Michelle Oude Groen 10281495
Why organizations should promote the use
ofSNSfor after-sales service and how they can
do this
Bachelor’s Thesis
Supervisor: A.C. Krawczyk Business Studies 2013-2014 25June 2014
Table of Contents
2 Theoretical Framework ... 5
2.1 What is Social Media? ... 6
2.1.1 Social Networking... 7
2.2 Age ... 8
2.2.1 Social Network Use by different Ages... 9
2.2.2 Only Privacy Concerns by different Ages ... 10
2.3 Organizational Benefits of After-Sales Customer Service through SNS ... 11
2.3.1 Why Organizations should invest in their After-Sales Customer Service ... 11
2.3.2 Why using SNS for After-Sales Customer Service is beneficial ... 12
3 Conceptual Framework ... 13
3.1 Age, Online Privacy Concerns and Use of SNS ... 14
3.1.1 Age and Use of SNS ... 14
3.1.2 Online Privacy Concerns as Mediator... 14
3.2 Age, Use of SNS and After-Sales Customer Service through SNS ... 15
3.2.1 Age and After-Sales Customer Service... 15
3.2.2 Use of SNS as Mediator ... 16
4 Research Design and Methodology ... 16
4.1 Data collection and sources... 17
4.2 The sample ... 18
4.3 Measurements ... 19
4.3.1 Age ... 20
4.3.2 Social Network Sites Use ... 20
4.3.3 Online Privacy Concerns ... 20
4.3.4 Willingness to use Customer Service through Social Media ... 20
4.3.5 Other variables ... 21
4.4 Data Analysis ... 21
4.5 Analyses and Predictions ... 21
4.5.1 Conceptual Model 1 ... 21 4.5.2 Conceptual Model 2 ... 22 5 Results ... 23 5.1 Correlations ... 23 5.1.1 Conceptual Model 1 ... 23 2
5.2.1 Conceptual Model 2 ... 24
5.2 Regression ... 25
5.2.1 Conceptual Model 1 ... 25
5.2.2 Conceptual Model 2 ... 26
6 Discussion... 27
6.1 Key Findings – Conceptual Model 1 ... 28
6.1.1 Hypothesis 1 ... 28
6.1.2 Hypothesis 2 ... 29
6.2 Key Findings – Conceptual Model 2 ... 31
6.2.1 Hypothesis 3 ... 31
6.2.2 Hypothesis 4 ... 32
6.3 Other implications and Limitations... 33
7 Conclusion ... 33
Bibliography ... 35
Appendix 1- Survey ... 39
Appendix 2 – SPSS Output ... 46
Table index Table 1: Internet Use by Age, Source: Statline.cbs.nl (2014) ... 9
Table 2: Social Network Use by Age, Source: CBS.nl (2013) ... 10
Table 3: Age Respondents ... 18
Table 4: Educational level Respondents ... 19
Table 5: Gender Respondents ... 19
Table 6: Hypotheses and measurements ... 23
Table 7: Conceptual Model 1 - Pearson Correlation (r) ... 24
Table 8: Conceptual Model 2 - Pearson Correlation (r) ... 25
Table 9: Conceptual Model 1 - Indirect effect (mediation) ... 26
Table 10: Conceptual Model 2 - Indirect effect (mediation) ... 27
Table 11: Summary findings hypotheses ... 33
Figure index Figure 1: Conceptual Model 1 ... 15
Figure 2: Conceptual Model 2 ... 16
Figure 3: Conceptual Model 1 - Regression ... 26
Figure 4: Conceptual Model 2 - Regression ... 27
1 Introduction
During the financial crisis organizations had to change their strategy. In most cases this meant cutting costs, which most of them started to do on marketing. They had to find cheaper ways to market their products in a cost-efficient way. The rise of social media provided multiple
opportunities to lower costsin each stage of marketing (Prez, 2014).
This is also the case for after-sales customer service. Even though this is relatively difficult to manage, the aftermarket can raise revenues to a large extent (Cohen, Agrawal & Agrawal, 2006, p.1). So by using after-sales customer service through social media,
organizations could have relatively low costs for direct end-consumer contact and thereby raise their revenues by being more efficient in comparison to the use of other communication tools (Kaplan &Haenlein, 2010, p.67).
But are customers willing to use this way of communication with customer service? In order for organizations to provide customers with optimal after-sales service, they need to know what drives which kinds of customers to use social media for their questions and complaints.This thesis will try to perceive whether these differences might occur because of differences between different ages. When knowing this, organizations can approach various age groups differently, which will help optimizing customer satisfaction.The possible concerns about online privacy will also be studied as people might be reluctant to use this form of after-sales service because they put their selves at risk when going online (Gross & Acquisti, 2005).
Even though a lot of research has been done on social media (e.g. Kaplan & Haenlein, 2010;van den Bighelaar & Akkermans, 2013), social media use in different age groups (e.g. Statline.cbs.nl, 2013;Lenhart et al., 2010), after-sales customer service (e.g. Cohen et al., 2006; Christopher, Payne & Ballantyne, 1991) and on the rise of social media usage for marketing within organizations (e.g. Culnan et al., 2010; Mangolds & Faulds, 2009), there is not much literature available on the research of the influence of age on after-sales customer service through social media. This thesis will try to combine these different kinds of literature and try to define to what extent different age groupsprefer to communicate through social media for after-sales customer service. The research question is as followed:
Does age affect a customer’s willingness to use Social Networking sites for after-sales service and what factors cause this willingness?
Social Networking sites are part of social media and will be explained in the theoretical framework.
The research will be theory-driven. The current literature on social media, Social Networking sites, customer service and age differences will be studied and reviewed and the following study will be based on this review. Current theories will help to give a thorough image of the current view on the different topics. The analysis of a questionnaire will help relate these to each other and draw a conclusion.
This thesis will start by analysing and reviewing the relevant theories as explained by existing literature in the second and third paragraph. Within this review an overview will be created of current theories and a conceptual framework will be introduced in order to
communicate patterns and visualize them. This will not only propose additions to the literature, but also provide a synthesis. This synthesis will help arrange and assemble the different elements to make a new statement (Rowntree, 1987 in Saunders, Lewis & Thornhill, 2009, p. 550) and present the hypotheses.
In the fourth paragraph the research design and methodology will be introduced. This part will explain the research method and justify these methodological choices. The overall design and the sample will be defined and the adequateness will be explained.
After this, the method of data collection will be explained, followed by its analysis, resulting in the final results. To create a link with the existing literature, paragraph six will aim to find its relationship with the results. In this paragraph the limitations and opportunities for future research will also be acknowledged. This will provide academics with new insights and give organizations a bigger grasp on customers’ needs. This thesis will end with a conclusion, in which an answer to the research question is tried to be found.
2 Theoretical Framework
In this paragraph the research topic will be thoroughly explained on the basis of existing literature. Thereby, this framework will help understanding and evaluating the given problem. This will be done by first defining the concept of social media. After this, social media use as well as online privacy concerns by different ages will be explained. The framework will end by clarifying why it is important for organizations to invest in after-sales service and why it can be beneficial to do this through social media.
2.1 What is Social Media?
When Tom Truscott and Jim Ellis created the Usenet in 1979, they meant to allow internet users to post their public messages on this worldwide discussion system (Kaplan &Haenlein, 2010, p.60). At the end of the 20thcentury, however, the internet was actually mainly used as a tool to gain information. Just after the beginning of the 21st century, the internet is notably used as a way to interact with others (van den Bighelaar & Akkermans, 2013, p.2), the way it was intended by Truscott and Ellis.
Nowadays, not only Usenet is available, but also innumerable other, improved, communication tools to interact with other internet users. Currently it is not only possible for individuals to create and post public messages, but multiple individuals can also continuously modify content in a participatory and collaborative fashion. This is made possible by the platform Web 2.0, which also created the evolution of social media (Kaplan & Haenlein, 2010, p.61). Social media is the group of online application, build on the foundations of Web 2.0, which allows the creation and exchange of UGS (User Generated Content). UGS is the collective name of all ways in which one can use social media.
The popularity of social media grew throughout the years, but started to grow extensively when access to the internet grew as well. During this time, popular social media like Facebook (2004) where introduced (Kaplan &Haenlein, 2010, p.60). Especially the last few years the use of social media has increased tremendously. In the Netherlands, for instance, the use of social media by 12-75 year olds, grew from 53% to 57% between 2011 and 2012 (Van den Bighelaar &Akkermans, 2013, p.5).
To define the difference between social media and other online communication tools, like news articles, the Organisation for Economic Cooperation and Development (OECD) developed in 2007 three requirements for an online application to be called UGS, social media. First of all, according to the publication requirement, content needs to be accessible to a (selected) group of people, so it has to be published on an accessible website or Social Networking site. These requirements makes sure private messaging, like email and MSN are excluded. According to the second requirement on creative effort, a certain amount of creative effort needs to be shown in the content, which means copying, e.g. news articles, does not count as social media. The last requirement states that the content has to be created outside of professional routines and practices, so no profit can be expected. This final requirement is getting harder to preserve and
would exclude the after-sales customer service through social media this study is about. Therefore, in this thesis, only the first two requirements are included in the definition of social media.
2.1.1 Social Networking
Social media is often confused with Social Networking, which is only a part of social media. Different authors use different definitions of both words, making it harder to understand existing literature on this topic. In this research the definition SNS (Social Networking sites) of Ellison and others (2007) is used, stating that Social Networking sites are web-based services build upon three main constructs; they create a bounded system in which individuals can construct a
(semi-)public profile, where they are able to see a list of other users with whom they are connected in some way and where they can view and search their own or others’ lists of connections within that system.
These SNS are, according to Kaplan & Haenlein (2010, pp 61-63), part of a combination of six different kinds of social media. They state that, next to SNS,social media consists of Blogs, Collaborative projects (e.g. Wikipedia), Content communities (e.g. YouTube), Virtual social worlds (e.g. Second Life) and Virtual game worlds (e.g. World of Warcraft).
These different types of social media are classified by social presence/media richness and self-presentation/self-disclosure. Social presence is the amount of visual, acoustic and physical contact, allowed between communication partners that can be reached. Media richness stands for the information that can be transmitted in a certain time frame, resolving ambiguity and
uncertainty. SNS are, like Content communities, classified as ‘medium’ in both, where Blogs are indicated lower and Virtual social worlds higher.
In self-presentation and self-disclosure, however, Social Networking sites are classified as ‘high’, which is equal to Blogs and Virtual social worlds and higher than Collaborative projects, Content communities and Virtual game worlds. Self-presentation is defined as the extent to which people can present themselves in cyberspace (Schau & Gilly, 2003, in Kaplan & Haenlein, 2010, p. 62), whereas self-disclosure stands for the revelation of personal information and the image he/she wants to give. Examples of personal information would be thought and feelings, but also likes and dislikes.
To sum up, SNS enable users to connect, which they can do through personal information profiles, for which they can invite others, e.g. friends, so they can also access their profiles and send messages between each other (Kaplan & Haenlein, 2010, p.63).
Even though there are a lot of available Social Networks, this research will only study two big Social Networks, given the time frame of the study, namely Facebook and Twitter. Both are explained in the following sections.
2.1.1.1 Facebook
Facebook enables the sharing of text-messages, but thereby also gives the possibility to share pictures, videos and other kinds of media (Kaplan &Haenlein, 2010, p.62). Users can create an online profile, accumulate ‘friends’ who can access it and join virtual groups, which are based on common interests. (Ellison, Steinfield & Lampe, 2007, pp.1143)
Whereas it was initially created by Mark Zuckerberg to keep in touch with other students at his university, Facebook is now one of the most popular and most used Social Networking applications (Kaplan & Haenlein, 2010, p.62). According to Kietzmann et al. (2011, p. 242) Facebook is now created for general masses and Kaplan & Haenlein state that the word ‘Facebook-addict’ has officially been added to the dictionary (2010, pp.64).
2.1.1.2 Twitter
Twitter is a microblogging Social Network that is centred around the exchange of messages that are mostly real-time updates and are meant to create awareness of certain (personal) issues (Kaplan & Haenlein in Kietzmann et al., 2011, p. 244). These messages are called ‘Tweets’, contain a maximum of 140 characters and show status updates of what users do, where they are, how they feel or links to other sites (Kietzmann et al., 2011, p. 242).
Twitter was founded eight years ago, in 2006, but has been growing extensively since. For example, Twitter had241 millionmonthly average users on 31st of December 2013, which shows a 30% year-over-year growth (Investor.twitterinc.com, 2014).
2.2 Age
Social Networking can be used by anyone who has access to internet. According to
Statline.cbs.nl (2014), 94% of all European citizens used the internet in 2012, of which 87% on a daily base. The Dutch had slightly more access, as 96% of them can use the internet, but this is not a substantial difference.
According to van den Bighelaar & Akkermans (2013, pp. 5,10), gender, origin and educational level do not show substantial differences in peoples’ opinion and activities on Social Networking. People of different ages, however, do have different opinions and behaviour
regarding the use of Social Networking and possible Privacy Concerns. This will be discussed in this paragraph.
2.2.1 Social Network Use by different Ages
As discussed before, both access to the internet in general and the rise of social media, as well as Social Networking sites, has only been going on for a small amount of years. Because of this, Kaplan & Haenlein (2010, p.61) refer to younger age groups as ‘digital natives’ and
‘screenagers’, since this generation grew up with the ability and willingness to use the internet and has substantial technical knowledge. This willingness can be seen in the actual active use of internet in 2012 compared to 2005 in Table 1, in which is shown that in 2005 most people of younger age groups already used the internet.
As shown, the active use of internet of older age groups has grown substantially within these seven years. Most remarkable is the internet use of elderly, 65-75 years, as it has almost doubled. However, internet use does decrease with age. The age group <12 is not included, as Social Networking sites have age restrictions that request a minimum age of 12 to register on their sites (Lenhart et al., 2010, p.17), making it not relevant to this research.
Table 1:Internet Use by Age, Source: Statline.cbs.nl (2014)
This is the same with the use of SNS, shown in
Table 2. Between the age of 12 and 25, 88% of all internet users also use Social Networks, whereas of all internet users between 65 and 75 years old, only 17% uses Social Networks. This could be explained by the positive effect SNS have on social capital. Because students, who are a big part of younger age groups, find this very important (Ellison, Steinfield & Lampe, 2007, p.1161). Even though SNS do not mainly
Age (in years) Active Use of Internet ( < 3 months ago)
2005 2012 12-25 98% 100% 25-45 90% 99% 45-65 72% 91% 65-75 34% 71% 9
Table 2: Social Network Use by Age, Source: CBS.nl (2013)
determine perceived social capital, there is however a positive effect (Valenzuals, Park & Kee, 2009, p. 893). According to Brandtzæg, Lüders & Skjetne (2010) younger age groups also
started to use SNS, because their friends used it. This could explain why relatively many younger age groups started to use SNS in a relatively short time. As older age groups also increasingly use SNS, the gap in use is getting smaller.
However, not only the use of Social Networks in general has increased, according to Lenhart et al. (2010, p.18), maintaining a profile on multiple SNS has also grown. Especially adults over 30 are increasingly more willing to keep their profiles up-to-date, with Facebook being most popular (73%).
2.2.2 Only Privacy Concerns by different Ages
Online concerns on privacy are discussed in a large amount of literature. To specify the definition of these concerns, the concept of Ellison and others is used (2008). They state that online privacy concerns consist of six elements, namely hacking and theft of identity, the use of personal information by third-parties, backtracking functions leading to surveillance-like structures, unwanted contact, harassment and stalking, rumours and gossip leading to damage reputations and the unaware sharing of personal information.
According to Dwyer et al. (2007) People often do not expect privacy within SNS or the perceived privacy is undefined. It is thereby hard for sites to deliver the same level of privacy that can be found online as diverse data protection mechanisms and policies are needed. Thereby, by using Social Networks and sharing content with friends, family and other internet users, a conflict arises between two aspects of SNS, namely the need for sociability (social capital) and privacy needs (Brandtzæg, Lüders & Skjetne , 2010, p.1007). When increasing one, the other aspect diminishes. Because of this, privacy concerns are discussed in a lot of existing literature on SNS.
Active Use of Facebook, Twitter and other SNS ( < 3 months ago) Age 2012 12-25 88% 25-45 66% 45-65 34% 65-75 17% 10
Gross & Acquisti (2005), for example, state that users of Social Networks can put themselves at risk, not only online (theft of identity), but also offline (stalking). On average, these privacy concerns are slightly above neutral (Dwyer et al., 2007).
While a lot of literature discusses gender differences in online privacy concerns (e.g. Fogel & Nehmad, 2009; Hoy & Milne, 2010) the concerns on this risks could, however, differ for different age groups. Remarkable is that younger age groups have a higher awareness of privacy concerns. They are more confident about their knowledge, understanding and use of Facebook (Brandtzæg, Lüders &Skjetne, 2010, p.1019). Nevertheless, according to Moore (2004, in Youn, 2009) young adolescents are more likely to be vulnerable to the information collection of e-marketing, as entertainment online is skilfullyblended with promotional messages.
Older age groups, on the other hand, are also concerned, but do often not know as well how to protect their profiles (Brandtzæg, Lüders &Skjetne, 2010, p.1019).As furthermore little research is done on the age-differences in privacy concerns, the question arises to what extent these differences occur and why.
2.3 Organizational Benefits of After-Sales Customer Service through SNS
When researching the difference in willingness to use after-sales customer service through Facebook and/or Twitter, it is beneficial to understand how and why this can be used in practice. First, the advantages of investing time and money into customer service and its after-sales is discussed. Secondly, it is explained for what reasons organizations should use Social Networks to increase these advantages. Both of these subjects are discussed in a lot of literature, as can be seen in the next paragraphs.
2.3.1 Why Organizations should invest in their After-Sales Customer Service
The importance of a customer-oriented business is discussed in a wide range of literature (e.g. Houston, 1986; Parasuraman, 1987). One of the reasons for this importance is that customer-oriented organizations are seen as executing their marketing strategies more successfully than other organizations (Brady & Croning, 2001, p. 248).
Within these strategies, relationship marketing is an important aspect. It consists of quality, marketing and customer service, which have to be brought into close alignment (Christopher et al., 1991).
In addition, Christopher et al. (1991) introduced the six markets model, representing all different markets an organization has to manage its quality in. One of these markets is ‘customer markets’. According to them, it is not only important to get new customers, but also to keep them. As sustaining existing relationships costs less, it is therefore important to properlyfocus on after-sales service.
A lot of organizations, however, do not know the potentials after-sales service can have. Mostly, they see investing in it as more difficult than manufacturing and do not realize they can gain extra revenue from it (Cohen et al., 2006). After-sales is often seen as being unpredictable and heterogeneous, needing revers logistics and a short response time.
Because of this, it is usually seen as causing more problems than creating benefits. This is, however, not true. According to Cohen et al. (2006, p.13) by providing support through after-sales service, organizations can create not only a low-risk revenue stream, but they can also differentiate from competitors by mindfully interacting with their customers. This will not only create competitive advantages, but will also help understand customer’s plans and thoughts, which will separate the organization even more from competitors. So, if after-sales service is organized properly it can be beneficial, it can create interaction with customers and can increase the organization’s competitive advantage.
2.3.2Why using SNS for After-Sales Customer Service is beneficial
When managing customer service, one of the possibilities is to use Social Networking sites. Social Networks open up the opportunity for organizations to talk with their customers, B2C, as well as customers talking to each other, C2C (not discussed in this thesis) (Mangold& Faulds, 2009, p. 359). Li and Bernoff (2008 in Hanna, Roham & Crittenden, 2011, pp. 268-260) defined five participants in using these SNS, namely creators, critics, collectors, joiners and spectators. Organizations can create content on Social Networks and consequently customers can either comment (critics), save and share (collectors), connect (joiners) or read (spectators) this content. Thereby, customers can also create their selves, for example when complaining about a product.
Subsequently, by using Social Networks, organizations can enlarge their accessibility and interacting with their customers, because they enable the creation of virtual customer
environments (Culnan, McHugh and Zubillaga, 2010, p. 243). These environments (VCEs) provide customers with the possibility to engage regularly with organizations and improve
relationships, which makes customers feel they ‘have something to say’. This increases the chance of them being loyal and willing to repurchase from the same organization.
The use of SNS in customer service does, however, mean organizations need to maintain these platforms very often. Thereby, they also lose control (customers can post and share freely) in deciding what is published Mangolds & Faulds, 2009, p. 359). Nevertheless, the advantages outweigh these disadvantages, because using SNS increases customer satisfaction, revenue and cost savings (Culnan et al., 2010).
According to Hanna et al. (2011, p. 268) traditional media tools constitute of a trade-off between consumer engagement and reach, while social media, which includes SNS, enables both. Above all, Mangold & Faulds (2009) even state that social media, are a hybrid element of the promotion mix, because it combines traditional integrated marketing communication tools with increased word-of-mouth communications. In short, it can be said that Social Networking sites do provide organizations with different opportunities that will benefit them in diverse ways.
This theoretical framework is concluded by emphasizing the importance of this thesis. A lot has been discussed on the use of Social Networks like Facebook and Twitter by individuals and for organizational practices and the privacy concerns related to this use. However, little literature is available on the age difference of this use and privacy concerns and thereby the willingness of customers to actually use SNS for after-sales customer service. Organizations can use this thesis to discover if different age groups have different opinions and behaviours on online privacy concerns, the use of Social Networking sites and the use of SNS for after-sales customer service.
3 Conceptual Framework
As discussed above, the use of Social Networking sites, age, privacy concern and willingness to use after-sales customer service through SNS are the most important constructs. Age is believed to be positively related to online privacy concern as well as willingness to use after-sales service through SNS, but seems to be negatively related to the use of Social Networking sites. Next to that, privacy concerns also seem to be negatively related to the amount of SNS use, which in turn is believed to be negatively related to a customer’s willingness to use Social Networking sites for after-sales service. To clarify this, the following paragraphs explain the conceptual framework of these constructs in this thesis.
3.1 Age, Online Privacy Concerns and Use of SNS
In a wide arrange of literature a negative relationship between age and the use of SNS has been discussed (e.g. Kaplan& Haenlein, 2010; Ellison, Steinfield & Lampe, 2007; CBS.nl, 2013). Thereby, after thoroughly reviewing the literature, privacy concerns can be believed to mediate this relationship. Mediation is a form of process analysis in which it is tried to find the variable that causes the observed effects. So in this case it is not tested whether one variable has an effect on another, but instead how it does (Judd & Kenny, 1981).
This paragraph will provide an explanation and will introduce the first two hypotheses.
3.1.1 Age and Use of SNS
As discussed above, age is believed to have a negative relationship with the amount anyone uses Social Networking sites. Even though it is believed that multiple constructs may influence these variables, it is still expected that this is the case. Therefore the relationship between age and the use of SNS will be tested, leading to the first hypothesis:
H1: Age is negatively related to the use of Social Networking Sites
This means it is expected that the older people are, the less they use Social Networking sites.
3.1.2 Online Privacy Concerns as Mediator
As explained, people often do not expect privacy on Social Networking sites or the perceived privacy is unknown, leading to possible risks online as well as offline (Dwyer et al.,2007; Gross & Acquisti, 2005). It is thereby discussed that difference occur through different ages. Not only regarding online privacy concerns, but also how different age groups might deal with it.
(Brandtzæg, Lüders &Skjetne, 2010; Moore, 2004, in Youn, 2009). This study will therefore test if different opinions and behaviours regarding privacy concerns mediate the relationship between age and the use of SNS. Therefore, the following hypothesis is introduced:
H2: Online Privacy Concerns mediate the relationship between Age and the use of Social Networking Sites
In Figure 1, an overview is shown of the first conceptual framework, representing the first 2 hypotheses.
Figure 1: Conceptual Model 1
3.2 Age, Use of SNS and After-Sales Customer Service through SNS
As the amount of organizations offering after-sales customer service through Social Networking sites is growing, it is beneficial to know which customers are willing to use it and what
influences their decision to do so. Unfortunately, no concrete information is available in existing literature. Therefore, the influence of age on this use will be researched in this thesis. Since in a large amount of literature the relationship between age and SNS use is discussed (e.g. Kaplan& Haenlein, 2010; Ellison, Steinfield & Lampe, 2007; CBS.nl, 2013), it is also expected that this influences the relationship between age and ones willingness to use after-sales through customer service. This will be explained in this paragraph and hypotheses 3 and 4 are introduced.
3.2.1 Age and After-Sales Customer Service
As stated above, the relationship between age and someone’s willingness to use Social
Networking sites for after-sales customer service is not yet discussed in most relevant literature. Because of this, the relationship of these two constructs will be tested. This will be done by testing the following hypothesis:
H3: Age is negatively related to the willingness to use After-Sales Customer Service through Social Networking Sites
This means it is expected that the older people are, the less willing they are to use after-sales customer service through SNS.
3.2.2 Use of SNS as Mediator
Whereas the use of Social Networking sites is a dependent variable in the first conceptual model, it will serve as a mediator in the second model. As said before, it has been proved that age influences the amount people use Social Networking sites, like Facebook and Twitter, to a great extent (Statline.cbs.nl, 2014). Because of this, it is expected that the relationship between age and willingness to use after-sales service through SNS is mediated by the use of SNS in general. Because of this the following hypothesis is tested:
H4: The Use of Social Networking Sites mediates the relationship between Age and the willingness to use After-Sales Customer Service through Social Networking Sites.
Figure 2shows the second conceptual model, which represents hypotheses 3 and 4.
Figure 2: Conceptual Model 2
4 Research Design and Methodology
This paragraph will explain how the previously explained four hypotheses are tested, how it is designed, which data was collected, how they were collected and how the four main constructs are measured.
According to Saunders et al. (2009, pp. 61, 151) a research can be quantitative or qualitative and inductive or deductive. To research whether different age groups have different viewpoints on
the use of social media for after-sales customer service, a quantitativea deductive study is executed. This means that the research tries to explain causal relationships between different variables, deducted from existing literature (Saunders et al., 2009, p. 125). In addition to this, it was also tried to find variables that cause these observed effects. As explained in the conceptual framework, mediation is used to execute this form of process analysis. Mediation provides the possibility to test how one variable has an effect on another by measuring indirect effects, which means one or more additional variables are taken into account. If the confidence interval of these indirect effects does not contain zero, it is likely that there is a mediation effect. This means that the additional variable causes the effect of one variable on another and is called a mediator(Judd & Kenny, 1981).
4.1 Data collection and sources
This research was conducted by surveying different, randomly selected, individuals, questioning their opinion and tendency on approaching the customer service of organizations through social media. Because this study is cross-sectional, a survey strategy is used, as a large amount of data can be collected within a short time frame (Saunders et al., 2009, p. 144). The collected data is also standardized, making it easier to compare and understand, giving more control over the process.
The surveys were collected within a time frame of one week. This provided the opportunity to get more participants than would be possible with interviews and it thereby granted sufficient independence when analysing the data (Saunders et al., 2009, p. 144). Moreover, the reliability and validity is also increased when using a standardized survey.
In order to be able to have all participants answer the same set of questions, the technique that is used is a questionnaire (deVaus, 2002 in Saunders et al., 2009, p. 360), in this case
digitalized with Qualtrics. This helps detect opinions and attributes as well as (intended) behaviours of participants (Saunders et al., 2009, p. 368).
This questionnaire started with an introduction. After this the participant was asked to answer some control and demographical questions to subdivide him or her to a certain age and find out if and, if yes, how often they use Social Networking sites. After this the participant was asked to evaluate several propositions, which were arranged by hypothesis and contained statements about their opinions and behaviour. Answering these questions was done using a Likert-scale, which means the respondent is asked to what extent they agree with certain
statements (Saunders et al., 2009, p. 378). This is done by using a 7-point rating scale, from ‘1’ corresponding with the lowest value (strongly disagree) to ‘7’ corresponding with the highest value (strongly agree). The survey can be found in Appendix 1.
Collection of these participants was done by using an online survey tool. The questionnaire was self-administered as it was internet-mediated, which is possible because personal perceptions are asked, so no guidance is needed.
Even though this did exclude respondents, those that do not use the internet, this will only benefit the research, because its main goal is to determine the factors influencing the use of after-sales customer service through Social Networking sites, not the use of internet. That is why this way of data collection is chosen to make sure that people that do not actively use the internet are be excluded from the research.
4.2 The sample
The survey was sent to a large amount of people as Saunders et al. (2009, p. 364) states that the response rate of online surveys is in general relatively low, namely 11% or lower. However, as explained earlier, surveys are more suitable than other forms of data collection given the relatively large amount of participants needed and the limited time frame.
Because of this restricted time frame, this research was eventually limited to 147
participants, of which 131 were usable, differing from 12 to 68 years old. As data from different ages was collected, it was necessary to make sure each age group is represented. The
demographics of the respondents can be found in Table 3, Table 4 and Table 5.
Table 3: Age Respondents
Age (in years) Percentage
10-20 21,4% 21-30 47,3% 31-40 6,1% 41-50 9,2% 51-60 12,2% 61-70 3,8% 18
Table 4: Educational level Respondents
Educational Level Percentage Primary School 3,1%
Vmbo 3,8%
Havo/Mbo 19,8%
Vwo/Hbo 58,0%
University 15,3%
Table 5: Gender Respondents
Gender Percentage Male 39,7& Female 60,3%
All participants were Dutch, making the results and conclusions most applicable for Dutch organizations to base their strategy on. The results of this research will help managers determine how to implement their strategy on after-sales customer service through SNS, taking different age groups into account. As Dutch people use social media in general relatively more than other EU-citizens, international organizations should be careful when making the conclusions
applicable for their national market (van den Bighelaar & Akkermans, 2013, p.10).
4.3 Measurements
People of different ages can or cannot be willing to use after-sales customer service through social media for different reasons. To find out whether this willingness differs between different age groups, four hypotheses are tested, as explained before. Furthermore, to make sure all variables used are reliable, Cronbach's Alpha is computed. This is done as the survey consists of multiple questions measuring the same thing. To ensure they are correlated, the reliability of the variables was tested by computing Cronbach’s Alpha. This alpha is acceptable when it is higher than 0.6, but is considered to be more acceptable between 0.7 and 0.8 or higher (Bland & Altman, 1997, George & Mallery, 2003).
In this paragraph the measurement of the four main constructs used in the hypotheses are explained. The used questionnaire can be found in Appendix 1.
4.3.1 Age
Age was measured by asking respondents what age they are, ranging from 12 to 85. As Facebook and Twitter are officially not possible for people younger than 12, they were not asked to fill in the questionnaire.
4.3.2Social Network Sites Use
The use of Social Networking sites was tested by asking de respondent how often they spend time actively using Facebook and/or Twitter. This means posting and sharing information and reading or responding to posts of others. Possibilities ranged from ‘never’ to ‘every hour, or more’.
4.3.3 OnlinePrivacy Concerns
Privacy on the internet and primarily with social media is often considered to be undefined and caries thereby low expectations (Dwyer et al., 2007, p.2). People often think privacy on online communication platforms is relatively low. For companies it is also harder to guarantee their customers privacy than it would be when using email or the telephone. Privacy concerns could influence whether someone will or will not use after-sales customer service through social media. This will be tested by asking the participants to what extent they agree with certain behaviour and certain opinions regarding their privacy on Social Networking sites. To test this, 8 questions were asked, using a 7-point Likert-scale with a range from ‘1’ (strongly disagree) to ‘7’ (strongly agree). Cronbach’s alpha for this scale is α=0,865. This is very high and therefore the scale is reliable and usable.
4.3.4Willingness to use Customer Service through Social Media
Since using SNS can increase customer satisfaction, revenues and cost savings (Culnan et al., 2010), this study will measure to what extent customers are willing to use this communication tool for after-sales service, as it will determine whether and for who organizations can invest in their Social Networking sites for their customer service. This willingness was measured in three different ways. First it was asked if the respondents already used this way of communicating with customer service for after-sales and, if yes, how often. Secondly, it was asked which ways of communicating they preferred, by asking them to categorize using email, telephone, mail, personally and SNS from most preferred to least preferred. Finally, 5 questions were asked to measure their willingness to start using SNS for customer service by using a 7-point Likert-scale,
ranging from ‘1’ (strongly agree) to ‘7’ (strongly disagree). Cronbach’s alpha for this scale is: α=0,884, which is relatively high, making the items reliable and therefore usable.
4.3.5 Other variables
To prevent no other variable influence the conclusions, the survey also included questions regarding the known possible control variables.
First of all, the participant was asked whether he/she knew Facebook and/or Twitter. This excludes people reluctant to approach a customer service through social media because they don’t even know it exists. Respondents that turned out not to know Facebook and/or Twitter were also excluded from the research.
Secondly, gender and education level were asked, to make sure both do not have any influence on the obtained results. Even though it was not expected they have an influence on the drawn conclusions, respondents were asked about their gender and education level as it is better to confirm these assumptions than to assume they are true.
4.4 Data Analysis
To analyse the collected data, the Statistical Package for the Social Sciences program (SPSS) was used. This program helped testing the hypotheses by running statistical tests of which reliability analysis, correlation and regression are most important. To run all regressions in one model at once a custom dialog developed by Hayes (2013) was used. As the effects of mediation were tested, model 4 was used in the dialog.
4.5 Analyses and Predictions
The data analysis started by testing the reliability and internal consistency of the constructs that were tested using multiple questions. This made sure all answers to the questions moved in the same direction and discussed the same construct. In the analysis, both conceptual models were analysed in four steps. In the following two paragraphs these four steps are discussed and de different types of statistical analysis used are explained.
4.5.1 Conceptual Model 1
In the first conceptual model, 3 variables are enclosed, namely age, online privacy concern and SNS use. In this model, age is the independent variable,SNS use is the dependent variable and online privacy concern is concerned to be the mediator. These constructs are analysed in SPSS in
four steps. First of all, it is tested if there is indeed, as expected in hypothesis 1, a negative relationship between age and the use of Social Networking sites. After this online privacy concern is tested in relation to age, the independent variable, which is expected to be positive. Thirdly, the relationship between the mediator and the dependent variable, privacy concern and SNS use is tested. Finally, the mediation effect of privacy concern is tested. This will provide results regarding hypothesis 2, of which it is expected the effect will be positive. This is done by comparing the direct effect with the indirect effect when SNS use is taken into account.
Additionally, the confidence interval of this interval is also analysed to see if this interval excludes zero and therefore increases the likelihood of a mediation effect.
4.5.2 Conceptual Model 2
The second conceptual model also contains 3 variables; age (independent variable), SNS use (mediator) and a customer’s willingness to use SNS for after-sales service (dependent variable). These variable will also be tested in four steps. First, hypothesis 3 will be tested, which means the relationship between age and a customer’s willingness to use SNS for after-sales service is measured. This effect is expected to be negative. Secondly, the relationship between SNS use and a customer’s willingness to use SNS for after-sales service is studied. It is expected that this relationship will be positive. After this, the same will be done for the relationship between age and SNS use, of which it is expected to be negative. Finally, the fourth hypothesis will be tested, by analysing the mediation effect of SNS use on age and a customer’s willingness to use SNS for after-sales service is measured, of which it is expected that the effect is positive. This is done in the same way hypothesis 2 is tested, by comparing the indirect effect with the direct effect and analysing its confidence interval. The analysis of mediation was also extensively explained in the introduction of this paragraph.
Table 6 provides an overview of each hypothesis with its effect, the used variables, the measurements and the statistical tests that will be done.
Table 6: Hypotheses and measurements
Effect Variables Measurements Statistical Tests
H1 Direct effect Age SNS Use
12-80 years
Never- Every hour or more
Correlation, Regression
H2 Indirect effect Age SNS Use Online Privacy Concerns
12-80 years
Never – Every hour or more 8 Likert scale (1-7) questions
Cronbach’s Alpha, Correlation, Hayes (regression)
H3 Direct effect Age Willingness
12-80 years
6 Likert scale questions
Correlation, Regression
H4 Indirect effect Age Willingness SNS Use
12-80 years
5 Likert scale (1-7) questions Never – Every hour or more
Cronbach’s Alpha, Correlation, Hayes (regression)
5 Results
This paragraph will provide the results gained from using SPSS to analyse the collected data. As explained, correlation and regressions (using Hayes) were used to do the statistical tests. All important relevant results will be introduced in this paragraph and all other output can be found in Appendix 2.
5.1 Correlations
First, it was tested to what extend the different variables in the two conceptual model relate to each other. This was done by testing for correlation using Pearson’s Correlation (r).
Thereby, as explained before, Cronbach’s Alpha was used to make sure all variables are reliable and multiple questions are correlated as some of them are supposed to measure the same thing. The Cronbach’s Alpha can be accepted when it is measured to be at least 0.6, but is more acceptable when it is higher than 0.7 (Bland & Altman, 1997, George & Mallery, 2003).
5.1.1 Conceptual Model 1
The first model measures the relationship between age, SNS use and online privacy concerns, which are respectively the independent, dependent and mediator variables. As explained, online privacy concern was measured by asking the respondent multiple questions to make sure all aspects of online privacy concern were involved. At first, the Cronbach’s alpha was α=0,854, which is high enough, but could be improved by removing the question that asked the respondent
to what extent online privacy concerns hold them back in using Social Networking sites. Therefore, the Cronbach’s Alpha used was α=0,865.
After this, Pearson Correlations were done, to define the relationship between all variables. These results can be found in Table 7. As demonstrated, a positive relationship was found between age and online privacy concern. In this case, the correlation is relatively low, but significant at a 95% level, r(131)=0,200; p<0,05. Age and SNS use are, as expected negatively related to each other. Their correlation is moderate and highly significant, r(131)=-0,305;
p<0,01.
However, no significant correlation was found between online privacy concerns and the use of SNS. The found effect was very small and thereby not significant at a 95% level,
r(131)=-0,060; ns.
Table 7: Conceptual Model 1 - Pearson Correlation (r)
1 2 3 Mean SD
1 Age (1) 0,200* -0,305** 29,9 14,07
2 Online Privacy Concern 0,200* (0,865) -0,060 5,121 1,097 3 SNS Use -0,305** -0,060 (1) 4,920 0,691 *= p<0,05; ** = p<0,01
The means and standard deviations are also provided in Table 7, as well as the
Cronbach’s Alpha, which is indicated on the diagonal. As age and SNS use were measured using only one question, their Cronbach’s Alpha is 1.
5.2.1 Conceptual Model 2
The second model measured the relationship between age (independent), willingness to use after-sales service through SNS (dependent) and SNS use (mediator). In this case, only the willingness was measured using multiple questions. The Cronbach’s Alpha of these questions was α=0,884, so it is sure they are measuring the same thing as this is acceptable and even relatively high (Bland & Altman, 1997, George & Mallery, 2003). This is shown in Table 8.
After this, the Pearson Correlations were also measured for this model. The results, including means and standard deviations, are provided in Table 8. As already mentioned in the previous paragraph, the relationship between age and SNS use is negative, moderate and highly significant, r(131)= -0,305; p<0,01.
As expected, a relationship was found between age and someone’s willingness to use aftersales service through SNS. This relationship is moderate and highly significant, r(131)=
-0,294; p<0,01. Nevertheless, no significant relationship was found between SNS use and this
willingness. The correlation is low and not significant at the 95% level, r(131)= 0,160; ns.
Table 8: Conceptual Model 2 - Pearson Correlation (r)
1 2 3 Mean SD
1 Age (1) -0,305** -0,294** 29,90 14,074
2 SNS Use -0,305** (1) 0,160 4,920 0,691
3 Willingness to Use After-Sales Service through SNS -0,294** 0,160 (0,884) 4,347 1,488 *= p<0,05; ** = p<0,01
5.2Regression
In order to further evaluate and explain the relationships between the different variables
measured, regression analysis were also carried out in SPSS. To facilitate this analysis, a custom dialog for mediation was integrated into SPSS. This custom dialog is made by Andrew Hayes (2013) and provides SPSS with an additional tool to measure mediation without doing separate statistical tests as it shows all relevant results at once. To make sure the results are easy to interpret, this dialog measures the unstandardized B-coefficient.
5.2.1 Conceptual Model 1
As explained, this model tests the relationship between the variables age, online privacy concern and use of SNS. By running the regression, additional information can be gathered on top of the correlations.
First of all, with an explained variance of 4 percent, the model shows a significant, negative effect of age on online privacy concerns, β=0,0156; p<0,05. This means that if age goes up by 1, the amount of online privacy concerns go down by 0,0156, so the older someone is, the more online privacy concerns he/she has.
Secondly, the relationship between age and use of SNS is found to be negative and highly significant, β=-0,0150; p<0,01, with an explained variance of 9,32 percent. This indicates that the younger someone is, the more he/she uses Social Networking sites.
Thirdly, the relationship between online privacy concern and the use of SNS turned out to be very small and not significant, β=0,0004, ns. Thereby, when online privacy concerns were
taken into account, the effect of age on use of SNS did not change. All these results can be found in Figure 3.
Figure 3: Conceptual Model 1 - Regression
To further explain the (lack of) mediation of online privacy concern, the output of Hayes (2013) in SPSS also shows the confidence interval of the indirect effect of age on use of SNS. If the confidence interval does not contain zero, it is likely that there is a mediation effect. In this case, the indirect effect of age on use of SNS by online privacy concern turned out to be very small, close to zero, but the confidence interval of 95% does not contain zero, which indicates that there probably is a mediation effect, but it is very small. These results are demonstrated in Table 9.
Table 9: Conceptual Model 1 - Indirect effect (mediation)
5.2.2 Conceptual Model 2
Conceptual Model 2 tested the relationship between age, use of SNS and someone’s willingness to use after-sales service through SNS.
As explained in the previous paragraph, the relationship between age and use of SNS was negative and significant with an explained variance of 9,32 percent, indicating that older people use SNS less often.
Indirect effect of Age on Use of SNS Effect
Confidence interval (95%) Lower bound Upper bound Online Privacy Concern 0,0000 -0,0014 -0,0019
Secondly, the relationship between age and willingness to use after-sales service through SNS was found to be very significant and negative, as expected (β=-0,0311; p<0,01). This relationship showed an explained variance of 8,64 percent. This indicates that the older people are, the less willing they are to approach customer service through Social Networking sites for after-sales questions or complaints.
Thirdly, the relationship between the use of SNS and willingness turned out to be not significant. However, when taken the use of SNS into account, the relationship between age and willingness to use after-sales service through SNS did change to β=0,0286; p<0,01, with an explained variance of 9,18 percent. All these results are shown in Figure 4.
Figure 4: Conceptual Model 2 - Regression
To further estimate this indirect effect, its confidence interval was determined. As can be seen in Table 10, the interval does contain zero, making it not probable that the use of SNS has a
mediating effect.
Table 10: Conceptual Model 2 - Indirect effect (mediation)
Indirect effect of Age on Willingness to use after-sales service through SNS Effect
Confidence interval (95%)
Lower bound Upper bound
Use of SNS -0,0025 -0,0096 0,0027
6 Discussion
The intention of this research was to find if and how age effects customer’s willingness to use after-sales service through SNS. This paragraph tries to define to what extent this question can be
answered, which is done by looking at the stated hypotheses. To determine to what extent the hypotheses can be rejected or confirmed, the results introduced in the previous paragraph are discussed. This helps to determine to what extent age effects this willingness and perhaps also how.
In this paragraph the contributions, limitations of this research and proposed directions for future research are also included for each model and hypothesis.
6.1 Key Findings – Conceptual Model 1
In this section, the results gained from analysing conceptual model 1 are discussed. This model contains the variables age (independent), SNS use (dependent) and online privacy concern (mediator).
6.1.1 Hypothesis 1
Age is negatively related to the use of Social Networking Sites
The results regarding the use of SNS by different ages are exactly as predicted. With a highly significant (99%) moderate correlation it turned out that when the respondent’s age goes up by 1, the use of SNS goes down by 0,0150 (R² = 0,0932), which confirms the first hypothesis.
This means that this research confirms the statement by Kaplan and Haenlein (2010, p.61) that younger people can be referred to as ‘digital natives’. The results are also in line with the information provided by CBS.nl (2014) regarding the active use of Facebook, Twitter and other Social Networking sites. Unfortunately, the age of the respondents in this research was not evenly distributed, making it harder to draw concrete conclusions, especially about the age group of 65-75, as only a small amount, less than 3,8%, of the respondents belonged in this group.
Thereby, because of the limited timeframe, it was not possible to confirm possible
motives respondents had to use SNS, so this research cannot be compared to Ellison et al. (2007) and Valenzuals et al. (2009) who state that SNS has a positive effect on social capital. It is however a possible explanation for the differences in use between different ages, as adolescents would find social capital more important.
This study does, however, confirms Brandtzæg et al. (2010) who state that the gap in SNS use between different age groups is getting smaller, as age and SNS use do significantly correlate in this research, but β=-0,0150, which is very small. There are significant
differencesbetween different age groups, but these differences turned out to be relatively small in this research, which contradicts information provided by CBS.nl (2013) about 2012 which affirms that differences between the age groups are relatively big. This is, amongst others, in line with Lenhart et al. (2010) who state that adults over 30 increasingly use online profiles like Facebook.
This research contributes to earlier studies as it proves that the gap in differences is getting smaller, but that age still has a significant impact on the use of SNS. Nevertheless, in future studies it could be beneficial to find out why the gap between age groups is getting smaller and what motives exists for this behaviour.
6.1.1.1 Managerial Practice
For managers, this information is very useful as it provides organizations with additional information when implementing their after-sales customer service into Facebook/Twitter. As it turned out, managers still need to take into account the different ages that are part of their target groups. Since younger people use Social Networks more often, it could be beneficial for
organizations to promote on Facebook/Twitter when younger people are part of their target groups. This is not the case for older people, so when an organization also target older age groups, they should probably not solely focus on promoting on SNS, but rather also use other channels like newspaper and commercials.
6.1.2 Hypothesis 2
Online Privacy Concerns mediate the relationship between Age and the use of Social Networking Sites
It was predicted that online privacy concerns would mediate the relationship between age and the use of SNS. As has been written in the results, the relationship between age and online privacy concerns turned out to be significant. When age goes up by 1, online privacy concerns go up by 0,0156. This means that older people are more concerned about their online privacy. This could be explained by the fact that younger people have higher awareness and knowledge of using SNS, while older people don’t know exactly how they can manage their online profiles (Brandtzæg et al., 2010, p.1019). Another explanation could be that younger people are more vulnerable and don’t always notice when their privacy is at stake (Moore 2004, in Youn, 2009).
As little concrete research is done on age differences in online privacy concerns, this study contributes to existing literature as it concretely confirms expectations of influences of age on online privacy concerns.
However, it turned out that no significant effect was found between online privacy concerns and SNS use, which means that the online privacy concerns people might have, do not influence the amount of time they will spent using SNS like Facebook and Twitter. It was, nevertheless, found that the possible mediation effect of online privacy concerns was close to zero, but likely. As an indirect effect was found, hypothesis 2 is confirmed. Because of this, it can be said that online privacy concerns in general have a very small influence on the
relationship between age and the use of Social Networking sites.
Future studies could research whether some components of online privacy concerns, as stated by Ellison and others (2008) have a more substantial relationship with SNS use and whether any differences between these components occur (e.g. solely the fear of identity theft or hacking). It would thereby be beneficial to find out which other variables do possibly mediate the effect of age on SNS use more substantially. Due to the limited timeframe in this research, that was not possible.
6.1.2.1 Managerial Practice
These results can be very beneficial for organizations as it provides additional information on the influence of age on online privacy concerns and the (very small) influence of these concerns on SNS use. First of all, it has been proved that age influences online privacy concerns negatively. This means that organizations can take this into account when building their website or SNS. They could for instance make sure that information about visitors of their website/SNS is protected and that their privacy is secured. By also providing clarification about this on the website, customers might feel more secure when visiting the website/SNS. This would be especially important when an organization has an older target group. This should, however, be researched more.
Thereby, it has been shown that these online privacy concerns have a small effect on the SNS use in general. This means that the online privacy concerns do influenceto what extent people use Social Networking sites, which makes it important for organizations to take it into account. However, even though older people have higher online privacy concerns, it is still
unlikely they will use SNS substantially less. A more secure experience on a particular Social Networking site could, however, make people choose one over another.
6.2 Key Findings – Conceptual Model 2
This paragraph discusses the results gained from analysing conceptual model 2. This model contains the variables age (independent), willingness to use SNS for after-sales customer service (dependent) and SNS use (mediator).
6.2.1 Hypothesis 3
Age is negatively related to the willingness to use After-Sales Customer Service through Social Networking Sites
As discussed before, after-sales can have a lot of potential for organizations (Cohen et al., 2006). Doing this through SNS does not only save costs (Culnan et al., 2010), it also provides
organizations with multiple opportunities they would not have without using SNS (e.g. Culnan et al., 2010; Hanna et al., 2011; Mangold & Faulds, 2009). It is, however, hard for organizations to invest in after-sales when it is unknown what variables determine a customer’s willingness to use SNS for after-sales service. This thesis studied the possibility of age influencing this willingness. The expectations were met, as age and customer’s willingness to use SNS for after-sales service have a significant correlation and it can be said that if age goes up by 1, this willingness goes down by 0,0311. Because of this, hypothesis 3 is confirmed. However, this could be studied more thoroughly, for example by finding out why older people are less willing to use after-sales service through SNS. Also, this study researches people’s willingness, but it is also beneficial to know why some people are willing to use it, but never actually do that. Unfortunately the limited possibilities of this thesis did not provide the opportunity to unravel this.
6.2.1.1 Managerial Practice
These findings are a contribution as they can be put to practice by organizations. Since age influences the willingness of customers to use SNS for after-sales, organizations can for example adapt their SNS to younger target groups, as they are the customers that are more willing to approach them through these sites. Another way to use these findings in practice, is that
organizations now know that they have to find a way to make SNS more interesting for older age
groups to use their after-sales through these channels or provide other communication channels for them.
6.2.2 Hypothesis 4
The Use of Social Networking Sites mediates the relationship between Age and the willingness to use After-Sales Customer Service through Social Networking Sites.
To find out why age and willingness are related to each other, the possible mediation effect of SNS use was tested. As discussed before, age and SNS use are indeed significantly related with a moderate correlation. The relationship between the use of SNS and willingness was, however, very small and not significant. This is remarkable as it could be expected that people who would use SNS more often, would also be more willing to use these websites for other than social purposes. Thereby, even though the effect of age on the use of after-sales service through SNS did change when taking the indirect effect of SNS use into account, this change was not
substantial. The meditation effect turned out to be very small and as the confidence interval did not contain zero, SNS use is not a mediator in this model and hypothesis 4 is rejected.
Unfortunately not much relevant literature is available on this subject. An explanation for this, however, might be that a lot of organizations don’t acknowledge the possibilities of customer service through SNS yet.
Due to the short time frame of this research, only the possibility of SNS use mediating this relationship was tested. Future research could focus on other variables that possibly influence the relationship between age and customer’s willingness to use SNS for after-sales.
6.2.2.1 Managerial Practice
These findings can be very beneficial for organizations as they will tell more precisely what factors influence the use of SNS for after-sales, which will help them create more accurate marketing techniques. The managerial practice of the findings about the relationship between age and SNS has already been discussed, but also the (lack of) relationship between SNS use and willingness and the lack of mediation of SNS can help managers. They now know, for example, that the use of SNS in general is not the factor withholding people to approach customer service through SNS. Because of this, organizations do not need to take this into account when
promoting after-sales service through Social Networking sites.
Table 11 provides a summary of all hypotheses and whether they were rejected or confirmed.
Table 11: Summary findings hypotheses
Hypothesis Findings
Age is negatively related to the use of Social Networking Sites Confirmed Online Privacy Concerns mediate the relationship between Age and the use of SNS Confirmed Age is negatively related to the willingness to use After-Sales Customer Service through SNS Confirmed The Use of SNSmediates the relationship between Age and the willingness to use After-Sales
Customer Service through SNS
Rejected
6.3 Other implications and Limitations
Implications and limitations relevant to the hypotheses are already discussed above. This section will discuss implications and limitation due to the entire research.
First of all, not much literature was available on some of the relationships that were tested, like the influence of SNS use on willingness to use after-sales service through SNS. This made it hard to draw conclusions or provide explanations for the found results.
Secondly, the questionnaire, used to collect the data, was newly created and therefore used for the first time, making the questionnaire overall less reliable and valid. However, by testing the validity of the questions (e.g. with Cronbach’s Alpha), this was partly solved.
Furthermore, the limited timeframe of the research did restrict the study to some extent. Only a limited amount of respondents could be reached and interviewed. Thereby, more
literature could be analysed and more statistical test could be done. This was also the case for the lack of financial resources that could be used to collect more data.
Finally, this thesis only researched the Social Networking sites Facebook and Twitter. Both SNS were thereby taken together. Future research could study the difference between these websites and also study different SNS.
7 Conclusion
This research tried to answer the following research question:
Does age affect a customer’s willingness to use Social Networking sites for after-sales service and what factors cause this willingness?
First it was tested whether the use of SNS in general was influenced by age and whether online privacy concerns mediate this relationship. As both are the case, it can be concluded that the older people are, the less time they spent using SNS. Thereby, online privacy concerns do mediate this relationship, which means that these concerns, possible among other factors, cause this relationship (Judd & Kenny, 1981).
After this, the willingness to use after-sales service through SNS was also taken into account and SNS use was used as mediator in this second model. It was found that age also influences this willingness, meaning that younger people are more willing to use SNS for after-sales service than older people. However, it turned out that the use of SNS does not cause this relationship, which implies that the amount of time spent using of Social Networking sites like Facebook and Twitter does not influence the effect of age on willingness to use after-sales service through SNS.
Because of these results it can be said that age does in fact influence a customer’s willingness to use after-sales service through SNS. The factors that were tested in this study, however, do not cause this relationship. Even though it was found that online privacy concerns influence the relationship between age and SNS use, SNS use did not cause the relationship between age and willingness to use after-sales service through SNS. Nevertheless, as explained before, interesting contributions to existing literature were found, which can be beneficial for future research.
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