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The use of models for detecting earnings management in the academic literature, using accrual and real manipulation, is commonplace. The purpose of the current study is to compare the power of these models in a United Kingdom (UK) sample of 19,424 firm-year observations during the period 1991–2018. The authors include artificially-induced manipulation of revenues and expenses between zero and ten percent of total assets to random samples of 500 firm-year observations within the full sample. The authors use two alternative samples, one with no reversal of manipulation (sample 1) and one with reversal in the following year (sample 2).

The authors include artificially induced manipulation of revenues and expenses between zero and ten percent of total assets to random samples of 500 firm-year observations within the full sample.

The authors find that real earnings manipulation models have lower power than accrual earnings manipulation models, when manipulating discretionary expenses and revenues. Furthermore, the real earnings manipulation model to detect overproduction has high misspecification, resulting in artificially inflating the power of the model. The authors examine an alternative model to detect discretionary expense manipulation that generates higher power than the Roychowdhury (2006) model. Modified real manipulation models (Srivastava, 2019) are used as robustness and the authors find these to be more misspecified in some cases but less in others. The authors extend the analysis to a setting in which earnings management is known to occur, i.e. around benchmark-beating and find consistent evidence of accrual and some forms of real manipulation in this sample using all models examined.

This study contributes to the literature by providing evidence of misspecification of currently used models to detect real accounts manipulation.

Based on the findings, the authors recommend caution in interpreting any findings when using these models in future research.

The findings address the earnings management literature, guided by the agency theory.

This paper attempts to develop a hybrid model using advanced data mining techniques for the detection of Stock Price Manipulation. The hybrid model detailed in this article elucidates the application of a Genetic Algorithm based Artificial Neural Network to classify stocks witnessing activities that are suggestive of potential manipulation.

Price, volume and volatility are used as the variables for this model to capture the characteristics of stocks. An empirical analysis of this model is carried out to evaluate its ability to predict stock price manipulation in one of the largest emerging markets – India, which has a large number of securities and significant trading volumes. Further, the article compares the performance of this hybrid model with a conventional standalone model based on Quadratic Discreminant Function (QDF).

Based on the results obtained, the superiority of the hybrid model over the conventional model in its ability to predict manipulation in stock prices has been established.

The classification by the proposed model is agnostic of the type of manipulation – action‐based, information‐based or trade‐based.

The market regulators can use these techniques to ensure that sufficient deterrents are in place to identify a manipulator in their market. This helps them carry out their primary function, namely, investor protection. These models will help effective monitoring for abnormal market activities and detect market manipulation.

Implementing this model at a regulator or SRO helps in strengthening the integrity and safety of the market. This strengthens investor confidence and hence participation, as the investors are made aware that the regulators implementing market manipulation detection techniques ensure that the markets they monitor are secure and protects investor interest.

This is the first time a hybrid model has been used to detect market manipulation.

The concept of modularity has gained considerable traction in technology studies as a way to conceive, describe, and innovate complex systems, such as product design or organizational structures. In the recent literature, technological modularity has often been intertwined with business model innovation, and scholarship has started investigating how modularity in technology affects changes in business models, both at the cognitive and activity system levels. Yet we still lack a theoretical definition of what modularity is in the business model domain. Business model innovation also encompasses different possibilities of modelling businesses, which are not clearly understood nor classified. We ask when, how, and if modularity theory can be extended to business models in order to enable effective and efficient modelling. We distinguish theoretically between modularity for technology and for business models, and investigate the key processes of modularization and manipulation. We introduce the basic operations of business modelling via modular operators adapted from the technological modularity domain, using iconic examples to develop an analogical reasoning between modularity in technology and in business models. Finally, we discuss opportunities for using modularity theory to foster the understanding of business models and modelling, and develop a challenging research agenda for future investigations.

Although software systems used to automate business processes have been becoming rather advanced, the existing practice of developing and modifying graphical process models in those software systems is still primitive: users have to manually add, change, or delete each node and arc piece by piece. Since such manual operations are typically tedious, time‐consuming, and prone to errors, it is desirable to develop an alternative approach. This paper aims to address this issue.

In this paper, a novel, human‐understandable process manipulation language (PML) for specifying operations (e.g. insertion, deletion, merging, and split) on process models is developed. A prototype system to demonstrate PML is also developed.

The paper finds that manipulation operations on process models can be standardized and, thus, can be facilitated and automated through using a structured language like PML.

PML can improve manipulation operations on process models over the existing manual approach in two aspects: first, using PML, users only need to specify what operations are to be performed on process models, and then a computer carries out specified operations as well as performs other routine operations (e.g. generating nodes and arcs). This feature minimizes user effort to deal with low‐level details on nodes and arcs. Second, using PML, users can systematically specify operations on process models, thus reducing arbitrary operations and problems in process models.

The purpose of this study is to examine the trade-off between accrual and real activity earnings manipulation by considering gender and punishment as one aspect of clawbacks.

To achieve the research objectives, experimental design research was used, involving 183 professional accountants in Indonesia. This was followed by interviews with board members of public companies in Indonesia.

After the adoption of clawbacks, the intention to manipulate accruals decreased more among women than among men. However, the possible unintended consequences of clawbacks, particularly an increase in real activity manipulation, did not differ between women and men.

There are still few studies that use experimental designs to examine the consequences of clawback. Our study is expected to provide a novel contribution to the literature on the consequences of clawbacks as we use an experimental method. Besides, previous research that tested the consequences of clawback, using both archival and experimental data, had not considered the gender aspect, thus prompting this study to fill the research gap related to the consequences of clawback adoption by including the gender variable.

In light of the challenge to attract skilled employees in times of dwindling labour supply, the purpose of this paper is to investigate how motivational person-job fit influences the preference for a job. Using a model based on prospect theory, person-job fit is operationalised on three motivational dimensions: extrinsic, enjoyment-based intrinsic, and prosocial intrinsic motivation. Public service motivation (PSM) is theoretically integrated into the model’s dimensions and conceptualised as a fuzzy subset thereof. Within the model, job seekers evaluate their fit with various jobs by comparing their personal motives to the jobs’ motivational incentives, compiling a preference order.

The model was tested using a completely randomised vignette experiment. In all, 102 master’s students had to indicate their preference among various randomly selected job vignettes. Certain vignettes presented to experimental group (EG) subjects were individually manipulated to provide either very good or very poor person-job fit. The manipulation’s effect on vignette preference was analysed using logistic regression.

EG subjects significantly preferred both well-fitting and random vignettes over misfitting ones. No significant preference was found comparing well-fitting and random vignettes. Coherent with prospect theory, the deterring effect of misfit appears to be stronger than the attraction effect of good fit, supporting the model.

Most previous research on motivational person-job fit focused on a single factor (e.g. PSM) when predicting job preference. This study, in contrast, incorporates multiple motivational dimensions, capturing that individuals usually pursue mixed motives.

The aim of this paper is to present the non‐model‐based multiple impedance control (NMIC) law for object manipulation tasks, which can be implemented with reasonable limited on‐line computations.

The multiple impedance control (MIC) is a model‐based algorithm that enforces a designated impedance on all cooperating manipulators, and the manipulated object itself. In this paper, the MIC law is modified to be implemented without using system dynamics. Therefore, this modified MIC law is a quick and more realistic algorithm for implementation in cooperating robotic systems, and so is called NMIC. Developing the NMIC law, error analysis shows that under the NMIC law all participating manipulators, and the manipulated object exhibit the same designated impedance behavior. Next, the proposed NMIC law is applied on an object manipulation task with three cooperating PUMA 560 manipulators while two of them are equipped with a remote compliant centre.

Developing the NMIC law, error analysis shows that under the NMIC law all participating manipulators, and the manipulated object exhibit the same designated impedance behavior. The obtained results show good tracking performance even in the presence of impacts due to contact with an obstacle, and also system flexibility.

The obtained results show good tracking performance even in the presence of impacts due to contact with an obstacle, and also system flexibility. These results reveal the merits of NMIC law as a non‐model‐based algorithm for object manipulation tasks, which can be implemented with reasonable limited on‐line computations.

The proposed NMIC law is applied on an object manipulation task with three cooperating PUMA 560 manipulators while two of them are equipped with a remote compliant centre.

The purpose of this paper is to study the control strategy of transition mode of the stopped-rotor (SR) aircraft under the condition of redundant control and complex aerodynamic characteristics.

This paper first proposes a transition strategy for the conversion between helicopter mode and fixed-wing mode. Then, aiming at the redundancy of the two control systems in the transition process, a control model is proposed, which greatly simplifies the control in conversion mode. Then, to facilitate the design of the control system, the Takagi-Sugeno model of the SR aircraft in transition mode is established. Finally, an explicit model tracking and tuning parameter stability augmentation control system is designed, so that the SR aircraft has a good stability during the transition process. Then, the outer loop control system of transition flight is designed.

The simulation results show that the control strategy proposed in this paper can realize the mode conversion well. It lays a solid foundation for the subsequent engineering flight test for the SR aircraft.

The work done in this paper provides ideas and methods for the flight control system design of SR aircraft in transition mode. The method of designing control model to solve the coordination of redundant control system is also applicable for other multimode aircraft, which provides a simple and convenient method for the multimode aircraft control.