“CUSTOMER ENGAGEMENT IN AN
OMNICHANNEL ENVIRONMENT: A
COMPARATIVE ANALYSIS OF
FACEBOOK AND INSTAGRAM BASED
UPON THE P2F MODEL”
Word count: 19.328
Senne Vermassen Student number: 01308585
Supervisor: Prof. dr. Sarah Steenhaut
Master’s Dissertation submitted to obtain the degree of:
Master of Science in Business Economics
Academic year: 2017 - 2018
“CUSTOMER ENGAGEMENT IN AN
OMNICHANNEL ENVIRONMENT: A
COMPARATIVE ANALYSIS OF
FACEBOOK AND INSTAGRAM BASED
UPON THE P2F MODEL”
Word count: 19.328
Senne Vermassen Student number: 01308585
Supervisor: Prof. dr. Sarah Steenhaut
Master’s dissertation submitted to obtain the degree of:
Master of Science in Business Economics
Academic year: 2017 - 2018
I
Confidentiality agreement
PERMISSION
I declare that the content of this Master’s Dissertation may be consulted and/or reproduced, provided
that the source is referenced.
Student’s name: Senne Vermassen
Signature:
II
Nederlandse samenvatting
De hedendaagse klant heeft zich onderworpen aan een totale metamorfose ten aanzien van zijn
traditionele voorganger. Waar klanten zich tot voor kort nog als één grijze massa voortbewogen op het
ritme van het bedrijf, heeft elk lid van die massa vandaag de dag een eigen gezicht en stem gekregen.
De achterliggende reden van deze ommekeer is te wijten aan de alsmaar groeiende kracht van de
digitale wereld. Meer bepaald, bieden sociale media zoals Facebook en Instagram een interactief
platform aan waarop klanten de mogelijkheid krijgen hun gedachten te spuien omtrent aanrakingen met
het bedrijf. Zodoende, betekent dit dat de machtsverhouding tussen bedrijven en klanten drastisch
gekeerd is ten voordele van de laatstgenoemden.
Om het hoofd te kunnen bieden aan deze cruciale veranderingen, dienen managers hun strategieën
ingrijpend te veranderen. Echter, gegeven de toegenomen complexiteit vanwege de overgang naar een
digitaal tijdperk, is dit beslist geen eenvoudige opdracht. Daarom is het aan de academici om hen hierin
wegwijs te maken, door een ruim arsenaal aan theorieën, conceptuele modellen en bruikbaar
gereedschap aan te bieden. In deze thesis, worden dusdanig twee veelbesproken onderwerpen
samengebracht, om zo een dergelijk inzetbaar instrument te bekomen.
Enerzijds behandelen we de thematiek rond klantbinding. Dit onderwerp heeft namelijk tot op heden
nogal wat discussie doen opwaaien tussen managers en academici en academici onderling. In de
literatuurstudie van dit werk gaan we daarom eerst op zoek naar raakvlakken tussen de verschillende
opinies, die vervolgens geconcretiseerd worden in vier stellingen. Bovendien zal getracht worden de
consensus verder te bevorderen door middel van het opstellen van een overkoepelende definitie.
Anderzijds, duiken we in de wereld van marketing kanalen. Meer bepaald, maken we duidelijk hoe een
graduele overgang van het gebruik van multi- naar Omnikanalen - die een geïntegreerde aanpak van
een veelheid aan traditionele en digitale kanalen impliceren - de deur opende naar verschillende
vormen van waarde creatie ten gunste van het bedrijf.
Als laatste, vormt het in elkaar vlechten van de bovengenoemde onderwerpen de apotheose van dit
eindwerk. Hieruit volgt namelijk de constructie van het P2F model, dat als doel voor ogen heeft een
nieuwe wending te geven aan de manier waarop marketing praktijken vandaag de dag op hun
effectiviteit beoordeeld worden.
Om vervolgens de daad bij het woord te voegen, wordt het voorgestelde model verder reeds in werking
gesteld in het onderzoek gedeelte van deze thesis. Meer bepaald, trachten we op basis van klantbinding
de meest complete en accurate vergelijking te maken van de verschillen in effectiviteit tussen Facebook
en Instagram, dé twee populairste sociale media van vandaag. Daarenboven, brengen we twee
mogelijke formats waarin de berichten op dergelijke platformen geplaatst worden, mee in rekening als
onafhankelijke variabelen. Ten einde het onderzoek vorm te geven, werd besloten om Volvo te hanteren
als onderzocht merk in de relatie tussen sociale media activiteit en klantbinding.
Uit onze resultaten volgt dat Instagram blijkt een sterker potentieel aan klantbinding te bezitten dan
Facebook. Daarnaast vinden we aanwijzingen voor de tendens dat video formats meer in staat zijn de
klant te engageren dan een afbeelding met opschrift. Als laatste benadrukken we tevens dat wanneer
het gaat om het meten van klantbinding, big-data technieken mogelijks een waardevol alternatief kunnen
bieden voor onze gebruikte survey-methode.
Kernwoorden: Klantbinding – Marketing kanalen – Sociale media – Facebook – Instagram – P2F
model - Formats
III
Preface
Starting off this academic journey, not fully knowing what to expect, I perceived it as just the most
evident next step in my education. I envisioned it as the simple continuation of my high school years.
Soon however, I became aware of the substantial increase of hard work I would have to invest in order
to handle the large amounts of study material. The theoretical aspect of college has thus proven itself
as quite the challenge. Yet, through hard work, it turned out to be achievable!
It is only now however, that I have come to realize that university has meant much more to me than
that. It has offered me the ideal platform to grow on a more personal level, to an extent that I hadn't
expected initially. The valuable skills attained in the numerous presentations, the countless group
works and my Erasmus experience in particular, have significantly contributed to my overall level of
confidence and self-knowledge.
However, one does not simply complete his master's degree on his own. Therefore, there are a few
people who I had wished to express some words of gratitude towards in the following paragraphs:
First of all, to my promotor, prof. dr. Sarah Steenhaut, who I could undoubtedly count on in the midst of
facing though obstacles throughout the process and who pushed me beyond what I imagined
achievable in writing a master’s dissertation.
To my best friend for almost twenty years, Guust, who always offered a listening ear to my overflowing
thoughts in times of both extreme joy and uttermost despair.
To my big brother, Flor, who set a tremendous example for me to not only succeed academically, but
also to follow my dreams and live life to the fullest.
To my amazing girlfriend Sarah, who I got together with in the first year of university and who stuck
with me through thick and thin along the ride. You often knew me better than I knew myself and kept
believing in me at all times. I love you.
And last but not least, to my wonderful parents. Without your unconditional love and support from the
first year of kindergarten up until the last year of university, I would have never been in the fortunate
place I am today. You consistently gave me every opportunity to do what I wanted to do and as a
result to become who I wanted to be. Therefore, I wanted to say: mom and dad, thank you, this one’s
for you!
Senne Vermassen
1st of January 2018
IV
Table of contents Page
CONFIDENTIALITY AGREEMENT ............................................................................ I
NEDERLANDSE SAMENVATTING ........................................................................... II
PREFACE ................................................................................................................. III
TABLE OF CONTENTS ............................................................................................ IV
LIST OF FIGURES .................................................................................................... VI
LIST OF TABLES .................................................................................................... VII
LIST OF USED ABBREVIATIONS ......................................................................... VIII
1 INTRODUCTION .................................................................................................. 1
2 LITERATURE SURVEY ....................................................................................... 2
2.1 A WALK THROUGH THE WONDER WORLD OF CUSTOMER ENGAGEMENT ................. 2
2.1.1 Back to the roots of CE: an historic overview ...................................................................... 2
2.1.2 Many perspectives, little consensus .................................................................................... 4
2.1.3 Bringing order to the chaos: recognized common ground in CE research.......................... 5
2.1.4 An overarching meaning singled out .................................................................................. 7
2.1.5 Conclusion ........................................................................................................................... 8
2.2 AN OMNIPOTENT SOLUTION FOR A MULTIDIMENSIONAL ISSUE .................................. 8
2.2.1 How multichannel marketing rose to the occasion .............................................................. 9
2.2.2 Exploring the ever changing MM landscape: from an offline to an online environment. ... 10
2.2.3 In search of MM crossovers: the proliferation of stand-alone empirical cases. ................ 11
2.2.4 The integration of a multitude of channels: from MM to OM ............................................. 12
2.2.5 Conclusion ......................................................................................................................... 14
2.3 COMBINING CONCEPTS: THE IDENTIFICATION OF A KNOWLEDGE GAP ................... 14
2.3.1 Introducing the P2F model ................................................................................................ 14
2.3.2 The weakness and strength of CE in an Omnichannel environment ................................ 16
2.3.3 Comparing the effectiveness of social media channels and their formatting ..................... 18
2.3.4 Research questions and hypotheses ................................................................................ 20
3 METHODOLOGY ............................................................................................... 21
3.1 GOAL OF THE RESEARCH ................................................................................................. 21
3.2 RESEARCH DESIGN ............................................................................................................ 22
3.2.1 Sample ............................................................................................................................... 23
3.2.2 Procedure .......................................................................................................................... 23
3.3 RESULTS .............................................................................................................................. 25
3.3.1 Scale validity analysis ........................................................................................................ 25
3.3.1.1 Factor analysis ........................................................................................................... 25
3.3.1.2 Internal consistency reliability analysis ...................................................................... 27
3.3.2 Hypotheses testing ............................................................................................................ 27
3.3.2.1 Descriptive statistics on the research question ......................................................... 27
V
3.3.2.2 The use of independent samples t-tests to identify general effects .......................... 28
3.3.2.3 The use of variance analysis to identify combined effects ........................................ 30
4 DISCUSSION ..................................................................................................... 32
4.1 CONCLUSIONS EMERGING FROM THE RESEARCH ....................................................... 32
4.2 DISCUSSION FROM THE RESEARCH RESULTS .............................................................. 32
4.3 LIMITATIONS AND SUGGESTIONS FOR FURTHER RESEARCH .................................... 34
4.4 MANAGERIAL IMPLICATIONS ............................................................................................. 35
5 CONCLUSIVE CONSIDERATION ..................................................................... 35
REFERENCES .......................................................................................................... IX
APPENDIX .............................................................................................................. XIV
SCHEMATIC OVERVIEW OF THE RESEARCH .............................................................................XIV
SURVEY ................................................................................................................... XV
1. SURVEY INTRODUCTION ............................................................................................................XV
2. CI MEASUREMENT ......................................................................................................................XV
2.1 Involvement towards cars.........................................................................................................XV
2.2 Involvement towards Volvo .....................................................................................................XVI
3. ASSIGNING RESPONDENTS TO EXPERIMENTAL/CONTROL GROUP(S) ............................XVII
3.1 Facebook allocation ............................................................................................................. XVIII
3.2 Instagram allocation .................................................................................................................XX
3.3 Stimuli .....................................................................................................................................XXII
3.3.1 Scenario 1: Facebook captioned image ......................................................................XXII
3.3.2 Scenario 2: Facebook video .........................................................................................XXII
3.3.3 Scenario 3: Instagram captioned image .....................................................................XXII
3.3.4 Scenario 4: Instagram video .........................................................................................XXII
3.3.5 Captioned image stimulus .......................................................................................... XXIII
3.3.6 Video stimulus ............................................................................................................. XXIII
4. CE MEASUREMENT ................................................................................................................. XXIV
5. SOCIO-DEMOGRAPHICS......................................................................................................... XXVI
VI
List of figures Page
Figure 1: The Evolution of Customer Management Figure……………...………………......………...…..3
Figure 2: : A comparison of the perceived and actual customer values between two customer when a
CE dimension has been wrongfully omitted…………………………………………………...……………...6
Figure 3: Customer's path to and off purchase…...……..…………………………………………………13
Figure 4: Proposed new marketing effectiveness model…………………………………....………….....15
Figure 5: Modified mass communications model…………………………………………………….…….17
Figure 6: The overview of a comparison between on- and offline marketing channels………………..17
Figure 7: Factiva Mentions per Major Topic in Popular Business Press……………………………...…18
Figure 8: A graphical representation of the intended research………………………………...…………23
Figure 9: Schematic overview of the research…………………………………………………………….XIV
Figure 10: Captioned image of a Volvo XC90…………………………………………………………...XXIII
Figure 11: Video of a Volvo XC90………………………………………………………………………...XXIII
VII
List of tables Page
Table 1: Factor loadings and communalities for 16 items of the CES……………………………………26
Table 2: Descriptive statistics for the three CES factors………………………………………….…...…..27
Table 3: Descriptive statistics on the measure of CE as a function of social media posting…………..28
Table 4: Independent samples t-test on the mean scores of CE between experimental groups and
control group…………………………………………………………………………………………………….28
Table 5: Cell sizes, means and standard deviations on the factorial design measuring CE…………..31
VIII
List of used abbreviations
CE………………………………………………Customer Engagement
CES……………………………………..Customer Engagement Scale
CIC……………………………………......Customer-Initiated Channel
FIC……………………………………………….Firm-Initiated Channel
FMCG…………….…………………….Fast-Moving Consumer Good
MM……………………………………………..Multichannel Marketing
MSI…………………...…………………...Marketing Science Institute
OM……………………...……………………..Omnichannel Marketing
P2F…………...…………………………………………Path-to-Feelings
P2P…………………………………………………….Path-to-Purchase
PII……………………………………Personal Involvement Inventory
RFM……………………………Recency/Frequency/Monetary Value
SEA………………………………………..Search-Engine Advertising
WTP………………………………………………….Willingness-to-Pay
[1]
1 Introduction
The way in which customers are managed has shifted drastically over the past decades. Customers are
no longer perceived as dependent cash-cows adding solely monetary value to a firm’s financial
objectives. Instead, customers are now seen as active contributors rather than passive subjects.
Examples of such contributions can be found in the field of customer acquisition and retention, product
innovation and marketing communication. (Malthouse, Haenlein, Skiera, Wege, & Zhang, 2013). In this
respect, we can identify a broader trend among firms of altering the focus from the objective of “selling”
to “emotionally connecting” with their customers. Hereby, the goal is to not only generate purchases,
but also to create a level of proactive engagement amongst customers that lasts for a lifetime (Pansari
& Kumar, 2016).
The shift of attitude towards customers has led to a continually increasing amount of interest in the
construct of “customer engagement” (CE). The Marketing Science Institute underscores CE as a key
research area that has the ability to develop a more enhanced academic insight into consumer behavior
in multiple environments (Marketing Science Institute, 2010). However, not only from an academic point
of view, but also from a practitioner’s perspective, the construct has gained significant interest. Take for
example the Belgian beer giant Anheuser-Busch, who is willing to spend more than $200 million annually
on the development of their engagement strategies, beginning in 2017 (Barris, 2015). In addition, a
study by Gallup (2013) shows that entirely engaged customers deliver 23 percent extra benefits in the
field of share-of-wallet, profitability, revenue, and relationship growth as opposed to non-engaged
customers. In all, these examples clarify that CE has prompted itself as a relevant concept worth
evaluating for professional marketers.
In sharp contrast to the illustrated importance of CE, the exact meaning of the construct has proven to
be far from determined. Over the past decade, many profound researchers provided valuable insights
into the matter, each with their own personal approach. However, no consensus between academics
and practitioners, nor between academics mutually, was ever reached. Harmeling, Moffett, Arnold and
Carlson (2017) warn that this diffuse variation in perceptions can become problematic due to a lack of
clarity and unambiguity regarding the concept. Therefore, the first aim of this dissertation is to make an
overview of the most significant existing definitions, ideas, and justifications used to examine the
construct, based on which four encompassing tenets are formed. By doing so, business professionals
as well as academics are provided with a useful guide that is able to show them the ropes in the complex
world of CE. Consequently, this conceptual guidance can be considered as the theoretical contribution
of this dissertation.
Next to the development of CE, Omnichannel marketing (OM) has almost simultaneously emerged as
a topic of high interest among marketing researchers. More specifically, next to traditional offline
marketing activities, the arrival of e-commerce has spawned an enormous amount of studies that
investigate the underlying drivers of online channel use (Lemon & Verhoef, 2016). Also in practice, the
expansion of online marketing channels has left its mark. In the United States for instance, online
advertising – in its many forms - has grown from $9.6 billion in 2004 to $72.3 billion in 2016 (Interactive
Advertising Bureau, 2017). However, despite this stunning growth by means of major investments,
companies remain to struggle with the integration of online marketing channels with their longer existent
offline channels. This is undesirable, as an inefficient incorporation of both channel types may negatively
affect the efficiency in which customers are led along their customer journey. In contrast, it is argued
that a well implemented Omnichannel strategy has the ability to strongly engage customers by means
of a seamless experience (Frazer & Stiehler, 2014).
This leads us to the second intent of this paper, that is to join together the CE construct and the OM
concept, with the purpose of enabling practitioners to compare the effectiveness of both online and
offline channels in terms of CE. By doing so, this thesis aims to come up with a coherent, logical and
[2]
applicable measuring model for both academicians as well as practitioners, that transcends a theoretical
classification of existing literature. After we conjoin the two identified concepts theoretically, the gathered
knowledge is then put into practice by means of a quantitative research, amplified with a qualitative
section. In summary, this part of the dissertation can be considered as its practical contribution.
More specifically, in this dissertation, we looked for the effect of online channel choice on the extent to
which a customer felt engaged towards a brand. The research was conducted for the car industry, as
Volvo was picked as the brand to investigate. Moreover, the effect was checked for by two different
formats: video and a captioned image.
This dissertation will consist of five major compartments. First of all, a detailed overview of the theoretical
concepts that were studied, will be provided in the literature study of the next section. This survey will
then be followed by the description and results of our conducted research, within the methodology
compartment. Thirdly, the ensuing results will be reflected upon within the discussion. At last, we will
end this thesis with a summarizing wind-up in the conclusive consideration section.
2 Literature survey
The literature study consists of four chapters. In what follows, the CE concept will first be addressed.
Secondly, we will move towards a better understanding of the concept of Omnichannel marketing. In
the third part, both CE and OM are combined in order to address an identified gap in literature. Lastly,
we will make use of the integration of both constructs, blended in the proposed P2F model, to make a
comparison of effectiveness within online marketing channels.
2.1 A walk through the wonder world of customer engagement
In the current decade, the biggest leap of progression in the management of customers has been made
in the field of CE (Lemon and Verhoef, 2016). The issue of how companies can win the minds and hearts
of consumers, turning them into fully committed customers, has been put on top of the agenda by
practitioners. Many challenging questions arose over this period of time. What does CE mean exactly?
How can CE contribute to a firm’s objectives? In which way can CE be effectively measured?
Consequently, academicians have been handed the task to find meaningful theories, concepts,
frameworks and tools that can help managers find the answers to those questions. In the following
sections, a comprehensive overview of the most prominent findings during this period of time will be
provided. Afterwards, the gathered knowledge will be condensed to its core, in an attempt to reveal the
essence of preexisting literature covering the CE construct. The eventual goal is to provide a conceptual
guide that assists practitioners in solving engagement related problems by reconciling the most
influencing CE researchers.
In this facet, this paper differentiates itself from previous research by searching for interrelationship in
the most prominent literature covering the CE theory, rather than delivering an umpteenth listing of
preexisting differences in between those works. By doing so, we answer to the call of Hollebeek,
Srivastava and Chen (2016) for more generalizable and less fragmented CE research. This demand
stems from the idea that a tendency for sparse research likely hinders and at least slows down CE
research, therefore possibly endangering its theoretical advancement (Hamerling et al., 2016).
2.1.1 Back to the roots of CE: an historic overview
In order to start understanding CE to a full extent, it is necessary to first of all take a look at the historical
context in which the concept grew. In general, a gradual shift in marketing focus from transaction to
relationship, to eventually engagement marketing has taken place (Pansari and Kumar, 2016). A
schematic representation of this transition can be found in Figure 1 below.
[3]
From the sixties up until the beginning of the nineties, emphasis in the relationship between firm and
customer was based upon the recency, frequency and monetary (RFM) value of the customer (Baier,
Ruf, & Chakraborty, 2002). This means that the most crucial task of a firm was to get as much money
out of each customer individually for as many times possible, regardless of the resulting attitude the
customer held towards the selling firm. Even though a first attempt was made to implement a more in-
depth analysis of the customer buying process during this period of time, by means of the integrated
Buying Behavior Process Models (Howard, Sheth, & Jagdish, 1969), the ‘money’ component – as
opposed to ‘feeling’ - stayed dominant in the interaction between both parties.
Further down the line, throughout the nineties and early 2000s, when the goals of companies slowly
changed, researchers followed by amplifying the ‘relationship marketing’ theory. Morgan and Hunt
(1994) as well as Berry (1995) made a case for this concept in a B2B and B2C setting respectively. The
main idea behind the theory is that feelings of trust and commitment sent out by companies were the
main drivers for firms wanting to obtain longer lasting, more satisfying and positive relations with their
customers. These long-term relationships were put in place with the aim of promoting efficiency,
productivity and effectiveness (Morgan and Hunt 1994). The major contribution of this line of thought
was that in comparison to the transaction theory, the focus of the marketer had been extended by
including emotions and perceptions associated with the overall buying experience (Lemon & Verhoef,
2016). On the other hand, the downfall of the relationship marketing theory was that it remained a one
sided conversation. As the firm pushed its trustworthiness and commitment onto the customer, it did not
leave much room for a possibility of exchange or conversation between both parties.
This is why the engagement theory jumped the scene at the beginning of the current decade. More
specifically, the CE theory emphasizes the customers’ ability to influence the firm in their part, through
a set of interactive brand-related dynamics (Brodie, Hollebeek, Juric, & Ilic, 2011). This means the
construct reveals an opportunity for creating a bilateral exchange of information between firm and
customer. In the evolution of CE management figure, Pansari and Kumar (2016) suggest that when a
relationship is satisfied and has emotional bonding, it then proceeds to the stage of “engagement.” This
implies that even though satisfaction had been described earlier on as an important concept in marketing
literature (e.g. Oliver, 1980; Bolton & Drew, 1991), it has only acquired its true meaning since the
introduction of the more recent engagement marketing theory. Despite the fact that many researchers
nowadays underline its importance, the wide variety of definitions and positions regarding CE
bamboozle its true meaning.
Figure 1:The Evolution of Customer Management Figure. Source: Pansari & Kumar (2016)
[4]
2.1.2 Many perspectives, little consensus
Both researchers as well practitioners have racked their brains in finding a meaningful definition of the
CE construct in the recent past. This resulted in an extensive, yet scattered landscape of CE
interpretations. Jaakkola and Alexander (2014) therefore made a first attemptof reconciling CE research
by splitting it up into two major viewing points from which the construct had been encountered up until
then.
The first perspective on CE accentuates its psychological component, both cognitively as well as
emotionally. One of the most influential researchers to describe the construct in such a manner has
been Bowden (2009), who conceptualizes CE as a psychological process that conjoins cognitive and
emotional aspects leading to the creation of loyalty amongst prospective as well as existing customers.
Brodie et al. (2011) follow this line of work. Remarkably, rather than seeing CE as a psychological
process in itself, the authors depict CE as a psychological state of mind that is the result of repetitive,
interactive and co-creative experiences with the firm. This means that CE is depicted as an outcome of
interactive experience, instead of a transitional stage towards an improved relationship between
customer and firm. Moreover, despite keeping the main emphasis on the cognitive and emotional
aspects of CE, the behavioral dimension of CE was added as a subpart. Notwithstanding the fact that
Hollebeek (2011) kept the notion of ‘state of mind’, the author refined the definition of Brodie et al. (2011)
by delaring this state is motivational, brand-related, and context-dependent. Patterson, Yu and De
Ruyter (2006) reinforce the psychological perspective of CE by defining it as a psychological state that
is characterized by a degree of vigor, dedication, absorption and interaction. Despite the slight
differences in approach, there is thread to be identified among the above-mentioned researchers. CE is
presented in a way in which the ‘thinking’ and ‘feeling’ of a customer prevails its ‘doing’ in the motivation
to move along their decision journey.
The second viewpoint from which the CE construct has been attempted to be described, stresses its
behavioral component. Van Doorn et al. (2010) noted that CE is a customer’s behavioral manifestation
towards a firm, beyond the act of purchasing. This manifestation resulted from the motivational drivers
of the customer. Remarkably, in their definition, by explicitly referring to its motivational drivers as the
starting point of CE, the aforementioned psychological component of CE has not been neglected. A
more transactional approach to a behavioral definition came from Vivek, Beatty, and Morgan (2012),
who construed CE as the intensity of participation in and connection with the activities of a firm. The
suggested interactivity that results from this definition is either initiated by the customer or by the firm.
Very important in this work is the addition of a social dimension to the preexisting cognitive, emotional
and behavioral aspects of CE. Moreover, the dominance of the behavioral component of CE has been
reaffirmed by Verhoef, Reinartz, and Krafft (2010) who also specify it as a behavioral manifestation
toward the firm that surpass transactions. In summary, we can identify that from the behavioral side of
view, CE can be seen as a deliberate action of the customer to associate itself with the brand, beyond
the act of buying products or services. If this is the case, than we can state that a customer’s ‘doing’
tends to become more important than its ‘thinking’ and ‘feeling’ in the path to purchase (P2P).
Although both sides of the ‘CE spectrum’ (psychological versus behavioral prevalence) recognize the
relevance of their counterpart, it has to be noted that the previously mentioned researchers haven’t
allowed sufficient room for reconciliation between both ways of thinking. For further and a more
advanced theoretical development of CE however, we suggest that it is of major importance to work
towards a happy medium between both extremities. Hamerling et al. (2016) confirm this concern by
expressing that without definitional precision, it may become very hard to operationalize and differentiate
CE from other marketing constructs. If compromise is the way towards a better understanding of CE, it
begs the question: how can a generally accepted consensus among parties concerned be reached?
[5]
2.1.3 Bringing order to the chaos: recognized common ground in CE research
I propose that a first step towards reconciliation can be attained by finding parallels among the existing
definitions. In doing so, this paper dissociates itself from previous research that has the urge to pick
sides in the CE conversation. Brodie et al. (2011) argued that in the developmental state of CE, the
construct was in need of multiple descriptions by means of varied research. This implies that the
construct initially urged alternate theoretical lenses through which to view the concept and its associated
dynamics. In contrast, we suggest that it is necessitous to find common ground within these perspectives
in order to guide the concept from its initial developmental state to a more mature phase. Therefore, in
the next paragraphs we will provide an overview of our four identified CE tenets, that seemingly exist
within each preceded description of the construct.
1. CE is a multidimensional construct that includes a cognitive, emotional, behavioral and social
aspect.
To the best of our knowledge, previous researchers can all agree on the fact that CE integrates multiple
areas of a customer’s set of human capabilities. The cognitive and emotional dimensions can be
classified as the psychological component, which co-operates with the behavioral component, as
opposed to dissociating itself from it. The more recently introduced social aspect refers to the interactive
capabilities of social media, whichprovide a conceptual similarity to the interactive nature of the CE
concept itself (Hollebeek, Glynn & Brodie, 2014). Kumar et al. (2010) inform us that customers may
draw an incorrect valuation (overvalued or undervalued), when not all of these dimensions of CE are
taken into account. This erroneous assessment of a customer’s value may therefore result in an
inappropriate allocation of resources by practitioners (Verhoef et al., 2010). The latter idea gives us an
additional motivation of why we find it so important to align an academic point of view in a theoretical
setting, with a practical point of view that exists in the working field.
Take for instance customer A and customer B who both have an equally positive cognitive and emotional
relationship towards a brand. . A graphical representation of this idea can be found in Figure 2 on the
next page. The brand itself takes only the cognitive, emotional and behavioral aspects of CE into
account. As a result of its contentment with the brand, customer A buys the brand’s products very
regularly. The brand identifies this repeated purchase as a sign of strong engagement with the brand,
based on that customer’s behavioral component (e.g. purchases). Customer B on the other hand buys
the brand’s products rather occasionally, but is a strong influencer and promoter of the brand on social
media. Since the brand does not incorporate the relevance of a social dimension of CE and form a
behavioral point of view customer A provides more value than customer B, the brand incorrectly
overvalues customer A. In practice however, by offering a strong social influence, customer B had
brought forward more impact to the brand’s objectives. Therefore, customer B, should have been
allocated with the most resources, as it was the biggest value creator.
[6]
Figure 2: a comparison of the perceived and actual customer values of two customer when a CE dimension is
wrongfully omitted.
2. CE goes beyond the act of purchasing based on the recognition of a need.
The second observed mutuality among existing definitions is the fact that CE implies a deeper interaction
process than the traditional shift from a pre-purchase stage - in which need recognition is the driver of
a consumer’s activity - to the purchase stage and eventually the evaluation of the purchased product in
the post-purchase stage. This mechanism, described by Howard and Sheth’s model (1969), highlights
the necessity of the arise of a need prior to the customer taking initiative to interact with the firm. This
contradicts more recent research that emphasizes the fact that CE goes beyond transactions.
Furthermore, tenet 2 is strengthened by the idea that CE is based on behaviors through which customers
make voluntary resource contributions to a firm’s objectives (Jaakkola & Alexander, 2014). However,
the exchange goes beyond what is fundamental to the transaction. The notion of voluntariness
emphasizes that customers do not need the firm to fulfill their desires, but rather spontaneously want to
reach out to the them. This depicts the active role of the customer in the contemporary relationship
between customer and firm.
3. CE is based upon a balanced interaction between the thinking, feeling and doing of a customer.
Instead of further fortifying the quarrel between a customer’s psychological component feeling on one
side and behavioral component on the other (as discussed in section 2.1.2), we recommend that both
customer activities are granted an equal weight of attention. Given the multidimensional nature of CE,
all of its components should be taken into account equally in order to obtain the full picture of one’s level
of CE. By leaning towards one end of the CE continuum, researchers unwillingly may mislead managers
in making an erroneous evaluation of their customers. An additional motivation for this consideration is
that foundational constructs such as CE must be conceptually broad enough in order to capture the true
underlying essence of the phenomenon (Suddaby ,2010), which suggests that a biased approach
towards CE may undermine its true meaning.
[7]
4. CE instigates the co-creation of value between customer and firm.
Value is considered a collaboratively created concept that stems from interaction between parties
through the exchange of resources (Grönroos and Voima 2012). These resources do not only include
goods and money (Michel, Brown, & Gallan, 2008), as they can also be considered intellectual resources
such as improved relationships. This means that the co-creation of value lays perfectly in line with the
CE construct, which analogically presumes that the relationship between customer and firm is
considered interactive beyond purchases. It follows that CE logically results in the co-creation of value
as a product of the relationship between customer and firm.
2.1.4 An overarching meaning singled out
After recognizing the four tenets of CE, it is meaningful for this thesis, as well as for future research, to
identify a definition that encompasses all four most fittingly. The reasoning for this idea is that the
utilization of one embracing definition in particular, provides more clarity and unambiguity for latter
research that aims to build on the proposed encompassing direction of CE research.
Despite acknowledging alternative valuable standpoints from which CE could possibly be described,
such as the S-D informed logic-informed CE of Hollebeek et al. (2016), we tend to follow the line of work
of Pansari and Kumar (p.4, 2016) who proclaim a holistic definition that describes CE as “the mechanics
of a customer’s value addition to the firm, either through direct or/and indirect contribution”. Direct
contributions consist of customer purchases, while indirect contributions refer to customer references,
influencing and knowledge addition.
The main reason we opt to follow such line of work is that we are of the opinion that it lays most in line
with our four identified tenets of CE. First of all, the definition directly confirms our first tenet, which
describes CE as a multidimensional construct, by underlining both direct and indirect contributions.
Secondly, what is important to recognize here is that purchases – in the form of direct contributions –
are considered an integral part of the CE construct, which therefore became a desired outcome of the
firm’s activities in itself. In this way the financial objectives of a firm are reflected within the construct of
CE an sich, rather than seeing CE as an intermediate step towards reaching those objectives. This can
be considered an affirmation of our second tenet. Furthermore, a customer’s direct contributions could
be viewed as its doing activity, while its indirect contributions refer to the both thinking and feeling. We
can recognize the validation of tenet 3 in this idea.
However, in order to come up with the most holistic definition of CE until date, we suggest that even this
definition remains too limited and therefore should be expanded. More specifically, by describing CE as
a “customer’s value addition to the firm”, we believe that the authors put too much emphasis on the one-
way value contribution of the customer towards the firm. Based upon our fourth tenet, we argue that in
fact, CE implies the co-creation of value and therefore the value contribution of the firm towards the
customer is ought to be at least as important. Consequently, we propose our own definition of CE, which
can be described as followed:
CE can be considered as the co-creation of value between customer and firm, which originates from the direct and indirect contributions made by the customer and results in a positively modified cognitive, emotional, behavioral and social state of that customer.
[8]
2.1.5 Conclusion
First of all it is made clear that CE did not pop up out of nowhere. It is the result of a process of shift in
focus by companies that react to a more active role of the consumer, which led to an opportunity of
interactive relationships. Thus, practitioners took the lead in heading towards CE marketing. Lagging
behind the changing approach of practitioners, academicians in the developmental phase then
abundantly brought forward a myriad of sparse meanings regarding the construct. This led to an overflow
of diverse suggestions whereupon researchers, nor practitioners could no longer see the wood for the
trees. This is why scholars such as Hollebeek et al. (2016) pled for more encompassing CE research.
By following the 4 proposed tenets of CE, academicians as well as practitioners can understand the
essence of CE much more easily and quicker. Because of this, the CE construct gets handed the
opportunity to evolve to a more mature stage in which practitioners and academicians, as well as
academicians mutually can agree upon its meaning. A self-identified holistic definition that includes all
four tenets, forms the apotheosis of this chapter based on which the next sections will be built upon.
2.2 An omnipotent solution for a multidimensional issue
In the first part of this literature survey, the CE construct has been recognized as one of the most
important developing concepts within recent marketing research. After going over the diverse point of
views from which the construct has been described in the past, I concluded that CE connotes the
creation of both customer and firm value which arises from the interactivity between both. This
interaction however, can solely be reached by means of channel use through which a firm communicates
with its customers. It follows that a marketing channel can be defined as a collection of exchange
relationships that co-create value in the acquisition, consumption, and disposition of products and
services (Pelton, Strutton and Lumpkin, 1997). In other words, CE and its accompanied value creation
can only arise when there are channels available as intermediating platforms, that are able to create
interactivity between the firm and customer.
Evidently, a firm nowadays possesses a wide range of possible channels to choose from through which
they can approach their customers, both on- and offline. In earlier works, researchers therefore started
considering the multichannel marketing (MM) concept, which investigated how the choices of multiple
channels across multiple phases of the customer experience impacted sales distinctively (e.g. De
Keyser, Schepers, & Konus, 2015; Konus, Verhoef and Neslin, 2008). Nowadays however,
Omnichannel marketing (OM) takes this insight a step further in trying to enable firms to make use of
two or more of the channels they decide to make us of, in an integrated way. This means that OM
strategies aim to provide outstanding shopper experiences by merging both a firm’s utilized on- and
offline channels in a highly effective and convenient way (Frazer & Stiehler, 2014). Due to this
recognized importance, OM has prompted itself as yet another upcoming phenomenon determined to
reform the marketing landscape.
In the following sections we move on towards a deeper understanding of OM by first making clear how
MM became an increasingly essential activity to the present-day marketer. Secondly, we recognize that
with the explosion of possibilities brought forward by the introduction of online channels, also came a
myriad of challenges. We will therefore take a look at what those challenges are and how OM could be
used to address them. At last, the integration of online and traditional channels will be looked at from a
two point of views: a basic and a more profound one.
[9]
2.2.1 How multichannel marketing rose to the occasion
The rise of the internet and its accessory popularity among customers to use it as an interactive platform
has had a tremendous impact on modern-day marketing. One of the major implications of the climbing
importance of online settings in the field of marketing, is a substantial increase in channels through
which firms are nowadays able to interact with their customers. Subsequently, firms have to adapt to
the constantly changing environment that results from this upsurge in channels. In exchange for the
effort firms put into managing multichannel customers, they in their part succeed in obtaining more value
per customer (Neslin & Shankar, 2009). The co-creative value that arises from this interaction can be
split up into two categories.
First of all, multichannel customers provide more value to a firm by means of profitability (Montaguti,
Neslin and Valentini, 2015). The reason behind the increased revenue that multichannel customers
bring forward compared to single-channel customers can be split up in three factors (Blattberg, Kim, &
Neslin, 2008; Neslin & Shankar, 2009). These causes are identified as self-selection, marketing, and
customer satisfaction. The self-selection reasoning refers to customers who purchase the firm’s
products or services occasionally. The more frequent a customer buys from the firm –thus self-selecting
itself-, the higher its profitability. The frequent buyer therefore tends to typically make use of more of the
available channels. The marketing explanation can be looked at from the idea that customers who
purchase products or services through multiple channels evidently get exposed to numerous different
marketing forms, which heightens their chance of providing more monetary value. Moreover,
multichannel customers see the fact that they can interact with the firm through a variety of channels as
an additional service. Therefore, they are perceived to be happier, which explains the customer
satisfaction motivation. In summary, we identify that multichannel customers generate more revenues
due to their increased interactivity with the firm.
Secondly, the expanded value addition of multichannel customers can be devoted to the fact that they
enjoy improved communication with the firm. Chen and Lamberti (2016) suggest that this second
component of MM benefits can be looked at from 2 perspectives. The first viewing point is illustrated by
the definition of MM proposed by Neslin et al. (2006), who describe a marketing channel as a contact
point through which the firm as well as the customer interact with one another. This implies that MM
solely encompasses reciprocal activities between firm and customers. In the second viewpoint, Keller
(2010) adds that besides interactive communication, mass-communication such as TV advertising can
also be considered within the MM concept. Both one-way and twi-way communications are accordingly
reflected upon in the latter idea.
When combining the aforementioned MM value benefits, it is clear to see that they account for both firm
and customer. The firm’s main advantage seems to remain mainly monetary. However, by means of
improved communication with the customer, firms also enjoy longer lasting and more loyal relationships
with their customers. By successfully synchronizing the channels through which firms operate,
customers in their turn benefit from MM because of its integrated communication possibilities.
Rangaswamy and Van Bruggen (2005) suggest that the convenience that results from this
synchronization creates superior service outputs, which makes it less likely for customers to switch over
to other firms. For instance, a firm that offers the customer services to look for a product online, buy it
in the store afterwards and request later services through their mobile application, has a higher chance
of retaining customers than its unsynchronized competitor. This reveals the true strength of MM and
suggests that both online and offline channels may play crucial factors in moving customers along their
decision journey.
[10]
2.2.2 Exploring the ever changing MM landscape: from an offline to an online
environment.
The contemporary addition of online to traditional channels has notably expanded the work field of
researchers as well as practitioners. This means that nowadays marketers possess a much more varied
and extensive set of marketing opportunities to work with. Whereas marketers of the past were solely
occupied with operating a small set of traditional channels, today’s marketers find themselves in the
midst of an overflow of new media, which influences when, where, and how customers pick their
preferred brands (Batra & Kelly, 2016).
To highlight a few of the traditional possibilities, marketers for instance, would make massive use of TV
broadcasting as a channel to raise brand awareness and create previously non-existing needs.
Additionally, they could set up direct mail campaigns to support existing customers and target potential
customers. Moreover, print ads could be utilized to symbolically publicize the values represented by the
firm. Marketers would also employ telephone marketing to retarget departed customers by offering
renewed deals and discounts (Batra & Kelly, 2016). Above all, these traditional marketing
communication channels aimed to reach an audience which was ought to be as large as possible.
Therefore, the old way of marketing is considered to be mainly oriented on mass advertising.
Nowadays however, practitioners can also communicate with customers through a set of online
platforms. Mainly the introduction of social media such as Facebook and Instagram have facilitated the
ability of companies to interact with their customers and target prospects. For example, companies can
now directly respond to messages regarding customer feedback or customer care through the
messages function of such media. Also, firms are able to monitor negative or positive buzz surrounding
their content put on social media, by directly anticipating on comments and working with influencers.
Moreover, firms have the possibility to segment their customers up front by making use of publicly
accessible information on the platform. Subsequently, this enables firms to target only those customers
within a desired segment. This shows us that the present-day marketer, in comparison to its traditional
predecessor, cannot only direct the narrative of its content, but also deliver a much more personalized
message to its target group.
However, it is not all roses when it comes to MM. The rise of the internet has also significantly
complicated the integration of the abundance of marketing channels (Lamberton & Stephen, 2016). As
a result, online marketing campaigns do not always turn out to be as fruitful as expected. The allocation
of a marketer’s budget to the firm’s numerous channels therefore has shown to be a risky and precarious
operation. With regards to this downside of MM in an online environment, it is essential for practitioners
to understand that the channels through which they interact with customers vary significantly in benefits
and costs. This insinuates that in most cases the usage of one channel is more appropriate than another
in a particular stage of the customer’s P2P (Lemon & Verhoef, 2016). Therefore, managers in the digital
era are in desperate of need models capable of measuring the contribution of multiple channels in order
to assist decisions regarding marketing budget allocation. In other words, the theoretical models brought
forward by academicians can allow practitioners to correctly determine how much money should be
spent on each of their considered channels, only when those models are aligned with the practitioner’s
goals in a complex environment.
[11]
In an effort to handle the above mentioned increase in quantity and complexity of marketing channels,
Li and Kannan (2014) decided to focus on the digital side of the emerging MM opportunities. Doing so,
the authors designed a measurement model that aims to analyze a customer’s consideration of, visits
through, and purchases at multiple online channels. The most valuable part of their work is that they
made a distinction between marketing channels by splitting them up in firm-initiated channels (FICs) and
customer initiated channels (CICs). This means that by means of online interactivity, customers not only
get approached by firms through FICs such as e-mail or display ads for example, but are now able to
approach firms in their part through the provided CICs. For instance, in case the customer wants to
report a complaint to the company, it can opt to do so directly via the online channels that are at hand.
This can be considered the core difference between online channels and traditional channels, that are
by definition exclusively firm-initiated. Consequently, this idea reveals the crucial strength of online
marketing channels.
Recent research into the biggest differences between FICs and CICs suggest that there is a growing
importance of the latter form of interacting with customers. This is caused by the idea that online FICs
are becoming increasingly unwanted due to a multitude of arguments (Blattberg et al., 2008). First of
all, Goldfarb and Tucker (2011) suggested that online FICs such as display ads show a lower chance
of noticeability among customers, compared to online CICs. Consequently, online FICs are less likely
to influence the customer’s brand awareness, as well as less probable of contributing to ad recall.
Interaction through online CICs on the other hand, has a strong potential to result in long-term
remembrance among customers. Secondly, CICs are generally conceived as less intrusive than the
more traditional FICs (Goldfarb and Tucker 2011). At last, CICs result in higher response rates than
FICs (Sarner & Herschel, 2008) because they inherently require a higher level of preliminary interest
from the customer.
2.2.3 In search of MM crossovers: the proliferation of stand-alone empirical cases.
With the multichannel marketing concept in mind it is particularly vital for the development of effective
metrics for practitioners, to not only consider online but also offline marketing channels. The reasoning
is that both media interact with one another (Srinivasan, Pauwels, and Rutz, 2016) and that traditional
media nowadays still prove to be relevant as they remain to take a large chunk of a manager’s marketing
budget. Despite the generally accepted importance of assimilating the online and traditional marketing
world, researchers continue to struggle to integrate these different ways of interacting with customers
into existing marketing mix models (Keller, 2016). Therefore, Lamberton and Stephen (2016) suggest
that the crossover between both worlds is in need of a more profound understanding.
Following the identified need for conciliation between on- and offline marketing channels, a tendency to
generate isolated cases of research arose among academics. Dinner, Van Heerde and Neslin (2014)
for example investigated how the interaction between traditional advertising on one side and online
display and paid search advertising on the other side, translated into sales. Their findings suggest that
there are strong cross-channel effects between both ways of advertising. Based on these effects, it
shows that search advertising in particular is more effective in terms of sales than traditional marketing.
Liaukonyte, Teixeira and Wilbur (2015) on the other hand looked for the effect between television
advertising and website traffic. More specifically, the authors wanted to find out how this interaction
could affect online shopping figures. As a result of their research, the writers found that there is a
significant relation between the researched offline and online channel. Moreover, action-focus content
(as opposed to information-focus content) proved to higher direct website traffic. Both contents however,
managed to have a positive net effect on eventual sales. Lastly, even more recent research seems to
continue to follow the trend of isolated research. Fossen and Schweidel (2016) researched the
relationship between television advertising and online word-of-mouth. Their findings suggest that
television advertising indeed has an influence on the volume of online word-of-mouth for the brand.
Although these empirical cases propose usable guidelines for practitioners seeking to use a certain set
of traditional and online marketing channels, they do not determine whether or not the eventual
integration of those channels, in possible combination with other marketing channels, results in an
[12]
efficient and effective whole. Therefore, Batra and Keller (2016) made a call for more encompassing
research that allow the design of more meaningful integrated marketing communication (IMC) models,
embedded in a broad multichannel context. Yet not only academics, but also practitioners need to
become more aware of the importance of well-designed IMC models. Towards that goal, the authors
composed seven criteria that can help marketers evaluate their integrated channel choice. The criteria
are called the “seven C’s” and consist of coverage, cost, contribution, commonality, complementarity,
cross-effects, and conformability. The found criteria imply that besides monetary maximization in terms
of the reduction of costs, the conciliation of a myriad of channels requires much more assessment and
interpretation of the marketer. This explains why the measurement of MM activities remains to be so
complex and therefore Keller (2016) prioritizes the development of more quantitative IMC models for
attribution analysis.
2.2.4 The integration of a multitude of channels: from MM to OM
Lately, researchers have started responding to this call for a deeper insight into MM crossovers by
searching for integrated effects of channel use. This can be considered the actual transition from MM
research to an OM approach. A first attempt has been made by De Haan, Pauwels and Wiesel (2016),
who followed the work of Li and Kannan (2014) by building upon the distinction between CICs and FIC
for six online marketing channels. The major addition of knowledge in this work however, is that the
authors controlled their findings for two traditional channels (television and radio). In doing so, a new
proposal of budget allocation across both online and offline advertising forms for practitioners was
provided. Hereby, the ultimate goal was to prevent managers from making decisions based on trial- and
error, own judgment and gut feeling (De Haan et al., 2016).
Furthermore, the researchers tested the effectiveness of simple attribution models such as the last-click
method. To date, the last-click attribution is one of the most favored metrics to assign a budget to multiple
online channels. It is still used by practitioners in the majority of the cases, due to its simple applicability
and easy use (Econsultancy, 2012). In essence, the last-click metric checks through which online
channel a customer visited the firm’s website before making a conversion. Based on this information,
practitioners then attribute their marketing budget in proportion to the channels that led to those
conversions. In other words, the online channels that brought forward the most conversions are
assigned with the most money and care. However, the authors’ findings suggest that this model cannot
longer be considered an accurate enough measurement, as it accounts for ten to twelve percent less
revenue than the status quo (De Haan et al., 2016). Although this work can be considered a first step
towards the integration of online and offline channels, it is obstructed by the fact that the authors control
their findings for traditional channels, rather than truly integrating those channels with their online
equivalent. Hereby, the urge for absolute unification remained to exist.
This last issue is where Srinivasan, Pauwels, and Rutz (2016) come to help. They contribute by
proposing and testing a conceptual framework that integrates the use of online and offline channels.
More specifically, the framework tests how online consumer activity, reflected in online marketing
channels, interacts with traditional marketing mix actions. Moreover, the way in which this interaction
drives the customer’s purchase decision and ultimately translates to sales is being investigated
(Srinivasan, Pauwels, and Rutz, 2016). In this way, the authors are able to compare the effectiveness
in terms of sales of traditional versus online marketing channels. Since this line of work corresponds
strongly with the goal of this thesis, the constructed framework will be explained into more detail.
The framework shown in Figure 3 below combines a set of traditional marketing actions with three
tangible online activities, which collectively derive into sales. The resulting sales are generated by two
effects. First of all, the traditional marketing actions have a direct impact on sales. This can be
considered the direct P2P. In this case, the use of traditional channel advertising such as television,
radio or newspaper advertisements can be thought of. Secondly, marketing mix activities have an
indirect impact on the online consumer activity, which in itself can indirectly lead towards more revenue.
The authors depicted this pathway as the indirect P2P.
[13]
Furthermore, the online consumer activities are divided in three channel options, based on the way in
which the firm managed to gain control over them. Either the firm owned the medium from the start,
either it paid for the medium, or it earned the medium through social media (Stephen and Galak, 2012).
A television commercial for instance, can generate more online interest by creating website visits.
Subsequently, a part of these visits will convert into purchases, which explains the last indirect effect.
The combination of traditional and online activities results in a P2P scheme that recognizes a cognitive,
emotional and behavioral component. These are respectively measured by paid search clicks and/or
visits to the firm’s website on one hand, and engagement that takes positive and negative expressions
on Facebook (like and unlike) into on the other hand. The scheme, presented in this way, depicts the
classic knowledge to feelings to action path (Srinivasan et al. 2010), which can be looked at as the
traditional way of thinking in marketing. Important to notice here, is that the cognitive aspect of the
scheme is measured by the outdated last-metric method (De Haan, Pauwels and Wiesel 2016). We will
reflect on this issue in section 2.3 of this thesis.
Figure 3: customer's path to and off purchase. Source: Srinivasan et al. (2016)
However, and maybe more importantly, the framework of Srinivasan, Pauwels, and Rutz (2016) also
provides us with another pathway for marketers to influence customers, namely the knowledge to action
to feelings sequence. Herewith, the authors build on the suggestion that multiple P2Ps are possible in
moving a customer along its customer journey (Vakratsas and Ambler, 1999). The idea reflected in this
alternative pathway implies that activities through traditional channels can lead to increased sales and
consequently to a combination of feelings found in the affective and cognitive components of the online
consumer activities, hence the reciprocal arrow between sales and online customer activity.
For instance, a customer can become aware of a product through a radio advertisement. Later on, when
the need arises, the customer picks this product above the alternatives of competitors because of its
remembrance of the ad. Because of the resulting positive experience the customer had with the product,
he or she then posted a positive review of it on social media. This can be considered an emerging key
insight into modern marketing in an omnichannel environment, since it implies that the end result of a
marketer’s activities may not solely imply the conversion to sales, but can translate into certain feelings
the customer holds towards the firm.
[14]
2.2.5 Conclusion
Although the questions of which channels a firm should pick to operate through and how they are ought
to be managed mutually, have been head-scratchers for marketers for many decades, the introduction
of online channels definitely complicated that puzzle. On the other hand, the possibilities brought forward
by those same channels seem to be endless, as success stories of effective online marketing campaigns
tend to be increasingly common. This leaves the present-day marketer wondering how it can optimize
the integration of its channels in such a way that it becomes viable within today’s complex online
environment. Simultaneously, the integration should focus on minimizing the risk of losing scare
resources by following simple metrics such as gut-feeling and the last-click method.
We argued that this issue is where academics should guide managers by handing them the right tools,
ready to be applied in a hyper digitalized climate, and therefore providing a significant competitive
advantage. In this chapter we proposed that the way academics should do this is not by following the
trend of doing stand-alone MM research with regards to a specific combination of channels, but rather
by following a broader OM vision that is able to bring forward complete models and provide firms with
the opportunity of integrating a myriad of marketing channels in an effective way. In an effort to elaborate
on our own propositions, we will therefore proceed in the following sections by composing and
suggesting such an integrative model.
2.3 Combining concepts: the identification of a knowledge gap
In the previous chapters of this dissertation we described the relevance of both the CE and OM concepts
in today’s marketing world from an academic standpoint, as well as a practical point of view. Hereby, on
one hand, we defined CE as the co-creation of value between customer and firm. On the other hand,
OM was described as a marketing approach aiming to generate such co-creative value between
customer and firm. Thus, it is made clear from these descriptions that based on the co-creative aspect,
both constructs taken together seem to imply a conceptual fit. However to the best of our knowledge,
this fit has not yet been thoroughly researched until date. Therefore, in the next sections we will start off
with constructing a framework that is able to address the identified knowledge gap. Secondly, we will
take a look at what the model can and cannot do for managers as well as academicians aiming to
integrate a multitude of marketing channels. At last, we will make use of the strength of the model in
order to come up with our own research ideas and its accessory hypotheses.
2.3.1 Introducing the P2F model
In the first chapter of this dissertation we learned that throughout the recent past of marketing research,
the activities of a marketer were judged upon the contribution made to their firm’s financial objectives.
This implies that practitioners were deemed to create value by means of increased sales or revenue per
customer, either through expanding RFM values or managing relationships. Later, in the early stages of
the newfangled era of CE, the construct gained attention as a crucial intermediate step towards sales.
More specifically, researchers suggested that in order to create revenues and thus more customer value,
practitioners should keep the cognitive, affective, behavioral and social components of CE in mind (Van
Doorn et al., 2010; Vivek et al., 2012; Hollebeek, 2011; Brodie et al. 2011; Verhoef et al., 2010). This
insight was concretized in the traditional idea of moving customers along their P2P by means of
interactive experiences through a multitude of channels.
[15]
Now, in a more holistic approach that is based on the four proposed tenets and suggested definition
presented within this thesis, we recognize that CE instigates the idea that value is co-created between
firm and customer and that sales are only one part of that value. This follows from the fact that through
the interactive platform that arises within the CE theory, besides buying, customers can additionally
provide indirect contributions by influencing, providing knowledge and referring (Pansari & Kumar,
2016). More specifically, customer influencing refers to the ability of social media users to affect one
another by promoting the firm. Knowledge addition on the other hand, implies the active feedback
activities of a customer that aim to improve the firm products or services. At last, customer referrals
concern customers exciting other potential customers who would otherwise had not been interested,
based on the provision of an incentive.
In figure 3 it shows that this more advanced approach suggests a new way of modeling marketing
effectiveness, which contradicts the traditional measurement model of P2P.
Figure 4: (a) Traditional marketing effectiveness model (P2P). (b) Proposed new marketing effectiveness model
(P2F).
Presented in this way, CE in itself has become a desirable outcome as a measure of marketing
effectiveness, which opposes the obsolete idea that CE is merely a transitional stage towards more
earnings. This sequential thought pattern aligns closely with Srinivasan, Pauwels and Rutz (2016), who
suggest that besides direct buying, there is also an indirect path towards value creation among
customers, namely the knowledge to action, to feelings sequence (see section 2.2.4). Whereas the
authors introduced this pathway as a variant of the traditional P2P scheme, we suggest that, based on
the self-proposed overarching definition of CE that composes our four identified tenets, this pathway is
a distinct concept. Therefore, we adopt it as the path to feelings, or P2F model.
By detaching the P2F model from the traditional P2P scheme, this model aims to offer a more complete
picture for researchers as well as practitioners wanting to measure major marketing activities, as it is
based on relevant findings within recent CE studies. Besides influencing, knowledge addition and
referencing, the purchases made by customers in this model are an expression of the customer’s
feelings towards the firm rather than a goal in itself, hence the naming. This means that whereas in the
P2P model, a customer that makes no purchases with the firm would therefore offer no additional value,
that same customer is still able to provide value in the P2F model through its influencing, knowledge
addition and/or referencing ability.
[16]
Although this model could subsequently be applied on a wide myriad of marketing actions, in this thesis
we chose to limit the P2F model to OM activities, as it is able to contribute to such line of work in three
ways (see chapter 2 of the preceded literature survey). First of all, we identified that the measurement
of effectiveness of marketing channel integration, as one of the most vital marketing activities of this
time, is still executed in terms of gained revenues rather than improved CE (Dinner et al., 2014;
Liaukonyte et al., 2015; Fossen and Schweidel, 2016). This means that the idea of P2P, as a direct
value creator in terms of gained revenues, continues to predominate OM research. However, Montaguti
et al. (2015) demonstrated that besides profitability, improved communication is an equally important
part of the value created between the firm and Omnichannel customer. In our proposed model, both of
these value components would be taken into consideration. Herewith, the added communication value
would take the form of customer influencing, knowledge addition and referencing. Secondly, the P2F
model allows researchers to move away from outdated effectiveness measurement techniques such as
the last-click method, criticized by De Haan et al. (2016), thus helping them moving towards a deeper
understanding of measuring OM activities. At last, the P2F model can provide an answer to the call of
Batra and Keller (2016) to develop new encompassing models, that are able to adequately optimize IMC
programs, by taking into account more factors than simply costs and profits.
Since the idea of merging CE with OM based on the P2F model had not been identified up until date, it
evidently has also not been researched yet. Therefore, in the following sections, we tend to elaborate
on the knowledge gap on the intersection between CE and MM, as we continue to explore it into depth.
2.3.2 The weakness and strength of CE in an Omnichannel environment
In chapter 2 of this thesis, we underscored the urgent need for measurement tools that are able to
compare the effectiveness of traditional channels with more recent online channels. As a result, our goal
within the dissertation is to investigate whether or not the CE construct, as part of the P2F model, could
fittingly serve as such a metric. Regarding this complex comparison, it is of major importance to find out
whether traditional channels are interactive to a minor degree in comparison to online channels, or
exclusively non-interactive. Indeed, this fine distinction determines the possibility of comparing
traditional channels with online channels by means of CE.
The idea follows from our self-proposed definition of CE that looks at the construct as ‘the co-creation of value between customer and firm, which originates from the direct and indirect contributions made by the customer and results in a positively modified cognitive, emotional, behavioral and social state of that customer’ (see section 2.1.4). The definition is based on the gradual shift of marketers from one-way transactional and relationship marketing, to the era of CE which is fundamentally based on two-way interaction (Pansari & Kumar, 2016). This means that if traditional channels can be depicted as non-interactive, the CE construct cannot be used as an adequate metric for those channels.
In an early work, Winer (2009) identified interactivity as one of the two key characteristics that can be
exclusively attributed to new media, with the other one being digitalism. This means that the interactivity
between customer and firm that nowadays exists within the online environment, is not applicable in a
traditional setting. The author illustrates these findings based on the figure of Hoffman and Novak (1996)
that compares the traditional mass communications model with the present-day mass communications
model. More specifically, the latter model sprouted from the introduction of online channels in paticular
and therefore implies interactivity (see Figure 5). Indeed, we can see that the reciprocal arrows that
solely exist within the modified mass communications model, seem to confirm the verdict.
[17]
Figure 5: (a) Traditional mass communications model. (b) Modified mass communications model. Source:
Hoffman and Novak (1996)
Moreover, our own extended research on CE dates back to Bowden (2009) at the earliest (see chapter
1 of the preceded literature survey). This suggests that there was no mention of the CE construct, as
described in academic literature, at the time that only traditional channels were available. It follows that
CE originated from a time in which online channels saw a significant rise in relevance. Furthermore, the
idea that traditional channels are to be seen as exclusively non-interactive is backed up by relevant
findings within MM research. In the distinction made between FICs and CICs (see section 2.2.2) Li and
Kannan (2014) noted that customer initiation, as a basic requirement of interactivity and consequently
CE, is a phenomenon that arose explicitly from the introduction of online channels in the field of
marketing. This means that CICs cannot be labeled as traditional channels, given they only seem to
exist within an online setting.
Conclusively, the ideas presented in the paragraphs above, from both a CE and MM standpoint, seem
to imply that CE cannot be considered as an adequate comparison basis for measuring traditional versus
online marketing channel effectiveness. Therefore, and to the best of my knowledge, the P2F model
cannot be of use for research within this field. In summary, a graphical representation of the
aforementioned ideas can be found in Figure 6 below.
Figure 6: The overview of a comparison possibility between traditional and online marketing channels based upon
their interactivity.
[18]
Now that the we know for which type of research the P2F model cannot be used, the question that
remains to exist is how it is then ought to be used in closing the knowledge gap that exists on the
intersection of CE and OM. Based on the preceding literature review and its representation in Figure 6
above, we notice that by means of interactivity between customer and firm, the CE construct allows us
to compare channels in an online environment. This means that, in contrast to the integration of both
traditional and online, the P2F model from an OM standpoint can be applied for the measurement of
effectiveness between a set of digital channels. It is therefore immediately clear that this is the true
power of CE in an Omnichannel environment.
2.3.3 Comparing the effectiveness of social media channels and their formatting
After proposing that it is not possible to conduct research within the field of comparing traditional with
online channels based on the P2F model, we continue focusing on the strength of CE in an OM
environment by considering online marketing channels solely. More specifically, Lamberton and
Stephen (2016) found that when it comes to online marketing, social media have been the most
discussed topic within business press over the last 15 years. (see Figure 7 below). This suggests that
the outcomes of interacting with customers through social platforms such as Facebook, Twitter and
Instagram, has mesmerized practitioners to a great extent over this period of time.
Figure 7: Factiva Mentions per Major Topic in Popular Business Press. Source: Lamberton and Stephen (2016)
However, despite its indisputable relevance to managers in the field of marketing, research in the field
of comparing the effectiveness of social media platforms remains to be fairly limited. This may be due
to the fact that the use and effects of social media are constantly changing, making it hard for
researchers to keep up with the newest trends in the resulting hyper-dynamic environment. For instance,
a study conducted by Statista (2017) found out that whereas Twitter, besides Facebook, used to be the
second biggest social media platform in 2014 with 255 million monthly users globally, in 2017 it has
been largely surpassed by Instagram with 800 million global active users per month. This shows that
within the time span of less than five years, the social media landscape has been subject to large-scale
evolutions.
[19]
In an attempt to address and explain this fast changing environment, we suggest that practitioners
should be able to compare the effectiveness of their social media activities at any given time. This would
allow these business professionals to permanently evaluate their social media strategy, thus enabling
them to revise, analyze and correct if necessary, judging upon the latest trends within the working field.
I propose that an answer to this problem can be provided by comparing the effectiveness of different
social media platforms based upon the P2F model, which allows managers to get a sense of how
engaged customers feel towards their distinctively used social channels. This would lead us to taking
the psychological, behavioral and social aspect of users into consideration, as an effectiveness metric
of social media use. Since Facebook and Instagram are the eminent platforms of today by monthly
usage (Statista, 2017), we suggest it could be worth comparing them mutually.
Moreover, we aim to elaborate on an identified call for more research within the field of formatting impact
on customer decisions (Batra & Kelly, 2016). In their work, the authors suggested that it could be worth
researching which types of formats have the most impact on a customer’s willingness to pay (WTP) and
are able to move the customer along its P2P the most efficiently. We would not only take into account
this suggestion, but genuinely build upon it by applying our proposed P2F model, as opposed to utilizing
the traditional P2P concept, as the effectiveness measurement tool. Consequently, we aim to provide a
more complete result of the effect of formatting by using CE as the metric for effectiveness as opposed
to sales.
Although research in the field of formatting impact on engagement is not new, my intended research
can contribute in multiple ways. First of all, practitioners implicitly seem to agree on the idea that a video
format is the prevailing formatting choice when it comes to engaging customers. This is why in practice,
a study by Magisto (2016) found out that the digital video marketing industry in the United States alone
is expected to reach $135 billion in 2017, which is roughly forty percent more than the budget allocated
to other formats of digital advertising. However, the preference for video marketing is often based on an
implicit gut feeling, which stems from trial and error and relatively simple rules among practitioners. Yet,
one of the core intentions of our research is to lead away managers from such subjective metrics and
guide them towards a deeper understanding of their activities through the use of the CE concept as
measured by objective standards.
Furthermore, the ambiguity of a video format as the prevailing tool to engage customers seems to be
dubious when reflecting on existing literature. Leung, Bai and Stahura (2015) checked the effectiveness
of formats on Facebook in terms of likes, comments and shares for the hotel industry in specific. Within
this sector, the biggest effect seemed to be generated by the image format in comparison to text, video
and web link. Moreover, Bonson, Royo and Ratkai (2015) compared the effectiveness of multiple
formats on the Facebook pages of local governments. Turns out, photo and text were far out the most
effective formats in engaging citizens. Then again, CE was measured in terms of likes, comments and
shares. At last Dolan, Conduit, Fahy and Goodman (2016) looked for the effect of different format types
on the engagement of customers within the wine industry. This study was conducted based upon the
same simple metrics based upon the users’ activity in terms of likes, comments and shares. The results
showed that a traditional status in the form of a text or web link was the number one engaging factor,
with video formatting coming in last place.
Based upon these findings, we do not only conclude that video formatting across multiple sector may
not be the king of engagement as opposed to what the practitioners’ gut feelings might tell them, but
also identify a trend among researchers of measuring engagement through simple metrics such as likes,
comments and shares. This implies that despite the extensive usage of social media in practice as tool
to foster CE (Lamberton & Stephen, 2016), up to now and to the best of my knowledge, there has been
no in-depth study yet of the relationship between social media use and CE.
[20]
2.3.4 Research question and hypotheses
As a result of the suggestive considerations made in section 2.3.3, we will set up and conduct a
quantitative research that (1) aims to find out whether posting on a social media platform truly impacts
a customer’s level of engagement beyond the act of liking, sharing and commenting, (2) elaborates on
a deeper understanding of measuring engagement by making use of the self-proposed P2F model that
is based on our self-proposed holistic definition of CE, (3) is able to compare the effectiveness of
distinctive social media platforms mutually and (4) is based on a critical standpoint of formatting as it
aims to draw practitioners away from simple metrics and subjective measures. The research question
that results from the identification of these four objectives can be investigated by means of 3 hypotheses,
each representing a level of analysis.
First of all, in an attempt to meet objective one and two, it is important to recall that the P2F model is not
able to make a comparison between a traditional and digital channel. Consequently, the model is ought
to be used in comparing digital channels solely. In our case we choose to limit digital channels to social
media in an attempt to address the fast changing environment in which those channels find themselves,
as well as the massive usage of these channels to engage customers from a practitioner’s standpoint.
Therefore, the overlapping level of our research concerns the main effect of social media usage on CE.
The research question that follows from this consideration can be articulated in the following way:
RQ: What is the impact of social media posting by a firm on a user’s level of engagement?
However, based on the preceding literature survey that led up to the identification of this research
question, we acknowledge that the answer to this question cannot be taken unambiguously as it is in
need of nuance in order to be explained thoroughly.
Therefore, when considering objective three, it is first of all important to find out whether there is a
difference of effect in between social media channels on a customer’s level of engagement. Yet, in order
to make such comparison, we first need to identify which types of social media channels are at hand in
today’s marketing world. To answer this issue, we follow the approach of Aichner and Jacob (2015) who
classify different categories of social media platforms, based upon their purpose and function. Within
their classification we opt to highlight the social networking channel and image-sharing channel as these
represent the most used social media platforms of today, namely Facebook and Instagram respectively.
In summary, we are thus aiming to look for a deeper explanation of our research question by looking at
the difference in effectiveness between a social networking channel and an image-sharing channel
The hypothesis for this refinement stems from preceded literature (Leung et al., 2013; Bonson et al.,
2014; Dolan et al., 2016) recognizing that across different sectors, images and text have a greater effect
in comparison to videos in terms of engaging customers on Facebook, the leading social networking
site. Given the fact that Instagram is the pre-eminent social media channel for sharing captioned images,
we translate the findings for Facebook to an image-sharing setting, in order to come up with the following
hypothesis:
H1: exposure to a post by a firm via an image-sharing channel, will have on average a more
positive influence on a user’s engagement level, compared to exposure to a post by a firm via a
social networking channel.
Secondly, in an effort to meet objective four, we believe that the answer to our research question can
be even further clarified by introducing the independent variable of formatting. In an attempt to do so,
the social networking and image-sharing channel need to be set apart in order to being able to identify
where the major differences in CE are situated within these respective channels. More specifically, we
aim to use two distinct posting formats, namely captioned image and video. Thus, this implies that we
identified an additional effect to be investigated in order to analyze our general research question. This
effect can be expressed as the difference in effectiveness in terms of CE for a social networking channel
and for an image-sharing channel respectively, based upon their posting formats.
[21]
The assumptions for this research question can be drawn up from the same argumentation used to
formulate H1. Hereby, we compose the following hypotheses:
H2a: exposure to a captioned image posted by a firm via a social networking channel, will have
on average a more positive influence on a user’s engagement level, compared to a video posted
by a firm via the same channel.
H2b: exposure to a captioned image posted by a firm via an image-sharing channel, will have on
average a more positive influence on a user’s engagement level, compared to a video posted by
a firm via the same channel.
At last, it is important to notice that customer involvement (CI) should be considered a key antecedent
of CE (Hollebeek et al. 2014), suggesting it has significant influence on the construct. Therefore, a
measurement of CI has to be taken into account as a moderating variable in the relationship between
social media usage and CE, in an effort to come up with the most accurate analysis of CE. Since CI is
deemed a crucial condition towards engaging customers, high-involved customers are supposed to
require less effort being converted into truly engaged contributors. If a customer on the other hand were
not to be involved with the firm from the get-off, the likeliness of being engaged towards it should be
expected to decrease substantially. Hollebeek et al. (2014) found evidence for this reasoning and state
that consumer involvement has a positive effect on CE. As a result of this insight, following hypothesis
arises:
H3: The effect of the posting of a firm on social media on a user’s level of engagement is stronger
for high-involved customers as compared to low-involved customers.
3 Methodology
After constructing our central research question and its accessory hypotheses, we will now discuss how
these preparations eventually were implemented into a specific research design. In the following
sections we will first recapitulate our objectives. Secondly, we will proceed by specifying the way in
which the research was set up from a respondent’s point of view. At last, the analysis of the gathered
data that resulted from our research will be presented in the results section.
3.1 Goal of the research
In summary of our research question and hypotheses, we wanted to look for the effect of both the social
media type and format type on a user’s level of CE, taking into account CI as a moderating variable.
This research will conclusively be brought to life through the use of a design that includes four
experimental groups, which are checked for by a control group. A graphical representation of this design
is illustrated in Figure 8.
[22]
Figure 8: A graphical representation of the intended research design.
However, before setting up the research it was necessary to first figure out which product category would
be adequately fitting enough to study the desired effects on CE. Considering this problem, it was
particularly meaningful for the study that the product category chosen for the eventual measurement of
CE was highly involving. Reason can be found in the idea that high-involvement products in comparison
to fast moving consumer goods (FMCGs) have a higher chance of generating digital consumer activity
(Srinivasan, Pauwels and Rutz 2016). Therefore, high-involvement product would provide us with a
more relevant basis in analyzing the relationships between our independent variables and CE. More
specifically, these product categories typically are characterized by a longer purchase decision
hierarchy, such is the case of consumer durables (Li and Kannan 2014). An example of a durable high-
involvement product category is a car. Subsequently, the research design will be set up around cars.
In order to keep the noise that could have an impact on the research limited, we chose to solely consider
one car brand in specific. This made sure that there was no difference in CE levels across respondents
as a result of feeling connected more to one car brand as compared to another. Volvo is a car brand
that has been very innovative in the recent past in terms of using their online platforms as a tool of
engaging a wide audience. For instance, in 2015, Volvo managed to hijack the Superbowl, which is the
most watched television broadcast in the United States, by setting up an enormous successful Twitter
campaign that challenged users to tweet the hashtag ‘VolvoContest’ during the commercial of any other
car brand in order to nominate a loved one of becoming eligible win a Volvo. By doing so, the brand
managed to change the Superbowl conversation from a couple of massively expensive thirty second
commercials on television, to an ongoing online Twitter contest that lasted the whole game (Grey, 2015).
Therefore, we chose to provide stimuli that considered Volvo as a car brand.
3.2 Research design
In finding the answers to our formerly introduced research question and hypotheses, we set up a survey-
based empirical research, making use of the online survey software Qualtrics. A comprehensive
understanding of the structure of our study can be attained by taking the schematic overview figure of
our research, attached to the Appendix, at hand. The survey consisted of five parts, which will be
explained more into detail in the following paragraphs. In what follows we will first address our ideal and
eventual sample characteristics, followed by the procedure in which the survey took place. Do note that
the software obliged respondents to answer all questions.
[23]
3.2.1 Sample
Prior to setting up and carrying out the eventual survey, we first needed to get ahold of the optimal pre-
composition of our sample, based upon the required amount and ideal distribution of the respondents.
Firstly, we calculated our minimal sample size based on the proportion formula of sample sizes (De
Pelsmacker & Van Kenhove, 2014). Although in practice a margin of error of five percent is often used
in comparing sample and population proportions, we allowed ourselves an extra percentage of error due
to a limited access to resources that would’ve enabled us to gather larger amounts of respondents,
compared to studies within a business setting. Making use of the formula of infinite populations at a
maximum acceptable margin of error of six percent, we concluded that we needed at least 267
respondents. In order to get an equal distribution across the five cells of our research design
(experimental groups and control group), this would imply that they were to contain at least 54
respondents each.
Moreover, we set two restrictions on our sample definition both geographically and by age. First of all,
we decided to conclude only European citizens in order to avoid as much bias as possible on the basis
of cultural differences among respondents. On the other hand, we did not want to limit ourselves to a
single country or region in an effort to gather as many respondents as possible. Secondly, we
determined to take into account only respondents between 18 and 39 years of age. The lower limit is
due to the fact that we are looking for engagement towards a car brand. Since minors in Europe are not
allowed to drive and they cannot be regarded as consumers of cars, we figured to leave them out of our
sample. In contrast, the upper limit is a consequence of the demographics of social media use. As we
were limited to sending-out the survey in Flanders (Belgium), we expected respondents to be living
mostly within this region. A study conducted by Imec (2016) showed that the relative percentages of
users within the age group from 18 to 39 years old in Flanders, are roughly the same for the Facebook
and Instagram. More specifically, across these ages, there tend to be twice the amount of Facebook
users (90%) as opposed to Instagram users (45%) within the population. As the older a user is, the less
likely he or she tends to be active on Instagram, the inclusion of older age classes would therefore lead
to an underrepresentation considering the use of Instagram. Moreover, the way in which social media
are used could vary considerably if we were to take older age classes into account (e.g. interactivity with
brands as opposed to family and friends).
After the survey was distributed, we managed to gather a total of 320 respondents. This implied that a
margin of error between sample and population proportions of 5.47% was attained, which represented
a value within the desired range. Roughly 150 of these respondents were collected organically, by
means of sharing the survey with friends and family on social media. We were able to accumulate the
other half of respondents by providing a snack as an incentive to students at the Faculty of Economics
and Business Administration at Ghent University.
3.2.2 Procedure
The survey started off with an introduction to the respondents. The subject of the survey was not
communicated in an effort to prevent respondents from being biased prior to answering the questions.
Nonetheless, the respondents were informed that there were no wrong answers and that they had to try
to answer the questions as subjectively as possible. In doing so, we aimed to reveal their true intentions
towards the later stimuli.
After advancing through the introduction, the respondents were then checked for their level of
involvement towards cars as well as Volvo. The reason this was done prior to posing any other questions
related to CE, is that according to Hollebeek (2014), CI is a not to be forgotten antecedent of CE and
therefore logically precedes the latter construct. Moreover, it was of our opinion that both involvement
towards cars as well as Volvo should be tested, as their eventual moderating effect could be different.
This follows from the idea that being involved towards cars does not explicitly indicates the respondent
feeling involved with Volvo and vice versa. Accordingly, an accurate and validated scale that was able
[24]
to measure how involved the respondents felt towards cars as well as Volvo, had to be sought for. The
Personal Involvement Inventory (PII) (Zaichowsky, 1994) gave us an adequate finding. It is a survey-
based scale that on a 7 point Likert scale and provides an idea of the extent to which consumers feel
involved with a certain product category or brand. The order in which the items of the scale presented
themselves was randomized in order to avoid order-effects. Moreover, six of the ten items were reverse-
scored with the purpose of reducing or acquiescence and boredom.
The next step included respondents declaring whether or not they were social media users. If not, the
respondent was automatically assigned to the control group as he or she could not be exposed to stimuli.
Those who were actual social media users then proceeded to the part in which they were asked to
indicate which social media platforms they specifically made use of, out of a list of ten possibilities. The
list was constructed based on the top ten social platforms by popularity.
The reason why respondents were asked to give indication of the platforms they were active on, prior
to getting exposed to stimuli, was that their answer determined which experimental group they would be
assigned to. More specifically, through self-selection, respondents stating they were active on both
Facebook and Instagram (besides other platforms) got drafted into the Instagram column. On the other
hand, respondents declaring only to be active on Facebook (besides other platforms except Instagram)
were assigned to the Facebook column. At last, respondents that indicated not to be active users of
Facebook nor Instagram besides other platforms, were automatically designated to the control group.
Due to the fact that we expected twice the amount of Facebook users as opposed to Instagram users
(see section 3.2.1) it was justifiable to assign the respondents that were active on both platforms to the
Instagram column. Furthermore, based upon which column they got assigned to, respondents were then
posed several questions regarding the way in which they used their designated platform. This specific
questioning was put in place for the purpose of obtaining a general view on how both Facebook and
Instagram were worked with by their users.
Afterwards, respondents assigned to the Facebook or Instagram column then got assigned to either an
image stimulus, a video stimulus or the control group. The probabilities of getting allocated to either one
of the subgroups within a column were 40%, 40% and 20% respectively. As opposed to randomizing
the allocation, the probabilities were put in place in order to avoid overcrowding the control group, since
this group occurred twice across the investigated platforms (see Appendix).
Consequently, across two columns, five subgroups were formed. In order to measure both the social
media type and format type as independent variables, these groups all got exposed to a different
stimulus: a Facebook image, a Facebook video, an Instagram image, an Instagram video or no stimulus
at all (control group). The content across stimuli was kept equal in an effort to isolate formatting as a
distinctive independent variable. Therefore, both the video and image were kept the same across
Facebook and Instagram. Moreover, in both cases, the video and image showed the same white Volvo
(see Appendix) being advertised for its safety, design and technology. What did differ across stimuli
were the scenarios attached to the image or video. These scenarios were put in place in order to put
the respondent in a real-life setting. The idea was to enable respondents to imagine them scrolling
through their social medium feed and encountering the post of Volvo. The eventual goal hereby was to
try and present a situation to the respondents that approached reality as much as possible.
Following the exposure to one of the respective stimuli which can be found in the Appendix, the
experimental groups were then separately tested for their level of engagement towards Volvo. For the
actual measurement of CE, we opted to utilize the Customer Engagement Scale (CES) of Pansari and
Kumar (2016), since this measurement scale aligned most closely with our earlier findings on CE and
the P2F model. In particular, the scale consisted of 16 items in a five-point Likert scale, grouped by the
four components of that measure CE: customer purchases, customer references, customer influencing
and customer knowledge. The order in which the questions were presented were randomized.
[25]
Moreover, we aimed to eliminate acquiescence by means of putting in a control question. More
specifically, this control question asked the respondent to tick off a specific answer option. If not, the
software would automatically return the following error message:
“WARNING!
Please take your time to fill in all questions honestly by carefully considering all possible answers.”
The aim here was evidently to prevent respondents from randomly ticking off the answers to each one
of our CES items. A more detailed version of the scale can be found in the Appendix. Do note however,
that due to its recency, the scale had not yet been officially validated. Therefore, further analysis and
validation was imperative.
As a last step, all respondents were asked to answer several questions related to their socio-
demographic status. The topics included sex, age, marital status, fondness of driving, having a driving
license and the amount of cars available within the household. These questions were put at the end of
the survey as a means to avoid scaring off potential respondents from the get go.
3.3 Results
After addressing the way in which the research was set up and how it was conducted from a
respondent’s point of view, we now move over to the description of our research outcomes. First of all,
we will focus on a primary check on the CES by means of a scale validity analysis. Secondly, we will
recapture our hypotheses and perform the adequate tests to examine whether they are to be confirmed
or not.
3.3.1 Scale validity analysis
Since the CES of Pansari and Kumar (2016), that is used to get a sense of a user’s level of CE, can be
depicted as a fairly new scale and therefore has not been validated yet, we deemed it essential to
primarily get a grasp of its validity. Therefore, before any other type of analysis on our data could be
executed, we opted to implement a factor analysis followed by an internal consistency reliability analysis.
3.3.1.1 Factor analysis
In our assumption, we follow the work of Pansari and Kumar (2016), who suggest that their self-
developed 16-item scale consists of 4 constructs: customer purchases, customer references, customer
influence and customer knowledge. In an effort to test this assumption for our sample, we used a
principal components method for our factor analysis. Moreover, since we assumed that the constructs
of the scale may be correlated to one another, we opted to utilize an oblique rotation for our analysis.
In order to determine the factorability of the 16 CE items we first took a look at the correlation between
items. Firstly, the determinant of 0.00009 shows us that the items were sufficiently related to each other.
More specifically, it was observed that all 16 items of the scale correlated at least .5 with at least one of
the other items. Moreover, none of the items were correlated more than 0.8 with another item, which
rules out the possible issue of multicollinearity. Secondly, the Kaiser-Meyer-Olkin measure of sampling
adequacy was .932, a value above the generally recommended value of .6. On top of that, The Bartlett’s
test of sphericity was statistically significant (χ2 (120) = 2888.18, p < .001). At last, the communalities
all had a value of more than .3, demonstrating that each item of the scale shared common variance with
any of the other items. As these three indicators suggested high factorability, a factor analysis was
perceived applicable.
Based on the criterion of Eigenvalues larger than 1, we extracted three factors, each explaining 46%,
12% and 8% respectively of the total variance. This implies that the three extracted factors cumulatively
explained 67% of the variance of all items combined. However, we can identify that the extraction of
[26]
three factors is not in line with the amount that was to be expected, namely the four factors of our initial
assumption. More specifically, we can see in Table 1 that whereas the items of customer purchases and
references indeed load on distinct factors, the items for influence and knowledge seem to load on the
same construct. We suggest that this occurrence is due to the content of the questions. When looking
at the items of the scale (see Appendix) we can first of all tell that items 1 to 4, regarding customer
purchases, examine the respondent’s buying intentions in an offline setting. Therefore, they load on the
same construct. The items measuring customer references, influence and knowledge on the other hand,
refer to the interaction intentions in an online setting and more specifically on a social platform.
Therefore, items 9 to 16, representing customer influence and knowledge, load on the same factor,
which we adopt as social media interactions. These interactions can be seen as interplay with other
platform users (influencing) as well as with the firm (knowledge addition). Despite the items aiming to
measure customer references (5 to 8) being additionally embedded in an online setting, the construct is
able to separate itself due to the important condition of an incentive. Therefore, we suggest that the
factor of customer references implies a reactive sensitivity towards incentives, rather than a true
intention of interacting with others on the social platform by means of referrals. Consequently, the label
of this factor was adapted to customer reference sensitivity.
As all the items of the scale contributed to a simple factor and met with the general criteria of a factor
loading higher than 0.4 on the designated factor and lower than 0.3 on the other factors, none of the
items were to be deleted.
Table 1: Factor loadings and communalities for 16 items of
the CE Scale (CES) of Pansari and Kumar (2016) (N=318)a
Components
Customer
purchases
Customer
reference
sensitivity
Customer social
media interaction
Communalities
Item1 .78 .63
Item2 .88 .77
Item3 .82 .69
Item4 .85 .75
Item5 -.79 .73
Item6 -.82 .72
Item7 -.69 .69
Item8 -.89 .72
Item9 .77 .60
Item10 .63 .61
Item11 .64 .59
Item12 .61 .65
Item13 .81 .64
Item14 .82 .63
Item15 .87 .70
Item16 .76 .59
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalization.
Factor loadings < .3 are suppressed.
a. Rotation converged in 5 iterations.
[27]
3.3.1.2 Internal consistency reliability analysis
To further verify the reliability of our extracted factors, we calculated the internal consistency by means
of Cronbach’s alphas. The alphas were good for customer purchases and customer reference
sensitivity, with scores of 0.86 and 0.87 respectively. The alpha for the customer social media interaction
factor could even be depicted as excellent, with a resulting Cronbach alpha of 0.91. No increases in
Cronbach’s alphas for any of the factors could have been achieved by eliminating an item. In summary,
our scale consisted of 3 factors with 4, 4 and 8 items respectively and could be seen as internally
consistent, suggesting its reliability. These findings can be retrieved in Table 2.
Table 2: Descriptive statistics for the three CES factors
(N = 318)
No of items Cronbach’s α
Customer purchases 4 0.86
Customer reference sensitivity 4 0.87
Customer social media interaction 8 0.91
3.3.2 Hypotheses testing
After preparing our scale for further analysis, we will now move over to the actual testing of the
hypotheses relevant for conducting the intended research. More specifically, we will rehearse every
effect that was to be investigated specifically, each time explaining which test was performed on it in
order to retrieve meaningful results. In what follows, we will first try to get an overview of our research
by means of a set of descriptive statistics. Secondly, we will shift to some pairwise comparisons by using
independent samples t-tests, with the aim of identifying stand-alone effects. At last, we will end up with
a conclusive variance analysis, in search of a more profound understanding of the combined effect
between our independent variables.
3.3.2.1 Descriptive statistics on the research question
In order to get a general, but rough first impression of the effect of the use of social media as a means
for positively influencing a user’s CE, we firstly ran a number of descriptive statistics comparing the
experiment groups with the control group. The results can be found in Table 3 below. Do note that prior
to running these descriptives, we executed a series of data cleaning steps . By means of a box plot we
looked for significant outliers within our CE distribution. However, none were to be found. Secondly, we
did delete two responses as they were incomplete, leaving us with a total of 318 respondents to be
analyzed.
[28]
Table 3: Descriptive statistics on the measure of CE as a function of social media
posting
CE score
N Mean
Std.
Deviation Minimum Maximum
Facebook video 69 34.42 10.55 16.00 66.00
Facebook image 72 31.83 10.17 16.00 65.00
Instagram video 60 38.65 9.79 18.00 66.00
Instagram image 62 36.61 11.16 16.00 66.00
Control group 55 39,85 12.38 16.00 64.00
Total 318 36,00 11.11 16.00 66.00
Note. The minimum and maximum score are 16 and 80 respectively.
Surprisingly, the absolute mean values of the CE scores of the experiment groups are all lower than the
mean CE score of the control group. At first glance, this would imply that getting exposed to a post of a
firm on social media, would result in a lower engagement as compared to the status quo. Therefore,
these means are unquestionably in need of further analysis and explanation. Furthermore, based on the
respective minimum and maximum values, the range of values of all groups tend to be quite similar,
reconfirming there were no significant outliers in the data of our CE scores.
3.3.2.2 The use of independent samples t-tests to identify general effects
Once we obtained a general view on the general effect that was to be investigated, we could then
proceed to conducting the relevant tests used to get a more profound insight of this effect, as this was
the eventual goal of our research in particular. In doing so, we first calculated a series of independent
samples t-test comparing the interval-scaled CE scores of our experimental groups with our control
group. The results can be found in Table 4 underneath.
Table 4: Independent samples t-test on the mean scores of CE between experimental
groups and control group
Mean Std. Deviation
t-value
p-value
Facebook video
Control group
34.42
36.00
10.55
12.38
-2.63 0.009
Facebook image
Control group
31.83
36.00
10.17
12.38
-3.90 < 0.001
Instagram video
Control group
38.65
36.00
9.79
12.38
-.58 0.562
Instagram image
Control group
36.61
36.00
11.16
12.38
-1.48 0.139
[29]
Based upon our above-mentioned results, we can conclude that whereas the average CE scores of the
control group do not significantly differ from the Instagram stimuli (both video and image), the average
CE scores of the Facebook both the video and captioned image stimuli do show significantly lower
values as opposed to the control group. This implies that on one hand, there is no effect of posting a
video nor an image on a user’s level of engagement on Instagram. On the other hand, posting a video
or a captioned image on Facebook negatively affects a user’s level of engagement as compared to the
status quo. These results did not lay in line with our expectations of the answer to our RQ.
Secondly, we calculated an independent samples t-test to identify a possible difference between the CE
scores of our two main independent samples, namely the Facebook group and the Instagram group.
This was done in an attempt to provide an answer to our first hypothesis. Do note that we did not yet
split up these groups based upon their formatting. The results indicated that users who got exposed to
a post by the firm via an image-sharing channel reported significantly higher CE scores (M = 37.6, SD
= 10.5) than did users who got exposed to a post by the firm via a social networking channel (M = 33.1,
SD = 10.4), t(261) = -3.49, single-tailed p = < .001. As expected, the image-sharing channel (Instagram)
generated a significantly higher mean CE score among users, as compared to a social networking
channel (Facebook), which confirms our first hypothesis.
After checking for the effect of platform type on CE between our two independent samples, we then
wanted to determine within these groups respectively whether or not formatting had a significant impact
on CE for each of the platforms distinctively. This would allow us to come up with an answer for the
second hypotheses of our research. In order to do so, we repeated two independent samples t-tests.
The results showed that on one hand the 72 respondents in the Facebook image group (M = 31.8, SD
= 10.2) and the 69 respondents in the Facebook video group (M = 34.4, SD = 10.6), demonstrated a
marginally significant difference in mean CE scores (t[139] = -1.48, single-tailed p = .07). On the other
hand, the 62 respondents in the Instagram image group (M = 36.6, SD = 11.2) and the 60 respondents
in the Instagram video group (M = 38.7, SD = 9.8) exhibited a non-significant difference in mean CE
scores (t[139] = -1.07, single-tailed p = 0.14). These results indicate that there are no significant
differences between the effects of formatting on a user’s level of engagement on Instagram, as well as
Facebook. Consequently, the bilateral hypotheses of H2 were denied. However, do note that the effect
of formatting within a Facebook context would have been significant if the significance level of
acceptance were to be raised to 0.1. Therefore, we conclude that a Facebook video has a marginally
significant more positive effect on a user’s level of engagement, as compared to a captioned image
posted on Facebook.
At last, in order to get a first glance at the moderating effect between our variables of customer
involvement, both towards cars and towards Volvo, we conducted two independent samples t-tests to
find differences in the means of CE scores between low –and high involved users. Firstly, we reverse
coded the 6 reverse scored items of the scale. Secondly, the cut-off point was determined by splitting
the scale of Zaichowsky (1994), that ranged from 10 to 70, in half. At last, respondents that had an
involvement score within a 5-point margin around the mid-point of the scale were left out, as we
perceived them indifferent towards the questioned construct. Consequently, respondents scoring less
than 35 on the scale were labeled as low-involved users, whereas respondents scoring more than 45
were classified as high-involved users. Regarding involvement towards cars, low-involved social media
users (M = 30.6, SD = 2.12) indeed reported significantly lower CE scores as opposed to high-involved
users (M = 38.4, SD = 0.75) , t(239) = -3.59, single-tailed p = < .001. With respect to involvement towards
Volvo, we found a similar effect with low-involved users scoring a mean CE score of 30.1 (SD = 1.08)
and high-involved users scoring a mean CE score of 41.1 (SD = 1.17), t(181) = -6.68, single-tailed p =
< .001. Based on the respective t-values of both tests we recognize that involvement towards Volvo has
a stronger significant moderating effect as opposed to involvement towards cars. Therefore, we will
utilize the latter mentioned moderator in further analysis of our research.
[30]
3.3.2.3 The use of variance analysis to identify combined effects
Now that we obtained insight on the effect of social media channel choice and formatting separately, we
figured it would be of major interest to observe the combined effect of both independent variables on
the mean CE scores of respondents. Moreover, in order to give meaning to the odd descriptive statistics
found in section x, the comparison of the combined research cells mutually, should be expanded with a
check for the control group. At last, the found effects should be further investigated by adding
involvement towards Volvo as a moderating variable to the mix.
This implies that we needed to conduct a factorial ANOVA to compare the main effects of type of
research category and Volvo involvement, and the interaction-effect between both variables on the
mean CE score of a user. The type of research category consisted of four levels (Facebook video,
Facebook image, Instagram video and Instagram image) and involvement included two levels (low-
involved and high-involved). The cell sizes, means and standard deviations of the 4x2 factorial design
are represented in Table 5 below. Whereas both main effects were statistically significant, the
interaction-effect turned out non-significant. On one hand the main effect of research category type
yielded an F ratio of F(3,156) = 2.9, p = 0.04 indicating a significant difference between a Facebook
video (M = 34.1, SD = 1.60), a Facebook image (M = 31.3, SD = 1.70), an Instagram video (M = 37.8,
SD = 2.00) and an Instagram image (M = 37.1, SD = 1.64). On the other hand the main effect of
involvement towards Volvo yielded an F ratio of F(1, 156) = 28.0, p < 0.001. This indicated that the mean
CE scores of low-involved users (M = 30.5, SD = 1.31) are significantly lower than those of the high
involved users (M = 39.7, SD = 1.15), which laid in line with H3. The interaction effect was non-
significant, F(3, 156) = 1.2, p > 0.05.
[31]
Table 5: Cell sizes, means and standard deviations on the 4x2 factorial
design aiming to measure CE
CE score
Research
category Volvo involvement N Mean
Std.
Deviation
Facebook video Low-involved towards
Volvo
20 28.45 8.29
High-involved towards
Volvo
23 39.65 11.89
Total 43 34.44 11.71
image
Low-involved towards
Volvo
20 26.15 6.45
High-involved towards
Volvo
18 36.50 13.98
Total 38 31.05 11.77
Instagram video Low-involved towards
Volvo
10 32.10 8.97
High-involved towards
Volvo
24 43.46 9.81
Total 34 40.12 10.80
image
Low-involved towards
Volvo
21 35.19 11.83
High-involved towards
Volvo
20 39.00 10.31
Total 41 37.05 11.14
Total Low-involved towards
Volvo
71 30.31 9.69
High-involved towards
Volvo
85 39.91 11.54
Total 156 35.54 11.73
Furthermore, post hoc Tukey HSD comparisons showed that the mean CE score for a Facebook
image was significantly lower than the mean CE score for an Instagram image (p = 0.02) and
Instagram video (p = 0.01) respectively. However, all the other pairwise comparisons between
research categories did not significantly differ from one another.
[32]
4 Discussion
4.1 Conclusions emerging from the research
First of all, our research suggests that when it comes to engaging users beyond the act of liking, sharing
and commenting, Instagram is a better performing social platform than Facebook. More specifically, a
video or a captioned image posted on Instagram contributes significantly more to a user’s level of
engagement than a captioned image posted on Facebook.
Secondly, we recognize two tendencies across the investigated social platforms. On one hand, our
research shows that for both platforms distinctively, it seems that video formatting tends to have a larger
positive effect on a user’s level of CE as compared to a captioned image. On the other hand, the effect
of formatting within a social platform is likely to be stronger for Facebook than for Instagram.
Conclusively, the choice of picking a video over a captioned image seems to be more important within
a social networking setting as opposed to an image-sharing environment. However, it needs to be noted
that more research in this area is needed in order to significantly affirm these trends.
4.2 Discussion from the research results
To the best of our knowledge, our research was the first in its kind to measure the result of marketing
activities - in our case social media channel integration - by means of direct and indirect contributions in
order to come up with a holistic result of CE. We did this by using our self-proposed P2F model as a
framework. In doing so, we merged the world of CE and OM in order to address the literature gap that
existed between both. Moreover, this research succeeded in making a comparison between the
effectiveness of Facebook and Instagram, the most used social media platforms of today.
Taking into account our global research question, we wanted to get a hold of the effect of the use of
social media by a firm on a user’s level of engagement. Notwithstanding the fact that the eventual goal
of our research was to provide a nuanced answer to this question, we found it valuable to obtain a
general view of this effect at the outset of our research. Consequently, this was done by comparing the
mean CE scores of our experiment groups with the CE score of our control group. Although the
differences in results proved not to be significant in case of the Instagram stimuli as opposed to the
Facebook stimuli, all of the experimental groups showed lower absolute mean values on their CE scores
as opposed to the control group. This would imply that firms posting content in a video or image format
on a social platform would not differ from the status quo in case of Instagram, and even have a negative
effect on a user’s level of engagement in the case of Facebook. Since these occurrence cannot be
justified from an academic point of view, nor from the standpoint of massive usage of social media in
practice to engage users, we are led to believe that the explanation of this manifestation has to be
sought elsewhere.
In our opinion, these odd outcomes are due to the utilized research method. Hereby, it needs to be
recalled that our survey-based approach put the respondents in an artificial setting. This made the
respondents very aware of the fact that they were being manipulated. Moreover, respondents tend to
not like to admit that they are being influenced within this environment. Both of these factors may have
contributed to raising response sensitivity, therefore having a none to negative impact on the mean CE
scores as opposed to the control group. In addition, we suggest that engagement towards a firm is
difficult to ascribe to a single manipulation and rather the result of multiple interactions with the firm
across social media platforms over time.
[33]
In these regards, we advocate the usage of a data-driven approach when it comes to measuring CE, as
this method enables the researcher to measure the true perceptions of a user rather than seeking for
superficial intentions. More specifically, we propose that for the measurement of direct, data mining
techniques such as the CLV method of Kumar, Venkatesan, Bohling, and Beckmann (2008) may still
provide a valuable alternative. As far as the measurement of indirect contributions, more recent research
in the field of text-mining may provide meaningful insights on the references, influencing and knowledge
addition value of users. Moreover, these techniques would not only be able to measure CE more
efficiently, but would also enable both researchers as well as practitioners to generate predictive models
of marketing activities on CE using the P2F model as a framework.
When taking a look at our first hypothesis, we anticipated a post of the firm via an image-sharing channel
having a stronger effect on a user’s level of engagement, as opposed to a post of the firm via a social
networking channel. Indeed, by comparing Facebook and Instagram, our research results show that this
is the case. However, we hypothesized this effect on the assumption that images would generate more
CE as compared to videos and that therefore Instagram would be the biggest engaging platform. Yet,
our own research now shows that there is tendency for video being a stronger engaging format as
opposed to a captioned image. Therefore, our hypothetical justification can no longer apply.
Now, the question that remains to exist is what did cause this effect. We suggest the biggest reason for
this outcome can be found in the fact that interacting with a firm on Instagram can be done more
anonymously as opposed to interacting with a firm on Facebook. More specifically, the activities of a
user on Facebook are subject to bigger public exposure in comparison to activities on Instagram.
Whereas on Facebook, the likes and comments of a user get displayed on the news feed of every one
of their friends, followers on Instagram do not receive notice of such interactions, unless they
coincidentally encounter the same post. We believe that this lack of anonymity on the Facebook platform
tends to withhold users from truly engaging with their beloved brands, as they might consider this an
infringement of their privacy. In summary, we recognize that despite CE goes beyond mere commenting,
sharing and liking, these factors still play a major role in terms of social media use. Therefore, we
propose that the sense of anonymity might be an important driver from a social media user's point of
view
Bearing in mind our second hypothesis, we stated that a captioned image would have had a more
positive effect on CE as opposed to a video format, and that this effect occur across both investigated
social platforms. On the contrary, the results showed that in fact there is a tendency for the opposite
effect. A video thus tends to create more engagement in comparison to a captioned image. In order to
being able to understand this, we first need to recapture the foundation of our hypothesis.
The assumption for our second exploratory effect was based on empirical research that investigated the
effect formatting had on social media engagement. Although no abundance of literature was found within
this field, Leung et al., (2013), Bonson et al. (2014) and Dolan et al. (2016) found that across different
sectors, images topped videos in engaging Facebook users. We suggest that our conflicting results can
be explained in two ways. First of all, we propose that the effect of formatting on the engagement of
social media users might be sector specific and therefore not generalizable. For instance, a moving
visual might be more appealing to users in the context of a car, as opposed to wines and hotels.
Secondly, given the fast-evolving nature of the social media environment, these papers, despite being
relatively recent, may already not be cooping well with the current trends regarding the effect of
formatting on CE. This means that whereas images may have topped videos in engaging users up until
two to four years ago, this trend may have already been reversed due to the social media dynamics that
took place in the meantime.
[34]
At last, our third hypothesis involved the moderating effect of customer involvement between our two
main effects of interest. Despite adding involvement towards Volvo as a moderator to all our main effects
as well as the combined effect, no significant interaction effects were found. Therefore, a further analysis
of this variable was not discussed. Nonetheless, our research showed that indeed low-involved users
were subject to lower CE scores as opposed to high-involved users, reconfirming the insight provided
by Hollebeek (2014).
4.3 Limitations and suggestions for further research
A first limitation of our research consists of the overrepresentation of the youngest age group in our
sample. Our socio-demographic distribution showed that roughly 78% of the sample consisted of
respondents within the age group of 18-24. In particular, we suggest that this could have caused bias
on the distribution of the CE scores. More specifically, we assume that the older people get, the more
sensitive they become towards risk avoidance. This implies that when it comes to a car, safety is likely
to become a more important characteristic as the person ages. Since safety can be seen as the most
important advertisement feature utilized by Volvo and this feature also came forward prominently in the
stimuli of our research, we suspect that the overrepresentation of the younger age group was the reason
of seeing lower CE scores to occur, as opposed to what was to be expected.
The attentive reader may have noticed that whereas at the end of chapter two of the literature study, we
discourage researchers of doing research in the field of OM in stand-alone fashion, we then proceed
with setting up a research that compared merely two social media channels by the end of chapter three.
This is due to the limitation that within the confines of a master’s dissertation, we lacked both time and
resources to further expand the research design. Moreover, this lack of resources implied that we were
not granted access to a real-life database of Volvo sales figures and/or social media details of their
customers. Therefore, we were only able to calculate the engagement scores of a random set of platform
users, rather than measuring the engagement intentions of real customers.
Taking all these limitations into consideration, we want to underline that the research conducted within
this dissertation should be considered a basic illustration of the type of research that could be done
making use of the P2F model as a framework and this from both a managerial as well as an academic
point of view. Therefore, we encourage future researchers to carry out larger-scale research in the area
of OM by adding more social media platforms to the mix such as Youtube and Snapchat. Moreover, we
suggest it could be of value to add other online channels to the research design, such as search engine
advertisements (=SEA) and e-mail marketing, in addition to social media channels. By doing so, the
marketer would be enabled to come up with a meta-analysis of the effectiveness of a firm’s total online
marketing activity.
Taking into account the ambiguity that remains to exist surrounding the effect of formatting on a user’s
level of engagement, we propose that first of all it could be worth doing research within this line of work
by taking into account multiple sectors in order to find meta-effects across sectors. Secondly, given the
hyper-dynamic environment of social media, we call for research that is able to keep track of time series
in order to keep up with the fast moving trends within social media use. Thirdly, besides formatting, the
content of a social media post could also be worth to be taken into consideration as an important variable
to explain differences of engagement across platform users. At last, we would like to mention that
whereas we assumed in section 2.2.3 that practitioners seemed to agree on the idea that video is the
prevailing engaging format based on a practical study, this idea could have been confirmed by
strengthening the quantitative study with a qualitative aspect which would check for the perception of
formatting effectiveness among a group of managers within the field. This qualitative study could have
been conducted by means of in-depth interviews.
[35]
4.4 Managerial implications
Despite the fact that Facebook has proved its value within the social media landscape for a longer period
of time and intermediately has developed an extensive tool to expose sponsored content to a specific
target group, our research shows that Instagram is significantly capable of engaging its users more.
More specifically, this effect is especially true when the target group consists of young people between
the ages of 18 and 39. Therefore, we encourage managers relying on Facebook as the prevailing social
platform within their social media strategy to also (and maybe even more) take Instagram into
consideration.
In addition, it is important to note that despite the fact that Instagram is the image-sharing channel par
excellence, videos nevertheless tend to generate the highest level of engagement, even within this
platform. This is why we recommend managers to predominantly post videos on their Instagram feed.
However, it needs to be noted that we are not convinced that posting solely video content on the platform
will maximize engagement levels, due to the potential negative effect of monotony.
Finally, we also want to discourage managers from putting an abundance of Facebook pictures on their
wall, as they tend to perform marginally worse than Facebook videos and significantly generate less
engagement than Instagram photos and videos.
5 Conclusive consideration
In today’s business world, all too often marketers from a consumer’s point of view are still being looked
at as money grabbing monsters eager to suck the dollars right out of their pockets. This is unfortunate,
as in fact it are the marketers who should be the ones providing firms deep, meaningful connections
with all of their customers and potential customers. I believe that the only way of changing the narrative
of how the marketers of today are being looked at, is by changing the way marketers themselves
approach their surroundings.
Instead of selling the idea of putting customers at the center point of gravity under the guise of “the
customer is always right” only to then chase as much revenues as possible in the short term, I propose
that marketers should truly build their strategy around the customer by focusing on the co-creation of
value between both parties. Hereby, it is of my opinion that following the latter kind of approach will
automatically lead to profit maximization as well as more satisfied relationships in the long term.
With the introduction of the P2F model presented within this dissertation, managers as well as
practitioners willing to pursue such vision, are provided with a framework that enables them to implement
a corresponding strategy.
[IX]
References
Aichner, T., & Jacob, F. (2015). Measuring the degree of corporate social media use. International
Journal of Market Research, 57(2), 257-276.
Baier, M., Ruf, K. M., & Chakraborty, G. (2002). Contemporary database marketing: concepts and
applications. Racom Communications.
Barris, M. (2015). Budweiser says Super Bowl digital strategy as important as ad buy. Mobile
Marketer. Retrieved August 12, 2017 from
http://www.mobilemarketer.com/cms/news/advertising/19580.html.
Batra, R., & Keller, K. L. (2016, November). Integrating Marketing Communications: New findings, new
lessons, and new ideas. American Marketing Association.
Berry, L. L. (1995). Relationship marketing of services—growing interest, emerging perspectives.
Journal of the Academy of Marketing Science, 23(4), 236-245
Blattberg, R. C., Kim, B. D., & Neslin, S. A. (2008). Why database marketing?. In Database Marketing
(pp. 13-46). Springer New York.
Bolton, R. N., & Drew, J. H. (1991). A multistage model of customers' assessments of service quality
and value. Journal of consumer research, 17(4), 375-384.
Bonsón, E., Royo, S., & Ratkai, M. (2015). Citizens' engagement on local governments' Facebook
sites. An empirical analysis: The impact of different media and content types in Western Europe.
Government Information Quarterly, 32(1), 52-62.
Bowden, J. L. H. (2009). The process of CE: a conceptual framework. Journal of Marketing Theory
and Practice, 17(1), 63-74.
Brodie, R. J., Hollebeek, L. D., Juric, B., & Ilic, A. (2011). CE: conceptual domain, fundamental
propositions, and implications for research. Journal of Service Research, 14(3) 252-271.
Chen, S., & Lamberti, L. (2016). Multichannel marketing: the operational construct and firms’
motivation to adopt. Journal of Strategic Marketing, 24(7), 594-616.
De Haan, E., Wiesel, T., & Pauwels, K. (2016). The effectiveness of different forms of online
advertising for purchase conversion in a multiple-channel attribution framework. International Journal
of Research in Marketing, 33(3), 491-507.
De Keyser, A., Schepers, J., & Konuş, U. (2015). Multichannel customer segmentation: Does the after-
sales channel matter? A replication and extension. International Journal of Research in Marketing,
32(4), 453-456.
De Pelsmacker, P., & Van Kenhove, P. (2014). Marktonderzoek: methoden en toepassingen.
Amsterdam: Pearson.
Dinner, Isaac M., Harald J. Van Heerde, and Scott A. Neslin (2014), “Driving Online and Offline Sales:
The Cross-Channel Effects of Traditional, Online Display, and Paid Search Advertising,” Journal of
Marketing Research, 51 (October), 527–45.
Dolan, R., Conduit, J., Fahy, J., & Goodman, S. (2016, February). Facebook for Wine Brands: An
Analysis of Strategies for Facebook Posts and User Engagement Actions. In 9th Academy of Wine
Business Research Conference (p. 457).
[X]
Econsultancy (2012). Quarterly digital intelligence briefing: Making sense of marketing attribution.
Retrieved January 7, 2018 from http://econsultancy.com/nl/reports/quarterly-digital- intelligence-
briefing-making-sense-of-marketing-attribution.
Fossen, B. L., & Schweidel, D. A. (2016). Television advertising and online word-of-mouth: An
empirical investigation of social tv activity. Marketing Science, 36(1), 105-123.
Frazer, M., & Stiehler, B. E. (2014, January). Omnichannel retailing: The merging of the online and off-
line environment. In Global Conference on Business & Finance Proceedings (Vol. 9, No. 1, p. 655).
Institute for Business & Finance Research.
Gallup (2014). The financial and emotional benefits of fully engaged bank customers. Retrieved
August 17, 2017 from http://www.gallup.com/opinion/gallup/173255/financial- emotional-benefits-fully-
engaged-bank-customers.aspx.
Goldfarb, A., & Tucker, C. (2011). Online display advertising: Targeting and obtrusiveness. Marketing
Science, 30(3), 389-404.
Grey (2015). The greatest interception. Retrieved January 8,, 2018 from
http://grey.com/global/work/key/new-york-volvo-interception/id/5647/.
Grönroos, C., & Voima, P. (2012). Making sense of value and value co-creation in service logic.
Hanssens, D.M. (2009). Empirical generalizations about marketing impact: What we have learned
from academic research. Marketing Science Institute.
Harmeling, C. M., Moffett, J. W., Arnold, M. J., & Carlson, B. D. (2017). Toward a theory of CE
marketing. Journal of the Academy of Marketing Science, 45(3), 312-335.
Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments:
Conceptual foundations. The Journal of Marketing, 50-68.
Hollebeek, L. (2011). Exploring customer brand engagement: definition and themes. Journal of
Strategic Marketing, 19(7), 555-573.
Hollebeek, L., Glynn M., & Brodie R. (2014), “Consumer Brand Engagement in Social Media: Concep-
tualization, Scale Development, and Validation,” Journal of Interactive Marketing, 28 (2), 149–65.
Homburg,
Hollebeek, L., Srivastava, R., Chen, T. (2016). S-D logic-informed CE: integrative framework, revised
fundamental propositions, and application to CRM. Journal of the Academy of Marketing Science
(2016) pp. 1-25.
Howard, J., Sheth A. & Jagdish, N. (1969). The theory of buyer behavior (No. 658.834 H6).
IAB (2017). 2016 Internet advertising revenue full-year report. Retrieved April 14, 2017 from
https://www.iab.com/wp-
content/uploads/2016/04/IAB_Internet_Advertising_Revenue_Report_FY_2016.pdf.
Imec (2016). Imec digimeter 2016. Retrieved January 6, 2017 from https://www.imec-
int.com/nl/digimeter.
Jaakkola, E., & Alexander, M. (2014). The role of CE behavior in value co-creation a service system
perspective. Journal of Service Research, 17(3), 247–261.
[XI]
Leung, X. Y., Bai, B., & Stahura, K. A. (2015). The marketing effectiveness of social media in the hotel
industry: A comparison of Facebook and Twitter. Journal of Hospitality & Tourism Research, 39(2),
147-169.
Li, H., & Kannan, P. K. (2014). Attributing conversions in a multichannel online marketing environment:
An empirical model and a field experiment. Journal of Marketing Research, 51(1), 40-56.
Magisto (2016). The size of video marketing. Retrieved December 23, 2017 from
https://www.magisto.com/video-market-size.
Keller, K. L. (2010). Brand equity management in a multichannel, multimedia retail environment.
Journal of Interactive Marketing, 24(2), 58-70.
Keller, K. L. (2016). Unlocking the Power of Integrated Marketing Communications: How Integrated Is
Your IMC Program?. Journal of Advertising, 45(3), 286-301.
Kumar, V. (2010). A customer lifetime value-based approach to marketing in the multichannel,
multimedia retailing environment. Journal of Interactive Marketing, 24(2), 71-85.
Kumar, V. (2013). Profitable CE: Concept, Metrics and Strategies.” SAGE Publications, India.
Kumar, V., & Pansari, A. (2016), Competitive Advantage Through Engagement. Journal of Marketing
Research, 53, 497–516.
Kumar, V., Venkatesan, R., Bohling, T., & Beckmann, D. (2008). Practice Prize Report—The power of
CLV: Managing customer lifetime value at IBM. Marketing science, 27(4), 585-599.
Lamberton, C., & Stephen, A. T. (2016). A thematic exploration of digital, social media, and mobile
marketing: research evolution from 2000 to 2015 and an agenda for future inquiry. Journal of
Marketing, 80(6), 146-172.
Lemon, K. N., & Verhoef, P. C. (2016). Understanding Customer Experience and the Customer
Journey. Journal of Marketing, 80(JM-MSI Special Issue), 1–62.
Liaukonyte, J., Teixeira, T., & Wilbur, K.C. (2015). Television Advertising and Online Shopping.
Marketing Science, 34(3), 311–30.
Malthouse, E. C., Haenlein, M., Skiera, B., Wege, E., & Zhang, M. (2013). Managing customer
relationships in the social media era: introducing the social CRM house. Journal of Interactive
Marketing, 27(4), 270-280.
Michel, S., Brown, S., & Gallan, A. (2008). An expanded and strategic view of discontinuous
innovations: Deploying a service-dominant logic. Journal of the Academy of
Marketing Science, 36(1), 54–66.
Montaguti, E., Neslin, S. A., & Valentini, S. (2015). Can marketing campaigns induce multichannel
buying and more profitable customers? A field experiment. Marketing science in press.
Morgan, R. M., & Hunt, S. D. (1994). The Commitment-Trust Theory of Relationship Marketing.
Journal of Marketing, 58(3) 20-38.
Nambisan, S. (2002). Designing virtual customer environments for new product development: Toward
a theory. Academy of Management Review, 27(3), 392-413.
[XII]
Neslin, S. A., & Shankar, V. (2009). Key issues in multichannel customer management: current
knowledge and future directions. Journal of interactive marketing, 23(1), 70-81.
Neslin, S. A., Grewal, D., Leghorn, R., Shankar, V., Teerling, M. L., Thomas, J. S., & Verhoef, P. C.
(2006). Challenges and opportunities in multichannel customer management. Journal of Service
Research, 9(2), 95-112.
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions.
Journal of marketing research, 460-469.
Pansari & V. Kumar (2016). CE: the construct, antecedents, and consequences. Journal of the
Academy of Marketing Science, 37, 1-32
Patterson, P., Yu, T., & De Ruyter, K. (2006, December). Understanding CE in services. In Advancing
theory, maintaining relevance, proceedings of ANZMAC 2006 conference, Brisbane (pp. 4-6).
Pelton, L. E., Strutton, D., & Lumpkin, J. R. (1997). Marketing channels. Chicago: Richard W. Irwin,
203-209.
Rangaswamy, A., & Van Bruggen, G. H. (2005). Opportunities and challenges in multichannel
marketing: An introduction to the special issue. Journal of Interactive Marketing, 19(2), 5-11.
Sarner, A., & Herschel, G. (2008). A checklist for evaluating an inbound and outbound multichannel
campaign management application. Report G00160776, Gartner, Stamford, CT.
Sedley, R. (2010). 4th Annual online CE report 2010.
Sethuraman, R., Tellis, G. J., & Briesch, R. A. (2011). How well does advertising work?
Generalizations from meta-analysis of brand advertising elasticities. Journal of Marketing Research,
48(3), 457–471.
Srinivasan, S., Pauwels K., & Rutz, O.J. (2016). Paths to and off Purchase: Quantifying the Impact of
Traditional Marketing and Online Customer Activity. Journal of the Academy of Marketing
Science,44(4),440–53.
Statista (2017). Instagram: number of monthly active users 2013-2017. Retrieved December 27, 2017
from https://www.statista.com/statistics/253577/number-of-monthly-active-instagram-users.
Statista (2017). Twitter: number of monthly active users 2010-2017. Retrieved December 27, 2017
from https://www.statista.com/statistics/282087/number-of-monthly-active-twitter-users.
Suddaby, R. (2010). Construct clarity in theories of organization. Academy of Management Review,
35(3), 346–357.
Vakratsas, D., & Ambler, T. (1999).How advertising works: what do we really know? Journal of
Marketing, 63(January), 26–43.
Van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. C. (2010). CE
behavior: Theoretical foundations and research directions. Journal of Service Research, 13(3), 253-
266.
Verhoef, P., Werner R., & Krafft M. (2010), “Customer 1211 Engagement as a New Perspective in
Customer Management,” Journal of Service Research, 13, 3, 247–52.
[XIII]
Vivek, S. D., Beatty, S. E., & Morgan, R. M. (2012). CE: Exploring customer relationships beyond
purchase. Journal of Marketing Theory and Practice, 20(2), 122-146.
Winer, R. S. (2009). New communications approaches in marketing: Issues and research directions.
Journal of Interactive Marketing, 23(2), 108-117.
[XIV]
Appendix
Schematic overview of the research
Figure 9: schematic overview of the research
[XV]
Survey
1. Survey introduction
Start of Block: Introduction
Intro Dear participant,
The next survey will take about 8 minutes of your time.
There are no right or wrong questions. Your personal ideas, perceptions and feelings are what matter.
The data will be analyzed anonymously.
Thank you for your participation,
Senne Vermassen
Ghent University
Click >> to start.
End of Block: Introduction
2. CI measurement
2.1 Involvement towards cars
Start of Block: Customer involvement measurement
In this section, I would like you to think about your perception of cars and answer the following
questions.
[XVI]
Car involvement To me cars are:
1 (0) 2 (1) 3 (2) 4 (3) 5 (4) 6 (5)
important
(1) o o o o o o o unimportant
boring (2) o o o o o o o interesting
relevant
(3) o o o o o o o irrelevant
exciting
(4) o o o o o o o unexciting
means
nothing (5) o o o o o o o means a lot
to me
appealing
(6) o o o o o o o unappealing
fascinating
(7) o o o o o o o mundane
worthless
(8) o o o o o o o valuable
involving
(9) o o o o o o o uninvolving
not
needed
(10) o o o o o o o needed
2.2 Involvement towards Volvo
Here, I would like you to think of the car brand Volvo and answer the following questions.
Volvo, to me is:
(Same scale as presented above)
End of Block: Customer involvement measurement
[XVII]
Start of Block: Any social medium usage
SM user Are you a user of any social medium?
o Yes (1)
o No (2)
End of Block: Any social medium usage
Start of Block: Which social media usage
3. Assigning respondents to experimental/control group(s)
Which of the following social media tools do you use? (Choose all that apply)
▢ Facebook (1)
▢ Instagram (2)
▢ Twitter (3)
▢ LinkedIn (4)
▢ Google+ (5)
▢ Youtube (6)
▢ Skype (7)
▢ Flickr (8)
▢ Snapchat (9)
▢ Other (please specify) (10) ________________________________________________
End of Block: Which social media usage
Start of Block: General condition Facebook
[XVIII]
3.1 Facebook allocation
Do you use Facebook primarily for business/academic or personal purposes?
o Business/academic usage (1)
o Personal usage (2)
o It's about 50/50 (3)
o I don't know (4)
access freq How often do you access your Facebook account? (On average)
o Hourly (1)
o Multiple times per day (2)
o Daily (3)
o Once per couple of days (4)
o Once per week (5)
o Once per month (6)
o Less than once per month (7)
[XIX]
How often do you post information you want to share on your Facebook account? (On average)
o Hourly (1)
o Multiple times per day (2)
o Daily (3)
o Once per couple of days (4)
o Once per week (5)
o Once per month (6)
o Less than once per month (7)
How often do you use Facebook to obtain information about a brand's products and services?
o Frequently (1)
o Sometimes (2)
o Rarely (3)
o Never (4)
How often do you use Facebook to like, comment or share posts of brands?
o Frequently (1)
o Sometimes (2)
o Rarely (3)
o Never (4)
End of Block: General condition Facebook
Start of Block: General condition Instagram
[XX]
3.2 Instagram allocation
Do you use Instagram primarily for business/academic or personal purposes?
o Business/academic usage (1)
o Personal usage (2)
o It's about 50/50 (3)
o I don't know (4)
How often do you access your Instagram account? (On average)
o Hourly (1)
o Multiple times per day (2)
o Daily (3)
o Once per couple of days (4)
o Weekly (5)
o Monthly (6)
o Less than once per month (7)
[XXI]
How often do you post information you want to share on your Instagram account? (On average)
o Hourly (1)
o Multiple times per day (2)
o Daily (3)
o Once per couple of days (4)
o Weekly (5)
o Monthly (6)
o Less than once per month (7)
How often do you use Instagram to obtain information about a brand's products and services?
o Frequently (1)
o Sometimes (2)
o Rarely (3)
o Never (4)
How often do you use Instagram to like, comment, or contact brands?
o Frequently (1)
o Sometimes (2)
o Rarely (3)
o Never (4)
End of Block: General condition Instagram
Start of Block: Photo stimulus Facebook
[XXII]
3.3 Stimuli
3.3.1 Scenario 1: Facebook captioned image
In this section I would like to imagine yourself in the following situation: "You are logged into your
Facebook account on any device. Scrolling down your news feed, you encounter posts, shares and
activity of your friends. Moreover, you get to see posts and shares of the pages you liked. Regularly
however, these messages are alternated by content of pages you did not like. These are sponsored
posts. At a given moment, one of those posts draws your attention."
Please read the caption and take a close look at the image below.
3.3.2 Scenario 2: Facebook video
In this section I would like to imagine yourself in the following situation: "You are logged into your
Facebook account on any device. Scrolling down your news feed, you encounter posts, shares and
activity of your friends. Moreover, you get to see posts and shares of the pages you liked. Regularly
however, these messages are alternated by content of pages you did not like. These are sponsored
posts. At a given moment, one of those posts draws your attention."
Please take a close look at the video below.
3.3.3 Scenario 3: Instagram captioned image
In this section I would like to imagine yourself in the following situation: "You are logged into your
Instagram account on any device. Scrolling down your home page, you encounter images and videos
of subjects you follow. These subjects consist of persons (friends, family, acquaintances, celebrities,
...) as well as brands. Regularly, you also encounter images and videos of subjects you don't follow.
These are sponsored posts. At a given moment, one of those posts draws your attention."
Please read the caption and take a close look at the image below.
3.3.4 Scenario 4: Instagram video
In this section I would like to imagine yourself in the following situation: "You are logged into your
Instagram account on any device. Scrolling down your home page, you encounter images and videos
of subjects you follow. These subjects consist of persons (friends, family, acquaintances, celebrities,
...) as well as brands. Regularly, you also encounter images and videos of subjects you don't follow.
These are sponsored posts. At a given moment, one of those posts draws your attention."
Please take a close look at the video below.
[XXIII]
3.3.5 Captioned image stimulus
Ins ph "Design, technology and safety. All in one with the New Volvo #XC60. Link in bio.
#MadeBySweden"
Figure 10: Captioned image of a Volvo XC90
3.3.6 Video stimulus
Figure 11: Video of a Volvo XC9
[XXIV]
4. CE measurement
Based on the image/video above, please provide an answer for the following statements:
Definitely not
(1)
Probably not
(2)
I don't
know (3)
Probably will
(4)
Definitely will
(5)
I would consider
buying the products of
Volvo in the near
future. (1) o o o o o
I feel like a purchase
with Volvo would make
me content. (2) o o o o o I feel like I would get
my money’s worth
when purchasing with
Volvo. (3) o o o o o
I feel like owning a
Volvo would make me
happy. (4) o o o o o I would promote Volvo
to my followers
because of possible
monetary referral
benefits provided by
the brand.* (5)
o o o o o
In addition to the value
derived from Volvo,
monetary referral
incentives would
encourage me to refer
Volvo to my followers.*
(6)
o o o o o
I would enjoy referring
Volvo to my friends
and relatives because
of possible monetary
referral incentives.* (7)
o o o o o
Given that I am the
owner of a Volvo, I
would refer my
followers on Instagram
to Volvo because of
possible monetary
referral incentives.* (8)
o o o o o
[XXV]
disclaimer * Volvo may provide monetary referral incentives. This means that if you refer Volvo to
multiple friends, you may be offered discounts or exclusive features on a purchase with Volvo.
I would actively
discuss Volvo on
Instagram. (9) o o o o o I would love talking
about my experience
with Volvo on
Instagram. (10) o o o o o
I would discuss the
benefits that I get from
Volvo with others on
Instagram. (11) o o o o o
I would feel part of
Volvo and mention it in
my conversations on
Instagram. (12) o o o o o
I would provide
feedback about my
experiences with
Volvo through
Instagram. (13)
o o o o o
I would provide
suggestions for
improving the
performance of Volvo
through Instagram.
(14)
o o o o o
I would provide
suggestions/feedbacks
about the new
product/services of
Volvo through
Instagram. (15)
o o o o o
I would provide
feedback/suggestions
for developing new
products/services for
Volvo through
Instagram. (16)
o o o o o
Please select
'probably not'. (17) o o o o o
[XXVI]
5. Socio-demographics
Start of Block: Socio-demographic info
What is your age?
o 18-24 (1)
o 25-31 (2)
o 32-39 (3)
Wat is your gender?
o Man (1)
o Woman (2)
Where do you live? (City/town)
________________________________________________________________
What is your employment status?
o Employed for wages (1)
o Self-employed (2)
o Out of work and looking for work (3)
o Out of work but not currently looking for work (4)
o Homemaker (5)
o Student (6)
o Unable to work (7)
[XXVII]
What is the highest degree or level of school you have completed? If currently enrolled, highest
degree received.
o No schooling completed (1)
o Elementary diploma (2)
o High school diploma (3)
o Bachelor's degree (4)
o Master's degree (5)
o Doctorate degree (6)
What is your marital status?
o Single, never married (1)
o Married or domestic partnership (2)
o Widowed (3)
o Divorced (4)
o Separated (5)
Are you in possession of a driving license?
o Yes (1)
o No (2)
[XXVIII]
How many cars does your household keep?
o 0 (1)
o 1 (2)
o 2 (3)
o 3 (4)
o More than 3 (5)
Display This Question:
If Are you in possession of a driving license? = Yes
How many years do you have your driving license?
________________________________________________________________
Display This Question:
If Are you in possession of a driving license? = Yes
In general terms, do you like driving?
o Yes (1)
o No (2)
o I am indifferent (18)
Display This Question:
If How many cars does your household keep? != 0
Are you the owner of one of the cars in your household?
o Yes (1)
o No (2)
Display This Question:
If Are you the owner of one of the cars in your household? = Yes
[XXIX]
Is the car you own a company car?
o Yes (1)
o No (2)
End of Block: Socio-demographic info
