most critical characteristics of Weibo, attracting many customers. Customers can browse some content they are interested in and enjoy leisure time on Weibo. The most valuable feature of Weibo is its social interaction. Customers can meet some like-minded friends on Weibo and discuss topics of interest with them. According to the official statistics of Weibo, the average daily browsing time of customers is 90 minutes, and 75% of Weibo login comes from wireless devices (Weibo, 2013). Therefore, Weibo uses social media brand experience as a research platform to explore the critical role of brand experience in CEB.

The Uses and Gratifications Theory

The U&G theory examines human consumption psychology, the social antecedents of consumption formation, and consumers’ attitudes and behavior after consumption from the consumption field perspective. This can be traced back to the traditional media analysis (Katz et al., 1973). The main idea of U&G theory is that consumers can meet different needs in different fields by consciously choosing the specific media they identify. These needs include customer information seeking, entertainment, and social needs (Diddi & LaRose, 2006; Rubin, 1985). It also proves that people’s consumption behavior is irrational and compulsive, but they choose their favorite media to meet their needs.

With the improvement and popularization of network technology and the factors influencing the improvement of new media platforms, the application of U&G theory to new media platforms has become a trend. Many scholars have proposed the use of social media from the perspective of this theory. For example, some scholars have proposed analyzing the potential explanatory power of the social networking site (SNS) platform consumer behavior prediction. In this respect, U&G theory applies to the online environment (Lee & Ma, 2012; Leung, 2013). Social media play an essential role in human life. Its function is to bring particular satisfaction to consumers because it provides a platform to consume, interact, share personalized media content and diversified information with other people (Diddi & LaRose, 2006; Dunne et al., 2010). There is much research on the satisfaction of SNS platforms. Lee et al. (2012) proposed that the pursuit of entertainment, information, and social interaction assumes that people use mobile media platforms to seek satisfaction. Lee and Ma (2012) believed that people share information on social media platforms to meet their social needs and enhance their social status (Lee & Ma, 2012). Additionally, Dunne et al. (2010) proposed that social network platforms can satisfy people in terms of entertainment, information search, maintenance of social relations, affirmation, and acceptance.

Based on previous studies, many recent studies have explored social media (such as SNS) from the perspective of U&G theory, which supports the applicability of this method in the network environment. It has potential explanatory power in predicting individual behavior in SNSs (Lee & Ma, 2012; Leung, 2013). This study uses Weibo, a representative social media platform in China, as the research object. Therefore, the U&G theory provides a reasonable theoretical basis for this study. Satisfaction comes from finding and sharing information through the content and by interacting with users. Therefore, there are three types of customer motivation for Weibo users: information seeking, entertainment, and social interaction.

Information seeking motivation and brand experience

Social media has the characteristics of rapid propagation and comprehensive coverage, providing information to customers with the best efficiency. Therefore, information seeking is an essential factor in attracting customers to experience social media (Archer-Brown et al., 2013). Thus, there are three types of customer motivation for Weibo users: information seeking, entertainment, and social interaction.

The information obtained from social media can provide customers with practical experience and satisfy them (Wang & Fesenmaier, 2004). Unlike other media advertising, customers want to obtain accurate, relevant, and prompt information from social media platforms (Sofia Martins & Patrício, 2013). They can search for and obtain accurate, relevant, and prompt information on social media and improve their trust (Chen et al., 2013). Hence, this study proposes the following hypothesis:

H1: Information-seeking motivation has a positive impact on customer brand experience.

Entertainment Motivation and Brand Experience

Social media in the personal field is famous for its entertainment value and pleasure (Xu et al., 2012). Recently, people have also been encouraged to use social media software to join the corporate community (Huy & Shipilov, 2012). Therefore, customers are satisfied and happy browsing social media. Previous studies have shown that entertainment value is one of the main factors that positively affect social media experience (Chin et al., 2003; Haridakis & Hanson, 2009). Entertainment value is the potential and intangible return social media consumers pursue (McLure Wasko & Faraj, 2000). It refers to the way people seek fun and relaxing experiences and pursue personal interests in their spare time. Generally, people can obtain entertainment resources through social media.

Enterprises should use social media platforms to provide a joyful entertainment experience and strengthen the relationship between customers and brands (Kang et al., 2014). Social media based on Web 2.0 technology enables customers to search for a richer entertainment experience than Web 1.0 technology (Habibi et al., 2014). Furthermore, entertainment experiences reflect social media engagement and promote customers to form a positive attitude toward posts (Shareef et al., 2019). Thus, Hypothesis 2 is as follows:

H2: Entertainment motivation has a positive impact on customer brand experience.

Social Interaction Motivation and Brand Experience

According to the interpersonal communication theory, interaction is a means of communication in interpersonal relationships (Katz et al., 1955), significantly impacting customers’ emotions (Verhoef et al., 2009). Social media provides new opportunities for social interaction among customers through chat, instant messaging, posting, and online comments. Social interaction meets the need for social contact by developing or maintaining communication with others (Walters & Horton, 2015). Previous studies have shown that social interaction plays an essential role in influencing social media use (Chiu & Huang, 2015; Smock et al., 2011).

Social media provides more social interaction opportunities for other brands, and customers can obtain social experiences from interaction (Huy & Shipilov, 2012). Customers can communicate and meet with like-minded people on a brand’s social media page (Larivière et al., 2013). Bolton (2011) believed that interaction is significant for co-creation. Carlson et al. (2019) claimed that the value of social interaction positively impacts customer meetings, communication, and business on mobile social media brand pages. Social media research reports have shown that customers can experience entertainment and satisfaction through communication and social media interaction (De Vries & Carlson, 2014; Jahn & Kunz, 2012; Shi et al., 2016). Based on this discussion, Hypothesis 3 is as follows:

H3: Social interaction motivation has a positive impact on customer brand experience.

Customer Engagement Behavior (CEB)

Customer engagement is a theory developed in Western management practices. The American advertising foundation was first proposed in 2006. From the perspective of customer engagement has the connotation and characteristics of “beyond loyalty,” “lasting relationship,” “co-creation value,” “participation,” and “interaction,” different from customer loyalty, participation, and involvement (Lu & Ho, 2020).

Van Doorn et al. (2010) described CE as unidimensional and conceptualize it as “the customer’s behavioral manifestation toward firm or a brand, beyond purchase, resulting from motivational drivers.” CE was defined as “behaviors through which customers make voluntary resource contributions having firm focus or a brand but go beyond what is fundamental to transactions, occur in interactions between the focal object and other actors, and result from motivational drivers” (Islam et al., 2019). Vivek (2009) regarded customer engagement as a kind of behavioral concept defined as the intensity of customer participation in the organization’s products, services, and activities provided by the and maintaining contact with it. He also highlighted that although customer engagement is also a behavior concept, it is different from customer participation. Customer participation mainly occurs in a transaction situation, while customer engagement occurs throughout the customer experience process. Regardless of whether there is an actual transaction, customer experience can occur. Thus, customer engagement includes activities and consequences beyond the scope of customer participation. Van Doorn et al. (2010) proposed the concept of customer engagement behavior (CEB). There is transaction behavior. That is, customers pay attention to brand or enterprise behavior. It is driven by motivation, not merely by buying. These behaviors may benefit the enterprise by writing favorable comments, helping others, word-of-mouth publicity, and voluntary suggestions. Alternatively, it may be disadvantageous to the enterprise. That is the organization’s public behavior toward the company. Although customer engagement focuses on brands or companies, it may target many participants, such as suppliers, the public, potential and existing customers, regulators, and company employees. The CEB also includes collaborative creation among customers. This can be achieved by sharing ideas, co-designing, or sharing the production of related products. Therefore, when customers participate spontaneously and independently, collaborative creation provides a customized customer brand experience (beyond the pre-determined choice in co-production). A collective creation behavior proposes suggestions to improve customer consumption experience and guide enterprises and customers to consume. CEB includes participation in the brand community, word-of-mouth recommendations, blog writing, online comments, and spontaneous product design suggestions (Kaur et al., 2020). Moreover, the brand’s content on the Internet can be forwarded for more people to know and understand the brand and ultimately help achieve brand promotion. Customers can influence other potential customers through online comments and answer questions of potential customers.

CEB is different in different environments. Based on social media and CEB as the dependent variables, this study mainly discusses the three aspects of customer reuse, customer feedback, and WOM intentions.

Customer reuse intention

Over the past decade, customer engagement has been a pioneering study of brand loyalty and customer purchases (Prentice & Correia Loureiro, 2017). When a customer experiences a brand, a strong psychological connection is formed (Hapsari et al., 2016), leading to customers’ repeated purchases or use of products of the brand. It may establish a long-term relationship with the brand (Vivek et al., 2012). Previous studies have shown that consumption experiences of customers have a significant impact on future repurchase behavior. Gounaris et al. (2007) highlighted that reuse intention is the core concept for maintaining the continuous relationship between brands and customers. In terms of reuse intention, the expected benefits come from experience mainly. Schivinski and Dabrowski (2016) also showed that customers’ communication experience on brand social media pages impacts brand purchase intention significantly. Experience has a positive effect on customer attitude, affecting customer satisfaction. Experienced customers tend to have better satisfaction and a more positive attitude than inexperienced customers (Chen, 2012). Previous studies have confirmed the influence of experience on the relationship between customer satisfaction and customer reuse intention (Khalifa & Liu, 2007). Thus, this research proposes the following hypothesis:

H4: Customer brand experience has a positive impact on the reuse intention of CEBs.

Customer feedback intention

CEB and customer loyalty are relatively similar. However, from the perspective of behavior, customer engagement emphasizes interaction beyond the transaction, having a broader range, such as customer feedback behavior. After the experience, customers propose reasonable opinions or suggestions on the brand. Customer feedback is usually anonymous, positive, negative, or mixed information (Walther, 2011). Nambisan and Watt (2011) found that customers spontaneously provide feedback and suggestions when satisfied with the brand. Other studies have shown that when customers are happy with their brand experience, they provide feedback and act as brand consultants. Their feedback can help brands identify strategies that have worked from the customers’ perspective (Eisingerich et al., 2014; Hollebeek et al., 2018; Pansari & Kumar, 2017). However, when the customer’s evaluation of the brand experience is negative, the customer may play mischief, not conducive to the customer’s appropriate behavior, such as not browsing the brand page and not providing feedback (Carlson et al., 2019). Therefore, this study proposes the following hypothesis:

H5: Customer brand experience has a positive impact on CEB feedback intentions.

Customer WOM intention

WOM is an essential variable for future actions (Kim et al., 2009). Previous studies have shown that the interaction between customers who do not merely share promotional information is an essential factor in customer decision-making (Feick & Price, 1987). WOM regarding products releases information related to the consumer experience through various means, the key determinant of good communication (Nie et al., 2019). WOM communication is also crucial for enterprises because customers evaluate the products and others’ feelings before choosing them (Belanche et al., 2020). Consequently, customers’ WOM is usually more attractive than other forms of communication (Chandler & Lusch, 2015; Villanueva et al., 2018). Siqueira et al. (2020) identified the positive effect of customer experience on WOM behavior. Chattopadhyay and Laborie (2005) found that if customers are satisfied with the service experience, they recommend it to their friends and intend to experience it again. Recommendation or WOM communication has become one of the most effective marketing tools. Comments on social media can affect the financial performance of other brands (Pansari & Kumar, 2017). Based on the above research on brand experience and WOM intention, this study predicts the following:

H6: Customer brand experience has a positive impact on the WOM intention of CEB.

The Mediating Effect of Customer Brand Experience

Emotional experience plays a vital role in guiding consumers’ future behavior and decision-making. As social media online content increases, creating a joyful consumer experience on social media or websites may reinforce a long-term brand relationship (Klaus, 2013). Jiang (2010) highlighted that experience results from customers and enterprises and impacts customer awareness and behavior. Chaohui Li and Qingjuan (2014) confirmed that brand experience plays a mediating role in the relationship between customer engagement in virtual brand community value co-creation and brand equity. Danhong Cai and Gong (2016) verified that brand experience partially mediates customer engagement and brand loyalty. Liye Zhu and Hao (2018) found that brand experience plays a mediating role in the relationship between customer engagement in two types of brand co-creation and brand commitment. Although several studies on brand experience have aroused people’s interest, brand experience in social media marketing has not been thoroughly studied. Researchers expect that brand experience on social media, like the traditional offline brand experience, will affect consumers’ behavioral responses. Therefore, researchers believe that the expectation of social media brand experience can mediate the relationship between customer motivation and CEB.

In consumer behavior theory, consumer motivation is the prime mover to promote consumer behavior and provide the direction and purpose to consumer behavior (Liu & Bakici, 2019). In addition, brand-related stimuli can be associated with any inherent characteristics of a brand signifying its absence or presence (Morrison & Crane, 2007). These characteristics bring experiences for customers and can be listed as the brand names (Srinivasan & Till, 2002), simple touchpoints such as order forms, customer motivation, application forms, and invoices (Coomber & Poore, 2013), and physical infrastructure (Hanna & Rowley, 2013). Brand-related stimuli (or clues) crucial have been considered by previous studies on experience’s concept in managing experiences to evoke the entire purchasing process (Berry et al., 2002). Customer satisfaction and brand loyalty are the two main consequences of brand experience. However, other variables, such as brand equity (Zarantonello & Schmitt, 2013) and purchase intention (Gabisch, 2011; Morgan-Thomas & Veloutsou, 2013), are important in extant literature. Previous studies have involved the impact of customer motivation on brand experience and brand experience on CEB. However, few studies have used brand experience as a mediator (Khan, Chelliah et al., 2019; Khan, Hollebeek et al., 2018; Khan & Rahman, 2015, 2017). Therefore, this study considers social media platforms in emerging countries as the research object to explore the mediating role of customer brand experience between customer motivation and CEB.

H7: Customer brand experience plays a mediating role in the influence of customer motivation on (H7a) reuse, (H7b) feedback, (H7c) WOM intentions.

Control Variable

This study introduced demographic characteristics as the primary control variables to further improve the model construction and the scale’s external validity. For example, the attention differences between men and women when using social media and that young people are more likely to have access to social media than other groups. The impact of the length of time spent using social media on potential variables also differs. Gender, age, and the time length of Weibo use every day were selected as the study’s control variables.

Materials and Methods

Theoretical Model

This study examines the impact of customer motivation on CEB in “social media” brands. It tests whether customers’ information-seeking, entertainment, and social interaction motivations positively impact customer brand experience and whether brand experience positively impacts the three dimensions of CEB (reuse, feedback, and WOM intentions). Figure 1 presents the research model.

Figure 1.

Research model.

Instrument

The project used to measure the structure of the study was adapted from previous studies. This study adopted some procedures to minimize the deviation of standard methods. First, the wording of items and questions should avoid ambiguity, be concise, and straightforward, and ensure no unfamiliar terms and complex grammar. Second, the physical distance between the same construct measures is considered, not adjacent to the exact construction items.

The three items used to measure information-seeking motivation in this study were adapted from Kankanhalli et al. (2005) and Park and Goering (2016). The three items provided by Diddi and LaRose (2006) and Voss et al. (2003) were used to measure entertainment motivation. Social interaction motivation was measured using three items adapted from Liu et al. (2016) and Whiting and Williams (2013). The brand experience was measured using three items adapted from Kim and Choi (2013). Reuse intention was measured using three items adapted from Chiu et al. (2014). Feedback intentions were measured by three items using the scale developed by Verleye et al. (2014). Purchase intention was measured using three items compiled by Ajzen (1991). Finally, the WOM intention was measured by Brown et al. (2005). All constructs were measured on a 7-Likert-scale from “strongly disagree” to “strongly agree.”

Sociodemographic measures included gender, age, average monthly income, and occupation. Gender was measured using a dichotomy. The age range and occupation were measured with a sequential scale, and the average monthly income was measured in local currency and then converted into U.S. dollars.

Data Collection

The back-translation method was followed to ensure translation validity, as the respondents were Chinese. First, a researcher whose native language was Chinese translated the items into Chinese. Moreover, another researcher translated these items back into English independently. The two researchers subsequently compared the two English versions and developed the items’ first Chinese version. We then invited five CEO researchers and a focus group of five Weibo members to check for ambiguity in format and wording. Similar to Facebook and Twitter in the USA, Sina Weibo is the premier social networking platform of China. Based on their feedback, minor modifications were made to improve the user-friendliness and comprehensiveness of the measurement items. Finally, the two initial translators checked this version and finalized the Chinese questionnaire.

Sina Weibo was chosen for this study because of its popularity. As such, it could provide insights into the best practices and serve as an appropriate context for investigating the motivation behind social commerce. Data were collected through the online survey. A survey hyperlink was placed on forums in Sina Weibo. Those people who had social commerce experience were only targeted for data gathering. Each participant’s Internet protocol address and demographic information were tracked and examined to ensure they submitted only one response. The formal questionnaire was issued between February and March 2020. A non-random that sampled survey was used by this study. The survey was conducted through online crowdsourcing platform of China, functioning similarly to Amazon’s Mechanical Turk. The online survey platform that was used in this study was the most representative one in China. Finally, through the questionnaire survey of 610 customers who had used Weibo, 45 invalid questionnaires with incorrect answers, inconsistent answers, and careless contents were eliminated, and 565 valid questionnaires were collected. The sampling efficiency was 92.6%.

Among the data samples collected, 37.7% were men, and 62.3% were women. According to the age distribution, 33.6% were under 20 years old, 58.2% were 20 to 39 years old, 7.8% were 40 to 59 years old, and 0.4% were over 60 years old. From the perspective of occupational distribution, 17.5% were civil servants and institutions, 21.1% were enterprise employees, 2.3% were self-employed, 53.6% were students, 0.7% were retired, 4.8% were unemployed, or others. According to the educational background distribution, 22.3% had high school education, 3.5% had a junior college education, 41.6% were undergraduates, and 32.6% were graduates or above. Regarding the economic level, 34.3% were below the average, 58.2% were above the average, and 7.4% were above the average. Regarding the average time of using Weibo daily, less than 1 hour accounts for 10.1%, 1 to 2 hours for 19.1%, 2 to 3 hours for 33.6%, 3 to 4 hours for 34%, and more than 4 hours for 3.2%. The statistics are listed in Table 1.

Table 1.

Demographics of the Survey Respondents (N = 565).

Item	Characteristic	Number of samples	Percentage
Gender	Male	213	37.7
Gender	Female	352	62.3
Age	20 or younger	190	33.6
	21–40	329	58.2
	41–60	44	7.8
	60 or older	2	0.4
Occupation	Institution and civil servant	99	17.5
	Enterprise staff	119	21.1
	Individual management	13	2.3
	Student	303	53.6
	Retire	4	0.7
	Unemployed or others	27	4.8
Education	High school and below	126	22.3
	College degree	20	3.5
	Bachelor degree	235	41.6
	Graduate or above	184	32.6
Economic level	Below average	194	34.3
	Middle level	329	58.2
	Above average	42	7.4
Average time of using Weibo every day	<1 hour	57	10.1
	1–2 hours	108	19.1
	2–3 hours	190	33.6
	3–4 hours	192	34.0
	>4 hours	18	3.2

Data Analysis and Results

Reliability and Validity

It is necessary to test the measurement model and evaluate the structural model to verify the tool’s reliability and validity. The test followed a two-step method recommended by Anderson and Gerbing (1988).

Amos 24.0 was used to evaluate the measurement and structural model proposed in this study. First, the normality of samples is a vital hypothesis based on covariance (Chin, 1998). Therefore, this study first evaluates the normality of samples by checking the skewness and kurtosis of the data. This sample’s skew values range from −0.709 to −0.519, indicating that the skew value is within the acceptable threshold of 3.0 (Kline, 1998). Simultaneously, the kurtosis values of the two samples are between − 1.190 and − 0.428, lower than the standard weight of 8 of Klein. Therefore, both samples conformed to a specific hypothesis.

Principal component factor analysis and confirmatory factor analysis (CFA) were used to evaluate the scale’s reliability and validity. The Kaiser–Meyer–Olkin (KMO) statistical value of this sample is .797, indicating that the data can be applied to factor analysis (Kaiser, 1974). Additionally, because a load of all indicators on the expected factors was higher than .6, while a load of other factors on all hands was lower than .4, this value’s appearance indicated that the scale had good convergence and discriminant validity (Chin et al., 1997).

The 7-factor measurement model is based on the CFA method to evaluate the reliability and validity of the structure. As shown in Table 2, the Cronbach’s α and overall reliability (C.R.) values for each structure were between .779 and .945, higher than the recommended reasonable threshold of .7. Simultaneously, convergent and discriminant validities were tested. The validity of convergence was verified by testing the extracted average variance (AVE) and index load. Table 3 shows that all the average values in the validation results were higher than the recommended level of .5. The results also showed that the standard load of all projects was higher than the required threshold of .7, and the significance was .001. Thus, the data also show that the measurement model has good convergence efficiency (Chin et al., 1997).

Table 2.

Results of Confirmatory Factor Analysis.

Construct	Measurement item	Standard loading***	AVE	CR	Cronbach’s α
Information seeking motivation (ISM)	Weibo is a good channel for information acquisition	0.796	0.696	0.873	.872
	The information on Weibo is accurate	0.861
	The information on Weibo is very useful to me	0.845
Entertainment motivation (EM)	Weibo can give me an exciting experience	0.903	0.692	0.870	.867
	Weibo adds happiness to my daily life	0.833
	I will be pleased after reading the content on Weibo	0.753
Social interaction motivation (SIM)	It’s terrific to communicate with other customers on Weibo	0.694	0.542	0.779	.775
	I can have better communication and interaction with other customers on Weibo	0.834
	The interaction with other customers on Weibo is outstanding	0.670
Brand experience (BE)	The experience on Weibo is excellent	0.795	0.588	0.810	.809
	I have a good experience in Weibo	0.712
	The overall experience on Weibo is outstanding	0.790
Reuse intention (RI)	I will continue to use Weibo	0.788	0.788	0.917	.913
	I will enjoy my leisure time in the future by using Weibo	0.949
	Using Weibo will satisfy me	0.917
Feedback intention (FI)	If I have any questions when using Weibo, I will give feedback	0.776	0.632	0.837	.837
	When I think of improving Weibo use quality, I will give feedback through the customer service center	0.819
	I will provide useful suggestions for Weibo	0.789
WOM intention (WOMI)	I will actively talk about Weibo to others	0.901	0.851	0.945	.945
	I will recommend Weibo to my acquaintances	0.923
	I will recommend Weibo to my friends	0.943

Note. All standard loadings were significant at ***p < 0.001.

Table 3.

Correlations Matrix.

Variable	Mean	SD	Correlation matrix
Variable	Mean	SD	1	2	3	4	5	6	7
1. ISM	4.220	1.948	.834
2. EM	4.137	1.993	.255	.832
3. SIM	4.713	1.808	.212	.101	.736
4. BE	3.717	1.582	.203	.168	.207	.767
5. RI	4.490	1.843	.230	.267	.167	.316	.888
6. FI	3.480	1.826	.331	.331	.147	.321	.138	.795
7. WOMI	3.647	2.047	.280	.261	.022	.416	.319	.161	.922

Note. Diagonal bold italics entries are values of the square roots average variance extracted (AVE); all others are correlations coefficients. M = mean, SD = standard deviation. ISM = information seeking motivation; EM = entertainment motivation; SIM = social interaction motivation; B.E. = brand experience; RI = reuse intention; F.I. = feedback intention; WOMI = WOM intention.

When the square root of each structure’s mean square deviation is greater than the correlation between structures, the validity of data discrimination can be illustrated (Fornell & Larcker, 1981). For example, Table 3 shows that the square root of AVEs in the two data groups is more significant than the correlation between the structures described in the off-diagonal terms. Therefore, the validity of the discrimination is verified.

The data collected in this study have a single source; therefore, the Harman single factor test was used to verify the deviation of the sample data. Seven factors were extracted from the data, explaining 79.413% of the variance. However, only 25.962% (<40%) of the sample had the most significant influencing factors. Therefore, the results showed no single factor controlling the total variance among the factors studied in this study, and there was no standard deviation.

After testing the validity and reliability of the measurement, this study tests the hypothesis proposed by AMOS 24.0. Table 4 shows the model fitting index’s actual and recommended values obtained after the original model is modified. These data prove that the model’s appropriate index is better than the recommended threshold, showing that the model fits the data well.

Table 4.

Measures of the Model Fit.

Fit index	X²/df	RMSEA	GFI	CFI	NFI	TLI
Recommended range	<3.84^a	<0.08^b	>0.90^a	>0.90^a	>0.90^a	>0.90^a
Model value	2.902	0.058	0.920	0.950	0.926	0.942

Note. RMSEA = root mean square error of approximation; GFI = the goodness of fit index; CFI = comparative fit index; NFI = normed fit index; TLI = non-normed fit index.

According to Bentler and Bonett (1980) and Lee et al. (2012).

According to Browne and Cudeck (1989) and Lee et al. (2012).

Hypotheses Testing

Path modeling was performed to test H1 to H6. The results show that information-seeking motivation (β = .130, p < .001), entertainment motivation (β = .131, p < .001), and social interaction motivation (β = .158, p < .01) positively affected brand experience. Therefore, H1, H2, and H3 are supported. This study believes that these findings can explain how customer motivation affects customer brand experience in social media. Brand experience positively affect reuse intention (β = .451, p < .001), feedback intention (β = .425, p < .001), and WOM intention (β = .704, p < .001). Thus, H4 to H6 are supported. Therefore, our conceptual model provides a reasonable explanation for the different CEBs. The results are presented in Table 5 and Figure 2.

Table 5.

Structural Model Results.

Hypotheses	Structural path	Estimate	SE	T value	p	Results
H1	Information seeking motivation → Brand experience	.130	.036	3.573	***	Supported
H2	Entertainment motivation → Brand experience	.131	.039	3.319	***	Supported
H3	Social interaction motivation → Brand experience	.158	.051	3.125	.002**	Supported
H4	Brand experience → Reuse intention	.451	.063	7.131	***	Supported
H5	Brand experience → Feedback intention	.425	.064	6.634	***	Supported
H6	Brand experience → WOM intention	.704	.077	9.198	***	Supported

p < .01. ***p < .001.

Figure 2.

Results of the research model tests.

This study used a bootstrapping method to test the mediating effect and found that brand experience plays a mediating role in customer motivation on CEB (Preacher & Hayes, 2008). Table 6 shows that the indirect effect of brand experience on the relationship between customer motivations and CEB is significant with a 95% bootstrap confidence interval, excluding 0. This finding shows that brand experience mediates the influence of customer motivation on CEB. Thus, H7a, 7b, and 7c are supported.

Table 6.

Analysis of Mediating Effect.

Independent variable	Mediator variable	Dependent variable	Effect	SE	Confidence interval	p-Value
Information seeking motivation	Brand experience	Reuse intention	0.059	0.020	[0.025, 0.106]	.001**
Entertainment motivation			0.059	0.022	[0.020, 0.110]	.003**
Social interaction motivation			0.071	0.026	[0.028, 0.133]	.000***
Information seeking motivation		Feedback intention	0.055	0.020	[0.023, 0.100]	.001**
Entertainment motivation			0.056	0.022	[0.019, 0.103]	.003**
Social interaction motivation			0.067	0.024	[0.027, 0.121]	.000***
Information seeking motivation		Word-of-mouth intention	0.092	0.030	[0.040, 0.161]	.001**
Entertainment motivation			0.092	0.033	[0.035, 0.163]	.003**
Social interaction motivation			0.111	0.036	[0.046, 0.191]	.000***
95% Bootstrap confidence intervals for indirect effect

p < .01. ***p < .001.

Control Variable Results

To improve the scale’s external validity and ensure the scientificity of the verification results, gender, age, and average time of using Weibo every day in demographic characteristics were selected as control variables in this study. According to the study results, gender has no significant impact on latent variables because male and female customers now use Weibo. Age has a considerable effect on reuse intention (β = .273, p < .01), but has no significant effect on other latent variables. As a social media platform, young people’s reuse behavior of Weibo is significantly higher than that of the elderly. Therefore, age significantly impacts reuse intention. Finally, the average time spent using Weibo every day had a significant impact on social media brand experience and WOM intention (β = .152, p < .05) and had no significant impact on other latent variables. The daily use time of Weibo can explain consumers’ dependence on social media and has different effects on brand experience and WOM. In the future, demographic characteristics should be considered as moderating variables to modify and improve this study’s conclusions further.

Discussion and Conclusions

With global and regional media development, social media with global influence and personalized characteristics has become a research topic attracting many scholars. Information communication between brands and customers has changed from asymmetric one-way communication to symmetrical two-way communication, realizing joint value creation. In social media brands, the influence of customer motivation on CEB has attracted increasing attention. This study selects Weibo, the leading social media platform in China, as the research brand and considers customers with Weibo experience as the research object to analyze the hypotheses proposed. The results are as follows.

General Discussion

First, customer’s information-seeking, entertainment, and social interaction motivations impact customers’ brand experience significantly. These findings are consistent with prior studies on social media (Leftheriotis & Giannakos, 2014; Liu & Bakici, 2019). Social interaction has the most significant impact on customers’ brand experiences. As a representative software for social media, this may be because Weibo performs best in social interaction functions.

Second, this study confirmed the positive impact of customer brand experience on CEB. Brand experience has a significant positive impact on customer reuse, feedback, and WOM intentions. These results are consistent with previous studies (Carlson et al., 2019; Islam & Rahman, 2016; Islam et al., 2018). Additionally, brand experience has the most significant impact on customers’ WOM intentions. Therefore, it is of great strategic significance to pay attention to CEB intention in this research. This is because of its impact on reuse intention and can provide a theoretical basis for promoting consumers’ recommendation behavior, such as feedback and WOM intentions, and ultimately help enterprises achieve profitable customer relationships (Kumar et al., 2010, 2016).

Third, this study also explored the mediating effect of customer brand experience on the relationship between customer motivation (i.e., information seeking, entertainment, and social interaction) and the three CEBs’ intentions (i.e., reuse, feedback, and WOM). Thus, the results help us better understand the incentive mechanism and consequences of customer brand experience. Additionally, this confirms previous studies. Therefore, the conceptual model in this research reasonably explains the different CEB.

Finally, gender, age, and average daily time of using Weibo were used as control variables. The results show that gender does not affect the brand experience, reuse intention, feedback intention, and WOM intention. Age controls customers’ reuse intention but does not affect the brand experience, feedback intention, and WOM intention. The average daily use of Weibo controls brand experience and WOM intention but does not affect reuse and feedback intentions.

Theoretical Implications

There is a surge of interest in customer engagement in academia and practice. Mobile social media technology provides further opportunities for the academic development of this concept. Therefore, this study contributes to the literature in several ways.

First, it provides a unique and meaningful model to test customer motivation’s collective influence on social media brand experience and CEB. This study is the first to link customer motivation conceptualization in social media networks with brand experience and CEB in the social media environment. Thus, this study’s contribution to marketing is to explain the relationship between customer motivation more accurately. The motivation mentioned in the survey covers a series of components and customer engagement intention (including trading and non-trading related behaviors) toward the brand in social media.

Second, this study further expands the discourse on customer brand experience in the context of mobile social media in emerging markets. This is realized by conceptualizing and testing the model with Chinese consumers. Based on the abundant literature on U&G theory, brand experience, and related viewpoints (Liu & Bakici, 2019; Rubin, 1985), this study confirms that customer motivation positively impacts brand experience. Customer brand experience positively impacts CEB. These components represent the brand experience valued by consumers and help social media enterprises evaluate the overall customer motivation index. As the customer brand experience occurs in the personal lifeworld and is subjective and constructed by society, research on customer motivation and related perspectives is worthy of attention. Therefore, in the social media environment, consumer motivation is directly affected by numerous stakeholders in the common field, such as other customers who use the brand and their social networks.

Third, in this research, attention to CEB has important strategic significance for research on the influence of customer purchase behavior and customer lifetime value. It provides a theoretical basis for promoting the value co-creation behavior of enterprises and consumers. This includes providing feedback and influencing customer behavior to help enterprises realize profitable customer relationships (Kumar et al., 2016). Consequently, this comprehensive approach involving the impact of customer motivation on CEB can help enterprises identify the psychological processes that lead to different forms of CEB and determine how to stimulate and manage these CEB effectively.

Management Implications

Regarding the academic research contribution, this study successfully confirmed the suitability of the U&G theory in explaining the influence of SNS content marketing on brand experience and CEB in China. And provide the following management implications.

First, enterprises need to meet customers’ motivation and improve their CEB. According to this study, when customers use social media, information seeking, entertainment, and social interaction motivations impact customer brand experience and CEB. Therefore, when using social media to carry out brand marketing, enterprises should pay full attention to customers’ motivational needs, enhance brand reuse intention, and improve brand competitiveness.

Second, managers should focus on maximizing the information, entertainment, and social interaction values of social media brands. For example, in such an emerging market, brands should formulate content marketing strategies and strive to provide customers the ability and knowledge to obtain the maximum information utility from products of the brand. Brands are positioned as allies of customers by engendering and sharing content focusing on meeting knowledge needs of customers mainly. Both customers and brands should strive for the same goal of maximizing the utility of services or products (Carlson et al., 2019). Thus, it is necessary for brands to create interactive content to attract customers to generate motivation. Social media practitioners should also pay attention to emotional values and entertainment. Therefore, designing content strategies and improving the emotional experience (fun, enjoyment, entertainment) through customer cultivation activities on social media platforms is an effective strategy. By customizing a social media information delivery strategy, which focuses on stimulating the emotional response synchronized with core information of the brand, social media managers can strengthen brand value by connecting customer emotion and entertainment content.

Finally, it is important to pay attention to the customer’s social media brand experience, enhance customer reuse intention, pay attention to the customer feedback content, and promote customers’ good reputation. Enterprises should pay more attention to brand experience, especially in developing and launching new products and services. WOM is a valuable tool in attracting new customers that may help potential customers gain trust by browsing positive suggestions issued by customers who have had positive experiences (Hennig-Thurau et al., 2004). Based on this study’s results, as an essential variable in customer reuse and satisfaction paths, social media brand experience has a necessary impact on business activities. Enterprise managers should pay full attention to improve customers’ brand experience to achieve the “win-win” of sales volume and public approval.

From the perspective of human communication behavior and psychological cognition, this study attempts to take “Weibo” as a brand and explore the influence of customer engagement under brand experience mediation. Thus, this study makes up for the lack of current and has a specific reference value and guiding significance for business activities to a certain extent.

Limitations and Future Research

This study’s results have a practical reference value for “social media” brands to stimulate reuse motivation of customers and affect CEB. Simultaneously, it has specific guiding significance for social media operators to establish a good brand image. However, like all studies, this study has some limitations, providing a starting point for the further research. First, this study uses a self-reported online survey to test active users’ and brand consumers’ perceptions rather than actual behavioral data. Second, the self-selection bias may influenced the results. Third, users with low use intensity and brand participation may have different experiences and views on social media platforms. Additional studies, including longitudinal data, can be used to explore whether the results can be epitomized.

Moreover, the research samples were from Chinese consumers using Weibo to follow their designated brands. This study researched brand of the “Weibo” social media platform but did not cover all social media platforms. Customer experiences that were based on social media platforms’ preferences will therefore be different. Future research could examine consumption experience of mobile social media in a broader range of various social media platforms and emerging market countries, especially in developed markets, to heighten their universality.

Second, this study is based on an online questionnaire collected by subjects with Weibo experience, which differs from the actual situation. It takes customer motivation as an independent variable and customer experience as a mediator variable. Future research can also examine the impact of a specific CEB on customer motivation over time, that is, the existence of a feedback loop. Such a survey is necessary to assess whether the results positively impact the brand and CEB. Future research can also explore whether customers’ active participation can make the brand experience valuable to other customers and the enterprise.

Third, in this analysis, demographic variables were not well explained for the test samples selected, as a vast customer group, whether customer characteristics are typical remains to be verified.

Footnotes

Acknowledgements

The authors would like to acknowledge the anonymous reviewers who provided insightful suggestions and comments on improving the quality of this manuscript.

Author Note

Yu Li is now affiliated to Shandong University of Finance and Economics, Jinan, China.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article:The research was funded by the Shandong Social Science Foundation [Grant No. 21DGLJ16] and the Doctoral foundation of Shandong Jianzhu University [Grant No. X20006S].

Ethical Statement

The authors of this paper have carefully studied the international research ethics, and this paper does not involve animal and human studies.

ORCID iD

Chunlan Jiao

References

Ajzen

(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.

Alloza

(2008). Brand engagement and brand experience at BBVA, the transformation of a 150 Years old company. Corporate Reputation Review, 11, 371–379.

Andangsari

E. W.

Gumilar

Godwin

(2013). Social networking sites use and psychological attachment need among Indonesian young adults population. International Journal of Social Science Studies, 1, 133–138.

Anderson

J. C.

Gerbing

D. W.

(1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103, 411–423.

Archer-Brown

Piercy

Joinson

(2013). Examining the information value of virtual communities: Factual versus opinion-based message content. Journal of Marketing Management, 29, 421–438.

Belanche

Flavián

Pérez-Rueda

(2020). Mobile apps use and WOM in the food delivery sector: The role of planned behavior, perceived security and customer lifestyle compatibility. Sustainability, 12, 4275.

Bentler

P. M.

Bonett

D. G.

(1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–606.

Berry

L. L.

Carbone

L. P.

Haeckel

S. H.

(2002). Managing the total customer experience. MIT Sloan Management Review, 43, 85–89.

Bolton

R. N.

(2011). Comment: Customer engagement: Opportunities and challenges for organizations. Journal of Service Research, 14, 272–274.

10.

Brakus

J. J.

Schmitt

B. H.

Zarantonello

(2009). Brand experience: What is it? How is it measured? Does it affect loyalty? Journal of Marketing, 73, 52–68.

11.

Browne

M. W.

Cudeck

(1989). Single sample cross-validation indices for covariance structures. Multivariate Behavioral Research, 24, 445–455.

12.

Brown

T. J.

Barry

T. E.

Dacin

P. A.

Gunst

R. F.

(2005). Spreading the word: Investigating antecedents of consumers’ positive word-of-mouth intentions and behaviors in a retailing context. Journal of the Academy of Marketing Science, 33, 123–138.

13.

Carlson

Rahman

M. M.

Taylor

Voola

(2019). Feel the VIBE: Examining value-in-the-brand-page-experience and its impact on satisfaction and customer engagement behaviours in mobile social media. Journal of Retailing and Consumer Services, 46, 149–162.

14.

Chandler

J. D.

Lusch

R. F.

(2015). Service systems: A broadened framework and research agenda on value propositions, engagement, and service experience. Journal of Service Research, 18, 6–22.

15.

Chang

P. L.

Chieng

M. H.

(2006). Building consumer–brand relationship: A cross-cultural experiential view. Psychology and Marketing, 23, 927–959.

16.

Chaohui Li

Y. J.

Qingjuan

B. U.

(2014). Research on the effect of customer participation in value co-creation of virtual brand community on brand equity – The mediating effects of brand experience. Journal of Marketing Science, 10, 109–124.

17.

Chattopadhyay

Laborie

J. L.

(2005). Managing brand experience: The market contact audit™. Journal of Advertising Research, 45, 9–16.

18.

Chen

Wang

Zhao

(2013). What drives content creation behavior on SNSs? A commitment perspective. Journal of Business Research, 66, 2529–2535.

19.

Chen

Y. Y.

(2012). Why do consumers go internet shopping again? Understanding the antecedents of repurchase intention. Journal of Organizational Computing and Electronic Commerce, 22, 38–63.

20.

Chin

W. W.

(1998). Issues and opinion on structural equation modeling. Information Systems Quarterly, 22(1), 19–24.

21.

Chin

W. W.

Gopal

Salisbury

W. D.

(1997). Advancing the theory of adaptive structuration: The development of a scale to measure faithfulness of appropriation. Information Systems Research, 8, 342–367.

22.

Chin

W. W.

Marcolin

B. L.

Newsted

P. R.

(2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189–217.

23.

Chiu

C. M.

Huang

H. Y.

(2015). Examining the antecedents of user gratification and its effects on individuals’ social network services usage: The moderating role of habit. European Journal of Information Systems, 24, 411–430.

24.

Chiu

C. M.

Wang

E. T. G.

Fang

Y. H.

Huang

H. Y.

(2014). Understanding customers’ repeat purchase intentions in B2C e-commerce: The roles of utilitarian value, hedonic value and perceived risk. Information Systems Journal, 24, 85–114.

25.

Coomber

Poore

(2013). From good to branded: Using immersive design to deliver a positive brand experience for British gas. Journal of Brand Strategy, 1, 318–326.

26.

Danhong Cai

M. Q.

Gong

. (2016). Research on the effect of customer engagement on brand loyalty: The mediation role of brand experience. Productivity Research, 8, 135–140.

27.

De Vries

N. J.

Carlson

. (2014). Examining the drivers and brand performance implications of customer engagement with brands in the social media environment. Journal of Brand Management, 21(6), 495–515.

28.

Diddi

LaRose

(2006). Getting hooked on news: Uses and gratifications and the formation of news habits among college students in an internet environment. Journal of Broadcasting & Electronic Media, 50, 193–210.

29.

Dunne

Á.

Lawlor

Rowley

. (2010). Young people’s use of online social networking sites – A uses and gratifications perspective. Journal of Research in Interactive Marketing, 4, 46–58.

30.

Eisingerich

A. B.

Auh

Merlo

(2014). Acta non verba? The role of customer participation and word of mouth in the relationship between service firms’ customer satisfaction and sales performance. Journal of Service Research, 17, 40–53.

31.

Evans

Krauthammer

(2011). Exploring the use of social media to measure journal article impact. AMIA . . . Annual Symposium Proceedings/AMIA Symposium. AMIA Symposium, 2011, 374.

32.

Feick

L. F.

Price

L. L.

(1987). The market maven: A diffuser of marketplace information. Journal of Marketing, 51(1), 83–97.

33.

Fornell

Larcker

D. F.

(1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18, 382–388.

34.

Gabisch

J. A.

(2011). Virtual world brand experience and its impact on real world purchasing behavior. Journal of Brand Management, 19, 18–32.

35.

Global Web Index. (2020). Social media marketing trends. Retrieved January 1, 2021, https://echidna.co/blog/top-6-social-media-marketing-trends-in-2020/

36.

Gounaris

S. P.

Tzempelikos

N. A.

Chatzipanagiotou

(2007). The relationships of customer-perceived value, satisfaction, loyalty and behavioral intentions. Journal of Relationship Marketing, 6, 63–87.

37.

Habibi

M. R.

Laroche

Richard

M. O.

(2014). Brand communities based in social media: How unique are they? Evidence from two exemplary brand communities. International Journal of Information Management, 34, 123–132.

38.

Hanna

Rowley

(2013). Place brand practitioners’ perspectives on the management and evaluation of the brand experience. The Town Planning Review, 84, 495–515.

39.

Hapsari

Clemes

Dean

(2016). The mediating role of perceived value on the relationship between service quality and customer satisfaction: Evidence from Indonesian airline passengers. Procedia Economics and Finance, 35, 388–395.

40.

Haridakis

Hanson

(2009). Social interaction and co-viewing with YouTube: Blending mass communication reception and social connection. Journal of Broadcasting & Electronic Media, 53, 317–335.

41.

Hennig-Thurau

Gwinner

K. P.

Walsh

Gremler

D. D.

(2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? Journal of Interactive Marketing, 18, 38–52.

42.

Holbrook

M. B.

Hirschman

E. C.

(1982). The experiential aspects of consumption: Consumer fantasies, feelings, and fun. Journal of Consumer Research, 9, 132–140.

43.

Hollebeek

L. D.

Macky

(2019). Digital content marketing’s role in fostering consumer engagement, trust, and value: Framework, fundamental propositions, and implications. Journal of Interactive Marketing, 45, 27–41.

44.

Hollebeek

L. D.

Srivastava

R. K.

Chen

(2018). Correction to: S-D logic–informed customer engagement: Integrative framework, revised fundamental propositions, and application to CRM. Journal of the Academy of Marketing Science, 47, 186–186.

45.

Huy

Shipilov

(2012). The key to social media success with in organizations. MIT Sloan Management Review, 54, 73–81.

46.

Islam

J. U.

Hollebeek

L. D.

Rahman

Khan

Rasool

(2019). Customer engagement in the service context: An empirical investigation of the construct, its antecedents and consequences. Journal of Retailing and Consumer Services, 50, 277–285.

47.

Islam

J. U.

Rahman

(2016). The transpiring journey of customer engagement research in marketing. Management Decision, 54, 2008–2034.

48.

Islam

J. U.

Rahman

Hollebeek

L. D.

(2018). Consumer engagement in online brand communities: A solicitation of congruity theory. Internet Research, 28, 23–45.

49.

Jahn

Kunz

W. H.

(2012). How to transform consumers into fans of your brand. SSRN Electronic Journal, 23(3), 344–361.

50.

Jiang

(2010). New service development and design based on customers’ experience. Scientific and Technology Management Research, 14, 146–148.

51.

Joo

Seok

Nam

(2020). The moderating effect of social media use on sustainable rural tourism: A theory of planned behavior model. Sustainability, 12, 4095.

52.

Kaiser

H. F.

(1974). An index of factorial simplicity. Psychometrika, 39, 31–36.

53.

Kang

Tang

Fiore

A. M.

(2014). Enhancing consumer–brand relationships on restaurant facebook fan pages: Maximizing consumer benefits and increasing active participation. International Journal of Hospitality Management, 36, 145–155.

54.

Kankanhalli

Tan

B. C. Y.

Wei

K. K.

(2005). Understanding seeking from electronic knowledge repositories: An empirical study. Journal of the American Society for Information Science and Technology, 56, 1156–1166.

55.

Kaplan

A. M.

Haenlein

(2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53, 59–68.

56.

Katz

Blumler

J. G.

Gurevitch

(1973). Uses and gratifications research. Public Opinion Quarterly, 37, 509.

57.

Katz

Lazarsfeld

P. F.

(1955). Personal influence : The part played by people in the flow of mass communications. Transaction Publishers.

58.

Kaur

Paruthi

Islam

Hollebeek

L. D.

(2020). The role of brand community identification and reward on consumer brand engagement and brand loyalty in virtual brand communities. Telematics and Informatics, 46, 101321.

59.

Khalifa

Liu

(2007). Online consumer retention: Contingent effects of online shopping habit and online shopping experience. European Journal of Information Systems, 16, 780–792.

60.

Khan

Hollebeek

L. D.

Fatma

Islam

J. U.

Rahman

(2019). Brand engagement and experience in online services. Journal of Services Marketing, 34, 163–175.

61.

Khan

Rahman

(2015). A review and future directions of brand experience research. International Strategic Management Review, 3, 1–14.

62.

Khan

Rahman

(2017). Brand experience and emotional attachment in services: The moderating role of gender. Service Science, 9, 50–61.

63.

Khan

M. J.

Chelliah

Khan

Amin

(2019). Perceived risks, travel constraints and visit intention of young women travelers: The moderating role of travel motivation. Tourism Review, 74, 721–738.

64.

Kim

Choi

(2013). The influence of customer experience quality on customers’ behavioral intentions. Services Marketing Quarterly, 34, 322–338.

65.

Kim

W. G.

Kim

H. B.

(2009). The effects of perceived justice on recovery satisfaction, trust, word-of-mouth, and revisit intention in upscale hotels. Tourism Management, 30, 51–62.

66.

Klaus

P. P.

(2013). New insights from practice: Exploring online channel management strategies and the use of social media as a market research tool. International Journal of Market Research, 55, 829–850.

67.

Kline

R. B.

(1998). Software review: Software programs for structural equation modeling: Amos, EQS, and LISREL. Journal of Psychoeducational Assessment, 16, 343–364.

68.

Kumar

Bezawada

Rishika

Janakiraman

Kannan

P. K.

(2016). From social to sale: The effects of firm-generated content in social media on customer behavior. Journal of Marketing, 80, 7–25.

69.

Kumar

Aksoy

Donkers

Venkatesan

Wiesel

Tillmanns

(2010). Undervalued or overvalued customers: Capturing total customer engagement value. Journal of Service Research, 13, 297–310.

70.

Larivière

Joosten

Malthouse

Van Birgelen

Aksoy

Kunz

W. H.

Huang

M. H.

(2013). Value fusion: The blending of consumer and firm value in the distinct context of mobile technologies and social media. Journal of Service Management, 24, 268–293.

71.

Lee

C. S.

(2012). News sharing in social media: The effect of gratifications and prior experience. Computers in Human Behavior, 28, 331–339.

72.

Lee

Chen

A. N.

Ilie

(2012). Can online wait be managed? The effect of filler interfaces and presentation modes on perceived waiting time online. MIS Quarterly, 36, 365–394.

73.

Leftheriotis

Giannakos

M. N.

(2014). Using social media for work: Losing your time or improving your work? Computers in Human Behavior, 31, 134–142.

74.

Leung

(2013). Generational differences in content generation in social media: The roles of the gratifications sought and of narcissism. Computers in Human Behavior, 29, 997–1006.

75.

Liu

I. L. B.

Cheung

C. M. K.

Lee

M. K. O.

(2016). User satisfaction with microblogging: Information dissemination versus social networking. Journal of the Association for Information Science and Technology, 67, 56–70.

76.

Liu

Bakici

(2019). Enterprise social media usage: The motives and the moderating role of public social media experience. Computers in Human Behavior, 101, 163–172.

77.

Liye Zhu

D. Y.

Hao

. (2018). Research on the effect of customer engagement in brand Co-creation on brand commitment in virtual brand community-the mediating effects of brand experience. Journal of Chinese Management, 15, 262–271.

78.

H. P.

H. C.

(2020). Exploring the impact of gamification on users’ engagement for sustainable development: A case study in brand applications. Sustainability, 12, 4169.

79.

McLure Wasko

Faraj

. (2000). “It is what one does”: Why people participate and help others in electronic communities of practice. The Journal of Strategic Information Systems, 9, 155–173.

80.

Morgan-Thomas

Veloutsou

(2013). Beyond technology acceptance: Brand relationships and online brand experience. Journal of Business Research, 66(1), 21–27.

81.

Morrison

Crane

F. G.

(2007). Building the service brand by creating and managing an emotional brand experience. Journal of Brand Management, 14, 410–421.

82.

Nambisan

Watt

J. H.

(2011). Managing customer experiences in online product communities. Journal of Business Research, 64, 889–895.

83.

Nie

Abd-Rabo

M. A.

Sun

Ren

(2019). A consumption behavior model with advertising and word-of-mouth effect. Journal of Nonlinear Modeling and Analysis, 1(4), 461–89.

84.

Noble

S. M.

Griffith

D. A.

Weinberger

M. G.

(2005). Consumer derived utilitarian value and channel utilization in a multi-channel retail context. Journal of Business Research, 58, 1643–1651.

85.

Novak

T. P.

Hoffman

D. L.

Yung

Y. F.

(2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing Science, 19, 22–42.

86.

Pansari

Kumar

(2017). Customer engagement: The construct, antecedents, and consequences. Journal of the Academy of Marketing Science, 45, 294–311.

87.

Park

D. Y.

Goering

E. M.

(2016). The health-related uses and gratifications of YouTube: Motive, cognitive involvement, online activity, and sense of empowerment. Journal of Consumer Health on the Internet, 20, 52–70.

88.

Preacher

K. J.

Hayes

A. F.

(2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879–891.

89.

Prentice

Correia Loureiro

S. M.

(2017). An asymmetrical approach to understanding configurations of customer loyalty in the airline industry. Journal of Retailing and Consumer Services, 38, 96–107.

90.

Rubin

A. M.

(1985). Uses and gratifications: Quasi-functional analysis. In Dominick

J. R.

Fletcher

J. E.

(Eds.), Broadcasting research methods. Allyn and Bacon (pp. 202–220).

91.

Schivinski

Dabrowski

(2016). The effect of social media communication on consumer perceptions of brands. Journal of Marketing Communications, 22, 189–214.

92.

Shareef

M. A.

Mukerji

Dwivedi

Y. K.

Rana

N. P.

Islam

(2019). Social media marketing: Comparative effect of advertisement sources. Journal of Retailing and Consumer Services, 46, 58–69.

93.

Shi

Chen

Chow

W. S.

(2016). Key values driving continued interaction on brand pages in social media: An examination across genders. Computers in Human Behavior, 62, 578–589.

94.

Siqueira

J. R.

Ter Horst

Molina

Losada

Mateus

M. A.

(2020). A Bayesian examination of the relationship of internal and external touchpoints in the customer experience process across various service environments. Journal of Retailing and Consumer Services, 53, 1–12.

95.

Smith

(2016). Marketing: 96 amazing social media statistics and facts. https://www.scribd.com/document/361264271/96Amazing-Social-Media-Statistics-and-Facts/

96.

Smock

A. D.

Ellison

N. B.

Lampe

Wohn

D. Y.

(2011). Facebook as a toolkit: A uses and gratification approach to unbundling feature use. Computers in Human Behavior, 27, 2322–2329.

97.

Sofia Martins

Patrício

. (2013). Understanding participation in company social networks. Journal of Service Management, 24, 567–587.

98.

Srinivasan

S. S.

Till

B. D.

(2002). Evaluation of search, experience and credence attributes: Role of brand name and product trial. Journal of Product & Brand Management, 11, 417–431.

99.

Van Doorn

Lemon

K. N.

Mittal

Nass

Pick

Pirner

Verhoef

P. C

. (2010). Customer engagement behavior: Theoretical foundations and research directions. Journal of Service Research, 13, 253–266.

100.

Verhoef

P. C.

Lemon

K. N.

Parasuraman

Roggeveen

Tsiros

Schlesinger

L. A.

(2009). Customer experience creation: Determinants, dynamics and management strategies. Journal of Retailing, 85, 31–41.

101.

Verleye

Gemmel

Rangarajan

(2014). Managing engagement behaviors in a network of customers and stakeholders: Evidence from the nursing home sector. Journal of Service Research, 17, 68–84.

102.

Villanueva

Yoo

Hanssens

D. M.

(2018). The impact of marketing-induced versus word-of-mouth customer acquisition on customer equity growth. In Hanssens

D. M.

(Ed.), Long-term impact of marketing: A compendium (pp. 427–459). World Scientific Publishing.

103.

Vivek

S. D.

(2009). A scale of consumer engagement [Doctoral dissertation]. Department of management & marketing, The University of Alabama.

104.

Vivek

S. D.

Beatty

S. E.

Morgan

R. M.

(2012). Customer engagement: Exploring customer relationships beyond purchase. The Journal of Marketing Theory and Practice, 20, 122–146.

105.

Voss

K. E.

Spangenberg

E. R.

Grohmann

(2003). Measuring the hedonic and utilitarian dimensions of consumer attitude. Journal of Marketing Research, 40(3), 310–320.

106.

Walters

N. T.

Horton

(2015). A diary study of the influence of Facebook use on narcissism among male college students. Computers in Human Behavior, 52, 326–330.

107.

Walther

J. B.

(2011). Theories of computer-mediated communication and interpersonal relations. In Knapp

M. L.

Daly

J. A.

(Eds.), The handbook of interpersonal communication (4th ed., pp. 443–479). SAGE.

108.

Wang

Wei

(2012). Social media peer communication and impacts on purchase intentions: A consumer socialization framework. Journal of Interactive Marketing, 26, 198–208.

109.

Wang

X.-W.

Cao

Y. M.

Park

(2019). The relationships among community experience, community commitment, brand attitude, and purchase intention in social media. International Journal of Information Management, 49, 475–488.

110.

Wang

Fesenmaier

D. R.

(2004). Towards understanding members’ general participation in and active contribution to an online travel community. Tourism Management, 25, 709–722.

111.

Weibo. (2013). Maximize your brand power with Weibo. https://www.doc88.com/p-9962186680242.html

112.

Whiting

Williams

(2013). Why people use social media: A uses and gratifications approach. Qualitative Market Research an International Journal, 16(4), 362–369.

113.

Ryan

Prybutok

Wen

(2012). It is not for fun: An examination of social network site usage. Information Management, 49, 210–217.

114.

Zarantonello

Schmitt

(2013). The impact of event marketing on brand equity: The mediating roles of brand experience and brand attitude. International Journal of Advertising, 32, 255–280.

Examining Customer Motivation and Its Impact on Customer Engagement Behavior in Social Media: The Mediating Effect of Brand Experience

Abstract

Keywords

Introduction

Literature Review and Hypotheses Development

Customer Brand Experience

The Uses and Gratifications Theory

Information seeking motivation and brand experience

Entertainment Motivation and Brand Experience

Social Interaction Motivation and Brand Experience

Customer Engagement Behavior (CEB)

Customer reuse intention

Customer feedback intention

Customer WOM intention

The Mediating Effect of Customer Brand Experience

Control Variable

Materials and Methods

Theoretical Model

Instrument

Data Collection

Data Analysis and Results

Reliability and Validity

Hypotheses Testing

Control Variable Results

Discussion and Conclusions

General Discussion

Theoretical Implications

Management Implications

Limitations and Future Research

Footnotes

Acknowledgements

Author Note

Declaration of Conflicting Interests

Funding

Ethical Statement

ORCID iD

References