Population and sampling

Consecutive to the above, the following stage in the research design is to compose a sample target. Sample in research is described as a chosen population section, which is conscientiously done to find generalizations about the entire population target (Sekaran, 2000; Schindler and Cooper, 2003; Bryman and Bell, 2007). In the current research, sample design depends on some key factors, as per Fowler (2002):

1. Sample selection- probability or non-probability method of sampling 2. Sample form-definite component of population sampled

3. Sample size- quantity of objects required

121 4.5.1 Sampling strategy

It is crucial to realise the significance of the respondents including background, prior to gathering data for collecting data relevant to the desired objectives. The important sampling methods are as per Bryman and Bell (2007): probability and non-probability related sampling. The first provides targeted strata within the population equal access to being selected, and in the second, the elements only have coincidental opportunities of getting chosen (Sekaran, 2000; Bryman and Bell, 2007).

For the current research, a probability method of sampling is selected for data collection, under which stratified sampling method is found to be most appropriate (Rungtusanatham et al., 2003; Saunders et al., 2012). Stratified sampling is a method designed to ensure that the sample has certain basic characteristics; usually that it represents the population of key variables (Jupp, 2006).

Stratified sampling (Rubinstein, 1981) is a variance reduction technique used in estimation. It consists of categorising the sample space to strata and estimating the yield in each stratum. It was initially developed by statisticians for use in sample surveys several years ago and has since been adapted for use in various research studies. As applicable with all sampling methods, stratified sampling is used if there is insufficient time or resources to conduct a census by collecting information from every member of a population. Through the stratified sampling method, the researcher can sample the smallest and inaccessible subsections in the target population representively.

This permits the researcher to sample even the exceptional strata of the given population. This technique results in statistical precision as compared to the technique of simple random sampling. This is due to the fact that the variability within the subsections is lower compared to the variations reflecting on the entire population. As it has a high statistical precision, it also means that this technique requires a small sample size thus saving time, money and effort of the researchers (Hammersley and Handscomb, 1975).

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The targeted population for this study are individual European client firms, represented by a senior decision maker, involved with IT offshoring decisions across various functions such as sourcing management, project/process management, finance, operations and technology/infrastructure management. Here, the job title, age/gender, organisation type/industry sector, country, size of the organisation in terms of staff strength is the relevant demographic information for stratified sampling. The response from every group is needed equally. On the other hand in the case of non-probability it is applied in situations where the research intends to generalise the results based on a definite cluster of individuals, like particular job function/title within a specific industry sector. Sekaran (2000) identifies that when more aspects than only generalisability needs to be studied, non-probability is preferred to probability. Thus, for the current research which aims to include a considerably large sample in a short time span, probability sampling is seen to be more beneficial.

4.5.2 Target population sample

Selecting of a precise population target is an important factor to the success of any research (Baker, 2002). Proper decisions for choosing particular locations limits for the research standardisation raising possible restrictions on hypotheses based conceptual framework. Hence, choosing the suitable locations facilitates the researcher in recommending an appropriate manner to examine the suggested hypotheses and theories for generating conclusions (Eisenhardt, 1989). Regarding the location, the aspect of choosing a suitable analysis unit depends on the complete population, Population has been described by Bryman and Bell (2007, p. 182) as “the world of components from where the sample is carefully chosen.” Population signifies the sample totality or constituents adhering to specific targets, like a group of companies, communities, schools, individuals, hospitals, associations, colleges, nationalities that having same features (Baker, 2002; Zikmund, 2003).

The population target chosen in the current research are senior management professionals working in various industry sectors involved with IT outsourcing

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decisions, in Europe. It is quite impractical and costly to incorporate entire population in this research. Consequently, a frame for selecting the sample is discussed in the following section.

4.5.3 Sample population and demography

Sampling framework is also known as the ‘working population’, which as per Zikmund (2003); Sekaran (2000); Bryman and Bell (2007); includes every unit in the population targeted in the sample. For the current research, selecting a relevant sampling amount is built on the specifications recommended as per Rice (1997) given below:

 Comprehensive frame refers to a type of accurate framework whereby the natures of the sample collected from population targeted are appropriate and representative.

 Competent frame refers to that frame which covers the entire population demography.

 Modern age frame facilitates sampling from updated records regarding targeted population.

 Easy to use frame refers to the frame where the population of interest sampling is simple to collect, practical but not duplicated.

For the present study, the sample population is based on relevant statistics and client data base available through secondary sources in the European IT offshoring industry, such as: Datamonitor, National Outsourcing Association-UK, TiE-UK, European Commission, UNCTAD, Gartner, IAOP and IT outsourcing specific business publications / quarterly magazines / industry reports such as the Blackbook of

Outsourcing, Outsourcing Magazine and Professional Outsourcing.

As gathering the entire population is impossible, costly and time-consuming (Sproull, 1995), in the current research, the sample population are individual professionals who represent IT offshoring functions in various industry sectors across Europe. Also, the sample population in the current study are full-time professionals. The purpose of the current study is to investigate effectiveness together with the soundness in conceptual framework and mitigating influence of

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the demographic factors like functional area, organisation type/size, job title/position, country/region and the industry sectors etc.

Table 4.5: Demographic composition of the sample

Demographic Group Demographic Group

Gender Male

Female

Annual revenue over $10B

$1B - $10B $500M - $1B $100M- $500M $50M- $100M $20M- $50M $10M- $20M

Job role CEO

CIO COO CTO PM/PMO Sourcing Head Finance Head Sales Head

Countries United Kingdom

Belgium France Netherlands Germany Luxembourg Switzerland Industry sectors Financial Services

Transportation Retail and CPG Logistics Manufacturing Telecom Media Hi Tech Others (SME/EPC)

Annual IT offshoring spend over $500M $250M - $500M $100M - $250M $50M - $100M $25M - $50M $10M - $25M $ 1M - $10M 4.5.4 Number of samples

To make a decision about the precise quantity for a sample is hazardous and complicated work. In this context, if the size of the sample is lower than the probable size, it results in increased chances of disaster convergence, inaccurate result (negative error variance estimated for a measured variable), and less exactness of an attribute (Comrey and Lee, 1992; Hair et al., 2006).

In the other hand, more than required size of the sample results in loss of money, time and process to collect the respondents’ answers (Zikmund, 2003; Bryman and Bell, 2007; Hair et al., 2006). This is why it is important to decide judiciously upon

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the proper sample size so that it can be standerdised across the population targeted.

The other strategy to select the size of the sample is determined by the data analysis techniques and methods according to Fowler (2002). Its suggested in literature (Hair et al., 2014), to check the size of the sample as it is essential to find options and strategies for data analysis methods that are suitable for this type of studies. Based on the characteristics of the conceptual framework, this study is directly related to multiple regression including statistical techniques. Data analysis literature underlines a certain guidelines that are designed to achieve accurate and authenticated results using Structural Equation Modeling (SEM) using partial least squares. From these few are deliberated for the current research and enumerated here

Roscoe (1975) suggested four rules for deciding and constituting a sample size:

1. Sample size (n): n>30 and n<500 are applicable for a lot of research.

2. Appropriate sample- divided into sub-samples, is a minimum sample size of 30 for each group.

3. In multivariate analysis-the size of the sample required should be 10 times the number of constructs in the framework.

4. In experimental research- size of the sample of about 10 to 20 respondents is enough having adequate control over the behaviour of the respondents.

In the current research, calculations were executed applying and Morgan’s and Krejcie (1970) principle and various literature based guidelines (Bentler and Chou, 1987; Comrey and Lee, 1992; Hair et al., 2014; Loehlin, 1992). The current study achieved a workable sample of 136 which was after the missing data treatment. The details are presented in the next chapter (section 5.3).