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Marketresearch using SEM print12022010

Marketing Objectives
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Marketing objectives are the targets that the
marketing department wishes to achieve.
These objectives should be compatible with
the firm’s overall objectives. E.g. if growth was
a firm’s objective then the marketing
department may consider entering an
overseas market or launching new products.
Marketing objectives should be SMART
Create customer satisfaction in a profitable
way
Source : IB Business & Management
Marketing Objectives may Include
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Maintain/ increase market share
Market leadership
Product positioning
Consumer satisfaction
Diversification
Market development
New product development
Product innovation
High market standing
Constraints on achieving marketing
objectives
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Finance
Costs of production
The size and status of the firm
Social issues
Time lags (liquidity problems)
Activities and reaction of competitors
The state of the economy
The political and legal environment
Market Research is ...
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“Process of systematically gathering, recording and
analyzing data and information about customers,
competitors and the market so as to create a business
plan, launch a new product or service, fine tune
existing products and services and expand into new
markets”
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(Source : The National Women’s Business Center
(NWBC))
Market research serves to identify the wants and
needs of customers. Market research involves
collecting primary and secondary data to gain some
insight into the structure of a market.
 (Source : IB Marketing and Management)
Why engage in Market Research
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Develop a detailed profile of the "ideal" consumer in the market
segment a business is trying to reach.
Be competitive, productive and profitable
Determine consumer interest and therefore create a business
plan or launch a new product or service tailored to the customers.
Fine tune existing products and services or expand into new
markets. Provide an understanding of how or why things are as
they are.
Determine which portion of the population will purchase a
product/services based on variables such as age, gender,
location and income levels
Why engage in Market Research
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To identify opportunities in the market
To minimizes the risk of doing business.
Uncover and identify potential problems
Evaluate business and its success
Why engage in Market Research
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Gives businesses up-to-date and accurate information.
Allows a business to see whether current products meet the
needs of customers. Customers tastes and preferences change
over time.
Allows businesses to improve their marketing by using a distinct
marketing mix for each customer target group (marker
segment).
Assesses potential customer reactions to a new product.
Gives businesses an understanding of the activities and
strengths used by their competitors
Helps businesses predict what is likely to happen in the future
Drawbacks of Market Research
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Information and findings are only as good as
the research methodology used. Directly
asking customers whether they are willing to
pay higher prices will generate obvious
responses. (Garbage In Garbage Out, GIGO)
Data and information can be inaccurate due to
bias
The cost of good market research is often very
high.
The Research Process
The market research process:
Problem
definition
More details of
this process on
pg. 102 of book,
Fig 4.1
Research
design
Data
collection
Data analysis
and interpretation
Presentation
of results
Copyright ©2005 by South-Western, a division of Thomson Learning. All rights reserved.
Explanatory vs. design science research
Design science
Explanatory research
research
The phenomenon
”out there”
to be created
Data
collected and analyzed
created and analyzed
Reasoning/research
hypothetico-deductive,
abductive,
design
inductive
practical syllogism
explanatory theory,
artifact (e.g.,
technology)
End product
prediction
Disciplinary basis
natural and social
science
engineering
Knowledge interest
cognitive
pragmatic
(Mikko Ketokivi)
Exploratory Research
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To uncover insights, to understand.
Definitions are loose, processes are flexible.
Samples are small and non-representative.
Uses qualitative data.
Your findings are provisional, and usually leave the door
open for additional research.
Methods used are:
 Discussions with experts.
 Pilot surveys.
 Case studies.
 Analysis of secondary data.
 Qualitative research.
Causal Research
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Analysing the Cause and Effect of a problem.
What is causing something and what is the
effect? E.g. Did an increase in price cause a
decrease in sales?
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Independent variable = cause
Dependent variable = effect
The effect may be a decrease in sales, but the cause may be an
increase in price, increased in competitors’ advertising,
decrease in competitors’ price, promotional activity in the
market, economic factors etc ….
Method used is:
Experimentation – You see if one variable effects another
variable.
Comparative Performance of Data
Collection Methods
Interview
Focus group
Telephone
Mail
Cost/response
High
Fairly high
Low
Very low
Speed
Fast
Fast
Very fast
Slow
Large
Large
Moderate
Moderate
Sample dispersion
Low
Low
High
High
Response rate
High
Very high
Fairly high
Low
Probing
High
High
Fairly high
None
Quantity of data
Brassington and Pettitt (2006)
Principles of Marketing
Why using SEM ? What is SEM ?
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A comprehensive statistical approach to
testing hypotheses about relations among
observed and latent variables.
Standard statistical Approaches: Ex.
Correlation, multiple regression, factor
analysis, ANOVA, MANOVA...
A general methodology for specifying,
estimating, fitting, and evaluating models of
relationships among variables.
What are Structural Equation Models?
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Also known as:
Covariance structure models
 Latent variable models
 “LISREL” models
 Structural Equations with Latent Variables
Special cases:
 ANOVA
 Multiple regression
 Path analysis
 Confirmatory Factor Analysis
 Recursive and Nonrecursive systems
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What are Structural Equation Models?
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SEM associated with path diagrams
intelligence
test 1
test 2
test 3
test 4
test 5
δ1
δ2
δ3
δ4
δ5
What are Structural Equation Models?
Latent variables, factors, constructs
Observed variables, measures, indicators,
manifest variables
Direction of influence, relationship from one variable
to another
Association not explained within the model
What are Structural Equation Models?
Depress 1
Family support
Self rated
closeness
δ1
Spousal
rating
δ2
Depress 2
Depress 3
depression
ζ1
Physical health
ζ2
Kids rating
δ3
Self rating
ε4
MD rating
ε5
# visits to MD
ε6
Notation
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η Latent Endogenous Variable
ξ Latent Exogenous Variable
ζ Unexplained Error in Model
x & y Observed Variables
δ & ε Measurement Errors
λ, β, & γ Coefficients
Notation
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Two components to a SEM
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Latent variable model
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Relationship between the latent variables
ηΒη
Γξ
ζ
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Measurement model
• Relationship between the latent and observed variables
xΛxξδ
yΛyηε
Example of SEM questions
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Control Factor in Broadband Research is a
latent variable with knowledge, facilitating
conditions (on TAM)
Control Factor and Customer Behaviour may
be affected by each other.
Others...
The impacts of economic development on
democracy may be indirect.
Characteristics
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SEM requires formal specification of a model
to be estimated and tested.
Confirmatory rather than exploratory (eg.
multiple regression) approach
inferential purpose
assess directional and non-directional
influences simultaneously
capability of assessing measurement error
Advantages of SEM
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reduces measurement error by having multiple
indicators of a latent variable
ability to test overall models and individual
parameters
ability to statistically compare nested and non-nested
models
ability to test models with multiple Dvs
ability to model mediator variables (processes)
ability to model error terms
ability to model relations across groups, across time
SEM Approaches
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2 major approaches, each based on the
researcher’s goals
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confirmatory factor analysis (CFA)
structural equation modeling (SEM)
For each major approach, one can take 1 of 3
additional methodological approaches
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strictly confirmatory
alternative models
model development
SEM Variables
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observed = measured, manifest, indicators
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latent = theoretical constructs
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variables that are defined by the observed
variables
goal is to model the commonality in the observed
variables
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can be items, subscales, or scales
sound like factor analysis?
and then look at relations between latent
variables
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sound like multiple regression?
How do data relate to learning?
multivariate
descriptive statistics
multivariate
data
modeling
SEM
univariate
descriptive statistics
univariate
data
modeling
Data
exploration,
methodology and
theory
development
abstract
models
Understanding of Processes
modified from Starfield and Bleloch (1991)
realistic
predictive
models
more detailed
theoretical
models