Pub Date : 2019-09-17DOI: 10.5220/0008064900620073
Hans Friedrich Witschel, Marco Peter, Laura Seiler, Soyhan Parlar, S. G. Grivas
In this work, we develop a case model to structure cases of past digital transformations which act as input data for a recommender system. The purpose of that recommender is to act as an inspiration and support for new cases of digital transformation. To define the case model, case analyses, where 40 cases of past digital transformations are analysed and coded to determine relevant attributes and values, literature research and the particularities of the case for digital change, are used as a basis. The case model is evaluated by means of an experiment where two different scenarios are fed into a prototypical case-based recommender system and then matched, based on an entropically derived weighting system, with the case base that contains cases structured according to the case model. The results not only suggest that the case model’s functionality can be guaranteed, but that a good quality of the given recommendations is achieved by applying a case-based recommender system using the proposed case model.
{"title":"Case Model for the RoboInnoCase Recommender System for Cases of Digital Business Transformation: Structuring Information for a Case of Digital Change","authors":"Hans Friedrich Witschel, Marco Peter, Laura Seiler, Soyhan Parlar, S. G. Grivas","doi":"10.5220/0008064900620073","DOIUrl":"https://doi.org/10.5220/0008064900620073","url":null,"abstract":"In this work, we develop a case model to structure cases of past digital transformations which act as input data for a recommender system. The purpose of that recommender is to act as an inspiration and support for new cases of digital transformation. To define the case model, case analyses, where 40 cases of past digital transformations are analysed and coded to determine relevant attributes and values, literature research and the particularities of the case for digital change, are used as a basis. The case model is evaluated by means of an experiment where two different scenarios are fed into a prototypical case-based recommender system and then matched, based on an entropically derived weighting system, with the case base that contains cases structured according to the case model. The results not only suggest that the case model’s functionality can be guaranteed, but that a good quality of the given recommendations is achieved by applying a case-based recommender system using the proposed case model.","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131988067","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 : 2019-09-17DOI: 10.5220/0008348202710276
C. Monsone, Eunika Mercier-Laurent, Jósvai János
Industry 4.0 aims in renewing processes using available technologies such as robots and other AI techniques implemented in IoT, drones, digital twins and clouds. This metamorphose impacts the whole industry ecosystems including people, information processing and business models. In this context, the accumulated knowledge and know-how can be reused but has also to evolve. This paper focus on the role of digital twins in transforming industrial ecosystems and discuss also the environmental impact.
{"title":"The Overview of Digital Twins in Industry 4.0: Managing the Whole Ecosystem","authors":"C. Monsone, Eunika Mercier-Laurent, Jósvai János","doi":"10.5220/0008348202710276","DOIUrl":"https://doi.org/10.5220/0008348202710276","url":null,"abstract":"Industry 4.0 aims in renewing processes using available technologies such as robots and other AI techniques implemented in IoT, drones, digital twins and clouds. This metamorphose impacts the whole industry ecosystems including people, information processing and business models. In this context, the accumulated knowledge and know-how can be reused but has also to evolve. This paper focus on the role of digital twins in transforming industrial ecosystems and discuss also the environmental impact.","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122740318","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 : 2019-09-17DOI: 10.5220/0008354503410348
P. Hellsten, Jussi Myllärniemi
Today many organizations have come to value knowledge as a production factor. Thus, there is a constant need for getting the information in and sorted. Business intelligence (BI) is a process for systematic acquiring, analyzing, and disseminating data and information from various sources to gain understanding about the business’s environment. This is required for supporting decisions for achieving organization’s business objectives. Literature has introduced models for planning and executing BI. However, as business environments and technologies evolve in a rapid pace, are the models still applicable? Not all recent issues are taken into consideration in the previous models. BI is considered to be integrated into business processes, so the similar evolution is expected to take place. There are two studies investigating BI instigating this study, but there are still questions to be answered. Literature on different models and findings of these studies were combined to form a vision to better match reality. Various issues like users’ active involvement, real-time analysis and presentation, and social media resources were brought up. Practitioners can use the approach to assess their current state of BI activities or planning the organization of BI program.
{"title":"Business Intelligence Process Model Revisited","authors":"P. Hellsten, Jussi Myllärniemi","doi":"10.5220/0008354503410348","DOIUrl":"https://doi.org/10.5220/0008354503410348","url":null,"abstract":"Today many organizations have come to value knowledge as a production factor. Thus, there is a constant need for getting the information in and sorted. Business intelligence (BI) is a process for systematic acquiring, analyzing, and disseminating data and information from various sources to gain understanding about the business’s environment. This is required for supporting decisions for achieving organization’s business objectives. Literature has introduced models for planning and executing BI. However, as business environments and technologies evolve in a rapid pace, are the models still applicable? Not all recent issues are taken into consideration in the previous models. BI is considered to be integrated into business processes, so the similar evolution is expected to take place. There are two studies investigating BI instigating this study, but there are still questions to be answered. Literature on different models and findings of these studies were combined to form a vision to better match reality. Various issues like users’ active involvement, real-time analysis and presentation, and social media resources were brought up. Practitioners can use the approach to assess their current state of BI activities or planning the organization of BI program.","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121218879","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 : 2019-09-17DOI: 10.5220/0008366403910397
M. A. Setiawan
In recent years, the world has witnessed how internet connectivity is exponentially growing in cities around the world. Universitas Islam Indonesia (UII) as one of biggest private universities in Indonesia is also seeing the similar trend like the rest of the world. With more than 700 high density access points and roughly 30,000 users, most of internet connectivity in campus is provided from WiFi access. After 802.1x WiFi authentication-method deployment, UII saw an opportunity to utilise WiFi metadata as a source of business intelligence. Previously, many business processes or managerial decisions in the university were decided by some hidden assumptions and approximations. These assumptions and approximations sometimes created sub-optimal managerial decisions. To improve the strategic decision, we proposed an evidence-based management based on WiFi data. We utilise this data to extract spatial knowledge, movement behaviour, seamless attendance record, and traffic analysis for marketing purpose. The results show promising result where many of university decision is helped by the result given from the knowledge extraction system. Managements can act faster as information is elicited from tacit knowledge within WiFi metada in real time and more accurate.
近年来，世界见证了互联网连接在世界各地城市的指数级增长。印尼伊斯兰大学(Universitas Islam Indonesia, UII)作为印尼最大的私立大学之一，也看到了与世界其他地区类似的趋势。校园内有700多个高密度接入点和大约3万用户，大部分互联网连接都是通过WiFi接入提供的。在802.1x WiFi认证方法部署之后，UII看到了利用WiFi元数据作为商业智能来源的机会。以前，大学中的许多业务流程或管理决策都是由一些隐藏的假设和近似决定的。这些假设和近似有时会产生次优的管理决策。为了完善战略决策，我们提出了基于WiFi数据的循证管理。我们利用这些数据提取空间知识、运动行为、无缝考勤记录和流量分析，用于营销目的。结果表明，知识抽取系统的结果对高校决策有一定的帮助。WiFi元数据中的隐性知识可以实时、准确地获取信息，管理层可以更快地采取行动。
{"title":"How 802.1x Enhances Knowledge Extraction from Large Scale Campus WiFi Deployment","authors":"M. A. Setiawan","doi":"10.5220/0008366403910397","DOIUrl":"https://doi.org/10.5220/0008366403910397","url":null,"abstract":"In recent years, the world has witnessed how internet connectivity is exponentially growing in cities around the world. Universitas Islam Indonesia (UII) as one of biggest private universities in Indonesia is also seeing the similar trend like the rest of the world. With more than 700 high density access points and roughly 30,000 users, most of internet connectivity in campus is provided from WiFi access. After 802.1x WiFi authentication-method deployment, UII saw an opportunity to utilise WiFi metadata as a source of business intelligence. Previously, many business processes or managerial decisions in the university were decided by some hidden assumptions and approximations. These assumptions and approximations sometimes created sub-optimal managerial decisions. To improve the strategic decision, we proposed an evidence-based management based on WiFi data. We utilise this data to extract spatial knowledge, movement behaviour, seamless attendance record, and traffic analysis for marketing purpose. The results show promising result where many of university decision is helped by the result given from the knowledge extraction system. Managements can act faster as information is elicited from tacit knowledge within WiFi metada in real time and more accurate.","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131022018","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 : 2019-09-17DOI: 10.5220/0008348902770284
N. Badr
Distributed ledger technology has seen its debut into communities of practice in healthcare where the reliance on knowledge sharing between participants postulates the foundations of secure and distributed knowledge, especially in some sensitive context, such as patient information. This knowledge is essential for the practice of care from patient contact to research, pharmaceutical supply chain, medication adherence and management of the plethora of bedside data into a collection of knowledge about the patient, essential to quality care. We introduce different schools of thought and implementation contexts of the distributed ledger technology or Blockchain. We provide an overview of Blockchain and Distributed Ledger Technology, focused on the Healthcare industry, as an initial assessment of the validity of an application of Distributed Ledger Technology in a specific knowledge management model to solve problems related to knowledge sharing in medical knowledge management systems. The paper summarizes some instances of most likely and unlikely uses of Blockchain in the healthcare setting. The paper also introduces a few use cases where some short-term benefits from such implementation.
{"title":"Blockchain or Distributed Ledger Technology What Is in It for the Healthcare Industry?","authors":"N. Badr","doi":"10.5220/0008348902770284","DOIUrl":"https://doi.org/10.5220/0008348902770284","url":null,"abstract":"Distributed ledger technology has seen its debut into communities of practice in healthcare where the reliance on knowledge sharing between participants postulates the foundations of secure and distributed knowledge, especially in some sensitive context, such as patient information. This knowledge is essential for the practice of care from patient contact to research, pharmaceutical supply chain, medication adherence and management of the plethora of bedside data into a collection of knowledge about the patient, essential to quality care. We introduce different schools of thought and implementation contexts of the distributed ledger technology or Blockchain. We provide an overview of Blockchain and Distributed Ledger Technology, focused on the Healthcare industry, as an initial assessment of the validity of an application of Distributed Ledger Technology in a specific knowledge management model to solve problems related to knowledge sharing in medical knowledge management systems. The paper summarizes some instances of most likely and unlikely uses of Blockchain in the healthcare setting. The paper also introduces a few use cases where some short-term benefits from such implementation.","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131237085","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 : 2019-09-17DOI: 10.5220/0008366003700376
Jussi Myllärniemi, Nina Helander, Samuli Pekkola
Understanding data-based value creation helps organizations to enhance its decision-making and to renew their business operations. However, organizations aiming to use modern data analytics face several severe challenges that are not usually so evident or visible beforehand. In this paper we study a Finnish manufacturing company’s data empowerment and information and knowledge management practices in order to identify the potential challenges related to modern data-based value creation within industrial context. The empirical data is consisted of group discussions, relevant data sets acquired from the case company’s information systems, and lastly, 12 thematic interviews of the key actors in the company in relation to service development. The study provides valuable insights for managing service development and decision-making and creates understanding on data-based value creation. Achieved understanding provides meaningful knowledge for organizations utilizing or having plans to utilize, for example, data analytic methods in their businesses.
{"title":"Challenges in Developing Data-based Value Creation","authors":"Jussi Myllärniemi, Nina Helander, Samuli Pekkola","doi":"10.5220/0008366003700376","DOIUrl":"https://doi.org/10.5220/0008366003700376","url":null,"abstract":"Understanding data-based value creation helps organizations to enhance its decision-making and to renew their business operations. However, organizations aiming to use modern data analytics face several severe challenges that are not usually so evident or visible beforehand. In this paper we study a Finnish manufacturing company’s data empowerment and information and knowledge management practices in order to identify the potential challenges related to modern data-based value creation within industrial context. The empirical data is consisted of group discussions, relevant data sets acquired from the case company’s information systems, and lastly, 12 thematic interviews of the key actors in the company in relation to service development. The study provides valuable insights for managing service development and decision-making and creates understanding on data-based value creation. Achieved understanding provides meaningful knowledge for organizations utilizing or having plans to utilize, for example, data analytic methods in their businesses.","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116183052","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 : 2019-09-17DOI: 10.5220/0008480701460156
Ying Zhao, T. Kendall, Riqui Schwamm
MarineNet is an US Marine Corps system that provides one-stop shop and 24/7 access to thousands of online courses, videos, and educational materials for the whole Marine Corps. The need for the e-learning organization is to identify the significant capabilities and measures of effectiveness (MoEs) for appropriate e-learning, and then design and identify how to collect and analyze the big data to achieve an effective integration of analytic within the MarineNet learning ecosystem. We show this as a use case and the sample data of the MarineNet CDET website on how to design MoEs that can guide how to collect big data, analyze and learn from users’ behavior data such as clickstreams to optimize all stakeholders’ interests and results for a typical e-organization. We also show the processes and deep analytics for exploratory and predictive analysis. The framework helps e-organization determine where investment is best spent to create the biggest impact for performance results.
{"title":"Measures of Effectiveness (MoEs) for MarineNet: A Case Study for a Smart e-Learning Organization","authors":"Ying Zhao, T. Kendall, Riqui Schwamm","doi":"10.5220/0008480701460156","DOIUrl":"https://doi.org/10.5220/0008480701460156","url":null,"abstract":"MarineNet is an US Marine Corps system that provides one-stop shop and 24/7 access to thousands of online courses, videos, and educational materials for the whole Marine Corps. The need for the e-learning organization is to identify the significant capabilities and measures of effectiveness (MoEs) for appropriate e-learning, and then design and identify how to collect and analyze the big data to achieve an effective integration of analytic within the MarineNet learning ecosystem. We show this as a use case and the sample data of the MarineNet CDET website on how to design MoEs that can guide how to collect big data, analyze and learn from users’ behavior data such as clickstreams to optimize all stakeholders’ interests and results for a typical e-organization. We also show the processes and deep analytics for exploratory and predictive analysis. The framework helps e-organization determine where investment is best spent to create the biggest impact for performance results.","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121016990","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 : 2019-09-17DOI: 10.5220/0008349602910298
Alexander Heußner, Moritz Höser, Sven Ziemer
In today’s data-centric world, the data-awareness challenge is a crucial touchstone to existing knowledge management technologies. Adaptive, stakeholder-centric knowledge modelling approaches provide a solid ground to tackle this challenge and open the door to enrich knowledge management by a socio-technological perspective. This paper proposes the use of a socio-technological approach to overcome the data-awereness challenge by treating knowledge on data as a crucial business asset. Here, a data awareness generating, iterative, incremental knowledge elicitation technique based on a multi-perspective, multi-modal diagrammatic knowledge representation language serves as proof of concept.
{"title":"Towards Data Awareness by Socio-technological Knowledge Management","authors":"Alexander Heußner, Moritz Höser, Sven Ziemer","doi":"10.5220/0008349602910298","DOIUrl":"https://doi.org/10.5220/0008349602910298","url":null,"abstract":"In today’s data-centric world, the data-awareness challenge is a crucial touchstone to existing knowledge management technologies. Adaptive, stakeholder-centric knowledge modelling approaches provide a solid ground to tackle this challenge and open the door to enrich knowledge management by a socio-technological perspective. This paper proposes the use of a socio-technological approach to overcome the data-awereness challenge by treating knowledge on data as a crucial business asset. Here, a data awareness generating, iterative, incremental knowledge elicitation technique based on a multi-perspective, multi-modal diagrammatic knowledge representation language serves as proof of concept.","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124551625","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 : 2019-09-17DOI: 10.5220/0008494204270434
Himanshu Joshi, Deepak Chawla
The study proposes a comprehensive model comprising of various relationships between antecedents to effective Knowledge Management (KM) and organizational performance. A review of literature besides a focus group discussion and a personal interview were used to design an instrument and propose seven hypotheses. Data was collected from 127 managers working in private sector organizations in India. To test the hypotheses, Structural Equation Modelling (SEM) analysis through Partial Least Squares (PLS) was used. The results indicate that although all the hypotheses had the desired positive sign, five out of them were significant. This paper presents empirical evidence of the role of KM planning and design (KMPD), KM implementation and evaluation (KMIE), Technology in KM (TKM), Culture in KM (CKM), Leadership in KM (LKM) and Structure in KM (SKM) in enhancing organizational performance. Further, improvements in organizational performance leads to improvements in financial performance.
{"title":"Knowledge Management and Its Impact on Organizational Performance in the Private Sector in India","authors":"Himanshu Joshi, Deepak Chawla","doi":"10.5220/0008494204270434","DOIUrl":"https://doi.org/10.5220/0008494204270434","url":null,"abstract":"The study proposes a comprehensive model comprising of various relationships between antecedents to effective Knowledge Management (KM) and organizational performance. A review of literature besides a focus group discussion and a personal interview were used to design an instrument and propose seven hypotheses. Data was collected from 127 managers working in private sector organizations in India. To test the hypotheses, Structural Equation Modelling (SEM) analysis through Partial Least Squares (PLS) was used. The results indicate that although all the hypotheses had the desired positive sign, five out of them were significant. This paper presents empirical evidence of the role of KM planning and design (KMPD), KM implementation and evaluation (KMIE), Technology in KM (TKM), Culture in KM (CKM), Leadership in KM (LKM) and Structure in KM (SKM) in enhancing organizational performance. Further, improvements in organizational performance leads to improvements in financial performance.","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125702032","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 : 2019-09-17DOI: 10.5220/0008355901300138
Takuya Washio, Takumi Ohashi, Miki Saijo
Agricultural industry needs to face both increasing demand from a growing population and transform in order to enhance its sustainability. Animal welfare, an aspect of this transformation, is still an unfamiliar concept for consumers in Japan, although this is expected to catch up with the global trend. Researchers have been working around the world to explore consumer behavior in markets, but few such studies have been performed in Japan. This study aimed to explore consumer behavior concerning high animal-welfare products in Japan, using the Theory of Planned Behavior (TPB). An online questionnaire was used to identify consumer characteristics and perceived attributes of high animal-welfare products among 620 consumers. We found that awareness of animal welfare was still low among Japanese consumers, and was not related to demographic characteristics. Two components out of three which are considered in TPB, attitude and social norm, were likely related to consumers’ willingness to purchase high animal-welfare products. Consumers’ empathy with, and psychological responses to, farmers and animals are suggested to be related to their willingness to purchase.
{"title":"Consumers' Willingness to Purchase High Animal-welfare Beef Products in Japan: Exploratory Research based on the Theory of Planned Behavior","authors":"Takuya Washio, Takumi Ohashi, Miki Saijo","doi":"10.5220/0008355901300138","DOIUrl":"https://doi.org/10.5220/0008355901300138","url":null,"abstract":"Agricultural industry needs to face both increasing demand from a growing population and transform in order to enhance its sustainability. Animal welfare, an aspect of this transformation, is still an unfamiliar concept for consumers in Japan, although this is expected to catch up with the global trend. Researchers have been working around the world to explore consumer behavior in markets, but few such studies have been performed in Japan. This study aimed to explore consumer behavior concerning high animal-welfare products in Japan, using the Theory of Planned Behavior (TPB). An online questionnaire was used to identify consumer characteristics and perceived attributes of high animal-welfare products among 620 consumers. We found that awareness of animal welfare was still low among Japanese consumers, and was not related to demographic characteristics. Two components out of three which are considered in TPB, attitude and social norm, were likely related to consumers’ willingness to purchase high animal-welfare products. Consumers’ empathy with, and psychological responses to, farmers and animals are suggested to be related to their willingness to purchase.","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127001441","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：