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Academic year: 2023

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Which brand related content pays off on social platforms in the Digital Era: The Moderating Role of Different Social-Platforms and Product Categories

Thesis Research

Faculty of Economics and Business

MSc Business Administration – Digital Marketing Track

Student name: Claudia den Houting SNR: 13316702

EBEC Approval Number: EC 20220124020103

Thesis Supervisor: dr. Umut Konus Coordinator: Karin Venetis

Word count: 15,544 (excluding reference list and table of content) Submission Date: January 27, 2022


1 Statement of originality

This document is written by Claudia den Houting who declares to take full responsibility for the content 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.

Amsterdam, 27 January 2022


2 Acknowledgement

I would like to thank Mr. Konus for his coaching and supervising during my thesis trajectory.

I feel blessed that I got the opportunity to get to know prof. Konus as, in my opinion, he is a fantastic thesis supervisor as well as someone with a lot of passion for the field he is

specialized in. I am incredibly thankful for his deep understanding of digital marketing and quantitative analyses. His research expertise and knowledge and skills of the digital marketing category are outstanding and have taught me a lot. In addition to that, he has made me feel comfortable with performing this research right from the start. To the best of my knowledge, both parties, meaning prof. Konus and me, were dedicated to getting the most out of this Master’s Thesis trajectory.

Furthermore, I would like to express gratitude to Ms. Venetis for the help at the start of the thesis project. More specifically, for the lectures and tutorials regarding the thesis proposal course, which aided me in starting the procedure flawless.

Last, and most importantly not least, I would like to thank my friends, family, and my boyfriend, Yves-Maurice Vork, for their exceptional help during my thesis trajectory. It wasn’t always the easiest because some sad and unforeseen private circumstances occurred.

Still, with their love, support, and exceptional help during the process, the trajectory was relatively painless and the outcome positive, resulting in one extensive scholarly paper.


3 Paper Abstract

In the recent years, both academic studies and business practice alike show the value of digital content as a driver of customer-brand engagement and better business outcomes. However, creating insights in the use of social media content to generate customer engagement remains to be accomplished. Marketers will fail without a good understanding of what content to communicate through which platform for what product category and what type of product.

Previous research has primarily relied on a singular product category and thus has been unable to identify how category and product characteristics play a role in generating online engagement.

This study investigates (i) the direct effects of the type of digital content on users’

engagement behaviour, and (ii) assesses the moderating effects of social media platform and product category and type on the link between each type of content and platform users’

engagement. Web Scraping was used to obtain on-site data from the social media networks Twitter and Instagram in order to test the research hypotheses. Thereafter, a regression analysis has been done to examine the causal relationship between the type of content,

platform, product categories and types, and engagement related outcomes. The findings reveal that the effectiveness of digital content on users’ engagement is determined by the type of content and moderated by the platform and product category and type. These findings aid in understanding how to effectively engage customers digitally, which influences online customer purchases and brand attitude.


Customer engagement, platform, social media, digital content


4 Table of content

Statement of originality ... 1

Acknowledgement ... 2

Paper Abstract ... 3

1. Introduction ... 7

2. Literature Review ... 10

2.1 Digital Content Marketing ... 10

2.2 Digital Content Types ... 12

2.3 Digital Content Types and Management on Social Media Platforms ... 15

2.4 Customer Engagement ... 18

2.5 Customer Engagement on Social Media Platforms ... 20

2.6 The Moderating Role of Product Category and Platform ... 23

2.6.1 Product Type and its Moderating Effect ... 24

2.6.2 Social Media and its Moderating Effect ... 25

2.7 Relevance and Expected Contributions ... 26

Managerial Implications ... 28

Theoretical Implications ... 29

3. Conceptual Framework and Hypotheses ... 31

3.1 Research Hypotheses ... 32

4. Data and methods ... 35

4.1 Description of Sample ... 35

4.2 Operationalization of variables ... 36

4.3 Description of research instrument(s) and procedures ... 42

5. Analysis and results ... 43

Multicollinearity Checks ... 43

Regression Analysis ... 43

Main Effects Analysis ... 44



Moderation effects analysis ... 46

Results ... 49

6. Discussion ... 53

Managerial implications ... 57

7. Conclusion ... 60

Limitations and directions for further research ... 60

Reference List ... 63

Appendices ... 69

Appendix 1 – Multicollinearity Detection ... 69

Ratio Likes/Followers ... 69

Ratio Comments/Followers ... 69

Engagement Rate (%) ... 70

Sentiment Score ... 70

Appendix 2 - Correlation Matrix ... 71

Appendix 3 – Main Effects Models ... 72

Model 1: All predictor variables and their main effects on the outcome variable Ratio Likes/Followers ... 72

Model 2: All predictor variables and their main effects on the outcome variable Ratio Comments/Followers ... 72

Model 3: All predictor variables and their main effects on the outcome variable Engagement Rate (%) ... 73

Model 4: All predictor variables and their main effects on the outcome variable Sentiment Score ... 73

Appendix 4 – Main Effects + Interaction Effects Models ... 74

Model 1: All predictor variables and interaction variables and their effects on the outcome variable Ratio Likes/Followers ... 74

Model 2: All predictor variables and interaction variables and their effects on the outcome variable Ratio Comments/Followers ... 75


6 Model 3: All predictor variables and interaction variables and their effects on the

outcome variable Engagement Rate (%) ... 76

Model 4: All predictor variables and interaction variables and their effects on the outcome variable Sentiment Score ... 77

List of Figures and Tables Table 1 ... 15

Table 2 ... 19

Table 3 ... 21

Table 4 ... 23

Table 5 ... 34

Table 6 ... 36

Table 7 ... 39

Table 8 ... 41

Table 9 ... 45

Table 10 ... 47

Table 11 ... 51

Figure 1 ... 11

Figure 2 ... 21

Figure 3 ... 31


7 1. Introduction

“We sought to develop relevant and snackable material that was both useful and amusing, based on data-driven insights. In this approach, the company intends to reach both existing customers and those who are considering purchasing a BMW but have not yet done so.” Jorg Poggenpohl, BMW’s Global Head of Digital Marketing, spoke at Mobil World Congress 2019. Mister Poggenpohl explained how BMW extends its content beyond automotive to engage more with (potential) customers (Arica, 2020).

Given the importance of digital content as a source to engage (potential) customers, it is crucial to note that future marketing strategies will be implemented in digital environments, particularly in social media and mobile contexts (Stephen, 2016). For a long time now, less and less is spent on traditional media, whereas more resources are spent on digital media (Dwivedi et al., 2021). The ease with which consumers may access the internet from anywhere via smartphones, and the rapid advancement of technology and the enormous growth in the digital advertising category, have increased consumer interest in digital content (Pektas & Hassan, 2020). Beyond that, digital content may be utilized for entertainment, which engages online customers, and for persuasion, which leads to sales, making digital content beneficial on multiple levels. As a result, social media platforms have become increasingly popular, with a third of the world’s population currently using them. However, without marketers understanding how to generate effective digital content on these platforms, such marketing investments will fail. As a result, brands’ online presence has become crucial for communicating and engaging with consumers through brand sites (Shahbaznezhad et al., 2021). On top of that, consumer engagement is one of the most commonly stated desired objectives (Lee, Hosanagar, & Nair, 2018).

Therefore, the online, digital era is getting more and more important for companies operating from all over the world. Content marketing is now used by both small and medium-sized


8 businesses, and huge enterprises such as Microsoft, Facebook, Google, Apple, and others (Baltes, 2015). Because of growth in Digital Content Marketing, also known as DCM, competition is intensified.

In fact, today’s consumers are spending no less than seven hours daily engaging with branded content online (DoubleVerify, 2020). In addition to that, brands nowadays are investing heavily in content marketing within digital communication channels, yet there is limited understanding of the effectiveness of this content on consumer engagement (Bowden and Mirzaei, 2021). Moreover, research has found that 96% of all consumers that discuss brands online fail to also engage with the brands’ social media profile (Smith, 2019), representing a lost opportunity to more fully engage consumers across different (branded) social media platforms (Bowden and Mirzaei, 2021). Consequently, this is a lost opportunity to improve companies’ profitability. Digital Customer Engagement (CE) reflects the customers’

investment in their brand-related interactions (Rasool, Shah, & Islam, 2020) and is of significant importance because it nurtures customer-brand relationships. Therefore, the current study dives into the effectiveness of different types of digital content on online customer engagement which has been found to be a key antecedent that influences customer purchases in online buying (Febrian, Bangsawan, Mahrinasari, & Ahadiat, 2020).

However, there is a lack of knowledge of the drive behind online customer engagement, and practitioners wouldn’t know if their digital content is as effective as they think. Understanding the motivation needed to induce the users’ active participation is necessary though, because the competitiveness of a social platform depends on the number of participants who share their knowledge and experiences on the platform (Ko, Kim, Taylor, Kim, & Kang, 2007).

With their research, Voorveld et al. (2018) found out that engagement and advertising evaluations are related in a highly context-specific way because the relationship is highly contingent on the platform. Each online platform provides a set of experience dimensions,


9 related to either how positively or negatively advertisements are evaluated. This suggests that companies communicating with their consumers through social media must select the most suitable social medium for their brands’ purposes to enhance the effectiveness of their content strategies.

Because of the increasing importance of Digital Content Marketing and the raising time consumers spend online (Dwivedi et al., 2021; DoubleVerify, 2020) in combination with the fact that not much is known about the actual drive behind customer engagement, the current study focuses on finding out what different types of digital content produce more online engagement in terms of likes, comments, and positive sentiment. The research has three purposes: (1) to study and compare the effect of different types of digital content on online customer engagement, (2) to research what kind of business outcomes are generated if companies share same similar content on different online platforms, and (3) to find out if these effects are different for various product categories and product types. Eventually, this study offers managers advice on establishing digital content and social media strategies by looking into the effect of several digital content types on customer engagement and the effect of the social media platform and product category and type herewith. Firms should be able to alter their strategy in response to this information, allowing them to make more money from the internet content they distribute.


10 2. Literature Review

The rise of digital marketing was one of the key transformations that traditional marketing underwent, necessitating a reassessment of marketing tactics for organizations that wished to remain competitive in the new digital era. As a result, content marketing has emerged as a critical component of a successful online marketing campaign and, as the most important digital marketing tool (Baltes, 2015). Multiple researchers have drawn on the rising importance of DCM in this digital era (Baltes, 2015; Dwiveda et al., 2021; Bowden and Mirzaei, 2021; Stephen, 2016), though not much attention is put on the impact of digital content on online customer engagement (Bowden and Mirzei, 2021; Chemela, 2019; Smith, 2019; Zyminkowska, 2018).

2.1 Digital Content Marketing

Authors define digital content as: ‘bit-based objects distributed through electronic channels’

(Koiso-Kanttila, 2004; Rowley, 2008). The term ‘electronic channels’ describes both wired and wireless networks. Practitioners frequently refer to digital content provided through wireless networks as “value added services” or “mobile services” (Koiso-Kanttila, 2004).

Kotler (2018: 167) defines content marketing as a marketing strategy that tries to develop engaging, helpful content for a target audience, distribute it, and allow users to comment on it.

Digital Content (DC) includes various content formats, such as videos, e-newsletters, ezines, podcasts, whitepapers, infographics, webinars, and virtual conferences (Fox, Nakhata, and Deitz, 2019; Hollebeek and Macky, 2019, as cited in Lou & Xie, 2021). Digital Content Marketing (DCM) is a crucial relationship marketing strategy, defined as “the management process responsible for discovering, predicting, and successfully delivering client

expectations” (Rowley, 2008, p.522). Category methods are continually altering to suit Google’s ranking algorithm, to eventually increase organic traffic to companies’ websites


11 (Moz, 2019). These changes in digitalization and electronic media have resulted in changes in aspects such as information collecting, communication with others, and making or

abandoning travel decisions, all of which have enhanced the relevance of online and web resources (Sing & Bhatia, 2016, as cited in Pektas & Hassan, 2020). As a result, it’s critical to improve DCM strategies based on greater consumer understanding (Dwivedi et al., 2021).

Furthermore, rather than corporations shoving or pushing content to customers, content marketing focuses on individuals proactively seeking out valuable brand content (Deighton and Kornfeld, 2009, as cited in Lou and Xie, 2021). Thus, DCM is now driven by customer knowledge instead of shouting to customers.

Figure 1

Visuals of business Digital Content Marketing

Desktop Twitter post of the KLM account Mobile Instagram post of the Louis Vuitton account


12 Useful, relevant, interesting, and current content is thought to be valuable in the digital era.

Multiple researchers and studies have stressed the distinction between DCM and advertising.

There is a general agreement about the aim of advertising, which is to foster sales in the short run. In contrast, DCM is defined as “the art of communicating with [prospective] customers without selling products” either overly or directly (Bicks, 2016, as cited in Hollebeek &

Macky, 2019). In Figure 1, two examples of Digital Content Marketing from business practice are given in which a distinction is made between desktop and mobile view. Both advertising and DCM aim to improve consumer views of brands and, as a result, sales. On the other hand, DCM achieves this by cultivating consumer engagement, trust, and connections, all of which are intended to cultivate sales indirectly and over time. Different marketing objectives,

methods, KPIs, and abilities than those associated with more traditional marketing approaches are required when transforming content marketing from “selling” to “helping” (Holliman &

Rowley, 2014).

2.2 Digital Content Types

Nowadays, the consumer is no longer merely a passive recipient of advertising content, but rather an active distributor (by sharing ad content with friends), contributor (by publicly commenting on ad content), and even producer (by (co-)creating ad content for advertisers (Hudders et al., 2019). The intended goal of a text (for example, to inform, entertain, or persuade) is an essential factor that shapes it (Caselli, Sprugnoli, & Moretti, 2021).

The creation of targeted content is beneficial to creating multidimensional brand engagement (Bowden and Mirzaei, 2021). The research of Bowden & Mirzaei (2021) found that

consumers are more likely to become involved if more contextual information about the brand is offered, as well as a “richer” brand narrative and dimensions. To add to that, Chemela (2019) found that content typology significantly impacts consumer engagement.


13 Over the years, researchers and scholars recognized different forms of content in the context of digital content marketing. Based on previous research, social media content that affects consumer engagement has been divided into three main categories: rational, interactional, and transactional (Shahbaznezhad et al., 2021), also named: informational, emotional and

commercial content (Tellis, MacInnis, Tirunillai, & Zhang, 2019). Rational content refers to informative, functional, educational or current events, interactional for example, means experiential, personal, employee, brand community, customer-relationship, or cause-related content and transactional refers to remunerative, brand resonance and sales promotion.

However, those studies showed mixed results. These disparate findings point to the necessity for more thorough theoretical and empirical research on the role of various content kinds in social media to comprehend its use and link with customer engagement (See Shahbaznezhad et al., 2021 for a clear overview of past results).

Lou and Xie (2021) classified digital content into four categories: informative, entertaining, social, and functional. Their study defines informative content as “those videos that provide relevant/timely/useful/valuable brand information” (Ducoffe, 1995, as cited in Lou & Xie, 2021). Furthermore, entertainment content is defined as “extremely

entertaining/enjoyable/exciting/pleasing”, while social value content encourages prospects or consumers to share what they’ve learned by reading or watching it because they believe it will make them popular, and, finally, the brand channel’s functional value is a “reliable medium for brand or product information” (Ming-Sung Cheng et al., 2009, as cited in Lou & Xie, 2021).

According to Pektas & Hassan (2020), consumers are wary of information provided in digital content, conducting research from several sources and comparing the results. People aim to capture consumers’ attention by making a difference, especially in the digital platform where the competition is fierce. In this scenario, building strong relationships with customers has


14 become unavoidable. Multiple studies draw on the findings that informational value may not necessarily be influential in the context of online content sharing (Hsiao, 2020). An

explanation could be the problem of information load. People may acquire too much

information at a given moment on the internet, despite the internet being an effective way for users to seek information. A surplus of information causes poor decision-making. Obermiller

& Spangenberg (1998), as cited in Pektas & Hassan (2020), defined advertising scepticism as

“the widespread tendency to doubt advertising promises” and assumed a fundamental market belief regarding persuasion and individual variability.

Drawing on previous literature, digital content types in this study are divided into three primary categories: (1) informative, (2) entertaining, and (3) persuasive (commercial related) content. As scholars have attempted to explore the impact of several content types on online customer engagement behaviour under these central themes, those form the basis in the current research. A summary of those prior previous studies may be found in Table 1.

Lambertini (2019) defines informative advertising as referring to a monopolist’s goal of increasing consumer density at every point along with a linear metropolis, whilst persuasive advertising refers to a monopolist’s goal of increasing their prices. This research distinguishes persuasive content as content emphasising discounts, loyalty offers and sales promotional content.

Previous research primarily focussed on a single platform or format, which has limited the understanding of the various ways digital content can be displayed and distributed to social media users. With the current research, all the three previously mentioned types of content (informative, entertaining, and persuasive) are thoroughly studied on a dual platform approach to see if and how they may enhance customer engagement.


15 Table 1

Main types of digital content and their characteristics

Types Of Digital Content Characteristics Authors

Rational / Informational Content that provides

relevant/timely/useful/valuable brand information and/or educational content.

Shahbaznezhad, Dolan, &

Rashidirad (2021); Tellis, MacInnis, Tirunillai, & Zhang (2019); and Lou and Xie (2021) Transactional / Persuasive /

Commercial / Functional

Content that direct calls to purchase. The promotional approach within social media content (e.g. sales promotion and brand resonance content).

Shahbaznezhad, Dolan, &

Rashidirad (2021); Gümüş (2017);

Tellis, MacInnis, Tirunillai, &

Zhang (2019); and Lou and Xie (2021)

Interactional / Emotional Experiential, personal, employee, brand community, customer relationship and cause-related content.

Shahbaznezhad, Dolan, &

Rashidirad (2021); and Tellis, MacInnis, Tirunillai, & Zhang (2019)

Entertaining Humorous, funny, and artistic content which is enjoyable, exciting and pleasing.

Gümüş (2017); and Lou and Xie (2021)

Social Social content encourages prospects or

consumers to share what they’ve learned by reading or watching it because they believe it will make them popular.

Lou and Xie (2021)

Leisure Any type of recreational content, such as competitions or games.

Gümüş (2017)

2.3 Digital Content Types and Management on Social Media Platforms

Because good content has no value unless and until it is pushed on social media, content marketing and social media are complementary. Social media is a platform that enables users to generate and share content with one another (Ansari, Ansari, Ghori, & Kazi, 2019). Social media has become essential for branding, as it provides brands with the ability to connect with consumers in a more dynamic and personalized manner (Singh & Mathur, 2019; Du Plessis, 2017; Ansari et al., 2019). Similarly, without a robust content plan, social media marketing will fail (Singh & Mathur, 2019). In the study of Singh & Mathur (2019), the authors state


16 that companies and entrepreneurs can use social networks to get a voice and engage with peers or [potential] customers.

Furthermore, the interactive nature of digitalization is reflected in the fact that consumers can now interact with the brand directly by responding to social media content messages through likes or comments (Hudders et al., 2019). The context of the content placed on a social media platform is vital to consider because customer profiles, and subsequently the customer-brand relationship, differ depending on the platform (Voorveld et al., 2015; Smith et al., 2015; Du Plessis, 2017). Distinct capabilities and characteristics of social media platforms translate into different customer experiences. Each platform has its own set of experience factors that influence how content is rated positively or negatively. As a result, many brands and companies employ numerous platforms.

Facebook, YouTube and Twitter are the dominant content providers on Social Media

(Williams, Crittenden, Keo, & McCarty, 2012). Moreover, Smith, Blzovich, & Smith (2015) found that Twitter is by far the most popular platform among service businesses. Second and third place, respectively, go to Facebook and YouTube. However, in later research, they found that Instagram ended up in the first three social networks (Kircova, Yaman, & Köse, 2018) together with Facebook and Twitter. Pelletier, Krallman, Adams, & Hancock (2020) found that Instagram is the most popular medium for entertainment motivation and social media co-creation with brands. This result is in line with one of the findings of Voorveld et al.

(2015) study showing that content or advertising on Instagram is seen as more entertaining than other platforms. Despite being the most prominent platform and network most widely utilized by marketers, Facebook has the lowest usage goals and co-creation.

Because Twitter is the most popular social media platform among service businesses (Smith et al., 2015), and Instagram is the most popular platform for entertainment motivation and social media co-creation with brands (Pelletier et al., 2020), those two platforms [Instagram


17 and Twitter] will be used in the current study. Their role is to test the moderating effect of social media platform on the direct relationship between type of digital content and online customer engagement.

Consumers today live in a world that is both content-rich and time-constrained (Tellis et al., 2019). Content marketing is a modern marketing paradigm with many long-term benefits, such as increasing brand loyalty by interacting with target audiences with meaningful content rather than using promotional approaches. Subsequently, content marketing for social media has become one of the most popular tactics for businesses and brands to enhance interaction and gain new followers on social media platforms (Du Plessis, 2017; Garcia, Pereira, &

Cairrão, 2021; Ansari et al., 2019). The findings of a study by Ansari et al. (2019) reveal that social media content marketing has a significant role in explaining the variance in customer purchase decisions.

The research of Tellis et al. (2019) discovered that the employment of informational appeals has a considerable detrimental impact on social sharing in general. On the other hand,

information-focused commercials have a favourable effect on social media sharing only when the product or purchase risk is high, such as when the product is new or when the price is high. In turn, ads that elicit pleasant emotions result in much more positive social sharing.

Because Twitter allows people to share content from other (business) accounts, whereas Instagram only allows you to share content in Instagram stories, this effect would be significantly stronger on Twitter than on Instagram. Upon that, ads that create emotional connections between the brand and the members of the brands’ social networks encourage the consumer’s brand loyalty and support (Gümüş, 2017). Another interesting outcome of the study of Tellis et al. (2019) is that a well-known brand name impedes social sharing, and when the brand name is displayed early or intermittently in the ad, it generates substantially less sharing than when the brand name is shown later.


18 Users of social media consume digital content posted by brands for various reasons, including utility (of persuasive message), entertainment, and leisure (Gümüş, 2017). Reasons for

consumers to follow and like brands online are: keeping up with discounts and promotions, the receiving of incentives, the provision of useful and entertaining content, reading other users’ comments, high number of followers of the brand, and friends following the brand (Gümüş, 2017). Customers in this age prefer content generated by others instead of brands for various reasons, the most important of which are emotional and the belief that it is more trustworthy (Garcia et al., 2021). Besides, participants in the study of Gümüş (2017) stated that negative comments regarding the brands affected them more than positive ones.

2.4 Customer Engagement

Today’s consumer has evolved from a poorly informed and passive consumer to one who is active, sociable, and engaged in company activities (Kuvykaitė & Tarutė, 2015). Engagement is a type of social and interactive behaviour that can be defined as a transitive state created through time in the appropriate engagement processes (Brodie et al. (2011) as cited in Kuvykaitė & Tarutė, 2015). In 2005-2006, the first studies of consumer engagement dimensionality were conducted, and they were similar in main aspects on consumer

engagement dimensions identified, namely: consumer’s focus, enthusiasm, and willingness to act and interact (Patterson et al., 2006 and Vivek, 2009, as cited in Kuvykaitė & Tarutė, 2015). Consumer engagement has frequently been studied either conceptually or in exploratory qualitative studies. In contrast, there appears to be a scarcity of quantitative studies, and just a few of those that do exist have tried to build or report meaningful and reliable consumer involvement scales (Dessart, Veloutsou, & Morgan-Thomas, 2016). Dessart et al. (2016) reconceptualized consumer engagement by the use of a multi-stage process and identified the following (sub-)dimensions of customer engagement: affective (enthusiasm and


19 enjoyment), cognitive (attention and absorption), and behavioural (sharing, learning, and endorsing). An overview of these (sub-)dimensions is given in Table 2.

Scholars conceived the notion of customer engagement and attempted to empirically confirm its measuring scales in the context of digital marketing. Digital developments and technology have forced a deeper examination of how engagement is formed, enabled, and sustained across brand-related communication channels. Depending on the characteristics and nature of the social media platform in issue, different channels may contribute to customer engagement in different ways (Bowden and Mirzaei, 2021). Online engagement is described as

“measuring undertaken activities such as click through rates, page views, etc. depending on the platform’s capabilities” (Cvijikj & Michahelles (2014), as cited in Vohra & Bardhwaj, 2016).

Table 2

Dimensions and sub-dimensions of customer engagement

Affective Cognitive Behavioural

Enthusiasm Attention Sharing

Enjoyment Absorption Learning


Digital Customer Engagement (CE) reflects the customers’ investment in their brand-related interactions (Rasool, Shah, & Islam, 2020), which is important because it nourishes customer- brand connections and promotes company profitability. However, most major brands have continual hurdles in developing compelling consumer engagement strategies (Dessart et al., 2016; Bowden & Mirzaei, 2021).


20 2.5 Customer Engagement on Social Media Platforms

Brands can use various features on social media platforms to engage with prospects and customers. Van Doorn et al. (2012) define customer engagement in the context of digital marketing as “A customer’s behavioural expressions that have a brand or business emphasis, beyond purchase.”. The concept of digital customer engagement is a type of active digital behaviour characterized by a high level of involvement of individuals with the content, brand, and person who posted the content on an online platform (Dhanesh, 2017, as cited in Silva, Farias, Grigg, & Barbosa, 2019). Depending on the setups of social media platforms, digital engagement can be monitored using a variety of indicators. Click-through rates (CTR), number of likes, number of comments, number of followers, duration of the interaction, sharing of a post, and creation of content related to the brand, can all be used as engagement metrics that enable businesses’ to engage with [potential] customers (Vohra & Bardwaj, 2016;

Silva et al., 2019). Silva et al. (2019) identified two primary forms of social media customer engagement: consumption (for example, likes and follows) and contribution (comments), allowing for varying levels of participation to be shown as a result of social media posting.

Consuming is defined as a type of behaviour in which the individual is restricted to viewing images and videos of the brand, liking the posts, following online topics, and downloading widgets, whereas contributing is defined as a type of behaviour based on the active

participation of the individual in the conversations involving the brand/product, such as commenting on social media posts (Valentini et al., 2018, as cited in Silva et al., 2019). An overview of different online customer engagement metrics divided by consumption and contribution is given in Table 3. The current study uses likes and comments to analyze the impact of different types of content on customer engagement.


21 Table 3

Customer engagement metrics of Social Media and their corresponding level of participation Level of participation: consumption Level of participation: contribution

Likes Comments

Followers Shares

Views Creation of content related to the brand

Duration of the interaction Click-through rates (CTR)

Downloading widgets Sentiment

Besides, this research additionally evaluates the comments’ emotion by using a sentiment analysis, which can identify whether the generated customer engagement is negatively or positively charged. The metrics being used are given in Figure 2 from more broad

[consumption] to narrow [contribution] towards the optimal outcome of social media customer engagement.

Figure 2

Funnel of the customer engagement metrics being used in the current study


22 Sentiment is an attitude, thought, or judgement prompted by feeling and can be positive, negative or neutral (Fang & Zhan, 2015). Sentiment analysis, also known as opinion mining, studies people’s feelings about certain things and analyses people’s opinions, sentiments, evaluations, attitudes, and emotions from written language (Liu, 2012; Fang & Zhan, 2015).

2.5.1 Social Media based Content and its influences on Customer Engagement As previously mentioned, the current study investigates the moderating role of the social media platforms Twitter and Instagram on the direct effect of digital content on customer engagement. Instagram was launched in 2010 and is designed exclusively for smartphones and tablets. Its goal is to allow users to share photographs and videos freely. The platform’s popularity spread to businesses, with 65 percent of the world’s major brands already having an account in 2013. Instagram, which is based on photo and video sharing, requires a

specialized device [smartphone or tablet] and features a dynamic timestamp, with each image displaying a continually changing representation of time. Businesses can use the platform to generate customer engagement in the form of likes, comments, and follows.

Twitter, the other medium employed in this study, is one of the most popular communication channels. Users can engage with friends and other people worldwide using a Twitter profile through a text message restricted to 140 characters known as a ‘tweet’. This message can be accompanied by photos, videos, URLs, and so on. ‘RT’ stands for ‘retweeting’ [sharing] a message on Twitter (Muñoz-Expósito et al., 2017). Aside from retweets, Twitter users can use four other features: likes (also known as ‘favorites’), comments, following, and incorporating (also named ‘quoting’) a tweet. Table 4 depicts an overview of the specific characteristics and significant metrics of Twitter and Instagram, used in this study.


23 Table 4

Specific characteristics of the platforms Twitter and Instagram and their customer engagement metrics

Specific characteristics Primary metrics

Twitter Messages of up to 140 characters. Retweets (share)

Both for personal and professional use. Likes

Sharing of other accounts’ content is made very easy. Replies Followers

Incorporate a tweet (quoting)

Instagram Postings only possible through the use of smartphones and tablets. Likes Postings exclusively containing either photo or video content. Comments Provides photo and video editing software in Algorithm. Followers

2.6 The Moderating Role of Product Category and Platform

In the current study, it is believed that product category and type and social media platform have a substantial impact on the direct effect of content type on online customer engagement.

Product categories are groups of products with comparable features and benefits. Products in the same category will have similar physical characteristics and benefits (Cambridge

Dictionary, n.d.). Research found that advertisers should carefully align platform character with (1) product type, (2) advertising or content goal, and (3) advertising message when selecting a platform for advertising or content purposes (Voorveld et al., 2015). In other words, different product types have different effects on social media when using these platforms for advertising or content purposes.

Although some scholars and researchers have looked into it (Voorveld et al., 2015;

Zyminkowska, 2018; Shahbaznezhad et al., 2021), research on the (in)direct effect of different product categories and product types on online customer engagement is scarce.

Therefore, the current study is keen to determine how product categories and product types moderate the relationship between the type of content and customer engagement. In the


24 present research a distinction is made between hedonic and utilitarian products within the (1) fashion, (2) electronics, and (3) travel industries.

2.6.1 Product Type and its Moderating Effect

Consumers see products in two basic types based on their purchasing reason or usage experience: utilitarian and hedonic. Though few products are solely utilitarian or entirely hedonistic, many do contain various degrees of utility and hedonism, with one taking precedence over the other (Chang, Chen, & Tan, 2012). Hedonic products are those that are purchased for the sake of enjoyment, fun, or pleasure, whereas utilitarian products are those that are acquired for their useful qualities (Ballester & Palazan, 2013; Sloot, Verhoef, &

Franses, 2002; Dhar & Wertenbroch, 1999, as cited in Jacob, 2021). Microwaves, minivans, and personal computers are examples of utilitarian products, whereas designer clothes, sports automobiles, and luxury watches are examples of hedonistic products (Chang, Cheng & Tan, 2012).

Several studies (Hsiao, 2020; Li et al., 2020; Kim et al., 2019; Jacob, 2021; Huang & Rust, 2021; Zyminkowska, 2018) have found a difference in digital marketing performance for hedonic products compared to utilitarian ones. There is ample evidence that both hedonic and utilitarian consumer motivations impact purchasing decisions (Pöyry, Parvinen &

Malmivaara, 2013). With their studies, Hisao (2020) and Barari, Ross & Surachartkumtonkun (2020) discovered a positive association between hedonic value and online customer

satisfaction. The beneficial impacts of commitment on both attitudinal and behavioural engagement are stronger for hedonic items than utilitarian ones, according to Barari et al.

(2020). Huang & Rust (2021) found that customers may rely on brands as choice heuristics when purchasing utilitarian products because technology aids consumers in making better- informed decisions.


25 Furthermore, the findings of Li et al. (2020) and Pöyry et al. (2013) show that consumers’

utilization of different path-to-purchase channels is dependent on the hedonic and utilitarian values of purchased products per retailer category. Consumers who make hedonic purchases, in particular, want to have fun, enjoy themselves, and feel good while buying; they prefer social media. Consumers buying for utilitarian purposes, on the other hand, prefer channels that allow for convenient and efficient comparison shopping. They prefer to utilize search engines, read more third-party reviews, compare prices on firms’ webpages, and browse more product pages on competing shops’ websites than hedonic purchasers. The findings of

Zyminkowska’s (2018) study confirm this idea, as that study discovered that the hedonic value dimension had a more positive influence on online consumer engagement than the utilitarian value dimension. Still, the latter had a beneficial impact as well. In addition, the advantages of an informational native ad are constrained by the product category. According to Kim, Lee & Lee (2019), an instructive native ad beat its entertaining counterpart in

consumer evaluations when the featured brand was perceived as a utilitarian product;

however, this advantage was lost when the featured brand was viewed as an hedonic product.

Thus, since the distinction between hedonic and utilitarian product values is an important driver of customer engagement and therefore in how customers respond to different (digital) marketing instruments, those two are being used in the current research as potential


2.6.2 Social Media and its Moderating Effect

Coelho, Oliveira & Almeida (2016) found that virtual social media platforms like Instagram are more effective when used to promote products, services, and prices. Given the findings of Pelletier et al. (2020) and Voorveld et al. (2015), which show that firms’ Instagram postings are seen as more entertaining and are more popular for co-creation with the brand, it is


26 reasonable to assume that entertaining content posted on Instagram generates more online customer engagement in terms of likes and comments. Particularly in terms of comments, which is a form of engagement defined as contribution (Silva et al., 2019). On the other hand, Tellis et al. (2019) discovered that informative brand content on social media platforms significantly increases sharing of that content. The findings of those studies demonstrate how social media-based content can influence different forms of online customer engagement.

As previously mentioned, the research of Voorveld et al. (2015) discovered that because the relationship between engagement and digital content (in the form of advertising) is mainly dependent on the platform, engagement and advertising evaluations are associated in a highly context-specific way. Each online platform has its own set of features related to how content that is posted on the platform is rated positively or negatively. To improve the success of businesses’ online content initiatives, firms connecting with their customers through social media platforms must choose the right platform for their brands’ needs. Since platform matters in brand-customer interactions, this factor will be a potential moderator in this study.

2.7 Relevance and Expected Contributions

In conclusion, brands’ online presence becomes more and more essential, and it is now more crucial than ever for companies to communicate and engage with consumers through brand sites (Chemela, 2019; DoubleVerify, 2020; Dwivedi et al., 2021). It’s not only the huge enterprises such as Facebook anymore who use content marketing but also the small and medium-sized businesses are implementing DCM in their strategies (Baltes, 2015).

Furthermore, one of the most typically stated desired objectives is consumer engagement (Lee, Hosanagar, & Nair, 2018). However, little is known about the impact of types of content on customer engagement (Bowden and Mirzaei, 2021, Chemela, 2019; Smith, 2019;

Zyminkowska, 2018; Voorveld et al., 2015). As a result, marketing investments in digital


27 content and social media strategies to generate engagement will fail if marketers do not grasp how to create effective content types for specific social media platforms. In fact, the research of Smith (2019) found that 96 percent of all consumers that discuss brands online fail to also engage with the social media profile of the specific brand.

Considering the importance of operating online in the digital era and the ever-rising numbers of online time spent by consumers, it might influence both practitioners and scholars when researching the impact of digital content on customer engagement. In addition to that, not only the type of digital content, but also the social media platforms are of influence on online customer engagement (Bowden & Mirzaei, 2021; Shahbeznezhad et al., 2021; Du Plessis, 2017; Garcia, Pereira, & Cairrão, 2021; Ansari et al., 2019). Previous studies concerning this topic mainly focussed on a single platform or format, which has limited the understanding of the several ways digital content is effective for generating consumer engagement and through which platforms. Shahbeznezhad et al. (2021), with their research, were the first to explore this topic on a dual platform (Facebook and Instagram). More research is needed though, in a broader range of businesses and product categories to understand better how to leverage social media content to promote customer engagement. Therefore, the current study strives to

determine and explore the effect of category and product variables in driving online

engagement. Additional, it might be that the impact of different types of content on customer engagement is different for several product categories. With this in mind, the following research question has been investigated in the current study:

What is the effect of different types of digital content on online customer engagement, and what is the impact of social media platforms, product types, and product categories



28 Managerial Implications

The current research has several important managerial implications.

▪ First of all, the outcomes could support the evidence that is previously found for the fact that practitioners must select the correct type of digital content to engage with companies’ prospects and customers. Brands will benefit from connecting with their consumers on the right time at the right platform, as customer engagement has been determined to be a crucial antecedent that influences customer purchases in online shopping (Febrian et al., 2020).

Besides, engaged customers are considerably more profitable for a company than regular customers (Pansari & Kumar, 2016, as cited in Barari et al., 2020).

▪ Besides, the effectiveness of the type of content on generating customer engagement could differ depending on the social medium used for posting and thus communicating. For example, it might be that the same advertisement has a different effect on customer engagement for Facebook compared to


▪ Lastly, it could be that the effect also differs within several product categories and product types. Maybe for Apple, entertaining online content improves customer engagement, whereas for Samsung, it has the opposite effect. Finally, this research provides managers with recommendations on developing digital content and social media strategies. Firms should be able to adjust their strategy in reaction to the study results, allowing them to make more profit from the online content they distribute.


29 Theoretical Implications

Not only will content marketers benefit from the study’s outcomes, but it also has some important empirical attributions to the existing literature.

▪ At first, it aims to contain insights if the type of digital content has different impacts on online customer engagement when posted on different social medium. This is important as nowadays, a lot of content is posted on social media platforms, whereas not much research is done on the moderating role of social media platforms on the relationship between digital content and

customer engagement. Shahbeznezhad et al. (2021) were the first to find evidence for differences in the effectiveness of different types of content on customer engagement through dual platform approach. The current study potentially confirms and extends those earlier findings.

▪ Second, this study examines how different product categories and types influence this effect. Some research investigated what social media platform works best for which product category (Smith et al., 2015), but it’s not enough to draw conclusions about different types of content posted on these platforms and within what product categories.

▪ Third, this research is the first to compare the effects of those different types of digital content on consumer engagement moderated by social media platform and product category. As Bowden & Mirzaei (2021) already stated, there is limited understanding of the effectiveness of content on customer engagement, and there is a high need for studies about this topic as it results in higher opportunities to fully engage consumers across different social media


30 platforms. This study extends the insights of the research of Shahbeznezhad et al. (2021) on how social media content should be used to generate engagement.


31 3. Conceptual Framework and Hypotheses

The conceptual framework related to the research question of this study is illustrated in Figure 3. As shown in the figure, the different platforms and product types and categories were studied first during the literature review. The clarification of the different platforms, product types, and product categories were used to set the scene.. Also, clarifying and defining the definitions and industries helped to set up different methods and measures. Lastly, the direct effect of the type of digital content on online customer engagement, moderated by platform and product type and category was researched and identified.

Figure 3

Research Conceptual Framework


32 3.1 Research Hypotheses

Multiple studies suggested that several channels may contribute to consumer engagement differently depending on the qualities and nature of the social media platform in question (Voorveld et al., 2015; Smith et al., 2015; Du Plessis, 2017; Bowden & Mirzaei, 2021).

Besides, Shahbaznezhad et al. (2021) discovered sufficient evidence to corroborate this in their research on the specific subject of air travel. As a result, it is expected that, within this study, the level of customer engagement changes depending on the type of social platform used.

Previous research into the motivations for using various social media platforms has provided an helpful foundation for understanding why engagement behaviours for different content types may differ across platforms. As earlier said, the methods used in studies of Voorveld et al. (2015), Smith et al. (2015) and Du Plessis (2017) allow the authors to show that the online platform used has a significant impact on digital engagement. The most important result of the Voorveld et al. (2015) study is that engagement and advertising valuations are associated in a highly context-specific manner because the relationship is primarily dependent on the

platform. Another research demonstrated that not only are the social media platforms

themselves unique but also advertising on each has a distinct profile (Voorveld et al., 2018).

Such researches show that depending on the social media platform, the type of social media material that is beneficial in generating consumer engagement may vary. Shahbaznezhad et al.

(2021) were the first authors to dive deeper into the question of how different types of digital content might be adapted to various platforms to enhance social media interaction and found significant evidence to support this. However, their study took place in a singular category.

The current study proposes that for both the fashion and the electronics category, the

relationship between the type of digital content and online customer engagement is moderated by the social media platform in a way that entertaining content is expected to have a stronger


33 and positive impact on customer engagement in comparison to informative-persuasive

content. The hypothesis related to this moderating role of social media platforms on the relationship between types of digital content and online engagement is as follows:

H1: Entertaining content is expected to have a more robust and positive impact on customer engagement (measured by likes, comments, and sentiment of the comments) in comparison to informative-persuasive content – and this positive effect is expected to be observed even stronger on Instagram in comparison to Twitter.

As mentioned previously, there is a scarcity of research on the (in)direct effect of various product categories on online customer engagement. Notwithstanding, Voorveld et al. (2015) found that different product kinds have varied consequences when using social media platforms for advertising or content. When it comes to purchasing, Wolfinbarger & Gilly (2003) discovered that electronic shoppers are more goal-oriented than experience-oriented.

As a result, informational and persuasive material are more likely to promote customer engagement than entertaining content. Another study (Zyminkowska, 2018) found significant results for the impact of hedonic value versus utilitarian value on customer engagement.

Results of the study illustrated that the hedonic value dimension has a higher positive effect on customer engagement. As a result, it’s expected that hedonic valued products’ digital content generates greater consumer engagement in the form of likes, comments, and positive sentiment.

These findings indicate that the influence of different types of digital content on consumer engagement is likely to differ for different product types and categories. To our knowledge, past literature has yet to identify how different types of content (e.g., informative versus


34 persuasive and entertaining) are best suited for different product types and categories to engage customers. The hypotheses related to this moderating role of product type and

category on the relationship between types of digital content and online customer engagement are as follows:

H2: For utilitarian products, informative and/or persuasive content is expected to have a stronger and positive impact on customer engagement (measured by likes, comments, and sentiment of the comments) in comparison to entertaining content – and this positive effect is expected to be pronounced even stronger for the electronics category in comparison to the fashion and the travel category.

H3: For hedonic products, entertaining content is expected to have a stronger and positive impact on customer engagement (measured by likes, comments, and sentiment of the comments) in comparison to informative or persuasive content – and this positive effect is expected to be pronounced even stronger for the fashion category in comparison to the electronics and the travel category.

An overview of the hypotheses and their corresponding directions is given in Table 5.

Table 5

Summary of hypotheses and expected directions

Independent Variable(s) Moderator Variable(s) Outcome Variable(s) Expected Directions Type of Content:


Type of Platform: Instagram Likes, comments, and sentiment of the comments


Type of Content:

Informative and/or persuasive

Product Type: Utilitarian and Product Category: Electronics

Likes, comments, and sentiment of the comments


Type of Content:


Product Type: Hedonic and Product Category: Fashion

Likes, comments, and sentiment of the comments



35 4. Data and methods

The current study used data mining and social media analytics through social media network research. “Data mining techniques are the process of extracting hidden knowledge from the web” (Injadat, Salo, & Nassif, 2016, p. 661). The social media network research contributed to collecting the data needed for this research.

4.1 Description of Sample

This study is a quantitative research study. In this research, an empirical investigation of the data of three international firms has been done. Those firms are highly active on their social media pages on both the platforms Instagram and Twitter. Herewith, the current study made use of a dual approach to research the effectiveness of different types of content on customer engagement through brand posts and the subsequent likes, comments, and sentiment of the comments. Not only were two separate social media platforms investigated, but the firms are also operating within three different product categories, namely: the fashion, the electronics, and the travel category. For those firms, a distinction is made between two types of products, utilitarian and hedonic, to verify the hypotheses of this research. A visualization of the sample structure used in the current research is given in Table 6.

To explore the research hypotheses within those two sectors and product types, on-site, secondary data of three (inter)national companies has been scraped from their social media platform accounts. This Web Scraping can be defined as “the construction of an agent to download, parse, and organize data from the web in an automated manner.” (Van den Broucke & Baesens, 2018). Data was captured of the last forty posts of the companies Louis Vuitton, Dell, and KLM on Instagram and Twitter including the corresponding engagement metrics of likes and comments. The process generated six sub-sets of data (3 companies x 2 platforms), allowing a systematic analysis of brand post strategy and liking/commenting


36 engagement behaviour as well as sentiment across and between brands and platforms. The unit of analysis in the data set is each brand post, represented by a row, and accordingly topics such as the engagement behaviour measurements sentiment score, number of likes, and

number of comments. During the sentiment analysis, an algorithm has been used to determine the sentiment of the text that is written in the comments. The sentiment of each comment is divided into three categories: positive, negative and neutral. Positiveness or negativeness is measured as the ratio of positive, negative, and neutral comments to each post’s total number of comments (Liu, 2012).

Table 6

Visualization of the sample structure

Platform Product Category Number of observations (posts)

Twitter Louis Vuitton 40 posts

Dell 40 posts

KLM 40 posts

Instagram Louis Vuitton 40 posts

Dell 40 posts

KLM 40 posts

4.2 Operationalization of variables

The current study gathered field data about firms and users’ activities and interactions on firms’ fan sites [Twitter and Instagram accounts] to capture the mutual communication between the firms and their [potential] customers. The variables are divided into two categories, namely: firm-centric and user-centric. In the study’s conceptual framework (Figure 3) the independent and moderating variables are the firm-centric variables. Those capture companies’ efforts in content generation (Shahbaznezhad et al., 2021). The


37 independent variables allowed us to investigate how the companies manage their digital content. On the other hand, the dependent variables show users’ reactions to firms’ content.

These are the user-centric variables and were measured in the form of customer engagement on social media fan pages (Hoffman & Fodor, 2010). The customer engagement in this research was measured by likes, comments and sentiment of the comments. The operationalization of the variables is shortly given in Table 8.

Independent variables

As mentioned before, the independent variables that were used in the current research are the type of content, divided into three sub-categories (informative, persuasive, and entertaining), platform, product type, and product category as well divided into three sub-categories

(fashion, electronics, and travel). A content analysis has been performed to operationalize the independent variable Type of Content. Content analysis is defined as

“analysis of the manifest and latent content of a body of communicated material (as a book or film) through classification, tabulation, and evaluation of its key symbols and themes in order to ascertain its meaning and probable effect.” (Krippendorff, 2018).

A qualitative approach was employed for the content analysis, which means that the social media postings of the monitored firms were carefully reviewed and assigned to different sub- categories. The posts were given codes by deconstructing the text into three categories:

informative, persuasive, and/or entertaining. Subsequently, the measures chosen for each variable fitted the unit of analysis’ conceptualizations as previously established in the literature review. All 240 posts were being intensively read and coded during the study. The content was coded in a binary manner meaning that the presence of a given sub-category was classified as 1, while its absence was coded as 0. As a result, an extensive overview was


38 created, with each posts’ measurements and codes shown alongside it. Posts may be rated 1 or 0 for the independent variables Type of Platform and Type of Category however, there were no chances of rating 1 for both sub-categories. Because nearly no posts had textual content for a product or brand that was both hedonic and utilitarian, the posts for the independent variable Type of Product were also coded either 1 or 0. On the contrary, it is possible that two or even three types of content could be identified in one post, so the maximum number of content items that any post had was 3; in other words, a post was coded with 1 for each content sub- category.

Dependent variables

The current study relied on a Web Scraping approach to operationalize the dependent

variables. The number of likes and comments was calculated by counting the platform users’

activities for each post. Table 7 provides a summary statistic of the outcome variables.

In total, this research used four different dependent variables: Ratio Likes/Followers, Ratio Comments/Followers, Engagement Rate (%), and Sentiment Score. Using multiple models with different outcome variables in a comparative manner, the current research considered different engagement outcomes into that might be triggered-influenced by different predictors or moderators. In total there were four models, each with a different engagement outcome, while these models had a similar predictor – moderator variables (X) set. Thus, only the outcome variable (Y) had been changed while running each model in the analysis stage. In the discussion section of the report, these differences are focussed on further.


39 Table 7

Summary statistic of dependent variables


Ratios were calculated since the observed firms and their linked social media profiles had a varying number of followers. The number of comments was divided by the number of followers to create a dependent variable called ratio comments/followers. Similarly, the number of likes was altered. In addition, the engagement rate was calculated by adding the total number of comments and likes on a post dividing this number by the number of

followers. The percentage was then calculated by multiplying this by 100. The ratios came out as too small values, which is very natural, however for this to not cause any problems when put-used in regressions and while running the analyses, the variables were twisted by multiplying them with 10,000.

To calculate a sentiment score for each brand post, the automatic process sentiment analysis of MonkeyLearn has been used. Each comment on a firms’ post has been run through MonkeyLearn to get a positive (1.0), neutral (0.5), or negative score (0.0). Afterwards, the scores for all comments of the brand post were added and divided by the total number of comments resulting in a sentiment score between 0 and 1 where 0 represents the most

Number of likes Number of comments Sentiment Score of comments

Twitter Louis Vuitton Sum 535,927 4,447 -

Average 13,398 111 0.36

Dell Sum 1,348 327 -

Average 34 8 0.13

KLM Sum 15,986 821 -

Average 399 21 0.28

Instagram Louis Vuitton Sum 4,300,249 27,026 -

Average 107,506 676 0.52

Dell Sum 92,848 3,090 -

Average 2321 77 0.53

KLM Sum 431,461 9,629 -

Average 10,787 241 0.64


40 negative score compared to 1 for the most positive one. As the usage of Emoji in text mining and sentiment analysis could affect the sentiment score (Ayvaz & Shiha, 2017), those were considered within the analysis.

Moderating variables

Moderating variables influence the strength or nature of the relationship between two

variables. Generally, a moderator is any variable that affects the relationship between two or more other variables, whereas moderation is the moderator’s effect on this relationship (Dawson, 2013). The current research hypothesises that the type of platform, type of product, and product category are potential moderating variables that affect the direct relationship between the variables type of content and customer engagement. Due to a large number of sub-categories of independent variables in the framework, dummy coding was employed for all three moderating variables to reduce the effect of unwanted variance.

Type of platform addresses the effect of the platform that has been used to post the content online. Previous research has found that the type of platform performs an important role in facilitating social media engagement behaviour (Shahbaznezhad et al., 2018). For the type of platform, posts placed on Instagram were labelled 1 and posts placed on Twitter as 0.

The second moderator was the type of product. Multiple studies found significant evidence that hedonic and utilitarian values are essential drivers of customer engagement

(Zyminkowska, 2018; Kim et al., 2019; Huang & Rust, 2021). The current study assumes that informative or persuasive content for utilitarian products would generate higher customer engagement outcomes. Subsequently, it is hypothesized that entertaining content improves customer engagement outcomes for hedonic products. A dummy variable has been created in which hedonic products were labelled 1 and utilitarian products as 0.



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