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When developing a new product, a buying firm solicits revenue sharing bids from two competing suppliers. Bidding behaviors of suppliers do not always align with predictions from rational agent models due to task uncertainty and bounded rationality, which could result in non-optimal supplier offers and ultimately hurt buying firm interests. This paper aims to discuss the aforementioned issues.

The authors built an analytical model that considers the impact of supplier technological risk, buyer–supplier coordination cost and supplier loss aversion on the optimal bid of the supplier. Next, using limited information processing capacity as a theoretic lens, the authors explore antecedents to the size of a focal supplier's bidding error, the absolute difference between the actual bid and the optimal bid. The authors used quantitative lab experimental data to test the hypotheses.

(1) Bounded rational bidders often fail to differentiate between relevant and irrelevant competitive information when placing bids, (2) loss aversion of a bidder significantly affects not only levels of bids, particularly for bidders with competitive disadvantages, but also sizes of the bidding error and (3) competitive information that has clearer performance implications are more influential in reducing sizes of bidding errors.

The results provide a comprehensive view of the bidding behaviors of a bounded rational supplier in an innovation outsourcing context with competition. With the results, managers now have a better understanding of behavioral influencers behind non-optimal supplier bids in an innovation outsourcing context.

This paper aims to identify factors that affect agricultural mortgage default and prepayment.

Using a sample of farm credit system loans, prepayment and default are modeled as competing risks with potentially non‐stationary covariates using a statistical/econometric technique called survival snalysis (SA).

The analysis suggests that the primary drivers of prepayment and default are the rate of interest charged by the lender at origination and the borrower's current ratio at origination. Tests of the existence of a geographic effect indicate that despite bank management belief to the contrary, branches may not be homogeneous.

This analysis would be improved if more data were available in an easily obtainable manner to control for unobserved heterogeneity. Unobserved heterogeneity or incomplete specification within a model can be problematic. Inferences among regression coefficients can be problematic in that the estimates have inflated variances and unreliable test statistics. In addition, more frequent measures of the time‐varying covariates could be obtained to improve upon the SA models presented above. Future analyses could also incorporate other sections of the agricultural credit association portfolio, as well as a comparison to variable rate notes. One other logical next step would be to obtain loan collateral values to obtain estimates of the exposure at default, and the loss given default, or the estimates needed for the advanced internal ratings based approach described in the Basel Accords.

This paper provides a method for lenders to measure and model mortgage termination, an important consideration for risk managers when determining capital adequacy described in the Basel Accords.

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.

The purpose of this paper is to investigate the extent and the human‐capital determinants of low‐wage mobility for labour market entrants in the UK and Germany.

Using panel data for the UK (BHPS) and Germany (GSOEP), a competing‐risks duration model is applied that allows the study of transitions from low pay to competing destination states: higher pay, self‐employment, unemployment and inactivity. Unobserved heterogeneity is tackled by a non‐parametric mass‐point approach.

It is found that low pay is only a temporary state for most young job starters. However, there is a small group of job starters that is caught in a trap of low pay, unemployment or inactivity. In the UK, job starters escape from low pay mainly by developing firm‐specific skills. In Germany, involvement in formal vocational training and the attainment of apprenticeship qualifications account for low pay exits.

Over the past decades, unemployment and low‐wage employment have emerged as major challenges facing young labour market entrants. While most empirical studies focus exclusively on the transition from low pay to high pay, the paper shows that a significant percentage of young entrants are caught in a low‐pay‐non‐employment trap. Moreover, it is shown that, depending on the institutional context, different types of human capital investments can account for a successful low‐pay exit.

The purpose of this paper is a review of updated evidence of a J-shaped association between alcohol consumption and the risk of coronary heart disease (CHD) and all-cause mortality in relation to public health issues to create a basis for sensible individual health deliberations.

A review of the evidence from the first observation of a J-shaped association between a moderate alcohol intake and CHD in 1926 to recent studies of the effect of healthy lifestyles (including moderate alcohol intake) on life expectancy free of cardiovascular disease (CVD), cancer and Type 2 diabetes. An update on the biological plausibility of the J-shaped association with focus on recent findings of the association of alcohol intake and blood lipid levels.

Plausible J-shaped relations between light to moderate alcohol consumption and the risk of CHD, CVD mortality and all-cause mortality have been found in a large number of robust epidemiological studies. Among the potential mechanisms underlying the proposed protective effects are higher levels of high-density lipoprotein lacking apolipoprotein C3, reduced platelet aggregability, increased level of endothelial cell fibrinolysis, increased insulin sensitivity and decreased inflammation.

The existence of a J-shaped association between alcohol consumption and the risk of CHD and all-cause mortality is based on observational evidence and accordingly challenged by a degree of uncertainty leading some public health circles to state: “there is no safe level of alcohol consumption.” The authors propose that communication on the pros and cons of alcohol intake should emphasize the nadir of a J-shaped curve as a healthy range for the general population while advice regarding the consumption of alcohol should be adjusted to factor in the risks and potential benefits for each individual patient considering age, sex, family history, personal drinking history and specific medical history.

The funding of innovation is explained by typical cost-based financial approaches. This paper breaks away from such tradition, and the purpose of this paper is to propose an alternative view where innovation funding decisions are strategic and concern interactions between actors – each with their own characteristics and strategic intentions – project features, and traits of the setting in which interactions take place.

This paper builds up an alternative framework to understand how innovation is financed by considering the interplay of innovation characteristics, the strategic reasons of project owners and funders, and the role of the matching environment and conditions. This proposal includes explanatory elements overlooked by extant theories. An illustrative case is presented to support the need for this proposal.

The framework proposed proves useful to better understand innovation funding cases where the traditional financial theory does not suffice.

Innovative companies may improve decision making about resource allocation to innovation; innovation funders may refine their decision-making criteria and implementation; and policy makers and practitioners need to devise better supporting strategies for innovative companies.

This proposal considers a continuum of funding options where supply/demand will match on the grounds of strategic decisions made during the interaction itself, under certain contextual conditions. Hence, it enriches the understanding of strategic decisions regarding firm capital structure and investment theory when it comes to funding innovation.

This study aims to identify and accurately assess the risk factors of competitors’ cooperation in the NPD project.

New product development (NPD) is essential to the survival of companies and surpassing other competitors. A key prerequisite for the success of an NPD project is the timing of new product delivery to the market. The main challenge faced by many project managers is the delay in execution and completion phases due to the complex nature and uncertainty of these projects. Rival companies' cooperation reduces the time spent on an NPD project which is an excellent way to reduce the risk of losing the market, but it increases other risk factors.

Based on the results, the security and confidentiality of innovation, the competitors attracting human resources and the company’s brand credibility factors were ranked higher than other factors and should be predicted and managed before cooperating with competitors.

This paper proposed a new model to assess risk factors in cooperation with rival companies in NPD projects. This model takes into account new parameters, for example, negative and positive risks, negative and positive passable risks and risk-based multi-objective optimization by ratio analysis plus full multiplicative form methodology for the rival companies cooperation in NPD projects. To evaluate the efficiency of the proposed model, a real case of the R&D unit of Iran Khodro Company was studied.

Insights into the origins of entrepreneurial activity are gained through a study of alternative mechanisms implicated in the tendency for children of the self-employed to be substantially more likely than other children to enter into self-employment themselves. I use unique life history data to examine the impact of parental self-employment on the transition to self-employment in Denmark and assess the different mechanisms identified in the literature. The results suggest that parental role modeling is an important source of the transmission of self-employment. However, there is little evidence to suggest that children of the self-employed enter self-employment because they have privileged access to their parent's financial or social capital, or because their parents’ self-employment allows them to develop superior entrepreneurial abilities.