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The purpose of this paper is to consider the influence of individual risk preferences on the effectiveness of incentive pay schemes, by examining the link between individual effort and risk aversion in situations where outcome uncertainty multiplies with effort. Such “multiplicative noise” situations are common, occurring whenever payment is awarded per success rather than per attempt.

The paper develops a theoretical model which predicts a negative risk aversion-effort link under multiplicative noise without a performance target (PT), and a weaker negative link once the target is introduced. This model is then taken to the data from a lab experiment where participants were randomly assigned to a control group, which received fixed pay, and a treatment group, which received a piece rate awarded with a certain probability, with and without a PT. Risk aversion is measured with a menu of lottery choices offered at the end of the experiment.

Compared to their peers in the control group, the more risk-averse participants in the treatment group put in progressively less effort in the absence of a PT. The introduction of a PT substantially weakens this negative risk aversion-effort link, so that there are no more significant differences in performance between the more and the less risk averse.

The paper’s findings speak to the empirical puzzles of incentive pay schemes backfiring and of the proliferation of PTs. The negative risk aversion-effort link may be one reason behind the failure of incentive schemes to deliver improved performance, whereas the weakening of this link may be one justification for the existence of PTs.

In the multiplicative noise environments, managers should take their workers’ risk preferences into account when designing incentive pay schemes. A PT may be a useful motivational tool for the risk-averse workers who are more likely to under-perform.

The multiplicative noise environment has been largely overlooked by the existing literature, yet it is common in practice. An example is the work of a sales agent who receives a bonus per sales which succeeds with a certain probability after each customer contact. This paper is one of the first to model, and test experimentally, worker performance in this environment.

To study the multiplicative perturbation of local C‐regularized cosine functions associated with the following incomplete second order abstract differential equations in a Banach space X u″(t)=A(I+B)u(t), u(0)=x, u′(0)=y,(*) where A is a closed linear operator on X and B is a bounded linear operator on X.

The multiplicative perturbation of exponentially bounded regularized C‐cosine functions is generally studied by the Laplace transformation. However, C‐cosine functions might not be exponentially bounded, so that the new method for the multiplicative perturbation of the nonexponentially bounded regularized C‐cosine functions should be applied. In this paper, the property of regularized C‐cosine functions is directly used to obtain the desired results.

The new results of the multiplicative perturbations of the nonexponentially bounded C‐cosine functions are obtained.

The new techniques differing from those given previously in the literature are employed to deduce the desired conclusions. The results can be applied to deal with incomplete second order abstract differential equations which stem from cybernetics, engineering, physics, etc.

The purpose of this paper is to study joint inventory and pricing strategy for a continuous inventory review system. While dynamic pricing decisions are often studied in the literature along with inventory management, the authors' aim in this study is to obtain a single long‐run optimal price; also to gain insight about how to obtain the optimal price and inventory control variables simultaneously and then the benefits of joint optimization of the inventory and pricing decisions over the sequential optimization policy often followed in practice.

A general (R;Q) policy system with fixed cost of ordering is modelled and then the case where unsatisfied demand is lost is studied. General forms of both the additive and multiplicative demand models are used to obtain structural results.

By showing optimality conditions on the price and inventory decision variables, two algorithms on how to obtain optimal decision variables, one for additive and another for multiplicative demand‐price model are provided. Through extensive numerical analyses, the potential profit increases are reported if the price and inventory problem are solved simultaneously instead of sequentially. In addition, the sensitivities of optimal decision variables to system parameters are revealed.

Although there are several studies in the literature investigating emergency price change models, they use arbitrary exogenous prices menus. However, the value of a price change can be better appreciated if the long‐run price is optimal for the system.

Very few researchers have investigated constant price and inventory optimization, and while there are several past studies demonstrating the benefits of dynamic pricing over a static one, there still are not many findings on the benefit of joint price and inventory optimization.

The purpose of this paper is to explore the curvilinear relationship between entrepreneurial orientation (EO) and innovation performance in ventures of Korea, where this topic has been unexplored.

This study used 1,837 ventures’ responses in a panel data of the 2015 Korea’s Venture Business Investigation Survey. The measurements of EO based on the Miller/Covin and Slevin scale were used from the survey. The author adopted and independently measured three different indicators of innovation performance: technology innovation, product innovation and sales growth.

The results of the regression analysis show the significant curvilinear relationships of EO with technology innovation and product innovation, while the relationship between EO and sales growth remains linear. The author also found that multiplicative EO construct explains the changes in R² better than the summative EO construct for the improvement of innovations in technology and product.

This paper provides empirical evidence on the EO–innovation performance relationships from Korean ventures – an underexplored area of research. It also takes into account the curvilinear relationships of summative and multiplicative EO constructs with different performance indicators. Future research can benefit from the use of the multiplicative EO estimation and multiple performance indicators to capture a clearer picture of the causal relationships.

The purpose of this article is to propose a detailed methodology to estimate, model and incorporate the non-constant volatility onto a numerical tree scheme, to evaluate a real option, using a quadrinomial multiplicative recombination.

This article uses the multiplicative quadrinomial tree numerical method with non-constant volatility, based on stochastic differential equations of the GARCH-diffusion type to value real options when the volatility is stochastic.

Findings showed that in the proposed method with volatility tends to zero, the multiplicative binomial traditional method is a particular case, and results are comparable between these methodologies, as well as to the exact solution offered by the Black–Scholes model.

The originality of this paper lies in try to model the implicit (conditional) market volatility to assess, based on that, a real option using a quadrinomial tree, including into this valuation the stochastic volatility of the underlying asset. The main contribution is the formal derivation of a risk-neutral valuation as well as the market risk premium associated with volatility, verifying this condition via numerical test on simulated and real data, showing that our proposal is consistent with Black and Scholes formula and multiplicative binomial trees method.

The purpose of this paper is to examine the relationship between the combined (multiplicative) effect of board governance and intellectual capital (IC) on firm performance.

This study is cross-sectional and follows a positivist view of testing pre-specified hypotheses. The study uses a respondent sample of 128 service firms operating in Kampala, directors or managers are the unit of enquiry. Structural equation modelling with analysis of moment structures is used for statistical modelling.

Board governance and IC make significant contributions to firm performance. However, their interaction is a significant booster to services sector firms’ performance in Uganda.

Although an attempt is made at controlling for common method variance in particular by proactive instrument design and testing, and usage of the Harman single factor analytical technique, its influence may not have been dealt away completely owing to failure to obtain a plausible common marker variable. Well, it is meaningful to identify the significant positive multiplicative effects of board governance and IC so as uncover what is needed in service firms to improve their performance.

Studies explaining firm performance via board governance only and which ignored the synergistic effects of board governance and IC have often missed the reality that the performance of the firm can significantly be improved by means of leveraging IC while simultaneously calling for effective board governance.

This chapter uses advance order data from an actual manufacturing shop to develop and test a forecast model for total demand. The proposed model made direct use of historical time series data for total demand and time series data for advance orders. Comparison of the proposed model to commonly used approaches showed that the proposed model exhibited greater forecast accuracy.

Decision analysis in management science employs concepts from economics such as utility functions and indifference curves. A utility function U models the “satisfaction” that a customer obtains from logistics service. Here U depends on two attributes (lead time, fill rate) whose values more directly represent customer service. The shipper can, at additional cost, improve either or both of these attributes. Constructs and maximizes various utility functions U given a total budget B for distribution service. Finds that without increasing the budget overall logistics service can often be improved from the customer’s point of view. Whether U is additive or multiplicative, a customer’s utility resulting from the optimal lead time and fill rate is typically 20 per cent higher than when those attribute levels are set intuitively (without reference to customer preferences and tradeoffs expressed by U). Gives some introduction to decision analysis (certainty equivalent, risk aversion, …) to aid in understanding the functional forms employed for U and methods of solution, rendering the paper more self‐contained.

To develop a new computational tool for predicting failure probability of structural/mechanical systems subject to random loads, material properties, and geometry.

High dimensional model representation (HDMR) is a general set of quantitative model assessment and analysis tools for capturing the high‐dimensional relationships between sets of input and output model variables. It is a very efficient formulation of the system response, if higher order variable correlations are weak and if the response function is dominantly of additive nature, allowing the physical model to be captured by the first few lower order terms. But, if multiplicative nature of the response function is dominant then all right hand side components of HDMR must be used to be able to obtain the best result. However, if HDMR requires all components, which means 2^N number of components, to get a desired accuracy, making the method very expensive in practice, then factorized HDMR (FHDMR) can be used. The component functions of FHDMR are determined by using the component functions of HDMR. This paper presents the formulation of FHDMR approximation of a multivariate limit state/performance function, which is dominantly of multiplicative nature. Given that conventional methods for reliability analysis are very computationally demanding, when applied in conjunction with complex finite element models. This study aims to assess how accurately and efficiently HDMR/FHDMR based approximation techniques can capture complex model output uncertainty. As a part of this effort, the efficacy of HDMR, which is recently applied to reliability analysis, is also demonstrated. Response surface is constructed using moving least squares interpolation formula by including constant, first‐order and second‐order terms of HDMR and FHDMR. Once the response surface form is defined, the failure probability can be obtained by statistical simulation.

Results of five numerical examples involving structural/solid‐mechanics/geo‐technical engineering problems indicate that the failure probability obtained using FHDMR approximation for the limit state/performance function of dominantly multiplicative in nature, provides significant accuracy when compared with the conventional Monte Carlo method, while requiring fewer original model simulations.

This is the first time where application of FHDMR concepts is explored in the field of reliability and system safety. Present computational approach is valuable to the practical modeling and design community, where user often suffers from the curse of dimensionality.

We provide a method for automatically extending small‐strain state‐update algorithms and their correspondent consistent tangents into the finite deformation range within the framework of multiplicative plasticity. The procedure, when it applies, operates at the level of kinematics and, hence, can be implemented once and for all independently of the material‐specific details of the constitutive model. The versatility of the method is demonstrated by a numerical example.