Evacuation Departure Timing during Hurricane Matthew

AbstractThis study investigates evacuation behaviors associated with Hurricane Matthew in October of 2016. It assesses factors influencing evacuation decisions and evacuation departure times for Florida, Georgia, and South Carolina from an online survey of respondents. Approximately 62% of the Florida sample, 77% of the Georgia sample, and 67% of the South Carolina sample evacuated. Logistic regression analysis of the departures in the overall time period identified variability in evacuation timing, primarily dependent on prior experience, receipt of an evacuation order, and talking with others about the evacuation order. However, using four logistic regressions to analyze differences in departure times by day shows that the only significant variable across the three main days of evacuation was our proxy variable for evacuation-order times. Depending on the day, other variables of interest include number of household vehicles, previous hurricane experience, and receipt of an evacuation order. Descriptive results show that many variables are considered in the decision to evacuate, but results from subsequent analyses, and respondents’ comments about their experiences, highlight that evacuation orders are the primary triggering variable for when residents left.

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Factors Influencing the Adoption of Cloud Computing by Small and Medium-Sized Enterprises (SMEs)

10.32920/ryerson.14661393 ◽

2021 ◽

Author(s):

Shima Ramezani Tehrani

Keyword(s):

Cloud Computing ◽

Regression Analysis ◽

Diffusion Of Innovation ◽

Online Survey ◽

Discriminant Validity ◽

Influential Factor ◽

Hype Cycle ◽

Factors Influencing ◽

Cloud Computing Adoption ◽

Convergent And Discriminant Validity

The main objective of this study is to determine the factors influencing the cloud computing adoption by Small and Medium sized Enterprises (SMEs). Based on two dominant theories in the field of diffusion of innovation, a conceptual model is proposed. In order to test the model empirically, an online survey was designed and launched. Decision makers of 101 SMEs agreed to participate in this survey. In order to evaluate the internal, convergent and discriminant validity of the instrument, factor analysis and reliability tests of panel data were performed. The logistic regression analysis was deployed to test the research hypotheses. The results of regression analysis reveal that decision maker’s knowledge about cloud computing is the main influential factor in adopting this technology. A comparison between two groups of cloud adopters and non-adaptors confirm the recent Gartner's hype cycle model for emerging technology (as discussed in chapter 7) indicating a high expectation from cloud computing in both groups.

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Factors Influencing the Adoption of Cloud Computing by Small and Medium-Sized Enterprises (SMEs)

10.32920/ryerson.14661393.v1 ◽

2021 ◽

Author(s):

Shima Ramezani Tehrani

Keyword(s):

Cloud Computing ◽

Regression Analysis ◽

Diffusion Of Innovation ◽

Online Survey ◽

Discriminant Validity ◽

Influential Factor ◽

Hype Cycle ◽

Factors Influencing ◽

Cloud Computing Adoption ◽

Convergent And Discriminant Validity

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An Investigation on Extrinsic and Intrinsic Rewards on Employee Retention among Gen Y: A Study in Malaysian Manufacturing Companies

Journal of Business & Economic Analysis ◽

10.36924/sbe.2020.3104 ◽

2020 ◽

Vol 3 (1) ◽

Keyword(s):

Regression Analysis ◽

Employee Retention ◽

Future Research ◽

Manufacturing Companies ◽

Extrinsic Reward ◽

Intrinsic Rewards ◽

Gen Y ◽

Intrinsic Reward ◽

Factors Influencing ◽

The Relationship

The aim of this study is to investigate the relationship between extrinsic and intrinsic reward on retention among Gen Y employees in Malaysian manufacturing companies. The data was collected from 113 respondents worked in manufacturing companies located in Seri Kembangan, Selangor using questionnaires. Multiple regression analysis was conducted to test the hypotheses. The results showed both extrinsic and intrinsic reward are the factors influencing retaining Gen Y in manufacturing companies. The discussion on the analysis, limitation of the study, recommendation for future research and conclusion were discussed at the end of this study. In a nutshell, it was proven extrinsic reward and intrinsic reward has contributed to the retention of Gen Y employees.

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PROFESSIONAL ATTRIBUTES OF INTERNAL AUDITORS IN RIAU

Jurnal Benefita ◽

10.22216/jbe.v3i2.2891 ◽

2018 ◽

Vol 3 (2) ◽

pp. 161

Author(s):

Satria Tri Nanda

Keyword(s):

Regression Analysis ◽

Audit Quality ◽

Hypotheses Testing ◽

Internal Auditors ◽

Factors Influencing ◽

Professional Attributes ◽

Professional Care ◽

Positive Effect

This research aims to identify the factors influencing the audit quality of auditors at Inspektorat Provinsi Riau. The population in this research were auditor, examiner, assistant examiner, and P2UPD (Pengawas Penyelenggara Urusan Pemerintah di Daerah) in charge at the Inspectorate in Riau Province and all Inspectorates in Districts and Cities in Riau Province. A total of 290 set of questionnaire were sent and a number of 184 of questionnaires were processed. Using regression analysis conducted by SPSS, the hypotheses testing analysis results show that experience, responsiveness, professional care, executive involvementt, planning and auditability have significant and positive effect on audit quality. These results indicate that the higher the levels of experience, responsiveness, professional care, executive involvement and auditability of Inspectorate officials, the better the quality of audit performed by the Inspectorate Officials of Riau Province.

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Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

Sustainability ◽

10.3390/su13073727 ◽

2021 ◽

Vol 13 (7) ◽

pp. 3727

Author(s):

Fatema Rahimi ◽

Abolghasem Sadeghi-Niaraki ◽

Mostafa Ghodousi ◽

Soo-Mi Choi

Keyword(s):

Regression Analysis ◽

Mean Squared Error ◽

Population Distribution ◽

Temporal Distribution ◽

Coefficient Of Determination ◽

Temporal Modeling ◽

Location Data ◽

Time Period ◽

Proposed Model

During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population distribution of the case study was investigated in the present study. In this regard, the number of generated trips and absorbed trips using the taxis pick-up and drop-off location data was calculated first, and the census population was then allocated to each neighborhood. Finally, the Spatial-temporal distribution of the population was calculated using the developed model. In order to evaluate the model, a regression analysis between the census population and the predicted population for the time period between 21:00 to 23:00 was used. Based on the calculation of the number of generated and the absorbed trips, it showed a different spatial distribution for different hours in one day. The spatial pattern of the population distribution during the day was different from the population distribution during the night. The coefficient of determination of the regression analysis for the model (R2) was 0.9998, and the mean squared error was 10.78. The regression analysis showed that the model works well for the nighttime population at the neighborhood level, so the proposed model will be suitable for the day time population.

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Factors Influencing Preparedness for Bioterrorism among Koreans

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18105401 ◽

2021 ◽

Vol 18 (10) ◽

pp. 5401

Author(s):

Yujeong Kim ◽

Eunmi Lee

Keyword(s):

Public Relations ◽

Online Survey ◽

Affective Response ◽

Coping Efficacy ◽

Front Line ◽

Personal Impact ◽

Societal Collapse ◽

Factors Influencing ◽

Perceived Seriousness ◽

Relevant Education

Bioterrorism is destructive enough to cause a societal collapse, and preparation for bioterrorism is imperative. This study aims to identify the factors influencing preparedness for bioterrorism among Koreans. A total of 1,050 subjects were included in the study, which were allocated according to region and age in proportion to population. An online survey was used to examine the following factors: participants’ general characteristics; cognitive factors including perceived probability, perceived seriousness, perceived personal impact, perceived coping efficacy, and perceived resilience; social–contextual factors including perceived governmental preparedness and perceived front-line preparedness; affective responses including affective response to terrorism and anxiety; and bioterrorism preparedness. The factors influencing the level of preparedness for bioterrorism included age, marital status, experience of bioterrorism education, perceived personal impact, perceived coping efficacy, perceived resilience, and perceived front-line preparedness. The factors that most significantly affected the level of preparedness for bioterrorism were perceived coping efficacy and perceived front-line preparedness, with an R2 of 41.4%. Relevant education and public relations programs should be strengthened to help citizens minimize their exposure and known to inform relevant institutions in the event of suspected bioterrorism, and front-line responders should cultivate their ability to respond to bioterrorism quickly and accurately.

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Labour-use Efficiency of Assam Gramin Vikash Bank: Branch- and District-level Analysis

Studies in Microeconomics ◽

10.1177/23210222211024374 ◽

2021 ◽

pp. 232102222110243

Author(s):

Mohuya Deb Purkayastha ◽

Joyeeta Deb ◽

Ram Pratap Sinha

Keyword(s):

Efficiency Score ◽

Significant Variable ◽

Data Envelopment ◽

Tobit Regression ◽

Second Stage ◽

Time Period ◽

Use Efficiency ◽

The Mean ◽

The Impact ◽

Observation Period

The present study estimated labour-use efficiency of 48 branches of Assam Gramin Vikash Bank at its branch level, covering three districts of Barak Valley, which falls under Silchar region of the bank for the time period from 2010–2011 to 2017–2018. The study applied data envelopment analysis for estimating labour-use efficiency. In the second stage, the study applied censored Tobit regression for determining the impact of several contextual variables on efficiency. The study reveals that the mean labour-use efficiency score of the selected branches is 76% when averaged for the in-sample branches over the observation period. Results of the Tobit regression identified cluster 2 and total business of the branches as the significant factors for determining efficiency and the number of employees as a significant variable influencing inefficiency. JEL Classifications: G2, G20, G21, J3

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Factors Affecting Secondary Traumatic Stress of Nurses Caring for COVID-19 Patients in South Korea

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18136843 ◽

2021 ◽

Vol 18 (13) ◽

pp. 6843

Author(s):

Mee Sun Lee ◽

Sujin Shin ◽

Eunmin Hong

Keyword(s):

Social Support ◽

Regression Analysis ◽

Traumatic Stress ◽

Secondary Traumatic Stress ◽

Direct Care ◽

Hierarchical Regression ◽

Factors Affecting ◽

Factors Influencing ◽

Affect Patient Care ◽

Significant Factors

The secondary traumatic stress (STS) of nurses caring for COVID-19 patients is expected to be high, and it can adversely affect patient care. The purpose of this study was to examine the degree of STS of nurses caring for COVID-19 patients, and we identified various factors that influence STS. This study followed a descriptive design. The data of 136 nurses who had provided direct care to COVID-19 patients from 5 September to 26 September 2020 were collected online. Hierarchical regression analysis was conducted to identify the factors influencing STS. Participants experienced moderate levels of STS. The regression model of Model 1 was statistically significant (F = 6.21, p < 0.001), and the significant factors influencing STS were the duration of care for patients with COVID-19 for more than 30 days (β = 0.28, p < 0.001) and working in an undesignated COVID-19 hospital (β = 0.21, p = 0.038). In Model 2, the factor influencing STS was the support of a friend in the category of social support (β = −0.21, p = 0.039). The nurses caring for COVID-19 patients are experiencing a persistent and moderate level of STS. This study can be used as basic data to treat and prevent STS.

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