1. Description of Method

The primary focus for the proposed research is higher educational institutions and research institutes within the Kuala Lumpur City-region. They are the primary targets for information. Among private providers of higher education, the target will be private universities and university colleges rather than colleges. Colleges do not normally conduct research and innovative work.

We are aiming at a minimum of 10 universities/university colleges and research institutes in the Kuala Lumpur City-region as respondent institutions and this 10 could come from the following:

•  Universiti Malaya

•  Universiti Kebangsaan Malaysia

•  Universiti Putra Malaysia

•  Universiti Islam Antarabangsa Malaysia

•  Universiti Sains Islam Malaysia

•  Universiti Teknologi Mara

•  Universiti Teknologi Nasional

•  Universiti Multimedia

•  Universiti Kuala Lumpur

•  Universiti Industri Selangor

•  Universiti Sains dan Teknologi Malaysia

•  Universiti Terbuka Malaysia

•  Universiti Tun Abdul Razak

•  Universiti Perubatan Antarabangsa

•  Universiti Teknologi Kreatif Limkokwing

•  Universiti Antarabangsa Al Bukhari

•  Pusat Antarabangsa Pendidikan Kewangan Islam

•  Asia e-University

•  Universiti Al-Madinah

•  Kolej Universiti Teknologi dan Pengurusan Malaysia

•  Kolej Universiti Islam Antarabangsa Selangor

•  Kolej Universiti Infrastructure Kuala Lumpur

•  Kolej Universiti Sunway

•  Kolej Universiti Antarabangsa Sedaya

•  Kolej Universiti Teknologi Antarabangsa Twintech

•  Kolej Universiti Teknologi dan Inovasi Asia Pasifik

•  Kolej Universiti Help

•  KolejUniversiti Pengurusan dan Keusahawanan Binary

•  Kolej Universiti Sains Perubatan Cyberjaya

•  Kolej Universiti INTI

•  Kolej Universiti Taylor

•  Kolej Universiti Antarabangsa Cosmopoint

•  Kolej Universiti Nilai Antarabangsa

•  University of Nottingham , Malaysia

•  Monash University Malaysia

Letters requesting participation will be sent to all 35 universities/university collges and those responding positively will be included in our list of respondent institutions.

In addition we will gather data from government departments and other agencies that support the learning region and ideopolis infrastructure component. Top on this list will be the Ministry of Higher Education, the Ministry of Science Technology and Innovation, Malaysia Technology Development Corporation, DBKL, Putrajaya Corporation.

It has to be appreciated at the outset that the data collection will involve the collection of both quantitative and qualitative data set.

The methods that will be adopted in this proposed research will comprise firstly, the collection and analysis of audio-visual materials, reports, documents and other statistics from government departments and agencies that support the “learning region” and “ideopolis” infrastructure component. These secondary data sources would normally have plans and other documented materials with details of infrastructure development over a time period, support services and fiscal and non-fiscal incentives that have been extended to universities and research institutions, incubators and other innovating units in the city-region. Data collected based on this document analysis can be both quantitative and qualitative in nature.

Second, through the use of a survey instrument to be designed and administered in face to face interviews with select personnel, basic information on surveyed entities will be compiled and analysed. (However, in most instances such data could be compiled from published and promotional materials of respondent institutions.) Face to face interviews will be necessary in cases where important statistics which define the institutions are not currently published. In some instances, the published statistics are not in the format that is useful to researchers and thus the need to communicate directly to respondent institutions for the relevant data.

Third, personal semi-structured interviews with at least 10 respondents comprising of the following: (1) holders of key posts in the management of the city of Kuala Lumpur, Kuala Lumpur's Conurbation, and Putrajaya; (2) vice-chancellors/rectors/directors/managers and other experts of universities, polytechnics and research institutes; (3) managers and experts of local research institutes; (4) experts and managers of institutions funding sciences, R&D and business start-up in Kuala Lumpur City-region; (5) experts from the Ministry of Higher Education; (6) representatives of innovation-based business enterprises, and (7) managers of the local technology industry. These personal interviews will provide detailed qualitative information on specific and policy-relevant matters. More importantly, these interviews will unravel interviewees' personal assessments of the situation (past, current and future). It is important to realise that while infrastructure provision may be adequate in terms of quantity, they may not be appropriate for the purpose on hand. Similarly, while support services may be available, these may not be delivered effectively. And, while policies are in placed, they may not be religiously implemented. Thus, while content and document analysis will provide data on the quantitative element of our enquiry, the qualitative dimension need to be probed through face to face interviews.

2. Flow Chart of Research Activities

See Appendix 1 for the Research Activities.

3. Gantt Chart of Research Activities

See Appendix 2 for Gantt Chart of Research Activities.

4. Milestones and Dates

Appendix 1: Research Activities Flowchart

Appendix 2: Gantt Chart of Research Activities

Appendix 3: Justification for purchasing NVivo7 for Qualitative Data Analysis and Transcriber Machines

Universiti Sains Malaysia does not have Nvivo7.

Choosing the main qualitative data analysis software packages can be difficult. To assist researchers in making this choice, knowledge about the differences among softwares are critical. The differences need to be examined along two dimensions: related to the qualities of the software and of the research project. The software dimension is structural design, and the project dimension is complexity. Software structure is dichotomised between structured, sequential, verbal versus visual, spatial, interconnected modes of operation. Projects are dichotomised between homogeneous sample, short timeframe, single data-type, single data analyst; versus multiple samples, longitudinal data, multiple data types and team data analysis. Based on the above consideration, NVivo7 is chosen for Nvivo7 is comprehensive and easy to learn (realising that RO will be doing data entry etc.). More importantly, the software can be used to analyze interviews, field notes, textual sources, and other types of qualitative or textbased data which will be collected in the proposed research.

There is sufficient pay-off in terms of enriched data analysis and more comprehensive development of coherent theoretical ideas and policy recommendations. Nvivo7 packages will help to automate and speed up some analysis tasks, allowing instant access to data once coded, facilitate more complex questioning of the data, and provide creative aids to stimulate both theoretical and policy development.

Two transcriber machines are required to transcribe transcripts of at least 30 personal interviews. This is a very laborious job.