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Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 December 2017) | Viewed by 146990

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Patricia Ordóñez de Pablos

E-Mail Website
Guest Editor
Department of Business Administration and Accountability, Faculty of Economics, The University of Oviedo (Spain), Oviedo, Spain
Interests: big data; knowledge management; human resource management; information technologies; intellectual capital.
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Miltiadis D. Lytras

E-Mail Website
Guest Editor
Computer Science Department, College of Engineering, Effat University, Jeddah, Saudi Arabia
Interests: cognitive computing; artificial intelligence; data science; bioinformatics; innovation; big data research; data mining; emerging technologies; information systems; technology driven innovation; knowledge management; semantic web
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Strategy management involves understanding the forces and causes that explain performance differences between organizations and economies, a result of different stocks of knowledge-based resources and capabilities. These resources are key for achieving and sustaining and competitive advantage for companies, economies and societies.

The Big Data and Data Analytics is a new paradigm for the integration of Internet Technology in the human and machine contexts. Now, we are able to transform raw data that are massively produced by humans and machines in to knowledge and wisdom capable of supporting smart decision making, innovative services, new business models, innovation, and entrepreneurship.

Aims of the Special Issue:

The Special Issue will explore the role of knowledge management strategies and tools to enhance the power of big data and help decision makers in today’s competitive economy.

The Special Issue will analyze the relation of knowledge management, big data and information technology towards a deeper understanding of their impact on economies and societies today.

Topics of interest

  • Big data analytics
  • Challenges and trends in knowledge and creative economics
  • Competitive strategy, knowledge-based view of the firm
  • Data and e-commerce
  • Ethical issues of big data
  • Future skills of knowledge workers
  • Human resource management, intellectual capital reporting
  • Human/relational/social/organizational capital
  • Innovation networks
  • Innovation, knowledge management and leadership
  • Intangible resources, sustainability, big data
  • Knowledge economics and neuroeconomics
  • Organizational learning, networked learning
  • Product and process innovation, technology and innovation management
  • Semantic big data
  • Social networks, gamification
  • Sustainability and organizational competitiveness
Prof. Patricia Ordóñez de Pablos
Dr. Miltiades D. Lytras
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Editorial

Jump to: Research

7 pages, 342 KiB  
Editorial
Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness
by Patricia Ordóñez de Pablos and Miltiadis Lytras
Sustainability 2018, 10(6), 2073; https://doi.org/10.3390/su10062073 - 19 Jun 2018
Cited by 24 | Viewed by 4693
Abstract
This Special Issue of Sustainability devoted to the topic of “Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness” attracted exponential attention of scholars, practitioners, and policy-makers from all over the world. Locating themselves at the expanding cross-section of [...] Read more.
This Special Issue of Sustainability devoted to the topic of “Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness” attracted exponential attention of scholars, practitioners, and policy-makers from all over the world. Locating themselves at the expanding cross-section of the uses of sophisticated information and communication technology (ICT) and insights from social science and engineering, all papers included in this Special Issue contribute to the opening of new avenues of research in the field of innovation, knowledge management, and big data. By triggering a lively debate on diverse challenges that companies are exposed to today, this Special Issue offers an in-depth, informative, well-structured, comparative insight into the most salient developments shaping the corresponding fields of research and policymaking. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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Research

Jump to: Editorial

20 pages, 1844 KiB  
Article
Social Networks Research for Sustainable Smart Education
by Miltiadis D. Lytras, Anna Visvizi, Linda Daniela, Akila Sarirete and Patricia Ordonez De Pablos
Sustainability 2018, 10(9), 2974; https://doi.org/10.3390/su10092974 - 21 Aug 2018
Cited by 67 | Viewed by 6224
Abstract
Social networks research has grown exponentially over the past decade. Subsequent empirical and conceptual advances have been transposed in the field of education. As the debate on delivering better education for all gains momentum, the big question is how to integrate advances in [...] Read more.
Social networks research has grown exponentially over the past decade. Subsequent empirical and conceptual advances have been transposed in the field of education. As the debate on delivering better education for all gains momentum, the big question is how to integrate advances in social networks research, corresponding advances in information and communication technology (ICT) and effectively employ them in the domain of education. To address this question, this paper proposes a conceptual framework (maturity model) that integrates social network research, the debate on technology-enhanced learning (TEL) and the emerging concept of smart education. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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27 pages, 1499 KiB  
Article
Internal Social Network, Absorptive Capacity and Innovation: Evidence from New Ventures in China
by Wei Shan, Chu Zhang and Jingyi Wang
Sustainability 2018, 10(4), 1094; https://doi.org/10.3390/su10041094 - 05 Apr 2018
Cited by 24 | Viewed by 4526
Abstract
This research investigates the impact of the internal social network on new venture’s innovation by building a comprehensive structural equation modeling (SEM) that integrates three streams of research: internal social network, innovation, and absorptive capacity. Based on a sample of 279 new ventures [...] Read more.
This research investigates the impact of the internal social network on new venture’s innovation by building a comprehensive structural equation modeling (SEM) that integrates three streams of research: internal social network, innovation, and absorptive capacity. Based on a sample of 279 new ventures from China, the current study’s results show that absorptive capacity plays a full mediating effect in the relationship of the internal social network and innovation. Particularly, among the skill set of absorptive capacity, a mere skill of knowledge acquisition does not guarantee an enhancement of new venture’s innovation. For new ventures to better utilize the social capital generated by the internal network in the process of innovation, they must focus more on the skills of knowledge digestion and knowledge application. The authors further separate the new ventures into two different sub-samples: the new venture supported by mature enterprises (M-type) and the independent new venture (I-type). This study’s findings indicate that the effect of the social network on innovation through knowledge digestion is greater in the M-type sample than in the I-type sample; internal social network heterogeneity in general plays a less important role in improving a new venture’s innovation than internal social network density, for both M-type and I-type new ventures. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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28 pages, 5057 KiB  
Article
Visualizing the Academic Discipline of Knowledge Management
by Peng Wang, Fang-Wei Zhu, Hao-Yang Song, Jian-Hua Hou and Jin-Lan Zhang
Sustainability 2018, 10(3), 682; https://doi.org/10.3390/su10030682 - 02 Mar 2018
Cited by 34 | Viewed by 7721
Abstract
The aim of this paper was to evaluate the research status of knowledge management (KM) and identify the characteristics of KM in the literature. We selected and studied in detail 7628 original research articles from the Web of Science from 1974 to 2017. [...] Read more.
The aim of this paper was to evaluate the research status of knowledge management (KM) and identify the characteristics of KM in the literature. We selected and studied in detail 7628 original research articles from the Web of Science from 1974 to 2017. Although many studies have contributed to the evolution of the KM domain, our results showed that a comprehensive bibliometric and visualization investigation was required. The literature on KM has grown rapidly since the 1970s. The United States of America, as the original contributing country, has also internationally collaborated the most in this field of study. The National Cheng Kung University has made the highest number of contributions. The majority of authors contributed a small number of publications. Additionally, the most common category in KM research was management. The main publications for KM research include Journal of Knowledge Management, and Knowledge Management Research & Practice. A keywords analysis determined that “knowledge sharing”, “innovation”, “ontology”, and “knowledge management” were consistent hotspots in knowledge management research. Through a document co-citation analysis, the intellectual structures of knowledge management were defined, and four emerging trends were identified that focus on new phenomenon, the practice of knowledge management, small and medium enterprises (SMEs) management based on knowledge perspective, innovation and performance, and big data-enabled KM. We also provide eight research questions for future studies. Our results will benefit academics, researchers, and research students who want to rapidly obtain an overview of knowledge management research. This study can also be a starting point for communication between academics and practitioners. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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19 pages, 2481 KiB  
Article
An Empirical Study on Visualizing the Intellectual Structure and Hotspots of Big Data Research from a Sustainable Perspective
by Feng Hu, Wei Liu, Sang-Bing Tsai, Junbin Gao, Ning Bin and Quan Chen
Sustainability 2018, 10(3), 667; https://doi.org/10.3390/su10030667 - 01 Mar 2018
Cited by 17 | Viewed by 4684
Abstract
Big data has been extensively applied to many fields and wanted for sustainable development. However, increasingly growing publications and the dynamic nature of research fronts pose challenges to understand the current research situation and sustainable development directions of big data. In this paper, [...] Read more.
Big data has been extensively applied to many fields and wanted for sustainable development. However, increasingly growing publications and the dynamic nature of research fronts pose challenges to understand the current research situation and sustainable development directions of big data. In this paper, we visually conducted a bibliometric study of big data literatures from the Web of Science (WoS) between 2002 and 2016, involving 4927 effective journal articles in 1729 journals contributed by 16,404 authors from 4137 institutions. The bibliometric results reveal the current annual publications distribution, journals distribution and co-citation network, institutions distribution and collaboration network, authors distribution, collaboration network and co-citation network, and research hotspots. The results can help researchers worldwide to understand the panorama of current big data research, to find the potential research gaps, and to focus on the future sustainable development directions. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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15 pages, 2348 KiB  
Article
Semantic Modeling of Administrative Procedures from a Spanish Regional Public Administration
by Francisco José Hidalgo López, Jose Emilio Labra Gayo and Patricia Ordóñez de Pablos
Sustainability 2018, 10(3), 633; https://doi.org/10.3390/su10030633 - 28 Feb 2018
Cited by 4 | Viewed by 4414
Abstract
Over the past few years, Public Administrations have been providing systems for procedures and files electronic processing to ensure compliance with regulations and provide public services to citizens. Although each administration provides similar services to their citizens, these systems usually differ from the [...] Read more.
Over the past few years, Public Administrations have been providing systems for procedures and files electronic processing to ensure compliance with regulations and provide public services to citizens. Although each administration provides similar services to their citizens, these systems usually differ from the internal information management point of view since they usually come from different products and manufacturers. The common framework that regulations demand, and that Public Administrations must respect when processing electronic files, provides a unique opportunity for the development of intelligent agents in the field of administrative processes. However, for this development to be truly effective and applicable to the public sector, it is necessary to have a common representation model for these administrative processes. Although a lot of work has already been done in the development of public information reuse initiatives and common vocabularies standardization, this has not been carried out at the processes level. In this paper, we propose a semantic representation model of both processes models and processes for Public Administrations: the procedures and administrative files. The goal is to improve public administration open data initiatives and help to develop their sustainability policies, such as improving decision-making procedures and administrative management sustainability. As a case study, we modelled public administrative processes and files in collaboration with a Regional Public Administration in Spain, the Principality of Asturias, which enabled access to its information systems, helping the evaluation of our approach. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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13 pages, 727 KiB  
Article
Transformational Training Programs and Quality Orientation of Employees: Does Employees’ Loyalty Matter?
by Nidal Fawwaz Al Qudah, Yang Yang and Muhammad Adeel Anjum
Sustainability 2018, 10(2), 465; https://doi.org/10.3390/su10020465 - 09 Feb 2018
Cited by 9 | Viewed by 6527
Abstract
Transformational training programs, employee loyalty and quality orientation of employees have been some of the important concerns for both academicians and practitioners for decades. Yet, little is known about their underlying relationship dynamics, especially in the context of higher education institutions. The pivotal [...] Read more.
Transformational training programs, employee loyalty and quality orientation of employees have been some of the important concerns for both academicians and practitioners for decades. Yet, little is known about their underlying relationship dynamics, especially in the context of higher education institutions. The pivotal aim of this study was to investigate the interplay of transformational training programs, loyalty and quality orientation of employees. For this, a causal model demonstrating the direct and indirect relationships of transformational training programs, employee loyalty and quality orientation was built and tested. Data for this study were collected from 212 (n = 212) academics (deans, head of departments and faculty members) from all private sector universities in Amman, Jordan, through a cross sectional survey. Results indicated that both direct and indirect effects of transformational training programs on quality orientation of employees were significant. More specifically, the positive effects that transformational training programs have on quality orientation of employees are through employee loyalty. This finding significantly advances the existing body of knowledge and implies that transformational training programs enhance employees’ loyalty which, in turn, escalates employees’ orientations towards quality. Hence, it is concluded that the objective of inculcating quality orientation amongst employees cannot be achieved with mere reliance upon transformational training programs. Several contextual factors, such as employee loyalty, should also be focused on and fostered to ensure the effects that training programs have on certain desirable outcomes. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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13 pages, 1406 KiB  
Article
Ontology Design for Solving Computationally-Intensive Problems on Heterogeneous Architectures
by Hossam M. Faheem, Birgitta König-Ries, Muhammad Ahtisham Aslam, Naif Radi Aljohani and Iyad Katib
Sustainability 2018, 10(2), 441; https://doi.org/10.3390/su10020441 - 08 Feb 2018
Cited by 7 | Viewed by 4231
Abstract
Viewing a computationally-intensive problem as a self-contained challenge with its own hardware, software and scheduling strategies is an approach that should be investigated. We might suggest assigning heterogeneous hardware architectures to solve a problem, while parallel computing paradigms may play an important role [...] Read more.
Viewing a computationally-intensive problem as a self-contained challenge with its own hardware, software and scheduling strategies is an approach that should be investigated. We might suggest assigning heterogeneous hardware architectures to solve a problem, while parallel computing paradigms may play an important role in writing efficient code to solve the problem; moreover, the scheduling strategies may be examined as a possible solution. Depending on the problem complexity, finding the best possible solution using an integrated infrastructure of hardware, software and scheduling strategy can be a complex job. Developing and using ontologies and reasoning techniques play a significant role in reducing the complexity of identifying the components of such integrated infrastructures. Undertaking reasoning and inferencing regarding the domain concepts can help to find the best possible solution through a combination of hardware, software and scheduling strategies. In this paper, we present an ontology and show how we can use it to solve computationally-intensive problems from various domains. As a potential use for the idea, we present examples from the bioinformatics domain. Validation by using problems from the Elastic Optical Network domain has demonstrated the flexibility of the suggested ontology and its suitability for use with any other computationally-intensive problem domain. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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26 pages, 391 KiB  
Article
Modeling and Quantifying User Acceptance of Personalized Business Modes Based on TAM, Trust and Attitude
by Jie Zhao, Suping Fang and Peiquan Jin
Sustainability 2018, 10(2), 356; https://doi.org/10.3390/su10020356 - 30 Jan 2018
Cited by 40 | Viewed by 6459
Abstract
With the rapid development of economics and social businesses, users’ business demand has changed a lot. More and more people want to personalize their business modes so that they can get better experiences in business and learning activities. The key factor of personalized [...] Read more.
With the rapid development of economics and social businesses, users’ business demand has changed a lot. More and more people want to personalize their business modes so that they can get better experiences in business and learning activities. The key factor of personalized business mode is to consider users’ individual needs on business activities, so that users can receive differentiated services. Users’ satisfaction on personalized services will effectively improve the consuming experience of users, which is helpful for business organizations to strengthen their competitive power in business environments. However, will users wish to participate in personalized businesses? This is a crucial issue for developing personalized businesses. Aiming to solve this problem, this paper analyzes the major factors influencing user acceptance of personalized business modes. Then, we propose a research model that enhances the TAM (Technology Acceptance Model) model with trust and attitude to depict the influence from several variables to user acceptance of personalized business modes. Further, we use the structural equation method to conduct an empirical analysis on questionnaire data from the Internet. The results in terms of many kinds of data analysis show that trust and the TAM factors (perceived usefulness and perceived ease of use) have significant influence on user acceptance of personalized business modes. In addition, there are partial intermediate relationships existing among the factors of the research model. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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15 pages, 391 KiB  
Article
Existing Knowledge Assets and Disruptive Innovation: The Role of Knowledge Embeddedness and Specificity
by Chunpei Lin, Baixun Li and Yenchun Jim Wu
Sustainability 2018, 10(2), 342; https://doi.org/10.3390/su10020342 - 29 Jan 2018
Cited by 19 | Viewed by 4508
Abstract
Disruptive innovation has created a significant impact on management practices and academia. This study investigated the impact of existing knowledge assets on disruptive innovation by analyzing the role of knowledge embeddedness and specificity. We conducted a hierarchical regression analysis by using survey data [...] Read more.
Disruptive innovation has created a significant impact on management practices and academia. This study investigated the impact of existing knowledge assets on disruptive innovation by analyzing the role of knowledge embeddedness and specificity. We conducted a hierarchical regression analysis by using survey data from 173 Chinese industrial firms to test the direct and indirect effects of knowledge embeddedness and specificity on disruptive innovation, which can be divided into outward-oriented and internal-oriented disruptive innovation. The results indicated that knowledge embeddedness not only played a positive role in knowledge specificity, but also had a positive effect on outward-oriented disruptive innovation. Furthermore, knowledge specificity exhibited opposite functions in outward-oriented and internal-oriented disruptive innovation. In addition, knowledge specificity mediated the relationship between knowledge embeddedness and outward-oriented (internal-oriented) disruptive innovation. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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10 pages, 942 KiB  
Article
Business Intelligence Issues for Sustainability Projects
by Mihaela Muntean
Sustainability 2018, 10(2), 335; https://doi.org/10.3390/su10020335 - 29 Jan 2018
Cited by 21 | Viewed by 8747
Abstract
Business intelligence (BI) is an umbrella term for strategies, technologies, and information systems used by the companies to extract from large and various data, according to the value chain, relevant knowledge to support a wide range of operational, tactical, and strategic business decisions. [...] Read more.
Business intelligence (BI) is an umbrella term for strategies, technologies, and information systems used by the companies to extract from large and various data, according to the value chain, relevant knowledge to support a wide range of operational, tactical, and strategic business decisions. Sustainability, as an integrated part of the corporate business, implies the integration of the new approach at all levels: business model, performance management system, business intelligence project, and data model. Both business intelligence issues presented in this paper represent the contribution of the author in modeling data for supporting further BI approaches in corporate sustainability initiatives. Multi-dimensional modeling has been used to ground the proposals and to introduce the key performance indicators. The démarche is strengthened with implementation aspects and reporting examples. More than ever, in the Big Data era, bringing together business intelligence methods and tools with corporate sustainability is recommended. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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23 pages, 2322 KiB  
Article
Exploring the Technological Collaboration Characteristics of the Global Integrated Circuit Manufacturing Industry
by Yun Liu, Zhe Yan, Yijie Cheng and Xuanting Ye
Sustainability 2018, 10(1), 196; https://doi.org/10.3390/su10010196 - 22 Jan 2018
Cited by 13 | Viewed by 6318
Abstract
With the intensification of international competition, there are many international technological collaborations in the integrated circuit manufacturing (ICM) industry. The importance of improving the level of international technological collaboration is becoming more and more prominent. Therefore, it is vital for a country, a [...] Read more.
With the intensification of international competition, there are many international technological collaborations in the integrated circuit manufacturing (ICM) industry. The importance of improving the level of international technological collaboration is becoming more and more prominent. Therefore, it is vital for a country, a region, or an institution to understand the international technological collaboration characteristics of the ICM industry and, thus, to know how to enhance its own international technological collaboration. This paper depicts the international technological collaboration characteristics of the ICM industry based on patent analysis. Four aspects, which include collaboration patterns, collaboration networks, collaboration institutions, and collaboration impacts, are analyzed by utilizing patent association analysis and social network analysis. The findings include the following: first, in regard to international technological collaboration, the USA has the highest level, while Germany has great potential for future development; second, Asia and Europe have already formed clusters, respectively, in the cooperative network; last, but not least, research institutions, colleges, and universities should also actively participate in international collaboration. In general, this study provides an objective reference for policy making, competitiveness, and sustainability in the ICM industry. The framework presented in this paper could be applied to examine other industrial international technological collaborations. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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22 pages, 3154 KiB  
Article
A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification
by Jie Hu, Shaobo Li, Jianjun Hu and Guanci Yang
Sustainability 2018, 10(1), 219; https://doi.org/10.3390/su10010219 - 16 Jan 2018
Cited by 29 | Viewed by 6505
Abstract
Various studies have focused on feature extraction methods for automatic patent classification in recent years. However, most of these approaches are based on the knowledge from experts in related domains. Here we propose a hierarchical feature extraction model (HFEM) for multi-label mechanical patent [...] Read more.
Various studies have focused on feature extraction methods for automatic patent classification in recent years. However, most of these approaches are based on the knowledge from experts in related domains. Here we propose a hierarchical feature extraction model (HFEM) for multi-label mechanical patent classification, which is able to capture both local features of phrases as well as global and temporal semantics. First, a n-gram feature extractor based on convolutional neural networks (CNNs) is designed to extract salient local lexical-level features. Next, a long dependency feature extraction model based on the bidirectional long–short-term memory (BiLSTM) neural network model is proposed to capture sequential correlations from higher-level sequence representations. Then the HFEM algorithm and its hierarchical feature extraction architecture are detailed. We establish the training, validation and test datasets, containing 72,532, 18,133, and 2679 mechanical patent documents, respectively, and then check the performance of HFEMs. Finally, we compared the results of the proposed HFEM and three other single neural network models, namely CNN, long–short-term memory (LSTM), and BiLSTM. The experimental results indicate that our proposed HFEM outperforms the other compared models in both precision and recall. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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26 pages, 3531 KiB  
Article
Data Governance Taxonomy: Cloud versus Non-Cloud
by Majid Al-Ruithe, Elhadj Benkhelifa and Khawar Hameed
Sustainability 2018, 10(1), 95; https://doi.org/10.3390/su10010095 - 02 Jan 2018
Cited by 31 | Viewed by 12319
Abstract
Forward-thinking organisations believe that the only way to solve the data problem is the implementation of effective data governance. Attempts to govern data have failed before, as they were driven by information technology, and affected by rigid processes and fragmented activities carried out [...] Read more.
Forward-thinking organisations believe that the only way to solve the data problem is the implementation of effective data governance. Attempts to govern data have failed before, as they were driven by information technology, and affected by rigid processes and fragmented activities carried out on a system-by-system basis. Until very recently, governance has been mostly informal, with very ambiguous and generic regulations, in siloes around specific enterprise repositories, lacking structure and the wider support of the organisation. Despite its highly recognised importance, the area of data governance is still underdeveloped and under-researched. Consequently, there is a need to advance research in data governance in order to deepen practice. Currently, in the area of data governance, research consists mostly of descriptive literature reviews. The analysis of literature further emphasises the need to build a standardised strategy for data governance. This task can be a very complex one and needs to be accomplished in stages. Therefore, as a first and necessary stage, a taxonomy approach to define the different attributes of data governance is expected to make a valuable contribution to knowledge, helping researchers and decision makers to understand the most important factors that need to be considered when implementing a data governance strategy for cloud computing services. In addition to the proposed taxonomy, the paper clarifies the concepts of data governance in contracts with other governance domains. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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568 KiB  
Article
Knowledge Creation Process and Sustainable Competitive Advantage: the Role of Technological Innovation Capabilities
by Chuanpeng Yu, Zhengang Zhang, Chunpei Lin and Yenchun Jim Wu
Sustainability 2017, 9(12), 2280; https://doi.org/10.3390/su9122280 - 11 Dec 2017
Cited by 60 | Viewed by 8968
Abstract
This study examines the relationship between the knowledge creation process and technological innovation capabilities, and analyzes their effect on a firm’s sustainable competitive advantage using a knowledge-based view theoretical framework. We conduct structural equation modeling analyses using survey data from 315 Chinese industrial [...] Read more.
This study examines the relationship between the knowledge creation process and technological innovation capabilities, and analyzes their effect on a firm’s sustainable competitive advantage using a knowledge-based view theoretical framework. We conduct structural equation modeling analyses using survey data from 315 Chinese industrial firms to test the direct and indirect effects of the knowledge creation process on sustainable competitive advantage. Technological innovation capabilities—operationalized to reflect the dimensions of process innovation capability and product innovation capability—are used as the mediating variable for explaining the relationship between the knowledge creation process and sustainable competitive advantage. The results indicate that the knowledge creation process does not have a significant direct effect on sustainable competitive advantage. Rather, the knowledge creation process can only influence the sustainable competitive advantage through the mediating effect of technological innovation capabilities completely. Consequently, the knowledge creation process favors the development of technological innovation capabilities for processes and products, because processes and products can lead to a sustainable competitive advantage. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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360 KiB  
Article
Top Management Teams’ Characteristics and Strategic Decision-Making: A Mediation of Risk Perceptions and Mental Models
by Tungju Wu, Yenchun Jim Wu, Hsientang Tsai and Yibin Li
Sustainability 2017, 9(12), 2265; https://doi.org/10.3390/su9122265 - 07 Dec 2017
Cited by 32 | Viewed by 8992
Abstract
Strategic decision-making is a key factor of sustainability and development in enterprises. Moreover, the top management team (TMT) of an enterprise constitutes the base for decision-making. This study employed structural equation modeling to analyze questionnaires regarding TMTs’ characteristics and strategic decision-making, and tested [...] Read more.
Strategic decision-making is a key factor of sustainability and development in enterprises. Moreover, the top management team (TMT) of an enterprise constitutes the base for decision-making. This study employed structural equation modeling to analyze questionnaires regarding TMTs’ characteristics and strategic decision-making, and tested the mediating effects of risk perceptions and mental models and the moderating effects of psychological ownership. We investigated 289 valid questionnaires on TMTs completed by representatives from enterprises in China and found risk perceptions and mental models that serve as a mediating factor and are affected by the TMTs’ characteristics and decision-making. We also found that psychological ownership exerts moderating effects between TMTs’ characteristics and decision-making. This paper concludes with a discussion of theoretical and managerial implications for enterprise owners. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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1391 KiB  
Article
Developing a Methodology of Structuring and Layering Technological Information in Patent Documents through Natural Language Processing
by Taeyeoun Roh, Yujin Jeong and Byungun Yoon
Sustainability 2017, 9(11), 2117; https://doi.org/10.3390/su9112117 - 17 Nov 2017
Cited by 20 | Viewed by 4434
Abstract
Since patents contain various types of objective technological information, they are used to identify the characteristics of technology fields. Text mining in patent analysis is employed in various fields such as trend analysis and technology classification, and knowledge flow among technologies. However, since [...] Read more.
Since patents contain various types of objective technological information, they are used to identify the characteristics of technology fields. Text mining in patent analysis is employed in various fields such as trend analysis and technology classification, and knowledge flow among technologies. However, since keyword-based text mining has the limitation whereby, when screening useful keywords, it frequently omits meaningful keywords, analyzers therefore need to repeat the careful scrutiny of the derived keywords to clarify the meaning of keywords. In this research, we structure meaningful keyword sets related to technological information from patent documents; then we layer the keywords, depending on the level of information. This research involves two steps. First, the characteristics of technological information are analyzed by reviewing the patent law and investigating the description of patent documents. Second, the technological information is structured by considering the information types, and the keywords in each type are layered through natural language processing. Consequently, the structured and layered keyword set does not omit useful keywords and the analyzer can easily understand the meaning of each keyword. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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2857 KiB  
Article
Big Social Network Data and Sustainable Economic Development
by Umit Can and Bilal Alatas
Sustainability 2017, 9(11), 2027; https://doi.org/10.3390/su9112027 - 07 Nov 2017
Cited by 34 | Viewed by 7786
Abstract
New information technologies have led to the rapid and effective growth of social networks. The amount of data produced by social networks has increased the value of the big data concept, which is one of the popular current phenomena. The immediate or unpredictable [...] Read more.
New information technologies have led to the rapid and effective growth of social networks. The amount of data produced by social networks has increased the value of the big data concept, which is one of the popular current phenomena. The immediate or unpredictable effects of a wide array of economic activities on large masses and the reactions to them can be measured by using social media platforms and big data methods. Thus, it would be extremely beneficial to analyze the harmful environmental and social impacts that are caused by unsustainable business applications. As social networks and big data are popular realms currently, their efficient use would be an important factor in sustainable economic development. Accurate analysis of people’s consumption habits and economic tendencies would provide significant advantages to companies. Moreover, unknown consumption factors that affect the economic preferences of individuals can be discovered and economic efficiency can be increased. This study shows that the numerous solution opportunities that are provided by social networks and big data have become significant tools in dynamic policy creation by companies and states, in solving problems related to women’s rights, the environment, and health. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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1639 KiB  
Article
Crowdsourcing Analysis of Twitter Data on Climate Change: Paid Workers vs. Volunteers
by Andrei P. Kirilenko, Travis Desell, Hany Kim and Svetlana Stepchenkova
Sustainability 2017, 9(11), 2019; https://doi.org/10.3390/su9112019 - 03 Nov 2017
Cited by 8 | Viewed by 4709
Abstract
Web based crowdsourcing has become an important method of environmental data processing. Two alternatives are widely used today by researchers in various fields: paid data processing mediated by for-profit businesses such as Amazon’s Mechanical Turk, and volunteer data processing conducted by amateur citizen-scientists. [...] Read more.
Web based crowdsourcing has become an important method of environmental data processing. Two alternatives are widely used today by researchers in various fields: paid data processing mediated by for-profit businesses such as Amazon’s Mechanical Turk, and volunteer data processing conducted by amateur citizen-scientists. While the first option delivers results much faster, it is not quite clear how it compares with volunteer processing in terms of quality. This study compares volunteer and paid processing of social media data originating from climate change discussions on Twitter. The same sample of Twitter messages discussing climate change was offered for processing to the volunteer workers through the Climate Tweet project, and to the paid workers through the Amazon MTurk platform. We found that paid crowdsourcing required the employment of a high redundancy data processing design to obtain quality that was comparable with volunteered processing. Among the methods applied to improve data processing accuracy, limiting the geographical locations of the paid workers appeared the most productive. Conversely, we did not find significant geographical differences in the accuracy of data processed by volunteer workers. We suggest that the main driver of the found pattern is the differences in familiarity of the paid workers with the research topic. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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820 KiB  
Article
Impacts of Leadership on Project-Based Organizational Innovation Performance: The Mediator of Knowledge Sharing and Moderator of Social Capital
by Junwei Zheng, Guangdong Wu and Hongtao Xie
Sustainability 2017, 9(10), 1893; https://doi.org/10.3390/su9101893 - 20 Oct 2017
Cited by 94 | Viewed by 11186
Abstract
With the increasing importance of leadership in project-based organizations, innovation is essential for the sustainable development of construction projects. Since few studies have explored the relationship between leadership and innovation in construction projects, this study fills this research gap and makes a significant [...] Read more.
With the increasing importance of leadership in project-based organizations, innovation is essential for the sustainable development of construction projects. Since few studies have explored the relationship between leadership and innovation in construction projects, this study fills this research gap and makes a significant theoretical contribution to the existing body of literature. Based on a knowledge-rated and resource-based view, this study aims to investigate various effects of different types of leadership on innovation performance in a construction project-based organization. Therefore, a theoretical model was constructed to explore the mediation mechanism and boundary condition of different types of leadership to improve innovation. The theoretical model was validated with empirical data covering project managers and engineers from the project-based organization in China via regression analysis and path analysis. The results show that transformational leadership and transactional leadership have some positively significant effects on knowledge sharing and innovation performance. Meanwhile, knowledge sharing partially mediates the relationship between transformational leadership and/or transactional leadership and innovation performance. Additionally, by considering different levels of social capital, transformational leadership is likely to have a strong positive impact on innovation performance through knowledge sharing. Our findings ensure a better understanding of the role of leadership, knowledge management, and social capital in the innovation process of construction projects. Therefore, project managers should promote a higher stimulation of a leadership behavior, encouraging knowledge management, and establishing the social capital, thus improving the innovation performance in the project-based organizations in construction projects. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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893 KiB  
Article
Role of Human Knowledge and Communication on Operational Benefits Gained from Six Sigma
by Jorge L. García-Alcaraz, Liliana Avelar-Sosa, Juan I. Latorre-Biel, Emilio Jiménez-Macías and Giner Alor-Hernández
Sustainability 2017, 9(10), 1721; https://doi.org/10.3390/su9101721 - 26 Sep 2017
Cited by 7 | Viewed by 4628
Abstract
Six Sigma (SS) is a production philosophy focused on human experiences and knowledge, aimed to minimize defects of products and services. The appropriate implementation of SS requires an education process, reliable data analysis, efficient didactic material, statistical techniques and human knowledge to improve [...] Read more.
Six Sigma (SS) is a production philosophy focused on human experiences and knowledge, aimed to minimize defects of products and services. The appropriate implementation of SS requires an education process, reliable data analysis, efficient didactic material, statistical techniques and human knowledge to improve communication and operational benefits. In this article, we present a structural equation model integrating those aspects as latent variables and relating them with ten hypotheses. Data for hypothesis validation were gathered among 301 manufacturing companies, and assessed using partial least squares (PLS) to estimate direct, indirect, and total effects. As results, we found that access to reliable information, trusted analysis and knowledgeable management are crucial for SS implementation at the problem definition stage. Likewise, to execute and control SS projects, it is important to be trained in statistical techniques through clear didactic materials. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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768 KiB  
Article
What Makes Firms Innovative? The Role of Social Capital in Corporate Innovation
by Se-Yeon Ahn and So-Hyung Kim
Sustainability 2017, 9(9), 1564; https://doi.org/10.3390/su9091564 - 03 Sep 2017
Cited by 30 | Viewed by 6946
Abstract
This paper offers a social capital explanation for the purported relationship between human capital investment and an organization’s innovation capability. We argue that social capital plays a mediating role in the relationship between the level of individual knowledge of employees and organizations’ innovation [...] Read more.
This paper offers a social capital explanation for the purported relationship between human capital investment and an organization’s innovation capability. We argue that social capital plays a mediating role in the relationship between the level of individual knowledge of employees and organizations’ innovation capabilities. The mediating mechanism is attributed to the role of social capital in knowledge exchange and combination that help enhance knowledge creation. Using survey data of 319 manufacturing firms in Korea, we conducted structural equation modeling (SEM) analysis to verify the mediating role of social capital in firms’ innovation performance. The results demonstrated that relational and cognitive dimensions of social capital are important mediators in realizing organizational innovation performance. Full article
(This article belongs to the Special Issue Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness)
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