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The purpose of this paper is to construct a novel grey filter model for image denoising and to solve the problems which exist in the image denoising filter method, in which the true intensity value of each noisy pixel cannot be predicted better.

Based on the definition of stepwise, the defects of traditional grey prediction models are found. A new grey filter model, named grey stepwise prediction model, is proposed. The new filter model for the image denoising is based on each noisy pixel's neighborhoods stepwise, which is the eight pixels around the noisy pixel, to predict its intensity value and to solve the problems which exist in the image denoising filter method.

The experiment results show that the improved filter model can effectively eliminate image noise, preserve the image's details and edges, increase SNR (signal‐to‐noise ratio) as well as PSNR (peak signal‐to‐noise ratio), reduce MSE (mean square error) and MAE (mean absolute error), and significantly improve the image's visual effect.

The new filter method exposed in the paper can be used to 8‐bit gray‐scale image denoising. The method can also be used to binary image denoising.

The paper succeeds in constructing a novel filter method for image denoding, and it is undoubtedly a new development in image recovery algorithm and grey systems theory.

The authors study the explanatory power of investor rationality and irrationality for value and momentum portfolios. We also examine the relationships during financial crisis events, namely, the US subprime mortgage crisis (2007–2009) and the European debt crisis (2011–2013).

This study examines the influence of investors’ rationality and irrationality on the US stock market, using the multiple linear regression model and the stepwise regression model. Technically, the stepwise regression uses the machine-learning technique, with specific testing methods — forward selection, backward selection and stepwise selection — to find the best-fit model, according to Akaike’s Information Criterion (AIC). Thus, in this study, we will show the best model, as tested by the stepwise regression model.

Our empirical results contribute to the importance of reasons and emotions for stock-market returns and conclude that rationality and irrationality simultaneously explain the value and momentum portfolios, as well as the ETF portfolios. Also, the rational and irrational explanatory powers differ, depending on portfolios and different periods. Rational factors usually explain the volatility of the return to a greater extent than irrational factors. Moreover, during a financial crisis, the irrational factors remarkably increase their importance in explaining returns, especially for the ETF portfolios.

We expect this study’s contribution will show not only academic contribution but also benefit many stakeholders in the financial market. Investors and traders can identify various irrational factors of trading — for example, taking a long position during the panic in the market following the indicators in the models. Managers also reconsider the cost of the company by adding irrational factors when computing the equity’s expected return. Similarly, stock exchanges can adequately adjust their circuit breaker during a pessimistic-investor period. Finally, regulators can evaluate a complete picture of the stock market by adding irrational factors into their considerations.

The purpose of this paper is to develop a criterion for selection of critical sub‐processes when all the sub‐processes cannot be taken up simultaneously for improvement. There exist various methods but the practitioners get utterly confused because of the existence of these multiple options. In this paper, the goal is to assist practitioners in the selection of the critical sub‐processes.

The authors discuss various statistical methods such as correlation and regression, simulation, basic statistics such as average, standard deviation, coefficient of variation % (C.V.%), etc. for the selection and identification of the critical sub‐processes. The strengths and weaknesses of these methods have been compared through empirical analysis based on real‐life case examples.

The stepwise regression and simulation have been found to yield identical results. However, from the perspective of application, stepwise regression has been found to be a preferred option.

The roadmap thus evolved for the selection of the critical sub‐processes will be of great value to the practitioner, as it will help them understand the ground reality in an unambiguous manner, resulting in a superior strategy for process improvement.

The purpose of this paper is to utilize the proposed function transformation to make the original data series meet the properties of smooth ratio being lessen and stepwise ratio deviation being reduced, so that to improve the accuracy of grey forecasting model.

According to the characteristics of anti-cotangent functional graph variation, the theory of functional transformation and grey system modeling, the authors proposed a grey model based on the transformation of Aarc cot x+B function.

The calculated result of practical example shows that the proposed method is both valid on improving fitting effectiveness and forecasting accuracy.

The proposed method in this paper can effectively improve the accuracy of forecasting of high-growth original data series (derivative of data series is not only greater than 1 but also increasing).

The paper succeeds in providing an effective function transformation to make the smooth ratio and stepwise ratio deviation reduced significantly.

This chapter concerns itself with the development of a regression model for determining the executive compensation of the AT&T CEO. The data observations for this model consist of a list of 21 comparable companies selected by the compensation committee of AT&T, its institutional investors, and AT&T advisors. A set of 24 financial variables for each of the companies is compiled as the data source for the regression model.

The frictional behavior of a Mo alloy‐coated piston ring sliding against cast iron cylinder bore was recorded as a function of temperature using a reciprocating tribotester and a fully formulated synthetic engine oil, with and without a friction modifier. It was observed that, as temperature increased in a stepwise mode, friction coefficients in the presence of MoDTC exhibited two local minimal values. Only one minimal friction coefficient value at 340–355 °C (μ = 0.065) was observed in the absence of MoDTC. Chemical characterization of worn tracks of the cylinder bore using reflected FTIR spectroscopy, Raman spectroscopy and ESCA, indicated that both the base stock and the tribological additives, ZDTP and MoDTC, experienced tribochemical reactions yielding MoO₃, MoS₂ and carbonaceous species as temperature ramped up stepwise. MoO₃ and MoS₂ reaction film formation are partially responsible for the local minimal friction coefficient found at the lower temperature and the ratio of the ordered carbon species accounts partially for the other local minimal friction coefficient found at the higher temperature.

The purpose of this paper is to seek answers to the question of the impact of different classes of strategy (generic and international) on firm performance in international markets.

Survey of 213 British SME exporters, using EQS.

The paper concludes that Porter's generic strategies have both a direct and an indirect impact through international marketing strategies on firm performance, and that the combined impact of the two levels yields better returns than either of them individually. Furthermore, it questions the wisdom of a stepwise approach to international markets and highlights the importance of a challenger strategy.

This research is limited to British SMEs and needs to be supplemented by research from other countries. Also, it explores the effect of only a limited number of confirmed international marketing strategies, excluding for instance the standardisation construct – a key construct in international marketing.

Managers may derive guidance in their planning by applying the model and the findings in their own deliberations.

Little agreement has been reached as to the impact of different international marketing strategies, let alone the classification of strategies themselves. This paper analyses firm strategy in two levels – generic strategies and five groups of international marketing strategies.

The purpose of this paper is to determine the key characteristics that determine housing prices in the USA. Data analytical models capable of predicting the driving forces of housing prices can be extremely useful in the built environment and real estate decision-making processes.

A data set of 13,771 houses is extracted from the 2013 American Housing Survey (AHS) data and used to develop a Hedonic Pricing Method (HPM). Besides, a data set of 22 houses in the city of San Francisco, CA is extracted from Redfin real estate brokerage database and used to test and validate the model. A correlation analysis is performed and a stepwise regression model is developed. Also, the best subsets regression model is selected to be used in HPM and a semi-log HPM is proposed to reduce the problem of heteroscedasticity.

Results show that the main driving force for housing transaction price in the USA is the square footage of the unit, followed by its location, and its number of bathrooms and bedrooms. The results also show that the impact of neighborhood characteristics (such as distance to open spaces and business centers) on the housing prices is not as strong as the impact of housing unit characteristics and location characteristics.

An important limitation of this study is the lack of detailed housing attribute variables in the AHS data set. The accuracy of the prediction model could be increased by having a greater number of information regarding neighborhood and regional characteristics. Also, considering the macro business environment such as the inflation rate, the interest rates, the supply and demand for housing, and the unemployment rates, among others could increase the accuracy of the model. The authors hope that the presented study spurs additional research into this topic for further investigation.

The developed framework which is capable of predicting the driving forces of housing prices and predict the market values based on those factors could be useful in the built environment and real estate decision-making processes. Researchers can also build upon the developed framework to develop more sophisticated predictive models that benefit from a more diverse set of factors.

Finally, predictive models of housing price can help develop user-friendly interfaces and mobile applications for home buyers to better evaluate their purchase choices.

Identification of the key driving forces that determine housing prices on real-world data from the 2013 AHS, and development of a prediction model for housing prices based on the studied data have made the presented research original and unique.

There is a tendency in team research to employ concepts of stepwise models, reaching from the primitive to the excellent, to suggest that a higher level of evolution is better than the basic and simple. This tendency includes typologies of teams. This article aims to question the relevance of this view.

Data were collected in three steps. In the first step, articles and books analyzing teams and teamwork from stepwise analytical models were collected. In the second step the collected data were classified into different themes. Each stepwise model was classified into one essential denomination. This classification resulted in eight themes. In the third step each theme was analyzed, which led to the fusion of some of the themes.

The conclusion is that a synchronous, complementary or mature team is not necessarily optimal. Contrary to this, a differentiated, sequential or multi team approach can be optimal for some purposes. Team research needs to establish a more open, inductive and critical attitude than at present.

The paper highlights the need to observe and use team theories in a balanced and critical way.

The purpose of this paper is to explore the role of emotions that consumers experience following service failures and to assess the effects of each of these emotions on important behavioral outcomes.

This paper extends the work of Wetzer et al. (2007) and draws upon the existing literature to test a series of research hypotheses tying emotions to four important behavioral outcomes primarily using stepwise regression.

When a service failure occurs, customers experience any of a variety of negative emotions. The effect on behavioral outcomes depends on the specific emotion experienced by the consumer. The current research, which benefits by using retrospective experience sampling, finds that frustration is the predominant emotion experienced by customers following service failure, but that anger, regret and frustration affect behavioral outcomes. Uncertainty also plays a role.

Future research should investigate the antecedents of propensity for emotions and predisposition toward industries, as well as the consequences of word-of-mouth (WOM) praise and WOM activity. Additionally, emotions could be examined by service stage. Several other moderators could be investigated, including severity, complaining behavior, repeat occurrence, service importance, remedies and forgiveness, product vs process failures, tenure, gender and age.

The current research emphasizes the importance of understanding which emotion is being experienced by a customer following service failure to identify the behavioral outcomes that will be most impacted. The specific managerial implications depend upon the specific emotional response experienced by the customer and are discussed separately for anger, regret and frustration. Service personnel must be trained to recognize and address specific customer emotions rather than to provide a canned or generalized response.

To date, there has been little, if any, systematic research into the effects of multiple discrete negative emotions on multiple desirable behavioral outcomes. The current study examines six discrete emotions. Predominant emotions are differentiated from emotional intensity. The behavioral outcomes of reconciliation and reduced share-of-wallet are added to the traditional outcomes of repatronage intentions and negative WOM. While existing research tends to rely on a scenario approach, this study uses the retrospective experience sampling method. The authors distinguish between mixed emotions and multiple emotions. The relative effects of disappointment and regret are examined for each of the four outcomes. Finally, importance-performance map analysis was applied to the findings to prioritize managerial attention. Numerous managerial and research implications are identified.