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2019 | Buch

Knowledge Management in Organizations

14th International Conference, KMO 2019, Zamora, Spain, July 15–18, 2019, Proceedings

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This book contains the refereed proceedings of the 14th International Conference on Knowledge Management in Organizations, KMO 2019, held in Zamora, Spain, in July 2019.

The 46 papers accepted for KMO 2018 were selected from 109 submissions and are organized in topical sections on: knowledge management models and analysis; knowledge transfer and learning; knowledge and service innovation; knowledge creation; knowledge and organization; information systems and information science; data mining and intelligent science; social networks and social aspects of KM; big data and IoT; and new trends in IT.

Inhaltsverzeichnis

Frontmatter

Knowledge Management Models and Analysis

Frontmatter
Enabling Technologies of Industry 4.0 and Their Global Forerunners: An Empirical Study of the Web of Science Database

Knowledge management in organizations brings many benefits for R&D operations of companies and corporations. This empirical study demonstrates the power of large database analyses for industrial strategies and policy. The study is based on the Web of Science database (Core Collection, ISI) and provides an overview of the core enabling technologies of Industry 4.0, as well as the countries and regions at the forefront of the academic landscape within these technologies. The core technologies and technologies of Industry 4.0 and Manufacturing 4.0 are: (1) Internet of Things and related technologies (2) Radio Frequency Identification (RFID), (3) Wireless Sensor Network (WSN), and (4) ubiquitous computing. It also covers (5) Cloud computing technologies, including (6) Virtualization and (7) Manufacturing as a Service (MaaS), and new (8) Cyber-physical systems, such as (9) Digital Twin-technology and (10) Smart & Connected Communities. Finally, important for the manufacturing integration Industry 4.0 enabling technologies are (11) Service Oriented Architecture (SOA), (12) Business Process Management (BPM), and (13) Information Integration and Interoperability. All these key technologies and technology drivers were analysed in this empirical demonstration of knowledge management.

Mikkel Stein Knudsen, Jari Kaivo-oja, Theresa Lauraeus
Scientometric Analysis of Knowledge in the Context of Project Management
Subject Area: (Knowledge Management and Project Management)

This research work carried out a meticulous scientometric analysis about the knowledge management in the context of project management, in order to build a detailed state of the art about the matter of study, allowing the identification of main elements investigated on the scientific literature about the knowledge on this context. Firstly; a theoretical framework was build, allowing the identification of concepts about knowledge management and scientometric analysis. Secondly; a methodology was constructed, by integrating analytics and measurement tools, 881 publications related to the knowledge management on projects were identified on the main databases, later, detailed bibliometric analysis were conducted in order to highlight the most investigated topics, main authors (Gemino, Carrillo and Reich) and sources with higher amount of documents and quotes on the scientific literature about the matter of study (International Journal of Project Management, Project Management Journal and Journal of Knowledge Management). Afterwards; the results of the scientometric analysis about the knowledge on projects were documented. And finally; conclusions were established and as future lines of research were identified the impact of knowledge management on project performance, leadership, management, and innovation.

César Rincón-González, Flor Nancy Díaz-Piraquive
Modeling the Colombian Swine Supply Chain from a Knowledge Management Perspective

The Colombian swine supply chain (CSSC) has a low level of national competitiveness compared to other supply chains such as coffee and fruit. While consumption of pork has raised in Colombia, most dealers are importing it from The United States and Canada, since farmers in those countries have received agricultural incentives to breed and commercialize pigs. Additionally, agribusiness have received technological developments to share information and develop the swine sector. This article aims to state theoretical Knowledge Management (KM) dimensions for CSSC that were built under authors’ assumptions on the literature. These were proposed to identify the competitiveness level in CSSC, because only two different kinds of measuring for swine competitiveness were found, but on the other hand, no model about Swine Supply Chain (SSC) was found. Perspectives of researching KM in CSSC would integrate stakeholders using a technological web platform which allows interchange of information among them.

Johanna Trujillo-Diaz, Flor Nancy Diaz-Piraquive, Milton M. Herrera, Jairo Gómez Acero
Entrepreneurship Knowledge Insights in Emerging Markets Using a SECI Model Approach

Entrepreneurship is an engine for economic growth, but generally the success rates are low [8, 10, 19], and they are even lower in developing countries where the relative cost of failure is even higher than in develop countries. There is little specific Latin American knowledge that has been incorporated to help reducing this failure rate, most of the entrepreneurial models have been developed for economies with advanced entrepreneurial ecosystems such has Silicon Valley, with economies and cultures that differ from developing economies, there is a lack of data and research on this topic. This research aims to use the SECI model approach in order to identify entrepreneurial practices for emerging countries that could improve the chances of success and to transfer these practices. The study shows that the use of a SECI model approach is very successful at an ecosystem level and that the entrepreneurial knowledge is related to the actual stage of the entrepreneurial journey of startups, also that there are significant differences in access to venture capital funds and angel investors in the Latin American Market and also a much less tolerance to failure in Chile and the region.

Dario Liberona, Aravind Kumaresan, Lionel Valenzuela, Cristian Rojas, Roberto Ferro

Data Mining and Intelligent Science

Frontmatter
Efficient Estimation of Ontology Entities Distributed Representations

Ontologies have been used as a form of knowledge representation in different fields such as artificial intelligence, semantic web and natural language processing. The success caused by deep learning in recent years as a major upheaval in the field of artificial intelligence depends greatly on the data representation, since these representations can encode different types of hidden syntactic and semantic relationships in data, making their use very common in data science tasks. Ontologies do not escape this trend, applying deep learning techniques in the ontology-engineering field has heightened the need to learn and generate representations of the ontological data, which will allow ontologies to be exploited by such models and algorithms and thus automatizing different ontology-engineering tasks. This paper presents a novel approach for learning low dimensional continuous feature representations for ontology entities based on the semantic embedded in ontologies, using a multi-input feed-forward neural network trained using noise contrastive estimation technique. Semantically similar ontology entities will have relatively close corresponding representations in the projection space. Thus, the relationships between the ontology entities representations mirrors exactly the semantic relations between the corresponding entities in the source ontology.

Achref Benarab, Jianguo Sun, Allaoua Refoufi, Jian Guan
Automatic Sleep Staging Based on Deep Neural Network Using Single Channel EEG

Sleep staging is the first step for sleep research and sleep disorder diagnosis. The present study proposes an automatic sleep staging model, named ResSleepNet, using raw single-channel EEG signals. Most of the existing studies utilize hand-engineered features to identify sleep stages. These methods may ignore some important features of the signals, and then influence the effect of sleep stage classification. Instead of hand-engineering features, we combine feature extraction and classification into an algorithm based on residual network and bidirectional long short-term memory network. In the proposed method, we develop a 22-layer deep network to automatically learn features from the raw single-channel EEG and classify sleep stages. Residual network can learn time-invariant features, and bidirectional long short-term memory can add learned transition rules among sleep stages to the network. The model ResSleepNet is tested on the Sleep-EDF database. We perform 10 experiments and get average overall accuracy of 90.82% and 91.75% for 6-state and 5-state classification of sleep stages. Experimental results show the performance of our model is better than the state-of-the-art sleep staging methods, and it yields high detection accuracy for identifying sleep stage S1 and REM. In addition, our model is also suitable for extracting features from other signals (EOG, EMG) for sleep stage classification.

Yongfeng Huang, Yujuan Zhang, Cairong Yan
Evolving Fuzzy Membership Functions for Soft Skills Assessment Optimization

This work proposes the design of a decision support tool able to guide the choices of any company HR manager in the evaluation of the profiles of PhD candidates. This paper is part of an ongoing research in the field of PhD profiling. The novelty here is an evolutionary fuzzy model, based on the Membership Functions (MFs) optimization, used to obtain the soft skills candidate profiles. The general aim of the project is the definition of a set of fuzzy rules that are very similar to those that a HR expert would otherwise have to calculate each time for each selected profile and for each individual skill.

Antonia Azzini, Stefania Marrara, Amir Topalović
Unsupervised Deep Clustering for Fashion Images

In many visual domains like fashion, building an effective unsupervised clustering model depends on visual feature representation instead of structured and semi-structured data. In this paper, we propose a fashion image deep clustering (FiDC) model which includes two parts, feature representation and clustering. The fashion images are used as the input and are processed by a deep stacked autoencoder to produce latent feature representation, and the output of this autoencoder will be used as the input of the clustering task. Since the output of the former has a great influence on the later, the strategy adopted in the model is to integrate the learning process of the autoencoder and the clustering together. The autoencoder is trained with the optimal number of neurons per hidden layers to avoid overfitting and we optimize the cluster centroid by using stochastic gradient descent and backpropagation algorithm. We evaluate FiDC model on a real-world fashion dataset downloaded from Amazon where images have been extracted into 4096-dimensional visual feature vectors by convolutional neural networks. The experimental results show that our model achieves state-of-the-art performance.

Cairong Yan, Umar Subhan Malhi, Yongfeng Huang, Ran Tao
A FCM, Grey Model, and BP Neural Network Hybrid Fashion Color Forecasting Method

In view of the low prediction accuracy of the existing fashion color prediction methods, this paper propose a fashion color forecasting method used the spring and summer women’s fashion color data released by the International Fashion Color Committee from 2007 to 2013. In preprocess stage, the Pantone color system is used as the color quantization basis, the fuzzy c-means is used to cluster the sample data at first, and a FCM algorithm is used to statistic the color categories in different time series. In forecasting stage, both the grey model and BP neural network are used respectively to construct the fashion color hue prediction model from the statistical results generated from FCM. In evaluation stage, the mean square error is used to compare the prediction effect. The results show that the grey model based on FCM has the smallest error and has the best prediction effect. The proposed model can be used to predict the future fashion color, which can help the apparel industry stakeholders to grasp the trend of the future fashion color and make design and production plan more effectively. The FCM and grey model hybrid prediction method shown in this model also can be used in other small sample data prediction scenario.

Ran Tao, Jie Zhang, Ze-Ping Lv, You-Qun Shi, Xiang-Yang Feng
Discovering Emerging Research Topics Based on SPO Predications

With the rapid growth of scientific literatures, it is very important to discover the implicit knowledge from the vast information accurately and efficiently. To achieve this goal, we propose a percolation approach to discovering emerging research topics by combining text mining and scientometrics methods based on Subject-Predication-Object (SPO) predications, which consist of a subject argument, an object argument, and the relation that binds them. Firstly, SPO predications are extracted and cleaned from content of literatures to construct SPO semantic networks. Then, community detection is conducted in the SPO semantic networks. Afterwards, two indicators of Research Topic Age (RTA) and Research Topic Authors Number (RTAN) combined by hypervolume-based selection algorithm (HBS) are chosen to identify potential emerging research topics from communities. Finally, scientific literatures of stem cells are selected as a case study, and the result indicates that the approach can effectively and accurately discover the emerging research topics.

Zhengyin Hu, Rong-Qiang Zeng, Lin Peng, Hongseng Pang, Xiaochu Qin, Cheng Guo
Top-N Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph Embedding

The traditional collaborative filtering recommendation algorithm only uses the item-user rating matrix without considering the semantic information of the item itself, resulting in a problem that the recommendation accuracy is not high. This paper proposes a Top-N collaborative filtering recommendation algorithm based on knowledge graph embedding. The knowledge graph embedding is used to learn a low-dimensional vector for each entity and relationship in the knowledge graph, while maintaining the structure and semantic information of the original graph in the vector. By calculating the semantic similarity between items, the semantic information of the item itself is incorporated into the collaborative filtering recommendation. The algorithm makes up for the defect that the collaborative filtering recommendation algorithm does not consider the knowledge information of the item itself, and enhances the effect of collaborative filtering recommendation on the semantic level. The experimental results on the MovieLens dataset show that the algorithm can get higher values on precision, recall and F1 measure.

Ming Zhu, De-sheng Zhen, Ran Tao, You-qun Shi, Xiang-yang Feng, Qian Wang
Talents Evaluation Modeling Based on Fuzzy Mathematics

Fuzzy synthetic evaluation is a comprehensive assessment method based on fuzzy mathematics. Using the jurisdiction degree theory in fuzzy mathematics, it transforms qualitative evaluation into quantitative evaluation, and has been widely applied in many fields. In order to make the recruitment process of HRM (Human Resource Management) more systematic and rational, this paper introduces a method to evaluate talents by applying fuzzy synthetic evaluation, and discussed how to implement such mathematical model in our prototype system based on J2EE. Furthermore, the enterprise evaluation process with customizable resume is also proposed in this paper.

Xiangyang Feng, Zhu Liang, Ran Tao, Youqun Shi, Ming Zhu

Knowledge and Service Innovation

Frontmatter
Understanding Consumers’ Continuance Intention Toward Self-service Stores: An Integrated Model of the Theory of Planned Behavior and Push-Pull-Mooring Theory

The development of self-service technologies (SSTs) has significantly changed the interactions between customers and enterprises. Similarly, traditional services are gradually being replaced. Self-service businesses are emerging one after the other, including self-service laundries, gas stations, car washes, ticketing machines, and even self-service stores. This is not merely a new trend, but a revolution in traditional consumption patterns and service models. Why do consumers continue to patronize self-service stores? Is the pushing force or the pulling force leading them to continue to switch from traditional shops to self-service stores? Or is this change the result of planned behavior or intention, determined by attitudes, subjective norms, and perceived behavioral control? This study integrates the theory of planned behavior and push-pull-mooring theory to determine the factors that influence consumers’ continuance intention toward self-service stores. Data was collected and analyzed, using structural equation modelling, from 231 consumers who accessed self-service car washes. Results showed that attitude was the most important factor affecting consumers’ continuance intention toward self-service stores. This was followed in order of relative importance by fun, habit, perceived behavioral control, and personal innovativeness. Subjective norms, low user satisfaction, perceived ease of use, and cost-savings did not affect consumers’ continuance intention toward self-service stores. Implications for theory and practice are being derived from these findings.

Shan-Shan Chuang, Hui-Min Lai
Public Innovation: Concept and Future Research Agenda

The complexity and uncertainty that increasingly characterize public issues in contemporary societies indicate the relevance of public innovation, which designates a collection of approaches for exploring, testing and validating new ideas that create added value for society. Despite its relevance, studies are still needed to go further in analyzing the literature built on the subject, and to identify new research agendas that can generate inputs to translate theories into practice. Hence, the purpose of this article is to analyze the concept of public innovation and establish a future research agenda about the topic, on the basis of a systematic literature review of documents published between 2004 and 2018 in the Web of Science® multi-disciplinary database. For this purpose, the data mining software Vantage Point® and the qualitative analysis software MAXQDA® were used to study 148 documents. The results show the need to deepen the construction of public innovation theory from the perspective of the actors who interact in its dynamics. Finally, from the methodological perspective, it was found relevant to study the topic using a triangulation of methods, and through developing longitudinal and comparative studies, in order to understand the conditioning factors and results of the network collaboration exercises implicit in public innovation processes.

Lizeth Fernanda Serrano Cárdenas, Yessika Lorena Vásquez González, Flor Nancy Díaz-Piraquive, Javier Eloy Guillot Landecker
Improvement on Subjective Weighing Method in Attribute Coordinate Comprehensive Evaluation Model

Attribute coordinate comprehensive evaluation model provides an evaluation method for allowing the evaluator to subjectively weigh the indexes of the evaluated object. Specifically, the process of weighing is implemented by rating the given sample data to reflect the evaluator’s psychological weight upon some indexes. However, if the evaluated object includes many indexes, it is difficult for the evaluator to intuitively judge and accurately rate the sample data, which causes the great possibilities of rating the samples randomly and further influencing the final evaluation results. To address the problem, the paper changes the quantitative rating mode into qualitative judgment, and then converts the qualitative judgment into psychological weight, and finally evaluates all objects by the attribute coordinate comprehensive evaluation method. The experiment result shows the effectiveness of the improved method.

Xiaolin Xu, Yan Liu, Jiali Feng
Conceptualization of Differences Between Entrepreneurs and Non-entrepreneurs of Undergraduate Emirati Students

Innovation is the transformation of the creative idea into real life project. This research is comparing between the perception of entrepreneurship between the creators of ideas who are still in the process of thinking and those who were able to transfer their ideas to real life projects. Before becoming an entrepreneur and during the first year of entrepreneurship are two critical stages that need further studies. This research is a focus group research which focuses on two groups; the first group is a group of undergraduate students who had creative ideas and worked on transferring those ideas into prototypes and tested those prototypes and the second group is a group of students who took further step to the real life market where they were able to open their business and start gaining returns on their investments. The conclusion of the study shows qualitative differences and the rationalization behind each one.

Rasha Abou Samra, Amal Al Ali, Shaikha Al Naqbi

Knowledge and Organization

Frontmatter
Knowledge Drivers of Japanese Foreign Direct Investment Location

With rising globalization, several countries have been expanding outside their borders through not only trade, but also foreign direct investment. In doing so, they establish subsidiaries in little-known distant countries where they must overcome knowledge gaps to be successful.This research examines some knowledge-related factors that affect the state location choices of Japanese investors when they established affiliates in the United States between 2003 and 2017. The results, constructed from an original database, showed that Japanese foreign direct investment favor states that are closer to Japan and with more direct flight access, which have higher industry concentration, and in which more Japanese firms are already located. These findings suggest that beyond economic considerations, Japanese firms select locations considered to be superior sources of knowledge, knowledge spillovers, and learning for foreign direct investment.These results are specific to Japanese investments in the United States over the past 15 years which tend to be concentrated in manufacturing and IT industries.

Remy Magnier-Watanabe
Research on the Mode of Cultural Integration After M&A—Based on Boundary Penetration Theory

Through the multi-case study of the cultural integration model after the M&As of three state-owned enterprises, this paper reveals how to carry out cultural integration under cultural differences after the merger of state-owned enterprises. Based on boundary permeability theory, we put forward three cultural integration mode: the absorption, promotion and separation. In conclusion, from the different types of state-owned enterprises’ mergers and acquisitions, we propose a theoretical framework for cultural differences identification, cultural matching, cultural integration, and ultimately cultural synergy. This paper broadens the application situation of the cultural integration pattern and provides some references for the reform of mixed ownership of state-owned enterprises.

Yongmei Cui, Xiangfei Fu, Ya Zhang
Cryptocurrency and Its Digital Panorama in the Colombian Government

Throughout recent years as well as in the use of cryptocurrencies factors that have been influenced by technologies such as Blockchain. This article investigates the current situation of the so-called cryptocurrencies in the international scene, as well as the acceptance, rejection or indifference position of different nations. Afterwards, the current situation of Colombia and its position with respect to the Cryptocurrencies is evaluated. Followed by this, a meet with a panel of experts is issued in order to provide a series of proposals that they evaluate and based on these a series of contributions regarding the concept of the cryptocurrency that is re-defined. Therefore, proceeding to characterize the actors involved in the transactions carried out with this medium. Finally, a brief recommendation is given to preserve the integrity of the Colombian users and a suggestion to form an interdisciplinary group under the concept of e-government that aims to investigate and observe the potential use of the applications of the technology that surrounds cryptocurrencies and their relationship with e-government.

Alejandro Vásquez, Jhon F. Bernal, Giovanni M. Tarazona
The SECI Model in Moroccan Context: A Case Study of Payment Solution Software Sector

Von Krogh [33] has emphasized that knowledge management community claimed the universality of some models in application and conception, Nonaka’s SECI model is one of them. Organizational knowledge creation theory has revolutionized knowledge management discipline through visualizing the conversion of individual tacit knowledge to organizational tacit dissemination. Few voices stated that contextual elements influence the applicability of this model. Consequently, the paradigm of universality is called into question. This article explores, to what extent the four modes of SECI Model exist in the Arab context, particularly in Morocco. For that purpose we used a single case research design, we collected and analyzed rich information from semi-structured interviews, documentation, and taking notes during non-participant observation in a multinational Moroccan company (payment solution software sector), the findings related to the four modes namely socialization, externalization, combination and internalization were very pivotal, explained in details in the rest of the article.

Meriem Talaskou, Lhacen Belhcen

Data Mining and Intelligent Science/Big Data and IOT

Frontmatter
Webpages Classification with Phishing Content Using Naive Bayes Algorithm

Phishing attacks cause people to be scammed and cheated because of the impossibility to visually detect fraudulent websites. As is known, the attack occurs from emails sent to collect or update information supposedly from an entity, there are also cases of phone calls or instant messages. There is ignorance of such attacks by people in general, which means that the user is not alerted, which means that he is not attentive to the digital certificates present on the page that authenticate the content of the same. For this reason, the web pages designed have required tools that counteract and alert the user of the “phished” webpages, which commit the theft of money from the account from which information has been provided.

Jorge Enrique Rodríguez Rodríguez, Víctor Hugo Medina García, Nelson Pérez Castillo
Artificial Intelligence Tool Penetration in Business: Adoption, Challenges and Fears

Artificial Intelligence (AI) and its promise to improve the efficiency of entire business value chains has been headlining newspapers for the last years. However, it seems that many companies struggle in finding the right tools and use cases for their distinct fields of application. Thus, the aim of the presented study was to evaluate the current state of machine learning and co in various European companies. Talking to 19 employees from various different industry sectors, we explored applicability of AI tools as well as human attitudes towards these technologies. Results show that AI implementations are still in their early stages, with a rather small number of viable use cases. Tools are predominantly bespoke and internally built, while off-the-shelf solutions suffer from a lack of trust in third party service providers. Although companies claim to have no intention of reducing the workforce in favor of AI technology, employees fear job loss and thus often reject adoption. Another important challenge concerns data privacy and ethics, which has grown in relevance with respect to recent changes in European legislation. In summary, we found that companies recognize the competitive advantage AI may attribute to their value chains, in particular when it comes to automation and increased process efficiency. Yet they are also aware of the rather social challenges, which currently inhibit the proliferation of AI-driven solutions.

Stephan Schlögl, Claudia Postulka, Reinhard Bernsteiner, Christian Ploder
Digital Twins Approach and Future Knowledge Management Challenges: Where We Shall Need System Integration, Synergy Analyses and Synergy Measurements?

We’re in the midst of a significant transformation regarding the way we produce products and deliver services thanks to the digitization of manufacturing and new connected supply-chains and co-creation systems. This article elaborates Digital Twins Approach to the current challenges of knowledge management when Industry 4.0 is emerging in industries and manufacturing. Industry 4.0 approach underlines the importance of Internet of Things and interactions between social and physical systems. Internet of Things (and also Internet of Services and Internet of Data) are new Internet infrastructure that marries advanced manufacturing techniques and service architectures with the I-o-T, I-o-S and I-o-D to create manufacturing systems that are not only interconnected, but communicate, analyze, and use information to drive further intelligent action back in the physical world. This paper identifies four critical domains of synergy challenge: (1) Man-to-Man interaction, (2) Man-to-Machine interaction, (3) Machine-to-Man interaction and finally (4) Machine-to-Machine interaction. Key conclusion is that new knowledge management challenges are closely linked to the challenges of synergic interactions between these four key interactions and accurate measurements of synergic interaction.

Jari Kaivo-oja, Osmo Kuusi, Mikkel Stein Knudsen, Theresa Lauraeus
Performances of OLAP Operations in Graph and Relational Databases

The increasing volume of data created and exchanged in distributed architectures has made databases a critical asset to ensure availability and reliability of business operations. For this reason, a new family of databases, called NoSQL, has been proposed. To better understand the impact this evolution can have on organizations it is useful to focus on the notion of Online Analytical Processing (OLAP). This approach identifies techniques to interactively analyze multidimensional data from multiple perspectives and is today essential for supporting Business Intelligence.The objective of this paper is to benchmark OLAP queries on relational and graph databases containing the same sample of data. In particular, the relational model has been implemented by using MySQL while the graph model has been realized thanks to the Neo4j graph database. Our results, confirm previous experiments that registered better performances for graph databases when re-aggregation of data is required.

Antonia Azzini, Paolo Ceravolo, Matteo Colella

Knowledge and Organization/Social Network and Social Aspect of KM

Frontmatter
Quality Measurement in Sterilization Processes at Healthcare Organization in Colombia Using Six Sigma Metrics

The article’s aim is to focus on the application of Six Sigma to measure defects and identify improvement opportunities as part of knowledge management in the sterilization department in a Colombian private hospital. The methodology was established in three stages: Recognize, Define and Measure as an exploratory study using data from all instrument sterilized (N = n = 12.846).Quality Management System’s information was considered to recognize the context because work team had not hired in the organization directly. Sigma level was calculated in three sterilization processes: autoclave, ethylene oxide and hydrogen peroxide using data from 2017 and 2018.Outcomes of this study provides information about behavior of sterilization processes to establish a baseline based on the historical data. Furthermore, employees require improve their skills and competences in statistics and data processing.Future works are related to reducing waste evidenced in excessive time of processing or storing, instrumental availability and knowledge management using lesson-learned in the processes as a result of Analyze and Control Stages of DMAIC cycle implementation.

Ivanhoe Rozo-Rojas, Flor Nancy Díaz-Piraquive, Mayra Samara Ordoñez-Díaz, Yasser de Jesús Muriel-Perea
Customer Knowledge Management: Micro, Small and Medium - Sized Enterprises in Bogotá - Colombia

The idea of Customer Knowledge Management (CKM) is quite new, especially linked to operations within an organization. In this context, it is required to recall 80’s worldwide concepts as Customer Relation Ship (CRM) or Customer Lifetime Value (CLV). CRMs were complex and focused on large companies in the 90’s. At the beginning, CRMs worked through connections in infrastructures; nonetheless, from 2010 it was normal to use Cloud Computing versions. CRMs arose in Colombia firstly in large companies, now it is available for micro, small and medium enterprises (MSME). There are approximately 2.5 million MSME operating in a competitive environment, pursuing their market share. Besides, customer loyalty appears to be a difficult issue as well. Hence, the present paper aims to identify the Customer Knowledge Management Strategies developed by Colombian MSME. The methodology incorporates primary data, through a validated instrument by experts. Research results confirm that MSME work on customer loyalty strategies without systematization or measurement technology. Thus, an opportunity emerges for MSME regarding the use of cloud computing or CKM.

Yasser de Jesús Muriel-Perea, Flor Nancy Díaz-Piraquive, Rubén González-Crespo, Trinidad Cortés Puya
Leveraging Knowledge Management to Promote Higher Education Programs Using Paid Social Media

Information and communication technologies have become the center of many aspects of our daily life, including education as we live in the era of digital revolution. The use of social network sites is well disseminated across businesses and organizations, but the adoption of paid social media to promote higher education institutions and to create awareness is scarce. This research is unique as it examines the results of Facebook ads campaigns promoting higher institutions’ teaching programs using the data envelopment analysis technique to assess their efficiency. We also used a Multidimensional Scaling technique (MDS) to graphically represent the analyzed ads to determine the variables affecting efficiency. The results show that investments on social paid advertising are an effective way to promote higher education centers.

Rebeca Cordero-Gutiérrez, Eva Lahuerta-Otero
The Topics Dynamics in Knowledge Management Research

The intellectual structure of an academic discipline can be viewed as a set of interacting topics evolving over time. Dynamics of those topics i.e. changes in their popularity and impact is the subject of special attention because it reflects a shift in actual researchers’ interest. This paper analyzes topics of knowledge management (KM) on the base of the topic modeling technique (namely Latent Dirichlet Allocation). Studying the flow of academic publications in 7 leading journals in 2010–2018, we identified 8 topics that concern different aspects of knowledge management science. Three topics, what focus on the social aspects of knowledge management (namely the context supporting knowledge transfer, the employees’ incentives to share knowledge, and innovation), grow in terms of popularity and impact. Opposite, popularity and impact of topics, which focus on the practice of the knowledge management and organizational learning also as on the impact of intellectual capital on performance, decline. It is consistent with the opinion of other researchers that in the contemporary flow of scientific publication role of KM is identified more as a social process than a management engineering method.

Yuri Zelenkov

Big Data and IOT

Frontmatter
Extending the UTAUT2 Model to Understand the Entrepreneur Acceptance and Adopting Internet of Things (IoT)

The aim of this empirical study is to explore and discuss the factors that affect entrepreneurs acceptance and adoption of the Internet of Things (IoT) using UTAUT2 model. The study data was collected using a survey that was distributed among Omani entrepreneurs in six months period. The results showed that the relationship between information technology knowledge and entrepreneurs acceptance and adoption of the IoT was supported, like most other hypothesized relationships in the study.

Ahmad Abushakra, Davoud Nikbin
Internet of Things Adoption: Empirical Evidence from an Emerging Country

The purpose of this study was to investigate factors affecting entrepreneurs’ intention to adopt Internet of Things (IoT) in Oman using UTAUT2 theory. Data for this study were collected from Omani entrepreneurs in a time period of five months. Results indicated that all hypothesized relationships are supported except for the effects of price value and effort expectancy on intention to adopt IoT.

Davoud Nikbin, Ahmad Abushakra
Design of an Identification System for Crop Monitoring as First Step to Implementing Precision Agriculture Technology: The Case of African Palm

The use of emerging technologies brings multiple benefits to the agricultural sector, not only at an economic level, but also in reference to sustainability and the use of natural resources. Some technological systems make it possible to, among others things, improve the monitoring process of crops through (1) the identification of pests and diseases in plants to implement corrective and preventive measures, (2) the planning of different activities and crop rotation times such as planting, collection, pollination, etc. having a direct impact on the product quality, the crop useful life, its productivity and the producer income. In Colombia, the monitoring of extensive crops such as African palm (465,985 ha) is still manually done, therefore this work proposes a prototype identification system, designed and validates to provide data to an information system for the monitoring of each plants in African palm crops as a starting point (data collection) to implement precision agriculture in this agro sector.

Jose Cruzado Jimenez, Katherine Andrea Cuartas Castro
Construing Microservice Architectures: State-of-the-Art Algorithms and Research Issues

Cloud Computing is one of the leading paradigms in the IT industry. Earlier, cloud applications used to be built as single monolithic applications, and are now built using the Microservices Architectural Style. Along with several advantages, the microservices architecture also introduce challenges at the infrastructural level. Five such concerns are identified and analysed in this paper. The paper presents the state-of-art in different infrastructural concerns of microservices, namely, load balancing, scheduling, energy efficiency, security and resource management of microservices. The paper also suggests some future trends and research domains in the field of microservices.

Amit V. Nene, Christina Terese Joseph, K. Chandrasekaran

Knowledge Transfer and Learning

Frontmatter
Knowledge Governance Helps Minimizing the Risks of External Knowledge Transfer

Initiatives to cooperate with external stakeholders are set up by more and more companies. The intention of those initiatives is to get a better understanding of market developments and new technologies very quickly. This should improve innovation processes which should lead a competitive advantage.Transferring knowledge from the company to external stakeholders is required in order to enable them to contribute in the innovation project effectively. This knowledge transfer incorporates risks in terms of knowledge leakage, which can easily harm an organization.This paper focusses on the risks of external knowledge transfer and potential countermeasures. Based on a literature review an empirical survey was conducted. A qualitative research approach was used to gain diversified opinions and insights on the research domain.The results show that the experts are highly aware of the potential risks that go in line with knowledge transfer initiatives for example in the context of open innovation settings. Countermeasures are proposed in literature and by the participants of the survey.Consequently, the study reveals that knowledge governance processes, that are part of a comprehensive knowledge management system, have to be implemented. They are intended to avoid unintentional knowledge leakage and guide knowledge transfer processes.

Reinhard Bernsteiner, Johannes Strasser, Christian Ploder, Stephan Schlögl, Thomas Dilger
Study of Entrepreneurial Students’ Perceptions of the Impact of Digital Literacy Skills on Their Future Career: Evidence from Tunisian Higher Education

Entrepreneurs need early access to information to seize the opportunities offered by the economic environment and avoid threats that could undermine their businesses. In the age of the internet economy, speed of access to information is dependent on the young entrepreneurs’ mastery of technological tools that promote access to the right information at the right time. The skills in digital literacy in terms of selection, interpretation and knowledge construction and also in terms of communication and insertion in the digital culture are essential to face the competition and to be competitive. The role of education is important here to prepare literate entrepreneurs.By conducting a qualitative study using a focus group technique with entrepreneurship students at the Tunisian University level, we aimed to deeply explore: (1) the familiarity of the students with Digital Literacy, (2) students’ perception of the knowledge acquired through their education programs and if it seemed to them sufficient compared to the competences that they perceived as necessary for their future job as entrepreneurs. The results show that the concept and the skills it contains were a discovery for the students. The focus group was also an opportunity for students to collectively reflect on their training and their ability to launch themselves as future entrepreneurs.

Souad Kamoun-Chouk
Relationship Between Context-Social and Academic Performance: First Notes

Context: Student performance based on interaction with virtual learning environments and traditional classroom.Problem: In the academy it is not clear what variables can influence the academic score, since there are different conclusions according to the context in analysis.Objective: To find academic and social variables that influence academic performance in virtual environments of learning and traditional classroom.Methodological Proposal: Apply data mining and correlation of variables as a first step to the identification of variables that influence academic score.Experiment: We worked with two datasets according to the context under study. The results of the first experiment showed 83% of effectiveness that the level of education and the number of previous credits of the student directly influence their performance, while the interaction with the virtual learning environment does not directly influence the score. The high ratings in this data set is difficult to classify. In the second experiment, we worked on a traditional classroom. The results showed that academic performance is not linked to alcohol consumption at the end or midweek. Free days have no relation to performance. For the case of gender it seems to be better that women have a university preparation to achieve a better score. In relation to health, women are more affected with absences. Finally, if there is a relationship between internet access and performance. The results of this work are not conclusive, they are only the first notes to determine and corroborate the influence of academic and social variables in the academic score.

Ortega C. Juan, Gómez A. Héctor, Villavicencio Alvarez Victor Emilio, Lozada T. Edwin Fabricio, Francisco R. Naranjo C
Knowledge Representation and Management for Precision Agriculture: A Case Study

Precision Agriculture (PA) means the use of information technology for the management of crop growing procedures in such a way that farming methods implementation is accurate, controlled and done on time so that maximum yield can be obtained while reducing the losses, eliminating health hazards and cutting down the input costs. Despite the significance of PA, its practical implementation is yet scarce in Pakistan. The successful implementation of PA depends on gathering, storing, and sharing knowledge being generated at various levels. The knowledge that is needed to be shared includes best practices at farming level, results of various crop monitoring mechanisms, and the latest research findings at research institutes. An efficient knowledge storing and sharing system ultimately results in better crop plans, high yields and cost reduction. Due to slow knowledge sharing processes, stakeholders especially the farmers get delayed information. Also, the process level integration, that is responsible for calculating agricultural indices, crop health monitoring parameters, and parameter estimation techniques require coupling of different Knowledge Management (KM) technologies. Common KM systems lack such capabilities thus result in overall reduced benefits. This paper proposes a KM framework through which knowledge can be readily stored and shared with all the stakeholders through process automation. The system being proposed has three layered architecture with organizational layer at the top, connected to process layer and resources through a conceptual layer. This fully integrated KM framework has been applied to Rice Research Institute (RRI) at KalaShah Kaku, Lahore. Automation of manual processes done at RRI has been achieved through the application of proposed KM framework and is one of the main contributions of this paper. The RRI study shows that real time analysis can be shared promptly with the stakeholders through efficient knowledge management. The proposed KM model is generic and can be customized for any other organization related to agriculture or otherwise.

Maryam Khalid, Habiba Saim, Zoha Qamar, Fahad Akhtar, Mian M. Awais

Information Systems and Information Science

Frontmatter
Case Studies on ISD Agility

Much attention is paid to information systems development (ISD) agility, which has positive consequences for ISD projects, teams, and their organizations. ISD agility enables organizations to react to ISD-related change with speed and flexibility while constantly contributing to the delivery of value via IS. This article investigates how IS departments maintain their continual readiness for ISD agility. Drawing on a dynamic capability perspective, we suggest that routines underlie ISD agility. The analysis of three high-performing IS departments identifies six aspects of routines conducive to ISD agility: continuous discovery and validation of customer needs, continuous evolution of IT-enabled products and services, resource optimization, continuous integration and deployment, continuous management of risk, and continuous learning. In light of microfoundations, individual competence and mindset, constructive dialogue, and structural arrangements are essential components of routines and ISD agility. Theoretical and practical insights are discussed.

Yi-Te Chiu, Houn-Gee Chen, Yu-Qian Zhu
The Future Use of LowCode/NoCode Platforms by Knowledge Workers – An Acceptance Study

Knowledge Workers have to deal with lots of different information systems to support daily work. This assumption leads to massive gaps in companies based on the complexity of legacy systems on one hand side and the development of the business processes on the other hand side. Many knowledge workers build their own shadow IT to get efficient process support without thinking about compliance, security, and scalability. One possible solution to deactivate this situation might be the idea of LowCode/NoCode platforms. The question is: Will knowledge workers be using this technology or are they not accepting the new trend? Therefore, the authors conducted a quantitative study based on an online questionnaire (N = 106) to check the acceptance of this upcoming technology for companies in the DACH region. The result of the study is a statement about the future willingness to use.

Christian Ploder, Reinhard Bernsteiner, Stephan Schlögl, Christoph Gschliesser
Neuro-Symbolic Hybrid Systems for Industry 4.0: A Systematic Mapping Study

Neuro-symbolic hybrid systems (NSHS) have been used in several research areas to obtain powerful intelligent systems. A systematic mapping study was conducted, searching studies published from January 2011 to May 2018 in three author databases defining four research questions and three search strings. With the results a literature review was made to generate a map with main trends and contributions about the use of NSHS in Industry 4.0. An evaluation rubric based on the work of Petersen et al. (2015) was applied too. In a first exploratory search 544 papers was found, but only 330 had relation with research theme. After this first classification a second filter was applied to identify repeated articles or which had not relevance for solve the research questions, obtaining 118. Finally, 50 primary studies was selected. This paper is a guide aimed at researching and obtaining evidence on the shortage of publications and contributions about the use of neuro symbolic hybrid systems applied in Industry 4.0 environment.

Inés Sittón, Ricardo S. Alonso, Elena Hernández-Nieves, Sara Rodríguez-Gonzalez, Alberto Rivas
Method for Identification of Waste in the Process of Software Development in Agile Teams Using Lean and Scrum

Waste in software development projects is defined as anything that consumes resources such as time, effort, room, and money without adding value to the customer. Methods and techniques to identify waste indicators, which are specific for each project, are applied to part of total interactions and the development phases; and spend analysts and developers’ time and effort. Therefore, this paper aims to define a method to identify waste within the software development process in Scrum teams, from data based on JIRA tool, which supports software development planning, management and controlling activities. According to the bibliographic review are defined: (i) indicators for types of waste according to Lean software development principles; (ii) JIRA’s attributes, mathematical operators, keywords, functions and reports related to such indicators. In the proposed method are defined requirements that establish a semantic relation between each indicator variables and formulas to the set of JIRA’S attributes, functions and keywords and, based on them, queries in JIRA Query Language are implemented to quantify the indicators. The method validation is performed using graphics that show queries results classified and grouped by project, indicator and type of waste, acquired from a software project base for a company in the Brazilian financial market. Through the quantitative analysis of results, it is possible to suggest a hypothesis for the occurrence of the types of observed wastes.

Márcio Trovão Bufon, Adriano Galindo Leal
Routing Protocols in Vehicular Ad-Hoc Networks: A Performance Evaluation

The presented article evaluates the routing protocols in connected Vehicular Ad-Hoc Networks (VANET) through 802.11p considering synthetic mobility models and vehicular traffic generators, Manhattan and Intelligent Driver Mobility (IDM) models were selected respectively. The following programs were installed on a Linux-based system to simulate the scenarios: SUMO for the traffic management, NS-2 for simulating the data network and MOVE for exporting the information from SUMO to NS-2. Proactive and reactive routing protocols classification was considered, to subsequently apply the DSDV and AOMDV protocols that proved to have better performance. In the simulated scenarios, a low, medium and high number of connections were used with two communication types: Vehicle to vehicle (V2V) and Vehicle to infrastructure (V2I) for VANET networks. The indicators that were analyzed to determine the performance of the protocols were Throughput, Packet delivery ratio relationship (PDR), Average End to End Delay and Normalized routing load (NRL). The study was found that for V2V communications, regardless of the connections or the mobility model, AOMDV or DSDV can be used since the difference in performance is minimal. The results indicate that the best protocol is AOMDV with a superiority to DSDV in most cases. It is concluded that the model closest to reality is IDM since it is based on a traffic generator while the Manhattan model, based on mathematical formulas offers ambiguous results.

Raúl Lozada-Yánez, Washington Luna-Encalada, Danni Tierra-Quispillo, Fernando Molina-Granja, Jonny Guaiña-Yungan
Design of a Competitive Intelligence System for the Meat Sector in Colombia Using Business Intelligence

As the economy progresses, globalization and competitiveness in companies, there is evidence of the need to evaluate information, especially external, to transform it into knowledge and make decisions, this allows not only the application of knowledge but the identification of threats and early opportunities of a company, to this study, recognition and application is called competitive intelligence.The proposed design of competitive intelligence system for the meat sector is developed from the research carried out at University, whose objective is to design competitive intelligence systems that respond to the business sector. Subsequently, the efforts made extend to develop a review of CI models, diagnose the sector, identify information needs, search for sources and collect input information and process the information that has validated sources, whose work generates the system design using Power BI, a well-known Microsoft business intelligence tool.

Miguel Ángel Ospina Usaquén, Víctor Hugo Medina García, Jorge Enrique Otálora

New Trends in KM and IT

Frontmatter
Digital Readiness Frameworks
Current State of the Art and Research Opportunities

Digital transformation requires major changes by introducing new strategies, processes and in particular information systems and technologies. However, enterprises and organization need to identify and assess their current status and transformation requirements. For this purpose, a variety of tools have been developed. We review those to better understand their scope, possible impact and credibility providing a threefold contribution by (a) structuring the research on digital readiness assessment models, (b) identifying gaps and (c) uncovering opportunities for further research.

Franziska L. V. Voß, Jan M. Pawlowski
Hybrid Artificial Intelligence B2B2C Business Application – Online Travel Services

Expert systems are commonly used artificial intelligence AI applications in business. They aim to solve complex problems with expert-level knowledge and rule-based techniques. However, a narrowly defined scope and existing structured knowledge base are necessary for an expert system. The case study concerns the deployment of a hybrid AI model in a business application that tries to overcome the limitations of a predefined domain of knowledge stored in the expert system. In addition to the data set or the algorithm, the case illustrates the successful use of a hybrid AI application as an effective business solution in B2B2C industries.

Eric Kin Wai Lau, Abel Zhao, Anthony Ko
Legal Aspects and Emerging Risks in the Use of Smart Contracts Based on Blockchain

Although the majority application of Blockchain Technologies (BT) are in the field of cryptocurrencies, they are gradually spreading to other services where a decentralized, reliable and immutable model makes sense. One of the fields where the use of Blockchain Technologies is spreading the most is in Smart Contracts, computer programs which are executed in the blockchains establishing a collection of clauses between the participating parties that agree to interact with each other and that are executed automatically at the moment in which these clauses are fulfilled. As it is a computer code, both the client and the service provider cannot misinterpret the agreed clauses, facilitating and verifying the agreement of the contract. This article will review existing applications and reviews the main vulnerabilities of Smart Contracts deployed within Blockchain Technologies. It also reviews the legal implications of the use of these technologies.

Yeray Mezquita, Diego Valdeolmillos, Alfonso González-Briones, Javier Prieto, Juan Manuel Corchado
Using Blockchain for Traceability in the Drug Supply Chain

The illegal trade of medication is a problem which has claimed lives all over the world. In spite of the efforts from different international institutions aiming to stop this situation, illegal sales continue to grow. The following article proposes the use of Blockchain technology as a solution to the traceability problems and lack of control in the drug trade, as well as the way different entities would participate in it.

Jennifer Cristina Molina, Daniela Torres Delgado, Giovanni Tarazona
Backmatter
Metadaten
Titel
Knowledge Management in Organizations
herausgegeben von
Prof. Lorna Uden
Prof. Dr. I-Hsien Ting
Juan Manuel Corchado
Copyright-Jahr
2019
Electronic ISBN
978-3-030-21451-7
Print ISBN
978-3-030-21450-0
DOI
https://doi.org/10.1007/978-3-030-21451-7