1. Discuss the difference between Delphi and Scenario methods, and identify in which circumstances you might use each.
1.1. Introduction:
When it comes to business and business operations, forecasting is one of the pivotal elements. This is because the growth and sustainability of a specific business is profoundly depended on how that business would adapt to changes, technological as well as consumer trend, in the future. As per Granger (2014) forecasting is a tool to understand future events, in his work ‘Forecasting in business and economics’, he defines the forecasting as a process of predicting or estimating a possible future event. When it comes to prediction or the process of predicting an event, it mainly emphasizes an imminent change that is approaching towards a business sector or an industry. The word change can be synonymous of the external or internal environment factors, which includes political, social, technological, legal, economical or even sometimes environmental.
The essay aims to learn, understand and establish the difference between the Delphi method and the Scenario method. In order to acquire that it is also important to understand the approach of qualitative forecasting and quantitative forecasting; since both the methods, Delphi and scenario comes under two of these fundamental approaches respectively. This was affirmed by Baker (2016) in his work, by asserting the importance of understanding both approaches fundamentally before learning and implementing any of the methods under those.
1.2. Qualitative Forecasting and the Quantitative Forecasting:
The business communities and the scholarly world are asserting on two fundamental types of forecasting or prediction method, and they are qualitative method and quantitative method. Gilliland (2011) asserts that both the approach has its own advantages and disadvantages, and is used in scenarios suitably more than its weightage as effective method; since various researches conducted on the both the approaches have bought forth different degree of accuracy while forecasting.
When it comes to qualitative approach, it is said the method is subjective, mainly due to its nature of forming the decision. According to Baker (2016), qualitative forecasting is done mainly on the basis of judgment and opinions exerted by the consumers or a expert of an area, and it is most effective when there is a lack or sometimes even absence of past records or data. It is also important to understand that qualitative forecasting is typically used as an application to long-range decision. One of the best examples of qualitative forecasting is the Delphi method.
Quantitative forecasting on the other side is used to predict the future events or scenarios by using past data. As Evans and Basu (2011) defined the quantitative forecasting process as a ‘prediction method derived from the past data’. Evan and Basu (2011) also points out that the quantitative forecasting is most effective and above appropriate where there are adequate numerical data available for the formation of decision. Forecasting an event or a scenario requires an affirmation of a pattern and these numerical data can be used to form that, as Frechtling (2012) asserted “With similar pattern emerging at a periodic juncture, a future forecasting can me made or formulated. At the same time Frechtling (2012) also highlights on the unpredictability of certain events too, by positing that all events cannot be predicted solely through numerical data. Quantitative forecasting is typically is typically used as an application of short or in other words intermediate range. One of the best examples of quantitative forecasting is the scenario method.
As discussed earlier, each method has its usage and advantages and above all the degree or the level of accuracies varies with each techniques.
According to Hyndman and Athanasopoulos (2014), the method of scenario is basically a ‘narrative prediction’ which explains the course of events that is potential to happen. They also assert that it has a similarity towards the cross-impact matrix method, which aid in recognizing the interrelationship of components within a system. For this reason the scenario method has the ability to elaborate or explain the affect of an event over the components and then on the system as a whole. This is the main reason Coates (2016) in his work defines ‘scenario method’ as a ‘script’ that has the capability of clarifying details of an events that are uncertain. In order to bring that clarity in the prediction or in the process of forecasting, the scenario integrates factors such as technology, preferences of the customers and sometimes even the shifts among the population (Hyndman and Athanasopoulos , 2014). For this reason, scenario method is able to table long term forecasting of the future events. Ogilvy (2015), says that the scenario method are usually presented with one positive along with a negative scenario, hence this would enable the decision makers to weigh each of the scenarios presented at them, and synthesize appropriate decisions. For example, a company might use scenario method to determine the future purchases, by analyzing past data and trends on sales.
Delphi method on the other side keenly relies on the expert advice and notions. As Cuhls (2017) asserted, the method is used to formulate and conceptualize a future event through key assumptions from a group of individuals, which belongs to a specific area or an expertise in a specific sector. The method was introduced by Norman Dalkey and Olaf Helmer in order to bring forth a solution towards a problem in the early American Military. The main focus of the Delphi method is to build a collective views and predictions from a group of experts through a systemic and mathematical manner. Cuhls (2017) terms the entire process as ‘iterative’ due to the effort that goes on to develop the prediction or the forecasting, and the entire procedure requires a facilitator for the collection and formulation of the qualitative data. In the most cases, Delphi method is used for the forecasting of science and technology trends, however, today the same method is used to evaluate and implement the stakeholder approaches for the development of participative policy making, especially in the developed countries.
1.4. Conclusion:
Delphi method and the scenario method come under qualitative forecasting approach and the quantitative forecasting approach respectively. Each of these methods is used in different scenarios, and offers different levels of accuracy. Delphi is mainly used in the formulation predictions that are long-range in nature, while the scenario is mainly used in the construction predictions that are short-range in nature.
2.1. Introduction:
Drucker (2014) in his book ‘Innovation and Entrepreneurship’ have defined entrepreneurship as an idea, object or even sometimes a practice that is accepted as new and revolutionary. At the same time Drucker (2014) also says that the revolutionary aspect is relative, since many of the innovative initiations are sometimes not perceived as revolutionary. A same perception was shared by Hedström and Wennberg (2017) who supports the notion put forth by Drucker (2014) on the aspect of revolution.
When it comes to adoption of an innovation, the attribute and also the way in which it is perceived by the people within a society, carries a significant important and role. As Nguyen et al (2016, p. 2476) asserted in their business journal, “The characteristics as well as the way an innovation is looked upon determines the rate of adoption. And this is the main reason in some cases the diffusion of an innovation takes time than it was relatively thought, and this was affirmed by Nguyen et al (2016, p, 2477) as he states, “innovation in some cases diffuse slowly and on other it diffuses rapidly.”
According to Lissoni (2016) the process of diffusion is when the communication of an innovation occurs through certain channels, in the course of time. It is through the individuals within the society or a social system that the entire communication process would happen. Lissoni (2016) also posits that the entire communication is considered as peculiar in nature that is concerned with the wide spreading of the messages that is looked upon as ideas that are new, which then is mostly acquired with certain level of uncertainty. When it comes to diffusion of ideas there are mainly four elements that constitutes its, and they are, the innovation itself; the channels where the communication flows; timeframe and finally the social system or the society.
The essay focuses to understand the diffusion and adoption of innovation in a greater detail, and for this it is important to comprehend the characteristics or the factors that determine the rate of adaption when it comes to innovation. Among various factors or the characteristics, five are chosen to evaluate comprehensively, and they are the compatibility, triability, observability, complexity, and its relative advantage.
Compatibility holds a profound importance in the adaptability and diffusion of an innovation. In fact, it is significant for the innovation to be perceived as stable and above all consistent. As Storey et al (2016) asserted; the compatibility aid in forming the perception of being consistent with the persisting values, potential utility and the experiences from the past, for an innovation. For this reason, a concept or an idea that is found incompatible with the persisting values, potential utility and the past experience would most probably fail to get adopted. As supported by Johannessen and Dolva (1994, p. 2011), “it is extremely difficult for an incompatible idea to be adoptable, in comparison to an adaptable idea; and at the same time it is also important to understand that the adoptability rate varies a highly in both the cases.” Therefore an idea that is incompatible to get adopted, it is necessary to adapt to a whole new value system. Johannessen and Dolva (1994) say that even adapting to a new value system would take time, which in turn contribute to the difficulty. For example, when a new incompatible software get launched in the market, which is not compatible with the general operating system such as Windows; it would take tremendous time to adopt and diffuse operating system that is compatible with that software. Hence compatibility always plays a significant role in an innovation.
Another important factor that influences the adoption and diffusion of an innovation is the trialability. Putzer and Park (2010) define traibility as the level to which the idea can be tested, mainly on a limited basis. As the term itself suggests, an innovation find its success when there is a possibility that it can be experimented within the conditions given. Putzer and Park (2010) states that the ideas that can be tested in an installment basis would usually have the higher adoptability rate than that of the ideas or an innovation that cannot be divided. Through the factor of triability, the idea projects lower uncertainty towards the people who are potential users.
Visibility or the observability is another important factor that influences the adoption and diffusion of an innovation. Johannessen and Dolva (1994) say observability determines the level to which the outcome of an innovation is able to be measured or in some case visible. It is important for a potential user or the individual to see the outcome of a specific innovation and is able to measure it, Johannessen and Dolva (1994) asserts that the visibility of the innovation determines the potential users’ adaptability. Hence more visible or observable the innovation becomes higher its chance to get adoptive. It is this observability then becomes the discussion matter among the potential individuals, who will be using the innovation later.
When an innovation possess difficulty in understanding it, as well as using it, the rate of adoption and diffusion becomes slower than that of the innovation that possesses simple attributes. Therefore complexity plays a greater role in adoption and diffusion process. Putzer and Park (2010) states that, innovations that are simple in nature will be adopted faster in the society, than the innovation that are complex in nature. The main reason Putzer and Park (2010) assert on this is the time requires for the people to adapt to the complex nature of the innovation, as they stated “adaptability rate would be slower due to its complex attribute.”
Relative advantage is another pivotal factor that influences the adoption and the diffusion of an innovation. As the name itself suggests, it is a notion among the users or the individuals within the society that a particular innovation has a better attribute and functioning than the other similar ideas or innovation in the market. According to Bayarçelik et al (2014) the relative advantage has many dimensions and can be measured in various aspects including economic term, social term, satisfactory term and convenience term. Due to these reasons, the objective advantage of an innovation gets overshadowed by the relative advantage. For example, a customer could buy a smart-phone with similar features of an I-phone with lower price from another brand, here the relative advantage through economic term is highlighted and taken into consideration by the individual.
2.3. Conclusion:
References:
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