1. Mark Piekarz, Ian Jenkins and Peter Mills
Risk and Safety Management in the Leisure, Events, Tourism and Sports Industries
Mark Piekarz, Ian Jenkins and Peter Mills

2. Chapter 4 – Risk Management Research, Models and Tool Application
(Part 1 - Research)

3. These relate to the chapter objectives in Chapter 4
To explain the difference between theories, tools and models.
To explain the basic principles of the research process and where data can be accessed from.

4. - This is the first lecture based around risk research, tools and analysis.
- Analysing risk is a key component of the practical risk process (H & S, project and strategic risk analysis)
- Analysis is most complex stage
- In order to analyse you need data, information and to apply models and tools.
- This lecture focuses just on the research methods which can be used for data collection.

5. AN OVERVIEW OF THE PROCESS STAGES AND THE METHODS OF RESEARCH WHICH CAN BE USED
What needs to be considered
Examples of research
Stage 1 - Context
-What is the nature of the organisation?
-Who, what, how and where of risk
-Draw on own/team experience and knowledge from team, individual meetings and interviews
-Review organisational objectives; audit of resources; review strategic plans; organisation history
-Meet with key stakeholders
-Delphi techniques
Stage 2 - Analysis
-Identifying key risks
-Identifying key hazards
-Identifying key causation factors
-Identifying any triggers
-Analysing the relationships of factors
-Identifying and analysing suitable indicators of causation
-Frequency profiling (see Chapter 3)
-Scanning of news events
- Interviewing and focus group discussions
-Observational analysis of practices/physical inspection of locations and equipment
-Research past case studies (see Chapter 11)
-Case study hindsight analysis
-Secondary data on accidents, reports, incidents, etc.
-Trade and journal publications
Stage 3 – Assessment
-Assessing type of impacts and severity
-Assessing likelihood
-Applying assessment scales to represent or categorise the risk (e.g. high, low, medium)
-Frequency of past events, statistical reports, journal articles (see Chapter 3)
-Risk registers, accident books, etc.
Stage 4 - Control
-Prioritising resources -Reviewing points where control measures can be put in place
Measures to reduce severity of impact
--Producing action checklists
-Case study analysis to review robustness of measures (see Chapter 11)
-Testing of procedures, such as via scenario training
Stage 5 – Monitor
-Review checklists, forms, assessments and analysis.
-Amend control measures
-Staff meetings, reviews, data monitored, scanning news and incident events

6. KEY CONCEPTS AND TERMS IN RESEARCH METHODS
Types of data:
Primary data relates to data which is collected first hand by the individual or organisation.
Secondary data relates to data which someone else has collected,
Quantitative, which refers to data which is expressed numerically, such as looking at accident data.
Qualitative, which can be more descriptive of an event or situation, such as reviewing a case study which looks at how an accident came about.
Types of variables:
Independent (the variable that can theoretically cause the independent variable to change - change the independent variable and measure the effects on the dependent.
Dependent (this is the variable affected).
Intervening (what modifies any changes).
Examples of methods of data collection: Interviews, team meetings or risk clinics: can use a variety of formal (Delphi techniques) and informal techniques (e.g. mind mapping) Observations and direct inspection of facilities, locations or countries (the latter is sometimes known as the grand tour approach). Interviews and meetings can have a formal structure with a set of pre-designed questions, or be informal, where ideas are encouraged to freely develop, or even a mix of the two.

7. KEY CONCEPTS 1: PROBABILITY AND HOW TO MAKE AN ASSESSMENT OF RISK
Professional judgments, heuristics or the guesstimate
Analysis of specific case studies
Identifying frequency of similar events
Identification of frequency of past events
Key approaches which vary in the degree of accuracy and the amount of research needed:
Professional judgements: Based on past experience s and knowledge (known as heuristics and explored in Chapter 7).
Analysis of specific case studies: Focus on one or two case studies in more detail, which can reveal insights into the factors of causation, the failures in systems and the critical paths which lead to the outcomes of the event.
Identifying frequency of similar events: For some activities which are new, or have little precedent, looking at the frequency of past events may not be an option, such as tour operators developing services in a new destination, or sports or adventure activities taking place in less familiar environments.
Identification of frequency of past events: Classic method, ground in positivist scientific methods. Here it can be a process of looking at the frequency of past incidents to try and: a) identify all the key risks, then b) build up an assessment of the likelihood of these risk events occurring in the future.
It should also be noted that whilst frequency and probability are sometimes used inter-changeably, these are in fact different concepts, as observing frequency is a means to build up probability estimates.

8. COLLECTING DATA
Examples of incident data to build up frequency profiles to inform probability and outcome assessments: 
accidents in a sport, which resulted in death or disability;
event or stadium accidents from around the world;
transport accidents in different countries and regions;
acts of criminality or terrorism;
incidents of natural disasters, from earthquakes, tsunamis, hurricanes, etc.;
disease outbreaks;
incidents of political instability, such as riots, demonstrations and strikes.
A great deal has been said about why it is important to collect data, with the previous section giving some indication of how data can be collected. The next critical point to consider is just where data can be collected from.
See chapter 4 for examples of databases which can be accessed.

9. CONTEXTUALISATION AND APPLICATION TO THE DIFFERENT SECTORS
At the end of the chapter in the book, a variety of examples are given to illustrate how these concepts can be applied to specific industry sector examples. The relevant sector examples can be selected and considered here.

10. Need to collect data to inform the analysis and make the tools work.
CONCLUSION
Need to collect data to inform the analysis and make the tools work.
The type of data used can depend on the risk assessment.
Research is vital in risk management, which can range from case studies to statistical incident data.

11. Name: Dr Mark Piekarz Email: m.piekarz@worc.ac.uk
Thank You
Name: Dr Mark Piekarz