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Open AccessArticle

Enhancing Marketing Performance Through Enterprise-Initiated Customer Engagement

Faculty of Organizational Sciences, University of Maribor, 4000 Kranj, Slovenia
Institue for Information and Process Management, FHS St. Gallen University of Applied Sciences, 9001 Sankt Gallen, Switzerland
Author to whom correspondence should be addressed.
Sustainability 2020, 12(9), 3931;
Received: 16 April 2020 / Revised: 3 May 2020 / Accepted: 7 May 2020 / Published: 11 May 2020


The exponential speed of technological advancements and the ever-changing needs of customers have changed the way enterprises engage with their customers. However, despite the increasing scholarly interest in the enterprise-initiated perspective of customer engagement (CE) in recent years, it remains unclear what drives enterprises to initiate customer engagement and how enterprise-initiated customer engagement enhances enterprise performance. Thus, the purpose of this study is to develop and evaluate a conceptual model that consists of drivers and outcomes of enterprise-initiated customer engagement. After developing a conceptual model based on previous conceptual approaches to customer engagement, a quantitative survey was undertaken to gather the data from business-to-customer micro, small, and medium-sized enterprises. The data were analyzed using the partial least square path modelling. The findings showed that external pressure and organizational readiness influence enterprise-initiated customer engagement. Enterprise-initiated customer engagement was also found to influence marketing performance. The empirical findings provide insights for managers that explain what drivers may influence enterprise-initiated customer engagement and what benefits they can expect. Overall, this research extends the understanding of the CE domain and provides additional insights for the drivers and outcomes of enterprise-initiated CE.
Keywords: customer engagement; enterprise perspective; drivers; marketing performance customer engagement; enterprise perspective; drivers; marketing performance

1. Introduction

The rapid pace of globalization, as well as the ever-changing needs of customers, the increasing competitive pressure, and the exponential speed of technological advancements, create a condition where businesses are struggling to gain competitive advantages. As both academicians and practitioners have recognized that customer engagement (CE) is an imperative facilitator for customer retention that further results in performance outcomes [1,2], enterprises try to provide better customer experiences [3] by increasingly deploying digital technologies (e.g., social media and e-commerce) [4].
The potential of CE has led to increasing [5] and persisting [6] scholarly interest in recent years. Extant research has provided several contributions by defining CE (e.g., [7,8]), proposing different CE dimensions (e.g., [9,10,11]), and investigating CE antecedents and consequences (e.g., [1,2,7,8,9,12]). Furthermore, extant research predominantly focuses on digital technologies [13], which, according to Vivek et al. [14], provide a range of ways that enterprises can use to interact with customers. Despite the aforementioned contributions, Alvarez-Milán et al. [13] pointed out that the CE research has only recently acknowledged enterprise-initiated CE perspective. Moreover, Beckers et al. [6] observed that the implications of enterprise-initiated CE on enterprise performance challenges practitioners and researchers to rethink what it means to proactively initiate and manage CE and provided several research directions. Recognizing the impotency of enterprises’ CE initiatives, this study refers to enterprise-initiated CE as “enterprises’ deliberate effort to guide customers’ voluntary contributions to its marketing functions, beyond a core, economic transaction” [15] (p. 312).
Existing research on the enterprise-initiated perspective of CE is limited to date. The term enterprise-initiated CE was introduced by Beckers et al. [6] in 2017 with the purpose of differentiating between customer-initiated and enterprise-initiated CE, which “occurs when firms adopt explicit strategies to stimulate CE” (p. 368). Similarly, Harmeling et al. [15] differentiate between CE and CE marketing, where the focus is on enterprise-initiated CE. Moreover, Alvarez-Milán et al. [13] propose a strategic CE marketing decision-making framework that enterprises are advised to follow. Even though researchers [6,15] provide evidence in support of the enterprise-initiated CE on enterprise performance outcomes and shareholder value, they encourage further exploration into the impact of enterprise-initiated CE. One of the recent studies [16] suggests that the impact of CE on the marketing performance that consists of operational performance (e.g., customer mind-set, customer behavior, and product-market performance) and enterprise performance (e.g., accounting and financial market performance) should be focused on. Additionally, Beckers et al. [6] also suggest that potential drivers that encourage enterprises to start with enterprise-initiated CE initiatives should be investigated.
Although several studies investigated the field of CE, most of them focused on customer-initiated CE. Enterprise-initiated CE as a relatively new domain remains much less explored. It is still unclear how enterprises approach enterprise-initiated CE, what the drivers are that encourage these approaches, and how enterprise-initiated CE influences enterprise performance. Consequently, enterprises that need to design and implement effective enterprise-initiated CE have scarce insights on what they need to take into consideration and what benefits they can expect.
Taking into consideration the aforementioned research gaps, the present study has the following research objectives: (1) to investigate what drives an enterprise to start CE initiatives; (2) to determine whether enterprise-initiated CE initiatives improve marketing performance. To address these research objectives, this study takes the following steps: firstly, based on the marketing literature review, we build a conceptual model that consists of enterprise-initiated CE, drivers, and marketing performance outcome constructs. Secondly, we formulate the hypotheses and test them empirically by conducting an online questionnaire among Slovenian micro, small, and medium-sized enterprises (SMEs).
Our study provides two main contributions to the emerging stream of research on enterprise-initiated research. First, we investigated the marketing performance consequences of enterprise-initiated CE. While the existing research investigated the net effect of enterprise CE initiatives, revenue benefits, and cost-saving, this study considers other metrics, including customer satisfaction, customer loyalty, sales value, and marketing share. Second, we followed Beckers et al.’s [6] suggestion and focused on providing insights into drivers that stimulate enterprise-initiated CE.
In the paper, we first develop a conceptual model of enterprise-initiated CE and formulate hypotheses based on the examined current relevant research. Then, we present data collection and data analysis procedures. After that, we discuss the results and contributions and suggest further research opportunities.

2. Theoretical Background

2.1. Enterprise-initiated Customer Engagement

The marketing domain literature on CE has, as a consequence of the rise of social media (SM), broadly debated the scope of CE and provided several CE conceptualizations (e.g., [2,7,8,9,17]). According to Beckers et al. [6], these studies have taken into consideration enterprise-initiated and customer-initiated CE in one CE construct. While existing research mainly conceptualizes customers as engagement subjects and enterprises as engagement objects, Alvarez-Milán et al. [13] argue that not only customers, but also enterprises, can initiate CE. The differentiation between customer-initiated CE and enterprise-initiated CE was also acknowledged by Harmeling et al. [15], who define enterprise-initiated CE as a “deliberate effort to motivate, empower, and measure a customer’s voluntary contribution to its marketing functions, beyond a core, economic transaction” (p. 312). This CE perspective is imperative because many enterprises that initiate CE [14] need to maintain it to provide positive customer experiences [18,19], which not only results in customer satisfaction, but also in increased revenue [6,8,20].
In their study, Pansari and Kumar [21] pointed out that confusion exists between CE and other customer relationship management (CRM) constructs. Therefore, studies on enterprise-initiated CE [6,15] focus on non-transactional behaviors that occur after the customer has made an initial purchase and already has experience with the enterprise. Nevertheless, according to Alexander and Jaakkola [22], this narrower conceptualization of CE provides an important perspective of CE. Beckers et al. [6] note that transactional behaviors have already been extensively studied in the CRM literature.
Non-transactional CE initiatives can be initiated by enterprises in two forms, including task-based engagement initiatives and experiential engagement initiatives [15]. Where task-based engagement initiatives guide customers to contribute to a single task defined by the enterprise (e.g., referring a customer), experiential engagement initiatives use events to stimulate independent customer contributions [15]. To ensure an overall positive customer experience, the enterprise should ensure that the customer is engaged in the best possible manner. Therefore, the conceptualization of non-transactional CE initiatives also needs to take into consideration different CE dimensions. According to Kumar et al. [17], the CE consists of customer purchases, customer referrals, customer influence, and customer knowledge. This comprehensive conceptualization includes different stages of customer relationships and takes into consideration enterprise perspective [23]. In our study, we focus on non-transactional CE dimensions: namely, customer referrals, customer influence, and customer knowledge management. Additionally, we focus on the enterprise perspective when exploring these CE behavior dimensions.
To encourage customer referrals, enterprises need to ask for referrals [17,24,25] and continuously engage with customers (e.g., involving them in conversations about their offerings, helping to solve problems, and answering questions). To provide better conditions for customers to exchange brand and product-related information (customer influence) enterprises activate brand ambassadors and opinion leaders in campaign activities [22,26] and provide online brand communities [7]. Enterprises can also gain customer knowledge by encouraging them to write a review and requesting customers’ individual opinions, tastes, or beliefs [27].

2.2. Enterprise-initiated Customer Engagement Drivers

Successful enterprise-initiated CE depends on the enterprises’ ability to identify and leverage their resources, including available engagement tools [15], while taking into consideration their customers as well as competitors. Several studies proposed models that investigate not only CE, but also CE drivers. For instance, Verhoef et al. [28] and van Doorn et al. [8] suggest three groups of drivers that can affect CE: customer-based, enterprise-based, and context-based. The majority of drivers that have been considered in the literature are customer-based. For instance, customer involvement and customer participation were found to have an effect on CE [29,30,31]. Furthermore, customer satisfaction [32], as well customer situational factors (e.g., perceived benefits and perceived resource requirements) [33], were associated with CE. As Vivek et al. [34] and So et al. [35] acknowledged in their studies on the impotency of enterprise-based drivers (e.g., organizational support and organizational socialization), several recent studies included enterprise-based drivers in their CE models. For instance, Groeger et al. [33] found that firm reputation plays an important role in CE. Furthermore, Wong and Merriless [36] identified brand orientation as an important driver for brand engagement. Among the context-based drivers, online engagement tools were usually considered in CE research (e.g., [9,37]). Despite the considerable research efforts in the CE domain, most studies have focused mainly on the customer’s point of view. Therefore, recent research on enterprise-initiated CE (e.g., [6,36]) suggests the drivers that encourage enterprises to start CE initiatives should be investigated.

2.3. Enterprise-initiated Customer Engagement Performance Outcomes

While the extent of CE research has focused mainly on customer-based performance outcomes, a little is known about enterprise performance outcomes [16]. In their conceptual model, Van Doorn et al. [8] suggested that several CE consequences for enterprise, including financial, reputational, and competitive, should be taken into consideration. Since then, little research has focused on different perspectives of enterprise performance. Therefore, Hollebeek et al. [16] pointed out in one of their recent studies that future research exploring the impact of CE on different enterprise performance aspects is needed and encouraged researchers to provide more generalizable findings.
The limited research examining CE from the enterprise perspective has focused on several enterprise performance aspects. For instance, Harmeling et al. [15] have provided business examples illustrating the effects of enterprise-initiated CE initiatives on enterprise performance, including revenue benefits and cost savings. Furthermore, Beckers et al. [6] investigate the market value consequences of CE initiatives and the mechanisms that drive this impact. They find that there are not only positive, but also negative effects of enterprise-initiated CE initiatives and suggest that different performance metrics should be assessed. Alvarez-Milán et al. [13] developed the CE marketing decisions, making a framework by integrating insights from enterprise-initiated [6,13] and other relevant CE (e.g., [2,5,38,39]) literature with findings from qualitative research. The framework consists of five facets: CE conceptualization, CE target, CE domain, CE experiential routes, and CE value. Concerning the CE value, they distinguish between customer interaction value (e.g., customer purchases) and customer multiplier value (e.g., customer referrals). Moreover, when conceptualizing enterprise performance, Braojos et al. [12] took into consideration the multidimensional construct proposed by Mithas et al. [40] and focused only on “elements that are theoretically supposed to be influenced directly by customer engagement” (p. 159). Therefore, among the four dimensions, they focused only on enterprise effectiveness (e.g., level of innovation) and customer-focused performance (e.g., customer satisfaction). Based on the empirical analysis, they also confirmed the relationship between online CE and enterprise performance.
In their recent study, Hollebeek et al. [16] noted that little is known about the marketing performance outcomes of CE and suggest following Katsikeas et al.’s [41] conceptualization of marketing performance. They analyzed the empirical studies published in the top 15 marketing journals from 1981 to 2014 and identified several operational and organizational performance dimensions, including customer mind-set performance, customer behavior performance, customer-level performance, product market performance, accounting performance, and financial-market performance.

3. Conceptual Model and Hypotheses Development

Following the studies focusing on the enterprise perspective of CE (e.g., [30,36]), we developed a conceptual model that consists of five constructs, as shown in Figure 1. Enterprise-initiated CE initiatives are a construct that is presented in the center of the model. The constructs on the left side of the model present drivers, and the construct on the right side of the model presents marketing performance. The assumptions regarding the relationships between individual research model constructs and developed hypotheses are presented in the following paragraphs.
Our study is based on the theoretical foundations proposed by van Doorn et al. [8]. As such, we considered all three groups of drivers proposed in their study. The customer-based group of drivers includes customer attitude, their consumption goals, resources, and perceived costs and benefits. As Doorn et al. [8] discuss these drivers more from the customer rather than the enterprise perspective, we aimed to fill this gap and focused on the CE factors from the enterprise point of view. With the rise of SM, customers are interacting with each other and they expect to interact with the enterprise in the same manner [42,43]. According to Kunz et al. [44], CE will increase if enterprise engagement activities “meet or exceed the customer’s expectations” (p. 177). Chathoth et al. [45] further pointed out that not only customer expectations, but also competitor actions need to be taken into consideration by the enterprise when trying to meet the customer’s expectations. Both customer pressure and competitor pressure can be perceived by enterprises as external pressure that motivates them to start CE initiatives. We therefore hypothesize:
Hypothesis 1 (H1).
External pressure has a positive effect on enterprise-initiated CE.
The enterprise-based group of drivers includes brand characteristics, enterprise reputation, enterprise size/diversification, enterprise information usage and processes, and industry. As already pointed out by Wong and Merrilees [36], brand orientation is an important determinant of brand engagement. Nevertheless, other enterprise-based determinants need to be considered. For instance, Gambetti and Graffigna [46] identified two main drivers related to CE based on thematic analysis: engagement strategy and strong relationships between employees and customers. The likelihood to improve CE by creating an engagement strategy focused on customers was also acknowledged by other recent studies (e.g., [44]). Employees can only engage with customers effectively if they understand CE goals and their responsibilities toward fulfilling these goals [1]. Furthermore, enterprises need to provide the adequate resources and skills necessary to regularly assess customer engagement-based changes and trends and immediately respond if needed [15,16,34]. We therefore hypothesize:
Hypothesis 2 (H2).
Organizational readiness has a positive effect on enterprise-initiated CE.
The context-based group of drivers is related to political/legal, economic/environmental, social, and technological aspects. It seems that emerging technologies, especially social media, have considerably changed the way enterprises interact with customers [47] and have been identified as a critical component for enterprise competitiveness and survival [48]. According to Kumar et al. [49], social media, among others, play an important role in increasing customer insights and marketing communication. Furthermore, social media enable enterprises to generate a useful and enjoyable environment that encourages customers to engage with them [50,51]. Moreover, while social media allows enterprises to reach a wide audience in a short time frame and hear what people say about a brand, several studies (e.g., [9,52,53]) pointed out that CE initiatives are more effective. Finally, enterprises that exploit social media for CE can increase enterprise competitive advantages [12]. We therefore hypothesize:
Hypothesis 3 (H3).
Social media have a positive effect on enterprise-initiated CE.
Our study also took into consideration Hollebeek et al.’s [16] suggestion and focused on the marketing performance outcomes of enterprise-initiated CE. Several dimensions of marketing performance were considered in recent studies. For example, customer mind-set and customer behavior are customer-based performances, which have been taken into consideration in several recent studies exploring online CE (e.g., [54,55]). Market share—as an item of product-marketing performance dimension—was widely used in empirical studies [41] and also in studies investigating the impact of customer involvement on performance outcomes (e.g., [56,57,58]). Similarly, studies that used product-marketing measures also measure the indicators related to profit and sales revenue that are related to the accounting performance dimension. To sum up, enterprise-initiated CE can improve customer-level, product-market, and accounting-related marketing performance indicators. Therefore, we hypothesize:
Hypothesis 4 (H4).
Enterprise-initiated CE has a positive effect on marketing performance.

4. Materials and Methods

First, the literature review was conducted in order to abreast new findings and emerging trends from recent research on CE. Based on the literature review, the conceptual model was developed and the hypotheses were formulated. Due to the limited number of quantitative studies that address enterprise-initiated CE, we chose a survey as a research instrument to validate the conceptual model. Survey data were analyzed using SPSS software for descriptive statistics and R software (Partial Least Square Path Modelling package (PLS-PM) [59]) for running the statistical tests. We used the PLS-PM method in this study because it is suitable for empirical research [60] and has already been used in CE research (e.g., [12]).

4.1. Instrument Development

When developing measurement scales for the proposed constructs, we took relevant marketing and information systems literature into consideration. The measures for drivers and outcome constructs were primarily adopted from previously validated scales and specified for the enterprise-initiated CE context. We conceptualized marketing performance outcomes as a global latent construct consisting of different performance dimensions (e.g., customer mind-set, customer behavior, product-marketing performance, and accounting performance) as researchers commonly do [41]. The new items were only developed for the enterprise-initiated CE construct. These items were drawn from the literature review (e.g., [15,17]) and based on the examination of other measures of the construct that already exist (e.g., [1]). Except for SM use, all items were measured with a five-point Likert type scale (1—strongly disagree, 5—strongly agree). Following Jayachandran et al. [61] and Trainor et al. [62], a single score to capture SM usage was used. If the respondents confirmed that their enterprise used SM for customer relationship management, they were asked to mark a check box next to the SM provided on the list (Facebook, YouTube, Twitter, Instagram, LinkedIn, and Jive). The marked items were then aggregated to determine a single score that showed how many SM used each enterprise. As the survey was conducted at the enterprise level, background information about the enterprise, ICT technologies for commercial activities, including the SM used in the enterprise, and demographic information about the respondent were also collected. Table 1 shows the constructs and measurement scales encompassed in this study.

4.2. Sample

The draft survey instrument was first reviewed by two experts with academic backgrounds (an expert in digital business and an expert in statistics) and pretested with nine business-to-customer (B2C) micro, small, and medium-sized enterprise (SME) managers/owners. Based on the feedback and recommendations, the survey instrument was refined [69].
Then, the introductory letter together with a link to the online survey was distributed among 2000 randomly selected SMEs from the Slovenian Business Register managed by the Agency of the Republic of Slovenia for Public Legal Records and Related Services. The survey was targeted at SME owners/managers who were considered to be involved in daily operations and strategic decisions in their enterprises. To increase a response rate the reminder e-mails were sent three days later.
Among the distributed questionnaires, 198 responses were received. After further screening, 36 responses were removed because not all required questions were answered. Out of 162 responses, 119 were from B2C SMEs and the remaining 43 from B2B SMEs. From those 43 B2B SME responses, only 29 were usable because they stated that their enterprises used at least one SM tool. As previous studies suggested that B2C CE activities differ from B2B activities [62] and that B2C enterprises need to be more cautious when launching CE initiatives [6], we decided to focus only on B2C SMEs. Therefore, we used responses from 119 B2C SMEs managers/owners for further analysis.
SMEs respondents were either directors/owners (31.1%) or managers (e.g., marketing manager or sales manager) (68.9%), which suggests that they were familiar with the values and ideas in an enterprise due to their position and, therefore, were the most appropriate respondents [62]. The majority (59.7%) of the respondents were females with at least high school qualifications. They represented a wide array of industry sectors, which included wholesale and retail trade (32.8%), manufacturing (16.8%), other service activities (10.1%), and accommodation and food service activities (5.9%), among others. The highest proportion of SMEs fell into the medium-sized category (37.8%), followed by small (35.3%) and micro (26.9%) enterprises.

5. Results

Before performing the data analysis, we determined the minimum sample size required to estimate the proposed model. Assuming an anticipated effect size of 0.10, a desired statistical power level of 0.80, five latent variables and 20 observed variables, and a probability level of 0.05, the minimum sample size for the model estimation was 100 [70]. Thus, the sample size of 119 responses was adequate to test the proposed model.

5.1. Measurement Model Evaluation

To ensure the properties of the instrument, the measurement model was assessed before the adoption of the structural model. The reliability and validity of the measurement model were done using the average variance extracted (AVE), Fornell’s composite reliability (CR), and Cronbach’s alpha [71,72]. To achieve an AVE value above the threshold and obtain a good-fitting model, the items with lower factor loading than 0.6 were removed. They were deleted one by one. First, the item ECE_a (activate brand ambassadors and opinion leaders in campaign activities) was removed from the enterprise-initiated CE construct, followed by items ECE_c (communicate success stories) and ECE_g (provide online space where customers connect, share their experience, and learn from each other). Table 2 demonstrates the measurement reliability of the scales. For all the constructs, the AVE was greater than 0.5, and the CR and Cronbach’s alpha were above the cut-off value of 0.7 [72].
The discriminant validity was examined using the Fornell–Larcker criteria. The correlations between each pair of constructs were lower than the square root of AVE for the relevant constructs (Table 3), thus, indicating discriminant validity. We also inspected cross-loading (Table A1 in Appendix A) and the result showed that no items cross-loaded higher on another construct than they did on their own. This additionally supported discriminant validity.
Before testing the structural model, the goodness of model fit was evaluated. Following Henseler et al.’s [73] suggestion, the standardized root mean square residual (SRMR) fit criterion was used to determine the approximate model fit. The SRMR value of 0.077 was obtained, which was below the cutoff value of 0.08 as proposed by Hu and Bentler [74].

5.2. Testing of Hypotheses

To evaluate the explanatory power of the model, the values of the beta coefficients, their level of significance, and the coefficient of determination (R2) were assessed. We used a bootstrap resampling procedure with 4999 bootstrap samples [73] to test the proposed model. The beta coefficients of the hypothesized relationships ranged from 0.129 to 0.682. Except for H3, all other hypotheses were supported by the data. The R2 value of enterprise-initiated CE was 0.275, while that of marketing performance was 0.465. Figure 2 presents the graphical depiction of the research model. The detailed information on the structural model evaluation is provided in Table 4.

6. Discussion

This study aimed to expand our understanding of the enterprise-initiated CE drivers and investigate how enterprise-initiated CE impacts marketing performance.
Our findings extend the existing knowledge regarding enterprise-initiated CE drivers. The first driver is an external pressure and consists of customer pressure and competitive pressure. In this study, the results show that the link between external pressure and enterprise-initiated CE was significant. This finding is aligned with previous CE studies (e.g., [44,45]), emphasizing the significance of customer expectations and competitor actions in CE. This means that customer requirements and behaviors, as well as evidence of how other competitors benefit from SM, use drive enterprise to initiate CE. First, as pointed out by Braojos-Gomez et al. [75] and Florin et al. [76], the customers can express their wants and needs and exert pressure on the enterprise to start using potential engagement tools that better support CE activities. Second, the competitive pressure can compel enterprises to engage with customers through different engagement tools [76]. This enables them to align their engagement activities with the practices of other competitors and, thus, help enterprises to survive in the competitive marketplace. Overall, to satisfy customers’ needs and to be able to compete with other businesses, enterprises need to start with initiatives that motivate customers to engage with them.
The second driver that was found to have influence on enterprise-initiated CE in this study is organizational readiness. This finding is coherent with existing studies (e.g., [1,15,46]) that imply the need for enterprises to be prepared for CE. To do so, managers in enterprises have to play an important role in driving enterprise-initiated CE [30]. By their commitment to SM strategy and the encouragement of their employees to follow SM objectives [2], enterprises can improve their CE processes. Furthermore, enterprises need to be able to quickly introduce new engagement tools into CE processes [30] and have knowledgeable personnel to support the utilization of new engagement tools into CE processes [44]. Thus, a combination of management support and technological readiness and skills drive enterprises to start engaging with their customers.
The third driver is SM use, for which we did not find evidence that significantly influences enterprise-initiated CE. This finding is inconsistent with earlier studies (e.g., [9,50,51]) asserting the significance of SM in CE. It seems that enterprises still have a lack of understanding of how engagement tools can support them when engaging with their customers. According to Chathath et al. [45], enterprises believe that they can achieve superior CE regardless of the use of engagement tools. Moreover, even though several studies have focused on online engagement tools (e.g., [9,77,78]) Alvarez-Milán et al. [13] pointed out that CE is also highly relevant in traditional (offline) environments. Namely, one of the informants in their study [13] argued that focusing solely on SM when engaging with customers and forgetting about the traditional way of CE, which occurs, for example, in psychical stores, could negatively affect competitive advantages. Thus, our finding implies that enterprises still rely more on traditional methods when they initiate CE. Nevertheless, they should not forget to take into consideration emerging engagement tools (online tools such as SM) that were identified as a critical component for enterprise competitiveness and survival [48].
Another result of this study indicates the positive effect of enterprise-initiated CE on marketing performance. This means that enterprise-initiated CE improves customer-level, product-market, and accounting performance outcomes. More specifically, the customers of the enterprises that initiate CE are more likely to speak positively about the brand and tend to be more pleased with the quality of service offered [79]. Furthermore, enterprise-initiated CE contributes to market share [2] and sales revenue growth [1]. Overall, all these marketing performance outcomes seem to be the consequence of enterprise-initiated CE. Thus, this study is consistent with the existing enterprise-initiated CE studies [12,36,80] and highlights the important role of enterprise-initiated CE in enhancing marketing performance.

6.1. Theoretical Contribution

This research contributes to the marketing literature in several ways. Firstly, we provide insights into the CE literature by focusing on the enterprise-initiated perspective of CE. When conceptualizing enterprise-initiated CE, the characteristics from several CE dimensions proposed by Kumar et al. [17] were considered, including customer referrals, customer influence, and customer knowledge. Nevertheless, the results of scale and model testing suggest that enterprise-initiated CE is a single dimension construct. This finding is consistent with France et al. [30], who pointed out that there may be only one real dimension of customer-brand engagement. Thus, even though the CE is a complex concept and several researchers (e.g., [7,14,81]) have, since 2010, suggested different multi-dimensional engagement constructs, it still seems that some researchers (e.g., [8,82]) consider CE as a single dimension construct.
Secondly, our study, as suggested by several recent studies [6,36], took a different perspective and focused on drivers that motivate enterprises to initiate CE. To the best of our knowledge, this study is the first that tried to take three groups of drivers proposed by Doorn et al. [8] into consideration while focusing on the enterprise perspective of CE. External pressure and organizational readiness were empirically revealed to be drivers of enterprise-initiated CE. Unfortunately, SM use was not empirically supported by this study.
Thirdly, beyond the influence of enterprise-initiated drivers, this study addresses the emerging area of interest. According to a recent study [16], little is known about the consequences of CE. Furthermore, even though the growing body of literature has focused on customer-based behavior and customer mindset, generalizable findings are still lacking [16]. Thus, this study provides evidence that enterprise-initiated CE has a significant impact on marketing performance. Without CE that is initiated by enterprises, the level of marketing performance may be lower compared to those of competitors.
Overall, the enterprise-initiated customer-based model extends the understanding of the CE domain. The model builds on the existing studies on enterprise-initiated CE and provides additional insights for the drivers and outcomes of enterprise-initiated CE.

6.2. Managerial Contributions

The empirical results of the research provide evidence that enterprises are forced by customers and competitors to start their CE initiatives. The passive role of the enterprise in the CE could harm relationships with customers, which may result in customers’ shift to a negative mindset and word of mouth. Moreover, if the enterprise neglects the best CE practices of their competitors, the customers’ perception of the quality of their service or product could decrease in comparison to the competing offers. Therefore, the enterprise needs to be prepared to start with CE initiatives. This means that enterprises must have an appropriate strategy and managers who are committed to this strategy and can motivate their employees to actively engage with their customers. Furthermore, the enterprise needs to be able to quickly introduce new engagement tools into the CE practice if necessary. Thus, the enterprise should provide necessary information technology and encourage employees to increase their CE skills. Moreover, enterprises should not forget that engagement tools are sometimes the only way to reach their customers. For example, in the case of the coronavirus disease (COVID-19) pandemic, many businesses all over the world were also shut down to curb the spread of the coronavirus. However, it seems that they stayed connected with their customers by increasing their SM and online presence.
Furthermore, this study implies that enterprises need to motivate and empower customers beyond the core economic transactions to maintain long-term and sustainable relationships. As sustainability aspects are gaining more and more importance, enterprises may also gain recognition from their customers by communicating their sustainable goals and operations towards preserving the environment and increasing benefits not just for customers, but also for their employees and society. By doing so, the enterprises will not only have satisfied customers, but they can also expect positive word of mouth. The positive recommendations cultivate a positive brand perception, which can lead to market share and sales revenue growth.
Additionally, this study provides insights into how enterprises actively and intentionally stimulate CE in several ways. For example, enterprises stimulate organic CE by including brand ambassadors and opinion leaders in campaign activities and communicating success stories. Furthermore, some enterprises provide an online space where they can ensure a prompt response to customers’ concerns and complaints. Other enterprises provide an online space where customers connect, share their experience, and learn from each other. Moreover, they proactively ask their recent customers to write a review or testimonial or to refer their brand to others. Additionally, they leverage innovative insights by requesting customers’ individual opinions, tastes, or beliefs. Overall, enterprises strive to proactively stimulate their customers’ engagement in every possible way, not only online (through SM), but also by using physical contact points.

7. Conclusions

Although enterprise-initiated CE is not a new marketing concept, most of the previous studies are conceptual or do not empirically explore CE-based relationships in a broader nomological network. Thus, our study investigates the drivers and outcomes of enterprise-initiated CE. The results demonstrate that external pressure and organizational readiness are the major drivers of enterprise-initiated CE—that is, higher levels of enterprise-initiated CE requires the ability and willingness of an enterprise to shift from its current way of operating. Furthermore, the higher level of enterprise initiated CE is associated with customer expectations and competitor actions. Importantly, the findings have managerial implications, as they guide enterprises to increase CE initiatives in a more systematic way, where they consider not only their customers, but also competitors and their resources. Nevertheless, deeper insights into additional drivers are needed to further clarify what drives enterprises to initiate their engagement with customers. Case studies with a more extensive analysis of enterprise-initiated CE drivers could shed light on groups of drivers that can affect engagement.
The results also indicate that enterprise-initiated CE affects marketing performance. Thus, this finding provides a major rationale for enterprises to conduct CE initiatives, yet the opportunity exists for further empirical demonstration of potential risks and sustainable aspects of enterprise-initiated CE. The results have also shown a strong correlation between organizational readiness and marketing performance, although this was not anticipated during the formulation of the hypotheses. As organizational readiness contributes to enterprise growth, further research should also consider linking these two constructs. Furthermore, this research provides insights on how enterprises are executing CE initiatives. The results reveal that enterprises not only encourage customers to complete a single task defined by the enterprise, but also motivate customers’ autonomous contributions. This implies that enterprises that combine task-based and experiential engagement initiatives are more likely to benefit from their CE initiatives. The results also indicate that enterprise-initiated CE is a single dimension construct. As recent research sees CE construct as multidimensional, further research that explores this phenomenon may provide additional insights. Further research could also target either large or B2B enterprises for a comparison of results.

Author Contributions

Conceptualization, M.M. and A.P.; methodology, M.M.; validation, M.M., H.-D.Z., and A.P.; formal analysis, M.M.; investigation, M.M. and A.P.; resources, M.M. and A.P..; data curation, M.M.; writing—original draft preparation, M.M. and A.P.; writing—review and editing, M.M., H.-D.Z., and A.P.; visualization, M.M. All authors have read and agreed to the published version of the manuscript.


This research was supported by the Slovenian Research Agency; Program No. P5-0018—Decision Support Systems in Digital Business.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Cross-loadings.
Table A1. Cross-loadings.
Notes: Item loadings on the assigned constructs are presented in bold.


  1. Kumar, V.; Pansari, A. Competitive Advantage through Engagement. J. Mark. Res. 2016, 53, 497–514. [Google Scholar] [CrossRef]
  2. Pansari, A.; Kumar, V. Customer engagement: The construct, antecedents, and consequences. J. Acad. Mark. Sci. 2017, 45, 294–311. [Google Scholar] [CrossRef]
  3. Duncan, E.; Fanderl, H.; Freundt, T.; Maechler, N.; Neher, K. Customer Experience: New Capabilities, New Audiences, New Opportunites; McKinsey&Company: New York, NY, USA, 2017; Available online: (accessed on 6 January 2020).
  4. Kiron, D. The Amplified Enterprise: Using Social Media to Expand Organizational Capabilities. MIT Sloan Manag. Rev. 2011, 37, 1–6. [Google Scholar]
  5. Roy, S.K.; Balaji, M.S.; Soutar, G.; Lassar, W.M.; Roy, R. Customer engagement behavior in individualistic and collectivistic markets. J. Bus. Res. 2018, 86, 281–290. [Google Scholar] [CrossRef]
  6. Beckers, S.F.M.; Van Doorn, J.; Verhoef, P.C. Good, better, engaged? The effect of company-initiated customer engagement behavior on shareholder value. J. Acad. Mark. Sci. 2017, 46, 366–383. [Google Scholar] [CrossRef]
  7. Brodie, R.J.; Hollebeek, L.D.; Juric, B.; Ilic, A. Customer Engagement: Conceptual Domain, Fundamental Propositions, and Implications for Research. J. Serv. Res. 2011, 14, 252–271. [Google Scholar] [CrossRef]
  8. Van Doorn, J.; Lemon, K.N.; Mittal, V.; Nass, S.; Pick, D.; Pirner, P.; Verhoef, P.C. Customer Engagement Behavior: Theoretical Foundations and Research Directions. J. Serv. Res. 2010, 13, 253–266. [Google Scholar] [CrossRef]
  9. Hollebeek, L.D.; Glynn, M.S.; Brodie, R.J. Consumer Brand Engagement in Social Media: Conceptualization, Scale Development and Validation. J. Interact. Mark. 2014, 28, 149–165. [Google Scholar] [CrossRef]
  10. Baldus, B.J.; Voorhees, C.; Calantone, R. Online brand community engagement: Scale development and validation. J. Bus. Res. 2015, 68, 978–985. [Google Scholar] [CrossRef]
  11. Schivinski, B.; Christodoulides, G.; Dabrowski, D. Measuring consumers’ engagement with brand-related social-media content: Development and validation of a scale that identifies levels of social-media engagement with brands. J. Advert. Res. 2016, 56, 64–80. [Google Scholar] [CrossRef]
  12. Braojos, J.; Benitez, J.; Llorens, J. How do social commerce-IT capabilities influence firm performance? Theory and empirical evidence. Inf. Manag. 2019, 56, 155–171. [Google Scholar] [CrossRef]
  13. Alvarez-Milán, A.; Felix, R.; Rauschnabel, P.A.; Hinsch, C. Strategic customer engagement marketing: A decision making framework. J. Bus. Res. 2018, 92, 61–70. [Google Scholar] [CrossRef]
  14. Vivek, S.D.; Beatty, S.E.; Morgan, R.M. Customer engagement: Exploring customer relationships beyond purchase. J. Mark. Theory Pract. 2012, 20, 127–145. [Google Scholar] [CrossRef]
  15. Harmeling, C.M.; Moffett, J.W.; Arnold, M.J.; Carlson, B.D. Toward a theory of customer engagement marketing. J. Acad. Mark. Sci. 2016, 45, 312–335. [Google Scholar] [CrossRef]
  16. Hollebeek, L.D.; Srivastava, R.K.; Chen, T. S-D logic-informed customer engagement: Integrative framework, revised fundamental propositions, and application to CRM. J. Acad. Mark. Sci. 2019, 47, 161–185. [Google Scholar] [CrossRef]
  17. Kumar, V.; Aksoy, L.; Donkers, B.; Venkatesan, R.; Wiesel, T.; Tillmanns, S. Undervalued or Overvalued Customers: Capturing Total Customer Engagement Value. J. Serv. Res. 2010, 13, 297–310. [Google Scholar] [CrossRef]
  18. Calder, B.J.; Hollebeek, L.D.; Malthouse, E.C. Creating Stronger Brands through Consumer Experience and Engagement. In Customer Engagement Marketing; Springer International Publishing: Cham, Switzerland, 2018; pp. 221–242. [Google Scholar]
  19. Lemon, K.N.; Verhoef, P.C. Understanding Customer Experience throughout the Customer Journey. J. Mark. 2016, 80, 69–96. [Google Scholar] [CrossRef]
  20. Kumar, V.; Choi, J.B.; Greene, M. Synergistic effects of social media and traditional marketing on brand sales: Capturing the time-varying effects. J. Acad. Mark. Sci. 2016, 45, 268–288. [Google Scholar] [CrossRef]
  21. Pansari, A.; Kumar, V. Customer Engagement Marketing. In Customer Engagement Marketing; Palmatier, R.W., Kumar, V., Harmeling, C.M., Eds.; Springer Nature: Berlin/Heidelberg, Germany, 2018; pp. 1–28. [Google Scholar]
  22. Alexander, M.; Jaakkola, E. Customer engagement behaviours and value co-creation. In Customer Engagement Contemporary Issues and Challenges; Brodie, R.J., Linda, D., Hollebeek, J.C., Eds.; Routledge Taylor and Francis Group: Abingdon, UK, 2015; pp. 21–38. [Google Scholar]
  23. Venkatesan, R. Executing on a customer engagement strategy. J. Acad. Mark. Sci. 2017, 45, 289–293. [Google Scholar] [CrossRef]
  24. Godes, D.; Mayzlin, D. Firm-created word-of-mouth communication: Evidence from a field test. Mark. Sci. 2009, 28, 721–739. [Google Scholar] [CrossRef]
  25. Schmitt, P.; Skiera, B.; Van den Bulte, C. Referral Programs and Customer Value. J. Mark. 2011, 75, 46–59. [Google Scholar] [CrossRef]
  26. Kozinets, R.; Wojnicki, A.C.; Wilner, S.J.S.; De Valck, K. Networked Narratives: Understanding Word-of-Mouth Marketing in Online Communities. HEC Res. Pap. Ser. 2009, 74, 71–89. [Google Scholar]
  27. Fuchs, C.; Schreier, M. Customer Empowerment in New Product Development. J. Prod. Innov. Manag. 2011, 28, 17–32. [Google Scholar] [CrossRef]
  28. Verhoef, P.C.; Reinartz, W.J.; Krafft, M. Customer Engagement as a New Perspective in Customer Management. J. Serv. Res. 2010, 13, 247–252. [Google Scholar] [CrossRef]
  29. Leckie, C.; Nyadzayo, M.W.; Johnson, L.W. Antecedents of consumer brand engagement and brand loyalty. J. Mark. Manag. 2016, 32, 558–578. [Google Scholar] [CrossRef]
  30. France, C.; Merrilees, B.; Miller, D. An integrated model of customer-brand engagement: Drivers and consequences. J. Brand Manag. 2016, 23, 119–136. [Google Scholar] [CrossRef]
  31. Wirtz, J.; Ramaseshan, B.; van de Klundert, J.; Canli, Z.; Kandampully, J. Managing brands and customer engagement in online brand communities. J. Serv. Manag. 2013, 24, 223–244. [Google Scholar] [CrossRef]
  32. Hollebeek, L.D. Demystifying customer brand engagement: Exploring the loyalty nexus. J. Mark. Manag. 2011, 27, 785–807. [Google Scholar] [CrossRef]
  33. Groeger, L.; Moroko, L.; Hollebeek, L.D. Capturing value from non-paying consumers’ engagement behaviours: Field evidence and development of a theoretical model. J. Strateg. Mark. 2016, 24, 190–209. [Google Scholar] [CrossRef]
  34. Vivek, S.D.; Beatty, S.E.; Dalela, V.; Morgan, R.M. A generalized multidimensional scale for measuring customer engagement. J. Mark. Theory Pract. 2014, 22, 401–420. [Google Scholar] [CrossRef]
  35. So, K.K.F.; King, C.; Sparks, B. Customer Engagement with Tourism Brands: Scale Development and Validation. J. Hosp. Tour. Res. 2014, 38, 304–329. [Google Scholar] [CrossRef]
  36. Wong, H.Y.; Merrilees, B. An empirical study of the antecedents and consequences of brand engagement. Mark. Intell. Plan. 2015, 33, 575–591. [Google Scholar] [CrossRef]
  37. Liu-Thompkins, Y.; Rogerson, M. Rising to Stardom: An Empirical Investigation of the Diffusion of User-generated Content. J. Interact. Mark. 2012, 26, 71–82. [Google Scholar] [CrossRef]
  38. Hollebeek, L.D. Exploring customer engagement: A multi-stakeholder perspective. In Customer Engagement; Routledge: New York, 2015; pp. 85–100. [Google Scholar]
  39. Maslowska, E.; Malthouse, E.C.; Collinger, T. The customer engagement ecosystem. J. Mark. Manag. 2016, 32, 469–501. [Google Scholar] [CrossRef]
  40. Mithas, S.; Ramasubbu, N.; Sambamurthy, V. How Information Management Capability Influences Firm Performance. MIS Q. 2011, 35, 237–256. [Google Scholar] [CrossRef]
  41. Katsikeas, C.S.; Morgan, N.A.; Leonidou, L.C.; Hult, G.T.M. Assessing Performance Outcomes in Marketing. J. Mark. 2016, 80, 1–20. [Google Scholar] [CrossRef]
  42. Trainor, K.J. Relating Social Media Technologies to Performance: A Capabilities-Based Perspective. J. Pers. Sell. Sales Manag. 2012, 32, 317–331. [Google Scholar] [CrossRef]
  43. Rainie, L.; Purcell, K.; Aaron, S. The Social Side of the Internet; Pew Research Center’s Internet & American Life Project: Washington, DC, USA, 2011; Available online: (accessed on 15 January 2020).
  44. Kunz, W.; Aksoy, L.; Bart, Y.; Heinonen, K.; Kabadayi, S.; Ordenes, F.V.; Sigala, M.; Diaz, D.; Theodoulidis, B. Customer engagement in a Big Data world. J. Serv. Mark. 2017, 31, 161–171. [Google Scholar] [CrossRef]
  45. Chathoth, P.K.; Ungson, G.R.; Harrington, R.J.; Chan, E.S.W. Co-creation and higher order customer engagement in hospitality and tourism services: A critical review. Int. J. Contemp. Hosp. Manag. 2016, 28, 222–245. [Google Scholar] [CrossRef]
  46. Gambetti, R.C.; Graffigna, G. The concept of engagement: A systematic analysis of the ongoing marketing debate. Int. J. Mark. Res. 2010, 52, 7. [Google Scholar] [CrossRef]
  47. Hansen, D.L. Exploring social media relationships. Horizon 2011, 19, 43–51. [Google Scholar] [CrossRef]
  48. Benitez, J.; Castillo, A.; Llorens, J.; Braojos, J. IT-enabled knowledge ambidexterity and innovation performance in small U.S. firms: The moderator role of social media capability. Inf. Manag. 2018, 55, 131–143. [Google Scholar] [CrossRef]
  49. Kumar, V.; Dixit, A.; Javalgi, R.R.; Dass, M. Research framework, strategies, and applications of intelligent agent technologies (IATs) in marketing. J. Acad. Mark. Sci. 2016, 44, 24–45. [Google Scholar] [CrossRef]
  50. Goel, L.; Johnson, N.; Junglas, I.; Ives, B. Predicting users’ return to virtual worlds: A social perspective. Inf. Syst. J. 2013, 23, 35–63. [Google Scholar] [CrossRef]
  51. Seol, S.; Lee, H.; Yu, J.; Zo, H. Continuance usage of corporate SNS pages: A communicative ecology perspective. Inf. Manag. 2016, 53, 740–751. [Google Scholar] [CrossRef]
  52. Libai, B.; Bolton, R.; Bügel, M.S.; de Ruyter, K.; Götz, O.; Risselada, H.; Stephen, A.T. Customer-to-Customer Interactions: Broadening the Scope of Word of Mouth Research. J. Serv. Res. 2010, 13, 267–282. [Google Scholar] [CrossRef]
  53. Harrigan, P.; Miles, M. From e-CRM to s-CRM. Critical factors underpinning the Social CRM activities of SMEs. Small Enterp. Res. 2014, 21, 99–116. [Google Scholar] [CrossRef]
  54. Rishika, R.; Kumar, A.; Janakiraman, R.; Bezawada, R. The Effect of Customers’ Social Media Participation on Customer Visit Frequency and Profitability: An Empirical Investigation. Inf. Syst. Res. 2013, 24, 108–127. [Google Scholar] [CrossRef]
  55. Ray, S.; Kim, S.S.; Morris, J.G. The central role of engagement in online communities. Inf. Syst. Res. 2014, 25, 528–546. [Google Scholar] [CrossRef]
  56. Anning-Dorson, T.; Hinson, R.E.; Amidu, M.; Nyamekye, M.B. Enhancing service firm performance through customer involvement capability and innovativeness. Manag. Res. Rev. 2018, 41, 1271–1289. [Google Scholar] [CrossRef]
  57. Anning-Dorson, T. Customer involvement capability and service firm performance: The mediating role of innovation. J. Bus. Res. 2018, 86, 269–280. [Google Scholar] [CrossRef]
  58. Feng, T.; Cai, D.; Zhang, Z.; Liu, B. Customer involvement and new product performance: The jointly moderating effects of technological and market newness. Ind. Manag. Data Syst. 2016, 116, 1700–1718. [Google Scholar] [CrossRef]
  59. Sanchez, G.; Trinchera, L.; Russolillo, G. plspm: Tools for Partial Least Squares Path Modeling (PLS-PM); Trowchez Editions: Berkeley, CA, USA, 2017. [Google Scholar]
  60. Henseler, J.; Ringle, C.M.; Sinkovics, R.R. The use of partial least squares path modeling in international marketing. In New Challenges to International Marketing (Advances in International Marketing, Volume 20); Sinkovics, R.R., Ghauri, P.N., Eds.; Emerald Group Publishing Limited: Bingley, UK, 2009; pp. 277–319. [Google Scholar]
  61. Jayachandran, S.; Sharma, S.; Kaufman, P.; Raman, P. The Role of Relational Information Processes and Technology Use in Customer Relationship Management. J. Mark. 2005, 69, 177–192. [Google Scholar] [CrossRef]
  62. Trainor, K.J.; Andzulis, J.M.; Rapp, A.; Agnihotri, R. Social media technology usage and customer relationship performance: A capabilities-based examination of social CRM. J. Bus. Res. 2014, 67, 1201–1208. [Google Scholar] [CrossRef]
  63. Sophonthummapharn, K. The adoption of techno-relationship innovations: A framework for electronic customer relationship management. Mark. Intell. Plan. 2009, 27, 380–412. [Google Scholar] [CrossRef]
  64. Premkumar, G.; Roberts, M. Adoption of new information technologies in rural small businesses. Omega 1999, 27, 467–484. [Google Scholar] [CrossRef]
  65. Mehrtens, J.; Cragg, P.B.; Mills, A.M. A model of Internet adoption by SMEs. Inf. Manag. 2001, 39, 165–176. [Google Scholar] [CrossRef]
  66. Iacovou, C.L.; Benbasat, I.; Dexter, A.S. Electronic Data Interchange and Small Organizations: Adoption and Impact of Technology. MIS Q. 1995, 19, 465. [Google Scholar] [CrossRef]
  67. Reinartz, W.; Krafft, M.; Hoyer, W.D. The Customer Relationship Management Process: Its Measurement and Impact on Performance. J. Mark. Res. 2004, 41, 293–305. [Google Scholar] [CrossRef]
  68. Demirci Orel, F.; Kara, A. Supermarket self-checkout service quality, customer satisfaction, and loyalty: Empirical evidence from an emerging market. J. Retail. Consum. Serv. 2014, 21, 118–129. [Google Scholar] [CrossRef]
  69. Callegaro, M.; Manfreda, K.L.; Vehovar, V. Web Survey Methodology; SAGE Publications: London, UK, 2015. [Google Scholar]
  70. Soper, D.S. A-Priori Sample Size Calculator for Structural Equation Models [Software]. Available online: (accessed on 5 February 2020).
  71. Chin, W.W. Issues and Opinion on Structural Equation Modeling. MIS Q. 1998, 22, 8–17. [Google Scholar]
  72. Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 1981, 18, 39. [Google Scholar] [CrossRef]
  73. Henseler, J.; Hubona, G.; Ray, P.A. Using PLS path modeling in new technology research: Updated guidelines. Ind. Manag. Data Syst. 2016, 116, 2–20. [Google Scholar] [CrossRef]
  74. Hu, L.T.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model. 1999, 6, 1–55. [Google Scholar] [CrossRef]
  75. Braojos-Gomez, J.; Benitez-Amado, J.; Javier Llorens-Montes, F. How do small firms learn to develop a social media competence? Int. J. Inf. Manag. 2015, 35, 443–458. [Google Scholar] [CrossRef]
  76. Foltean, F.S.; Trif, S.M.; Tuleu, D.L. Customer relationship management capabilities and social media technology use: Consequences on firm performance. J. Bus. Res. 2019, 104, 563–575. [Google Scholar] [CrossRef]
  77. Brodie, R.J.; Ilic, A.; Juric, B.; Hollebeek, L. Consumer engagement in a virtual brand community: An exploratory analysis. J. Bus. Res. 2013, 66, 105–114. [Google Scholar] [CrossRef]
  78. Hennig-Thurau, T.; Malthouse, E.C.; Friege, C.; Gensler, S.; Lobschat, L.; Rangaswamy, A.; Skiera, B. The Impact of New Media on Customer Relationships. J. Serv. Res. 2010, 13, 311–330. [Google Scholar] [CrossRef]
  79. Kumar, V. Profitable Customer Engagement: Concept, Metrics and Strategies; SAGE Publications: New Delhi, India, 2013. [Google Scholar]
  80. De Vries, N.J.; Carlson, J. Examining the drivers and brand performance implications of customer engagement with brands in the social media environment. J. Brand Manag. 2014, 21, 495–515. [Google Scholar] [CrossRef]
  81. Rather, R.A.; Sharma, J. Dimensionality and Consequences of Customer Engagement: A Social Exchange Perspective. Vis. J. Bus. Perspect. 2019, 23, 255–266. [Google Scholar] [CrossRef]
  82. Jaakkola, E.; Alexander, M. The Role of Customer Engagement Behavior in Value Co-Creation. J. Serv. Res. 2014, 17, 247–261. [Google Scholar] [CrossRef]
Figure 1. Conceptual model.
Figure 1. Conceptual model.
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Figure 2. Results of the hypotheses testing.
Figure 2. Results of the hypotheses testing.
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Table 1. Constructs and corresponding measurement items.
Table 1. Constructs and corresponding measurement items.
External pressure (EXP)Customers’ requirements indicate that an organization needs to use social media (EXP_a)
Customers’ behaviors indicate that an organization needs to use social media (EXP_b)
Our organization has seen other companies benefit from social media (EXP_c)
Organizational readiness (ORE)My organization has established clear business objectives concerning customer acquisition, retention, and expansion and has communicated these objectives to all employees (ORE _a)
Top management has clearly indicated their commitment to a social media strategy (ORE _b)
Our organization is capable of the quick introduction of new information technology into the process of customer relationship management (ORE _c)
Our organization has knowledgeable personnel to provide technical support for the utilization of computer technology in building customer relationships (ORE _d)
SM use (SMU)Social media use[61,62]
Enterprise-initiated CE
Activate brand ambassadors and opinion leaders in campaign activities (ECE_a)
Inform customers about special offers (ECE_b)
Communicate success stories (ECE_c)
Provide prompt correspondence to customer requests (ECE_d)
Provide online space where customers connect, share their experience, and learn from each other (ECE_e)
Encourage customers to write a review/testimonial (ECE_f)
Encourage customers to refer your business to others (ECE_g)
Leverage innovative insights by requesting customers’ individual opinions, tastes, or beliefs (ECE_h)
Researcher defined, informed by [1,17,67]
Marketing performance (MPE)Our customers are pleased with the quality of service provided by our organization (MPE_a)
Our customers often speak positively about our organization (MPE_b)
The market share has grown (MPE_c)
The sales revenue has grown (MPE_d)
Table 2. Convergent validity of the measurement model.
Table 2. Convergent validity of the measurement model.
ConstructItemsItems MeanSDFactor LoadingsAVECRCronbach’s Alpha
External pressure (EXP)EXP_a4.0760.9130.9340.7880.8780.864
Organizational readiness (ORE)ORE_a3.7061.0360.9020.6920.9450.858
SM use (SMU)SMU2.8401.2751.0001.0001.0001.000
Enterprise-initiated CE (ECE)ECE_b3.7731.0530.6880.5440.7230.790
Marketing performance (MPE)MPE_a3.5040.9460.7600.6910.8780.848
Table 3. Discriminant validity evaluation using Fornell–Larcker criteria.
Table 3. Discriminant validity evaluation using Fornell–Larcker criteria.
Notes: Numbers on diagonal (given in bold) are square roots of AVE.
Table 4. Effect analysis.
Table 4. Effect analysis.
RelationshipsDirect EffectsIndirect EffectsTotal Effects
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