复杂环境下企业适应性领导行为研究

竞争环境的复杂性要求企业领导行为具有较强的适应性。在复杂适应系统刺激-反应原理的基础上,构建复杂环境下企业适应性领导行为模型,指出在政府、市场、企业等组织内外部环境因素作用下,企业的适应性领导行为由文化识别、知识管理、风险管理和协同创造4个方面构成,并对员工绩效、组织承诺和员工满意度等领导有效性指标产生影响。在问卷调查的基础上,采用因子分析、相关分析、层次回归和逐步回归等方法对调查数据进行统计分析。实证研究结果表明,企业适应性领导行为对员工绩效、组织承诺、员工满意度等结果变量有不同程度的影响;企业文化、领导-成员交换关系与企业适应性领导行为和领导有效性存在相关关系,并且作为调节变量对企业适应性领导行为与领导有效性之间的关系起调节作用。企业应从适应性领导行为层面和调节变量层面加强管理,构建有效领导行为机制,提高环境变化中的领导有效性。     

Leadership across hierarchical levels: Multiple levels of management and multiple levels of analysis

We examined differences in leadership influence processes, perceptions, and multiple levels-of-analysis effects between close and distant charismatic and contingent reward leadership across three hierarchical levels in 13 Korean companies. Multi-source data revealed that followers' commitment to the leader mediated relationships between leadership and followers' attitudinal, behavioral, and performance outcomes in close situations, but not in distant relationships. Leadership at higher levels of management was positively related to leadership at the next lower level, which in turn related to follower outcomes at the lowest echelon. Multivariate within and between analysis indicated multiple-level effects differing by leader–follower distance and for the variables of interest.     

Effective Leadership in Superior-Subordinate Dyads

Abstract

This paper describes and experimentally demonstrates the main tenets of an operant theory of leadership. Leadership is characterized in the current paper as involving problem solving operant behavior (Cerutti, 1989; Skinner, 1969) in a social context (Skinner, 1953). The theory was assessed under two experimental analogs modeled from generic formal organizational bureaucratic leader-follower role relations. Under a minimal leadership contingency (MLC) leaders and followers in N = 4 dyads interacted via button pressing and trigger pulling responses, respectively, and they received feedback on counters located on response panels in their separate rooms. Under the MLC every leader button press added a point worth money to one of the follower's counters but the leader received no points worth money based on follower responses. A leadership contingency (LC) was identical to the MLC except that for every 19th follower trigger pull the leader received a point worth money. As anticipated, high rates of leader-follower interaction evolved in all dyads under the LC and appreciably lower rates occurred under the MLC as leader button pressing extinguished under the MLC with repeated exposures to the two contingencies presented in ABABAB fashion. Results were discussed in terms of the theory and data as they may be related to assessment and maintenance of leader-follower interactions and performance in OBM lab and field experiments.     

中国情境下的精神型领导模型与测量研究

针对基于西方文化提出的精神型领导概念,试图在中国情景下验证其模型与测量工具的适用性.在文献梳理的基础上,通过探索性因子分析和验证性因子分析发现,中国情景下的精神型领导内容包括愿景和利他之爱2个维度,愿景维度由9个测量题目构成,利他之爱由11个测量题目构成.经过实证研究,构建了中国文化情景下精神型领导的二维模型,并开发了具有较高信度和效度的测量工具.     

Incomplete Preferences Under Uncertainty: Indecisiveness in Beliefs versus Tastes

We investigate the classical Anscombe–Aumann model of decision‐making under uncertainty without the completeness axiom. We distinguish between the dual traits of “indecisiveness in beliefs” and “indecisiveness in tastes.” The former is captured by the Knightian uncertainty model, the latter by the single‐prior expected multi‐utility model. We characterize axiomatically the latter model. Then we show that, under independence and continuity, these two models can be jointly characterized by means of a partial completeness property.     

Deterrence and Risk Preferences in Sequential Attacker–Defender Games with Continuous Efforts

Most attacker–defender games consider players as risk neutral, whereas in reality attackers and defenders may be risk seeking or risk averse. This article studies the impact of players' risk preferences on their equilibrium behavior and its effect on the notion of deterrence. In particular, we study the effects of risk preferences in a single‐period, sequential game where a defender has a continuous range of investment levels that could be strategically chosen to potentially deter an attack. This article presents analytic results related to the effect of attacker and defender risk preferences on the optimal defense effort level and their impact on the deterrence level. Numerical illustrations and some discussion of the effect of risk preferences on deterrence and the utility of using such a model are provided, as well as sensitivity analysis of continuous attack investment levels and uncertainty in the defender's beliefs about the attacker's risk preference. A key contribution of this article is the identification of specific scenarios in which the defender using a model that takes into account risk preferences would be better off than a defender using a traditional risk‐neutral model. This study provides insights that could be used by policy analysts and decisionmakers involved in investment decisions in security and safety.     

Leadership and business model innovation in late internationalizing SMEs

Drawing on the distinction between small-scale and large-scale business model innovation, and between directive and empowering leadership, we examine how CEOs in SMEs lead business model innovation during the process of internationalization. Building on eight cases of Japanese manufacturing SMEs, we develop a theoretical framework pointing to two different patterns in the articulation between CEO leadership style and business model innovation. We show that small-scale business model innovation led by directive leadership results in a timelier foreign market entry. However, in order to increase international sales, large-scale business model innovation is required. This is facilitated by an empowering leadership style of the CEO.     

Guidelines for Use of the Approximate Beta‐Poisson Dose–Response Model

For dose–response analysis in quantitative microbial risk assessment (QMRA), the exact beta‐Poisson model is a two‐parameter mechanistic dose–response model with parameters and , which involves the Kummer confluent hypergeometric function. Evaluation of a hypergeometric function is a computational challenge. Denoting as the probability of infection at a given mean dose d, the widely used dose–response model is an approximate formula for the exact beta‐Poisson model. Notwithstanding the required conditions and , issues related to the validity and approximation accuracy of this approximate formula have remained largely ignored in practice, partly because these conditions are too general to provide clear guidance. Consequently, this study proposes a probability measure Pr(0 < r < 1 | , ) as a validity measure (r is a random variable that follows a gamma distribution; and are the maximum likelihood estimates of α and β in the approximate model); and the constraint conditions for as a rule of thumb to ensure an accurate approximation (e.g., Pr(0 < r < 1 | , ) >0.99) . This validity measure and rule of thumb were validated by application to all the completed beta‐Poisson models (related to 85 data sets) from the QMRA community portal (QMRA Wiki). The results showed that the higher the probability Pr(0 < r < 1 | , ), the better the approximation. The results further showed that, among the total 85 models examined, 68 models were identified as valid approximate model applications, which all had a near perfect match to the corresponding exact beta‐Poisson model dose–response curve.     

Testing Hypotheses About the Number of Factors in Large Factor Models

In this paper we study high‐dimensional time series that have the generalized dynamic factor structure. We develop a test of the null of k₀ factors against the alternative that the number of factors is larger than k₀ but no larger than k₁>k₀. Our test statistic equals max_{k0<k k1}(γ_k−γ_k+1)(γ_k+1−γ_k+2), where γ_i is the ith largest eigenvalue of the smoothed periodogram estimate of the spectral density matrix of data at a prespecified frequency. We describe the asymptotic distribution of the statistic, as the dimensionality and the number of observations rise, as a function of the Tracy–Widom distribution and tabulate the critical values of the test. As an application, we test different hypotheses about the number of dynamic factors in macroeconomic time series and about the number of dynamic factors driving excess stock returns.     

研发团队社会资本对创新绩效作用路径——心理安全和学习行为整合视角

一方面,研发团队作为企业创新活动的基本单位,对提升企业创新能力起着关键作用;另一方面,如何有效管理研发团队以促进创新,在现实中又面临严峻挑战.基于团队心理安全和学习行为整合视角,以151个研发团队585个研发成员为样本,实证研究研发团队社会资本对创新绩效的作用路径.研究结果表明:研发团队3维社会资本(结构、认知和关系)对创新绩效均具有显著的积极影响,团队学习行为部分中介团队心理安全与创新绩效的关系;团队心理安全部分中介团队社会资本与学习行为的关系;团队心理安全和学习行为分别部分中介团队结构资本、认知资本与创新绩效的关系,完全中介团队关系资本与创新绩效的关系.     

Two New Conditions Supporting the First‐Order Approach to Multisignal Principal–Agent Problems

This paper presents simple new multisignal generalizations of the two classic methods used to justify the first‐order approach to moral hazard principal–agent problems, and compares these two approaches with each other. The paper first discusses limitations of previous generalizations. Then a state‐space formulation is used to obtain a new multisignal generalization of the Jewitt (1988) conditions. Next, using the Mirrlees formulation, new multisignal generalizations of the convexity of the distribution function condition (CDFC) approach of Rogerson (1985) and Sinclair‐Desgagné (1994) are obtained. Vector calculus methods are used to derive easy‐to‐check local conditions for our generalization of the CDFC. Finally, we argue that the Jewitt conditions may generalize more flexibly than the CDFC to the multisignal case. This is because, with many signals, the principal can become very well informed about the agent's action and, even in the one‐signal case, the CDFC must fail when the signal becomes very accurate.     

Dynamic Preference for Flexibility

We consider a decision maker who faces dynamic decision situations that involve intertemporal trade‐offs, as in consumption–savings problems, and who experiences taste shocks that are transient contingent on the state of the world. We axiomatize a recursive representation of choice over state contingent infinite horizon consumption problems, where uncertainty about consumption utilities depends on the observable state and the state follows a subjective Markov process. The parameters of the representation are the subjective process that governs the evolution of beliefs over consumption utilities and the discount factor; they are uniquely identified from behavior. We characterize a natural notion of greater preference for flexibility in terms of a dilation of beliefs. An important special case of our representation is a recursive version of the Anscombe–Aumann model with parameters that include a subjective Markov process over states and state‐dependent utilities, all of which are uniquely identified.     

An Optimization‐Based Framework for the Identification of Vulnerabilities in Electric Power Grids Exposed to Natural Hazards

This article proposes a novel mathematical optimization framework for the identification of the vulnerabilities of electric power infrastructure systems (which is a paramount example of critical infrastructure) due to natural hazards. In this framework, the potential impacts of a specific natural hazard on an infrastructure are first evaluated in terms of failure and recovery probabilities of system components. Then, these are fed into a bi‐level attacker–defender interdiction model to determine the critical components whose failures lead to the largest system functionality loss. The proposed framework bridges the gap between the difficulties of accurately predicting the hazard information in classical probability‐based analyses and the over conservatism of the pure attacker–defender interdiction models. Mathematically, the proposed model configures a bi‐level max‐min mixed integer linear programming (MILP) that is challenging to solve. For its solution, the problem is casted into an equivalent one‐level MILP that can be solved by efficient global solvers. The approach is applied to a case study concerning the vulnerability identification of the georeferenced RTS24 test system under simulated wind storms. The numerical results demonstrate the effectiveness of the proposed framework for identifying critical locations under multiple hazard events and, thus, for providing a useful tool to help decisionmakers in making more‐informed prehazard preparation decisions.     

Information in Securities Markets: Kyle Meets Glosten and Milgrom

This paper analyzes models of securities markets with a single strategic informed trader and competitive market makers. In one version, uninformed trades arrive as a Brownian motion and market makers see only the order imbalance, as in Kyle (1985). In the other version, uninformed trades arrive as a Poisson process and market makers see individual trades. This is similar to the Glosten–Milgrom (1985) model, except that we allow the informed trader to optimize his times of trading. We show there is an equilibrium in the Glosten–Milgrom‐type model in which the informed trader plays a mixed strategy (a point process with stochastic intensity). In this equilibrium, informed and uninformed trades arrive probabilistically, as Glosten and Milgrom assume. We study a sequence of such markets in which uninformed trades become smaller and arrive more frequently, approximating a Brownian motion. We show that the equilibria of the Glosten–Milgrom model converge to the equilibrium of the Kyle model.     

项目团队成员怠工行为修正的理论模型

项目团队并不总能实现预期目标,项目团队中存在的某些问题属于怠工行为的表现,项目团队中怠工行为修正亟待研究。采用定性研究方法,按照行为修正分析模式,以行为修正理论、态度改变理论、态度与行为理论、认知平衡理论等为理论基础,经理论与文献研究提出:项目团队成员怠工行为与项目团队绩效的关系、工作态度在工作情境认知与怠工行为之间中介作用关系、团队文化认知在工作情境认知与工作态度之间调节作用关系,从而构建起项目团队成员怠工行为修正的理论模型。     

Illustration of a Method to Incorporate Preference Uncertainty in Benefit–Cost Analysis

Benefit–cost analysis is widely used to evaluate alternative courses of action that are designed to achieve policy objectives. Although many analyses take uncertainty into account, they typically only consider uncertainty about cost estimates and physical states of the world, whereas uncertainty about individual preferences, thus the benefit of policy intervention, is ignored. Here, we propose a strategy to integrate individual uncertainty about preferences into benefit–cost analysis using societal preference intervals, which are ranges of values over which it is unclear whether society as a whole should accept or reject an option. To illustrate the method, we use preferences for implementing a smart grid technology to sustain critical electricity demand during a 24‐hour regional power blackout on a hot summer weekend. Preferences were elicited from a convenience sample of residents in Allegheny County, Pennsylvania. This illustrative example shows that uncertainty in individual preferences, when aggregated to form societal preference intervals, can substantially change society's decision. We conclude with a discussion of where preference uncertainty comes from, how it might be reduced, and why incorporating unresolved preference uncertainty into benefit–cost analyses can be important.     

A Robust Approach to Risk Assessment Based on Species Sensitivity Distributions

The guidelines for setting environmental quality standards are increasingly based on probabilistic risk assessment due to a growing general awareness of the need for probabilistic procedures. One of the commonly used tools in probabilistic risk assessment is the species sensitivity distribution (SSD), which represents the proportion of species affected belonging to a biological assemblage as a function of exposure to a specific toxicant. Our focus is on the inverse use of the SSD curve with the aim of estimating the concentration, HCp, of a toxic compound that is hazardous to p% of the biological community under study. Toward this end, we propose the use of robust statistical methods in order to take into account the presence of outliers or apparent skew in the data, which may occur without any ecological basis. A robust approach exploits the full neighborhood of a parametric model, enabling the analyst to account for the typical real‐world deviations from ideal models. We examine two classic HCp estimation approaches and consider robust versions of these estimators. In addition, we also use data transformations in conjunction with robust estimation methods in case of heteroscedasticity. Different scenarios using real data sets as well as simulated data are presented in order to illustrate and compare the proposed approaches. These scenarios illustrate that the use of robust estimation methods enhances HCp estimation.     

A Production–Inventory Model for a Push–Pull Manufacturing System with Capacity and Service Level Constraints

We study a hybrid push–pull production system with a two‐stage manufacturing process, which builds and stocks tested components for just‐in‐time configuration of the final product when a specific customer order is received. The first production stage (fabrication) is a push process where parts are replenished, tested, and assembled into components according to product‐level build plans. The component inventory is kept in stock ready for the final assembly of the end products. The second production stage (fulfillment) is a pull‐based assemble‐to‐order process where the final assembly process is initiated when a customer order is received and no finished goods inventory is kept for end products. One important planning issue is to find the right trade‐off between capacity utilization and inventory cost reduction that strives to meet the quarter‐end peak demand. We present a nonlinear optimization model to minimize the total inventory cost subject to the service level constraints and the production capacity constraints. This results in a convex program with linear constraints. An efficient algorithm using decomposition is developed for solving the nonlinear optimization problem. Numerical results are presented to show the performance improvements achieved by the optimized solutions along with managerial insights provided.     

Liquidity in Asset Markets With Search Frictions

We develop a search‐theoretic model of financial intermediation in an over‐the‐counter market and study how trading frictions affect the distribution of asset holdings and standard measures of liquidity. A distinctive feature of our theory is that it allows for unrestricted asset holdings, so market participants can accommodate trading frictions by adjusting their asset positions. We show that these individual responses of asset demands constitute a fundamental feature of illiquid markets: they are a key determinant of trade volume, bid–ask spreads, and trading delays—the dimensions of market liquidity that search‐based theories seek to explain.