Sampling

An important practice in designing a good survey is sampling which is the process of selecting a reasonably sufficient number of respondents that can represent the whole population (Salant and Dillman, 1994, Fowler, 2009). In many research cases it is possible to collect and analyse data from the whole population. However, it does not mean that it will provide more useful and reliable outputs than from the data collected from a sample that represents that population (Saunders et al., 2009). The results generated through a survey that has implemented a good sampling technique in which the respondents are good representation of the target population will be equally useful and reliable (Yu and Cooper, 1983).

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The use of sampling is important because it is unlikely that researchers can collect and analyse every bit of data available due to limitations caused by time, money and access (Saunders et al., 2009). Hence, the rationale behind sampling is to gain efficiency as it involves less time and money (Salant and Dillman, 1994). The sampling design for the quantitative data collection will involve the determination of the sampling method, sampling frame, sample size and selection of key informants.

Probability and non-probability sampling are the two sampling techniques broadly used in research. Probability sampling technique was used to select the sample for the survey as it is characterised by equal chances of each individual case being selected (Creswell, 2008). It minimises selection bias and hence, the results from this sample can be generalised to the population (Hair et al., 2003, Creswell, 2008, Saunders et al., 2009, Fowler, 2009, Creswell, 2014). The individual companies were selected randomly based on a random number table. The target population for this study was 558 and the sample size as calculated in the next section was 215. To generate a simple random sample of that population, a numbered list of population was acquired by sequentially numbering the companies from 1 to 558. A random number table was generated in Microsoft Excel and the first 215 companies were selected (see Appendix VIII) that constituted a simple random sample of that population (Fowler, 2009, Creswell and Clark, 2011).

While probability sampling technique was used for selecting the survey respondents, non- probability sampling based on the researcher’s subjective judgement was used as a sampling technique for selecting the interview respondents (Zikmund et al., 2014). Non-probability sampling techniques are used due to many reasons such as the time and costs involved, extreme difficulty in obtaining probability samples and the need to study a particular sample out of a population (Bryman, 2016). Since the aim is to get in-depth information about the topic under study, it will be wise to select people that are assumed to provide the best help in understanding the phenomenon (Creswell, 2008). This technique suits best to fulfil the need of this study to collect in-depth information from a small number of respondents through interviews (Saunders et al., 2009). Convenience (purposeful) sampling which is a form of non-probability sampling was used in particular as it allows the researcher to select eligible participants who can provide rich information; are willing and available in a most convenient and economical way (Teddlie and Yu, 2007, Creswell, 2008, Zikmund et al., 2014). In particular, maximal variation sampling was used to select participants that differ on some

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traits, such as manufacturers, retailers and LSPs (Creswell, 2008). The target population of this study comprise majority of manufacturers and it is easy for the researcher to select more manufacturers for interviews. The use of Maximal variation sampling will minimise this bias by avoiding the selection of manufacturers only.

It was expected that the number of participants for the interviews will be much less than that of questionnaire survey as the main objective of conducting the interviews was to provide better understanding of, and complementary information to the quantitative data. According to Kvale (1996), interviews can be conducted until a point of saturation is reached where further interviews are unlikely to generate new knowledge. In this study, fifteen large and medium sized firms from the sampling frame were chosen for interviews. The size of the firm was determined based on the total number of employees. Large and medium firms were chosen as the potential respondents for interviews because: 1) they were most likely to comprise of supply chains with a number of national and international members; 2) they were expected to have knowledge about supply chain management and inter-organisational relationships; and 3) with the availability of sufficient funding, they were likely to use new technologies for business operations (Yigitbasioglu, 2010, Singh, 2011). Small firms in Nepal are most likely to have little knowledge about the importance of SCM, organisational relationships and information technology, and may not provide useful insights for this study. There were three databases available, consisting of companies that are relevant to this study. The first list consisted of industries registered in the Ministry of Industry, Government of Nepal and the second list consisted of the associate members of the Federation of Nepalese Chamber of Commerce & Industries (FNCCI). Since only few logistics companies were listed in these two databases, a third list was considered which consisted of logistics service providers only. It is always advisable to use the most comprehensive list available as the sampling frame in order to minimise the coverage error (Salant and Dillman, 1994). Two databases amongst the three were selected as the sampling frame.

The list of associate members of the Federation of Nepalese Chamber of Commerce & Industries (FNCCI) was chosen as one of the sampling frame because it is an up-to-date list consisting of active members (Federation of Nepalese Chambers of Commerce & Industries, 2015). FNCCI was established in 1965 and is represented in almost all national councils/boards/committee/policy advisory bodies concerned with business and industry. FNCCI membership list consists only of those companies with paid up capital of more than

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ten million Nepali Rupees (USD 96,645.00) (Federation of Nepalese Chambers of Commerce & Industries, 2015). This means that the list does not include small sized industries as their members. Furthermore, it provides current contact details of all the members.

On the contrary, the list obtained from the Ministry of Industry, Government of Nepal had many drawbacks. Firstly the database consisted of the list of companies registered since 1972 which means that amongst the listed companies there were companies registered four decades ago. According to Government of Nepal Ministry of Industry (2015), most of the information provided in the database has been collected during the time of registration only and are in the process of updating it. This signifies that the information provided is not up-to-date and may contain wrong information. Moreover, it is unclear whether all the companies listed are still operating or not. Secondly, the database does not provide any contact information of the companies. It is uncertain that the contact details of every sample member can be located. Thirdly, the list comprises of 62.5% of small, 26.3% of medium and 11.2% of large size companies which clearly means that the random sampling will result in the selection of mostly small sized companies. This will cause the medium and the large sized companies that are more likely to provide better information regarding the topic of interest, to be under- represented.

The membership roster of Nepal Freight Forwarders Association (NEFFA) was considered as a sampling frame for the logistics service providers which consisted of 113 general members in 2015. NEFFA is a national organisation of freight forwarders in Nepal which was established in 1998 as a non-political, non-profit making and non-government association (Nepal Freight Forwarders Association, 2016). Its main objective is to safeguard the rights and privileges of freight forwarders and transportation entrepreneurs of Nepal (Nepal Freight Forwarders Association, 2016).

Choosing key informants is another important aspect while selecting a sample that could yield results generalisable to an entire population. A key informant according to Campbell (1955), is the one who is well informed about the issue under study and at the same time has the ability to communicate with the researcher. Campbell’s criteria were used in this study to select key informants. The top decision makers of the focal firms were selected as key informants with knowledge about their supply chain partners, processes and important trading partners (John and Reve, 1982). Most of the company details obtained from the above- mentioned lists provide the contact details of the top decision makers. The contact details of

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the decision makers of the remaining firms can be obtained by contacting their offices. When looking at the business culture of Nepal, most of the small and medium sized businesses are entrepreneur owned which means that the owner of the business is responsible for looking after the business, maintaining the business relationships and improving the performance of the firm (Biggs et al., 2000, Thagurathi, 2007). In other cases, managing directors, CEOs, logistics or supply chain managers would be the key informants with sufficient knowledge about the issues covered in the survey.