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A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

The growing number of people with multiple problems in different areas of life means that cross-domain interprofessional collaboration is becoming increasingly important. This study aims to focus on interprofessional collaboration between professionals from social work and financial and employment service organizations in The Netherlands. This type of cross-domain collaboration is still mostly superficial, and limited empirical knowledge is available about its beneficial factors.

The interprofessional collaboration model of Mulvale et al. (2016) was used as theoretical background for the research methodology and to reflect on the findings of our study. Data was collected through a qualitative study among professionals (N = 18) from social work and financial and employment service organizations in three different Dutch municipalities.

Similar team-level collaboration mechanisms of Mulvale et al.’s (2016) model were seen within this study. Joint client meetings were very beneficial for the interprofessional collaboration. Further beneficial factors include the decision-making process, team vision, client as an equal member, open communication and the appointment of a coordinator.

Further special attention is needed to determine how best to allocate co-ordinating tasks, and how organizational and policy contexts affect the functioning of interprofessional collaborative teams.

This study offers an empirical view on a cross-domain collaboration between social work and financial and employment service organizations, by using Mulvale et al.’s (2016) model. In addition, this study also offers special attention to the role of the client in interprofessional collaborative teams.

The study integrated understanding by design-Internet of Things (UbD-IoT) education with design thinking and computational thinking to plan and design an IoT course. Cross-domain application examples were employed to train students in problem-understanding, deep thinking and logical design for IoT applications.

In this study, the UbD model was integrated with design thinking and computational thinking in the planning and design of an IoT course. The examples of cross-domain applications were used to train students to understand a problem by engaging themselves in deep thinking and helping them think and design logically for an IoT application.

The UbD-IoT learning design greatly decreased students' overall cognitive load. UbD-IoT learning has a significant impact on the performance of computational thinking in problem-solving and problem-understanding. The impact of UbD-IoT learning on logical thinking and program learning cognition in students needs to be verified.

The results of this study have shown that the UbD model is effective in reducing the cognitive load of a learning course and also strengthens T-competencies in the lateral skills of computational thinking, critical problem-solving, logical thinking and creative thinking.

Drawing on the whole-life perspective of career development and the conservation of resources theory, the authors consider whether self-initiated expatriates' (SIEs’) cultural intelligence (CQ) is a general, cross-domain resource that helps SIEs gain resources in the work and nonwork domains. The authors contend that CQ will be associated with greater levels of organizational and community embeddedness, which in turn will facilitate their career satisfaction. The authors also propose the role of perceived host country community diversity climate as an environmental condition that, when low, strengthens the relationships between CQ and organizational and community embeddedness.

The authors examine the study hypotheses based on two distinct samples of SIEs (Sample 1: 169 Asian SIE professionals; Study 2: 147 SIE academics).

SIEs' CQ positively relates to their organizational and community embeddedness, which in turn is associated with greater levels of career satisfaction. The authors also find that SIEs with high CQ are more likely to experience community embeddedness and career satisfaction when they perceive that the host country community diversity climate is low.

First, this study goes beyond existing literature that rarely examines nonwork inputs to SIE career success. Second, extending previous CQ research with a strong organizational focus, the authors investigated how CQ influences SIEs' work and nonwork embeddedness. Third, the authors found that the absence of a peripheral ecological condition, perceived host country community diversity climate, may strengthen the direct relationship between CQ and embeddedness and the indirect relationship between CQ and career satisfaction.

This paper examines the framing of digital transformation. The research questions are specified as follows: what are the different types of framing strategies in response to digital transformation? How do the strategies differ across organizations? Theoretically, the authors draw on the framing perspective to emphasize the use of linguistic frames in shaping innovation and change processes. Empirically, the authors choose to study the Taiwanese sectors, including publicly governed entities, traditional private business or technology-based ventures.

The authors’ approach combines topic modeling and qualitative analysis. Using data collected from newspaper and magazine articles, the authors employ topic modeling to generate a set of distinctive framings that Taiwanese actors typically adopt to motivate and justify their digital move. The authors also conduct personal interviews to qualitatively complement the authors’ topic modeling analysis and to identify the rationale behind the linguistic framings and the strategic differences brought about by the various organizations.

The authors identify five topics that the Taiwanese actors commonly used in the framing of digital transformation. These topics or frames are labeled as cross-domain coordination, market demand, intelligent technology, global trend and competition and digital innovation. The practical use of the framings is contingent on organizational characteristics. Furthermore, the authors show how the framings can be classified as either positive framing (e.g. winning the next war) or negative framing (e.g. innovate or die), generally applicable to organizations around the world struggling to cope with digital disruption.

The authors’ study has two research implications. First, the authors extend the appreciation of the digital transformation from the usual concern with technological and business model innovations to linguistic or framing practices. Second, the authors enrich the framing analysis by emphasizing a practice or contingency perspective based on sector difference. The findings are subject to the limitations of the choice of only established and reputable media outlets, the diatextual reading and filtering of useful articles for topic modeling analysis and the use of world frequency to account for frame significance.

The authors shift actors' attention from improving technical efficiency to acquiring linguistic resources in the pursuit of digitalization. For example, framing the digital transformation in terms of creating a market orientation calls for not only real consumer power but also strategic discursive competence that enables the move to change. The findings also point out that practitioners can enlarge the scope of their agency rather than being trapped in the habituated routine of practices. Despite social embeddedness, organizations are more often widely connected and built enough to call for more of the cognitive frames to appeal to heterogeneous stakeholders.

The authors study contributes to the literature by developing a linguistic or socio-cognitive view of digital transformation strategy that is capable of expanding organizational attention toward change and innovation. The authors explore menus of strategic frames employed by actors in response to digital transformation. We also address the application of a machine-learning tool such as topic modeling to explore the socio-cognitive dimensions of digital transformation. Furthermore, the analysis leads us to identify the outcomes or effects – either positive or negative – that move beyond the particular Taiwanese case to explain the framing of digital transformation in general.

Effective project learning can prevent projects from repeating the same mistakes; however, knowledge codification is required for project-to-project learning to be up-scaled across the temporal, geographical and organisational barriers that constrain personalised learning. This paper explores the state of practice for the structuring of codified project learnings as concrete boundary objects with the capacity to enable externalised project-to-project learning across complex boundaries. Cross-domain reconceptualisation is proposed to enable further research and support the future development of standardised recommendations for boundary objects that can enable project-to-project learning at scale.

An integrative literature review method has been applied, considering knowledge, project learning and boundary object scholarship as state-of-practice sources.

It is found that the extensive body of boundary object literature developed over the last three decades has not yet examined the internal structural characteristics of concrete boundary objects for project-to-project learning and boundary-spanning capacity. Through a synthesis of the dispersed structural characteristic recommendations that have been made across examined domains, a reconceptualised schema of 30 discrete characteristics associated with boundary-spanning capacity for project-to-project learning is proposed to support further investigation.

This review makes a novel contribution as a first cross-domain examination of the internal structural characteristics of concrete boundary objects for project-to-project learning. The authors provide directions for future research through the reconceptualisation of a novel schema and the identification of important and previously unidentified research gaps.

This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces.

This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framework to gradually adapt AI models’ knowledge while reducing personal data transmission and retention. The framework uses three different federated learning strategies to rapidly learn from different new data sources while minimizing personal data transmission and retention to a central machine.

The findings confirm that the proposed CFL framework can help minimize the use of personal data without compromising the AI model's performance. The gradual learning strategies help develop AI-enabled video surveillance that continuously adapts for long-term deployment in public spaces.

This study makes two specific contributions to advance the development of AI-enabled video surveillance in public spaces. First, it examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Second, it proposes a CFL framework to minimize data transmission and retention for AI-enabled video surveillance. The study provides comprehensive experimental results to evaluate the effectiveness of the proposed framework in the context of facial expression recognition (FER) which involves large-scale datasets.

At the forefront of current research is the investigation of how big data analytics capability (BDAC) and artificial intelligence capability (AIC) can enhance performance in concert. Therefore, current study intended to conduct more deep research into emerging phenomena and attempts to cover the gap by exploring how entrepreneurial orientations (EO) emphasize the use of two emerging capabilities under the moderating role of environmental dynamism which in turn augment co-innovation and hotel performance.

Data were collected from four-star and five-star hotels located in Kula Lumpur and Langkawi in Malaysia. A total of 260 responses were obtained from IT staff and senior managers with the assistance of a Manpower agency for data analysis. The hypotheses were examined by analyzing the data using PLS-SEM technique through Smart PLS 3 software.

The result revealed that EO has a positive and significant effect on co-innovation (CIN). Additionally, the BDAC and AIC have been tested and proven to be potential mediators between EO and CIN. Also, environmental dynamism as moderator has positive and significant effect on BDAC and co-innovation performance, however, not significant impact on AIC and co-innovation performance. Lastly, findings displayed positive and significant moderated mediation impact of environmental dynamics on BDAC and CIN with hotel performance, but not significant influence on AIC and co-innovation with hotel performance. For theoretical corroboration of the research findings, the current study integrated EO, resource-based view theory and contingent dynamic capabilities (CDC), because neither single stance can explicate an extant research framework.

This study anticipated the several implications for the entrepreneurs of hospitality industry. Managers are recommended to invest in the entrepreneurial traits of the employees/organizations and make strategic readjustment of their capabilities for sustained business performance.

The study goes beyond the normal inquiry by investigating moderated mediation impact of environmental dynamism between two emerging capabilities, co-innovation and hotel performance relationships. Another novelty of this study is to culminate the exploitation and adoption of emerging IT-based capabilities in cross domains of management, entrepreneurship, information systems management within the hotel industry.

Although work-related antecedents of abusive supervision are well-known, knowledge on the cross-domain antecedents of this destructive leadership behavior is scarce. Accordingly, this study aims to investigate off-the-job supervisors’ after-work experiences that may influence their work behavior. The authors explore how and when a supervisor’s poor recovery experiences lead to abusive supervisory behaviors through a negative start-of-workday mood for high vs low supervisor sleep quality.

The authors conducted a single-source, three-phase field study (N = 422) to test the proposed moderated mediation model for participants from a large telecommunications company located in Anhui province, People’s Republic of China.

Poor recovery experiences in the supervisor’s personal life can spill over to their work domain and provoke abusive supervisory behavior through the mediating effect of a negative start-of-workday mood. Moreover, a supervisor’s good night’s sleep (i.e. first-stage moderator) serves as a key mitigating factor to diminish the negative start-of-workday mood resulting from a lack of relaxation, mastery experiences and control experiences (except for the lack of psychological detachment from work) and lessens abusive supervision.

This study contributes to the literature examining off-the-job events to understand the antecedents of abusive supervision that are beyond organizations’ control but significantly influence workplace behaviors, showing that not all antecedents of abusive supervision exist in the workplace; some are transferred from the home domain through nonvisible moods. Finally, the inclusion of sleep quality as a first-stage moderator provides insights on preventing abusive supervision caused by nonwork-related events. This adds a unique dimension to the abusive supervision literature by highlighting factors in the home domain that can prevent negative spillovers to the work domain. The authors conclude with some theoretical and practical implications for researchers and practitioners.