Problem Recognition

Problem Structuring

Research Design

Data Collection (Surveys, Requirements Elicitation, experiments, focus groups etc.)

Data Analysis, generating, interpreting results

(PILOT STUDY followed the FULL SCALE study)

Writing up results and recommendations

Implementation

The Research Process

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

Conceptual Framewrok

• Identify Key Concepts

• Define the Key Concepts

• Operationalise the Concepts

• Explore systematic relationship between the concepts.

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.

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

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.

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

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

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 J&J such as Woolworths, Coles.

• Prefer a system solution to the problem.

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.

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.

Primary Data

Research Methods for collecting Primary Data

• Exploratory: Focus Groups, Pilot Studies.

• Sample surveys

• Experimental studies

Definitions

• Respondent: the person who answers an interviewer’s 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 population;method of data collection based on responses from a representative sample of individuals from a population of interest.

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.

Systematic Error

• Non-response error• Self-selection bias• Response Bias

- Deliberate Falsification

- Unconscious Misrepresentation

- Acquiescence Bias

- Interviewer Bias

- Social Desirability Bias

Types of surveys

• Cross sectional

• Longitudinal

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 availableDisadvantages:• 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.

Uses of Secondary Data

• Fact finding

• Trends in the economy, markets etc.

• Exploratory analyses

• Building and testing analytical (mathematical, econometric, forecasting etc.) models

Types of Secondary Data

• Internal – generated by the organisation’s 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

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.

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 taster’s ranking of 10 wines

Ranking of candidates from a job interview

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.

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

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.