The commonly adopted project management approach is the stage-gate model, which is not always the convenient approach to innovation projects. The paper objective is to present a qualitative analysis of existing project management approaches and to propose a new hybrid model for effective management of innovation programs based on traditional project management approaches, agile methods to involve the customer, and then lean approach to eliminate waste. The results were illustrated by a new model based on the Japanese P2M (program and project management for enterprise innovation) guide, then combine it with Agile Industrial Scrum method and the agile 3S (scheme, system, and service) model of P2M, and finally with some lean tools and techniques oriented towards the innovation and project management context. Finally, an application case was illustrated where the researchers present the planning of the application of the proposed model on an innovation program in medical waste management field.
{"title":"Adapting P2M Framework for Innovation Program Management Through a Lean-Agile Approach","authors":"Fatima-Zahra Eddoug, R. Benabbou, J. Benhra","doi":"10.4018/ijitpm.318125","DOIUrl":"https://doi.org/10.4018/ijitpm.318125","url":null,"abstract":"The commonly adopted project management approach is the stage-gate model, which is not always the convenient approach to innovation projects. The paper objective is to present a qualitative analysis of existing project management approaches and to propose a new hybrid model for effective management of innovation programs based on traditional project management approaches, agile methods to involve the customer, and then lean approach to eliminate waste. The results were illustrated by a new model based on the Japanese P2M (program and project management for enterprise innovation) guide, then combine it with Agile Industrial Scrum method and the agile 3S (scheme, system, and service) model of P2M, and finally with some lean tools and techniques oriented towards the innovation and project management context. Finally, an application case was illustrated where the researchers present the planning of the application of the proposed model on an innovation program in medical waste management field.","PeriodicalId":375999,"journal":{"name":"Int. J. Inf. Technol. Proj. Manag.","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116670002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The literature revealed approximately 50% of IT-related projects around the world fail, which must frustrate a sponsor or decision maker since their ability to forecast success is statistically about the same as guessing with a random coin toss. Nonetheless, some project success/failure factors have been identified, but often the effect sizes were statistically negligible. A pragmatic mixed methods recursive approach was applied, using structured programming, machine learning (ML), and statistical software to mine a large data source for probable project success/failure indicators. Seven feature indicators were detected from ML, producing an accuracy of 79.9%, a recall rate of 81%, an F1 score of 0.798, and a ROCa of 0.849. A post-hoc regression model confirmed three indicators were significant with a 27% effect size. The contributions made to the body of knowledge included: A conceptual model comparing ML methods by artificial intelligence capability and research decision making goal, a mixed methods recursive pragmatic research design, application of the random forest ML technique with post hoc statistical methods, and a preliminary list of IT project failure indicators analyzed from big data.
{"title":"Mining Project Failure Indicators From Big Data Using Machine Learning Mixed Methods","authors":"K. Strang, N. Vajjhala","doi":"10.4018/ijitpm.317221","DOIUrl":"https://doi.org/10.4018/ijitpm.317221","url":null,"abstract":"The literature revealed approximately 50% of IT-related projects around the world fail, which must frustrate a sponsor or decision maker since their ability to forecast success is statistically about the same as guessing with a random coin toss. Nonetheless, some project success/failure factors have been identified, but often the effect sizes were statistically negligible. A pragmatic mixed methods recursive approach was applied, using structured programming, machine learning (ML), and statistical software to mine a large data source for probable project success/failure indicators. Seven feature indicators were detected from ML, producing an accuracy of 79.9%, a recall rate of 81%, an F1 score of 0.798, and a ROCa of 0.849. A post-hoc regression model confirmed three indicators were significant with a 27% effect size. The contributions made to the body of knowledge included: A conceptual model comparing ML methods by artificial intelligence capability and research decision making goal, a mixed methods recursive pragmatic research design, application of the random forest ML technique with post hoc statistical methods, and a preliminary list of IT project failure indicators analyzed from big data.","PeriodicalId":375999,"journal":{"name":"Int. J. Inf. Technol. Proj. Manag.","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127500913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
According to the market research firm Tractica, the global artificial intelligence software market is forecast to grow to 126 billion by 2025. Additionally, the Gartner group predicts that during the same time as much as 80% of the routine work , which represents the bulk of human hours spent in today's project management (PM) activities, can be eliminated because of collaboration between humans and smart machines. Today's PM practices rely heavily on human input. However, that is not the optimum use of the human project manager's intuitive, innovative, and creative abilities. Many aspects of a project manager's work could be managed by machines that utilize AI/ML approaches to address nonroutine and predictive tasks. This paper describes IT project management (ITPM) processes and associated tasks and identifies the AI/ML approaches that can support them.
{"title":"A Proposal for Research on the Application of AI/ML in ITPM: Intelligent Project Management","authors":"Anoop Mishra, A. Tripathi, D. Khazanchi","doi":"10.4018/ijitpm.315290","DOIUrl":"https://doi.org/10.4018/ijitpm.315290","url":null,"abstract":"According to the market research firm Tractica, the global artificial intelligence software market is forecast to grow to 126 billion by 2025. Additionally, the Gartner group predicts that during the same time as much as 80% of the routine work , which represents the bulk of human hours spent in today's project management (PM) activities, can be eliminated because of collaboration between humans and smart machines. Today's PM practices rely heavily on human input. However, that is not the optimum use of the human project manager's intuitive, innovative, and creative abilities. Many aspects of a project manager's work could be managed by machines that utilize AI/ML approaches to address nonroutine and predictive tasks. This paper describes IT project management (ITPM) processes and associated tasks and identifies the AI/ML approaches that can support them.","PeriodicalId":375999,"journal":{"name":"Int. J. Inf. Technol. Proj. Manag.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129939355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Investing in the stock market has never been an easy task. This paper develops a stock recommendation and trade assistance that uses the past performance of the stock to predict its future performance using linear regression model. Linear regression model has given an accuracy of 99.8% as compared to support vector machine (SVM) which resulted into an accuracy of 94.6%. Data set used under the study was extracted from the historic stock data of reliance industries limited (RIL). To analyze whether to buy or sell the stock, four financial algorithms, namely Bollinger bands, moving average convergence/divergence indicator (MACD), money flow index (MFI), and relative strength index (RSI) are employed to find the composite result. Moreover, sentiment analysis of the news depending upon the earning calls and the annual general meetings is done to provide an overall stock and market sentiment analysis. In-depth balance sheet analysis of the company is also done using various instruments to make the trade assistance more accurate. The values for WACC, D/E ratio, and NPV obtained are 14.99, 0.76, and 8.9 lakh crores for RIL.
{"title":"Stock Recommendation and Trade Assistance","authors":"Archana Purwar, Indu Chawla, Sarthak Jain, Rahul Malhotra, Dhanesh Chaudhary","doi":"10.4018/ijitpm.313423","DOIUrl":"https://doi.org/10.4018/ijitpm.313423","url":null,"abstract":"Investing in the stock market has never been an easy task. This paper develops a stock recommendation and trade assistance that uses the past performance of the stock to predict its future performance using linear regression model. Linear regression model has given an accuracy of 99.8% as compared to support vector machine (SVM) which resulted into an accuracy of 94.6%. Data set used under the study was extracted from the historic stock data of reliance industries limited (RIL). To analyze whether to buy or sell the stock, four financial algorithms, namely Bollinger bands, moving average convergence/divergence indicator (MACD), money flow index (MFI), and relative strength index (RSI) are employed to find the composite result. Moreover, sentiment analysis of the news depending upon the earning calls and the annual general meetings is done to provide an overall stock and market sentiment analysis. In-depth balance sheet analysis of the company is also done using various instruments to make the trade assistance more accurate. The values for WACC, D/E ratio, and NPV obtained are 14.99, 0.76, and 8.9 lakh crores for RIL.","PeriodicalId":375999,"journal":{"name":"Int. J. Inf. Technol. Proj. Manag.","volume":"1312 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127437710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Given the proliferation of websites which act as digital channels for life insurance companies, a competitive situation has emerged with each vying for the web user's attention and patronage. Web efficacy is vital for creating an impressive online experience and gaining customer patronage. To facilitate the understanding of website managers on specific aspects which matter the most to customers, this study employs the best-worst method to evaluate the importance of various criteria employed by the web users to assess these digital options. Additionally, using four life insurance websites (LIC, SBI Life, HDFC Life Insurance, and Max Life Insurance) as alternatives, the study helps illustrate the competitive position of the websites based on key criteria: trust, visual appeal, innovativeness, information fit-to-task, tailored information, response time, intuitive operations, and relative advantage. The results of this study are easily interpretable and can provide key insights on the specific attributes in a comparative manner for website administrators and managers.
鉴于作为寿险公司数字渠道的网站的激增，竞争局面已经出现，每个网站都在争夺网络用户的关注和赞助。网络效能对于创造令人印象深刻的在线体验和获得客户惠顾至关重要。为了促进网站管理者对客户最重要的具体方面的理解，本研究采用最佳最差方法来评估网络用户评估这些数字选项所采用的各种标准的重要性。此外，本研究以四家寿险网站(LIC、SBI life、HDFC life insurance和Max life insurance)为研究对象，从信任度、视觉吸引力、创新性、资讯适合度、资讯定制、回应时间、操作直觉和相对优势等关键标准，说明这些网站的竞争地位。本研究的结果很容易解释，并且可以以比较的方式为网站管理员和管理者提供关于特定属性的关键见解。
{"title":"Perceived Website Efficacy for Life Insurance Companies: Insights From a Best-Worst Method","authors":"Anupriya Kaur","doi":"10.4018/ijitpm.313631","DOIUrl":"https://doi.org/10.4018/ijitpm.313631","url":null,"abstract":"Given the proliferation of websites which act as digital channels for life insurance companies, a competitive situation has emerged with each vying for the web user's attention and patronage. Web efficacy is vital for creating an impressive online experience and gaining customer patronage. To facilitate the understanding of website managers on specific aspects which matter the most to customers, this study employs the best-worst method to evaluate the importance of various criteria employed by the web users to assess these digital options. Additionally, using four life insurance websites (LIC, SBI Life, HDFC Life Insurance, and Max Life Insurance) as alternatives, the study helps illustrate the competitive position of the websites based on key criteria: trust, visual appeal, innovativeness, information fit-to-task, tailored information, response time, intuitive operations, and relative advantage. The results of this study are easily interpretable and can provide key insights on the specific attributes in a comparative manner for website administrators and managers.","PeriodicalId":375999,"journal":{"name":"Int. J. Inf. Technol. Proj. Manag.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122921985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Gupta, S. Bharti, Nikhlesh Pathik, Ashutosh Sharma
Nowadays, a major concern for most retail banks is the risk that originates from customer fluctuation and that increases the cost of almost every financial product. In this work, the authors compared different approaches and algorithms to predict the relevant features that affect the customer churn, which means we can find ways to reduce the customer churn and create financial inclusion. This research was conducted by applying different machine learning techniques like decision tree classifier, random forest classifier, AdaBoost classifier, extreme gradient boosting, and balancing data with random under-sampling and random oversampling. The authors have also implemented AutoML to further compare different models and improve the accuracy of the model to predict customer churn. It was observed that applying AutoML highest accuracy model gave the accuracy of 97.53% in comparison to that of the decision tree classifier, which was 93.48% with the use of low processing power. Important features were ‘total transaction amount' and ‘total transaction count' to predict customer churn for a given dataset.
{"title":"Predicting Churn of Credit Card Customers Using Machine Learning and AutoML","authors":"R. Gupta, S. Bharti, Nikhlesh Pathik, Ashutosh Sharma","doi":"10.4018/ijitpm.313422","DOIUrl":"https://doi.org/10.4018/ijitpm.313422","url":null,"abstract":"Nowadays, a major concern for most retail banks is the risk that originates from customer fluctuation and that increases the cost of almost every financial product. In this work, the authors compared different approaches and algorithms to predict the relevant features that affect the customer churn, which means we can find ways to reduce the customer churn and create financial inclusion. This research was conducted by applying different machine learning techniques like decision tree classifier, random forest classifier, AdaBoost classifier, extreme gradient boosting, and balancing data with random under-sampling and random oversampling. The authors have also implemented AutoML to further compare different models and improve the accuracy of the model to predict customer churn. It was observed that applying AutoML highest accuracy model gave the accuracy of 97.53% in comparison to that of the decision tree classifier, which was 93.48% with the use of low processing power. Important features were ‘total transaction amount' and ‘total transaction count' to predict customer churn for a given dataset.","PeriodicalId":375999,"journal":{"name":"Int. J. Inf. Technol. Proj. Manag.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125139048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A lucid representation of the hidden structure of real-world application has attracted complex network research communities and triggered a vast number of solutions in order to resolve complex network issues. In the same direction, initially, this paper proposes a methodology to act on the financial dataset and construct a stock correlation network of four stock indexes based on the closing stock price. The significance of this research work is to form an effective stock community based on their complex price pattern dependencies (i.e., simultaneous fluctuations in stock prices of companies in a time series data). This paper proposes a community detection approach for stock correlation complex networks using the BAT optimization algorithm aiming to achieve high modularity and better-correlated communities. Theoretical analysis and empirical modularity performance measure results have shown that the usage of BAT algorithm for community detection proves to transcend performance in comparison to standard network community detection algorithms – greedy and label propagation.
{"title":"Detecting Community Structure in Financial Markets Using the Bat Optimization Algorithm","authors":"K. Aggarwal, Anuja Arora","doi":"10.4018/ijitpm.313421","DOIUrl":"https://doi.org/10.4018/ijitpm.313421","url":null,"abstract":"A lucid representation of the hidden structure of real-world application has attracted complex network research communities and triggered a vast number of solutions in order to resolve complex network issues. In the same direction, initially, this paper proposes a methodology to act on the financial dataset and construct a stock correlation network of four stock indexes based on the closing stock price. The significance of this research work is to form an effective stock community based on their complex price pattern dependencies (i.e., simultaneous fluctuations in stock prices of companies in a time series data). This paper proposes a community detection approach for stock correlation complex networks using the BAT optimization algorithm aiming to achieve high modularity and better-correlated communities. Theoretical analysis and empirical modularity performance measure results have shown that the usage of BAT algorithm for community detection proves to transcend performance in comparison to standard network community detection algorithms – greedy and label propagation.","PeriodicalId":375999,"journal":{"name":"Int. J. Inf. Technol. Proj. Manag.","volume":"498 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123057761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The online banking system is the new trend in the developing digital world. The transferring of a large amount of currency in a millisecond is leading to fast accessing of the banking system as it saves more time at the online payment and digital shopping. The increase in rate of use of banking credit and debit card leads to a large amount of fraud in the field of finance. Machine learning has the new discovering faces in the field of the finance. So, this research work proposed a hybrid model using the logistic regression, multilayer perceptron, and the XgBoost. The study involves both the balance and imbalance dataset to conclude the result based on the accuracy precision and recall. The results show that accuracy of the model is 100%, and precision, recall, and F1-scores are 95.63%, 99.99%, and 97.76% respectively.
{"title":"A Hybrid Machine Learning Approach for Credit Card Fraud Detection","authors":"Sonam Gupta, Tushtee Varshney, Abhinav Verma, Lipika Goel, A. Yadav, Arjun Singh","doi":"10.4018/ijitpm.313420","DOIUrl":"https://doi.org/10.4018/ijitpm.313420","url":null,"abstract":"The online banking system is the new trend in the developing digital world. The transferring of a large amount of currency in a millisecond is leading to fast accessing of the banking system as it saves more time at the online payment and digital shopping. The increase in rate of use of banking credit and debit card leads to a large amount of fraud in the field of finance. Machine learning has the new discovering faces in the field of the finance. So, this research work proposed a hybrid model using the logistic regression, multilayer perceptron, and the XgBoost. The study involves both the balance and imbalance dataset to conclude the result based on the accuracy precision and recall. The results show that accuracy of the model is 100%, and precision, recall, and F1-scores are 95.63%, 99.99%, and 97.76% respectively.","PeriodicalId":375999,"journal":{"name":"Int. J. Inf. Technol. Proj. Manag.","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131728437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A change in FDI inflow is noticed across the globe. G-7 economies, as representative of developed economies, are fronting with a sharp decline in foreign direct investment inflows in the entire world's FDI inflow, while BRICS, a representative of developing economies, is getting more of the world as a whole's FDI inflow. FDI is a significant economic development variable that has substantially impacted the economic growth of economies. Past trends of FDI inflow into BRICS and G-7 economies showed that BRICS economies had noticed a higher compounded average annual growth rate in FDI compared to G-7 economies in the preceding periods. The best-suited ARIMA model's anticipated value of FDI inflow shows an increasing trend in BRICS and a steady and dropping trend in the G-7. Comparative results of the predicted values of FDI inflow showed that BRICS would have positive FDI inflow while the G-7 would follow a declining trend. The study's findings shall help foreign investors identify the investment opportunities and their future course of action in selecting an investment destination.
{"title":"FDI Inflow in BRICS and G7: An Empirical Analysis","authors":"Somesh Sharma, Manmohan Bansal, A. K. Saxena","doi":"10.4018/ijitpm.313443","DOIUrl":"https://doi.org/10.4018/ijitpm.313443","url":null,"abstract":"A change in FDI inflow is noticed across the globe. G-7 economies, as representative of developed economies, are fronting with a sharp decline in foreign direct investment inflows in the entire world's FDI inflow, while BRICS, a representative of developing economies, is getting more of the world as a whole's FDI inflow. FDI is a significant economic development variable that has substantially impacted the economic growth of economies. Past trends of FDI inflow into BRICS and G-7 economies showed that BRICS economies had noticed a higher compounded average annual growth rate in FDI compared to G-7 economies in the preceding periods. The best-suited ARIMA model's anticipated value of FDI inflow shows an increasing trend in BRICS and a steady and dropping trend in the G-7. Comparative results of the predicted values of FDI inflow showed that BRICS would have positive FDI inflow while the G-7 would follow a declining trend. The study's findings shall help foreign investors identify the investment opportunities and their future course of action in selecting an investment destination.","PeriodicalId":375999,"journal":{"name":"Int. J. Inf. Technol. Proj. Manag.","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122743935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The study examines the factors affecting the performances of the Indian banking sector, especially after the global financial crisis. The sample constitutes a total of 33 scheduled commercial banks (SCBs) that were operative in India during the period extending from 2002 to 2016 by employing a panel data model. It also reports that leverage and management efficiency as internal determinants do have a significant impact, while inflation as an external determinant affects the bank's profitability. The Indian banking industry has been less affected by the influence of external factors as compared to profitability.
{"title":"\"Soar\" or \"Sore\": Examining and Reflecting on Bank Performance During Global Financial Crisis - An Indian Scenario","authors":"S. Mohanty, A. Mahendra, Santosh Gopalkrishnan","doi":"10.4018/ijitpm.313662","DOIUrl":"https://doi.org/10.4018/ijitpm.313662","url":null,"abstract":"The study examines the factors affecting the performances of the Indian banking sector, especially after the global financial crisis. The sample constitutes a total of 33 scheduled commercial banks (SCBs) that were operative in India during the period extending from 2002 to 2016 by employing a panel data model. It also reports that leverage and management efficiency as internal determinants do have a significant impact, while inflation as an external determinant affects the bank's profitability. The Indian banking industry has been less affected by the influence of external factors as compared to profitability.","PeriodicalId":375999,"journal":{"name":"Int. J. Inf. Technol. Proj. Manag.","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120922061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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