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The purpose of this case study was to complement existing weeding and retention criteria beyond the most used methods in academic libraries and to consider citation counts in the identification of important scholarly works.

Using a small sample of items chosen for withdrawal from a small liberal arts college library, this case study looks at the use of Google Scholar citation counts as a metric for identification of notable monographs in the social sciences and mathematics.

Google Scholar citation counts are a quick indicator of classic, foundational or discursive monographs in a particular field and should be given more consideration in weeding and retention analysis decisions that impact scholarly collections. Higher citation counts can be an indicator of higher circulation counts.

The authors found little indication in the literature that Google Scholar citation counts are being used as a metric for identification of notable works or for retention of monographs in academic libraries.

Due to ongoing inequities in the social determinants of health and systemic barriers, homelessness continues to be a significant concern that disproportionately impacts racialized communities. Despite constituting a small proportion of the population, Black individuals are over-represented among people experiencing homelessness in many Canadian cities. However, although Black homelessness in Canada is a pressing issue, it has received limited attention in the academic literature. The purpose of this paper is to examine the reported prevalence of Black homelessness across Canada.

By consulting enumerations from 61 designated communities that participated in the 2018 Nationally Coordinated Point-in-Time Count and two regional repositories – one for homeless counts supported by the government of British Columbia and another from the Rural Development Network – this paper reports on the scale and scope of Black homelessness across Canada.

Significantly, these reports demonstrate that Black people are over-represented among those experiencing homelessness compared to local and national populations. These enumerations also demonstrate significant gaps in the reporting of Black homelessness and inadequate nuance in data collection methods, which limit the ability of respondents to describe their identity beyond “Black.”

This research provides an unprecedented examination of Black homelessness across Canada and concludes with recommendations to expand knowledge on this important and under-researched issue, provide suggestions for future iterations of homeless enumerations and facilitate the development of inclusive housing policy.

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization.

Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT).

Methods procedures, process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods.

Results, observations, and conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

The bank is termed as an imperative part of the marketing economy. The failure or success of an institution relies on the ability of industries to compute the credit risk. The loan eligibility prediction model utilizes analysis method that adapts past and current information of credit user to make prediction. However, precise loan prediction with risk and assessment analysis is a major challenge in loan eligibility prediction.

This aim of the research technique is to present a new method, namely Social Border Collie Optimization (SBCO)-based deep neuro fuzzy network for loan eligibility prediction. In this method, box cox transformation is employed on input loan data to create the data apt for further processing. The transformed data utilize the wrapper-based feature selection to choose suitable features to boost the performance of loan eligibility calculation. Once the features are chosen, the naive Bayes (NB) is adapted for feature fusion. In NB training, the classifier builds probability index table with the help of input data features and groups values. Here, the testing of NB classifier is done using posterior probability ratio considering conditional probability of normalization constant with class evidence. Finally, the loan eligibility prediction is achieved by deep neuro fuzzy network, which is trained with designed SBCO. Here, the SBCO is devised by combining the social ski driver (SSD) algorithm and Border Collie Optimization (BCO) to produce the most precise result.

The analysis is achieved by accuracy, sensitivity and specificity parameter by. The designed method performs with the highest accuracy of 95%, sensitivity and specificity of 95.4 and 97.3%, when compared to the existing methods, such as fuzzy neural network (Fuzzy NN), multiple partial least squares regression model (Multi_PLS), instance-based entropy fuzzy support vector machine (IEFSVM), deep recurrent neural network (Deep RNN), whale social optimization algorithm-based deep RNN (WSOA-based Deep RNN).

This paper devises SBCO-based deep neuro fuzzy network for predicting loan eligibility. Here, the deep neuro fuzzy network is trained with proposed SBCO, which is devised by combining the SSD and BCO to produce most precise result for loan eligibility prediction.

The study examines the relationship between news intensity, media sentiment and market microstructure invariance-implied measures of trading activity and liquidity of Chinese property developer stocks during the 2020–2022 Chinese property sector crisis.

The authors adopt the extension of the news article invariance hypothesis, which is a generalization of the market microstructure invariance conjecture, from January 2020 to January 2022 to test specific quantitative relationships between the arrival rate of public information, trading activity and a nonlinear function of a proxy for the probability of informed trading. Empirical tests are based on a dataset of 22,412 firm-day observations and two count-data models to correct for overdispersion and the excess number of zeros. Seventy-five stocks of Chinese companies from the property development industry (including the China Evergrande Group) were included in the sample.

The authors reject the news article invariance hypothesis but document a positive and significant relationship between the flow of public information and risk liquidity. Additionally, the authors find that the proxy for informed trading activity is positively related to the arrival rates of public information from October 2021 to January 2022.

The findings support the hypothesis that negative (positive) media sentiment induces significant deterioration (insignificant improvement) in stock liquidity. The authors find that an increase in the number of news articles about a company corresponds to a higher liquidity of Chinese property developers' stocks after controlling for media sentiment.

Mobile applications affect our everyday activities and have become more and more information centric. Effort estimation for mobile application is an essential factor to consider in the development cycle. Due to feature complexities and size, effort estimation of mobile applications poses a continued challenge for developers. This paper attempts to adapt COSMIC Function Point and Unified Modeling Language (UML) techniques to estimate the size of a given mobile application. The COSMIC concepts capture data movements of the functional processes whereas the UML class analyzes them. We utilize the Use Case Diagrams, sequence diagrams and class diagrams for mapping the Function user requirements for sizing mobile applications. We further present a new size measurement technique; Unadjusted Mobile COSMIC Function points (UMCFP) to get the functional size of mobile application using Mobile Complex Factors as an input. In this study eight mobile applications were analyzed using UMCFP, Function Point Analysis and COSMIC Function Point. The results were compared with the actual size of previous Mobile application projects.

The ideology of this article is to study the performance concerns of SDN Controllers, with the help of developed SRGM and thereby obtain its optimal testing duration. The effect of undetected uncertainty in the parameter values have also been catered in the proposal.

These uncertainties in the parameter values are studied as the risk of not meeting desired set of requirements, whose removal causes additional cost. Considering these two constructs as attributes of MAUT, the controller's optimal testing duration is obtained.

The article focuses towards obtaining the optimal duration for which the SDN Controllers must be tested. It was observed that the inculcation of risk-attribute has provided the higher utility value as compared to any other existing scenarios.

Plenty of SRGM have been proposed in the literature which talks about the testing stop time determination problems. But, none of them have considered the impact of risk of not meeting the requirements (reliability) along with cost to obtain its testing stop time. Further, validation of the proposed model in presented with the help of two releases versions of SDN controller platform, ONOS, entitled as “Kingfisher” and “Loon” and has acquired promising results.

The purpose of this paper is to examine the role of short sellers in foreign direct investment (FDI) decisions. Drawing on threat rigidity theory, the authors argue that short sellers pose a threat to chief executive officers (CEOs) by exerting downward pressure to target firms’ stock prices. That threat will evoke rigid managerial responses that hinder new FDI activities. The authors also posit that CEOs will be less reactive to short sellers’ threats when they are generalist CEOs who have extensive general work experience or when they serve as the board chair.

The authors collect data from S&P 1,500 firms, and the final sample consists of 717 firms and 6,930 firm-year observations from 1998 to 2016. The authors use an Arellano and Bond generalized method of moments static linear probability panel data model and an instrumental panel count data model to test the hypotheses.

The findings support the hypotheses and suggest that CEOs who are under more pressure from short sellers engage in fewer new FDI activities. The negative impact of short sellers on FDI decisions is less salient when CEOs have general work experience or are the chairperson of the board.

This study contributes to the international business research by stressing the need to consider the role of short sellers in firm internationalization decisions.

Although being fired up about changes such as firm expansion, chief executive officers (CEOs) have a hard time with changes that involve divesting businesses or downsizing operations. This study aims to examine how a particular psychological process – regulatory focus – serves as a managerial exit barrier in the context of store closings in the US retail industry. This study also examines how a particular corporate governance mechanism, the board of directors, moderates the relationship between CEO regulatory focus and divestment activity.

This study content-analyzed letters to shareholders to measure the regulatory focus of retail CEOs and used negative binomial regression to test the effect of the CEO’s regulatory focus and board independence on store closure activity.

The two motivation orientations – promotion and prevention – focuses have distinct effects on store closure decisions. As predicted, promotion-focused CEOs, who value attainment and growth, resist “pulling the plug.” Conversely, prevention-focused CEOs, who are more sensitive to losses, are more inclined to close stores. Independent boards decrease the CEOs’ resistance to “pull the plug” only when necessary, which is the case when CEOs have less vigilant tendencies.

This study contributes to the strategy and marketing literature. It examines an individual-level antecedent of store closure decisions and responds to the call for research on the effect of regulatory focus on divestment decisions.

Leaders themselves can be a source of resistance to change. The findings suggest the importance of boards hiring CEOs psychologically aligned with the firms’ strategic priorities. Promotion-focused CEOs may be a better fit for companies engaged in growth and acquisition. By contrast, prevention-focused CEOs may be a better fit for firms involved in retrenchment and restructuring. Independent boards still have the power to influence CEO decisions in the case of a misfit, as the findings suggest.

This study examines divestment decisions during the “retail apocalypse” and provides empirical evidence for the existence of managerial exit barriers, first introduced by Michael Porter.

This study investigates differences and commonalities in data production, sharing and reuse across the widest range of disciplines yet and identifies types of improvements needed to promote data sharing and reuse.

The first authors of randomly selected publications from 2018 to 2019 in 20 Scopus disciplines were surveyed for their beliefs and experiences about data sharing and reuse.

From the 3,257 survey responses, data sharing and reuse are still increasing but not ubiquitous in any subject area and are more common among experienced researchers. Researchers with previous data reuse experience were more likely to share data than others. Types of data produced and systematic online data sharing varied substantially between subject areas. Although the use of institutional and journal-supported repositories for sharing data is increasing, personal websites are still frequently used. Combining multiple existing datasets to answer new research questions was the most common use. Proper documentation, openness and information on the usability of data continue to be important when searching for existing datasets. However, researchers in most disciplines struggled to find datasets to reuse. Researchers' feedback suggested 23 recommendations to promote data sharing and reuse, including improved data access and usability, formal data citations, new search features and cultural and policy-related disciplinary changes to increase awareness and acceptance.

This study is the first to explore data sharing and reuse practices across the full range of academic discipline types. It expands and updates previous data sharing surveys and suggests new areas of improvement in terms of policy, guidance and training programs.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2021-0423.