An Investigation of How the Nature of Financial Management and Ethical Behavior Affects Business Performance

Week One

The main aim of the lectures this week is to get you to think about research, the complexities involved and the decisions you need to make in terms of a robust and appropriate approach. I might have over-stressed the philosophy of research and paradigms a little, but I hope you can now see how you can start thinking about the ‘reality’ around you and the objective/subjective continuum through new angles.

Start to think about possible topics for your own research (I provided a session on techniques and sources for generating ideas). At this stage, don't close down on topics just yet… get creative and start making lists of potential topics… don’t worry if they’re ‘right’ or ‘wrong’ at this stage. Once you’ve narrowed it down to a few topics, think about what sort of paradigm might be most suitable…. This will then lead to an overall research approach and design. We'll revisit topic selection in week 3.

I’ve put some past dissertation titles on Blackboard. I should stress again that these titles are in no way recommendations; some of them are poorly worded and constructed. Use them to spark ideas but don’t get too hung up on them.

The session on the literature review should have helped to cement a wide range of sources. Do take the opportunity to download the ABS journal guide and start to look for journals in your chosen field.

Week Two

We started this week with an additional session on 'how to choose a topic'. I hope that this has helped to spark ideas and to get you thinking about possible topics. It's still early days, so keep an open mind ... also start to think about possible sources of data at the same time, as a topic without access to suitable data may change your decision. Once you have a topic in mind, remember to make sure that a) the scope is very specific, and b) you have clear and unambiguous aims and objectives. The biggest issue that I see year after year with research proposals are where the scope is unclear and/or too broad. You also need to make sure that your topic is aligned to your degree programme, RISK AND FINANCE, the main thing is to show us your understanding of research methods.

We then looked at sampling... you will need to show a clear approach to sampling for both quantitative and qualitative approaches. Developing a plan for research is all about making decisions, so be sure to defend your choice(s) for sampling (ie. weigh up the pros and cons of the possibilities open to you).

You should now have a greater insight into how to collect qualitative data. Interviews can be appropriate, however don't neglect alternatives. You'll see next week that some research strategies (methodologies) favour certain methods for collecting data.

We finished the week by looking at ways of analysing qualitative data... you'll see that many research strategies draw on similar approaches for analysing the data. We started with content analysis as a way of segmenting the data. Then we looked at a very typical form of segmenting via categorisation... breaking the data down into codes and categories, then looking for themes and relationships. Due to the interpretive nature, analysing qualitative data can be more an art than science, and it's something that you need to practise in order to become proficient.

WEEK THREE

This week, we moved on to explore various qualitative methodologies (or strategies)… these are over-arching ways of ‘doing’ qualitative research that each have subtle differences to make them more or less suitable, depending on the topic/question and data. Recall that many of the methodologies draw on similar ways of collecting data and even more so, similar ways of analysing that data (coding, themes, data displays and drawing conclusions).

We started with Ethnography which has its roots in Anthropology… drawing primarily on participant observation (but other data sources can be incorporated), in the natural setting, Ethnography is useful for understanding behaviour, culture and human interaction with the environment.

Action Research was next up, and we explored how this can be used to facilitate change within an organisation. Very much ‘mode 2’, applied research where you as a researcher work with a team or teams to actually plan and bring about organisational change. A challenging type of research as you have a dual role; but it can be very rewarding to see the outcome of your research develop in real time.

We then looked at Case Study… you may recall the three types and the role of theory in distinguishing between explanatory and exploratory case studies. This approach to research can draw on both quantitative and/or qualitative data… be careful to fully justify the use of case study if that is what you decide on as it can often be seen as the ‘catch all’ and not robustly carried out. I emphasised the need for a clear and single unit of analysis to assist with the case development and scope.

The final qualitative approaches this week have been archival research and Grounded Theory. Archival research is more to do with the source(s) of data relating to a particular instance in time and/or organisational setting. You would analyse the data along similar lines to any quantitative or qualitative analysis techniques. I realise that the example I used for Grounded Theory was perhaps too complex for what may be needed just to get an idea for it. GT is really good for discovering why people do the things they do. It’s very much a discovery… it can be exciting and painful in equal measure… you need to be persistent!

We wrapped up the session by looking at risk, ethics and writing up your research.

WEEK FOUR

Briefly, the layers of the research onion. Although there was some repetition here, I hope it was useful to you to see how two different researchers talk about research. This is important because many issues in research--though not all!--are a matter of interpretation and point of view, and this makers the dissertation module unlike your other modules. The main goal of this first lecture was to encourage you to see research as a positive, well-motivated approach to solving real-world problems. And I want you to develop the confidence to have your own point of view on research. This will help you to pick a topic with confidence. 

In the second lecture, we were less reflective and focused on some more concrete, technical things that you will have to face in your dissertation. We introduced the concept of sampling [fancy way of saying that the researcher picks a subset of the whole target population s/he wants to study], and defined different kinds of sampling. You need to learn these different kinds. For your dissertation, you need to be able to choose the appropriate kind of sampling, and justify your choice. Of course, you may choose to not sample but do a census, if your target population is small, or if you will not gather your own data. We also talked about secondary data, which includes the topic of big data. I want you to be as critical of big data as you need to be of any method or idea that is fashionable! 

WEEK FIVE

We started this week by continuing to talk about data (as we had done in Week 4). We focused now on how to gather data by making questionnaires. Questionnaires are a very broad category of quantitative methods, including for example our well-known surveys. Except for discussing some technical terms, the focus was on the big power, and hence responsibility, you all have when creating a questionnaire: There are so many ways, such as visual salience and verbal framing, in which the participants can be influenced to give biased responses! Please always think twice before committing to a questionnaire design. Pilot testing is indispensable. I took the opportunity to link verbal framing to behavioural-science claims that people systematically think irrationally, and posted on the discussion forum a debate on the issue. We first discussed types of data (categorical and numerical; and their sub-types)--you have to know which type(s) you gathered in your dissertation. We also touched on coding, which should be done carefully. The bulk of the lecture focused on exploratory statistics. In contrast to numerical statistics--which we will discuss next week--no hypotheses are tested or regression, or other mathematical, models are run. Rather, the idea is to use intuitive graphs, tables, and figures to gain insight into the data, and form hypotheses (which will be tested later, as just said). It was fun to discuss some particular graphics with you, and to see how opinions and tastes can differ!

WEEK SIX

This was our week of numerical statistics. This statistics is in contrast to exploratory statistics, which used visuals; numerical statistics uses numbers.

As such, numerical statistics tends to be more suitable for quantitative research. Still, students with an interest in qualitative research should also know some basics of numerical statistics:

Numerical statistics can be descriptive or inferential. Descriptive statistics refers to the computation of measures of central tendency and dispersion. Central tendency is measured by averages such as mean, median and mode and dispersion is measured by range, variance, standard deviation, and so on. These measures give us a first idea about our data based on the participants we have sampled. 

But descriptive statistics does not allow us to make rigorous inferences from our sample about the target population. For example, if we sample, say, 50 out of 5,000 employees in a company and measure their job satisfaction under two different managers, A and B, and find that what does that say about the satisfaction of all 5,000 employees under A and B? Is it the same or different? Such are the reasons why inferential statistics was developed. 

Inferential statistics is, in some ways, the most challenging material we covered together. Please do review, and try to understand significance testing (t test).