This study proposed an alternative model specification to increase accuracy of conceptualization of the definition of customer engagement and provided theoretical justification for the model in specific social media contexts. The proposed model is a formative construct based on theoretical contexts and observational data. The construct comprises six formative first-order dimensions, namely “influencing behaviors,” “participation in activities,” “customer knowledge sharing,” “feedbacks,” “helping other customers,” and “customer-to-customer interaction.” The study findings offer a basis for identifying indicators of distinct dimensions in the proposed construct. Three models with different conceptualization specifications were estimated and subsequently compared using survey data.

Customer engagement (CE) has commonly defined as a psychological state or process that leads to customer loyalty (Brodie, Hollebeek, Juric, & Ilic, 2011). CE research has received increasing attention due to its critical role in luring favorable customer experience and outcomes such as brand trust, affection, and future purchase intention (Harrigan, Evers, Miles, & Daly, 2017; So, King, Sparks, & Wang, 2016). Despite scholars’ continuous efforts in advancing the CE field of study, several limitations remain unaddressed. First, empirical research focuses primarily on antecedents and consequences of CE that are derived from individual dispositions (Harrigan, et al., 2017); thus, customer actual behavioral outcomes of CE are generally unexplored. Second, most, if not all, empirical research investigates the nomological network of CE based on individual-level factors (Khan, Rahman, & Fatma, 2016; So, King, & Sparks, 2014). Such an individual-level approach is important as it builds the necessary foundation of the CE domain of study. Yet, the roles of organizational strategic position are largely ignored, while organizational-level situational factors are rarely considered. This research aims to bridge the aforementioned research gaps by constructing both individual-level dispositions and organizational-level situational factors into an integrated framework. In particular, this research seeks to explore the roles of two organizational strategic initiatives – service environment and brand equity – on customer engagement and its impact on customer behaviors.

In this paper we explore the concept of gamification and its business applications in the Russian companies. Gamification has been introduced in 2003 and since that time it has acquired a wide recognition as an efficient tool to enhance front-office and back-office business processes increasing performance and boosting engagement of the participants. Gamification refers to the use of game elements and designs in non-game environments. As a result customers and employees involved stay more focused and motivated to accomplish the chosen goal. We explore customer engagement practices (gamification) of the Russian companies including application areas, funding and perceived efficiency of these initiatives and their shifts over time. Our analysis is based on two waves of data collection: 2015 and 2018, as the result we outline four groups of practices based on the scope of the gamification techniques used and variety of the business processes involved. Also we provide comparative analysis and observe changes in gamification use over time.

Introduction The retail environment, which is offering special experience to consumers based on customized consumer lifestyle, creates customer value from voluntary customer engagement. In recent study, it is shown that customer engagement is becoming an important factor which determines the characteristics of customer behavior in the retail and hospitality industries. However, the study of customer engagement has mainly focused on its performance in marketing field ( Hapsari, Clemes, and Dean, 2017; Kumar and Pansari, 2016) and most researches have handled the concept of customer engagement from the perspective of online environment(Shin and Byun, 2016; Jeon, 2016). Theoretical Development This study aims to investigate the psychological motivation for customer engagement and to examine the underlying factors of customer behavior in offline retail environment based on experience economy theory and Self-Determination Theory(SDT). First, this study investigates the relationship between perceived psychological benefits (autonomy, competence, and relatedness) and intrinsic motivation. Also, this study tried to analyze the relationship between intrinsic motivation and customer engagement (conscious participation, enthusiasm, and social interaction). Second, we tried to examine the relationship between customer engagement and customer creation value (functional value, hedonic value, and social value). Thirdly, we suggested the effect of customer creation value on customer purchasing behavior (shopping memories, customer satisfaction, word-of-mouth, and revisit intention). In addition, we attempted to find the mediating effect of the hedonic value between customer engagement and shopping memories; customer engagement and customer satisfaction. Futhermore, we investigated the mediating effect of shopping experience between hedonic value and customer satisfaction. Finally, We discussed the managerial implication for differentiated competitive advantage in the experience-based retail environment. Research Design and Model Testing To test the research hypotheses and our research model, we conducted questionnaire survey from the respondents who have ever been to the major experience-based shopping malls within 6 months. Through the confirmatory factor analysis, reliability and validity of the study constructs were verified. By using the structural equation model, research hypotheses were tested and most research hypotheses were statistically significant and accepted. The final research model also showed the statistical significance with the goodness-of-fit indices. Result and Conclusion As shown in the results of this study, the experience-based retail environment leads to higher customer engagement and increase the customer’s hedonic value and reinforce positive shopping memories. Specifically, the experience-based retail environment is offering psychological benefits and customers enjoy experience itself. During the shopping experience, the customers are motivated for customer engagement. The managerial implications of the study results for the corporate managers in the retail and/or hospitality industries were also discussed.

Hospitality and tourism industries have recognized that engaging with customers via social media is now a critical element of their marketing strategy. Given the high variability of success with which firms have been attracting customer interest online, businesses are clearly struggling to determine exactly the right methods to use the newest technologies. This study presents a predictive model of attributes for online posts that evoke a high level of customer engagement. The contribution to the literature is a unique set of features that have significant impact on customer engagement, using a big-data set to support findings. In accordance with theories originating from Social Belongingness and Brand Community Marketing, findings indicate that appeals to a sense of community belonging have a significant impact on customer engagement in social media. Specifically, communities have idiomatic vocabularies consisting of “activation words” that are especially effective for engaging customers on social media. This has both theoretical implications in that it constitutes a large-scale, real-world confirmation of belongingness hypotheses, and managerial implications in that it suggests best practices for maintaining an online presence.

The customer engagement construct has been widely investigated in the marketing literature since 2005. Scholars in hospitality and tourism have perceived the importance of this construct and tried to propose both conceptual framework (e.g., So, King, Sparks, & Wang, 2016) and measurement scale to capture this phenomena (e.g., So, King, & Sparks, 2014). However, there is no consensus in many issues such as conceptualization (Dijkmans, Kerkhof, & Beukeboom, 2015) and dimensionality (Romero, 2017). In addition, this construct is relatively new in hospitality and tourism. Hence, the direction for future research and what has been done in the past are indispensable for researchers since it reduces research fragmentations in the future. The study aim is to use existing works in hospitality and tourism literature with the systematic literature review to summarize facts and address the future research. SCOPUS and ISI were employed as the main databases to search and identify the relevant articles. A total of 19 out of 590 documents was identified and selected to analyze and classify based on types of research, country, and journal. In addition, the issues of theoretical background, conceptual framework, conceptualization, dimensionality, statistical analysis, key contributors are summarized respectively. Finally, this study addresses the scope of potential future research in a realm of hospitality and tourism.

The power of social media is colossal considering that the number of worldwide users is expected to grow even more in the future reaching 2.95 billion by 2020. Because of the apparent customer engagement in these platforms, companies spend on average 11% of marketing budgets on social media and this expenditure is expected to grow to 19% over the next five years (CMO Survey, 2017). However, the main challenge the companies are facing is how to convert the social media investments into effective marketing and contribution to company’s performance. The customer engagement (CE) in social media catches a strong attention from scholars (Brodie et al., 2013) as well as experts of online marketing (Dessart, Veloutsou, & Morgan-Thomas, 2016). Even though there is a significant progress in the conceptual (Van Doorn et al., 2010) and empirical (Brodie et al., 2013) analysis of CE, its clear understanding remains still insufficient. Customer engagement’s definition requires more attention as there is inconsistency in the terms because of a lack of agreement on the terminology (van Doorn et al., 2010). Noticeable differences exist concerning also the measurement of CE and what exactly this phenomenon encompasses (Dessart et al., 2016). Particularly, the empirical studies show incongruity in the number and the nature of the dimensions (Sprott et al., 2009; Brodie et al., 2013). To investigate the complex and emergent occurrence of CE in social media, this research endorses a managerial-oriented approach using rich qualitative data from three different sources covering a variety of views for different social media platforms (41 companies/24 advertising/communication agencies, and 10 research/consulting firms). The results illustrate the gaps among customer engagement’s conceptions, the customer engagement dimensionality, and the metrics of social media performance beyond customer engagement.

In today’s highly dynamic tourism and hospitality environment, the role of customer engagement (CE) in customer experience and value is receiving increasing attention from practitioners and academics (Harrigan, Evers, Miles, & Daly, 2017). Despite this interest, scholarly analysis into the concept and its associated elements has been limited to date. For these reasons, the objective of this study is to present a science mapping approach to analysing the thematic evolution of customer engagement, specifically in the tourism/hospitality and marketing industries. The study applies a bibliometric approach combining co-citation analysis with co-word analysis to reveal and visualize the evolution of customer engagement in the hospitality and tourism areas. Specifically, authors use the SciMat software in order to discover the most important research themes and its conceptual evolution. This technique returns a set of clusters, which can be understood as conglomerates of different scientific aspects. They allow researchers the analysis of the research topics’ dynamic evolution by measuring continuance across consecutive sub-periods. Authors followed the ranking of hospitality and tourism journals considered by Gursoy and Sandstrom (2016) and, the marketing journal ranking developed by Hunt Reimann and Schilke (2009) as criteria for journal selection process. This study has considered the Web of Science (WoS) as the main academic database for collecting research contributions. Findings indicate symptoms of a research field in constant evolution that has not yet reached a stage of maturity. Initially, customer engagement was seen as an important element, but its examination was scarce and has gradually come to be recognized as a key goal within organizations to serve as a basis for the development of various study models. The results obtained from this study will enable future authors studying customer engagement to focus their studies more effectively.

As information technology advanced, customers’ service consumption process heavily shifted to the online environment, particularly social media and mobile. A new topic, customer engagement, has emerged along with the fast advancement of social media. Customer engagement refers to a type of customer behavior that is beyond purchase such as spreading word-of-mouth, providing recommendations to family and friends, interacting with fellow customers, and writing online reviews (Bijmolt et al., 2010; Verhoef, Reinartz, & Krafft, 2010). This study introduces the Flow theory, which is especially important to understand consumers’ online experiences, with the intention to shed light on how to better engage consumers in the hospitality industry (Bilgihan, Okumus, Nusair, & Bujisic, 2014; Hoffman & Novak, 2009). The purpose of this study is to examine the antecedents of flow and further investigate its influence on positive attitude and continuance intention among restaurant social media users. This study specifically examines restaurant customers who use social media through their smart phones in searching information and sharing experiences with others. A self-administered questionnaire was developed and a Structure equational modeling (SEM) was employed to test the proposed hypotheses. Study results support the hypotheses, indicating the importance of creating flow to increase customer engagement. Academically, this study contributes to the limited body of literature on flow experience and customer engagement in the hospitality context. Additionally, it provides practical insights for hospitality marketers on how to gain competitive advantages by strategically managing customer engagement on social media marketing through flow.

Introduction Although hotel employees are trained to deliver the best service, service failures may happen at any time because service is delivered by people to people (Susskind, 2002). Moreover, customers are more impressed by failed services than good services (Titz, 2001). According to the recovery paradox, customers have higher satisfaction level after experiencing a service failure if they receive satisfactory service recovery or compensation (McCollough & Bharadwaj, 1992). With the development of information communication technology and mobile device, customers can receive personalized services in recent days (Migacz, Zou, & Petrick, 2018). They also can easily share their experience on the online review platforms such as TripAdvisor, as well as select hotels based on shared online reviews (Liu & Park, 2015; Nieto-Garaía, Muñoz-Gallego, & González-Benito, 2017). Therefore, it is important for hotel managers to understand the mechanisms for service failure and recovery strategy. Thus, this study aims to examine the relationship between different emotion, customer engagement and brand loyalty under the context from the luxury hotels in China that different service failure compensation strategies are adopted. Particularly, the following two research questions are aimed to be addressed: First, do emotions (anger, regret and helplessness) significantly affect hotel brand loyalty through customer engagement? Second, does compensation type (immediate vs. delayed) significantly affect customer engagement and hotel brand loyalty based on customers’ emotions? The results of this study will benefit industry practitioners for formulating effective service failure recovery strategies. Theoretical frameworks and hypotheses development Stimulus-Organism-Response framework Stimulus-Organism-Response (S-O-R) framework is a commonly used form of behavioral research in which events or occurrences are said to be the result of certain stimulus leading to a certain response, following a set of organism processes (Kim & Lennon, 2013; Mehrabian & Russell, 1974). In behavioral research, the S-O-R theory explains “how” something happens and a variance theory describes “why” (Chiles, 2003). We adopted the S-O-R framework in an attempt to explain the effect of the compensation types (immediate vs. delayed) on hotel brand loyalty. In our research model, customer engagement is used an intervening construct on the causal relationship between emotions of customer (anger and regret as a retrospective emotions, helplessness as a prospective emotion) (Gelbrich, 2010) and hotel brand loyalty. Customer engagement is composed of multidimensional concepts of identification, enthusiasm, attention, absorption, and interaction (So, King, & Spark, 2014). Our model thus explains four basic processes of relationship impact on service failure as “stimulus”, emotions and customer engagement as “organism”, and hotel brand loyalty as “response”. This study also emphasizes compensation type as “moderator”. The model shows how to enhance the understanding of emotions that affect hotel brand loyalty through customer engagement based on the moderating effect of compensations type. Customer engagement It is important for a firm to manage customers to improve a firm’s performance. Customer management has transformed from customer transactions, to relationship marketing, and then engaging customers (Pansari and Kumar 2017). There are different definition about customer engagement and most of them define customer engagement as the activity of the customer toward the firm. For example, Pansari and Kumar (2017) define customer engagement as how customer contributes to the firm by “the mechanics of a customer’s value addition to the firm, either through direct or/and indirect contribution.” Vivek et al. (2012) define customer engagement as “the intensity of an individual’s offerings or organizational activities, which either the customer or the organization initiates” (p.127). It has been discussed that customer engagement has been affected by customer emotion and also has significant impact on behaviour intention and brand loyalty. However it has not been discussed under service failure context and when different types of compensation strategies are employed. This study therefore aims to explore this mechanics. Under hospitality context, So, King and Sparks (2014) develop five factors to measure customer engagement: identification, enthusiasm, attention, absorption, and interaction. Since this study also examine hotel guest customers, we adopt the scale of So et al. (2014) due to its comprehensiveness and consistent context. Service failure and emotion Customer emotion is an important antecedent of customer engagement. Currently firms have been shifted their focus from selling products to emotional connection with their customers (Pansari and Kumar 2017). Positive emotion may enhance customer engagement and thereby improve customer loyalty. But when service failure occurs, customers have different negative emotions including anger, frustration, helplessness, regret amongst others. These negative emotions of customers disappoint customers themselves and reduce customer loyalty. Different emotions may have different impact on customer engagement. Anger often refers to the attributes of others such as the service providers (Weiner, 1985) whereas regret often refers to the service failure locus of customer himself/herself such as the customer is regret to choose this service provider (Roseman, 1991). Both anger and regret refer to retrospective emotions and when customer would like to solve questions they may also negative emotion of helplessness which is called prospective emotions (Davidow, 2003; Gelbrich, 2010). This study aims to examine and differentiate the impact of two retrospective emotions of anger and regret and one prospective emotions of helplessness. The first hypothesis is therefore proposed: H1: Anger has negative impact on customer engagement. H2: Regret has negative impact on customer engagement. H3: Helplessness has negative impact on customer engagement. Service failure compensation Though service providers aim to deliver zero fault service, it is inevitable service failure may occur that may bring customers anger and dissatisfaction and damage the customer loyalty thereby. It is found that compensation is an effective way to comfort and delight the dissatisfied customers. Therefore, it is important to formulate effective compensation strategy when service failure occurs. Different compensation strategies such as monetary or nonmonetary (Fu et al. 2015), immediate or delayed compensation (Boshoff, 1997; Davidow, 2003), may be suitable to different contexts/situations. According to prospect theory, a customer is risk-reverse in case of gains. A customer may value products available now more than products obtained in the future due to the higher certainty of the former. Similarly, immediate compensation has less uncertainty than delayed compensation, and therefore is supposed to have higher value. Therefore customers with anger are assumed to have higher customer engagement when immediately compensated. On the other hand, regret customers attribute failure to himself/herself and therefore less expect compensation. The immediate compensation may lead to unfair and thereby less effect than delayed compensation. Therefore immediate compensation may not always be superior over the delayed one under different contexts. We therefore propose the second hypothesis: H1a: Compensation type (immediate vs. delayed) moderates the relationship between anger and customer engagement. H2a: Compensation type (immediate vs. delayed) moderates the relationship between regret and customer engagement. H3a: Compensation type (immediate vs. delayed) moderates the relationship between helplessness and customer engagement. Brand loyalty Brand loyalty refers to the loyalty of a customer toward the brand both behaviourally and attitudinally (Dick and Basu 1994; Li and Petrick 2008; So, King, Sparks, and Wang 2013). It is a key goal of marketing activities, and its antecedents have been extensively examined such as satisfaction, perceived quality, received value, and brand trust, amongst others. Customer engagement, as the activity of a customer toward to a firm, is naturally viewed to influence brand loyalty. This study therefore adopts brand loyalty as the consequence of customer engagement. Furthermore, we would like to examine if compensation types have moderating effect between customer engagement and brand loyalty. We therefore propose below two hypotheses: H4: customer engagement has positive impact on brand loyalty. H4a: Compensation type (immediate vs. delayed) moderates the relationship between customer engagement and brand loyalty. The research model is shown in Figure 1 where all hypotheses are demonstrated. Our research model is developed based on the S-O-R framework in which emotions are antecedent of customer engagement, and customer engagement impacts hotel brand loyalty. This research model also shows the moderating effects of compensation types has on causal relationships between the aforementioned constructs. Methodology Scenario design Scenario based questionnaire is designed to obtain quantitative data for analysis. Based on the interview with hotel managers/operators, one service failure scenario and two compensation scenarios (immediate and delayed) are designed. In-depth interviews with a couple of hotel managers and guests were conducted to verify the realisation of the scenarios formulated. The questionnaire begins with a screening question: in the previous 12 months have you ever had experience staying in a four- or five-star hotel? The survey would only continue if the answer is “yes”. Then the participant is asked to write down the name of this hotel and read the below service failure scenario thereby. Service failure scenario: Imagine you have checked into this hotel again. During your stay in hotel, you send your coat for laundry. It is a nice coat and you bought it a year ago with the price of 1000RMB. However when you collect the cleaned coat, you notice that there is a damage on your coat which makes you cannot dress this coat anymore. You therefore call the service counter for complain. Immediate and delayed compensation scenarios were designed as follows: Immediate compensation scenario: after 15 minutes, the duty manager of the hotel went to our hotel and expressed his sincere apology. You showed him about the damage and informed him the original price of your coat. The manager offered you the cash compensation with the original price of your coat and you agree with this. After half an hour you received 1000RMB cash as the compensation. Delayed compensation scenario: after 15 minutes, the duty manager of the hotel went to your room and expressed his sincere apology. You showed him about the damage and informed him the original price of your coat. The manager said according to the hotel policy, they need to check how this happened and confirm the price of your coat first before making the compensation for you. After two weeks you left the hotel, you received 1000RMB compensation which is transferred into your bank account directly. Participant emotion is measured after the participants read the service failure scenario and before they read the compensation scenario. Each participant is randomly assigned to be involved in one compensation scenario only. Customer engagement and hotel brand loyalty are measured after the compensation happened. Variable measurement Customer engagement is measured using 25-item scale developed by So et al. (2014) in which five factors are involved: identification, enthusiasm, attention, absorption, and interaction. Particularly, identification is measured by four attributes: “When someone criticizes this brand, it feels like a personal insult”, “When I talk about this brand, I usually say we rather than they”. “This brand’s successes are my successes”. “When someone praises this brand, it feels like a personal compliment”. Enthusiasm is measured by five attributes: “I am heavily into this brand”. “I am passionate about this brand” “I am enthusiastic about this brand” “I feel excited about this brand” “I love this brand”. Attention is measured by five attributes: “I like to learn more about this brand” “I pay a lot of attention to anything about this brand” “Anything related to this brand grabs my attention” “I concentrate a lot on this brand” “I like learning more about this brand” . Absorption is measured by five attributes: “When I am interacting with the brand, I forget everything else around me” “Time flies when I am interacting with the brand” “When I am interacting with brand, I get carried away” “When interacting with the brand, it is difficult to detach myself” “In my interaction with the brand, I am immersed” “When interacting with the brand intensely, I feel happy”. Interaction is measure by five attributes: “In general, I like to get involved in brand community discussions” “I am someone who enjoys interacting with likeminded others in the brand community” “I am someone who likes actively participating in brand community discussions” “In general, I thoroughly enjoy exchanging ideas with other people in the brand community” “I often participate in activities of the brand community”. Three emotion of anger, regret and helplessness are included as the measurement of emotion. Particularly, according to Gelbrich (2010), three attributes are adopted to measure anger “I would feel angry with the hotel/hotel employees”, “I would feel mad with the hotel/hotel employees”, and “I would feel furious about the hotel/hotel employees”. Three statements are employed to measure regret (Tsiros & Mittal 2000): “I would feel sorry for choosing this hotel”, “I regretted choosing this hotel”, and “I should have chosen another hotel”. Four statements are used to measure helplessness (Gelbrich 2010): “I would feel helpless”, “I would feel lost”, “I would feel defenceless”, and “I would feel stranded.” Five statements are used to measure brand loyalty (So, King, Sparks, & Wang 2013): “I would say positive things about this brand to other people.” “I would recommend this brand to someone who seeks my advice.” “I would encourage friends and relatives to do business with this brand.” “I would consider this brand my first choice to buy services.” “I would do more business with this brand in the next few years.” A seven-point Likert scale ranging from 1 (=disagree strongly) to 7 (=agree strongly) is adopted for all measurement. Data collection and analysis method In-depth interview with managers from upscale hotels and customers will be used to finalize scenarios. Opinions of academic experts will be used to revise variable measurements and questionnaires. Convenience sampling method will be adopted to obtain about 400 respondents who has experience of staying at four- or five-stars hotels in China in the previous year. Regarding with data analysis, Partial least square structural equation modelling (PLS-SEM) is used to test the hypotheses proposed. Expected results The manipulation check has been conducted to verify the scenarios designed. The negative relationship between emotions and customer engagement are expected and compensation timing (delayed or immediate) may moderate this relationship. Most importantly, it is expected that this moderating effect varies when different emotions and customer engagement are examined. Contributions The theoretical contributions have three folders. Firstly, this study first considers compensation timing into the examination of relationship between different negative emotions and customer engagement, after service failure occurs. Secondly, this study adopts stimulus-organism-response theory to explore the mechanism how service failure could be well recovered by relationships of different negative emotions, effective compensation type, customer engagement, and brand loyalty. Thirdly, this study applies second order factor for the measurement of customer engagement and also divides negative emotions into retrospective and prospective ones to shed light on customer engagement in the context of service failure and compensation. The practical implication of this study will benefit industry practitioners for their formulation of compensation strategies. Especially as the development of big data, hotel industry is able to adopt different strategies for individuals to maximize customer experience. The findings of this study could propose different strategies for different situations/individuals thereby.

Employee innovation is critical to business success and draws knowledge and ideas from customer engagement (CE). In particular, customer interaction, a key aspect of CE, offers opportunities for, and act as a source of, hotel employees’ innovative behaviors (Jaakkola & Alexander, 2014; Li & Hsu, 2016). Focusing on the hotel industry, this study investigates the role of customer interaction, positive affect, and employee motivations in enhancing employees’ innovative behaviors. A structural model was developed based on relevant literature and pilot tested on data collected via a quantitative survey of 196 hotel employees who were in a position requiring interactions with customers. The findings provide support for the proposed model and suggest customer interaction, positive affect, and intrinsic and extrinsic motivations as influential factors impacting on employees’ innovative behaviors directly and/or indirectly. This pilot study forms the basis of a larger project modeling the customer interaction - employee innovation relationship (findings to be presented at the conference). The study contributes to the limited literature on innovation through enhancing CB and employee emotional welfare, addressing the call to strengthen research on the antecedents and outcomes of CB (So, King, Sparks, & Wang, 2016). Practically, the results highlight a need for hotels’ benefit and reward systems to incorporate measures of employee performance in relation to CB.

Customer engagement has become a prominent issue in hospitality and tourism industry. However, customer engagement, is not easily defined or uniformly measured because a number of diverse factors must be considered. While meeting planners are important customers to CVBs, their engagement with CVBs has rarely been studied. Therefore, the purpose of this study was to develop and test a model of meeting planners’ engagement in CVBs empirically. Survey data were collected from a variety of meeting planners, which resulted in 305 usable responses for data analysis. Two step analyses (measurement model and structural model) were implemented. In addition, the moderating effect of reputation and familiarity were examined among the paths of the constructs. The results showed that the hypotheses were supported while familiarity has less salient impact than reputation on customer engagement in the event industry.

A company’s collaborative competence to seamlessly engage with its customers within the service industry becomes one of the primary sources of competitive advantage. Increasingly more customers expect more personalized and contextualized services to maximize the value they can obtain from each transaction, and companies are actively seeking opportunities to listen and learn from customers to enhance their value proposition. Scholars in the service marketing literature suggest that service recovery encounter represent one of the situations where a company can effectively leverage its collaborative competence to restore the company-customer relationship damaged due to an initial service failure. One of the important benefits customers can obtain from actively engaging in recovery is enhanced control. However, they are required to put additional personal resources which count as an increased cost for customers. Also, depending on the nature of a collaborative outcome (positive vs. negative), customers tend to ascribe responsibility differently (more credit to oneself for a positive result and less blame for an adverse result). This study attempts to 1) investigate the role of enhanced control and increased self-effort in customers’ equity perception toward a service recovery encounter and 2) examine how attributional bias intervene in the process. A 2 x 2 web-based experiment with a service recovery scenario was designed and administered to collect the data. The results indicate that when the resulting outcome is not desirable, the increased self-effort from customers counteracts the positive effect of enhanced control. When the recovery outcome is positive, on the other hand, customers with a low level of involvement in recovery process tend to overestimate their self-effort. However, such effect does not exist for customers with a high level of engagement. This study contributes to the extant literature by contemplating adverse impacts of effort and positive impacts of empowering customers with more control. It also provides valuable insight into the usefulness of customer engagement as a pre-emptive recovery strategy.

Since the late 1980s, information communication and technology (ICT) have reshaped the landscape of the tourism industry (Buhalis & Law, 2008). Thanks to the Web 2.0 technology, tourism practitioners have never been this close to their customers over social media platforms. According to Kaplan and Haenlein (2010), social media refers to “a group of Internet-based applications which build on the ideological and technological foundations of Web 2.0 and that allow the creation and exchange of User Generated Content” (p. 61). In line with this definition, electronic social networks, user-generated content aggregators, as well as location-based applications are all typical social media platforms, across which enable customers to create, edit, and share content. The increasingly growing social media platforms have greatly facilitated implementations of customer engagement strategies for organizations. As a psychological state, customer engagement is featured by interactive customer experiences with an organization, which encourage psychological, emotional, and physical investment a customer has in the organization (Harrigan, Evers, Miles, & Daly, 2017). In the tourism and hospitality context, customer engagement strategies are as critical in strengthening customer loyalty, trust, and brand evaluations (So, King, & Sparks, 2016). Useful insights have been gained relating to conceptualization and measurement scale of customer engagement, organizational and cultural obstacles to consumer engagement within hotel organizations (Chathoth et al., 2014), customer engagement in a social media context alongside the process of recognition (Cabiddu et al., 2014). Underlying the practical and theoretical significance of customer engagement lies the subjective nature of views on the social media platforms. Goh, Heng, and Lin (2014) recognized that engagement in social media brand communities positively lead to enhanced purchase expenditures through embedded information and persuasion. Quantitively, the persuasive effect of user generated information is at least 22 times more than that of marketer’s in terms of marginal effect. Although previous research has examined consequences of consumer engagement, there has been less attention paid to its causes. Meanwhile, as far as Brodie et al. (2011) were concerned, the persistency of consumer-brand engagement is contingent on an assessment of tangible and intangible costs against possible benefits such as product news and offers. Therefore, identification of these benefits can offer supplementary insights into current literature of consumer engagement. The current study utilizes the self-determination theory to uncover how engagement in social media activities is facilitated by consumers’ intrinsic motivators and what psychological benefits can consumer obtain from such engagement, as either psychological state or process (Brodie et al., 2011). Research subjects in this study are Chinese social media users. According to eMarketer’s (2017) estimated that more than 80 percent of Internet users in China (i.e., around 626 million people) accessed social networks regularly in 2017. The importance of tapping this massive market can never be overestimated.

Despite the increasing attention to customer engagement (CE) by practitioners and academics, extant studies have largely been restricted to conceptualized relationships without empirical testing (So, King, & Sparks, 2014). Drawing on social identity theory and social exchange theory, this study develops a research model delineating the relationship between customer identification (CI), CE and customer purchasing behaviors in virtual communities. The model was tested with structural equation modeling and survey data from 513 members of two virtual tourism communities. Results indicate that both customer-community identification and customer-customer identification (constituting two classifications of CI) have directly positive effect on customer engagement attitude, on which the duration of membership in a community has a moderating effect. Additionally, customer-customer identification influences customer engagement behavior directly and positively, of which customer engagement attitude is the psychological foundation. Finally, customer engagement attitude and customer engagement behavior will promote customers’ purchasing behaviors. The contribution of this paper is that CE has been empirically validated to compose of customer engagement attitude and customer engagement behavior these two separate variables, and CI is testified to be an antecedent rather than a dimension of CE, in line with the standpoints proposed by Algesheimer, Dholakia, and Herrmann (2005).Through the current investigation, empirical studies into the concept connotation and formation mechanism of CE are enriched, and the insight into customer behavior management and CE marketing is intensified.

The channel transformation to omni-channel is currently in progress in the retail industry. For the progress to occur, facilitating meaningful experiences of customers in their customer journeys, capturing such experiences through various channels and touch points, and then analyzing the information acquired as big data are required (Lemon and Verhoef, 2016). With the increase in the number of customer experiences being observed through the internet and mobile communication, the focus is now on engagement. However, there have not yet been many studies conducted to deliberate comprehensively on how the engagements of behavioral aspects captured through various channels and the evaluation indicators of customers, as represented by the RFM or LTV, are related in a broader sense. The purpose of this research is to clarify the relational structure from a comprehensive perspective that are not constrained by monetary amounts. This paper showed results using data from the retailer. This research is divided broadly into two stages. The first stage identifies the engagements of behavioral aspects and the relationship between the respective behaviors, as well as the typification of behavioral patterns. The second stage involves clarifying the relationship between the customer’s evaluation indicators and engagement behaviors. The engagement behaviors are basically correlated with RFM, however authors found that there is no relationship between specific engagement behavior and RFM in the group of low rank customers. On the other hand, using different types of services or shops from the core business strengthens the customer relationship. Finally, the relationship between the types of engagement behaviors and the respective customer evaluation indicators is presented in the conclusion.

Customers’ opinions on social network platforms are known to influence peer behaviour (Bai, 2011; Eirinaki, Pisal, & Singh, 2012). Customers are also known to be more engaged in sharing their experiences by writing online reviews and recommendations that may be useful to others (Cantallops & Salvi, 2014; Tang & Guo, 2015; Xu & Li, 2016). Actually, user-generated content (UGC) on social network platforms has emerged as an important source for understanding and managing consumers’ expectations, particularly using automated and semi-automated knowledge extraction techniques from text such as text mining and sentiment analysis (Zhang, Zeng, Li, Wang, & Zuo, 2009). This research analyses dimensions of online customer engagement and associated concepts in customers’ reviews through (i) a global sentiment analysis using positive, neutral and negative sentiments and (ii) a topic-sentiment analysis to capture latent topics in online reviews. Furthermore, it examines what influences customers to contribute their online reviews, beyond the features of each focal company or brand. The research methodology is based on a text mining approach, using the MeaningCloud tool. The study focuses on Yelp.com reviews and includes a random sample of 15,000 unique reviews of restaurants, hotels and nightlife entertainment in eleven cities in the USA. An innovative customer engagement dictionary is created, based on previously validated scales using known dimensions of engagement, experience, emotions and brand advocacy, and extended using WordNet 2.1 lexical database. The research findings reveal a high impact of the engagement cognitive processing dimension and hedonic experience on customers’ review endeavour. The study results further indicate that customers seem to be more engaged in positively advocating a company/brand than the contrary. The findings will help social network managers to reinforce their platforms.

This study focuses on the digital generation in China and their engagement in social media to co-create values with firms. The study employed a qualitative research approach to first develop a social media co-creation value scale. This was followed by motivational analysis of social media engagement to co-create values. A spectrum of utilitarian and hedonic motives related to value co-creation behaviors via social media were then identified. Many theoretical and practical implications are provided based on the study findings.

This study discusses how firm-customer interactions impact customer engagement behaviors in the area of hospitality and tourism services. In the current study, two research questions are raised to answer: 1) what values are perceived by customers through firm-customer interactions during various service encounters? 2) how customer engagement behaviors are led by the customer perceived values? To test the proposed model, a quantitative approach is adopted. Amazon Mechanical Turk is used as data collection platform to collect responses with the aid of Qualtrics as questionnaire development tool. To reach the maximum scope of hospitality and tourism services, hotel guests, restaurant patrons, travelers, airline customers, and theme park travelers are included in the sample. This study adopts a quantitative approach to investigate the factors that contribute to the varied customer engagement behaviors (i.e. online ratings, online reviews, online blogging, and customer-to-customer interactions). Successful industry practices demonstrate that customer engagement brings many benefits and opportunities to maintain business sustainability and profitability.

To investigate the value co-creation process in wellness tourism, this study constructed a structural equation model of customer interactions with (1) the environment, (2) service employees, and (3) other customers relating to customer-perceived value and customer engagement. Empirical data were collected from 528 survey respondents who were at wellness tourism resorts. The results reveal that all three types of interaction have positive effects on customer-perceived value, and that perceived value positively affects customer engagement. Based on this finding, management recommendations for wellness tourism service enterprises are given.