Selecting and Developing a Suitable Topic

Introduction

The event study method relies on a sudden release of news to the market that the investors react to, pricing in the future outcomes from the study and pricing these into the current price. Generally positive events, those improving the company's long-term prospects, result in a positive bump or change in the stock returns. Those that are negative, with overall negative influences on the firm's success, result in a negative change in stock returns.

The need or a sudden and surprising news release that might influence the stock returns suggests two key issues.

• 1.

  There must be news and a surprise. (We include more details are below about how to take an otherwise unsurprising project and use it as the basis for an event study.)

• 2.

  The issue must be ‘large’ enough to make a discernible impact on the overall company value. There are many interesting phenomena that may not be large enough to dent corporate costs or revenues and are, therefore, less likely to be suitable for study with this method.

Many operations and supply chain studies use primary data collections (Töyli et al., 2008) based on surveys or interviews (Fawcett et al., 2014). But, the range of different topics where the use of secondary data can be quite valuable and beneficial for the study. For operations and supply chain subjects, we are often interested in sustainability outcomes. However, when speaking with a manager or sending them a survey, is a high probability that the managerial perception of outcomes or performance may not actually correlate with the genuine reality. Often with a survey, for example, we use a Likert-type scale where we ask participants to report on the firm’s ability to meet various performance metrics, a form of ‘soft measure’ (Töyli et al., 2008). One good reason for using a survey approach to evaluate this type of measure is that participants and their employers will probably be prepared to report on outcomes and performance using Likert -type scales, as no confidential data needs to be revealed. However, the use of a ‘hard measure’ such as actual data from the company would be vastly more accurate and would provide more insight. However, the drawback is that many companies would be reluctant and unwilling to share these types of data. Secondary data often allows us to evaluate financial performance in terms of this reliable, secondary data, and it is becoming increasingly relevant in supply chain research in the 2000-2010 decades (Ellinger et al., 2011; Hendricks & Singhal, 2003, 2009).

There have also been calls for more use of secondary data in supply chain management research (Roth, 2007; Töyli et al., 2008). The research often proves easier to collect them primary data, and it often has a more objective nature, which helps improve the reliability of findings. Overall, a body of research (note, not necessarily from a single researcher or research team, but overall) needs to cover a range of methods and data for long-term success (Boyer & Swink, 2008).

Another important factor is the issue of social desirability bias (Walker et al., 2012). This refers to a manager’s propensity to enhance their performance by making a self-evaluation. It is particularly an issue when using perceptual data and interviews and survey research to self-report soft measures (Bertrand & Mullainathan, 2001; Dam & Petkova, 2014). Most times, the researchers have not addressed the issue itself, and perception and soft measures are still commonly used for performance measures (Carter & Easton, 2011). The issue is important when considering environmental and sustainability performance, where there is a strong emphasis on improving outcomes and is often used in marketing materials.