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Purpose How to improve the resilience of service firms in the crisis, such as the COVID-19 epidemic, to maintain a sustainable competitive advantage becomes a growing concern worldwide. Digital platform capability (DPC) provides a series of opportunities and advantages for service firms to shape resilience in the crisis. This study aims to clarify the effect and mechanism of DPC on service firms’ resilience, and provides a new mediator (strategic learning [SL]), as well as two boundary conditions (legal inefficiency [LIE] and legal incompleteness [LIC]). Design/methodology/approach Questionnaires were used to obtain firm data, and executives answered these key questions. Data from 293 service firms during the COVID-19 period were used for hypothesis testing. Findings DPC was positively related to the adaptive capacity (AC) and planning capacity (PC) of service firms. SL mediated the positive effect of DPC on the AC and PC of service firms. The positive effect between DPC and SL was weakened when LIE and LIC were high. Practical implications This study suggests that it is a very desirable measure to improve DPC to gain organizational resilience (OR) in the crisis. In addition, a SL process in the crisis is crucial, because service firms need to absorb key strategic information from digital platforms to cope with uncertainty. The services firms need to realize that the benefits of DPC will be weakened in the dysfunctional institutional environment of LIE and LIC. Originality/value To the best of the authors’ knowledge, this study is the first to link the DPC with the resilience of service firms, and provides a new explanation mechanism and some boundary conditions for this important relationship. Furthermore, this study takes a step forward, because these efforts respond to the widespread call of the literature on digitalization and OR, and provide new insights for understanding digital resilience.

PurposeThis study aims to examine how and when digital platform capabilities drive technological innovation from a strategic information perspective, regarding information flow and information quality as mediators and business intelligence analytics as a moderator.Design/methodology/approachThis study engaged corporate executives in surveys and obtained 182 firm data and then tested the hypotheses using linear regression models and the robustness using the structural equation model.FindingsDigital platform capabilities drive corporate technological innovation through access to strategic information, i.e. strategic information mediates between digital platform capabilities and technological innovation. Business intelligence analytics increases the influence of strategic information on technological innovation.Practical implicationsThis study underlines the importance for companies to construct digital platform capabilities to access strategic information to achieve technological innovation and the role of business intelligence analytics in processing strategic information.Originality/valueThis study finds a new perspective, strategic information, to explain the mechanisms by which digital platform capabilities drive firms’ technological innovation. In addition, the robustness of the resource-based view in understanding digital platform capabilities is stressed.

PurposeThis study aims to examine how and when leader bottom-line mentality (BLM) affects employee innovation (EI) in the context of Chinese organisations in which psychological contract breach (PCB) is a mediator and moral identity (MI) is a moderator.Design/methodology/approachThe authors distributed the questionnaires to technology and culture firms in 2 waves and obtained a sample of 308 employees in 56 teams. The authors used the hierarchical linear model (HLM) and bootstrap method for the hypothesis testing.FindingsThe authors conclude that leader BLM negatively influences EI. Specifically, leader BLM leads mainly to low EI by increasing PCB amongst employees. Moreover, MI plays a moderating role in the above mechanism, i.e. the higher the MI degree of employees, the stronger the mediating role of PCB and the stronger the negative impact of leader BLM.Practical implicationsThis study argues that BLM is necessary for firm development, but paying too much attention to BLM may achieve the opposite effect. Reducing BLM or buffering the negative impact of BLM through various measures (e.g. human resource management) is appropriate for managers.Originality/valueThe authors discover a new mechanism and boundary condition, i.e. leader BLM has a negative impact on EI through PCB and MI strengthens the effect of leader BLM.

The overwhelming parameters and computation consumption of deep neural networks limit their applicability to a single computing node with poor computing power, such as edge and mobile devices. Most previous works leverage model pruning and compression strategies to reduce DNN parameters for resource-constrained devices. However, most model compression methods may suffer from accuracy loss. Recently, we find that combine many weak computing nodes as a distributed system to run large and sophisticated DNN models is a promising solution for the issue. However, it is essential for the distributed system to design distributed DNN models and inference schemes, one of the great challenges of distributed system is how to design an efficient distributed DNN model for data parallelism and model parallelism, and communication overhead is also another critical performance bottleneck for distributed DNN model. Therefore, in this article, we propose DFSNet framework (Dividing-Fuse neural Network with Searching Strategy) for distributed DNN architecture. Firstly, the DFSNet framework includes a joint ”dividing-fusing” method to convert regular DNN models into distributed models that are friendly for distributed systems. This method divides the conventional DNN model in the channel dimension, and sets a few special layers to fuse feature-map information from different channel groups for accuracy improvement. Since the fusion layers are sparse in the network, they do not increase too much extra inference time and communication overhead on the distributed nodes, but they can maintain the accuracy of distributed neural networks significantly. Secondly, considering the architecture of distributed computing nodes, we propose a parallel fusion topology to improve the utilization of different computing nodes. Lastly, the popular weight-sharing neural architecture search (NAS) technique is leveraged to search the position of fusion layers in the distributed DNN model for high accuracy and finally generate an efficient distributed DNN model. Compared with the original network, our converted distributed DNN achieves better performance (e.g. 1.88% precision boosting in ResNet56 on CIFAR-100 dataset, and 1.25% precision improving in MobileNetV2 on ImageNet dataset). In addition, most layers of DNN have been divided into different distributed nodes on channel dimension, which is particularly suitable for distributed DNN architecture with very low communication overhead.

The past few years have witnessed the dramatic increase in layers of convolutional neural networks (CNN). Most studies focused on the CNN’s vertical structure design (e.g. residual structure, creating short paths architecture from early layers to later layers in vertical connections), but few people pay their attention to the process of feature generation and extraction in a single convolutional layer in CNN. In this paper, we find the non-feature suppression phenomenon in the process of extracting features. On the basis of this, we proposed an orthogonal approach named HSC (Horizontal Shortcut Connections) to improve feature representation fusion and computational efficiency for CNN. Especially, our HSC approach can effectively reduce interference overhead of non-feature areas and enhance the information fusion for depthwise convolution and group convolution which are the key blocks in lightweight neuron network. At HSC layer, the feature-maps of all preceding layer are properly connected with our strategy in horizon direction to constitute features and then produce a new representation which are used as input feature-maps passed on subsequent layers. Our HSC block can be plugged into convolution neural networks that include group convolution or depewise convolution, and can effectively improve accuracy of convolutional networks with slight additional computational cost. We evaluate our design on the popular lightweight neural networks and standard CNN structure. Compared with existing methods, we can achieve 1.63% accuracy improvement for MobileNet v2 on CIFAR-10 dataset and up to 3.70% accuracy improvement on CIFAR-100 dataset by adding HSC block after depthwise convolution, and 2.80% accuracy improvement on ImageNet dataset. For Mobilenet v3-small, we can achieve 0.8% accuracy improvement on ImageNet dataset. In order to prove the improvement effect of group convolution, the standard convolution is changed manually to group convolution and then the HSC block is added after group convolution, we can achieve 4X to 6X FLOPs improvement while maintaining the accuracy of neural networks. Notably, on ILSVRC- 2012, our method reduces more than 43% FLOPs on ResNet-50 without accuracy declines and reduces 60.1% FLOPs on ResNet-50 with 0.44% accuracy declines.We also present primary hardware experiment results when HSC framework running on special hardware platform.

Purpose This study aims to explore the influence mechanism of digital platform capability on firm performance in the business-to-business (B2B) context. This study draws on the core competence theory and the resource-based view and includes resource identification, resource allocation and intrapreneurship into the research framework. Design/methodology/approach Data were collected from 167 B2B firms with survey questionnaires in central, eastern and southeastern coastal areas of China. The firms were mainly involved in e-commerce, manufacturing, service industry and internet technology. Participants were mainly middle and senior managers with a comprehensive grasp of their firms’ information. Findings This study found that digital platform capability has a positive impact on a B2B firm’s performance. Resource identification, resource allocation and intrapreneurship play a chain mediating role between digital platform capability and firm performance. That is, digital platform capability could promote employee intrapreneurship through resource identification and resource allocation, thereby improving firm performance. Practical implications Aiming to gain performance, firms should pay attention to the construction of digital platforms, increase venture capital investment and provide more resources to support intrapreneurship. Originality/value Based on empirical evidence, to the best of authors’ knowledge, this is the first attempt to link digital platform capability and firm performance in the B2B context of emerging markets, providing a new perspective to clarify its relationship mechanism.