Mapping the customer centric weather index insurance service design using quality function deployment

PurposeThe present paper aims to propose a framework on weather index insurance (WII) service design by using quality function deployment (QFD).Design/methodology/approachThis study utilizes QFD technique to propose a customer oriented framework on WII service design. In initial phase, customer and design requirements were gathered to derive the relationship between customers' and managers' voice for construct the house of quality (HOQ). Later on, prioritized customer and design requirements as QFD outcome were utilized to develop the action plan matrix in order to suggest the future action plans.FindingsThis study proposed a customer centric framework on WII service design to address the customer requirements. Findings show that adequate claim payments, hassle free prompt claim payment and transparency in losses computation are prioritized customer requirements with highest importance rating, whereas, accurate claim estimation, claim management system and advancement of technology are prioritized service design necessities with highest importance rating.Research limitations/implicationsThe proposed WII service design can enhance the quality of WII service by attain the higher standards of WII service in order to completely satisfy the customers.Practical implicationsThe proposed WII service design can provide a solution to the problems faced by WII industry by improve the customer's service experience and satisfaction.Originality/valueBased on best of author's knowledge, this paper first proposed a framework on WII service design by integrating customer and design requirements by using QFD.

Modelling the barriers of weather index insurance service adoption integrating expert mining and ISM Fuzzy-MICMAC

PurposeThe purpose of this study is to explore and prioritize the barriers that affect weather index-insurance (WII) adoption among customers by utilizing interpretive structural modelling (ISM) and fuzzy-MICMAC.Design/methodology/approachThis paper utilized the combined approach in two phases. In first phase comprehensive literature study and expert mining method have been performed to identify and validate WII adoption barriers. In second phase, ISM has been utilized to examine the direct relationships among WII adoption barriers in order to develop a structural model. Further, fuzzy-MICMAC method has been utilized to analyse indirect relationships among barriers to explore dependence and driver power.FindingsThis study has identified 15 key barriers of WII adoption among customers and developed a structural model based on binary direct relationship using ISM. Later, the outcomes of ISM model have been utilized for analysing the dependence and driver power of each WII adoption barriers in cluster form using fuzzy-MICMAC. The customer awareness related WII adoption barrier are mainly at the top level, WII demand related barriers are in the centre and WII supply related barriers at the bottom level in ISM model.Practical implicationsThe findings offered important insights for WII insurers to understand mutual relationships amongst WII adoption barriers and assists in developing strategy to eliminate dominant key barriers in order to enhance their customer base.Originality/valueBased on best of author's knowledge this paper firstly integrates the ISM fuzzy-MICMAC method into identification and prioritization of barriers that affects WII adoption among customers.

The potential of machine learning for weather index insurance

Weather index insurance is an innovative tool in risk transfer for disasters induced by natural hazards. This paper proposes a methodology that uses machine learning algorithms for the identification of extreme flood and drought events aimed at reducing the basis risk connected to this kind of insurance mechanism. The model types selected for this study were the neural network and the support vector machine, vastly adopted for classification problems, which were built exploring thousands of possible configurations based on the combination of different model parameters. The models were developed and tested in the Dominican Republic context, based on data from multiple sources covering a time period between 2000 and 2019. Using rainfall and soil moisture data, the machine learning algorithms provided a strong improvement when compared to logistic regression models, used as a baseline for both hazards. Furthermore, increasing the amount of information provided during the training of the models proved to be beneficial to the performances, increasing their classification accuracy and confirming the ability of these algorithms to exploit big data and their potential for application within index insurance products.

Do high-resolution satellite indices at field level reduce basis risk of satellite-based weather index insurance?

PurposeSatellite-based weather index insurance has recently been considered in order to reduce the high basis risk of station-based weather index insurance. However, the use of satellite data with a relatively low spatial resolution has not yet made it possible to determine the satellite indices free of disturbing landscape elements such as mountains, forests and lakes.Design/methodology/approachIn this context, the Normalized Difference Vegetation Index (NDVI) was used based on both Moderate Resolution Imaging Spectroradiometer (MODIS) (250 × 250 m) and high-resolution Landsat 5/8 (30 × 30 m) images to investigate the effect of a higher spatial resolution of satellite-based weather index contracts for hedging winter wheat yields. For three farms in north-east Germany, insurance contracts both at field and farm level were designed.FindingsThe results indicate that with an increasing spatial resolution of satellite data, the basis risk of satellite-based weather index insurance contracts can be reduced. However, the results also show that the design of NDVI-based insurance contracts at farm level also reduces the basis risk compared to field level. The study shows that higher-resolution satellite data are advantageous, whereas satellite indices at field level do not reduce the basis risk.Originality/valueTo the best of the author’s knowledge, the effect of increasing spatial resolution of satellite images for satellite-based weather index insurance is investigated for the first time at the field level compared to the farm level.

Determinants of Participation of Smallholder Farmers in Weather Index Insurance in Embu County, Kenya

The study examined how multiple factors influence participation of farmers in Weather Index Insurance WII in Embu County, Kenya. Data wer e collected from a sample of 401 smallholders following multi stage sampling technique The study employed the Cragg ’s Double Hurdle model in determining factors that influence participation and extent of participation in WII. Results revealed th at short rain season, household size, land size, perception of the household head on WII , owners h ip of a mobile phone a nd location of the farm were important factors in explaining participation in WII. The distance to a registered agro veterinary products outlet, insurance premium , group membership, the weather station in Runyenjes station and distan c e to the local weather station influenced probability to participate negatively. Similarly, ownership of mobile phone had a positive influence on the extent of participation in WII while the size of the household, distance to a registered agro veterinary p roducts outlet and land size were significant with a negative influence. The findings of this study highlight the importance of shaping farmers’ perceptions to wards WII, promotion of policies that allow for access and use of information and communication t echnologies ( such as mobile phones by the farming households as a pathway to providing smart so lutions to smallholder farmers in dealing with weather rela ted risks . Further, the research recommends for development of policies that would ensure modest WII insurance premiums that are aligned to the unique need s of the smallholder farmers.

Weather index insurance will offset heat-induced rice loss under global warming

Global warming threatens food security through causing increasing and severe yield losses from heat extremes, especially for smallholder rice-cropping farmers in Asia. Weather index insurance (WII) could transfer weather-related risks, secure farms’ income, and recover agricultural systems. Under future warming scenarios, however, the related studies are still scarce. Here, compared with the historical period (1961-2010), heat-induced loss will approximately increase by up to 5%, 18%, and 26% at 2100 under three shared socioeconomic pathways of CMIP6, respectively. As an ex-ante strategy, county-specific WII will improve farmers’ income by up to 13% and stabilize it by up to 36%, even though the pure premium rate of WII will increase by 10% at 2050 and by 30% at 2100. For the first time, our study proves WII is one effective adaptation strategy for the most susceptible farmers under global warming and has the potential to be applied for other crops and countries.

Does the Mutton Sheep Weather Index Insurance Promote the Mutton Sheep Industry?A Study based on the Endogenous Switching Regression Model

In 2018, as the first livestock weather index-based insurance product for grassland animal husbandry, the mutton sheep weather index insurance was officially listed as a subsidy agricultural insurance by the Government of the Inner Mongolia Autonomous Region, which was later in 2019 implemented throughout the XilinGol League of the autonomous region. Employing the endogenous switching regression model, this study investigates the factors influencing herdsmen's purchasing decisions, as well as the impact of mutton sheep weather index insurance on the mutton sheep industry scale, which are accomplished based on 308 survey data from herdsmen in the XilinGol League. The empirical results reveal that the age, age squared, pasture area and neighbors' purchasing intention constitute the significant influencing factors of the herdsmen's purchasing decisions, while the pasture area and net pastoral income affect the scale of mutton sheep farming prominently. Furthermore, according to the ATT results, the mutton sheep weather index insurance produces an insignificant impact on the sheep farming scale of herdsmen. The conclusions of this study suggest that the mutton sheep weather index insurance is not contrary to the cattle-increasing, sheep-reducing policy in the Inner Mongolia Autonomous Region, which also provide a solid theoretical basis for the promotion of the insurance throughout the autonomous region.

Toward the development of a weather index insurance for rice farmers in the coastal region of Myanmar

Climate change is expected to exacerbate damage to agricultural production from natural disasters. Examination of measures to adapt to the damage represents an urgent matter for agriculture. A multidisciplinary research project aimed at providing effective information related to development a weather index insurance (WII) system was conducted for rice farmers in a coastal region of Myanmar to achieve sustainable rice farm management in the country, which is among the world’s poorest and most disaster prone. For lower income countries, WII is one adaptation measure to mitigate damage by climate change. Using remote sensing and statistical data, changes in tracks of cyclones in the Bay of Bengal, the duration of damage by cyclone disasters, and areas affected by saltwater intrusion were analyzed to ascertain risk levels for disasters in the target area: Labutta township in the Ayeyarwady region. Furthermore, demand analysis of WII using discrete choice experiments, a question-based statistical survey method, revealed that farmers’ demand of WIIs for cyclone landfall, flood, and drought is relatively greater than that for saltwater intrusion. This finding indicates that saltwater intrusion might not be a crucially important matter for farmers who cultivate rainfed rice, whereas inland water floods caused by cyclone landfall and drought caused for changing the weather patterns represent a threat for these farmers. Results of econometric model analysis for designing a WII indicate that if a regular farmer in the township were to pay 41.5 US dollars per year to purchase WII for flood damage, their expected income will be stable.