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In the digital age, enterprise social media (ESM) has been broadly used to break the barrier of space and time for knowledge sharing involving two activities—knowledge donating and knowledge collecting. Yet, hardly any study has thus far explored how and when knowledge donating and knowledge collecting through ESM impact employees’ innovative work behavior (IWB). Therefore, drawing on the social exchange theory, this study aims to introduce relational social capital as a mediator and performance climate as a moderator to examine differential impact mechanisms of knowledge donating and knowledge collecting through ESM on employee IWB.

First, we collected 225 survey data from employees who used ESM at work. Then, according to the two-stage procedure of the partial least square structural equation modeling method, we established and analyzed a moderated mediation model based on the context of ESM.

Results demonstrate that knowledge donating through ESM has a positive relationship with IWB, but knowledge collecting through ESM has no such relationship. Through the mediation of relational social capital, both knowledge donating and knowledge collecting through ESM have indirect positive relationships with IWB, but knowledge collecting through ESM has a greater one. Moreover, when employees perceived a relatively high-performance climate, the positive relationships of knowledge donating and knowledge collecting through ESM with IWB via relational social capital would be stronger.

On the one hand, organizational managers should not only focus on different innovation benefits caused by knowledge donating and knowledge collecting through ESM but also should realize differences in the direct/indirect effect of knowledge donating and knowledge collecting through ESM on IWB. On the other hand, employees need to proactively enhance their innovative ability by considering knowledge donating/collecting channels (e.g. ESM), gaining successful competitiveness. Moreover, both managers and employees should realize that different innovation benefits of knowledge sharing activities through ESM can be further strengthened when establishing a high-performance climate.

Different from prior literature on knowledge sharing or ESM, this study contributes to analyzing the differential effects of two dimensions (i.e. knowledge donating and knowledge collecting) of knowledge sharing on employees' IWB in the context of ESM.

The emerging nonstandard employment (i.e. gig work) makes gig workers face a series of forms of labor insecurity. Prior studies focus on the linkage between gig work insecurity and precariousness. However, how gig workers and platforms jointly handle gig work insecurity has been so far overlooked. To this end, this study aims to explore how gig platforms and workers jointly cope with the insecurity of the gig work model.

Building upon the JD-R model, this study used a double-level perspective to hypothesize how gig platforms and workers jointly cope with gig work insecurity. Second, 248 questionnaire data were collected from workers who worked for several gig platforms (e.g. Meituan, Eleme, DidiTax, Zhihu and Credamo) in China. Third, the analysis method based on the partial least squares structural equation model (PLS-SEM) was employed to test the study theoretical model.

Empirical findings show that gig workers can cope with gig work insecurity by crafting their work; gig platforms' formalization governance not only reduces gig work insecurity but also helps gig workers address it by more easily crafting their work.

Gig workers do always have not enough job resources and motivation to work hard. Gig workers merely rely on job crafting to cope with the insecurity of the gig work model, which is insufficient. Gig platforms should also formalize their current governance mechanisms, which can supplement gig workers' job resources and reduce their job demands so as to help them cope with such gig work insecurity.

These results advance the understanding of the joint roles of gig platforms and workers in addressing gig work insecurity and improve governance effectiveness and value of gig platforms.

Digital platform work monitored by algorithms is increasingly supplementing or substituting standard employment. Though gig workers are faced with the vulnerable, fragile and precarious digital platform work environment, the reason why gig workers remain highly willing to show good task performance has been so far unexamined. Building upon the reciprocity of the social exchange theory, this study aims to explore the antecedents and boundary condition of facilitating gig workers’ task performance.

First, to minimize common method variance, decline spurious mood effects and ensure data robustness, we conducted a two-wave time-lagged survey and collected 269 survey responses from gig workers on different gig platforms in China (e.g. Meituan, Eleme, Didi, Credamo, Zaihang) at two time nodes. Second, abiding by two stage procedures of the PLS-SEM (partial least square structural equation model) approach, we analyzed a moderated mediation model in the digital platform work context.

Results present that both platform work remuneration and flexibility help gig platforms develop an affective trust relationship with gig workers, thus encouraging them to repay the platform by performing platform tasks well. Algorithmic monitoring shows a “double-edged sword” moderating role since it weakens the indirectly positive relationship between platform work remuneration and task performance via affective trust but enhances the indirectly positive relationship between platform work flexibility and task performance via affective trust.

Understanding the importance of remuneration and flexibility in developing affective trust can help platforms design effective human resource management (HRM) strategies that enhance worker motivation of maintaining high engagement and performance under precarious working conditions. Additionally, optimizing the “double-edged sword” moderating role of algorithmic monitoring makes it more humanized, enhancing the efficiency with these HRM strategies and making both workers and platforms beneficial.

These findings offer an affective trust-based explanation for the mechanism of maintaining high work performance motivation in the nonstandard and precarious employment from the social exchange perspective, while understanding the (de)humanized aspect of algorithmic monitoring by revealing its “double-edged sword” moderating role.

This study focuses on perceived overload from environmental stimuli and individual psychology and behavioral interactions. It constructs a theoretical model with overload as the key stressor based on the stressor-strain-outcome (SSO) model. The authors argue that system feature overload (SFO), information overload, and social overload lead to two psychological strains: fear of missing out (FoMO) and fatigue among users of short video platforms, affecting their discontinuous usage intentions.

To test the hypotheses, the authors conducted a questionnaire survey on 412 users' short video platform usage and empirically tested the constructed model using the research tool SmartPLS 3.3.2.

The results of data analysis showed that most of the hypotheses were supported. Specifically, system feature overload, information overload and social overload all positively affected FoMO. However, SFO and information overload significantly affected fatigue. There was no significant relationship between social overload and fatigue. In addition, both FoMO and fatigue negatively influenced users' discontinuous usage intentions.

The current research on user behavior in information systems tends to focus on the influence in the positive direction and less on the negative direction. The research on discontinuous usage intention (DUI) is a very new research topic. This research studies the influencing factors of users' discontinuous behavior from the perspective of perceptual overload. It provides a unique view for future short video platform user behavior research, with significant theoretical contributions and essential practice for short video platform operators to improve services.

Being interested in developing a natural preservative for the prolongation of shrimp shelf life, this study aims to evaluate the Sargassum horneri extracts for their antioxidant effect, copper reducing power, copper chelating activity and polyphenol oxidase (PPO) inhibitory activity, as well as explored their potential preservative activity in white leg shrimp.

The antioxidant properties of Sargassum horneri 75% ethanol aqueous extracts were measured by 2,2-diphenyl-1-picryl hydrazyl (DPPH), 2,2-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), ferric reducing antioxidant power (FRAP) and PPO inhibitory activity. Sample III displayed the highest antioxidant activity, PPO inhibitory activity, copper reducing power and copper chelating activity, so it was selected for further studying its impact on shrimp's quality changes such as pH value, total volatile basic nitrogen (TVB-N), thiobarbituric acid reactive substances (TBARS), melanosis and sensory score during storage at 4°C.

Among all three samples, sample III containing 49.88% total phenolic contents was selected for the further study on the prevention of quality loss and melanosis of shrimp, because it yielded the highest antioxidant activity, PPO inhibitory activity, copper reducing power and copper chelating activity than the other two samples (samples II and IV). It was found that shrimp treated with 0.2% sample III had the lowest melanosis scores throughout the storage (p < 0.05). During ten days of storage at 4°C, shrimp treated with 0.2% sample III had a higher score in sensory properties (color, segments, texture and odor), compared with the control, 0.1% kojic acid and 0.1% sample III treated shrimp (p < 0.05). The results indicated that the treatment of shrimp with 0.2% sample III could extend the shelf life to eight days at 4°C.

The extracts of Sargassum horneri displayed potent PPO inhibition, antimicrobial and antioxidant properties. Importantly, the 75% extracts of Sargassum horneri could extend the shelf life. Considering these results, the extracts of Sargassum horneri may be used as natural preservatives in the food industry after further evaluation of their other properties such as toxicity, as well as provide a choice target to source natural products intended for cosmetics.

The originality of this paper lies in the development and application of the extracts of edible algae. To extend the shelf life of seafood, the polyphenolic-rich extract of Sargassum horneri provided another possible selection.

This study aims to design an optimized algorithm for low-cost pedestrian navigation system (PNS) to correct the heading drift and altitude error, thus achieving high-precise pedestrian location in both two-dimensional (2-D) and three-dimensional (3-D) space.

A novel heading correction algorithm based on smoothing filter at the terminal of zero velocity interval (ZVI) is proposed in the paper. This algorithm adopts the magnetic sensor to calculate all the heading angles in the ZVI and then applies a smoothing filter to obtain the optimal heading angle. Furthermore, heading correction is executed at the terminal moment of ZVI. Meanwhile, an altitude correction algorithm based on step height constraint is proposed to suppress the altitude channel divergence of strapdown inertial navigation system by using the step height as the measurement of the Kalman filter.

The verification experiments were carried out in 2-D and 3-D space to evaluate the performance of the proposed pedestrian navigation algorithm. The results show that the heading drift and altitude error were well corrected. Meanwhile, the path calculated by the novel algorithm has a higher match degree with the reference trajectory, and the positioning errors of the 2-D and 3-D trajectories are both less than 0.5 per cent.

Besides zero velocity update, another two problems, namely, heading drift and altitude error in the PNS, are solved, which ensures the high positioning precision of pedestrian in indoor and outdoor environments.

Although digital transformation (DT) has emerged as an important phenomenon for both research and practices, the influences remain inconclusive and inadequate. The emerging artificial intelligence (AI) technologies further complicate the understanding and practices of DT while understudied yet. To address these concerns, this study takes a process perspective to empirically investigate when and how digital-intelligence transformation can improve firm performance, aiming to enrich the literature on digital-intelligence transformation and strategic information systems (IS) field.

Drawing on the dynamic capability view and business agility, we took a process perspective to conceptualize and empirically examine the influence of digital-intelligence transformation and the process characteristics. Taking a continuous panel dataset of listed Chinese firms covering 2007 to 2020, we investigated digital-intelligence transformation’s effect on firm performance and the moderating roles of three strategic aspects: pace, scope and rhythm.

This study found that digital-intelligence transformation positively affects firm performance and is moderated by the characteristics of transformation processes (i.e. pace, scope and rhythm). Specifically, the high-paced and rhythmic transformation processes facilitate the positive relationship, while the large scope undermines the benefits of transformation. These relationships hold across various endogeneity and heterogeneity analyses.

Our findings provide valuable implications for digital-intelligence transformation and strategic IS field. First, this study enriches existing literature on digital-intelligence transformation by empirically investigating the influence from a process perspective. Moreover, this study provides insights into a comprehensive understanding of the complexity of digital-intelligence transformation and the influences of AI. Finally, this study provides practical implications on how to make digital-intelligence transformation to benefit firm performance.

As a highly knowledge-intensive activity, digitalization is changing the construction industry landscape and is encouraging construction firms to explore the transformation. This study establishes a new theoretical model aimed at examining the impact of three types of intellectual capital (IC) on digitalization through the lens of knowledge-based view and explores how IC and digitalization influence sustainability performance from the triple bottom line principles.

A questionnaire survey was conducted to collect data from Chinese construction firms using convenience sampling. A total of 181 valid responses were obtained. Then, a partial least squares structural equation modelling (PLS-SEM) technique was executed through Smart PLS 3.0 software. The measurement model was assessed to ensure reliability and validity, and the structural model was analysed to test the proposed hypotheses.

The empirical results confirm the positive impact of IC on digitalization and digitalization on sustainability performance. Moreover, digitalization plays a significant mediating role in the relationship between IC and sustainability performance.

The results provide empirical evidence supporting the different roles of IC and digitalization in improving sustainability. The findings contribute to enhancing the understanding of digitalization practices from the perspective of IC and provide theoretical and managerial implications for sustainability issues in the context of the construction industry.

The purpose of this paper is to provide new insights for managing knowledge reuse in terms of the duality of innovator personality. Continuously developing new products is crucial for firms to maintain and enhance their competitive advantages. However, the limited and highly specialized knowledge can cause innovators of firms to face difficulties in the process of new product development (NPD). In this setting, knowledge reuse becomes a solution that may benefit innovators to overcome the innovation dilemma. Given the fact that innovators with different personality are likely to form incongruent cognitions and affection on knowledge reuse, thus subsequently affecting the performance of NPD, there is an urgent need to investigate the effects of innovator personality in the entire process of knowledge reuse.

This paper exploits five-factor model (FFM) of personality to comprehensively investigate the dual effects of innovator personality in managing knowledge reuse based on the two distinct sets of knowledge reuse initiation and implementation.

By using the data from 981 innovators of knowledge-intensive firms in China, this study finds that the FFM traits of conscientiousness and agreeableness had opposing effects on initiation and implementation of knowledge reuse. While the FFM traits of emotional stability and openness to experience both positively affect the knowledge reuse initiation and implementation. Moreover, the FFM traits of extraversion benefit the shaping of knowledge reuse initiation whereas encumbering the implementation of knowledge reuse.

First, this study reveals the different roles of cognitive and affective traits of personality in shaping knowledge reuse. Second, this study exposes the role of innovator personality in determining the performance effects of knowledge reuse implementation. Third, this study highlights the dual effects of innovator personality in managing knowledge reuse. This study offers evidence for arranging the innovators with appropriate FFM traits in various stages of knowledge reuse.

This paper aims to investigate the antecedents of recombinant innovation from the perspective of ego–network dynamics, and further disentangle whether ego–network stability or ego–network expansion is more conducive to recombinant innovation under heterogeneous knowledge base.

This paper uses 1,801 patent data in China’s biotechnology field as a sample and adopts fixed effects regression model to examine the effects of ego–network dynamics on recombinant innovation and further uses the Wald tests to discern which ego–network dynamic is more conducive to recombinant innovation under heterogeneous knowledge base.

The empirical results indicate that ego–network dynamics have a positive impact on recombinant innovation. Specifically, for firms with high knowledge breadth and high knowledge depth as well as high knowledge breadth and low knowledge depth, ego–network stability is more conducive to recombinant innovation. By contrast, for firms with low knowledge breadth and high knowledge depth, recombinant innovation benefits more from ego–network expansion. As for firms with low knowledge breadth and low knowledge depth, both ego–network stability and ego–network expansion can promote recombinant innovation, while the effects are not significant.

This research may enlighten managers to choose suitable ego–network dynamics strategies for recombinant innovation based on their knowledge base.

This research not only contributes to the literature on recombinant innovation by revealing the impact of different ego–network dynamics on recombinant innovation but also contributes to network dynamics theory by exploring whether ego–network stability or ego–network expansion is more conducive to recombinant innovation under a heterogeneous knowledge base.