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The present research examines the benefits of implementing knowledge management (KM) principles in the Jordanian banking sector to enhance performance. The study emphasizes the significance of Artificial Intelligence (AI) and how Jordanian banks utilize it to improve the quality of customer service they provide. This study targets managers at all levels and focuses on the Jordanian banking sector as its research environment. A questionnaire is created to gather information from a random sample to achieve the research's objectives. The study involves a sample of 250 managers. Additionally, the research adopts a descriptive methodology, and SPSS is used to analyze the data. The statistical findings provide robust evidence for the importance of performance expectations, social influence, and perceived risk in influencing consumer intentions. Marketers and decision-makers within the banking industry can leverage these insights to shape their long-term strategies for effectively utilizing and maximizing AI technology in the banking sector. Furthermore, by providing policymakers and practitioners of Jordanian commercial banks with insight into the variables influencing user satisfaction, the findings will help these complex institutions operate more effectively.

Purpose: Start-up businesses have attracted considerable attention regarding the new approach in the modern economy The present study was conducted to investigate the factors affecting the improvement of customers’ online shopping experience in e-commerce start-ups in the healthcare sector.Method: This is an applied research conducted as a descriptive-survey. The target population includes all customers who used the electronic start-up services in the healthcare sector; out of which a sample of 384 individuals was selected using the Morgan table. A self-administered questionnaire was developed to collect the data which were analyzed using partial least squares and Smart-PLS software.Findings: All research hypotheses were confirmed, and it was proved that factors such as customer respect, customer enjoyment, importance of time, information security, convenience, perceived experience, valuable experience, and perceived image experience have a positive effect on improving customers’ online shopping experience in e-commerce start-ups.Conclusion: Applying the proposed model helps e-start-ups increase their performance   by eliminating their shortcomings and boosting their strengths.

This study investigates Ghanaian students' adoption of Mobile Learning (ML) by extending the technology acceptance model with a subjective norm variable. Specifically, this study focuses on the moderating effect of gender using the Measurement Invariance of Composite Models for the analysis. The study used a purposive sampling technique to collect the data for the study from sec-ond-year diploma students at the University of Professional Studies in Accra. SmartPLS 3.3.3 was used to analyze the data from 330 respondents. The findings of the study suggest that perceived ease of use, perceived usefulness, and subjective norm have a significant influence on the behavior-al intention to adopt mobile learning for the complete data set. In addition, the results suggest that the impact of the subjective norm was not significant for female students but for male students. Also, the impact of perceived ease of use and perceived usefulness on behavioral intention were insignificant. Furthermore, the findings suggest that behavioral intention influences students' actual use of mobile devices to access learning materials. Finally, gender moderates the relationship be-tween subjective norms and behavioral intention. The findings demonstrate group heterogeneity, therefore, investigations on technology adoption must always incorporate group dynamics to un-derstand how different groups respond to its adoption. The findings of the study hold significance for both policy and research implications.

Purpose: This study extends the current understanding of customer engagement by examining the impact of relationship bonding and relationship quality on customer engagement in value creation for online taxis.Method: A quantitative research design was used to validate the hypotheses proposed in this research. Customer engagement has become an important metric for measuring the quality of relationships between brands and their customers. Despite this, there has been limited research into how relationship bonds affect the effectiveness of building such a relationship in online taxi services. A survey was conducted using the convenience method among 600 users of online transportation services in the city of Urmia, Iran to test the theoretical model. Structural equation modelling in software Amos 23 were used in this study to test the research hypotheses. Findings: Findings showed that relationship bonding (financial, social, and structural) positively affects the quality of online relationships. Moreover, it was found that the quality of online relationships had a positive impact on all four aspects of customer engagement value (lifetime value, influence value, customer knowledge value, and customer referral value).Conclusion: In order to demonstrate the promotion of customer engagement through relationship bonds and online relationship quality, the study adds new data to the literature on online taxis services. Online taxi services are able to offer open innovation structures to help them   increase customer engagement, gather innovative ideas and integrate them into their operations. In this regard, in order to enrich ideas, customers who have successfully come up with new ideas should be rewarded.

Heart failure is a severe medical ailment that significantly impacts patients’ well-being and the healthcare system. For improved results, early detection and immediate treatment are essential. This work aims to develop and evaluate predictive models by applying sophisticated ensemble learning techniques. In order to forecast heart failure, we used a clinical dataset from Kaggle. We used the well-known ensemble techniques of bagging and random forest (RF) to create our models. With a predicted accuracy of 82.74%, the RF technique, renowned for its versatility and capacity to handle complex data linkages, fared well. The bagging technique, which employs several models and bootstrapped samples, also demonstrated a noteworthy accuracy of 83.98%. The proposed model achieved an accuracy of 90.54%. These results emphasize the value of group learning in predicting cardiac failure. The area under the ROC curve (AUC) was another metric to assess the model’s discriminative ability, and our model achieved 94% AUC. This study dramatically improves the prognostic modeling for heart failure. The findings have extensive implications for clinical practice and healthcare systems and offer a valuable tool for early detection and intervention in cases of heart failure.

Green information technology is in the spotlight for organizations, helping them save money by using information technology (IT) to achieve the highest efficiency and thus reduce environmental impacts. One of the ways that can help organizations planning for deploying green IT is to evaluate green information technology maturity (GITM). Previous studies have referred to various criteria for green IT evaluation, most of which are qualitative criteria that are difficult to measure and evaluate in ambiguous conditions. The main objective of this study is to identify crucial criteria that affect the GITM level and to design a fuzzy inference system to assess the GITM level in any organization. While using a Mamdani Inference system, inputs can be verbal expressions or crisp values, and the output shows the level of maturity of green information technology. Since green IT knowledge is not modeled in previous studies, modeling it in the current study is a valuable step for organizations confused about various factors they should consider for going green. The main system criteria are the conditions of the data center, office environment, work practice, procurement, and corporate citizenship. Due to the generality of the model used for the knowledge base system development, organizations can use this system for the green IT maturity level determination. The presented inference system helps organizations understand their status of being IT green and plan for the following steps to accomplish their desired maturity level. The proposed inference system has been tested, validated, and used to determine the maturity level of Tehran municipality.

Purpose: The purpose of this research was to provide a model for Validating and Presenting Brand-Customer Interaction Model in Digital Platform in the Cosmetics Industry.Method: This research was applied in its purpose and utilized a mixed approach (qualitative and quantitative). In this regard, this study was conducted with the aim of presenting and validating the brand-customer interaction model in Instagram Platform. The present study is a descriptive survey in terms of its practical-developmental purpose and data collection method. The statistical population in the qualitative section includes marketing professors and cosmetics industry managers, 20 of whom were selected by purposive sampling. The statistical population in the quantitative section also includes customers of cosmetics and health products, 384 participants were selected using the convenience sampling. The data collection tools were a semi-structured interview and a researcher-made questionnaire. First, thematic analysis method was used to analyze the expert interviews. Next, the identified pattern was validated using partial least squares method. Thematic analysis and partial least squares were done with MaxQDA software and Smart PLS software, respectively.Findings: The criterion to achieve data saturation has been to achieve repetition in extracting codes. 235 codes were identified in the open coding stage. Finally, three overarching themes, eight organizing themes, and 49 basic themes were obtained through axial coding. Based on the structural equation model, the proposed model was fitted and confirmed.Conclusion: Based on the results, effective marketing and digital content marketing are the basic elements of the model, which increase brand recognition and brand identity among customers by increasing interaction with customers. Brand recognition and identity contribute to positive word-of-mouth marketing, which in turn affects brand positioning on Instagram. Finally, in this way, it is possible to create a constructive and interactive brand-customer relationship.

This study investigates the relationship between the user interface and problem-solving towards the continuous intention to use the services. New products or services will always face tough challenges for the customer, especially when the new procedures require them to learn and change some behaviors. Chatbots are also facing the same situation in Malaysia, where customers refuse to accept using chatbots to represent their physical presence. To understand customer behaviors, a quantitative survey was designed. Four hundred twenty-two data were collected from the online survey method. As per the results, the predictors of chatbot continuous intention are user interface and problem-solving. Apart from that, this study also measures the role of mediator, namely trust and customer satisfaction. This study contributes to unique academic and practical insights that can be used to explore the effectiveness of chatbots. The results revealed that both predictors were significant towards the continuous intentions. Besides, the role of the mediator was found to be significant and relevant in the relationship between trust and customer satisfaction and customer satisfaction and trust towards continuous attention.

Purpose: This research aimed to identify and evaluate the risks of content production in the governmental digital libraries of Tehran.Method: In terms of essence, this research is synthetic (library studies, qualitative and quantitative), and regarding purpose, it is an applied one.  In the first part, using the studies of the research literature, a set of indicators related to the risks of content production in the governmental libraries of Tehran was obtained. The second part of this research involved a fuzzy Delphi approach that was conducted at two stages among 20 experts. In this stage, a researcher-made questionnaire based on the indicators obtained from the first stage was used. The third part of the research was a survey-analysis conducted with a quantitative method using a questionnaire made out based on the results of qualitative stage with three criteria: the probability of occurrence, effect intensity, and the inability of organization to respond and was distributed among 100 managers and experts of governmental digital libraries in Tehran.Findings: In this research, the risks of content production were identified and fell into nine major categories (human force, environmental factors, infrastructure, protection and maintenance,  creators`  technical rights, integration, content evaluation of sources and authors and information security).Conclusion: The results of this evaluation show that digital libraries are not exactly at their best level when it comes to their responses to the risk of human resources, authors' rights, and integration risks.

Purpose: The purpose of this research was to provide a model for predicting time series of financial information based on the Lyapunov representation of information using chaos theory.Method: This research is applied in its purpose, which is conducted using a quantitative approach. The research ranks as descriptive-causal accounting research based on actual information in companies' financial statements. The research method is the "post-event" type and was carried out using chaos theory and Saida's method based on the Lyapunov view.Findings: The findings showed that during the ADF test, the null hypothesis was rejected at a level of less than 5% type 1 error and 95% confidence, and it shows that the data is not static. During the substitution analysis test and its significance level, the behavior of the time series of the main financial information is significantly different compared to their substitutes. The obtained value was calculated to describe the production process of all data sets for μ = 2, ApEnMax equal to 0.65 and rMax equal to 0.32, and for μ = 3, ApEnMax equal to 0.6 and rMax equal to 0.44. The value of the Lyapunov profile in stability at a certain point is less than zero and in the limited cycle of stability is equal to zero and in chaos, it is greater than zero and smaller than ∞, and in noise it is equal to ∞.Conclusion: The results show that higher returns, encourage investors to invest and increase the flow of capital. It is believed that companies' stock returns are a function of systematic risk, and systematic risk represents the changes in the return rate of a share compared to the changes in the return rate of the entire stock market.

Information technology (IT) governance has become a crucial area of study because of the growing corpus of research in the area. This study aims to conduct both a literature review and bibliometric analysis focusing on three specific research questions: (RQ1) examining the trends in publications and citations, (RQ2) recognizing the most impactful countries, journals, and authors, and (RQ3) investigating popular research areas and trending topics in IT governance. Data surrounding IT governance research were obtained through the Scopus database, with a total of 1,510 documents published from 1995 to February 2023. The extracted documents were analysed using Harzing’s Publish or Perish, VOSviewer, and Bibliometrics sub-tool Biblioshiny software. Publications have continued to rise since 2004 when the United States has had the greatest influence over IT governance publication and research collaboration, while Indonesia is the most prolific. Primary sources on IT governance research were taken from conference proceedings related to IT topics. As for authors, Pereira R from Portugal is at the top of the list while Van Grembergen W from Belgium is the most influential. This study is the first to identify and propose themes and current hotspots in IT governance research which serves as a guide for future researchers and professionals.

Purpose: The purpose of this research was to provide a model for dynamic knowledge management in Iranian government organizations.Method: This research is applied in terms of purpose, survey-exploratory in terms of approach, and qualitative in terms of type. The statistical population of this research was a group of experts, including university professors, and senior managers of the Zanjan Province Industry, Mining and Trade Organization, and based on theoretical sampling, 11 of them were selected as a statistical sample and were interviewed. The approach adopted in this research to analyze the interviews was theme analysis.Findings: The results of the research led to the presentation of a new model in the field of dynamic knowledge management in Iranian government organizations, including five main variables (including: promotional levers of dynamic knowledge management, intra-organizational factors of dynamic knowledge management, extra-organizational factors of dynamic knowledge management, executive levers of dynamic knowledge management, and expected consequences of dynamic knowledge management).Conclusion: The results of this research showed that if dynamic knowledge management is implemented well in Iran's government organizations, it can be expected that organizational productivity and agility and the satisfaction of stakeholders will improve.

This research delves into the socio-economic aspects of online gambling in Iran, exploring the underlying cultural drivers that have contributed to its rapid growth in recent years. Online gambling's prevalence is intricately linked to cultural norms and values, both overt and covert, which have shaped its emergence in the country. Despite the religious, legal, and cultural taboos associated with gambling in Iran, this study investigates the cultural motifs that have challenged these obstacles, particularly among the youth. To unravel the psychological mechanisms at play among young online gamblers, this study adopts a qualitative approach, employing in-depth interviews with 17 male online gamblers aged between 20 and 40, alongside participatory observations. The data is analyzed thematically, with a focus on cultural motifs that reinforce mental accounting biases, drawing from the principles of behavioral economics. Notably, the central theme that emerges from this analysis is the "psychology of success", a worldview that influences the mindset and actions of individuals engaged in online gambling. The findings of this study reveal a significant thematic alignment between the teachings of the psychology of success and the cultural elements that bolster cognitive biases in the speech and conduct of online gamblers. The psychology of success, which emphasizes a sense of agency and empowerment, appears to drive cultural change and contributes to the widespread acceptance of online gambling despite its illegitimate status. Furthermore, the study highlights a connection between the prevalence of online gambling and the devaluation of the national currency.

Getting highly accurate output in biomedical data processing concerning biomedical signals and images is impossible because biomedical data are generated from various electronic and electrical resources that can deliver the data with noise. Filtering is widely used for signal and image processing applications in medical, multimedia, communications, biomedical electronics, and computer vision. The biggest problem in biomedical signal and image processing is developing a perfect filter for the system. Digital filters are more advanced in precision and stability than analog filters. Digital filters are getting more attention due to the increasing advancements in digital technologies. Hence, most medical image and signal processing techniques use digital filters for preprocessing tasks. This paper briefly explains various filters used in medical image and signal processing. Matlab is a famous mathematical, analytical software with a platform and built-in tools to design filters and experiment with different inputs. Even though this paper implements filters like, Mean, Median, Weighted Average, Guassian, and Bilateral in Python to verify their performance, a suitable filter can be selected for biomedical applications by comparing their performance.