Using Convergent Parallel Mixed Methods and Datasets for Science, Technology, and Innovation Policy Dynamics Research in Indonesia

The application of mixed methods has been widely implemented in several studies, particu-larly in the field of public policy; however, the implementation of convergent parallel mixed methods has been limited. Thus, such methods are appropriate to reveal the science, technology, and innovation (STI) policy dynamics in Indonesia during the 1945–2020 period, as policy dynamics research attempts to reveal the evolution of the changes regarding the policy itself. The following five concepts are analyzed through convergent parallel mixed methods: 1) regime/government change, 2) institu-tional change/transformation, 3) change in policy issuance, direction, and content, 4) actor role and existence, and 5) policy object input and output. This article discusses the method details, from the paradigm, research dataset, and technique selection for collecting and analyzing research data to the research implementation.


INTRODUCTION
Policy dynamics is defined as the evolution of policy changes, which have implications for regime/government policies toward institutions, actors, and systems within a certain period. Based on this definition, the five concepts are as follows: 1) regime/government change, 2) institutional change/transformation, 3) change in policy issuance, direction, and content, 4) actor role and existence, and 5) policy object input and output.
This concept is in accordance with the notion that understanding policy dynamics has been unlimited to the positivist conception of the public policy process as a policy cycle (Bardach 2011). The object of policy in this study conveys the science, technology, and innovation (STI) system or acknowledged as STI policy in Indonesia.
The research questions are as follows: How were the STI policy dynamics in Indonesia from 1945to 2020? What policy transformation is appropriate to improve STI policy performance for enhancing Indonesia's competitiveness?
Both questions are addressed with a conceptual framework and research design, as illustrated in Figure 1. Before explaining further about the research design, the author initially conveys the position of the study based on research paradigm. Research paradigm refers to a perspective or belief to understand and examine problems (Kuhn and Hacking 2012) or acknowledged as "the philosophical worldview proposed in the study" (Creswell 2014). In this research, the paradigm used is pragmatism generated from actions, situations, and consequences based on solutions to existing problems (Morgan 2007). The pragmatism paradigm applies mixed methods as a choice of research methods, techniques, and procedures (Creswell 2014;Morgan 2007). Moreover, the pragmatism paradigm frequently appears in the context of social, historical, political, and dynamic changes (Brierley 2017). This understanding is in line with the research objectives of STI policy dynamics in Indonesia for the 1945-2020 period, in which the historical aspects and dynamics of change are highlighted in this research.

RESEARCH METHOD
This study applies the mixed methods, widely used in public administration research or public policy. Mixed methods not only combine the direction of data collection and analysis but also include mix qualitative and quantitative approaches as part of the research process (Creswell and Plano Clark 2011); thus, the implementation of mixed methods in public administration research can clarify the analysis of complex problems and establish a strong and comprehensive knowledge basis Hendren et al. (2018). This notion is in line with conceptual extraction from research, in which studies on STI policy dynamics comprise five complex concepts: 1) STI policies and regulations, 2) science and technology system resources and innovation, 3) actors, their roles, and their interactions with the STI system, 4) output performance and impact of the STI system, and 5) science and technology ecosystem and innovation. In ad- dition to these five concepts, an explanation is required regarding the second research question, which is the design of policy transformation. Therefore, one concept is added to the research design (diagrammatically, as illustrated in Figure 1).
Convergent parallel mixed methods are thus appropriate for this research because of their nature of providing flexibility for researchers to collect and combine qualitative and quantitative data under the same period, further combined with the information in the overall interpretation of the results. Thus, the methods can provide a comprehensive analysis of research problems (Creswell 2014).

RESEARCH DATASET
The dataset comprises two data attribute types: 1) numeric, in the form of statistical data on human resources, research budgets, population levels (people aged 30-34 with college graduate education) and 2) categorical, in the form of ordinal narratives, such as the funding structure of the research and innovation budget in Indonesia-direct funding of government RD institutions and universities and competitive funding in the STI sector-interactions among actors and their relationship patterns. Data requirements, sources, and types are depicted in Table 1.

Data collection and analysis techniques
Data collection and analysis techniques have adjusted to the new norms in terms of research data collection, obtained during/after the COVID-19 pandemic (Lupton 2020;Ocean Nexus Center 2020). The data collection and analysis techniques in this study ( Figure 2) are as follows: a. Regulatory Survey (Li et al. 2017) (1945) to President Joko Widodo (2020). Furthermore, the data are analyzed through content analysis (Putera 2012;Tight 2019a). Content analysis applies the frame of the STI policy (policy instruments) (12 classifications of policy instruments exist for the STI system in Indonesia) and maps actors through stakeholder analysis (Varvasovszky 2000). b. Secondary Data Research (Heaton 2012;Tight 2019b) is the collection of data from previously published sources. Secondary data are 1) financial statistical data "budget allocation for science and technology and government (RD and university) and private sector budget for Gross Domestic Product (GDP)" and 2) statistical data "funding structure of the research and innovation budget in Indonesia-direct funding government RD institutions and universities and competitive funding in the STI sector; 3) statistical data on the number (availability) of science and technology and innovation of human resources per one million population; 4) statistical data on the number of new doctoral graduates; 5) statistical population level (people aged 30-34 with college graduate education); 6) statistical data on the number of science technology parks, science parks, and technology parks; 7) technology product trade balance data; and 8) comparative study data on science and technology, including innovation systems from several countries, such as South Korea and Malaysia. Data are further analyzed using supplementary analysis technique (Heaton 2004(Heaton , 2012, assorted analy- sis (Dale et al. 2008), and comparative research. Supplementary analysis, which reuses secondary data, aims at generating new analysis/understanding of existing data. Assorted analysis is generally a technique that combines secondary data with additional primary data obtained from interviews or observations. c. Text Data Mining is similar to "Big Data Methods" (Oswald and Putka 2017) conducted by collecting data from large databases and creating data clusters according to research needs. In this study, text data mining applies big data from the Scopus database for international publications, and the Intellectual Property Database, WIPO, and ASEAN databases for patents and other intellectual properties. The following data are obtained from the Scopus database: 1) data on Indonesian international scientific publications from 1945 to 2020 and 2) data on the top 10% of most cited publications from 1945-2020. Furthermore, data are analyzed using a bibliometric approach and visualized with VOSviewer (van Eck and Waltman 2011). Meanwhile, the intellectual property database is utilized to obtain intellectual asset data (numbers of patent applications, PVP, trade secrets, and copyrights). Furthermore, intellectual asset data are analyzed through a patentometrics approach (Li et al. 2020; Lukman and Rianto 2019). d. Interviews are conducted face-to-face (when possible), but the author of this study also prepares for interviews to be conducted online. Online interviews are preferred as an appropriate way to collect data during a pandemic (Lupton 2020), in which the medium used adjusts to the sources. Data collection under this method is performed to obtain data on: 1) narrative of the funding structure of the research and innovation budget in Indonesia-direct funding of government RD institutions and universities and competitive funding in the STI sector; 2) narrative of funding allocation priorities; 3) narrative growth and development of STI system vehicles, such as science technology parks, science parks, and technology parks; 4) actor narratives, roles, and interactions in the STI system; and 5) narrative of diversity and subsystem interactions formed by actors to the environment of each period of government. The collected data are further coded and clustered according to the needs of the analysis. e. Expert Elicitation or referred to as an Expert Opinion Survey is the facilitation of the quantitative expression of subjective judgment of a person's expertise regarding facts or values; it is typically utilized when data or interview models cannot provide the required information comprehensively (Colson and Cooke 2018). This mode of data collection is performed by online questionnaire or email (Singh et al. 2019). The collected data are further analyzed through (social) network analysis. Data are taken from the mapping results of the policy content analysis in Number 1 above through the stakeholder analysis matrix. In addition, primary data are obtained through an Expert Opinion Survey and online interviews with experts in the field of STI policy and management.

4.
Output performance and impact of STI system during the period from 1945 to 2020 Secondary Indonesian international scientific publication data CSV file (.csv) or Excel Workbook (.xlsx) 2lData are obtained from the database of international journals (Scopus). We have access to the Scopus database. To find international publications written by researchers from Indonesia, we use the "AFFILCOUN-TRY ("Indonesia") data query in the Scopus database and previously published publications "Increased number of Scopus articles from Indonesia from 1945 to 2020: An analysis of international collaboration and a comparison with other ASEAN countries from 2016 to 2020" (Putera et al. 2022 Data are obtained from the mapping results of the policy content analysis in Number 1 above through the stakeholder analysis matrix. In addition, primary data are obtained through an Expert Opinion Survey and online interviews with experts in the field of STI policy and management.
Answering Research Objective: 2. Design of policy transformations that are conducted to improve the performance of STI system policies for increasing Indonesia's competitiveness 6.
STI system policy transformation design Primary and secondary Narratives of the five conceptual dynamics of STI system policies in Indonesia, coupled with the analysis of comparative country studies OpenDocument text (.odt) and PDF/A or PDF (.pdf) Data are retrieved from the five conceptual dynamics of STI system policies in Indonesia, Expert Opinion Survey, online interviews with experts in the field of STI policy and management, and secondary data from books/journals/reports on science and technology systems-innovations from South Korea and Malaysia.

Research stages
Stage 1 is initiated from the Regulatory Survey of legal documents, with a period of policy documents from the era of President Soekarno (1945) to President Joko Widodo (2020). The stage is simultaneously progressed to Text Data Mining/"Big Data Methods" on the Scopus Database for international publications, the Intellectual Property Database, WIPO, and ASEAN databases for patents and other intellectual properties. Data collection results at this stage are later analyzed using the analytical techniques described in the previous subchapter.
Stage 2 is conducted by considering the regulatory survey results in the form of a stakeholder analysis matrix used for interviews. This stage involves expert elicitation/expert opinion surveys to obtain data related to the funding structure of the research and innovation budget, funding allocation priorities, STI growth and development, interactions among actors in the helix models, and interactions among subsystems in ecosystems. At this stage, secondary data research is also performed to obtain resource data on STI systems and trade balance data for technology products. Furthermore, the collected data are analyzed by applying analytical techniques in accordance with the previous subchapter.
Stage 3 is performed after the analysis results of the five conceptual dynamics of the STI system policies in Indonesia have been identified. The next step is taken to obtain secondary data in the form of a comparative study of the STI system implementation in South Korea and Malaysia. Data are further constructed into a policy transformation design while conducting expert elicitation/expert opinion surveys.
The research stages are illustrated on figure 3.

CONCLUSIONS
The study of STI policy dynamics in Indonesia serves as the evolution of policy changes, presenting the implications for institutions, actors, and systems over a certain period. Therefore, mixed methods are appropriate to reveal policy dynamics. Specifically, convergent parallel mixed methods are proper for this research because of their nature of providing flexibility for researchers to collect and combine qualitative and quantitative data under the same period.