Differentiation between B2B and B2C companies on LinkedIn

Differentiation between B2B and B2C

companies on LinkedIn: A content

analysis for high customer engagement

Master Thesis

MSc Business Administration – Digital Business track

University of Amsterdam

Amsterdam Business School

Author: Sophie Louise Koopmans

Student number: 10556419

Date of submission: June 22nd, 2018

Supervisor: Dr. Shameek Sinha

MASTER THESIS – MSc DIGITAL BUSINESS SOPHIE LOUISE KOOPMANS, 10556419

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Statement of Originality

This document is written by Student Sophie Louise Koopmans who declares to take full

responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources

other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion

of the work, not for the contents.


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Abstract

This study researches to what extent there is a difference in customer engagement on LinkedIn

between B2B and B2C companies, and what role social media content has in generating

engagement. Posts from six company pages on LinkedIn were collected and a content analysis

was conducted over the resulting 572 posts. Each post was assigned one of the twelve content

types and one of the three relationship types. The data was quantitatively analyzed. The results

indicate that there is indeed a difference between business models: B2C company posts generate

higher customer engagement than B2B posts. There are also significant differences between

content types: counts of five types differ significantly between B2B and B2C. Concerning the

relationship phases (awareness, acquisition and retention), B2B companies post more

awareness-related posts, and B2C companies post more acquisition-related posts. There are no

significant differences in engagement between the relationship phases. For both content types

and relationship phases, there is no significant moderating role for business models on customer

engagement. Moreover, no three-way effect between business models, content types and

relationship phases has been found. This study narrows the knowledge gap in B2B social media

marketing research and the methodology for content analyses on social media is further

developed. Furthermore, it helps practitioners to make deliberate decisions on social media

strategy and allows them to select the right content types on social media for their respective

business model to generate engagement. Concluding, some directions for future research are

proposed.

Keywords: social media strategy, LinkedIn, B2B, B2C, content analysis, relationship phases,

customer engagement


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Contents

1. Introduction .............................................................................................................. 8

1.1 Expected contributions to theory and practice ..................................................... 9

2. Theoretical Framework .......................................................................................... 11

2.1 Social media use in business .............................................................................. 11

2.2 B2B vs. B2C ....................................................................................................... 12

2.3 Customer engagement ........................................................................................ 13

2.4 Content type ....................................................................................................... 15

2.5 Relationship phases ............................................................................................ 19

2.5.1 Awareness ................................................................................................... 20

2.5.2 Acquisition .................................................................................................. 21

2.5.3 Retention ..................................................................................................... 21

2.6 Relationship between Content Type and Relationship Phase ............................ 23

2.7 Conceptual Framework ...................................................................................... 26

3. Methodology .......................................................................................................... 27

3.1 LinkedIn ............................................................................................................. 27

3.2 Sample ................................................................................................................ 28

3.3 Measurements ..................................................................................................... 29

3.3.1 Business Model ........................................................................................... 29

3.3.2 Content Type ............................................................................................... 29

3.3.3 Relationship Phase ...................................................................................... 30

3.3.4 Customer Engagement ................................................................................ 31

3.4 Procedure ............................................................................................................ 32

3.4.1 Data Collection ........................................................................................... 32

3.4.2 Content Analysis ......................................................................................... 33

3.5 Reliability ........................................................................................................... 34

3.6 Data Analysis ..................................................................................................... 34


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4. Results .................................................................................................................... 35

4.1 Reliability ........................................................................................................... 35

4.2 Result per hypothesis ......................................................................................... 36

5. Discussion .............................................................................................................. 47

5.1 Business models ................................................................................................. 47

5.2 Content types ...................................................................................................... 48

5.3 Relationship Phases ............................................................................................ 51

5.4 Missing three-way effect .................................................................................... 52

5.5 Limitations ......................................................................................................... 56

5.6 Theoretical and Managerial Implications ........................................................... 57

6. Conclusion ............................................................................................................. 58

6.1 Future research ................................................................................................... 60

7. References .............................................................................................................. 61

8. Appendix ................................................................................................................ 67

8.1 Coding guidelines second coder ......................................................................... 67

8.2 Descriptives tables .............................................................................................. 69

8.3 Content type preferences .................................................................................... 71

8.4 Ranking of most used content types ................................................................... 72


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Table of Figures

1. Figure 1 Conceptual model of variables business model, content type and customer

engagement ................................................................................................................... 19

2. Figure 2 Simplified customer journey in relationship phases ...................................... 19

3. Figure 3 Conceptual model of variables business model, relationship phase and

customer engagement ................................................................................................... 23

4. Figure 4 Conceptual model of variables business model, content type and relationship

phase ............................................................................................................................. 26

5. Figure 5 Conceptual Framework .................................................................................. 26

6. Figure 6 Interaction plot of the effect of business model on the relationship between

content type and customer engagement ....................................................................... 42

7. Figure 7 Interaction plot of the effect of business model on the relationship between

relationship phase and customer engagement .............................................................. 45


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Table of Tables

1. Table 1 Sample of selected companies ........................................................................ 29

2. Table 2 Coding sheet Content Types ........................................................................... 30

3. Table 3 Descriptives of the engagement scores per business model groups ................ 36

4. Table 4 Descriptives and chi square results of content types ....................................... 38

5. Table 5 Descriptives and chi square results of broad content categories ..................... 39

6. Table 6 Games Howell Post Hoc Test: Mean differences ........................................... 41

7. Table 7 Descriptives and chi square results ................................................................. 43

8. Table 8 Hochberg's GT2 Post Hoc test results ............................................................. 44

9. Table 9 Results of the two-way independent ANOVA ................................................ 45

10. Table 10 Partial Associations: Effect of each variable in the loglinear model ............ 47

11. Table 11 The Content Decision Table: Matrix with preferences of content types over

other types. ................................................................................................................... 49

12. Table 12 Top 10 of the most engaging content types for customers. ........................... 50

13. Table 13 Descriptives: Counts of posts in each category in the loglinear analysis ..... 54

14. Table 14 Overview of hypotheses and corresponding results ...................................... 55

15. Table 15 Coding sheet: Content Categories ................................................................. 67

16. Table 16 Coding Sheet: Relationship Phases ............................................................... 68

17. Table 17 Descriptives of customer engagement for each content type. Sorted on

Means. .......................................................................................................................... 69

18. Table 18 Descriptives of engagement scores per group ............................................... 70

19. Table 19 Results of engagement comparing content types .......................................... 71

20. Table 20 Ranking of content types of the data set per business model ........................ 72


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1. Introduction

Social media is everywhere and the number of users keeps growing. LinkedIn has passed the

500 million users (Awan, 2017). The advancement of technology in the last decades is making

the world more connected. The new domain of digital marketing is becoming ubiquitous and is

expanding (Brennan & Croft, 2012; Peters, Chen, Kaplan, Ognibeni, & Pauwels, 2013).

Social media is an important component of digital marketing. Social media platforms are

defined as ”forms of electronic communication through which users create online communities

to share information, ideas, personal messages, and other content” (Merriam-Webster, 2018).

Nowadays, social media have become indispensable in business. In 2015, already 96% of

businesses globally used social media (Statista, 2015). Facebook, Twitter, Instagram and

LinkedIn are the most popular examples of social media marketing channels.

Some industries are frontrunners in the digital marketing domain. Especially in business

to consumer (B2C) marketing, social media grew from an underestimated channel to one that

marketers can’t do without (Brennan & Croft, 2012; Peters et al., 2013; Rodriguez, Peterson,

& Krishnan, 2012). This caused an increase in research in B2C digital marketing. While

research in B2C social media marketing is advancing and becoming more mature, research on

business to business (B2B) marketing is far behind and still in its embryonic stage (Salo, 2017).

The shortage in research cannot be the result of a lack of size or relevance of the B2B industry,

since B2B transactions account for 42% of reported US revenues in 2010 (Lilien, 2016). As

more businesses are using social media, marketing scholars are urgently calling for further

research on the distinction between B2B and B2C social media (Lilien, 2016; Mora Cortez &

Johnston, 2017; Salo, 2017).

The interest of social media usage differs between the business to business and business

to consumer contexts. B2B companies are more focused on customer relationship management,

whereas B2C companies’ main goal is to create brand awareness (Michaelidou, Siamagka, &


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Christodoulides, 2011). Hoffman and Fodor (2010) proposed to forget the traditional

(monetary) measures of return on investment (ROI) as a measure of performance, but instead

assess customer engagement, as customer engagement might better predict the likelihood of

payoff in the long term. The common focus on “show me the return” by companies is too much

oriented on the short term and dismisses the qualitative opportunities that social media might

bring (Hoffman & Fodor, 2010). Thus, performance comes through customer engagement;

digital marketers should focus on increasing engagement instead of directly trying to increase

sales through social media. The goal of social media is not selling; it’s engaging.

Customers can engage with companies on social media by liking and commenting on

firm-generated content or by creating content about companies (user-generated content). Social

media content is textual, visual and aural content that is posted on a social media platform by

any member, including textual messages, images, audio, videos, website links, and news

articles. Content plays a crucial role in the engagement of customers. Good content engages

customers, but the question is what content works for B2B and B2C companies.

There has been little research on how the different company contexts influence customer

engagement on social media, or if there is any difference in customer engagement between B2B

and B2C contexts at all. And what influence does the type of content that firms posts on its

social media company pages have on customer engagement? This study addresses this query.

It attempts to answer the following research question:

To what extent does customer engagement on social media differ in the B2B

and B2C contexts, and what role does content play in this relationship?

1.1 Expected contributions to theory and practice

First, this study adds to the literature about the distinction of B2B and B2C markets. B2B and

B2C differ in such a degree that B2C measurements cannot bluntly be copied into B2B


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applications (Swani, Brown, & Milne, 2014). A distinction in business models must be made

for future research to progress. Also, this study contributes to narrowing the B2B knowledge

gap proposed by Lilien (2016). Lilien studied the imbalance between the volume of B2B and

B2C research and uncovered opportunities and challenges of B2B digital marketing. Also,

academics are calling for more explanatory research in the industrial marketing field (La Placa

& da Silva, 2016). This study extends the limited quantified knowledge on customer

engagement on social media in the B2B and B2C context. The knowledge can be used in

practice to develop targeted social media strategies for firms in B2B and B2C markets.

Second, this study might encourage more (non-)social media using companies to seize

the opportunities of this type of marketing and adapt their marketing strategies on their business

model. In a study on the usage, perceived barriers and the measurement of effectiveness of the

social networks of small and medium-sized enterprises (SMEs) in the UK, Michaelidou,

Siamagka and Christodoulides (2011) found that only 27% of the B2B SMEs were actively

using social media networks. 53% of the social media-using participating companies indicated

that they did not measure the effectiveness of social media at all, mostly due to their lack of

knowledge of the right metrics. 61% of them did claim to consider evaluating the metrics in the

near future (Michaelidou et al., 2011). This thesis attempts to reduce the lack of knowledge and

provides tools and guidance for academics and practitioners to increase the effectivity and

performance of social media usage for companies.

Third, the results might provide direction for both B2B and B2C companies on which

content works best for their specific customer: businesses or consumers. This research looks at

what objective the content supports and which type of content increases customer engagement.

With this insight, marketers can increase overall customer engagement by posting B2B- or

B2C-specific content, which will benefit companies in the long run (Hoffman & Fodor, 2010).


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The structure of this research is as follows. First, extant literature on social media content

and customer engagement is discussed to build a conceptual framework and form hypotheses.

Second, the methodology of this research is described. Third, the results are discussed in the

sequence of the hypotheses. Forth, the results are deliberated on in the discussion section,

limitations are uncovered, and theoretical and practical implications are revealed. To conclude,

the main research question is answered in the conclusion and directions for future research are

proposed.

2. Theoretical Framework

2.1 Social media use in business

Social media allows firms to communicate and engage more extensively online with customers

than in the past. Social media marketing offers a solution for the declining effectiveness of

traditional marketing techniques (Holliman & Rowley, 2014). Despite the potential, social

media is not as much used as traditional digital tools like email and websites in business, and is

still underutilized by industrial companies (Karjaluoto, Mustonen, & Ulkuniemi, 2015).

The extent to which companies and employees use social media for professional purposes

depends on several factors: technology savviness, personal use and the use of social media in

work processes. Taking insights from different studies together suggests that the steps to

increase social media usage in business are, firstly, to let employees get social media savvy

personally, e.g. encourage them to make a personal account (Keinänen & Kuivalainen, 2015;

Schultz, Schwepker, & Good, 2012); secondly, to implement social media network usage in the

internal organization; after which, thirdly, the reach can be expanded externally to customers

(Karjaluoto et al., 2015). The literature that provides these insights does not distinguish between


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different business models. However, B2B and B2C companies are likely to act differently on

social media.

2.2 B2B vs. B2C

Extant research has studied the differences in customers, companies and sales people between

B2B and B2C. First, customers of B2B companies have different motivations to use social

media than customers of B2C companies. Customers of B2B companies are professionals who

represent their employer. The customer and company in the B2B context build a professional

relationship. Customers of B2C companies, on the other hand, represent themselves. They buy

products or use services for their own personal needs. The objectives of the customer to use

social media are to find product and service information, share their experiences or join in co-

creation on social media (Constantinides, Schepers, & De Vries, 2015; Moore, Hopkins, &

Raymond, 2013).

Second, B2B and B2C companies themselves differ. The general objectives of companies

for using digital channels are to create awareness, enhance customer relationships and support

sales (Karjaluoto et al., 2015). These key objectives are different for B2B companies than for

B2C companies. For B2C companies, the most important objective to use social media is to

create brand awareness by consumers (Michaelidou et al., 2011). Furthermore, providing

information, having interaction and doing sales is also important for B2C companies

(Constantinides et al., 2015). On the other hand, attracting new customers and cultivating

customer relationships are the most important objectives of social media for B2B companies.

While creating brand awareness is the main objective in the B2C context, its only third for B2B

companies (Michaelidou et al., 2011).

Third, B2B sales people seem to use social media more than B2C sales people. A study

found that when comparing social media usage of B2B and B2C sales people, the utilization


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percentage of B2B salesmen was significantly higher than the number of B2C sales people on

many social media platforms (Moore et al., 2013). This would imply that the adoption rate of

social media is higher for B2B sales people than for B2C sales people. It can be explained by

the relationship-oriented selling practices of B2B sales people. For B2B sales people, quality

of relationships is more important, while B2C sales people use social media to reach a large

quantity of potential customers (Moore et al., 2013).

2.3 Customer engagement

These differences between B2B and B2C markets in customers, companies, and its sales people

are expected to endure in customer engagement. But what is customer engagement? Scholars

do not seem to agree on one general definition as customer engagement is a very broad concept

(Brodie, Hollebeek, Jurić, & Ilić, 2011; Malthouse, Haenlein, Skiera, Wege, & Zhang, 2013;

Sashi, 2012). The much used definition of customer engagement proposed by Brodie et al.

(2011) is: “a psychological state, which occurs by virtue of interactive customer experiences

with a focal agent/object within specific service relationships”. It emphasizes the interactive

nature of social media on which both sides (company and customer) can create content and

interact.

To examine the effectiveness of the interactions, social media is becoming more data

driven. Marketers utilize social Customer Relationship Management (CRM) systems to collect

data on customer engagement, from which they can gather insights on how to reach new

customers and enhance relationships more efficiently and effectively (Trainor, Andzulis, Rapp,

& Agnihotri, 2014). These insights improve relationships and facilitate integration of the

obtained information in the sales process (Rodriguez, Peterson, & Ajjan, 2015). Keeping

consumers engaged is crucial to increase the value of interactive firm-consumer relationships.


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Barger & Labrecque (2013) identified four types of social media users based on their

customer engagement: bystanders, followers, participants and advocates. Bystanders stumble

upon posts or mentions of a brand, but do not interact. Followers neither engage with a brand,

but do search for them on social media and actively follow their posts. Participants engage and

interact with a brand by liking or commenting on their posts. Lastly, advocates both interact

with and actively promote a brand by creating and uploading their own positive content (Barger

& Labrecque, 2013).

Customers of B2C companies have found their way to social media and are more likely

to be participants or advocates. According to Keinänen and Kuivalainen (2015), B2B customers

are still fairly passive on social media, as they use it as a source of information and do not

actively comment nor open a discussion. Therefore, most B2B customers can be classified as

bystanders and followers. Companies should think about how to activate business customers

on social media and turn them into participants and advocates to get the true value from the

platforms.

Cuillierier (2016) argues that customer engagement is more important for B2B firms,

because client bases are smaller and customers buy in large volumes or do larger transactions,

as compared to B2C firms. Maintaining good relations with customers and retaining customer

engagement should therefore be more critical to B2B firms than to B2C firms. Also, B2B sales

people are more focused on building relationships with the help of social media and are utilizing

the platforms more for these purposes (Moore et al., 2013) than B2C sales people. It is expected

that customers reciprocate the efforts of B2B salespeople. Therefore, it is hypothesized that:

Hypothesis 1: Customer engagement is higher on B2B company posts than on B2C

company posts on LinkedIn.


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2.4 Content type

Besides the objectives of firms to post on social media networks, content of posts (firm-

generated content) is controlled by firms and might have an influence on customer engagement.

Content on social media is created by corporate, employee, professional and civilian users.

Firms have limited direct control what customers see and do on social media, but firms do have

an (indirect) influence on the customer engagement of a post. B2B companies influence

customer engagement directly with corporate user accounts by adding new content,

participating in discussions, and removing content with corporate accounts (Huotari et al.,

2015). So, the content companies post is important for customer engagement.

Some types of content might work better than others to involve customers. There haven’t

been many studies on which content works on social media and which doesn’t. In other

disciplines of digital marketing has been some more research. For instance, in search engine

advertising (SEA) stating a call to action in an search engine advertisement’s content has a

positive effect on the engagement (click through rate) (Rutz & Trusov, 2011). Another study

found that increasing the interactivity of a post was found to have a strong effect on customer

engagement on posts, measured by likes, comments and shares (Luarn, Lin, & Chiu, 2015).

Semantics, writing style and evidence type of content also influence engagement (Haans,

Raassens, & van Hout, 2013), but are out of the scope of this study. This study is primarily

focused on subjects of content.

The individual content types can be divided into three broad categories: promotion,

reputation, and events. The content types in the promotion category promote products and

services from the companies. They are directly related to sales. The promotion category

includes the content types promotion/discount, contests, and products and services. With the

second category, reputation posts, a company can influence the image a customer has of the

company, and present the company as an industry expert. The reputation posts include posts


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about awards, CSR, knowledge sharing, reports, industry news, company information, and

workforce/recruitment. In the third overarching category, content types are related to events,

which include content types like presence at internal or external events, conferences or award

shows. As there is no prior study which provides a categorization, the following categorization

is based on assumptions.

The type of content used may be dependent on the business model. For example, B2C

firms may make more use of certain types of content than B2B firms, like content to enhance

awareness among customers. Because the objectives for using social media differ for B2B and

B2C, it is expected that business to business and business to consumer companies emphasize

different types of content.

For instance, I expect that B2C companies engage more in promotional activities like

(publicly announced) discounts and contests. B2B companies are less likely to engage in these

types of promotions, as their individual customers have to some extent more bargaining power

than B2C customers, because the transactions in the B2B industries are larger and the products

and services are more personalized on the client’s preferences (Cuillierier, 2016). B2B

marketers rely more on (personal) emotional appeals than functional appeals (Swani et al.,

2014). On the other side, B2C customers have less bargaining power as prices and product

specifications are commonly fixed. Also, the relationship with a brand is less personal

(Homburg, Klarmann, & Schmitt, 2010) and B2C companies must rely more on mass

communication channels, like social media, to promote their products, because the quantity of

potential customers is larger than for B2B companies (Moore et al., 2013). Therefore, I

hypothesize:

Hypothesis 2a: B2C companies post more promotional content than B2B companies.


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Customers may be more responsive to a “1+1 for free!” promotional post than to “Did

you know [fact] about our product?” as a product introduction post. The type of content of posts

may influence the amount of likes and comments on posts. It is expected that some types of

content interact better with either B2B or B2C customers than other types. I expect that the

content types that are popular with B2B or B2C marketers, are also more engaging for their

customers. The business model works as a moderator on the relationship between content type

and customer engagement. So, in line with the previous hypothesis that B2C companies post

more promotional content, it is expected that these posts generate higher engagement for this

target group. Therefore:

Hypothesis 3a: B2C customers are more engaged in promotional content than B2B

customers.

Content types in the reputation category are important for both B2B and B2C companies

(Smaiziene & Jucevicius, 2009). Building a positive brand reputation on social media causes

an increase in firm performance (Swani et al., 2014), which is an objective of all companies.

However, having a good corporate reputation and posting about this might be slightly more

important to B2B companies. Especially in the professional services industry, reputation is very

important to retain loyal customers (Walsh, Beatty, & Holloway, 2015). Though, I expect the

difference between B2B and B2C to be only small.

Hypothesis 2b: B2B companies post slightly more posts to increase reputation than B2C

companies.

Again, in line with the reasoning of the hypothesis 2b about content, I expect the following:


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Hypothesis 3b: B2B customers are slightly more engaged in reputation posts than B2C

customers.

Furthermore, as B2B companies are more focused on individual relationships with customers

and can adapt their efforts to some degree of personalization, B2B companies are more involved

in organizing, joining and promoting events. During events, they can meet and engage with

customers on a more personal level. On the other hand, B2C companies have a less personalized

relationship with their customers, and due to the relatively large size of their customer base,

they are less likely to post about events to reach their individual customers. Therefore, it is

expected that B2B companies post more event-related posts to engage customers than B2C

companies.

Hypothesis 2c: B2B companies post significantly more event-related content than B2C

companies.

Once again, in line with the reasoning of the hypothesis about content, I expect the following:

Hypothesis 3c: B2B customers are more engaged with event-related posts than B2C

customers.

Throughout the theoretical framework of this paper, the conceptual framework will be build up

out of smaller parts. The full conceptual framework is given in the end. Figure 1 shows a

conceptual model that connects the (sub) hypotheses 1, 2 and 3. The moderating effect of


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business models on the relationship between content type and customer engagement is left out

of this figure.

Figure 1 Conceptual model of variables business model, content type and customer engagement

2.5 Relationship phases

As mentioned before, firms post content on social media to support their relationship objectives:

raising (brand) awareness, doing customer acquisition or enhancing customer retention

(Michaelidou et al., 2011). These objectives form a customer’s simplified journey (see Figure

2). Companies can create content on social media targeted at customers in one of the three

phases of the customer journey. A relationship that is still in the awareness phase targets

potential customers who do not know or barely have knowledge about a brand. In the

acquisition phase, potential customers are encouraged to buy a product or use a service. The

potential customers might never have used the brand before or only incidentally. In the retention

phase, relationships with customers are present, and companies like to invigorate the

relationships and encourage customers to re-use the brand. Below, all relationship phases are

further explained.

Figure 2 Simplified customer journey in relationship phases

Awareness Acquisition Retention


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2.5.1 Awareness

Customers gain awareness through exposure to a brand or product. Hoffman and Fodor (2010)

describe brand awareness in the social media environment as: “every time a person uses an

application designed by or about the company, the company gains increased exposure to its

brand, often in highly relevant contexts.” The exposure enhances and strengthens associations

of the brand in the customers’ minds. Awareness posts are not necessarily personalized, at best

segmented, because in general the first awareness messages are meant for a wide audience. The

goal of raising awareness is to inform customers about the existence of a brand, product or

service, not yet actively acquiring or converting them as customers. That is the next step in the

customer journey. In traditional marketing, television advertisements or billboards are

examples of ways to create customer awareness. On social media, this could be online

advertisements, (sponsored) posts, mentions or events.

In B2C markets, social media are used for one-to-many communications and posts are

quite general. In B2B markets the focus is a slightly more personal, even in this first stage where

potential customers get acquainted with brands and products. The social media channels can be

used for one-to-many, but also one-to-one communications. It is not likely that B2C marketers

contact individual customers to let them know about a certain new product. The return on (time)

investment is too low for B2C, while in B2B ‘cold calling’ on social media may be worth the

time investment. On the other hand, the main objective for B2C marketers to use social media

is to create awareness (Michaelidou et al., 2011). Accordingly, they will focus their social media

strategy on awareness posts and aim to reach many potential customers with one message (one-

to-many marketing). Therefore, I expect that B2C companies post more awareness posts.

Hypothesis 4a: B2C companies post more awareness-related posts than B2B companies.


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2.5.2 Acquisition

In the acquisition phase, companies, which already created awareness, start encouraging

prospective customers to buy a product or use a service. This may be the first time a customer

converts, or a customer has incidentally bought the product or service before, without reaching

the retention phase. Online acquisition efforts include running promotions about a product or

service or giving discounts on e.g. Facebook or LinkedIn. In traditional marketing, this could

be promotions in brochures, on television or in magazines. An important difference between

traditional and online marketing is the ability to improve targeting and personalization in online

marketing (Malthouse et al., 2013). For instance, a web shop could run personalized ads of

products a customer viewed online, but did not buy yet. This is an example of retargeting.

Another distinction between B2B and B2C in the acquisition phase is the difference in

the origin of demand. For B2B, demand is derived by a subsequent customer; the buyer is not

likely to be the end user, while in B2C demand is driven by the specific tastes, emotions and

preferences of the end customer (Lilien, 2016; Mora Cortez & Johnston, 2017). Furthermore,

the buying decision process differs between the two markets. Where in B2C the decision maker

is most likely the same person as the consumer, in B2B the decision making process is more

complex and involves more stakeholders (Mora Cortez & Johnston, 2017). For example, a

consumer of a product (an employee) does not make the buying decision, but his/her boss does.

This implies that it is more about quantity of demand in the B2C markets and more about quality

of relationships in the B2B markets. Therefore, it is hypothesized:

Hypothesis 4b: B2B companies post more acquisition-related posts than B2C companies.

2.5.3 Retention

In the retention phase, customers are aware of a company’s offerings and have been

successfully acquired as a customer. The goal of retention is to encourage them to return and


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remain in an ongoing relationship with the company, preferably becoming loyal to the brand.

Firms can retain existing customers by keeping them satisfied and engaged. Customer

relationship management is very important in this stage. In online marketing, firms use cookies

and re-marketing techniques to remind customers of the brand, product or service (Iankova,

Davies, Archer-Brown, Marder, & Yau, 2018). The relationship and communication is

becoming even more personal to create a bond with customers and let them know their business

appreciated. With the remarketing technologies, it is possible to automate the process, yet make

the messages personal. When the customer is successfully retained, the customer enters the

loyalty loop and stays with the company. Once the customer has entered the loyalty loop, it is

important to maintain the relationship and nurture the customer, otherwise the customer may

become unsatisfied and switch to a competitor’s product or service (Thaichon & Quach, 2015).

Hence, the retention phase is mainly about keeping the customer satisfied. In B2B, this is

done by creating strong buyer-seller relationships. In B2C, buyer-seller relationships are weaker

(Homburg et al., 2010), so customers base their choice to stay loyal to a brand on different

factors. As B2B marketers are focused on building relationships with customers on social

media, more than B2C marketers, I expect that B2B marketers post more retention posts than

B2C marketers.

Hypothesis 4c: B2B companies post more retention-related posts than B2C companies.

In the acquisition and retention stages of the customer journey, relationships are established to

some extent, and customers might feel more connected to a company’s brand. The relationship

is more personal than in the awareness phase. Because of this feeling of connectedness, I expect

higher customer engagement on acquisition and retention posts, which implies that B2B

customers are more engaged than B2C customers. Therefore, I hypothesize:


23

Hypothesis 5a: Acquisition and Retention content generates higher Customer

Engagement than Awareness content.

If hypotheses 4b, 4c, and 5a are true, this might indicate a moderating relationship of business

model on the connection between relationship phase of the content and engagement. Therefore,

I additionally hypothesize:

Hypothesis 5b: Relationship content posted by B2B companies create higher engagement

than relationship content by B2C companies.

Figure 3 shows a conceptual model that connects the (sub) hypotheses 1, 4 and 5. The

moderating effect of business model on the relation between relationship phases and customer

engagement is left out of this figure.

Figure 3 Conceptual model of variables business model, relationship phase and customer engagement

2.6 Relationship between Content Type and Relationship Phase

The two variables relationship phase and content type are studied separately in this study.

Although the relationship phase and content type of posts might match in some instances, like

a ‘product introduction’ promotion always targets the acquisition objective, there may also be

promotions that rewards only existing customers, and therefore targets the retention of


24

customers. This way, the same content category promotion can target two different relationship

phases. Interestingly, which types of content and relationship phases are connected, might

deviate between the B2B and B2C markets. What content types can be linked to certain

relationship types in which markets? Marketers could use this information to determine what

content to create when they aim to influence customers in a certain relationship phase,

according to their business model.

Few scholars studied the connection of content types to the relationship phase, so there is

little academic knowledge in this area. This study is the first to address this question. Using the

two business models (B2B and B2C), the three broad content categories (promotion, reputation

and events) and the three relationship phases (awareness, acquisition and retention), I framed

hypotheses based on intuition.

I expect the types of promotional posts that the two different business markets use to

differ. B2C companies probably use promotional posts to incentivize potential customers to buy

the products by offering discounts, targeting the acquisition phase. B2B companies are less

keen on giving discounts, as mentioned before. B2B companies probably use promotional posts

to introduce and inform customers about products or services, targeting the awareness and

retention phases. Therefore, I hypothesize:

Hypothesis 6a: Promotion posts in the B2C context mainly target the acquisition phase.

Hypothesis 6b: Promotion posts in the B2B context mainly target the awareness and

retention phases.

Next, reputation posts are presumably used by B2C companies to raise awareness about the

company’s products and capabilities, mainly creating trust in the brand. Therefore, reputation


25

posts by B2C companies are likely to be focused on the awareness phase. On the other hand,

B2B companies use reputation posts for raising awareness with potential customers, but also

use reputation for retention purposes. To retain customers and keep the company-customer

relationship positive, B2B company would want to profile themselves as experts in their field

of work by sharing reports and industry news. These content types would be less relevant for

B2C customers to increase a B2C company’s reputation. Therefore, I hypothesize:

Hypothesis 6c: Reputation posts in the B2C contexts mainly target the awareness phase.

Hypothesis 6d: Reputation posts in the B2B context mainly target the awareness and

retention phases.

Finally, event-related posts could also have different purposes in the B2B and B2C contexts. If

a B2C company would, for example, organize an event for its customers, it is probably to

increase awareness about the brand by e.g. handing out free samples. On the contrary, B2B

companies would organize events to invite existing customers for retention purposes, for

example inviting customers to conferences. Therefore, I expect that B2B and B2C companies

would use event-related posts to reach customers in different relationship phases. I hypothesize:

Hypothesis 6e: Event posts in the B2C context mainly target the awareness phase.

Hypothesis 6f: Event posts in the B2B context mainly target the retention phase.


26

Figure 4 shows a conceptual model of hypothesis 6 that connects the variables business model,

content type and relationship phase. It is expected that there is a sequential relationship between

the three variables.

Figure 4 Conceptual model of variables business model, content type and relationship phase

2.7 Conceptual Framework

The conceptual framework below (Figure 5) shows all the variables and hypotheses (excluding

the moderating effects) schematically. The three previous conceptual models are merged into

one. This research ultimately studies the relationship between B2B and B2C business models

and customer engagement on social media. It also looks at the different types of content and

how these types of content are related to relationship phases. Next, the method of how these

subjects were investigated is discussed.

Figure 5 Conceptual Framework


27

3. Methodology

The purpose of the study is to assess the influence of business model and content on customer

engagement. This study had an explanatory, cross-sectional research design. The data was

measured at a specific point in time, and the sample was selected based on existing differences

rather than random allocation (Labaree, 2009). For obtaining data on content of social media

posts, this study used the methodological framework of content analysis (White & Marsh,

2006).

3.1 LinkedIn

The social medium assessed in this study is LinkedIn (www.linkedin.com). LinkedIn claims to

be world’s largest professional network with more than 546 million users in more than 200

countries and territories worldwide. The mission is to connect the world’s professionals to make

them more productive and successful (Linkedin Corporation, 2018). Individuals can display

their resumes publicly online and link with other professionals. Companies can create corporate

pages where they can upload posts (‘updates’), communicate vacancies and display information

about the company. Members can opt-in to follow company pages.

Companies can also create corporate accounts on other social media platforms like

Facebook, Twitter and Instagram. In this study, LinkedIn was chosen as the focal social media

platform, because members represent both themselves (targeted by B2C’s) and companies

(targeted by B2B’s) as they use the medium for professional purposes and communication.

LinkedIn is the most important social media platform to follow a company professionally

(Keinänen & Kuivalainen, 2015). Also, B2B marketers indicated that LinkedIn was the most

important platform for them, however, B2C marketers preferred Facebook (Social Media

Examiner, 2017). Since part of the contribution of this study is to narrow the B2B knowledge

gap, the preference of B2B marketers was given more weight. Furthermore, the number of

http://www.linkedin.com/


28

studies on LinkedIn is limited, so our academic contribution would be more relevant when

studying LinkedIn instead of Facebook.

3.2 Sample

The LinkedIn posts (‘updates’) of 6 companies were assessed. The selection included 3 B2B

and 3 B2C firms (see Table 1). The companies were selected according to several criteria.

Firstly, the company had to report at least an annual revenue of €100 million. Because of this

revenue level, it is assumed that these companies have professional digital marketing

employees/departments, so professional marketers create content on the LinkedIn page.

As B2B and B2C companies mostly operate in different sectors, getting all the sampled

companies from only one sector is not feasible. Therefore, the companies in the sample all

operated in different sectors. An advantage of this method of sampling is that the sample was

not biased by one specific sector and the results are generalizable across sectors. On the other

hand, a disadvantage of this sampling method is that it is harder to find significant results.

The LinkedIn profiles of the companies also had to meet several criteria. The selected

companies had to be active on LinkedIn for at least one year and posted at least 27 times in the

last six months (average of once a week). This would imply that the marketers had experience

on writing content for LinkedIn. Furthermore, the company’s LinkedIn page had to have at least

10,000 followers, so there were enough LinkedIn members to engage. The content of company

pages was also assessed to check if they used LinkedIn as a marketing and sales tool, not merely

a recruitment tool, so maximum 25% of the lasts 50 posts should could have recruitment

purposes (i.e. the page cannot exclusively contain content about vacancies and recruitment

activities). The posts had to be in either Dutch or English. Lastly, if the company had both B2B

and B2C divisions, it was checked that the LinkedIn content was not targeted at both B2B and

B2C customers, but exclusively at one group. Following these requirements and using the


29

LinkedIn search tool, the following six companies were selected: Accenture, Maersk Line,

Adyen, KLM Royal Dutch Airlines, Citroën and Louis Vuitton (Table 1).

Table 1 Sample of selected companies

Company

Name

Industry Country Business

Model

LinkedIn

Page Name

Number of followers

(10-4-2018)

Accenture Management

Consulting

Netherlands B2B Accenture the

Netherlands

19460

Adyen Financial Services Netherlands B2B Adyen 29482

Maersk Line Logistics and

Supply Chain

Denmark B2B Maersk Line 283988

Citroën Automotives France B2C Citroën 47485

KLM Royal

Dutch Airlines

Airlines/Aviation Netherlands B2C KLM Royal

Dutch Airlines

437128

Louis Vuitton Luxury Goods &

Jewelry

France B2C Louis Vuitton 623467

3.3 Measurements

3.3.1 Business Model

Different business models determine in which business companies are and who they sell to. In

the sample, only business to business (B2B) and business to consumer (B2C) are compared. A

company’s business model is identified simply by looking at who their customers are:

individual consumers (B2C) or other businesses (B2B)?

3.3.2 Content Type

The type of content of a post can differ in many aspects. Previous content analyses came up

with categorizations of social media posts, for example Parsons (2011) identified and used 20

different categories of content posted by brands on Facebook. However, content is specific per

social media platforms, therefore, the coding sheets of other researchers could not be reused.

Based on adjusts of the existing coding sheets of Parsons (2011) and Ashley and Tuten (2015),

the coding sheet in Table 2 was created. Each post received the best fitting content code.

https://www.linkedin.com/company/2677373/









30

Table 2 Coding sheet Content Types

Content type Broad category Description

1 Awards and

Congratulations

Events Notification of award won by company

2 Company Information Reputation History, announcement

3 Contests Promotion Call to action, do [this] and win …,

4 CSR Reputation CSR initiative by company/employee

5 Events and Conferences Events Post about event/conference company

representatives are attending

6 Industry news Reputation Happenings in industry

7 Knowledge Sharing Reputation Sharing knowledge, but not directly related to

product/service

8 Other Other Holiday greetings

9 Products and Services Promotion Introduction of product/service

10 Promotion and discount Promotion 2+2 for free, 20% discount if…

11 Reports Reputation Announcement report by company or industry-

related

12 Workforce/Recruitment Reputation Vacancy announcement or business course, or

reasons why the company is a good employer

3.3.3 Relationship Phase

The next variable, relationship phase, is a more abstract variable. Some guidelines have been

made to identify the phase of a post and to ensure reliability in future studies. The descriptions

are also included in the coding sheet in Appendix 8.1 Table 16.

• An awareness post is targeted to introduce customers to a brand, product or service. The

content is non-specific and does not encourage explicitly to buy the

brand/product/service.

• An acquisition post is targeted at potential customers to buy a product or service for the

first time. The customer may be encouraged by a (first time) promotion or a discount.


31

• A retention post is targeted at existing customers. The post could mention a loyalty

program or promotion, an invitation to events for customers, complementary services

or the name of an existing customer as a best practice or case study of their work.

The phases of a post were sometimes mutually inclusive, for example when a post targeted both

new and existing customers (both acquisition and retention). However, only the best fit was

assigned to a post for analytical purposes. Furthermore, posts about recruitment and job

opportunities were excluded in this measurement, as they do not target customers in any phase

of the customer journey. These posts received the code None.

3.3.4 Customer Engagement

The customer engagement measurements are based on existing social media metrics. The

number of likes is a metric for low engagement and number of comments is medium

engagement (Neiger et al., 2012). There are many other social media metrics for engagement,

but only these two are used, because they are publicly available and retrievable. To get one

general engagement score, the number of comments were weighted twice as heavy as the

number of likes. To control for the number of followers per company page in the sample, as

more followers increases the number of impressions and thus the possibility to engage, the

number of likes and the number of comments were divided per 10,000 followers to standardize

the metrics. Thus, the engagement score of a post was calculated as follows (formula 1):

(1) 𝐸𝑛𝑔𝑎𝑔𝑒𝑚𝑒𝑛𝑡 𝑆𝑐𝑜𝑟𝑒 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑙𝑖𝑘𝑒𝑠 + (𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑜𝑚𝑚𝑒𝑛𝑡𝑠 × 2)

(𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑓𝑜𝑙𝑙𝑜𝑤𝑒𝑟𝑠/10000)

Number of shares is also a widely used metric for customer engagement across social media

platforms, but as of February 2018 LinkedIn does not longer show the number of shares on

posts or provide the count. LinkedIn states: “The share count on its own doesn’t fully reflect

the impact that a piece of content delivers, and we encourage publishers and other content

creators to leverage the [share tool] as a way to drive conversation and engage with members


32

on LinkedIn.” Therefore, only the number of likes and comments were used as measurements

for customer engagement.

Logically, the longer a post has been online, the more exposure it could get and the more

time there was to like or comment on the post. However, the time a post has been online was

not considered in the data analysis of engagement. LinkedIn only displays recent posts on the

timeline of users and it is assumed that users mostly engage with posts that appear on their

timeline. From own experience, most likes and comments are given within a few days after

posting the message. Research on Twitter messages confirmed that the majority of shares

happens within one week (Priem & Costello, 2010). Therefore, it is unlikely that a three-month-

old post received significantly more likes and comments than a one-month-old post. To control

for recently posted updates which are still appearing on timelines, the post sample excluded

posts younger than one week.

3.4 Procedure

3.4.1 Data Collection

After the sample was successfully composed, the collection of data started. The unit of analysis

was the independent posts. For each company in the sample, the last 100 qualifying posts

including the engagement data were collected. A post qualified when it met all the following

criteria:

1. The post should contain at least three words (besides optionally an ‘attachment’ like

e.g. a website link, report, photo, video).

2. The post must be generated by the official account of the company (firm generated

content).


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3. Posts posted in the seven days prior to the date of data collection were excluded.

Customer engagement is not reliable yet, because not all followers had the chance to

see the post, as explained before.

The collection was done either by using the LinkedIn API which collected several statistics for

each post separately, or it was done manually by copying/pasting the data in Microsoft Excel.

The first method had preference, but the LinkedIn API had the limitation that the data collector

must be an administrator of the LinkedIn company page. Not all companies in the sample had

provided this access to the company’s LinkedIn page, so these companies’ data had to be

collected manually. After the content of the post, date, number of likes, number of comments,

and number of followers of the company page were documented, the content analysis could

begin.

3.4.2 Content Analysis

To obtain data on each post about the variables Relationship Phase and Content Type, each post

had to be assessed on its content. According to Riff, Lacey and Fico (2014), “Quantitative

content analysis is the systematic and replicable examination of symbols of communication,

which have been assigned numeric values according to valid measurement rules, and the

analysis of relationships involving those values in statistical methods, to describe the

communication, draw inferences about its meaning, or infer from the communication to its

context, both of production and consumption.”

Due to the manageable size of the sample of posts, the content of the LinkedIn post was

coded and analyzed on content type and then categorized in a relationship phase, both manually.

As the content of the post in the data file (Excel spreadsheet) only captured text and not

interactive content, the content analysis was done based on the online version of the posts, and

not just the text in the data file. To check whether the post was not altered in the time between


34

the data collection and the content analysis, the text of the post in the dataset and the text online

were compared. If the post was altered, the post was excluded from the dataset.

The data was collected in different formats for the purpose of data analysis. For the

business model variable, each company and corresponding posts received a categorical value

of 0 (B2B) or 1 (B2C). For the relationship phase variable, each post was assessed on each

category and received a 1 (Awareness), 2 (Acquisition) or 3 (Retention). For the content type

variable, each post was assessed on each type and received the corresponding number 1 to 12

(see Table 2). Engagement was measured as an interval variable, and the number of likes and

comments were recorded as the number displayed on the LinkedIn website.

3.5 Reliability

Before testing our hypotheses, the reliability of the acquired data was tested. The whole dataset

was coded by one researcher (also the author of this paper). To guarantee reliable results and

reduce the possibility of bias, one additional researcher was trained on the coding method and

was asked to code 50 random posts on content type and on relationship phase. According to

intercoder reliability research, the twice coded sample had to be at least 10% of the full sample

(±30 in this study) or 50 units (Lombard, Snyder-Duch, & Bracken, 2002). To assess intercoder

reliability, Krippendorff’s Alpha had to be at least 0.67 (De Swert, 2012; Krippendorff, 2004).

This reliability test was done to ensure that the coding of the main researcher isn’t biased and

the research can be replicated.

3.6 Data Analysis

The data was quantitatively analyzed in the IBM SPSS software program. To analyze

hypothesis 1, the relation between business model and customer engagement, an independent

t-test was conducted. The hypotheses 2a, 2b and 2c about the relation between business model


35

and content type were assessed by conducting a 2X12 Pearson’s chi-square test. However, when

the assumptions of the chi-square test were violated, the test would be replaced by a Fisher’s

exact test. This would be the case if counts were less than 5. After the 2X12 chi square, each

category would be analyzed separately with a 2X1 chi square test. Hypotheses 3a, b and c about

the relation between content type and customer engagement were tested with a one-way

independent ANOVA and a factorial ANOVA to add business model as a moderator.

Hypotheses 4a, b and c were analyzed with a 2X3 chi-square test to look for a statistical link

between business model and relationship phase and 2X1 chi square tests for individual

differences in business models. Hypothesis 5a about the association of relationship phase of

posts to the engagement was analyzed with a one-way independent ANOVA. Hypothesis 5b,

which studies the moderating effect of business model on the relation between relationship

phase and customer engagement, was assessed by conducting an independent factorial

ANOVA. Lastly, the influence of the business model on the relationship between content type

and relationship phase was analyzed by conducting loglinear analysis.

Furthermore, as content analysis is also seen as a qualitative research method (White &

Marsh, 2006), the quantitative results were complemented to offer additional insights with

qualitative results such as frequencies, means and proportions. This was especially relevant for

the descriptive statistical results of the chi-square tests. Descriptive statistics indicate an

association, but are not modeling techniques and can therefore not predict values of the outcome

variable. The reader should keep this in mind when interpreting the results.

4. Results

4.1 Reliability

Before starting statistical analyses, the reliability of the coding was assessed. The results of the

Krippendorff’s Alpha showed that intercoder reliability was moderate for the content type


36

coding (α = 0.72) and on the border of acceptable for relationship phase coding (α = 0.69). As

a reference, a Krippendorff’s Alpha of 0.67 is acceptable and 0.80 is high (Krippendorff, 2004).

Thus, the dataset was reliable enough to proceed with the analysis according to the standards

of Hayes and Krippendorff (2007).

4.2 Result per hypothesis

Hypothesis 1

First, it was tested whether there is an association between the business model type and the

level of customer engagement. In the independent t-test, Levene’s test showed equal variance

could not be assumed (F (1, 513) = 28.06, p = .00). On average, posts from B2C generated

higher engagement (M = 16.90, SD = 12.04) than B2B companies (M = 11.73, SD = 08.13)

(see Table 3). This difference, -5.17 BCa 95% CI [-6.95, -3.39], was significant, t (446.77) = -

5.70, p = .00. It represented a medium-sized effect, Cohen’s d is 0.50. Hypothesis 1 stated that

customer engagement is higher on B2B company posts than on B2C company posts on

LinkedIn. However, the contrary was found. B2C customers were significantly more engaged.

Table 3 Descriptives of the engagement scores per business model groups

Business Model N M SD SE

B2B 259 11,734 8,1284 0,5050

B2C 256 16,903 12,044 0,7527

Note: 5 outliers were removed.

Hypothesis 2

To research the sub hypotheses 2a, b and c about the relationship between business model and

content type, a Chi Square test was conducted over the counts of the separate content types and

an additional Chi Square test over the broad content categories.


37

First, the Chi Square with the twelve content types was conducted. According to the descriptive

of frequencies (Table 4), the category Promotion and Discounts had a value of 0 for the B2B

business model. This broke the assumption that each value should be at least 1 and no more of

the expected counts should be less than 5 (Field, 2013). To avoid losing a radical reduction of

test power, this category was excluded from the Chi Square test. B2B companies never posted

about this and B2C companies 8 times. We can infer from this that B2C companies used

promotional posts more than B2B companies, but it could not be statically tested. Also, the

category Other was excluded from the Chi Square test. The category was not relevant as it did

not represent a single type of posts, but contained the random posts that could not be

categorized. The number of posts labeled Other was low (total count is 5), so most posts of the

data set could be classified in one of the other categories.

The 10X2 Chi Square test results showed that there was a significant association between

business model and content type (χ2(9) = 65.06, p < .001). None of the assumptions were

violated. Cramer’s V had a value of 0.341, which means that the effect size is large. The

conducted z-test within the 10X2 Chi Square showed that the proportions of some of the

individual categories were significantly different between the two business models.

To specify which content types were used significantly more by one type of company,

2X1 Chi Square tests were conducted over the remaining ten categories (Table 4). Significant

associations were found for the categories Contests (χ2(1) = 9.00, p = .003), Industry News

(χ2(1) = 27.46, p < .001), Knowledge Sharing (χ2(1) = 6.63, p = .01), Products and Services

(χ2(1) = 13.26, p < .001), and Workforce and Recruitment posts (χ2(1) = 7.69, p = .006). B2B

companies posted significantly more about industry news and shared more knowledge than

B2C companies, while B2C companies posted significantly more contests, products and

services, and workforce and recruitment posts.


38

There were no significant associations for Awards and Congratulations (χ2(1) = 0.00, p =

1.00), Company Information (χ2(1) = 0.02, p = .89), CSR (χ2(1) = 0.57, p = .45), Events and

Conferences (χ2(1) = 0.05, p = .81), and Reports (χ2(1) = 2.31, p = .13). This implied that either

B2B or B2C did not post more about awards and congratulations, company information, CSR,

events and conferences, or reports than the other company type.

Table 4 Descriptives and chi square results of content types

Content Type Broad Category Counts

Chi Square Tests

B2B B2C Total χ2 df p-value

Awards and Congratulations Events 9 9 18

0.00 1 1.00

Company Information Reputation 25 26 51

0.02 1 .889

Contests Promotion 2 14 16

9.00 1 .003**

CSR Reputation 16 12 28

0.57 1 .450

Events and Conferences Events 39 37 76

0.05 1 .819

Industry News Reputation 33 2 35

27.46 1 .000***

Knowledge Sharing Reputation 64 38 102

6.63 1 .010**

Products and Services Promotion 51 95 146

13.26 1 .000***

Promotions and Discounts Promotion 0 8 8

- - -

Reports Reputation 22 13 35

2.31 1 .128

Workforce/Recruitment Reputation 36 16 52

7.69 1 .006**

Other None 1 4 5

1.80 1 .180

* p < .05, ** p < .01, *** p < .001

Second, the content types were lumped into the broad content categories and an additional

chi square tests were conducted. There were significant associations found for the broad content

categories Promotion (χ2(1) = 24.09, p = .00) and Reputation (χ2(1) = 26.14, p = .00), but no

association for Events (χ2(1) = 0.04, p = .84). The descriptives showed that B2C companies did


39

significantly more promotion posts, B2B companies more reputation posts, and that there was

no significant difference in counts for the event category (see Table 5).

Table 5 Descriptives and chi square results of broad content categories

Broad Category Counts

Chi Square Tests

B2B B2C Total

χ2 df p-value

Promotion 53 117 170

24.09 1 .000***

Reputation 196 107 303

26.14 1 .000***

Events

48 48 94

.04 1 .837

* p < .05, ** p < .01, *** p < .001

With these results, the sub hypotheses were assessed. Hypothesis 2a stated that B2C

companies post more promotional content than B2B companies. The types Contests, Products

and Services, and Promotions and Discounts fall in this category. The findings presented that

contests, and products and services showed significant results that B2C companies indeed

posted more promotional content. It seemed like the same applies to promotions and discounts,

but this was not significantly tested. The broad content category confirmed that B2C companies

posted significantly more promotional content than B2B companies. Overall, the hypothesis is

accepted.

Hypothesis 2b stated that B2B companies post slightly more posts to increase reputation

than B2C companies. The types CSR, Knowledge Sharing, Reports, Workforce and

Recruitment, Company Information and Industry News fall in this category. Indeed, the

hypothesis was accepted for the categories industry news and knowledge exchange. No

significant results were found for CSR, Reports, and Company Information. However,

Workforce and Recruitment was significantly more posted by B2C companies. The broad

content category Reputation confirmed that B2B companies posted more reputational posts.


40

Given that the hypothesis mentioned that B2B’s post slightly more than B2C, this hypothesis is

accepted, keeping in mind that this did not account for Workforce and Recruitment.

Hypothesis 2c stated that B2B companies post significantly more event-related content

than B2C companies. The types Events and Conferences, and Awards and Congratulations fall

in this category. However, the results showed no significant results for these two content types.

Neither did the broad content category Events. Therefore, the hypothesis is rejected.

Hypothesis 3

To test the sub hypotheses of hypothesis 3, whether there is a relationship between content types

and customer engagement and a possible moderating effect of business models, a factorial

ANOVA was conducted. However, Levene’s test showed a significant result (p = 0.004), so the

assumption of homogeneity of variance was violated. Therefore, first a one-way independent

ANOVA was conducted with a Welch test to look at the relation between content type and

customer engagement. The results (Welch’s F (10, 72.5) = 26.68, p < .001) showed there were

significant differences in customer engagement between the content types. The Games-Howell

Post Hoc test exposed that some categories generated either significantly higher or lower

engagement than other types of posts. See Table 6 for the results over all categories. The most

notable results were that the content category promotions and discounts generated to lowest

engagement, and knowledge sharing posts didn’t perform very well either. Posts with company

information performed the best.

To find out whether there is an interaction effect between the business model and content

types, a two-way independent ANOVA was conducted. To account for the violated

homogeneity of variance, the data of the dependent variable data had to be transformed by

taking the square root of the engagement score to meet the homogeneity of error variance

assumption. After this, the result of Levene’s test was that homogeneity of error variance could

be assumed (p = .46). There was a significant main effect of the business model on customer


41

engagement, F (1, 499) = 12.93, p = .007. Just like in the one-way independent ANOVA, there

was also a significant main effect of the content type of a post on customer engagement, F (10,

499) = 11.69, p = .00.

Table 6 Games Howell Post Hoc Test: Mean differences

Prom

oti

on

an

d D

isco

un

t

Co

nte

sts

Even

ts a

nd

Co

nfe

ren

ces

Aw

ard

s a

nd

Co

ngra

tula

tio

ns

CS

R

Pro

du

cts

an

d S

erv

ices

Kn

ow

led

ge

Sh

arin

g

Rep

orts

Co

mp

an

y I

nfo

rma

tio

n

Ind

ust

ry

New

s

Oth

er

Promotion and Discount 0 -13,08 -13,59*** -18,58*** -9,10*** -13,81*** -6,72** -14,11*** -22,22*** -10,65*** -19,88

Contests

0 -0,51 -5,50 3,98 -0,73 6,36 -1,03 -9,13 2,43 -6,80

Events and Conferences

0 -4,99 4,49 -0,22 6,87* -0,52 -8,63 2,94 -6,29

Awards and Congratulations

0 9,48 4,77 11,86* 4,47 -3,64 7,93 -1,30

CSR

0 -4,71 2,38 -5,01 -13,12* -1,55 -10,78

Products and Services

0 7,09** -0,30 -8,41 3,16 -6,08

Knowledge Sharing

0 -7,39* -15,50*** -3,93 -13,16

Reports

0 -8,11 3,46 -5,77

Company Information

0 11,56 2,33

Industry News

0 -9,23

Other

0

* p < .05, ** p < .01, *** p < .001

However, no interaction effect was found between the content type of a post and the

business model of the company placing the post, on the customer engagement (F (9, 499) =

1.02, p = .42). This indicated that it did not matter for customer engagement whether a B2B or

B2C company posted a certain type of content. This means that hypotheses 3a, 3b and 3c are

not supported, as there was no interaction effect and we could not distinguish between B2B and

B2C. Figure 6 displays the interaction plot.


42

Figure 6 Interaction plot of the effect of business model on the relationship between content type and customer engagement

Hypothesis 4

Hypotheses 4a, 4b and 4c looked at the link between the business model and the relationship

phase which the companies targeted with their post. The majority of the posts targeted

awareness (67%), then acquisition (30%) and only 4% retention. The results of a 2X3 Chi

Square test showed that there was a significant association between the business models and

the relationship phases (χ2(2) = 33.20, p < .001). None of the assumptions were violated. The

effect size was medium; Cramer’s V = 0.25 (Cohen, 1988).

The conducted z-test showed that the proportions of all three relationship phases were

significantly different. Therefore, 2X1 Chi Square tests were also conducted to specify the

relationships of the individual relationship phases with the business models (Table 7).

The first 2X1 Chi Square test showed a significant association between the business

model and the relationship phase Awareness (χ2(1) = 5.63, p = .02). The frequencies implied

that B2B companies posted more awareness raising content than B2C companies. However,

hypothesis 4a stated that B2C companies post more awareness-related posts than B2B


43

companies. The results showed the opposite, so hypothesis 4a is refuted: B2B companies posted

more awareness-related posts.

Furthermore, there was also a significant association between the business model and the

relationship phase Acquisition (χ2(1) = 18.69, p < .001). B2C companies posted significantly

more acquisition content than B2B companies. Hypothesis 4b stated that B2B companies post

more acquisition-related posts than B2C companies. Again, the hypothesis is refuted and the

opposite was found.

Finally, there was a significant association between the business model and the

relationship phase Retention (χ2(1) = 8.91, p = .003), which showed that B2B companies post

significantly more retention content than B2C companies if one looked at the frequencies.

Hypothesis 4c stated that B2B companies post more retention-related posts than B2C

companies. This hypothesis is confirmed and therefore accepted.

Table 7 Descriptives and chi square results

Relationship Phase Counts

Chi Square tests

B2B B2C Total

χ2 df p-value

Awareness 194 150 344

5.63 1 .018*

Aquisition 51 105 156

18.69 1 .000***

Retention 18 4 22

8.91 1 .003***

* p < .05, ** p < .01, *** p < .001

Hypothesis 5

To test whether there was an association between relationship phase and customer engagement,

a one-way independent ANOVA was conducted. Levene’s test showed that homogeneity of

variance could be assumed (p = .89). The one-way ANOVA showed that there was no

significant effect of the relationship phase on the height of the customer engagement score,


44

F (2, 519) = 0.40, p = .67. Also, Hochberg’s GT2 post hoc test did not show any significant

results (Table 8). This indicated that there was no association between the relationship phase of

a post and customer engagement. Hypothesis 5a stated that acquisition and retention content

generates higher customer engagement than awareness content, which is not statically

significant and therefore the hypothesis is rejected.

Table 8 Hochberg's GT2 Post Hoc test results

Relationship Phase

Awareness Acquisition Retention

Awareness 0 -0,52045 2,23205

Acquisition

0 2,75250

Retention

0

* p < .05, ** p < .01, *** p < .001

To study hypothesis 5b, the moderating effect of business models, an additional two-way

independent ANOVA was conducted. To comply with the assumptions of this test, the data of

the dependent variable data again had to be transformed by taking the square root of the

engagement score to meet the homogeneity of error variance assumption. After this, the result

of Levene’s test was that homogeneity of error variance could be assumed (p = .13). For the

descriptives, see Appendix 8.2 Table 18. The statistical results showed there was no significant

main effect of the relationship phase of the customer that the company targeted with the post

on customer engagement, F (2, 516) = 1.04, p = .35. This was in accordance with the results of

hypothesis 5a. The two-way ANOVA also did not find a significant main effect for the business

models on customer engagement (F (1, 515) = 3.34, p = .07), but it was close to the alpha value

of .05 to be significant (Table 9). Furthermore, there was also no interaction effect between the

relationship phase and the business model type on the height of the customer engagement score,

F (2, 516) = 2.49, p = .08. This indicated that the variables relationship phase and business


45

model were independent of each other and did not interact. The hypothesis that relationship

content posted by B2B companies create higher engagement than relationship content by B2C

companies, must therefore be rejected. However, note that the significance of the interaction

effect was close to the alpha value of .05, and that the plot does intuitively point to a small

interaction effect (Figure 7). This may be a good starting point for future research.

Table 9 Results of the two-way independent ANOVA

Two Way Independent ANOVA

Source SS df MS F p-value

Relationship Phase 4,506 2 2,253 1,043 0,353

Business Model 7,223 1 7,223 3,343 0,068

Interaction Effect 10,765 2 5,383 2,491 0,084

Within 1114,804 516 2,16

Total 7904,462 522

Note: R Squared = ,060 (Adjusted R Squared = ,051)

Figure 7 Interaction plot of the effect of business model on the relationship between relationship phase and customer

engagement


46

Hypothesis 6

To conduct the loglinear analysis to look at relationship between content type and relationship

phase specified by business model, the data had to be altered significantly. To satisfy the

assumption that not more than 20% of the frequencies are lower than 5 and that all expected

frequencies must be higher than 1, the content categories were merged into the four broad

content categories identified in Table 2. After this, all data entries with ‘None’ or ‘Retention’

as relationship phase were excluded, since the assumption that every frequency had to be higher

than 1 was violated in these groups. This meant that we could not make any statistical inferences

about these categories. After the alterations 496 out of the initial 567 data entries remained.

This was still a significant data set; therefore, it was decided to continue with the loglinear

analysis. Now all assumptions were met.

The three-way loglinear analysis showed that not all effects in the model could be

retained. The likelihood ratio of the model including all three variables was insignificant (χ2(2)

= 2.38, p = .30).

The results showed significant two-way effects for the variables business model and

broad content types (χ2(2) = 31.20, p = .00) and broad content types and Relationship phases

(χ2(1) = 284.41, p = .00). Business model and Relationship phase were not significantly

associated(χ2(1) = 0.88, p = .36). Also, there were main effects for broad content types (χ2(2) =

74.9, p = .00) and relationship phase (χ2(1) = 69.92, p = .00), but not for business models (χ2(1)

= 0.13, p = .72) (Table 10).

It could be concluded that the three-way interaction made no significant contribution to

the model. All the sub hypotheses of 6 should therefore be rejected.


47

Table 10 Partial Associations: Effect of each variable in the loglinear model

Effect df Partial Chi-Square Sig.

Business Model * Broad Content Category 2 31,204 0,000***

Business Model * Relationship Phase 1 0,884 0,347

Broad Content Category * Relationship Phase 2 284,414 0,000***

Business Model 1 0,129 0,719

Broad Content Category 2 74,086 0,000***

Relationship Phase 1 69,917 0,000***

* p < .05, ** p < .01, *** p < .001

5. Discussion

5.1 Business models

This study shows that customers of B2C companies are significantly more engaged on LinkedIn

than B2B customers, although the opposite was hypothesized. B2C customers liked and

commented more on posts by company pages than B2B customers. This is in line with the

insights of Keinänen and Kuivalainen (2015), as they argued that B2B customers are more

passive on social media and do not actively engage, even though B2B sales people themselves

are more active on social media. The efforts on social media are apparently not reciprocated by

B2B customers. Furthermore, B2B customers were expected to be more active, because

acquiring and retaining business relationships was more important in the B2B sectors

(Michaelidou et al., 2011). However, our other results show that there is no relationship between

the relationship phases (awareness, acquisition and retention) and customer engagement, which

demonstrates that the motivations to facilitate business relationships does not influence

customer engagement.


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5.2 Content types

As the content types of LinkedIn posts were analyzed, the results showed that the types of

content posted by the two company types differs. Of the identified twelve content types on

LinkedIn, five types differ significantly between B2B and B2C. B2B companies posted

significantly more about industry news and shared more knowledge than B2C companies, while

B2C companies post significantly more contests, products and services, and workforce and

recruitment posts. The frequencies of the other seven types (awards and congratulations,

company information, CSR, events and conferences, reports, promotions and discounts, and

other) do not significantly differ between B2B and B2C.

The content types were divided in three broader content categories: promotion, reputation

and events. Overall, it was found that B2B companies aim to increase their reputation more

than B2C’s, which supports the findings of Walsh et al. (2015) stated in the theoretical

framework that reputation is more important for B2B companies. Furthermore, B2C companies

promote their products and services more. Events were not significantly used by one party more

than the other. Although from the latter broad category it was expected that B2B would post

more about events, there was no difference between the two business models. What did stand

out about event posts during the content coding, was that B2C companies sponsored many

events which they could use for marketing purposes. B2B companies were more active at sales

events, like conferences, at which they were physically present.

The top 3 most used content types for B2B in this study were knowledge sharing (21%),

products and services (17%) and events and conferences (13%). The top 3 for B2C companies

are products and services (35%), knowledge sharing (14%) and events and conferences (35%)

(see Table 20 in Appendix 8.4 for the top 10). Yet, what stands out is the differences in counts

between the twelve categories. By far, the most popular content types were products and

services (count: 146) and knowledge sharing (count: 102) (out of the 572 posts, see Table 17 in


49

Appendix 8.2). The average number of posts per content type is 47. Thus, the posts on a

LinkedIn page are not very diverse, and social media managers could alternate the types more.

The study identified that significant differences in the customer engagement for the

different content types exist. These differences are displayed in the Content Decision Table

(Table 11). This table can be used in deciding on what type of content a social media content

creator should use, because some are better than others. Important is to note that the table can

only be read horizontally. For example, CSR performs better than promotions and discounts

(green), is not better or worse than contests (yellow), and worse than company information

posts (red).

Table 11 The Content Decision Table: Matrix with preferences of content types over other types.

The Content Decision Table

Is the choice better than the alternative?

Better Neutral Worse

Choice

Alt

ern

ati

ve

Pro

moti

on a

nd

Dis

coun

t

Con

test

s

Ev

ents

and

Con

fere

nce

s

Aw

ard

s an

d C

ong

ratu

lati

on

s

Co

mp

any

In

form

atio

n

Indu

stry

New

s

Oth

er

CS

R

Pro

du

cts

and

Ser

vic

es

Kn

ow

led

ge

Sh

arin

g

Rep

ort

s

Promotion and Discount

Contests

Events and Conferences

Awards and Congratulations

CSR

Products and Services

Knowledge Sharing

Reports

Company Information

Industry News

Other Note: Only read this table in horizontal direction, from choice to alternative.


50

The content type with the lowest customer engagement was promotions and discounts,

although you must keep in mind that the sample of this category was small, which could be the

reason why the posts performed significantly worse than almost all other categories. Also,

knowledge sharing isn’t a good choice when you want high customer engagement. Surprising,

because it was a frequently used content type; it was the most used content type by B2B and

second most used by B2C companies (Table 20 in Appendix 8.4). This insight should teach

marketers that they shouldn’t create too many knowledge sharing posts, as it generates low

engagement.

Furthermore, there is not really one content type that outperforms all other types of

content (no content type has many green cubes in the Content Decision Table on the horizontal

line). The top 10 content types (Table 12) shows which content types are most engaging.

Therefore, it could be concluded that content writers should stay away from promotions and

discounts and knowledge sharing posts; from most remaining content types, one isn’t much

better than another, with some exceptions, which are displayed in Table 11 marked as red or

green cubes.

Table 12 Top 10 of the most engaging content types for customers.

Rank Content Types

1 Company Information

2 Awards and Congratulations

3 Reports

4 Products and Services

5 Events and Conferences

6 Contests

7 Industry News

8 CSR

9 Knowledge Sharing

10 Promotion and Discount

Note: Based on the descriptive statistics

in appendix 8.4 Table 17


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Furthermore, there was no interaction effect of business models on the relationship

between content type and customer engagement. This means that we cannot, for example, say

that a company information post creates higher customer engagement than a CSR post for B2B

companies, but not for B2C companies. Therefore, the Content Decision Table and the top 10

content types for customer engagement are the same for both business models. Apparently, the

preferences for content types do not significantly differ between B2B and B2C customers.

5.3 Relationship Phases

We also looked at which part of the simplified customer journey (relationship phases) posts

targeted. The study shows that the use of relationship phases differs between the two business

models.

Although the opposite was hypothesized based on the literature of Michaelidou et al.

(2011), B2B companies post more awareness-related posts than B2C companies, and B2C

companies more acquisition-related posts. Although the objective for using social media by

B2C companies is raising awareness and by B2B companies is more acquisition-related

(Michaelidou et al., 2011), the actual use of content types and the objectives are surprisingly

not aligned. It could be the case that LinkedIn company pages covers only a small part of the

LinkedIn strategies, followed up by other actions on LinkedIn to reach the objective. For

instance, the objective of a B2B company is to acquire new customers. The company page is

utilized to ‘plant a seed’ in the potential customer’s mind by using awareness posts, after which

social media marketers follow up on the engaged potential customers who liked a certain post,

by sending them a direct message. The objective of using LinkedIn is still to acquire customers,

though the company page is not used for this. The posts in the company page have a more

supporting role in the social media strategy. Also, using multiple channels, including digital

and traditional, to reach and communicate with customers is the most profitable strategy.


52

Multichannel customers have higher rates of profitability, consistently across industries and

geographies (Honts, Klimek, & Meyer, 2016). LinkedIn is definitely not the only marketing

and sales channel, and may be just a part of an extensive social media marketing strategy.

Furthermore, it may be more difficult for B2B companies to create acquisition related

posts than for B2C’s. The products and services of B2B companies are more often customized

for customers, while the products and services of B2C companies are more standard and high

volume. That makes it easier to create an acquisition post for a product or service, because they

can be very specific in details about the product and the deal. This may explain part of the

unexpected result that B2B companies post less acquisition posts than B2C companies.

For retention-related posts our expectation was confirmed; B2B companies have more

posts in the category. However, the number of retention posts was very small in the sample.

Moreover, it was expected that the relationship phases acquisition and retention generated

higher customer engagement than awareness posts, but we couldn’t find any significant

differences. There is no relation between the relationship phase of a post and the customer

engagement. This indicates that a customer is not more or less engaged with a certain

relationship type of a post than another.

There is also no interaction effect found of business model on the relationship between

relationship phase and customer engagement. However, the interaction effect (p = .084) was

close to being significant, and the interaction plot (Figure 7) showed some interaction. It is very

interesting though, because there is no main effect from the relationship phase on customer

engagement. However, this could also be due to the small sample size of retention posts.

5.4 Missing three-way effect

The results showed there is no significant effect between the business model, content types and

relationship phases. This means there is no three-way relationship, it is not possible to predict,


53

for example, that if a B2B company posts a promotional post, the relationship phase targeted

would likely be the acquisition phase.

However, when looking at the descriptives (Table 13), many of the hypotheses do come

out as true. Hypothesis 6a stated that promotion posts in the B2C context mainly target the

acquisition phase. This seems to be true since B2C companies posted 95 acquisition posts, only

20 awareness and 2 retention posts. The difference in count between acquisition posts and the

other categories is very large. B2C companies did many product promotions by mentioning

their products. These posts were like advertisements. Hypothesis 6b stated that promotion posts

in the B2B context mainly target the awareness and retention phases. This does not seem to be

true; surprisingly, B2B companies post mostly promotion messages targeted at the acquisition

phase (34 posts). There were only 6 awareness posts and 13 retention posts.

Hypothesis 6c stated that reputation posts in the B2C contexts mainly target the awareness

phase. This seems to be true, since there were 90 reputational posts targeted at customers in the

awareness phase, and only 1 in both the acquisition and retention phase. Hypothesis 6d stated

that reputation posts in the B2B context mainly target the awareness and retention phases. This

seems to be partly true; indeed, B2B companies posted many reputational awareness posts (149)

and only 8 acquisition and 4 retention posts. The hypothesis is not correct for the expectation

of retention.

Hypothesis 6e stated that event posts in the B2C context mainly target the awareness

phase. This seems to be true, since 37 posts were on awareness and only 8 on acquisition. Lastly,

hypothesis 6f stated that event posts in the B2B context mainly target the retention phase. This

is not true; most B2B event posts targeted the awareness phase (37), 9 posts in the acquisition

phase and none the retention phase.

Thus, these hypotheses based on common sense are in general correct, but are not

statistically confirmed. It should be noted that there were quite some alterations on the data set


54

to be able to do the statistical test. I would be valuable to repeat the loglinear analysis with a

more complete data set.

In short, this research studied the connections between business models, content types,

relationship phases and customer engagement on LinkedIn. The results showed some surprising

results, but also confirmed many expectations. For a full overview of all the hypotheses and

their outcomes, Table 14 summarizes the hypotheses and the results.

Table 13 Descriptives: Counts of posts in each category in the loglinear analysis

Broad Content Type Business Model Relationship Phase Count

Promotion

B2B

Awareness 6

Acquisition 34

Retention 13

B2C

Awareness 20

Acquisition 95

Retention 2

Reputation

B2B

Awareness 149

Acquisition 8

Retention 4

B2C

Awareness 90

Acquisition 1

Retention 1

Events

B2B

Awareness 38

Acquisition 9

Retention 1

B2C

Awareness 37

Acquisition 9

Retention 0


55

Table 14 Overview of hypotheses and corresponding results

Number Hypothesis Result

Accepted = Result is significant Refuted = Opposite is significant Rejected = No significant result

1 Customer engagement is higher on B2B company posts than on B2C company posts

on LinkedIn.

Refuted

2a B2C companies post more promotional content than B2B companies. Accepted

2b B2B companies post slightly more posts to increase reputation than B2C companies. Accepted

2c B2B companies post significantly more event-related content than B2C companies. Rejected

3a B2C customers are more engaged in promotional content than B2B customers. Rejected

3b B2B customers are slightly more engaged in reputation posts than B2C customers. Rejected

3c B2B customers are more engaged with event-related posts than B2C customers. Rejected

4a B2C companies post more awareness-related posts than B2B companies. Refuted

4b B2B companies post more acquisition-related posts than B2C companies. Refuted

4c B2B companies post more retention-related posts than B2C companies. Accepted

5a Acquisition and Retention content generates higher Customer Engagement than

Awareness content.

Rejected

5b Relationship content posted by B2B companies create higher engagement than

relationship content by B2C companies.

Rejected

6a Promotion posts in the B2C context mainly target the acquisition phase. Rejected*

6b Promotion posts in the B2B context mainly target the awareness and retention

phases.

Rejected

6c Reputation posts in the B2C contexts mainly target the awareness phase. Rejected*

6d Reputation posts in the B2B context mainly target the awareness and retention

phases.

Rejected

6e Event posts in the B2C context mainly target the awareness phase. Rejected*

6f Event posts in the B2B context mainly target the retention phase. Rejected

* Descriptives show otherwise


56

5.5 Limitations

Some limitations are associated with the current study.

First, although the sample size of the LinkedIn posts of six different companies was

extensive, the sample was too small for some statistical tests to be conducted at full strength.

Adaptations, like the exclusion of a category, had to be done to meet the assumptions for some

tests. With a larger sample, some of these problems could have been bypassed, but this was not

possible in this study due to time and access constraints. A solution would be to use the

LinkedIn API for all company pages to collect data, but the researchers will have to be

authorized to access the data by each company separately.

Second, technology and digital industries are fastmoving, and what is hot and trending

this year, could be obsolete the next. Likewise, in social media networks, the features and

content are constantly changing. In the last couple of years, LinkedIn has grown and added and

removed features. Some literature used in this study is not very recent, but in my opinion the

insights are still valid. However, the reader should keep in mind that some parameters might

have become invalid during the pace of this study. Legislation for digital and data-intensive

companies is also currently changing, like the General Data Protection Regulation (GDPR) that

has taken effect in May 2018 in the EU. The effect on data sharing and the functions of the

APIs is still uncertain, but could be changing in the near future.

Third, the content types identified and used in this study are not applicable to every social

network. Also, if this study would be repeated, some content types may be lumped to get

stronger statistical results and validity. Moreover, each post was assigned only one content type

and one relationship phase, to satisfy the assumption of chi square tests where all units in the

sample should be independent. In practice, in many posts the content and/or relationship types

overlap, i.e. are not mutually exclusive. A post with the mention of an event may as well include


57

a product discount, which allows the post to receive two labels. However, in this study the best

option was chosen in such situations, which may cause a dispersion with reality.

5.6 Theoretical and Managerial Implications

As expected in the introduction, this study adds new insights to the literature on the distinction

between the B2B and B2C markets. There is a difference in the level of engagement of B2B

and B2C customers and in which content they are engaged. These insights are new to this

branch of literature. Also, the content categorization helps advance the methodology of content

analysis on social media. Surprisingly, there are few studies which specifically focus on the

content of social media messages. The categorization in Table 2 will still need to be adapted,

but will be a good starting point for future studies.

In practice, this study helps managers better understand the distinction between the B2B

and B2C markets. It makes them aware that for each business model a different online strategy

might be needed for optimal results. The better they engage their customers, the better the long

term (financial) results of the company will be (Hoffman & Fodor, 2010).

Social media managers are better equipped to development an effective social media

strategy after reading this study. For instance, it makes it easier for social media managers to

choose other companies to benchmark their performance of social media pages with. Many

social media networks have the option to see how other companies are performing. On

LinkedIn, the tool ‘Companies to track’ is available at the Analytics page of an administrator.

Social media managers should now be able to make a better selection of which companies they

should track to compare their company page performance with; it has been revealed that

comparing their results with those of company pages with other business models is deceitful.

Companies should only benchmark themselves with companies with the same business model,

otherwise it would be comparing apples with oranges.


58

Furthermore, social media managers can use the information on content types to make a

substantiated choice of which type of content they should choose for a message. Also, if a social

media manager is considering two types and must make a choice, he can use the Content

Decision Table (Table 11) to determine which content will work best to get the highest customer

engagement.

LinkedIn positions itself as the social network for professionals and businesses. Because

customer engagement on B2C company pages is higher than on B2B company pages, it implies

that LinkedIn members are representing themselves more than they are representing the

company they work for. They are more active in liking and commenting on posts they enjoy

personally than they enjoy professionally. So, although LinkedIn positions itself as the

professional social network, this study refutes the intuitive idea that LinkedIn is made for

professionals only. Therefore, the insights may be valuable for LinkedIn itself, as an

opportunity for business development is uncovered. LinkedIn could focus more on B2B

businesses and facilitate their optimal use of LinkedIn, and equalize the value B2B and B2C

companies obtain from their platform.

6. Conclusion

The lack of studies about the distinction between B2B and B2C companies in digital marketing

led to the need for a quantitative and explanatory study in this field. LinkedIn is a widely used,

but barely studied social medium for professionals. This study identifies differences between

the two business models and finds which content works best to generate high customer

engagement. Therefore, the results of this study can be used by social media managers to

establish an effective social media strategy on LinkedIn. The main research question of this

study was to find out to what extent customer engagement on social media differs in the B2B

and B2C contexts, and what role content plays in this relationship.


59

The findings suggest that there is a significant difference in customer engagement

between B2B and B2C companies. Posts of B2C companies on LinkedIn company pages

receive significantly more likes and comments than B2C companies’ posts.

In addition, the type of content also differs between the two business models. B2C

companies post more promotional content and B2C companies more content that establishes a

reputation. Both business models post about events, but there were no significant differences in

counts. Although some content types generate significantly less or more engagement than other

types, there is no significant influence of the business models on the customer engagement per

content type.

The study also made a distinction in the phase of the relationship that the posts target:

awareness, acquisition or retention. B2B companies posts more awareness-related posts, B2C

companies post more acquisition-related posts, and lastly, B2B companies use more retention-

related posts, although previous literature showed that B2C companies are more focused on the

awareness phase, and B2B companies on the acquisition and retention phases (Michaelidou et

al., 2011). However, there is no link between the relationship phases and customer engagement,

so the phase does not influence engagement. The results neither show a significant interaction

effect of the two business models on the connection between relationship phases and customer

engagement. Finally, there is no three-way effect between the business models, content types

and relationship phases.

To conclude, this study shows some interesting findings, even though not all hypothesis

could be answered with significant results. It has been established that there are indeed

differences between business to business and business to consumer companies on social media,

and also in the content they can use and post.


60

6.1 Future research

The findings suggest various avenues for future research. Scholars may repeat this research on

other social media platforms to establish that there’s a difference in content and engagement

per platform, or that the content preferences of customers are the same throughout social media

platforms. An advantage of repeating this study on other social media could be that their APIs

are more accessible (likes Facebook’s API), and data collection will be easier.

Future researchers may also take a deeper look into the three-way effect of business

models, content types and relationship types as the descriptives confirmed out hypotheses, but

the loglinear analysis did not. In this research, the data set for the loglinear analysis had to be

adapted unfortunately. It would be very interesting to see if the hypotheses are confirmed with

a more extensive data set.

Furthermore, although there is no significant difference in customer engagement

between posts of different relationship phases in this study, this might actually point to an

association, which will need further research. Hypothetically, if it’s the case that only

converting (acquisition stage) or converted (retention stage) customers engage with acquisition

and retention posts, and non-converted (awareness stage) followers exclusively engage with

awareness posts, then converted customers are actually much more active, since this group is

likely to be only a fraction of the total followers of a LinkedIn company page. This means that,

for example, a group of 1000 non-converted followers would account for the same number of

likes and comments on awareness posts as a group of 100 converted followers on acquisition

and retention posts. A study with two groups of customers (converted and non-converted)

would lead to further insights into the motivations of a (potential) customer to engage on social

media.


61

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8. Appendix

8.1 Coding guidelines second coder

The coding guidelines were given to the second coder for the reliability test.

Table 15 Coding sheet: Content Categories

Content type Description Example

1 Promotion/discount Posts which mention prices or discount

marketing campaigns

• 2+2 for free,

• 20% discount if…,

• KLM werelddealweken

2 Contests Asking a member to take a quiz, guess

an object, etc. Asking for user

generated content in the comment

section.

• Do [this] and win …,

• Take a quiz…,

• Guess what product this

is…?

3 Events / Conferences Mention of an event where the

company attended, or an invite to join

an event. Preferably with place and

time mentioned. No business courses!

• Come see us at booth …,

• Watch our show at 19.00

on 1-1-2011,

• We were present at

[conference]

4 Awards /

Congratulations

Congratulating someone or something,

or announcement of winning an award.

• Congratulations to Timmy

for being nominated for…,

• We’ve won the award…

5 CSR Sustainability related post, charity,

voluntary work

• We reduced water usage

by ...

• Donate to this charity…,

• Our employees

volunteered at ...

6 Products/Services Mentioning of a product or service • See our [new product] …,

• Go to our website to buy

[product name]

7 Knowledge Sharing Sharing knowledge, without explicitly

promoting a product or service.

• Top 10 tips,

• Things to do in this city...,

• Did you know that …

• Whitepapers

8 Reports Mentioning of a report, annual

numbers, infographics with numbers

etc.

• Download our annual

report…,

• This year our revenue

grew to…


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9 Workforce/Recruitment Vacancy announcement or business

course, or reasons why the company is

a good employer.

• Meet our employee...

• we are looking for new

employees…

10 Company Information Partnership announcements (not related

to a very specific product), birthdays of

founders, facts about the company,

history stories

• We like to announce our

partnership with …

• Happy birthday to our

founder …

11 Industry news News articles about company/industry,

Announcements about industry

• We were featured in

WSJ…

• The consultancy industry

is doing …

12 Other Holiday greetings (without promotion

of specific product), or other random

posts

• Merry Christmas

Table 16 Coding Sheet: Relationship Phases

Relationship Phase Description Example

1 Awareness General (marketing messages), fun

facts, blogs, top 10 tips.

Presenting the company as an

industry expert.

• What to do when you are in

Bangkok

• Ten things companies should

look at when applying

blockchain

2 Acquisition Related to encouragement to buy a

specific product.

Or when a social media post is

directed to ‘you’ instead of genera.

• Our new car model…

• Ensure that your company’s AI

will be a success with these key

consideration

3 Retention Offering something to existing

customers, or mentioning an

existing customer as a business

case/example

• [Company] is our new customer

• We did a project for [company]

and we increased its ...

4 None Everything with

workforce/recruitment content code

(these posts are not directed at

customers but at (potential)

employees)

• Meet harry

• Join our business course


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8.2 Descriptives tables

Table 17 Descriptives of customer engagement for each content type. Sorted on Means.

Engagement Score

Content Types N Mean Std. Dev Std. Error Minimum Maximum

Company Information 51 24,56 21,90 3,07 3,52 113,22

Other 5 22,22 8,29 3,71 12,56 32,22

Awards and Congratulations 18 20,92 11,94 2,81 7,15 51,26

Reports 35 16,45 9,02 1,52 1,01 41,69

Products and Services 146 16,15 10,93 0,90 0,35 65,08

Events and Conferences 76 15,93 8,90 1,02 0,00 42,69

Contests 16 15,42 16,69 4,17 3,22 71,14

Industry News 35 12,99 10,52 1,78 2,01 48,28

CSR 28 11,44 8,26 1,56 1,02 28,18

Knowledge Sharing 102 9,06 15,39 1,52 0,00 149,40

Promotion and Discount 8 2,34 1,84 0,65 0,48 5,89

Total 520 15,09 13,72 0,60 0,00 149,40

Note: the category Workforce/Recruitment is excluded; its target group are not customers but potential employees,

therefore, customer engagement is not a valid measure.


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Table 18 Descriptives of engagement scores per group

Relationship Phase Business Model Squared Engagement Score

Mean Std. Deviation N

B2B 3,353 1,35038 194

Awareness B2C 3,8718 1,65127 150

Total 3,5792 1,50895 344

B2B 2,9125 1,31646 51

Acquisition B2C 4,0367 1,39918 105

Total 3,6692 1,46715 156

B2B 3,1469 1,86795 18

Retention B2C 3,0859 1,52912 4

Total 3,1358 1,77743 22


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8.3 Content type preferences

Table 19 Results of engagement comparing content types

Content Type Description

Promotion and

Discounts

Promotion and discounts have very low engagement as opposed to most other content

types. It generated significantly lower engagement than events and conferences, awards

and congratulations, CSR, products and services, knowledge sharing, reports, company

information, and industry news.

Contests Contests generated average engagement, nothing significantly lower or higher compared

to other categories.

Events and

Conference

Events and conference posts generate significantly higher engagement than promotions

and discounts, and knowledge sharing.

CSR CSR performs better than promotions and discounts, but worse than company

information posts.

Products and

Services

Products and services created significantly higher engagement than knowledge sharing

and promotions and discounts.

Knowledge

Sharing

Knowledge sharing generates significant lower results compared to events and

conferences, awards and congratulations, products and services, reports, and company

information, but higher than promotions and discounts.

Reports Posting about reports creates higher engagement than promotions and discounts, and

knowledge sharing posts.

Company

Information

Sharing company information generates significantly more engagement than promotions

and discounts, CSR, and knowledge sharing.

Industry News Industry news only generates higher engagement than promotions and discounts.

Other Other didn’t generate significantly higher or lower engagement than any other type, but

this could be due to the low number of posts that were assigned ‘Other’, but also because

Other contains the most random posts that couldn’t be assigned to any other category.


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8.4 Ranking of most used content types

Table 20 Ranking of content types of the data set per business model

Rank B2B

B2C

1 Knowledge Sharing 21% Products and Services 35%

2 Products and Services 17% Knowledge Sharing 14%

3 Events and Conferences 13% Events and Conferences 14%

4 Workforce/Recruitment 12% Company Information 9%

5 Industry News 11% Workforce/Recruitment 6%

6 Company Information 8% Contests 5%

7 Reports 7% Reports 5%

8 CSR 5% CSR 4%

9 Awards and Congratulations 3% Awards and Congratulations 3%

10 Contests 1% Promotions and Discounts 3%

11 Other 0% Other 1%

12 Promotions and Discounts 0% Industry News 1%

Documents

Differentiation between B2B and B2C companies on LinkedIn