Impact of Catastrophe on Shareholder Value

Chapter 3: Methodology

Introduction

The effect of catastrophe on shareholder value is a significant area of concern in the business world, especially during these times characterized by uncertainties and risks that businesses are likely to encounter when undertaking their business operations (Vijayakumar 2011). With regard to the impact of catastrophe on the shareholder value, there is the need to undertake a detailed review of the post impact, in terms of stock price and share volume, of firms that have witnessed catastrophes. This is helpful in assessing the effectiveness of response strategies adopting by organizations during cases of catastrophes, and distinguishing recoverers and non-recoverers (Beaumont 2012). Effective response strategies are characterized by exceeding the pre-catastrophe share price and volume. This study used the dependent variables of share price and share volume to assess the cumulative impact that catastrophes have on the shareholder value, which can be either positive or negative. In addition, the effective management of the outcomes of catastrophes was another dependent variable in assessing the effect that catastrophes have on the shareholder value. The methodology section of this report provides a discussion of the research design utilized and the corresponding data collection methods and justification. This chapter also discusses the data collection and analysis methods used to facilitate the achievement of the study objectives.

Research Design

Fisher (2007) asserts that the research design serves as the blue print for any study and helps in addressing the research questions, relevant data, the data to collect, and how to analyze the findings. Ruane (2005) points out that the research design can take the form of either qualitative or quantitative research designs, or incorporate both designs according to the structure of the research questions and the topic under study. Qualitative research methods make use of analysis and evaluation of qualitative data with the aim of attempting to answer the research questions and reaching a conclusion. On the other hand, quantitative research design involves the analysis and evaluation of quantitative data using statistical variables and methods in order to infer conclusion. Ruane (2005) asserts that qualitative research designs are helpful in the explication of concepts, definitions, metaphors, symbols, descriptions and characteristics of the topic being researched whereas qualitative research design utilizes numerical statistics to answer the research question and determine the relationships existing between various variables under study. Given the nature of this study, which is mainly explorative and correlational, the research design utilized was quantitative design with the data collected from secondary sources.

Fisher (2007) points out that qualitative research design entails grouping data into several categories and assessing the patterns and correlations existing among the various categories of data. In the context of this study, quantitative study entailed secondary research that relied on numerical data involving the pre-catastrophe and post-catastrophe share price and share volume of a company faced with a catastrophe in the recent past. The data collected was helpful in reaching post ad hoc conclusions, wherein the study utilized a grounded strategy whereby the theoretical models and empirical findings discussed in the literature review section were anticipated as the findings of the study (Neuman 1997). In this study, pre-catastrophe and post-catastrophe share price and share volume formed the dependent variables whereas the independent variable was the occurrence of a catastrophe in a company’s history. The pre-catastrophe and post-catastrophe share price and share volumes were compared to determine the cumulative impact that catastrophes have on the shareholder value for an organization. Data relating to pre-catastrophe and post-catastrophe share price and share volumes were obtained from secondary sources documented by other researchers and scholars in peer-reviewed published journal articles, books, annual reports of the company, and the share prices and share volumes reported by various stock exchange markets. In addition, the cumulative impact of the catastrophe will be used in determining the effectiveness of the response strategy used by the organization in managing the catastrophe (Ritchie & Lewis 2003).

Target Group

This research used a case study of the BP oil spill of 2010, which is the largest marine accident involving oil spills recorded in the history of the petroleum industry (Beaumont 2012). The oil spill was the most controversial because it resulted in immense damage to wildlife and marine habitat, and the tourism and fishing industries in the Gulf basin (Vijayakumar 2011). The choice of this case study was because of the extensive media coverage and the sensitive nature of the catastrophe, which implies that it was highly likely to culminate to substantial effects on the shareholder value. This presents an ideal case study for assessing the effects that catastrophes have on the shareholder value, especially the share volumes and share prices, and assess the effectiveness of BP in managing the catastrophe (Mitroff 2005).

Data Collection Methods

According to Nardi (2003), data collection is an essential prerequisite for any study because it plays a pivotal role in ascertaining the success of the research by offering means for reaching at conclusions. Data can be collected from two sources including primary and secondary data. Primary data is collected by the researcher using direct contact with participants whereas secondary data refers to the data documented by other researchers and scholars relating to the topic under study (Maxwell 1996). As aforementioned, this study relied on secondary sources of data that were relevant in meeting the aims of the current study. The collection of secondary data involved mainly internet and library research. Commencing with the key words such as “crisis management, shareholder value, and effects of catastrophes on shareholder value”, it was relatively easy to spot materials that are relevant to the topic under study. The main source for open books included Amazon, Questia, Athens and Google Books. Internet search was also used in gathering supplementary data from websites such as the Wall Street Journal, Huffington Post, Sage Pub, Oil and Gas Financial Journal and Google Scholar. Data relating to share prices and share volume were collected from the New York Stock Exchange (NYSE) and London Stock Exchange (LSE), which are the major stock exchanges involved in the sale and purchase of BP stocks (Beaumont 2012).

Numerous search terms were used when searching for secondary data, which included the exclusion and inclusion criteria. The inclusion criteria consisted of data sources that focused only on crisis management, effect of catastrophe on shareholder, and the relationship between crisis management, and shareholder prices and shareholder volume. The criteria for exclusion included materials that were more than five years old.

Analysis and Presentation of Data

Ruane (2005) points out that data analysis entail assessing, modeling and converting the collected data in order to come up with any helpful information, make meaningful conclusions, and support the decision-making process. In the context of this study, data analysis had the primary aim of making conclusions in order to achieve the study objectives and answer the study questions. Because the study was quantitative in nature, inferential and descriptive statistics form a significant component of the data analysis process. Fisher (2007) asserts that descriptive statistics help in describing and summarizing the gathered data using statistical quantities. Descriptive statistics are vital in determining the existing correlations and patterns in the gathered data. A significant limitation of descriptive statistics is that they are not deployed in making conclusions although they are essential in the presentation and interpretation of data. Inferential statistics, on the other hand, make use of statistical variables to generalize findings relating to the study. In this current study, generalizations will be derived from the impact of the BP oil spill on BP’s share prices and share volumes, which will be used to denote the general impacts of catastrophes on shareholder value.

Data analysis techniques will comprise mainly of bivariate and multivariate data analysis, which are used in determining the patterns of relationship existing between several variables. For instance, bivariate analysis will be used to determine the relationship between the occurrence of a catastrophe and the share prices, which will used in denoting the effect that catastrophes have on share prices and volumes.

 

 

 

 

 

 

 

 

 

 

 

References List

Beaumont, P 2012, ‘Gulf’s dolphins pay heavy price for Deepwater oil spill’, The Guardian, 31     March 2012.

Fisher, C 2007, Researching and writing a dissertation, Pearson Education Limited, Edinburgh.

Laurel, B 2003, Design research: methods and perspectives, MIT Press, New York.

Maxwell, J 1996, Qualitative research design: An interactive approach, Sage, Thousand Oaks.

Mitroff, I 2005, Why some companies emerge stronger and better from a crisis: Seven essential     lessons for surviving disaster, AMACOM, New York.

Nardi, P 2003, Doing survey research- A guide to quantitative methods, Pearson Education Inc,    Boston.

Neuman, W 1997, Social research methods: Qualitative and quantitative approaches, Allyn and   Bacon, Boston.

Ritchie, J & Lewis, J 2003, Qualitative research practice: a guide for social science students and   researchers, London, Sage Publications.

Ruane, JM 2005, Essentials of Research methods: a guide to social science research., Blackwell    Publishers, Malden, MA.

Vijayakumar, H 2011, British Petroleum oil spill crisis and aftermath: Corporate governance        and communication at BP during the disaster, GRIN Verlag, New York.