This study investigates the application of market segmentation on E-commerce canceled orders. It uses a transnational dataset that contains transactions of an online retail store during a year. The analysis process includes 1) an exploratory data analysis on the canceled orders which makes up a considerably amount of the dataset to show their characteristics. 2) a production segmentation that utilize the k-means clustering to create 5 product clusters. 3) a customer segmentation with k-means clustering using the production segments and customer features which results in 7 segments. In the process, the study compares silhouette scores and applies principal component analysis to optimize the number of clusters. The conclusion shows that market segmentation serves as an effective tool to distinguish products and consumers with different characteristics and help make suggestions to businesses. Also, including attitudinal features into the analysis process will result in improved customer profiles.
{"title":"Analysis on E-commerce Order Cancellations Using Market Segmentation Approach","authors":"Jingyi Ye","doi":"10.1145/3450588.3450596","DOIUrl":"https://doi.org/10.1145/3450588.3450596","url":null,"abstract":"This study investigates the application of market segmentation on E-commerce canceled orders. It uses a transnational dataset that contains transactions of an online retail store during a year. The analysis process includes 1) an exploratory data analysis on the canceled orders which makes up a considerably amount of the dataset to show their characteristics. 2) a production segmentation that utilize the k-means clustering to create 5 product clusters. 3) a customer segmentation with k-means clustering using the production segments and customer features which results in 7 segments. In the process, the study compares silhouette scores and applies principal component analysis to optimize the number of clusters. The conclusion shows that market segmentation serves as an effective tool to distinguish products and consumers with different characteristics and help make suggestions to businesses. Also, including attitudinal features into the analysis process will result in improved customer profiles.","PeriodicalId":150426,"journal":{"name":"Proceedings of the 2021 4th International Conference on Computers in Management and Business","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129517483","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}
E. Kuncoro, H. Saroso, Darjat Sudrajat, Anisa Larasati, Dennis Moeke
As a hub in multimodal transport, there are multiple stakeholders take part in the successful of dry port. This research proposed that the commitment among these stakeholders in terms of cost is essential to the dry ports’ performance. In addition, this research examined that collaborative supply chain as mediation between stakeholders’ cost commitment and dry port firms’ performance. The results showed that the indirect effect is larger than direct effect, which support the partial mediation effect. Finally, this research discussed the implication and limitation of this research.
{"title":"Why Supply Chain Collaboration Matters for Indonesian Dry Port Firms?","authors":"E. Kuncoro, H. Saroso, Darjat Sudrajat, Anisa Larasati, Dennis Moeke","doi":"10.1145/3450588.3450601","DOIUrl":"https://doi.org/10.1145/3450588.3450601","url":null,"abstract":"As a hub in multimodal transport, there are multiple stakeholders take part in the successful of dry port. This research proposed that the commitment among these stakeholders in terms of cost is essential to the dry ports’ performance. In addition, this research examined that collaborative supply chain as mediation between stakeholders’ cost commitment and dry port firms’ performance. The results showed that the indirect effect is larger than direct effect, which support the partial mediation effect. Finally, this research discussed the implication and limitation of this research.","PeriodicalId":150426,"journal":{"name":"Proceedings of the 2021 4th International Conference on Computers in Management and Business","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123344340","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}
Early diagnosis of maturity carried out by experts in laboratory tests is often not applicable for fast and inexpensive implementation. Using deep learning, an image of various fruits used as data input. Training deep learning models requires large, hard-to-come datasets to perform the task in order to achieve optimal results. In this study. There are 4 research objects, namely apples, oranges, mangoes, and tomatoes used totaling around 9000 training data. Data were trained using 200 epoch iterations using the transfer learning method with the VGG16 models. At the top layer of both models, the same MLP is applied with several parameters, data is converted from RGB to L * a * b with the aim of being a color descriptor on the fruit. Trained using CNN VGG16 with the transfer learning method. The Dropout 0.5 shows the best performance of experiment with 4 scenario that used different technique and show result the best performance with an average score of accuracy rate from scenario 4 is 92%.
专家在实验室测试中对成熟度进行早期诊断，往往不适用于快速和廉价的实施。使用深度学习，将各种水果的图像用作数据输入。训练深度学习模型需要大量的、难以获得的数据集来执行任务，以获得最佳结果。在这项研究中。研究对象有4个，分别是苹果、橘子、芒果和西红柿，总共使用了大约9000个训练数据。使用VGG16模型的迁移学习方法对数据进行200 epoch迭代训练。在这两个模型的顶层，同样的MLP应用了几个参数，数据从RGB转换为L * a * b，目的是作为水果的颜色描述符。使用CNN VGG16进行迁移学习训练。Dropout 0.5显示了使用不同技术的4个场景的最佳性能，并显示了场景4的平均准确率得分为92%的最佳性能。
{"title":"Transfer Learning for Classification of Fruit Ripeness Using VGG16","authors":"Asep Nana Hermana, Dewi Rosmala, M. G. Husada","doi":"10.1145/3450588.3450943","DOIUrl":"https://doi.org/10.1145/3450588.3450943","url":null,"abstract":"Early diagnosis of maturity carried out by experts in laboratory tests is often not applicable for fast and inexpensive implementation. Using deep learning, an image of various fruits used as data input. Training deep learning models requires large, hard-to-come datasets to perform the task in order to achieve optimal results. In this study. There are 4 research objects, namely apples, oranges, mangoes, and tomatoes used totaling around 9000 training data. Data were trained using 200 epoch iterations using the transfer learning method with the VGG16 models. At the top layer of both models, the same MLP is applied with several parameters, data is converted from RGB to L * a * b with the aim of being a color descriptor on the fruit. Trained using CNN VGG16 with the transfer learning method. The Dropout 0.5 shows the best performance of experiment with 4 scenario that used different technique and show result the best performance with an average score of accuracy rate from scenario 4 is 92%.","PeriodicalId":150426,"journal":{"name":"Proceedings of the 2021 4th International Conference on Computers in Management and Business","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121290014","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}
Counterfeiting is one of the biggest threats to the current RFID-based supply chain industry. There are several blockchain solutions suggested which preserve RFID tag uniqueness and bring all the parties to a common platform. However, they are based on some common assumptions such as an adversary may need a large number of tags to retrieve the product details. Besides, these tags can be easily cloned post supply chain and reused for counterfeiting. Lack of proper mechanisms to preserve trade secrets such as volumes and supplier relationships is also a major challenge faced by most of these systems in this competitive market. We propose a novel product ownership tracking system based on blockchain for the supply chain industry which solves these problems while allowing access to the trade secrets only to its supply chain participants. The proposed protocol is based on Shamir's threshold Scheme where a secret key is used to decrypt the trade secrets and its shares are distributed among the supply chain parties during the ownership transfer process. Product verification is done by the consumer as well with the help of the retailer at the time of sale.
{"title":"Avoiding Counterfeits and Achieving Privacy in Supply Chain: A Blockchain Based Approach","authors":"Prem Ratan Baranwal","doi":"10.1145/3450588.3450597","DOIUrl":"https://doi.org/10.1145/3450588.3450597","url":null,"abstract":"Counterfeiting is one of the biggest threats to the current RFID-based supply chain industry. There are several blockchain solutions suggested which preserve RFID tag uniqueness and bring all the parties to a common platform. However, they are based on some common assumptions such as an adversary may need a large number of tags to retrieve the product details. Besides, these tags can be easily cloned post supply chain and reused for counterfeiting. Lack of proper mechanisms to preserve trade secrets such as volumes and supplier relationships is also a major challenge faced by most of these systems in this competitive market. We propose a novel product ownership tracking system based on blockchain for the supply chain industry which solves these problems while allowing access to the trade secrets only to its supply chain participants. The proposed protocol is based on Shamir's threshold Scheme where a secret key is used to decrypt the trade secrets and its shares are distributed among the supply chain parties during the ownership transfer process. Product verification is done by the consumer as well with the help of the retailer at the time of sale.","PeriodicalId":150426,"journal":{"name":"Proceedings of the 2021 4th International Conference on Computers in Management and Business","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114641888","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}
Paraskevi Tsoutsa, Omiros Iatrellis, O. Ragos, P. Fitsilis
Services that collaborate alongside other services or systems for performing tasks, need to be aware of either predetermined or other abrupt and unexpected behaviors in other to adapt theirs. The service behaviors we consider are performed in smart city domains which are dynamic environments where continuously new services appear, that usually have a large variation in the way they perform the same task. We use roles having teamwork behavior to represent the composition procedure in such domains, and we model the team of services through the individual behavior of each participant as well as their group goal. In this paper, we present a framework, which consists of the approach and IT system in order to serve the choreography between a large number of heterogeneous services so as to achieve a seamless and cooperative environment suitable for a smart city. This enables composite city services to adapt their behavior during execution and themselves intervene from inside the team if a possible unexpected behavior happens during the service activity, in order to run proactively and avoid obstacles or collisions. A scenario from a smart city domain illustrates that, services having different teamwork abilities are composed to a new one which inherits teamwork features and combines them to something novel.
{"title":"A Framework for developing Teamwork Enabled Services in Smart City Domains","authors":"Paraskevi Tsoutsa, Omiros Iatrellis, O. Ragos, P. Fitsilis","doi":"10.1145/3450588.3450595","DOIUrl":"https://doi.org/10.1145/3450588.3450595","url":null,"abstract":"Services that collaborate alongside other services or systems for performing tasks, need to be aware of either predetermined or other abrupt and unexpected behaviors in other to adapt theirs. The service behaviors we consider are performed in smart city domains which are dynamic environments where continuously new services appear, that usually have a large variation in the way they perform the same task. We use roles having teamwork behavior to represent the composition procedure in such domains, and we model the team of services through the individual behavior of each participant as well as their group goal. In this paper, we present a framework, which consists of the approach and IT system in order to serve the choreography between a large number of heterogeneous services so as to achieve a seamless and cooperative environment suitable for a smart city. This enables composite city services to adapt their behavior during execution and themselves intervene from inside the team if a possible unexpected behavior happens during the service activity, in order to run proactively and avoid obstacles or collisions. A scenario from a smart city domain illustrates that, services having different teamwork abilities are composed to a new one which inherits teamwork features and combines them to something novel.","PeriodicalId":150426,"journal":{"name":"Proceedings of the 2021 4th International Conference on Computers in Management and Business","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123524251","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}
D. H. Syahchari, E. Kuncoro, H. Saroso, Darjat Sudrajat, E. V. Zanten
A dry port (or land port) is an inland area or intermodal port directly connected to a seaport. Cikarang Dry Port, as one of the best performing dry ports among other dry ports in Indonesia, has only contributed 18% to the loading and unloading volume at Tanjung Priok Port. This study aims to examine the impact of supply chain collaboration and Service Stakeholder Commitment on the performance of dry port companies. The data was collected through a questionnaire that included 55 responses from Cikarang Dry port and maritime logistics companies in Jakarta. The hypotheses were tested by multiple regression. The results of this study confirm that Collaboration in the Supply Chain and the Commitment of Stakeholders in the Service have a positive impact on the performance of port companies. This study provides inspiration for managers to recognize the positive results of supply chain collaboration between service stakeholder engagement organizations to improve port performance in the port supply chain.
{"title":"Effect of Supply Chain Collaboration and Service Stakeholder Commitment on Dry Port Firm Performance","authors":"D. H. Syahchari, E. Kuncoro, H. Saroso, Darjat Sudrajat, E. V. Zanten","doi":"10.1145/3450588.3450602","DOIUrl":"https://doi.org/10.1145/3450588.3450602","url":null,"abstract":"A dry port (or land port) is an inland area or intermodal port directly connected to a seaport. Cikarang Dry Port, as one of the best performing dry ports among other dry ports in Indonesia, has only contributed 18% to the loading and unloading volume at Tanjung Priok Port. This study aims to examine the impact of supply chain collaboration and Service Stakeholder Commitment on the performance of dry port companies. The data was collected through a questionnaire that included 55 responses from Cikarang Dry port and maritime logistics companies in Jakarta. The hypotheses were tested by multiple regression. The results of this study confirm that Collaboration in the Supply Chain and the Commitment of Stakeholders in the Service have a positive impact on the performance of port companies. This study provides inspiration for managers to recognize the positive results of supply chain collaboration between service stakeholder engagement organizations to improve port performance in the port supply chain.","PeriodicalId":150426,"journal":{"name":"Proceedings of the 2021 4th International Conference on Computers in Management and Business","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123557459","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}
P. Brandtner, Chibuzor Udokwu, Farzaneh Darbanian, T. Falatouri
The increased amount of data being generated in virtually every context provides huge potential for a variety of organisational application fields, one of them being Supply Chain Management (SCM). The possibilities and use cases of applying this new type of data, i.e. Big Data (BD), is huge and a large body of research has already been conducted in this area. The current paper aims at identifying the understanding and the applications of BD not from an academic but a practitioners’ point of view. By applying expert interviews, the main aim is to identify (i) a definition of Big Data from SCM practitioners’ point of view, (ii) current SCM activities and processes where BD is already used in practice, (iii) potential future application fields for BD as seen in SCM practice and (iv) main hinderers of BD application. The results show that Big Data is referred to as complex data sets with high volumes and a variety of sources that can't be handled with traditional approaches and require data expert knowledge and SCM domain knowledge to be used in organisational practical. Current applications include the creation of transparency in logistics and SCM, the improvement of demand planning or the support of supplier quality management. The interviewed experts coincide in the view, that BD offers huge potential in future SCM. A shared vision was the implementation of real-time transparency of Supply Chains (SC), the ability to predict the behavior of SCs based on identified data patterns and the possibility to predict the impact of decisions on SCM before they are taken.
{"title":"Applications of Big Data Analytics in Supply Chain Management: Findings from Expert Interviews","authors":"P. Brandtner, Chibuzor Udokwu, Farzaneh Darbanian, T. Falatouri","doi":"10.1145/3450588.3450603","DOIUrl":"https://doi.org/10.1145/3450588.3450603","url":null,"abstract":"The increased amount of data being generated in virtually every context provides huge potential for a variety of organisational application fields, one of them being Supply Chain Management (SCM). The possibilities and use cases of applying this new type of data, i.e. Big Data (BD), is huge and a large body of research has already been conducted in this area. The current paper aims at identifying the understanding and the applications of BD not from an academic but a practitioners’ point of view. By applying expert interviews, the main aim is to identify (i) a definition of Big Data from SCM practitioners’ point of view, (ii) current SCM activities and processes where BD is already used in practice, (iii) potential future application fields for BD as seen in SCM practice and (iv) main hinderers of BD application. The results show that Big Data is referred to as complex data sets with high volumes and a variety of sources that can't be handled with traditional approaches and require data expert knowledge and SCM domain knowledge to be used in organisational practical. Current applications include the creation of transparency in logistics and SCM, the improvement of demand planning or the support of supplier quality management. The interviewed experts coincide in the view, that BD offers huge potential in future SCM. A shared vision was the implementation of real-time transparency of Supply Chains (SC), the ability to predict the behavior of SCs based on identified data patterns and the possibility to predict the impact of decisions on SCM before they are taken.","PeriodicalId":150426,"journal":{"name":"Proceedings of the 2021 4th International Conference on Computers in Management and Business","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133676370","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 current world economy and science are in an era of rapid development, and Beijing is experiencing chronic air pollution. The air quality is important to the travel of people, development of enterprise and normal operation of traffic. PM2.5 and PM10 are the main components which cause the air pollution, and it's very meaningful to predict their concentration in the air [1]. Although some traditional models (like basic linear regression) have been proposed to predict the content of PM2.5/PM10, the quantities of variables included to predict the concentration are few and it executes with low efficiency and low accuracy. In the big data era, it's necessary to build the model which can execute the big data kinds and sets. With the adequate data sets from different meteorological stations in Beijing, we can use the more abundant variables such as mass of SO2, NO2, wind direction and other weather observations to predict the content of PM2.5/PM10. We build the machine learning models with higher efficiency, accuracy and stronger learning ability, whose primary algorithms include: multiple linear regression, decision tree, boosting and random forest based on decision tree and neural network. The result demonstrates that the prediction effect of the models is based on neural network and ensemble learning. Boosting performs best among these models, which achieves R-square 84.2% and 75.7% on the test set for the PM2.5 and PM10, respectively.
{"title":"The Application of Machine Learning Models in the Prediction of PM2.5/PM10 Concentration","authors":"Xinzhi Lin","doi":"10.1145/3450588.3450605","DOIUrl":"https://doi.org/10.1145/3450588.3450605","url":null,"abstract":"The current world economy and science are in an era of rapid development, and Beijing is experiencing chronic air pollution. The air quality is important to the travel of people, development of enterprise and normal operation of traffic. PM2.5 and PM10 are the main components which cause the air pollution, and it's very meaningful to predict their concentration in the air [1]. Although some traditional models (like basic linear regression) have been proposed to predict the content of PM2.5/PM10, the quantities of variables included to predict the concentration are few and it executes with low efficiency and low accuracy. In the big data era, it's necessary to build the model which can execute the big data kinds and sets. With the adequate data sets from different meteorological stations in Beijing, we can use the more abundant variables such as mass of SO2, NO2, wind direction and other weather observations to predict the content of PM2.5/PM10. We build the machine learning models with higher efficiency, accuracy and stronger learning ability, whose primary algorithms include: multiple linear regression, decision tree, boosting and random forest based on decision tree and neural network. The result demonstrates that the prediction effect of the models is based on neural network and ensemble learning. Boosting performs best among these models, which achieves R-square 84.2% and 75.7% on the test set for the PM2.5 and PM10, respectively.","PeriodicalId":150426,"journal":{"name":"Proceedings of the 2021 4th International Conference on Computers in Management and Business","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128993485","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 supply chain is a thriving industry where numerous parties have different interests. Subsequently, the immense volume of data produced is difficult to audit. Some information can be lost or intentionally distorted in the process. Blockchain as an open, public, borderless, neutral, and censorship-resistant architecture can significantly complement supply chains. A new supply chain architecture is proposed in this work, where the tokenized directed acyclic hypergraph (DAG) represents real-world production processes. An anti-aerosol respirator manufacturing is used as an illustration example. By tokenizing all parts of multi-component products, supply chain data is automatically timestamped and secured. Moreover, the DAG design allows one to trace-back all the elements of the final product to their origin. Blockchain can formally audit the entire supply chain without the need to go from place to place. A single incorruptible operations log creates an enabling environment for an unbiased reputation system to emerge.
{"title":"Logging Multi-Component Supply Chain Production in Blockchain","authors":"Y. Madhwal, Ivan Chistiakov, Y. Yanovich","doi":"10.1145/3450588.3450604","DOIUrl":"https://doi.org/10.1145/3450588.3450604","url":null,"abstract":"The supply chain is a thriving industry where numerous parties have different interests. Subsequently, the immense volume of data produced is difficult to audit. Some information can be lost or intentionally distorted in the process. Blockchain as an open, public, borderless, neutral, and censorship-resistant architecture can significantly complement supply chains. A new supply chain architecture is proposed in this work, where the tokenized directed acyclic hypergraph (DAG) represents real-world production processes. An anti-aerosol respirator manufacturing is used as an illustration example. By tokenizing all parts of multi-component products, supply chain data is automatically timestamped and secured. Moreover, the DAG design allows one to trace-back all the elements of the final product to their origin. Blockchain can formally audit the entire supply chain without the need to go from place to place. A single incorruptible operations log creates an enabling environment for an unbiased reputation system to emerge.","PeriodicalId":150426,"journal":{"name":"Proceedings of the 2021 4th International Conference on Computers in Management and Business","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121841209","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}
Business intelligence (BI) is a highly regarded tool used to enhance decision-making and business procedures. Numerous studies argue for its effectiveness. However, the role of end users in ensuring efficient and effective BI solutions has received little attention in the literature. This paper presents a study of interviews with four BI end users to identify their influence on the efficiency of BI solutions. The interviews depart from theories within human-computer interaction (HCI) and information architecture (IA) to reveal users’ perspectives on actively engaging with BI solutions in everyday work tasks. The qualitative interviews demonstrate that users recognize the potential of working with BI and that engaged users support the efficient use of BI solutions in professional organizations. The study concludes that more research should be conducted in this field to increase our understanding of users as critical factors in the efficient use of BI solutions in organizations.
{"title":"The Role of End Users in Efficient Business Intelligence Solutions: A Preliminary Study","authors":"Marianne Buus Jorgensen, Tobias Christensen, Tanja Svarre","doi":"10.1145/3450588.3450590","DOIUrl":"https://doi.org/10.1145/3450588.3450590","url":null,"abstract":"Business intelligence (BI) is a highly regarded tool used to enhance decision-making and business procedures. Numerous studies argue for its effectiveness. However, the role of end users in ensuring efficient and effective BI solutions has received little attention in the literature. This paper presents a study of interviews with four BI end users to identify their influence on the efficiency of BI solutions. The interviews depart from theories within human-computer interaction (HCI) and information architecture (IA) to reveal users’ perspectives on actively engaging with BI solutions in everyday work tasks. The qualitative interviews demonstrate that users recognize the potential of working with BI and that engaged users support the efficient use of BI solutions in professional organizations. The study concludes that more research should be conducted in this field to increase our understanding of users as critical factors in the efficient use of BI solutions in organizations.","PeriodicalId":150426,"journal":{"name":"Proceedings of the 2021 4th International Conference on Computers in Management and Business","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117280202","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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