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PurposeThis paper presents the systematic process framework to develop the suitable crop insurance for each agriculture farming region which has individual differences of associated crop, climate condition, including applicable technology to be implemented in crop insurance practice. This paper also studies the adoption of new insurance scheme to assess the willingness to join crop insurance program.Design/methodology/approachCrop insurance development has been performed through IDDI conceptual framework to illustrate the specific crop insurance diagram. Area-yield insurance as a type of index-based insurance advantages on reducing basis risk, adverse selection and moral hazard. This paper therefore aims to develop area-yield crop insurance, at a provincial level, focusing on rice insurance scheme for the protection of flood. The diagram demonstrates the structure of area-yield rice insurance associates with selected machine learning algorithm to evaluate indemnity payment and premium assessment applicable for Jasmine 105 rice farming in Ubon Ratchathani province. Technology acceptance model (TAM) is used for new insurance adoption testing.FindingsThe framework produces the visibly informative structure of crop insurance. Random Forest is the algorithm that gives high accuracy for specific collected data for rice farming in Ubon Ratchathani province to evaluate the rice production to calculate an indemnity payment. TAM shows that the level of adoption is high.Originality/valueThis paper originates the framework to generate the viable crop insurance that suitable to individual farming and contributes the idea of technology implementation in the new service of crop insurance scheme.

Delisting has raised an attention in recent years. This is because delisting may post a negatively direct or indirect impact on shareholders, managers, employees, firms, and other stakeholders. Therefore, the needs to understand the symptoms of financial distress in a company and to be able to predict the firm delisting are crucial. The study global objective is to provide a financial operation analysis of The Stock Exchange of Thailand (SET) delisting and listing firms and to uncover the essential variables and risks which may be helpful in monitoring their corporate governance and financial strength. This study uses logistic regression to predict the delisting and listing status. The data used in this study are drawn from the annual reports filed by SET listed firms in Thailand. The sample consists of SET-listed firms in Thailand operating from 2005 to 2011. Scope of data included in this study is listed firms from all industry groups and all sectors except financials industry group classification structured as banking, finance and securities, and insurance sectors. There are many ratios, representing risks, statistically have an effect on delisting. Risks which are likely to affect firm delisting are liquidity risk, operating efficiency risk, profitability risk, leverage risk, credit risk, and financial insolvency risk. The result shows that net working capital to total asset, debt to equity, and gross profit margin statistically significantly have an impact on a delisting of SET-listed firms. Important variables as mentioned above show that liquidity risk, leverage risk, and profitability risk have crucial impacts on firm delisting. The -2 Log likelihood is equal to 17.322, Cox and Snell R2 is equal to 0.509, and Nagelkerke R2 is equal to 0.896. Logistic regression model above is used to classify the delisting and listing during the year of 2005-2010. The result shows that logistic regression model is able to predict 99.1% correctly classifying listing or surviving firms and 90% correctly classifying delisting firms. In addition, early warning model of logistic regression is able to predict 100% correctly classifying listing or surviving firms and 83.3% correctly classifying delisting firms during their operation in the year of 2011.

Purpose– The purpose of this paper is to explore the adoption of a mobile insurance claim system (M-insurance) and develops a framework for the adoption of M-insurance by consumers.Design/methodology/approach– This study assesses mobile technology for claim management through the lens of the technology acceptance model (TAM) and diffusion of innovation (DOI) models as a major guideline, using exploratory research through in-depth interviews with four executive experts who are first movers in mobile claim motor insurance in Thailand. Semi-structured interviews and open-ended questions were used to conduct group interviews of insurance consumers who mostly use smartphones. The data were collected in a qualitative research approach from Thai insurance consumers (n=177), and contents were classified and analysed to gain strong insights into respondent opinions, comments, attitudes, behaviour, and experiences.Findings– The results indicate that the external (social) factors influence attitude and behaviour of consumers which link to their intention to adopt M-insurance. These external factors include: preference for face-to-face service; confidence of insurers in accepting claim; and risk of claim knowledge that might cause legal issues among others. In application, the findings shall meaningfully enhance insurer firms’ improvement of adoption rate and development of future features and functions of M-insurance.Research limitations/implications– This study is based on insurance consumers in each region of Thailand but focuses only on mobile claim management for motor insurance. Although the findings bring new insight and understanding of consumer preferences and behaviours, they were not tested statistically.Practical implications– The study has practical implications for motor insurance claimants who are concerned over the complicated policy conditions, the perspective risk of claim knowledge and fault admission, and the on-site investigation by surveyor for another party. These are the guidance impediments to overcome M-insurance adoption improvement.Originality/value– Previously, TAM and DOI approaches have been employed to study general adoption of M-banking by quantitative research which confirmed descriptive data and tested the hypothesis, but neglected crucial data. However, M-insurance is different from M-banking in term of features and functions, purpose and process of usage, and legal liability. Therefore, this study is one of a few empirical studies that attempt to identify insightful factors to consumer uptake of M-insurance which is in its early stage and lacks an underpinning TAM model. This study contributes by identifying insights of “pull” factors to successfully develop M-insurance in Thailand.

This paper introduces a Decision Tree Learner as an early warning system for classification of the non-life insurance companies according to their financial solid as strong, moderate, weak, or insolvency. In this study, we ran several experiments to show that the proposed model can achieve a good result using standard 10 fold crossvalidation, split train and test data set, and separated test set. The results show that the method is effective and can accurately classify the solvency position.