2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
The role of emotions in customer buying decisions: An analysis of
Telecommunication Industry, Sri Lanka.
Pradeep Randiwela
p_randiwela@yahoo.co.uk
University of Colombo-Sri Lanka
Tharanga S. Widanachchi
tharangasw@gmail.com
July 2-3, 2013
Cambridge, UK
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ABSTRACT
Emotions perform different functions and operate at different levels under various stages of
customer decision making. As those are often represented in subtle, complex ways and diverse
range of other techniques, with a better understanding on marketing triggers that makes the
purchase decision, obviously will lead towards productive action and more eager customers.
The respective study targets towards analyzing the role of emotions in customer buying
process in the Telecommunication industry while focusing on the real time customer feedback.
The overall process scans and quantifies the emotional states of customers on their purchase
decisions and determines the level of influence that each state has contributed to the scenario
given the industry. The main objective of this study is to assess the rate of influence generated
via different emotional states and to classify them based on their gravity at buying decisions.
This will ultimately provide a broad image about the emotional influence generated targeting the
industry of the country as a whole and will guide each player in the arena with an attractive
source of market intelligence in achieving their strategic marketing goals.
In carrying out the study online social media sources of five giant Telco players in the
market has been selected and posts were extracted from those sources with a balance. Emotion
mining software has been used for classifying and weighing the customer reviews in its essence
while examining the polarity of the respective opinions as to generate the real time scenario.
INTRODUCTION
Emotions exert and exceptionally powerful force on human behaviour. Strong emotions can
cause actions that someone might not normally perform, or avoid situations that generally enjoy.
In psychology, emotion is described as a complex state of feeling that results in physical and
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psychological changes which influences the thought and behaviour. It carries more weight than
reason for most of the actions and they are about gut reaction and influence the cause and effect.
Therefore emotions play the role of motivator and can be identified as the root of motivation.
Everything that happens has a considerable relationship with the emotion and holds a giant share
in the event of buying. It can be the most supportive lever for any type of business environment
with a clear understanding of the real scenario of emotion in the current market place.
In assessing the role of emotion within any particular industry, the social media sources
such as “twitter”, “facebook”, etc., hold a key position in capturing the genuine feedback of the
market. Using emotion mining tools and other statistical mechanisms, emotions can be
measured, which illustrate how people feel about a brand. This can be the starting point of the
journey of finding ways to manage each marketing attempts for any type of scenario. This also a
measurement of the companies target towards what they need to deliver and what has been
captured by the end consumer in a psychological way. The emotional categories and their roles
in the market arena, therefore makes the biggest impact to the success of any kind of brand in the
market and also determines its position in the industry.
Therefore it is vital to observe and understand the emotions of the market place towards
the respective industry which could help with long and short term communications and build up
their ethics in practice. Sri Lanka’s Telecommunication industry is one of the most competitive
markets in the region with several operators competing for a total addressable population of 21.7
million. This makes the arena most hard and vulnerable for any kind of player as emotions itself
makes a key risk to Telecom operators in the country. Therefore in fulfilling the gap of market
knowledge with the help of social media data sources and with all the background stuff in hand,
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this respective study focuses on the role of emotions in the customer buying decisions with
regards to the Telecommunication industry of Sri Lanka.
Research Objectives
a. To assess the rate of influence generated via different emotional states with regards to the
Telecommunication Industry, Sri Lanka
b. To come up with a growing model based on their gravity at buying decisions
REVIEW OF LITERATURE
Emotion as well its role in the industry has been extracted and prioritized in the existing
marketing arena where the result would be the key turning point for any type of market oriented
attempts within the given industry. In backing up the studies carried out on this subject area,
various researchers has come up with different set of theories and explanations which lays a
strong foundation for this respective study.
In general, physiological theories suggest that
responses within the body are responsible for emotions while the neurological theories argue that
activity within the brain leads to emotional responses. Whereas according to the cognitive
theories, thoughts and other mental activities play an essential role in the formation of emotions.
According to the James-Lang theory of emotion proposed by famous psychologists,
James and Lange, (1884), emotions occur as a result of physiological reactions to the events. As
per the theory, external stimulus leads towards a physiological reaction and emotional reaction is
dependent upon the way that someone interprets those physical reactions. In contributing to the
emotional theories, Canon and Bard, (1920), has introduced a psychological explanation of
emotion through their Cannon-Bard theory of emotion while challenging the James-Lang theory
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of emotion. According to the creators the theory states that it stimulus that we react and
experience the associated emotion at the same time. It is suggested that emotions result when the
thalamus sends a message to the brain in response to a stimulus, resulting in a physiological
reaction according to their further explanations.
Furthermore, when discussing about theories of emotion, Schachter-Singer theory or the
two factor theory explains the cognitive theory of emotion.
According to the researchers,
Schachter and Singer, (1962), the physiological arousal occurs first and then the individual must
identify the reason behind this arousal in order to experience and label it as an emotion. Along
with the identification of emotion theories the need of having a better classification of emotions
also aroused. It made the task of researchers difficult as that the experiences are so complex and
involve so many different factors, so distinguishing one emotion from another is a lot like
drawing lines of sand in the desert. It can be hard to determine where one emotion ends or
another begins. In addition, each individual experience is often comprised of multiple emotions
at once, which add another dimension of complexity to the respective emotional experience.
Despite of the difficulty, psychologists have attempted to come up with more depth theories on
emotion classifications during the past period.
Among the set of leading and well established theories, Ekman, (1977), invented his list
of basic emotions after doing research on many different cultures. Across all cultures studied,
Ekman found six basic emotions; namely “Anger”, “Disgust”, “Fear”, “Happiness”, “Sadness”
and “Surprise”. Ekman has added some further set of emotions types to the already published list
in the 1990s including “Amusement”, “Contempt”, “Contentment”, “Embarrassment”,
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“Excitement”, “Guilt”, “Pride in achievement”, “Relief”, “Satisfaction”, “Sensory pleasure” and
“Shame”
By adding another dimension to the study area, another famous researcher Plutchik,
(1980), has created a new conception of emotions. According to the researcher, it is being called
as the “wheel of emotions” because it demonstrated how different emotions can blend into one
another and create new emotions. Plutchik first suggested eight primary bipolar emotions: joy
versus sadness; anger versus fear; trust versus disgust; and surprise versus anticipation. There,
Plutchik identified more advanced emotions based on their differences in intensities.
The
“Parrots theory of emotion” in 2001 is also a challenging result for the existing in the arena and it
has been recognized as the most nuanced classification of emotions. In weighing the literature in
a comparable manner, it can be clearly extracted that there is a lot of disparity on how
researchers choose to group different emotions. Therefore there is a sequence of research works
to be done in order to dig the knowhow related to the subject area, while clearly covering all the
scenarios.
RESEARCH METHOD
Research Design
The study is organized in a way that the results can be captured through a depth analysis where
they can be measured via different angels. This respective design encourages the research
outcomes to be organized in a manner that they depict the actual scenario in the selected research
arena. While practising the primary data collection method, the planned survey was carried out
based on the social media sources of five giant Telco players in the country. This rescues the
need of having an unbiased as well as the genuine data source targeting the ultimate reliability of
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the study. Identification of the gravity of customer emotions towards the Telco industry will then
no longer be dependable as the inputs are from general public who are voluntarily expressing and
sharing their views with regards to the industry as a whole.
Variable identification
The respective study is based on two separate assessment path ways including assessing the
polarity and valuing the emotion categories in order to identify its role in the industry. Therefore
through comprehensive analysis on the leading emotion classification theories, the classification
of basic emotions by Ekman, P has been selected as the most spirited categorization of emotions
in order to carry out the study in the most fruitful manner. As a further extension to the Ekmans’
model “Anticipation” factor also has been taken out from the wheel of emotions explained by
Pultchik, R and has been amended to the model in conducting the research. Figure 1, depicts the
conceptual framework used for this respective study.
Figure 1: Conceptual Framework
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Sample Selection
The research employs the non-probability sampling in carrying out the study and therefore
purposive sampling has been used for this research as the sampling method. Though there are
several players in the Telecommunication industry in Sri Lanka, the most leading five companies
were chosen for the study as they holds the majority of the share in the market, considering as a
whole. Through the social media pages of the selected Telco players, customer write-ups and
comments were extracted within a period of four months in a sequential manner, without judging
or manipulating the message that each respective write-up delivers. Among the collected sample
of 726 write-ups 533 write-ups were chosen for the study by judging the relevancy of each writeup towards the Telco industry. There the judgmental sampling method has also been merged into
the purposive sampling method, considering the requirement.
Data Analysis
In analysing the data two approaches has been practised as to get the real outcome of the study.
As the first approach the data sample has been distributed among four carefully selected
annotators for evaluations (used as the training survey) according to their expert opinions. Along
with the first approach the study has used widely used emotion analysis software,
“SentiStrength” (Thelwall et al. 2010) classifier.
This was built especially to cope with
sentiment detection in short informal text and combines a lexicon-based approach with more
sophisticated linguistic rules. The same data set has been fed into the software and received the
result of the second approach of the respective study. In SentiStrength, it detects the positive and
negative emotion in write-ups and develops workarounds for lack of grammar and spelling while
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harnessing emotion expression and classifies each comment as positive (1-5) AND negative (15) scale with 1 denoting no sentiment and 5 denoting high sentiment value.
In summing up the data analysis work from two path ways; the outcomes of the training
survey has been analysed using mean, standard deviation and one-way ANOVA evaluations.
Whereas the respective data set was carefully compared with the outcome of the emotion mining
software, “SentiStrength” in justifying the answers in more reliable manner. This study also
covers a comprehensive percentage analysis on each factor to elaborate the scenario in a very
much descriptive manner.
DISCUSSION
Based on the thorough statistical analysis conducted on research data, the polarity of the emotion
in customer buying can be expressed via two separate approaches. Below includes the outcomes
via the training survey as well as through SentiStrength, by analysing their final evaluations as a
whole.
Figure 2: Emotional polarity distribution
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Figure 3: Emotional polarity summary
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The above results generated through SentiStrength depicts that the role of emotion in customer
buying shows a significant biasness towards the positive polarity. When comparing the outcome
with the training survey obtained from four different annotators, it draws the reliable conclusion
as that those results justify the outcomes of SentiStrength with a strong statistical backing up for
the respective result. Error! No text of specified style in document.
Table 1: Descriptive Statistics
Variable
Mean
Happy
233.25
Surprise
Anticipation
Fear
Sadness
Anger
Disgust
StDev
24.55
46.00
134.00
14.50
104.00
108.00
121.00
18.83
32.73
12.97
32.78
46.64
55.92
According to the above statistics generated via training survey, the study discloses the positive
polarity of emotions in customer buying with reference to the Telco industry in Sri Lanka. With
a mean value of 233.25 “happy” holds the highest position in emotion classification while other
positive range variables supports the result by strengthening the result furthermore.
In-order to establish the above findings in a more statistically reliable way the one-way
analysis of variance (ANOVA) test has been applied to the scenario. It allows in determining if
one variable has a significant effect on any other variable in the emotion classification, under the
study.
Table 2: One-way ANOVA test
Source DF
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SS
MS
F
Factor
7
153606
21944 20.40
Error
24
25813
1076
Total
31
179419
P
0.000
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A significantly low p-value of “0” with a 95% confidence interval resulting from a one-way
ANOVA test indicates that there is a significant difference between the outcomes retrieved with
regards to different variables of the emotion classification model used. Therefore the ultimate
result can be taken as reliable for a sustainable conclusion.
In assessing the rate of influence generated via different emotional states in the used
classification, below illustrates their gravity at buying decisions.
Table 3: Gravity of emotional states at buying decisions
Emotion
Happy
Surprise
Anticipation
Fear
Sadness
Anger
Disgust
Average of emotion instances
found in 533 write-ups
233.25
46
134
14.5
104
108
121
43.76
8.63
25.14
2.72
19.51
20.26
22.70
Percentage (%)
Accordingly, the emotion states belong to positive polarity leads the classification although with
a considerable attraction towards “Disgust”, “Anger” and “Sadness” statuses. The “Fear” and
“Surprise” hold the least positions from the two respective polarity groups where a crucial
marketing point lies. Although the polarity level is attracted towards positivity, the “Surprise”
factor would gain much advantage in the industry in a competitive market arena, and therefore
should be looked after carefully.
CONCLUSION
Emotion itself plays a giant role in any given scenario as all human acts based upon a certain
kind of emotional influence. Therefore, any of the strategic marketing attempts by particular
player in a given industry would definitely have a biggest impact from those emotional decisions
of the ultimate customer. This made the urgency of obtaining a carefully assessed as well as a
reliable identification on the role of emotions in customer buying decisions with regards to the
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Telecommunication Industry, Sri Lanka, considering the tight competitive environment and the
limited market share.
The study reveals that generally, there is a positive emotional locale towards the Telco
industry of Sri Lanka where, a considerable tendency towards “Disgust”, “Anger” and “Sadness”
and “Fear” also exists in the atmosphere. Although the “Happiness” dominates the research
outcome, still there is a considerable portion of “anticipation” also present in the environment
inviting the giants to think out of the ladder in their service delivery. Along the way, by showing
another side of the coin, the “Surprise” variable has a comparably less value in the emotion
classification. This is generating an alarm towards the players in getting the best marketing
advantage which the outcome delivers silently.
Analysing the role of emotion in customer buying decisions itself is a controversial topic
as measuring the level of influence generated via different emotional variables is a tough task by
its every mean. Although the chosen data source; the social media pages, delivers a real time
genuine feedback, it is critical to get the real emotional picture at it every essence. Therefore, this
respective study is open for further research with different data sources and assessing tools as
well as this invites a common marketing model in identification of emotional influence with
regards to the leading industries.
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