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The aim of this paper is to research the effectiveness of different forms of performance management and to trace the factors which influence these effectiveness. In order to better emphasize the context dimensions, it also aims to study two municipal processes in the Dutch public sector, i.e. treatment of building permits and the establishment of environmental policy papers.

The study analyses 57 Dutch municipalities on performance management.

The research shows that management should not intervene nor should it focus on the process itself, contrary to what is normally thought and done. This study also points out that the type of management on output is more important for the effectiveness than the context of the processes. The conclusion may be drawn that process control will lead to less effectiveness in almost all cases. Effective output management should be confined to a combination of input and output control. Although the general management has the tendency to engage in the way processes are conducted, it will need to resist this tendency as much as possible. In addition, intermediate control is ineffective. In the investigated processes more capacity was needed when the general management exercised intermediate control, regardless of the mode. This conclusion is at odds with the widely endorsed desirability and need for intermediate control (monitoring). Control ex ante, in contrast, increases effectiveness in every case. The impression arises that opting for less control (only ex ante and not on processes) makes the type of management more effective.

Output management will have to take the context into account, but also give decentralized managers sufficient degrees of freedom. In order to decentralize adequately, the correct type of management will need to be chosen. Although most authors endorse the importance of context and believe the possibility of output management to be almost completely dependent on this context, this study shows that the effectiveness of output management is virtually entirely dictated by the chosen type of management, no matter the context. The type of management allocating as much freedom to decentralized management as possible, offers the best basis for effective action. Paradoxically, general management could maximize its effectiveness by interfering as little as possible with the process at stake.

This paper aims to propose output feedback-based control algorithms for the flight control system of a scaled, un-crewed helicopter in its hover flight mode.

The proposed control schemes are based on H_∞ control and composite nonlinear control. The gains of the output feedback controllers are obtained as the solution of a set of linear matrix inequalities (LMIs).

In the proposed schemes, the finite-time convergence of system states to trim condition is achieved with minimum deviation from the steady-state. As the proposed composite nonlinear output feedback design improves the transient response, it is well suited for a scaled helicopter flight. The use of measured output vector instead of the state vector or its estimate for feedback provides a simple control structure and eliminates the need for an observer in real-time application. The proposed control strategies are relevant to situations in which a simple controller is essential due to economic factors, reliability and hardware implementation constraints.

The proposed control strategies are relevant to situations in which a simple controller is essential due to economic factors, reliability and hardware implementation constraints. They also have significance in applications where the number of measurement quantities needs to be minimized such as in a fully functional rotor-craft unmanned aerial vehicle.

The developed output feedback control algorithms can be used in small-scale helicopters for numerous civilian and military applications.

This work addresses the LMI-based formulation and solution of an output feedback controller for a hovering un-crewed helicopter. The stability and robustness of the closed-loop system are proved mathematically and the performance of the proposed schemes is compared with an existing strategy via simulation studies.

This study proposes a predictive neural network model reference decoupling control method for the coupling problem between the leg joints of hydraulic quadruped robots, and verifies its decoupling effect..

The machine–hydraulic cross-linking coupling is studied as the coupling behavior of the hydraulically driven quadruped robot, and the mechanical dynamics coupling force of the robot system is controlled as the disturbance force of the hydraulic system through the Jacobian matrix transformation. According to the principle of multivariable decoupling, a prediction-based neural network model reference decoupling control method is proposed; each module of the control algorithm is designed one by one, and the stability of the system is analyzed by the Lyapunov stability theorem.

The simulation and experimental research on the robot joint decoupling control method is carried out, and the prediction-based neural network model reference decoupling control method is compared with the decoupling control method without any decoupling control method. The results show that taking the coupling effect experiment between the hip joint and knee joint as an example, after using the predictive neural network model reference decoupling control method, the phase lag of the hip joint response line was reduced from 20.3° to 14.8°, the amplitude attenuation was reduced from 1.82% to 0.21%, the maximum error of the knee joint coupling line was reduced from 0.67 mm to 0.16 mm and the coupling effect between the hip joint and knee joint was reduced from 1.9% to 0.48%, achieving good decoupling.

The prediction-based neural network model reference decoupling control method proposed in this paper can use the neural network model to predict the next output of the system according to the input and output. Finally, the weights of the neural network are corrected online according to the predicted output and the given reference output, so that the optimization index of the neural network decoupling controller is extremely small, and the purpose of decoupling control is achieved.

Prior research has emphasised the importance of the early phases of construction projects, as well as the difficulties of procuring engineering services – especially due to the uncertainties. Despite that, studies on the public procurement of engineering services are scarce. Although scholars have shown that uncertainty may affect the choice of control modes, the level of uncertainty that characterises services is not addressed by the two task characteristics: knowledge of the transformation process and output measurability. The purpose is to investigate organisational control in public procurement of engineering services.

The existing control model was adjusted in this study by conceptually adding uncertainty as a third aspect to the two task characteristics. A single case study of the Swedish Transport Administration was used. The empirical data, comprising 14 interviews with managers from the client and engineering consulting companies, were analysed using flexible pattern matching and visual mapping approaches and then illustrated using the model.

The public client did not base its choice of control modes on uncertainty, but rather on the other two task characteristics. Consequently, the service providers argued that the chosen control modes reduced their creativity, increased their financial risks and caused unclear responsibilities. This study therefore shows that uncertainty is an important factor to consider in the choice of control modes, both from a theoretical perspective and from the service providers' point of view. The developed model may therefore be useful for researchers as well as practitioners.

This study is the first attempt to add uncertainty as a task characteristic when choosing control modes. The results contribute to the scarce control literature regarding the procurement of engineering services for construction projects and the procurement of other services with high uncertainty.

The purpose of this paper is to derive the output predictor for a stationary normal process with rational spectral density and linear stochastic discrete-time state-space model, respectively, as the output predictor is very important in model predictive control. The derivations are only dependent on matrix operations. Based on the output predictor, one quadratic programming problem is constructed to achieve the goal of subspace predictive control. Then an improved ellipsoid optimization algorithm is proposed to solve the optimal control input and the complexity analysis of this improved ellipsoid optimization algorithm is also given to complete the previous work. Finally, by the example of the helicopter, the efficiency of the proposed control strategy can be easily realized.

First, a stationary normal process with rational spectral density and one stochastic discrete-time state-space model is described. Second, the output predictors for these two forms are derived, respectively, and the derivation processes are dependent on the Diophantine equation and some basic matrix operations. Third, after inserting these two output predictors into the cost function of predictive control, the control input can be solved by using the improved ellipsoid optimization algorithm and the complexity analysis corresponding to this improved ellipsoid optimization algorithm is also provided.

Subspace predictive control can not only enable automatically tune the parameters in predictive control but also avoids many steps in classical linear Gaussian control. It means that subspace predictive control is independent of any prior knowledge of the controller. An improved ellipsoid optimization algorithm is used to solve the optimal control input and the complexity analysis of this algorithm is also given.

To the best knowledge of the authors, this is the first attempt at deriving the output predictors for stationary normal processes with rational spectral density and one stochastic discrete-time state-space model. Then, the derivation processes are dependent on the Diophantine equation and some basic matrix operations. The complexity analysis corresponding to this improved ellipsoid optimization algorithm is analyzed.

The purpose of this paper is to describe a generic method and tool for assessing the reliability and robustness of the product development process.

By extending the integrated definition for function modelling (IDEF0)‐based modelling approach, the paper demonstrates how to calculate the effectiveness of the process and the quality of the process output based on the quality of inputs, the controls and the tools used within the process. To illustrate and validate the proposed approach, it is applied to a case study of a product development process incorporating incomplete, fuzzy and uncertain inputs and resources.

Demonstrates the effectiveness of the tool in providing a quantified assessment of the process as well as its ability to identify those critical areas which will yield a significant improvement in the outcome of the product development process.

The technique is a valuable tool to assess the robustness and sensitivity of the process to changes in the quality of inputs, controls and tools, and can be integrated into businesses processes and management systems, and used as a tool to support continuous business and manufacturing decisions at any point of time.

E-commerce content platforms are a typical type of multi-sided platform that combines transactions with social media platforms. To solve the managerial dilemma of balancing the tension between control and autonomy of influencers' output performance, this study aims to investigate how exercising output controls through performance rewards and performance punishments impact the quantity and quality of influencers' content generation.

Choosing JD WeChat Shopping Circle as the research context and leveraging the introduction of a double commission subsidy policy and a removal policy as quasi-natural experiments, this study applied the difference-in-differences (DID) method to empirically test hypotheses.

Performance rewards incentivize influencers to generate high-quality content, but such incentivizing effects attenuate over time. Performance punishment drives influencers to generate expected pieces of high-quality content, and such safeguarding effects accentuate over time.

This study proposes output controls as an important form of governance mechanism in multi-sided platforms and substantiates how rewards and punishments as two facets of incentives affect complementors' behaviors. In addition, by distinguishing performance-contingent rewards from completion-contingent rewards, this study helps resolve the mixed findings on the effects of monetary rewards in the user-generated content (UGC) literature.

Considers an application of adaptive control policy to dynamic input‐output systems of Japanese large‐scale industrial (primary, secondary and tertiary) sectors by neural networks. The adaptive control policy has three steps. The first is to obtain the optimal control policy such that the minimization of the weighted sum of the squared deviation between the actual targets and the desired subject to econometric models is achieved. The second is to determine the optimal outputs for each industrial sector through dynamic input‐output system under the optimal control policies. The third is to obtain the network outputs by neural network algorithm through the controlled output equations derived from DIO system. We consider what affects the outputs if the optimal control policy was adopted, and how the change of industrial structure has occurred after the bubble burst in 1990s in Japan during 1985 through 1993, and we predict the future of the industries up to 2010 by using DIO linked to the final demand econometric models of the Japanese industrial sectors by simulation.

In many countries, governmental organizations decentralized their organization structures, leading to an increased relevance of performance information. This paper identifies two approaches to measure performance in the not‐for‐profit sector: an output‐focused and a throughput‐focused approach. Furthermore, the paper sets a contingency framework concerning the applicability of these two approaches. The two most important contingent variables, i.e., whether output can be identified and whether activities are repetitive, determine the possibilities to apply the two approaches. If it is possible to apply both approaches, the ambiguity of organizational objectives, the hierarchical position of information users and the relevance of efficiency and quality are additional contingent variables that influence the approach to assess performance. Finally, the paper identifies how the approach to measure performance relates to management control types. If none of the two approaches is applicable, the organization has to rely on a management control type without an explicit system to measure performance, i.e., political or judgmental control. Routine or trial‐and‐error control fit with both approaches to measure performance. Expert and intuitive control are control types that rely on output‐based approaches to measure performance.

This study sets out to empirically investigate the effect of salesforce output control on perceived job autonomy, customer-oriented selling behaviours and sales performance.

Data are gathered from 704 salespeople and their visiting customers in Ghana. The hypotheses are tested using the structural equations modelling technique (SEM).

According to the findings of the study, output control has a significant and positive impact on perceived job autonomy. It also discovers that perceived job autonomy improves both customer-directed problem solving and adaptive selling behaviours. Furthermore, the study finds that customer-directed problem solving and adaptive selling behaviours both improve sales performance. Moreover, the study uncovers that perceived job autonomy mediates the relationship between output control and customer-oriented selling behaviours, whereas both customer-oriented selling behaviours mediate the relationship between perceived job autonomy and sales performance.

The current study provides both practical and theoretical insights into salesforce control dynamics, job autonomy, adaptive selling behaviour, customer-directed problem-solving behaviour and sales performance. The findings have important implications for sales organisations because they can assist sales managers in determining the best type of salesforce control systems to deploy and highlight the strategic role job autonomy plays in enhancing sales performance.

The current study shows how output control can influence salespeople's perceived job autonomy, adaptive selling and customer-directed problem-solving behaviours, and how these can improve sales performance.