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The purpose of this paper is to develop recommendations for measures assessing intercultural training needs for international human resource management. Based on scientific as well as application‐oriented criteria the aim is to select six measures assessing general intercultural competencies and with the help of behaviour ratings in interactive intercultural exercises to evaluate the psychometric quality and practicability of the questionnaires for training purposes.

Psychometric quality including prognostic power of ICSI, FLCS, NVCCS, ISAS, TIHK, and SIB was tested by correlating subjects' questionnaires results with observations of their actual behaviour in intercultural exercises.

Satisfying psychometric quality and prognostic validity of almost all measures was found. Especially TIHK, ICSI, and FLCS score well for assessing training needs whereas the results for SIB were problematic.

The study observed student participants of the same culture. Replication studies should examine large samples of different cultural backgrounds and manager populations.

Human resource managers may benefit from gaining knowledge about which measures to use for identifying employee's weaknesses in intercultural competence in order to create tailor‐made training interventions.

The study is the first providing information about the psychometric quality, including predictive power, of six measures, that assess intercultural skills to detect intercultural training needs.

Managers working for multinational companies need to be culturally sensitive, as do those responsible for training methods and materials in different cultural settings. The issues associated with the effectiveness of applying training and development techniques and processes across different cultures are focused on, specifically: the need for human resource development professionals to be sensitive of cultural factors in different nationalities in their training activities; the need for cross‐cultural training for managers; and the impact of cultural factors on the processes in training and development.

Some organizations invest a great deal of time and effort in elaborate training programmes designed to improve the so‐called “soft” skills of managing. Yet assessing the effectiveness of such initiatives has been rare. Indeed, some trainers have argued that such assessments are misleading. Recent developments in the use of survey feedback have provided a technique for pre‐ and post‐training assessments. A study, at a leading business school, was designed to assess the impact of interpersonal skills training on top managers. The evaluation of the training was based on subordinate feedback of 252 executives from 48 organizations, conducted before, and six months after, the training programme took place. The results indicate significant impact on some, but not all, of the competencies and skills under study.

The bank is termed as an imperative part of the marketing economy. The failure or success of an institution relies on the ability of industries to compute the credit risk. The loan eligibility prediction model utilizes analysis method that adapts past and current information of credit user to make prediction. However, precise loan prediction with risk and assessment analysis is a major challenge in loan eligibility prediction.

This aim of the research technique is to present a new method, namely Social Border Collie Optimization (SBCO)-based deep neuro fuzzy network for loan eligibility prediction. In this method, box cox transformation is employed on input loan data to create the data apt for further processing. The transformed data utilize the wrapper-based feature selection to choose suitable features to boost the performance of loan eligibility calculation. Once the features are chosen, the naive Bayes (NB) is adapted for feature fusion. In NB training, the classifier builds probability index table with the help of input data features and groups values. Here, the testing of NB classifier is done using posterior probability ratio considering conditional probability of normalization constant with class evidence. Finally, the loan eligibility prediction is achieved by deep neuro fuzzy network, which is trained with designed SBCO. Here, the SBCO is devised by combining the social ski driver (SSD) algorithm and Border Collie Optimization (BCO) to produce the most precise result.

The analysis is achieved by accuracy, sensitivity and specificity parameter by. The designed method performs with the highest accuracy of 95%, sensitivity and specificity of 95.4 and 97.3%, when compared to the existing methods, such as fuzzy neural network (Fuzzy NN), multiple partial least squares regression model (Multi_PLS), instance-based entropy fuzzy support vector machine (IEFSVM), deep recurrent neural network (Deep RNN), whale social optimization algorithm-based deep RNN (WSOA-based Deep RNN).

This paper devises SBCO-based deep neuro fuzzy network for predicting loan eligibility. Here, the deep neuro fuzzy network is trained with proposed SBCO, which is devised by combining the SSD and BCO to produce most precise result for loan eligibility prediction.

The State University of New York at Buffalo, Cornell University, The University of Rochester, and Syracuse University joined forces with Ford Foundation support to provide a summer experience for administrative interns. The program's central focus was the development of educational leaders. Interns from each of the four universities were brought to Cornell University. Interns and professors from each university in the program were viewed as a social system, providing an opportunity for participants to diagnose their own social behavior. Two factors contributed to the process: the creation of an “open” climate to foster inquiry and a high degree of cooperation among staff and interns. The first two weeks of the summer experience were devoted to sensitivity training which accomplished several related objectives: more accurate perceptions of self; increased accuracy in perceiving the effect and affect of one's behavior on others; greater understanding of interaction between groups and inductively derived understanding of social and behavioral theories. An instrument for evaluation was prepared by the interns. Among the “high points” they identified, two items were most frequently and about evenly mentioned: sensitivity training and day‐to‐day relationships. The reactions of the interns as a whole was an affirmation of the experience as a meaningful contribution to their personal and professional growth.

Describes a comprehensive 14‐week predeparture training programme and an on‐location orientation session that companies may find useful when they transfer personnel to foreign assignments. Increasing numbers of Western professionals go to foreign countries on assignment. Their success or failure in these assignments largely hinges upon their adaptation to the new set of living and working conditions. A well designed training programme can enhance the chances for success and minimize failures.

The purpose of this paper is to examine the essential abilities service employees need to deliver an exceptional service that delights customers and to identify effective methods to train exceptional service employees.

The authors conducted in-depth interviews with seven senior butlers and three human resource managers from four of the most renowned five-star hotels in Taiwan. Interviews with the two groups of participants provided good triangulation and allowed us to gain different perspectives and to obtain a more holistic understanding of the research topic.

This study systematically organized the essential abilities required to deliver delightful service into three hierarchical levels: professionalism, the ability to respond to customers’ emotions and hidden needs and build bonds with them, and the ability to deliver one-stop service. The authors propose that the most effective training method is to develop a customer-oriented service climate. Such an environment will enable service employees to be naturally molded into exceptional service personnel. Several training methods are identified to build a customer-oriented service environment within a company.

This study focused on the staff of five-star hotels. The proposed service standards and abilities may only be applicable to high-end service providers. Furthermore, this study used only a qualitative research method (in-depth interviews) to develop a preliminary training model to foster outstanding service employees. This model can be further verified using a quantitative method and a larger number of participants in a future study.

This research provides contributions to the literature on delightful service and human resources management.

Fraud transaction detection has become a significant factor in the communication technologies and electronic commerce systems, as it affects the usage of electronic payment. Even though, various fraud detection methods are developed, enhancing the performance of electronic payment by detecting the fraudsters results in a great challenge in the bank transaction.

This paper aims to design the fraud detection mechanism using the proposed Harris water optimization-based deep recurrent neural network (HWO-based deep RNN). The proposed fraud detection strategy includes three different phases, namely, pre-processing, feature selection and fraud detection. Initially, the input transactional data is subjected to the pre-processing phase, where the data is pre-processed using the Box-Cox transformation to remove the redundant and noise values from data. The pre-processed data is passed to the feature selection phase, where the essential and the suitable features are selected using the wrapper model. The selected feature makes the classifier to perform better detection performance. Finally, the selected features are fed to the detection phase, where the deep recurrent neural network classifier is used to achieve the fraud detection process such that the training process of the classifier is done by the proposed Harris water optimization algorithm, which is the integration of water wave optimization and Harris hawks optimization.

Moreover, the proposed HWO-based deep RNN obtained better performance in terms of the metrics, such as accuracy, sensitivity and specificity with the values of 0.9192, 0.7642 and 0.9943.

An effective fraud detection method named HWO-based deep RNN is designed to detect the frauds in the bank transaction. The optimal features selected using the wrapper model enable the classifier to find fraudulent activities more efficiently. However, the accurate detection result is evaluated through the optimization model based on the fitness measure such that the function with the minimal error value is declared as the best solution, as it yields better detection results.