Wen Chen, Roman Hohl and Lee Kong Tiong
The purpose of this paper is to present the development of cumulative rainfall deficit (CRD) indices for corn in Shandong Province, China, based on high-resolution weather…
Abstract
Purpose
The purpose of this paper is to present the development of cumulative rainfall deficit (CRD) indices for corn in Shandong Province, China, based on high-resolution weather (county, 1980-2011) and yield data (township, 1989-2010) for five counties in Tai’an prefecture.
Design/methodology/approach
A survey with farming households is undertaken to obtain local corn prices and production costs to compute the sum insured. CRD indices are developed for five corn-growth phases. Rainfall is spatially interpolated to derive indices for areas that are outside a 25 km radius from weather stations. To lower basis risk, triggers and exits of the payout functions are statistically determined rather than relying on water requirement levels.
Findings
The results show that rainfall deficits in the main corn-growth phases explain yield reductions to a satisfying degree, except for the emergence phase. Correlation coefficients between payouts of the CRD indices and yield reductions reach 0.86-0.96 and underline the performance of the indices with low basis risk. The exception is SA-Xintai (correlation 0.71) where a total rainfall deficit index performs better (0.87). Risk premium rates range from 5.6 percent (Daiyue) to 12.2 percent (SA-Xintai) and adequately reflect the drought risk.
Originality/value
This paper suggests that rainfall deficit indices can be used in the future to complement existing indemnity-based insurance products that do not cover drought for corn in Shandong or for CRD indices to operate as a new insurance product.
Details
Keywords
Jiyoung Lee, Ningyang Ocean Wang and Rebecca K. Britt
When facilitating transmission of health information from government officials to the public, social media employs algorithms that selectively expose users to specific…
Abstract
Purpose
When facilitating transmission of health information from government officials to the public, social media employs algorithms that selectively expose users to specific perspectives, even for accurate health-related information from official sources. The purpose of this study was to explore impact of algorithm-driven comments characterized by different emotional tones (i.e. positive vs. negative vs. mixed) on users’ perceptions of credibility of corrective information to examine misinformation about flu vaccines aimed at young adults. Additionally, this study explored how prior misinformation credibility acted as an intervening variable in shaping the impact of algorithmically generated comments with diverse emotional tones on credibility of corrective information, with algorithm credibility serving as a mediator.
Design/methodology/approach
An online experiment was conducted with 275 participants recruited from Amazon Mechanical Turk (MTurk). Young adults from the USA aged between 18 and 35 years who were also users of Instagram were eligible for participating in this study as this study utilized Instagram platform for stimuli.
Findings
Results highlighted a diminished impact of algorithm-generated negative comments on perceived credibility of corrective information. Additionally, individuals with high misinformation credibility demonstrated a stronger tendency to trust algorithms featuring negative comments, underscoring the significant impact of algorithm-driven negativity in shaping trust dynamics for this group. Notably, credibility of the algorithm among individuals with high misinformation credibility did not translate into increased credibility for corrective information. This suggests that strategically designing algorithms to emphasize supportive or diverse opinions can be an effective approach to alleviate potential negative consequences associated with accurate information.
Originality/value
This research signifies the initial effort to disentangle the dynamics between negativity bias and cue routes within the algorithmic framework, shaping individuals’ perceptions of credibility of accurate health-related information contingent on accompanying comments. In the context of social media platforms that embrace diverse opinions, it emphasizes the critical necessity for tailored algorithmic strategies to effectively deliver accurate information.