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Builds on the work of Damodaran (1993) and Brisley and Theobald (19967) on measuring the speed with which stock markets convert information into price changes by using a simpler model of the price adjustment coefficient and applying it to 1988‐1966 data from the Hong Kong, US and Japanese markets and the Morgan Stanley Capital International indexes. Explains the methodology and presents the detailed results, which show that the Hong Kong adjustment is similar to the US and Japan for systematic and for all information; although the range of adjustment speeds depends on the sector and composition of the indexes. Makes many comparisons between the three markets and suggests that this method of describing market efficiency could provide a more consistent and objective ranking of worlds capital markets.

Two of the primary growth strategies for Major League Soccer (MLS) have been team expansion and the construction of soccer-specific stadiums. Therefore, the purpose of this paper is to determine the relationship between these factors and game-specific MLS spectator attendance.

Two multiple regression models, one using multi-level mixed effects linear regression and another using interval regression, were developed to explain the variation in attendance utilizing the two factors of interest along with other control factors that have been identified as attendance determinants in previous literature. Game-specific data were collected for five MLS seasons, 2007-2011.

The two regression models explained approximately 40 percent of the variation in spectator attendance and the results showed that expansion teams and soccer-specific stadiums were significantly related to attendance. However, the effect of soccer-specific stadiums was minimized due to the extreme success of the Seattle Sounders in drawing about twice as many fans as the next highest drawing franchise, yet playing in an American football stadium.

While many of the standard factors such as the presence of holidays and novelty players, competition from other professional teams, and day of week, competition from other professional teams; team quality failed to show significance. Expansion teams drew better than incumbent teams and the impact from soccer-specific stadia is weak given the success of the Seattle franchise (and possibly negative when excluding Seattle). Censoring of the dependent variable had a discernible impact on many of the attendance factors.

These findings may be useful to managers of MLS and their teams along with other professional teams and/or leagues that are investigating the use of either team expansion or the construction of new facilities to increase spectator attendance.

This is the first study to investigate the relationship between expansion and new stadium construction in MLS over multiple years. The results indicate that MLS’s decision to use team expansion and the construction of soccer-specific stadiums has been beneficial with respect to spectator attendance.

The purpose of this paper is to propose an uncertain regression model with an intrinsic error structure facilitated by an uncertain canonical process.

This model is suitable for dealing with expert's knowledge ranging from small to medium size data of impreciseness. In order to have a rigorous mathematical treatment on the new regression model, this paper establishes a series of new uncertainty concepts sequentially, such as uncertainty joint multivariate distribution, the uncertainty distribution of uncertainty product variables and uncertain covariance and correlation based on the axiomatic uncertainty theoretical foundation. Two examples are given for illustrating a small data regression analysis.

The uncertain regression model is formulated and the estimation of the model coefficients is developed.

The paper is devoted to a regression model to handle a small amount of data with mathematical rigor.

The theory and the methodology of the uncertain canonical process regression is proposed for the first time. It addresses the practical challenges of small data size modelling.

Nowadays, evaluation objects are becoming more and more complicated. The interval grey numbers can be used to more accurately express the evaluation objects. However, the information distribution of interval grey numbers is not balanced. The purpose of this paper is to introduce the central-point triangular whitenization weight function to solve the clustering process of this kind of numbers.

A new expression of the central-point triangular whitenization weight function is presented in this paper, in terms of the grey cluster problem based on interval grey numbers. By establishing the integral mean value function on the set of interval grey numbers, the application range of grey clustering model is extended to the interval grey number category, and, in this way, the grey fixed weight cluster model based on interval grey numbers is obtained.

The model is verified by a case which reveals a high distinguishability, validity and practicability.

This model can be used in many fields, such as agriculture, economy, geology and medical science, and provides a feasible method for evaluation schemes in performance evaluation, scheme selection, risk evaluation and so on.

The central-point triangular whitenization weight function is introduced. The method reflects the thought “make full use of the information” in grey system theory and further enriches the system of grey clustering theory as well as expands the application scope of the grey clustering method.

The purpose of this paper is to examine citizens’ willingness to pay (WTP), in relation to having a professional first-tier football club in a medium-sized Danish municipality, when tangible economic benefits such as economic growth and/or inbound migration produced by these are absent.

Using the contingent valuation method on survey respondents, the study examines factors affecting WTP using binary logistic regression and interval regression and further extrapolates the WTP from the sample to the municipal population.

Citizens significantly value having a first-tier football club in their municipality even when tangible benefits are absent, although a large proportion of respondents stated to be against the municipality being financially involved in professional team sports clubs (PTSC). WTP is largely driven by interest in sports and the local football club. It is argued that the findings cannot be generalized across contexts.

There can be circumstances where public subsidy of PTSCs is beneficial to economic welfare. However, authorities should be careful in their evaluation of whether to subsidize PTSCs.

The study expands on existing research by informing respondents about the lack of tangible benefits produced by PTSCs, hereby focusing on WTP on an informed basis.

The purpose of this paper is to focus on high segments of the Italian olive oil and wine markets. The main goal is to compare the role and the effectiveness of the certification of origin in the creation of value in the two selected markets. Moreover, the authors investigate how different quality clues in the olive oil and wine sectors are related to prices.

To meet the goal the authors estimate two separate hedonic price models where the price of the product is regressed over different quality clues some of which are sector specific and some are common to the two sectors. The models are estimated on data which come from two of the major Italian guides chosen for their well established reputation and for the richness of information.

The results indicate that: product origin and the relative certification schemes play a relevant role in the formation of prices in both markets; while the olive oil price seems to be more sensitive to farm location than to the certification of origin, the opposite happens for the wines; the higher segments of the Italian olive oil market is increasingly sophisticated and follows the main tendencies established in the quality wine markets where many quality attributes are intensely active.

First, it should be kept in mind that results for higher market segment may not hold for different segments where relevant quality clues may be different. Second, reader should be aware that comparability of the two samples is constrained by limited data availability for the olive oil sector compared to the wine sector.

This study represents one of the first attempts to compare the role of the certification of origin in the creation of value in the Italian agro-food markets.

The purpose of this paper is to investigate Chinese consumers’ willingness-to-pay (WTP) for fair trade coffee given different amount of product information. Although coffee is becoming more popular in China, the concept of fair trade is often found unfamiliar to most Chinese consumers.

A total of 564 consumers were interviewed in Hubei, China. The key survey question asked consumers’ willingness to purchase a cup of fair trade coffee compared to a traditional cup of coffee. A modified payment card approach was used to elicit WTP. Before answering the purchase question, respondents were randomly assigned to one of three different information scenarios: basic definition, impact on sustainability and the environment, and information including both environmental and social implications.

Results indicated that consumers were generally willing to pay additional amount for fair trade coffee. Information played an important role in determining what types of consumers were responsive to fair trade coffee. Furthermore, the amount of information provided and consumer WTP did not follow a linear relationship.

Results obtained in this study are useful for coffee marketers to better target their promotion strategies.

In contrast to China's fast growing coffee market, little is known about consumer preferences and far less on fair trade coffee. This study is the first of its kind to understand Chinese consumers’ preferences for coffee in general and for fair trade coffee in specific.

We explore the predictive capacity of short-horizon time preference decisions for long-horizon investment decisions. We use experimental evidence from a sample of Canadian working poor. Each subject made a set of decisions trading off present and future amounts of money. Decisions involved both short and long time horizons, with stakes ranging up to 600 dollars. Short horizon preference decisions do well in predicting the long-horizon investment decisions. These short horizon questions are much less expensive to administer but yield much higher estimated discount rates. We find no evidence that the present-biased preference measures generated from the short-horizon time preference decisions indicate any bias in long-term investment decisions. We also show that individuals are heterogeneous with respect to discount rates generated by short-horizon time preference decisions and long-horizon time preference decisions.

The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less rationality and accuracy of the Risk Priority Number. The current study proposes a machine learning–enhanced FMEA (ML-FMEA) method based on a popular machine learning tool, Waikato environment for knowledge analysis (WEKA).

This work uses the collected FMEA historical data to predict the probability of component/product failure risk by machine learning based on different commonly used classifiers. To compare the correct classification rate of ML-FMEA based on different classifiers, the 10-fold cross-validation is employed. Moreover, the prediction error is estimated by repeated experiments with different random seeds under varying initialization settings. Finally, the case of the submersible pump in Bhattacharjee et al. (2020) is utilized to test the performance of the proposed method.

The results show that ML-FMEA, based on most of the commonly used classifiers, outperforms the Bhattacharjee model. For example, the ML-FMEA based on Random Committee improves the correct classification rate from 77.47 to 90.09 per cent and area under the curve of receiver operating characteristic curve (ROC) from 80.9 to 91.8 per cent, respectively.

The proposed method not only enables the decision-maker to use the historical failure data and predict the probability of the risk of failure but also may pave a new way for the application of machine learning techniques in FMEA.

The increasing flexibility and discontinuity of labor relations have been associated with the development of new forms of psychological contracts as well as the development of more self-directed and mobile career attitudes. The purpose of this paper is to examine the relationship between the forms of psychological contract and protean/boundaryless career attitudes on the one hand and organizational citizenship behaviors (OCB) on the other.

In total, 458 employees of three large Italian organizations were sampled through a self-report questionnaire. Zero-order correlations were carried out to examine the associations between study variables while dominance analysis, along with multiple linear regression, was used for evaluating their unique contribution with respect to OCB.

OCB were positively predicted by relational and balanced psychological contracts, protean career attitude and boundaryless mindset.

Organizations must pay particular attention to the content of the psychological contract and the career attitudes of their employees because they influence their willingness to carry out OCB.

The results add new evidence to the careers literature in terms of boundary conditions with regard to the effects of protean and boundaryless career attitudes as well as different forms of psychological contracts.