scholarly journals Research on the Relationship Between H Index Ranking Evaluation Method and Comprehensive Ranking Based on Big Data Statistics

2021 ◽  
Vol 1952 (4) ◽  
pp. 042074
Author(s):  
Shufen Yang
Keyword(s):  
Big Data ◽  
Evaluation Method ◽  
H Index ◽  
Data Statistics ◽  
The Relationship

Dave Snowden on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 12-17 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theory And Practice ◽  
Content Type ◽  
Face To Face ◽  
The Relationship ◽  
A Company ◽  
Practical Implications ◽  
Scientific Officer

Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.

scholarly journals The Relationship Between Tumor Budding, Tumor Microenvironment, and Survival in Patients with Primary Operable Colorectal Cancer

2019 ◽  
Vol 26 (13) ◽  
pp. 4397-4404 ◽  
Author(s):  
Hester C. van Wyk ◽  
Antonia Roseweir ◽  
Peter Alexander ◽  
James H. Park ◽  
Paul G. Horgan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Tumor Microenvironment ◽  
Evaluation Method ◽  
Venous Invasion ◽  
Staging System ◽  
Consensus Conference ◽  
Tumor Budding ◽  
Inflammatory Cell Infiltrate ◽  
Cancer Specific Survival ◽  
The Relationship

Abstract Background Tumor budding is an independent prognostic factor in colorectal cancer (CRC) and has recently been well-defined by the International Tumour Budding Consensus Conference (ITBCC). Objective The aim of the present study was to use the ITBCC budding evaluation method to examine the relationship between tumor budding, tumor factors, tumor microenvironment, and survival in patients with primary operable CRC. Methods Hematoxylin and eosin-stained slides of 952 CRC patients diagnosed between 1997 and 2007 were evaluated for tumor budding according to the ITBCC criteria. The tumor microenvironment was evaluated using tumor stroma percentage (TSP) and Klintrup–Makinen (KM) grade to assess the tumor inflammatory cell infiltrate. Results High budding (n = 268, 28%) was significantly associated with TNM stage (p < 0.001), competent mismatch repair (MMR; p < 0.05), venous invasion (p < 0.001), weak KM grade (p < 0.001), high TSP (p < 0.001), and reduced cancer-specific survival (CSS) (hazard ratio 8.68, 95% confidence interval 6.30–11.97; p < 0.001). Tumor budding effectively stratifies CSS stage T1 through to T4 (all p < 0.05) independent of associated factors. Conclusions Tumor budding effectively stratifies patients’ survival in primary operable CRC independent of other phenotypic features. In particular, the combination of T stage and budding should form the basis of a new staging system for primary operable CRC.

Evaluation of Product Preference Using Virtual Prototyping: Case Study of an Automobile Interior

2002 ◽  
Vol 46 (6) ◽  
pp. 740-744 ◽  
Author(s):  
Chul Woo Kim ◽  
Jungchul Park ◽  
Myung Hwan Yun ◽  
Sung H. Han ◽  
Hee-Dong Ko
Keyword(s):  
Virtual Reality ◽  
Interior Design ◽  
Evaluation Method ◽  
Product Evaluation ◽  
Steering Wheel ◽  
Subjective Impression ◽  
Design Elements ◽  
Design Alternatives ◽  
Virtual Prototypes ◽  
The Relationship

The objective of this study was to develop a product evaluation method applicable to virtual prototypes and to apply the method to automobile interior design. Considering that virtual reality-based product prototypes could represent design alternatives comparable to physical prototypes, prototypes developed in virtual reality environments were employed as design alternatives. After a procedure to evaluate virtual prototypes was developed specifically for a virtual reality environment, the procedure was applied to the problem of automobile interior design. 34 subjects evaluated 32 different virtual prototypes generated from the combination of design element variations. Four categories of subjective impression were used to evaluate the 32 virtual prototypes: luxuriousness, comfort, harmoniousness, and controllability. ANOVA and multiple linear regression analysis were performed to specify design elements critical to customer preference and to interpret the relationship between design elements and subjective impressions. As the result, the shapes of frontal area including crash pad and center fascia, door trim and steering wheel were selected as important variables related to subjective impressions. The proposed evaluation method for virtual prototypes could be utilized as an alternative way of identifying the relationship between subjective impressions and design elements.

Big data analytics capabilities and organizational performance: the mediating effect of dual innovations

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaofeng Su ◽  
Weipeng Zeng ◽  
Manhua Zheng ◽  
Xiaoli Jiang ◽  
Wenhe Lin ◽  
...  
Keyword(s):  
Big Data ◽  
Organizational Performance ◽  
Empirical Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Mediating Effect ◽  
Content Type ◽  
Mediating Role ◽  
The Relationship

PurposeFollowing the rapid expansion of data volume, velocity and variety, techniques and technologies, big data analytics have achieved substantial development and a surge of companies make investments in big data. Academics and practitioners have been considering the mechanism through which big data analytics capabilities can transform into their improved organizational performance. This paper aims to examine how big data analytics capabilities influence organizational performance through the mediating role of dual innovations.Design/methodology/approachDrawing on the resource-based view and recent literature on big data analytics, this paper aims to examine the direct effects of big data analytics capabilities (BDAC) on organizational performance, as well as the mediating role of dual innovations on the relationship between (BDAC) and organizational performance. The study extends existing research by making a distinction of BDACs' effect on their outcomes and proposing that BDACs help organizations to generate insights that can help strengthen their dual innovations, which in turn have a positive impact on organizational performance. To test our proposed research model, this study conducts empirical analysis based on questionnaire-base survey data collected from 309 respondents working in Chinese manufacturing firms.FindingsThe results support the proposed hypotheses regarding the direct and indirect effect that BDACs have on organizational performance. Specifically, this paper finds that dual innovations positively mediate BDACs' effect on organizational performance.Originality/valueThe conclusions on the relationship between big data analytics capabilities and organizational performance in previous research are controversial due to lack of theoretical foundation and empirical testing. This study resolves the issue by provides empirical analysis, which makes the research conclusions more scientific and credible. In addition, previous literature mainly focused on BDACs' direct impact on organizational performance without making a distinction of BDAC's three dimensions. This study contributes to the literature by thoroughly introducing the notions of BDAC's three core constituents and fully analyzing their relationships with organizational performance. What's more, empirical research on the mechanism of big data analytics' influence on organizational performance is still at a rudimentary stage. The authors address this critical gap by exploring the mediation of dual innovations in the relationship through survey-based research. The research conclusions of this paper provide new perspective for understanding the impact of big data analytics capabilities on organizational performance, and enrich the theoretical research connotation of big data analysis capabilities and dual innovation behavior.

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