Pub Date : 2022-03-28DOI: 10.46272/2587-8476-2022-13-1-94-109
A. Boyashov
Having evolved since the 1990s into a political and ideological concept, global governance has become one of the priorities of the program documents of EU states and institutions, today formulated as “rules-based order”. A distinctive feature of this concept is the “blurring” of interstate interaction on the world stage in favor of non-state and supranational interaction. Under global governance, there is allegedly no hierarchy between state actors and nonstate or supra-state actors. This article critically examines this thesis with the help of the sociological theory of diff erentiation. According to diff erentiation theory, the state does not remain on the sidelines of global governance but takes the lead in the hierarchy of levels of world politics. The fi nal part of the article deals with the principles of EU coordination in the UN system. To promote ideas of global governance, the EU aims at major reform of the UN through intertwining intergovernmental interaction with nongovernmental interaction. This model is used especially often when non-core issues (e.g., human rights or climate in the Security Council) are considered in a UN body, allowing for broader participation in negotiations and, from a strategic perspective, a revision of the UN Charter. The main conclusion of the article is that the EU’s actions in the UN system refute the position of global governance that there is no hierarchy between the interstate and supranational levels of world politics. Despite the active promotion of global governance by the EU states, the EU itself is based on interstate coordination and a rigid hierarchy among states, as well as between states and nongovernmental actors.
{"title":"European Union in Promotion of Global Governance in the UN System","authors":"A. Boyashov","doi":"10.46272/2587-8476-2022-13-1-94-109","DOIUrl":"https://doi.org/10.46272/2587-8476-2022-13-1-94-109","url":null,"abstract":"Having evolved since the 1990s into a political and ideological concept, global governance has become one of the priorities of the program documents of EU states and institutions, today formulated as “rules-based order”. A distinctive feature of this concept is the “blurring” of interstate interaction on the world stage in favor of non-state and supranational interaction. Under global governance, there is allegedly no hierarchy between state actors and nonstate or supra-state actors. This article critically examines this thesis with the help of the sociological theory of diff erentiation. According to diff erentiation theory, the state does not remain on the sidelines of global governance but takes the lead in the hierarchy of levels of world politics. The fi nal part of the article deals with the principles of EU coordination in the UN system. To promote ideas of global governance, the EU aims at major reform of the UN through intertwining intergovernmental interaction with nongovernmental interaction. This model is used especially often when non-core issues (e.g., human rights or climate in the Security Council) are considered in a UN body, allowing for broader participation in negotiations and, from a strategic perspective, a revision of the UN Charter. The main conclusion of the article is that the EU’s actions in the UN system refute the position of global governance that there is no hierarchy between the interstate and supranational levels of world politics. Despite the active promotion of global governance by the EU states, the EU itself is based on interstate coordination and a rigid hierarchy among states, as well as between states and nongovernmental actors.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":"14 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90060844","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}
Being experiential commodities, it becomes difficult to make any judgment about hotels or attractions before their utilization. This is where the reviews provided by guests/tourists play an influential role. Therefore, it becomes imperative to study in-depth characteristics of reviewers through which such valuable information is diffused, and also classifying them into various categories based on it. This study adopts a two-stage methodology to segment reviewers based on the reviewer as well as review characteristics. In the first stage, factors that help in evaluating a reviewer are formulated using factor analysis. Later on, cluster analysis is performed for the segmentation of reviewers. Finally, the obtained reviewers' segments are validated using external validation methods. The study comes up with various implications that could be profitable for business managers in selecting the reviewer community.
{"title":"Segmenting Reviewers Based on Reviewer and Review Characteristics","authors":"H. Sharma, A. Aggarwal","doi":"10.4018/ijban.303115","DOIUrl":"https://doi.org/10.4018/ijban.303115","url":null,"abstract":"Being experiential commodities, it becomes difficult to make any judgment about hotels or attractions before their utilization. This is where the reviews provided by guests/tourists play an influential role. Therefore, it becomes imperative to study in-depth characteristics of reviewers through which such valuable information is diffused, and also classifying them into various categories based on it. This study adopts a two-stage methodology to segment reviewers based on the reviewer as well as review characteristics. In the first stage, factors that help in evaluating a reviewer are formulated using factor analysis. Later on, cluster analysis is performed for the segmentation of reviewers. Finally, the obtained reviewers' segments are validated using external validation methods. The study comes up with various implications that could be profitable for business managers in selecting the reviewer community.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41776034","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}
We propose two analytical models to characterize the relationship between technological upgrading and innovation in the oil & gas industry. The first one is an “optimization model” which focuses on the trade-offs between profit maximization and environmental compliance cost. The other has been developed based on “predator-prey” model which captures the dynamics of biological systems. Our study contributes to the strategic planning process for sustainable development by providing the insight that optimal allocation process is determined by multiple operational factors, including a firm’s competitive ranking among its industrial competitors, industrial consent on the concurrent rate of return on capital investment, the projected demand of oil & gas in future, and a change in environmental compliance cost. Further, we add to the robustness of the optimal allocation process by providing binding conditions of the set of solutions.
{"title":"Analytical Models to Characterize Trade-Offs Between Technological Upgrading and Innovation","authors":"C. Liu, Young Chan","doi":"10.4018/ijban.288515","DOIUrl":"https://doi.org/10.4018/ijban.288515","url":null,"abstract":"We propose two analytical models to characterize the relationship between technological upgrading and innovation in the oil & gas industry. The first one is an “optimization model” which focuses on the trade-offs between profit maximization and environmental compliance cost. The other has been developed based on “predator-prey” model which captures the dynamics of biological systems. Our study contributes to the strategic planning process for sustainable development by providing the insight that optimal allocation process is determined by multiple operational factors, including a firm’s competitive ranking among its industrial competitors, industrial consent on the concurrent rate of return on capital investment, the projected demand of oil & gas in future, and a change in environmental compliance cost. Further, we add to the robustness of the optimal allocation process by providing binding conditions of the set of solutions.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48409145","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}
J. B. Awotunde, R. Ogundokun, E. Adeniyi, S. Misra, G. J. Ajamu
The COVID-19 epidemic has triggered unmatched impairment to businesses globally. There are unmeasurable financial influences in the short-term and long-term and have causes intangible destruction within businesses. This study investigates the adoption and utilization of e-business during COVID-19 by both organizations and the general populaces. The study used a questionnaire-based survey to collect data from top managers of business organizations and their clients. SPSS was used to analyze the adoption factors. The outcomes presented that embracing e-business can assist to reduce the spread of COVID-19 and can reduce the physical ways of doing business. The findings of this study will help strategy makers, companies, and officials in making better decisions on the implementation of e-business. This will reduce the rapid spread of community transmission since ordering goods and services can easily be done virtually without physical contact, which goes in line with the social distance policy and in return boost the country’s economy
{"title":"The Adoption and Utilization of Electronic Business in Response to the Global Economy During COVID-19","authors":"J. B. Awotunde, R. Ogundokun, E. Adeniyi, S. Misra, G. J. Ajamu","doi":"10.4018/ijban.288518","DOIUrl":"https://doi.org/10.4018/ijban.288518","url":null,"abstract":"The COVID-19 epidemic has triggered unmatched impairment to businesses globally. There are unmeasurable financial influences in the short-term and long-term and have causes intangible destruction within businesses. This study investigates the adoption and utilization of e-business during COVID-19 by both organizations and the general populaces. The study used a questionnaire-based survey to collect data from top managers of business organizations and their clients. SPSS was used to analyze the adoption factors. The outcomes presented that embracing e-business can assist to reduce the spread of COVID-19 and can reduce the physical ways of doing business. The findings of this study will help strategy makers, companies, and officials in making better decisions on the implementation of e-business. This will reduce the rapid spread of community transmission since ordering goods and services can easily be done virtually without physical contact, which goes in line with the social distance policy and in return boost the country’s economy","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46574279","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}
Studies concerning Big Data patents have been published; however, research investigating Big Data projects is scarce. Therefore, the objective of this study was to conduct an exploratory analysis of a patent database to collect information about the characteristics of registered patents related to Big Data projects. We searched for patents related to Big Data projects in the Espacenet database on January 10, 2021, and identified 109 records.. The textual analysis detected three word classes interpreted as (i) a direction to cloud computing, (ii) optimization of solutions, and (iii) storage and data sharing structures. Our results also revealed emerging technologies such as Blockchain and the Internet of Things, which are utilized in Big Data project solutions. This observation demonstrates the importance that has been given to solutions that facilitate decision-making in an increasingly data-driven context. As a contribution, we understand that this study endorses a group of researchers that has been dedicated to academic research on patent documents.
{"title":"A Patent Analysis on Big Data Projects","authors":"G. Grander, L. F. Silva, E. D. Gonzalez","doi":"10.4018/ijban.288516","DOIUrl":"https://doi.org/10.4018/ijban.288516","url":null,"abstract":"Studies concerning Big Data patents have been published; however, research investigating Big Data projects is scarce. Therefore, the objective of this study was to conduct an exploratory analysis of a patent database to collect information about the characteristics of registered patents related to Big Data projects. We searched for patents related to Big Data projects in the Espacenet database on January 10, 2021, and identified 109 records.. The textual analysis detected three word classes interpreted as (i) a direction to cloud computing, (ii) optimization of solutions, and (iii) storage and data sharing structures. Our results also revealed emerging technologies such as Blockchain and the Internet of Things, which are utilized in Big Data project solutions. This observation demonstrates the importance that has been given to solutions that facilitate decision-making in an increasingly data-driven context. As a contribution, we understand that this study endorses a group of researchers that has been dedicated to academic research on patent documents.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43484659","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}
This article investigates the impact of data-complexity and team-specific characteristics on machine learning competition scores. Data from five real-world binary classification competitions hosted on Kaggle.com were analyzed. The data-complexity characteristics were measured in four aspects including standard measures, sparsity measures, class imbalance measures, and feature-based measures. The results showed that the higher the level of the data-complexity characteristics was, the lower the predictive ability of the machine learning model was as well. Our empirical evidence revealed that the imbalance ratio of the target variable was the most important factor and exhibited a nonlinear relationship with the model’s predictive abilities. The imbalance ratio adversely affected the predictive performance when it reached a certain level. However, mixed results were found for the impact of team-specific characteristics measured by team size, team expertise, and the number of submissions on team performance. For high-performing teams, these factors had no impact on team score.
{"title":"The Impact of Data-Complexity and Team Characteristics on Performance in the Classification Model","authors":"V. Pungpapong, Prasert Kanawattanachai","doi":"10.4018/ijban.288517","DOIUrl":"https://doi.org/10.4018/ijban.288517","url":null,"abstract":"This article investigates the impact of data-complexity and team-specific characteristics on machine learning competition scores. Data from five real-world binary classification competitions hosted on Kaggle.com were analyzed. The data-complexity characteristics were measured in four aspects including standard measures, sparsity measures, class imbalance measures, and feature-based measures. The results showed that the higher the level of the data-complexity characteristics was, the lower the predictive ability of the machine learning model was as well. Our empirical evidence revealed that the imbalance ratio of the target variable was the most important factor and exhibited a nonlinear relationship with the model’s predictive abilities. The imbalance ratio adversely affected the predictive performance when it reached a certain level. However, mixed results were found for the impact of team-specific characteristics measured by team size, team expertise, and the number of submissions on team performance. For high-performing teams, these factors had no impact on team score.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45194085","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}
Container ships transport a large number of valuable cargoes, and there is a demand for less expensive and faster transportation options. Weather, vessel type, and the nature and amount of the goods are all external elements that might impact container handling times, which are directly related to overall port stay time. In this scope, container terminal operations could be optimised with the help of historical data which provides access to classification and prediction of the cargo handling operations. In this study, the real-time data of a container terminal operation is analysed with different machine learning techniques along with the Fuzzy C-Means clustering method. The results show that Fuzzy C-Means clustering has a positive impact on the explanatory power of models in container terminal operations. The research revealed that an increase in wind speed influences cargo handling time for mobile cranes.
{"title":"Analysing the Effects of Weather Conditions on Container Terminal Operations Using Machine Learning","authors":"Üstün Atak, Tolga Kaya, Yasin Arslanoğlu","doi":"10.4018/ijban.298016","DOIUrl":"https://doi.org/10.4018/ijban.298016","url":null,"abstract":"Container ships transport a large number of valuable cargoes, and there is a demand for less expensive and faster transportation options. Weather, vessel type, and the nature and amount of the goods are all external elements that might impact container handling times, which are directly related to overall port stay time. In this scope, container terminal operations could be optimised with the help of historical data which provides access to classification and prediction of the cargo handling operations. In this study, the real-time data of a container terminal operation is analysed with different machine learning techniques along with the Fuzzy C-Means clustering method. The results show that Fuzzy C-Means clustering has a positive impact on the explanatory power of models in container terminal operations. The research revealed that an increase in wind speed influences cargo handling time for mobile cranes.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47189247","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}
In this study, the most suitable normalization techniques for the multi-criteria decision making (MCDM) method proposed by Biswas and Saha were compared and a real situation was analyzed. In the study, the financial performance of the top 10 companies on the FORTUNE 500 list for 2019 was evaluated using seven financial ratios and five well-known normalization techniques. The results have shown that the max normalization procedure generated the most consistent results for Biswas and Saha’s MCDM method. The study is the first to test the suitability of different normalization techniques for the MCDM method proposed by Biswas and Saha. Also, this paper provides decision support that can be used for the selection of the best normalization techniques for other MCDM methods.
{"title":"The Influence of Statistical Normalization Techniques on Performance Ranking Results","authors":"Nazlı Ersoy","doi":"10.4018/ijban.298017","DOIUrl":"https://doi.org/10.4018/ijban.298017","url":null,"abstract":"In this study, the most suitable normalization techniques for the multi-criteria decision making (MCDM) method proposed by Biswas and Saha were compared and a real situation was analyzed. In the study, the financial performance of the top 10 companies on the FORTUNE 500 list for 2019 was evaluated using seven financial ratios and five well-known normalization techniques. The results have shown that the max normalization procedure generated the most consistent results for Biswas and Saha’s MCDM method. The study is the first to test the suitability of different normalization techniques for the MCDM method proposed by Biswas and Saha. Also, this paper provides decision support that can be used for the selection of the best normalization techniques for other MCDM methods.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47919000","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}
Up to the present, various methods such as Data Mining, Machine Learning, and Artificial Intelligence have been used to get the best assess from huge and important data resource. Deep Learning, one of these methods, is extended version of Artificial Neural Networks. Within the scope of this study, a model has been developed to classify the success of tele-marketing with different machine learning algorithms especially with Deep Learning algorithm. Naïve Bayes, C5.0, Extreme Learning Machine and Deep Learning algorithms have been used for modelling. To examine the effect of class label distribution on model success, Synthetic Minority Oversampling Technique have been used. The results have revealed the success of Deep Learning and Decision Trees algorithms. When the data set was not balanced, the Deep Learning algorithm performed better in terms of sensitivity. Among all models, the best performance in terms of accuracy, precision and F-score have been achieved with the C5.0 algorithm.
{"title":"Machine Learning Approach and Model Performance Evaluation for Tele-Marketing Success Classification","authors":"F. Koçoğlu, Şakir Esnaf","doi":"10.4018/ijban.298014","DOIUrl":"https://doi.org/10.4018/ijban.298014","url":null,"abstract":"Up to the present, various methods such as Data Mining, Machine Learning, and Artificial Intelligence have been used to get the best assess from huge and important data resource. Deep Learning, one of these methods, is extended version of Artificial Neural Networks. Within the scope of this study, a model has been developed to classify the success of tele-marketing with different machine learning algorithms especially with Deep Learning algorithm. Naïve Bayes, C5.0, Extreme Learning Machine and Deep Learning algorithms have been used for modelling. To examine the effect of class label distribution on model success, Synthetic Minority Oversampling Technique have been used. The results have revealed the success of Deep Learning and Decision Trees algorithms. When the data set was not balanced, the Deep Learning algorithm performed better in terms of sensitivity. Among all models, the best performance in terms of accuracy, precision and F-score have been achieved with the C5.0 algorithm.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49087883","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}
Marcos Paulo Valadares de Oliveira, Kevin McCormack, Marcelo Bronzo, P. Trkman
Decision makers are exposed to an increasing amount of information. Algorithms can help people make better data-driven decisions. Previous research has focused on both companies’ orientation towards analytics use and the required skills of individual decision makers. However, each individual can make either analytically based or intuitive decisions. We investigated the characteristics that influence the likelihood of making analytical decisions, focusing on both analytical orientation and capabilities of individuals. We conducted a survey using 462 business students as proxies for decision makers and used partial least squares path modeling to show that analytical capabilities and analytical orientation influence each other and affect analytical decision-making, thereby impacting decision quality and decision regret. Our findings suggest that when implementing business analytics solutions, companies should focus on the development not only of technological capabilities and individuals’ skills but also of individuals’ analytical orientation.
{"title":"The Effect of Individual Analytical Orientation and Capabilities on Decision Quality and Regret","authors":"Marcos Paulo Valadares de Oliveira, Kevin McCormack, Marcelo Bronzo, P. Trkman","doi":"10.4018/ijban.288510","DOIUrl":"https://doi.org/10.4018/ijban.288510","url":null,"abstract":"Decision makers are exposed to an increasing amount of information. Algorithms can help people make better data-driven decisions. Previous research has focused on both companies’ orientation towards analytics use and the required skills of individual decision makers. However, each individual can make either analytically based or intuitive decisions. We investigated the characteristics that influence the likelihood of making analytical decisions, focusing on both analytical orientation and capabilities of individuals. We conducted a survey using 462 business students as proxies for decision makers and used partial least squares path modeling to show that analytical capabilities and analytical orientation influence each other and affect analytical decision-making, thereby impacting decision quality and decision regret. Our findings suggest that when implementing business analytics solutions, companies should focus on the development not only of technological capabilities and individuals’ skills but also of individuals’ analytical orientation.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49217260","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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