Using Panel Data to Understand the Dynamics of Human Behavior in Response to Flooding

Insights into the dynamics of human behavior in response to flooding are urgently needed for the development of effective integrated flood risk management strategies, and for integrating human behavior in flood risk modeling. However, our understanding of the dynamics of risk perceptions, attitudes, individual recovery processes, as well as adaptive (i.e., risk reducing) intention and behavior are currently limited because of the predominant use of cross-sectional surveys in the flood risk domain. Here, we present the results from one of the first panel surveys in the flood risk domain covering a relatively long period of time (i.e., four years after a damaging event), three survey waves, and a wide range of topics relevant to the role of citizens in integrated flood risk management. The panel data, consisting of 227 individuals affected by the 2013 flood in Germany, were analyzed using repeated-measures ANOVA and latent class growth analysis (LCGA) to utilize the unique temporal dimension of the data set. Results show that attitudes, such as the respondents’ perceived responsibility within flood risk management, remain fairly stable over time. Changes are observed partly for risk perceptions and mainly for individual recovery and intentions to undertake risk-reducing measures. LCGA reveal heterogeneous recovery and adaptation trajectories that need to be taken into account in policies supporting individual recovery and stimulating societal preparedness. More panel studies in the flood risk domain are needed to gain better insights into the dynamics of individual recovery, risk-reducing behavior, and associated risk and protective factors.     

The Impact of Skinner's Verbal Behavior on Organizational Behavior Management

In the book Verbal Behavior, Skinner provided a comprehensive, behavioral account of language. While the impact of Skinner's analysis on empirical research has been examined broadly, this review of the literature focused on studies relevant to organizational behavior management (OBM). Both empirical and nonempirical journal articles in OBM were analyzed, along with several influential books in the field. The results of this review indicate that the conceptual framework provided in Verbal Behavior has had virtually no impact on empirical research in OBM and very limited impact on conceptual work. Potential reasons for this lack of influence are discussed, and further research on verbal behavior in organizations is encouraged.     

基于委托-代理模型的外部兼职问题研究

本文利用委托-代理模型对员工有无从事外部兼职活动进行了比较分析,在此基础上建立了内部职务扩大化的委托-代理模型,并与兼职模型进行了比较,指出两种不同激励机制对员工和企业的影响,为企业制定有效的激励合同提供理论依据.     

Prioritizing and Monitoring Concurrent Project Work: Effects on Switching Behavior

Project switching occurs when a multi‐project worker shifts his/her attention from one project to another before completing the first project. In this study, we study the effects of two areas of management policy on project switching behavior, project prioritization, and work monitoring. We conduct a controlled experiment to evaluate direct and combined effects of prioritization, scheduled progress checks, and managerial progress checks on project switching behavior in a distributed, multi‐project work environment. We use computerized tasks constituting multiple projects as a means of efficiently simulating a project work setting. Working professionals served as subjects for the experiment, thereby enabling us to control for experience and other individual differences that may vary across workers in real‐world projects. We find that clarifying priorities has little overall effect on the prevalence of switching in our multi‐project setting, while the presence of managerial progress checks has significant and distinct impacts, driving up switch tendencies. Interestingly, various attributes of the timing of these monitoring events also significantly impact the likelihood that workers will switch in response to these event triggers. We discuss the implications of these findings for managerial practice and for future research.     

Using Contingent Markdown with Reservation to Profit from Strategic Consumer Behavior

We examine a contingent price markdown (CM) mechanism with guaranteed reservation under which a retailer sells multiple units to forward‐looking consumers who arrive over time according to a Poisson process. During the early part of the selling season, each arriving consumer can either purchase a unit by paying the regular price or reserve a unit at the discount price. Reserved units can only be claimed later when the number of guaranteed reservations meets a pre‐specified threshold, or at the end of the selling season, whichever comes first. Immediately after the number of guaranteed reservations meets the pre‐specified threshold, the retailer will reduce its selling price to the discount price so that all subsequent arriving consumers can take immediate possession by paying the low price. We study the consumer purchasing behavior in equilibrium when the retailer adopts such a selling mechanism, and compare the performance of our mechanism against two benchmarks: fixed price (FP) and pre‐announced discount (PD). Through an extensive numerical analysis, we identify market conditions under which CM dominates both FP and PD in terms of the retailer's revenue and consumer's surplus. Finally, through a fluid approximation to the stochastic model, we simplify the computation of the equilibrium strategy and the optimal revenues, and verify that the key insights obtained from the stochastic model still hold.     

Interstitial Lung Diseases in the U.S. Mining Industry: Using MSHA Data to Examine Trends and the Prevention Effects of Compliance with Health Regulations, 1996–2015

Given the recent increase in dust‐induced lung disease among U.S. coal miners and the respiratory hazards encountered across the U.S. mining industry, it is important to enhance an understanding of lung disease trends and the organizational contexts that precede these events. In addition to exploring overall trends reported to the Mine Safety and Health Administration (MSHA), the current study uses MSHA's enforcement database to examine whether or not compliance with health regulations resulted in fewer mine‐level counts of these diseases over time. The findings suggest that interstitial lung diseases were more prevalent in coal mines compared to other mining commodities, in Appalachian coal mines compared to the rest of the United States, and in underground compared to surface coal mines. Mines that followed a relevant subset of MSHA's health regulations were less likely to report a lung disease over time. The findings are discussed from a lung disease prevention strategy perspective.     

Application Development Using Fault Data

We develop a general model for software development process and propose a policy to manage system coordination using system fault reports (e.g., interface inconsistencies, parameter mismatches, etc.). These reports are used to determine the timing of coordination activities that remove faults. We show that under an optimal policy, coordination should be performed only if a “threshold” fault count has been exceeded. We apply the policy to software development processes and compare the management of those projects under different development conditions. A series of numerical experiments are conducted to demonstrate how the fault threshold policy needs to be adjusted to changes in system complexity, team skill, development environment, and project schedule. Moreover, we compare the optimal fault threshold policy to an optimal release‐based policy. The release‐based policy does not take into account fault data and is easier to administer. The comparisons help to define the range of project parameters for which observing fault data can provide significant benefits for managing a software project.     

Speed and Accuracy of Data Analysts' Behavior Using Methods of Equal Interval Graphic Data Charts,Standard Celeration Charts,and Statistical Process Control Charts

Abstract

In response to a debate concerning effectiveness of alternative data handling methods in the hands of experts, the exploratory research reported here was conducted. Experts worked individually except for the SPC expert who was assisted by another expert. Two equal interval graphing experts, two standard behavior or celeration charting experts, and one team of SPC experts were presented with four sets of data that they graphed one point at a time. Their task was to plot, analyze, and identify the location of an intervention within each data set. A total of N = 8 series were plotted and analyzed by each analyst. Average percent of interventions correctly identified by equal interval, celeration, and SPC chartists were respectively, 63%, 32%, and 25%. Rank order of mean hours spent on the exercises was from fewest to most hours, celeration chartists, equal interval chartists, and charting SPC charting team. Regardless of method used, poorer quality baseline data in data series resulted in poorer performances among chartists. Implications are discussed in terms of future research into charting behavior and practical consequences of establishing or failing to establish “good” baseline control prior to introducing interventions in the OBM tradition.     

Big Data and Predictive Analytics in Fire Risk Using Weather Data

The objective of this article is to study the impact of weather on the damage caused by fire incidents across the United States. The article uses two sets of big data—-fire incidents data from the National Fire Incident Reporting System (NFIRS) and weather data from the National Oceanic and Atmospheric Administration (NOAA)—to obtain a single comprehensive data set for prediction and analysis of fire risk. In the article, the loss is referred to as “Total Percent Loss,” a metric that is calculated based on the content and property loss incurred by an owner over the total value of content and property. Gradient boosting tree (GBT), a machine learning algorithm, is implemented on the processed data to predict the losses due to fire incidents. An R² value of 0.933 and mean squared error (MSE) of 124.641 out of 10,000 signify the extent of high predictive accuracy obtained by implementing the GBT model. In addition to this, an excellent predictive performance demonstrated by the GBT model is further validated by a strong fitting between the predicted loss and the actual loss for the test data set, with an R² value of 0.97. While analyzing the influence of each input variable on the output, it is observed that the state in which a fire incident takes place plays a major role in determining fire risk. This article provides useful insights to fire managers and researchers in the form of a detailed framework of big data and predictive analytics for effective management of fire risk.     

新股发行抑价的两种模型检验

本文采集自2000年1月—2001年8月共193个新上市公司的样本,就我国新股发行抑价对Rock模型和信号传递假设模型的适用性进行了检验。检验结果表明这些模型都不适用。     

Reconfiguring the Supply Network Using Current Performance Data

Reconfiguration of the supply chain network from time to time is essential for businesses to retain their competitive edge. This paper presents a methodology for reconfiguration of an existing supply chain network. The methodology is characterized by two decision levels. In the first level, the current network performance is evaluated and efficient practices are identified. In the next level, a model that incorporates efficient practices is developed to reconfigure the network. This integrated methodology allows for decision maker (DM) input throughout the process. The methodology has been implemented and tested in the reconfiguration of an outbound petroleum supply chain network for CountryMark Cooperative, Inc. In this case study, Data Envelopment Analysis (DEA) is used to analyze current operations and an integer programming (IP) model that incorporates efficiency metrics is developed for selection of distribution facilities and allocation of resources to the facilities. Use of this methodology can lead to improved operations and reduced operating expenses.     

The Design of a Knowledge-Based Decision Support System to Support the Information Analyst in Determining Requirements

Decision support system (DSS) researchers and designers continue to look for unstructured organizational tasks where there is a critical need for intelligent computer-based support. One such decision task is information requirements determination. Requirements determination is recognized as the most crucial phase of the systems development life cycle. Unfortunately, most methodologies and CASE tools focus only on how to specify the requirements once they are determined. There is very little computer support for the process of determining requirements. This paper discusses the conceptual design and development of a knowledge-based DSS to support information analysts in the critical decision task of determining requirements for the design of effective information systems. The expert modeling support system has the expertise to assist the analyst in studying the organization as a whole and in modeling the system under study in the context of the overall organization's goals and needs. The focus of the paper is on the problems associated with building the knowledge base component of the intelligent decision support system. A prototype implementation of the system is described.     

Combining Administrative and Survey Data to Derive Small‐area Estimates Using Loglinear Modelling

Abstract. Structure‐preserving estimation (SPREE) is currently used to derive small‐area estimates of unemployment in New Zealand using data from the Household Labour Force Survey and the Ministry of Social Development. Noble et al. (Journal of Official Statistics 18: 45–60, 2002) advocate loglinear modelling as a major improvement and substitute for SPREE. The algorithm, however, is difficult to implement in SAS, the common statistical platform for the public sector, because of three major problems: (1) their way of writing the design matrix is incompatible with the ‘Proc Genmod’ procedure in SAS; (2) an important step in estimating cell frequencies from survey margins is unclear in the modelling procedure; and (3) the user has to manually write the design matrix of the model. This paper resolves these problems, provides novel SAS programs for implementing the approach, and discusses the implications.     

Fitting Second-Order Finite Mixture Models to Data with Many Censored Values Using Maximum Likelihood Estimation

Finite mixture models, that is, weighted averages of parametric distributions, provide a powerful way to extend parametric families of distributions to fit data sets not adequately fit by a single parametric distribution. First-order finite mixture models have been widely used in the physical, chemical, biological, and social sciences for over 100 years. Using maximum likelihood estimation, we demonstrate how a first-order finite mixture model can represent the large variability in data collected by the U.S. Environmental Protection Agency for the concentration of Radon 222 in drinking water supplied from ground water, even when 28% of the data fall at or below the minimum reporting level. Extending the use of maximum likelihood, we also illustrate how a second-order finite mixture model can separate and represent both the variability and the uncertainty in the data set.     

Hiring Behavior Analysts: Free Gifts at a Booth Increase Verbal Contacts with a Recruiter,but Not Serious Job Inquiries

ABSTRACT

The job market for certified behavior analysts currently is excellent, which poses a serious challenge for organizations looking to hire such individuals. We evaluated whether the provision of small giveaways at a recruitment booth set up at two behavior-analysis conferences and at a university career fair influenced the relative number of attendees who verbally contacted a recruiter for an organization looking to hire certified and prospective behavior analysts. We also examined whether the provision of gifts influenced the relative number of attendees who left an e-mail address requesting further contact about possible employment. An alternating-treatments design was used to compare the giveaways and no-giveaways conditions. The giveaways items significantly increased the number of attendees who spoke with the recruiter, but not the number who requested further contact. These findings provide support for the use of giveaways items to generally attract attention to a recruiter, but further research is needed to determine whether their use increases applications for employment.     

Using Scorecards and a Lottery to Improve the Performance of Behavior Technicians in Two Autism Treatment Clinics

ABSTRACT

Performance scorecards are tools that measure staff performance across multiple criteria to produce a single, overall performance score, which can be used as feedback for employees. While scorecards are considered a low-cost and effective approach to improve performance, published research demonstrating the effectiveness of scorecards is limited. The current study attempted to first demonstrate the effectiveness of scorecard feedback to improve performance, then, evaluate the added effects of linking the scorecard feedback to a lottery. Current results suggest scorecard feedback can modestly improve average employee performance, and linking the scorecard to a lottery may result in further performance improvements.     

Quantifying Uncertainty in a Risk Assessment Using Human Data

A call for risk assessment approaches that better characterize and quantify uncertainty has been made by the scientific and regulatory community. This paper responds to that call by demonstrating a distributional approach that draws upon human data to derive potency estimates and to identify and quantify important sources of uncertainty. The approach is rooted in the science of decision analysis and employs an influence diagram, a decision tree, probabilistic weights, and a distribution of point estimates of carcinogenic potency. Its results estimate the likelihood of different carcinogenic risks (potencies) for a chemical under a specific scenario. For this exercise, human data on formaldehyde were employed to demonstrate the approach. Sensitivity analyses were performed to determine the relative impact of specific levels and alternatives on the potency distribution. The resulting potency estimates are compared with the results of an exercise using animal data on formaldehyde. The paper demonstrates that distributional risk assessment is readily adapted to situations in which epidemiologic data serve as the basis for potency estimates. Strengths and weaknesses of the distributional approach are discussed. Areas for further application and research are recommended.     

Changing Corporate Governance in Response to Negative Media Reports

We study how organizations change their corporate governance in response to negative publicity in the media. We build on insights from the literature on interpersonal trust to theorize how organizations respond to different types of trust‐damaging information. We suggest that organizations are likely to replace key individuals involved in the corporate governance process when trust‐damaging information provides evidence of low integrity. In contrast, organizations are likely to make changes in how the governance process is organized when trust‐damaging information provides evidence of low benevolence. We test our hypotheses by using data on publicly traded Korean firms from 2006 to 2013. Our results provide general support for our argument about corporate governance changes that organizations initiate in response to different types of trust‐damaging information. We also explore how foreign ownership and state ownership moderate organizational responses to trust‐damaging information.     

Identification in Differentiated Products Markets Using Market Level Data

We present new identification results for nonparametric models of differentiated products markets, using only market level observables. We specify a nonparametric random utility discrete choice model of demand allowing rich preference heterogeneity, product/market unobservables, and endogenous prices. Our supply model posits nonparametric cost functions, allows latent cost shocks, and nests a range of standard oligopoly models. We consider identification of demand, identification of changes in aggregate consumer welfare, identification of marginal costs, identification of firms' marginal cost functions, and discrimination between alternative models of firm conduct. We explore two complementary approaches. The first demonstrates identification under the same nonparametric instrumental variables conditions required for identification of regression models. The second treats demand and supply in a system of nonparametric simultaneous equations, leading to constructive proofs exploiting exogenous variation in demand shifters and cost shifters. We also derive testable restrictions that provide the first general formalization of Bresnahan's (1982) intuition for empirically distinguishing between alternative models of oligopoly competition. From a practical perspective, our results clarify the types of instrumental variables needed with market level data, including tradeoffs between functional form and exclusion restrictions.     

Bayesian Methodology for Model Uncertainty Using Model Performance Data

A simple and useful characterization of many predictive models is in terms of model structure and model parameters. Accordingly, uncertainties in model predictions arise from uncertainties in the values assumed by the model parameters (parameter uncertainty) and the uncertainties and errors associated with the structure of the model (model uncertainty). When assessing uncertainty one is interested in identifying, at some level of confidence, the range of possible and then probable values of the unknown of interest. All sources of uncertainty and variability need to be considered. Although parameter uncertainty assessment has been extensively discussed in the literature, model uncertainty is a relatively new topic of discussion by the scientific community, despite being often the major contributor to the overall uncertainty. This article describes a Bayesian methodology for the assessment of model uncertainties, where models are treated as sources of information on the unknown of interest. The general framework is then specialized for the case where models provide point estimates about a single‐valued unknown, and where information about models are available in form of homogeneous and nonhomogeneous performance data (pairs of experimental observations and model predictions). Several example applications for physical models used in fire risk analysis are also provided.