Preventing successful assassination attacks by terrorists: an environmental criminology approach

2017 ◽  
Vol 3 (3) ◽  
pp. 173-191 ◽  
Author(s):  
Marissa Mandala ◽  
Joshua D. Freilich
Keyword(s):  
Regression Models ◽  
Situational Crime Prevention ◽  
Binary Logistic Regression ◽  
Situational Factors ◽  
Environmental Criminology ◽  
Prevention Measures ◽  
Content Type ◽  
Logistic Regression Models ◽  
Global Terrorism Database ◽  
Practical Implications

Purpose The purpose of this paper is to use an environmental criminology and situational crime prevention (SCP) framework to study global assassinations carried out by terrorists. The authors set forth a series of hypotheses to explain successful and unsuccessful assassination incidents. Design/methodology/approach The authors use assassination data from the Global Terrorism Database from 1970 to 2014 to estimate a series binary logistic regression models. Findings Results indicate that various situational factors contribute to successful assassinations, such as target types, weapon types, total fatalities, and injuries. Practical implications These findings suggest that environmental criminology and SCP are valuable in developing prevention measures that thwart and disrupt attempted assassinations by terrorists. Originality/value Criminology has yet to apply environmental criminology and SCP to assassinations, a tactic often used by terrorists. This paper thus extends the existing assassination, terrorism, and criminology literature by applying this framework to assassinations performed by terrorists.

The impact of intellectual capital on start-up expectations

2016 ◽  
Vol 17 (4) ◽  
pp. 654-674 ◽  
Author(s):  
Diego Matricano
Keyword(s):  
Logistic Regression ◽  
Intellectual Capital ◽  
Regression Models ◽  
Empirical Studies ◽  
Binary Logistic Regression ◽  
Research Trend ◽  
Content Type ◽  
Logistic Regression Models ◽  
Start Up ◽  
The Impact

Purpose According to an emerging research trend, which seeks to apply the concept of intellectual capital (IC) to the field of entrepreneurship, the purpose of this paper is to test whether IC can affect the start-up expectations of aspiring entrepreneurs. Design/methodology/approach Binary logistic regression models, based on empirical data derived from the Global Entrepreneurship Monitor website and referring to Italy over the years 2005-2010, are used to test the influence of IC (comprising human, structural and relational capital) on start-up expectations. Findings Binary logistic regression models reveal robust results. Human, structural and relational capitals affect start-up expectations in Italy. Only in 2010 did structural capital fail to do so. Research limitations/implications This study has three main limitations. The first concerns the need for further research to confirm the influence of IC on start-up expectations. The second concerns in-depth, more exhaustive analyses that cannot be carried out due to the use of second- hand data. The third deals with the reference only to Italy, over a limited time-span (2005-2010). Originality/value To the best knowledge of the author, this is one of the first empirical studies that investigate whether IC can affect start-up expectations. Results revealed by the regression models might steer other scholars’ interest toward this research path (linking IC and entrepreneurship) that has not yet been properly considered.

Disrupting Terrorist Assassinations Through Situational Crime Prevention

2017 ◽  
Vol 64 (12) ◽  
pp. 1515-1537 ◽  
Author(s):  
Marissa Mandala ◽  
Joshua D. Freilich
Keyword(s):  
Logistic Regression ◽  
Crime Prevention ◽  
Random Sample ◽  
Target Location ◽  
Situational Crime Prevention ◽  
Binary Logistic Regression ◽  
Environmental Criminology ◽  
Global Terrorism ◽  
Global Terrorism Database

This article uses environmental criminology and situational crime prevention (SCP) to devise a series of hypotheses to determine the factors that distinguish successful from unsuccessful assassination incidents. We analyzed a random sample of 100 successful and 100 unsuccessful assassination incidents from the Global Terrorism Database (GTD) that occurred between 2005 and 2014. We then consulted open sources to create new SCP variables that we added to the original GTD data. The hypotheses were tested in a binary logistic regression. Results show that successful assassinations are associated with several SCP measures, including weapon type, fatalities, terrorist proximity to target, and attack and target location.

Hitting (or missing) the mark

2019 ◽  
Vol 42 (3) ◽  
pp. 474-489 ◽  
Author(s):  
Christopher M. Donner ◽  
Nicole Popovich
Keyword(s):  
Regression Models ◽  
Design Methodology ◽  
Police Department ◽  
Situational Factors ◽  
Descriptive Statistics ◽  
Predictive Utility ◽  
Content Type ◽  
Hit Rate ◽  
Multiple Regression Models ◽  
Practical Implications

Purpose The purpose of this paper is to examine police shooting accuracy and the factors that influence whether officers hit, or miss, their intended target. Design/methodology/approach Descriptive statistics explore both incident-level and hit rate shooting accuracy in single officer/single suspect shooting incidents in the Dallas Police Department between 2003 and 2017. Multiple regression models analyze the predictive utility of officer, suspect and situational factors on the two accuracy outcomes. Findings Consistent with prior research, the results demonstrate that officers are often inaccurate in officer-involved shooting (OIS) incidents. Additionally, several factors emerged as significant predictors of shooting accuracy. Practical implications The results are discussed in terms of the practical implications for training and accountability. Originality/value It has been more than a decade since the last academic study investigated this important topic using actual OIS data. Acknowledging the general dearth of this literature, this study explores what factors contribute to shooting accuracy.

scholarly journals Can Cut-Off-Values for Tumor Size or Patient Age in Breast Ultrasound Reduce Unnecessary Biopsies or is it all About Bi-rads?– A Retrospective Analysis of 763 Biopsied T1-Sized Lesions

2017 ◽  
Vol 03 (03) ◽  
pp. E94-E98 ◽  
Author(s):  
Laura Holzer-Fruehwald ◽  
Matthias Meissnitzer ◽  
Michael Weber ◽  
Stephan Holzer ◽  
Klaus Hergan ◽  
...  
Keyword(s):  
Regression Models ◽  
Breast Lesion ◽  
Binary Logistic Regression ◽  
Lesion Size ◽  
Breast Lesions ◽  
Predictive Values ◽  
Logistic Regression Models ◽  
Independent Variables ◽  
Oval Shape ◽  
Histological Result

Abstract Aims and Objectives To assess whether it is possible to establish a size cut-off-value for sonographically visible breast lesions in a screening situation, under which it is justifiable to obviate a biopsy and to evaluate the grayscale characteristics of the identified lesions. Materials and Methods Images of sonographically visible and biopsied breast lesions of 684 patients were retrospectively reviewed and assessed for the following parameters: size, shape, margin, lesion boundary, vascularity, patient’s age, side of breast, histological result, and initial BI-RADS category. Statistical analyses (t-test for independent variables, ROC analyses, binary logistic regression models, cross-tabulations, positive/negative predictive values) were performed using IBM SPSS (Version 21.0). Results Of all 763 biopsied lesions, 223 (29.2%) showed a malignant histologic result, while 540 (70.8%) were benign. Although we did find a statistically significant correlation of malignancy and lesion size (p=0.031), it was not possible to define a cut-off value, under which it would be justifiable to obviate a biopsy in terms of sensitivity and specificity (AUC: 0.558) at any age. Lesions showing the characteristics of a round or oval shape, a sharp delineation and no echogenic rim (n=112) were benign with an NPV of 99.1%. Conclusion It is not possible to define a cut-off value for size or age, under which a biopsy of a sonographically visible breast lesion can be obviated in the screening situation. The combination of the 3 grayscale characteristics, shape (round or oval), margin (circumscribed) and no echogenic-rim sign, showed an NPV of 99.1%. Therefore, it seems appropriate to classify such lesions as BI-RADS 2.

Service quality and global competitiveness: evidence from global service firms

2017 ◽  
Vol 27 (6) ◽  
pp. 1058-1080 ◽  
Author(s):  
Wenbin Sun ◽  
Jing Pang
Keyword(s):  
Service Quality ◽  
Service Industry ◽  
Situational Factors ◽  
Service Firms ◽  
Moderating Effects ◽  
Multiple Sources ◽  
Content Type ◽  
Global Competitiveness ◽  
The Relationship ◽  
Practical Implications

Purpose The purpose of this paper is to explore the relationship between service quality and firms’ global competitiveness in the service industry. A set of moderating effects is formulated to further reveal how the relationship varies under different situations. Design/methodology/approach This paper tests the model with data collected from multiple sources such as World’s Most Admired Companies and COMPUSTAT. Two types of robust regressions for panel data are employed in the empirical model estimation. Findings Service quality is found to significantly drive global competitiveness. Specifically, its impact is stronger for large service firms and when the global environment is characterized as low munificence, high dynamism, or high complexity. Practical implications The paper provides a set of implications for managers of service firms regarding global expansion and quality management. It generates useful guidelines of maximizing the power of service quality when a firm’s global competitive advantage is considered. Originality/value This paper takes the first attempt to formulate service quality’s influence on firm’s global competitiveness with a consideration of specific situational factors.

Women’s Autonomy and Support for Wife Beating: Findings From a Population-Based Survey in Jordan

2010 ◽  
Vol 25 (3) ◽  
pp. 409-419 ◽  
Author(s):  
Natalia Linos ◽  
Marwan Khawaja ◽  
Mohannad Al-Nsour
Keyword(s):  
Regression Models ◽  
Binary Logistic Regression ◽  
Population Based ◽  
Married Women ◽  
Wife Beating ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Logistic Regression Models ◽  
Hypothetical Scenario ◽  
Population Based Survey

The aim of this study is to examine attitudes among married women toward wife beating and to investigate the hypothesis that female individual empowerment is associated with such attitudes within a broader context of societal patriarchy in Jordan. The study uses data from a cross-sectional survey of a representative sample of married women (n = 5,390) conducted in 2002. Associations between acceptance of wife beating and several women’s empowerment variables, including decision-making power, as well as other risk factors were assessed, using odds ratios from binary logistic regression models. The key finding is that the vast majority (87.5%) of Jordanian women believe that wife beating is justified in at least one hypothetical scenario, and justification is negatively associated with empowerment variables and some demographic, geographic, and socioeconomic factors.

scholarly journals Application of Logistic Regression Models for the Marketability of Cucumber Cultivars

Agronomy ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 17 ◽  
Author(s):  
Manuel Díaz-Pérez ◽  
Ángel Carreño-Ortega ◽  
José-Antonio Salinas-Andújar ◽  
Ángel-Jesús Callejón-Ferre
Keyword(s):  
Weight Loss ◽  
Logistic Regression ◽  
Shelf Life ◽  
Regression Models ◽  
Chilling Injury ◽  
Binary Logistic Regression ◽  
Fruit Weight ◽  
Binary Logistic Regression Analysis ◽  
Cucumis Sativus L ◽  
Logistic Regression Models

The aim of this study is to establish a binary logistic regression method to evaluate and select cucumber cultivars (Cucumis sativus L.) with a longer postharvest shelf life. Each sample was evaluated for commercial quality (fruit aging, weight loss, wilting, yellowing, chilling injury, and rotting) every 7 days of storage. Simple and multiple binary logistic regression models were applied in which the dependent variable was the probability of marketability and the independent variables were the days of storage, cultivars, fruit weight loss, and months of evaluation. The results showed that cucumber cultivars with a longer shelf life can be selected by a simple and multiple binary logistic regression analysis. Storage time was the main determinant of fruit marketability. Fruit weight loss strongly influenced the probability of marketability. The logistic model allowed us to determine the cucumber weight loss percentage over which a fruit would be rejected in the market.

Copa America: a resource-based theory of football talent

2019 ◽  
Vol 25 (3/4) ◽  
pp. 176-191
Author(s):  
Peter Omondi-Ochieng
Keyword(s):  
Logistic Regression ◽  
Design Methodology ◽  
Binary Logistic Regression ◽  
Content Type ◽  
Independent Variables ◽  
Football Team ◽  
Practical Implications ◽  
Resource Based Theory

Purpose Guided by the resource-based theory, the purpose of this study was to predict the role of football talent in the Federation Internationale de Football Association (FIFA) rankings of the men’s national football teams in the Copa America zone. Design/methodology/approach The study used archival data of Copa American national football teams. The dependent variable was FIFA rankings, and the independent variables were football talent (measured by the stocks of amateur footballers, professional footballers and football officials). Statistical analysis was performed using Kendall tau statistic and binary logistic regression. Findings The binary logistic regression results indicated that FIFA rankings were statistically and significantly associated with the stock of football officials and professional footballers – but not amateur footballers. The predictive model explained 80 per cent of the variance. Research limitations/implications The study focused exclusively on the stock of football talent in each nation, and not alternative determinants of national football team competitiveness as economic power and quality of professional football leagues, among others. Practical implications The stocks of professional footballers and football officials are valuable sources of competitive advantage (CA) in national football team rankings. Originality/value The study highlighted the uniqueness and distinctiveness of a nation possessing large stocks of professional footballers which can boost the CA and rankings of Copa American national football teams.

Innovation in the aftermath of downsizing: evidence from the threat-rigidity perspective

2020 ◽  
Vol 49 (9) ◽  
pp. 1859-1877
Author(s):  
José Fernández-Menéndez ◽  
Óscar Rodríguez-Ruiz ◽  
José-Ignacio López-Sánchez ◽  
María Isabel Delgado-Piña
Keyword(s):  
Regression Models ◽  
Negative Impact ◽  
Content Type ◽  
Logistic Regression Models ◽  
Marketing Innovation ◽  
Different Types ◽  
Threat Rigidity ◽  
The Impact ◽  
Research Show ◽  
Innovation Outcomes

PurposeThe purpose of this paper is to study how job reductions affect product innovation and marketing innovation in a sample of 2,034 Spanish manufacturing firms in the period 2007–2014.Design/methodology/approachPoisson and logistic regression models with random effects were used to analyse the impact of downsizing on some innovation outcomes of firms.FindingsThe results of this research show that the stressful measure of job reductions may have unexpected consequences, stimulating innovation. However downsizing combined with radical organisational changes such as new equipment, techniques or processes seems to have a negative impact on product and marketing innovation.Originality/valueThis research has two original features. First, it explores the unconventional direction of causality from the planned elimination of jobs to innovation outputs. Secondly, the paper looks at the combined effect of downsizing and other restructuring measures on different types of innovation. Following the threat-rigidity theory, we assume that this combination represents a major threat for survivors that leads to lower levels of product and marketing innovation.

scholarly journals The sensitivity of modeled snow accumulation and melt to precipitation phase methods across a climatic gradient

2019 ◽  
Vol 23 (9) ◽  
pp. 3765-3786 ◽  
Author(s):  
Keith S. Jennings ◽  
Noah P. Molotch
Keyword(s):  
Logistic Regression ◽  
Air Temperature ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Snow Accumulation ◽  
Phase Method ◽  
Logistic Regression Models ◽  
Temperature Thresholds ◽  
Snow Cover Duration ◽  
Precipitation Phase

Abstract. A critical component of hydrologic modeling in cold and temperate regions is partitioning precipitation into snow and rain, yet little is known about how uncertainty in precipitation phase propagates into variability in simulated snow accumulation and melt. Given the wide variety of methods for distinguishing between snow and rain, it is imperative to evaluate the sensitivity of snowpack model output to precipitation phase determination methods, especially considering the potential of snow-to-rain shifts associated with climate warming to fundamentally change the hydrology of snow-dominated areas. To address these needs we quantified the sensitivity of simulated snow accumulation and melt to rain–snow partitioning methods at sites in the western United States using the SNOWPACK model without the canopy module activated. The methods in this study included different permutations of air, wet bulb and dew point temperature thresholds, air temperature ranges, and binary logistic regression models. Compared to observations of snow depth and snow water equivalent (SWE), the binary logistic regression models produced the lowest mean biases, while high and low air temperature thresholds tended to overpredict and underpredict snow accumulation, respectively. Relative differences between the minimum and maximum annual snowfall fractions predicted by the different methods sometimes exceeded 100 % at elevations less than 2000 m in the Oregon Cascades and California's Sierra Nevada. This led to ranges in annual peak SWE typically greater than 200 mm, exceeding 400 mm in certain years. At the warmer sites, ranges in snowmelt timing predicted by the different methods were generally larger than 2 weeks, while ranges in snow cover duration approached 1 month and greater. Conversely, the three coldest sites in this work were relatively insensitive to the choice of a precipitation phase method, with average ranges in annual snowfall fraction, peak SWE, snowmelt timing, and snow cover duration of less than 18 %, 62 mm, 10 d, and 15 d, respectively. Average ranges in snowmelt rate were typically less than 4 mm d−1 and exhibited a small relationship to seasonal climate. Overall, sites with a greater proportion of precipitation falling at air temperatures between 0 and 4 ∘C exhibited the greatest sensitivity to method selection, suggesting that the identification and use of an optimal precipitation phase method is most important at the warmer fringes of the seasonal snow zone.

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