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THE BUCHAREST UNIVERSITY OF ECONOMICS STUDIES

PHD THESISPresented and publicly defended by the author:

Gabriel Gh. Jipa

Title of the PhD Thesis:

Modeling and research of the motivation influence on buying behaviour

PhD Supervisor: Prof. Univ. Dr. NICOLAE TEODORESCU

July 18th, 2019, Bucharest

MARKETING DOCTORAL SCHOOL

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Motivation modeling and research

To understand the interactions

and effects of motivation with other

processes in mobile application

buying behaviour context.

Public Transportation, Singapore 2019, Personal archive

First Iphone launched 2007, aiming to transform lives*

*Source: https://www.youtube.com/watch?v=wGoM_wVrwng

Pokémon Go, a mobile app game,

reached 2 Billion USD revenue.

(Forbes, 2018)

https://www.youtube.com/watch?v=wGoM_wVrwng

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Decisional problem

Choice

TargetAudience

AppstoresDistribution

Vendors

Buyer/ Consumer

•Difficult choice•Free versus paid• Instant accessibility

• Difficult Search

https://www.apple.com/ios/app-store/

https://www.apple.com/ios/app-store/

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Research subquestions

Identification of paths of effect between motivation

and other processes.

Propose a conceptual model that explores the effects of

motivation ,exogenous ,endogenous factors, behavioral intention

Empirical validation of the proposed model.

Can we explore buyers’ opinions to identify potential

needs and motives?

Motivation

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Motivation: Historical perspective

• Theories concerned with the content /

classification

• Theories concerned with the process

• Motivation in isolation or integrated in a process

• Contemporary approach in motivation – positive/

flow / wellbeing. Call for another grand theory.

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Motivation and buyer behavior

Motivational research rise

and abandonment

Grand theories and their critiques and support

Too generic versus too specialized

research

Lack of taxonomy and ontology acceptance -

including terminology

Motivation in e-commerce/

mobile buyer behavior

Technology adoption influences buyer

behavior ?

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Modern needs from research – Why mobile apps

App-driven society: 30 hours per month

Apps downloads: 205.4 billion in 2018 1. Buyer and consumer difference

2. Application stores = apps supermarket

1. Digital substitutes of goods and services

2. Digital ecosystem

3. Universal instant coverage

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Buyer’s challenge – Which mobile app?

• Control of distribution

• Entry of new players – LINE,

Wechat, Kakao

App Store generated 93% more revenue than Google Play in Q3, 2018

Projected market revenue for 2020 is 188.9

Billion USD

• Google’s Android open source mobile operating

system, 73.24%

• Apple IOS recording 25.3%, with a user base of

1.4Bn smart mobile devices

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Vendor’s challenge

What are the Needs that

top revenue generating

apps satisfies?

Difference in profiles

based on appstores

activity

Rank Overall

revenue

Apple Appstore

revenue

Google App Store

revenue

1 Netflix Netflix Tinder

2 Tinder Tencent Video Google drive

3 Tencent Video Tinder Pandora

4 iQIYI iQIYI LINE

5 Pandora Kwai BIGO Live

6 Kwai Youtube Netflix

7 Youtube Pandora Azar

8 LINE Youku Kakao Talk

App-driven society: 30 hours per month

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Downloads

Difference in

generating

revenue:

1. Behavioral

Data

2. Charges

Rank Overall downloads

Apple Appstore

Downloads

Google App Store

Downloads

1 WhatsApp Tiktok (musik.ly) WhatsApp2 Messenger Youtube Messenger 3 Facebook WhatsApp Facebook4 Tiktok (musik.ly) Instagram Tiktok (musik.ly)5 Instagram Messanger Instagram6 UC Browser Facebook SHAREit7 SHAREit Google Maps UC Browser8 Youtube Netflix Vigo Video9 Snapchat Gmail Snapchat

No utilitarian, productivity or efficiency apps are in TOP 9 ranking

(that excludes the Gaming category).

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Research Methodology

1. Qualitative

2. Quantitative

3. Mixed methods -

exploratory

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Methodology: Data Collection and Analysis

Target population

definition: • White collar worker

(Prinz, 2015)

• Age: 21-70 years

• Guidelines from

ESOMAR and GDPR

• International research

Control variables:

• Experience

• Age

• Gender

• Country of residence

Qualitative research:

Interviews

Qualitative research: WAT

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Projective techniques, Interviews

Word Association TestN=10

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Results- projective techniques, N=10

StimulusReason Useful Easier

ValueTime

Response

(extract)

money good simpler money consuming

money benefits simple benefits resources

Satisfaction Trust, help Comfort Satisfaction Fun

Appreciation Needed Simple Appreciation Life

money get simplify time is money

Money or

return

Help Simple Benefit Value

Money Helpful Shortcut Worth Money

Money Helpful Doesn’t mean

best

Money Freedom

Money Good Simplification impact No patience

money good simpler money consuming

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The conceptual model

1. Constructs common to TAM, TAM2,

TAM3, UTAUT and Howard-Sheth,

EKB, Newman and Gross Theory of

consumption values. Mobile

adoption research.

2. Constructs leveraged the grand

theories of buyer behavior

3. Construct leveraged from

psychology (knowledge

accessibility and information

retrieval, perceptual distortion )

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Motivation in validated model

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Data Collection: validated N=360, 577 total responses

• Self managed online survey instrument, no

parceling per item (LimeSurvey), 57 items.

• Challenge to estimate the total population size

(smartphone users being estimated at 2.71Bn)

• Judgment non-probabilistic sampling method

• Individuals contacted: 2731

• Response rate= 21.11% (total)

• Response rate = 13.21% (complete)

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Data analysis flow

Based on (Gaskin, 2019; Arbuckle, 2012; Byrne, 2000) and own research

Modeling technique is SEM ;Tools: SPSS Statistics and SPSS AMOS ; Dataiku DSS; Pythion3.5

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Data Analysis quantitative research. Geocoding. N=360

1. Romania, # respondents 242 (100

females, 142 males),

2. United States # respondents 10,

3. Poland # respondents 9,

4. United Kingdom # respondents 9,

5. Russia # respondents 8,

6. Canada # respondents 8,

7. Czech Republic # respondents 8,

8. Germany # respondents 8,

9. Croatia # respondents 7

• 1 respondent : Jordan, Netherlands,

Qatar, Singapore, Switzerland, Taiwan,

Belgium, Brazil, Cyprus, Greece,

Indonesia Map: Country of survey filling based on reverse geo-coding (IP)

N=34 countries and 1 untraceable

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Data Analysis, quantitative research N=360

Respondents:

• Delete apps when they need,

• Look for alternative when the app they want is not compatible or supported.

The respondents are able to explain why they install apps:

• utilitarian services because of their convenience.

• Curiosity, information search, stay informed, and leisure are also drivers.

• Getting skillful using mobile apps is easy , proficient in using apps; the user experience is

positive, helping them to be efficient and achieve tasks.

They realize that selective attention affects them due to the

volume of information and they do not hesitate to install

app once motivated.

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EFA and CFA , quantitative research N=360

• Measurement model (EFA) to be

confirmed in SPSS AMOS

• CFA suggest that a second order

factor SOF– Perception (KA +

MB) should be proposed . This is

aligned with literature. Proposed SOF: (KA + MB)

For N size, factor loadings > .3 are relevant

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Results – Structural Model and its variants

Model A: Initial model derived from theoretical constructs and hypothesis

Model F: Selected Model, derived from Model A

Model B: Male. Model based on gender. Female model is not stable statistically because N=117

Model C: Romania Model based on Country of residency (N=241). The other cluster (all countries,

excluding Ro) are not stable statistically (N=119 <min sample size)

Model D TAM. Reduced model, without perceptual second order factor

Model D TAM Significant. Reduced model, without perceptual second order factor (Perception, KA, MB)

Model E (with Control) Model F with control variables added

Optional

Model F-optimized: a model was optimized to correct the CFI deviation based on factor loading, resulting a

simplified factors structure:; CMIN/DF 1.580 Between 1 and 3 Excellent; CFI 0.907 >0.90 Acceptable; SRMR 0.062

<0.08 Excellent; RMSEA 0.025 <0.06 Excellent; PClose 1.000 >0.05 Excellent.

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Validated model (F)

1. The model proposes direct effects

between motivation categories states

and other endogenous and

exogenous factors

2. The model proposes a feedback loop

from behavioral intention and

perceptual factors

3. Motivational states (a Gestaltist

view) are derived form TAM

(Technology Acceptance Model) and

its variants

4. Howard Sheth problem solving

context: Routinized (RPS), Limited

(LPS), Extensive (EPS).

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Selected Structural Model F: N =360, “white collars”

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Results: Motivation

1. Motivation latent factors includes Communication, Entertainment And Social Utility

Motivation; Only Social utility (SUM) has a significant positive effect on Behavioral

Intention, being activated by Direct Inhibitors (time, contextual, value for money).

2. Entertainment motivation are highly influenced by Social (Exogenous) and linked

(EM→CM)

3. Perceived Ease of Use (Intrinsic motivation type) is not generating a strong effect on

Perceived usefulness or Behavioral Intention as expected in TAM based literature.

That suggests that “productions” are created instead of declarative knowledge. Still is

highly influenced by Perception while direct inhibitors have a significant negative

effect.

4. Perceived Usefulness generates a positive effect on Behavioral intention and

accumulates effects from Exogenous, activated by Direct Inhibitors and SUM

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Results: Motivational states and categories

Social Utility Motivation factor is determined by Services, News, Brand Trust

and Curiosity-leisure (highest effect, intrinsic motivation). Factor loadings in [0.7-

0.91] interval.

Communication Motivation is determined by Competitiveness, Communicate

care and Meet new people (highest), with [0.67:73] factor loadings

Entertainment Motivation is determined by hedonic, kill time and boredom

(highest), with [0.75:0.87] factor loadings

Perceived Usefulness, extrinsic motivation is measured by Reward,

Productivity and Quality of life (highest) with effects [0.66:0.75]

Perceived Ease of Use, intrinsic motivation is measured by Skillful and

Mobile app use (highest) with effects [0.83:0.84]

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Results: Exogenous factors

Exogenous – Social factor is described by by Reference Groups

(Highest), Social and Subjective Norm- influencers with loadings of

[0.69:0.80].

Direct inhibitors as Time Pressure, Unexpected situational (highest

effect) and Value for money, with loadings in [0.54:0.72] interval.

Unexpected situational requires intensive cognitive effort from

perception and knowledge accessibility.

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Results: Perception and its latent factors

Sensitivity - Distortion Bias or MB factor is measured by

Ambiguous evidence- heuristic processing, Need for cognitive closure-

decisiveness, H-based filtering, Anchoring (Highest) and Perceptual

vigilance loadings of [0.33:0.57].

Knowledge Accessibility is measured by Information Salience,

Inferences (Productions), Information Frequency (highest effect),

Information recency and First supporting evidence with loadings in

[0.39:0.68] interval.

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Results: controls and sub-groups

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Male, N=241

Romanian, N=243

Sub-group shows slight differences of effects, weaker than Model F between

Perceived Usefulness and Intention but stronger Social Utility Motivation

effect on Intention. They have the strongest effect of Direct Inhibitors on SUM,

that suggest a strong contextual activation path for Intention. Also the Direct

Inhibitors have the weakest effect on Perceived Ease of Use (-0.16) but Highest

on Perceived Usefulness (0.62)

Supports the F Model findings but indicates stronger effect of Perceived

Usefulness →Behavioral Intent and weaker Social Utility Motivation →

Behavioral intent. Direct Inhibitors effect on Social Utility Motivation is

reduced. Direct Inhibitors have a very negative high influence on Perceived

Ease of Use

Others

The feedback loop from intention to perception is strong in all models, weakest

being the Male only. Female and Non-Romanian respondents sample size is too

small

Results: Sub-groups

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Results: Controls

• Age and Gender do not significantly influence the model’s morphology, based

on current data. Age and Experience are not necessarily corelated (based on the

current dataset and target population).

• Self-assessed experience influences the behavioral intention (seen as a

motivation state, strong antecedent of behavior ) but is not correlated with

Exogenous factors.

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Results: exploratory analysis using NLP

Open-ended answers

analyzed using

machine learning.

Tools: • Python/ word2vec

embedding (Mikolov,

2013) at Google using

Gensim (Rehurek and

Sojka, 2010 )

• Glove vectors pre-trained

on Wikipedia for

comparison

• Tensorboard projection

visualization/ tensorflow

model

• Unsupervised machine

learning

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Results: exploratory analysis using NLP

Can we analyze opinions (attitudes) with alternative content analytics approach?

Question: “Please write down the

reasons why you would pay for a for

mobile application and also why you

would not pay for a mobile application”

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Exploratory analysis using word embedding (vector)

• N=231 respondents.

• Respondents’ distribution is:

32.41% Female and 67.59

Male.

• Largest participation was

recorded for Romania

67.04%, followed by United

States 2.77%, Poland

2.49% and UK 2.49% from

34 countries.

http://127.0.0.1:6006/#projector&run=.

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WAT-like analysis using word embedding

*Glove Stanford NLP is a pretrained vector modelhttps://nlp.stanford.edu/pubs/glove.pdf

N=231 respondents,

cluster belonging.

Term ' similar', cluster #1

-Glove* Top3 [('example',0.901),

('instance',0.890), ('such',0.886)]

-Own Corpus Top3 [('free',0.963),

('charge',0.963), ('available',0.958)]

https://nlp.stanford.edu/pubs/glove.pdf

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Sentence completion – next word prediction using word embedding

Sentence to

probe

Related words or concepts and cosine distance

Interpretation

I pay if I

see

value.???

[('but',0.006), ('helps',0.006), ('just',0.006), ('really',0.006), ('because',0.005),

('something',0.005), ('s',0.005), ('install',0.005), ('bring',0.005), ('useless',0.005)]

Interpretation:

The outcome indicates related words or concepts as utilitarian motivation as well as very close distance.

Word “Useless” is surprisingly found.

Time is

…???

[('no',0.007), ('money',0.006), ('help',0.006), ('work',0.006), ('very',0.006),

('helps',0.006), ('then',0.006), ('at',0.005), ('expensive',0.005), ('way',0.005)]

Interpretation:

Is interesting to note “that time is” money or expensive, work, help can be generated out of unsupervised

machine learning process from vector numerical representationSentence completion test (similar to Next word prediction)., predict_output_word method in Gensim using word2vec

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Exploratory analysis using word embedding

N=231, Association prediction, using nearest_similarity_cosmul() method

Terms related Interpretation and predicted word

("useful", "value",

"features"); ?

“useful is related to value, as features is related to

applications”

("time", "money",

"value"); ?

” time is related to money, as clear is related to

value

Relational similarity provides association (called semantic similarity) by using the computed

distance between word vectors. As in Paris is related to France as ….? is related to Italy. A

corpus strained on Wikipedia content will identify Rome as one of the predicted words.

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Exploratory analysis using chatbot

N=231, Association prediction, using TFIDF similarity in Python

-A chatbot is a software program that

implements a conversational interface

- The model was built on TF/ IDF

vectors created using NLTK and

Scikit-learn

-Retrieves the most significant

respondent sentence based on the

question asked

- [Q: do you like games? TFDIDF-MODEL: i will not pay for

games. ]

- [Q: will you pay for fun? TFDIDF-MODEL: i will not pay for

fun or to kill the time.]

- [Q: do you like to spend money for entertainment? TFDIDF-

MODEL: i'll not pay for entertainment applications (like

games).]

.

- [Q: do you pay or look for free alternatives? TFDIDF-

MODEL: i would not pay if it is free.]

- [Q: are apps expensive? TFDIDF-MODEL: if it's not too

expensive then i can pay.]

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Exploratory analysis: Latent Topics

N=231, topic keywords, using trained LDA Gensim Model in Python

• Topic 1: • Hedonic and entertainments

• Topic 2 • Value for money (or free)

• Topic 3 • Utilitarian

The sentence “i pay for value of the games” generates the following

distribution:

[(Topic 3: 0.166), (Topic 2: 0.166), (Topic 1: 0.666)]

Topic 3: ('0.059*"need" + 0.027*"help" + 0.024*"valu" + 0.022*"dont" +

0.020*"realli" + 0.019*"provid" + 0.018*"get" + 0.015*"servic" + 0.013*"solv"

vis2.html

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Motivation

activation

Social

factorsEvaluation

of OptionsBehaviora

s intent

Motivation (Social

Utility) has a

significant positive

effect on Behavioral

Intent and is

influenced by

situational

circumstances (DI)

and a weak positive

effect on the

Perceived Usefulness,

a motivational state.

Social factors

remains a key

influencing factors

of perceived

usefulness, as well

as information

recency, frequency

and salience giving

the challenges in

problem solving

for finding new

apps.

There are interactions

between motivational

categories and

exogenous factors, and

motivation cannot be

simply reduced but

studied in context.

White collars and

“production ”like

behavioral change in

managing the

application lifecycle

Conclusions

Perceptual processes

( knowledge

accessibility and

perceptual

distortions) are

highly influenced by

situational factors, as

unexpected situations

and through a

feedback loop by

Behavioral intention

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Personal contribution

1. Development and Proposal of an original, multi disciplinary TAM-extended

model to include Perception with effects from Howard Sheth Model and

principles of knowledge accessibility (Wyer)

2. Development ( based on the critical literature review) of new scales for

measuring the proposed constructs (knowledge accessibility and perceptual

bias

3. Applying the idea of using multiple domain techniques (from NLP, a subset

of Artificial Intelligence) for supporting the exploratory analysis (similar to

projective techniques) with natural language processing, developing own code

using academic recognized libraries.

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Thesis’s Significant Original Contribution to Knowledge

• Combine ideas from old grand theories across multiple disciplines

(Psychology, Marketing and IT) into a new proposed cognitive model.

• Bring new perspective of applicability of Howard-Sheth,

EKB, TAM and Learning theories

• Has a generalization potential by extending the target population from

“White collars” or limit to a geography

• Proposes new scales for a series of factors

• Identified that Perceived Ease of Use do not influence the Perceived

Usefulness or Behavioral intention - Suggesting a behavioral change or

“productions” from effort-aware declarative knowledge memory

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Conclusions: Managerial implications

1. Understand the social influence, information accessibility

(recency, frequency, awareness) importance to support

mobile app discovery and adoption.

2. Improve the marketing mix or pricing strategy

3. Understand users’ opinions and reviews

4. Prepare for more disruption and digital substitute services and

a more globalized economy

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Future research directions

1. Different target population’s definition (with different experience or

profile) or to study narrower categories of mobile apps.

2. Future extension to understand behavior in “supermarket “type of

apps as Facebook, Line in Japan and Taiwan or Kakao Friends in

Korea

3. A deeper understanding of relationship between perception, learning

and means-end chain in active memory.

4. Extending the motivational categories and studying mobile phone

addiction and flow states (Csikszentmihalyi, 2014)

5. While active search generates cognitive effort, a future research

direction would be to extend the proposed model to understand how

and where users search for a mobile app

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Closing remarks

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