Promoting Customer Loyalty and Satisfaction in Financial Institutions through Technology Integration: The Roles of Service Quality, Awareness, and Perceptions

Iqbal, Kamran; Munawar, Hafiz Suliman; Inam, Hina; Qayyum, Siddra

doi:10.3390/su132312951

Cited by 13 publications

(7 citation statements)

References 91 publications

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“…According to previous study, the effect of service quality is positive on customer loyalty, so quality means improved customer loyalty. Similar findings have been shown by Iqbal et al (2021) [15], where service quality and customer loyalty had a positive linear correlation resulting from digitalization. Their findings are also consistent with the findings of this research, where digitalization showed a positive impact on service quality and customer loyalty.…”

Section: -4-verification Of Hypothesis H3supporting

confidence: 86%

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The Effect of Digitalization on the Quality of Service and Customer Loyalty

et al. 2022

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This research aims to measure the effect of digitalization on service quality through the SERVQUAL model (tangibility, reliability, responsiveness, assurance, and empathy) and customer loyalty. Also, we analyze the relationship between customer loyalty and their demographics. A quantitative method was used to achieve the objectives through a structured questionnaire, where part of the research sample was 400 clients of Kosovo banks. Results show that digitalization positively affects service quality and customer loyalty based on the OLS model. According to the T-test, there was no significant difference in customer loyalty between the genders. There has been a significant difference in loyalty between clients’ ages following the one-way ANOVA test. According to the Kruskal Wallis test, it also resulted in a significant difference between levels of education. This study will provide banks with feedback on the importance of digitalization and its correlation with their customers’ quality of service and loyalty. In this form, they decide to make even greater investments in digitalization by satisfying customer demands and creating loyal customers. Doi: 10.28991ESJ-2022-06-06-04 Full Text: PDF

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Section: -4-verification Of Hypothesis H3supporting

confidence: 86%

“…Customers' use of products and services, as retaining existing ones has a lower cost than the benefit of gaining new customers. This is considered a motivation for the banking sector to create customer loyalty by providing quality services [15].…”

Section: -Introductionmentioning

confidence: 99%

The Effect of Digitalization on the Quality of Service and Customer Loyalty

et al. 2022

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“…Besides focusing on functional reliability products, business entities also focus on customer quality perception (Vinh et al 2021). A high-quality perception gives a competitive edge to a firm and promotes sustainable relationships with customers (Iqbal et al 2021).…”

Section: Perceived Qualitymentioning

confidence: 99%

Promotion strategies in consumer paradox towards with word of mouth brand equity

Jalees¹,

Kazmi²,

Zaman³

et al. 2023

Rev. Bras. Mark.

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Objective of the study: This study is unique because it has incorporated two concepts, brand equity, and WOM, in a model to understand how the factors assimilate and improve consumers' understanding of WOM and brand equity. The study have focused on the local allied brand. Methodology:The study used the mall intercept method to collect data based on a self-administered questionnaire. The study distributed 415 blank questionnaires and received 403 filled questionnaires. The study have used Smart PLS for statistical analysis that included reliability and validity analyses, Findings:The study have used Smart PLS version 3.3 for statistical analysis, and test propositions relations to the consumer psychology, the study results supported seven hypotheses. The study did not find support on (i) perceived quality and WOM and (ii) satisfaction and WOM. Similarly, of the seven indirect hypotheses, the study also did not find support for three, which are (i) Association mediates awareness and brand equity, (ii) WOM mediates perceived Quality and brand equity, and (iii) WOM mediates satisfaction and brand equity. Theoretical Contributions:The study has tested a multi-mediating relationship which will provide more insight into the discussed phenomenon. It is the first study incorporating four drivers of WOM and four drivers of brand equity in a single study. The study adopted a holistic approach by testing seven direct, nine indirect, and one multi-mediating relationship Significance of the study: Perhaps, this is the first study that has developed a new model containing four variables associated with brand equity and another four related WOM in one study. Past studies have examined brand image and WOM with a narrow perspective. Most of them have used about seven or eight relationships. The study has taken a holistic approach and examined nine direct and seven mediating relationships. A holistic approach is necessary as the study has four antecedents related to WOM and four related to brand equity.

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“…Text mining is known as one of the best available techniques that can be applied to achieve automatic relation extraction from the published literature and articles. Text mining is being used widely for relation extraction, for instance protein-protein interactions (PPIs) and genedisease relationships, and therefore a corpus [30] is formed specifically for the DDI extraction task [31][32][33]. The DDI extraction task is very similar to relationship extraction studied in depth in the text-mining domain as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Big Data Management in Drug–Drug Interaction: A Modern Deep Learning Approach for Smart Healthcare

Salman

Munawar

Latif

et al. 2022

BDCC

Self Cite

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The detection and classification of drug–drug interactions (DDI) from existing data are of high importance because recent reports show that DDIs are among the major causes of hospital-acquired conditions and readmissions and are also necessary for smart healthcare. Therefore, to avoid adverse drug interactions, it is necessary to have an up-to-date knowledge of DDIs. This knowledge could be extracted by applying text-processing techniques to the medical literature published in the form of ‘Big Data’ because, whenever a drug interaction is investigated, it is typically reported and published in healthcare and clinical pharmacology journals. However, it is crucial to automate the extraction of the interactions taking place between drugs because the medical literature is being published in immense volumes, and it is impossible for healthcare professionals to read and collect all of the investigated DDI reports from these Big Data. To avoid this time-consuming procedure, the Information Extraction (IE) and Relationship Extraction (RE) techniques that have been studied in depth in Natural Language Processing (NLP) could be very promising. Since 2011, a lot of research has been reported in this particular area, and there are many approaches that have been implemented that can also be applied to biomedical texts to extract DDI-related information. A benchmark corpus is also publicly available for the advancement of DDI extraction tasks. The current state-of-the-art implementations for extracting DDIs from biomedical texts has employed Support Vector Machines (SVM) or other machine learning methods that work on manually defined features and that might be the cause of the low precision and recall that have been achieved in this domain so far. Modern deep learning techniques have also been applied for the automatic extraction of DDIs from the scientific literature and have proven to be very promising for the advancement of DDI extraction tasks. As such, it is pertinent to investigate deep learning techniques for the extraction and classification of DDIs in order for them to be used in the smart healthcare domain. We proposed a deep neural network-based method (SEV-DDI: Severity-Drug–Drug Interaction) with some further-integrated units/layers to achieve higher precision and accuracy. After successfully outperforming other methods in the DDI classification task, we moved a step further and utilized the methods in a sentiment analysis task to investigate the severity of an interaction. The ability to determine the severity of a DDI will be very helpful for clinical decision support systems in making more accurate and informed decisions, ensuring the safety of the patients.

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Promoting Customer Loyalty and Satisfaction in Financial Institutions through Technology Integration: The Roles of Service Quality, Awareness, and Perceptions

Cited by 13 publications

References 91 publications

The Effect of Digitalization on the Quality of Service and Customer Loyalty

The Effect of Digitalization on the Quality of Service and Customer Loyalty

Promotion strategies in consumer paradox towards with word of mouth brand equity

Big Data Management in Drug–Drug Interaction: A Modern Deep Learning Approach for Smart Healthcare

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