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The purpose of this research is to explore how an innovation organization's orientation toward the digital economy influences its position within R&D networks. By using institutional theory, the study aims to forecast market changes and understand how organizations can navigate the digital economy to secure essential resources and minimize dependencies.

This study employs a longitudinal panel dataset with 11,763 entries from 1995 to 2018, covering strategic emerging industries in China to analyze the impact of digital economy orientation on R&D networks. Utilizing advanced statistical models, it assesses the role of the legal environment as a moderator. This methodological approach facilitates a robust examination of the nexus between digital orientation and network dynamics within the context of institutional theory.

The study reveals that an organization's digital economy orientation enhances its centrality in R&D networks but reduces its control over structural holes. The legal environment negatively moderates the impact of digital economy orientation on network centrality, while positively influencing the relationship with network structural holes. These findings offer new insights into how institutional forces shape the strategic positioning of organizations in R&D collaborations.

This research offers a fresh perspective on the digital economy's impact on R&D networks, particularly in the Industry-University-Research (IUR) context. It extends the discourse by integrating institutional theory to elucidate the adaptation of R&D networks in the digital era. By identifying the legal environment as a moderator, the study provides a nuanced understanding of the strategic alignment within networks influenced by digital advancements. The unique focus on China's R&D networks presents a valuable contribution to the global discussion on digital integration and innovation ecosystems, highlighting the intersection of policy, academia, and industry in shaping research and development trajectories.

Three-way decision (3WD) and probabilistic rough sets (PRSs) are theoretical tools capable of simulating humans' multi-level and multi-perspective thinking modes in the field of decision-making. They are proposed to assist decision-makers in better managing incomplete or imprecise information under conditions of uncertainty or fuzziness. However, it is easy to cause decision losses and the personal thresholds of decision-makers cannot be taken into account. To solve this problem, this paper combines picture fuzzy (PF) multi-granularity (MG) with 3WD and establishes the notion of PF MG 3WD.

An effective incomplete model based on PF MG 3WD is designed in this paper. First, the form of PF MG incomplete information systems (IISs) is established to reasonably record the uncertain information. On this basis, the PF conditional probability is established by using PF similarity relations, and the concept of adjustable PF MG PRSs is proposed by using the PF conditional probability to fuse data. Then, a comprehensive PF multi-attribute group decision-making (MAGDM) scheme is formed by the adjustable PF MG PRSs and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. Finally, an actual breast cancer data set is used to reveal the validity of the constructed method.

The experimental results confirm the effectiveness of PF MG 3WD in predicting breast cancer. Compared with existing models, PF MG 3WD has better robustness and generalization performance. This is mainly due to the incomplete PF MG 3WD proposed in this paper, which effectively reduces the influence of unreasonable outliers and threshold settings.

The model employs the VIKOR method for optimal granularity selections, which takes into account both group utility maximization and individual regret minimization, while incorporating decision-makers' subjective preferences as well. This ensures that the experiment maintains higher exclusion stability and reliability, enhancing the robustness of the decision results.

Our study investigates the influence of peer performance on the earnings management decisions within publicly traded Indian companies. There is mixed evidence in the literature, with the impact of peer performance on earnings management in emerging markets being notably underexplored. Additionally, the study explores whether robust corporate governance mechanisms can mitigate earnings management practices. Our study offers policy insights into these areas.

Our study used a longitudinal panel dataset from 2011 to 2020, utilising idiosyncratic returns of peer firms as an external measure of peer performance. This approach is further enhanced by the usage of alternative discretionary accrual metrics, which could be a robust measure for both market leaders and followers.

Our study employs two distinct methods, accrual and real earnings management, to assess earnings management. The findings indicate that peer performance triggers earnings management within peer groups, showcasing managerial opportunism in financial reporting to align with peer achievements. Furthermore, the evidence suggests that robust corporate governance effectively curtails earnings management, especially in industries where peer influence is significant.

Our study offers valuable insights for regulators, highlighting that enhancing the institutional framework with stringent corporate governance mechanisms can effectively reduce earnings management in companies within emerging markets.

The paper is a novel attempt in emerging markets to show that managers engage in opportunistic reporting to align with the performance of their peers and that governance strategies effectively mitigate these practices in such markets.

The purpose of this paper is to clarify the ontological assumptions regarding the concept of agency and sociality within business networks in the Industrial Marketing and Purchasing Group (IMP) research by refining these assumptions with a relational sociological (RS) perspective. This paper reinforces the robustness of the actors-resources-activities (ARA) model with an in-depth investigation of the actor dimension, where local interactions between interdependent individuals play a central role in building common futures within business networks through organisational reflexivity.

This conceptual paper investigates the social ontology of research. It challenges the implicit assumptions of IMP research regarding agency and sociality within business networks with a problematisation strategy (Sandberg and Alvesson, 2011). Combining IMP views on agency with the RS perspective, it sets this combined framework as an alternative for the analysis of sustainability and ethics within business networks.

Combining IMP research and an RS perspective allows us to extend the knowledge of sociality within business networks, highlighting the centrality of meaning sharing in the process of network change. By focusing on symbolic interaction processes, an RS perspective contributes to a deeper theoretical understanding of the relationship between local communication and business network patterns. Combined with an IMP perspective on agency, it provides researchers with an alternative conceptual framework for examining sustainability by considering ethics and leadership dialectically.

RS is still an emerging stream within sociology, characterised by diverse views. Not all relational sociologists, as scientists, feel obliged to engage with sustainability research. Thus, the paper is a two-sided invitation to IMP researchers and relational sociologists to delve into the adaptation processes in business networks in highly uncertain environments.

RS focusing on the centrality of communication in local interactions, business network researchers can show that organisational leaders are not the ones with a charismatic vision isolated from any natural and social environment; rather, they are the people with “the capacity to assist the group to continue acting ethically, creatively and courageously in the unknown” (Stacey,2013).

Adopting an RS perspective on agency in business networks can help managers and researchers determine how business networks can be managed in a more sustainable way. Combined with a dialectical and processual understanding of ethics, the IMP-RS perspective emphasises day-to-day local communication practices within and between organisations that challenges microeconomic views on nature, strategy, ethics and leadership. This paper thus places the social at the centre of sustainability approaches.

From an RS perspective, business networks are analysed as patterns of interactions between many organisations and individuals. The value of this conceptual paper is in showing that change within business networks is negotiated through local interactions and symbolic communication between individuals. Thus, it suggests the need to combine the individual and the organisational levels to analyse agency within business networks and to examine the adaptation of business networks to sustainability.

Reynolds-averaged Navier–Stokes (RANS) models often perform poorly in shock/turbulence interaction regions, resulting in excessive wall heat load and incorrect representation of the separation length in shockwave/turbulent boundary layer interactions. The authors suggest that this can be traced back to inadequate numerical treatment of the inviscid fluxes. The purpose of this study is an extension to the well-known Harten, Lax, van Leer, Einfeldt (HLLE) Riemann solver to overcome this issue.

It explicitly takes into account the broadening of waves due to the averaging procedure, which adds numerical dissipation and reduces excessive turbulence production across shocks. The scheme is derived based on the HLLE equations, and it is tested against three numerical experiments.

Sod’s shock tube case shows that the scheme succeeds in reducing turbulence amplification across shocks. A shock-free turbulent flat plate boundary layer indicates that smooth flow at moderate turbulence intensity is largely unaffected by the scheme. A shock/turbulent boundary layer interaction case with higher turbulence intensity shows that the added numerical dissipation can, however, impair the wall heat flux distribution.

The proposed scheme is motivated by implicit large eddy simulations that use numerical dissipation as subgrid-scale model. Introducing physical aspects of turbulence into the numerical treatment for RANS simulations is a novel approach.

The purpose of this paper is mainly to develop the adjoint method within the method of magnetic moment (MMM) and thus, to provide an efficient new way to solve topology optimization problems in magnetostatic to design 3D-magnetic circuits.

First, the MMM is recalled and the optimization design problem is reformulated as a partial derivative equation-constrained optimization problem where the constraint is the Maxwell equation in magnetostatic. From the Karush–Khun–Tucker optimality conditions, a new problem is derived which depends on a Lagrangian parameter. This problem is called the adjoint problem and the Lagrangian parameter is called the adjoint parameter. Thus, solving the direct and the adjoint problems, the values of the objective function as well as its gradient can be efficiently obtained. To obtain a topology optimization code, a semi isotropic material with penalization (SIMP) relaxed-penalization approach associated with an optimization based on gradient descent steps has been developed and used.

In this paper, the authors provide theoretical results which make it possible to compute the gradient via the continuous adjoint of the MMMs. A code was developed and it was validated by comparing it with a finite difference method. Thus, a topology optimization code associating this adjoint based gradient computations and SIMP penalization technique was developed and its efficiency was shown by solving a 3D design problem in magnetostatic.

This research is limited to the design of systems in magnetostatic using the linearity of the materials. The simple examples, the authors provided, are just done to validate our theoretical results and some extensions of our topology optimization code have to be done to solve more interesting design cases.

The problem of design is a 3D magnetic circuit. The 2D optimization problems are well known and several methods of resolution have been introduced, but rare are the problems using the adjoint method in 3D. Moreover, the association with the MMMs has never been treated yet. The authors show in this paper that this association could provide gains in CPU time.

The study delves into the influence of wear cycles on these parameters. The purpose of this paper is to identify characteristic patterns of σ_RS and ε_PEEQ that discern varying wear situations, thereby contributing to the enrichment of wear theory. Furthermore, the findings serve as a foundational basis for nondestructive and in situ wear detection methodologies, such as nonlinear ultrasonic detection, known for its sensitivity to σ_RS and ε_PEEQ.

This paper elucidates the wear mechanism through the lens of residual stress (σ_RS) and plastic deformation within distinct fretting regimes, using a two-dimensional cylindrical/flat contact model. It specifically explores the impact of the displacement amplitude and cycles on the distribution of residual stress and equivalent plastic strain (ε_PEEQ) in both gross slip regime and partial slip regimes.

Therefore, when surface observation of wear is challenging, detecting the σ_RS trend at the center/edge, region width and ε_PEEQ distribution, as well as the maximum σ_RS distribution along the depth, proves effective in distinguishing wear situations (partial or gross slip regimes). However, discerning wear situations based on ε_PEEQ along the depth direction remains challenging. Moreover, in the gross slip regime, using σ_RS distribution or ε_PEEQ along the width direction rather than the depth direction can effectively provide feedback on cycles and wear range.

This work introduces a novel perspective for investigating wear theory through the distribution of residual stress (σ_RS) and equivalent plastic strain (ε_PEEQ). It presents a feasible detection theory for wear situations using nondestructive and in situ methods, such as nonlinear ultrasonic detection, which is sensitive to σ_RS and ε_PEEQ.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0005/

This study aims to enhance the prediction accuracy of hydroelectricity consumption in China, with a focus on addressing the challenges posed by complex and nonlinear characteristics of the data. A novel grey multivariate prediction model with structural optimization is proposed to overcome the limitations of existing grey forecasting methods.

This paper innovatively introduces fractional order and nonlinear parameter terms to develop a novel fractional multivariate grey prediction model based on the NSGM(1, N) model. The Particle Swarm Optimization algorithm is then utilized to compute the model’s hyperparameters. Subsequently, the proposed model is applied to forecast China’s hydroelectricity consumption and is compared with other models for analysis.

Theoretical derivation results demonstrate that the new model has good compatibility. Empirical results indicate that the FMGM(1, N, a) model outperforms other models in predicting the hydroelectricity consumption of China. This demonstrates the model’s effectiveness in handling complex and nonlinear data, emphasizing its practical applicability.

This paper introduces a scientific and efficient method for forecasting hydroelectricity consumption in China, particularly when confronted with complexity and nonlinearity. The predicted results can provide a solid support for China’s hydroelectricity resource development scheduling and planning.

The primary contribution of this paper is to propose a novel fractional multivariate grey prediction model that can handle nonlinear and complex series more effectively.

In order to solve the decision-making problem that the attributive weight and attributive value are both interval grey numbers, this paper tries to construct a multi-attribute grey decision-making model based on generalized greyness of interval grey number.

Firstly, according to the nature of the generalized gresness of interval grey number, the generalized weighted greyness distance between interval grey numbers is given, and the transformation relationship between greyness distance and real number distance is analyzed. Then according to the objective function that the square sum of generalized weighted greyness distances from the decision scheme to the best scheme and the worst scheme is the minimum, a multi-attribute grey decision-making model is constructed, and the simplified form of the model is given. Finally, the grey decision-making model proposed in this paper is applied to the evaluation of technological innovation capability of 6 provinces in China to verify the effectiveness of the model.

The results show that the grey decision-making model proposed in this paper has a strict mathematical foundation, clear physical meaning, simple calculation and easy programming. The application example shows that the grey decision model in this paper is feasible and effective. The research results not only enrich the grey system theory, but also provide a new way for the decision-making problem that the attributive weights and attributive values are interval grey numbers.

The decision-making model proposed in this paper does not need to seek the optimal solution of the attributive weight and the attributive value, and can save the decision-making labor and capital investment. The model in this paper is also suitable for the decision-making problem that deals with the coexistence of interval grey numbers and real numbers.

The paper succeeds in realizing the multi-attribute grey decision-making model based on generalized gresness and its simplified forms, which provide a new method for grey decision analysis.

Knowing financial and economic information beforehand benefits in planning and developing policies for every country especially for a developing country like Thailand and for other Asian countries. Unfortunately, missing data or non-response plays an essential role in many areas of studies including finance and economics. Eradication of missing data in a proper way before further analysis can gain remarkable outcomes and can be effective for planning policies. This review on the generalized regression estimators for population total can be applied to financial, economic and other data when missing data are present.

The generalized regression estimators for estimating population total, including the variance estimators under unequal probability sampling without replacement with missing data are explored under the reverse framework. Applications to financial and economic data in Thailand are also reviewed.

The review of literatures related to the proposed estimator shows the best performance, giving smaller variances in all scenarios.

The generalized regression estimators can assist in estimating financial and economic data that contain missing values with different missing mechanisms and can be used in other applications which help gain more superior estimators.