A model of goal dynamics in technology-based organizations

The purpose of the present work is to build a suitable system dynamics model for goal dynamics in organizations, as proposed by Barlas and Yasarcan (2008). The proposed model does not bear any ambition of being exhaustive: the main objective of this paper is to propose a model of goal dynamics in which Goal Setting, Management by Objectives and Training are viewed as human resource practices able to enhance workers’ goal commitment, and therefore, improve organizational performance. In the first part of this paper, an analysis of the Goal Setting Theory and the role of goal setting practices, in bettering worker's performance, are stressed. In the second part, a case-study, the causal loop and a quantitative model of goal dynamics in organizations are described. In the third part, behaviour reproduction testing, optimization analysis for parameter estimation and scenario analysis are presented. Limitations of the present research and conclusions are finally discussed.     

Asteroid Risk Assessment: A Probabilistic Approach

Following the 2013 Chelyabinsk event, the risks posed by asteroids attracted renewed interest, from both the scientific and policy‐making communities. It reminded the world that impacts from near‐Earth objects (NEOs), while rare, have the potential to cause great damage to cities and populations. Point estimates of the risk (such as mean numbers of casualties) have been proposed, but because of the low‐probability, high‐consequence nature of asteroid impacts, these averages provide limited actionable information. While more work is needed to further refine its input distributions (e.g., NEO diameters), the probabilistic model presented in this article allows a more complete evaluation of the risk of NEO impacts because the results are distributions that cover the range of potential casualties. This model is based on a modularized simulation that uses probabilistic inputs to estimate probabilistic risk metrics, including those of rare asteroid impacts. Illustrative results of this analysis are presented for a period of 100 years. As part of this demonstration, we assess the effectiveness of civil defense measures in mitigating the risk of human casualties. We find that they are likely to be beneficial but not a panacea. We also compute the probability—but not the consequences—of an impact with global effects (“cataclysm”). We conclude that there is a continued need for NEO observation, and for analyses of the feasibility and risk‐reduction effectiveness of space missions designed to deflect or destroy asteroids that threaten the Earth.     

Bootstrap Determination of the Co‐Integration Rank in Vector Autoregressive Models

This paper discusses a consistent bootstrap implementation of the likelihood ratio (LR) co‐integration rank test and associated sequential rank determination procedure of Johansen (1996). The bootstrap samples are constructed using the restricted parameter estimates of the underlying vector autoregressive (VAR) model that obtain under the reduced rank null hypothesis. A full asymptotic theory is provided that shows that, unlike the bootstrap procedure in Swensen (2006) where a combination of unrestricted and restricted estimates from the VAR model is used, the resulting bootstrap data are I(1) and satisfy the null co‐integration rank, regardless of the true rank. This ensures that the bootstrap LR test is asymptotically correctly sized and that the probability that the bootstrap sequential procedure selects a rank smaller than the true rank converges to zero. Monte Carlo evidence suggests that our bootstrap procedures work very well in practice.     

On the Asymptotic Sizes of Subset Anderson–Rubin and Lagrange Multiplier Tests in Linear Instrumental Variables Regression

We consider tests of a simple null hypothesis on a subset of the coefficients of the exogenous and endogenous regressors in a single‐equation linear instrumental variables regression model with potentially weak identification. Existing methods of subset inference (i) rely on the assumption that the parameters not under test are strongly identified, or (ii) are based on projection‐type arguments. We show that, under homoskedasticity, the subset Anderson and Rubin (1949) test that replaces unknown parameters by limited information maximum likelihood estimates has correct asymptotic size without imposing additional identification assumptions, but that the corresponding subset Lagrange multiplier test is size distorted asymptotically.     

PERCEIVED ENVIRONMENTAL UNCERTAINTY AS A MEDIATOR OF EXPECTANCY THEORY PREDICTIONS: SOME PRELIMINARY FINDINGS

Expectancy theory has been tested in a variety of research settings with somewhat mixed results. One possible explanation for the sometimes low predictive ability of the model is the presence of environmental uncertainty. It was hypothesized that the greater the perceived environmental uncertainty, the greater the uncertainty regarding expectancy estimates. Hence, the predicted relationship between the level of motivation and job performance was expected to vary with the level of perceived uncertainty. Empirical results are presented supporting the hypothesis.     

Decision-Making with Heterogeneous Sources of Information

In any model the values of estimates for various parameters are obtained from different sources each with its own level of uncertainty. When the probability distributions of the estimates are obtained as opposed to point values only, the measurement uncertainties in the parameter estimates may be addressed. However, the sources used for obtaining the data and the models used to select appropriate distributions are of differing degrees of uncertainty. A hierarchy of different sources of uncertainty based upon one's ability to validate data and models empirically is presented. When model parameters are aggregated with different levels of the hierarchy represented, this implies distortion or degradation in the utility and validity of the models used. Means to identify and deal with such heterogeneous data sources are explored, and a number of approaches to addressing this problem is presented. One approach, using Range/Confidence Estimates coupled with an Information Value Analysis Process, is presented as an example.     

Technische Softwaredokumentation unter ?konomischen Gesichtspunkten – Ein formal-deduktiver Ansatz

Only a very low priority seems to be given to software documentation. For example, about 80% of the software systems in production worldwide are badly documented. In contrast empirical studies confirm that an adequate technical documentation reduces the error rate of subsequent changes during software maintenance. This leads to the question about the optimal scale of investment in technical documentation in any software project. In research, this decision problem has been largely neglected. Since underlying cause-effect relationships may be empirically hard to detect, this paper proposes a new deductive approach, which compares the present value of payments for the documentation and bug fixing. The basis of this model is the phenomenon of error propagation from one component to adjacent components through its dependencies. As a result the following hypothesis can be inferred: It is always beneficial to document too much rather than comparatively too little even for an arbitrarily low quality of documentation and for any required rate of interest. This hypothesis shows robustness even against some changes in assumptions.     

THE DEMAND FOR LIQUID ASSETS,CORPORATE SAVING,AND INTERNATIONAL CAPITAL FLOWS

The recent period of capital outflows from emerging economies has coincided with an increase in their corporate saving. In this paper, we model corporate saving as a demand for liquid assets by credit‐constrained firms in a dynamic open‐economy macroeconomic model. We find that the implications of this model are very different from standard models, because the demand for foreign bonds is a complement to domestic investment rather than a substitute. We show that this complementarity is at work when an emerging economy is on its convergence path or when it has a higher TFP growth rate. This framework is consistent with a number of stylized facts found in high‐growth, high‐investment emerging economies.     

Determining attribute weights using mathematical programming

Group decisions are an important element of successful knowledge management in organizations. Such decisions are difficult to make, however, especially when they involve a large set of attributes that require decision-makers to develop rankings. This paper presents a goal programming model for determining constrained regression estimates of attribute weights. The model is developed using pair-wise comparison ratings that are derived by using triads of the attributes. In addition, metrics are presented for measuring individual and group consensus. A specific application to the health care industry is presented to illustrate results that are obtained from the model.     

A Study of the Promotion to Partner Process in a Professional Services Firm: How Women are Disadvantaged

Increasing numbers of women are attracted to careers in the professional services. However, when their progress is considered to partner positions, it is found that they are not advancing to the levels anticipated. When the literature in relation to the partnership promotion process is explored, we find explanatory models are rare, and rarer yet is work that considers the impact of sex bias on the process. The article adds to the limited work available by presenting findings from a behavioural process perspective through an empirical study with male and female management consultants in a professional services firm which indicates that the promotion to partner process is indeed sex biased. Two areas of disadvantage for women are identified: the presence of a self-managed career advancement process necessitating a proactive approach to demonstrating individual contribution; and the need to ‘fit’ a prevailing model of success within the firm which is a masculine model and is more problematic for women. The article calls for a differentiated treatment of the glass ceiling phenomenon, capable of capturing disadvantage accruing from societally based factors and sector-based factors. The implications of the findings for future research and professional service firms are discussed.     

A Multinomial‐Dirichlet Model for Analysis of Competing Hypotheses

Analysis of competing hypothesis, a method for evaluating explanations of observed evidence, is used in numerous fields, including counterterrorism, psychology, and intelligence analysis. We propose a Bayesian extension of the methodology, posing the problem in terms of a multinomial‐Dirichlet hierarchical model. The yet‐to‐be observed true hypothesis is regarded as a multinomial random variable and the evaluation of the evidence is treated as a structured elicitation of a prior distribution on the probabilities of the hypotheses. This model provides the user with measures of uncertainty for the probabilities of the hypotheses. We discuss inference, such as point and interval estimates of hypothesis probabilities, ratios of hypothesis probabilities, and Bayes factors. A simple example involving the stadium relocation of the San Diego Chargers is used to illustrate the method. We also present several extensions of the model that enable it to handle special types of evidence, including evidence that is irrelevant to one or more hypotheses, evidence against hypotheses, and evidence that is subject to deception.     

LABOR‐MARKET HETEROGENEITY,AGGREGATION, AND POLICY (IN)VARIANCE OF DSGE MODEL PARAMETERS

Data from a heterogeneous‐agents economy with incomplete asset markets and indivisible labor supply are simulated under various fiscal policy regimes and an approximating representative‐agent model is estimated. Preference and technology parameter estimates of the representative‐agent model are not invariant to policy changes and the bias in the representative‐agent model’s policy predictions is large compared to predictive intervals that reflect parameter uncertainty. Since it is not always feasible to account for heterogeneity explicitly, it is important to recognize the possibility that the parameters of a highly aggregated model may not be invariant with respect to policy changes.     

Estimating Conditional Probabilities of Terrorist Attacks: Modeling Adversaries with Uncertain Value Tradeoffs

Many analyses conducted to inform security decisions depend on estimates of the conditional probabilities of different attack alternatives. These probabilities are difficult to estimate since analysts have limited access to the adversary and limited knowledge of the adversary’s utility function, so subject matter experts often provide the estimates through direct elicitation. In this article, we describe a method of using uncertainty in utility function value tradeoffs to model the adversary’s decision process and solve for the conditional probabilities of different attacks in closed form. The conditional probabilities are suitable to be used as inputs to probabilistic risk assessments and other decision support techniques. The process we describe is an extension of value‐focused thinking and is broadly applicable, including in general business decision making. We demonstrate the use of this technique with simple examples.     

Daily work pressure and exposure to bullying-related negative acts: The role of daily transformational and laissez-faire leadership

The present study integrates the work environment hypothesis and the effort-reward imbalance model to argue that work-related antecedents of workplace bullying are moderated by the day-to-day leadership practices of one’s immediate leader. Specifically, we propose that individuals’ daily experiences of work pressure are positively related to their daily experiences of bullying-related negative acts. Moreover, we claim that this relationship is weaker on days when those individuals report high (vs. low) levels of transformational leadership behaviour, and stronger on days when they report high (vs. low) levels of laissez-faire leadership behaviour. To test these three hypotheses, we asked 61 naval cadets on a tall ship sailing from Northern Europe to North America to fill out a diary questionnaire for 36 days yielding 1509 observations. The results of multilevel analyses supported our hypothesis of a positive relationship of cadets’ daily reports of work pressure with their daily reports of bullying-related negative acts. In addition, laissez-faire leadership behaviour (but not transformational leadership behaviour) moderated the work pressure–bullying-related negative acts relationship. Our findings support the assumption that laissez-faire leadership is an important component in the development of conflict escalation and workplace bullying, while transformational leadership is not. We discuss theoretical as well as practical implications of these findings.     

Why Youths Drop out of High School: The Impact of Preferences,Opportunities, and Abilities

In this paper, we develop and structurally estimate a sequential model of high school attendance and work decisions. The model's estimates imply that youths who drop out of high school have different traits than those who graduate—they have lower school ability and/or motivation, they have lower expectations about the rewards from graduation, they have a comparative advantage at jobs that are done by nongraduates, and they place a higher value on leisure and have a lower consumption value of school attendance. We also found that working while in school reduces school performance. However, policy experiments based on the model's estimates indicate that even the most restrictive prohibition on working while attending high school would have only a limited impact on the high school graduation rates of white males.     

Influence of Safety Measures on the Risks of Transporting Dangerous Goods Through Road Tunnels

Quantitative risk assessment (QRA) models are used to estimate the risks of transporting dangerous goods and to assess the merits of introducing alternative risk reduction measures for different transportation scenarios and assumptions. A comprehensive QRA model recently was developed in Europe for application to road tunnels. This model can assess the merits of a limited number of "native safety measures." In this article, we introduce a procedure for extending its scope to include the treatment of a number of important "nonnative safety measures" of interest to tunnel operators and decisionmakers. Nonnative safety measures were not included in the original model specification. The suggested procedure makes use of expert judgment and Monte Carlo simulation methods to model uncertainty in the revised risk estimates. The results of a case study application are presented that involve the risks of transporting a given volume of flammable liquid through a 10-km road tunnel.     

基于价值创造视角的互联网企业核心资源及能力研究

尽管网络经济已经成为国民经济的重要组成部分,但至今尚未形成一套适用于网络经济特征的体系化的竞争战略理论。为了弥补这一不足,本文通过对网络经济理论和战略管理理论的整合,借鉴AMIT&ZOTT提出的虚拟交易的四维价值空间理论框架,并将其推广应用到WEB2.0企业,从价值创造的角度揭示了网络企业的"3+1"核心资源和能力的构成体系。理论研究表明3种核心资源--原创内容资源、匹配数据资源、关系网络资源和1种核心能力--汇聚资源能力,对企业核心价值和竞争优势的形成具有正向的影响作用。为了检验假设,本文以北京市海淀区的248家网络企业为样本,采用结构方程模型分析方法进行分析。实证结果表明:原创内容资源、匹配数据资源和关系网络资源对核心价值和竞争优势的产生具有显著作用;而汇聚资源能力对原创内容资源、匹配数据资源和关系网络资源的形成具有显著的正效应。实证数据部分支持了本文的原始假设。     

The Effects of Publication Selection on Test Probabilities and Estimator Distributions

Editorial decisions, based in part on reported hypothesis test results, affect the probabilities associated with those results: the probabilities of Type I and Type II errors thus become different for readers than for authors. The distributions of published parameter estimates are similarly affected. A framework for studying the consequences of test-based information filtering is developed and illustrative examples are provided. The examples indicate that filtering can markedly distort the power functions of hypothesis tests and can induce large estimator biases and increases in mean square error. It is argued that test-based filtering is relevant not only to journal publication but to other forms of information dissemination as well.     

Integrated Uncertainty Analysis for Ambient Pollutant Health Risk Assessment: A Case Study of Ozone Mortality Risk

The U.S. Environmental Protection Agency (EPA) uses health risk assessment to help inform its decisions in setting national ambient air quality standards (NAAQS). EPA's standard approach is to make epidemiologically‐based risk estimates based on a single statistical model selected from the scientific literature, called the “core” model. The uncertainty presented for “core” risk estimates reflects only the statistical uncertainty associated with that one model's concentration‐response function parameter estimate(s). However, epidemiologically‐based risk estimates are also subject to “model uncertainty,” which is a lack of knowledge about which of many plausible model specifications and data sets best reflects the true relationship between health and ambient pollutant concentrations. In 2002, a National Academies of Sciences (NAS) committee recommended that model uncertainty be integrated into EPA's standard risk analysis approach. This article discusses how model uncertainty can be taken into account with an integrated uncertainty analysis (IUA) of health risk estimates. It provides an illustrative numerical example based on risk of premature death from respiratory mortality due to long‐term exposures to ambient ozone, which is a health risk considered in the 2015 ozone NAAQS decision. This example demonstrates that use of IUA to quantitatively incorporate key model uncertainties into risk estimates produces a substantially altered understanding of the potential public health gain of a NAAQS policy decision, and that IUA can also produce more helpful insights to guide that decision, such as evidence of decreasing incremental health gains from progressive tightening of a NAAQS.