The influence of webcare in the after-‐sales
phase on customer retention
‘A comparison of customer retention, customer satisfaction, affective commitment and
calculative commitment, of customers that use different after-‐sales contact channels’
Master Thesis Jeroen Osinga
The influence of webcare in the after-‐sales
phase on customer retention
‘A comparison of customer retention, customer satisfaction, affective commitment and
calculative commitment, of customers that use different after-‐sales contact channels’
Jeroen Osinga
Business Administration -‐ Marketing Management Master Thesis
Rijksuniversiteit Groningen (University of Groningen) October 2012 Jeroen Osinga Friesestraatweg 322 9718 NT Groningen Telephone: 06-‐14280900 E-‐mail: jeroen_osinga@outlook.com Student number: 2042193
First supervisor: drs. J. (Hans) Berger Second supervisor: E. de Haan, MSc. External supervisor: H. Latenstein van Voorst
Management Summary
One relatively new aspect of marketing that obtained a lot of attention in recent years, both in research (e.g. Neslin et al., 2006; Verhoef et al., 2007; Neslin and Shankar, 2009) and in practice, is multichannel customer management. Prior literature distinguishes several customers’ decision stages with regard to this concept, namely need recognition, information search, purchase and after-‐sales (Neslin et al., 2006). However, up to now most research in multichannel marketing has focused on the phases of information search and purchase (e.g. Verhoef et al., 2007; Kumar and Venkatesan, 2005; Konus et al., 2008). In particular, the after-‐sales phase has been underexposed. Therefore, this study investigates effects of companies’ after-‐sales activities.
The effectiveness of these activities is based on customer retention rates in this study, since customer retention rates are an appropriate after-‐sales performance metric. Retention rates are “the chance that the account will remain with the vendor for the next purchase” (Jackson, 1985), and have been
increasingly important since research showed that improved customer retention has the largest impact on customer value, compared to improved margins and reduced acquisition costs (Gupta et al., 2004). Especially retention rates of customers that are using the webcare channel to interact with companies in the after-‐sales phase are under investigation in this study, and are compared with retention rates of customers that are using the callcenter and customers that are not contacting companies in the after-‐ sales phase.
From a company perspective, webcare can be defined as: “The act of engaging in online interactions with (complaining) consumers, by actively searching the web to address consumer feedback (e.g., questions, concerns and complaints)” (Van Noort and Willemsen, 2012). These interactions are often a response to online word-‐of-‐mouth, which is very important for today’s companies. Twitter-‐users send hundreds of millions of messages daily, of which 19% contains in some way a message about a brand (Jansen et al., 2009).
Retention rates in this study are based on a scale developed by Gustafsson et al. (2005) (Appendix 1), which assumes that customer retention depends on customer satisfaction, affective commitment, and calculative commitment. The rates are measured on a 7-‐point Likert scale.
A multivariate linear regression with customer retention as dependent variable, and (independent) dummy variables for webcare-‐usage and callcenter-‐usage shows that customer retention rates of webcare-‐users are significantly higher on a 7-‐point Likert scale than retention rates of customers that are not contacting the company in the after-‐sales phase (Table 1.1). Moreover, customer satisfaction rates, affective commitment rates, and calculative commitment rates of webcare-‐users are significantly higher than those of no-‐contact customers.
In addition, the regression results show that retention rates of callcenter-‐users are significantly lower than retention rates of customer that are not contacting the company in the after-‐sales phase (Table 1.1). Furthermore, it is found that customer satisfaction rates and affective commitment rates of callcenter-‐users are significantly lower than those of customers that are not contacting the company in the after-‐sales phase. Only calculative commitment rates of callcenter-‐users are not significantly lower than calculative commitment rates of no-‐contact customers.
B Standard Error P-‐value
Constant 3.990 0.070 <0.001
Webcare usage 0.460 0.182 0.012
Callcenter usage -‐ 0.363 0.094 <0.001
Table 1.1: Customer retention regression coefficients
Apparently, the callcenter of KPN is not performing very well with respect to the important after-‐sales objective of customer retention. One possible reason for the relative low scores of callcenter-‐customers may be that these customers are contacting the company because they are dissatisfied about the company in the first place. Logically, their satisfaction rates and retention intentions are lower than those of customers that are not contacting the company in the after-‐sales phase. However, in contrast to the webcare channel, the callcenter is not able to take away the dissatisfaction. Therefore, it seems to be advantageous for companies to aim for a higher webcare-‐usage among customer in the after-‐sales phase.
Preface
This preface can, in some way, also be seen as an afterword, since the completion of this master thesis marks the end of my school career. A new phase of life, with new challenges, lies ahead.
My school career can be characterized as a quest for the right track, with several side roads on its way. After finishing secondary school, I made several successful and less successful stops at the studies of Business Economics and Communication on the Hanzehogeschool in Groningen, and finally I successfully entered a Business Administration pre-‐master program on the University of Groningen, and
subsequently, the Master of Marketing Management.
During this period, a lot of people supported me and I want to thank some of them in particular. Of course, I want to thank my parents, brother, sister and friends. In addition, I want to thank my
supervisor Hans Berger, and my second supervisor Evert de Haan, for their assistance during the process of writing this master thesis. Their suggestions and feedback were very valuable and useful.
Furthermore, I want to thank Hester Latenstein van Voorst, Thom Kokhuis and Inge Brandt from KPN for their indispensable help. Finally, I want to thank YOU for reading this thesis.
Table of contents
Management summary 3
Preface 5
Table of contents 6
1. Introduction 8 1.1 Background 8 1.2 Problem statement 10 1.3 Structure 11 1.4 Scope 11 2. Theoretical framework 12 2.1 Literature overview 12 2.1.1 Word-‐of-‐mouth 12 2.1.2 Online word-‐of-‐mouth 13 2.2 Development of hypotheses and conceptual model 15 2.2.1 Linking webcare to customer retention rates 16 2.2.2 Linking callcenter-‐usage to customer retention rates 20 2.2.3 The moderating effect of webcare platform type 23
2.2.4 Conceptual model 25
3. Research design 27
3.1 Research method 27
3.2 Introduction company 27
3.4 Scale development 29 3.4.1 Contact with company in the after-‐sales phase 29 3.4.2 Degree of independence of platform type 30
3.4.3 Customer retention 30
3.5 Plan of analysis 31
3.5.1 Multiple regression analyses 31
4. Results 33
4.1 General results 33
4.1.1 Non-‐response bias 33
4.1.2 Comparison of groups 35
4.2 Cronbach’s alpha 38
4.3 Multiple regression analysis 38
4.3.1 Model specification 38
4.3.2 Model estimation customer satisfaction 39 4.3.3 Model estimation affective commitment 39
4.3.4 Model estimation calculative commitment 40 4.3.5 Model estimation customer retention 41
1
Introduction
In this first chapter, the background problem of this master thesis and its practical and academic relevance are introduced and are placed into context. Additionally, the problem statement of the study is presented. Finally, the structure of the rest of the paper is described.
1.1 Background
Marketing is “the art of attracting and keeping profitable customers” (Kotler and Armstrong, 1996). In addition, a profitable customer can be described as “a person, household, or company whose revenues over time exceed, by an acceptable amount, the company costs of attracting, selling and serving that customer” (Kotler and Armstrong, 1996).
One relatively new aspect of marketing that obtained a lot of attention in recent years, both in research (e.g. Neslin et al., 2006; Verhoef et al., 2007; Neslin and Shankar, 2009) and in practice, is multichannel customer management. Neslin et al. (2006) define this concept as “the design, deployment and evaluation of channels through which firms and customers interact, with the goal of enhancing customer value through effective customer acquisition and retention”. From this point of view, a channel is “a contact point between the customer and the company”.
Important reasons for the growing interest in multichannel management are the findings that multichannel consumers offer higher revenues, they have a higher share of wallet, a higher past customer value and, in addition, multichannel consumers have a higher probability of staying active (Kumar and Venkatesan, 2005). In other words: the retention probabilities of multichannel consumers are higher. This is an important issue since improved retention rates may lead to increased customer lifetime values (Berger and Nasr, 1998), in particular in the telecommunications industry (Mattersion, 2001). Channels that are often used and evaluated with regard to multichannel management are: brick and mortar stores, Internet and catalogues (e.g. Verhoef et al., 2007).
and multichannel customer management in particular (Neslin et al., 2006), by pointing out, respectively, the activities of ‘keeping profitable customers’ and ‘customer retention’.
A reason for the absence of the after-‐sales phase in previous literature is provided by Konus et al. (2008) by mentioning the fact that the after-‐sales phase is left out of their analysis, because the use of after-‐ sales activities remains unusual in channels and categories in their study. Therefore, adding the after-‐ sales phase to their analysis could have led to unreliable results. However, in the end of their article these researchers encourage other researchers to focus on after-‐sales in the future, since this could possibly lead to an improved understanding of (multi)channel behaviour of consumers.
The use of after-‐sales with regard to several channels, such as the Internet channel, has been increasing in recent years and, therefore, is worth studying. Hence, this study aims to contribute to knowledge about behaviour of companies and consumers in the after-‐sales phase. In particular, customers that are using certain contact channels (i.e. webcare and callcenter) in the after-‐sales phase are compared with customers that are not contacting companies in the after-‐sales phase.
As, of course, is well-‐known, the advent of the Internet has huge importance for companies and, in particular, for their (multichannel) marketing programs and marketing activities. Consequently, an increasingly important, but often underdeveloped contact point between customer and company in the after-‐sales phase is the Internet channel and, as part thereof, the concept of webcare. This concept is highly related to the phenomenon of online word-‐of-‐mouth, which cannot be ignored by today’s companies.
Technological developments have enabled consumers to exchange experiences with organizations online with other consumers. As a result, customer complaints are now shared on social network sites, review sites and blogs (Ward and Ostrom, 2006), which enables (dis)satisfied customers to share their thoughts about companies with millions of other consumers. This is as well mentioned by Jansen et al. (2009) when they found that 19% of all tweets on Twitter contain in some way a message about a brand. At this moment, Twitter has 140 million users who send 340 million messages a day and this number is multiplying constantly. Other social media such as Facebook have comparable figures (Wikipedia, 2012), which demonstrates the enormous importance of social media for companies nowadays.
anticipate on, and in some cases even respond to, online consumer interactions. These responses are also named ‘webcare’ (Van Noort and Willemsen, 2012).
Several prior studies have shown positive relationships between webcare in the after-‐sales phase and performance metrics such as customer brand evaluations (Van Noort and Willemsen, 2012) and brand equity (Breitsohl et al., 2010). Nevertheless, although the link between after-‐sales activities and the objective of customer retention seems to be obvious, and despite the fact that the concepts of customer retention and retention rates have been increasingly important in recent years (Gupta et al., 2004), research with regard to effects of webcare in the after-‐sales phase on customer retention has been missing so far. Therefore, this study aims to contribute to knowledge about this topic.
Another well-‐known, and more traditional, customer contact channel in the after-‐sales phase is the callcenter. Prior research has described that interactions between customers and callcenter agents are used for cross-‐selling activities and maintaining customer satisfaction and customer loyalty (Askin et al., 2007). Consequently, the link between callcenter-‐usage in the after-‐sales phase and the objective of customer retention is evident. This study intends to contribute to knowledge about this topic as well.
1.2 Problem statement
In order to investigate the effects of webcare interactions and callcenter interactions between companies and customers in the after-‐sales phase, the following problem statement is under investigation in this study:
“What are the differential effects of 1) webcare interactions, 2) callcenter interactions, and 3) no contact, between companies and customers in the after-‐sales phase on customer retention, and how is the influence of webcare interactions on customer retention moderated by independence of webcare platform type?
In order to study this problem statement, retention rates of customers who use the webcare channel to contact companies in the after-‐sales phase, customers who use the callcenter channel, and customer who are not contacting companies in the after-‐sales phase, are compared.
The outcomes of this study provide important theoretical and managerial insights about advantages, disadvantages, and tactics with regard to customer-‐contact through different channels in the after-‐sales phase. Since retention rates are increasingly important in mature industries, and in particular in the telecommunications industry (Mattersion, 2001), it is worthwhile to prove a relationship between webcare in the after-‐sales phase and customer retention in the case of a Dutch telecommunications provider. Furthermore, it is useful from a tactical point of view to study the differential effects of different platform types of webcare.
1.3 Structure
The remainder of this report is structured as follows: The next chapter provides a broad literature study with regard to the concepts under investigation. Furthermore, it contains a presentation of hypotheses and, in addition, a conceptual model. The third chapter contains the research design, divided into a description of the research method, data collection and a plan of analysis. Then, the fourth chapter describes the results of the research, and the fifth chapter presents conclusions. Finally, limitations of this study and areas for further research are described.
1.4 Scope
The research in this report is limited to customers of a Dutch telecommunications provider. Only customers in the mobile telecommunications market are under investigation, because Mattersion (2001) describes that retention rates are particularly important in this industry.
Furthermore, retention rates of webcare-‐users, callcenter-‐users and customers without contact with the company in the after-‐sales phase, are compared. Customers that are using other contact channels, such as e-‐mail or stores, and multichannel-‐customers in the after-‐sales phase are beyond the scope of this thesis.
2
Theoretical framework
This second chapter consists of two parts. The purpose of the first part of the chapter is to provide a broad literature overview with regard to the concept of (online) word-‐of-‐mouth, which is a main motive for companies’ webcare deployment. Additionally, in the second part of this chapter, an explanation about how to link webcare to customer retention is provided. Furthermore, a clarification about how to compare webcare-‐users with callcenter-‐users and customers without contact with companies in the after-‐sales phase is given. Moreover, the suggested moderating effect of ‘independence of webcare platform type’ is introduced. Finally, the second part of this chapter contains a description of hypotheses and a conceptual model that are studied in this report.
2.1 Literature overview
Here, a description of the concepts of word-‐of-‐mouth and online word-‐of-‐mouth and their importance for companies is provided.
2.1.1 Word-‐of-‐mouth
There have been numerous studies in the last decades that mention the importance of word-‐of-‐mouth for companies and the huge potential effectiveness of word-‐of-‐mouth in comparison with regular and traditional marketing methods, such as advertising.
In an early study, Katz and Lazarsfeld (1955) almost 60 years ago showed that word-‐of-‐mouth was the most important source of information on purchase decisions for most households in the world at that time. More recently, word-‐of-‐mouth has still been perceived as an effective, but complicated, aspect of marketing, since it has frequently been called the “world’s most effective, yet least understood
marketing strategy” (Misner, 1999; Trusov et al., 2009). Besides, there exists a lot of research that shows the importance of understanding word-‐of-‐mouth with regard to firms marketing activities.
Liu (2006) defines word-‐of-‐mouth as “informal communication among consumers about products and services”. This phenomenon can either be positive of negative. Positive word-‐of-‐mouth gives a direct or an indirect recommendation with regard to product or service consumption. Negative word-‐of-‐mouth is typically about product or service denigration, rumour or private complaining.
reason for this high percentage is the finding that word-‐of-‐mouth is assumed to be more reliable than other sources of information, such as advertising (Liu, 2006).
More recently, Chen et al. (2011) investigated effects of positive word-‐of-‐mouth and negative word-‐of-‐ mouth, and found that negative word-‐of-‐mouth is more influential than positive word-‐of-‐mouth. Trusov et al. (2009) confirmed the importance of word-‐of-‐mouth when they found that word-‐of-‐mouth has a strong impact on new customer acquisition. According to these researchers, word-‐of-‐mouth marketing is attractive for companies, since it creates possibilities to acquire customers with lower costs and faster delivery than traditional advertising.
Villanueva et al. (2008) contributed to demonstrating word-‐of-‐mouth importance for companies when they found that customers acquired through word-‐of-‐mouth provide twice the customer lifetime value of customers acquired through regular channels (e.g. broadcast media, direct mail). According to these authors, customers acquired through word-‐of-‐mouth may actually function as a salesperson for the company, and word-‐of-‐mouth has demonstrated to be more persuasive than traditional marketing (the latter is mentioned by Brown and Reingen (1987) and Herr et al. (1991) as well).
Although most of the abovementioned research on word-‐of-‐mouth focussed on the process of customer acquisition, Villanueva et al. (2008) concentrate on the process of customer retention as well, by
mentioning that companies with customer bases that are primarily based on word-‐of-‐mouth face a higher long-‐term profitability, and in addition, have to spend less on customer retention. This is an important finding since the research in this study aims to find a relationship between companies’ responses towards online word-‐of-‐mouth among customers and customer retention rates. Before we go deeper into the relationship between these responses and customer retention, we first describe the concept of online word-‐of-‐mouth and its differences with ‘traditional’ word-‐of-‐mouth.
2.1.2 Online word-‐of-‐mouth
Online word-‐of-‐mouth is defined in prior literature as “any positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet” (Hennig-‐Thurau et al., 2004). This definition has similarities with Liu’s (2006) earlier mentioned general definition of word-‐of-‐mouth since both definitions cover the elements of consumer interaction about the company. However, the definition about online word-‐of-‐mouth makes clear that the online aspect represents the opportunity to interact with a multitude of people and institutions. As mentioned in the introduction of this report, Twitter-‐ users send hundreds of millions of messages daily, of which 19% contains in some way a message about a brand (Jansen et al., 2009). Given the enormous online developments of the last years it is not unlikely that this number has grown since the publication of the study of Janssen et al. (2009). Therefore, the described impacts of ‘traditional’ word-‐of-‐mouth in the previous paragraph may be much larger with regard to online word-‐of-‐mouth. As a consequence, online word-‐of-‐mouth is considerably more important to companies than the so-‐called ‘traditional’ word-‐of-‐mouth.
Consumers’ activities and motives with regard to online word-‐of-‐mouth
Complaining of consumers is changing from a private to a public area (Ward and Ostrom, 2006). Lots of consumers nowadays show their aversion towards companies on consumer-‐complaints websites. Six particular consumer activities with regard to online complaints can be distinguished (Ward and Ostrom, 2006):
• presenting organizations’ faults as betrayals of customer rights which is worth public complaining
• intensifying the significance of the damage
• stereotyping firm directors as evil betrayers of innocent consumers
• referring to the criticisms of other customers to attribute blame to the organization • presenting themselves as ‘crusaders’ struggling for the dignity of all customers
• encouraging other customers to see themselves as a group, united in their opposition to the organization.
In addition, consumers have several motives to engage in online word-‐of-‐mouth. The most important motives are (Hennig-‐Thurau et al., 2004):
• consumers’ concern for other consumers • the opportunity to improve their self-‐worth.
Several researchers (e.g. Anderson, 1998; Bowman and Narayandas, 2001) find evidence for a U-‐shaped function with regard to online word-‐of-‐mouth, which demonstrates that primarily very satisfied
customers and very dissatisfied customers are likely to participate in online word-‐of-‐mouth.
Additionally, Bowman and Narayandas (2001) found that loyal customers are more likely to engage in online word-‐of-‐mouth. However, these loyal customers were more likely to participate in word-‐of-‐ mouth when they were dissatisfied. As a consequence, these word-‐of-‐mouth interactions were often negative.
In summary, with regard to consumers’ online word-‐of-‐mouth activities and motives, consumers might aim to demonstrate their power in order to influence others and gain revenge (Ward and Ostrom, 2006).
Effects of online word-‐of-‐mouth
Effects of online word-‐of-‐mouth have been increasingly under investigation. One of the most important findings in this respect is the finding that online word-‐of-‐mouth has a causal influence on consumer purchasing behaviour, and that this is especially the case with customers’ reviews of products (Chevalier and Mayzlin, 2006).
Furthermore, Liu (2006), in a study of word-‐of-‐mouth patterns with regard to movie marketing, found that the growing amount of online word-‐of-‐mouth increases the likelihood of consumers using word-‐of-‐ mouth in their decision making with regard to products or services.
Finally, Godes and Mayzlin (2004) mention an advantage of online word-‐of-‐mouth for companies, namely that online word-‐mouth interactions provide an easy and inexpensive way to measure word-‐of-‐ mouth.
2.2 Development of hypotheses and conceptual model
2.2.1 Linking webcare to customer retention rates
Prior research shows that just hoping that the storm of (negative) online word-‐of-‐mouth will blow over is not suitable in the current competitive environment (Van Laer and De Ruyter, 2010). As stated earlier, the Internet channel offers various possibilities for consumers to share their views, preferences, or experiences with other consumers. This may often lead to negative word-‐of-‐mouth, and as a
consequence, create a threat to companies (Ward and Ostrom, 2006). However, the Internet channel offers opportunities for firms as well, namely to take advantage of word-‐of-‐mouth marketing (Trusov et al., 2009). One commentary stated that: “Instead of tossing away millions of dollars on Superbowl advertisements, fledgling dot-‐com companies are trying to catch attention through much cheaper marketing strategies such as blogging and word-‐of-‐mouth campaigns” (Whitman, 2006).
Customer-‐initiated online contacts are now becoming more and more regular because of changing attitudes and technologies (Bowman and Narayandas, 2001). The concept of webcare is an important tool with regard to this topic.
Van Noort and Willemsen (2012) define webcare as: “The act of engaging in online interactions with (complaining) consumers, by actively searching the web to address consumer feedback (e.g., questions, concerns and complaints)”.
Research suggests that webcare, as a reaction to online word-‐of-‐mouth, offers the possibility for companies to involve customers in the service experience even more than just the basic frequently asked-‐question (FAQ) interactions, and that a growing amount of (young) customers prefer contact with online agents rather than contact with human agents (Köhler et al., 2011). Therefore, the possible advantages of webcare for companies are twofold. In the first place, webcare offers opportunities to participate in general online discussions about the company and, thus, offers opportunities to deal with online word-‐of-‐mouth. Secondly, webcare provides options for companies to handle customers’
questions and complaints online.
retention.
In addition, Breitsohl et al. (2010) state that webcare has a positive influence on brand equity.
Furthermore, they suggest as well that satisfactory company responses to online complaints might be crucial with regard to customer retention. However, they do not investigate this suggestion in their study either.
The earlier mentioned definition of multichannel customer management states that a goal of
multichannel management is enhancing customer value, partly through effective customer retention (Neslin et al., 2006). Additionally, it is found that multichannel shoppers have higher retention probabilities in general (Kumar and Venkatesan, 2005).
In particular, several links between customer retention and companies’ usage of the Internet channel have been suggested in prior literature. For example, Boehm (2008) found that usage of the Internet channel has a strong positive impact on customer retention. In particular, companies’ websites appear to perform well with regard to retention (Verhoef and Donkers, 2005).
Therefore, a positive link between webcare and customer retention rates may be expected in this study as well.
Retention rates are “the chance that the account will remain with the vendor for the next purchase” (Jackson, 1985). This definition suggests that retention rates apply to the period between a certain purchase and a potential subsequent purchase. Consequently, the concept of customer retention relates for a large part to the after-‐sales phase. This makes retention rates a suitable performance measure with regard to after-‐sales activities. Therefore, in this study effects of company/customer interactions in the after-‐sales phase are examined based on customer retention rates.
Customer retention has gained increasing attention in recent years. Gupta et al. (2004) showed that improved customer retention has the largest impact on customer value, compared to improved margins and reduced acquisition costs. Furthermore, these researchers found that a 1% improvement in
retention rates improves firm value with 5%, and the impact of retention on customer value is higher in mature markets. The latter is an important finding is the light of this study, since we focus on the Dutch telecommunications market which is clearly a mature market.
In summary, we can state that customer retention rates are a suitable after-‐sales performance metric, especially in the case of this study.
Gustafsson et al. (2005) and Wetzels et al. (1998) describe three main drivers of customer retention, namely: customer satisfaction, affective commitment, and calculative commitment.
Customer satisfaction can be defined as “a customer's overall evaluation of the performance of an offering to date” (Johnson and Fornell, 1991), and is supposed to have a strong positive influence on customer loyalty, and thus on customer retention, among an extensive range of products and services categories (Fornell 1992; Fornell et al. 1996). For example, Bolton (1998) and Gustafsson et al. (2005) found positive relationships between customer satisfaction and customer retention in the
telecommunications industry, which suggests that increasing customer satisfaction increases customer retention is this industry.
Commitment can be defined as: “a desire to maintain a relationship” (Moorman et al., 1993; Morgan and Hunt, 1994). Additionally, a distinction can be made between affective commitment (i.e. “an emotional variable that develops through the degree of personal involvement that a customer has with a company” (Garbarino and Johnson, 1999; Morgan and Hunt, 1994)) and calculative commitment (i.e. “a more rational, economic-‐based dependence on product benefits due to an absence of choice or switching cost” (Anderson and Weitz, 1992; Dwyer et al., 1987; Heide and John, 1992)).
Verhoef (2003) found that especially affective commitment has a direct influence on customer retention and relationship development.
A positive relationship between webcare contact and customer satisfaction is expected for several reasons in this study.
First, the benefits of online channels, such as better opportunities for personalized marketing, and a greater flexibility and convenience for the customer, compared to other channels (Srinivasan et al., 2002; Wind & Rangaswamy, 2001), apply to the after-‐sales phase as well. These benefits have proven to be a positive influence on customer satisfaction (Shankar et al., 2003: Goldsmith & Freiden, 2004). Second, thanks to webcare deployment companies may expect that consumers sympathize with the company because it proves that they are sensitive to consumers concerns, and take their problems seriously (Van Laer and De Ruyter, 2010).
Fourth, in a service context, overall customer satisfaction is similar to overall evaluations of service quality (Gustafsson et al., 2005). Consequently, companies’ accurate webcare services may increase customer satisfaction.
Finally, as mentioned before, a growing amount of (young) customers prefer contact with online agents rather than contact with human agents (Köhler et al., 2011).
In line with these findings, customer satisfaction rates of customers that have participated in a webcare interaction in the after-‐sales are expected to be significantly higher than customer satisfaction rates of customers without contact with the company in the after-‐sales phase. Therefore, we hypothesize:
H1a: Webcare contact (versus no contact) with the company in the after-‐sales phase is positively
related to customer satisfaction.
The earlier mentioned opportunities for personalized marketing are expected to influence affective commitment of customers towards companies, since affective commitment is defined as ‘an emotional variable that develops through the degree of personal involvement that a customer has with a company’ (Garbarino and Johnson, 1999; Morgan and Hunt, 1994). As the name suggests, personalized marketing and customization can accommodate a high degree of personal involvement (Duray et al., 2000). Moreover, a positive link between webcare contact and affective commitment can be expected because prior research has shown that affective commitment and exhibiting word-‐of-‐mouth behaviour are positively related. This is the case for both positive word-‐of-‐mouth and negative word-‐of-‐mouth (Harrison-‐Walker, 2001). Contact with webcare employees is obviously part of word-‐of-‐mouth behaviour, since webcare is often a response to online word-‐of-‐mouth.
In line with these findings, affective commitment rates of customers that have participated in a webcare interaction in the after-‐sales are expected to be significantly higher than affective commitment rates of customers without contact with the company in the after-‐sales phase. Therefore, we hypothesize:
H1b: Webcare contact (versus no contact) with the company in the after-‐sales phase is positively
Important drivers of calculative commitment are the company’s location advantages versus other companies (Gustafsson et al., 2005). Since customers are able to contact webcare agents from their seats behind their computers at home, companies that are using webcare obviously have location advantages versus companies that are not using webcare.
Moreover, calculative commitment is positively related with customer satisfaction in service contexts (Wetzels et al., 1998), and webcare contact is expected to positively affect customer satisfaction, and consequently, calculative commitment in this study.
In line with these findings, calculative commitment rates of customers that have participated in a webcare interaction in the after-‐sales are expected to be significantly higher than calculative
commitment rates of customers without contact with the company in the after-‐sales phase. Therefore, we hypothesize:
H1c: Webcare contact (versus no contact) with the company in the after-‐sales phase is positively
related to a customer’s calculative commitment towards the company.
Hong and Lee (2005) found that a timely webcare response to online complaining is not only able to solve the customers’ problems, but can also increase customer loyalty and, thus, customer retention. In line with this finding, and in line with our expectations about the positive influence of webcare contact on customer satisfaction, affective commitment, and calculative commitment, which are the main drivers of customer retention (Gustafsson et al., 2005), customer retention rates of customers that have participated in a webcare interaction in the after-‐sales are expected to be significantly higher than customer retention rates of customers without contact with the company in the after-‐sales phase. Therefore, we hypothesize:
H1d: Webcare contact (versus no contact) with the company in the after-‐sales phase is positively
related to customer retention.
2.2.2 Linking callcenter-‐usage to customer retention rates
A more traditional customer contact channel in the after-‐sales phase is the callcenter.
for handling customers’ questions and complaints. Furthermore, callcenters are used for the purposes of customer acquisition and customer retention (Askin et al., 2007).
However, despite these advantages of callcenters for companies, and the suggested goal of customer retention, mainly disadvantages for customers are distinguished in prior literature, which are supposed to harm customer satisfaction, affective commitment, calculative commitment, and thus, customer retention.
Customer satisfaction is expected to be negatively related to callcenter usage for several reasons. First, in many companies it appears that there is often no callcenter employee available to instantly answer the call and customers are frequently placed on hold with numerous other customers waiting in front of them (Askin et al., 2007).
Second, callcenters provide minimal flexibility and convenience to the customer, compared to webcare, (Srinivasan et al., 2002; Wind & Rangaswamy, 2001) since webcare users can respond to the company whenever they want, which makes it more comfortable for them. In contrast, callcenter users have to handle their complaints and questions in the relatively limited time that they are in a conversation with the callcenter employee.
Third, contacting a callcenter is not in all cases for free, which may decrease customers’ satisfaction, because customer satisfaction is defined as: “a customer's overall evaluation of the performance of an offering” (Johnson and Fornell, 1991).
Fourth, a growing amount of (young) customers prefer contact with online agents rather than contact with human agents (Köhler et al., 2011).
Finally, the fact that callcenter interactions are not public might decrease the motivation of employees, compared to webcare employees, to take away customer dissatisfaction. Webcare interactions are, in principle, visible for the whole world and, consequently, inspire employees to do their best job possible. Callcenter employees do not have this additional motivation.
In line with these findings, customer satisfaction rates of customers that have participated in a
H2a: Callcenter contact (versus no contact) with the company in the after-‐sales phase is
negatively related to customer satisfaction.
Compared to the webcare channel, the callcenter channel is supposed to provide fewer opportunities for personalized marketing, because callcenter interactions are generally shorter, which accommodates a lower degree of personal involvement (Duray et al., 2000). This negatively influences a customer’s affective commitment towards the company (Garbarino and Johnson, 1999; Morgan and Hunt, 1994). Consequently, it is expected that callcenter employees are less able to ensure customers’ affective commitment, compared to their webcare colleagues.
Moreover, since callcenter usage has no clear link to word-‐of-‐mouth behaviour, callcenter-‐users are expected to have a lower affective commitment than webcare-‐users, because word-‐of-‐mouth behaviour and, consequently, webcare-‐usage, is positively related to affective commitment (Harrison-‐Walker, 2001).
In line with these findings, affective commitment rates of customers that have participated in a
callcenter interaction in the after-‐sales are expected to be significantly lower than affective commitment rates of customers without contact with the company in the after-‐sales phase. Therefore, we
hypothesize:
H2b: Callcenter contact (versus no contact) with the company in the after-‐sales phase is
negatively related to a customer’s affective commitment towards the company.
Callcenter usage is expected to decrease customers’ calculative commitment for three reasons. First, calling a callcenter is not always for free, and calculative commitment is ‘a more rational, economic-‐based dependence’ (Anderson and Weitz, 1992; Dwyer et al., 1987; Heide and John, 1992). Second, location advantages are an important driver of calculative commitment, and callcenters are not always easy to reach due to waiting times.
In line with these findings, calculative commitment rates of customers that have participated in a callcenter interaction in the after-‐sales are expected to be significantly lower than calculative
commitment rates of customers without contact with the company in the after-‐sales phase. Therefore, we hypothesize:
H2c: Callcenter contact (versus no contact) with the company in the after-‐sales phase is
negatively related to a customer’s calculative commitment towards the company.
Despite advantages for companies and the suggested goal of customer retention, mainly disadvantages of customer usage of callcenters are distinguished in prior literature. In line with this finding, and in line with the expectations about the negative influence of callcenter interactions on customer satisfaction, affective commitment, and calculative commitment, callcenter contact is expected to have a negative influence on customer retention, since customer satisfaction, affective commitment, and calculative commitment are the three most important drivers of customer retention (Gustafsson et al., 2005). In other words: customer retention rates of callcenter-‐users are expected to be lower than customer retention rates of customers that are not contacting the company in the after-‐sales phase.
Therefore, we hypothesize:
H2d: Callcenter contact (versus no contact) with the company in the after-‐sales phase is
negatively related to customer retention.
2.2.3 The moderating effect of webcare platform type
Research shows that effects of webcare deployment on brand evaluations are moderated by the type of platform on which the webcare deployment takes place. Van Noort and Willemsen (2012) and Lee et al. (2011) distinguish brand-‐generated platforms (e.g. company website) and consumer-‐generated
on dependent platforms, is more often seen as unpleasant by consumers, and Fournier and Avery (2011) described that webcare intervention on independent platforms is more often seen as disturbing and intrusive. Moreover, in particular companies’ websites appear to perform well with regard to retention (Verhoef and Donkers, 2005), which suggests that dependent platforms are appropriate for retention purposes.
Webcare on independent platforms, compared to webcare on dependent platforms, is more often a reaction to consumer complaining, and to a lesser extent about consumers’ questions. Therefore, consumers on independent platforms are expected to be dissatisfied about the company in the first place, and this dissatisfaction may be enlarged by the fact that webcare on independent platforms can be seen as unpleasant, disturbing and intrusive (Van Noort and Willemsen, 2012; Fournier and Avery, 2011).
Therefore, with regard to the moderating effect of independence of platform type, we hypothesize:
H3a: Independence of webcare platform type decreases the effect of webcare contact with the
company in the after-‐sales phase on customer satisfaction.
Customers that are using dependent platforms are supposed to have a larger personal involvement towards the company than customers that are using independent platforms for their questions and complaints, because going to a company website, creating an account, and asking your question on the right place, takes more effort than just talking about/to the company through (independent) social media. As mentioned earlier, personal involvement is an important driver of affective commitment (Garbarino and Johnson, 1999; Morgan and Hunt, 1994).
Therefore, we hypothesize:
H3b: Independence of webcare platform type decreases the effect of webcare contact with the
company in the after-‐sales phase on affective commitment.
Dependent platforms, such as company websites, have location advantages versus independent
