Un modelo para la implementación de soluciones empresariales inteligentes con base en el nivel de madurez en inteligencia de negocios: una experiencia iraní

The purpose of this research is to provide a basis for choosing the approach to implementing business intelligence systems based on the maturity of the organization. This research not only recommends considering the level of business intelligence maturity while planning for the implementation, but also recommends the use of IT governance levers in order to increase the level of efficient usage of such systems. In this research, while analyzing the subject by experts in the Delphi Fuzzy method, these ideas have been reviewed and verified in nearly 109 organizations responding to the questionnaires. The result of this research is a fuzzy inference engine that suggest the best execution package required, by entering data on the size and maturity of the organization. Packages containing the appropriate implementation methodology, products at that maturity level and, consequently, IT governance processes and requirements to that level of maturity.El propósito de esta investigación es proporcionar una base para elegir el enfoque para implementar sistemas de inteligencia de negocios basados en la madurez de la organización.Esta investigación no solo recomienda considerar el nivel de madurez de la inteligencia de negocios a momento de planear la implementación, sino también el uso de instrumentos de política pública de tecnologías de la información (TI) para aumentar el nivel del uso eficiente de dichos sistemas. En esta investigación, al analizar el tema por expertos en el método Delphi Fuzzy, estas ideas han sido revisadas y verificadas en casi 109 organizaciones que respondieron a los cuestionarios. El resultado de esta investigación es un motor de inferencia difusa que sugiere el mejor paquete de ejecución requerido, al ingresar datos sobre el tamaño y la madurez de la organización. Los paquetes que contienen la metodología de implementación adecuada, los productos en ese nivel de madurez y, en consecuencia, los procesos y requisitos de gobierno de TI a ese nivel de madurez

Drivers Affecting Bitcoin Adoption as a Payment Mechanism in the Tourism Industry

The authors received no financial support for the research, authorship and, or publication of this article.While travelers' desire to visit the world's most remote places has grown, the inefficiency of global payments indicates a significant barrier to tourism growth. As an emerging, decentralized, and borderless digital innovation, Bitcoin technology seems to have the ability to serve as a payment alternative and address such fundamental inefficiencies. On the other hand, bitcoin adoption can only happen when tourists and business owners choose to operate bitcoin simultaneously. The study has developed a novel Bitcoin Collaborative Network and Tourism Collaborative Network model to examine Bitcoin adoption factors. Then a fuzzy DEMATEL method was applied to the factors influencing the adoption domain, as identified based on an extensive literature review, in-depth interviews, and an international Delphi process. The study offered a model for the heterogeneous collaborative network of Bitcoin and Tourism (BCN and TCN), revealing that Perceived Usefulness is the most influencing criterion and the most prominent variable in Bitcoin Adoption. Bitcoin Technological Complexity, Government Regulatory, and Bitcoin Awareness are the factors that give the highest impacts. Also, Bitcoin's Technological Complexity is the most significant factor in bitcoin adoption. The findings might assist businesses in adopting a new market expansion strategy and benefiting from technological spillover, while government officials can explore new supporting legislation.publishersversionpublishe

Governmental origin: why NTBFs grow in a transitional economy

The NTBFs have attracted growing interest from most of the transitional economies as they are seen as an important source of greater value added creation while being characterised with higher rate of return on capital. Shedding light on the growth determinants of new technology-based firms not only helps managers to accomplish organisational goals but also assists policymakers in devising effective strategies. The role of individual, organisational as well as environmental factors in the development of the new technology-based firms has been separately addressed by many researchers. The simultaneity of these factors leads to diverse configurations, each of which envisaging different growth paths for the firm. The aim of this paper is to identify the growth paths for the new technology-based firms. To this end, Some interviews were conducted with the managers of the developed new technology-based firms in Iran (as a transitional economy) and the key themes governing the growth pattern of this group of firms have been identified using the thematic analysis, while possible growth paths for these firms were established by means of qualitative comparative analysis. The designed questionnaires were distributed among 22 developed firms and 8 underdeveloped firms for the period 2013–2015 and the obtained data were analysed using the FSQCA software, which led us to the development of dominant growth path for new technology-based firms. Based on the findings of this paper and factors affecting the growth of firms, two growth paths are suggested for the new technology-based firms, of which the one with greater role for government is more likely to take place. Communication with government officials and lobbying groups in the field of science and technology as the key customer in transitional economies is critical to the corporate growth, which has been identified as a sufficient condition for this research

A comparison of artificial intelligence algorithms in diagnosing and predicting gastric cancer: a review study

Today, artificial intelligence is considered a powerful tool that can help physicians identify and diagnose and predict diseases. Gastric cancer has been the fourth most common malignancy and the second leading cause of cancer mortality in the world. Thus, timely diagnosis of this type of cancer could effectively control it. This paper compares AI (artificial intelligence) algorithms in diagnosing and predicting gastric cancer based on types of AI algorithms, sample size, accuracy, sensitivity, and specificity.  This narrative-review paper aims to explore AI algorithms in diagnosing and predicting gastric cancer. To achieve this goal, we reviewed English articles published between 2011 and 2021 in PubMed and Science direct databases. According to the reviews conducted on the published papers, the endoscopic method has been the most used method to collect and incorporate samples into designed models. Also, the SVM (support vector machine), convolutional neural network (CNN), and deep-type CNN have been used the most; therefore, we propose the usage of these algorithms in medical subjects, especially in gastric cancer

Effective factors in dealing with industrial crises caused by widespread virus outbreaks

Background: The global spread of the COVID-19 virus has not only posed a severe threat to public health but has also triggered a profound economic crisis affecting numerous industries. Addressing and mitigating the economic repercussions of the pandemic is imperative to prevent further damage. While prior research has explored the negative impacts of the COVID-19 pandemic on various industries, there has been a notable gap in understanding the specific factors that influence how industrial crises stemming from viral outbreaks are managed. Methods: This article undertakes a comprehensive investigation into these influential factors. Through in-depth interviews with industry experts, we identified a set of 30 pivotal variables in this context, forming the basis of an initial model. Subsequently, a questionnaire was administered to a cohort of one hundred managers and industry experts to assess these variables. Employing exploratory factor analysis, we categorized the 30 variables into six distinct categories: producer-related factors, supplier-related factors, distributor-related factors, retailer-related factors, consumer-related factors, and government-related factors. Results: Our findings revealed several strategic considerations for effectively addressing industrial crises in the face of viral outbreaks. These include the importance of building trust with customers in emerging markets, streamlining the adoption of digital technologies by customers, enhancing the customer relationship management process, prioritizing awareness, concern, and environmental consciousness, and providing support to consumers during times of viral spread. Conclusion: To effectively navigate industrial crises triggered by the widespread dissemination of viruses like COVID-19, businesses and governments should prioritize strategies that align with the identified factors. By focusing on these key areas, industries can bolster their resilience and minimize the adverse effects of such crises, ultimately contributing to economic stability and recovery in the face of global health emergencies

Predicting students' performance using machine learning algorithms and educational data mining (a case study of Shahed University)

The purpose of this research is to investigate the effective factors in predicting the academic performance of undergraduate students in the classification of four classes. To achieve this goal, the study follows the CRISP data mining method. The data set was extracted from the NAD educational system for the bachelor's degree in Shahed University for the entry of the years 2011 to 2021. 1468 records were used in data mining. First, the effective features on students' academic performance were extracted. Modeling was done using Rapidminer9.9 tool. To improve classification performance and satisfactory prediction accuracy, we use a combination of principal component analysis combined with machine learning algorithms and feature selection techniques and optimization algorithms. The performance of the prediction models is verified using 10-fold cross-validation. The results showed that the decision tree algorithm is the best algorithm in predicting students' performance with an accuracy of 84.71%. This algorithm correctly predicted the graduation of 77.88% of excellent students, 85.26% of good students, 84.69% of medium students, and 85.96% of weak students based on the final GPA

Developing the LARG-Effective Supply Chain Model Using a System Dynamics Approach

Nowdays, supply chain specialists are looking for the integrated development of the supply chain model in order to increase the competitiveness, effectiveness and reduce the problems in the supply chain. They always seek to identify and develop this process so that they can cover more aspects of the chain. Adding effectiveness indicators along with LARG indicators and using the basics of the dynamic system to improve the efficiency of the supply chain is one of the innovations of this study. At first, by using research literature and studies, 12 headings of indicators were selected as LARG-E indicators. Then, with the Fuzzy Delphi method, the relationships and importance of each of these components were determined, and more important variables were modeled for further investigation. With using the concepts of dynamic systems, causal loops were drawn. Then, to check the function of the model, dynamic hypotheses were developed with the opinion of experts. In the next step, the flow diagram of the model and also the validation tests of the proposed model were done. Finally, by examining the outputs obtained from the proposed scenarios, it was found that most of variables have better behavior in LARG-E approach.IntroductionIn recent years, with the addition of various competitions in the world markets, many researches have been conducted to use new technologies and researches in order to improve the production process and increase the effectiveness of these competitions as much as possible (Mohghar et al., 2017). All the goals that work in this field increase the competitiveness of the organization. This competition is by reducing costs, being present in the market and satisfying the customer. To increase profits, protect the environment, keep the markets stable and meet the expectations of customers, organizations should be provided using the existing environments in a set of customers (Pisha et al., 2016). Use chain management requires the use of new facilities and improvements to previous findings such as lean, agile, resilience and green to increase speed and competitiveness, selection and decision-making to achieve the organization and maximum effectiveness.Today, supply chain specialists are looking for the integrated development of the supply chain model to increase the effectiveness and efficiency of the supply chain in order to increase competitiveness and reduce supply chain problems. In this case, there is a consensus among experts that there is no comprehensive model. All the mentioned cases make it inevitable to design a comprehensive and effective model for the supply chain. The verifiable issue is the conflicts and the non-alignment of all the indicators of the paradigms with each other. LARG paradigms, without considering the spirit of effectiveness in each supply chain, cannot fully protect it against continuous changes in the competitive market arena. A comprehensive model that pays particular attention to effectiveness while implementing LARG paradigms has not been examined in the literature review and the consensus of experts. Therefore, in this research, we are looking to design a comprehensive model in a LARG-effective manner so that the effect of various LARG-effective indicators on the performance of the supply chain can be investigated. The integration of LARG paradigms has been studied a lot so far, but its development is based on the concepts of innovation effectiveness of this research, and in this way, the dynamic system approach was used.Materials and MethodsTo formulate a LARG supply chain, first the framework, indicators and variables of each LARG paradigm were extracted from the research literature, then in order to develop them with effective concepts, the effective supply chain was studied. In order to implement the fuzzy Delphi approach, based on the effectiveness indicators extracted from the subject literature and LARG supply chain approaches, operational indicators were provided to the experts participating in the research in the form of a questionnaire via email and after initial coordination. After collecting the completed questionnaires, fuzzification operations, fuzzy averaging and then de-fuzzification were performed. The results were brought to the attention of the participants and they were asked to apply their desired changes according to the obtained results. This approach reached the saturation stage in the third round and there was no change in the opinions of the participants and the consensus of the panel experts was the final and trusted output of the Delphi method. Finally, according to these weights, 9 quantitative variables had the highest importance and were used for dynamic modeling. The simulation stage is done with the help of software and Nasim. According to the features of modeling based on system dynamics, this approach was chosen as the main research tool in this study because there are linear relationships between the variables and there are nested feedbacks between the variables of the subsystems, the importance of simultaneously improving the performance in different layers of the producer, supplier and distributor. Which is one of the goals of this research, with this approach, it can be a very suitable tool for decision-making by the senior managers of the organization.Discussion and ResultsOrganizations are trying to improve their competitiveness by adopting Lean, Resilient, Green and Agile strategies; But as it was said, the implementation of these paradigms, which sometimes have conflicting results, requires a new integration and index to align the goals. So far, many researches have been done by merging two or more paradigms, the combination of all 4 paradigms called LARG has greatly helped to improve the performance of supply chains, but in this research, in order to improve the conflicts between paradigms, a new concept of spiritual effectiveness was given to the supply chain. Understanding the dynamics of applying the above four strategies and their effectiveness was done using the dynamic systems approach. In this research, the indicators of the LARG supply chain were defined based on theoretical foundations and interviews with experts; then the effectiveness indicators were placed next to them. These indicators were implemented in the printing and ink industry. In this way, an effective LARG integrated system was defined; then, using a dynamic model, dynamic hypotheses were first defined and state and flow diagrams were drawn. After correctness of the model and validation of the model, two scenarios were examined for 8 important variables. After applying the scenarios, the performance of LARG and effective LARG was compared. By applying each scenario in the designed model, it was possible to check the effect of new indicators on the variables and their behavior.ConclusionsAs a result, if the components of the effective supply chain are properly integrated with the LARG concepts, they integrate the conflict that may exist between the LARG paradigms and play the role of synchronization and improvement as a ruler and standard. The effective management of the LARG supply chain may not be defined as an independent variable, but it is a result of variables and indicators that improved performance in most cases

Comparing the Efficacy of Surfactant Administration by Laryngeal Mask Airway and Endotracheal Intubation in Neonatal Respiratory Distress Syndrome

Objectives: This study aimed to compare the effcacy of surfactant administration by laryngeal mask airway (LMA) and endotracheal intubation in the management of respiratory distress syndrome (RDS) in preterm infants. Materials and Methods: In a prospective interventional study in NICU at Al-Zahra hospital, 50 premature infants with gestational age of 33-37 weeks and birth weight of 1800 g or more who needed surfactant replacement therapy for RDS were randomly allocated to 2 groups. Twenty-fve neonates in ETT group received surfactant by endotracheal intubation and the LMA were used for the administration of surfactant in 25 neonates (LMA group). Results: The mean gestational age in LMA group was 32.88±1.32 and it was 33.76±2.12 weeks in ETT group (P=0.15). The mean RDS score was not statistically different 2 two groups, 7.68±0.80 vs. 7.24±1.17 (P=0.79). Mechanical ventilation was needed for 1 neonate in the LMA group and 3 infants in the ETT group (P=0.16). After surfactant administration, the mean FiO 2 requirements to maintain oxygen saturation between% 88 to 92% showed a statistically signifcant decrease in both groups. The needed FiO2s were 0.60±0.12 and 0.57±0.12 before surfactant therapy and decreased to 0.42±0.15 and 0.36±0.10 after surfactant administration in LMA and ETT groups, respectively (P<0.001). No choking or vomiting occurred during surfactant therapy in either group. Conclusions: Based on our fndings, the LMA may be a safe and effective alternative way for surfactant administration in late preterm infants. Future multicenter studies are recommended for determining safety and effcacy of LMA in preterm infants

Systematic Review Focusing on Financial Technology Machine Learning and Customer Experience and Providing Framework for Future Research

Technological innovation in the financial industry created the financial technology ecosystem. With the advent of artificial intelligence, the technology and financial worlds are intertwined to allow smarter financial processes to enable managers to make smarter decisions. It is not a fixed method of using the machine and accurate prediction of the test results using the machine algorithms is challenging. Much research has been done on the specific management of the customer experience, but research on financial technology in the artificial intelligence and machine industry in the sense of constructing a theory that can create a customer experience is a subject that pays less attention to. . This article, by reviewing 75 articles and summarizing it in 41 research articles, has examined the subject of the present study. In order to predict the presentation of theory, research method is a fundamental theory. The purpose of this article is to cover the gap of studies through which a research path is studied and the field of financial technology and artificial intelligence is examined. Findings show that what is done in extraordinary networks can be divided into five main parts of innovation. The findings provide a good way to address some of the issues in financial and artificial technology research for knowledge management experience through the possibility of providing a customer performance model

Research & Development of Digital Marketing and Innovation in Commercial Automotive Industry

Automotive industry particularly, commercial automotive industry, ranks as a key industry in the economic growth. The necessity of investigating the research & development(R & D) activities of digital marketing and innovation in the form of a dynamic system in automotive industry based on the 3 variables: empowerment of supply network, of product innovation, and of digital marketing is quite undisputed. The present research has been done with a view to identifying and evaluating the cause-and-effect interdependent relations governing the variables of R & D of digital marketing and innovation in commercial automotive industry. The research is typically applied, and has been done using the descriptive-survey method. The research community consisted of 50 experts; all with acceptable academic backgrounds and years of experience as executive managers and marketeers in the R & D of automotive industry. To analyze the data, the views of some elected experts on automotive industry, along with Delphi fuzzy and Dematel method were applied. Our findings showed that the variable “Intensity of R & D of digital marketing and innovation” has the most effect on the other variables. The variable “Empowerment of supply network” with the score of 3,25 has the largest amount of interaction with the other variables. Also, the variable “ Empowerment of R & D in digital marketing and innovation” with the score of 1,08 has the smallest amount of interaction with the other variables