BRM_Unit-3 business research for mba git

Measurement & Scaling Techniques
Measurement means assigning number or other
symbols to characteristics of objects according to
certain pre-specified rules. Here we don’t
measure objects but some characteristic of it.
Thus we do not measure consumer but only their
perceptions, attitudes, preferences, or other
relevant characteristics.
Scaling may be considered as an extension of
measurement.
Example: Consider scaling from 1 to 100 for
locating consumers according to the
characteristics “attitude toward department
store.”

Primary Scales of Measurement
• Nominal Scale: A scale whose number serve only as labels or tags
for identifying and classifying objects with a strict one-to-one
correspondence between the numbers and the objects.
Example: The numbers assigned to the respondents in a study
constitute a nominal scale, social security numbers, numbers
assigned to football players.
Market Research Examples: Identifying Brand numbers, store
types, sex classification.
• Ordinal Scale: A ranking scale in which numbers are assigned to
objects to objects to indicate the relative extent to which some
characteristics is possessed. Thus it is possible to determine
whether an object has more or less of a characteristics than some
other object.
Examples: Quality rankings, rankings of teams in tournaments,
socio-economic class, & occupational status.
Marketing Research Examples: Relative attitudes, opinions,
market positions, and preference ranking. Rank order of most
preferred companies.

• Interval Scale: A scale in which the numbers are used to
rate objects such that numerically equal distances on the
scale represent equal distances in the characteristics.
 Example: Temperature scale.
 Marketing Research Example: Customer preference of a
brand, store, company on a 7 point rating scale etc.
• Ratio Scale: The highest scale which allows the researcher
to identify or classify objects, rank order the objects, and
compare intervals or differences. It is also meaningful to
compute ratios of scale values.
 Example: Height, weight, age etc.
 Marketing Research Example: Sales, costs, market
share, and number of customers.

• Nominal Scale: Permissible statistics for nominal
scale are:
 Descriptive: Percentages, Mode.
 Inferential: Chi-square, binomial test
• Ordinal Scale: Permissible statistics for ordinal scale
are:
 Descriptive: Percentages, Mode.
 Inferential: Chi-square, binomial test
• Interval Scale: Permissible statistics for interval scale
are:
 Descriptive: Range, mean, standard deviation.
 Inferential: Product-moment, correlation, t-tests, ANOVA,
regression, factor analysis
• Ratio Scale: Permissible statistics for ratio scale are:
 Descriptive: Geometric mean, harmonic mean.
 Inferential: Coefficient of variation.

Examples:
a. Nominal: Numbers assigned to runners:
7, 11, 3
b. Ordinal: Rank order of winners:
1st
place, 2nd
place, 3rd
place
c. Interval: Performance Rating on a 0-to-10 scale:
8.2, 9.1, 9.6
d. Ratio: Time to finish in seconds:
15.2, 14.1, 13.4

Rating Scales
• Simple Attitude Scales
• Category Scales
• Likert Scales
• Semantic Differential
• Numerical Scales
• Constant-Sum Scale
• Stapel Scale
• Graphic Rating Scale
• Thurstone Equal-Appearing Interval Scale
• Paired Comparison

Rating Scales
• Simple Rating Scale: Simple rating scales help in attitude rating by asking
an individual whether he agree or disagree with a statement or respond to a
single question.
Example: Think of your present work. What is it like most of the time?
Fascinating – Yes / No?
Routine- Yes / No?
Satisfying- Yes / No?
• Category Scale: An attitude scale consisting of several response categories
to provide the respondent with alternative ratings.
Example: How often is your supervisor courteous and friendly to you?
 Never
 Rarely
 Sometimes
 Often
 Very often

Rating Scales
• Likert Scale: A measure of attitudes designed to allow respondents to indicate
how strongly they agree or disagree with carefully constructed statements that
range from very positive to very negative toward an attitudinal object.
Examples: Mergers and acquisitions provide a faster means of growth than internal
expansion.
Strongly Strongly
Disagree Disagree Uncertain Agree Agree
(1) (2) (3) (4) (5)
• Semantic Differential: An attitude measure consisting of a series of seven-
point bipolar rating scales allowing response to a concept.
Example: How do you rate Dell Laptops?
Modern__ : __ : __ : __ : __ : __ : __ Old-Fashioned
Costly __ : __ : __ : __ : __ : __ : __ Reasonable
Fast __ : __ : __ : __ : __ : __ : __ Slow

Rating Scales
• Numerical Scale: An attitude rating scale similar to a semantic differential
except that it uses numbers instead of verbal descriptions as response options to
identity response positions.
Example: Now that you’ve had your automobile for about one year, please tell us how
satisfied you are with your Hero Honda Splendor
Extremely Extremely
Satisfied 7 6 5 4 3 2 1 Dissatisfied
• Constant-Sum Scale: A measure of attitudes in which respondents are asked to
divide a constant sum to indicate the relative importance of attributes.
Example: Suppose you had Rs3000/- in benefits per month. How much you would you like
to allocate to salary, medical insurance, and retirement plan? Divide Rs3000/- according to
your preference.
Salary _______
Medical Insurance _______
Retirement plan _______

Rating Scales
• Stapel Scale: An attitude measure that places a single adjective in the center of
an even number of numerical values.
Example: To measure attitudes toward a supervisor:
+3
+2
+1
Supportive
-1
-2
-3
• Graphical Rating Scale: A measure of attitude consisting of a graphic
continuum that allows respondents to rate an object by choosing any point on
the continuum.
Example: Rate your new Yamaha YZ 16 on this rating scale, where “0” being the least
rating and “10” being the most best rating.
--- 0 1 2 3 4 5 6 7 8 9 10 ----

Rating Scales
• Thurstone Equal-Appearing Interval Scale: Louis Thurstone, an early pioneer in
attitude research, developed the concept that the attitude vary along the continua and
should be measured accordingly. It was a complex process having 2 stages.
Stage1: Here it is a ranking operation, performed by judges, who assign scale values to
attitudinal statements.
Stage2: This consists of asking subjects to respond to the attitudinal statements.
Since this scale is too complex it has become less popular now a days and it is rarely
utilized.
• Paired Comparison Scale: A measurement technique that involves presenting
the respondent with two objects and asking the respondent to pick the preferred
object. More than two objects may be presented, but comparisons are made in
pairs.
Example: You think which of these two brands of adhesive bandages are better? Or are
both the same?
Handi-plast is better ___________
Band-Aid is better ___________
They are the same ___________

Multidimensional Scaling (MDS)
A class of procedures for responding
perceptions and preferences of respondents
spatially by means of a visual display.
Here perceived or psychological
relationships among stimuli are represented
as geometric relationships among points in
a multidimensional space. These geometric
representations are often called spatial
maps.

MDS has been used in marketing to identify:
• The number and nature of dimensions
consumers use to perceive different
brands in the market place.
• The positioning of current brands on these
dimensions.
• The positioning of consumer’s ideal brand
on these dimensions.

MDS provides information to help marketing
decisions regarding:
– Image measurement
– Market segmentation
– New product development
– Assessing advertising effectives
– Pricing analysis
– Channel decisions
– Attitude scale construction

MDS Input Data
Perception Preferences
Direct Derived
(Similarity judgments) (Similarity judgments)

Direct Approach:
In direct approaches to gathering perception data, the respondents are asked to
judge how similar or dissimilar the various brands or stimuli are, using own
criteria. Here respondents are often required to rate all possible pairs of brands or
stimuli in terms of similarity on a Likert Scale. These data are referred to as
similarity judgements.
Example:
Very Very
Dissimilar Similar
Pepsodent vs. Colgate 1 2 3 4 5
Aqua-fresh vs. Pepsodent 1 2 3 4 5
Pepsodent vs. Cibaca 1 2 3 4 5
…
…
Colgate vs. Aqua-fresh 1 2 3 4 5

Derived Approach:
In MDS, attribute-based approaches in collecting perception
data requiring the respondents to rate the stimuli on the
identical attributes using semantic differential or likert scale.
Example: The different brands of toothpaste may be rated on
attributes such as these:
Whitens teeth ___ : ___ : ___ : ___ : ___ Does not whiten teeth
Prevents tooth decay ___ : ___ : ___ : ___ : ___ Does not prevent tooth decay
...
…
…
Pleasant tastin ___ : ___ : ___ : ___ : ___ Unpleasant tasting

Criteria for Good Measurement
A measurement scale can be assessed on its:
• Accuracy- Accurate results
• Reliability- Consistent results
• Validity- True data (which gives true data)
• Generalizability- Should indicate population
characteristics (can be able to generalize)

Reliability
• Reliability is the degree to which an
assessment tool produces stable and
consistent results.
• way of assessing the quality of the
measurement procedure used to collect data
• e.g., IQ, emotional intelligence, etc

Questionnaire Design
A questionnaire is a formalized set of
questions arranged in a logical order for
eliciting information.
Parts of questionnaire
– Identification data. It includes name, age, address, etc. try to know
about the informant
– Classification data. It includes designation, income and family size.
– Sought data.

Relevance and accuracy are two basic criteria a
questionnaire must meet if it is to achieve the
researcher’s purposes. To achieve these ends, a
researcher who systematically plans a
questionnaire’s design will be required to make
several decisions- typically, but not necessarily,
in the order listed below:
1. What should be asked?
2. How should each question be phrased?
3. In what sequence should the questions be arranged?
4. What questionnaire layout will best serve the research
objectives?
5. How should the questionnaire be pre-tested? Does the
questionnaire need to be revised?

1. What should be asked?
The problem definition and objectives will indicate which type of
information must be collected to answer the manager’s questions;
different types of questions may be better at obtaining certain types of
information than others.
Further, the communication medium used for data collection –
telephone interviews, personal interview, or self administered survey.
Questionnaire Relevancy: A questionnaire is relevant if no unnecessary
information is collected and if the information that is needed to solve the
business problem is obtained.
Questionnaire Accuracy: Once the researcher has decided what should be asked,
the criterion of accuracy becomes the primary concern.

2. How questions should be framed?
There are many ways to phrase questions and many standard question
formats:
• Open-Ended Response: A question that poses some problem and asks the respondents to
answer in his own words.
Examples: What things do you like most about your bike?
What names of dish TV s you can think offhand?
What comes to mind when you look at this advertisement?
• Fixed-Alternative questions: A question in which the respondent is given specific limited-
alternative responses and asked to choose the one closest to his or her own viewpoint.
Examples: Do you think that Pulsar has got best aesthetics (looks)?
Yes ______ No ______
Do you think Reliance Big TV has got best picture quality?
Yes ______ No ______

Fixed Alternative Questions:
• Simple-dichotomy questions: A fixed-alternative question that requires the respondent to
choose one of two alternatives.
Did you make any long-distance calls last week?
Yes _____ No ______
• Determinant-choice questions: A type of fixed-alternative question that requires a
respondent to choose one (and only one) response from among several possible
alternatives.
Please give us some information about your journey by VRL bus? The traveled in the:
Sleeper class 2-Tier AC 3-Tier AC
• Frequency-determination question: A types of fixed-alternative question that asks for an
answer about general frequency of occurrence.
How frequently do you travel by Trains:
Every day ……………………………………………
Once a week…………………………………………
2-4 times a week ……………………………………
5-6 times a week…………………………………….
Less than once a week……………………………..
Never …………………………………………………

Fixed Alternative question:
• Attitude Rating Scales: Measures used to rate attitudes, such as the Likert scale,
Semantic differential and Stapel scale are also fixed question scales.
• Checklist Question: A type of fixed-alternative question that allows the
respondent to provide multiple answers to a single question.
Example: Please check which of the following sources of information about investments
you regularly use, if any.
 Personal advise of your broker (s)
 Brokerage newsletters
 Brokerage research reports
 Investment advisory service (s)
 Conversations with other investors
 Reports on the internet
 None of these
 Others (please specify) _____________________________

Question Phrasing
Art of Asking Questions:
• Avoid Complexity: Use simple, Conversational Language
• Avoid Leading and Loaded questions:
1. Leading: A question that suggests or implies certain answers.
2. Loaded question: A question that suggests a socially desirable answer or is emotionally
charges.
1. Counter-biasing Statement: An introductory statement or preface to a question that
reduces a respondent’s reluctance to answer potentially embarrassing questions.
To know the “Income” or “Qualification” which are sensitive to some people. You can used counter-
biasing statements.
Example:
“To help classify your answers, we’d like to ask you a few questions. Again, your answers will be kept in strict
confidence”
2. Split-ballot technique: A technique to control for response bias. Two alternatives
phrasings of the same question are utilized for respective halves of the sample to yield a
more accurate total response than would be possible if only a single phrasing were utilized.
Example: A study of small car buying behavior for example, gave one-half of the sample of imported-car
purchasers a questionnaire in which they were asked to agree or disagree with the statement.
“Small Indian cars are cheaper to maintain than small imported cars”

Question Phrasing
Art of Asking Questions:
• Avoid Ambiguity: Be as specific as possible
• Avoid Double-Barreled Items: A question that may include bias
because it covers two issues at once.
Example: Please indicate if you agree or disagree with the following
statement: “I have called in sick or left work to play golf”
• Avoid Making Assumptions:
Example: Consider the following question:
Should Sherry continue its excellent gift-wrapping program?
This question contains the implicit assumption that people believe the
gift-wrapping program is excellent.
• Avoid Burdensome Questions that may Tax the Respondents Memory:
Example: Do you recall any commercials on that programs? If yes
What brands were advertised?

Primary & Secondary Data
• Primary Data
Primary data are the fresh data collected directly from the
field. They are the first hand data.
Important methods of collecting primary data are:
 Direct observation method
 Personal interview method
 Information through correspondence
 Method of questionnaire

Primary & Secondary Data
• Secondary Data
Secondary data are the data which the investigator does not collect
directly from the field. They are the data which he borrows from others
who have collected them for some or other purpose.
Important sources of Secondary Data:
 Published sources
a. Reports & communications of central & state government departments.
b. Reports & communications of international bodies like UNO, IMF etc
c. Publications of banks, research institutions, administrative offices etc.
d. Magazines and the news papers.
e. Websites of various organizations on the internet.
 Unpublished sources
a. Records maintained as government offices, municipal offices, panchayat
offices.
b. Records maintained by research institutions, research scholars etc.

Secondary Data
Classification of Secondary Data
Secondary Data
Internal External
Ready to Use Requires Published Computerized Syndicated
Further Processing Materials Databases Services
Syndicated Services: Syndicated services are referred to those companies that
collect and sell common pool of data of known commercial value, designed to
service information needs shared by a number of clients.
Using these services is frequently less expensive than collecting primary data.

Advantages and Disadvantages of Primary Data
• Advantages:
 It gives solution for problem at hand
 It gives a precise solution
 Here the researcher can collect according to the data requirement
 It gives researcher an opportunity to get more ideas about the research.
• Disadvantages:
 The collection cost of primary data is always high
 It is time consuming
 More people might be needed.

Advantages and Disadvantages of Secondary Data
• Advantages
 It is rapid and easy to collect
 Its collection cost is relatively low
 It is not time consuming
 It helps in developing an approach to the problem
 It helps in formulating the research design
• Disadvantages
 Since they are actually taken for other problems they will be less useful to
the problem at hand
 It may lack accuracy
 It may not be current data

Hypothesis
• Statistical Hypothesis:
A statistical hypothesis is a claim (assumption or belief) about
an unknown population parameter value.
Types of Hypothesis:
• Null Hypothesis
• Alternative Hypothesis

Hypothesis
• Null Hypothesis
The hypothesis which is being tested for possible rejection is
called Null Hypothesis. It is denoted by Ho.
• Alternative Hypothesis
The hypothesis which is accepted when the null hypothesis is
rejected is called Alternative Hypothesis. It is denoted by H1.

Procedural Steps of Testing Hypothesis
1. State the null and alternative hypothesis
2. State the level of significance
3. Establish a critical region (Rejection Region)
4. Select the suitable test static( t value )
5. Formulate a decision rule t cal with t tab

Steps in Testing of Hypothesis
Step 1: State null & alternative hypothesis:
Example:
Ho : There is no difference between the average scores of boys &
girls
H1 : The average score of boys differ significantly when compared to
the score of girls.
Ho : There is no difference between the sales figures of three different
stores
H1 : There is a significant difference between the sales figures of
three different stores.

Step 2: State the level of significance:
It is the predicted upper limit for the probability of rejection
of the Null hypothesis when it is actually true. It is denoted by
‘Alpha’.
The predicted values of ‘Alpha’ are:
0.05 or 0.01
i.e. 5% or 1%

Step 3: Establish a Critical Region
The normal distribution curve there will be acceptance &
critical region and both the regions are mutually exclusive.
The critical region is the range of sample statistic
values within which if values of sample static falls then null
hypothesis is rejected.

Step 4: Select the suitable test statistic
The test static are classified into two types:
1. Parametric Tests
2. Non-parametric Tests
Parametric Test: They are more powerful because there the data are derived
from interval & ratio measurements
Non-parametric Test: They are used to test hypothesis with nominal & ordinal
data.
Sample Size Population Parameter
known
Population Parameter
Unknown
n>30 Normal distribution Normal distribution
n<=30 Normal distribution T-distribution

Step 5: Formulate a Decision Rule
Here compare the calculated value of the test static with
the critical value (standard table value)
1. Accept H0 if the test static value falls within area of
acceptance.
2. Reject otherwise.
The conclusion reached by hypothesis testing must be
expressed in terms of the marketing research problem.

Errors in Testing of Hypothesis
Decision Actual
(H0 is true)
Actual
(H0 is false)
Accepting H0 Correct decision Wrong decision
(Type II Error)
Rejecting H0 Wrong decision
(Type I Error)
Correct decision

Errors in Testing of Hypothesis
• Type I Error:
Rejecting H0 when it is actually true is called Type I Error.
• Type II Error:
Accepting H0 when it is actually false is called Type II Error.

Parametric & Non-parametric Test
• Parametric Test:
Hypothesis-testing procedures that assume that the variables
of interest are measured on at least an interval scale or an
ratio scale.
• Non-parametric Test:
Hypothesis-testing procedures that assume that the variables
are measured on a nominal or ordinal scale.

Hypothesis Tests
Parametric Tests Non-parametric Tests
One Sample Two Sample One Sample Two Sample
Independent Sample Paired Samples
Independent Samples Paired Samples
•t-test
•z-test
•Two-Group
t-test
•z-test
•Paired
t-test
•Chi-square
•K-S
•Runs
•Binomial
•Chi-square
•Mann-Whitney
•Median
•K-S
•Sign
•Wilcoxon
•McNemar
•Chi-square

Parametric Tests
• T-test: A univariate hypothesis test using the t
distribution , which is used when the standard
deviation is unknown and the sample size is
small.
Example:
One sample t-test
Independent sample t-test
Paired sample t-test

Parametric Tests
• Z-test: A univariate hypothesis test using the
standard normal distribution.
Example:
One sample z-test
Two independent sample z-test
• F-test: A statistical test of the equality of the
variances of two populations. Here F-statistic is
compared as the ratio of two sample variances.

Non-Parametric Tests
Rank Sum Tests:
• Mann-Whitney U-test: A statistical test for a variable
measured on an ordinal scale, comparing the difference in the
location of two populations based on observations from two
independent sample.
• Kruskal-Wallis test (H-test):This test is conducted in a way
similar to the U test. This test is used to test the null
hypothesis that ‘k’ independent random samples come from
identical universes against the alternative hypothesis that the
means of these universes are not eqqual.

Sampling
Sampling: The process of using a small number
of items or parts of a larger population to make
conclusions about the whole population.
Sample: A subset, or some part of a larger
population.
Population: A complete group of entities sharing
some common set of characteristics.

Sampling
• Sample Frame: The list of elements from which a sample
may be drawn is called sample frame. Ex: The list of all
members of city cricket association, the list of students
who are studying MBA will be a sample frame.
• Sampling Frame Error: Error that occurs when certain
sample elements are not listed or available and are not
represented in the sampling frame.
• Sampling Unit: A single element or group of elements
subject to selection in the sample.
Example: If an airline, wishes to sample passengers, every
25th name on a complete list of passengers may be taken.
In a random digit dialing, a sample unit will be telephone
numbers.
• Random sampling error: It is the difference between the
sample result and the result of a census conducted by the
identical procedures.

Steps in Sampling Process:
• Defining the target population
• Select a sampling frame
• Determine if a probability or non probability
sampling method will be chosen
• Plan procedure for selecting sampling units
• Determine sample size
• Select actual sampling units
• Conduct field work

Types of Sampling:
• Probability sampling
• Non-probability sampling.

Probability Sampling
Simple random sampling: In this method every number or element of the
population has an equal and independent chance of being selected again and
again when is sample is drawn from the population.
Ex: Selecting 4 MBA students to study their behavior
Stratified sampling: This method is useful when the population consists of a
number of heterogeneous sub populations and the elements within a given sub
population are relatively homogenous compared to the population as a whole.
Ex: Heterogeneous sub population- “Strata” or “Group”
High Income group
Middle Income group
Low Income group
Cluster sampling: The population is divided in to groups those groups are called as
clusters and study of these clusters is known as Cluster Sampling.
Ex: An engineering college will have different departments and to study
students behavior we pick 2 students from each group / cluster is called cluster
sampling.

Probability Sampling
Multistage sampling: The given population is heterogeneous, so it is
broken into two which will give you homogenous data. Those data is
called clusters or strata. This method of study is Multistaged sampling.
Ex: If you are doing census then you divide people into urban,
semi-urban groups which will be your strata. You can also divide people
into different age groups that you can arrange systematically & study.
Systematic Sampling: A sampling procedure in which an initial starting
point is selected b a random process and then every nth number on the
list is selected.
Ex: Suppose a researcher wants to take a sample of 1000 from a
list consisting of 2,00,000 names of companies. With Systematic sampling,
every 200th name from the list would be drawn.

Non-Probability Sampling:
Convenience sampling: Any thing which is convenient that is related to
your friends, relatives etc. so that data can be collected conveniently. This
method is called convenience sampling.
Quota sampling: A non probability sampling procedure that ensures that
certain characteristics of a population sample will be represented to the
exact extent that the investigator desires.
Ex: An interviewer may fix a quota that out of 100 questionnaires
70 has to be men amd 30 has to be female.
Judgment sampling: A non probability sampling technique in which an
experienced individual selects the sample based upon some appropriate
characteristic of the sample members.
Snow-balling: A sampling procedure in which initial respondents are
selected by probability methods and additional respondents are obtained
from information provided by the initial respondents.

Errors in Sampling:
• Conscious or unconscious bias in the selection of a sample
• Deliberate selection of a non-representative sample
• Substitution of an item in place of the one chosen in a
random sampling.
• Incomplete coverage of the units in the sample.
• Defective process of selection
• Faulty work during the collection of information and
• Incorrect methods of analysis.

General Guidelines to
Writing a Literature Review
• Introduce the literature review by pointing
out the major research topic that will be
discussed
• Identify the broad problem area but don’t be
too global (for example, discussing the history of
education when the topic is on specific instructional strategy)
• Discuss the general importance of your topic
for those in your field

• Don’t attempt to cover everything written on
your topic
• You will need to pick out the research most
relevant to the topic you are studying
• You will use the studies in your literature
review as “evidence” that your research
question is an important one

• It is important to cover research relevant to
all the variables being studied.
• Research that explains the relationship
between these variables is a top priority.
• You will need to plan how you will structure
your literature review and write from this
plan.

Organizing Your Literature Review
• Topical Order—organize by main topics or
issues; emphasize the relationship of the
issues to the main “problem”
• Chronological Order—organize the
literature by the dates the research was
published
• Problem-Cause-Solution Order—Organize
the review so that it moves from the
problem to the solution

Organizing Your Literature Review
• General-to-Specific Order—(Also called the
funnel approach) Examine broad-based
research first and then focus on specific
studies that relate to the topic
• Specific-to-General Order—Try to make
discuss specific research studies so
conclusions can be drawn

• After reviewing the literature, summarize
what has been done, what has not been
done, and what needs to be done
• Remember you are arguing your point of
why your study is important!
• Then pose a formal research question or
state a hypothesis—be sure this is clearly
linked to your literature review
Literature Review
Literature Review

Literature Review
• All sources cited in the literature review
should be listed in the references
• To sum, a literature review should include
introduction, summary and critique of
journal articles, justifications for your
research project and the hypothesis for
your research project

Common Errors Made in Lit
Reviews
• Review isn’t logically organized
• Review isn’t focused on most important facets
of the study
• Review doesn’t relate literature to the study
• Too few references or outdated references
cited
• Review isn’t written in author’s own words
• Review reads like a series of disjointed
summaries
• Review doesn’t argue a point
• Recent references are omitted

Writing the Literature Review
Plagiarism includes (Galvan, pg. 89):
1. Using another writer’s words without proper citation
2. Using another writer’s ideas without proper citation
3. Citing a source but reproducing the exact word without
quotation marks
4. Borrowing the structure of another author’s
phrases/sentences without giving the source
5. Borrowing all or part of another student’s paper
6. Using paper-writing service or having a friend write the
paper

BRM_Unit-3 business research for mba git

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