scholarly journals Exploring the human factor in cyber-enabled and cyber-dependent crime victimisation: a lifestyle routine activities approach

2020 ◽  
Vol 30 (6) ◽  
pp. 1665-1687
Author(s):  
Naci Akdemir ◽  
Christopher James Lawless
Keyword(s):  
Logistic Regression ◽  
Binary Logistic Regression ◽  
Routine Activities ◽  
Public Access ◽  
Future Research ◽  
Control Group ◽  
Binary Logistic Regression Analysis ◽  
Data Set ◽  
Content Type ◽  
Online Activities

PurposeThe purpose of this study was to explore human factors as the possible facilitator of cyber-dependent (hacking and malware infection) and cyber-enabled (phishing) crimes victimisation and to test the applicability of lifestyle routine activities theory (LRAT) to cybercrime victimisation.Design/methodology/approachA mixed methods research paradigm was applied to address the research questions and aims. The data set of Crime Survey of England and Wales (CSEW) 2014/2015 and 42 semi-structured interviews conducted with victims of cybercrime and non-victim control group participants were analysed via binary logistic regression and content analyses methods.FindingsThis research illustrated that Internet users facilitated their victimisation through their online activities. Additionally, using insecure Internet connections and public access computers emerged as risk factors for both cyber-enabled and cyber-dependent crime victimisation. Voluntary and involuntary personal information disclosure through social networking sites and online advertisement websites increased the likelihood of being a target of phishing. Deviant online activities such as free streaming or peer-to-peer sharing emerged to increase the risk of cyber-dependent crime victimisation.Research limitations/implicationsThe binary logistic regression analysis results suggested LRAT as a more suitable theoretical framework for cyber-dependent crime victimisation. Future research may test this result with models including more macro variables.Practical implicationsPolicymakers may consider implementing regulations regarding limiting the type of information required to login to free Wi-Fi connections. Checking trust signs and green padlocks may be effective safeguarding measures to lessen the adverse impacts of impulsive buying.Originality/valueThis study empirically illustrated that, besides individual-level factors, macro-level factors such as electronic devices being utilised to access the Internet and data breaches of large companies also increased the likelihood of becoming the victim of cyber-enabled and cyber-dependent crime.

Resource-based view of innovation activity in SMEs: an empirical analysis based on the global competitiveness project

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lívia Lukovszki ◽  
András Rideg ◽  
Norbert Sipos
Keyword(s):  
Logistic Regression ◽  
Literature Review ◽  
Empirical Analysis ◽  
Binary Logistic Regression ◽  
Point Of View ◽  
Innovation Activity ◽  
Data Set ◽  
Content Type ◽  
Resources And Capabilities ◽  
The Impact

Purpose The purpose of this study is to identify the corporate functions that contribute most to the innovation success of SMEs with limited resources. After a systematic literature review, the authors used a unique primary data set of 784 SMEs from eight countries. Descriptive statistics and binary logistic regression were used to show the data set peculiarities. The logistic regression targeted the presence of innovative products and services in sales by 11 dummy variables and 4 principal factors describing SMEs’ different resources and capabilities. Design/methodology/approach The authors developed a resource-based product innovation model that is synthesising the impact of the company resources and capabilities and of the innovation activity of the company on the actual innovation performance. The authors carry out an empirical analysis of the characteristic features of innovation activity in an international sample of SMEs. Findings The results show that two corporate functions play a crucial role in the effectiveness of innovation for SMEs as follows: management and research and development (R&D). In addition, although of lesser importance, the effect of the marketing function also appears significant. The binary logistic regression had 84.2% of explanatory power. Originality/value From a scientific point of view, the SME-focussed, complex and synthesising RBV model of innovation construction and literature review can be used as a reference point for future researches. From a practical point of view, the analysis is useful for those SMEs, which want to gain a competitive advantage through innovation. Indeed, the results show that in the case of SMEs, a company wishing to innovate must invest in three corporate functions for innovation to be effective as follows: management, R&D and marketing.

A fuzzy DEMATEL – Fuzzy Binary Logistic Regression approach to evaluate and prioritize risks and simulated annealing optimization algorithm (an empirical study in energy projects)

2020 ◽  
Vol 13 (5) ◽  
pp. 1025-1050
Author(s):  
Reyhane Hashemi ◽  
Reza Kamranrad ◽  
Farnoosh Bagheri ◽  
Iman Emami
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Binary Logistic Regression ◽  
Fuzzy Decision Making ◽  
Fuzzy Dematel ◽  
Data Set ◽  
Content Type ◽  
Energy Projects ◽  
High Level ◽  
Level Of Risk

PurposeThe aim of this paper is to predict and minimize the risks of oil, gas and petrochemical projects. Besides, reducing the likelihood of occurrence and minimizing risks impact on the projects to reduce the probable costs and improve the economic situation is another purpose of this paper.Design/methodology/approachThis paper provides a fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) – a technique that assist to solve decision-making problems – and IP (Impact & Probability) table methods to identify and analyze critical risks in energy projects, and then fuzzy Binary Logistic Regression (BLR) in order to predict the probability of each level of risk for more efficient risk management in projects. Furthermore, in this paper, the fuzzy BLR (FBLR) is optimized such that the probability of a high level of risk for the implementation of the project has been minimized using meta-heuristic algorithm.FindingsThe results from the point of view of experts show that combination of fuzzy DEMATEL with FBLR approach as well as using SA algorithm, in order to optimize the high level of risks, can provide a smart approach to managing risks with more success.Practical implicationsThe application of the proposed method is illustrated via a real data set from energy projects.Originality/valueWe propose combined fuzzy DEMATEL and FBLR methods to predict and optimize the risks of the energy projects, which is the innovation of this paper.

Examining the Characteristics That Differentiate Jihadi-Associated Cyberattacks Using Routine Activities Theory

2021 ◽  
pp. 089443932110233
Author(s):  
Thomas J. Holt ◽  
Noah D. Turner ◽  
Joshua D. Freilich ◽  
Steven M. Chermak
Keyword(s):  
United States ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
The United States ◽  
Routine Activities ◽  
Routine Activities Theory ◽  
Future Research ◽  
Binary Logistic Regression Model

This study applies routine activities theory to determine whether the characteristics of jihadi-inspired web defacements in the United States vary from all other defacements performed against IP addresses hosted within the United States from 2012 to 2016. We focus on target suitability variables and use a sample of over 2.2 million defacements reported by the independent website Zone-H. We estimated a binary logistic regression model and found that jihadi cyberattacks were rare among all the defacements performed in this 5-year period. Additionally, these findings demonstrated jihadists were more likely to target organizational websites and utilized specific attack methods compared to all other defacers. We contextualize our findings and outline a number of avenues for future research.

scholarly journals Factors Affecting Intention to Disclose HIV Status among Adult Population in Sarawak, Malaysia

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Aren Sinedeh Lemin ◽  
Md Mizanur Rahman ◽  
Cliffton Akoi Pangarah
Keyword(s):  
Logistic Regression ◽  
Adult Population ◽  
Binary Logistic Regression ◽  
Future Research ◽  
Binary Logistic Regression Analysis ◽  
Cluster Sampling ◽  
Hiv Status ◽  
Factors Affecting ◽  
Significant Difference ◽  
Hiv Aids

Background. Disclosure of HIV-positive status is an essential prerequisite for the prevention and care of person living with HIV/AIDS as well as to tackle hidden epidemic in the society. Objective. To determine the intention to disclose the HIV/AIDS status among adult population in Sarawak, Malaysia, and factors affecting thereof. Methods. This cross-sectional community-based study was conducted among adult population aged 18 years and above in Sarawak, Malaysia. A gender-stratified multistage cluster sampling technique was adopted to select the participants. A total of 900 respondents were successfully interviewed by face-to-face interview using interview schedule. Stepwise binary logistic regression models were fitted in SPSS version 22.0 to identify the factors associated with the disclosure of HIV/AIDS status. A p value less than 0.05 was considered as statistically significant. Results. The mean (SD) age of male and female respondents was 41.57 (13.45) and 38.99 (13.09) years, respectively. A statistically significant difference of intention to disclosure of HIV status was found between males and females (p<0.05). A stepwise binary logistic regression analysis revealed that age, occupation, knowledge on HIV transmission, and content of discussion about HIV/AIDS appeared to be potential predictors for male respondents to disclose HIV status, while ethnicity and content of discussion on HIV/AIDS were found to be important predictors among the female respondents (p<0.05). Conclusion and Recommendation. Though the study did not depict the national prevalence of disclosure of HIV/AIDS status, the findings of the study would provide an important basic information for programme intervention, policy, and future research agenda.

scholarly journals Endovascular stroke treatment in orally anticoagulated patients: an analysis from the German Stroke Registry-Endovascular Treatment

2020 ◽  
Author(s):  
Clemens Küpper ◽  
◽  
Katharina Feil ◽  
Frank Arne Wollenweber ◽  
Steffen Tiedt ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Endovascular Treatment ◽  
Vitamin K ◽  
Logistic Regression Analysis ◽  
Binary Logistic Regression ◽  
Control Group ◽  
Binary Logistic Regression Analysis ◽  
Stroke Registry ◽  
Good Outcome

Abstract Background Endovascular treatment (ET) in orally anticoagulated (OAC) patients has not been evaluated in randomized clinical trials and data regarding this issue are sparse. Methods We analyzed data from the German Stroke Registry-Endovascular Treatment (GSR-ET; NCT03356392, date of registration: 22 Nov 2017). The primary outcomes were successful reperfusion defined as modified thrombolysis in cerebral infarction (mTICI 2b-3), good outcome at 3 months (modified Rankin scale [mRS] 0–2 or back to baseline), and intracranial hemorrhage (ICH) on follow-up imaging at 24 h analyzed by unadjusted univariate and adjusted binary logistic regression analysis. Additionally, we analyzed mortality at 3 months with adjusted binary logistic regression analysis. Results Out of 6173 patients, there were 1306 (21.2%) OAC patients, 479 (7.8%) with vitamin K antagonists (VKA) and 827 (13.4%) with non-vitamin K antagonist oral anticoagulation (NOAC). The control group consisted of 4867 (78.8%) non-OAC patients. ET efficacy with the rates of mTICI 2b-3 was similar among the three groups (85.6%, 85.3% vs 84.3%, p = 0.93 and 1). On day 90, good outcome was less frequent in OAC patients (27.8%, 27.9% vs 39.5%, p < 0.005 and < 0.005). OAC status was not associated with ICH at 24 h (NOAC: odd’s ratio [OR] 0.89, 95% confidence interval [CI] 0.67–1.20; VKA: OR 1.04, CI 0.75–1.46). Binary logistic regression analysis revealed no influence of OAC status on good outcome at 3 months (NOAC: OR 1.25, CI 0.99–1.59; VKA: OR 1.18, CI 0.89–1.56) and mortality at 3 months (NOAC: OR 1.03, CI 0.81–1.30; VKA: OR 1.04, CI 0.78–1.1.37). Conclusions ET can be performed safely and successfully in LVO stroke patients treated with OAC. Clinical trial registration-URL http://www.clinicaltrials.gov. Unique identifier: NCT03356392.

DARK TRIAD AND UTILITARIAN CHOICES: A STUDY FROM TURKEY

2021 ◽  
Vol 6 (11) ◽  
pp. 35-46
Author(s):  
Murat BOLELLİ
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Online Survey ◽  
Binary Logistic Regression ◽  
Dictator Game ◽  
Dark Triad ◽  
Future Research ◽  
Binary Logistic Regression Analysis ◽  
Research Areas ◽  
Positive Effect

Purpose of this study is to examine effects of dark triad (narcissism, Machiavellianism and psychopathy) on the utilitarian choices. Data is collected from university students with an online survey using convenience method for sampling. To measure research variables, a version of dictator game that presents a salary raise case to participants and Short Dark Triad Scale (SD3) is used. Reliability analysis is conducted to SD3 which returned 0.611, 0.651 and 0.684 Cronbach’s alpha values for Dark Triad personality traits. Binary logistic regression analysis is used to test research hypotheses. Findings indicate that psychopathy has a significant and positive effect on the utilitarian choice whereas Machiavellianism and narcissism has not. Implications of the results are discussed and future research areas are suggested.

scholarly journals Risk factors for poor outcomes of spontaneous supratentorial cerebral hemorrhage after surgery

2021 ◽  
Author(s):  
Kangwei Zhang ◽  
Lai Wei ◽  
Xiang Zhou ◽  
Baoqing Yang ◽  
Jinxi Meng ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cerebral Hemorrhage ◽  
Treatment Plan ◽  
Binary Logistic Regression ◽  
Control Group ◽  
Binary Logistic Regression Analysis ◽  
Midline Shift ◽  
Difference Analysis ◽  
The Poor ◽  
Poor Outcomes

Abstract Objective This study aimed to explore the factors affecting the outcomes of spontaneous supratentorial cerebral hemorrhage 90 days after surgery. Methods A total of 256 patients with spontaneous supratentorial intracerebral hemorrhage underwent craniotomy evacuation of hematoma. The control group included 120 patients who received conservative treatment. The patients were divided into two subgroups based on a bifurcation of the modified Rankin Scale (mRS) 90 days after clinical therapeutics: good outcome (mRS score 0–3) and poor outcome (mRS score 4–6). The differences in clinical and imaging data between the two subgroups were analyzed. Based on difference analysis results, a binary logistic regression model was constructed to analyze the influencing factors related to poor outcomes. Results The difference analysis results in the surgery group showed statistically significant differences in age, sex, Glasgow Coma Score (GCS) on admission, coronary atherosclerosis, smoking, stroke history, blood glucose, D-dimer, hematoma size, deep cerebral hemorrhage, midline shift, hematoma burst into the ventricle, vortex sign, island sign, and black hole sign. Binary logistic regression analysis showed that deep cerebral hemorrhage, midline shift, and age > 58 years independently correlated with the poor outcomes of patients after surgery. The binary logistic regression results of the control group showed that age > 58 years and GCS ≤ 8 independently correlated with the poor outcomes of patients. Conclusions Deep cerebral hemorrhage, midline shift, and age > 58 years significantly increased the risk of adverse prognosis in patients after surgery. The findings might help select the clinical treatment plan and evaluate the postoperative prognosis of patients.

Attitudes towards accounting and intention to major in accounting: a logistic regression analysis

2018 ◽  
Vol 8 (4) ◽  
pp. 459-475 ◽  
Author(s):  
Rita Amoah Bekoe ◽  
Godfred Matthew Yaw Owusu ◽  
Charles Gyamfi Ofori ◽  
Anthony Essel-Anderson ◽  
Edem Emerald Welbeck
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Business Students ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Analysis ◽  
Accounting Profession ◽  
Content Type ◽  
Business Educators ◽  
High Level

PurposeThe purpose of this paper is to examine the attitude of business students towards the accounting profession and investigate the relationship between students’ attitude and their intention to pursue a degree in accounting.Design/methodology/approachA self-administered survey was used to collect data from students from the University of Ghana Business School (UGBS). A total of 457 questionnaires were used in the empirical analysis. A binary logistic regression analysis technique was employed to analyse the data.FindingsThe logistic regression analysis demonstrates that intrinsic interest in the accounting discipline, prior exposure to accounting at the senior high level and the desire to pursue professional accounting qualification in future are good predictors of students’ intention to major in accounting. The results also indicate family members, course instructors and other referent group play a crucial role in influencing students’ intention to pursue a career in accounting.Practical implicationsThe findings of this study have important implications for the professional accountancy body and business educators interested in addressing the skill shortage in the accounting profession.Originality/valueThis study does not only examine students’ attitude towards the accounting discipline but also investigates how such attitudes influence intentions to major in accounting.

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

scholarly journals Predicting Burn Mortality Using a Simple Novel Prediction Model

2021 ◽  
Author(s):  
Sneha Sharma ◽  
Raman Tandon
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Logistic Regression Analysis ◽  
Operating Characteristic ◽  
Binary Logistic Regression ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Binary Logistic Regression Analysis

Abstract Background Prediction of outcome for burn patients allows appropriate allocation of resources and prognostication. There is a paucity of simple to use burn-specific mortality prediction models which consider both endogenous and exogenous factors. Our objective was to create such a model. Methods A prospective observational study was performed on consecutive eligible consenting burns patients. Demographic data, total burn surface area (TBSA), results of complete blood count, kidney function test, and arterial blood gas analysis were collected. The quantitative variables were compared using the unpaired student t-test/nonparametric Mann Whitney U-test. Qualitative variables were compared using the ⊠2-test/Fischer exact test. Binary logistic regression analysis was done and a logit score was derived and simplified. The discrimination of these models was tested using the receiver operating characteristic curve; calibration was checked using the Hosmer—Lemeshow goodness of fit statistic, and the probability of death calculated. Validation was done using the bootstrapping technique in 5,000 samples. A p-value of <0.05 was considered significant. Results On univariate analysis TBSA (p <0.001) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score (p = 0.004) were found to be independent predictors of mortality. TBSA (odds ratio [OR] 1.094, 95% confidence interval [CI] 1.037–1.155, p = 0.001) and APACHE II (OR 1.166, 95% CI 1.034–1.313, p = 0.012) retained significance on binary logistic regression analysis. The prediction model devised performed well (area under the receiver operating characteristic 0.778, 95% CI 0.681–0.875). Conclusion The prediction of mortality can be done accurately at the bedside using TBSA and APACHE II score.

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