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حوزه‌های تخصصی
ادبیات اقتصاد تاریخ تربیت بدنی جغرافیا حقوق روانشناسی زبان شناسی علم اطلاعات و دانش‌شناسی علوم اجتماعی علوم اسلامی علوم انسانی علوم تربیتی علوم سیاسی فلسفه و منطق مدیریت مطالعات زنان هنر و معماری
اخبار مدیریت تئوری های مدیریت گروههای ویژه مدیریت آموزشی مدیریت استراتژیک مدیریت بازرگانی مدیریت دانش و IT مدیریت دولتی مدیریت سازمانی و منابع انسانی مدیریت صنعتی مدیریت مالی – حسابداری نقد و بررسی کتاب
پیش بینی، اجرا و ارزیابی تکنولوژی تکنولوژی و توسعه حقوق مالکیت سرمایه گذاریهای خارجی و انتقال تکنولوژی سیستم های پشتیبانی تصمیم گیری سیستمهای خبره کلیات مدلهای انتقال تکنولوژی مدیریت دانش مدیریت فناوری اطلاعات منطق فازی و هوش مصنوعی
ترتیب بر اساس: جدیدترینپربازدید‌ترین
فیلتر های جستجو: فیلتری انتخاب نشده است.
نمایش ۱ تا ۲۰ مورد از کل ۲٬۷۹۴ مورد.
۱.

An Intelligent Heart Disease Prediction by Machine Learning Using Optimization Algorithm(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۶, Issue ۱, ۲۰۲۴ 167 - 181

کلید واژه ها: Optimization algorithm Cardiovascular disease Prediction Gradient Descent Machine Learning Neural Networks deep learning

حوزه های تخصصی:
تعداد بازدید : ۱۲ تعداد دانلود : ۱۳
Heart and circulatory system diseases are often referred to as cardiovascular disease (CVD). The health and efficiency of the heart are crucial to human survival. CVD has become a primary cause of demise in recent years. According to data provided by the World-Health-Organization (WHO), CVD were conscientious for the deaths of 18.6M people in 2017. Biomedical care, healthcare, and disease prediction are just few of the fields making use of cutting-edge skills like machine learning (ML) and deep learning (DL). Utilizing the CVD dataset from the UCI Machine-Repository, this article aims to improve the accuracy of cardiac disease diagnosis. Improved precision and sensitivity in diagnosing heart disease by the use of an optimization algorithm is possible. Optimization is the process of evaluating a number of potential answers to a problem and selecting the best one. Support-Machine-Vector (SVM), K-Nearest-Neighbor (KNN), Naïve-Bayes (NB), Artificial-Neural-Network (ANN), Random-Forest (RF), and Gradient-Descent-Optimization (GDO) are just some of the ML strategies that have been utilized. Predicting Cardiovascular Disease with Intelligence, the best results may be obtained from the set of considered classification techniques, and this is where the GDO approach comes in. It has been evaluated and found to have an accuracy of 99.62 percent. The sensitivity and specificity were likewise measured at 99.65% and 98.54%, respectively. According to the findings, the proposed unique optimized algorithm has the potential to serve as a useful healthcare examination system for the timely prediction of CVD and for the study of such conditions.
۲.

Brain Tumor Image Prediction from MR Images Using CNN Based Deep Learning Networks(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۶, Issue ۱, ۲۰۲۴ 44 - 60

کلید واژه ها: Brain tumour Magnetic Resonance Images (MRI) deep learning CNN SVM Image reorganization

حوزه های تخصصی:
تعداد بازدید : ۷ تعداد دانلود : ۸
Finding a brain tumor yourself by a human in this day and age by looking through a large quantity of magnetic-resonance-imaging (MRI) images is a procedure that is both exceedingly time consuming and prone to error. It may prevent the patient from receiving the appropriate medical therapy. Again, due to the large number of image datasets involved, completing this work may take a significant amount of time. Because of the striking visual similarity that exists between normal tissue and the cells that comprise brain tumors, the process of segmenting tumour regions can be a challenging endeavor. Therefore, it is absolutely necessary to have a system of automatic tumor detection that is extremely accurate. In this paper, we implement a system for automatically detecting and segmenting brain tumors in 2D MRI scans using a convolutional-neural-network (CNN), classical classifiers, and deep-learning (DL). In order to adequately train the algorithm, we have gathered a broad range of MRI pictures featuring a variety of tumour sizes, locations, forms, and image intensities. This research has been double-checked using the support-vector-machine (SVM) classifier and several different activation approaches (softmax, RMSProp, sigmoid). Since "Python" is a quick and efficient programming language, we use "TensorFlow" and "Keras" to develop our proposed solution. In the course of our work, CNN was able to achieve an accuracy of 99.83%, which is superior to the result that has been attained up until this point. Our CNN-based model will assist medical professionals in accurately detecting brain tumors in MRI scans, which will result in a significant rise in the rate at which patients are treated.
۳.

Efficient NetB3 for Enhanced Lung Cancer Detection: Histopathological Image Study with Augmentation(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۶, Issue ۱, ۲۰۲۴ 98 - 117

کلید واژه ها: Lung cancer Convolutional Neural Network (CNN) Histopathological Images Transfer Learning Lung Cancer Detection

حوزه های تخصصی:
تعداد بازدید : ۶ تعداد دانلود : ۷
Cancer is an abnormal cell growth that occurs uncontrollably within the human body and has the potential to spread to other organs. One of the primary causes of mortality and morbidity for people is cancer, particularly lung cancer. Lung cancer is one of the non-communicable diseases (NCDs), causing 71% of all deaths globally, and is the second most common cancer diagnosed worldwide. The effectiveness of treatment and the survival rate of cancer patients can be significantly increased by early and exact cancer detection. An important factor in specifying the type of cancer is the histopathological diagnosis. In this study, we present a Simple Convolutional Neural Network (CNN) and EfficientNetB3 architecture that is both straightforward and efficient for accurately classifying lung cancer from medical images. EfficientnetB3 emerged as the best-performing classifier, acquiring a trustworthy level of precision, recall, and F1 score, with a remarkable accuracy of 100%, and superior performance demonstrates EfficientnetB3’s better capacity for an accurate lung cancer detection system. Nonetheless, the accuracy ratings of 85% obtained by Simple CNN also demonstrated useful categorization. CNN models had significantly lower accuracy scores than the EfficientnetB3 model, but these determinations indicate how acceptable the classifiers are for lung cancer detection. The novelty of our research is that less work is done on histopathological images. However, the accuracy of the previous work is not very high. In this research, our model outperformed the previous result. The results are advantageous for developing systems that effectively detect lung cancer and provide crucial information about the classifier’s efficiency.
۴.

The Influence of Social Media Marketing Activities on Purchase Intention: A Study of the E-Commerce Industry(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۶, Issue ۱, ۲۰۲۴ 201 - 216

کلید واژه ها: Social media marketing purchase intention brand awareness

حوزه های تخصصی:
تعداد بازدید : ۷ تعداد دانلود : ۶
This paper sought to examine the impact of perceived Social Media Marketing Activities (SMMAs) on customer purchase intention via brand awareness in an online context. An online questionnaire was used to collect data from 188 samples. The data were analyzed using the structural equation modeling approach, and the research hypotheses were examined using SEM. The study measured SMMAs through personalization, customer community, and live video. The results revealed that SMMAs were insignificant towards brand awareness and purchase intention. The result also stated that brand awareness does not mediate the relationship between SMMA and purchase intention. However, brand awareness was found to affect purchase intention positively. The current study introduces the stimulus–organism–response model as a theoretical support to examine SMMAs of e-commerce to customers' purchase intention via brand awareness.
۵.

Comparative Study on Different Machine Learning Algorithms for Neonatal Diabetes Detection(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۶, Issue ۱, ۲۰۲۴ 5 - 26

کلید واژه ها: Voting Classifiers Meta-Classification Technique Diabetes Risk Prediction Biomedical Clinical Risk Factors Random Forest Logistic regression Gradient Boosting Support Vector Machines

حوزه های تخصصی:
تعداد بازدید : ۷ تعداد دانلود : ۷
This paper gives a performance analysis of multiple vote classifiers based on meta-classification methods for estimating the risk of diabetes. The study's dataset includes a number of biological and clinical risk variables that can result in the development of diabetes. In the analysis, classifiers like Random Forest, Logistic Regression, Gradient Boosting, Support Vector Machines, and Artificial Neural Networks were used. In the study, each classifier was trained and evaluated separately, and the outcomes were compared to those attained using meta-classification methods. Some of the meta-classifiers used in the analysis included Majority Voting, Weighted Majority Voting, and Stacking. The effectiveness of each classifier was evaluated using a number of measures, including accuracy, precision, recall, F1-score, and Area under the Curve (AUC). The results show that meta-classification techniques often outperform solo classifiers in terms of prediction precision. Random Forest and Gradient Boosting, two different classifiers, had the highest accuracy, while Logistic Regression performed the worst. The best performing meta-classifier was stacking, which achieved an accuracy of 84.25%. Weighted Majority Voting came in second (83.86%) and Majority Voting came in third (82.95%).
۶.

An Accurate Prediction Framework for Cardiovascular Disease Using Convolutional Neural Networks(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۶, Issue ۱, ۲۰۲۴ 27 - 43

کلید واژه ها: Deep-Learning CNN Heart-Disease Prediction Cardiovascular disease Accuracy

حوزه های تخصصی:
تعداد بازدید : ۹ تعداد دانلود : ۷
Cardiovascular-Diseases (CVD) are a principal cause of death worldwide. According to the World-Health-Organization (WHO), cardiovascular illnesses kill 20 million people annually. Predictions of heart-disease can save lives or take them, depending on how precise they are. The virus has rendered conventional methods of disease anticipation ineffective. Therefore, a unified system for accurate illness prediction is required. The study of disease diagnosis and identification has reached new heights thanks to artificial intelligence. With the right kind of training and testing, deep learning has quickly become one of the most cutting-edge, reliable, and sustaining technologies in the field of medicine. Using the University of California Irvine (UCI) machine-learning (ML) heart disease dataset, we propose a Convolutional-Neural-Network (CNN) for early disease prediction. There are 14 primary characteristics of the dataset that are being analyzed here. Accuracy and confusion matrix are utilized to verify several encouraging outcomes. Irrelevant features in the dataset are eliminated utilizing Isolation Forest, and the data is also standardized to enhance accuracy. Accuracy of 98% was achieved by employing a deep learning technique.
۷.

Breast Cancer Classification through Meta-Learning Ensemble Model based on Deep Neural Networks(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۶, Issue ۱, ۲۰۲۴ 61 - 87

کلید واژه ها: Deep-Learning Meta-Learning EL CNN Breast-Cancer Classification

حوزه های تخصصی:
تعداد بازدید : ۹ تعداد دانلود : ۸
Predicting the development of cancer has always been a serious challenge for scientists and medical professionals. The prompt identification and prognosis of a disease is greatly aided by early-stage detection. Researchers have proposed a number of different strategies for early cancer detection. The purpose of this research is to use meta-learning techniques and several different kinds of convolutional-neural-networks(CNN) to create a model that can accurately and quickly categorize breast cancer(BC). There are many different kinds of breast lesions represented in the Breast Ultrasound Images (BUSI) dataset. It is essential for the early diagnosis and treatment of BC to determine if these tumors are benign or malignant. Several cutting-edge methods were included in this study to create the proposed model. These methods included meta-learning ensemble methodology, transfer-learning, and data-augmentation. With the help of meta-learning, the model will be able to swiftly learn from novel data sets. The feature extraction capability of the model can be improved with the help of pre-trained models through a process called transfer learning. In order to have a larger and more varied dataset, we will use data augmentation techniques to produce new training images. The classification accuracy of the model can be enhanced by using meta-ensemble learning techniques to aggregate the results of several CNNs. Ensemble-learning(EL) will be utilized to aggregate the results of various CNN, and a meta-learning strategy will be applied to optimize the learning process. The evaluation results further demonstrate the model's efficacy and precision. Finally, the suggested model's accuracy, precision, recall, and F1-score will be contrasted to those of conventional methods and other current systems.
۸.

Effectiveness of AI-Driven Knowledge Management System in Improving the Performance of Banking Sector in Jordan(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۶, Issue ۱, ۲۰۲۴ 182 - 200

کلید واژه ها: artificial intelligence (AI) Banking sector Customer Satisfaction Jordan Knowledge Management service quality

حوزه های تخصصی:
تعداد بازدید : ۵ تعداد دانلود : ۵
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.
۹.

The Moroccan Health Data Bank: A Proposal for a National Electronic Health System Based on Big Data(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۶, Issue ۱, ۲۰۲۴ 79 - 97

کلید واژه ها: National Electronic Health System Big Data Health Information System survey Moroccan Health Data Bank MHDB

حوزه های تخصصی:
تعداد بازدید : ۵ تعداد دانلود : ۷
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.
۱۰.

Clinical Healthcare Applications: Efficient Techniques for Heart Failure Prediction Using Novel Ensemble Model(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۶, Issue ۱, ۲۰۲۴ 149 - 166

کلید واژه ها: Machine Learning Heart failure Cardiovascular Diseases Ensemble learning Healthcare

حوزه های تخصصی:
تعداد بازدید : ۵ تعداد دانلود : ۹
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.
۱۱.

Managing Customer Trust and Satisfaction on Chatbots in the Retail Industry(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۶, Issue ۱, ۲۰۲۴ 217 - 236

کلید واژه ها: Chatbots User Interface Problem-Solving Intention

حوزه های تخصصی:
تعداد بازدید : ۸ تعداد دانلود : ۶
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.
۱۲.

Chronic Kidney Disease Risk Prediction Using Machine Learning Techniques(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۶, Issue ۱, ۲۰۲۴ 118 - 134

کلید واژه ها: Machine Learning CKD Prediction SVM RF Data Analysis

حوزه های تخصصی:
تعداد بازدید : ۸ تعداد دانلود : ۱۱
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.
۱۳.

Performance Comparison of Different Digital and Analog Filters Used for Biomedical Signal and Image Processing(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۶, Issue ۱, ۲۰۲۴ 135 - 148

کلید واژه ها: Digital Filters Biomedical Data Signal Processing Medical Image Processing Noise Removal Preprocessing

حوزه های تخصصی:
تعداد بازدید : ۸ تعداد دانلود : ۷
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.
۱۴.

Sustainable Decision-Making Model: Loyalty Points Through Email Communication With Real Option Valuation(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۵, Issue ۲, ۲۰۲۳ 46 - 58

کلید واژه ها: Sustainable Investment Net Present Value Real Option Valuation

حوزه های تخصصی:
تعداد بازدید : ۵۷ تعداد دانلود : ۵۵
Nowadays, many companies cannot see the digital investment that plays a main role in the IR 4.0. Therefore, this study is investigating the study of investment as plays a critical role in an analytical activity to assess the benefits and costs of an investment and can be used as an investment justification. Traditional investment appraisal uses a financial approach where the benefits and costs are quantified in a certain amount of value for money and then compared in value. Moreover, this study is revealed the fruitful outcomes because revealed the investment valuation method with NPV (Net Present Value) and ROV (Real Option Valuation). ROV is an alternative to financial valuation. Seeding from the same philosophy as Financial Option, ROV has advantages in handling the flexibility, risk, and volatility that may occur from an investment. Thus, ROV is considered more able to appreciate an investment that has these characteristics. Investment appraisal with ROV is better able to appreciate investment than traditional financial methods, as shown by ROV's NPV results in the case of marketing with Loyalty points through email communication as a digital investment that are greater than ordinary NPV. This is because ROV can appreciate flexibility in investments that have choices of investment plans in the future
۱۵.

Predicting Court Judgment in Criminal Cases by Text Mining Techniques(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۵, Issue ۲, ۲۰۲۳ 204 - 222

کلید واژه ها: Legal Judgment Prediction text mining Sentiment Analysis Emotions Analysis Machine Learning

حوزه های تخصصی:
تعداد بازدید : ۹۰ تعداد دانلود : ۷۳
What is clear is that judges usually judge cases based on their knowledge, experience, personality, and sentiment. Due to high pressures and stress, it may be difficult for them to carefully examine documents and evidence, which leads to more subjective judgments. Legal judgment prediction with artificial intelligence algorithms can benefit judicial bodies, legal experts, and litigants as well as judges. In this research, we are looking at predicting legal sentences in drug cases involving the purchase, possession, concealment, or transportation of illicit drugs, using machine learning methods, and the effect of sentiment and emotions in case texts on predicting the severity of whipping, fines, and imprisonment. So, the text documents of 6000 Persian drug-related cases were pre-processed and then the translation of the NRC Glossary of Emotions and sentiment was used to give each item a score for positive or negative sentiment and a score for emotion. Then machine learning methods were used for modeling. BERT, TFIDF+Adaboost, and Skipgram+LSTM+CNN methods had the highest accuracy, respectively. Also, evaluation criteria were analyzed in situations where sentiment scores, emotional scores, or both were used in the prediction process along with judicial texts. Finally, it was found that the use of sentiment and emotion scores improves the accuracy of legal judgment predictions for all three types of sentences and that sentiments have a greater impact on the accuracy of legal judgment predictions than emotions
۱۶.

Application of Grouped MCDM Technique for Ranking and Selection of Laptops in the Current Scenario of COVID-19(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۵, Issue ۳, ۲۰۲۳ 1 - 16

کلید واژه ها: MCDM AHP BWM TOPSIS Laptop Selection

حوزه های تخصصی:
تعداد بازدید : ۵۰ تعداد دانلود : ۴۸
  In the modern technological age, laptops are widely used for doing various day-to-day activities and getting updates all around us. The COVID-19 situation is playing a vital role in a dynamic shift in buyer behavior with multiple personal computing devices at home. Prioritizing and selecting appropriate laptop devices is difficult because there are several options of laptops that are available in the market, and these are equipped with the latest features to do gaming, designing, attending online classes, and performing office and other everyday tasks. There are multiple selection criteria that are complex in nature. MCDM (Multiple Criteria Decision Making) approaches can handle and analyze these complicated criteria. By using MCDM techniques, decision-making can be done to select the top-ranked alternative from among the available alternatives. This paper exhibits a group of two MCDM techniques; Best Worst Method (BWM) and Analytical Hierarchy Process (AHP), which have been used to evaluate relative weights of considered conflicting criteria such as brand, price, storage capacity, RAM, processor, weight, touch screen, Bluetooth, and screen size, and these weights are used in the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for ranking and selecting the best product of laptops.
۱۷.

رابطه مهارت های مدیریت کوانتومی ادراک شده با اینرسی سازمانی: نقش میانجی سرمایه فکری و یادگیری سازمانی (مورد مطالعه: کارکنان دانشگاه کاشان)(مقاله علمی وزارت علوم)

نویسنده:
منبع: مدیریت دانش سازمانی سال ششم پاییز ۱۴۰۲ شماره ۲۲ 123 - 148

کلید واژه ها: مهارت های مدیریت کوانتومی اینرسی سازمانی سرمایه فکری یادگیری سازمانی

حوزه های تخصصی:
تعداد بازدید : ۸۷ تعداد دانلود : ۶۳
تحقق رشد و توسعه پایدار دانشگاه ها و موسسات آموزش عالی می تواند به کمک مهارت های مدیریت کوانتومی، یادگیری سازمانی و سرمایه فکری در دانشگاه ها ایجاد گردد. زیرا دانشگاه ها، مکان هایی برای تولید، سازماندهی و انتقال دانش اند که در این فرایند نقش کارکنان به عنوان سرمایه های فکری و انسانی، مشهود و حیاتی است. بر همین اساس، هدف پژوهش حاضر، بررسی تاثیر مهارت های مدیریت کوانتومی ادراک شده بر کاهش اینرسی سازمانی با نقش میانجی سرمایه فکری و یادگیری سازمانی بود. نوع پژوهش، توصیفی- همبستگی و جامعه آماری شامل کارکنان دانشگاه کاشان بود که از میان آن ها و از طریق جدول مورگان 150 نفر به عنوان نمونه انتخاب شدند. ابزار پژوهش، چهار پرسشنامه مهارت های مدیریت کوانتومی، اینرسی سازمانی، سرمایه فکری و یادگیری سازمانی بود. پایایی پرسشنامه ها از طریق ضریب آلفای کرونباخ برای مهارت کوانتومی 70/0، اینرسی سازمانی 75/0، سرمایه فکری 87/0 و یادگیری سازمانی 90/0 برآورد شد. تحلیل داده ها در سطح توصیفی و استنباطی با استفاده از نرم افزارهای آماری SPSS و اسمارت پی ال اس انجام شد. نتایج نشان داد میانگین متغیرهای مهارت های کوانتومی، سرمایه فکری و یادگیری سازمانی، بالاتر از نمره ملاک و میانگین اینرسی سازمانی، پایین تر از نمره ملاک بود. ضرایب مسیر نشان داد سرمایه فکری با (51/0-=Beta)، مهارت کوانتومی با (26/0-=Beta) و یادگیری سازمانی با (50/0-=Beta) روی اینرسی سازمانی، تاثیر منفی و معنادار دارد. مهارت کوانتومی با (42/0=Beta) روی سرمایه فکری و با (45/0=Beta) روی یادگیری سازمانی تاثیر مثبت و معنادار دارد. به علاوه نقش میانجی یادگیری سازمانی و سرمایه فکری در تاثیر مهارت های کوانتومی در کاهش اینرسی سازمانی، تایید شد.
۱۸.

Digital Tools of Marketing Strategies in Hotel Branding(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۵, Issue ۱, ۲۰۲۳ 62 - 88

کلید واژه ها: Digital Marketing Digital tools marketing strategies brand Hotel Branding

حوزه های تخصصی:
تعداد بازدید : ۲۴۲
The condition of the hotel's competitiveness is a strong brand. The introduction of digital marketing in the strategy of hotel branding creates new opportunities for hotels when interacting with guests through digital channels. The purpose of this study is to develop theoretical and practical measures to improve the effectiveness of marketing strategies in hotel branding using digital tools. To achieve the goal of the study was conducted research on targeted branding some of the largest hotel chains. The results of the analysis showed that in the process of branding at each stage the corresponding goals have achieved by means of advertising, marketing, public relations management, personnel selection, corporate culture.This study substantiates the main tools of the strategy of the Digital Marketing and Sales. The brands need to constantly monitor changes in market positions and audience sentiment using all the features and channels. The priorities should be implemented by performing key tasks, in particular such astimely measurements as far as brand experience has a positive effect on customer satisfaction and loyalty.
۱۹.

تحلیل علی هم افزایی دانش در شرکت های دانش بنیان با رویکرد تلفیقی مدل سازی ساختاری تفسیری و معادلات ساختاری (مورد مطالعه: پارک علم و فناوری یزد)(مقاله علمی وزارت علوم)

نویسنده:
منبع: مدیریت دانش سازمانی سال ششم پاییز ۱۴۰۲ شماره ۲۲ 49 - 80

کلید واژه ها: شرکت های دانش بنیان مدل سازی ساختاری تفسیری مدل سازی معادلات ساختاری مدیریت دانش هم افزایی دانش

حوزه های تخصصی:
تعداد بازدید : ۸۰ تعداد دانلود : ۶۱
امروزه شرکت های دانش بنیان می توانند با هم افزایی دانش در مراحل ادغام و اکتساب دانش، قابلیت های پویا را در خود ایجاد و تقویت نمایند. از این رو هدف از انجام این پژوهش تحلیل و بررسی عوامل اثرگذار بر هم افزایی دانش در شرکت های دانش بنیان پارک علم و فناوری یزد است. به منظور انجام پژوهش در ابتدا عوامل تاثیرگذار بر شکل گیری هم افزایی دانش در درون شرکت های دانش بنیان شناسایی گردید. در ادامه با استفاده از رویکرد مدل سازی ساختاری تفسیری، عوامل شناسایی شده ساختاربندی شدند. به منظور اعتبارسنجی مدل مفهومی تحقیق، از رویکرد معادلات ساختاری و از نرم افزار SmartPls3 استفاده گردید جامعه آماری این پژوهش را متخصصین، خبرگان و کارکنان شرکت های دانش بنیان در پارک علم و فناوری یزد تشکیل داده اند. روش نمونه گیری در این پژوهش در بخش معادلات ساختاری، روش نمونه گیری در دسترس بوده است. در این پژوهش در بخش معادلات ساختاری، تعداد 186 پرسشنامه تکمیل و مورد تجزیه و تحلیل قرار گرفت. در سطح ششم و آغازین مدل، عامل زمینه های محیطی، در سطح پنجم عامل رهبری مدیریت و در سطح چهارم عامل فرهنگ سازمانی قرار گرفته اند. در سطح سوم مدل عوامل منابع، چشم انداز و استراتژی و سرمایه اجتماعی جای گرفته اند. در سطح دوم عوامل آموزش و تعهد کارمندان و نهایتاً در سطح پایانی عامل فناوری اطلاعات قرار گرفته اند. زمینه های دیگر نتایج این پژوهش می-توان به تأثیر آموزش و تعهد کارکنان شرکت های دانش بنیان پارک علم و فناوری یزد بر فناوری اطلاعات اشاره کرد.
۲۰.

بررسی تاثیر رهبری دانش محور بر توسعه منابع انسانی با نقش میانجی رفتارهای کاری نوآورانه (مورد مطالعه: سازمان هاي دانش بنیان فعال در پارك علم فناوري لرستان)(مقاله علمی وزارت علوم)

نویسنده:
منبع: مدیریت دانش سازمانی سال ششم پاییز ۱۴۰۲ شماره ۲۲ 21 - 48

کلید واژه ها: رهبری دانش محور توسعه منابع انسانی رفتارهای کاری نوآورانه

حوزه های تخصصی:
تعداد بازدید : ۱۲۰ تعداد دانلود : ۹۲
پژوهش حاضر به بررسی تاثیر رهبری دانش محور بر توسعه منابع انسانی با نقش میانجی رفتارهای کاری نوآورانه  می پردازد. این پژوهش در زمره پژوهش های کمی و همچنین از حیث فلسفه تحقیق دارای رویکرد قیاسی است. جامعه آماری این پژوهش 120 نفر از کارشناسان سازمان های دانش بنیان فعال در پارک علم فناوری لرستان هستند که با استفاده از جدول مورگان 92 نفر به روش تصادفی ساده به عنوان نمونه انتخاب شدند. ابزار گردآوری اطلاعات در این پژوهش پرسشنامه استاندارد ویتالا (2004)، شای و همکاران (2004) و جانسن (200) است که روایی و پایایی آن با استفاده از روش اعتبار محتوا و آلفای کرونباخ تایید شده است. در این پژوهش برای بررسی و آزمون فرضیه ها، رویکرد مدل سازی معادلات ساختاری و نرم افزار spss و pls به کار رفت. یافته ها نشان داد که در سطح اطمینان0/95  رهبری دانش محور تاثیر مثبت و معنادار بر توسعه منابع انسانی دارد. همچنین رهبری دانش محور تاثیر مثبت و معنادار بر رفتارهای کاری نوآورانه دارد. نتایج پژوهش مبین آن است که رفتارهای کاری نوآورانه نقش میانجی در تاثیر رهبری دانش محور بر توسعه منابع انسانی دارد

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