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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.

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 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 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: The key to startup success is a brand-centric approach that combines brand leadership strategy with creative, targeted, and cost-effective business methods. In the brand-centered approach, all company processes revolve around the development of brand identity in continuous interaction with customers, to create long-term competitive advantages for the brand. The purpose of this research is to investigate the impact of entrepreneurship-based branding in cyberspace on the performance of startups.Method: The current research is of an applied type and with a mixed method and a sequential qualitative-quantitative approach, first qualitative and then quantitative data was collected and analyzed. Due to the small size of the population, sampling was not done and all 52 people were examined. Smart PLS software was used to analyze quantitative data and test research hypotheses and implement structural equation modeling. Confirmatory factor analysis was also used to test the significance of the relationships and the fit of the obtained measurement models. To analyze the data and test the research hypotheses, the structural equation modeling method was applied.Findings: Qualitative findings showed that the branding model based on entrepreneurship in cyberspace includes six main themes and 18 sub-themes. Causal factors include risk-taking categories, business situation analysis, and brand content capabilities; Background factors include the categories of brand organizational structure, market orientation, and brand orientation; intervening conditions include the categories of legal infrastructure, customer characteristics, and economic uncertainty; the central category includes the three categories of brand vision depiction, brand integrity and entrepreneurial capabilities; strategies including three categories of brand positioning, strategic planning for branding and value creation; And the consequences include three categories of competitive advantage, strategic brand development and brand identity development. Quantitative findings showed that the causal conditions of branding with a score of 26.719 has a positive and significant effect on entrepreneurial branding; Entrepreneurial branding with a score of 4.873 on value-based brand positioning; And value-based positioning with a score of 29.960 has a positive and significant effect on the performance of startups.Conclusion: The results indicate that the branding model based on entrepreneurship in cyberspace and its impact on the performance of startups includes six main themes and 18 sub-themes, and branding based on entrepreneurship in cyberspace has a positive and significant effect on the performance of startups.

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.

Purpose: The purpose of this study was to prioritize the factors affecting consumer-buying behavior in online social media marketing.Method: The analysis and validation of indicators were conducted using the mixed method of structural equation modeling and partial least squares. Additionally, the IPMA matrix was utilized to determine the importance and type of performance of each factor. The research data were influenced by consumer information, predominantly consisting of women with a master's degree aged between 36 and 50 years. The study employed a non-probability sampling method common for online surveys, and 466 individuals were examined using Cohen's effect size formula.Findings: The research findings indicated that consumer engagement was the most important factor, and ease of use exhibited the highest level of performance in the overall social media model. Furthermore, consumer engagement was identified as the most important factor, while electronic word-of-mouth (EWOM) demonstrated the highest performance level on Instagram. Conversely, EWOM was deemed the most important factor, with consumer engagement displaying the highest performance level on Telegram. These results can be utilized by marketers to influence consumer purchasing behavior and craft online marketing strategies accordingly.Conclusion: Companies utilizing online social media platforms are advised to enhance consumer engagement and performance by implementing short-term techniques and effective strategies, such as incorporating online chat functionalities in the user environment. Instagram, being the most widely used social media platform and an online shopping hub for consumers, showcased acceptable performance in terms of consumer engagement. Hence, companies need to take measures to elevate performance levels to match their importance. In the case of Telegram, EWOM emerged as the most crucial factor among others, with a commendable performance rating. Businesses can bolster their Customer Relationship Management (CRM) units and enhance WOM initiatives to the fullest extent possible.

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.

This work serves to propose a national electronic health system based on the Big Data approach. First of all, we assessed the practice of health information systems (HIS) in Morocco and their obstacles. We performed a survey that was founded on 24 questions to specify the necessary details on this topic. This study shows that there is a primary need for the establishment of an HIS that facilitates the control, analysis, and management of health data in Morocco. For this reason, we have proposed the implementation of the Moroccan Health Data Bank (MHDB). This system will be based on powerful big data technologies that save, manage, and process health data with greater efficiency. The information present in this proposed system can provide the necessary resources for several actors to exploit this wealth, which is embodied in this massive data. We have developed a general description of the MHDB, its components, its conceptual architecture, and an example of a use case.

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.

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.

Purpose: The field of providing information and the method of publishing information has permanently been subject to modification and alteration, and as we know, with the introduction of computer technology in the field of librarianship and the change in the way of providing services, the imperative task of information dissemination has been reserved for librarians. The dissemination of information technology into the education system has led to the emergence of a new concept called e-learning. The task of libraries is to provide infrastructures for information retrieval through the production of integration, organization, and distribution of knowledge. The methods that libraries employ for e-learning include learning collections, tools, and facilities necessary for studying, improving the quality of reference services, and ensuring universal access to books. Therefore, this research aims to identify the factors influencing e-learning in information retrieval.Method: This current research is developmental was conducted using a qualitative approach and grounded theory. The population of the study consisted of 20 experts in the field of information technology in public libraries across the country, who were selected through purposive non-probability sampling. Data were collected through interviews and analyzed through three stages of open, axial, and selective coding, based on which the research model was designed.Findings: The findings showed that e-learning in information retrieval contains of six main dimensions, 45 sub-dimensions, and 85 concepts, which are presented in a paradigm model including causal factors (quality of e-learning website design, quality of education in e-learning environments, employees' enthusiasm for developing knowledge capabilities, employees' belief in continuous training), contextual factors (suitable technical equipment (hardware, software, and network), availability of necessary infrastructure, high accessibility, periodic purchase of required equipment, ease of learning and simplicity of teaching platforms (instructors)), interveners (competence of instructors in course delivery, course development based on indigenous data, organizational culture and atmosphere), central factors (strategies for enhancing the quality of e-learning in information retrieval, processes for improving the quality of e-learning in information retrieval, infrastructures for improving the quality of e-learning in information retrieval), strategic factors (existence of an appropriate incentive system for promoting knowledge sharing (systemic strategy), security of software used (educational approach), efficient support and feedback mechanisms (responsiveness strategy)), and consequences (consistency of goals and plans, adequate understanding of e-learning, formulation of standards, capability in information retrieval).Conclusion: Based on the dimensions, main factors, and sub-factors, the impacts of e-learning in information retrieval can be measured and managed in the studied community.

In today’s world, there is a notable focus on incorporating disabled people within the community and the workplace. Advancements in artificial intelligence (AI) have the potential to significantly impact empowering individuals with disabilities to enhance inclusivity and autonomy for this demographic in the era of Society 5.0. This paper aims to offer a bibliometric analysis of the increasing number of publications addressing the potential impact of AI on disabled individuals and their future employment. To conduct this analysis, Scopus served as the bibliographic source, using Disability, Employment, and Future Workforce as the search terms, yielding 203 publications on the subject of study from 1973 to 2024. The analysis was conducted using VOS viewer. The results indicate that as Society 5.0 evolves, advancements in AI have the potential to significantly empower individuals with disabilities, enhancing inclusivity and autonomy for this demographic.

In healthcare, a diagnosis is reached after a thorough physical assessment and analysis of the patient's medicinal history, as well as the utilization of appropriate diagnostic tests and procedures. 1.7 million People worldwide lose their lives every year due to complications from chronic kidney disease (CKD). Despite the availability of other diagnostic approaches, this investigation relies on machine learning because of its superior accuracy. Patients with chronic kidney disease (CKD) who experience health complications like high blood pressure, anemia, mineral-bone disorder, poor nutrition, acid abnormalities, and neurological-complications may benefit from timely and exact recognition of the disease's levels so that they can begin treatment with the most effective medications as soon as possible. Several works have been investigated on the early recognition of CKD utilizing machine-learning (ML) strategies. The accuracy of stage anticipations was not their primary concern. Both binary and multiclass classification methods have been used for stage anticipation in this investigation. Random-Forest (RF), Support-Vector-Machine (SVM), and Decision-Tree (DT) are the prediction models employed. Feature-selection has been carried out through scrutiny of variation and recursive feature elimination utilizing cross-validation (CV). 10-flod CV was utilized to assess the models. Experiments showed that RF utilizing recursive feature removal with CV outperformed SVM and DT.