A common problem with launching new products is one of
reaching a decision as to what options, and how many options one offers. Whilst a company may
be anxious to meet the needs of as many market segments as possible, it has to ensure that the
segment is large enough to enable him to make a profit. It is always easier to add products to the
product line but much more difficult to decide which models should be deleted. One technique for
evaluating the options which are likely to prove successful is the unity-sum-gain approach.
The procedure is to begin with a list of features which might possibly be offered as ‘options’ on the
product, and alongside each you list its retail cost. A third column is constructed and this forms an
index of the relative prices of each of the items. The table below will help clarify the procedure. For
the purposes of this example the basic reaper is priced at $20,000 and some possible ‘extras’ are
listed along with their prices.
The total value of these hypothetical ‘extras’ is $7,460 but the researcher tells the farmer he has an
equally hypothetical $3,950 or similar sum. The important thing is that he should have considerably
less hypothetical money to spend than the total value of the alternative product features. In this
way the farmer is encouraged to reveal his preferences by allowing researchers to observe how he
trades one additional benefit off against another. For example, would he prefer a side rake
attachment on a 3 metre head rather than have a transporter trolley on either a standard or 2.5m
wide head? The farmer has to be told that any unspent money cannot be retained by him so he
should seek the best value-for-money he can get.
In cases where the researcher believes that mentioning specific prices might introduce some form
of bias into the results, then the index can be used instead. This is constructed by taking the price
of each item over the total of $ 7,460 and multiplying by 100. Survey respondents might then be
given a maximum of 60 points and then, as before, are asked how they would spend these 60
points. In this crude example the index numbers are not too easy to work with for most
respondents, so one would round them as has been done in the adjusted column. It is the relative
and not the absolute value of the items which is important so the precision of the rounding need
not overly concern us.
Figure 3.6 The unity-sum-gain technique
Item Additional Cost ($s) Index Adjusted Index
2.5 wide rather than standard 2m 2,000 27 30
Self lubricating chain rather than belt 200 47 50
Side rake attachment 350 5 10
Polymer heads rather than steel 250 3 5
Double rather than single edged cutters 210 2.5 5
Transporter trolley for reaper attachment 650 9 10
Automatic levelling of table 300 4 5
The unity-sum-gain technique is useful for determining which product features are more important
to farmers. The design of the final market version of the product can then reflect the farmers’
needs and preferences. Practitioners treat data gathered by this method as ordinal.
Noncomparative scales
Continuous rating scales: The respondents are asked to give a rating by placing a mark at the
appropriate position on a continuous line. The scale can be written on card and shown to the
respondent during the interview. Two versions of a continuous rating scale are depicted in figure
3.7.
Figure 3.7 Continuous rating scales
When version B is used, the respondent’s score is determined either by dividing the line into as
many categories as desired and assigning the respondent a score based on the category into
which his/her mark falls, or by measuring the distance, in millimetres or inches, from either end of
the scale.
Whichever of these forms of the continuous scale is used, the results are normally analysed as
interval scaled.
Line marking scale: The line marked scale is typically used to measure perceived similarity
differences between products, brands or other objects. Technically, such a scale is a form of what is
termed a semantic differential scale since each end of the scale is labelled with a word/phrase (or
semantic) that is opposite in meaning to the other. Figure 3.8 provides an illustrative example of
such a scale.
Consider the products below which can be used when frying food. In the case of each pair,
indicate how similar or different they are in the flavour which they impart to the food.
For some types of respondent, the line scale is an easier format because they do not find discrete
numbers (e.g. 5, 4, 3, 2, 1) best reflect their attitudes/feelings. The line marking scale is a
continuous scale.
Itemised rating scales: With an itemised scale, respondents are provided with a scale having
numbers and/or brief descriptions associated with each category and are asked to select one of
the limited number of categories, ordered in terms of scale position, that best describes the
product, brand, company or product attribute being studied. Examples of the itemised rating scale
are illustrated in figure 3.9.
Figure 3.9 Itemised rating scales
Itemised rating scales can take a variety of innovative forms as demonstrated by the two illustrated
in figure 3.9, which are graphic.
Whichever form of itemised scale is applied, researchers usually treat the data as interval level.
Semantic scales: This type of scale makes extensive use of words rather than numbers.
Respondents describe their feelings about the products or brands on scales with semantic labels.
When bipolar adjectives are used at the end points of the scales, these are termed semantic
differential scales. The semantic scale and the semantic differential scale are illustrated in figure
3.11.
Figure 3.11 Semantic and semantic differential scales
Likert scales: A Likert scale is what is termed a summated instrument scale. This means that the
items making up a Liken scale are summed to produce a total score. In fact, a Likert scale is a
composite of itemised scales. Typically, each scale item will have 5 categories, with scale values
ranging from -2 to +2 with 0 as neutral response. This explanation may be clearer from the
example in figure 3.12.
Figure 3.12 The Likert scale
Strongly
Agree
Agree Neither Disagree Strongly
Disagree
If the price of raw materials fell firms
would reduce the price of their food
products.
1 2 3 4 5
Without government regulation the
firms would exploit the consumer.
1 2 3 4 5
Most food companies are so concerned
about making profits they do not care
about quality.
1 2 3 4 5
The food industry spends a great deal
of money making sure that its
manufacturing is hygienic.
1 2 3 4 5
Food companies should charge the
same price for their products
throughout the country
1 2 3 4 5
Likert scales are treated as yielding Interval data by the majority of marketing researchers.
The scales which have been described in this section are among the most commonly used in
marketing research. Whilst there are a great many more forms which scales can take, if students
are familiar with those described in this chapter they will be well equipped to deal with most types
of survey problem.
There are four levels of measurement: nominal, ordinal, interval and ratio. These constitute a
hierarchy where the lowest scale of measurement, nominal, has far fewer mathematical properties
than those further up this hierarchy of scales. Nominal scales yield data on categories; ordinal
scales give sequences; interval scales begin to reveal the magnitude between points on the scale
and ratio scales explain both order and the absolate distance between any two points on the scale.
The measurement scales, commonly used in marketing research, can be divided into two groups;
comparative and non-comparative scales. Comparative scales involve the respondent in signalling
where there is a difference between two or more producers, services, brands or other stimuli.
Examples of such scales include; paired comparison, dollar metric, unity-sum-gain and line
marking scales. Non-comparative scales, described in the textbook, are; continuous rating scales,
itemised rating scales, semantic differential scales and Likert scales.
Determine combinations of types of data to best inform objectives
Good research usually uses a combination of all source types, but the sources will depend on the
purpose of your research. As part of your research proposal, you should have identified a range of
sources of information and methods of collecting that information.
Gathering data to help market research needs can come from:
Internal organisation records o Sales records o Customer feedback questionnaires
Marketing intelligence o Market trends o Competition SWOT analysis
Secondary marketing research
Primary marketing research
Combinations of data will support each other.
Make sure your objectives are clear and think very carefully how you will answer your research
questions? What information do you need from what sources? Consider how customer surveys
could support the results of a competitor analysis. What information do you need is the question?
More than one source to support your information will make it all the more valid.
Identify and evaluate suitable data gathering methods
There are numerous methods in which to obtain data, best practice principles state that a variety
of these methods should be used in collecting your research data.
Developing questions
Authors, Burns and Bush believe there are five ‘should’ and eleven ‘should nots ‘of question
wording. Should
The question should be focused on a single issue or topic
The question should be brief
All respondents should interpret the question the same way
The question should use the respondent’s core vocabulary
The question should be grammatically simple, in a sentence if possible
Should nots
Assume criteria that are not obvious
Be beyond the respondent’s ability or experience
Use a specific example to represent a general case
Ask respondent to recall specifics when only generalities will be remembered
Require the respondent to guess a generalisation
Ask for details that cannot be related
Use words that overstate the condition
Have ambiguous wording
Be ‘double-barrelled’ (asking two questions at the same time)
Lead the respondent to a particular answer
Have ‘loaded’ (use of subtle emotional appeal) wording or phrasing.
These are the main types of questions:
Advantages and disadvantages of using open ended and multiple choice questions? Open ended
questions:
Multiple choice questions:
Regardless of the type of question used, there should be an opportunity for respondents to
provide an answer to every question. If numerous questions are left blank because the
respondents felt the answers provided did not apply to their situations, the study’s findings will be
weakened.
Sequencing and layout decisions
The format and order of questions should be set out to gain and maintain the respondents’
cooperation and also to make it easy for the interviewer to administer. Four basic guidelines for
sequencing a questionnaire are:
Open the interview with an easy and non-threatening question
Questionnaires should flow smoothly / logically from one question to the next
With most topics you should start with broad, general questions and move toward the
more specific
Sensitive questions should not be placed at the beginning of the questionnaire
Remember in preparing a questionnaire YOU must decide:
What questions to ask
The type of the questions
The wording of the questions
The ordering of the question
What assumptions you make
Each question should be considered very carefully before it is used to see that it contributes to the
research objectives.
Remember that the purpose of the survey is to gather information that meets your research
purpose. You need to present your research information in a way that is useful in the decision
making process. Ensure that your survey allows for suitable presentation of this information.
Why should we use one or other of these methods? What are the particular advantages and
disadvantages of data collection?
Determining the Research Design16
A research design encompasses the methodology and procedure employed to conduct scientific
research. Although procedures vary from one field of inquiry to another, identifiable features
distinguish scientific inquiry from other methods of obtaining knowledge. In general, scientific
researchers propose hypotheses as explanations of phenomena, and design research to test these
hypotheses via predictions which can be derived from them.
The design of a study defines the study type, research question and hypotheses, independent and
dependent variables, and data collection methods . There are many ways to classify research
designs, but some examples include descriptive (case studies, surveys), correlational (observational
study), semi-experimental (field experiment), experimental (with random assignment), review, and
meta-analytic, among others. Another distinction can be made between quantitative methods and
qualitative methods.
16 Source: Boundless, as at https://www.boundless.com/sociology/textbooks/boundless-sociologytextbook/
sociological-research-2/the-research-process-26/determining-the-research-design-168-
7446/, as on 4th September, 2017.
The Scientific Method is an Essential Tool in Research
This image lists the various stages of the scientific method.
Quantitative Methods
Quantitative methods are generally useful when a researcher seeks to study large-scale patterns of
behavior, while qualitative methods are often more effective when dealing with interactions and
relationships in detail . Quantitative methods of sociological research approach social phenomena
from the perspective that they can be measured and quantified. For instance, socio-economic
status (often referred to by sociologists as SES) can be divided into different groups such as
working-class, middle-class, and wealthy, and can be measured using any of a number of
variables, such as income and educational attainment.
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