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The purpose of this paper is to assess the performance of competing methods and model selection, which are non‐trivial issues given the financial implications. Researchers have adopted various methods including statistical models and machine learning methods such as neural networks to assist decision making in direct marketing. However, due to the different performance criteria and validation techniques currently in practice, comparing different methods is often not straightforward.

This study compares the performance of neural networks with that of classification and regression tree, latent class models and logistic regression using three criteria – simple error rate, area under the receiver operating characteristic curve (AUROC), and cumulative lift – and two validation methods, i.e. bootstrap and stratified k‐fold cross‐validation. Systematic experiments are conducted to compare their performance.

The results suggest that these methods vary in performance across different criteria and validation methods. Overall, neural networks outperform the others in AUROC value and cumulative lifts, and the stratified ten‐fold cross‐validation produces more accurate results than bootstrap validation.

To select predictive models to support direct marketing decisions, researchers need to adopt appropriate performance criteria and validation procedures.

The study addresses the key issues in model selection, i.e. performance criteria and validation methods, and conducts systematic analyses to generate the findings and practical implications.

Based on user-generated content from a Chinese social media platform, this paper aims to investigate multiple methods of constructing user profiles and their effectiveness in predicting their gender, age and geographic location.

This investigation collected 331,634 posts from 4,440 users of Sina Weibo. The data were divided into two parts, for training and testing . First, a vector space model and topic models were applied to construct user profiles. A classification model was then learned by a support vector machine according to the training data set. Finally, we used the classification model to predict users’ gender, age and geographic location in the testing data set.

The results revealed that in constructing user profiles, latent semantic analysis performed better on the task of predicting gender and age. By contrast, the method based on a traditional vector space model worked better in making predictions regarding the geographic location. In the process of applying a topic model to construct user profiles, the authors found that different prediction tasks should use different numbers of topics.

This study explores different user profile construction methods to predict Chinese social media network users’ gender, age and geographic location. The results of this paper will help to improve the quality of personal information gathered from social media platforms, and thereby improve personalized recommendation systems and personalized marketing.

Despite the fact that shame is recognized as a significant factor in clinical encounters, it is under-recognized, under-researched and under-theorized in health prevention, assessment and cross-cultural contexts. Thus, this study aims to investigate the psychometric properties of the most widely used scale, the “Other as Shamer Scale” (OAS), to assess the risk and proclivities of external shame in adults. As in health care, there is a barrier between what is known through research in one culture and what is acceptable in practice in another culture.

The Urdu version was prepared using the standard back-translation method, and the study was conducted from June 2021 to January 2022. The translation and adaptation were completed in four steps: forward translation, adaptation and translation, back translation, committee approach and cross-language validation. The sample, selected through the purposive sampling method, is comprised of 200 adults (men = 100 and women = 100), with an age range of 18–60 years (M = 28, SD = 5.5), spanning all stages of life. The Cronbach's alpha reliability and factorial validity of the OAS were assessed through confirmatory factor analysis and Pearson correlation analyses. Internal consistency and test–retest reliability (at a two-week interval) were used to evaluate the reliability. Statistical analyses were performed using Statistical Package for Social Sciences (version 22) software.

Preliminary analysis revealed that the overall instrument had good internal consistency (Urdu OAS a = 0.91; English OAS a = 0.92) as well as test–retest correlation coefficients for 15 days (r = 0.88). The factor loading of all items ranged from 0.69 to 0.9, which explained the significant level and indicated the model's overall goodness of fit.

Findings suggest that this scale has significant psychometric properties and the potential to be used as a valid, reliable and cost-effective clinical and research instrument. This study contributes to scientific knowledge and helps to develop and test indigenous cross-cultural instruments that can be used to examine external shame in Pakistani people.

The purpose of this paper is to report data empirically linking competencies of individual leaders to business profitability and demonstrate that competencies are cross‐culturally valid.

Participants in the initial competency study were 15 business unit managers identified as high performing. Data were collected using Critical Incident Interviews that were systematically coded using thematic analysis to identify the presence of competencies. Competencies identified were then adapted into a behaviourally‐based questionnaire used in a follow‐up validation study. Participants in the validation study (n=70) were managers from North America and two European countries who were participants in a management development program. Boss ratings of competencies were then correlated with business unit profitability.

A set of competencies was identified as predictive of unit profit growth in managers in both North America and the European Union. Subsequent regression analysis showed that 17 per cent of the variance in business unit profitability could be accounted for by four competencies, specifically team leadership, developing others, achievement orientation, and impact and influence. Cross‐cultural validity was demonstrated to the degree that similar competencies predicted performance in both North America and the European Union as evidenced by the correlation between boss rating of subordinate competencies and profit growth.

The initial study using Critical Incident Interviews was conducted with a small sample size and did not employ a comparison group of average performers.

Initial competency research using empirical methods should be used to help focus competency models used for selection, feedback, training, and performance management.

The study is one of the few published studies that link competencies to business unit profitability. The paper demonstrates that competencies have a degree of cross‐cultural validity.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

The purpose of this paper is to develop and validate cross‐culturally a short‐form, consumers' need for uniqueness (CNFU) scale. The length of the original scale (31 items) might have hindered its diffusion in research when questionnaire length and respondent fatigue are major considerations.

The paper uses survey‐based data from Israel, Slovenia, and the Palestinian Authority and uses a combination of statistical techniques, such as EFA, CFA, and structural equation modeling.

In general, support was found for the cross‐cultural reliability and validity of the new, short‐form CNFU scale.

Future research can use the short‐form scale with additional confidence in its cross‐cultural reliability and validity.

First, since CNFU appears not to be culturally bound, marketers can identify cross‐country segments of high‐CNFU individuals and use standardized marketing campaigns to reach them. Second, marketers of unique products can use the antecedents identified in this study to develop and encourage CNFU. Third, the findings can be used to design advertising campaigns such as by emphasizing the social context of consumption of high‐uniqueness products.

An original and first presentation of a cross‐cultural validation of a parsimonious CNFU scale.

Prior studies have paid close attention to the impact of political risk on financial markets. Following this strand of literature, this paper aims to focus on the causality link between political shocks and their impacts on emerging stock markets.

This paper highlights an innovative counterfactual model for political risk assessment. Based on a natural experiment, i.e. the Taiwan Strait Crisis in 1995-1996, this study utilizes one data-driven approach, e.g. the synthetic control methods (SCMs), to estimate causal impact of this political shock on Taiwan’s stock market.

Major findings in this study are consistent with existing literature on the price of political risk, e.g. political uncertainty commands a risk premium. The SCM estimations suggest that Taiwan’s stock prices dramatically underperformed its newly industrialized peers and other developed markets during the crisis. The SCM results are statistically significant and robust to various cross-validation tests.

Findings in this study indicate that political risks could generate enormous impacts on emerging financial markets. In particular, political uncertainty following new geopolitical dynamics requires proper identification and assessment.

To the author’s knowledge, this paper is the first rigorous counterfactual study to the causality relationship between political uncertainty and stock prices in emerging markets. This paper is distinct from previous studies in applying a data-driven approach to combine the features of learning from others (cross-sectional) and learning from the past (time series).

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization.

Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT).

Methods procedures, process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods.

Results, observations, and conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

The purpose of this paper is to address a solution to the problem of defect recognition from images using the support vector machines (SVM).

A SVM‐based multi‐class pattern recognition system has been developed for inspecting commonly occurring fabric defects such as neps, broken ends, broken picks and oil stain. A one‐leave‐out cross validation technique is applied to assess the accuracy of the SVM classifier in classifying fabric defects.

The investigation indicates that the fabric defects can be classified with a reasonably high degree of accuracy by the proposed method.

The paper outlines the theory and application of SVM classifier with reference to pattern classification problem in textiles. The SVM classifier outperforms the other techniques of machine learning systems such as artificial neural network in terms of efficiency of calculation. Therefore, SVM classifier has great potential for automatic inspection of fabric defects in industry.