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The polynomial dimensional decomposition (PDD) method is a popular tool to establish a surrogate model in several scientific areas and engineering disciplines. The selection of appropriate truncated polynomials is the main topic in the PDD. In this paper, an easy-to-implement adaptive PDD method with a better balance between precision and efficiency is proposed.

First, the original random variables are transformed into corresponding independent reference variables according to the statistical information of variables. Second, the performance function is decomposed as a summation of component functions that can be approximated through a series of orthogonal polynomials. Third, the truncated maximum order of the orthogonal polynomial functions is determined through the nonlinear judgment method. The corresponding expansion coefficients are calculated through the point estimation method. Subsequently, the performance function is reconstructed through appropriate orthogonal polynomials and known expansion coefficients.

Several examples are investigated to illustrate the accuracy and efficiency of the proposed method compared with the other methods in reliability analysis.

The number of unknown coefficients is significantly reduced, and the computational burden for reliability analysis is eased accordingly. The coefficient evaluation for the multivariate component function is decoupled with the order judgment of the variable. The proposed method achieves a good trade-off of efficiency and accuracy for reliability analysis.

The purpose of this study is to examine categorization leakage from employees in service encounters in terms of indications that the customer has been categorized as either poor or rich. Given that customers perceive themselves as belonging to one of these two categories, leakage can result in perceptions of the categorization as either correct or incorrect, and the specific purpose is to assess the impact of such outcomes on customer satisfaction.

Two between-subjects experiments were used to manipulate service employees’ leakage of categorization clues; the participants were subject to leakage comprising clues that they had been categorized as either poor or rich. The participants’ self-perceived membership in the poor and rich categories was used as a measured factor.

The results indicate that customers are indeed sensitive to how they are categorized in service encounters. More specifically, when categorization in terms of the categories poor and rich was leaked to the customer, being correctly categorized (either as poor or rich) was more satisfying than being incorrectly categorized. In addition, given the valenced charge of these two categories, the results indicate that the category charge per se also influences satisfaction.

The present study adds employee categorization leakage to the existing literature dealing with employee-related factors affecting customer satisfaction in service encounters.

Balancing accuracy and efficiency is an important evaluation index of response surface method. The purpose of this paper is to propose an adaptive order response surface method (AORSM) based on univariate decomposition model (UDM).

First, the nonlinearity of the univariate function can be judged by evaluating the goodness of fit and the error of curve fit rationally. Second, combining UDM with the order analysis of separate component polynomial, an easy-to-implement AORSM is proposed. Finally, several examples involving mathematical functions and structural engineering problems are studied in detail.

With the proposed AORSM, the orders of component functions in the original response surface can be determined adaptively and the results of those cases in this paper indicate that the proposed method performs good accuracy, efficiency and robustness.

Because just the cases with single failure mode and single MPP are studied in this paper, the application in multi-failure mode and multi-MPP cases need to be investigated in the coming work.

The nonlinearity of the univariate in the response surface can be determined adaptively and the undetermined coefficients of each component function are obtained separately, which reduces the computation dramatically.

Drawing on an evolutionary perspective, prior studies have revealed that conspicuous consumption by men has signaling functions (i.e. signaling the consumer’s positive mate qualities such as status and ability to acquire resources) for mate attraction. However, it is unclear whether conspicuous consumption of luxury products by women has a function in mate attraction. The purpose of this paper is to investigate the effect of mate attraction goal on women’s interest in conspicuous consumption and the possible mediating effect of the attractiveness enhancement need in this effect.

A survey and two experimental studies were conducted in which 354 Chinese female undergraduates participated. In the survey, the respondents’ desire to have a romantic partner was measured; in the two experiments, the participants’ mate attraction goals were primed. The authors followed the literature to measure dependent variables (i.e. consumption measures), but the specific consumption items were adapted to meet the purpose of the current research. The authors analyzed the data from the three studies through analysis of variance, regression analysis and bootstrapping.

Young women with a strong (vs weak) desire for a romantic partner reported a high level of attractiveness enhancement needs, thereby indicating a higher willingness to pay (WTP ) for conspicuous items that can enhance their attractiveness (Studies 1 and 3). Furthermore, activating young women’s mate attraction goal can also increase their WTP for conspicuous items (Studies 2 and 3) and attractiveness enhancement items (Study 3) but not inconspicuous luxury product (e.g. underclothes) (Study 3). These findings suggested that young women consider conspicuous consumption of certain products as a means of enhancing attractiveness to acquire a desired mate.

This research identifies a novel function of conspicuous consumption: young women, especially those who do not have a romantic partner, may use conspicuous consumption of certain products to satisfy their attractiveness enhancement needs and, ultimately, to attract an ideal mate.

This study proposes the use of cumulative prospect theory (CPT) to predict over- and under-estimation of risks and the counteractive adjustment in a cold chain context. In particular, the purpose of this paper is to address the importance of the socio-demographic characteristics of an individual in influencing risk attitude and the analysis of measurable risk probability.

This study uses CPT as the basis to develop a decision analysis model in which the two functions of value editing and probability weighting are nonlinear to adequately determine the flexible risk attitudes of individuals, as well as their prospects with numerous outcomes and different probabilities. An experiment was conducted to obtain empirical predictions, and an efficient Markov Chain Monte Carlo algorithm was applied to overcome the nonlinearity and dimensionality in the process of parameter estimation.

The respondents overweigh the minor cold chain risks with small probabilities and behave in a risk-averse manner, while underweighting major events with larger ones, thereby leading to risk-seeking behavior. Judgment distortion regarding probability was observed under risk decision with a low probability and a high impact. Moreover, the findings indicate that factors, such as gender, job familiarity and confidentiality significantly influence the risk attitudes and subjective probability weighting of the respondents.

The findings fit the framework of CPT and extend this theory to deal with human risk attitudes and subjective bias in cold chains. In particular, this study enhances the literature by providing an analysis of cold chain risk from both the human decision-making and managerial perspectives. Moreover, this research determined the importance of the socio-demographic characteristics of an individual to explain the variability in risk attitudes and responses.

Managers must consider the issues of flexible risk attitude and subjective judgment when making choices for risk mitigation strategies. Given the focus on counteractive adjustment for over- and under-estimated risk, firms could evaluate cold chain risk more accurately, and thereby enhance their resilience to risky events while reducing the variability of their performance.

The current study is the first to materialize the phenomena of over- and under-estimation of cold chain risks, as well as to emphasize the different characteristics for loss aversion and judgment distortion at the individual level.

This paper aims to examine the mediating role of attitudinal loyalty in the relationship between satisfaction and customer behavioral intentions such as willingness to pay more and internal and external complaining responses. It also seeks to examine the nonlinear effects in the relationship between satisfaction, attitudinal loyalty and behavioral intentions.

The paper adopted the structural equation modeling approach to test the hypotheses (sample size 202). It used Marsh et al.'s unconstrained method to test latent quadratic effects in the conceptualized relationships.

The results support the fully mediating role of attitudinal loyalty in the relationship between satisfaction and behavioral intentions. The paper also finds partial support for nonlinear effects in the relationship. Results support nonlinearity, and in particular diminishing sensitivity, in the link from attitudinal loyalty to willingness to pay more.

The paper adds to the existing literature by detangling the complex relationships between satisfaction, attitudinal loyalty and behavioral intentions such as willingness to pay more and external and internal complaining responses. In particular, this is the first study to simultaneously examine the nonlinear effects of attitudinal loyalty on multiple behavioral intentions constructs. This study also establishes the superiority of a fully mediated model, in which satisfaction affects behavioral intentions through attitudinal loyalty, over a partially mediated model.

Based on univariate dimension-reduction model, this study aims to propose an adaptive anisotropic response surface method (ARSM) and its high-order revision (HARSM) to balance the accuracy and efficiency for response surface method (RSM).

First, judgment criteria for the constitution of a univariate function are derived mathematically, together with the practical implementation. Second, by combining separate polynomial approximation of each component function of univariate dimension-reduction model with its constitution analysis, the anisotropic ARSM is proposed. Third, the high-order revision for component functions is introduced to improve the accuracy of ARSM, namely, HARSM, in which the revision is also anisotropic. Finally, several examples are investigated to verify the accuracy, efficiency and convergence of the proposed methods, and the influence of parameters on the proposed methods is also performed.

The criteria for constitution analysis are appropriate and practical. Obtaining the undetermined coefficients for every component functions is easier than the existing RSMs. The existence of special component functions is useful to improve the efficiency of the ARSM. HARSM is helpful for improving accuracy significantly and it is more robust than ARSM and the existing quadratic polynomial RSMs and linear RSM. ARSM and HARSM can achieve appropriate balance between precision and efficiency.

The constitution of univariate function can be determined adaptively and the nonlinearity of different variables in the response surface can be treated in an anisotropic way.

This paper aims to investigate the debated nexus of financial inclusion (FI) and financial stability (FS) in a comprehensive way, with several indicators of FI, considering nonlinearity and cross-country heterogeneity.

The authors introduce several indexes for FI by applying principal component analysis (PCA) and explore their impact on stability for a sample of 108 countries and subsamples based on income grouping as well as for pre- and post-crisis episodes over the period 2004–2017. To address the heterogeneity and endogeneity, the authors use the two-step quantile regression (2SQR), three-stage least square (3SLS) and two-step system-GMM (System-GMM).

The findings reveal that the relationship of FI and stability depends on the measurement of FI used and the heterogeneity of different macroeconomic factors. Besides, there is nonlinearity, irrespective of the measurement of inclusion used. The findings also confirm that the effect of FI is more prominent in countries with strong governance. The results are robust to several robustness validations, which could be useful for policymakers to align the divergence of these policies and ensure FS while expanding access to formal financial services.

This study makes an attempt to explore the reasons behind the debated empirical findings of the existing literature by revisiting the nexus using several disaggregated indexes, each representing individual dimension and a multidimensional index, examine the possible nonlinearity and investigate the conditioning effect of different macroeconomic factors that might play a significant role in this relationship.

It is well‐known from the literature that locational externalities influence the price formation of residential property. This effect is usually studied empirically with the hedonic price models, by including various neighbourhood and proximity variables in the model. These regression based techniques have, however, been criticised for a number of reasons. The arguments pertain partly to technical issues such as model flexibility, functional discontinuity and nonlinearity, and data quality, and partly to more fundamental problems regarding the nature of the value formation process. The criticism has attracted experiments with new modelling approaches, each of which adds something substantial to the hedonic approach. The study comprises two parts: it first highlights the rationale behind each broad approach composed of specific modelling techniques currently available, and then demonstrates an improvement of the demand side analysis by applying the analytic hierarchy process. This method enables quantification of qualitative expert judgements, and may lead to conclusions that go beyond the purely economic value framework.

Previous research that investigated the effects of currency depreciation on the trade balance assumed that the adjustment of all variables in a given model is in linear fashion. The authors wonder if introduction of nonlinearity in the adjustment of some variables such as the exchange rate can shed additional light on evidence of the J-curve. The new approach also allows to test whether exchange rate changes have symmetric or asymmetric effects on the trade balance. Estimates of a trade balance model for Canada, China, Japan, and the USA reveal that the effects are indeed asymmetric. The paper aims to discuss these issues.

The methodology is based on linear and nonlinear ARDL approach.

When nonlinearity is introduced into testing approach for the J-curve, more evidence is found in support of the J-curve.

The models are estimated using aggregate trade flows of each country with the rest of the world, hence they suffer from aggregation bias. Using trade flows at bilateral level and at commodity level are highly recommended for future research.

This is the first paper that applies nonlinear ARDL approach to test the short-run and long-run effects of currency depreciation on the trade balance.