Problem Recognition

1
The Research Process
Problem Recognition
Problem Structuring
Research Design
Data Collection (Surveys, Requirements
Elicitation, experiments, focus groups etc.)
(PILOT STUDY followed the FULL SCALE study)
Data Analysis, generating, interpreting results
Writing up results and recommendations
Implementation
2
Problem Recognition/Selecting the Research Topic

• Personal Interest
• Suggested by Research/Practitioner Literature
• Emergence of a new technology
• Perceptions of discrepancy between desired and
  actual state
• Management Directives and Policies
• Social Concerns/Popular Issues

3
Conceptual Framewrok

• Identify Key Concepts
• Define the Key Concepts
• Operationalise the Concepts
• Explore systematic relationship between the
  concepts.

4
Specific Research Questions

• Main Considerations
• -Specificity and answerability can the questions
  be answered through research?
• Scale and Scope in relation to needs, available
  resources.
• Resource Adequacy in Relation to available time.

5
Research Strategy and Design

• Data gathering methods
• - Type of method to be used.
• - Type of data to be gathered.
• - Pilot Study
• Data analysis methods
• Budget and timetable
• Reporting the results

6
Employee Self-Service (ESS) Module of PeopleSoft
ERP system (Univ. of Sydney)

• System Development and Testing completed.
• Need to decide on university-wide roll out and a
  strategy doing this.

7
Reducing Cycle Time for New Product Development
at Bosch

• Average cycle time for new product
  development/product redesign was 18 months need
  to compress it to 9-12 months

8
3G Wireless Applications for the Univ. of Sydney

• 3G wireless technology emerging as the foundation
  for mobile applications in a range of domains.
• The Major Projects Group at the university wants
  to
• Make an assessment of the feasibility and
  viability of the technology and the applications
  it can offer
• Identify potential applications that the uni
  might benefit from.
• Develop business cases for these applications

9
Decision Support System application for Johnson
Johnson

• Need to decide on how much to spend on a variety
  of special promotions at large retail outlets of
  JJ such as Woolworths, Coles.
• Prefer a system solution to the problem.

10
Primary Data

• Data gathered and assembled for the specific
  research project at hand.
• Primary data gathered through observations, focus
  groups, experiments, field studies etc.
• Format could be numeric, text, image, video,
  sound recordings.
• Source may be internal or external to an
  organisation.

11
Secondary Data

• Secondary data are data collected and assembled
  for a purpose other than the project at hand, but
  may be useful for the project.
• Source may be internal or external to an
  organisation.
• Typical sources include
• Australian Bureau of Statistics, Australian Stock
  Exchange, Reserve Bank of Australia, OECD, UN,
  National Archives, AC Nielsen (UPC scanner data),
  Austrade etc.

12
Primary Data

• Research Methods for collecting Primary Data
• Exploratory Focus Groups, Pilot Studies.
• Sample surveys
• Experimental studies

13
Definitions

• Respondent the person who answers an
  interviewers questions or the person who
  provides answers to written/printed questions in
  self-administered surveys.
• Sample survey indicates that the purpose of
  contacting the respondents is to obtain a
  representative sample of a target
  populationmethod of data collection based on
  responses from a representative sample of
  individuals from a population of interest.

14
Types of Errors in Survey Data

• Random Sampling Error
• Systematic Error (Bias) arising from some
  imperfect aspect of the research design or errors
  in the execution of the research.

15
Systematic Error

• Non-response error
• Self-selection bias
• Response Bias
• - Deliberate Falsification
• - Unconscious Misrepresentation
• - Acquiescence Bias
• - Interviewer Bias
• - Social Desirability Bias

16
Types of surveys

• Cross sectional
• Longitudinal

17
Advantages of Secondary Data

• In some situations, useful for clarification and
  to define a research problem more sharply
  exploratory research
• Lower cost of research
• Time saving- data readily available
• Disadvantages
• Data may be outdated
• Units of analysis and measures may not be
  appropriate.
• Difficulties in combining multiple sec. Data
  sources
• Lack of information to verify the accuracy of
  data.

18
Uses of Secondary Data

• Fact finding
• Trends in the economy, markets etc.
• Exploratory analyses
• Building and testing analytical (mathematical,
  econometric, forecasting etc.) models

19
Types of Secondary Data

• Internal generated by the organisations
  accounting systems
• External, Proprietary commercial organisations
  like IDC, Dow Jones, Standard and Poors etc.
  routinely gather data which can be purchased.
• Other external Government and other public
  agencies

20
Types of measurement scales

• Nominal data are measurements that simply
  classify the units being measured ( of a sample
  or the population) into categories.
• Eg. Gender in census data, post code of
  residential units, political party affiliation of
  individuals, industrial classification code of
  businesses.

21
Types of measurement scales (contd.)

• Ordinal data are measures that enable the units
  to be ordered (ranked) with respect to the
  variable of interest no indication of how much.
• Eg. A wine tasters ranking of 10 wines
• Ranking of candidates from a job interview

22
Types of measurement scales (contd.)

• Interval Data Measurements that enable the
  determination of how much (greater or lesser) the
  characteristic being measured is possessed by the
  unit than another
• Interval scale subsumes ordinal scale but it also
  tells us how far apart the units are with respect
  to the characteristic (or attribute) of interest.
• Always numerical but there is no knowledge of a
  zero point (origin) on the measurement continuum.

23
Interval Scale

• Examples
• -Measurement of temperatures (in celsius) at
  which sample of 30 pieces of heat-resistant
  plastic begins to melt.
• - Scores of high school students in a
  standardised test

24
Ratio Scale

• Ratio scale data are data are measurements that
  enable the determination of how many times the
  attribute or characteristic being measured is
  possessed by the unit
• Eg. Sales revenues of 50 firms, bonus payments to
  managers, unemployment rates for the past 60
  months etc.
• Always numerical and the zero point is defined.