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The aim of this research is twofold: first, to get more insights on digital maturity to face the emerging 4.0 augmented scenario by identifying artificial intelligence (AI) competencies for becoming hybrid employees and leaders; and second, to investigate digital maturity, training and development support and HR satisfaction with the organization as valuable predictors of AI competency enhancement.

A survey was conducted on 123 participants coming from different industries and involved in functions dealing with the ramifications of Industry 4.0 technologies. The sample has included predominately small-to-medium organizations. A quantitative analysis based on both exploratory factor analysis and multiple linear regression was used to test the research hypotheses.

Three main competency clusters emerge as facilitators of AI–human interaction, i.e. leadership, technical and cognitive. The interplay among these clusters gives rise to plastic knowledge, a kind of moldable knowledge possessed by a particular human agent, here called hybrid. Moreover, organizational digital maturity, training and development support and satisfaction with the organization were significant predictors of AI competency enhancement.

The size of the sample, the convenience sampling method and the geographical context of analysis (i.e. California) required prudence in generalizing results.

Hybrids’ plastic knowledge conceptualized and operationalized in the overall quantitative analysis allows them to fill in the knowledge gaps that an AI agent-human interplay may imply, generating alternative solutions and foreseeing possible outcomes.

There is evidence that quality improvement projects developed with the participation of patients and family members are more likely to result in a sustainable change. To identify the intervening factors is an important step in promoting and supporting patient and family members’ engagement.

A survey was carried out with 90 hospitals. A total of 35 intervening factors were evaluated by the healthcare professionals from the quality area using a Likert scale. Factor analysis was applied to identify the relationship among the factors and cluster analysis and the standardized scores for each new latent variable were obtained to observe the association between them and hospitals profile. Cluster analysis allowed to group the hospitals with similar responses and to analyze whether there was any association with the profile of the institutions.

A total of ten intervening factors are identified: two in the financial dimension, five in the structural and three in the personal and cultural. The standardized scores of latent variables suggest that the financial factors could be affected by the hospital capacity. The structural factors could be impacted by the accreditation status, location (region) and administrative control (ownership). And the personal and cultural factors could be by the location and dominant organizational culture. All of factors are influenced by the performed quality management activities. The cluster analysis allowed the identification of three groups in the financial dimension, and four in the other two dimensions. Except for the accreditation status in the personal and cultural dimension, no evidence of association between the groups and the variables raised to characterize the profile of the hospitals was found.

The study contributed to identify the relationship among the intervening factors turning possible to simplify and reduce them more comprehensively than those originally identified in the literature and at the same time maintaining the representativeness of the original variables.

Based on the strong feature representation ability of the convolutional neural network (CNN), generous object detection methods in remote sensing (RS) have been proposed one after another. However, due to the large variation in scale and the omission of relevant relationships between objects, there are still great challenges for object detection in RS. Most object detection methods fail to take the difficulties of detecting small and medium-sized objects and global context into account. Moreover, inference time and lightness are also major pain points in the field of RS.

To alleviate the aforementioned problems, this study proposes a novel method for object detection in RS, which is called lightweight object detection with a multi-receptive field and long-range dependency in RS images (MFLD). The multi-receptive field extraction (MRFE) and long-range dependency information extraction (LDIE) modules are put forward.

To concentrate on the variability of objects in RS, MRFE effectively expands the receptive field by a combination of atrous separable convolutions with different dilated rates. Considering the shortcomings of CNN in extracting global information, LDIE is designed to capture the relationships between objects. Extensive experiments over public datasets in RS images demonstrate that our MFLD method surpasses the state-of-the-art methods. Most of all, on the NWPU VHR-10 dataset, our MFLD method achieves 94.6% mean average precision with 4.08 M model volume.

This paper proposed a method called lightweight object detection with multi-receptive field and long-range dependency in RS images.

This research addresses a need in early childhood education for evidence-based teaching strategies that build emotional self-regulation skills in young children. The intervention assessed in this study focused on increasing the emotion vocabulary of preschool-aged students.

This mixed-methods, quasi-experimental study evaluated the impact a dialogic reading approach combined with direct instruction of emotion words during a shared book-reading activity had on students' emotion vocabulary knowledge. The study was conducted in a licensed daycare center in a suburb of Chicago, Illinois, with ten four- and five-year-old students. Pre- and post-session surveys assessed the intervention's impact on the students' receptive and expressive vocabulary knowledge, and observation notes captured the students' responses to the intervention activities.

The results showed significant increases with small to medium effect sizes between the students’ pre- and post-session survey scores for both receptive and expressive emotion vocabulary knowledge, a strong positive correlation between the level of student engagement during the intervention and their emotion vocabulary assessment scores, and the impact other variables had on the intervention’s effectiveness.

This research provides information on a culturally adaptable and quickly learned teaching strategy that could be used to build emotional self-regulation skills in the early childhood classroom.

This research uniquely applies this intervention as a universal strategy with preschool-aged children.

Natural environments and imagery are known to have a myriad of effects on people’s physical and psychological states. However, little is known about how exposure to nature-related imagery can influence consumers’ motivational states. This research investigates the effect of exposure to nature on consumers’ regulatory focus. More specifically, this paper proposes that consumers exposed to nature will exhibit stronger promotion-oriented focus and weaker prevention-oriented focus, and as such, these consumers will prefer promotion-framed marketing messages over prevention-framed ones. This paper aims to explore a mediating mechanism and a boundary condition for this effect.

A series of six experiments, including an Implicit Association Test, examined the effect of exposure to images of natural objects and scenes (in contrast with non-nature imagery) on consumers’ regulatory focus and whether they experienced regulatory fit when encountering promotion-framed (vs prevention-framed) advertising messages.

The results revealed that consumers exhibited lower prevention-focused and higher promotion-focused motivational orientation after exposure to nature. Furthermore, exposure to nature led consumers to experience more regulatory fit with promotion-oriented marketing messages than prevention-oriented ones. This study found that natural environments offer urban consumers a reprieve from their day-to-day life, which mediates the effect of exposure to nature on regulatory focus. This study investigated the boundary condition of engaging in maintenance of nature (e.g. mow the grass) in which the effects of nature on regulatory focus were attenuated.

This study used text and pictures related to nature as a way to expose the online participants to nature. Future research may use field studies with participants in real natural settings, with expectation of stronger effects. Second, this study examined mostly urban American participants. There may be cultural differences or living situations (e.g. living “off the grid” and in the “wild”) that influence people’s relationship with nature. Future research may examine how these differences can affect the influence of exposure to nature on motivational orientation.

The findings have direct implications for marketing managers and other related stakeholders. Exposing urban consumers to nature – even images of nature – they become more receptive to promotion-framed advertisements and marketing communications (vs prevention-framed messaging).

Little is currently known about how exposure to nature can influence psychological processes such as motivational orientation. This research contributes to the understanding of consumers’ responses to nature-related imagery in advertising and the effect that nature imagery has on consumers’ motivational orientation. This research also contributes to the body of work on regulatory focus by identifying a novel context in which consumers’ motivational orientation can be influenced.

The response of the receiver to a voiced patient safety concern is frequently cited as a barrier to health professionals speaking up. The authors describe a novel Receiver Mindset Framework (RMF) to help health professionals understand the importance of their response when spoken up to.

The framework draws on the broader receiver-focussed literature and integrates innovative findings from a series of empirical studies. These studies examined different receiver behaviour within vignettes, retrospective descriptions of real interactions and behaviour in a simulated interaction.

The authors' findings indicated that speaking up is an intergroup interaction where social identities, context and speaker stance intersect, directly influencing both perceptions of and responses to the message. The authors' studies demonstrated that when spoken up to, health professionals poorly manage their emotions and ineffectively clarify the speaker's concerns. Currently, targeted training for receivers is overwhelmingly absent from speaking-up programmes. The receiver mindset framework provides an evidence-based, healthcare specific, receiver-focussed framework to inform programmes.

Grounded in communication accommodation theory (CAT), the resulting framework shifts speaking up training from being only speaker skill focussed, to training that recognises speaking up as a mutual negotiation between the healthcare speaker and receiver. This framework provides healthcare professionals with a novel approach to use in response to speaking up that enhances their ability to listen, understand and engage in point-of-care negotiations to ensure the physical and psychological safety of patients and staff.

Building on the relationship marketing and stimulus-organism-response (SOR) theory, the purpose of this paper is to study the impact of the integrated store service quality (ISSQ) on the omnichannel customer lifetime value (CLV). The mediating role of customer commitment (affective, normative and continuance) and relationship program receptiveness with the moderating role of customer relationship proneness were relied upon to better understand the omnichannel customer profitability metric (CLV).

The study is descriptive and relies upon the cross-sectional data collected using the self-administered structured questionnaires from 785 omnichannel shoppers. A purposive sampling technique was performed in the study. Structural equation modeling was performed using the SMART-PLS 4.0 software to analyze the data.

The results indicate that omnichannel customer commitment (affective, normative and continuance) differentially mediates the relationship between ISSQ and relationship program receptiveness, subsequently impacting the omnichannel CLV. The customer relationship proneness significantly and positively moderated the relationships between different dimensions of customer commitment and relationship program receptiveness.

The study relied upon the cross-sectional data from the Indian population aged above 18 years for testing the proposed model. Further studies could test the model across different populations to generalize the study results.

This study addresses the need to investigate the omnichannel retail store customer profitability and their relationship performance with the store. By testing the customer relationship management model in the omnichannel retail store context, this study is the first to show that ISSQ will impact the customer profitability and relationship performance metric (CLV) through omnichannel customer commitment and relationship program receptiveness. The moderating effect of customer relationship proneness on a few proposed hypotheses was also tested to give managerial recommendations.

This study aims to investigate the determinants of employees’ readiness to adapt to the change from the conventional banking system to the Islamic banking system in Libya. The determinants identified by this research include supervisors’ support, trust in leadership, participatory management, employees’ involvement in the change, the attitude of top management towards the change, openness towards the change and nature of change.

The study adopted a quantitative approach by using a self-administered questionnaire survey where a total of 482 sets of questionnaires were distributed manually of which 316 completed questionnaires were found to be usable. The structural equation modelling techniques were used to test and validate the proposed model.

Statistically, the study found that supervisor support, employees’ involvement in the change process and openness towards change showed significant relationships on employees’ readiness to perform their jobs in Libyan banks where the conventional financial system had been changed to the Islamic banking system. However, on the contrary, trust in leadership, participatory management and the attitude of top management towards the change process were found to have no significant relationship towards employees’ readiness. In addition, a significant moderation effect of the nature of change has been found on the relationship between openness to change and employees’ readiness for change.

Determinants of employees’ readiness for the transformation process identified in the present study can be used in assessing the employees’ readiness before implementing any change. In fact, openness and willingness towards change were found to be significant factors in employees’ readiness in the transformation process. Therefore, banks can use this information as one of the important criteria in recruiting employees. Open-minded and receptive attitudes towards change could be the quality of employees that banks should look for.

Overall, this study can be considered as novel because its findings will prompt other researchers to conduct and expand similar research in this field. Specifically, the findings of this research could facilitate the Central Bank of Libya in identifying their readiness gaps and organisational obstacles that stalled the move of conventional banks in Libya to migrate into the Islamic banking system.

This aims to explore the relationship between the agile methods barriers in large-scale contexts and the benefits for business, team and product and process, exploring the organizational readiness (OR) mediating role.

We propose a theoretical model through survey-based research, applying partial least square structural equation modelling.

We confirmed that OR mediating effect on the relationship between agile methods barriers and team benefits. We operationalized OR in a broader context that embeds the strategic alignment of large-scale agile implementation, considering variables such as organizational structure and culture.

The data are cross-sectional rather than longitudinal, which limits temporal interpretations of the associations between agile methods and organizational issues.

The findings offer a way forward for organizations already using or planning to implement agile management to understand the pathway towards achieving the expected benefits. Our study also unveils the importance of looking at OR when implementing such a complex change in management from traditional to large-scale contexts.

Our results show the significant and positive influence of agile method on all three benefit variables (team, business, product and processes). Furthermore, we identified the significant and positive mediating role of OR on the relationship between agile method and team benefits.

The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage, lack of effective features, and small sample sizes, resulting in low fault identification accuracy and slow efficiency. The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.

First, to address the impact of background noise on the accuracy of anomaly signals, the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD) method is used to eliminate strong noise in pipeline signals. Secondly, to address the strong data dependency and loss of local features in the Swin Transformer network, a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed. This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities. Thirdly, to address the sparsity and imbalance of anomaly samples, the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.

In the pipeline anomaly audio and environmental datasets such as ESC-50, the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods. Additionally, the model achieved 98.7% accuracy on the preprocessed anomaly audio dataset and 99.0% on the ESC-50 dataset.

This paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model, addressing noise interference and low accuracy issues in pipeline anomaly detection, and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.