Symbolic computation for approximating distributions of some families of one and two-sample nonparametric test statistics

A conditional saddlepoint approximation was provided by Gatto and Jammalamadaka (1999) for computing the distribution function of many test statistics based on dependent quantities like multinomial frequencies, spacing frequencies, etc. The considerable complexity of the formulas involved can be bypassed by symbolic computation. This article illustrates the effectiveness of symbolic computation to evaluate the saddlepoint approximation for the likelihood ratio, the exponential score, and the Wald-Wolfowitz test statistics. The case of composite hypotheses is also discussed.     

Bertrand competition with asymmetric costs: a solution in pure strategies

Theory and Decision - We consider a Bertrand duopoly with homogeneous goods and we allow for asymmetric marginal costs. We derive the Myopic Stable Set in pure strategies as introduced by Demuynck...     

Satisfaction and Tourism Expenditure Behaviour

In the literature, the quantification of the effect of satisfaction on tourists’ expenditure behaviour has not been extensively studied. This research aims to fill in this gap, providing additional information about this crucial relation by analysing it from a microdata perspective. In particular, the Fuzzy Double-Hurdle model, a new model which combines the well-known Double-Hurdle model and the fuzzy set theory, is suggested and presented, both technically and by means of a real case study. The proposed model gathers the advantages of the Double-Hurdle model and the fuzzy set theory together producing a suitable model for the analysis of censored observations in presence of imprecise data. Specifically, the Double-Hurdle model allows to efficiently estimate the average values of a non-negative, non-normally distributed variable characterised by high frequency of zero values, as tourists’ expenditure can be, considering the two-stages nature of the decision process. On the other end, the inclusion of the fuzzy set theory in the regression model allows to cope with the imprecision of both collected information (i.e. levels of satisfaction) and kind of measurement used (i.e. Liker-type scale). The results will help tourism managers to more accurately evaluate the efficacy of their policies and marketing strategies in enhancing tourists’ satisfaction and, consequently, in increasing the level of spending at the destination.

     

Correction to: Do women directors contribute to R&D? The role of critical mass and expert power

Journal of Management and Governance - An amendment to this paper has been published and can be accessed via the original article.     

The Resource-Infrastructure-Environment Index for Measuring Progress: An Application to Australia, Mexico and the US

The resource-infrastructure-environment (RIE) index was proposed as an alternative measure of progress which was then employed to: (1) compare the aggregate (single summary) index findings between Australia (mid-industrialised nation), Mexico (emerging economy), and the US (highly industrialised nation); and (2) compare the RIE index against the gross domestic product (GDP), human development index (HDI) and genuine savings (GS) measure. This paper builds on the previous work by assessing the seven themes and 21 dimensions which comprise the RIE index for the three aforementioned nations, as well as the associated policy implications. The results identified Australia’s strength in the human resource and infrastructure themes. For Mexico, strong contributions came from the natural and generated resource themes as well as the physical environment theme, while the US performed strongly in the infrastructure themes. The comparative results of the US and Mexico illustrated that it is possible to achieve high levels of progress without an excessive reliance on high levels of production and income.     

A neural network clustering model for miscellaneous components production planning

The growing importance of providing service to customers, e.g. post-sale assistance, supplying of spare parts, upgrading and integration of new elements in installed systems, enhances the importance of planning and management of upgrading parts in most manufacturing industries. These parts are generally characterized by high technical heterogeneity and have a highly variable and difficult to forecast demand. In some areas (especially the most dynamic, e.g. high-tech products), these kinds of components are quite common, and represent a very strong relation between the manufacturing firm and the market. These parts are generally too many to be efectively supported on a planning database system with individual records and too heterogeneous ( and sometimes with a too high value) to be supported all together in a single record. In this paper, we want to study the application of adaptive techniques for the clustering of these components in classes based on the similarities in their market behaviour in order to build an optimal database for planning production and supplying of these components.     

Uniformly most powerful unbiased test for shoulder condition in point transect sampling

Point transect sampling is a well-known methodology for estimating wildlife population density. In this context, the usual approach is to assume a model for the detection function. Thus, the estimate depends on the shape of the detection function. In particular, the estimation is influenced by the so-called shoulder condition, which guarantees that detection is nearly certain at small distances from the observer. For instance, the half-normal model satisfies this condition, whereas the negative exponential model does not. Testing whether the shoulder condition is consistent with data is a crucial issue. In this paper we propose the uniformly most powerful unbiased test for the shoulder condition in the exponential mixture model of the half-normal and the negative exponential. Critical values of the proposed test are calculated for large samples by means of asymptotic distribution theory and for small samples via Monte Carlo simulations. Finally, a case study is presented.     

Trust and partner selection in social networks: An experimentally grounded model

This article investigates the importance of the endogenous selection of partners for trust and cooperation in market exchange situations, where there is information asymmetry between investors and trustees. We created an experimental-data driven agent-based model where the endogenous link between interaction outcome and social structure formation was examined starting from heterogeneous agent behaviour. By testing various social structure configurations, we showed that dynamic networks lead to more cooperation when agents can create more links and reduce exploitation opportunities by free riders. Furthermore, we found that the endogenous network formation was more important for cooperation than the type of network. Our results cast serious doubt about the static view of network structures on cooperation and can provide new insights into market efficiency.     

Business Performance Analytics: exploring the potential for Performance Management Systems

Business Performance Analytics (BPA) entails the systematic use of data and analytical methods (mathematical, econometric and statistical) for performance measurement and management. Although potentially overcoming some traditional diagnostic issues related to Performance Management Systems (PMS), such as information overload, absence of cause-effect relationships, lack of a holistic view of the organisation, research in the field is still in its infancy. A comprehensive model for operationalising analytics for diagnostic and interactive PMS is still lacking. Adopting an action research approach, this paper addresses this gap and develops a five-step framework applied to a company operating in the construction industry. The results show that in addition to encouraging dialogue, BPA can contribute to identifying critical performance variables, potential sources of risk and related interdependencies. A number of critical issues in implementing data-based approaches are also highlighted, including data quality, organisational competences and cultural shifts.