Search results

This paper aims to explore the impact of supply chain centralities on sales performance moderated by reputation and stock listing.

Empirical data on supply chain relationships were drawn from the context of electronics and electrical appliance industries in Tokyo, sourced from Teikoku Data Bank in year 2017–2018. On average, the authors analyzed an industrial network of 4,181 focal firms with 3.6 and 3.8 supplier and customer ties, respectively, using social networks and moderated regression analyses.

The authors find that in-(out-)degree, closeness and betweenness centralities are positively related to the focal firm’s annual sales performance. Hubs and authorities as ways of measuring reputation are found to not directly affect performance; hubs negatively moderate the relationship between betweenness and performance. Stock-listing was also found to negatively moderate the relationship between in-degree centrality and performance.

This study adds to existing literature by conducting a supply network analysis in a new industrial context, introducing a new method for assessing firm reputation in supply networks and showing how the structural characteristics of supply networks influence business performance.

Therapeutic communities (TCs) use social learning between peers in treating substance abuse. One mechanism for fostering social learning is peer affirmations for prosocial behavior. The purpose of this study is to use consistency of affirmations as a test of whether social learning does occur.

Using the results of a social network survey of 50 women in a corrections-based TC, the authors compared affirmations and nonprogrammatic compliments exchanged between residents as two directed social networks. The authors evaluated consistency of judgment using the hubs and authorities algorithm, and tested to see whether more senior residents are more likely to be hubs, thereby showing more consistent judgment.

More senior residents show greater consistency with peers in program affirmations but not in nonprogrammatic compliments. Hub status in the network of affirmations increases most rapidly in the first 200 days of residence, with slower increases thereafter.

This study is limited to one survey of the women in one TC. The external validity of the findings is therefore unclear. The results suggest that social learning of TC principles and prosocial behavior does occur and that it is not simply a function of popularity among peers. This seems to happen most rapidly in the first 200 days, suggesting that programs much shorter than six months may limit this process.

This is the first use of the hubs and authorities algorithm with a social network drawn from a therapeutic community and the first attempt to verify social learning through a social network analysis.

This research paper aims to better understand the network structure of higher education in North America. It draws on a relationally networked dataset of 1,292 degree-granting colleges and universities in North America to develop a modularity class approach to categorizing colleges and universities based on their own self-defined peer networks and assesses the utility of the modularity class approach as well as several measures of network centrality for predicting offerings of new curricular fields. Results show that not all measures of network centrality equally predict organizational change outcomes, with hub/authority position being most important. Additionally, results show that an empirically derived modularity class approach to categorizing organizations has important strengths in relation to more typical approaches based on prestige or perceived organizational characteristics. The approaches detailed in this paper will be useful for future analysts seeking to explain the spread of innovations and behavior across the higher education institutional field, as well as those seeking to understand clustering and organizational divergence.

Considers the problem of improving the performance of Web access by proposing a reconstruction of the internal link structure of a Web site in order to match the quality of the pages (measured in terms of their link importance in the Web space – global ranking) with the popularity of the pages (measured in terms of their importance recognized by Web users – local metrics). Provides a set of simple algorithms for local reorganization of a Web site, which results in improving users’ access to quality pages in an easy and quick way.

The purpose of this study is to improve the egocentric search speed for important documents in neighbouring blogs.

This paper presents a rapid egocentric search scheme that narrows down the search space to more important blogs. To determine which blogs are more valuable among a user's neighbouring blogs, a heuristic function is developed that predicts the authority scores on the basis of the local information of the blog. The proposed approach improves the speed of the egocentric search process and the quality of retrieved documents.

A blog is a new medium that is receiving considerable attention. Its links enable one to acquire information about social relations between bloggers in a blog space, and these relations reflect bloggers' interests. Therefore, the ability to search documents in linked blogs is significant for bloggers. An egocentric search method is proposed to search for documents in such neighbouring blogs. However, it takes considerable time to find the most valuable documents in a user's neighbouring blogs when many blogs are linked to that user's blog.

This study shows that the number of neighbouring blogs, which are linked to a blog with trackbacks and comments, is important for estimating the authority of a blog. In the experimental results this method performs about five times faster than the egocentric search using a breadth‐first search strategy in searching for the top 5 per cent of the most important documents in the neighbouring blogs.

This study aims to identify the characteristics of the maritime shipping network in Northeast Asia as well as compare the level of port connectivity among these container ports in the region. In addition, this study analyses the change in role and position of 20 ports in the region by clustering these ports based on connectivity index and container throughput and route index.

This study employs Social Network Analysis (SNA) to delineate the international connectivity of major container ports in Northeast Asia. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to identify each port's connectivity index and container throughput index, and the resulting indexes are employed as the basis to cluster 20 major ports by fuzzy C-mean (FCM).

The results revealed that Northeast Asia is a highly connected maritime shipping network with the domination of Shanghai, Shenzhen, Hong Kong and Busan. Furthermore, both container throughput and connectivity in almost all container ports in the region have decreased significantly due to the coronavirus disease 2019 (COVID-19) pandemic. The rapid growth of Shenzhen and Ningbo has allowed them to join Cluster 1 with Shanghai while maintaining high connectivity, yet decreasing container throughput has pushed Busan down to Cluster 2.

The originality of this study is to combine indexes of SNA into connectivity index reflecting characteristics of the maritime shipping network in Northeast Asia and categorize 20 major ports by FCM.

The personal learning environment driven approach to learning suggests a shift in emphasis from a teacher‐driven knowledge‐push to a learner‐driven knowledge‐pull learning model. One concern with knowledge‐pull approaches is knowledge overload. The concepts of collective intelligence and the Long Tail provide a potential solution to help learners cope with the problem of knowledge overload. The paper aims to address these issues.

Based on these concepts, the paper proposes a filtering mechanism that taps the collective intelligence to help learners find quality in the Long Tail, thus overcoming the problem of knowledge overload.

The paper presents theoretical, design, and implementation details of PLEM, a Web 2.0 driven service for personal learning management, which acts as a Long Tail aggregator and filter for learning.

The primary aim of PLEM is to harness the collective intelligence and leverage social filtering methods to rank and recommend learning entities.

This study reviews the methods found in the literature for the ranking of authors, identifies the pros and cons of these methods, discusses and compares these methods. The purpose of this paper is to study is to find the challenges and future directions of ranking of academic objects, especially authors, for future researchers.

This study reviews the methods found in the literature for the ranking of authors, classifies them into subcategories by studying and analyzing their way of achieving the objectives, discusses and compares them. The data sets used in the literature and the evaluation measures applicable in the domain are also presented.

The survey identifies the challenges involved in the field of ranking of authors and future directions.

To the best of the knowledge, this is the first survey that studies the author ranking problem in detail and classifies them according to their key functionalities, features and way of achieving the objective according to the requirement of the problem.

Purpose — The purpose of this discussion is, first, to review the concept of truth claim and how it forms the framework for four research traditions: science, social science, law, and judgments of excellence. Then, the operational mechanisms of networks are reviewed. The discussion concludes by introducing three philosophic perspectives that might deepen the meanings nascent in the concept of “search.”

Methodology/approach — The methodology includes a historical approach to outline brief but sufficient definitions for how truth claims are built in four established research traditions. Each tradition is then analyzed with a view to testing its methods. The tests suggest a number of pathways to reframe search engine results in order to evaluate their relationship to the previously established types of truth claims.

Findings — The findings constitute an outline of the research traditions in the four areas of science, social science, law, and judgments of excellence. These are followed by a review of the current configurations of networks, their infrastructures, and their capabilities, including a brief section on the importance of search engine mechanisms. Crawling, indexing, and then ranking form the operational mechanisms that search engines employ in delivering search results. It is clear that each operation introduces logical problems. Then, the final sections outline three widely ranging philosophic perspectives on the nature of search: (1) an aesthetic theory of indexing, (2) understanding search from the psychology of learning, and (3) an exploration of the relationship between performativity and recent economic models of how data accumulates in today's world.

Research implications — It is suggested that exploration of a deeper philosophical perspective will assist library and information science (LIS) scholars to reframe Web search in ways that allow linkages to the established research traditions.

Originality/value of the paper — The idea of testing the “truth claim” as connected to traditional research methods was presented initially by Rall (2002, 2004). This area has been neglected in the literature as many Internet scholars find that the philosophy of research methodologies remains outside of their knowledge base. Overall, LIS scholars have focused on information seekers, on the politics of search engines, as well as documenting the computational problems that are present in search engine results. The consideration of how truth claims are formed and subsequently tested will allow LIS researchers to explore the linkages between their current studies and the established frameworks of scholarly research.