Les regulacions restrictives

The quantitative research design tests the research question relating to whether there are distinctive differences between family and non-family firms in the SME manufacturing sector of the United Kingdom, with a particular focus on innovation. As this study explores how doxa, fields and habitus can be used to explore the nature of familiness, a decision was made to use a dataset of SMEs, rather than larger firms, in which familiness can more easily be observed. This is because larger firms are more likely to hire non-family managers and these formal management mechanisms dilute the family influence (Sonfield & Lussier, 2009b; Zhang & Ma, 2008).

The data selected for the quantitative analysis is the dataset “SN 6856 Small Business Survey, 2010-2012” (Department for Business Innovation and Skills, 2013a). This is a large-scale representative telephone survey of SME business owners in the United Kingdom,

commissioned by the Department for Business Innovation and Skills, the Scottish Government and Invest NI. This is the most recent and the most comprehensive survey of the SME

Methodology Page 94 United Kingdom. As such, it represents the largest, most reliable dataset which was readily available for this research. Data of this quantity and quality could not otherwise have been obtained within the timescales of this research project.

Given the confidential nature of individual company records, SN 6856 is not available for public access. Therefore, the researcher applied for and was granted “Secure Researcher Status” and was required to complete data confidentiality and data security training. The evidence of authorisation of researcher status from the UK Data Service is provided in Volume 2.

The dataset “SN 6856 Small Business Survey, 2010-2012” consists of a raw SPSS file with accompanying codebook lookups. The SPSS file contains the results of 30 minute interviews, conducted between June and September 2012, with a representative sample of United Kingdom SME employers. The results of the research are securely stored by the UK Data Service and the owner of the dataset is the Department of Business Innovation and Skills. The complexity of ownership and storage rights for this dataset has added to the rigor and effort required for this analysis. Within each of the four United Kingdom nations, the sample was stratified by industry sector within employee size band, which makes the dataset particularly relevant to the scope of this research: the dataset enabled the identification of family firms and non-family firms within industry sector (manufacturing) and employee size band (SME). The survey used Standard Industrial Classification codes from 2007 (Companies House, 2015) to classify industry sector. The original purpose of the Small Business Survey 2012 was to understand key enterprise indicators amongst SMEs and SME owners, including recent turnover and growth of the firm, the business owner’s use of business support, their experience of accessing finance, their intentions to grow turnover and employment and, crucially for this report, their innovation practice and capability (Department for Business Innovation and Skills, 2013a). The

Methodology Page 95 comprehensive and reliable sample of SME firms in the United Kingdom. Furthermore, this dataset has not been used for further analysis beyond the BIS Research Paper published in 2013 (Department for Business Innovation and Skills, 2013a). As such, the analysis set out in

subsequent chapters represents a contribution to knowledge as being the first detailed analysis of manufacturing firms (both family and non-family) ever performed on SN6856.

The weakness of this dataset is that it is surprisingly small for a survey that is used to form United Kingdom government policy and represents only 0.1% of the total population of

businesses. SN6856 consists of 5723 records from an estimated total of 5.24 million businesses (Department for Business Innovation and Skills, 2013a). However, the random sample

approach, which selected 6000 records at random from the Inter-Departmental Business Register (IDBR) database, and further sample stratification, is considered to have produced the most representative sample SMEs across the United Kingdom (Department for Business Innovation and Skills, 2014d).

The first stage in the quantitative analysis was to perform a descriptive statistical analysis using SPSS in order to compare the differences between family and non-family firms across variables relating to firm characteristics and business performance. The analysis took place within the UK Data Service secure environment, as is required by their confidentiality requirements (UK Data Service, 2014). The UK Data Service offers a choice of SPSS or Excel for data analysis. SPSS is considered to be more suitable for analysis of large datasets and more complex statistical analyses (Pallant, 2007), such as the exploratory factor analysis and chi-squared techniques used in this study.

This initial quantitative phase will compare key business performance metrics, such as profit, turnover, firm size and age. In order to answer the research question relating to innovation in family firms, innovation indicators will also be compared. These include variables such as the

Methodology Page 96 introduction of new product and services, the investment in innovation, the intention to

innovate in future, and the adoption of industry best practice. More general variables, such as training, staff development, and interaction with support organisations, will provide an understanding of how family and non-family firms differ in activities relating to innovation. This initial statistical analysis will confirm whether family firms are indeed different to non- family firms across a number of key variables relating to innovation and business performance. This analysis will exclude firms with zero employees as a one-person firm is insufficiently complex field for exploring the nature of familiness (Zahra, 2003; Zellweger et al., 2010). The operation of doxa and habitus also requires the study of a field containing more than one individual (Bourdieu et al., 1993).

The second stage of the quantitative analysis is to conduct both Exploratory Factor Analysis and Parallel Analysis to produce a statistically-derived conceptual grouping of the variables of interest. This conceptual grouping will reduce the large number of variables to a smaller number of conceptual groupings, or factors. These factors will then be analysed for Bourdieusian themes, such as whether family firms’ relationship with customers can be explained through doxa, whether their approach to training and staff development can be explained through habitus and whether their willingness to interact with the outside world can be explained through the concept of fields.

The third and final stage of the quantitative analysis is to use the results of the descriptive statistics and factor analysis in order to develop of a number of hypotheses. These hypotheses, will relate the key aspects of familiness to each other. These hypotheses will be tested using a chi-squared technique. These three stages of quantitative analysis (descriptive statistical analysis, factor analysis, and chi-squared testing) will together answer the research question relating to whether familiness exists. The quantitative analysis will also start to explore the extent to which Bourdieusian themes can explain the nature of familiness.

Methodology Page 97 This section has described how the quantitative analysis will firstly use descriptive statistics to analyse the extent to which family firms differ from non-family firms in the SME

manufacturing sector of the United Kingdom. The quantitative analysis will start with a

statistical analysis to describe key business metrics, including the profit, turnover, firm size and age, as well as innovation-related activities, such as staff training, interaction with other fields, the extent of product and process innovation, and how firms finance innovation. Then the exploratory factor analysis and parallel analysis will combine the large number of variables studied in the quantitative analysis to identify key factors of interest in relation to the family firms. In this way, the key constructs of familiness will be identified from the exploratory factor analysis and parallel analysis. The final stage in the quantitative design is to develop hypotheses based on the factor analysis and on the descriptive statistical analysis. The

hypotheses will be tested using the chi-squared technique. At the same time as the quantitative data collection and analysis is taking place, the qualitative data collection and analysis will also take place. The qualitative research design will now be discussed.