Yunmei Liu, Changling Li and Zichun Gao
With the development of Web2.0 and publishing digitalization, traditional libraries and evaluation citation system can no longer indicate academic paper influence validly…
Abstract
Purpose
With the development of Web2.0 and publishing digitalization, traditional libraries and evaluation citation system can no longer indicate academic paper influence validly. Therefore, it is necessary to construct smart library and find the evaluation effect of Internet metrics-Usage.
Design/methodology/approach
This study puts forward four indexes of scholars’ evaluation based on Usage (total Usage (U), average Usage rate (U/N), hu-index and pu-index), which refer to citation indexes, takes the 35 high-output scholars in the field of library and information science in the WoS database as examples, analyzes performance of different scholars evaluation indexes based on Usage and compares the differences and correlations between “citation indicators” and “usage indicators.”
Findings
This study results show that pu-index is the strongest index to evaluate scholars. Second, there is a high correlation and strong mechanism based on time dependence and interactions between Usage and citation. Third, compared to “citation indicators”, the “usage indicators” has a larger numerical value and wider measurement range, which can break the time limitation of citation, and scientifically evaluate young scholars and newly published paper by scholars.
Originality/value
This paper proposes the pu-index – a relatively superior mathematical model for Usage and provides reference for the scholars’ evaluation policy of the smart library. This model can not only provide fair evaluation conditions for young scientists but also shorten the evaluation effect of the time lag of cited indicators. In addition, the “usage indicators” in this paper are new scientific evaluation indicators generated in the network environment. Applying it to the academic evaluation system will make the research papers widely accepted by the public and will also encourage scientists to follow the development of the Internet age and pursue research with equal emphasis on quantity and quality.
Details
Keywords
Abstract
Purpose
As the global emphasis on environmental consciousness intensifies, many corporations claim to be environmentally responsible. However, some merely partake in “greenwashing” – a facade of eco-responsibility. Such deceptive behavior is especially prevalent in Chinese heavy-pollution industries. To counter these deceptive practices, this study aims to use machine learning (ML) techniques to develop predictive models against corporate greenwashing, thus facilitating the sustainable development of corporations.
Design/methodology/approach
This study develops effective predictive models for greenwashing by integrating multifaceted data sets, which include corporate external, organizational and managerial characteristics, and using a range of ML algorithms, namely, linear regression, random forest, K-nearest neighbors, support vector machines and artificial neural network.
Findings
The proposed predictive models register an improvement of over 20% in prediction accuracy compared to the benchmark value, furnishing stakeholders with a robust tool to challenge corporate greenwashing behaviors. Further analysis of feature importance, industry-specific predictions and real-world validation enhances the model’s interpretability and its practical applications across different domains.
Practical implications
This research introduces an innovative ML-based model designed to predict greenwashing activities within Chinese heavy-pollution sectors. It holds potential for application in other emerging economies, serving as a practical tool for both academics and practitioners.
Social implications
The findings offer insights for crafting informed, data-driven policies to curb greenwashing and promote corporate responsibility, transparency and sustainable development.
Originality/value
While prior research mainly concentrated on the factors influencing greenwashing behavior, this study takes a proactive approach. It aims to forecast the extent of corporate greenwashing by using a range of multi-dimensional variables, thus providing enhanced value to stakeholders. To the best of the authors’ knowledge, this is the first study introducing ML-based models designed to predict a company’s level of greenwashing.
Details
Keywords
Rajan Yadav, Anurag Tiruwa and Pradeep Kumar Suri
The growing use of internet-based learning (IBL) platforms in institutions of higher education is producing profound changes in the traditional teaching learning process…
Abstract
Purpose
The growing use of internet-based learning (IBL) platforms in institutions of higher education is producing profound changes in the traditional teaching learning process worldwide. This paper aims to identify and understand the ways in which higher education institutions draw benefits by the use of such means, synthesizing the literature research.
Design/methodology/approach
The study synthesized the literature research by using a mixed method approach in which both Web of Science (WoS) and bibliographic techniques were used to retrieve the relevant data base.
Findings
The comprehensive review of the literature suggests that communication technology (CT), massive open online courseware (MOOCs), social networking sites (SNSs), blogs, real simple syndication (RSS) and YouTube are creating new possibilities and avenues of collaborative learning by transforming the traditional class and teacher-centric system.
Research limitations/implications
Multiplicity of the IBL platforms and rapid technological obsolesce are some of the limitations of this paper.
Originality/value
The findings of this study are highly useful in developing a strategic framework to accelerate the integration of IBL platforms to make teaching learning process more interactive and informative.