Online Engagement Among Restaurant Customers: The Importance of Enhancing Flow for Social Media Users

Abstract

The increased usage of digital communication technologies has transformed online engagement into a key aspect of the modern customer experience in the hospitality industry. The flow theory is especially important for understanding customer engagement in the online environment. The purpose of this study is to examine the antecedents of flow and to investigate its influence on the positive attitudes and continuance intentions among the users of social media. The study’s results show that challenge, information quality, and system quality all play significant roles in flow; and flow leads to positive attitudes and continuance intentions, which indicates the importance of creating flow to increase customer engagement. Academically, this study contributes to the limited body of literature on the flow experience and customer engagement in the hospitality context. Additionally, it provides practical insights how to gain competitive advantages by strategically managing customer engagement with social media marketing through flow.

Keywords

continuance intention customer engagement flow positive attitude social media

Introduction

As information technology has advanced, customers’ service consumption has shifted heavily to the online environment. Marketers are constantly increasing their digital marketing channels, particularly social and mobile media (Bart, Stephen, & Sarvary, 2014). Social network penetration worldwide is constantly increasing; 71% of Internet users are social network users as of 2017 (Statista, 2019). Social media is one of the most popular online activities with higher engagement rates and expanding mobile possibilities (Bart et al., 2014; Stephen & Bart, 2015). Overall, social media has created a new platform for businesses to inform, engage, and connect with their customers and has become a very important part of successful business marketing (Lamberton & Stephen, 2016).

A new topic, customer engagement, has emerged along with the fast advancement of social media. Customer engagement refers to a type of activity that is beyond purchasing, such as word-of-mouth activity, recommendations to family and friends, interactions with fellow customers, information search, writing reviews, and other similar activities (Bijmolt et al., 2010; Van Doorn et al., 2010; Verhoef, Reinartz, & Krafft, 2010). In fact, the topic of customer engagement is receiving substantial attention from both marketing academics and practitioners (Brodie, Hollebeek, Juric, & Ilic, 2011; Brodie, Ilic, Juric, & Hollebeek, 2013; Hollebeek, Glynn, & Brodie, 2014; Rehnen, Bartsch, Kull, & Meyer, 2017; Vivek, Beatty, & Morgan, 2012).

The increased usage of digital communication technologies worldwide has transformed online engagement into a key aspect of the modern customer experience (Nusair, Bilgihan, & Okumus, 2013). Modern customers want to be engaged because they prefer instant communication and rely heavily on electronic word-of-mouth (T. Zhang, Omran, & Cobanoglu, 2017). Accordingly, service providers are pursuing a variety of customer engagement strategies through numerous social media platforms. However, up to now, the research on how to best facilitate customer engagement on social media platforms is still rather scant (Cabiddu, Carlo, & Piccoli, 2014). How should marketers design their social media platforms to motivate customer engagement? What type of promotional activities should be provided to encourage customer participation? To answer these questions, this study introduces the Flow theory with the intention of shedding light on how to better engage customers in the virtual world.

Flow is defined as a psychological state in which an individual feels pleasant when he or she is absolutely involved and absorbed in an activity (Csikszentmihalyi, 1975). It can be considered an indicator of online customer engagement (Mollen & Wilson, 2010). In fact, the research shows that flow influences a variety of consequences in the virtual world. Thus, the flow theory is especially important for understanding customers’ online experiences (Agarwal & Karahanna, 2000; Bilgihan, Okumus, Nusair, & Bujisic, 2014; Hoffman & Novak, 2009; Novak, Hoffman, & Duhachek, 2003; Novak, Hoffman, & Yung, 2000; Nusair & Parsa, 2011). Unfortunately, researchers argue that flow, as the optimum online customer condition, is an underdeveloped area (Bilgihan et al., 2014). Regardless of all the strong interest, the empirical academic work in the hospitality field is scarce, and it is not clear what factors could lead to better flow among social media users (Kaur, Dhir, Chen, & Rajala, 2016).

To bridge the above gap, this study aims to examine the antecedents of flow and its influence on positive attitudes and continuance intentions among social media users. Academically, this study is expected to contribute to the field of customer engagement in hospitality services through the lens of the flow theory. Additionally, this study provides practical insights for hospitality marketers on how to gain competitive advantages by strategically managing customer engagement with social media marketing through flow.

Literature Review

Social Media and Customer Engagement

Social media is a form of digital media that facilitates content creation, interactive information, and collaboration on social networks. It allows users to easily produce, manage, edit, access, and link to content and/or other individuals/businesses in various forms (Ashley & Tuten, 2015; Tuten & Solomon, 2017). The overall consumer experience is now highly influenced by interactions through these networks since consumers are spending more time on social media (Wilcox & Stephen, 2013). As a dominant digital communication channel, social media is significantly transforming the marketing environment (Lamberton & Stephen, 2016) in which it particularly sets itself apart from traditional media by creating a culture of engagement (Tuten & Solomon, 2017; Wilcox & Stephen, 2013). Businesses stay engaged and enhance customer relationship through mutual communications via social networks, and customers stay engaged with each other by exchanging information and sharing their experiences (Tuten & Solomon, 2017; Wilcox & Stephen, 2013).

The popularity and importance of social media in the modern business world has resulted in the emergence of a new topic called customer engagement (Hudson & Thal, 2013). Brodie et al. (2011) describe customer engagement as a “psychological state, which occurs by virtue of interactive customer experiences with a focal agent/object within specific service relationships” (p. 258). In general, customer engagement implies a customer’s behavioral manifestation that arise beyond transactions (Van Doorn et al., 2010). From this perspective, online engagement indicates a variety of activities and behaviors that occur in the online environment and includes searching and using consumer-generated content, contributing and creating content or blogging, participating in online communities and platforms (sharing information, posting reviews, and responding to comments), spreading word of mouth, and marketing on social media (Cabiddu et al., 2014; Chan & Guillet, 2011; Park & Allen, 2013; Van Doorn et al., 2010).

The recent research demonstrates the positive influence of customer engagement on businesses, such as enhanced customer relationships and brand attitude and higher future purchasing intentions (e.g., Brodie et al., 2013; Hollebeek et al., 2014; Tussyadiah, Kausar, & Soesilo, 2015). For example, So, King, Sparks, and Wang (2016) conducted two studies on retail brands and concluded that customer engagement has a significant impact on customer–brand relationship quality and customer loyalty. The hospitality and tourism research have also started paying more attention to the topic of customer engagement. So, King, and Sparks (2014) developed a 25-item scale to capture the concept of customer engagement including identification, enthusiasm, attention, absorption, and interaction, by using hotel and airline customers. Furthermore, So, King, and Sparks (2015) discuss three types of customer engagement (online reviews, social networks, and travel blogs) and argue that customer engagement plays a vital role in enriching and expanding customers’ travel experiences. Unfortunately, the literature on how to best facilitate customer engagement is rather limited. In the next section, this study introduces the flow theory that will help shed light on how to better manage customer engagement on social media platforms.

Flow Theory

Flow is considered a vital construct to understand customers’ experiences (Hoffman & Novak, 2009; Novak et al., 2000). First introduced by Csikszentmihalyi (1975), the flow theory argues that customers’ experience will be the most positive when they reach the flow state. Flow, also known as optimal experience, refers to the psychological state in which an individual feels pleasant when he or she is absolutely involved and absorbed in an activity. Flow results in “intense engagement, distorted sense of time, loss of self-consciousness, and heightened motivation” (Pelet, Ettis, & Cowart, 2017, p. 116), and is the most enjoyable experience a person can feel (Chen, 2006; Csikszentmihalyi, 1975, 1988). When people reach the flow state, they neglect all other thoughts because they are intensively engaged in the activity. People consider the flow state to be intrinsically rewarding and appreciate the flow experience because they have full control of their actions without distraction and feel time passes faster as they are completely engaged in the process (Mollen & Wilson, 2010).

While flow is experienced in a wide variety of activities in our daily lives, it exists especially strong in the online environment (e.g., general web usage and web navigation, online shopping, online gaming, and social networking). Specifically, individuals reach a high flow state when they are searching for information online because they have a clear objective and their attention is focused (Chen, 2006; Hoffman & Novak, 2009). Using the web is usually intrinsically enjoyable so users often lose track of time and become cognitively locked-in, which increases the level of engagement for online users significantly (Chen, 2006; Hoffman & Novak, 1996, 2009; Kaur et al., 2016). In fact, flow experiences in an online environment are known to enhance customer engagement (Carlson, de Vries, Rahman, & Taylor, 2017; Mollen & Wilson, 2010). For example, Carlson et al. (2017) found that flow is an influencing driver for increasing customer engagement. Furthermore, H. Zhang, Gupta, and Zhao (2014) found that flow experience in the social media environment increases users’ intention to participate in online activities, such as spreading word-of-mouth and sharing information with friends. Moreover, flow experiences are associated with various behavioral consequences that benefit businesses (Carlson et al., 2017). Reasoning from this fact, flow is a vital construct to understand online customer engagement and the provision and management of flow experience on social media has much to offer. In the next section, this study introduces the antecedents and consequences of flow because it is important for companies to recognize the environment via web-based attributes for customers to experience flow and how it enables favorable consumer behavior.

Antecedents of Flow

Considering the characteristics of social media, this study summarizes the numerous antecedents of flow into two dimensions: personal and technical (Bridges & Florsheim, 2008; Huang, 2003; Koufaris, 2002; Novak et al., 2000, 2003; Skadberg & Kimmel, 2004). The personal dimensions include challenge, skill, and interactivity, whereas the technical dimension includes information quality and system quality.

The level of the challenge and skill often facilitates the flow state. Challenge refers to the user’s opportunity for action and relates to the level at which users find it difficult to manage the tasks involved (Novak et al., 2000). On social media platforms, new features or functions, display modifications, and a variety of performance options (e.g., winning games, entering contests, etc.) are all reasons why users may feel challenged in achieving their goals (Pelet et al., 2017). Csikszentmihalyi (1975) argues that users reach the flow state only when they feel challenged because this usually stimulates excitement and enhances concentration on the task. In addition, flow tends to occur when there is a balanced amount of challenge: If there is too much challenge, users usually get frustrated, but they lose interest if there is no challenge at all (Csikszentmihalyi, 1975, 1988, 1990). Novak et al. (2000, 2003) further test the flow theory and establish that challenge is a significant factor that creates compelling flow experiences online. More recent empirical studies have also found that challenge facilitates the achievement of a flow state (Bridges & Florsheim, 2008; Koufaris, 2002; Pelet et al., 2017; Skadberg & Kimmel, 2004). Thus, the following hypotheses are proposed:

Hypothesis 1₀: Challenge will not positively influence flow.

Hypothesis 1a: Challenge will positively influence flow.

Skill refers to the user’s ability to deal with the tasks while navigating online (Novak et al., 2000). Csikszentmihalyi (1975) suggests that skill is also an imperative factor that affects the process of optimal experience and is often matched with the level of challenge. Users stretch their own capabilities when they have to perform at high levels of skill. In this process, they are more likely to learn new things, increase self-esteem and personal complexity, and enjoy the moment (Csikszentmihalyi & LeFerve, 1989). Users only experience flow when a task exceeds the threshold value of skill but coordinates well with their own skills. For example, when a task requires a high level of skill, the user can feel anxious. On the other hand, when the users’ skill level exceeds the skill needed for a task, they feel bored (Csikszentmihalyi, 1988, 1990; Novak et al., 2000). The research reports that users felt more alert, concentrated, satisfied, and happy when they perceive a high level of skill (Csikszentmihalyi & LeFerve, 1989). Additional studies provide empirical support that skill contributes to flow (Bridges & Florsheim, 2008; Ghani & Deshpande, 1994; Kaur et al., 2016; Koufaris, 2002; Novak et al., 2000; Skadberg & Kimmel, 2004). Thus, the following hypotheses are proposed:

Hypothesis 2₀: Skill will not positively influence flow.

Hypothesis 2a: Skill will positively influence flow.

Interactivity refers to the sharing of information through intercommunication and engaging with others. Interactivity includes both personal interaction and technical interaction (access online contents; Hoffman & Novak, 1996; Novak et al., 2000). Interactivity is an essential feature of modern media as customer experience is now highly influenced by interactions mediated through the web. Interactivity is also the most distinctive attribute of social media that distinguishes it from traditional media (Ashley & Tuten, 2015; Wilcox & Stephen, 2013). Interactive functionalities, such as Internet discussion groups or reading and posting reviews, excite users by enhancing their subjective feeling of having control that makes navigating online interesting (Novak et al., 2000). Several studies emphasize interactivity as a critical factor that influences the flow state (Huang, 2003; Skadberg & Kimmel, 2004; Wu & Chang, 2005). Thus, the following hypotheses are proposed:

Hypothesis 3₀: Interactivity will not positively influence flow.

Hypothesis 3a: Interactivity will positively influence flow.

The technical dimensions comprise the quality of the information and the system. According to DeLone and McLean (2004), these two crucial dimensions determine the success of information systems and further influence user satisfaction and their usage intention. Information quality refers to the user’s evaluation of the system’s performance in providing information. It reflects the relevance, sufficiency, accuracy, and completeness of information. Information quality is a crucial component for users because it is simply one of the foremost reasons why they access social media (Stephen & Bart, 2015). When the content is poor, users are likely to form negative perceptions (Zhou, Li, & Liu, 2010), which may undermine the users’ flow experience by resulting in inadequate engagement and pleasure (Lai, Chen, & Chang, 2014). In contrast, users may lose track of time when they focus on information browsing and become deeply captivated by high-quality information (Zhou, 2013). Gao, Bai, and Park (2014) find that rich, objective, value-added, and timely produced information significantly affects customers’ ability to concentrate better that then leads to the flow state, while other studies find that information quality is a significant determinant of the flow experience (Jung, Perez-Mira, & Wiley-Patton, 2009; Zhou et al., 2010). Thus, the following hypotheses are proposed:

Hypothesis 4₀: Information quality will not positively influence flow.

Hypothesis 4a: Information quality will positively influence flow.

System quality refers to the user’s evaluation of the performance of system features. In the online environment, system quality reflects the reliability, convenience, functionality, and response time of information systems (DeLone & McLean, 2004). Such technological features are critical in supporting online user experiences (Kang & Namkung, 2016). For example, users get easily irritated if the network is unstable and the website functions are not user-friendly. Especially, if users have to wait a long time to receive responses and share information, they will get distracted that results in an undesirable experience (Gao et al., 2014; Zhou et al., 2010). Guo and Poole (2009) also discover that the perceived difficulty negatively affects flow experience for online shopping. These system quality values affect the user’s flow experience such as enjoyment and concentration (Gao et al., 2014; Zhou, 2013; Zhou et al., 2010). Thus, the following hypotheses are proposed:

Hypothesis 5₀: System quality will not positively influence flow.

Hypothesis 5a: System quality will positively influence flow.

Consequences of Flow

Flow produces relevant marketing outcomes (Hoffman & Novak, 2009). The research has found that flow is a significant determinant of a positive attitude (Chen, 2006; Korzaan, 2003). Attaining a positive attitude is vital in a highly competitive marketing environment because it is ultimately related to the customers’ behavioral intention (Bart et al., 2014). Studies have shown that online users of travel websites develop positive attitudes by experiencing flow (Skadberg & Kimmel, 2004), and the flow experience is a significant determinant of their attitudes toward the website and the company (Mathwick & Rigdon, 2004). Generally, social media users who experience flow are more likely to develop a positive attitude toward the service provider because the occurrence was extremely pleasurable (Chen, 2006). Thus, the following hypotheses are proposed:

Hypothesis 6₀: Flow will not lead to a positive attitude.

Hypothesis 6a: Flow will lead to a positive attitude.

There is also a significant relation between flow and subsequent behavioral outcomes (Agarwal & Karahanna, 2000; Hoffman & Novak, 2009; Hsu & Lu, 2004). When users experience online flow, they are willing to revisit the website as often as possible and spend additional time to reexperience that state because it is so pleasurable (Bridges & Florsheim, 2008; Ilsever, Cyr, & Parent, 2007). Continuance intention reflects a social media user’s behavioral intention to revisit the website and continuously use a product or service. Thus, higher continuance intention levels potentially lead to increase in revisit intentions, brand relationship, and even revenues (Needles & Thompson, 2013). Ultimately, flow may lead to higher purchasing intentions and loyalty that can further generate productive states (Hausman & Siekpe, 2009). For example, studies found that flow lead to a significant increase in purchasing (Korzaan, 2003), impulsive buying (Koufaris, 2002), transaction intentions (Wu & Chang, 2005), and brand loyalty (Carlson & O’Cass, 2011). This study measures continuance intention as a behavioral outcome as it is relatively difficult to measure actual revisit or continuance usage (Zhou, 2013). Thus, the following hypotheses are proposed:

Hypothesis 7₀: Flow will not positively influence continuance intention.

Hypothesis 7a: Flow will positively influence continuance intention.

Attitude is the positive or negative feeling toward a service provider. A positive attitude is a competitive advantage because it leads to favorable intentions (Ajzen & Fishbein, 2005; Attia, Aziz, & Friedman, 2012). Customers are more likely to repurchase a product or continue visiting a service provider when they have a positive attitude toward the brand and the experience (Ajzen & Fishbein, 2000, 2005). The positive attitude driven from online flow increases the customers’ desire to continue the relationship with the brand (Bilgihan et al., 2014). Thus, the last hypotheses are proposed and the model is presented (see Figure 1):

Hypothesis 8₀: A positive attitude will not positively influence continuance intention.

Hypothesis 8a: A positive attitude will positively influence continuance intention.

Figure 1

Study Model

Method

Due to the intangible characteristics of service and its higher perceived risk, online engagement especially plays an important role in influencing consumers in the hospitality industry (Berezina, Bilgihan, Cobanoglu, & Okumus, 2015). In particular, restaurants have been increasingly implementing customer engagement strategies through social media (Bilgihan, Peng, & Kandampully, 2014; DiPietro, Crews, Gustafson, & Strick, 2012) because it fits especially well with the industry’s structure as a relatively low-cost marketing tool (DiPietro et al., 2012; Needles & Thompson, 2013). Ultimately, online engagement through social media affects the overall customers’ dining experiences from awareness through purchase to satisfaction and loyalty (Hajli, 2014; Needles & Thompson, 2013). Accordingly, this study specifically focuses on restaurant customers who use social media to search for dining information. This study targets those who have used social media when searching for dining-out information within the past three months. This study develops a self-administered questionnaire to collect data. Data were collected through an online data collection agency in South Korea. A total of 516 valid responses were collected.

Survey Instrument

The self-administered questionnaire comprises three sections. The first section asks general questions about participants’ overall usage of social media, such as usage frequency, average daily usage time, favorite social media platforms, and the type of information participants usually search for. The next section captures the main variables in this study such as challenge, skill, interactivity, information quality, and system quality. All items are adapted from an extensive review of the literature and are measured using a 5-point Likert-type scale, where 1 indicates “strongly disagree” and 5 indicates “strongly agree.” The last section includes questions on the participants’ demographic information such as gender, marital status, age, education, and annual income.

Personal characteristics of social media are measured with the following three dimensions: challenge, skill, and interactivity. Challenge has four items (e.g., searching for dining-out information on social media challenges me; Bridges & Florsheim, 2008; Hoffman & Novak, 1996; Koufaris, 2002; Novak et al., 2000; Pelet et al., 2017). Skill has four items (e.g., I feel competent in searching for dining-out information on social media; Bridges & Florsheim, 2008; Hoffman & Novak, 1996; Kaur et al., 2016; Koufaris, 2002; Novak et al., 2000). Interactivity has four items (e.g., I can communicate in real time when I search for dining-out information on social media; Hoffman & Novak, 1996; Huang, 2003; Novak et al., 2000; Skadberg & Kimmel, 2004; Wu & Chang, 2005). Technical characteristics of social media are measured with the following two dimensions: information quality and system quality. Information quality comprises four items (e.g., the dining-out information on social media is accurate; Gao et al., 2014; Zhou, 2013; Zhou et al., 2010). System quality comprises four items (e.g., social media is reliable when I search for dining-out information; Gao et al., 2014; Zhou, 2013; Zhou et al., 2010). A complete list of measurement items is provided in a table later in the text.

Although some studies measure flow as a unidimensional construct, the majority of scholars agree on the following three dimensions of flow: concentration (being absorbed intensively), enjoyment (feeling fun), and time distortion (losing track of time; Agarwal & Karahanna, 2000; Koufaris, 2002; Kwak, Choi, & Lee, 2014); Pelet et al., 2017). Therefore, the current study also operationalizes the concept of flow through three dimensions. Concentration contains three items (e.g., I concentrate fully when I search for dining-out information on social media). Enjoyment contains four items (e.g., it is exciting when I search for dining-out information on social media). Time distortion contains three items (e.g., time goes by quickly when I search for dining-out information on social media). (Agarwal & Karahanna, 2000; Csikszentmihalyi, 1990; Ghani & Deshpande, 1994; Koufaris, 2002; Kwak et al., 2014; Novak et al., 2000; Pelet et al., 2017; Zhou et al., 2010).

Positive attitude is measured with four items (e.g., searching for dining-out information on social media is a pleasant experience; Hoffman & Novak, 1996; Huang, 2003; Korzaan, 2003; Skadberg & Kimmel, 2004). Continuance intention is measured with four items (e.g., I will continue to search for dining-out information on social media; Agarwal & Karahanna, 2000; Zhou, 2013).

Data Analysis Method

This study uses the statistical programs of SPSS 24 and AMOS to analyze the data. A descriptive analysis is conducted to understand the participants’ overall usage of social media and their demographic information. A reliability analysis is performed to check internal consistency. A second-order confirmatory factor analysis (CFA) is first performed on the three dimensions of flow to increase parsimony. Then, the CFA with maximum likelihood is performed to assess the overall model’s fit and validity. Last, structural equational modeling is performed to test the model.

Results

Sample Profile

Table 1 summarizes the sample profile. Approximately 48% are male and 52% are female. Roughly 51% are single (N = 262) and 49% are married (N = 254). Slightly more than 30% are in their 20s (N = 159), 30s (N = 156), or 40s (N = 161), and less than 10% are in their 50s (N = 40). The majority hold a bachelor’s degree (64.9%, N = 335) and more than 70% (N = 438) earn an annual income of under $50,000.

Table 1

Sample Profile

	N	%
Gender
Male	250	48.4
Female	266	51.6
Age (years)
20-29	159	30.8
30-39	156	30.2
40-49	161	31.2
50 and older	40	7.8
Marital status
Single	262	50.8
Married	254	49.2
Highest education
High school degree	53	10.3
Associate degree	73	14.1
Bachelor’s degree	335	64.9
Graduate degree	55	10.7
Annual income
Under $20,000	146	28.3
$20,000-$29,999	145	28.1
$30,000-$39,999	93	18.0
$40,000-$49,999	54	10.5
$50,000-$59,999	45	8.7
Over $60,000	33	6.4
Total	516	100.0

Confirmatory Factor Analysis

First, a second-order CFA is performed on the three dimensions of flow to increase parsimony. The CMIN/degree of freedom (χ²/df = 2.886) is lower than 3; the comparative fit index (CFI = 0.980), the normed fit index (NFI = 0.970), and the Tucker–Lewis index (TLI = 0.969) are higher than the recommended value of 0.9. The root mean square error of approximation (RMSEA = 0.061) is lower than the recommended value of 0.08. The goodness-of-fit index (GFI = 0.964) is higher than the recommended value of 0.9, and thus has a good initial fit (Fornell & Larcker, 1981; Hair, Black, Babin, Anderson, & Tatham, 2009). Additionally, all of the standardized factor loadings are significant at p < .001 and greater than .05, the composite reliability values of all constructs exceed the minimum criterion of 0.7, and the average variance extracted of all constructs exceeds the minimum criterion of 0.5 (Fornell & Larcker, 1981; Hair et al., 2009). Thus, the second-order CFA results support the three dimensions of flow (concentration, enjoyment, and time distortion) to be used as latent variables in the model (see Table 2). Next, the overall CFA results are shown in Table 3. The results of the CFA indicate a good initial model fit (χ² = 852.749, df = 398, p < .001, χ²/df = 2.143, GFI = 0.897, adjusted GFI = 0.871, CFI = 0.951, NFI = 0.912, TLI = 0.942, RMSEA = 0.047; see Table 3).

Table 2

Second-Order Confirmatory Factor Analysis

			Standardized Factor Loadings	SE	CR	AVE
Flow	Concentration (0.730)	I ___when I search for dining-out information on social media.	0.712	0.323	0.847	0.584
		(1) concentrate fully
		(2) am totally absorbed in what I am doing	0.886	0.195
		(3) do not think about other things	0.723	0.470
		(4) do not hear anything	0.643	0.601
	Enjoyment (0.759)	Searching for dining-out information on social media is ___.
		(1) fun	0.684	0.267	0.935	0.784
		(2) enjoyable	0.832	0.166
		(3) exciting	0.889	0.123
		(4) interesting	0.828	0.171
	Time Distortion (0.821)	___ when I search for dining-out information on social media.
		(1) Time seems to go by very quickly	0.818	0.224	0.879	0.709
		(2) Sometimes I lose track of time	0.897	0.175
		(3) I often spend more time than I had intended	0.734	0.428

Note. SE = standard error; CR = composite reliability; AVE = average variance extracted. Standardized factor loadings for second-order CFA are in parentheses.

Table 3

Confirmatory Factor Analysis

		α	Standardized Factor Loadings	SE	CR	AVE
Challenge (CHL)	Searching for dining-out information on social media ___.
	(1) challenges me	0.879	0.682	0.398	0.900	0.695
	(2) allows me to perform my best abilities		0.833	0.249
	(3) is a good way to test my skills		0.878	0.208
	(4) stretches my capabilities to further limits		0.821	0.289
Skill (SKL)	I ___ for dining-out information on social media.
	(1) am competent at searching	0.879	0.653	0.345	0.916	0.734
	(2) have sufficient knowledge on search techniques		0.795	0.238
	(3) am better than others at searching		0.841	0.211
	(4) know how to search		0.896	0.137
Interactivity (INT)	I can ___ when I search for dining-out information on social media.
	(1) easily interact with other users	0.889	0.720	0.354	0.903	0.702
	(2) communicate on real-time		0.750	0.411
	(3) discuss with others		0.863	0.213
	(4) easily exchange information		0.902	0.141
Information Quality (IQ)	The dining-out information on social media is ___.
	(1) what I need	0.820	0.698	0.242	0.891	0.674
	(2) accurate		0.697	0.284
	(3) up-to-date		0.652	0.292
	(4) comprehensive		0.811	0.177
System Quality (SQ)	Social media ___ when I search for dining-out information.
	(1) has advanced functions	0.821	0.657	0.265	0.902	0.687
	(2) provides fast responses		0.722	0.238
	(3) is easy to use		0.841	0.145
	(4) is reliable		0.684	0.265
Flow (FL)	(1) concentration	0.847	0.586	0.403	0.825	0.617
	(2) enjoyment	0.890	0.853	0.111
	(3) time distortion	0.853	0.627	0.393
Positive Attitude (PA)	Searching for dining-out information on social media is ___.
	(1) good idea	0.841	0.735	0.213	0.920	0.742
	(2) wise idea		0.773	0.216
	(3) desirable		0.807	0.178
	(4) pleasant experience		0.762	0.217
Continuance Intention (CI)	I ___ to search for dining-out information on social media in the future.
	(1) am willing	0.866	0.728	0.244	0.908	0.713
	(2) will continue		0.818	0.186
	(3) will recommend my friends and family		0.673	0.307
	(4) will make it my first choice		0.794	0.180

Note. SE = standard error; CR = composite reliability; AVE = average variance extracted.

Reliability is supported as Cronbach’s alphas are higher than 0.7. Convergent validity exists because the composite reliability values of all constructs exceed the minimum criterion of 0.7, and all of the standardized factor loadings are significant at p < .001 and greater than .05. Additionally, the average variance extracted of all constructs exceeds the minimum criterion of 0.5, so the measures share at least half of their variation with the latent variable (Fornell & Larcker, 1981; Hair et al., 2009). There are no correlations greater than 0.7 between the factors, and the squared correlation coefficients for the corresponding interconstructs are less than the AVEs (see Table 4; Anderson & Gerbing, 1988; Fornell & Larcker, 1981), which indicates satisfactory discriminant validity.

Table 4

Correlations Among Latent Variables

Factor	CHL	SKL	INT	IQ	SQ	FL	PA	CI
CHL	0.695	0.189	0.228	0.114	0.112	0.339	0.181	0.167
SKL	0.435	0.734	0.411	0.348	0.316	0.336	0.176	0.228
INT	0.477	0.641	0.702	0.277	0.219	0.284	0.154	0.171
IQ	0.337	0.59	0.526	0.674	0.561	0.423	0.376	0.432
SQ	0.335	0.562	0.468	0.749	0.687	0.373	0.376	0.449
FL	0.582	0.58	0.533	0.65	0.611	0.617	0.503	0.555
PA	0.426	0.42	0.393	0.613	0.613	0.709	0.742	0.699
CI	0.409	0.477	0.413	0.657	0.67	0.745	0.836	0.713

Note. CHL = Challenge; SKL = Skill; INT = Interactivity; IQ = Information Quality; SQ = System Quality; FL = Flow; PA = Positive Attitude; CI = Continuance Intention. The boldface numbers indicate the AVEs. The underlined numbers indicate the squared correlation coefficients.

Structural Equational Modeling

The results of the alternative goodness-of-fit statistics support the appropriateness of the structural model (χ²/df = 2.31, GFI = 0.89, adjusted GFI = 0.86, CFI = 0.94, NFI = 0.94, TLI = 0.93, RMSEA = 0.05). Given the satisfactory fit of the final model, the estimated structural coefficients are examined. The overall results of the structural model are summarized in Table 5 and Figure 2.

Table 5

Structural Model Relations

Hypothesis	Hypothesized Path	Path Coefficients (β)	SE	t	p	Results
1a	Challenge → Flow	0.323	0.028	6.470***	0.000	Supported
2a	Skill → Flow	0.089	0.035	1.517	0.129	Rejected
3a	Interactivity → Flow	0.021	0.030	0.387	0.699	Rejected
4a	Information quality → Flow	0.381	0.066	4.492***	0.000	Supported
5a	System quality → Flow	0.265	0.069	3.502***	0.000	Supported
6a	Flow → Positive attitude	0.777	0.087	9.746***	0.000	Supported
7a	Flow → Continuance intention	0.395	0.092	5.141***	0.000	Supported
8a	Positive attitude → Continuance intention	0.542	0.084	7.145***	0.000	Supported

p < .05. **p < .01. ***p < .001.

Figure 2

Structural Model

The results partially support the hypothesized relation between social media characteristic dimensions and flow. Challenge (β = 0.323, t = 6.470, p < .001) is positively significant for flow and supports Hypothesis 1a. However, skill (β = 0.089, t = 1.517, p > .05) and interactivity (β = 0.021, t = 0.387, p > .05) are not significant for flow. Thus, these results reject Hypotheses 2a and 3a. The hypothesized relation between technical characteristics and flow are supported.

Information quality (β = 0.381, t = 4.492, p < .001) and system quality (β = 0.265, t = 3.502, p < .001) are significant, which supports Hypotheses 4a and 5a. Flow shows a significantly positive relation with a positive attitude (β = 0.777, t = 9.746, p < .001), which supports Hypothesis 6a. Flow also has a significantly positive relation with continuance intention (β = 0.395, t = 5.141, p < .001), which supports Hypothesis 7a. Last, positive attitude positively affects continuance intention (β = 0.542, t = 7.145, p < .001), which supports Hypothesis 8a. Overall, challenge, information quality, and system quality explain approximately 74% of flow (squared multiple correlations [SMC] = 0.738); challenge, information quality, system quality, and flow explain approximately 60% of a positive attitude (SMC = 0.603); and challenge, information quality, system quality, flow, and a positive attitude explain approximately 78% of continuance intention (SMC = 0.782).

Discussion

Flow is a critical construct to understand online customer engagement as it has a significant positive impact on enhancing engagement levels (Carlson et al., 2017). In order to understand how to better facilitate customer engagement strategies in an online environment, this study proposes an integrated model that examines the personal and technical dimensions of social media that affect flow, and how flow affects positive attitude and continuance intention among restaurant customers. The study’s results show that challenge, information quality, and system quality are significant determinants of flow. Challenge is usually developed in situations where an individual has a clear objective (Csikszentmihalyi, 1990). Customers have a purpose when they search for dining-out information on social media, thus the results are consistent as hypothesized and challenge positively influences flow. When customers search for dining-out information on social media, they expect to acquire accurate and up-to-date information quickly and easily. If the social media platform is poorly designed and difficult to use, customers may feel the service provider lacks the ability to offer quality service. Also, if the information is insufficient, irrelevant, and out-of-date, customers may have doubts about the service provider. Consequently, these findings support Hypotheses 1a, 4a, and 5a.

Surprisingly, the study’s results fail to find a significant relation between skill and flow (Hypothesis 2a) as well as one between interactivity and flow (Hypothesis 3a). Although these findings conflict with some of the results in the studies that find that skill and interactivity are significant (Hoffman & Novak, 1996; Huang, 2003; Koufaris, 2002; Novak et al., 2000; Wu & Chang, 2005), these conflicts may be due to the sample and context (searching dining-out information on social media) in the current research. For example, searching for dining-out information is comparatively easy and may not require such a high level of skill. Therefore, it is likely that the participants in this study were already competent in such a task as more than 90% indicated that they had been using social media for more than 6 months. In addition, the majority of the participants indicated that their average usage of social media to search for dining-out information is 30 minutes. In this relatively short time, it may not be necessary for customers to highly interact with others.

Regarding the consequences of flow, the results demonstrate that flow is a significant determinant of a positive attitude and continuance intention, and positive attitude is a significant determinant of continuance intention for restaurant diners (Ajzen & Fishbein, 2005; Attia et al., 2012; Bridges & Florsheim, 2008; Hoffman & Novak, 1996; Huang, 2003; Ilsever et al., 2007; Korzaan, 2003; Mathwick & Rigdon, 2004; Skadberg & Kimmel, 2004; Zhou et al., 2010). When customers experience flow, they generate a positive attitude toward the service provider and become more willing to continuously visit the brand’s social media site. When diners have a positive attitude toward the brand and have higher continuance intention levels to the brand’s social media site, they are more likely to visit the restaurants with higher purchase intentions (Needles & Thompson, 2013). Consequently, the findings support Hypotheses 6a, 7a, and 8a.

Overall, the results indicate that it is necessary for restaurant businesses to recognize which attributes are influential in creating flow and it is important to manage the flow experience in their customer engagement strategies through social media.

Theoretical Contributions

This study mainly provides contribution to theory by testing a model that provides insight to create an optimal flow state for restaurant customers, which influences customer engagement and positive outcomes. First, this study extends the customer engagement literature in the context of hospitality management. Social media is now considered as a crucial customer engagement platform that provides businesses with opportunities to create value. Therefore, research on customer engagement in the online environment, especially through social media, is receiving much more attention as its significant impact increases globally. However, the amount of empirical research in the hospitality field has not kept in pace with its growth in the business environment. This study contributes to theory development in online customer engagement regarding the nature of the flow relationship, and specifically in the contextual condition of the social media environment of restaurants.

Next, the majority of the studies on restaurant customers’ online activity focus on the technology acceptance model, information system success model, and the Triandis mode; thus, applying a relatively novel theory in the hospitality field, the flow theory, is meaningful. To the best of the authors’ knowledge, the current study is the first empirical study in the hospitality context that examined customer engagement through the lens of the flow theory and examined how flow experiences in a social media environment may affect restaurant diners. In addition, the existing studies on flow mainly pay attention to two specific antecedents: challenge and skill. Considering the characteristics of social media as a digital media, the current study investigates the technical dimensions as well. This study adds unique value by incorporating both the personal (challenge, skill, interactivity) and technical (information quality, system quality) dimensions that affect flow and investigates the antecedents from a holistic standpoint, which has rarely been attempted in the hospitality research. Furthermore, this study provides empirical evidence on the importance of using flow theory in the field of hospitality services. This study demonstrates that flow positively affects attitude and continuance intention in the context of searching for dining-out options, and personal and technical dimensions can influence flow. This conclusion lends support to the flow theory, which explains the relations between flow and customer experience (Csikszentmihalyi, 1975).

Practical Implications

This study also provides valuable implications to industry practitioners. Numerous restaurants are using social media for a variety of purposes. Our study revealed that information quality is the most important predictor of flow, therefore, restaurants need to especially focus on providing high-quality information through their social media platforms. Surprisingly, many restaurants overlook this basic issue. Customers usually do not spend a lot of time when they search for dining-out information on social media so the experience should be fast and stress free. Restaurants should optimize pertinent information like hours of operation, contact information, and address should be easily accessed on the profile page. The restaurant’s uniform resource locator should be available on the profile page so customers can easily navigate through different sites. Any changes to relevant information must be updated and announced immediately so customers do not get confused. For example, some restaurants have different operating hours on holidays but do not update this most rudimentary information on their social media websites. It is a good idea for restaurants to announce on their social media platforms when they are sold out of their most popular items. Customers also want useful and credible information that is detail oriented. Adding filtering techniques can also provide customers with flexibility and allow them to filter out irrelevant information. Today’s customers look for all sorts of information, from menu ingredients to the furniture brand in the restaurant. Therefore, restaurants need to attentively have the most up-to-date communication and try to deliver extra information as well.

Besides information quality, challenge is another important predictor of flow for restaurant social media users, marketers need to implement content and functions on their social media platforms that challenge the customers. For example, restaurants can recurrently introduce the latest widgets on their social media websites and constantly implement new features to provide enough challenge to arouse the customers. Furthermore, instead of just giving out free coupons, restaurants could create simple games for their social media followers and offer different types of exclusive coupons to the winners. They can also offer limited edition menus for followers only if they win contests or pop-quizzes that ask questions related to the restaurant or the food they are searching for. Whatever ideas restaurants come up with, they should make their customers feel challenged in some way and spark their interest consistently. However, it is also crucial to recognize that while customers want to be challenged, they do not want the experience to be excessively difficult.

Simultaneously, restaurants should provide high-quality systems on their social media platforms. Restaurant customers will easily feel frustrating and inattentive when navigating through social media platforms becomes time consuming and unreliable. Restaurants are highly encouraged to conduct structured testing regularly to ensure high-quality technology on site responsiveness, compliance, speed, and more. Overall, restaurant marketers should clearly understand that other than the food, the customers’ experience on their social media website can be extremely influential.

Additionally, social media sites should be designed attractively with advanced functions by adding visually appealing photos and videos as they can get customers to imagine themselves at the restaurant. Most important, customers want immediate responses that are reliable. For example, if a customer inquiries about a reservation for a special occasion, restaurants should be able to respond right away with all the possible information. If restaurants are unusually busy during certain times and days and cannot react immediately, the social media platform should have some type of function to inform their customers that they will get back to them within a given time and give assurance that they will not be ignored.

Overall, the findings indicate that flow positively affects attitude and continuance intention, thus restaurant marketers should implement strategies on their social media platforms where they can create flow for their customers. Restaurants need to constantly provide innovative elements on their social media platforms to trigger the customers’ attention and curiosity. Furthermore, restaurants should get a more complete picture of their customers and try to make recommendations that relate to the customers’ interests so they can be deeply involved when they are searching for information on social media.

Study Limitations and Future Recommendations

The findings of this study cannot be generalized to the entire population as data are collected from South Korea. Additionally, this study does not reflect the different types of social media platforms in searching dining-out information. Customers’ behaviors are likely to differ depending on the different types of social media (e.g., social community, social entertainment, social commerce, social publishing). Also, this study only included restaurant customers who search for dining-out information: This fact embraces the possibility that not all of the participants may necessarily experience flow due to the relatively shorter average time they spend on social media.

While interest in examining flow experience increases, ambiguity still exists in both the conceptualization and operationalization of flow. Based on the findings from the current study, future studies are highly encouraged to explore new antecedents of flow, such as incorporating the unique features of hospitality industry or different personality characteristics across various consumer groups. Moreover, future studies should examine the flow experience specifically in the domain of hospitality and develop explicit measurement scales. Further investigation of the flow experience on social media by its type and different objectives is also expected to provide particularized practical insights in managing social media marketing.

Concluding Summary

Flow emerged as a critical construct to understand online customer engagement. This study proposes an integrated model that examines the antecedents of flow and its influence on positive attitudes and continuance intentions among social media users. The results of this study show that challenge, information quality, and system quality significantly affect flow, while flow further positively influences positive attitudes and continuance intentions. These findings indicate the importance of creating flow to increase customer engagement for hospitality businesses. Academically, this study provides contribution as it is the first empirical study in the hospitality context that examined customer engagement through the lens of the flow theory. Practically, this study provides practical insights for hospitality businesses in strategically managing their social media marketing by creating and handling flow.

Footnotes

Authors’ Note:

First two authors are principal authors and contributed equally to this article.

ORCID iD

Michelle (Myongjee) Yoo

Bomin Kim (email: bominbyme@gmail.com), PhD, is currently the marketing director at Grand Coteau, where she is in charge of marketing and distribution of Korean wine to luxury hotels in Seoul. Michelle (Myongjee) Yoo (email: myoo@cpp.edu), PhD, is an associate professor at The Collins College of Hospitality Management in California State Polytechnic University Pomona (Cal Poly Pomona). Wan Yang (email: wanyang@cpp.edu), PhD, is an associate professor at The Collins College of Hospitality Management in California State Polytechnic University Pomona (Cal Poly Pomona).

References

Agarwal

Karahanna

(2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24, 665-694.

Ajzen

Fishbein

(2000). Attitudes and the attitude–behavior relation: Reasoned and automatic processes. In Stroebe

Hewstone

(Eds.), European review of social psychology (pp. 1-33). Chichester, England: Wiley.

Ajzen

Fishbein

(2005). The influence of attitudes on behavior. In Albarracin

Johnson

B. T.

Zanna

M. P.

(Eds.), The handbook of attitudes (pp. 173-221). Mahwah, NJ: Erlbaum.

Anderson

J. C.

Gerbing

D. W.

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

Ashley

Tuten

(2015). Creative strategies in social media marketing: An exploratory study of branded social content and consumer engagement. Psychology & Marketing, 32, 15-27.

Attia

A. M.

Aziz

Friedman

B. A.

(2012). The impact of social networks on behavioral change: A conceptual framework. World Review of Business Research, 2, 91-108.

Bart

Stephen

A. T.

Sarvary

(2014). Which products are best suited to mobile advertising? A field study of mobile display advertising effects on consumer attitudes and intentions. Journal of Marketing Research, 51, 270-285.

Berezina

Bilgihan

Cobanoglu

Okumus

(2015). Understanding satisfied and dissatisfied hotel customers: Text mining of online hotel reviews. Journal of Hospitality Marketing & Management, 25, 1-24.

Bijmolt

Leeflang

Block

Eisenbeiss

Hardie

Lemmens

Saffert

(2010). Analytics for customer engagement. Journal of Service Research, 13, 341-356.

10.

Bilgihan

Okumus

Nusair

Bujisic

(2014). Online experiences: Flow theory, measuring online customer experience in e-commerce and managerial implications for the lodging industry. Information Technology & Tourism, 14(1), 49-71.

11.

Bilgihan

Peng

Kandampully

(2014). Generation Y’s dining information seeking and sharing behavior on social networking sites. International Journal of Contemporary Hospitality Management, 26(3), 349-366.

12.

Bridges

Florsheim

(2008). Hedonic and utilitarian shopping goals: The online experience. Journal of Business Research, 61, 309-314.

13.

Brodie

R. J.

Hollebeek

Juric

Ilic

(2011). Customer engagement: Conceptual domain, fundamental propositions, and implications for research. Journal of Service Research, 14, 252-271.

14.

Brodie

R. J.

Ilic

Juric

Hollebeek

(2013). Consumer engagement in a virtual brand community: An exploratory analysis. Journal of Business Research, 66, 105-114.

15.

Cabiddu

Carlo

M. D.

Piccoli

(2014). Social media affordances: Enabling customer engagement. Annals of Tourism Research, 48(5), 175-192.

16.

Carlson

de Vries

N. J.

Rahman

M. M.

Taylor

(2017). Go with the flow: Engineering flow experiences for customer engagement value creation in branded social media environments. Journal of Brand Management, 24, 334-348.

17.

Carlson

O’Cass

(2011). Creating commercially compelling website-service encounters: An examination of the effect of website-service interface performance components on flow experiences. Electronic Markets, 21, 237-253.

18.

Chan

N. L.

Guillet

B. D.

(2011). Investigation of social media marketing: How does the hotel industry in Hong Kong perform in marketing on social media websites? Journal of Travel & Tourism Marketing, 28, 345-368.

19.

Chen

(2006). Flow on the net-detecting web users’ positive affects and their flow states. Computers in Human Behavior, 22, 221-233.

20.

Csikszentmihalyi

(1975). Beyond boredom and anxiety. San Francisco, CA: Jossey-Bass.

21.

Csikszentmihalyi

(1988). Motivation and creativity: Toward a synthesis of structural and energistic approaches to cognition. New Ideas in Psychology, 6, 159-176.

22.

Csikszentmihalyi

(1990). Flow: The psychology of optimal experience. New York, NY: HarperCollins.

23.

Csikszentmihalyi

LeFerve

(1989). Optimal experience in work and leisure. Journal of Personality and Social Psychology, 56, 815-822.

24.

DeLone

W. H.

McLean

E. R.

(2004). Measuring e-commerce success: Applying the DeLone and McLean information systems success model. International Journal of Electronic Commerce, 9(1), 31-47.

25.

DiPietro

R. B.

Crews

T. B.

Gustafson

Strick

(2012). The use of social networking sites in the restaurant industry: Best practices. Journal of Foodservice Business Research, 15, 265-284.

26.

Fornell

Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39-50.

27.

Gao

Bai

Park

(2014). Understanding sustained participation in virtual travel communities from the perspectives of is success model and flow theory. Journal of Hospitality & Tourism Research, 41, 475-509.

28.

Ghani

J. A.

Deshpande

S. P.

(1994). Task characteristics and the experience of optimal flow in human–computer interaction. Journal of Psychology, 128, 381-391.

29.

Guo

Y. M.

Poole

M. S.

(2009). Antecedents of flow in online shopping: A test of alternative models. Information Systems Journals, 19, 369-390.

30.

Hair

J. F.

Black

W. C.

Babin

B. J.

Anderson

R. E.

Tatham

R. L.

(2009). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.

31.

Hajli

M. N.

(2014). A study of the impact of social media on consumers. International Journal of Market Research, 56, 387-404.

32.

Hausman

A. V.

Siekpe

J. S.

(2009). The effect of web interface features on consumer online purchase intentions. Journal of Business Research, 62(1), 5-13.

33.

Hoffman

D. L.

Novak

T. P.

(1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of Marketing, 60(3), 50-68.

34.

Hoffman

D. L.

Novak

T. P.

(2009). Flow online: Lessons learned and future prospects. Journal of Interactive Marketing, 23, 23-34.

35.

Hollebeek

L. D.

Glynn

M. S.

Brodie

R. J.

(2014). Consumer brand engagement in social media: Conceptualization, scale development and validation. Journal of Interactive Marketing, 28, 149-165.

36.

Hsu

(2004). Why do people play on-line games? An extended tam with social influences and flow experience. Information and Management, 41, 853-868.

37.

Huang

(2003). Designing website attributes to induce experiential encounters. Computer in Human Behavior, 19, 425-442.

38.

Hudson

Thal

(2013). The impact of social media on the consumer decision process: Implications for tourism marketing. Journal of Travel & Tourism Marketing, 30, 156-160.

39.

Ilsever

Cyr

Parent

(2007). Extending models of flow and e-loyalty. Journal of Information Science & Technology, 4(2), 3-22.

40.

Jung

Perez-Mira

Wiley-Patton

(2009). Consumer adoption of mobile TV: Examining psychological flow and media content. Computers in Human Behavior, 25, 123-129.

41.

Kang

Namkung

(2016). Restaurant information sharing on social networking sites: Do network externalities matter? Journal of Hospitality & Tourism Research, 40, 739-763.

42.

Kaur

Dhir

Chen

Rajala

(2016). Flow in context: Development and validation of the flow experience instrument for social networking. Computers in Human Behavior, 59, 358-367.

43.

Korzaan

M. L.

(2003). Going with the flow: Predicting online purchase intentions. Journal of Computer Information Systems, 43(4), 25-31.

44.

Koufaris

(2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13, 205-223.

45.

Kwak

K. T.

Choi

S. K.

Lee

B. G.

(2014). SNS flow, SNS self-disclosure and post hoc interpersonal relations change: Focused on Korean Facebook user. Computers in Human Behavior, 31, 294-304.

46.

Lai

H. M.

Chen

C. P.

Chang

Y. F.

(2014). Determinants of knowledge seeking in professional virtual communities. Behaviour & Information Technology, 33, 522-535.

47.

Lamberton

Stephen

A. T.

(2016). A thematic exploration of digital, social media, and mobile marketing: Research evolution from 2000 to 2015 and an agenda for future inquiry. Journal of Marketing, 80(6), 146-172.

48.

Mathwick

Rigdon

(2004). Play, flow, and the online search experience. Journal of Consumer Research, 31, 324-332.

49.

Mollen

Wilson

(2010). Engagement, telepresence and interactivity in online consumer experience: Reconciling scholastic and managerial perspectives. Journal of Business Research, 63, 919-925.

50.

Needles

Thompson

G. M.

(2013). Social media use in the restaurant industry: A work in progress. Cornell Hospitality Report, 13(7), 6-16.

51.

Novak

T. P.

Hoffman

D. L.

Duhachek

(2003). The influence of goal-directed and experiential activities on online flow experiences. Journal of Consumer Psychology, 13, 3-16.

52.

Novak

T. P.

Hoffman

D. L.

Yung

Y. F.

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

53.

Nusair

Bilgihan

Okumus

(2013). The role of online social network travel websites in creating social interaction for Gen Y travelers. International Journal of Tourism Research, 15, 458-472.

54.

Nusair

Parsa

H. G.

(2011). Introducing flow theory to explain the interactive online shopping experience in a travel context. International Journal of Hospitality & Tourism Administration, 12, 1-20.

55.

Park

S. Y.

Allen

J. P.

(2013). Responding to online reviews: Problem solving and engagement in hotels. Cornell Hospitality Quarterly, 54, 64-73.

56.

Pelet

Ettis

Cowart

(2017). Optimal experience of flow enhanced by telepresence: Evidence from social media use. Information & Management, 54, 115-128.

57.

Rehnen

L. M.

Bartsch

Kull

Meyer

(2017). Exploring the impact of rewarded social media engagement in loyalty programs. Journal of Service Management, 28, 305-328.

58.

Skadberg

Y. X.

Kimmel

J. R.

(2004). Visitors’ flow experience while browsing a Web site: Its measurement, contributing factors and consequences. Computers in Human Behavior, 20, 403-422.

59.

K. K. F.

King

Sparks

(2014). Customer engagement with tourism brands: Scale development and validation. Journal of Hospitality & Tourism Research, 38, 304-329.

60.

K. K. F.

King

Sparks

(2015). Extending the tourism experience. In Brodie

R. J.

Hollebeek

L. D.

Conduit

(Eds.), Customer engagement: Contemporary issues and challenges (pp. 236-254). London, England: Routledge.

61.

K. K. F.

King

Sparks

B. A.

Wang

(2016). Enhancing customer relationships with retail service brands: The role of customer engagement. Journal of Service Management, 27, 170-193.

62.

Statista. (2019). Statistics and market data about the Internet. Retrieved from https://www.statista.com/markets/424/internet/

63.

Stephen

A. T.

Bart

(2015). Social media marketing: Principles and strategies. Idaho Falls, ID: Stukent.

64.

Tussyadiah

S. P.

Kausar

D. R.

Soesilo

P. K. M.

(2015). The effect of engagement in online social network on susceptibility to influence. Journal of Hospitality & Tourism Research, 42, 210-223.

65.

Tuten

T. L.

Solomon

M. R.

(2017). Social media marketing (3rd ed.). Thousand Oaks, CA: Sage.

66.

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.

67.

Verhoef

P. C.

Reinartz

W. J.

Krafft

(2010). Customer engagement as a new perspective in customer management. Journal of Service Research, 13, 247-252.

68.

Vivek

S. D.

Beatty

S. E.

Morgan

R. M.

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

69.

Wilcox

Stephen

A. T.

(2013). Are close friends the enemy? Online social networks, self-esteem, and self-control. Journal of Consumer Research, 40, 90-103.

70.

Chang

(2005). Towards understanding members’ interactivity, trust, and flow in online travel community. Industrial Management & Data Systems, 105, 937-954.

71.

Zhang

H., Y.

Gupta

L. S.

Zhao

. (2014). What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences. Information & Management, 51, 1017-1030.

72.

Zhang

Omran

B. A.

Cobanoglu

(2017). Generation Y’s positive and negative eWOM: Use of social media and mobile technology. International Journal of Contemporary Hospitality Management, 29, 732-761.

73.

Zhou

(2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54, 1085-1091.

74.

Zhou

Liu

(2010). The effect of flow experience on mobile SNS users’ loyalty. Industrial Management & Data Systems, 110, 930-946.