Sampling Procedures and Data Sources

A sample is “a smaller set of cases a researcher selects from a larger pool and generalises to the population” (Neuman 2006, p.219). A sampling is a “process of selecting a sufficient number of elements from the population so that by studying the sample, and understand the properties or characteristics of the sample subjects, it would then be possible to generalize the properties or characteristics to the population elements” (Sekaran 2000, p.268). Strauss and Corbin (1998, p.202) emphasise the aim of the purpose or theoretical sampling is “to maximise opportunities to compare [different situations] to determine how a category varies in terms of its properties and dimensions”. That is, a good sample must be chosen at random, large, and unbiased (Hussey & Hussey 1997). The sampling process in this research study is divided into a number of steps: defining the population, selecting the sample frame and unit, choosing the sampling technique, deciding on the sample plan, and determining the sample size (Luck & Rubin 1987; Kinnear & Taylor 1996; Churchill 1999; Zikmund 2003; Neuman 2006). The population is the entire group of individuals that this research study needs to investigate and the element is a single member of that population (Zikmund 2003; Bryman 2004; Davis 2005; Burns & Bush 2006; Neuman 2006). The target population within the entire group of individuals is the specific pool of cases that need to be investigated (Zikmund 2003; Neuman 2006). The sampling frame includes a specific list that closely approximates all the elements in the population from which the sample may be drawn, that is, the “working population” (Sekaran 2003; Zikmund 2003; Davis 2005; Burns & Bush 2006; Neuman 2006). The sample frame for this research study comprised all small and medium firms, which are listed in the Dubai Chamber of Commerce and Industry commercial database. The sampling unit includes a single element or group of elements under discussion to be selected in the sample and is divided into primary and secondary stages (Zikmund 2003). It involves listing and describing specific units of analysis for data collection (Davis & Cosenza 1993).

4.5.4.1 Sampling Technique and Plan

The sample survey is conducted to achieve a representative sample of the target population by contacting individuals and respondents (Bryman 2004), which can be a “method of primary data collection based on communication with representative sample of individuals” (Zikmund 1997,

p.202). The sampling techniques have various considerations that are: necessity, effectiveness, and time and cost limitations (Saunder, Lewis & Thornhill 2000; Sekaran 2003; Sarantakos 2005). The sampling technique is divided into probability sampling and non-probability sampling (Cooper & Schindler 2003; Sekaran 2003; Zikmund 2003; Davis 2005; Burns & Bush 2006; Neuman 2006) (see Appendix C). The probability sampling focuses on the techniques that produce a highly representative sample (Neuman 2006). The goal is to collect a representative sample and a small unit collection from a population and to produce an accurate generalisation. Examples include: simple random, systematic, stratified, and cluster (Saunders, Lewis & Thornhill 2003; Zikmund 2003; Berg 2004). The probability sampling applies statistical means to select the sample, which reflects a more technical superiority and reduces sampling bias and error (Sekaran 2003; Zikmund 2003). However, the non-probability sampling focuses on how a sample and a small collection of cases or units describe social phenomena (Neuman 2006). The goal is to collect specific cases, events, or actions and elucidates and deepens the understanding of the process of social life and its context. Examples include: haphazard, quota, purposive, snowball, deviant case, sequential, and theoretical (Saunders, Lewis & Thornhill 2003; Zikmund 2003; Berg 2004). In the non-probability sampling, the chosen item of the population is unknown and the judgment of the investigator impacts the selection of a sampling unit (Sekaran 2003; Zikmund 2003). The sampling techniques should fit the research methodological approach, by this means in this research study, the stratified random sampling of various SMEs in the Dubai market was used.

The sampling plan concerns the development of specific procedures and operational methods in selecting the sample (Zikmund 2003; Davis 2005) that can be followed to avoid potential errors (Davis & Cosenza 1993). In this research study, the primary sampling frame consisted of a total of 16,300 firms whose names were obtained from the Dubai Chamber of Commerce and Industry commercial database along with employment sizes and economic activities (DCCI 2010). The sample was stratified by employment size because firms were geographically diverse populations and this allowed enough variance with respect to the determinants under study (Aragon-Sanchez & Sanchez-Marin 2005; Homburg & Jensen 2007). Data collection was carried out using a sample design that follows the principles of stratified sampling in the target population. The sampling procedure involves a process of stratification through dividing the sample into mutually exclusive subgroups or strata according to some common characteristics that are relevant and meaningful to the context of the research study, and then randomly sampling from each group (Zikmund 2003). The outcome yields a richer source and has a smaller standard of error that ensures homogeneity

in each stratum and heterogeneity among all strata (Cavana, Delahaye & Sekaran 2001). In this research study, the population was segmented from the DCCI database according to employment size. It was divided into three groups: 1 to 9, 10 to 199, and more than 200 employees (DCCI 2010). Only two groups: 1 to 9 and 10 to 199 employees were selected according to this research study. The sample size distribution over the specified strata was carried out using a proportional affixation criterion where the sample of firms in each stratum is proportional to the relative weight of the stratum in relation to the population (Aragon-Sanchez & Sanchez-Marin 2005). Within each stratum, the selection was conducted by simple random sampling. The sample size was 600 firms, considering an overall maximum error of 5% with a 95% level of confidence.

This research study draws on a sample of small and medium firms from a range of industries on the basis of their contributions to the local economy and in the modern economy most industries are technology-driven creating innovation challenges for emerging markets (North & Smallbone 2000; Calantone, Cavusgil & Zhao 2002; Blumentritt & Danis 2006; Szirmai, Naude & Goedhuys 2011). Various manufacturing and service industries were included in this sample to generalise beyond particular industries, to the population of SMEs, to produce unbiased final results, and to accommodate for the nature of this research study (Dawes 2000; Scozzi, Garavelli & Crowston 2005; Martinez-Roman, Gamero & Tamayo 2011). It is to contrast the hypothesised conceptual model related to the firm’s innovative behaviour in a specific context (Montalvo 2006; Marcati, Guido & Peluso 2008). Further, the randomly selected firms from the commercial database were contacted by telephone and by email to ask for their participation. The sampling frame contained a target respondent of owners/managers of SMEs operating in the Dubai market who are directly involved with their firms as SMEs usually reflect the personalities of their owners/managers (Kets de Vries 1977). The rationale for selecting individuals with senior-level responsibilities as key informants was based on the fact that their values and philosophies influence their firms’ strategic direction, innovation activities, and businesses performances (Covin & Slevin 1990; Kumar, Stern & Anderson 1993), which is comparable to the owners and/or the managers of SMEs. They often rely on self-reports and tend to provide reliable and objective data (Podsakoff & Organ 1986).

4.5.4.2 Sample Size

A sample size is the number of observations that are included in the research study (Cooper & Schindler 2003; Zikmund 2003) and the “absolute size of the sample that is important, not its size relative to the population,” (Ticehurst & Veal 2000, p.164). Neuman (2006) argues that the best

sample size depends on the degree of accuracy required, the degree of variability and diversity in the population, and the number of different constructs examined simultaneously when analysing data. Sample size can be determined by precision and confidence (Sekaran 2003). Precision is how close the research study estimate is to the true population as a function of the range of variability in the sampling distribution of the mean while confidence is how true the research study estimate is to the population, that is, the greater the precision required the larger the sample size needed. The confidence level can range from 0% to 100% where a 95% confidence level (significant level of p ≤ 0.05) is the conventionally accepted level for most social sciences and business research studies (Cavana, Delahaya & Sekaran 2001; Sekaran 2003; Burns & Bush 2006). The contributing element of a sample size involves the magnitude of population correlations, number of constructs, level of analysis details, level of result precision, and availability of times and budgets (Ticehurst & Veal 2000; Tabachnick & Fidell 2001). The minimum sample size is “to have at least five times as ... the number of variables to be analysed, and the more acceptable sample size would have a 10:1 ratio” (Hair et al. 2006, p.112). To select an optimum sample size, Roscoe (1975) emphasises that between 30 and 500 is suitable for most research studies; however, Green (1991) argues that it is based on the number of independent latent variables in the conceptual model and later Bartlett, Kotrlik, and Higgins (2001) recommend that it is five to ten times more than the number of independent constructs for multivariate research. Hoelter (1983) recommends that the respondent sample size is between 100 and 200 when a quantitative method is used. The sample size plays an important role in the proposed data analysis technique of around 100 and 200 responses in the case of advanced statistical methods, such as the Partial Least Squares in the structural equation modelling technique (Bagozzi 1997; Chin & Newsted 1999; Hair et al. 2006). A sample size in a range of 150 to 400 is suggested that is subject to considerations of model complexity, missing data, and error variance of questions and items (Hair et al. 2006; Manning & Munro 2007).

As this research study relates to the activities of small and medium firms, firms with over 200 employees were eliminated according to the DCCI classification. In order to assess the level of innovation practices and business growth performance, firms in the sample would have to originate in the Dubai market and operate on a full-time basis. It was decided to eliminate small and medium firms with the “establishment” legal status from the sampling frame. The rationale was based on the DCCI figures that show approximately 45% of the small and medium population comprises of one individual who is only liable for the firm’s debts and is only interested in day-to- day activities of wholesale, retail trading, and repairing services (DCCI 2010). The commercial

database was further screened for SMEs, which were branches and franchises of foreign offices, gone out of business, merged with other firms, acquired by other firms, refused to participate for confidentiality reasons, and were no longer operating in the local marketplace. According to these above criteria, a sample size of 600 SMEs was randomly drawn using a disproportionate stratified sampling technique from the DCCI commercial database, which covered a range of economic activities and industries (DCCI 2010). That was 600 survey questionnaires were distributed to the respondents of SMEs with the expectation of obtaining a high response rate (Burns 1994).