Pub Date : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978684
R. Jiang
A typical multi-criteria decision analysis (MCDA) problem aims to rank a set of alternatives according to a set of criteria. The problem deals with selection of criteria, determination of criteria weights, normalization of criteria scores and aggregation of normalized criteria scores. The focus of this paper is on the normalization method. Most MCDA methods (e.g., AHP and TOPSIS) use a linear normalization method. Its main drawback is that the “magnitudes” of the normalized criteria scores of different criteria are different in terms of average. The difference in magnitude actually changes the relative importances of criteria so that the final rankings of alternatives may not appropriately reflect the preference of decision makers. To address this issue, a novel normalization method is proposed. The proposed normalization method uses a Gaussian value function to transform the criteria scores to interval (0, 1). The parameters of the value function are determined so that the average and variance of the normalized criteria scores are equal to pre-specified constants. A real-world dataset is used to illustrate the advantages of the proposed normalization method.
{"title":"A Novel Normalization Method for Using in Multiple Criteria Decision Analysis","authors":"R. Jiang","doi":"10.1109/IEEM44572.2019.8978684","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978684","url":null,"abstract":"A typical multi-criteria decision analysis (MCDA) problem aims to rank a set of alternatives according to a set of criteria. The problem deals with selection of criteria, determination of criteria weights, normalization of criteria scores and aggregation of normalized criteria scores. The focus of this paper is on the normalization method. Most MCDA methods (e.g., AHP and TOPSIS) use a linear normalization method. Its main drawback is that the “magnitudes” of the normalized criteria scores of different criteria are different in terms of average. The difference in magnitude actually changes the relative importances of criteria so that the final rankings of alternatives may not appropriately reflect the preference of decision makers. To address this issue, a novel normalization method is proposed. The proposed normalization method uses a Gaussian value function to transform the criteria scores to interval (0, 1). The parameters of the value function are determined so that the average and variance of the normalized criteria scores are equal to pre-specified constants. A real-world dataset is used to illustrate the advantages of the proposed normalization method.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115127200","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}
Pub Date : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978652
Elok Pitaloka, N. Masruroh, Shi-Woei Lin
A growing number of studies in the newsvendor problem provided evidence that subjects' ordering behavior deviates from the optimal order quantity. Furthermore, almost all existing studies only engage students as subjects, and it leaves an essential question of whether the insights of those studies can be applied in real business practices. In this empirical study, we experimented with investigating decision biases in the newsvendor setting and presenting a structured comparison of the order decisions made by managers and students. We also proposed a Decision Support System as a debiasing strategy to prevent the order decision bias. To provide a piece of evidence about the effectiveness of the proposed DSS, we experimented with comparing the ordering behavior, before and after DSS implementation. This study found that both managers and students showed demand chasing bias, but the magnitude of the bias differed significantly. This study also showed that an informational DSS generally improved inventory decision-making performance in terms of adjustment behavior and long-term profitability.
{"title":"Decision Bias in the Newsvendor Problem: On the Comparison of Managers and Students as Newsvendors with Decision Support System as Debiasing Strategy","authors":"Elok Pitaloka, N. Masruroh, Shi-Woei Lin","doi":"10.1109/IEEM44572.2019.8978652","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978652","url":null,"abstract":"A growing number of studies in the newsvendor problem provided evidence that subjects' ordering behavior deviates from the optimal order quantity. Furthermore, almost all existing studies only engage students as subjects, and it leaves an essential question of whether the insights of those studies can be applied in real business practices. In this empirical study, we experimented with investigating decision biases in the newsvendor setting and presenting a structured comparison of the order decisions made by managers and students. We also proposed a Decision Support System as a debiasing strategy to prevent the order decision bias. To provide a piece of evidence about the effectiveness of the proposed DSS, we experimented with comparing the ordering behavior, before and after DSS implementation. This study found that both managers and students showed demand chasing bias, but the magnitude of the bias differed significantly. This study also showed that an informational DSS generally improved inventory decision-making performance in terms of adjustment behavior and long-term profitability.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115387672","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}
Pub Date : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978681
Leif Sundberg, Katarina Lindblad-Gidlund, L. Olsson
The purpose of this paper is to assess the digital maturity of the manufacturing industry in a Swedish region. Data is collected using a survey conducted among the manufacturing industry in the region. Variables are based on prior research on digital maturity and Industry 4.0, and analyzed using descriptive and inferential statistical analysis. An initial finding was that several of the small organizations within the manufacturing industry does not have a basic digital presence in the form of a website, email or social media accounts, which calls for alternative approaches when assessing and developing digital maturity among these actors. The results from the survey reveal that perceived potential of digitalization and organizational enablers are ranked higher than actual operationalizations in the form of technology implementations and projects. Moreover, the digital maturity varies on variables such as organization size, location of customer base, and level of technological output. Organizations with a high degree of female employees perceive a higher digital maturity concerning some variables, which is an interesting subject for further studies. The overall conclusion is that a large part of the industrial sector in the region has not implemented anything that resemble the concept of Industry 4.0 in the literature.
{"title":"Towards Industry 4.0? Digital Maturity of the Manufacturing Industry in a Swedish Region","authors":"Leif Sundberg, Katarina Lindblad-Gidlund, L. Olsson","doi":"10.1109/IEEM44572.2019.8978681","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978681","url":null,"abstract":"The purpose of this paper is to assess the digital maturity of the manufacturing industry in a Swedish region. Data is collected using a survey conducted among the manufacturing industry in the region. Variables are based on prior research on digital maturity and Industry 4.0, and analyzed using descriptive and inferential statistical analysis. An initial finding was that several of the small organizations within the manufacturing industry does not have a basic digital presence in the form of a website, email or social media accounts, which calls for alternative approaches when assessing and developing digital maturity among these actors. The results from the survey reveal that perceived potential of digitalization and organizational enablers are ranked higher than actual operationalizations in the form of technology implementations and projects. Moreover, the digital maturity varies on variables such as organization size, location of customer base, and level of technological output. Organizations with a high degree of female employees perceive a higher digital maturity concerning some variables, which is an interesting subject for further studies. The overall conclusion is that a large part of the industrial sector in the region has not implemented anything that resemble the concept of Industry 4.0 in the literature.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116705873","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}
Pub Date : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978677
Mahmood Ali, Mayar Tarbulsi, Asim Majeed
The advancement in information technology is transforming the business environment. The logistics industry is a major beneficiary since it has enabled them to better coordinate and integrate their operations while ensuring realtime tracking. However, the implementation of new technology introduces a unique set of challenges. This paper presents a case study with the main objective to understand the challenges an organisation face in implementing a freight tracking system. Adopting qualitative methods, in-depth interviews are conducted with the implementation team, users and major stakeholders. The in-depth analysis suggests that minimising user resistance and change management strategy forms the basis of successful implementation. In addition, establishing new rules and procedure, learning and development environment, accountability and communication are the pillars for the smooth transition to the new system.
{"title":"Challenges in Implementing Transportation Tracking System in Saudi Arabia","authors":"Mahmood Ali, Mayar Tarbulsi, Asim Majeed","doi":"10.1109/IEEM44572.2019.8978677","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978677","url":null,"abstract":"The advancement in information technology is transforming the business environment. The logistics industry is a major beneficiary since it has enabled them to better coordinate and integrate their operations while ensuring realtime tracking. However, the implementation of new technology introduces a unique set of challenges. This paper presents a case study with the main objective to understand the challenges an organisation face in implementing a freight tracking system. Adopting qualitative methods, in-depth interviews are conducted with the implementation team, users and major stakeholders. The in-depth analysis suggests that minimising user resistance and change management strategy forms the basis of successful implementation. In addition, establishing new rules and procedure, learning and development environment, accountability and communication are the pillars for the smooth transition to the new system.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117205095","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}
Pub Date : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978509
Andrés Morales-Forero, S. Bassetto
In this paper, a semi-supervised methodology for anomaly detection and diagnosis is proposed. The approach combines techniques of non-parametric statistics, quality control, and deep learning to provide a tool that allows an adequate and online detection of faults in a production system and a diagnosis of the factors associated with the failure. We propose a semi-supervised neural network for detection and a particular control chart called Open Up for the diagnosis. This neural network is composed of the adjustment of an autoencoder followed by a Long Short-Term Memory model (LSTM). Open Up is used in the last stage to identify the variables associated with the anomaly. This proposal achieves a high correct classification rate using real data of a monitoring system in paper manufacturing and simulated data from the Tennessee Eastman Process.
{"title":"Case Study: A Semi-Supervised Methodology for Anomaly Detection and Diagnosis","authors":"Andrés Morales-Forero, S. Bassetto","doi":"10.1109/IEEM44572.2019.8978509","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978509","url":null,"abstract":"In this paper, a semi-supervised methodology for anomaly detection and diagnosis is proposed. The approach combines techniques of non-parametric statistics, quality control, and deep learning to provide a tool that allows an adequate and online detection of faults in a production system and a diagnosis of the factors associated with the failure. We propose a semi-supervised neural network for detection and a particular control chart called Open Up for the diagnosis. This neural network is composed of the adjustment of an autoencoder followed by a Long Short-Term Memory model (LSTM). Open Up is used in the last stage to identify the variables associated with the anomaly. This proposal achieves a high correct classification rate using real data of a monitoring system in paper manufacturing and simulated data from the Tennessee Eastman Process.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117238483","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}
Pub Date : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978852
Hanan Yakubu, C. Kwong
As markets become increasingly competitive, most businesses have adopted modern practices that helps them to enhance the competitiveness of their products. Such practices involve the use of internet though which companies gain insights into the concerns of their customers. For instance, the proliferation of e-commerce websites has enabled consumers to voice their opinions on the products they have purchased. This study proposes a methodology for modelling customer satisfaction (CS) based on online reviews using a new multigene genetic programming based fuzzy regression (MGGP-FR). Polynomial structures of CS models were developed by employing the multigene genetic programming method. The fuzzy coefficients of the polynomial structures were then determined using the fuzzy regression analysis. The proposed method was illustrated using an electronic hair dryer as a case study. The validation test results indicated that MGGP-FR the outperformed the genetic programming based fuzzy regression and the fuzzy regression analysis in terms of prediction errors.
{"title":"Multigene Genetic Programming Based Fuzzy Regression for Modelling Customer Satisfaction Based on Online Reviews","authors":"Hanan Yakubu, C. Kwong","doi":"10.1109/IEEM44572.2019.8978852","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978852","url":null,"abstract":"As markets become increasingly competitive, most businesses have adopted modern practices that helps them to enhance the competitiveness of their products. Such practices involve the use of internet though which companies gain insights into the concerns of their customers. For instance, the proliferation of e-commerce websites has enabled consumers to voice their opinions on the products they have purchased. This study proposes a methodology for modelling customer satisfaction (CS) based on online reviews using a new multigene genetic programming based fuzzy regression (MGGP-FR). Polynomial structures of CS models were developed by employing the multigene genetic programming method. The fuzzy coefficients of the polynomial structures were then determined using the fuzzy regression analysis. The proposed method was illustrated using an electronic hair dryer as a case study. The validation test results indicated that MGGP-FR the outperformed the genetic programming based fuzzy regression and the fuzzy regression analysis in terms of prediction errors.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127117043","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}
Pub Date : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978893
Annika Hasselblad
This exploratory study identifies two existing conceptions of welfare technology identified from policy and practice. By using policy documents and previous research together with a survey of primary care managers and students, this paper identifies one wider and one narrower conception. The wider tends to include the whole society, making it difficult to specify end-user requirements, while the narrower focuses on the more vulnerable people in society, which can exclude possible users.
{"title":"Welfare Technology Policy and Practice - A Conceptual Analysis","authors":"Annika Hasselblad","doi":"10.1109/IEEM44572.2019.8978893","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978893","url":null,"abstract":"This exploratory study identifies two existing conceptions of welfare technology identified from policy and practice. By using policy documents and previous research together with a survey of primary care managers and students, this paper identifies one wider and one narrower conception. The wider tends to include the whole society, making it difficult to specify end-user requirements, while the narrower focuses on the more vulnerable people in society, which can exclude possible users.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124876773","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}
Pub Date : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978863
T. Bigler, P. Baumann, M. Kammermann
Direct marketing has become a fundamental advertising method in many industries. In direct marketing, companies target specific customers with personalized product offers. By optimally assigning customers to direct marketing activities, the effectiveness of direct marketing campaigns can be greatly increased. In this paper, we study a real-world customer assignment problem of a leading telecommunications provider in Switzerland. The planning problem contains many business and customer-specific constraints that have not yet been covered in the literature. We propose a binary linear programming formulation that solves instances involving up to one million customers and over 100 direct marketing activities to optimality in short running time. The novel formulation delivers substantially better solutions in terms of expected profit than the current practice at the company.
{"title":"Optimizing Customer Assignments to Direct Marketing Activities: A Binary Linear Programming Formulation","authors":"T. Bigler, P. Baumann, M. Kammermann","doi":"10.1109/IEEM44572.2019.8978863","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978863","url":null,"abstract":"Direct marketing has become a fundamental advertising method in many industries. In direct marketing, companies target specific customers with personalized product offers. By optimally assigning customers to direct marketing activities, the effectiveness of direct marketing campaigns can be greatly increased. In this paper, we study a real-world customer assignment problem of a leading telecommunications provider in Switzerland. The planning problem contains many business and customer-specific constraints that have not yet been covered in the literature. We propose a binary linear programming formulation that solves instances involving up to one million customers and over 100 direct marketing activities to optimality in short running time. The novel formulation delivers substantially better solutions in terms of expected profit than the current practice at the company.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124886714","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}
Pub Date : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978872
O. Olanrewaju
Industry participation is pivotal to the economic growth of any nation. However, its energy demand, if not managed can cripple the economy. South Africa recently experienced load shedding resulting in the decreased of manufacturing and mining outputs among others dragging down the economic growth. This study focused on understanding those factors responsible for the energy consumption in the following industrial sub-sectors: basic chemicals, non-metallic minerals, basic iron and steel, basic non-ferrous metals and other manufacturing industries, between 1994 and 20016 through the application of Logarithmic Mean Divisia Index (LMDI), a form of Index Decomposition Analysis (IDA). These factors are the activity, structure and intensity. Among the three factors, activity was the most responsible for the increase in the amount of energy consumed whereas intensity factor contributed to minimizing energy consumption. Structural effect contributed minimally to the consumption of energy. The results implied concentrating more on policies that would affect the activity effect.
{"title":"Analysing Impacts Responsible for South Africa's Energy Consumption: LMDI Application","authors":"O. Olanrewaju","doi":"10.1109/IEEM44572.2019.8978872","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978872","url":null,"abstract":"Industry participation is pivotal to the economic growth of any nation. However, its energy demand, if not managed can cripple the economy. South Africa recently experienced load shedding resulting in the decreased of manufacturing and mining outputs among others dragging down the economic growth. This study focused on understanding those factors responsible for the energy consumption in the following industrial sub-sectors: basic chemicals, non-metallic minerals, basic iron and steel, basic non-ferrous metals and other manufacturing industries, between 1994 and 20016 through the application of Logarithmic Mean Divisia Index (LMDI), a form of Index Decomposition Analysis (IDA). These factors are the activity, structure and intensity. Among the three factors, activity was the most responsible for the increase in the amount of energy consumed whereas intensity factor contributed to minimizing energy consumption. Structural effect contributed minimally to the consumption of energy. The results implied concentrating more on policies that would affect the activity effect.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123807039","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}
Pub Date : 2019-12-01DOI: 10.1109/IEEM44572.2019.8978849
Keiya Mori, F. Takeda
This study examines how target companies react to the Internet flaming and how the reactions affect their stock prices, based on the 154 flaming events targeting Japanese listed companies from 2009 to 2018. Among 154 flaming events, target companies ignored the flaming and did not take any actions in 80 cases while actions were taken in 74 events. These actions include 49 official apologies, 18 objections, and 7 deletions of comments without appropriate apologies. Using a probit model, we demonstrate that flamed companies are more likely to take actions if their stock prices drop immediately after the flaming, the incident is published in the newspaper, or they have higher PBR or sales growth. We also show whether the effect dies down in the short term depends on responses of the flamed companies. When a company apologizes or deletes comments, its stock price tends to decrease significantly immediately after the outbreak of the flaming, but this decrease does not continue after a few days. In contrast, when the company objects to the flaming, its stock price starts to decline a few days after the flaming outbreak and continues to further decline.
{"title":"Corporate Responses to Internet Flaming: Evidence from Japan","authors":"Keiya Mori, F. Takeda","doi":"10.1109/IEEM44572.2019.8978849","DOIUrl":"https://doi.org/10.1109/IEEM44572.2019.8978849","url":null,"abstract":"This study examines how target companies react to the Internet flaming and how the reactions affect their stock prices, based on the 154 flaming events targeting Japanese listed companies from 2009 to 2018. Among 154 flaming events, target companies ignored the flaming and did not take any actions in 80 cases while actions were taken in 74 events. These actions include 49 official apologies, 18 objections, and 7 deletions of comments without appropriate apologies. Using a probit model, we demonstrate that flamed companies are more likely to take actions if their stock prices drop immediately after the flaming, the incident is published in the newspaper, or they have higher PBR or sales growth. We also show whether the effect dies down in the short term depends on responses of the flamed companies. When a company apologizes or deletes comments, its stock price tends to decrease significantly immediately after the outbreak of the flaming, but this decrease does not continue after a few days. In contrast, when the company objects to the flaming, its stock price starts to decline a few days after the flaming outbreak and continues to further decline.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125279736","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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