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This study aimed to investigate the underlying boundary conditions under which boundary-spanning behaviour has a positive or negative effect on innovative behaviour.

A multi-wave and multi-source research design was adopted to collect data. Data were analysed using the multilevel structural equation modelling and latent moderated structural equation approach.

The results showed that boundary-spanning behaviour was significantly and negatively associated with employees' innovative behaviour via ego depletion when employees' intrinsic motivation or organisational support was low. Additionally, boundary-spanning behaviour was significantly and positively associated with employees' innovative behaviour via ego depletion when employees' intrinsic motivation or organisational support was high.

This research suggests that the consequences of boundary-spanning behaviour are conditional, explaining the contrasting conclusions in this regard.

The purpose of this paper is to provide a fast and accurate network for spatiotemporal action localization in videos. It detects human actions both in time and space simultaneously in real-time, which is applicable in real-world scenarios such as safety monitoring and collaborative assembly.

This paper design an end-to-end deep learning network called collaborator only watch once (COWO). COWO recognizes the ongoing human activities in real-time with enhanced accuracy. COWO inherits from the architecture of you only watch once (YOWO), known to be the best performing network for online action localization to date, but with three major structural modifications: COWO enhances the intraclass compactness and enlarges the interclass separability in the feature level. A new correlation channel fusion and attention mechanism are designed based on the Pearson correlation coefficient. Accordingly, a correction loss function is designed. This function minimizes the same class distance and enhances the intraclass compactness. Use a probabilistic K-means clustering technique for selecting the initial seed points. The idea behind this is that the initial distance between cluster centers should be as considerable as possible. CIOU regression loss function is applied instead of the Smooth L1 loss function to help the model converge stably.

COWO outperforms the original YOWO with improvements of frame mAP 3% and 2.1% at a speed of 35.12 fps. Compared with the two-stream, T-CNN, C3D, the improvement is about 5% and 14.5% when applied to J-HMDB-21, UCF101-24 and AGOT data sets.

COWO extends more flexibility for assembly scenarios as it perceives spatiotemporal human actions in real-time. It contributes to many real-world scenarios such as safety monitoring and collaborative assembly.

This study aims to explore how communication visibility affects employees’ work engagement from the negative perspective of employees’ perceived overload in the context of enterprise social media (ESM) and the role of ESM policies in the relationship between communication visibility and perceived overload.

This study examines how communication visibility (i.e. message transparency and network translucence) affects employees’ perceived overload (i.e. information overload and social overload), which in turn affects employees’ work engagement, and how ESM policies moderate the relationship between communication visibility and perceived overload. Partial least squares (PLS) analysis was conducted on data gathered from 224 ESM users in workplaces.

Communication visibility has significant positive impacts on perceived overload, perceived overload has significant negative impacts on work engagement and ESM policies negatively moderate the relationships between communication visibility and perceived overload, except for the relationship between message transparency and social overload.

The findings provide new insights for organizational managers to formulate ESM policies to mitigate perceived overload and guidance for ESM developers to improve ESM functions to alleviate perceived overload.

This study provides empirical evidence to explain the role of communication visibility and perceived overload in employees’ work engagement, which contributes to the existing literature on the negative impacts of communication visibility.

This study aims to explore whether and how task characteristics affect employee agility in the context of enterprise social media (ESM).

Adopting the social network ties perspective, this study examines how task characteristics (i.e. task complexity, task interdependence and task non-routineness) affect employee agility by promoting their social network ties (i.e. instrumental ties and expressive ties) and how ESM visibility moderates their relationships. Data gathered from 341 ESM users in workplaces were analyzed using Smart-PLS 3.2.

First, task complexity, task interdependence and task non-routineness have positive effects on instrumental and expressive ties, which in turn influences agility; Second, instrumental ties have a stronger effect on employee agility relative to expressive ties; Finally, ESM visibility positively moderates the effects of task complexity and task non-routineness on social network ties.

The findings provide guidance for organizational managers on how to use task characteristics and ESM to improve employee agility, as well as insights for social media designers to optimize ESM functions to improve agility.

This study provides empirical evidence to explain the roles of task characteristics and social network ties in influencing employee agility, thus clarifying the inconsistent findings in extant research. The moderating effects of ESM visibility on the relationships between task characteristics and social network ties are also examined, thus providing further insights on the positive role of ESM in organizations.

Under the background of low consumer financial knowledge and accumulated credit card liabilities, this study investigates the relationship between financial knowledge and responsible credit card behavior using data from the 2019 China Household Finance Survey (CHFS). From the perspective of consumer economic well-being, this study defines accruing credit card debt to buy houses and cars when loans with lower interest rates are available as irresponsible credit card behavior.

This study uses probit regressions to examine the association between financial knowledge and responsible credit card behavior because the dependent variable is a dummy variable. To alleviate endogeneity problems, this study uses instrument variables and Heckman’s two-step estimation. Furthermore, to explore the potential mediators in this process, this study follows the stepwise regression method. Finally, this study introduces interaction terms to examine whether this association differs in different groups.

The results indicate that financial knowledge is conducive to increasing the probability of responsible credit card behavior. Mediating analyses reveal that the roles of financial knowledge occur by increasing the degree of concern for financial and economic information and the propensity to plan. Moderating analyses show that the effects of financial knowledge on responsible credit card behavior are stronger among risk-averse consumers and in regions with favorable digital access.

This study measures responsible credit card behavior from the perspective of the consumer’s well-being, which enriches practical implications for consumer finance. Furthermore, this study explores the potential mediators influencing the process of financial knowledge that affects responsible credit card behavior and identifies moderators to conduct heterogeneous analyses, which helps comprehensively understand the nexus between financial knowledge and credit card behavior. By achieving these contributions, this study helps to curb the adverse effects of irresponsible credit card behavior on consumers’ well-being and the economic system and helps policymakers promote financial knowledge to fully prevent irresponsible credit card behavior.

This paper aims to investigate whether and how enterprise social media (ESM) affordances affect employee agility.

Adopting self-determination theory (SDT), this study examines a model in which the four ESM affordances (i.e. visibility, association, editability and persistence) impact employee agility through the three basic psychological needs satisfaction (i.e. perceived autonomy, perceived relatedness and perceived competence) of employees. Mplus 7.4 was used to analyze survey data gathered from 304 employees who used ESM in the workplace.

The authors’ findings show that all four ESM affordances contribute to perceived relatedness and perceived competence; visibility and association affordances also have positive impacts on perceived autonomy; and all three psychological needs satisfaction positively impact employee agility.

First, this study adapted SDT to explore how ESM influences employee agility. Second, this study enriches the relevant research on the antecedents of employee agility and also provides new evidence and theoretical support for employee agility. Third, this study effectively expands the antecedents and outcomes of employee basic psychological needs satisfaction in the domain of ESM and agility.

According to extensive analysis, employee agility is influenced by teamwork, coordination and the organizational environment. However, less consideration has been given to the role of work stressors (challenge, hindrance) in influencing employee agility. To address this research gap, this study sheds light on how the use of enterprise social media (ESM) for social and work purposes influences employee agility through work stressors.

This research also explores how ESM visibility enhances the interaction between work stressors and employee agility by using primary data obtained from Chinese workers. A total of 377 entries were analyzed using AMOS 24.10 tools. All the hypotheses were tested using structural equation modeling (SEM).

The findings revealed that ESM use (social and work) negatively impacts challenge and hindrance work stressors. The results also reflect that challenge stressors have a significant impact on employee agility, whereas hindrance stressors are negatively related to it. Furthermore, the outcome also indicated that increased ESM visibility reinforces the connection between challenge stressors and employee agility. However, ESM visibility did not indicate a significant moderating impact on the link between hindrance stressors and employee agility.

This study describes how ESM usage effects agility of stressed employees. This research also explores how ESM visibility improves the interaction between work stressors and employee agility. The study results contribute to growing research on social media and employee agility and suggest several points of guidance for managers.

In the big data era, image compression is of significant importance in today’s world. Importantly, compression of large sized images is required for everyday tasks; including electronic data communications and internet transactions. However, two important measures should be considered for any compression algorithm: the compression factor and the quality of the decompressed image. In this paper, we use Frei-Chen bases technique and the Modified Run Length Encoding (RLE) to compress images. The Frei-Chen bases technique is applied at the first stage in which the average subspace is applied to each 3 × 3 block. Those blocks with the highest energy are replaced by a single value that represents the average value of the pixels in the corresponding block. Even though Frei-Chen bases technique provides lossy compression, it maintains the main characteristics of the image. Additionally, the Frei-Chen bases technique enhances the compression factor, making it advantageous to use. In the second stage, RLE is applied to further increase the compression factor. The goal of using RLE is to enhance the compression factor without adding any distortion to the resultant decompressed image. Integrating RLE with Frei-Chen bases technique, as described in the proposed algorithm, ensures high quality decompressed images and high compression rate. The results of the proposed algorithms are shown to be comparable in quality and performance with other existing methods.