Past, present and future of bank marketing: a bibliometric analysis of International Journal of Bank Marketing (1983–2020)

PurposeThis study aims to provide an overview of bank marketing through a retrospection of the International Journal of Bank Marketing (IJBM), the leading journal for bank marketing.Design/methodology/approachThis study conducts a bibliometric analysis to analyze the performance and intellectual structure of bank marketing literature curated through IJBM between 1983 and 2020.FindingsThis study sheds light on the growing influence and impact of IJBM on the field of bank marketing through six major clusters (themes): relationship marketing and service quality in banking and financial services, consumer behavior in banking and financial services, customer satisfaction and loyalty in banking and financial services, electronic or online banking and financial services, Islamic banking and financial services, and service failure and recovery in banking and financial services.Research limitations/implicationsThough this study offers a state-of-the-art overview of bank marketing through the lens of IJBM, the insights remain limited to the accuracy and availability of bibliographic data of the journals from Scopus.Originality/valueTo the best of the authors' knowledge, this study represents the first objective assessment of bank marketing and IJBM. Thus, this study should be useful to past and prospective authors, editorial board members, editors, readers and reviewers to gain a one-stop understanding about bank marketing through the contributions of IJBM.

Peran mediasi self-efficacy pada work engagement dan perceived organizational support

Excellent human resources in the organization are seen as the principal capital for the company to improve the company’s role, function, and competitiveness in facing developments and changes both internally and externally. Companies with high-stress levels need superior employees that motivated, passionate about work and loyal to the organization. These needs can refer to the characteristics of employees who are engaged with their work. However, there are still many dynamics of psychological constructs that have not been revealed that can increase work engagement. The purpose of this study was to determine whether self-efficacy can be a mediator on the effect of perceived organizational support (POS) on work engagement. The research was conducted through a survey method taken from 103 bank marketing employees. The collected data were analyzed through the structural equation model (SEM) and showed an indirect effect of perceived organizational support on work engagement (ab = 0.13; p≤0.001). This result shows that self-efficacy partially mediates the relationship between perceived organizational support (POS) and work engagement and the research hypothesis is acceptable. Keywords: SEM, work engagement, perceived organizational support, self-efficacy

Density-based classification with the DENCLUE algorithm

<p><span>Classification of information is a vague and difficult to explore area of research, hence the emergence of grouping techniques, often referred to Clustering. It is necessary to differentiate between an unsupervised and a supervised classification. Clustering methods are numerous. Data partitioning and hierarchization push to use them in parametric form or not. Also, their use is influenced by algorithms of a probabilistic nature during the partitioning of data. The choice of a method depends on the result of the Clustering that we want to have. This work focuses on classification using the density-based spatial clustering of applications with noise (DBSCAN) and DENsity-based CLUstEring (DENCLUE) algorithm through an application made in csharp. Through the use of three databases which are the IRIS database, breast cancer wisconsin (diagnostic) data set and bank marketing data set, we show experimentally that the choice of the initial data parameters is important to accelerate the processing and can minimize the number of iterations to reduce the execution time of the application.</span></p>

PREDIKSI TINGKAT KESUKSESAN PROMOSI BANK DENGAN ALGORITMA DNN

Telemarketing is one effective way for promoting products. However, it is often difficult to measure the success of telemarketing. Therefore, a way to predict the success rate of telemarketing, and hence strategies could be planned to increase the success rate. In this study, we evaluate several implementations of machine learning for prediction the success of telemarketing. The evaluated methods are Deep Neural Network (DNN), Random Forest, and K-nearest neighbor (K-NN). We validate our experiments using 10-fold cross validation and our experiments show that DNN with 3 hidden layers outperforms other methods. Accuracy of 90% is achieved with the DNN. It is better than Random Forest and KNN that achieve accuracies of algorithm and 88% and 89%.Keywords— Bank Marketing, DNN, KNN, Random Forest.

Analisis Optimasi Algoritma Klasifikasi Naive Bayes menggunakan Genetic Algorithm dan Bagging

The increasing demand for credit applications to banks has motivated the banking world to switch to more sophisticated techniques for analyzing the level of credit risk. One technique for analyzing the level of credit risk is the data mining approach. Data mining provides a technique for finding meaningful information from large amounts of data by way of classification. However, bank marketing data is a type of imbalance data so that if the classification is done the results are less than optimal. The classification algorithm that can be used for imbalance data types can use naïve Bayes. Naïve Bayes performs well in terms of classification. However, optimization is needed in order to obtain more optimal classification results. Optimization techniques in handling imbalance data have been developed with several approaches. Bagging and Genetic Algorithms can be used to overcome imbalance data. This study aims to compare the accuracy level of the naïve Bayes algorithm after optimization using the bagging and genetic algorithm. The results showed that the combination of bagging and a genetic algorithm could improve the performance of Naive Bayes by 4.57%.

Analysis Of The Use Of Marketing Tools At The Current Stage Of Development Of The Banking Services Market

In order to understand the success of the world banks, we will analyze the marketing tools used by foreign specialists in the field of banking. At the same time, it is necessary to define the concepts of "bank marketing" and "bank marketing tools". Bank marketing is the process of regularly improving and improving the efficiency of the bank's activities with the help of a certain set of tools within the framework of the interaction marketing concept and taking into account the market strategy based on the opinion, preferences and needs of consumers.

Data Mining for Potential Customer Segmentation in the Marketing Bank Dataset

Direct marketing is an effort made by the Bank to increase sales of its products and services, but the Bank sometimes has to contact a customer or prospective customer more than once to ascertain whether the customer or prospective customer is willing to subscribe to a product or service. To overcome this ineffective process several data mining methods are proposed. This study compares several data mining methods such as Naïve Bayes, K-NN, Random Forest, SVM, J48, AdaBoost J48 which prior to classification the SMOTE pre-processing technique was done in order to eliminate the class imbalance problem in the Bank Marketing dataset instance. The SMOTE + Random Forest method in this study produced the highest accuracy value of 92.61%.

Customer loyalty and value anticipation: does perceived competition matter?

PurposeDrawing on customer value theory and the demanding nature of today's customers, this paper examines the moderating effects of competition, as perceived by customers, on the nexus between customer value anticipation (CVA), satisfaction and loyalty.Design/methodology/approachUtilizing data from the Ghanaian banking sector, which has been going through some reforms that are changing the banking landscape, the study analyzes data from 587 customers. Respondents were drawn from a cluster of banks within an enclave with different types of customers and epitomize the competitive nature of Ghana's banking sector.FindingsCVA drives customer satisfaction, attitudinal loyalty and behavioral loyalty among bank customers. However, between attitudinal and behavioral loyalty, customers will be more behaviorally loyal to banks that successfully anticipate their needs than they would be in attitude. The relationships between CVA and satisfaction and loyalty are such that the level of competition among sector players does not alter the effect; thus, when a bank is able to anticipate customer value, customers are going to stay loyal to such a bank irrespective of the competitive offers.Originality/valueAlthough the impact CVA has on satisfaction and loyalty is justified in the existing literature, extant research has not systematically examined the influence of external boundary and situational effects on the potency of anticipating customer value in detail. The current study shows the effect of competition on CVA and customer behavioral outcome. The study further concludes that irrespective of competition, banks that are perceived to be high on CVA will have their customers being loyal. This is very important in the development of bank marketing and product innovation strategies.