Enterprise Systems Adoption and Firm Performance in Europe: The Role of Innovation

Despite the ubiquitous proliferation and importance of Enterprise Systems (ES), little research exists on their post-implementation impact on firm performance, especially in Europe. This paper provides representative, large-sample evidence on the differential effects of different ES types on performance of European enterprises. It also highlights the mediating role of innovation in the process of value creation from ES investments. Empirical data on the adoption of Enterprise Resource Planning (ERP), Supply Chain Management (SCM), Customer Relationship Management (CRM), Knowledge Management System (KMS), and Document Management System (DMS) is used to investigate the effects on product and process innovation, revenue, productivity and market share growth, and profitability. The data covers 29 sectors in 29 countries over a 5-year period. The results show that all ES categories significantly increase the likelihood of product and process innovation. Most of ES categories affect revenue, productivity and market share growth positively. Particularly, more domainspecific and simpler system types lead to stronger positive effects. ERP systems decrease the profitability likelihood of the firm, whereas other ES categories do not show any significant effect. The findings also imply that innovation acts as a full or partial mediator in the process of value creation of ES implementations. The direct effect of enterprise software on firm performance disappears or significantly diminishes when the indirect effects through product and process innovation are explicitly accounted for. The paper highlights future areas of research.Enterprise Systems; ERP; SCM; CRM; KMS; DMS; IT Adoption; Post-implementation Phase; IT Business Value; Innovation; Firm Performance; Europe

Kriging for Interpolation in Random Simulation

Whenever simulation requires much computer time, interpolation is needed. There are several interpolation techniques in use (for example, linear regression), but this paper focuses on Kriging.This technique was originally developed in geostatistics by D.G.Krige, and has recently been widely applied in deterministic simulation.This paper, however, focuses on random or stochastic simulation.Essentially, Kriging gives more weight to 'neighbouring' observations.There are several types of Kriging; this paper discusses - besides Ordinary Kriging - a novel type, which 'detrends' data through the use of linear regression.Results are presented for two examples of input/output behaviour of the underlying random simulation model: A perfectly specified detrending function gives the best predictions, but Ordinary Kriging gives quite acceptable results; traditional linear regression gives the worst predictions.simulation;statistics;stochastic processes;methodology;linear regression

Application-driven Sequential Designs for Simulation Experiments: Kriging Metamodeling

This paper proposes a novel method to select an experimental design for interpolation in simulation.Though the paper focuses on Kriging in deterministic simulation, the method also applies to other types of metamodels (besides Kriging), and to stochastic simulation.The paper focuses on simulations that require much computer time, so it is important to select a design with a small number of observations.The proposed method is therefore sequential.The novelty of the method is that it accounts for the specific input/output function of the particular simulation model at hand; i.e., the method is application-driven or customized.This customization is achieved through cross-validation and jackknifing.The new method is tested through two academic applications, which demonstrate that the method indeed gives better results than a design with a prefixed sample size.experimental design;simulation;interpolation;sampling;sensitivity analysis;metamodels

The Innovation Performance of Foreign Affiliates: Evidence from Dutch Manufacturing Firms

innovation, performance, manufacturing, firms

Customized Sequential Designs for Random Simulation Experiments: Kriging Metamodelling and Bootstrapping

This paper proposes a novel method to select an experimental design for interpolation in random simulation.(Though the paper focuses on Kriging, this method may also apply to other types of metamodels such as linear regression models.)Assuming that simulation requires much computer time, it is important to select a design with a small number of observations (or simulation runs).The proposed method is therefore sequential.Its novelty is that it accounts for the specific input/output behavior (or response function) of the particular simulation at hand; i.e., the method is customized or application-driven.A tool for this customization is bootstrapping, which enables the estimation of the variances of predictions for inputs not yet simulated.The new method is tested through the classic M/M/1 queueing simulation.For this simulation the novel design indeed gives better results than a Latin Hypercube Sampling (LHS) with a prefixed sample of the same size.simulation;statistical methods;bootstrap

On the Marshall - Jacobs Controversy It takes two to tango

The literature is inconclusive as to whether Marshallian specialization or Jacobian diversification externalities favour regional innovativeness. The specialization argument poses that regional specialization towards a particular industry improves innovativeness in that industry. Regional specialization allows for knowledge to spill over among similar firms. By contrast, the diversification thesis asserts that knowledge spills over between firms in different industries, causing diversified production structures to be more innovative. Building on an original database, we address this controversy for the Netherlands. We thereby advance on the literature by providing a two-level approach, at the region’s and the firm’s level. At the regional level, we compare specialized with diversified regions on numbers of accommodated innovators. At the firm level, we establish causalities between externalities and degree of innovativeness. The results suggest Marshallian externalities: specialized regions accommodate increased numbers of innovating firms and, consistently, incumbent firms’ innovativeness increase with regional specialization. Once the product has been launched, innovators in diversified Jacobian regions prove more successful in commercial terms than innovators in specialized Marshallian regions.Industrial clusters; innovation; knowledge externalities

Constrained Optimization in Simulation: A Novel Approach

This paper presents a novel heuristic for constrained optimization of random computer simulation models, in which one of the simulation outputs is selected as the objective to be minimized while the other outputs need to satisfy prespeci¯ed target values. Besides the simulation outputs, the simulation inputs must meet prespeci¯ed constraints including the constraint that the inputs be integer. The proposed heuristic combines (i) experimental design to specify the simulation input combinations, (ii) Kriging (also called spatial correlation mod- eling) to analyze the global simulation input/output data that result from this experimental design, and (iii) integer nonlinear programming to estimate the optimal solution from the Krig- ing metamodels. The heuristic is applied to an (s, S) inventory system and a realistic call-center simulation model, and compared with the popular commercial heuristic OptQuest embedded in the ARENA versions 11 and 12. These two applications show that the novel heuristic outper- forms OptQuest in terms of search speed (it moves faster towards high-quality solutions) and consistency of the solution quality.

Constrained optimization in simulation: a novel approach.

This paper presents a novel heuristic for constrained optimization of random computer simulation models, in which one of the simulation outputs is selected as the objective to be minimized while the other outputs need to satisfy prespeci¯ed target values. Besides the simulation outputs, the simulation inputs must meet prespeci¯ed constraints including the constraint that the inputs be integer. The proposed heuristic combines (i) experimental design to specify the simulation input combinations, (ii) Kriging (also called spatial correlation modeling) to analyze the global simulation input/output data that result from this experimental design, and (iii) integer nonlinear programming to estimate the optimal solution from the Kriging metamodels. The heuristic is applied to an (s, S) inventory system and a realistic call-center simulation model, and compared with the popular commercial heuristic OptQuest embedded in the ARENA versions 11 and 12. These two applications show that the novel heuristic outperforms OptQuest in terms of search speed (it moves faster towards high-quality solutions) and consistency of the solution quality.

Monotonicity-Preserving Bootstrapped Kriging Metamodels for Expensive Simulations

Kriging (Gaussian process, spatial correlation) metamodels approximate the Input/Output (I/O) functions implied by the underlying simulation models; such metamodels serve sensitivity analysis and optimization, especially for computationally expensive simulations. In practice, simulation analysts often know that the I/O function is monotonic. To obtain a Kriging metamodel that preserves this known shape, this article uses bootstrapping (or resampling). Parametric bootstrapping assuming normality may be used in deterministic simulation, but this article focuses on stochastic simulation (including discrete-event simulation) using distribution-free bootstrapping. In stochastic simulation, the analysts should simulate each input combination several times to obtain a more reliable average output per input combination. Nevertheless, this average still shows sampling variation, so the Kriging metamodel does not need to interpolate the average outputs. Bootstrapping provides a simple method for computing a noninterpolating Kriging model. This method may use standard Kriging software, such as the free Matlab toolbox called DACE. The method is illustrated through the M/M/1 simulation model with as outputs either the estimated mean or the estimated 90% quantile; both outputs are monotonic functions of the traffic rate, and have nonnormal distributions. The empirical results demonstrate that monotonicity-preserving bootstrapped Kriging may give higher probability of covering the true simulation output, without lengthening the confidence interval.Queues