Knowledge diffusion path analysis of data quality literature: A main path analysis

The knowledge trajectory and structure of the supply chain integration: a main path and cluster analysis

Purpose

Supply chain integration (SCI) dominates supply chain strategy and is receiving increasing academic attention. The purpose of this paper is to provide a systematic review of the knowledge trajectory and structure of the SCI field.

Design/methodology/approach

Based on 3,533 papers extracted from the Web of Science (WoS), this paper adopts the main path analysis (MPA) method to detect three distinct knowledge development trajectories. Coupling-based clustering is combined with MPA to reveal three critical subfields.

Findings

The findings show that the definition, content and dimensions of SCI lack unified conclusions. The influencing factors and performance consequences of SCI are long-standing research elements. Building theoretical models and integrated systems and applying blockchain technology to improve SCI are the key research contents. The intertwining of collaboration and SCI cannot be ignored, and the green SCI may be a hot topic in the future.

Research limitations/implications

This study explores knowledge in the SCI field based on the limited literature collected by WoS rather than all published papers. The omissions of some relevant papers and books may exist.

Practical implications

The study methodology provides a framework for similar studies in the future, and the results help researchers to get a comprehensive picture of the knowledge trajectory and structure of the SCI field.

Originality/value

Compared to existing reviews, MPA combines cluster analysis to develop a synthetic framework of the knowledge trajectory and structure in the SCI domain. It contributes to a systematic review of the development of SCI.

The Academic Development Trajectories of the Lean Production Based on Main Path Analysis Method

Enterprises looking to be competitive are constantly looking for a continuous increase in productivity, quality, and level of services. With the development of the industry 4.0 concept, manufacturers are more confident about the new advantages of automation and systems integration. Lean management is a well-developed and empirically proven managerial strategy. Combining lean and industry 4.0 practices seems to be a necessary evolutionary step to further raise the level of operational excellence. This study applied the main path analysis method to explore the development trend of lean management in the academic field. First, this study adopted the Scopus database to collect relevant papers, then analyzed their overall development trajectory by using Main Path 437 software, and used the g-index and h-index to identify more influential journals. Next, this study clustered the papers with similar topics into several groups, and then used Wordle software to present the keywords of each group in a word cloud that serves as a reference for naming. Thus, the top five groups obtained are as follows: “Lean production concept and application”, “Lean Six Sigma concept and application”, “Lean system integration and application”, “Lean construction concept and application”, and “Lean healthcare concept and application”. Finally, this study provides explanations and conclusions on each group’s development trajectory, as well as research recommendations in the field of lean production. The findings can serve as guides for industry, government, and academia as they develop future lean production development strategies. This study utilized an integrated analysis approach to successfully and effectively depict the trajectory of lean production development and applications, identify future research and development directions, and generate technological forecasts.

Examining Knowledge Diffusion in the Circular Economy Domain: a Main Path Analysis

The circular economy (CE) field has recently attracted significant interest from academics and practitioners. CE represents a departure from the linear economy, which is characterised by unsustainable resource production and consumption. The growing number of publications necessitates a comprehensive analysis of this field. This is the first systematic examination of the knowledge base and knowledge diffusion pathways in the CE domain. We analyse a Web of Science dataset containing 5431 articles published between 1970 and 2020. To create a comprehensive review of the CE domain, we conducted a keyword co-occurrence network analysis. We examined four distinct types of main paths using the main path analysis (MPA) technique: forward, backward, global, and key-route. According to the analyses, CE research focuses on six primary research themes: CE and sustainability, bioeconomy, CE practices, lifecycle assessment and industrial symbiosis, construction activities, and waste management. In addition, the MPA demonstrates that the CE literature has recently focused on Industry 4.0 technologies and their contribution to CE. This is the first attempt to depict the genealogy of CE research so that scholars can comprehend the domain's evolutionary structure, identify hot topics, and capture the history, development status, and potential future directions of CE research.

Tracing knowledge evolution flows in scholarly restaurant research: a main path analysis

Restaurant research has received significant attention globally. This article aims to examine the evolution and the knowledge structure of restaurant research over the past decades. We also investigate the restaurant research hotspots and knowledge diffusion paths based on 1489 articles extracted from the Web of Science database. Furthermore, we conduct a keyword co-occurrence network analysis and four different types of main path analyses to scrutinize the historical formation of the restaurant research. Results revealed that restaurant research mainly focused on five research themes: consumer behavior, consumer satisfaction, social media, green restaurants, and authenticity. While consumer behavior has been the mainstream topic, the focus of this line of research has recently shifted from traditional to luxury and ethnic restaurants. Furthermore, our analysis has detected several recent changes in response to the COVID-19 pandemic. By examining the knowledge structure of restaurant research, we reveal its knowledge diffusion paths and provide avenues for future research in this vast and interdisciplinary research field.

Knowledge Diffusion of the Internet of Things (IoT): A Main Path Analysis

The Internet of Things (IoT) is a concept that has attracted significant attention since the emergence of wireless technology. The knowledge diffusion of IoT takes place when an individual disseminates his knowledge of IoT to the persons to whom he is directly connected, and knowledge creation arises when the persons receive new knowledge of IoT, which is combined with their existing knowledge. In the current literature, several efforts have been devoted to summarising previous studies on IoT. However, the rapid development of IoT research necessitates examining the knowledge diffusion routes in the IoT domain by applying the main path analysis (MPA). It is crucial to update prior IoT studies and revisit the knowledge evolution and future research directions in this domain. Therefore, this paper adopts the keyword co-occurrence network and MPA to identify the research hotspots and study the historical development of the IoT domain based on 27,425 papers collected from the Web of Science from 1970 to 2020. The results show that IoT research is focused on IoT applications for smart cities, wireless networks, blockchain technology, computing technologies, and AI technologies. The findings from the MPA address the need to explore the knowledge evolution in the IoT domain. They also provide a valuable guide to disseminate the knowledge of IoT among researchers and practitioners, assisting them to understand the history, present and future trends of IoT development and implementation.

A Data-Driven Approach to Trace the Development of Lean Construction in Building Projects: Topic Shift and Main Paths

Due to the varied ideas of lean philosophy adopted in the construction industry, it is challenging to trace the development of lean philosophy in terms of how the field evolved by adopting the lean ideas and how the topic shifted. However, it is challenging to extract useful information from the massive body of literature and to trace the development of Lean Construction in Building Projects. Previous studies have conducted longitudinal analyses of scientific areas depending on the authors’ interpretation and explanation, which is time-consuming and labor-intensive. To address this concern, this study proposes a data-driven approach integrating N-gram extraction, citation analysis, and a global key-route algorithm to trace the development. Based on the collected literature of Lean Construction in Building Projects, N-grams were extracted as topics from the raw texts of titles, abstracts, and keywords, and the shifts in topics were measured. Then, the references were extracted from the literature to create a citation network to represent the knowledge flows, and the global key-route algorithm was used to identify the most valuable flows reflecting the main paths of the development. The results illustrate how Lean Construction in Building Projects evolved and how the topics shifted, providing an exciting opportunity to reveal this development by using a data-driven approach rather than personal judgments. The findings can help us to understand that the field of Lean Construction in Building Projects was driven and motivated not only by the “lean theory”, but also by problems in the practice of building projects. Moreover, lean theory leads to flourishing research on informatization, and BIM will be an important tool to better achieve lean thinking in construction.

Investigating Company’s Technical Development Directions Based on Internal Knowledge Inheritance and Inventor Capabilities: The Case of Samsung Electronics

This paper proposes a new method to analyze technical development directions of a company using knowledge persistence-based main path analysis and co-inventor network analysis. Main path analysis is used for identifying internal technical knowledge flows and inheritances over time within a company, and knowledge persistence-based main path analysis can well identify major knowledge streams of each sub-domain within a relatively small knowledge network generated by one company without omission of significant inventions. A co-inventor network analysis is used for identifying key inventors who can be represented as the major technical capabilities of a company. The method is a meaningful attempt in that it applies knowledge persistence-based main path analysis to analyzing a company’s internal technical development and combines the two approaches to provide the information on both base technical capabilities and new technical characteristics. To test the method, this paper conducted an empirical study of Samsung Electronics. The results show that the method generated major knowledge flows and identified key inventors of Samsung Electronics. In particular, the method can identify the base technical knowledge as the ‘backbone’ and newly injected knowledge as ‘fresh blood’ for forecasting future technical development. Based on the identified clue information, this paper forecasted the potential future technologies for each sub-domain of Samsung Electronics with technical keywords and descriptions.

Mapping the Territorial Adaptation of Technological Innovation Systems—Trajectories of the Internal Combustion Engine

Besides the rise of sustainable technologies, successful sustainability transitions crucially depend on the phase-out of unsustainable ones. However, the detailed dynamics of declining technological innovation systems (TIS) remain vague. Thus, based on the new TIS life cycle framework, we investigate how the technological dimension of a mature TIS adapts to increasing transformational pressures towards its decline. Considering the internal combustion engine (ICE) as a suitable research case, we measure the technological adaptation as changes in the dominant technological trajectory over time and across TIS territories. Empirically, this is operationalised by a main path analysis in patent citation networks, using 221,700 patents to cover the period from 10 January 1901 until 31 January 2019. Our results not only point to considerable shifts in the direction of technological development over time but also highlight stark differences across the three major car markets. Most notably, in contrast to USA and Japan, where hybrid powertrains have become the dominant alternative powertrains, the dominant trajectory in the EU territory points to an ongoing commitment towards diesel technology. In essence, our results highlight the importance of path dependency and connectivity of the knowledge search process as well as selective forces on the innovation system level, which have been neglected by related empirical studies. Conceptionally, our analysis demonstrates that the technological adaptation process is influenced by specific developments during a time period and heterogenous territorial dynamics within the TIS. Consequently, future TIS studies might consider spatially heterogeneous development cycles as well as possible mechanisms to establish an international trajectory towards sustainability goals.

Do main paths reflect technological trajectories? Applying main path analysis to the semiconductor manufacturing industry

It has been proposed that main path analysis can be used to identify technological trajectories in patent-citation networks. In this paper, the method is applied to a network composed of one million US patents and eight million citations in order to trace the backbone of the technological trajectory of the semiconductor manufacturing industry. An in depth discussion of the method is presented, focusing on the many parameters that can be adjusted while applying it and on the consequences of adjusting any of them. Moreover, and differently from other papers on the subject, the result of the algorithm is analysed to determine if it indeed represents the most important technological contributions to the trajectory or if it is merely a collection of relevant and connected patents. This is made easier by the fact that the semiconductor industry has a clear and widely known technological trajectory that spans more than 50 years, Moore's law.

Exploring the knowledge development trajectories of the supply chain finance domain: a main path analysis

Purpose

Supply chain finance (SCF), which is able to manage financial flows along the supply chains effectively, has received wide attention from all over the world. Faced with the increasing number of outputs, the purpose of this paper is to investigate the SCF development over the past decades effectively, including the hot topics, knowledge diffusion trajectories and structure.

Design/methodology/approach

This paper adopts the keyword co-occurrence cluster and main path analysis (MPA) including four types of main paths, studying the historical development of SCF based on 2,233 papers retrieved from Web of Science during 1970–2019.

Findings

The results show that: (1) the research focuses on several aspects, including trade credit, supply chain management, procurement, health financing and sustainability, etc. and (2) trade credit financing has been the mainstream and the research focus has shifted from one-level trade credit to two-level trade credit. Recently, there is a trend to use game-theoretic models to find the best solutions for members in the supply chain.

Originality/value

This paper addresses the need to investigate the knowledge evolution in the SCF domain. It provides a framework to study the knowledge diffusion trajectories and structure, which helps scholars to handle thousands of papers effectively and deepen their understanding of the history, present and future trends of SCF development.

Mapping the technology evolution path: a novel model for dynamic topic detection and tracking

Identifying the evolution path of a research field is essential to scientific and technological innovation. There have been many attempts to identify the technology evolution path based on the topic model or social networks analysis, but many of them had deficiencies in methodology. First, many studies have only considered a single type of information (text or citation information) in scientific literature, which may lead to incomplete technology path mapping. Second, the number of topics in each period cannot be determined automatically, making dynamic topic tracking difficult. Third, data mining methods fail to be effectively combined with visual analysis, which will affect the efficiency and flexibility of mapping. In this study, we developed a method for mapping the technology evolution path using a novel non-parametric topic model, the citation involved Hierarchical Dirichlet Process (CIHDP), to achieve better topic detection and tracking of scientific literature. To better present and analyze the path, D3.js is used to visualize the splitting and fusion of the evolutionary path. We used this novel model to mapping the artificial intelligence research domain, through a successful mapping of the evolution path, the proposed method’s validity and merits are shown. After incorporating the citation information, we found that the CIHDP can be mapping a complete path evolution process and had better performance than the Hierarchical Dirichlet Process and LDA. This method can be helpful for understanding and analyzing the development of technical topics. Moreover, it can be well used to map the science or technology of the innovation ecosystem. It may also arouse the interest of technology evolution path researchers or policymakers.

Reviewing ISO Compliant Multifunctionality Practices in Environmental Life Cycle Modeling

The standard ISO 14044:2006 defines the hierarchical steps to follow when solving multifunctionality issues in life cycle assessment (LCA). However, the practical implementation of such a hierarchy has been debated for twenty-five years leading to different implementation practices from LCA practitioners. The first part of this study discussed the main steps where the ISO hierarchy has been implemented differently and explored current multifunctionality practices in peer-reviewed studies. A text-mining process was applied to quantitatively assess such practices in the 532 multifunctional case studies found in the literature. In the second part of the study, citation network analysis (CNA) was used to identify the major publications that influenced the development of the multifunctionality-debate in LCA, i.e., the key-route main path. The identified publications were then reviewed to detect the origins of the different practices and their underlying theories. Based on these insights, this study provided some “food for thought” on current practices to move towards consistent methodology. We believe that such an advancement is urgently needed for better positioning LCA as a tool for sustainability decision-making. In particular, consistent allocation practices could be especially beneficial in bioeconomy sectors, where production processes are usually multifunctional, and where current allocation practices are not harmonized yet.

Major Knowledge Diffusion Paths of Megaproject Management: A Citation-Based Analysis

This article integrates social network analysis and main path analysis to investigate progress in megaproject management (MPM) from the perspective of knowledge diffusion. After measuring three major knowledge diffusion paths of MPM, the authors find that MPM is mainly driven by a set of problems and puzzles. The findings provide an exciting opportunity to advance existing understanding of MPM from an alternative angle of knowledge diffusion that considers the underlying associations among publications. Moreover, this article employs quantitative methods to examine citation data of publications, thus providing more unbiased and in-depth analysis to illustrate the development of MPM.

Evaluating the Quality and Usability of Open Data for Public Health Research: A Systematic Review of Data Offerings on 3 Open Data Platforms

CONTEXT

Government datasets are newly available on open data platforms that are publicly accessible, available in nonproprietary formats, free of charge, and with unlimited use and distribution rights. They provide opportunities for health research, but their quality and usability are unknown.

OBJECTIVE

To describe available open health data, identify whether data are presented in a way that is aligned with best practices and usable for researchers, and examine differences across platforms.

DESIGN

Two reviewers systematically reviewed a random sample of data offerings on NYC OpenData (New York City, all offerings, n = 37), Health Data NY (New York State, 25% sample, n = 71), and HealthData.gov (US Department of Health and Human Services, 5% sample, n = 75), using a standard coding guide.

SETTING

Three open health data platforms at the federal, New York State, and New York City levels.

MAIN OUTCOME MEASURES

Data characteristics from the coding guide were aggregated into summary indices for intrinsic data quality, contextual data quality, adherence to the Dublin Core metadata standards, and the 5-star open data deployment scheme.

RESULTS

One quarter of the offerings were structured datasets; other presentation styles included charts (14.7%), documents describing data (12.0%), maps (10.9%), and query tools (7.7%). Health Data NY had higher intrinsic data quality (P < .001), contextual data quality (P < .001), and Dublin Core metadata standards adherence (P < .001). All met basic "web availability" open data standards; fewer met higher standards of "hyperlinked to other data."

CONCLUSIONS

Although all platforms need improvement, they already provide readily available data for health research. Sustained effort on improving open data websites and metadata is necessary for ensuring researchers use these data, thereby increasing their research value.