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This paper investigates Chinese agricultural insurance agents willingness to offer (WTO) livestock insurance based on the variations of eight main attributes of livestock insurance.

This study implements discrete choice experiments (DCE) with actual insurance agents who design, sell and operate livestock insurance in China. The choice experiment of this study is based on the D-optimal approach, a six-block design, with 15 cards per block and two choices per card. The sample size was 211. Econometrics results are based on conditional and mixed logit models.

The authors find that the subsidy effect is enormous; a one level increase of subsidy leads to 3.166 times higher probability to offer. This subsidy effect is important as it confirms the endogenous structure between price and quantity in insurance offering, where subsidy does not only incentivize demand but also the supply. Another main factor of insurance investigated is the impact of different coverage types on agents' WTO. The authors find that agents prefer mortality insurance the most, followed by revenue insurance and profit insurance, while Index-Based Livestock Insurance (IBLI) is the least preferred to offer. Agents' knowledge about these newer types of insurance supports their WTO as well; thus, proper education is necessary to promote the more advanced types of livestock insurance.

A limitation is that in the presence of COVID 19, and administrative issues at the local level, the sample was not randomly drawn. Nonetheless, the authors believe that there is enough diversity across participants, insurers and provinces and have done sufficient robustness checks to support results and conclusions.

This study provides further validation for the DCE research method that could potentially be applied to different analyses: using choice experiments to study insurers and reveal their preferences, through combinations of various levels of core attributes for insurance products. The findings and contribution are critical to the reform and improvement of livestock insurance in China and for insurance markets more broadly. The authors find that insurers do not place equal weights or values on insurance product attributes and do not view types of insurance equally. In other words, while farmers may hold different preferences about the type of insurance they demand, the results suggest that insurers also hold preferences in the type of insurance they sell.

So far as the authors are aware, this is the first DCE designed around the supply of insurance products with the subjects being insurance agents, marketers and executives.

This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.

This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.

There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.

The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.

In this study, the authors examined demand-side credit in rural China with the aims of understanding attribute preferences and the willingness of farmers to pay for credit.

The authors implemented an in-the-field discrete choice experiment (DCE) using a D-optimal block (6 × 9 × 3) design applied to 420 farm households across five Chinese provinces (Shandong, Sichuan, Shaanxi, Jiangsu and Henan) in the summer and fall of 2018. The DCE included six attributes including the interest rate, term of loan, type of loan, type of repayment, type of institution and mobile banking services.

Conditional and mixed logit results indicated a downward sloping credit demand curve with variable elasticity across regions. Provincial willingness-to-pay (WTP) indicators suggested that farmers were willing to pay a premium for long-term ( 0.03–0.687%) and low collateral credit loans ( 0.79–2.93%). Also, four of five provinces indicated a preference for loan amortization rather than lump-sum payment. Interestingly, in comparison to the Agricultural Bank of China (ABC), only farmers in Shandong, Sichuan and Shaanxi indicated a preference for rural credit cooperatives (RCCs)/banks and the Postal Savings Bank of China (PSBC). Another quite surprising result was bank services, in our case, access to mobile banking did not appear to induce WTP for agricultural credit. While conditional and mixed logit regression coefficients were similar (and therefore robust), the authors found that there was substantial heterogeneity across attribute preferences on term of loan, type of loan and amortization. Preferences for type of lender and mobile banking were generally homogenous. This result alone suggested that lenders should consider offering a suite of credit products with different attributes in order to maximize the potential pool of borrowers. While there were some differences across provinces, farmers appeared to be indifferent to lenders, and it did not appear that offering banking services such as mobile banking had any bearing on credit decisions.

This paper presents a first step in using in-the-field choice experiments to better understand rural finance in China. Although the sample size satisfies conventional levels of significance and rank conditions, the authors caution against attributing results to China as a whole. Different provinces have different institutional structures and agricultural growing conditions and economies and these effects may differentially affect WTP for credit. Although by all indications farmers were aware of credit, not all farmers, in fact a minority, actually borrowed from a financial institution. This is not unusual in China, but for these farmers, the DCE was posed as hypothetical. Likewise, the study’s design was based on a generic credit product typical of rural China, and the authors caution against making inferences about other products with different attributes and risk structures.

This study is motivated by the rapidly changing dynamic in China's agricultural economy. With specific reference to new laws and regulations about the transfer of land use rights (LURs), China's agricultural economy is undergoing significant and rapid change which will require better understanding by policy makers, lenders and practitioners of the changing credit needs of farmers, including the new and emerging class of commercial farmers.

To the best of the authors’ knowledge, the authors believe that the result provided in this paper present the first use of in-the-field DCE and are the first to be reported in either the English or Chinese literature on rural credit product design.

The purpose of this paper is to develop a better understanding of the factors that influence a shipper's decision to choose air cargo as a mode of shipment.

A disaggregate multinomial discrete choice model is developed using freight shipment survey data to identify critical factors influencing air cargo mode choice. Disaggregate revealed preference data is obtained from surveying 347 manufacturers, freight forwarders, and other third-party service providers.

The empirical model developed in this research shows that the rate of shipment, time of transit, cost-per-pound shipped, quantity shipped, perishability and delay rate of the mode are significant factors that influence mode choice.

The discrete choice model developed can be improved by taking into account logistics costs not considered in this research. Perhaps more in-depth surveys of the shippers and freight forwarders are needed. Additionally, improving the mode choice model by including stated preference data and subsequently incorporating service quality latent variables would be beneficial.

Identifying the sensitivity of the shippers to various factors influencing mode selection enables transportation planners make better demand forecast for each mode of transportation.

This paper extends previous mode choice studies by analyzing mode selection between air cargo and other modes. Better forecasting is achieved by replacing the logit model with probit, heteroscedastic extreme value and mixed logit models.

The main purpose of this study is to examine the possibility of prediction of Greek takeover targets that belong to the industrial sector, emphasizing the econometric methodology and the prediction test.

The study uses a sample of 51 targets and 290 non‐targets exclusively from Greek industry over the period 1997‐2005. In order to achieve a better predictive accuracy the paper uses a new econometric methodology, the bootstrap mixed logit and different (more advanced) techniques of prediction test and choice of cutoff values.

The results exhibit that bootstrap mixed logit has significant and valuable predictive ability with respect to the classical conditional logit model. Furthermore, the predictive accuracy is higher than the results of other studies (e.g Palepu and Espahbodi and Espahbodi).

The main contribution of this study is the application of the bootstrap mixed logit in analyzing Greek takeovers. The results change the prediction variables as well as the determinants of the takeover target characteristics for the Greek industry. This is meaningful, not only for the investors that seek to increase the value of their fortune through acquisitions, but also for the managers that can detect if their firm might be considered a takeover target.

This paper aims to introduce and illustrate how discrete choice experiments (DCEs) can be used by accounting researchers and present an agenda of accounting-related research topics that might usefully benefit from the adoption of DCEs.

Each major phase involved in conducting a DCE is illustrated using a capital budgeting case study. The research agenda is based on a review of experimental research in financial accounting, management accounting and auditing.

DCEs can overcome some of the problems associated with asking decision-makers to rank or rate alternatives. Instead, they ask decision-makers to choose an alternative from a set. DCEs arguably better reflect the realities of real-world decision-making because decision-makers need to make trade-offs between all of the alternatives relevant to a decision. An important advantage that DCEs offer is their ability to calculate willingness-to-pay estimates, which can enable the valuation of non-market goods. Several streams of experimental accounting research would appear well-suited to investigation with DCEs.

While every effort has been made to ensure that this illustration is as generic to as the many potential studies as possible, it may be that researchers seeking to utilise a DCE need to refer to additional literary sources. This study, however, should serve as a useful starting point.

Accounting researchers are expected to benefit from reading this article by being: made aware of the DCE method and its advantages; shown how to conduct a DCE; and provided with an agenda of accounting-related research topics that might usefully benefit from application of the DCE methodology.

It is the authors’ understanding that this is the first article directed to accounting academics regarding the conduct of DCEs for accounting research. It is hoped that this study can provide a useful platform for accounting academics to launch further research adopting DCEs.

The aim of this paper is to evaluate the practices of economic intelligence used by Portuguese firms and to identify the attributes that may increase the probability of their adoption.

A questionnaire was designed and addressed to the CEOs of Portuguese firms. The authors used the mixed logit method to select a number of significant variables that influence the use of economic intelligence by firms in the sample.

From the results, the authors concluded that firm size, information and environmental scanning connected to the markets, social networks, economic diplomacy and public policies, namely clusters and industrial policies in the context of competitive intelligence, were some of the attributes relevant in this study. It is concluded that the probability of firms adopting competitive intelligence practices lies in two spheres: in orientations of business policy and strategy and in public policies that improve business context in the perspective of competitive intelligence.

The different categories of attributes that explain the existence of economic intelligence practices are relatively limited when compared with studies made outside Portugal. This derives from specific factors tied to Portuguese entrepreneurial culture.

This paper contributes to the literature on this area of research. One of the innovations introduced here was the design of a conceptual model proposal integrating business and public policy approaches connected to the competitive intelligence and, consequently, the capacity to formulate entrepreneurial strategies and public policies geared for the adoption of competitive intelligence procedures.

The purpose of this study is to analyze the preferences of potential Japanese cruise ship tourists and identify the factors influencing their participation in cruise ship tourism. In the analysis, preference for cruise ports in East Asia is also examined.

The behavioral model of potential cruiser is developed through a mixed ordered logit approach. The data are collected by means of the stated preference method with the application of a Web-based questionnaire. Multiple answers are collected from each respondent. Hence, panel effects between answers are considered so as to obtain a robust model.

The results show that Nagasaki and Hong Kong ports are preferred, and other domestic ports, namely, Kobe, Kagoshima, and Naha are also relatively popular places to visit. However, potential Japanese cruisers are reluctant to visit two South Korean destinations which are frequently selected as cruise lines by avoiding Cabotage rule. Besides, shorter cruise duration and lower prices increase the possibility of participation in cruise tourism, particularly for working people. Retirees tend to have less interest in cruise tourism. However, Japanese-related services will increase retirees’ intentions to participate in cruise tourism.

This study attempts to analyze potential cruisers’ behavior toward cruise ship tourism in Japan and discusses how to increase the number of cruisers participating in cruise ship tourism. In this vein, repeat behavior should also be analyzed. Repeat behavior contributes to the maintenance and increase in cruisers in Japan.

There is no study on potential cruiser’s behavior analysis in Japan which is the emerging country as cruise market. Thus, the number of potential cruisers is expected to be high. This study reveals that potential cruisers’ preferences on cruise ship services (e.g. duration, price, on board services, etc.), which are separately analysed for working ages and retirees. Besides, preferences on port of calls in East Asian context are revealed. These results are useful for cruise ship industries, especially for cruise lines.

The purpose of this paper is to develop a credit‐scoring model as an aggregate valuation procedure that integrates various financial and non‐financial factors and thereby improves small to medium‐sized enterprises' (SMEs) knowledge about their default risk.

Using panel data from a representative sample of Portuguese SMEs operating in the food or beverage manufacturing sector, this paper develops a logit scoring model to estimate one‐year predictions of default.

The probability of non‐default in the next year is an increasing function of profitability, liquidity, coverage, and activity and a decreasing function of leverage. Smaller firms and those with just one bank relationship have a higher probability of default. The findings suggest that a main bank has incentives to engage in hold up by increasing margins that ex post are too high.

Because SMEs differ from large corporations in their credit risk (e.g., riskier, lower asset correlations), this study has implications for both banks and supervisory actors. Banks should consider qualitative variables when setting internal systems and procedures to manage credit risk. Supervisory institutions should claim mixed credit ratings to determine regulatory capital requirements.

This paper offers a new model, focused specifically on SMEs, and explores the role of financial and non‐financial factors in determining internal credit risks.

We describe the application of a nested logit function for modelling consumer brand choice using household transaction data from the Indian market. This is unique since it is one of the first attempts to integrate disparate consumer information sources available at various levels of aggregation towards developing a prediction model for brand market share in India. We test the usefulness of the model for forecasting brand market share in the premium detergents market in Mumbai, India. The results of the model building exercise reveal the importance of advertising, specifically the role of ad message in influencing brand choice. It is concluded that such modelling initiatives show significant returns for market planning exercises in developing markets. However, the need for streamlining the collection of market data and its subsequent organization in a form that can help develop more portent prediction models is apparent.