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Drawing on the Health Belief Model (HBM), this study aims to investigate the roles of health beliefs (i.e. perceived susceptibility, perceived severity, perceived benefits, perceived barriers, health self-efficacy and cues to action) in promoting college students’ smartphone avoidance intention.

Empirical data were collected through a cross-sectional survey questionnaire administered to 4,670 student smartphone users at a large university located in Central China. Further, a two-step Structural Equation Modeling was conducted using AMOS 22.0 software to test the hypothesized relationships in the research model.

Analytical results indicate that (1) perceived susceptibility, perceived severity, perceived benefits and health self-efficacy positively influence users’ smartphone avoidance intention; (2) perceived barriers negatively influence smartphone avoidance intention, while (3) cues to action reinforce the relationships between perceived susceptibility/perceived benefits and smartphone avoidance intention, but attenuate the relationships between perceived barriers/health self-efficacy and smartphone avoidance intention.

This study demonstrates that HBM is invaluable in explaining and promoting users’ smartphone avoidance intention, thereby extending extant literature on both HBM and smartphone avoidance.

Research on smartphone avoidance is still in a nascent stage. This study contributes to the field by offering a fresh theoretical lens for pursuing this line of inquiry together with robust empirical evidence.

Research on the commitment to customer service (CCS) typically considers either trainable behavior or external stimuli such as financial incentives vital to CCS. Utilizing the cultural context of Confucian Asia, this study proposes a novel approach that shifts the focus towards the antecedents of the informal institutional environment.

This research considers four informal institutions typical for Confucian Asia about their influence on CCS: power distance, perceived individual independence, openness to change, and informal network ties. Hypotheses are tested in a structural equation model using data obtained from a South Korean subject pool.

Results show that informal institutions like power distance and network ties, and mediators like perceived individual independence and openness to change are positively related to CCS. Power distance and network ties also have a direct positive effect on openness to change. Moreover, power distance negatively affects perceived individual independence.

The authors' findings contribute to the service management literature by showing that a given CCS of service employees can be explained by antecedents of the company's informal institutional environment.

From a human resource perspective, the informal institutional environment should be taken into account when establishing a supporting organizational culture and designing management training programs.

This research introduces the institutional view to services management research, focusing on the role that informal institutions play. In particular, factors like power distance and network ties that influence CCS are tested for the first time.

The purpose of this paper is to present a deep ensemble neural network model for the detection of forest fires in aerial vehicle videos.

Presented deep ensemble models include four convolutional neural networks (CNNs): a faster region-based CNN (Faster R-CNN), a simple one-stage object detector (RetinaNet) and two different versions of the you only look once (Yolo) models. The presented method generates its output by fusing the outputs of these different deep learning (DL) models.

The presented fusing approach significantly improves the detection accuracy of fire incidents in the input data.

The computational complexity of the proposed method which is based on combining four different DL models is relatively higher than that of using each of these models individually. On the other hand, however, the performance of the proposed approach is considerably higher than that of any of the four DL models.

The simulation results show that using an ensemble model is quite useful for the precise detection of forest fires in real time through aerial vehicle videos or images.

By this method, forest fires can be detected more efficiently and precisely. Because forests are crucial breathing resources of the earth and a shelter for many living creatures, the social impact of the method can be considered to be very high.

This study fuses the outputs of different DL models into an ensemble model. Hence, the ensemble model provides more potent and beneficial results than any of the single models.