THE EFFECT OF AFFECTIVE VERSUS COGNITIVE INFORMATION ON PRODUCT
INNOVATION JUDGEMENT
A Thesis
Presented to the Faculty
Of ISM University of Management and Economics
In Partial Fulfillment of the Requirements for the Degree of
Master of International Marketing
by
Justina Unguraitytė
May 2015
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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Abstract
Purpose – the aim of this research is to evaluate the effect of different types of information –
affective versus cognitive - on product innovation’s judgements. Furthermore, the effect of
main moderators (affective vs. cognitive information) was aimed to be investigated through
the product innovation attributes and attitude toward product, which were taken in the
research as the main pridictors.
Theoretical findings – theoretical analysis uncovered that the product innovation
judgment is mostly associated with the cognitive process where customers use information
with the purpose to reduce the uncertainty. Despite this, increasing importance of innovations
in a highly competitive market caused that both scholars and practitioners had engaged in a
long maintained investigations with the purpose of more comprehensive understanding of
process that customers use while judge product innovations. Due to this, it was found that
significant findings of psychologists and neuroscience specialists caused the increasing
interest to investigate the process of product innovation judgment in the perspective of
affective process.
Methodology approach – a quantitative research method an experiment was applied
with the interaction of moderation effect. After the pre-experiment was conducted one
product innovation with two types (affective vs. cognitive) of advertisements were selected
for the main experiment to perform.
Research findings – findings indicated that affective information provided in the way
of advertisement had more positive (as opposed to cognitive) influence on the effect between
product innovation attributes, attitude toward product and overall product innovation
judgment. Keywords – affective information, cognitive information, information behavior,
product innovation judgment, product innovation adoption, affective-based ads., cognitivebased ads., product innovation attributes, attitude toward innovation.
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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Table of contents
Abstract………………………………………………………………………………………..2
Table of contents……………………………………………………………………………....3
List of tables…………………………………………………………………………………...6
List of figures………………………………………………………………………………….7
Abbreviations………………………………………………………………………………….8
Introduction…………………………………………………………………………………....9
Literature review……………………………………………………………………………..13
Key concepts identification………………………………………………………………13
Body of literature………………………………………………………………………...14
Innovation………………………………………………………………………………..14
Defining innovation……………………………………………………………………...14
Classification of innovations…………………………………………………………….16
Development of successful innovation…………………………………………………..18
Innovation judgment……………………………………………………………………..20
Innovation adoption process……………………………………………………………..20
Innovation attributes……………………………………………………………………..25
Affective and cognitive information……………………………………………………..28
Defining information and information behavior…………………………………………28
Affective and cognitive decision making regarding information use……………………30
Interplay of cognitive and affective factors………………………………………………32
Affective and cognitive communication strategies for successful innovation judgment...34
Problem definition………………………………………………………………………..39
Summary of the theorethical conceptualization………………………………………….39
Problem identification……………………………………………………………………42
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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Research methodology………………………………………………………………………44
Research model development and hypothesis formulation……………………………..44
Research method………………………………………………………………………..50
Experiment selection…………………………………………………………………....50
Analytical framework for moderation effect…………………………………………....50
Research process………………………………………………………………………..52
Pre-experiment………………………………………………………………………….53
Design of the randomized manipulation check…………………………………………53
Affective and cognitive information as advertisements………………………………...53
Selection of product innovations and advertisements…………………………………..54
Aim and hypothesis of manipulation check…………………………………………….57
Sample and procedure of manipulation check………………………………………….57
Result of the manipulation check……………………………………………………….58
Experimental design…………………………………………………………………….63
Identification of moderators roles………………………………………………………63
Methodology of experiment…………………………………………………………….64
Techniques for statistical data analysis…………………………………………………64
Procedure of experiment………………………………………………………………..65
Empirical research results…………………………………………………………………..66
Research model representativeness and multivariate data assumptions………………...66
Results of experiment…………………………………………………………………...75
Comparing moderation affect of affective and cognitive information to product
innovation judgment……………………………………………………………………76
Effect of affective information to product innovation judgment (as opposite to cognitive
information) with predictors of product innovation attributes…………………………77
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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Effect of affective information to product innovation judgment (as opposite to cognitive
information) with predictors of attitude toward product…………………………....77
Conclusion of the research results…………………………………………………..78
Discussion…………………………………………………………………………….....80
Synthesis of research findings…………………………………………………….....80
Implications for theoretical understanding of product innovation judgment………..82
Implications for theoretical understanding of affective and cognitive information....83
Implications for innovation management…………………………………………....85
Limitations…………………………………………………………………………...86
Suggestions for future research……………………………………………………...86
Conclusion……………………………………………………………………………….88
References……………………………………………………………………………….90
Appendices……………………………………………………………………………..102
5
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List of tables
Table 1. Classification of innovations…………………………………………………….....17
Table 2. Overview of product innovations attributes analyzed in literature………………....39
Table 3. Overview of the most recent studies supporting the significant role of affective
factors in customers information use and decision making………………………………….41
Table 4. Hypotheses formulation…………………………………………………………….46
Table 5. Product innovations for manipulation checks……………………………………....55
Table 6. Advertisements for manipulation checks…………………………………………...56
Table 7. Abbreviations of advertisements…………………………………………………....56
Table 8. Hypothesis of manipulation check……………………………………………….....57
Table 9. Student’s criteria for testing hypotheses of Innovation1’s’s advertisements……….60
Table 10. Student’s criteria for testing hypotheses of Innovation2 advertisements………….61
Table 11. Student’s criteria for testing hypotheses of Innovation3 advertisements………….61
Table 12. Descriptive statistics of evaluations affective-based and cognitive-based
advertisements…………………………………………………………………………….….63
Table 13. Hypotheses of the simple model…………………………………………………..68
Table 14. Correlations in regards with the simple model…………………………………....69
Table 15. Multivariate data of the simple model…………………………………………….70
Table 16. Hypothesis of interaction terms between product innovation attributes…………..72
Table 17. Multivariate data of the improved model………………………………………….73
Table 18. The effect of affective vs. cognitive information on product innovation judgment..
………………………………………………………………………………………………..76
Table 19. Evaluation of the hypotheses……………………………………………………...79
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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List of figures
7
Figure 1. Main differences of ‘System 2’ and ‘System 1’ suggested by D. Kuhgneman…...37
Figure 2. Direct and indirect effect of Ad-evoked feelings to product or brand attitude…...38
Figure 3. Research model…………………………………………………………………...47
Figure 4. Statistical moderator model applied for the present study..……………………..51
Figure 5. Research process outline……………………………………………………….....53
Figure 6. Product innovation judgment with the simple model…………………………......68
Figure 7. Innovation judgment with the improved model…………………………………..71
Figure 8. Final results of experiment (moderation effect of Ads3affective vs. Ads3cognitive
through product innovation attributes and attitude toward product)………………………..75
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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Abbreviations
IDM
Innovation Diffusion model
TAM
Technology Acceptance theory
DPM
Innovation – Decision Process model
IB
Information behavior
SMH
Somatic Marker Hypothesis
ISP
Information Search process
SBIT
Social – Information Technology model
VS.
Versus
ADS.
Advertisements
8
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Introduction
Challenges that companies are facing in an extremely competitive, politically and
economically uncertain world became one of the main reasons why the extensive spectrum of
scientific studies and practices regarding the importance of the innovations started to grow
dramatically. In respect to this, companies must find the ways to innovate in order to respond
to the changing customer demands, lifestyles or simply for taking into account all
opportunities offered by technologies and the changing market (Rowley, Beregheh,
Sambrook, 2011). Consequently, the market started to be occupied by a variety of radically
new products that suggest for the customers to believe in the prominence of the endless range
of new human’s needs which are sometime artificially created by marketers. This has
particularly caused the situation where numbers of marketing discussions evolved with the
aim to understand the processes that consumers experience while trying to evaluate
innovations.
Much of the existing studies support the approach that innovation adoption is directly
related to the uncertainty reduction process (Davis, 1989; Flight, D’Souza & Allaway, 2011;
Art, Frambach & Bijmolt, 2011). It is clear from the previous researchers, that customers
tend to analyze risks and benefits (Saaksjarvi & Morel, 2010) while gathering and
considering information with the purpose of making the evaluation decision of the innovation
(Roger, 2003). In addition, even feelings are perceived as having a significant influence on
human’s decision making, although many previous researchers maintained their central focus
on the analytical process in the innovation adoption (Wood & Moreau, 2006). Subsequently,
in such a global knowledge and technological economy innovation started to be seen as key
success factor for creating a strong competitive advantages (Johanessen & Skaalsvik, 2015;
Ettlie, Groves, Vance & Hess, 2014; Inauen & Schenker-Wicki, 2012; Hassanien&Dale,
2012; Gremyr, Lofbero, Witell, 2010; Lidgren, Saghaug, Knudsen, 2009; Salavou, 2004;
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Johnson, 2001; Johannessen, Olsen, Lumpkin, 2001; Evangelista, McKinnon, Sweeney
2013). Due to this, the need for comprehensive exploration of processes in which customers
engage in order to assess innovation is defined as one of the highest priorities of recent
scholars.
Needs to be mentioned, that growing academic interest in understanding the effect of
both affective and cognitive factors in the innovation adoption process is the meaningful
contribution not only to the innovation adoption literatures but also for a totally new sphere
of neuromarketing, because clues of psychology and neuroscience in the mentioned topic is
recognized in numbers of studies (Kings & Slovic, 2014). Meanwhile, from the practical
perspective it helps for a professional marketing specialist to understand what makes the
biggest influence to the customer’s decisions to adopt or not to adopt the innovation.
Accordingly, such information might play a fundamental role for preparing marketing
strategy for future innovations.
This research investigates the effect of affective and cognitive information on the
product innovation judgment. Currently, one side of the researchers explains innovation
adoption as a bottom-up cognitive process, other side tries to find alternatives while building
their approach on more affective factors. However both sides support classical models such
as Roger’s innovation diffusion theory (2003), theory of planned behavior (Ajzen, 1991), the
technology acceptance model (Davis, 1989) or theory of reasoned action (Fishbein & Ajzen,
1975; Sheppard, Hartwick & Warshaw, 1988) who claim that during the adoption process
customers gather and consider information in order to make adoption decision. In respect to
this, recently little attempts were conducted with the aim to explore how information
provided in different types – affective versus cognitive – effects the overall product
innovation judgments. Partly, the present study will fill this gap while continuing the work
started by one of the most recent studies implemented by Kings and Slovic (2014); there
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central focus was to explore affect heuristic in product innovation judgment, but no
manipulations with real product communication to the overall evaluation of the product
innovation were done.
The underlying research question is: what is the effect of cognitive versus affective
information on product innovation judgment? Findings of the study contribute for both
theoretical understanding of consumer’s evaluations of product innovations as well as
affective and cognitive information. Obviously, it provides for marketers useful practical
findings which might help to prepare effective communication strategy for the certain
innovation.
Hence, the aim of the research is to explore the effect of different types of
information - affective versus cognitive - on product innovation’s judgements. This aim leads
to the objectives provided below:
1. To provide comprehensive literature review on the theoretical grounding of the
innovation judgment and affective and cognitive factors in terms of information use.
2. To design and conduct empirical research in order to examine how information
provided in different style (affective versus cognitive) effects the judgment of product
innovation.
3. To explore the results of empirical research providing statistical analyzes of the date
received.
4. To discuss managerial implications of the research findings and provide the
guidelines for the future research and practical recommendations for marketers.
The experimental research going to be adopted to the study. The method enables the
researcher to control and manipulate independent variables that effect experimental group
which is not possible by using survey or past data (Soo & Oo, 2014). The latter is extremely
important in exploration of the specificities of customer’s behavior in terms of their reactions
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to technological product innovations. Moreover, design of experiments is defined as powerful
tool for generating valid, supportable findings for improving various of process (Firka 2011).
Following this, the method really can help to reach useful insights for marketers that are
engaged in the creation process of innovations.
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Literature Review
Key Concepts Identification
The chapter of the literature review is based on numbers of objectives that need to be
achieved. Working definitions of the key concepts ‘innovation judgement” and “affective
and cognitive information” are going to be identified through taking into account varieties of
definitions and conceptualizations in the relevant prior and most recent literature. Need to
empathize that significantly increased attention of academics to the mentioned concepts
caused the extensive line of approaches that require to be specified for the context of
affective and cognitive information impact to the product innovation judgment. In particular,
for the purpose to answer the research question different types of literature such as research
studies, theoretical articles and editions, reviews or case studies are going to be taken into
considerations.
Consequently, the first chapter consists of four main subsections with the aim to
ensure the consistent way to arrive to a comprehensive understanding of key concepts.
Accordingly, the purpose of the innovation section is to clearly define the concept
“innovation” in order to ensure the comprahensive discussion in the second section where
analysis will be continued with the concept “innovation judgment” . Analysis will be
conducted through a review of the literature, which discusses topics from the innovation
definitions itself to the overall innovation adoption process. While the third section is
dedicated for the concept “affective and cognitive information” to delineate through
sequential analyzes regarding widespread approaches of customer information behavior to
affective and cognitive constructs. The section closes with conclusions of theoretical
conceptualizations, which leads to the main problem identification that is tend to be solved
for the purpose of this research.
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Body of Literature
Innovation. Defining innovation. Challenges that companies are facing in such
extremely competitive, politically and economically uncertain world became one of the main
reasons why the amount of scientific studies regarding various types of innovations started to
grow dramatically. Companies must find the ways to innovate in response to changing
customer demand and lifestyles, or simply for turning to account all opportunities offered by
technologies, changing market places, structures and dynamics (Rowley, Beregheh,
Sambrook, 2011). Consequently, in such a global knowledge and technological economy
innovation started to be seen as key success factor because of its ability to create a strong
competitive advantages (Johanessen & Skaalsvik, 2015; Ettlie, Groves, Vance & Hess, 2014;
Inauen & Schenker-Wicki, 2012; Hassanien & Dale, 2012; Gremyr, Lofbero, Witell, 2010;
Lidgren, Saghaug, Knudsen, 2009; Salavou, 2004; Johnson, 2001; Johannessen, Olsen &
Lumpkin, 2001; Evangelista, McKinnon, Sweeney, 2013). Due to the widespread recognition
of the importance of innovation, numerous of researchers started to provide various
approaches regarding definitions of innovation and its types. Following early literature
(Roger, 2003) innovation can be described as “an idea, practice, or object that is perceived as
new by an individual or other unit of adoption”, should be underlined that this definition is
strongly supported by numerous of current researchers too (King & Slovic, 2014; Flight,
D’Souza, Allaway, 2011; Saaksjarvi, 2003). The same time Doran (2012) presents innovation
as “an iterative process initiated by the perception of a new market and/or new service
opportunity for a technological-based invention which leads to the development, production
and marketing tasks striving for the commercial success of the invention”. From this
perspective, innovation process can be considered as iterative and with different levels of
innovativeness (Garcia & Calantone, 2002). In this respect, the first introduction of
innovation is not the same as reintroduction of an improved one (Doran, 2012). Several
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authors provide one more approach and define innovation from two perspectives “as a
transversal organizational process, or as an outcome embodied in the final offer and referred
to as product innovation” (Fort-Rioche & Ackerman, 2013). In a relationship to this, the
distinction between product innovativeness from organizations’ point of view and from the
market perspectives must be identified (Fort-Rioche & Ackerman, 2013; McNally, Cavusgil,
Calantone, 2010; Calantone, Chan, Cui, 2006). Even more concepts of innovations could be
provided, however such a diversity of approaches creates ambiguity and confusions, while
consensual definition would help to avoid numerous of miscommunications in both academic
and business fields (McAdam, Raid & Gibson (2004); Rowley et al., 2011).
Need to be empathized that most prevailing definitions of innovation more or less are
focused on the newness and novelty. Hence one more shortcoming such as a lack of
accordant measures of innovation’s newness can arise (Johannessen et al., 2001; Blyth,
1999). Accordingly, following both early and the most receant literature the concept of
newness can be explained by individuals’ reaction to a new idea (Roger, 1983; Hristov &
Reynolds, 2015). the mentioned reaction directly correlates with five perceived attributes
(relative advantage, compatibility, complexity, trialability, and observability) of
innovativeness by which customers supposedly judge innovation during the decision-making
process (Roger, 1962). Whereas more later studies suggest that newness consists of several
components that are addressed by three sub-questions: what is new, how new, and new for
whom? (Johannessen et al., 2001). In this respect, innovation should be seen as a single
continuum, which includes all three mentioned aspects. Although the approach more
exceptionally empathizes the importance of economic unit, that has the possibility to evaluate
the level of innovations’ newness regarding scope and size (new for whom). While another
scholar (Salavau, 2004) provides even more distinct perception regarding measurement of
innovation and assumes newness as a subjectively defined, incorporated element of
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innovation rather than some kind of measurement. Subsequently, the term innovativeness is
conceived as a key negotiable expression and not necessarily is used interchangeably with
innovation, but it also can be distinguished from the other and refer to some kind of measure
(Salavau, 2004). In correlation with this, innovativeness is the one, which indicates the level
of newness of the innovation and should be measured taking into account different types of
innovations (Salavau, 2004; Doran, 2012; Garcia & Calantone, 2002; Herrman, Tomczak,
Befurt, 2006). This has particularly been the case for the different types of innovation to be
identified by extensive spectrum of literature.
Classification of innovations. Nearly all comprehensive reviews agree that two types
of innovation could be distinguished: “radical” or “incremental” (Hristov & Reynolds, 2015;
Szekely & Strebel, 2013; Doran, 2012; Herrman et al., 2006; Dewar & Dutton, 1986; Inauen
& Schenker-Wicki, 2011; Bigliardi, Dormio, Galati, 2012). Should be mentioned that such
classification is applied for both products or services innovation (Inauen & Schenker-Wicki,
2012). The present study will contribute to the broader scientific understanding regarding
product innovation, due to this all further analysis is going to be provided from the product
rather than service point of view. To this extent, radical product innovation refers highly new
experiences to the customers and new technological solutions to the companies (Herrman et
al., 2006; Aboulnasr, Narasimhan, Blair & Chandy, 2008). When radical or otherwise
discontinuous innovation occurs, significant level of changes appears in the whole industry
(Rowley et al., 2011). Subsequently, such kind of innovation has a conspicuous influence for
both sides: producers are facing the possibility of new sources of competitive advantage
while for customers it brings major changes in their everyday lives regarding economic and
social aspects (Heiskanen et al., 2007). Meanwhile, the incremental innovation takes into
account minimal improvements regarding technologies and changes in product value for the
customers (Herrman et al., 2006). On the basis of one of the latest research paper (Ponnam,
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Sreejesh, Balaji, 2015) even 90% of all new products can be identified as incremental and
usually come from the conservative strategies that are focused on small changes in such
aspects as product features, price, design, main functions etc.
On the other hand, radical and incremental types of innovations are typical extremes
that lead to even more deeper debates in terms of understanding what is beyond them
(Boregheh et al., 2011). In this respect, one of the relatively recent models suggested by
Francis and Bassant (2005) distinguishes four categories for variant types of product (can be
applied for services too) innovations: product, process, position and paradigm (Table 1).
Table 1. Classification of innovations
Product innovations
Process innovation
Position innovation
Paradigm innovation
Changes in the
Changes in the way
Changes in the
Changes in the
product/service
product/service is
context in which
business model of
features, elements.
developed and
product/service are
organization
delivered.
presented to the
regarding
customers.
product/service
provided.
Note. From ‘Targeting innovation and implications for capability development,’ by Francis
D. and Bessant J., 2005, 25(3), p.171 – 83.
The most unique point of such classification is the basis on which innovation types
are assorted. Instead of central focus on the extent of changes (level of changes made into
the industry) regarding tangibility and visibility of innovation, the concept encompasses all
types of innovations used within companies (Baregheh, Rowley, Sambrook & Davies, 2012).
In addition, this classification is the only one, which includes such unconventional types of
innovation as position and paradigm that can lead to major transformations in the market
(Baregheh et al, 2012; Rowley et al, 2011).
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For instance, position innovation can cause even the emergence of new market
(Francis & Bessant, 2005), because it is directly related to the modification of the
understanding about products while trying to reach another market or new customers’ group
(Rowley et al., 2011). In the same time, paradigm, which has some parallel with radical
innovation, reframes the way the product is perceived (e.g. “Low – cost” airlines) in the
market and means significant changes in the industry (Rowley et al., 2011).
Development of successful innovation. It is no wonder that such widespread
recognition of the importance of innovation by both academics and practitioners has
particularly led to rapidly growing body of literature analyzing what kind of innovation has
the greatest potential to create a sustainable competitive advantage. Numerous of examples in
the market have shown that even the first mover position that was done by huge corporations
such as IBM and its personal computer, NOKIA with their 7650 smartphone and numbers of
others, does not guarantee the success of innovation (Basu, 2014). In addition, due to the
approach of several authors (Kekäle & Kola-Nyström, 2007) the innovativeness and success
leads to the major paradox in business strategy, because the higher level of products’ novelty
the higher possibility to fail instead of sustain long-term growth of the company.
Consequently, extensive spectrum of approaches in terms of different ways to
innovate started to be analyzed. Recently open innovation could be noted as one of the most
discussed concepts on this topic and is conceived as an opposite model of the vertical
integration (Duarte & Sarkar, 2011; Bellantuano, Pontrandolfo & Scozzi, 2013; Scott &
Chaston, 2013; Inauen & Schenker-Wicki, 2012; Schroll & Mild, 2011).
While vertical integration model perceives R&D division as a strategicall core in the
creation process of innovation (Chesbrough & Appleyard, 2007; Duarte & Sarkar, 2011), the
open innovation is in a correlation with external collaboration (Duarte & Sarkar, 2011). After
having analyzed the impact of the open innovation method on innovation performance,
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Inauen and Schenker-Wicki (2011) argued that vertical or otherwise called “closed”
innovation has reached its limits, because of the rapid increases of tradeability in
technologies and intellectual property. This goes hand in hand with premeditated exploration
and exploitation of numbers of sources for innovation comes into being (West & Gallagher,
2006). Conceptually, open innovation in contrast with the vertical one, is seen as a an open
system with various ways to use the collaboration of external and internal knowledge
together, instead of being closed only with the capabilities of R&D division (Duarte &
Sarkar, 2011; Jarnepaa & Warnick, 2011). Hence, open system is built on the possibility to
apply expertise and technology competencies that are not available inside the company which
is directly related to both reduced cost of innovation and sharing the risks (Ballantuano et al.,
2013; Jarnepaa & Warnick, 2011; Bigliardi et al., 2012). Meanwhile, Kutvonen (2011)
proposes to distinguish even six groups of strategic objectives that companies are tend to
target for achieving long-term benefits through external collaboration: gaining new
knowledge, learning from knowledge transfer, multiplication of own technologies,
controlling technological trajectories, external exploitation as a core business model and
higher control power over the market environment.
While trying to make it clear what is the main purpose of open innovation, the
research study conducted by Scott and Chaston (2013) could be taken into account. These
researchers analyzed the performance of companies in Peru in terms of the engagement in the
open system. Statistically significant relationship between companies’ results and its
involvement in external collaboration were noted. Accordingly, it is reasonable to claim that
in the twenty-first century open innovation has become one of the most effective ways to
achieve sustaining business performance (Chesbrough, 2007).
In addition, the mentioned study confirmed the statement of Lichtenthaler and
Lichtenthaler (2009) who stated that the prime benefit of using open system considers to be
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the possibility to enhance companies’ new product development activities. In this line of
thought, firms that are operating in developed economies such as Perus’, are seeking to
enhance their performances not only by new products, but also by improving all other areas
that are related to their elaborations (Scott & Chaston, 2013).
However, collaboration with partners, customers, suppliers, competitors and all other
possible external knowledge and technological sources is still perceived as sufficiently risky
(Gillier, Kazakci & Piat, 2012). It is so, because partners should be both: different enough in
order to provide effective contributions for the mutual purpose from both sides and still have
something in common for being able to understand each other.
Innovation judgment. Innovation adoption process. Extensive literature reviews
and studies were conducted with the central focuses on the innovation adoption process
(Plewa, Troshani, Francis & Rampersad, 2012; Sääksjärvi & Morel 2010; Azadegan & Teich,
2010; Flight et al., 2011; Cui, Bao, Chan, 2009; King & Slovic, 2014; Seligman 2006;
Hirunyawipada & Paswan, 2006). There is no doubt, that understanding the overall process
of how individuals evaluate innovations’ attributes and what are the main factors influencing
their decisions, significantly contributes to the companies success while commercializing
different types of innovations (Haggman, 2009; McCoy, Badinelli, Koebel & Thabet, 2010).
Despite the rapidly growing body of recent studies, early models such as Rogers’s (
2003) innovation diffusion (IDM) or Davis’s (1989) technology acceptance theory (TAM)
still reported as being a starting point for nowadays studies (Sheng, Zolfagharian, 2014;
Sheikhshoaei & Oloumi, 2011; Elwood, Changchit, Cutshall, 2006; Kuo & Lee 2009).
Nevertheless, much of the recent discussions still appear to be based on trying to identify the
possible limitations of the prior theories and uncovering new important insights in order to
enhance the perception of innovation adoption process.
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For instance, Seligman (2006) states the IDM as a linear, decision and consequences
focused progression, rather than the method that would enable to understand the adopter’
mental process. On the contrary, TAM together with one more early theory of Planed
Behavior (Ajzen, 1991), is presented by the scholar, as being more behavioral focused. This
assumption comes from the main purpose of the TAM which is to analyze the effect of
external variables on internal customers’ attitudes, beliefs and intensions (Elwood et al.,
2006, Cui, Bao, Chan 2009; Kuo & Lee 2009). In regard to TAM, two aspects -perceived
usefulness and perceived ease of use – are assumed as the core while explaining customer’s
attitude and intensions toward innovation. Despite the fact, that the theory was supported by
extensive spectrum of studies, some limitations still appears to be highlighted. For instance,
due to some other researchers Elliot and Loebbecke (2000), one of the main shortcoming of
the TAM is the fact that theory does not include such aspects as social, institutional and
personal as having an influence to the adoption process. Consequently, some researchers
have approached the misgivings regarding individual acceptance needs, which is
underestimating while applying TAM (Plewa et al., 2012). Following this approach, IDM can
be assumed as being even more concentrated on the individual’s perception because of the
possibility to evaluate customer’s standpoints toward relative advantage, trialability,
observability, complexity and compatability of innovation significantly affect its adoption
(Roger, 2003). Taking this into account, recently extensive line of literature reviews appears
to be focused on empathizing the importance of deeper understanding of individuals’ mental
process while adapting innovations (Kings & Slovic, 2014; Seligman, 2006; Pereira, 2002).
The vast majority of recent researchers agree on the Rogers’ “Innovation – Decision
Process’’ model (IDPM) (Seligman, 2006)which implies the progression of activities that
occur during the innovation adoption process (Seligman, 2006). The model consists of five
stages. Accordingly, Rogers (2003) claims, that firstly customers are concentrated on the
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
PRODUCT INNOVATION JUDGMENT
22
evaluations of the knowledge they gain about the innovation. To this extent, innovation
attributes, for instance, advantage, compatibility, and complexity are started to be of the
interest to individual (Azadegan & Teich, 2010). The second stage considers the persuasion
level, which goes hand in hand with individuals concerns to receive even more information in
terms of innovation. Conceptually, the third stage is dedicated for making a decision. In
respect to this, individual makes a judgment of innovation regarding its advantages or
disadvantages, evaluates possible consequences of acceptance or rejection of the presented
innovation. The fourth stage, is called implementation and implies individual’s engagement
to the innovation which involves adopter’s efforts to learn more about the usefulness of a new
item. Finally, the stage confirmation is build upon the decision finalization while seeking to
be warranted about decision already made.
However such five stages model of innovation started to be criticized because of its
linear approach to customer decision-making. For instance, Pereira (2002) argue that
innovation adoption process cannot be modeled on decision-focused progression through
certain stages because it makes a potential adopter as a black box. On the contrary to this,
Pereira (2002) suggest to investigate innovation adoption process through a sensemeking
model which is defined as a cyclical process of extracting information about innovation from
stimuli. Compering the mentioned model with Roger’s five stages, sensemakeing one is
based on the evolution of the adopter’s mental framework rather than explaining innovation
adoption through series of activities through which the potential adopter goes during the
innovation-decision process. In particular, Roger’s innovation-decision process shows the
progress of activities during the overall process while sensemkaing model clarify the mental
mechanics at each stage. For better understanding, the example of the first knowledge stage
can be taken. Sesnmakeing model claims, that potential adopter gain knowledge about
innovation from stimuli, which is a result of the environment surrounding an individual.
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
PRODUCT INNOVATION JUDGMENT
23
Taking this into account potential adopter use his/her social contacts or other elements of the
environment in order to gain understanding if innovation is the way to meet his/her needs. On
the other hand, such attempts to explain innovation adoption process require deeper
understanding of customer behavior from psychology point of view. However, the
alternative model still agrees on the fact, that information use is unavoidable in order to make
a judgment of innovation.
According to the fact that the main purpose of this study is to explore the effect of
cognitive versus affective information types on innovation judgment, deeper analysis of prior
studies regarding the role of information in the innovation adoption process is essential. How
it was mentioned above, following the Rogers’s (2003) series of adoption stages, customers
gather and consider information in order to make analytical judgment of the risks and
benefits of innovation. To this extent, the adoption process can be perceived as “a future
event with uncertain outcomes’’ (Sääksjärvi & Morel 2010) where risk and uncertainty
considered as being the main barriers for favorable adoption decisions (Hoeffler, 2003; King
& Slovic, 2014). Subsequently, due to the information collected customers increase
understanding of an innovation, which goes hand in hand with the evaluation of its benefits
(Hoeffler, 2003). This has particularly been the case, why extensive spectrum of research
correlates adoption process to the reduction of uncertainty through a bottom-up cognitive
operations in which customers consider information available (King & Slovic, 2014).
Accordingly, Kuo & Lee, 2009) has successfully applied TAM in order to point out the
relationship between information and perceived ease of use. Based on the study conducted,
two crucial points such as need-centric and task-centric should be taken into considerations in
order to ensure the effectiveness of information. Conceptually, need-centric reports the
significant importance to be focus on providing information in as much comprehensible way
as it is possible for the purpose to enhance the perception of usefulness. Whilst, task-centric
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
PRODUCT INNOVATION JUDGMENT
24
factor is suppose to be for targeting the certain group of customers. The importance of the
need-centric orientation can be supported by the approach, which is based on the fact, that
information enable potential adopter to re-assess and mentally overcome risks associated with
the innovation (Flight et al., 2011).
From this perspective, the hypothesis that information is negatively related to risk and
complexity supposed to be confirmed. However, the study conducted by Flight et al., (2011)
pointed that the negative coefficient certainly exists, but it is not strong enough to affirm the
significant linkage. Considerably, substantial amount of questions regarding the role of
information in terms of innovation attributes in the adoption process still require deeper
analyzes. Taking this into account, despite the fact that much researcher has been carried out
for conceptualizing the innovation adoption decision as a bottom-up cognitive process (Wood
& Moreau, 2006), growing body of the recent studies started to suggest an alternative
explanation for early judgments of product innovations (King & Slovic, 2014). According to
the study of the Pham et al. (2001) the affective innovation judgments can be perceived as
being more faster, more consistence across individuals and even more predictive in terms of
targeted customer’s minds than cognitive-based evaluation. Due to this, some authors argue
that the role of affective reactions assume to be as a dominant in the process of product
innovation judgments (King & Slovic, 2014; Baumeister, Vohs, DeWall &Zhang, 2007;
Lowenstein, Weber, Hsee & Welch, 2001). As a result, the present research considers the
different influence of the affect-based and cognitive-based information toward innovation
product judgments.
Innovation attributes. Many studies have been conducted in order to understand the
main aspects of the innovation adoption process that can provide valuable support for the
companies commercializing their innovations. It can generally be said that the majority of the
most recent studies are still based on the theories of Ewerett M. Rogers that first were
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
PRODUCT INNOVATION JUDGMENT
25
recognized in 1962 and still hold the highest position in terms of innovation topics (Kapoor,
Dwivedi, Williams 2014). As the inventor of diffusion of innovation theory, scholar claimed
that diffusion is the process by which innovation is communicated through various channels
to members of the social system (Roger, 2003). Accordingly, Rogers identified five main
attributes of innovation – relative advantage, compatibility, complexity, trialability,
observability - that have been found as having significant influence on the rate of adoption.
For the purpose of making deeper analysis regarding the relationship of innovation
characteristics and its adoption process, an enormous volume of studies was conducted on the
basis of Roger’s five attributes (Adams, Transfield, Denyer, 2013; Kapoor et al., 2014).
To this extent, Tornatzky and Klein (1982) after performing review and meta-analysis
of seventy-five articles have distinguished three attributes – compatibility, relative
advantages and complexity – as having statistically significant relationship with the
innovation adoption. Nevertheless, the researchers have done considerably more work, while
adding even 25 supplementary characteristics of innovation to the five attributes of Rogers.
However, it should be empathized that this already thirty – three years old study primarily
was implemented not for adding new attributes. The main purpose was to explore the
relationship of innovation characteristics with both: innovation – adoption development and
participative decision – making (Tornatzky and Klein, 1982).
However, taking into account that the study of Tornatzky and Klein was conducted
almost thirty-three years ago, it is no wonder that until these days numbers of the revisions of
innovation attributes were done by many researchers. Consequently, in 1991 Moore and
Beinbasat have successfully performed one more study in this area regarding attributes from
both Rogers and Tornatzky and Klein lists. As a result, researchers have identified three new
characteristics – image, voluntariness and result demonstrability – as needed to be
incorporated while analyzing innovation adoption process. However, in 1997 Agarwal and
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
PRODUCT INNOVATION JUDGMENT
26
Prasad have presented interesting findings that in some cases can be perceived as contrary to
the approach of Moore and Beinbasat. Scholars argue that attributes identified by Moore and
Beinbasat cannot be introduced as significant predictors of innovation acceptance. In this
respect, Agarwal and Prasad have noticed that the effect of different attributes to the
innovation adoption depends on two dependent variables: current and future usage. Due to
this, such attributes as compatibility, visibility, trialability and voluntariness were perceived
as having the biggest influence to the current use. Meanwhile, with regard to the future use,
result demonstrability was recognized as working in concert with concept of relative
advantage and has statistically significant impact on the future use intensions. Following this
approach, to communicate the tangibility of innovations’ benefits becomes extremely
important for the organizations’ performance.
Based on the growing literature on innovations importance to the overall company’s
success in the twenty-first century, researchers have recently questioned the need for
exploring innovations attributes and their impact to the consumer’s decision to adopt
innovations in nowadays perspective. One of the most resent studies regarding the
innovations’ attributes was conducted by Kapoor, Dwivedi and Williams (2014). The
primarily purpose of the study was to identify the main changes and possible progress in use
of innovation attributes in the last 15 years (attributes that were shortlisted since 1996 until
mid of 2011). To this extent, scholars decided to explore all twenty-eight attributes that were
identified by prior researchers Tornatzki and Kleen (1982) as well as More and Beinbasat
(1991). However, the research has been proceeded with the list of eight attributes which had
been developed using the results of meta-analysis that were based on 223 publications and
the elimination of all attributes that were discussed in less than ten studies. The results of the
study were concluded with the fact that the most frequently mentioned attribute was noted to
be the ease of operations. Following the study, from the customers perspectives, ease of
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
PRODUCT INNOVATION JUDGMENT
27
operation is associated with the minimal need of physical and mental efforts to be used in
order to make a particular innovation to operate. In addition, it was found that ease of
operation consider to be assumed as an opposite to the complexity attribute and had the
influence on the perception of usefulness. On the other hand, even the ease of operation in
numbers of studies was identified as having a significant influence on the innovation
adoption process, but the mentioned attribute not necessarily plays a substantial role in this
matter. For instance, it was revealed that innovations of e-government services do not include
ease of operations to the list of attributes as having essential impact on the innovation
adoption. Continuing with other discussed attributes - visibility, social approval,
demonstrability, and image – it can be concluded that the latter are directly related with the
positive impact on adoption. The same time riskiness and voluntariness can cause both
effects: positive and negative. It should be empathized, the cost was linked only with the
negative impact on innovation adoption.
One of the most recent studies was conducted by King and Slovic (2014). Researchers
have recently questioned the need to explore the affect heuristic in early judgments of
product innovations. Consequently, in order to reach the study’s primarily purpose, which
was mentioned above, scholars decided to distinguished two attributes – riskiness and
benefits – as having a significant influence on the innovation adoption process. The central
focus on these certain attributes was based on the approach provided by prior study (Arts,
Frambach, Bijmolt, 2011), which generalized consumer innovation adoption process, by
providing deep meta-analysis regarding widespread models in terms of innovation adoptions
(e.g. Rogers’ (2003) innovation diffusion model, Davis’ (1989) the technology acceptance
model etc.). As a result, it was come to agreement that innovation adoption process is
directly related with the process of uncertainty reduction. Accordingly, in regards with
innovation attributes customers are tend to assess both benefits and risk they are going to
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PRODUCT INNOVATION JUDGMENT
28
encounter while adapting innovation. King and Slovic (2014) empathize that the risk can be
perceived as a “salient”attribute, which has a significant influence while adapting
innovations. It is so, because from the customers’ perspectives the benefit of innovation due
to its newness is considered as being uncertain or simply unproven. Subsequently, the risk
attribute is noted as being one of the most ‘salient’ attributes, which arise from the factor of
uncertainty and can significantly influence the innovation adoption process. In addition, it
must be mentioned, that scholars (King and Slovic, 2014) have found, that the assessments of
attributes – riskiness and benefit – are interrelated and can be directly effected by both
cognitive and affective decision making.
Affective and cognitive information. Defining information and information
behavior. It is no wonder, that once the decision to launch an innovation is made, the second
stage of new product development is the combination of production and marketing activities
(Garrido-Rubio & Polo-Redondo, 2005). The current study will contribute to the marketing
part which is noted as being one of the most substantial and expensive determinants of
innovation success (Avlonitis & Papastathopoulou, 2000). It is fact, that marketing involves
product communication activities that play extremely important role in innovation adoption
process while the target audience is trying to gather information in order to make a decision
(Roger, 2003). Consequently, to investigate the relationship between information provided
during the product communication and decisions made by customers is significantly
important for commercializing innovations.
Extensive spectrum of studies has been carried out in the field of human behavior
regarding information (Burford & Park 2014, Robson & Robinson, 2013; Hepworth, 2007).
As a result, the term ‘’information behavior’’ (IB) evolved and started to be widely analyzed
by researchers with the aim to predict how human is tend to approach and handle
information (Davenport, 1997). The scholars provided various suggestions for the purpose to
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PRODUCT INNOVATION JUDGMENT
29
define IB as many precise as it is possible (Mutshewa, 2007), while this study is based on the
explanation introduced by Wilson (2000):
Information Behavior is the totality of human behavior in relation to sources and
channels of information, including both active and passive information seeking, and
information use. Thus, it includes face- to-face communication with others, as well as
the passive reception of information as in, for example, watching TV advertisements,
without any intention to act on the information given (p.49).
In addition, for the purpose to be even more precise in accordance with the aims and
objectives of the study the term “information” here will take the form of “message”
(Shenton, 2004) to the customers about product innovation. Following the approach of
Wilson (2000) the central focus on the present study is going to be on information use and
potentially passive reception to it. It is so because, according to the fact that the product
innovation should be new for the customers (Johannessen et al., 2001), an assumption can be
made, that target audience simply had no idea that the product exist before the advertisement
has reached them. From this perspective, customers are tend to gather and consider
information very carefully, because of the high uncertainty level in terms of possible risks
and not confirmed benefits of the innovation (Kings & Slovic, 2014; Huy, Svein & Olsen,
2012; Saaksjarvi et al., 2010). Subsequently, any insights about the process how individual
move him or her to evaluate one product better than other assume to be a significant
contribution to the marketer’s success (Werth & Foerster, 2007). Despite the fact, that the
abundant of the literature investigating human information behavior regarding innovation
adoption decision process place cognitive approach as a dominant (Wood & Moreau, 2006),
the recent studies have been started to actively empathize the significant role of affective
factors in customer’s decision making (Savolainen, 2015; Vinhas, Silva, Faridah &Alwi,
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PRODUCT INNOVATION JUDGMENT
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2006; Werth & Foerster, 2007; Kings & Slovic, 2014; Mazaheri, Richard & Laroche, 2012).
In line with Werth & Foerster (2007) the comprehensive evaluation of customers behavior
cannot assume cognitive as a dominant one and affective factors need to be taken into more
accountable considerations because of its ability to influence consumer behavior significantly
in an unconscious way. In accord to the information behavior, one of the prior study (Clore,
Schwarz & Conway, 1994) has identified the correlation between both negative and positive
emotions impact to the different customer’s information – processing strategies. This
correlation was supported by Savolainen (2015) who following the approach of Nahl (2007)
once again revealed the energizing role of the affective factors in respect to cognitive one.
Consistent with Nahl (2007) it was empirically proved that successful information behavior
depends on regulation of negative and positive affective forces on individuals in information
use. Taking into account the present research considers how customers form product
innovation judgments using information provided in both cognitive and affective styles.
Affective and cognitive decision making regarding information use. Owning to the
fact, that nowadays one of the most fascinating discussions surrounding scholars is the role of
emotions and rationality in customers’ decision- making (Hess & Bacigalupo, 2011). It is no
wonder that numbers of ways to present the main idea of cognitive and affective factors were
identified. However, first of all, in order to clarify the affective and cognitive factors from
terminological point of view needs to be empathized, that due to psychology both factors are
assigned to the components of mind (Ranganathan, Madupu, Sen & Brooks, 2013).
Consequently, it can generally be said that both of these components is directly related with
information use, which can be perceived as a raw material for the individual’s mind, (Kaye
D., 1995) and is recognized as playing important role in the customers innovation adoption
process (Roger, 2003; Kuo & Lee, 2009; Flight, D’Souza & Allaway, 2011). Based on the
approach of Youn (2000), both cognitive and affective factors influence human’s decision
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PRODUCT INNOVATION JUDGMENT
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making by taking into account different elements. Subsequently, affect involves feelings,
emotions and moods, the same time cognition refers rational thinking, understanding and
interpreting information (Youn, 2000). In the context of process, due to Huitt and Chain
(2005), cognition can be conceived as “ the process of coming to know and understand; the
process of encoding, storing, processing, and retrieving information”, while the affective
process is based on “the emotional interpretation of perceptions, information, or knowledge”.
Due to the definitions provided it can generally be said, that cognition answer to the question
of “what is the meaning of the information provided?”, meanwhile affective can be defines
by the question “ How human feels about this information?”.
Characterizing the cognitive factor in the decision making perspectives, it can be
noted that it is in a strong correlation with rationalism and rational decision making
(McGrath, 2006) because it is directly related with perception, reasoning and judgments
(Kim, Chan & Chan, 2007). Whereas affective factor serves individual’s subconscious level
of mind and works as the internal motivator to appeal the things that provides us positive
associations and good feelings (Williamson, 2002). In this respect, emotion considered to be
the core element in affective perspectives (Law, Wong & Yip, 2012) and plays important role
in customer’s information use (Kings & Slovic, 2014) . It is so, because individuals are more
focus and more often recall the information, which is consistent with one’s mood state
(Mattila & Wirtz, 200). As a result, can be claimed that feelings are able to provide customers
with a significant source of information for evaluative judgments of an innovation attributes
by creating positive or negative feelings (King & Slovic, 2014). For instance, specific risk of
an innovation can be unnoted by customers, because of the positive feelings created by
compelling communication type of an innovation with positive emotional cues such as
uplifting music or happy people. The assertion, that feelings provide valuable information,
which is used by costumers in order to facilitate their decisions, is supported by somatic
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PRODUCT INNOVATION JUDGMENT
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marker hypothesis (SMH) (Damasio, 1996). SMH fundamentally is based on the notion that “
bioregulatory signals, including those that constitute feeling and emotion, provide the
principal guide for decisions” (Bachara, Damasio H. & Damasio A.R., 2000). SMH holds
that positive and negative feelings associated, for instance, with information provided have a
direct impact to the extent to which customers prefer one option to another (Kings & Slovic,
2014).
Taking everything into account, numbers of scholars have assumed cognitive and
affective factors as being even as an antagonist (Armony & Vuilleumier, 2013) that are
fighting for the main role in the customer’s decision making. Despite this, some researchers
have approached the question to analyze the interplay between these to components of
human’s mind and even started to support the assumption, that emotion itself can be
perceived as being rational, because it is able to set the goals of actions (De Sousa, 2009).
Consequently, scholars have recently questioned the need to investigate the relationship
between cognitive and affective factors in terms of information seeking and use for the
purpose to elaborate what the role each of the factor is playing in customers behavior
(Savoleinen, 2015).
Interplay of cognitive and affective factors in information use. Abundance of
articles still are tend perceive cognitive and affective factors as separate entities (Savoleinen,
2015). The assumption is supported by Wilson (1999) who has analyzed thirteen major
models of information behavior, and only several of them were identified as taking both
factors into sufficiently detail analyzes. Following the approach of the recent sholar
Savoleinen (2015), models of Kuhlthau and Nahl, that have been developed independently
since the 1990s, should be conceived as the most sophisticated and the only one that provides
detail conceptualization of cognitive, and affective factors in information seeking and use. In
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addition, both of the models do not consider mentioned factors as a separate entities and the
main empathizes is placed on their interplay.
Due to the Fulton (2009) the model Information Search Process (ISP) introduced by
Kuhlthau in 1991 plays a significant role while forming “cognitive-affective approach”. ISP
is based on the psychological theories developed by Kelly (1963) and Bruner (1973), where
the central focus is concentrated on the nature of human thinking, feeling and acting as a
construction process (Kughlthau, 2004). The model consider affective factor as a
“Symptoms” that are caused by cognitive factors arrived from negative or positive feelings
such as anxiety, doubt or satisfaction. Need to be empathized, that the mood is assigned to the
affective-cognitive factor and reflects individual’s attitude toward information. Whereas,
cognitive factor is interpreted as thoughts that are in a relation with the task at hand and
choices made. Subsequently, the interplay of cognitive and affective factors is based on the
uncertainty principles “Uncertainty is a cognitive state that commonly causes affective
symptoms of anxiety and lack of confidence” (Kuhlthau, 1993). As a result, Kuhlthau (2004)
argues that affective factors direct cognition throughout the process of information search and
use. On the basis of psychological theory provided by Kelly (1963), feelings directly impact
the process of constructing meaning about the information gathered.
The Social – Information Technology model (SBIT) proposed by Nahl is reported as
being a significant contribution to the perception of the affective factors in terms of
information behavior (Savoleinen, 2015). The main purpose of SBIT is to reveal the
interdependency of cognitive and affective factors in processing and using information (Nahl,
2007). The model is based on the fact, that information behavior is based in individual’s
biological procedures that are continuous and consists of sensorimotor, cognitive and
affective processes. To this extent, affective factor is perceived as playing a substantial role
in the first phase of information use and is dominant on bi-polar scale with regard to the
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PRODUCT INNOVATION JUDGMENT
34
evaluation of information (e.g good – bad etc.). While cognitive factor appears to be active in
the second phase of information use. Accordingly, in the context of interplay of affective and
cognitive factors, SBIT claims that cognitive appraisal is caused by affective procedures that
work as motivator and regulator for cognitive procedures to occur.
To sum up, basically the core difference of ISP and SBIT is based on the role of
affective factor, which is significantly stronger in SBIT model where affective procedures are
reported as a dominant one and suppose to be the main reason for cognitive action to be
started. In contrast to the approach of ISP, SBIT does not place such priorities to the affective
factor and place cognitive (thoughts) and cognitive-affective factors (mood) as prior to
affective one.
Affective and cognitive communication strategies for successful innovation
judgment. In a subsection “defining information and information behavior” we provide the
definition of information and how customers tend to use it, however any clues about the way
information reaches the customers are not provided. However, for the purpose of this
research it is important to look into it directly from the perspective of the innovation adoption
process. From this perspective, information goes hand in hand with communication strategies
(Roger, 1995). Consequently, communication can be described as a process of information
transfer and construction of the meaning of information shared (Blyth, 2002). As a result, it is
no wonder that companies put a lot of efforts in order to provide the information about
innovation in as much effective way as it is possible. In essence, extensive line of researchers
suggest that appropriate introduction to the innovation is one of the key success factors for
achieving a favorable performance of innovation (López & Sicilia, 2013).
Existing literature, which is exploring innovation adoption process, argues that
advertisement is still maintained to be the most effective communication tool for introducing
innovation (Manchanda, Xie & Youn, 2008; Narayanan, Manchanda & Chintagunta, 2005).
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According to the fact that active discussions evolved in terms of affective and cognitive
factors in information use and overall innovation adoption process, advertisements started to
be created on the basis of the process that potential adopter uses in order to evaluate the
innovation.
Active considerations regarding what plays a dominant role, (affective or cognitive
information use in innovation judgment) leads to one more question, which asks on what
basis – emotional or rational appeals - advertisements should be built on.
The way consumer accepts advertisement started to be analyzed years ago. For
instance, Batra and Ray (1986) argue that cognitive responses regarding advertisements and
information provided in it cannot be assumed as playing a dominant role and affective
responses should be taken into serious considerations by researchers. In this regard, the
matter is to understand which way to appeal to the customer is more affective in shaping
consumers attitude towards the product (Batra & Ray, 1986; Crites, Fabrigar, Petty 1994;
Gil-Saura & Ruiz-Molina, 2008). Consequently, emotional or rational appeal can cause
positive or negative attitude towards the product what is directly related to the customer’s
decision to adopt or not a certain product (Marin, Pizzinatto & Giuliani, 2014).
Currently, despite the fact that the number of studies explain customer’s behavior in respect
of cognitive process, significantly increased number of researchers started to support the
dominant role of emotions in successful advertising (Marin et al., 2014). A comprehensive
understanding of the overall process how human mind works became the main reason why
cognitive or rational based advertisements started to give the way for affective or simply
feeling-focused ones (Wood, 2014). Hence the interaction of psychology and neuroscience in
marketing sphere assumed to be obvious.
Nobel prize laureate psychologist Daniel Kahneman contributed the marketing field
by providing profound insights about human decision-making and judgment process that was
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PRODUCT INNOVATION JUDGMENT
36
based on number of researches conducted and presented in his work “Thinking Fast and
Slow” (Bazerman, 2011). Kahneman proposes that the two cognitive processes exist
(Figure 1). First is called “system 1” and is built on the initiative, automatic and associative
fast reaction. Latter system characterized as being slow for learning and not requiring any
efforts. While the “system 2” is based on conscious mental process that requires efforts in
making certain judgments. However, Kahneman asserts that human being is not used to
thinking hard and tends to rely on a plausible judgment that comes to the mind on the spot
(Wood, 2014). In addition, Koghneman agrees with the idea that emotional arousal is able to
reduce the possibility to accept rational-counter arguments while increasing the accessibility
of the thoughts that are based on the immediate emotion. Need to be empathized that the
scholar asserts that the ability to evaluate information provided using “system 2” depends on
the quantity of information available. Consequently, according to Wood (2014) such mental
mechanism can be applied for customer’s reactions to the advertisements and their later
decision making.
System 2







Slow
Conscious
Reflective
Deliberative
Analytical
Rational
Logical
System 1






Fast
Unconscious
Impulsive
Associative
Automatic
Emotional
Figure 1. Main differences of System 2 and System 1 suggested by D. Kuhgneman. From: “How Implicit
Association Measurements Lead to Explicit Business Result at Dr. Aaron Reid at IIe X”, 2014. Retrieved from:
http://www.sentientdecisionscience.com/tapping-system-1-processing-reveals-deep-consumer-insights/
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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Continuing, when the information about the product in advertisement is introduced on
the emotional basis (“system 1”), it makes customer feel more positive and leads to the
resistance of any effortful considerations (“system 2”) in terms of information provided.
According to Wood (2012), who dedicates the majority of his research to investigate
emotion’s role in advertising, “system 1” is the one which is willingly used by the customers
while evaluating advertisements. In addition, one more scholar Reid (2014) provides
estimations that were conducted by psychologist and claims that in general 95% of the human
thoughts occur in “system 1”. Following Wood (2012) “emotion not only influences what we
pay attention to but automatically channels our thoughts and makes certain associations
accessible to us, simplifying decisions and guiding the judgments we make” (p. 32).
Furthermore, the researcher argues that lack of emotions can even interfere the customer’s
process of decision making. As a result emotional appeal to the customers evokes positive
feelings that in a direct or indirect way has a significant positive effect on customers attitude
towards product or brand (Figure 2) (Geuens, De Pelsmacker & Pham, 2014). According to
Geuens et al., the direct positive effect on the customer’s attitude can be identified in two
ways as showed the Figure 2. As a result, the first way is conducted when emotional based
advertisements can awaken positive thoughts by stimulating favorable feelings about the
brand. The second way for shaping customer’s expected attitude towards the product or brand
is by automatic process of evaluative conditioning evoked by emotional advertisements.
Taking this into account one of the main aims of the marketers became to stimulate as much
positive emotions as it is possible in order to generate customer’s satisfaction that would be
reflected into more positive attitude towards the product (Marin et al., 2014).
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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a
Ad-evoked feelings
Attitude toward Ad
b
a
Attitude toward product or brand
b
Direct effect
a
Indirect effect
Figure 2. Direct and indirect effect of Ad-evoked feelings to product or brand attitude. From:
“Do pleasant emotional Ads Make Consumers Like Your Brand More? “, Emotional Ad, 6(1),
40-45.
As a result traditional hierarchy-of-effects model which is based the sequential
process where customers move from unawareness to awareness, from awareness to
understanding, from understanding to persuasion, from persuasion to purchase is noted as
being more convenient because of its applicability for pre-testing and tracking research rather
than being more informative and useful for marketers (Wood, 2012).
Problem Definition
Summary of the theoretical conceptualization. The main aim of this section is to
summarize the significant contributions of previous researchers in the context of the main
aim of the present study.
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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According to the literature review it can generally be said that majority of the recent
innovation adoption studies still is built on the basis of classical models such as TAM
(Davis 989) or IDPM (Roger, 2003). It is important to notice that none of the researchers
argue against the fact that innovation adoption is directly related with the process there
potential adopters search and use information about innovation. From this perspective
consumers consider information with respect to innovation attributes regarding which
numbers of discussions occurred and have been already reviewed in this chapter (Table 2).
Table 2
Overview of product innovation’s attributes analyzed in the literature
Authors
Number
of
attributes
5
Analyzed attributes listed
Tornatzky & Klein,
(1982)
9
Relative advantage, compatability, complexity, communicability, divisibility,
profitability, social approval, trialability, observability.
Moore & Beinbasat,
(1991)
Saaksjarv & Morel
(2010)
Flight, D’Souza &
Allaway (2011)
3
Image, voluntarines, result demonstrability.
3
Performance risk, relative advantage, compatability.
15
Kapoor, Dwivedi &
Williams (2014)
8
Observability, communicability, trialability, personal compatability, social
compatability, social advantage, volition, relative atvantage by attributes, relative
economic advantage, customizability, product performance, complexity-in-use,
complexity-in-design, category risk, discontinuity.
Ease of operations, image, cost, riskness, visibility, voluntariness, result
demonstrability, social approval.
Kings & Slovic (2014)
2
Roger, (1962)
Relative advantage, compatability, complexity, trialability, observability.
Risk , benefis
Despite the fact that numbers of innovation attributes are considered by different
scholars and in different times of period some of the attributes remain irreplaceable years
after years. Without doubts relative advantages, risk, complexity and compatibility can be
assigned to the list of irreplaceable innovation attributes. The long maintained interest of
latter attributes can be correlated with the fact, that extensive line of researchers support the
approach that innovation adoption goes hand in hand with uncertainty reduction process.
Consequently customers suppose to consider information about innovation in order to
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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40
evaluate risk, which also correlates with complexity to use totally new product or
innovation’s ability to be compatible to his/her personal life. Moreover, following the
analysis of prior research conducted, potential adopter always try to assess how new product
could satisfy his/her needs, consequently the attribute relative advantage always is involved
in the innovation adoption process.
However, should be empathized that sufficient number of most current studies started
to provide critique approach toward traditional explanation of innovation adoption process
which more a less is based on cognitive process. Scholars started to provide empirically
proved arguments regarding dominant role of affective process in innovation judgment.
How it was mentioned above, the vast majority of researchers agree that information
plays extremely important role while potential adopter try to evaluate innovation. Due to this
the term information behavior was taken into considerations in order to ensure the clear
understanding of customer’s information use process. Extensive line of discussions of
quantitative and qualitative empirical works, reviews shows that understanding of
information use works together with customer’s decision-making. It means that decisions are
effected by the way the information was used by the potential adopter. Consequently, the
numbers of researchers questioned the need for discussion in terms of mental process that
customers experience while evaluating information that leads to a decision regarding
innovation judgment. Based on the literature review it can be said that exist sufficient amount
of research asserting the dominant or at least the same weight role of affective process in the
innovation adoption context comparing with cognitive process. Table 3 shows an overview of
studies that actively support the approach, that cognitive process do not play a dominant role
in customers information use and decision making, and the significant considerations
regarding the role of affective factors and its interplay with cognitive one should be taken
into considerations.
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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Table 3
Overview of most recent studies that support the significant role of affective factors in customers
information use and decision-making
Authors
Approach
Comment
Savolainen, 2015
Cognitive factor in information use cannot bee seen as
Approach is based on detail analysis
playing a dominant role. In order to have holistic
and comparison of Kuhlthau’s and
picture of information use the interplay of both factors
Nahl’s models. The latters are
– affective and cognitive – should be discussed.
identified as pioneering approaches in
information seeking and use.
King & Slovic, 2014
Potential adopter of innovation often relies upon their
Authors conducted three experimental
overall affective impression while evaluating risks and
studies that were based on the
benefits of product innovation.
customers judgment of two main
innovations attributes: risk and benefit.
Mazaheri, Richard &
Support the idea that customer’s emotions such as
Lab experiment was conducted for
Laroche, 2012
pleasure, arousal and dominance has a significant
testing online consumer behavior
impact on customers attitude toward object and
regarding the effect of emotions.
intention to buy.
Mattila & Wirtz, 2000
Claim that affective factors (emotional) effects
Analyze the effect of affective factors
individual judgments of products or services.
in the context of prepurchase and
Distinguish such factors as pleasure and arousal as
postpurchase process in order to test
playing a significant role in product or services
the effect of customers emotional
evaluation process.
responses to overall judgment of the
product/service service and his/her
intention to choose this product/service
one more time.
Lakomski & Evers,
Argue that emotions plays a dominant role in rational
The approached is based on the
2010
decision making. Suggest to replace traditional rational
neuroanatomy of emotion applying
decision-making theories to theory of emotional
naturalistic philosophy of human
decisions that is assume to be more realistic from the
thoughts and experience and his ability
biologically point of view.
to value the things on the basis of his
neuroanatomy.
Currently, many questions frequently arise in order to understand the role of feelings
and rationality during the process of information evaluation. However, lack of studies can be
identified that would directly analyze the affective and cognitive information use in terms of
innovation adoption process. Due to this, the present study is going to fill this gap while
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42
connecting process such as information use, cognitive and affective procedures that potential
adopter experience and innovation adoption.
Coming back to the main aim of the present study, both of the mentioned way can be
applied for promoting innovation, because radical product innovations can be suggested not
only by the well known brands but by totally new creators such as start ups too.
Problem identification. The review of literature has shown that innovation adoption
is perceived as an uncertainty reduction process. Due to this risk and relative advantages
became innovation attributes that were approved by number of studies as being the core
variables according which potential adopter makes his/her judgment. In addition, extensive
line of researchers agree that in order to reduce the uncertainty level and to make a positive
decision toward innovation, customers need to be provided with a reasonable amount of
information. Consequently there is no consensus in academia world regarding the mental
process that customers experience while evaluating risks and benefits of innovation.
Discussions’ main purpose is to understand which process (cognitive or affective) is playing
the dominant role while the potential adopter is making his judgment. At the same time
researchers investigating the direct effect of advertisement suggest number of empirically
proved evidence that emotional based advertisement has significantly more positive effect on
customers decision making than one that has rational appeal to the target audience. As a
result, the fact that advertisement is approved as the most effective communication tool for
providing information about innovation to the potential adopter (Manchanda et al., 2008;
Narayanan et al., 2005) rises one more question: is it possible to reduce uncertainty level
using emotional advertising? Taking everything into account what was mentioned above it
becomes obvious that in order to understand what mental process potential adopter uses in
evaluation process of innovation it becomes important to find out how information provided
in an affective or cognitive way effects innovation judgment. Hence the main question still
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unanswered is: what is the effect of affective versus cognitive information use on product
innovation judgment?
Although previous studies have attempted to answer the question what process occurs
in customers’ mind while they try to evaluate innovation, obvious lack of studies that would
investigate the direct effect of information provided in both affective and cognitive ways
exists. Despite the fact that the number of researches was conducted with the purpose to
understand the differences of effects of emotional and rational advertisements, deficit of
studies that would be specially adapted to the innovation adoption sphere is noted. As a result
current state of researchers’ and marketers’ knowledge in terms of mental process that
potential adopter experiences is still limited, because the topic is strongly related not only to
marketing sphere but interaction of psychology and neuroscience specialists becomes
increasingly necessary. Consequently, it becomes clear that causal relationship between main
concepts of the current study -“affective and cognitive information” and “ innovation
judgment” - is still not covered sufficiently.
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Research Methodology
The chapter of the research methodology presents research model and provides main
arguments, based on the findings of literature analysis, why it is reasonable to apply it in
order to test hypothesis that are framed with the aim to answer the research question. Later
the research method is selected for the purpose to conduct developed research model in as
much effective way as it is possible. The overall plan of the research will be outlined in order
to be always on the track while trying to reach empirically approved findings that would be
beneficial from both theoretical and practical point of view. Moreover, how it will be
uncovered in the overall plan of the research, the chapter involves implementation of
manipulation checks that enable to be more insured in terms of the validity of the research
model. Finally, the overall process of empirical testing of hypothesis concludes the chapter.
Consequently, in order to be clear how the results of the research were gained the section
consists of detail introduction of the research methodology, techniques of statistical data
analysis, the role of main moderators and procedure of the study.
Research Model Development And Hypothesis Formulation
Research model is presented in the Figure 5 and is constructed according to the literature
analysis of previous studies in the field and obviously with respect to the research question:
“what is the effect of cognitive versus effective information on product innovation
judgment?”. The model is based on the construct of multivariate regression analysis with the
moderation effect (affective-based ads vs. cognitive-based ads.).
Consequently, the model suggests that affective and cognitive information being as a
moderator addresses how product innovation attributes and attitude toward brand (product)
influences product innovation judgment.
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As a result four innovation attributes such as compatibility, relative advantage,
construct of risk and complexity should be involved between predictors for two main reasons.
Firstly, mentioned attributes, following the literature review, are empirically approved as
being one of the strongest predictors regarding innovation adoption. Secondly, the
information, which is going to be provided in two ways – affective-based ads and cognitive–
based ads has the real possibility to raise associations with the attributes listed. It means that
information will be informative enough to form even the primary approach of the potential
adopter regarding innovations’ relative advantage, risk, complexity and compatibility.
In addition the model involves one more predictor, which is attitude toward product.
The latter is added because of reasons, that are uncovered in the vast majority of theories of
innovation adoption process and studies that investigated customer’s responses toward
affective or cognitive advertisements. Taking into account such theories as Roger’s
innovation diffusion (2003), the technology acceptance model (Davis, 1989) or the theory of
planned behavior (Ajzen,1991) or simply with reference to the overall literature analysis
made in this research we can come to a conclusion that potential adopters consider
information which form their attitude toward innovation (Kings & Slovic, 2014). Moreover,
according to the fact that the present research will manipulate with information provided in
different kind of advertisements – one will be affective-based and the other - cognitive-based
– it is important to involve brand attitude which is undoubtedly is one of the most frequently
used variables for evaluating customer’s responses toward advertisements (Batra & Ray,
1986). In general, the involvement of attitude as a predictor in the research model can be
reasoned even by the approach of Crite, Fabriger and Petty (1994) who investigated the effect
of affective and cognitive properties to individual’s attitude and support the position that
“attitude is assumed to be evaluative summary judgments that can be derived from
qualitatively different types of information e.g affective and cognitive”. According to the fact
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46
that brands of product innovations that are going to be judged are not well known it is more
likely that attitude toward brand will be transformed to attitude toward product. Such
situation is not an exception in the process of product innovation judgment, because varieties
of innovation types that have been already presented in the literature analysis show that
innovations are not necessarily created under the well-known brands. For instance, number of
success stories of startup business model reveals that on many occasions, potential adopters
need to judge product innovations regarding their attitude toward product rather than brand.
As a result, it can generally be said, that the construct of the model is dedicated to assess
innovation judgment, which is affected by product innovation’s attributes (variables that are
most frequently used for understanding potential adopter’s decisions) and attitude toward
brand (product) (variable that most frequently is used for evaluating customers responses
toward different types of advertisements) moderated by information provided in affective or
cognitive ways. Consequently, the following hypotheses are identified (Table 4):
Table 4. Hypotheses formulation
H1
Compatibility is directly related to the product innovation judgment
H2
Relative Advantage is directly related to the product innovation judgment
H3
Risk and Complexity is directly related to the product innovation judgment
H4a
Interaction of Compatibility and Relative Advantage is directly related to the product innovation judgment
H4b
Interaction of Compatibility and Risk and Complexity is directly related to the product innovation judgment
H4c
Interaction of Relative Advantage and Risk and Complexity is directly related to the product innovation judgment
H5
Attitude toward brand (Product) is directly related to the product innovation judgment
H6
Affective-based and Cognitive-based Ads is directly related to the product innovation judgment
H7
Affective-based ads (as opposed to cognitive based ads) have a positive moderating influence on the effect between
product innovation attributes and product innovation judgment
H8
Affective-based ads (as opposed to cognitive based ads) have a positive moderating influence on the effect between
attitude toward product and product innovation judgment
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The hypotheses are constructed on the basis of research problem that has clearly
revealed the need to compare effect of information, which would be communicated through
different kind of advertisements. This research is going to test the fact if the process of
innovation judgment, which is comparable with uncertainty reduction process, can be more
positively effected by information provided on emotional basis rather than rational
information which, from the first sight, can be perceived as having higher possibility to
Product innovation attributes
reduce the risks related to the innovation.



Compatibility:
Personal compatibility
Social compatibility
Social advantage
Relative Advantage:

Product performance
H1
H2
H4
Innovation
judgment
H7
Risk and complexity:

Complexity - in - use

Discontinuity
H6
Affective vs.
Cognitive ads
H3
Affective vs.
Cognitive ads
H8
Affective vs.
Cognitive ads
Attitude toward product:

Usefulness

Importance

Pleasure

Nice or not
H5
Figure 3. Research model
As it was mentioned above, innovation adoption process is inseparable from the
innovation attributes that were analyzed in an extensive line of prior and most recent research
and is used by potential adopters in order to evaluate the innovation provided. Review of the
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studies that have already been conducted showed that attributes such as relative advantage,
risk, complexity and compatibility are one of the most frequently tested and analyzed by the
scholars trying to explore the potential adopter’s decisions making regarding innovation
judgment. According to the fact, that all these attributes are involved in the research model it
becomes vitally important to provide clear definitions of each of them for the purpose of
understanding the main role of each attribute in the model.
Compatibility. The main aim of the compatibility attribute is to evaluate how well the
innovation fits to the potential adopter’s personal life and social environment (Flight et al.,
2011). Due to this, it can be defined as a degree to which potential adopter doubts that
innovation is compatible with his/her values, needs and existing experience (Saaksarjvi et al.,
2015). As a result, the more innovation is compatible the more it fits to potential adopter’s
personal and social life (Roger, 2003). The present study involves the current attribute into its
predictor’s lists because of nowadays market, which is already crowded by wide range of
different kind of innovations. Consequently to evaluate only perceived benefits and risks
cannot be representative enough for testing hypothesis, because the overflowing offers in the
market provoke customers to personalize and evaluate them in the context of their personal
and social life.
Relative advantage. Roger (2003) defines relative advantage as a degree of potential
adopter’s uncertainty level regarding product innovation’s ability to be better than already
existing products in the market. Accordingly, Roger (2003) defines relative advantage as a
rate of benefits and costs perceived by potential adopter. In other words, individual evaluates
the promises of innovation and tries to make his/her position in terms of expected benefits
and possible risks (expected product performance). Despite the fact, that Roger did not
distinguish risk as a separate attribute, as was shown in the literature review, numbers of
scholars put it in their lists of innovation attributes as one of the most important predictor for
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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49
the outcomes to test. Consequently, the present study uses relative advantage for exploring
the strength of perceived advantages of product innovation while the perceived risks are
checked additionally.
Risk and Complexity. Flight et al. (2011) who had investigated characteristic-based
innovation adoption process took risk and complexity in one construct defining risk as the
customer’s conceived probability of potential failure of innovation while meeting his/her
needs. Accordingly, in terms of risk attribute, customer simply tries to estimate how much it
is likely that innovation will not be able to satisfy his/her expectations. Due to this, risk goes
hand in hand with individual’s doubts to adopt or not the innovation and obviously with the
overall rate of innovation judgment. Moreover, it is discussed that risk can be divided into
numbers of kinds (Rijsdijk & Hultink, 2003), as a result it should be empathized, that the
present study uses the performance risk which is most relevant to the product innovation and
is conceptualized as definitions provided above (Saaksarjvi et al., 2015). The same time
complexity, can be conceived as a subjective interpretation of the risk (Dowling & Staelin,
1994) or the degree of difficulties of learning to use the innovation (Flight et al., 2011). The
present study uses the construct of risk and complexity that both following the previous
research conducted are noted as being negatively related to the positive judgments of product
innovations.
To conclude regarding the research model, the latter is based on the moderation
effect, which leads to the fact that manipulation will occur with two independent variables
(affective-based ads and cognitive-based ads ) in order to test the effect of identified
predictors (product innovation attributes and attitude toward product) to the product
innovation judgment. Subsequently, manipulation with such moderators will enable us to
gather date for empirical testing of hypothesis formulated with the purpose to reach the main
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purpose of the present study, which is to evaluate the effect of affective vs. cognitive
information on product innovation judgment.
Research Method
Experiment selection. In this research a quantitative research method an experiment
is going to be applied for several reasons. First of all, currently existing studies that are
related to the present research problem have been conducted while applying experiment. For
instance, Batra and Ray (1986) use experiment for investigating affective responses as a
supplement to cognitive ones in the acceptance of advertisements, while King and Slovic
(2014) carried out an experiment in order to evaluate what is the impact of affect heuristic in
early judgments of product innovation. Taking this into account, experiment is already
approved as being an affective way for empirical evidence to be found in terms of exploration
of affective and cognitive processes. Secondly, from theoretical point of view, an experiment
is perfectly suitable for testing hypothesis where causal relationship can be identified
between two or even more variables (Patzer,1996). Consequently, an experiment is
frequently conducted in marketing research in order to reach the condition where
manipulation occurs with one or more independent variable for the aim to test the hypothesis
regarding dependent variable. In respect to this, such methodological approach is applied for
the present research question because the manipulation is needed to evaluate the effect of
affective and cognitive information to the product innovation judgments.
Analytical framework for moderation effect. Following the research model the
experiment is based on the construct of multivariate regression analysis with the moderator
interaction. Hence, the effects of moderators are going to be tested. Moderator can be defined
as qualitative or quantitative variable that influences the direction of the relation between an
independent or predictors variables (Reuben and Baron, 1986). In addition, moderator can
affect the strength of the mentioned relation. Accordingly the analytical framework of
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moderator can be presented as in the Figure 3 provided below. Following the Figure 3 where
the theoretical moderator model is applied for the present research, it is obvious that in this
case the qualitative moderator directs the relation between predictors. Moreover, three causal
paths (a;b;c) are identified as having impact to the outcome. Due to this the path a is noted as
a factor which is not in a control while the path b is considered as effect of controllability.
Need to be mentioned, that using such an analytical framework for testing moderator effect,
the hypothesis can be supported only in the case when the interaction of path c , which is the
interplay of predictor and moderator, is significant. In addition, it is eligible that moderator
variable would not be in a correlation with both predictor and dependent variable in order to
ensure the clearly interpretable interaction. Another specialty of the moderator is the fact that
it always behaves as independent variable. Generally, in the current research, affective-based
ads and cognitive-based ads being as a moderator address how innovation attributes and
attitude toward product influence product innovation judgment.
Moderator model
b
c
Predictor
X
Moderators
Innovation attributes
+
Attitude toward brand
(Product)
a
Predictor
Moderator
Moderator model applied for the present
Outcome
Variable
Affective-based Ads
and Cognitive-based
Ads
(Innovation
attributes + Attitude
toward product)
x
Affective-based Ads
and Cognitive-based
Ads
a
b
Innovation
judgment
c
Figure 4. Statistical moderator model applied for the present study. From “The Moderator – Mediator
Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical
considerations”, by M. B. Reuben, D.A. Kenny, 1986, Journal of personality and Social Psychology,
51(6), p. 1174
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Research process. For the purpose to come to the answer of the research problem
consistently, the overall research plan was prepared. As shown in Figure 4 the theoretical
foundation for the empirical work has been done in the previous chapter, where development
of main concepts were implemented in respect to the studies conducted until the need of this
research was identified, i.e. product innovation attributes that are used by potential adopter in
order to judge the product innovation were identified through numbers of research performed
as well as affective and cognitive process in terms of information behavior were analyzed.
Consequently, theoretical considerations have brought to the main research question. Next
the research model was constructed and the research method, in order to perform me model,
was selected. In order to ensure the validity of the research model randomize manipulation
check had to be carried out for moderators (independent variables) – affective-based ads and
cognitive-based ads. The empirical testing of the hypothesis was performed, after that the
independent variables have been statistically approved as being suitable for taking the role of
moderators. The data collected has been checked on multivariate regression analysis with
moderator’s interaction for the aim to provide statistically proven findings of the research.
For the conclusion, discussion about research’s contribution to both academic and marketing
management fields is presented the same as some limitations and suggestions for the future
research are identified.
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
PRODUCT INNOVATION JUDGMENT
Identification and
conceptualization of
key concepts
Key concepts
identification
Theoretical
conceptualization
Main problem,
research question and
hypothesis
identification
Empirical testing
of hypothesis
Research model
and hypothesis
identification
Research method
and analytical
framework for
moderation effect
manipulation check
implementations
53
Discussion and
Conclusion
Theoretical findings
and implications
Contribution to
marketing
specialists
Limitations & Future
Research
Empirical testing of
hypothesis
Figure 5. Research process outline.
Pre-experiment
Design of randomized manipulation check. Affective and cognitive information as
advertisements. There is no concrete criteria according to which affective and cognitive
information can be differentiated. Several authors underline that the level of presentation
considered to be one of the most widely-spread ways to distinct what kind of information affective or cognitive – is presented (Crite et al., 1994). Consequently, following Crite et al.
(1994) who conducted the research in order to investigate the scale for the evaluation of
affective and cognitive information, as a whole describes affective information as the one
which is assessed by the customers at a very general level, e.g. “happiness or sadness”, while
cognitive information is more often evaluated at an object-specific level e.g. “grass is green”.
According to the fact, that advertisement is still assumed to be the most effective way to
present the information to the potential adopters of product innovations (Manchanda et al.,
2008; Narayanan et al., 2005), the present research uses this presentation type for empirical
testing of hypotheses. However, it is important to check if the selected advertisements
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(selected way to present information) are really perceived as being affective-based or
cognitive-based by the customers. Consequently, in order to be sure, that the moderators
(affective-based ads vs. cognitive-based ads) are really suitable for the research manipulation
check is required to be conducted.
Selection of the product innovations and advertisments. Product innovations were selected
taking into account three main criteria. Firstly products needed to be as new for the
Lithuanian market as it is possible. In other words, no marketing campaign has been
conducted in the Lithuanian market and innovations can not be older than one year in order to
minimize the possibility that customers have already had the possibility to be familiar with
innovations through any personally used channels. Secondly, product innovations had to be
assigned to the same level regarding customer’s involvement. In order to avoid prices as a
significant influencing factor it was decided to use product innovations that are more likely to
be described to the low-involvement goods rather than high-involvement. Thirdly, product
innovations needed to have advertisements that could be applied to the present research in
terms of affective and cognitive nature. As a result two product innovations – vessyl cup and
butterfleye camera were selected from the international list of innovative startups in 2014.
While the third startup – Rockety Bike Lamp – was created in Lithuania and the present
research was the first time when this innovation has been shown to the potential adopters.
Short introductions to the each of the innovations are provided in Table 5.
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Table 5. Product innovations for manipulation checks
Innovation
Description
Research name
Innovation1
Vessyl cup
Sensor-packed cup can automatically determine what kind
Innovation1
of liquid you have poured inside it, as well as report the
calorie content of whatever you poured.
Innovation2
Rockety Bike Lamp
Product provides light for a daily commute and extra
Innovation2
security for possible situations of a bike theft. Lamp has a
single LED light, battery to provide power, dynamo to
recharge system, GPS module for tracking features and
GSM module to maintain Internet connection.
Innovation3
Butterfleye camera
for home security
The product can detect peoples’ faces and animals, and be
Innovation3
connected with smart devices that would let you be aware
of what is happening in your house at any place you are.
Continuing, for the each of the selected product innovation two types of
advertisements needed to be selected - affective-based ads and cognitive-based ads.
According to the fact, that real product innovations were decided to be tested, videos were
taken as being the best representatives of affective-based ads for Innovation1 and
Innovation2. While for representing the cognitive-based ads, the print was approved as
having the biggest possibility to present Innovation1 and Innovation2 in a cognitive nature.
At the same time, the producer of Innovation3 had two different videos available that
perfectly met our requirements in terms of affective-based ads and cognitive-based ads. The
latter combination (two videos) was expected to be the best (two videos) choice, because of
the decreased possibility that the results can be influenced not only by the nature of
advertisements (affective and cognitive) but by the format of advertisement too.
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Consequently, such selection of advertisements (all the advertisements you can see in the
Table 6) for all the innovations enabled not only to reach the main aim of manipulation
check, but also allowed to detect if there are significant differences in the respondent’s
evaluations of cognitive-based ads provided in two different formats - audio and descriptive.
Moreover, selection of the two different formats could become the reason to eliminate
Innovation1 and Innovation2 from the exper
iment because of the aim to avoid any external influence factors such as different
formats of affective-based ads and cognitive-based ads to the outcomes.
Table 6. Advertisements for manipulation checks
Innovation
Affective-based Ads
Cognitive-based Ads
Innovation1
https://www.youtube.com/watch?v=lu4ukHmXK
FU
https://www.surveymonkey.com/s.aspx?sm=gnbNj2g
X6bLnU1aLO7vk8g%3d%3d
Innovation2
https://vimeo.com/113736749
Password: lempute
https://www.surveymonkey.com/s/QMCX8G2
Innovation3
https://www.youtube.com/watch?v=6kCsztbSLo
A
https://www.youtube.com/watch?v=jlUu46Y8tWI
In order to avoid confusion regarding products’ innovations and advertisements
selected, abbreviations of advertisements used in the following research are provided in
Table 7.
Table 7. Abbreviations of advertisements
Advertisement
Innovation1
Innovation2
Innovation3
Affective-based
Ads1affective
Ads2affective
Ads3affective
Cognitive-based
Ads1cognitive
Ads2cognitive
Ads3cognitive
Aim and hypothesis of the manipulation check. It is fact, that only after providing
statistical evidence that tested advertisements (that are the selected as the way to provide
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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information about the product innovation) can be indicated as affective and cognitive the
research’s moderators are identified. Consequently the main aim of the manipulation check is
provided below as well as hypothesis in the Table 8.
Aim: to evaluate if different types of the advertisements (affective-based ads and
cognitive-bases ads) can be considered as cognitive or affective.
Table 8. Hypothesis of munipulation check.
Ha
Ads1affective is more affective ads than Ads1cognitive
Hb
Ads1affective is affective-based advertisement
Hc
Ads1cognitive is more cognitive-based advertisement
Hd
Ads2affective is more affective ads than Ads2cognitive
He
Ads2affective is affective-based advertisement
Hf
Ads2cognitive is more cognitive-based advertisement
Hg
Ads3affective is more affective ads than Ads3cognitive
Hh
Ads3affective is affective-based advertisement
Hj
Ads3cognitive is more cognitive-based advertisement
Sample and procedure of manipulation check. Sample: Each advertisement was
required to be evaluated by 80 respondents who were first and second year students from
ISM University of management and economics. Such sample was selected in order to avoid
any extraneous influences that would occur because of different ages, social class etc. and
would cause unrepresentative data.
Procedure: Using online survey respondents were randomly assigned to receive one
advertisement in terms of three different innovations without having possibility to see and
compare two different advertisements about the same product. One group of 86 participants
read approximately 100 words print (Ads1cognitive) of Innovation1 that was cognitive in nature.
Different group of 84 participants evaluated the Ads1affective of the same innovation, which
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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duration was 3 minutes 45 seconds, created in affective nature. Ads1affective enabled
participants to see real situations of the product consumptions and provided the same
information, which was presented in the print for the other group but using real creator team
as presenters. 80 participants evaluated the approximately 100 words print (Ads2cognitive) of
Innovation2. The group of 83 participants evaluated the Ads2affective. The latter group had to
watch 1 minute 25 seconds advertisement with the same information provided as in the print,
but with strong emotional clues (story telling, music, real artist etc.). Needs to be mentioned,
that the Ads2affective, contrary to Ads1affective, did not have artists as presenters, due to this the
information was written on the screen in the context of emotional scenario of advertisement.
Finally, 84 participants got the Ads3cognitive. Participants had to watch totally informative, 1
minute 51 second video, where a man’s voice (it was not possible to use the presenter)
provided main information about Innovation3 while showing it at the front of the screen.
Different group of 83 participants were asked to watch Ads3affectiv. In Ads3affective real artists
and a woman as a main presenter, enabled participants to imagine how Product3 could be
applied to their everyday life situations. Consequently, in total 6 different objects were tested
on a 7-point scale anchored at “Emotional” to “Logical”, “Imaginative” to “Informative” and
“Feeling” to “Thinking”.
Results of manipulation check. To evaluate if different types of the provided
information can be considered as cognitive or affective information, student’s criteria for one
sample and Student’s criteria for two independent samples were calculated for each of the
objects (Table 5; Table 7; Table 9). Student’s criteria for two independent samples are used
for testing if two sets of data statistically different in the context of normal distribution
(Čekanavičius, Murauskas 2003, p.172 -175)
𝑡=
𝑥̅ − 𝑦̅
𝑛𝑚(𝑛 + 𝑚 − 2)
√
𝑛+𝑚
√(𝑛 − 1)𝑠𝑥2 + (𝑚 − 1)𝑠𝑦2
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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
𝑥̅ , 𝑦̅ are means of samples

𝑠𝑥2 , 𝑠𝑦2 are standard deviations of samples

nm are sample sizes.
59
The value of t-test is compared with student’s distribution value with (n+m-2) degrees
of freedom. Student’s criteria for one sample tests if the sample mean differs from the
particular estimation.
𝑡=

𝑥̅ is the mean of sample

a is estimation of mean

𝑠 2 is standard deviation of sample

n is sample sizes.
𝑥̅ − 𝑎
√𝑠 2 /𝑛
The value of t-test is compared with student’s distribution value with (n-1) degrees of
freedom. While means, standard deviations and t-tests results for all six information types
were listed in the Table 12.
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Table 9. Student’s criteria for testing hypotheses of Innovation1 advertisements
Measures
Student's
Student's criteria for one
criteria for two
sample
Student's criteria for
sample
Ads1affective
one sample Ads1cognitive
4
4
a (indifferent level)
t (t-test)
10,87
15,12
111,02
k (degrees of freedom)
157
83
85
α (significant level)
0,05
0,05
0,05
1,98
1,99
1,99
t_α/2 (k) (Student
distribution)
The data provided in the Table 9 shows, that all hypotheses for Ads1affective and
Ads1cognitive are statistically supported. Accordingly, first of all the hypotheses were tested
comparing Ads1affective and Ads1cognitive with each other. Due to this, it can be stated that
Ads1affective is more emotional than Ads1cognitive (10,87>1,98). Moreover, it was statistically
confirmed that Ads1affective is affective-based advertisement (15,12>1,99) as well as
Ads1cognitive is statistically approved as being cognitive-based advertisement (111,02>1,99).
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Table 10. Student’s criteria for testing hypotheses of Innovation2 advertisements
Student's
Measures
criteria for two
Student's criteria for
Student's criteria for
sample
one sample Ads2affective
one sample Ads2cognitive
4
4
a (indifferent level)
t (t-test)
12,95
344,76
665,82
k (degree of freedom)
170
82
99
α (significant level)
0,05
0,05
0,05
1,97
1,99
1,98
t_α/2 (k) (Student
distribution)
As with the Innovation1, the Table 10 statistically supports all hypotheses of
Innovation2 advertisements. It can be claimed that Ads2affective is more emotional than
Ads2cognitive (12,95>1,97). Furthermore, it can be generally said that Ads2affective is able to
represent the affective-based advertisements (344,79>1,99), as well as Ads2cognitive can be
noted as being cognitive-based advertisements (665,82>1,98).
Table 11. Student’s criteria for testing hypotheses of Innovation3 advertisements
Student's
Measures
criteria for two
Student's criteria for
Student's criteria for
sample
one sample Ads3affective
one sample Ads3cognitive
4
4
a (indifferent level)
t (t-test)
11,87
123,46
380,04
k (degree of freedom)
151
80
83
α (significant level)
0,05
0,05
0,05
1,98
1,99
1,99
t_α/2 (k) (Student
distribution)
The situation with the Innovatio3 is totally the same as with the previous product
innovations. Following the Table11 it can be asserted that Ads3affective is more emotional than
Ads3cognitive (11,87>1,98). In addition, it was statistically confirmed that Ads3affective is
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affective-based advertisement (123,46>1,99) as well as Ads3cognitive is statistically proved as
being cognitive-based advertisement (380,04>1,99).
As expected, after comparing the means (Table 12) all advertisements provided in
affective nature was perceived by respondents significantly more emotional rather than
logical, more imaginative rather than informative and more feeling rising rather than
encouraging to think. Need to be mentioned, that as the most affective-based advertisement
was supported the Ads2affective with the total average score of 4,96. The last position in terms
of level of emotions is taken by Ads1affective with the total average score of 4,45. Continuing
with cognitive-based advertisement, as the most logical, informative and encouraging to
think is recognized Ads3cognitive with the final score of 1,99. In addition, needs to be
mentioned that such result has just supported the assumption, that print provided in cognitive
nature is not necessarily needed to be interpreted as more cognitive than video only because
of its descriptive format. This has particularly been the case why it was decided to remove
Innovations1 and Innovations2 from experiment in order to minimize the possibility for the
additional interpretations of the final results caused by different formats of affective and
cognitive information placed in the moderator role.
Consequently, according to the results it can be asserted that information provided in
affective and cognitive natures is statistically supported as being representative of different
kind of information and empirically can be approved as a moderator for the experiment
designed for the purpose to evaluate the effect of affective and cognitive information type to
product innovation judgment.
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Table 12. Descriptive statistics of evaluations affective-based and cognitive-based advertisements
Affective information
Product
1
Cognitive information
Logical/
Informative/
Thinking/
Final
Logical/
Informative/
Thinking/
Final
Emotional
Imaginative
Feeling
result
Emotional
Imaginative
Feeling
result
Mean
4,79
4,02
4,54
4,45
2,07
2,17
2,05
2,10
St.dev.
1,69
1,93
1,74
1,58
1,28
1,42
1,55
1,21
Signific
yes
yes
yes
yes
yes
yes
yes
yes
84
84
84
84
86
86
86
86
ance*
Respon
dents
Product
Mean
5,20
4,75
4,92
4,96
2,46
2,90
2,28
2,55
2
St.dev.
1,42
1,60
1,63
1,30
1,33
1,71
1,45
1,19
Signific
yes
yes
yes
yes
yes
yes
yes
yes
83
83
83
83
80
80
80
100
Mean
4,52
4,46
4,49
4,49
2,02
2,12
1,83
1,99
St.dev.
1,65
1,73
1,75
1,52
1,22
1,48
1,28
1,15
Signific
yes
yes
yes
yes
yes
yes
yes
yes
81
81
81
81
84
84
84
84
ance*
Respon
dents
Product
3
ance*
Respon
dents
Experimental Design
Identification of moderators roles. After receiving the results of the multiple check
it was decided to conduct the experiment only with the Product3. Such a decision mainly is
based for the purpose to avoid any external interactions that would cause the deviation of the
main research question. Due to this, even the selected ads of Product1 and Product2 was
statistically approved as being representatives of affective and cognitive information, the
different types of ads – video and print – possibly could have an influence to the result of the
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main experiment. As a result, according to the fact that the main aim of the present research
is to manipulate with different nature of ads (affective vs. cognitive) rather than types of ads
(audio vs. descriptive), ads of Product1 and Product2 needed to be eliminated. In addition,
the ads of Product3 were statistically confirmed, as being able to represent affective - based
and cognitive - based ads being in the same video format that will let to ensure the
representativeness of the moderation effect. Consequently, the role of the main moderators in
the research are taken by Ads3affective and Ads3cognitive (Table 6 or Appendix 3).
Methodology. The sample (N) of the experiment was 277 participants. More women
participated (63,2%) and participants mean age was 22. Participants were randomly assigned
to one of the two advs – Ads3affective (N=136) versus Ads3cognitive (N=141). Each participant
needed to evaluate Product3 responding to 24 questions on a 7 – point scale provided in a
controlled laboratory environment using a computer-based survey (online software
Qualtrics).
Techniques for the statistical data analysis. According to the fact, that the model of
experiment is based on the moderation - affective versus cognitive information as moderators
- effects, multivariate linear regression analysis is going to be applied. Need to be empathized
that in cases of moderation effects, all predictor variables and their interaction term play
extremely important role in the overall model estimations seeking to improve interpretation
of regression coefficients. Taking into account the model of the present research, product
innovation attributes – compatibility, relative advantage, risk and complexity – as well as
attitude toward product will take the role of predictors. In addition, interaction terms will be
estimated between product innovation attributes because the latter tend to correlate amongst
each other. Furthermore, for the purpose to be convinced that the selected model will reach
statistically significant R2 score (how much variance the model of product innovation
judgment is able to explain) more simple model without interaction term among product
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innovation attributes will be tested and compared with the selected one. Multivariate linear
regression analysis will be conducted using predictive analytics software SPSS.
Procedure of experiment. After the manipulation check was conducted it was
decided to continue the experiment only with one product innovation BC in order to ensure
the representative outcomes and eliminate all possible external factors that would cause the
deviation from the main research question. Consequently, two ads in video format with 2
minutes durations were randomly presented to the participants. Both advertisements had the
same information in terms of quantity and quality, but it was provided in different nature –
affective and cognitive. As it was mentioned above, both advertisements were statistically
supported as being representatives of affective and cognitive information types. As a result,
firstly after watching the video participants were asked to answer four questions related with
attitude toward product on a 7 – point scale anchored at “Useful” to “Useless”, “Pleasant” to
“Unpleasant”, “Nice” to “Awful”, “Important” to “Unimportant”. The survey is continuing
with questions dedicated to evaluate product innovation through its attributes. Participants
were asked to complete 7 – point scale adapted for different 19 questions dedicated to capture
affective evaluations of product innovations attributes. The questionnaire has been prepared
on the basis of the research conducted by Flight et al (2011), which has successfully
developed a scale for measuring innovation attributes (Appendix 5). Finally, the participants
completed the survey with the question dedicated to find out the participant’s intention to use
the innovation (“Would you use it or not?”) on a 7 – point scale anchored at “I would
definitely use” to “I would definitely not use”.
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Empirical Research Results
It is fact that the research aim is to evaluate the effect of affective vs. cognitive information
on product innovation judgment in as much comprehensive way as it is possible in order to
ensure the validity of the results. Consequently, for the purpose to receive results that would
provide meaningful findings about moderation effect (Ads3affective vs. Ads3cognitive), the
interaction term between predictors (compatibility, relative advantage, risk and complexity)
suppose to be estimated. It is so, because according to the literature analysis, the assumption
can be made, that product innovation attributes interacting together can influence the overall
judgment of product innovation. Subsequently, the chapter mainly consists of three parts: the
results of the test of the research model representativeness, the final results of experiment and
finally the short summary with regard to research question and hypothesis tested.
Research Model Representativeness And Multivariate Data
As it was mentioned above, in order to be convinced that the constructed model statistically
proved as being representative the latter model should be empirically compared with the
simplified one. Subsequently, it will be tested if it is statistically significant to estimate
interaction term between selected product innovation attributes (compatibility, relative
advantage, risk and complexity) in order to evaluate the moderation effect of Ads3affective vs.
Ads3cognitive. The score R2 of the two models (with interaction terms and without it) is going to
be compared for the purpose to be as much precise as it is possible. The score R2 shows how
much variance in the product innovation judgment model is explained, due to this it is
expected that the research model with interaction terms among product innovation attributes
has higher R2 and this difference (between R2) is statistically significant. Consequently, the
comparison of the score R2 will be estimated using F test:
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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𝐹𝑓−𝑟,𝑁−𝑓−1 =
67
(𝑁 − 𝑓 − 1)(𝑅𝑓2 − 𝑅𝑟2 )
(𝑓 − 𝑟)(1 − 𝑅𝑓2 )
Where:

f - the number of parameters in the model with interaction effects

r - the number of parameters in the model without interaction effects

N - is sample size
Taking everything into account what was mentioned above, in order to use F test for
proving the representativeness of the constructed research model numbers of estimations
need to be performed for both models.
For the following estimations, according to the fact, that affective and cognitive
information are qualitative variables therefore their effect type codes them so that we could
use the comparison with grand mean. Due to this, in the present research 1 codes the
Ads3affective, while -1 indicates Ads3cognitive. As a result, dummy coding (e.g. cognitive
information = 0 and affective information =1) is not used in order to be more precise and
avoid calling one group as a control ones.
Based on the multivariate regression the model that not includes interaction term
between products innovations attributes has been specially constructed for the present
research. Due to the fact, that this chapter is dedicated to discuss the results, the latter model
(later will be called as a simple model) is expressed in the way of the mathematical formula
provided in the Figure 6. Consequently, simple model would evaluate product innovation
judgment with moderation effect of Ads3affective vs. Ads3cognitive as follows in the Figure 6
using multivariate regression analysis:
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=α+
(1) + β1* Attitude +
(1) + β2 * Compatibility +
(2) + β3 * [Relative advantage] +
(3) + β4 * [Risk and complexity]
Figure 6. Product Innovation judgment with the simple model
The simple model would be able to test hypothesis as shows the Table 13:
Table 13. Hypotheses of the simple model
H1
Compatibility is directly related with product innovation judgment
H2
Relative Advantage is directly related with product innovation judgment
H3
Risk and Complexity is directly related with product innovation judgment
H5
Attitude toward is directly related with product innovation judgment
H6
Affective-based and Cognitive-based Ads is directly related with product innovation judgment
H7
Affective-based ads (as opposed to cognitive based ads) have a positive moderating influence on the
effect between product innovation attributes and product innovation judgment
H8
Affective-based ads (as opposed to cognitive based ads) have a positive moderating influence on the
effect between attitude toward product and product innovation judgment
In order to evaluate the simple model, first of all correlations of predictors and
outcomes need to be estimated (Table 14). In addition, estimations of correlations provide the
first meaningful findings related with the main aim of the present research.
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Table 14. Correlations in regards with the simple model
Attitude
Relative
Risk and
Innovation
advantage
complexity
judgment
Compatibility
Attitude
1,000
0,752
0,596
0,231
0,690
Compatibility
0,752
1,000
0,596
0,320
0,670
Relative advantage
0,596
0,596
1,000
0,362
0,457
Risk and complexity
0,231
0,320
0,362
1,000
0,243
Innovation judgment
0,690
0,670
0,457
0,243
1,000
Secondly, the multivariate regression analysis was conducted in order to test the
hypothesis of the first model. Following the data of the Table 14 it can be claimed that all
hypotheses are statistically supported with the one-tale 5% significance level. Subsequently,
estimations of coefficients being greater than zero and σ value (the probability that
coefficient value significantly differs from zero) being lower than desired 5% significance
level indicates that attitude toward product and all four product innovations attributes are
directly related with product innovations judgments. Moreover, it can be claimed that product
innovation judgment has the strongest relationship with attitude toward product (0,690). This
leads to the fact that the general evaluation that customers make about the product innovation
has the strongest impact to the overall product innovation judgment. Considering product
innovations attributes, unexpectedly none of the most frequently by the researchers tested
attributes such as relative advantage (0,457) and risk and complexity (0,243) was identified
as having the strongest relationship with the product innovation judgment. While
compatibility (0,670) showed significantly higher correlation to the product innovation than
three others, already discussed innovation attributes. Need to mention that the latter attribute
was not stated as being frequently tested by previous researchers and was added to the model
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only as an additional predictor in the respect of the increasing customers need for
personalization.
Continuing with correlations, as Table 14 indicates all the correlation values are
significant with 5% two-tails significant level. In addition, for the purpose to decrease the
correlation standardized variables have been used. Due to this, standardized value is created
deducting the average value from the original value and dividing the difference by standard
error using the formula as follows:
𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑡𝑖𝑧𝑒𝑑 𝑣𝑎𝑙𝑢𝑒 =
𝑂𝑟𝑖𝑔𝑖𝑛𝑎𝑙 𝑣𝑎𝑙𝑢𝑒 − 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑣𝑎𝑙𝑢𝑒
𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑒𝑟𝑟𝑜𝑟
After the correlation values were standardized the multivariate regression model of
the simple model was evaluated as provided in the Table 15.
Table 15. Multivariate data of the simple model
Variable
Value (β)
Standard Error
σ (Sig.,)
Constant
0,635
0,557
0,025
Attitude
0,563
0,104
0,000
Compatibility
0,503
0,120
0,000
Relative advantage
0,310
0,093
0,007
Risk and complexity
0,590
0,081
0,000
As the results of the multivariate regression show, all variables are approved as being
statistical significant with one-tale 5% significance level (σ < 5%). In addition it can be
asserted that the quantity of variance that the simple model is able to explain is equal to R2 =
0,520.
After the estimations of the simple model are done, the model with interaction term
between product innovation attributes (latter will be called as improved model) and
moderation effect of Ads3affective vs. Ads3cognitive need to be evaluated. Consequently, the
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mathematical expression of the research model, which has been already presented in the
previous chapter as being constructed on multivariate regression model is provided in the
Figure 7. The Figure 7 shows that the interacted variables are described as follows:
Interacted variable = Variable * [Affective or cognitive information]
=α+
+ β1* [Affective of cognitive information] +
+ β2* Attitude + β3 * [Interacted attitude] +
+ β4 * Compatibility + β5 * [Interacted compatibility] +
+ β6 * [Relative advantage] + β7 * [Interacted relative advantage] +
+ β8 * [Risk and complexity] + β9 * [Interacted risk and complexity]
+ β10 * [Compatibility and Relative advantage] + β 11 * [Interacted Compatibility and
Relative advantage]
+ β12 * [Compatibility and Risk and complexity] + β 13 * [Interacted Compatibility and Risk
and complexity]
+ β14 * [Relative advantage and Risk and complexity] + β15 * [Interacted Relative
advantage and Risk and complexity]
+ β16 * [Compatibility and Relative advantage and Risk and complexity] + β 17 *
[Interacted Compatibility and Relative advantage and Risk and complexity]
Figure 7. Innovation judgment with the improved model
Consequently the main difference between the simple model and the improved model can be
expressed by three additional hypotheses (Table 16) that can be tested by the improved model
as well as all other hypothesis identified for this research:
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Table 16. Hypothesis of interaction terms between product innovation attributes
4a
Interaction of Compatibility and Relative Advantage is directly related to the product innovation
judgment
H4b
Interaction of Compatibility and Risk and Complexity is directly related to the product innovation
judgment
H4c
Interaction of Relative Advantage and Risk and Complexity is directly related to the product innovation
judgment
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According to the fact, that correlation of predictors are already estimated, the results
of multivariate regression of improved model is presented in the Table 17.
Table 17. Multivariate data of the improved model
Variable
Value (β)
Standard Error
σ (Sig.)
(α )Constant
0,391
0,017
0,000
Affective or cognitive information
0,211
0,104
0,000
Attitude
0,754
0,158
0,000
Interacted attitude
0,196
0,086
0,037
Compatibility
0,949
0,172
0,000
Interacted compatibility
0,135
0,013
0,000
Relative advantage
0,272
0,099
0,007
Interacted relative advantage
0,067
0,014
0,006
Risk and complexity
0,203
0,048
0,015
Interacted risk and complexity
0,107
0,017
0,000
Compatibility and Relative advantage
0,382
0,114
0,001
Interacted Compatibility and Relative advantage
0,076
0,011
0,005
Compatibility and Risk and complexity
0,343
0,147
0,020
Interacted Compatibility and Risk and complexity
0,161
0,015
0,000
Relative advantage and Risk and complexity
0,341
0,140
0,016
Interacted Relative advantage and Risk and complexity
0,061
0,014
0,007
Compatibility and Relative advantage and Risk and
0,204
0,091
0,026
0,141
0,031
0,040
complexity
Interacted Compatibility and Relative advantage and Risk
and complexity
Following the data provided in the Table 17, all the hypotheses of the improved model
are supported. As a result, it can generally be said that product innovation attributes and
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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attitude toward product moderated by Ads3affective vs. Ads3cognitive are directly related to the
product innovation judgment, because all values gained are statistically significant using onetail 5% significant level (σ >5%). Needs to be mentioned, that the improved model approved
the assumptions made after the estimations of correlations of predictors. Due to this, attitude
toward product can be distinguished as having the strongest influence to the product
innovation judgment with the value of 0,754. Consequently, it can be stated that the quantity
of variance that the improved model explains is equal to R2 = 0,609.
To sum up, after the multivariate regression analysis has been performed for both
simple and improved models the comparison of the scores R2 was implemented while
applying the F test formula, which was introduced at the beginning of the section.
The following F test results were gained:
𝐹=
259 ∗ 0,079
= 4,0235 > 0,449 = 𝐹13,267 (0,05)
13 ∗ 0,391
In respect to the results, it can be noted that F test value is significantly higher than F
distribution value with 5% probability. Taking this into account, we can come to a conclusion
that the model with interaction term between innovation attributes (improved model) has
statistically higher representativeness than the model without interactions terms involved
(simple model). Consequently, after the model is statistically proved the effect of moderators
– Ads3affective vs. Ads3cognitive are going to be analyzed in the second section.
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Result of Experiment
After the model was proved as being representative to analyze the effect of Ads3affective vs.
Ads3cognitive to product innovation judgment, the Figure 8 shows how the results of
multivariate regression analysis are applied to the mathematical expression of the research
model.
Innovation judgment = 0,391 (α) +
+ 0,211 (β1 ) * [Affective of cognitive information] +
+0,754 (β2 ) * Attitude + 0,196 (β3) * [Interacted attitude] +
+ 0,949 (β4 ) * Compatibility + 0,135(β5) * [Interacted compatibility] +
+ 0272 (β6) * [Relative advantage] + 0,067(β7) * [Interacted relative advantage] +
+ 0,203 (β8) * [Risk and complexity] + 0,107 (β9)* [Interacted risk and complexity] +
+0,382 (β10) * [Compatibility and Relative advantage] + 0,076(β11) * [Interacted
Compatibility and Relative advantage]
+ 0,343 (β12)* [Compatibility and Risk and complexity] + 0,161(β13) * [Interacted
Compatibility and Risk and complexity]
+ 0,341 (β14)* [Relative advantage and Risk and complexity] + 0,061(β15) *
[Interacted Relative advantage and Risk and complexity]
+ 0,204 (β16 )* [Compatibility and Relative advantage and Risk and complexity] +
0,141(β17) * [Interacted Compatibility and Relative advantage and Risk and
complexity]
Figure 8. Final results of experiment (moderation effect of Ads3affective vs. Ads3cognitive through product innovation
attributes and attitude toward product)
Comparing the affect of affective vs. cognitive information to the product
innovation judgment. For the purpose to compare the effect of affective and cognitive
information to the product innovation judgment the data collected with the moderator
Ads3affective (was coded by 1) and the data gathered with the Ads3affective (was coded with -1),
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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were applied into the regression model. Consequently after summarizing the results of the
experiment (Table 18), it becomes obvious that Ads3affective have statistically significant more
positive moderating effect on the overall product innovation judgment than Ads3cognitive.
These results are supported by the fact that participants that judged product innovation on
affective nature based information had the 3,89 average value of the product innovation
judgment while the average value of other group of participants is 3,02 (using information
provided in cognitive nature). Student’s test confirms that the difference between these
averages is statistically significant at 5% one-tail significance level. In addition, should be
mentioned, that Ads3affective caused 3,3 times better evaluation of the product innovation than
Ads3cognitive, before the moderation effect to the predictors has occurred (proportion of
constants 0,602/0,180).
However, in order to test the main hypothesis of the research the results need to be
compared in regards with product innovation attributes as well as attitude toward product.
Table 18. The effect of affective vs. cognitive information on product innovation judgment
Ads3affective
Ads3cognitive
(β)
(β)
Variable
(α) (Constant)
0,602
0,180
Attitude
0,950
0,558
Compatibility
1,084
0,814
Relative advantage
0,339
0,205
Risk and complexity
0,310
0,096
Compatibility and Relative advantage
0,458
0,306
Compatibility and Risk and complexity
0,504
0,182
Relative advantage and Risk and complexity
0,402
0,280
Compatibility and Relative advantage and Risk and complexity
0,345
0,063
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Effect of affective information to product innovation judgment (as opposite to
cognitive information) with predictors of product innovation attributes. It was
statistically confirmed that interaction between product innovation attributes has statistically
significant influence on the product innovation judgment. Consequently, the Table 18
involves values of the moderated effect of separate product innovation attributes as well as
moderated effect of interaction between product innovation attributes. The results provided in
the Table 18 shows that the hypothesis H7 - affective-based ads (as opposed to cognitive
based ads) have a positive moderating influence on the effect between product innovation
attributes and product innovation judgment - is statistically confirmed. It is fact, that
participant who evaluated the product innovation with respect to affective information tended
to perceive it as less risky and complex (affective: 0,339; cognitive: 0, 096). In addition,
participants who randomly received affective information assessed relative advantage of the
product innovation with higher scores in contrast to participants who got the cognitive
information (affective: 0,339; cognitive: 0,205). The same situation occurred with the
compatibility attribute, the affective information moderated the effect where participants
conceived product innovation as being more easily applied to their personal life and social
environment, while cognitive information caused lower compatibility scores assigned by
participants (affective: 1,084; cognitive: 0,814).
Effect of affective information to product innovation judgment (as opposite to
cognitive information) with predictors of attitude toward product. How it was noted in
the literature analysis and statistically confirmed by the model of the present research,
attitude toward product has a direct effect to the product innovation judgment. As the Table
16 shows, the hypothesis - H8 affective-based ads (as opposed to cognitive based ads) have a
positive moderating influence on the effect between attitudes toward product to the product
innovation judgment - was statistically approved too. Due to this, it was confirmed that
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participants after having randomly received affective information tended to form more
positive attitude toward product that caused better overall evaluation of the product
innovation judgment (affective: 0,950; cognitive: 0,558).
Conclusion of the research results. To sum up, the research model has been
successfully applied and the result of the experiment has supported all the hypotheses.
Consequently, it was statistically approved that a moderator variable (affective or cognitive
based ads) changes the strength of an effect between predictors (attitude toward product and
product innovation attributes) and the product innovation judgment (intension to use). Taking
this into account it can be claimed that the effect of affective information (as opposite to
cognitive) significantly contributes on the strength of this relationship. In addition, affective
information as apposite to cognitive being as a moderator does not tend to change its
direction in terms of its effect. It means, that affective information cause not only more
positive attitude toward product (as opposite to cognitive information) and better evaluation
of product innovation attributes, but even strengthen the possibility of the customer’s
intention to use the product innovation that is indicated by better score of product innovation
judgment. Moreover, the results of the research have showed, that attitude toward product is
statistically approved as being the strongest predictor of product innovation judgment while
the moderation effect of affective vs. cognitive information occur. Furthermore, the results
confirmed the importance of interaction term of the product innovation attributes. It was
statistically approved, that moderator of affective information significantly effect stronger
and more positive interaction not only between the product innovations attributes and product
innovation judgment (intension to use), but also interaction among product innovation
attributes. Consequently, the results show that the relationship between product innovation
attributes and product innovation judgment is stronger, when affective information effect not
each of the product innovation separately, but their interaction terms.
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Table 19. Evaluation of the hypotheses
H1
Compatibility is directly related to the product innovation judgment
Accepted
H2
Relative Advantage is directly related to the product innovation judgment
Accepted
H3
Risk and Complexity is directly related to the product innovation judgment
Accepted
H4a
Interaction of Compatibility and Relative Advantage is directly related to the product
Accepted
innovation judgment
H4b
Interaction of Compatibility and Risk and Complexity is directly related to the product
Accepted
innovation judgment
H4c
Interaction of Relative Advantage and Risk and Complexity is directly related to the
Accepted
product innovation judgment
H5
Attitude toward brand (Product) is directly related to the product innovation judgment
Accepted
H6
Affective-based and Cognitive-based ads is directly related to the product innovation
Accepted
judgment
H7
Affective-based ads (as opposed to cognitive based ads) have a positive moderating
Accepted
influence on the effect between product innovation attributes and product innovation
judgment
H8
Affective-based ads (as opposed to cognitive based ads) have a positive moderating
influence on the effect between attitudes toward brand (product) to the product innovation
judgment
Accepted
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Discussion
The chapter discusses the main findings of the present research in the context of previous
studies conducted. In addition, the contributions of the findings are considered with regard to
the theoretical implications for both concepts of the present research: product innovation
judgment and affective and cognitive information. Necessarily, the chapter presents practical
implications of the findings for marketing practitioners from the innovation management
field. For the conclusion, limitations of the present research are identified as well as
suggestions for the future researches are provided.
Synthesis Of Research Findings
The aim of the present research was to evaluate the effect of affective versus cognitive
information on product innovation judgment. For this purpose, the research model was
constructed on the basis of theoretical analysis of the previous researches and conducted
applying experiment as a research method for the empirical testing of hypothesis with a
sample of Lithuanian consumers. The results of the research showed that information
provided in an affective nature (affective-based ads) caused more positive product innovation
judgment than information received in a cognitive-nature (cognitive-based ads).
Consequently, findings indicate important insights about customer’s behavior regarding
product innovation judgment for both theoretical and practical implications.
While making a synthesis of the research findings, need to mention, that much of the
attention was dedicated for identifying reliable moderators in order to ensure the validity of
the research model and overall findings. Due to this, based on the findings from the pervious
researchers it was decided to apply advertisement as the best type for the communication of
information for the product innovations (Manchanda et al., 2008; Narayanan et al., 2005).
The empirical testing of advertisements of three product innovations (Innovation1,
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Innovation2, Innovation3) has led to the assumption that marketing tools that are using to
create a certain nature (affective or cognitive) of advertisements, in other words a certain
nature to communicate the information, can be successfully applied for the different kinds of
advertisements such as video and print. Hence, the results of the pre-experiment denied the
predictions, that the tested print, because of it’s descriptive format, will be evaluated by the
participants as being more logical, informative and encouraging to think than only one
cognitive-based video. As a result, even all of the empirically assessed advertisements
created in a cognitive nature were statistically approved as being cognitive-based ads, the
most logical, informative and encouraging to think was conceived video instead of two print
tested. Consequently, the results supported the early approach provided by Crite et al (1994),
which claims that the only one way to distinguish affective and cognitive information is to
evaluate the level of presentation, which not necessarily depends on the advertisement type or
any other kind of communication. However, in order to minimize the possibility to deviate
from the main purpose of the research, date of the main experiments were gathered using
only one advertisement type (video ads) for communicating information in two different
nature (affective and cognitive).
Subsequently, the main findings of the experiment confirmed the assumption, that
customers are tend to use affective instead of cognitive process while evaluating information
in order to make innovation judgment. This was statistically approved after manipulation
with two different advertisements of the same product, same duration and the same quality
and quantity of information about the innovation but in different presentation nature
(affective and cognitive). Consequently, such findings can be explained while taking into
account findings of the researchers conducted in psychology and neuroscience field, who
claim that customers are more willing to make plausible judgment that comes to the mind on
the spot rather than being to be involved in logical thinking (Wood, 2014). Due to this, as the
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findings of the present research show customers’ information behavior which leads to their
decision making can be effected by emotional arousal.
Implications For Theoretical Understanding Of Product Innovation Judgments
Numbers of literature investigating product innovation adoption analyzed it with a central
focus on cognitive process (Wood & Moreau, 2006). However, the need for alternative
understanding what process customers really use for judging the innovation become obvious
for numbers of reasons. First of all, extensive line of scholars and practitioners started to
empathize the increasing importance of the innovations, which is noted to be one of the most
effective ways to create a strong competitive advantage in a saturated, extremely competitive
markets. Due to this, currently scholars have been working more broadly to understand the
customer behavior in the context of psychology and neuroscience, because it started to be
extremely difficult to predict customer’s decisions relying only on the assumptions that are
based on logical explanations. In addition, researchers from the advertising field provided
numbers of empirical evidences showing even the dominant role of affective instead of
cognitive based advertisements while influencing the customer to make a positive decision
toward product. Taking everything into account the present research contributed to the
theoretical understanding of product innovation judgment while providing empirical
evidences that even the process is conceived as an uncertainty reduction ones it is not
necessarily are based on cognitive process.
Findings of the present research indicate that potential adopter who evaluates
information about product innovation in order to reduce uncertainty and make a decision can
be influenced by the way in which information is communicated. Specifically, findings
support the approach of King and Slovic (2014), which is based on the fact that overall
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judgment of the product innovation is influenced by customer’s affective evaluation of risk
and benefit.
The contribution of this paper includes the development of innovation adoption
process, while deeper investigating the first stage in the adoption process, which consist of
customers’ information evaluation about the innovation (Roger, 2003). Need to empathize,
the approach that product innovation adoption is inseparable with information assessment is
supported by numbers of scholars (Plewa, Troshani, Francis & Rampersad, 2012; Sääksjärvi
& Morel 2010; Azadegan & Teich, 2010; Flight et al., 2011; Cui, Bao, Chan, 2009; King &
Slovic, 2014; Seligman 2006; Hirunyawipada & Paswan, 2006; Arts et al., 2011).
Consequently, each of the finding related to this stage of the adoption process become even
more important because it was proven in numbers of studies as playing extremely important
role for customer’s decisions. Moreover, researchers have long maintained an interest to
understand product innovation attributes that are assumed to be the main measures in order to
evaluate customer’s decisions regarding product innovation judgment (Flight et al.,). Taking
this into account, findings of the present research show how affective and cognitive
information effect four, approved as one of the most valuable product innovation attributes:
compatibility, relative advantage, risk and complexity.
Implications For Theoretical Understanding Of Affective And Cognitive Information
Extensive spectrum of studies has been carried out for the purpose to understand customer’s
behavior in terms of information use. Due to this, even the term “information behavior” was
identified and started to be widely analyzed by academics in order comprehend what process
individual proceeds for using information. The findings of the present research contribute to
the deeper understanding of how customer’s information behavior is influenced by the
information provided in different nature – affective and cognitive. Findings of the present
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research support psychological theory, which claims that emotional arousal (specificity of
affective information) is able to reduce the possibility that rational arguments (septicity of
cognitive information) will play a dominant role in customer’s decision making (Wood,
2014). Hence, the results of the experiment showed that affective information has caused
more positive judgment of product innovation than cognitive information, and the different of
judgment was statistically significant (judgment caused by affective information was 3 time
higher). Moreover, need to empathize, that manipulation with different kind of information
was conducted in the product innovation judgment which is conceived as being uncertainty
reduction process where potential adopter use information in order to understand risks and
benefits of the innovation (Hoeffler, 2003; King & Slovic, 2014; Saaksarjvi et al., 2015 ).
Consequently, the results of the present research demonstrated that affective information was
able even to reduce the perceived risk (product innovation was evaluated as being 3 times
less risky and complex) and increase the perceived benefit (product innovation was evaluated
as being 1,7 times more beneficial). Accordingly, it can be claimed, that cognitive
information which is based on a rational, exclusively clear presentation of the product
innovation does not encourage customers to feel more informed and save in terms of possible
risk of the innovation. Due to this, the present research support the approach that emotions
aroused by the affective information makes customer to feel more positive which leads to
more fast, unconscious and associative evaluation of information and more positive attitude
toward product. Furthermore, results of the present research approved one more approach
from psychology field which is based on the fact, that lack of emotions can even cause the
interfere in customers decision making process (Wood, 2012). As a result, cognitive
information can has even the harmful effect on innovation judgment rather than helping to
reduce the feelings of uncertainty. Therefore, all tested predictors of product innovation
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judgment – product innovation attributes and attitude toward product – were assessed with
significanly lower scores after providing cognitve information.
Implications For Innovation Management
Marketing part is stated to be one of the most substantial and expensive determinants of
innovation success (Avlonitis & Papastathopoulou, 2000). Consequently, extensive line of
nowadays companies still trying to find the answer what is the key factor for launching a
successful innovation. Due to this, meaningful findings of the present research leads
marketers specialists to better understanding of the customer behavior in terms of two
different communication strategies for the product innovations. Understanding how customer
interprets information provided in two different nature could become the basis for the future
launch campaigns for most radical or simple incremental innovations. Findings uncover for
the marketers the specificities of customer’s decision making in regards with product
innovation judgment from the psychological point of view, while empathizing the dominant
role of the affective factors. Subsequently, the research provides significant insights about
most important product innovation attributes that can help the marketer to prepare as much
effective characteristic-based description of product innovation as it is possible. This
knowledge allows marketers to become more customer oriented while selecting the best
communication strategy for reaching the target audience and influencing their positive
decision toward innovation in as much effective way as it is possible. Hence, using findings
of the present research can help marketers to create more value for the company as well as for
the customers who do not want to be attacked by the information, which leads them to bigger
considerations, rather than make their decision process to be easier.
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Limitations
As with any research the present study has limitation. First and foremost limitation is related
to the reliance of the samples during the experiment implementation. The samples of the preexperiment were drawn from students at a single university in order to ensure the
homogeneity. However, the main experiment was conducted with significantly less
homogenous because of the purpose to reach the representative sample size. Such a situation
is not enable to ensure, that all external factors such us different age, social class etc. are
totally eliminated in order to be sure that the final results are impacted only by the
manipulation of the main moderators (affective-based ads and cognitive-based ads).
A second limitation is the modest quantity of moderators tested. Because of the
selected types advertisements and the results of the pre-experiment it was decided to make
experiment only with one innovation with the aim to gain results that would answer to the
main question of the research without any deviations, supposedly caused by external factors
such as different format of advertisements (video vs print).
Suggestions For Future Research
There is no doubt that there is a need for continued research in this field. First of all future
research needs to examine and test effect of affective and cognitive information on the
product innovations that be assigned to the high-involvement product category. There is even
the theoretical possibility, that the judgment of high-involvement product innovations can
receive statistically different effect made by affective and cognitive information than the
present study showed with product innovations that require fewer customers’ involvement to
be adopted. Such assumptions can be made because of the different level of uncertainty, due
to this findings of the future research would help to compare the process that customers use
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in order to judge different type of product innovations with the effect of affective and
cognitive information.
In addition, future research can contribute to the present research while testing the
effect of affective and cognitive information on product innovation with the wider range of
moderators. Manipulation with a larger amount of moderators would help to confirm the
approach of the present study or would uncover more important insights for both theoretical
and managerial implications in the discussed field.
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Conclusion
The aim of the present research was to evaluate the effect of affective versus cognitive
information on product innovation judgment. For this purpose the research model was
constructed on the basis of multivariate regression analysis with the moderation effect. The
experiment was applied as the most suitable research method for the present research because
of its specificity to test causal relationship between two or even more variables. In order to
ensure the validity of the experiment, pre-experiment were conducted with the aim to
empirically test and select the most suitable moderators – affective-based Ads and cognitivebased Ads.
According to the results of the study, two major issues have been identified: affectivebased Ads statistically approved as having more positive influence on the effect between
product innovation attributes and product innovation judgment as well as affective-based Ads
statistically approved as having more positive influence on the effect between attitude toward
product and product innovation judgment. It is important to empathize, that advertisement
was selected as a communication type for the purpose to ensure the most effective way to test
the effect of information provided in two different natures. Consequently, the results are
significant for both academic research and marketing practitioners.
For the theoretical implications the present study contributes while providing
alternative explanation of the product innovation judgment process with the dominant
role of affective information rather than cognitive ones. Moreover, the study clearly
indicates the link between marketing and psychology science in regards with deeper
understanding of customer behavior in product innovation judgment in the
perspectives of affective and cognitive information use. In addition, findings of the
results are applicable even for the developing deeper theoretical understanding about
the customer’s responses toward advertisements certainly in context of product
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innovation judgment. From the marketing management perspectives, practitioners are
provided by numbers of meaningful insights about customer’s behavior in terms two
different communication strategies (affective information versus cognitive) for the
product innovations. Due to this, these findings can even become the basis for the
affective-based launch campaigns to be prepared for the future innovations.
To sum up, the main aim of the research is achieved. The experiment resulted in
the acceptance of all the hypothesis of the research. It was statistically confirmed that
affective information cause more positive (as apposite to cognitive information) overall
judgment of product innovation with the product innovation attributes and attitude toward
product as predictors. Should be to mention, that further research needs to be conducted in
order to ensure sufficient variety of ways (e.g. more different ads of different products etc.) in
which affective and cognitive information could be provided. In addition, the effect of
affective and cognitive information on product innovation judgment should be examined
involving into the experiment product innovations that would be assigned to the highinvolvement product category for the purpose to investigate weather the type of product
innovation has a statistical significant influence on the overall judgment.
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Appendices
Appendix 1. Print of Ads1cognitive (Product1)
Sensor-packed cup can automatically determine what kind of liquid you’ve poured inside it,
as well as report the calorie content of whatever you poured. As a result it will help you to
manage your weight, stay hydrated, regulate caffeine, curb sugar, build muscle or even for
having better sleep by knowing the right time to intake the beverage before the sleep. Cup is
synchronized with smartphone app that shows how much and what kind of drinks you have
already used during the day. Cup’s design is very stylish and you can choose from different
colors. Moreover, it is very comfortable to use, because the shape of it is adapted for holding
the cap even with one hand. The cup has the cover, which can be opened with the easy press
and make it to be easily used in any place. The cap can easily be washed, because the shape
has not any unnecessary trappings.
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Appendix 2. Print of Ads2cognitive (Product2)
Rockety is a product that combines two functionalities – providing light for a daily commute
and extra security for possible situations of a bike theft.
Lamp has a single LED light, battery to provide power, dynamo to recharge system, GPS
module for tracking features and GSM module to maintain Internet connection.
Every Rockety Bike Lamp is synchronized with smartphone app that shows real time position
on a map. Application is also used to switch between two modes – Calm (lamp simply tracks
your movement with requests once in a minute) and Panic (lamp reacts immediately after
bike is moved, sends panic notification to smartphone and tracks location every 3 seconds
providing bike movement direction).
Appendix 3. Access to the Ads3affective and Ads3cognitive
Ads3affective : https://www.youtube.com/watch?v=6kCsztbSLoA
Ads3cognitive : https://www.youtube.com/watch?v=jlUu46Y8tWI
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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Appendix 4. Photo of Product3
104
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Appendix 5. Questionnaire constructed on the basis of Fligh et al., (2011) and Batra & Ray
(1986)
Part 1 questions – attitude toward product
useful /useless
important /unimportant
pleasant/unpleasant
nice /awful
Part 2 questions - compatibility
personal compatibility:
1. this product compliments other products currently owned by the potential adopter
2. this product is in – keeping with potential adopter‘s self image
3. this product fits into potential adopter‘s existing lifestyle or social class
social compatability:
1. using this product is socially acceptance
2. adopting this product would be met with approval by friends and family
3. many of my friends would want to own/use this product
social advantage:
1. it would be socially prestigious to own/use this product
2. this product has great social reward associated with its use
3. people would be impressed with my use of this product
Part 3 questions – relative advantage
1. this product will do what it claims
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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2. this product will perform reliably and consistently
3. i am confident that this product will perform as expexted
4. this product is truly new
Part 4 questions – risk and complexity
1. this product does not require a lot of time to learn how to use it
2. is not needed to have special skills to use the product
3. the product does not require high general knowledge about how to use
1. this product contains a new cutting edge technology
2. this product is new to the world or industry
3. this product provides radically new product benefits or features
Part 5 questions - innovation judgment
I would definitely use/I would definitely not use
106
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THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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108
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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109
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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110
Appendix 4. SPSS data multivariate of predictors’ correlations
Correlations
Attitude
Attitude
Pearson Correlation
Pearson Correlation
Compatibili
e
y
ent
,752**
,596**
,231**
,690**
,000
,000
,002
,000
277
277
277
277
277
,752**
1
,596**
,320**
,670**
,000
,000
,000
1
Sig. (2-tailed)
N
Relative_advantag Risk_and_complexit Innovation_judgm
Compatibility
Sig. (2-tailed)
,000
N
277
277
277
277
277
,596**
,596**
1
-362**
,457**
Sig. (2-tailed)
,000
,000
,000
,000
N
277
277
277
277
277
,231**
,320**
,362**
1
,243**
complexity Sig. (2-tailed)
,002
,000
,000
N
271
277
277
277
277
,690**
,670**
,457**
,243**
1
Sig. (2-tailed)
,000
,000
,000
,001
N
277
277
277
277
ty
Relative_a Pearson Correlation
dvantage
Risk_and_ Pearson Correlation
Pearson Correlation
Innovation_
judgment
**. Correlation is significant at the 0.01 level (2-tailed).
,001
277
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
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111
Appendix 5. SPSS multivariate date of the simple model
Model Summary
Model
R
,728a
1
R Square
,530
Adjusted R
Std. Error of the
Square
Estimate
,520
1,328632251
a. Predictors: (Constant), Attitude, Compatibility, Relative_advantage,
Risk_and_complexity
ANOVAb
Model
1
a.
Sum of Squares
df
Mean Square
Regression
360,939
4
90,235
Residual
319,513
272
1,765
Total
680,452
276
Predictors: (Constant), Attitude, Compatibility, Relative_advantage, Risk_and_complexity
Appendix 6. SPSS multivariate date of coefficients standatization
F
Sig.
51,117
,000a
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
PRODUCT INNOVATION JUDGMENT
112
Appendix 6. SPSS multivariate date of coefficients standatization
Correlations
Attitude
Attitude
Pearson
Compatibility
Relative_advant Risk_and_comple Innovation_judg
age
xity
ment
,752**
,596**
,231**
,690**
,000
,000
,002
,000
277
277
277
277
277
,752**
1
,596**
,320**
,670**
,000
,000
,000
1
Correlation
Sig. (2-tailed)
N
Pearson
Compatibil
Correlation
Sig. (2-tailed)
,000
N
277
277
277
277
277
,596**
,596**
1
-362**
,457**
Sig. (2-tailed)
,000
,000
,000
,000
N
277
277
277
277
277
,231**
,320**
,362**
1
,243**
Sig. (2-tailed)
,002
,000
,000
N
271
277
277
277
277
,690**
,670**
,457**
,243**
1
_judgment Sig. (2-tailed)
,000
,000
,000
,001
N
277
277
277
277
ity
Relative_a Pearson
dvantage Correlation
Risk_and_ Pearson
complexity Correlation
Pearson
Innovation
,001
Correlation
**. Correlation is significant at the 0.01 level (2-tailed).
Appendix 7. SPSS multivariate date of the simple model
Model Summary
277
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
PRODUCT INNOVATION JUDGMENT
Model
R
R Square
,728a
1
Adjusted R
Std. Error of the
Square
Estimate
,530
,520
113
1,328632251
a. Predictors: (Constant), Attitude, Compatibility, Relative_advantage,
Risk_and_complexity
ANOVAb
Model
1
Sum of Squares
df
Mean Square
Regression
360,939
4
90,235
Residual
319,513
272
1,765
Total
680,452
276
F
Sig.
,000a
51,117
a. Predictors: (Constant), Attitude, Compatibility, Relative_advantage, Risk_and_complexity
b. Dependent Variable: Innovation_judgment
Coefficientsa
Standardized
Unstandardized Coefficients
Model
1
B
Std. Error
(Constant)
,635
,557
Attitude
,563
,104
Compatibility
,503
Relative_advantage
Risk_and_complexity
Coefficients
Beta
t
Sig.
1,969
,025
,336
5,406
,000
,120
,243
4,203
,000
,310
,093
,122
2,473
,007
,590
,081
,240
4,732
,000
a. Dependent Variable: Innovation_judgment
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
PRODUCT INNOVATION JUDGMENT
114
Appendix 8. SPSS multivariate date of coefficients standatization
Coefficientsa
Standardized
Unstandardized Coefficients
Model
1
B
Coefficients
Std. Error
(Constant)
,635
,557
Attitude
,563
,104
Compatibility
,503
Relative_advantage
Risk_and_complexity
Beta
t
1,969
,025
,336
5,406
,000
,120
,243
4,203
,000
,310
,093
,122
2,473
,007
,590
,081
,240
4,732
,000
a. Dependent Variable: Innovation_judgment
Appendix 7 SPSS multivariate date of final results of the experiment
Model Summary
Model
1
R
Adjusted R
Std. Error of the
Square
Estimate
R Square
,780a
,609
,570
1,258167414
a. Predictors: (Constant), Affective_or_cognitive_information, Attitude,
Interacted_attitude, Compatibility, Interacted_compatibility,
Relative_advantage, Interacted_relative_advantage,
Risk_and_complexity, Interacted_risk_and_complexity,
Compatibility_and_Relative_advantage,
Interacted_Compatibility_and_Relative advantage,
Compatibility_and_Risk_and_
omplexity, Interacted_Compatibility_and_Risk_and_complexity, Relative
advantage_and_Risk_and_complexity,
Interacted_Relative_advantage_and_Risk_and_complexity,
Compatibility_and_Relative_advantage_and_Risk_and_complexity,
Interacted_Compatibility_and_Relative_advantage_and_Risk_and_com
plexity
Sig.
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
PRODUCT INNOVATION JUDGMENT
115
ANOVAb
Model
1
Sum of Squares
df
Mean Square
Regression
414,510
17
24,383
Residual
265,942
259
1,583
Total
680,452
276
F
Sig.
15,403
,000a
a. Predictors: (Constant), Affective_or_cognitive_information, Attitude, Interacted_attitude,
Compatibility, Interacted_compatibility, Relative_advantage, Interacted_relative_advantage,
Risk_and_complexity, Interacted_risk_and_complexity, Compatibility_and_Relative_advantage,
Interacted_Compatibility_and_Relative advantage, Compatibility_and_Risk_and_complexity,
Interacted_Compatibility_and_Risk_and_complexity, Relative
advantage_and_Risk_and_complexity, Interacted_Relative_advantage_and_Risk_and_complexity,
Compatibility_and_Relative_advantage_and_Risk_and_complexity,
Interacted_Compatibility_and_Relative_advantage_and_Risk_and_complexity
b. Dependent Variable: Innovation_judgment
THE EFFECT OF AFFECTIVE VS. COGNITIVE INFORMATION ON
PRODUCT INNOVATION JUDGMENT
116
Coefficientsa
Model
1
Unstandardized
Standardized
Coefficients
Coefficients
B
(Constant)
Std. Error
0,391
,017
Affective_or_cognitive_information
,211
,104
Attitude
,754
Interacted_attitude
t
Sig.
Beta
28,940
,000
,110
3,298
,000
,158
,393
4,774
,000
,196
,086
,085
1,793
,037
Compatibility
,949
,172
,495
5,508
,000
Interacted_compatibility
,135
,013
,065
6,023
,000
Relative_advantage
,272
,099
,135
2,486
,007
Interacted_relative_advantage
,067
,014
,036
2,520
,006
Risk_and_complexity
,203
,048
,106
2,182
,015
Interacted_risk_and_complexity
,107
,017
,034
5,158
,000
Compatibility_and_Relative_advantage
,382
,114
,147
2,997
,001
Interacted_Compatibility_and_Relative advantage
,076
,011
,361
2,593
,005
Compatibility_and_Risk_and_complexity
,343
,147
,216
2,064
,020
Interacted_Compatibility_and_Risk_and_complexity
,161
,015
,105
31,098
,000
Relative advantage_and_Risk_and_complexity
,341
,140
,211
2,440
,016
Interacted_Relative_advantage_and_Risk_and_co
,061
,014
,039
2,437
,007
,204
,091
,207
2,248
,026
,141
,031
,073
1,755
,040
mplexity
Compatibility_and_Relative_advantage_and_Risk_
and_complexity
Interacted_Compatibility_and_Relative_advantage_
and_Risk_and_complexity
a. Dependent Variable: Innovation_judgment