Benefitting from virtual customer environments: An empirical study of customer engagement
Introduction
An increasing amount of companies have started to make use of their own online customer platform to have customers engage with the company and with each other (Mathwick et al., 2008, Van Doorn et al., 2010, Wagner and Majchrzak, 2007). These so-called virtual customer environments (VCEs), defined here as company-hosted electronic platforms that facilitate digital communications between customers and company employees (Nambisan, 2002), have been described as effective, reliable and low-cost digital platforms to maintain relationships with customers as well as to provide customer service (Das, 2003). Previous research has confirmed this potential of VCEs by demonstrating successful practices across industries such as automobiles (Füller, Bartl, Ernst, & Muhlbacher, 2004), e-commerce (Dholakia, Blazevic, Wiertz, & Algesheimer, 2009), sports equipment (Füller, Jawecki, & Mühlbacher, 2007) and software (Jeppesen & Frederiksen, 2006).
Despite the rise and attributed advantages of VCEs only little is known about the drivers of customer engagement (CE) within VCEs. This lack of knowledge is quite remarkable given that establishing an engaged population of VCE users has been labeled as a prerequisite for companies to achieve VCE success (e.g., see Bishop, 2007, Hagel, 1999). Also from a conceptual and contextual perspective, studying the drivers of CE in VCE settings seems of high interest. Reflecting customers behavioral manifestations, beyond transactional behavior, that have a firm or brand focus and that are derived from motivational drivers (Van Doorn et al., 2010, p. 254), CE has been put forward as a relatively renewed concept that integrates a multitude of non-transactional behaviors such as customer retention, referral/word-of-mouth, supporting other customers, and co-creation (Kumar et al., 2010, Verhoef et al., 2010, Vivek et al., 2012). These kinds of behavior are typically observed in VCEs, where customers exhibit loyalty (Rosenbaum & Massiah, 2007), share their thoughts and opinions (Hennig-Thurau, Gwinner, Walsh, & Gremler, 2004), help other customers (Verhagen, Nauta, & Feldberg, 2013), and may assist the company in improving/designing products (Füller et al., 2007). Overall, this underlines the relevance of studying CE in a VCE context.
This study aims to answer the question what drives VCE users to become engaged with the VCE? To answer this question we develop and validate a model grounded in uses and gratifications theory (UGT) (Katz, Blumler, & Gurevitch, 1973). Drawing upon UGT we examine the role of four types of perceived VCE benefits as direct determinants of CE intentions: cognitive benefits, social-integrative benefits, personal-integrative benefits, and hedonic benefits. These four benefits represent basic behavioral needs underlying people’s use of new media and online technology (Nambisan & Baron, 2007), and are included as core constructs in the model to address the elementary reasons why customers make use of VCEs. To gain more understanding of how customers show engagement through VCEs we add eight VCE-specific characteristics to the model. These VCE characteristics are, following recent suggestions for adding contextual richness to grounded research models (see Hong, Chan, Thong, Chasalow, & Dhillon, 2013), incorporated as indirect determinants of engagement intentions, mediated through the four aforementioned benefits.
This paper intends to make several contributions. First, the adoption of CE as key concept implies that we add to the underexplored field of CE research. Despite its assumed relevance, theoretical and empirical studies into CE are scarce (e.g., see Van Doorn et al., 2010, Verhoef et al., 2010) and more research, especially in interactive environments, is openly called for (Brodie, Ilic, Juric, & Hollebeek, 2013). Second, we shed light on the drivers of CE intentions by modeling and testing the four benefit types as identified in UGT as determinants. As such, we conceptually integrate the research streams on CE and UGT and empirically assess the value of this integration in terms of explanatory power. Third, by including VCE-specific characteristics as determinants of VCE benefits, and thereof CE intentions, we aim to assist VCE managers and designers in prioritizing their development efforts. The significance and the magnitude of the effects found may serve as actionable guidelines to improve the perceived benefits of VCEs and lead to more engaged customers, as such positively influencing the success of VCEs.
The remaining of this paper is organized as follows. First, we elaborate on the theoretical background of our study and report on a systematic review of the VCE literature. Then, we introduce our research model, discuss its nomological considerations, and postulate the hypotheses. Next, using survey data collected via three different VCEs in The Netherlands, we estimate and cross-validate our model, report on the findings, and discuss the theoretical and practical implications. Finally, interesting avenues for further research are suggested.
Section snippets
Customer engagement
Characteristic for the CE concept is that it extends the value a customer has for a company (Kumar et al., 2010; Marketing Science Institute, 2010). Instead of viewing customer value as equivalent of transactional value (e.g. the monetary value of the purchase), CE substantiates that the value customers deliver to an organization goes ‘beyond the purchase’ and stems from a multitude of other behavioral manifestations, which have a firm or a brand focus (Kumar et al., 2010, Vivek et al., 2012).
Research model and hypotheses
Fig. 1 displays our research model. Following our research objectives and in line with UGT, the four benefits are modeled as determinants of CE intention, that is, customers’ willingness to perform a multitude of non-transactional behaviors, with both other customers and representatives of the organization, that deliver value to the organization (Kumar et al., 2010, Wagner and Majchrzak, 2007). The eight selected VCE characteristics are linked to this theoretical backbone as determinants of the
Procedure
An online survey was conducted to collect the data. Three independent samples were collected from three VCEs facilitated by well-known mobile telecommunication providers in The Netherlands. Each of these VCEs services registered customers by the provision of customer service, by giving a platform of feedback, and by having customers interact with each other. Our decision to collect data within the telecom industry was supported by the fact that companies within this highly competitive industry
Results
To analyze the three datasets we initially estimated the research model by making use of covariance-based structural equation modeling (Amos 20, IBM SPSS). The resulting fit indices, however, indicated unacceptable fit with the data, also after removing a few conflicting items. In such instances, especially when models contain a relatively large number of constructs and items (Chin, 2010), it is common practice to make use of partially least squares (PLS) structural equation modeling (Hair,
Key findings
The empirical results of this study lead to three key findings. First, taking the entire model structure into consideration, the analyses provide strong and robust support for the overall assumption that VCE-specific characteristics lead to perceived VCE benefits, and thereof CE intention. Except for the variance explained of cognitive benefits for VCE 2 (28%), all explained variances are 35% or, mostly, higher. Furthermore, 27 of the 36 beta coefficients across the three datasets are higher
Acknowledgement
The authors would like to state their gratitude to inSided for their support in the data collection in this study. In particular we thank Martine van Deursen and Joris Dieben.
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