Delphi offered a systematic forecasting method relying on the gathered opinions of professionals and experts. The method provided an interactive approach that relied d on a structured group of experts, providing accurate feedback and judgment compared with other methods (Rowe & Wright, 2001). The purpose is to control the exchange of information anonymously iteratively to conclude average estimates and feedback. An American nonprofit global think tank corporation RAND simply describe Delphi as an elicit and refined group judgment (Dalkey, 1969).
The concept emphasized that a group of minds deliver better judgment and feedback than a single mind, leading to more accurate forecasting (Grime & Wright, 2014). The technique relies on the gathered responses that may look more suitable in the qualitative research method. The application of Delphi encompasses several principles to orchestrate and utilize group opinions. Grime & Wright (2014) highlighted six different values focusing on the group’s characteristics and the applied procedures. The first two points emphasize the mature domain knowledge and heterogeneity of the involved experts, thus opening a controversial discussion on evaluating and defining a person being an expert (Baker, Lovell, & Harris, 2006). Thirdly, the number should be between five and twenty at most. The fourth point stressed considering the estimated averaged rational illustrated from the gathered feedback. The next value demanded an iterative process to ensure the stability and consistency of the responses. Generally, the maturity of the circling iterations is quantified by three rounds as an acceptable practice. Lastly, the forecast concluded through equally distributed estimation across the entire group.
Designing the question in this method should provoke the feedback reasoning to gain concrete responses, perceive relevant materials, and, consequently, ensure gathering any missing piece of information, leading to a better assessment (Dalkey & Helmer, 1963). Grime & Wright (2014) recommended framing the questions in a balanced manner and avoiding irrelevant information to obtain concise feedback. Additionally, the content should be away from any emotive terms and presented clearly and straightforward. While the mentioned principles lack consistency in empirical applications, Delphi groups resulted in effective forecasting and judgmental opinions far from other approaches. However, according to Grime & Wright (2014), the challenge that impacts the method’s effectiveness is the human element that may involve biased opinions, whether individually or at the group level. Eventually, the results may not necessarily produce low quality and inaccuracy and do not necessitate a presumption of such phenomena influencing the delivered outcome.
The S-curve referred to Sigmond Growth Curve to forecast development or a project. An S-curve method is a scientific approach that delivers accurate results, yet it is not commonly used (Tidd & Bessant, 2020). According to Christensen (1992), S-curve’s application does not present a uniform industry phenomenon, but instead applied, specifically on the firm level. It represents an inductive theory for technological improvements through measuring the performance in a given period. The most published studies considered technology maturity at the industry level.
Comparing Delphi to S-Curve can be identified due to gathering the information for decision making or analysis. While Delphi relies on qualitative design methods to interact with a particular population or sample to gather opinions and different perspectives, S-Curve adopts the quantitative approach to analyze the available data over time. Although Delphi may require some statistical analysis to conclude a more generalized outcome across the sampled actors, the results are based on people’s opinions and different perspectives. Thus, the decision-making process can be generalized on a larger scale. For S-Curve, the outcome is explicitly related to the analyzed sample and only generalizes in the same context and relevant to the same environment.
References
Baker, J., Lovell, K., & Harris, N. (2006). How expert are the experts? An exploration of the concept of ‘expert’ within Delphi panel techniques. Nurse researcher, 14(1).
Christensen, C. M. (1992). Exploring the limits of the technology S‐curve. Part I: component technologies. Production and operations management, 1(4), 334-357. doi:/10.1111/j.1937-5956.1992.tb00001.x
Dalkey, N. C. (1969). THE DELPHI METHOD: AN EXPERIMENTAL STUDY OF GROUP OPINION. RAND CORP SANTA MONICA CALIF.
Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9(3), 458-467.
Grime, M. M., & Wright, G. (2014). Delphi Method. Wiley state ref: Statistics reference online, 1-6.
Rowe, G., & Wright, G. (2001). Expert opinions in forecasting: the role of the Delphi technique. In Principles of forecasting (pp. 125-144). Springer, Boston, MA.
Tidd, J., & Bessant, J. R. (2020). Managing innovation: integrating technological, market, and organizational change. Wiley.
Artificial Intelligence Humanity 