C AUSAS DE MERMA PARA LA FAMILIA MARE ( MATERIALES RESISTENTES )

Participants in this research are employees of all six pharmaceutical companies, with a large percentage of these being pharmacists or scientists. However, given that the pharmaceutical firms are very large, often with multiple departments, some participants were professionals from other departments including the accounting, information technology, and logistics departments amongst others.

With this spread of participants, the study was able to capture more accurately the nature of interactions between the specific bureaucratic context and the moral identities of its employees. Also since it can be argued that every employee, regardless of position within the firms is exposed to ethical issues in different ways; they all are potential participants in this study.

To narrow down the population to a sample able to capture the essence of this research, sampling had to be applied. According to Lohr, (1999), the sample should be “representative in the sense that each sampled unit will represent the characteristics of a known number of units in the population”. Thus, there are two broad categories of sampling methods: probability sampling or random sampling and non-probability sampling or non-random sampling (Latham, 2007). The choice of which sampling method to be used depends on the goals of the researcher. As MacNealy, (1999) suggested, when a researcher needs to have a certain level of confidence in the data collection, probability sampling should be employed. As such, Frey, Botan and Kreps (2000) further explained that the difference between both sampling methods differs in “how confident we are about the ability of the sample to represent the population” (pg.126). Since a fairly decent level of confidence is required in this research, probability-sampling methods were adopted. Probability sampling allows every unit of a population the equal chance of being selected hence; it eliminates the danger of researcher’s bias in the selection process (Frey, 2000). There are four types of probability sampling as follows: simple random sampling, systematic random sampling, stratified random sampling and cluster sampling. The table below summarises the selection strategy of each of these types of probability sampling:

Table 4.2 – Probability Sampling Methods

Type of

Sampling

Selection Strategy

Simple Each member of the study population has an equal probability of being selected

Systematic Each member of the population is first listed. Then, sampling begins with a random start, then members are

selected at equal intervals

Stratified Each member of the study population is assigned to a group, and then simple random sampling is employed in selecting sample.

Cluster Each member is first assigned to a group, then groups are selected at random and all members of selected cluster are included in the sample.

Source: Adopted from Henry, (1990) and Latham, (2007)

In this study, three of the probability sampling types, simple, cluster and stratified sampling were employed in different instances across the three different case groups. The choice of sampling method in each case was dependent on a number of reasons: access through an internal contact, the cooperation of firm with the researcher and the willingness of volunteer participants in the research process. In almost all the case groups in this study, a combination of different probability sampling methods were employed even within the same participating firms. For instance, in five of the six firms studied in all three case groups, having an internal contact made access easier and as such the leads of such internal contacts were followed in randomly selecting participants for the study. This can be argued to be some form of cluster sampling in which members of the organisation were first categorized into the ‘friends network’ of the internal lead before random selection of all those within that network ensued. But this approach immediately raises bias concerns in which those within that friend’s network may all have similar opinions about the firm and would therefore give similar responses to the researcher’s questions, thereby generating a lopsided dataset. The researcher was aware of this and reduced this tendency by not relying only on the ‘friend’s network’ of the internal leads alone, but by using his people skills to interact and engage other ‘neutral’ employees within the firms to extend the participants for the research. This obtained extra interviews outside of the ‘friends’ network’ and easily controlled this likely bias problem. Besides, it was very interesting to note that even within the networks of the internal leads, there were obviously noticeable variations in their responses to almost all the questions asked. This further implied that the likely bias that could skew dataset in one particular direction was

significantly reduced in this research process.

The internal lead approach is very similar to the snowball method, a type of non- probability sampling method except that as group members identified additional members to be included in the sample, the researcher still had the choice of randomly selecting from among this group and others outside of such groups. For instance, in all five firms accessed through internal leads, the researcher capitalised on physically being granted access into the firm’s premises to gain legitimacy by chatting with other employees using the names of previous participants, and in some cases, the continual physical presence of the researcher in the firm created an informal familiarity with some of the staff who had been seeing the researcher entering offices and moving about freely. As earlier explained this gave the researcher opportunity to get more participants for the study, which turned out to be a good way of controlling bias all around. Since the additional participants were not particularly within the network of the internal lead, and the researcher had no bias in selection whatsoever as simple random sampling was employed in such cases. Simple random sampling also known as straight random sampling, as MacNealy, (1999) explained requires that each member of a population stand an equal chance of being selected. Thus, each member of the population is “selected one at a time, independent of another and without replacement; once a unit is selected, it has no chance of being selected again” (Fowler, 1993:14). This was the case with the extra participant secured by the researcher, as the researcher simply randomly walked up to employees, explained the research and was often granted audience. In cases where participants turned down the researcher’s proposal, it was often on the grounds of time, since the interview process and filling of survey questionnaires often lasted up to two hours and beyond, a lot to demand of an individual out of their busy time schedules. As such, a combination of both simple and cluster sampling were employed within these firms.

However in one of the firms in case-group three, where the top management had granted the researcher full access to all members of staff, stratified sampling was employed. In this case, employees were first split into different groups based on

participant from each of the departments to participate in the study. Also, in one of the firms used in this study, the snowball technique was employed with the researcher relying on the lead of group members to identify additional members to be included in the sample. In such organisation the snowball method was employed, access was highly restricted and with the help of an internal lead who had presented the researcher as his personal guest, several interviews were conducted by strictly following the lead’s choice of additional participants in the study. Although it is understood from literature (Henry, 1990; Fowler, 1993; Lohr, 1999) that each of these sampling techniques could be used to achieve different outcomes, adopting them in this research was strictly based on reduction of selection bias. As earlier explained, the use of multiple sampling techniques within the firms enabled the researcher reach a wider range of participants with whom the researcher had no prior contact nor were within the network of in internal leads thereby lending greater credibility to the data collected from all participants. Secondly, the adoption of different sampling techniques in each firm was in response to the different internal scenarios presented by each firms. Since the core strategy to gain access was to be pragmatic, the researcher had to devise the best possible ways of selecting participants in each of the firms but as each firm was a different environment, it called for different sampling strategies. As such, the researcher could not have stuck to a particular sampling method but had to be flexible and adaptive to different scenarios posed in the firms.

A total of 36 participants took part in this study as detailed in table 4.3 below:

Table 4.3: Summary of Participants

s/n Participant Job Role Background

1 A1a Pricing and Access Manager Pharmacy

2 A1b Sales Manager Pharmacy

3 A1c Marketing Manager Pharmacy

4 A1d Logistics Manager Biology

5 A1e Fleet Manager Engineering

6 A1f IT Manager Computer Science

8 A2a Sales Representative Sciences

9 A2b Medical Representative Pharmacy

10 A2c Country Manager Pharmacy

11 A2d Senior medical representative Pharmacy

12 I1a Human Resource Manager Management

13 I1b Sales representative Pharmacy

14 I1c Medical representative Bio-Chemistry

15 I1d Senior Medical representative Pharmacy

16 I1e Medical Representative Bio-Chemistry

17 I2a Regional Manager Pharmacy

18 I2b Sales representative Chemistry

19 I2c Senior medical representative Pharmacy

20 I2d Medical Representative Bio-Chemistry

21 N1a IT Manager Computer Engineering

22 N1b Operations Manager Pharmacy

23 N1c Product Manager Industrial Chemistry

24 N1d Human Resources Officer Management

25 N1e Administrative Assistant Marketing

26 N1f Audit Manager Accountancy

27 N1g Corporate Services Manager Journalism

28 N1h Administrative Assistant Secretarial Studies

29 N1i Regulatory Officer Pharmacy

30 N2a Brand Manager Pharmacy

31 N2b Regional Manager Chemistry

32 N2c Trade Marketing Marketing

33 N2d Trade Business Manager Pharmacy

34 N2e Sales Executive Pharmacy

35 N2f Sales Executive Chemistry

36 N2g Business Development

Manager

Pharmacy