��������������������������
demonstrates a very optimistic picture about the potential adoption of SO even when the perceived utility of the vehicle is poor . This simulation leads to a conclusion that SO ( SOGAS ) will overtake conventional gasoline ( GAS ) platform before the end of the simulation ( 2046 ), and the annual ethanol consumption will exhibit a strong growth trajectory .
However , with the unfavorable WOM turned on ( S = 1 ), the simulation shows that this scenario does not guarantee SO success and results only in the temporary mitigation of the downward ethanol consumption trend ( see Figure 8 ). As soon as the marketing program that was promoting the new vehicles ends after 10 years ( 2024 ), the unfavorable consumer experience takes over and drives down vehicle sales .
In the scenarios explored in this research , 100 % of the adverse effect on market share of SO vehicles observed between Figure 7 and Figure 8 is due to unfavorable WOM because the buyer utility U 1
( Eq . ( 1 )) of the SO vehicle was set to have a ������ relative value versus competing platforms . This means that the SO platform is a clear winner in the absence of unfavorable WOM . Therefore , the negative market change observed in the simulation with full unfavorable WOM sensitivity ( S = 1 ) is solely due to low customer satisfaction level affected by inferior post purchase user utility U 2
( Eq .( 2 )). While in a general market unfavorable WOM will not be the only mechanism influencing market change , this paper focuses on establishing the need and providing arguments to directly consider unfavorable WOM as a distinct phenomenon affecting market growth . It does not negate the role of also considering the classic market mechanisms utilized in traditional models and policy development .
Results & Analysis
The specific conditions that lead to
loop dominance in a given time interval are dependent on the exogenous inputs . Consequently analysis focuses on qualitative observations and explanations that provide insight into policy formation .
Based on the model structure and analysis , the marketing and consumer views , in part derived by the utility of the fuel choice , were identified as the most important factors in affecting the views of potential adopters and total ethanol demand . Lack of empirical data on the consumer perception exists on how to relate availability and price of fuel and performance of SO vehicles on the different fuel options to consumer experience . Therefore , the link was intentionally broken and a range of inputs spanning the entire factor space tested using multivariate Monte-Carlo analysis . This identified the ranges under which the different loops dominate and inform where the system boundaries need to be and where the stakeholders will need to operate in order to achieve sustained ethanol consumption increases .
Scenario runs with varying marketing ranging between $ 0 and $ 500 million / year over a 10-year period were performed and the consumer view was varied . The entire consumer response space ( -1 to + 1 ) was mapped showing ethanol consumption and SO vehicles ( see Figure 9 ). Final consumer experience is always equal to 1 and the trajectory is second order polynomial .
Marketing is needed to initiate sales , but sees rapidly diminishing returns . Concurrently , the marketing impact is highly affected by consumer view . This can result in the unintended consequence of growth then collapse . The effect is pro-
125