Jung-Ran Park, Lorraine L. Richards and Andrew Brenza
The purpose of this paper is to highlight the potential strengths and weaknesses of the BIBFRAME bibliographic model and outline its purpose and key features. In addition, it…
Abstract
Purpose
The purpose of this paper is to highlight the potential strengths and weaknesses of the BIBFRAME bibliographic model and outline its purpose and key features. In addition, it discusses specific aspects of the model with respect to the pre-existing models of bibliographic description.
Design/methodology/approach
A review of source and secondary materials regarding BIBFRAME was undertaken, and a comparison of the conclusions derived from this literature was made to the pre-existing models of bibliographic description.
Findings
If the BIBFRAME Initiative can overcome what will likely be some significant challenges to the development and implementation of the model, BIBFRAME appears to be poised to become the next standard of bibliographic description and exchange for the library community.
Research limitations/implications
The findings and conclusions of this paper are based upon an in-depth literature review, not on theoretical or empirical derivations or examples. As a result, further research of both theoretical and empirical natures need to be developed.
Practical implications
BIBFRAME may well become the next standard of bibliographic description and exchange for the library community, leading to significant changes in cataloging practices over the years.
Social implications
To the extent that BIBFRAME can expand discovery mechanisms, the vast array of information currently available to information seekers will open up in previously unthought of ways.
Originality/value
This paper synthesizes a literature that was developed during a more preliminary design of the bibliographic model BIBFRAME and adds to the literature by discussing newer capabilities that have been designed into BIBFRAME 2.0.
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Keywords
Andrew Brenza, Michelle Kowalsky and Denise Brush
This paper aims to develop a better understanding of student worker perceptions of academic libraries in an effort to improve student worker training, position structure and…
Abstract
Purpose
This paper aims to develop a better understanding of student worker perceptions of academic libraries in an effort to improve student worker training, position structure and, ultimately, their perceptions of the library. The value of student reference assistants extends well beyond the completion of assigned library tasks. Specifically, student reference assistants can be important community voices and advocates for academic libraries, spreading, by word-of-mouth, the kinds of library services available to students and their relevance to the academic community. Consequently, it is essential to gain an understanding of how student workers perceive the academic library as a result of their employment.
Design/methodology/approach
This paper uses an informal, anonymous online survey of student reference assistants to gather information on student worker perceptions of the library. Questions focus on student workers’ understanding of library services and resources as well as on the library’s mission and purpose. An analysis of student responses is provided in light of four “ideal” perceptions which the librarians hold for all students.
Findings
Overall, student worker responses to the survey generally aligned with the ideal perceptions. However, misalignments suggest the need for training that focuses on the development of a broader understanding of the library’s role in the academic community.
Practical implications
The paper can help academic librarians gain insight on how student workers understand the role of the library within the larger academic community and how positive student worker perceptions can be leveraged for outreach purposes.
Originality/value
This paper examines an important aspect of student worker perception that has generally been overlooked in the literature.
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Mushtaq Hussain Khan, Navid Feroze, Junaid Ahmed and Mahzar Mughal
Earlier studies used conventional time-series models to forecast the impact of the Covid-19 pandemic on stock market performance. This study aims to provide a more flexible model…
Abstract
Purpose
Earlier studies used conventional time-series models to forecast the impact of the Covid-19 pandemic on stock market performance. This study aims to provide a more flexible model that offers more robust estimation features, such as incorporating additional information (prior) about the model parameters, capturing the evolving behavior of the parameters over time and being able to include several covariates using a spike and slab prior, within the context of the Covid-19 shock and its effect on stock market performance.
Design/methodology/approach
Empirically, this paper compares autoregressive integrated moving average (ARIMA) models and the proposed Bayesian structural time-series (BSTS) models regarding their forecasting accuracy for airline and petroleum stocks in the five countries most affected by the Coronavirus, namely, Brazil, France, India, Russia and the USA. In addition, the authors estimate the difference between the pre- and post-intervention periods of the observed series of stock prices and a simulated time-series that would have occurred without the extreme event of Covid-19, using intervention analysis under the best-performing models.
Findings
The forecasting results, based on the trend, seasonality and regression components, demonstrate that BSTS models respond faster to the diverse needs of time-series analysis in unprecedented and crisis conditions compared to ARIMA models. Therefore, the authors use intervention analysis under BSTS models to examine the impact of Covid-19 intervention on stock market performance. The authors find that the Covid-19 shock had an adverse effect on the stock markets of the selected countries. The impact was more pronounced in the Brazilian market, where the average weekly prices of airline and petroleum stocks plummeted by 76% and 29%, respectively.
Originality/value
To the best of the authors’ knowledge, no prior study has carried out intervention analysis under BSTS models to forecast the impact of Covid-19 intervention on stock market returns. This study attempts to fill this methodological gap in the literature.