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Microfoundations have become an effective approach for capability scholars to explore the heterogeneity of organizational results. Since the early pioneering work of scholars such as Felin and Foss, the microfoundations of strategic organization had not been extensively studied until 2010. The theoretical and empirical literature associated with the microfoundations of dynamic capabilities has developed rapidly. However, the diversity and fragmentation of micro-elements lead to a lack of a relatively consistent understanding of microfoundations, the study purpose is to aggregate the associated scattered terminology into a cohesive discussion.

A systematic literature review was conducted on research papers published between 2000 and 2022 using a hybrid methodology that included bibliometric and content analysis.

The authors suggest that this line of research can be divided into three stages. The study further develop a framework delineating the main components and mechanism involved in the microfoundations of dynamic capabilities, which in turn help us distill research gaps and opportunities for future work.

The authors construct a framework that can serve as a coherent research platform for further knowledge development. In the framework, the authors highlight that the research of group constructs, culture and leadership, data-driven topics are valuable for our understanding of the microfoundations of dynamics capabilities.

This study proposes that the three core underpinnings of dynamic managerial capabilities (DMCs) – managerial cognition, managerial human capital and managerial social capital – represent individual-level micro-foundations that influence corporate social responsibility (CSR). It further explores the interaction mechanism between the three underpinnings in influencing CSR, and their influence depends on the technological turbulence caused by big-data-related technologies.

This study uses a quantitative research method and partial least squares structural equation modelling (PLS-SEM) to test the relationship between latent factors based on a sample of 270 Chinese top managers.

The three core underpinnings of DMCs are positively related to CSR. Managerial human capital and managerial social capital mediate the relationship between managerial cognition and CSR. Technological turbulence's moderating effects are also tested. Specifically, technological turbulence amplifies the positive relationship between managerial cognition, managerial human capital and CSR but negatively moderates the relationship between managerial social capital and CSR.

Why are some firms more willing to participate in CSR than others mainly depend on the fact that the actual participants of CSR are the top managers who formulate strategies and implement CSR plans. This study, grounded in the DMCs framework and the upper echelons perspective, is arguably the first to link DMCs' three core underpinnings and CSR, and further explore the multiple drivers' mechanisms and boundary conditions. This study contributes to individual micro-foundation of CSR literature, and advances the understanding of whether and how top managers influence CSR engagement.

Cause-related marketing (CRM) focuses on the use of marketing tools to publicize a firm’s corporate social responsibility (CSR) activities. Drawing on legitimacy theory, the purpose of this paper is to investigate the impact of CRM-led CSR in international business-to-business (B2B) markets. In particular, the authors examine the relationship between supplier CRM-led philanthropic CSR reputation and foreign customer business engagement in an international B2B setting. The authors also test how the foreign customer’s host-country sustainable development level moderates this relationship.

The authors collect and analyze dyadic data from multiple sources including: dyadic data from a supplier and its 90 foreign customers; the supplier’s internal company records; and publically available data.

The authors find that supplier CRM-led philanthropic CSR reputation positively affects foreign customer business engagement. Furthermore, the authors find that this positive relationship is stronger when host-country environments are characterized by achieving higher level of environmental well-being development. In contrast, this positive relationship is weaker when the foreign customer host-country environment is characterized by achieving higher level of economic well-being development.

The authors examine that impacts of CRM-led CSR in international B2B markets and differentiate the contingent roles of foreign customer host-country sustainable development in moderating such impacts.

In contrast to existing studies, this paper aims to propose that digital transformation does not depend on a single condition; rather, it depends on the interaction between internal and external factors of a firm. Therefore, the aim of this paper is to examine the effect of a combination of internal and external factors on a firm's digital transformation intention.

An empirical analysis on a sample of 112 Chinese small- and medium-sized firms was conducted by applying smart-PLS and fuzzy set qualitative comparative analysis (fsQCA).

The results of smart PLS show that external pressures (institutional and market pressures) and human capital have a positive impact on corporate digital transformation intentions. From a combination perspective, the results of the fsQCA show that there are five causal conditions that lead to high digital transformation intention. In contrast to the net effect, the results of fsQCA show that different combinations of states of internal (human capital, organizational culture and technological capital) and external elements (institutional and market pressures) of the firm are likely to stimulate digital transformation intention.

This study provides empirically based insights into firms' digital transformation intentions and advances the current understanding of the drivers and inhibitors of digital transformation. Unlike most current research, which tends to focus on the net effect of factors influencing the digital transformation of enterprises, this study focuses on identifying the core elements influencing enterprises' digital transformation intention, especially the joint effect of different factors, both internal and external to the enterprise. The combined SEM and fsQCA findings of this paper not only enrich the existing theories on digital transformation but also have high value in guiding the digital transformation of firms.

The purpose of this study is to explore the impact of service quality on the formation of destination brand equity through customer satisfaction at a winery, from the perspective of Chinese wine tourists.

This study utilized a survey research design. A convenience sample of 311 visitors to a major winery located in Yantai, China, was surveyed, and 265 useable questionnaires were analyzed. To analyze the data, the study used partial least squares structural equation modeling (PLS-SEM).

The results of the study reveal that service quality at a winery is a significant determinant of winery satisfaction among Chinese wine tourists, which in turn affects the brand equity of a wine tourism destination.

This study contributes to the growing body of literature focusing on identity-based branding in the context of wine tourism. As such, this study brings together knowledge of a place branding dimension (i.e. destination brand equity), satisfaction and tourism experience at a winery.

The results suggest that the road to favorable assessments of a wine destination brand (macro level) go through a satisfying experience at a winery (micro level). Therefore, the need to co-create the wine experience through various stakeholders' involvement is crucial for the success of wine tourism.

Extant wine studies often highlight western wine tourists' behavior and examine central behavioral constructs such as winery service quality and satisfaction. This study extends previous research by: (1) investigating the issue from Chinese wine tourists' perspective and (2) integrating the destination brand equity of a wine region to current investigations that commonly focus on the service quality of a winery and wine tourists' satisfaction.

A binary-tree subdivision method (BTSM) for numerical evaluation of weakly singular integrals with discontinuous kernel in the three-dimensional (3D) boundary element method (BEM) is presented in this paper.

In this method, the singular boundary element is split into two sub-elements and subdivided recursively until the termination criterion is met and the subdivision is stopped. Then, the source point is surrounded by one or more spherical cavities determined by the discontinuous kernel function. The sub-elements located in spherical cavities will be eliminated, and the regular triangular or rectangle elements are employed to fill the spherical cavities.

With the proposed method, the obtained sub-elements are automatically refined as they approach the source point, and they are “good” in shape and size for standard Gaussian quadrature. Thus, the proposed method can be used to evaluate singular integrals owing discontinuous kernel function accurately for cases of different element shapes and various source point locations.

Numerical examples show that the BTSM is suitable for planar and curved elements of arbitrary regular or irregular shape at various source point locations, and the results have much better accuracy and robustness than conventional subdivision method (CSM) when the kernel function is discontinuous.

For many pattern recognition problems, the relation between the sample vectors and the class labels are known during the data acquisition procedure. However, how to find the useful rules or knowledge hidden in the data is very important and challengeable. Rule extraction methods are very useful in mining the important and heuristic knowledge hidden in the original high-dimensional data. It can help us to construct predictive models with few attributes of the data so as to provide valuable model interpretability and less training times.

In this paper, a novel rule extraction method with the application of biclustering algorithm is proposed.

To choose the most significant biclusters from the huge number of detected biclusters, a specially modified information entropy calculation method is also provided. It will be shown that all of the important knowledge is in practice hidden in these biclusters.

The novelty of the new method lies in the detected biclusters can be conveniently translated into if-then rules. It provides an intuitively explainable and comprehensive approach to extract rules from high-dimensional data while keeping high classification accuracy.

The purpose of this paper is to predict the effect of bra pad specifications on breast deformation during jumping using a finite element (FE) method. Breast deformation is a key concern for women during exercise and can be effectively controlled with sports bras. In most studies, the deformation of breasts when wearing a sports bra is measured using motion capture devices to judge their effectiveness. However, the operation of such devices is highly complex and time-consuming. Computer-aided technology is an efficient way to simulate these experiments.

In this study, the breast model was obtained using three-dimensional (3D) scanning. Assembling models were obtained for FE analysis using reverse engineering and computer-aided design (CAD) software. The breast deformation results were obtained by completing pre-processing, solving and post-processing in the FE simulation software. To extend the application of these models, pads of different sizes and thicknesses within the bra were constructed to simulate the effect of pads on breast deformation.

The calculated root mean square errors were <1%, which indicated good agreement between the FE and experimental data in all the models. Nipple deformation was always the largest in most models. The smallest deformation occurred at the superior position of breasts in all models. In addition, larger pads were not effective in reducing breast deformation; however, thicker pads were.

The method developed in this study provides an effective way to predict breast deformation in multiple positions and is convenient for designing compression bras.

Widely used in micro‐position devices and vibration control, the piezoelectric actuator exhibits strong hysteresis effects, which can cause inaccuracy and oscillations, even lead to instability. If the hysteretic effects can be predicted, a controller can be designed to correct for these effects. This paper aims to present a neural network hysteresis model with an improved Preisach model to predict the output of piezoelectric actuator.

The improved Preisach model is given: A wiping‐out memory sequence is defined that is along a single axis only and at the same time the ascending and the descending extreme points are separated. The extended area variable is calculated according to wiping‐out memory sequence. The relationship between the two inputs (the extended area variable and variable rate of input signal) and the hysteresis output is implemented with a neural network to approximate the hysteresis model for the piezoelectric actuators.

Some experiments are carried out with a piezoelectric ceramic (PST150/7/40 VS12) and the results show the neural network hysteresis model can reliably predict the hysteretic behaviours in piezoelectric actuators.

The improved Preisach model is a simple model that is implemented by a neural network to reliably predict the hysteretic output in piezoelectric actuators.

ETL format is a newly proposed CAD format, which is both simple enough that CAD models in this format can be easily sliced, and capable of describing solids with arbitrarily complex surface details. This paper aims to provide a method for slicing CAD models in ETL format.

The proposed slicing method is based on a method for slicing CAD models in colour STL format. Affine mapping method is used to calculate the colour of the intersection polygon of a voxel in the slicing result volume dataset, and a triangular facet that constitutes the geometry of the described object, when the facet has texture mapping definition.

The proposed slicing method is simple and robust. Implementations have demonstrated its feasibility.

The proposed slicing method is meaningful to preparing data for the rapid formation of models with complex surface details, including colour and textures.