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This paper generalizes the quadratic framework introduced by Le Courtois (2016) and Sumpf (2018), to obtain new credibility premiums in the balanced case, i.e. under the balanced squared error loss function. More precisely, the authors construct a quadratic credibility framework under the net quadratic loss function where premiums are estimated based on the values of past observations and of past squared observations under the parametric and the non-parametric approaches, this framework is useful for the practitioner who wants to explicitly take into account higher order (cross) moments of past data.

In the actuarial field, credibility theory is an empirical model used to calculate the premium. One of the crucial tasks of the actuary in the insurance company is to design a tariff structure that will fairly distribute the burden of claims among insureds. In this work, the authors use the weighted balanced loss function (WBLF, henceforth) to obtain new credibility premiums, and WBLF is a generalized loss function introduced by Zellner (1994) (see Gupta and Berger (1994), pp. 371-390) which appears also in Dey et al. (1999) and Farsipour and Asgharzadhe (2004).

The authors declare that there is no conflict of interest and the funding information is not applicable.

This work is motivated by the following: quadratic credibility premium under the balanced loss function is useful for the practitioner who wants to explicitly take into account higher order (cross) moments and new effects such as the clustering effect to finding a premium more credible and more precise, which arranges both parts: the insurer and the insured. Also, it is easy to apply for parametric and non-parametric approaches. In addition, the formulas of the parametric (Poisson–gamma case) and the non-parametric approach are simple in form and may be used to find a more flexible premium in many special cases. On the other hand, this work neglects the semi-parametric approach because it is rarely used by practitioners.

There are several examples of actuarial science (credibility).

In this paper, the authors used the WBLF and a quadratic adjustment to obtain new credibility premiums. More precisely, the authors construct a quadratic credibility framework under the net quadratic loss function where premiums are estimated based on the values of past observations and of past squared observations under the parametric and the non-parametric approaches, this framework is useful for the practitioner who wants to explicitly take into account higher order (cross) moments of past data.

Branching is not the only way for foreign banks to enter a national market, and it is impractical when there are informational and cultural barriers and asymmetries among countries. The purpose of this paper is to analyze the determinants of cross-border branching in the Latin American banking sector, a region with regulatory disparity and political and economic instability, offering elements to a grounded strategic decision.

This study uses data from six Latin American countries. To account for the preponderance of zero counts, classes of zero-inflated models are applied (Poisson, negative binomial, and mixed). Model fit indicators obtained from differences between observed and estimated counts are used for comparisons, considering branches in each region established by banks from every other foreign region of the sample.

Branching by foreign banks is positively correlated with the population, GDP per capita, household disposable income, and economic freedom score of the host country. The opposite holds for the unemployment rate and entry regulations of the host country.

Few paper address cross-border banking in emerging economies. This paper analyzes cross-border branching in Latin America in the context of the current financial integration and bank strategy. Econometrically, its pioneering design allows modeling of inflation of zeros, over-dispersion, and the multilevel data structure. This design allowed testing of a novel country-level variable: the host country’s economic freedom score.

The purpose of this paper is to evaluate investor behaviour related to the timing of selling financial assets based on an intuitive evaluation of the current market trend and growth expectation.

The experiment involved 1,052 volunteer participants who made decisions about stock sales in an environment that simulated a home broker platform to negotiate stocks. Zero-inflated regression models were used.

The results show that investors’ attitudes, or beliefs, determine whether they will buy or keep risky assets in their investment portfolios; they may decide to sell such assets, even though market shows an upward trend. Such results make a new contribution to behavioural finance within the context of prospect theory and the disposition effect.

The originality of this paper lies in the use of new and innovative techniques (zero-inflated Poisson and negative binomial regression models) applied to real data obtained experimentally.

Este artigo estuda o comportamento de investidores relacionado ao momento da venda de ativos financeiros com base em uma avaliação intuitiva da tendência atual do mercado e da expectativa de crescimento.

Nosso experimento envolveu 1,052 participantes voluntários que tomaram decisões sobre a venda de ações em um ambiente que simulava uma plataforma de corretagem para negociação. Foram utilizados modelos de regressão inflacionados de zeros.

Os resultados mostram que as atitudes ou crenças dos investidores determinam se eles comprarão ou manterão ativos de risco em suas carteiras de investimento; eles podem decidir vender esses ativos, mesmo que o mercado mostre uma tendência ascendente. Tais resultados constituem uma nova contribuição para o campo das finanças comportamentais, dentro do contexto da teoria do prospecto e do efeito disposição.

A originalidade deste artigo reside no uso de técnicas novas e inovadoras (modelos de regressão Poisson e binomial negativo inflacionados de zeros) aplicadas a dados reais obtidos experimentalmente.

Successful strategies for maintenance require good decisions and we commonly use stochastic reliability models to help this process. These models involve unknown parameters, so we gather data to learn about these parameters. However, such data are often difficult to collect for maintenance applications, leading to poor parameter estimates and incorrect decisions. A subjective modelling approach can resolve this problem, but requires us to specify suitable prior distributions for the unknown parameters. This paper considers which priors to adopt for common maintenance models and describes the method of predictive elicitation for determining unknown hyperparameters associated with these prior distributions. We discuss the computational difficulties of this approach and consider numerical methods for resolving this problem. Finally, we present practical demonstrations to illustrate the potential benefits of predictive elicitation and subjective analysis. This work provides a major step forward in making the methods of subjective Bayesian inference available to maintenance decision makers in practice. Practical implications. This paper recommends powerful strategies for expressing subjective knowledge about unknown model parameters, in the context of maintenance applications that involve making decisions.

This study aims to examine and understand the impact of reverse logistics adoption on firms' digitalization and collaboration activities. Specifically, leveraging the knowledge-based view, this study examines how adopting sustainable logistic practices (reverse logistics) prepares firms to embrace digitalization and encourages them to collaborate with other organizations.

The study used longitudinal survey data from two waves (2017 and 2019) from the Mannheim Centre for European Economic Research. The authors used the negative binomial regression analyses to test the impact of reverse logistics adoption on the digitalization and inter-organizational collaboration dependent count variables.

The study's findings highlight the usefulness of reverse logistics in enabling digitalization and inter-organizational collaboration. The results show that the firms investing in sustainable supply chains will be better positioned to nurture digitalization and inter-organizational collaboration.

For resource-bound managers, this study provides an important insight into prioritizing activities by highlighting how reverse logistics can facilitate digitalization and collaboration. The study demonstrates that the knowledge generated by reverse logistics adoption can be an essential pillar and enabler toward achieving firms' digitalization and collaboration goals.

The study is among the first to examine the effect of reverse logistics adoption on firm activities that are not strictly associated with the circular economy (digitalization and collaboration). Utilizing the knowledge-based view, this study reports on the additional benefits of reverse logistics implementation previously not discussed in the literature.

To obtain optimal deliverables, more and more crowdsourcing platforms allow contest teams to submit tentative solutions and update scores/rankings on public leaderboards. Such feedback-seeking behavior for progress benchmarking pertains to the team representation activity of boundary spanning. The literature on virtual team performance primarily focuses on team characteristics, among which network closure is generally considered a positive factor. This study further examines how boundary spanning helps mitigate the negative impact of network closure.

This study collected data of 9,793 teams in 246 contests from Kaggle.com. Negative binomial regression modeling and linear regression modeling are employed to investigate the relationships among network closure, boundary spanning and team performance in crowdsourcing contests.

Whereas network closure turns out to be a negative asset for virtual teams to seek platform feedback, boundary spanning mitigates its impact on team performance. On top of such a partial mediation, boundary spanning experience and previous contest performance serve as potential moderators.

The findings offer helpful implications for researchers and practitioners on how to break network closure and encourage boundary spanning with the establishment of facilitating structures in crowdsourcing contests.

The study advances the understanding of theoretical relationships among network closure, boundary spanning and team performance in crowdsourcing contests.

This paper explores how the manufacturer’s strategic choice (acquisition or investment) impacts product quality in a supply chain comprising two complementary suppliers and a common manufacturer.

The manufacturer faces six strategic choices to improve product quality: acquiring or investing in the high-capable supplier, the low-capable supplier, or both. As the Stackelberg leader, the manufacturer determines which strategy is adopted, while suppliers are separately responsible for components’ quality and wholesale prices. The authors use game theory and calculate the model with Mathematica.

The authors develop analytical models to analyze how acquisition costs, investment proportions, component importance and quality improvement coefficients influence decision-makers. The results show that the highest quality may not benefit the manufacturer. Investing in or acquiring a low-capable supplier is better than a high-capable supplier under certain conditions. If the gaps between two suppliers’ quality improvement coefficients and the importance of two components are dramatic, the manufacturer should choose an investment strategy.

This study contributes to the complementary supply chain management by comparing two kinds of strategies-acquisition and investment, with a high-capable supplier and a low-capable supplier.

This article aims to quantify and analyse empirically how the geographic dispersion of a firm's supply chain impacts on intra‐firm supply chain performance.

Generalised linear modelling is utilised to analyse a sample of 95 large manufacturing companies operating in Finland.

Results indicate that the increased geographic dispersion of the upstream supply chain results in higher costs of warehousing and logistics administration. On the downstream side, inventory costs, inventory days of supply, and cash‐to‐cash cycle time tend to increase due to geographically dispersed sales network. Increased geographic dispersion in the upstream and downstream supply chain results in the decline of perfect orders, and increases order fulfilment cycle time. However, the increased dispersion of the production network reduces order fulfilment cycle time. The results also indicate that the larger the firm, the better it can alleviate the negative implications of dispersion on perfect order fulfilment. Make‐to‐stock companies suffer less from the supply chain dispersion related delays in comparison to companies that utilise more pull‐type production and inventory strategies.

Research limitations include the cross‐sectional nature of the data, the concentrated geographic origin of the respondents, and the small sample size.

Building on the multidisciplinary body of prior literature on geographic dispersion, the research provides quantified insights into the general principles of international supply chain design in the presence of a performance related trade‐off between the dispersion and centralisation of operations across the tiers of the supply chain. Contributions are made to the discussions on supply chain complexity, international sales portfolio diversification and international purchasing.

Data mining registers of transactions allows for benchmarking customer's evaluation strategies. The purpose of this paper is to provide information on the application of different approaches to explore this kind of data.

Traditionally, heuristics based on variables such as recency, frequency, and monetary (RFM) value of transactions are used to determine the best customers. In this paper, a new form of directly combining the values of these variables is compared to an approach based on fitting a stochastic model. This last model is a mixture of a model for the number of transactions and another for the value spent. The new direct form of evaluation is based on computing the joint probability of maximizing quality indicators.

Good fit of the different models tested to the series of individual data as well as coherent predictions are registered. Patterns found provide empirical confirmation of results that theoretically should be expected.

These results are valid for a particular supermarkets network in a Brazilian city. The inner consistency of the results, nevertheless, and the coherence of the results obtained with what was expected, encourage application to other places and sectors of activity.

The results obtained show clearly the effectiveness of the approach based on RFM value measurement.

The models studied are applied for the first time for the kind of data treated, where determination of which customers remain active is a problem of special interest.

This research presents a framework that allows project managers to predict the next critical paths (CP(s)) and to take extra care when planning and executing those activities that have the potential to cause changes in a project's current CP(s).

The method presented here is based on an assessment of each activity's contribution to the overall schedule variance, which involves assigning a probability distribution function to each activity duration in the project. A sensitivity analysis is also carried out, which forms the basis of identifying which activity most affects the project completion date and therefore will have the greatest effect in changing the CP.

The authors’ analysis reveals that the most appropriate probability density function (PDF) for the targeted project is the normal distribution. However, the aim of this work is not to determine the most suitable distribution for each activity but rather to study the effect of the activity distribution type on the CP prediction. The results show that the selection of the appropriate probability distribution is very important, since it can impact the CP prediction and estimated project completion date.

This research work proposes a delay analysis scheme which can help the project manager to predict the next CP and to improve performance by identifying which activity is the bottleneck. On the other hand, the simplicity arises from the fact that this method does not require any expensive machines or software to generate results.