Quantitative research approach
Regardless to the subject or area of study, there are two types of research to choose from: qualitative or quantitative study. The first can have a strong positivist position, meaning that it usually assumes that reality exists independently of the observer, and hence the researcher work is to discover theories and laws which explain this reality (Easterby-Smith, Thorpe & Jackson, 2015). On the other hand, quantitative research is less strong positivist meaning that it accepts that reality cannot be directly accessed. Therefore, the researcher must deal with the nature of this reality indirectly through conducting surveys with larger sample sizes of individuals, organizations, or activities. According to our thesis topic, most of the data for absorptive capacity can be easier analysed and collected. Therefore, this type of data will normally be expressed in quantitative form (Easterby-Smith, Thorpe & Jackson, 2015). Furthermore, in the general area of absorptive capacity, most of the studies screened during our literature review were qualitative studies. However, if we want to measure a specific component for absorptive capacity, it would be much easier to use survey or secondary data. This last is our case.
Most research areas in business and management have data collected by other people. Individuals and organizations usually save and keep various types of data in order to monitor past and present performance, for protection purposes, and as regulatory reasons (e.g. personal tax records). It can be interesting to work with data collected for other reasons (Easterby-Smith, Thorpe & Jackson, 2015). Secondary data study is usually depending on the analysis of existing data that have a relation to the research topic in question. Secondary data are usually used by researchers as it is cheaper and takes less time to be collected, thus, a lot of money and time will be saved otherwise they would have to spend it in collecting primary data. Using secondary data can return to the researcher many advantages, which are as follows:
•Secondary data can assist in the process of clarifying, identifying, and redefining the research problem in positions when the main problem in a research study cannot be defined or its defined in different meanings and ways, therefore, utilizing secondary data can assist to clear the confusion with a coherent definition of the research problem.
•Secondary data might have a solution to the research problem, which might not need the collecting of a primary data each time. Several times, it might happen that accurate data for a current research is already available as secondary data collected for other purposes, therefore, it is not necessary to start conducting primary data collection again.
•Secondary data capable of providing other methods that could be used for primary research, therefore, generate needed information for better creativity, moreover, secondary data can give insight into the tools for identifying industry trends, potential customers, and languages usage. This previous knowledge will contribute and assets in the design and progress of a current research, therefor, provides a better opportunity for creativity.
•Secondary data can be classified based on source, category, database format, and medium as shown the figure below. Source data is usually available within the organization, either to be internal, such as financial and accounting reports, or external coming from outside the company, like publications and trade manufacturers’ associations. Classification of data by category depends on the type of the source where the data have been collected. Secondary data can be also classified by the database format and content. Moreover, secondary data may be found in different mediums, either as hard copies, or online and internet sources.
Characteristics of the used dataset
For our thesis we have decided to use secondary data from a database (Carlo Altomonte & Tommaso Aquilante, 2012. “The EU-EFIGE/Bruegel-Unicredit dataset,” Working Papers, Bruegel 753, Bruegel) collected in 2010 and containing the data from the timespan 2007-2009 of firms in 7 European Countries (Austria, Germany, France, Hungary, Italy, Spain and UK).
The database has been collected within the framework of the EFIGE project (European Firms in a Global Economy: internal policies for external competitiveness). It has received the support of Directorate General Research of the European Commission through its 7th Framework Programme and coordinated by Bruegel.
The peculiarity of this dataset is that for the first time in EU it includes and combines different measures of firms’ international activities such as data on exports, outsourcing, FDI and imports, by using both quantitative and qualitative information. This information is including 150 items that range from R&D and innovation, to labour organisation as well as also financing and pricing behaviours.
The dataset is free accessible in two truncated versions. Truncated 1 only contains EFIGE survey data, firm level (log) TFP in 2008 and 2014 and average TFP growth for the years 2001-2007, 2008-2009 and 2010-2014. No balance sheet figures are included. Information are truncated and aggregated to make firms’ identities undetectable. Regional and industry fixed effects are present for each firm but also anonymised.
Truncated 2 contains Balance sheet figures merged with the Truncated 1 set of data. For the purpose of our research we have used Truncated 1 dataset.
The dataset was constructed by distributing a questionnaire (available at http://bruegel.org/wp-content/uploads/2015/06/QST_International_Final_.pdf) to 135000 firms in the 7 aforementioned countries. The sampling method used by the researchers was based on different specified criteria. Their main goal was to distribute the questionnaire to a large number of firms with above 10 employees. First criterion was that for large countries (Germany, France, Italy, Spain and the UK) they expected to have responses from 3000 firms, 500 firms for smaller countries (Austria and Hungary), with a total number of 16,000 questionnaires distributed for each country according to the specific sampling method. Second criterion was to have a minimum response rate of 85-90% for five to ten key questions on the survey, the total responses they collected were 14,759 responses according to the table shown below.
For Family involvement variable we have considered all the values above and including 1 to 999999. By doing this, we have made a subsample constituted by firms in the 7 countries having family members in the middle management. This means that we can test if and how family involvement (i.e. number of family members in the firm) has an effect on filing patents by the firm in the timespan 2007-2009. To do so we use the following variable included in the dataset.
1.1 Tentative research proposals.
2. Literature review
2.1 The organization of the research
2.2 Family firms and Innovation
2.3 Absorptive Capacity
2.4 Absorptive Capacity and patents
2.5 Absorptive Capacity in family firms
2.6 Conclusions drawn from the literature review
3.1 Quantitative research approach
3.2 Characteristics of the used dataset
4.1 Logistic Curve Versus Regression Line
4.2 SPSS software
6.1 Limitations and future research
6.2 Practical Implications
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Absorptive Capacity in Family Firms