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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.

Drawing on the work design growth model (WDGM), this paper aims to explore the relationship between job complexity and employee creativity through feedback-seeking and the moderating effect of team leaders with a growth creative mindset.

The authors used an online survey to test the hypotheses. Data was collected in three waves from 74 supervisors and 349 paired employees in China.

Job complexity had a positive association with employees’ feedback-seeking, which further linked to employee creativity. This indirect effect was stronger in work teams with leaders endorsing a growth creative mindset.

Job complexity has become prevalent in organizations today. Taking daily complexity as a resource for nurturing employee creativity may balance organizations’ costs on formal training and give them more initiatives in long-term development. In addition, as the growth creative mindset is relatively easy to assess and change, it may bring insights in terms of creativity development.

By empirically testing the behavioural mechanism of WDGM, the learning and development perspective of work design offers a new explanation of the relationship between job complexity and employee creativity. The authors further extend WDGM by identifying leaders’ growth creative mindset to be a boundary condition.

Consumers interacting with smart wearable devices is on the rise in the current health-AI market, which offers a great opportunity for companies to execute interactive marketing. However, this opportunity is mainly reliant on consumers' use of smart wearable devices. This paper aims to develop a model considering health and privacy factors to elucidate consumers' use of smart wearable devices for unleashing their full potential in interactive marketing.

The authors collected 250 samples via an online survey to validate the smart wearable devices usage model that elucidates factors that stimulate consumer usage, including privacy concerns, health consciousness and consumer innovativeness. The authors used structural equation modeling and multi-group analysis to test the hypotheses.

Privacy concerns of consumers have a negative effect on smart wearable devices usage, while health consciousness positively impacts consumers' usage of smart wearable devices. Consumer innovativeness indirectly affects smart wearable devices usage via effort expectancy. Experienced consumers are less sensitive to the performance expectancy but more affected by effort expectancy regarding smart wearable devices.

The present study contributes to the literature stream of health-AI usage by unraveling the impacts of privacy concerns and health consciousness and examining the moderating role of prior experience. The findings suggest marketers in the health-AI industry should endeavor to build transparent and sound privacy protection mechanisms and promote smart wearable devices by fostering health awareness of potential consumers.

The purpose of the research is to explore how small and medium enterprises (SMEs) in central China achieve intelligent transformation through the use of artificial intelligence (AI). Because of unequal resource allocation, constraints on the intelligent transformation of SMEs in central China are different from those in economically and technologically well-developed coastal provinces. Hence, the authors focus on SMEs in central China to identify drivers of and barriers to intelligent transformation.

The interview data were collected from 66 SMEs across 20 industries in central China. To verify the validity of the data collection method, the authors used two methods to control for retrospective bias: multi-level informants and enterprises' AI project application materials (Wei and Clegg, 2020). The final data were validated without conflicts. Next, the authors cautiously followed a two-step approach recommended by Venkatesh et al. (2010) and used NVivo 11.0 to analyze the collected text data.

SMEs in central China are enthusiastic about intelligent transformation while facing both internal and external pressures. SMEs need to pay attention to both internal (enterprise development needs, implementation cost, human resources and top management involvement) and external factors (external market pressure, convenience of AI technology and policy support) and their different impacts on intelligent transformation. However, constrained by limited resources, SMEs in central China have been forced to take a step-by-step intelligent transformation strategy based on their actual needs with the technological flexibility method in the short term.

Considering the large number of SMEs and their importance in promoting China's economic development and job creation (SME Bureau of MIIT, 2020), more research on SMEs with limited resources is needed. In the study, the authors confirmed that enterprises should handle “social responsibility” carefully because over-emphasizing it will hinder intelligent transformation. However, firms should pay attention to the role of executives in promoting intelligent transformation and make full use of policy support to access more resources.

The purpose of this paper is to provide a modified welding image feature extraction algorithm for rotating arc narrow gap metal active-gas welding (MAG) welding, which is significant for improving the accuracy and reliability of the welding process.

An infrared charge-coupled device (CCD) camera was utilized to obtain the welding image by passive vision. The left/right arc position was used as a triggering signal to capture the image when the arc is approaching left/right sidewall. Comparing with the conventional method, the authors’ sidewall detection method reduces the interference from arc; the median filter removes the welding spatter; and the size of the arc area was verified to reduce the reflection from welding pool. In addition, the frame loss was also considered in the authors’ method.

The modified welding image feature extraction method improves the accuracy and reliability of sidewall edge and arc position detection.

The algorithm can be applied to welding seam tracking and penetration control in rotating or swing arc narrow gap welding.

The modified welding image feature extraction method is robust to typical interference and, thus, can improve the accuracy and reliability of the detection of sidewall edge and arc position.