Proxémica

Having formed a clear understanding of the nature of this research project and the epistemological perspective that has been adopted, the design of the research can be considered. Cooper and Schindler (2003, p. 146) describe research design as ‘the blueprint for the collection, measurement and analysis of data’ and identify five essentials of research design.

1. The design is an activity and time based plan.

2. The design is always based on the research question.

3. The design guides the selection of sources and types of information.

4. The design is a framework for specifying the relationship among the study’s variables. 5. The design outlines procedures for every research activity.

Saunders et al., (2003) propose the following framework for deciding upon a research design. 1 Return to the research question/s and objectives; decide on a research paradigm;

2 Decide upon a research strategy i.e. what approach/es and method/s will be used to gather the data; consider strategies used in extant studies;

104 3 Consider the constraints on the research and the possible preclusion of specific

strategies;

4 Consider the possibility for, and advantages of, combining different research methods; 5 Identify the threats to reliability and validity contained in the research design.

According to this framework, the second stage of research design is to decide on how to gather data. Data may be primary, secondary or tertiary (Blaikie, 2012). Primary data is that which has been generated by the researcher, secondary data is that which has been generated by another researcher and tertiary data is that which has been collected and summarised by another researcher. Unless the researcher has access to original data sets, data that has been summarised, manipulated or categorised is considered to be tertiary (Blaikie, 2012). According to Saunders et al (2009), secondary data has the disadvantage of being collected to achieve research objectives that do not match those of the current research – this may also have a detrimental effect on the way that data is presented. Conversely, secondary data has the advantage of being unobtrusive, requiring fewer resources to collect than primary data and facilitating the contextualisation and comparison of primary data as well as the overall research. Secondary information and tertiary data has been included in the literature review to guide the research, identify gaps in the research agenda and to facilitate the formation of several hypotheses. They were also used to inform the methodology, the design of the research instrument and to provide a context within which to compare the research findings. Consideration was given to the way in which primary data was collected as well as the type of primary information that is most appropriate. Initially, the researcher needed to decide between monitoring or interrogation/communication data collection methods. As this study is based on decision making behaviour which takes place in private areas such as in people’s homes, monitoring was not a viable option. Respondents were invited to participate in the research, implying that an interrogation/communication method is necessary.

3.2.1 Quantitative or Qualitative Methods?

Primary data has two forms; quantitative and qualitative. Saunders et al (2012, p. 161) state that:

‘‘Qualitative’ is often used as a synonym for any data collection technique or data analysis procedure that generates or uses non-numerical data and that ‘quantitative’ is often used as

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a synonym for a data collection technique or data analysis procedure that generates or uses numerical data”.

Walle (1997) states that the quantitative approach is rigorous and scientific, whereas the qualitative approach is less rigid and employs flexible tools of investigation. Saunders et al (2012) argue that qualitative research is generally associated with an interpretive philosophy as the focus is on the subjective opinions of respondents. Neuman (2011) states that qualitative research is more appropriate for exploratory research, where an inductive approach is required to gain a richer understanding of the context before theories and hypotheses can be created. By comparison, Saunders et al (2012) state that quantitative research is generally associated with the (post) positivist philosophy and the deductive approach as the highly structured methodology allows for the objective testing of prescribed hypotheses.

The identification of the information sources that are used at each stage of the decision making process does not call for rich data; objective data collected from large numbers of respondents is more valuable to in order to increase the validity of the findings. The implication of this is that quantitative research is the most appropriate approach given the nature of this study. Lee et al (2007) applied a quantitative approach to data collection and data analysis when researching their paper entitled ‘Tourists Search for Different Types of Information’. Money and Crotts (2003) also used quantitative collection and analysis techniques in their research on ‘The Effect of Uncertainty Avoidance on Information Search, Planning and Purchase of Travel Vacations’. Indeed, quantitative research is the most prevalent approach adopted when studying vacation decision makers’ choice of information search. This also supports the argument for adopting quantitative techniques for this research. The quantitative data collection method is also consistent with the Post Positivist philosophy and the deductive approach, thus adding support to the adoption of this method within this research.

However, consideration has been given to the part of this research which intends to identify why specific information sources are used at different stages of the decision making process. This is distinct from attempting to identify why consumers search for information when faced with decisions which involve risk. As has been discussed in Chapter 2, in purchase situations which involve risk, (such as those relating to the vacation decision) risk is reduced through reducing uncertainty

106 (Quintal et al, 2010), and uncertainty is reduced through the acquisition of information. Chapter 2 also discusses the way in which different information sources are perceived by the decision maker and the influence of this perception on information source engagement. Commercial sources, for example, are perceived to hold more risk due to the lower levels of trust attributed to them by the decision maker (Money and Crotts, 2003). Personal information sources which generate information for the decision maker in response to their enquiry can provide more detailed information than non- personal information sources which only contain generic information. The perceived benefits and drawbacks of the individual information sources have been established in previous research; the key issues being trust (that the information is honest and unbiased), the accuracy of information they provide (how up to date it is), the accessibility of the information source and the financial value that they offer. While this has already been established, it will be beneficial to identify whether the perceptions of the respondents of this research are consistent with general theory; therefore it is necessary to include what Saunders et al (2012) call ‘qualitative numbers’. The extent to which respondents agree with previous research findings about the trustworthiness, accuracy of information, accessibility and financial value offered by information sources can be gathered numerically through rating questions.

Consideration has also been given to the most appropriate technique for testing the composite decision making model presented in Chapter 2. Decrop (2010) adopted a longitudinal, qualitative research technique to investigate the formation of choice sets within the vacation decision making process, whereas Crompton’s (1992) paper presented a model which summarised widely agreed choice sets into a theoretical decision making process model. Both models provided explanations as to why vacation choice alternatives were either rejected or included for further consideration along the decision making process. The choice sets in the composite model put forward in this research represent an amalgamation of the two models upon which it is based, and combines the explanations to create a comprehensive description of the dynamics of choice alternatives within the decision making process. The composite model is hypothetical and tests were be applied as part of the primary research to discern whether choice alternatives are rejected or included for further consideration in accordance with the framework or not. This was achieved through a series of closed questions, i.e. those in which a predetermined set of responses is presented to the respondent. For example, according to the composite model that was tested, choice alternatives are excluded from the active information search stage because they are unavailable, inept or otherwise inferior to the alternatives being researched.

107 There are four types of data that can be collected through the quantitative technique: nominal, ordinal, interval and ratio. Nominal data are a type where information can be categorised and the categories are ‘mutually exclusive and collectively exhaustive’ (Cooper and Schindler, 2003, p. 223). Examples of nominal data required in this study are gender and whether a respondent used a particular information source or not. Although nominal data are the weakest of the four types they suggest no order, no distance relationships and have no arithmetic origin (Cooper and Schindler, 2003, p. 225), they can be useful to identify patterns in data. Differences in levels of education have, for example, been found to influence the decision making process (Park, Wang and Fessenmaier 2011), giving rise to the need for such nominal data. Ordinal data also enable information to be categorised, but unlike nominal data, the categories can be put in a ranked order. However, ordinal data must follow the transitivity postulate (i.e. that if a is greater than b and b is greater than c, a must be greater than c) and the differences between the categories may vary (a may be a lot greater than b, but b may only be a little greater than c). Agreement scales are commonly used in social science research to obtain ‘qualitative numbers’ as Saunders et al (2012) describe them. Agreement scales are effective tools for identifying constructs such as the level of trust ascribed to an information source by a decision maker. Interval data, like ordinal data, is also put in ranked order; however, unlike ordinal data the intervals between the categories are equal. Temperature is the classic example of interval data; the difference between 10 and 20 degrees is the same as the difference between 20 and 30 degrees. Because interval data has consistency between the categories, unlike nominal or ordinal data, it allows for the calculation of the mean value which in turn allows for greater statistical testing (Lee and Lings, 2008). We cannot say, however, that 20 degrees is twice as hot as 10 degrees as the zero point is arbitrary and not a true zero. However, where Likert-type scales are correctly designed, e.g. with balanced options from ‘Strongly Disagree’ (1), ‘Disagree’ (2), ‘Neither Disagree Nor Agree’ (3), ‘Agree’ (4) and ‘Strongly Agree’ (5)‘, all labelled and numbered, the responses are widely regarded in social science as interval data (Nardi, 2003; De Vellis, 2012). Ratio data incorporate all of the powers of previous data types plus the provision of absolute zero or origin (Cooper and Shindler, 2003, p. 228). Saunders et al (2012) use income as an example of ratio data and state that if profits are $300,000 one year followed by $600,000 the next, it can be said that profits have doubled because there is a true zero.

According to Lee and Lings (2008), the data type that is collected is guided by the information sought. It is normally recommended to use the most precise scale possible, but ‘it’s usually the case

108 that more powerful scales are harder for respondents to fill in’ (Lee and Lings, 2008, p. 147). This is why it is important to understand what will be done with the data before it is collected.