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Drawing on the knowledge-based view, this study investigates how IT–business alignment influences business model design via organizational learning and examines the moderating role of data-driven culture in the relationship between IT–business alignment and business model design via organizational learning.

Using multi-respondent survey data collected from 597 Chinese firms, mediation and moderated mediation analyses were used to examine this study's hypotheses.

The mediation test results revealed organizational learning served as a mediator between IT–business alignment and two types of business model design (i.e. novelty- and efficiency-centered). In addition, data-driven culture strengthened the indirect effects of IT–business alignment on these two types of business model design via organizational learning.

This study extends current understandings of the relationship between IT–business alignment and business model design by revealing the mediating role of organizational learning and investigating its indirect effects under various degrees of data-driven culture. As such, it contributes to the literature on the business model and IT–business alignment and provides insights for managers seeking to achieve the expected business model design.

Despite the potential influence of data analytics capability on servitization, the understanding of the underlying mechanisms of this influence remains unclear. This study aims to explore how data analytics capability affects servitization by examining the mediation effect of bricolage and the conditional role of innovation orientation.

This study employs the moderated mediation method to examine the proposed research model with archival data and multiple-respondent surveys from 1,206 top managers of 402 manufacturing firms in the Yangtze River Delta area in China.

Bricolage partially mediates the positive relationship between data analytics capability and servitization, and innovation orientation positively moderates this effect.

Manufacturers can leverage bricolage to materialize data analytics capability for servitization. Manufacturers should also pursue an innovation orientation to fully glean the benefits of bricolage in transforming data analytics capability into servitization.

This study opens the black box of how data analytics capability affects servitization by revealing the underlying mechanism of bricolage and the boundary condition role of innovation orientation for this mechanism. It offers valuable insights for practitioners to leverage data analytics to improve servitization through developing bricolage and cultivating a culture of innovation orientation.

This study investigates the different effects of the (mis)alignment between servitization strategies (i.e. product-oriented and customer-oriented services) and inter-organizational information technology (IT) capabilities (i.e. IT reconfiguration and integration) concerning the environmental performance of servitized manufacturing firms.

Using hierarchical regression analysis, this study examines the theoretical model based on multi-respondent survey data from 1,149 senior managers of 383 manufacturers in China.

Product-oriented services have a non-significant effect on environmental performance, whereas customer-oriented services exert a significantly positive effect. The alignment between product-oriented services and IT reconfiguration, and that between customer-oriented services and IT integration, improve environmental performance. In contrast, the misalignment between product-oriented services and IT integration, and that between customer-oriented services and IT reconfiguration, hurt environmental performance.

This study elucidates the different effects of the (mis)alignment between servitization strategies and inter-organizational IT capabilities on environmental performance. It also resolves the inconsistency regarding the implications of servitization on environmental performance.

Firms are increasingly depending on supplier portfolios in the quest for firm innovation. However, whether concentrated supplier portfolios are beneficial to innovation remains highly disputed. This study aims to investigate the effect of supplier portfolio concentration on firm innovation and the contingencies that shape this effect.

The authors build on the knowledge search view to theorize a U-shaped effect of supplier portfolio concentration on firm innovation and further propose that the U-shaped effect is contingent on financial slack and growth opportunities. The authors collected panel data from 1,320 manufacturing firms in China. The negative binomial regression analyses were performed to test the hypotheses.

Supplier portfolio concentration has a U-shaped effect on firm innovation. This U-shaped effect is weakened and flipped by financial slack but strengthened by growth opportunities.

The findings extend current understandings of the influence of supplier portfolio on firm innovation by clarifying the U-shaped effect of supplier portfolio concentration on innovation and the circumstances under which supplier portfolio concentration is more effective for firm innovation.

This paper aims to explore the influence of information technology (IT) capability (i.e. IT exploration and exploitation) on service innovation and ultimately financial performance in manufacturing firms. Moreover, this paper examines the contingent role of cross-functional integration (CFI) on the IT capability–service innovation relationship.

This study employs seemingly unrelated regression to test the theoretical model based on survey data from 121 manufacturers in China.

IT exploration is positively related to radical and incremental service innovations, whereas IT exploitation is only positively related to radical service innovation. CFI positively moderates the relationship between IT exploitation and service innovation. Radical and incremental service innovations are positively related to manufacturers' financial performance.

This study contributes to existing information systems and servitization literature by uncovering the varying effects of IT exploration and exploitation on radical and incremental service innovations and by revealing the different contingent roles of CFI in moderating the above effects in the manufacturing context.

This paper aims to evaluate the effectiveness of the reforming Chinese arbitration judicial review process and supplement the corresponding suggestions and analyze the practical trends of Chinese arbitration.

This paper presents considerable evidence that includes the latest empirical data and iconic cases to demonstrate the Chinese judicial system’s acts of internationalizing Chinese arbitration. This paper then elaborates the Chinese Supreme People’s Court (hereinafter SPC) recent reforms of the mechanisms of arbitration judicial review.

The SPC’s efforts to coordinate Chinese arbitration practice with international standards are effective and fruitful. However, even after recent reforms, there are still inherent deficiencies and important omissions that hinder the efficiency of Chinese arbitration.

The major contributions of this paper are providing latest empirical data to evaluate effectiveness of current Chinese arbitration judicial review and analyzing latest SPC’s legal interpretations.

This paper aims to improve the diversity and richness of haptic perception by recognizing multi-modal haptic images.

First, the multi-modal haptic data collected by BioTac sensors from different objects are pre-processed, and then combined into haptic images. Second, a multi-class and multi-label deep learning model is designed, which can simultaneously learn four haptic features (hardness, thermal conductivity, roughness and texture) from the haptic images, and recognize objects based on these features. The haptic images with different dimensions and modalities are provided for testing the recognition performance of this model.

The results imply that multi-modal data fusion has a better performance than single-modal data on tactile understanding, and the haptic images with larger dimension are conducive to more accurate haptic measurement.

The proposed method has important potential application in unknown environment perception, dexterous grasping manipulation and other intelligent robotics domains.

This paper proposes a new deep learning model for extracting multiple haptic features and recognizing objects from multi-modal haptic images.

This study aims to examine the impact of lean manufacturing (LM) on the financial performance of companies affected by emergency situations. It additionally explores the role of advanced manufacturing technologies (AMTs) in complementing LM to enhance financial performance in emergency and non-emergency situations.

Both survey and archival data were collected from 219 manufacturing companies in China. With longitudinal data collected before and after an emergency situation (i.e. Typhoon Rumbia), regression analysis was conducted to investigate the effects of LM and AMTs on financial performance in different contexts.

Our results reveal an inverted U-shaped relationship between LM and financial performance in the context of emergency. We also found that AMTs exerted a positive moderation effect on the inverted U-shaped relationship, indicating high levels of AMTs that mitigated the inefficiency of LM in coping with supply chain emergencies.

Through simultaneous investigation of LM and AMTs as bundles of practices and their fit with different contexts, this study takes a systems approach to fit that advances the application of contingency theory in the Operations Management literature to more complex patterns of fit.

This study illuminates how AMTs support LM practices in facilitating organizational performance in different contexts. Specifically, this study unravels the interaction mechanisms between AMTs and LM in influencing financial performance in emergency and non-emergency situations.

For an enterprise, it is essential to win as many customers as possible. The key to successfully winning customers is often determined by understanding the personality characteristics of the object of communication in order to employ an effective communication strategy. An enterprise needs to obtain the personality information of target or potential customers. However, the traditional method for personality evaluation is extremely costly in terms of time and labor, and it cannot acquire customer personality information without their awareness. Therefore, the manner in which to effectively conduct automated personality predictions for a large number of objects is an important issue. The paper aims to discuss these issues.

The diverse social media that have emerged in recent years represent a digital platform on which users can publicly deliver speeches and interact with others. Thus, social media may be able to serve the needs of automated personality predictions. Based on user data of Facebook, the main social media platform around the world, this research developed a method for predicting personality types based on interaction logs.

Experimental results show that the Naïve Bayes classification algorithm combined with a feature selection algorithm produces the best performance for predicting personality types, with 70-80 percent accuracy.

In this research, the dominance, inducement, submission, and compliance (DISC) theory was used to determine personality types. Some specific limitations were encountered. As Facebook was used as the main data source, it was necessary to obtain related data via Facebook’s API (FB API). However, the data types accessible via FB API are very limited.

This research serves to build a universal model for social media interaction, and can be used to propose an efficient method for designing interaction features.

This research has developed an approach for automatically predicting the personality types of network users based on their Facebook interactions.

This study aims to reveal the essential characteristics of nonstationary signals and explore the high-concentration representation in the joint time–frequency (TF) plane.

In this paper, the authors consider the effective TF analysis for nonstationary signals consisting of multiple components.

To make it, the authors propose the combined multi-window Gabor transform (CMGT) under the scheme of multi-window Gabor transform by introducing the combination operator. The authors establish the completeness utilizing the discrete piecewise Zak transform and provide the perfect-reconstruction conditions with respect to combined TF coefficients. The high-concentration is achieved by optimization. The authors establish the optimization function with considerations of TF concentration and computational complexity. Based on Bergman formulation, the iteration process is further analyzed to obtain the optimal solution.

With numerical experiments, it is verified that the proposed CMGT performs better in TF analysis for multi-component nonstationary signals.