Muhammed Jisham, Vanitha Selvaraj and Abin John
Driven by the explosive growth of artificial intelligence, WealthTech has played a pivotal role in reshaping the wealth management industry in recent years. Within this context…
Abstract
Purpose
Driven by the explosive growth of artificial intelligence, WealthTech has played a pivotal role in reshaping the wealth management industry in recent years. Within this context, this study aims to explain the antecedents of users’ continuance intention to use the WealthTech platform by integrating the technology continuance theory (TCT), task-technology fit (TTF) and digital nudging.
Design/methodology/approach
To empirically test the research model, an online survey was conducted among 337 investors who had previously used WealthTech platform. The authors used partial least squares structural equation modeling (PLS-SEM) to assess the research model and test the hypotheses.
Findings
PLS-SEM results show that the proposed model has moderate explanatory power in explaining WealthTech continuance intention. The results also found that attitude, digital nudging and satisfaction are important drivers in promoting WealthTech continuance intention. According to importance performance map analysis, digital nudging, expectation confirmation and satisfaction are critical factors in explaining continuance intention, which require managerial action.
Originality/value
To the best of the authors’ knowledge, this is one of the earliest studies that analyze the determinants of WealthTech continuance intention by integrating TCT with TTF and digital nudging. The study’s findings highlight the importance of fit factors and digital nudging in promoting successful WealthTech services.
Details
Keywords
Adamu Gayus Kasa, Matthew Egharevba and Ajibade Jegede
This paper aims to present the continuous Nigerian Government’s failure to protect the lives and property of its citizens against the incessant itinerant herders’ violence…
Abstract
Purpose
This paper aims to present the continuous Nigerian Government’s failure to protect the lives and property of its citizens against the incessant itinerant herders’ violence, despite its numerous programs in attempts to end the carnage. It sought also to examine the relationship between this government’s failure to meet its responsibility and the ineluctable self-defense mechanisms adopted by the people of Plateau State, Nigeria.
Design/methodology/approach
The research was both quantitative and qualitative. The study was conducted in four of the 17 Local Government Areas of the state: Bassa, Jos-south, Riyom and Barkin Ladi. A sample size of 400 was determined using Yamane Taro’s sampling size formula. Four hundred respondents were interviewed using a Google questionnaire (found at this link: https://forms.gle/tu96ZDwP85e8JsGu8). In this study, a total of seven key informant interviews and nine focus group discussions were conducted.
Findings
The finding revealed that most indigenous ethnic groups were dissatisfied with the government’s handling of the nomadic herders’ aggression. Therefore, 99.1% of Berom, 99.0% of Irigwe and 92.9% of other ethnicities argued that the government’s failure to protect them is a tacit permission for self-defense. On the contrary, 60.0% of the Fulani were satisfied with the government’s strategies in ending the aggression and 95.0% of them argued that the government’s failure to protect its citizens is not an implied permission for self-defense. It was also found that a relationship exists between the government’s lack of capacity to end the nomadic herders’ aggression and implied consent for self-defense in Plateau State, Nigeria.
Originality/value
This is a research paper that uses primary data. The findings are germane to ending the challenge of recurrent aggression of nomadic herders on other Nigerians. The study concludes that the government must live up to its responsibility of the protection of its citizens’ lives and property, failure to do so is an implicit permission to the citizens to defend themselves. It also recommended that the government should return displaced people to their communities.
Details
Keywords
Shokoofa Mostofi, Sohrab Kordrostami, Amir Hossein Refahi Sheikhani, Marzieh Faridi Masouleh and Soheil Shokri
This study aims to improve the detection and quantification of cardiac issues, which are a leading cause of mortality globally. By leveraging past data and using knowledge mining…
Abstract
Purpose
This study aims to improve the detection and quantification of cardiac issues, which are a leading cause of mortality globally. By leveraging past data and using knowledge mining strategies, this study seeks to develop a technique that could assess and predict the onset of cardiac sickness in real time. The use of a triple algorithm, combining particle swarm optimization (PSO), artificial bee colony (ABC) and support vector machine (SVM), is proposed to enhance the accuracy of predictions. The purpose is to contribute to the existing body of knowledge on cardiac disease prognosis and improve overall performance in health care.
Design/methodology/approach
This research uses a knowledge-mining strategy to enhance the detection and quantification of cardiac issues. Decision trees are used to form predictions of cardiovascular disorders, and these predictions are evaluated using training data and test results. The study has also introduced a novel triple algorithm that combines three different combination processes: PSO, ABC and SVM to process and merge the data. A neural network is then used to classify the data based on these three approaches. Real data on various aspects of cardiac disease are incorporated into the simulation.
Findings
The results of this study suggest that the proposed triple algorithm, using the combination of PSO, ABC and SVM, significantly improves the accuracy of predictions for cardiac disease. By processing and merging data using the triple algorithm, the neural network was able to effectively classify the data. The incorporation of real data on various aspects of cardiac disease in the simulation further enhanced the findings. This research contributes to the existing knowledge on cardiac disease prognosis and highlights the potential of leveraging past data for strategic forecasting in the health-care sector.
Originality/value
The originality of this research lies in the development of the triple algorithm, which combines multiple data mining strategies to improve prognosis accuracy for cardiac diseases. This approach differs from existing methods by using a combination of PSO, ABC, SVM, information gain, genetic algorithms and bacterial foraging optimization with the Gray Wolf Optimizer. The proposed technique offers a novel and valuable contribution to the field, enhancing the competitive position and overall performance of businesses in the health-care sector.