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Although digitalization in the workplace is burgeoning, tools are needed to facilitate personalized learning in informal learning settings. Existing knowledge recommendation techniques do not account for dynamic and task-oriented user preferences. The purpose of this paper is to propose a new design of a knowledge recommender system (RS) to fill this research gap and provide guidance for practitioners on how to enhance the effectiveness of workplace learning.

This study employs the design science research approach. A novel hybrid knowledge recommendation technique is proposed. An experiment was carried out in a case company to demonstrate the effectiveness of the proposed system design. Quantitative data were collected to investigate the influence of personalized knowledge service on users’ learning attitude.

The proposed personalized knowledge RS obtained satisfactory user feedback. The results also show that providing personalized knowledge service can positively influence users’ perceived usefulness of learning.

This research highlights the importance of providing digital support for workplace learners. The proposed new knowledge recommendation technique would be useful for practitioners and developers to harness information technology to facilitate workplace learning and effect organization learning strategies.

This study expands the scope of research on RS and workplace learning. This research also draws scholarly attention to the effective utilization of digital techniques, such as a RS, to support user decision making in the workplace.

This study initiated an investigation of how the Macau–Zhuhai tourism cooperated and discussed how Macau and Zhuhai could join hands to develop tourism in the region. The study demonstrated an approach for destination marketing organizations to explore online tourist-generated content and to understand tourists' perceptions of the destination image (DI).

A total of 1,291,057 reviews (535,317 for Macau and 755,740 for Zhuhai) were collected, analyzed and examined to determine how the DI s of Macau and Zhuhai changed during the period of 2015–2019 based on tourist-generated content on travel websites (TripAdvisor, Ctrip.com and Qyer.com) through a text-mining approach.

The result revealed that Macau and Zhuhai were in a hybrid of competition and collaboration on tourism DI s. First, Macau and Zhuhai competed in hotel and catering industry. Macau was appealing to international tourists and provided high-end and prestigious offerings; while Zhuhai was impressed by cost-effective accommodation and food. Second, Macau diversified industrial structure with diverse “Tourism, Leisure and Recreation” and “Culture, History and Art” more than Zhuhai did. Meanwhile, Macau should balance the different demands of international and Chinese tourists. Third, complementary potentials were found in natural resources, urbanization technology and tourism innovation and related projects.

The research provides valuable insights for policymakers and industrial managers on their endeavors to develop DIs. Policymakers should be able to develop supportive mechanisms and tourism facilitators to promote industrial collaboration and mutual DIs. Managers could refer to the components in the changing DIs and identify the developmental gaps and cooperation potentials in their targeted areas.

The research fulfills the gap in regional tourism studies on Macau, in which the evaluation on synergetic influence and neighbor effect from Zhuhai has been underexplored. Facilitated by up-to-date data mining techniques, the study contributes to both DI and coopetition literature in tourism marketing; and this should inspire further studies on the antecedences of DI changes, resolutions to the competing interests and DIs of different stakeholders in different forms of strategic cooperation in regional tourism. The employment of DIs is an explicit demonstration of tourists' immersion and values attached to the destination, providing effective cues on the status of coopetition.

This paper aims to study the issues of adaptive fuzzy control for a category of switched under-actuated systems with input nonlinearities and external disturbances.

A control scheme based on sliding mode surface with a hierarchical structure is introduced to enhance the responsiveness and robustness of the studied systems. An equivalent control and switching control rules are co-designed in a hierarchical sliding mode control (HSMC) framework to ensure that the system state reaches a given sliding surface and remains sliding on the surface, finally stabilizing at the equilibrium point. Besides, the input nonlinearities consist of non-symmetric saturation and dead-zone, which are estimated by an unknown bounded function and a known affine function.

Based on fuzzy logic systems and the hierarchical sliding mode control method, an adaptive fuzzy control method for uncertain switched under-actuated systems is put forward.

The “cause and effect” problems often existing in conventional backstepping designs can be prevented. Furthermore, the presented adaptive laws can eliminate the influence of external disturbances and approximation errors. Besides, in contrast to arbitrary switching strategies, the authors consider a switching rule with average dwell time, which resolves control problems that cannot be resolved with arbitrary switching signals and reduces conservatism.

With the increasing use of crowdfunding platforms in raising funds, it has become an important and oft-researched topic to analyze the critical factors associated with successful or failed crowdfunding. However, as a major subject of crowdfunding, medical crowdfunding has received much less scholarly attention. The purpose of this paper is to explore how contingency factors combine and casually connect in determining the success or failure of medical crowdfunding projects based on signal theory.

The paper adopts the crisp-set qualitative comparative analysis to analyze the causal configurations of 200 projects posted on a leading medical crowdfunding platform in China “Tencent Donation.” Five anecdotal conditions that could have an impact on the outcome of medical crowdfunding campions were identified. Three relate to the project (funding duration, number of images and number of updates) and two relate to the funding participants (type of suffer and type of fund-raiser).

The results show that diversified configurations of the aforementioned conditions are found (six configurations for successful medical crowdfunding projects and four configurations for failed ones).

Despite the fact that there are a considerably large number of medical crowdfunding projects, relatively few researches have been conducted to investigate configurational paths to medical crowdfunding success and failure. It is found that there are certain combinations of conditions that are clearly superior to other configurations in explaining the observed outcomes.

The purpose of this paper is twofold. First, to propose an operational model for aircraft maintenance routing problem (AMRP) rather than tactical models that are commonly used in the literature. Second, to develop a fast and responsive solution method in order to cope with the frequent changes experienced in the airline industry.

Two important operational considerations were considered, simultaneously. First one is the maximum flying hours, and second one is the man-power availability. On the other hand, ant colony optimization (ACO), simulated annealing (SA), and genetic algorithm (GA) approaches were proposed to solve the model, and the upper bound was calculated to be the criteria to assess the performance of each meta-heuristic. After attempting to solve the model by these meta-heuristics, the authors noticed further improvement chances in terms of solution quality and computational time. Therefore, a new solution algorithm was proposed, and its performance was validated based on 12 real data from the EgyptAir carrier. Also, the model and experiments were extended to test the effect of the operational considerations on the profit.

The computational results showed that the proposed solution algorithm outperforms other meta-heuristics in finding a better solution in much less time, whereas the operational considerations improve the profitability of the existing model.

The authors focused on some operational considerations rather than tactical considerations that are commonly used in the literature. One advantage of this is that it improves the profitability of the existing models. On the other hand, identifying future research opportunities should help academic researchers to develop new models and improve the performance of the existing models.

The experiment results showed that the proposed model and solution methods are scalable and can thus be adopted by the airline industry at large.

In the literature, AMRP models were cast with approximated assumption regarding the maintenance issue, while neglecting the man-power availability consideration. However, in this paper, the authors attempted to relax that maintenance assumption, and consider the man-power availability constraints. Since the result showed that these considerations improve the profitability by 5.63 percent in the largest case. The proposed operational considerations are hence significant. Also, the authors utilized ACO, SA, and GA to solve the model for the first time, and developed a new solution algorithm. The value and significance of the new algorithm appeared as follow. First, the solution quality was improved since the average improvement ratio over ACO, SA, and GA goes up to 8.30, 4.45, and 4.00 percent, respectively. Second, the computational time was significantly improved since it does not go beyond 3 seconds in all the 12 real cases, which is considered much lesser compared to ACO, SA, and GA.

Multiple-source disturbances exist in the polarization sensor, which severely affect the sensor accuracy and stability. Hence, the disturbance analysis plays a vital role in improving the sensor orientation performance. This paper aims to present a novel sensor error model, a disturbances quantitative analysis, a calibration and performance test of polarization sensor based on a polarizing beam splitter.

By combining with the sensor coefficient errors, the Azimuth of Polarization (AoP) error model and the Degree of Polarization (DoP) error model are established, respectively. In addition, the multiple-source disturbances are classified, while the influence on the orientation accuracy is quantitative analyzed. Moreover, the least square optimization algorithm is employed to calibrate the sensor coefficients. Finally, an outdoor test is carried out to test the sensor long-term accuracy.

The theoretical analysis and numerical simulations illustrate that the sensor accuracy is closely related to the disturbances. To eliminate the influence of the disturbances, the least square optimization algorithm, which can minimize the sum of squares of the residual difference of AoP and DoP, is used to calibrate the sensor coefficients. The outdoor test indicates that the sensor can maintain long-term accuracy and stability.

The main contribution of this paper is to establish a novel sensor error model, where the sensor coefficient errors are introduced. In addition, the disturbances are classified and analyzed to evaluate the orientation accuracy of the sensor.

This era is an era of social media (SM); thus, it is an essential tool for communication among individuals and organizations. The excessive use of SM by employees has raised many questions about their job performance. Therefore, there is a dire need to investigate the effects of SM use on an employee’s job performance mediated by knowledge exchange. Furthermore, the purpose of this paper is to examine how the organization’s SM rules can moderate the relationship between personal and work-related use of SM with information sharing and obtaining information.

Quantitative methodology was used and randomly 1,200 questionnaires data were collected physically from the employees of the public and private sectors in Pakistan. To examine the hypothesized relationships, partial least squares (PLS), rather than covariance-based structural equation modeling, was used to analyze the data. For this reason, multivariate technique, Smart PLS-3.2.1, was used for data analysis.

The findings of this study demonstrated that personal and work-related use of SM could enhance employees’ job performance through knowledge exchange, and SM rules have adverse impacts on the relationships between SM use and knowledge exchange.

This study provides a novel model for the investigation of whether SM use affects employees’ job performance. Furthermore, it will help the policy makers and researchers regarding the management of SM use at work.

It is worth mentioning that in supply chain management (SCM), managerial decisions are often based on optimization of resources. Till the early 2000s, supply chain optimization problems were being addressed by conventional programming approaches such as Linear Programming, Mixed-Integer Linear Programming and Branch-and-Bound methods. However, the solution convergence in such approaches was slow. But with the advent of Swarm Intelligence (SI)-based algorithms like particle swarm optimization and ant colony optimization, a significant improvement in solution of these problems has been observed. The purpose of this paper is to present and analyze the application of SI algorithms in SCM. The analysis will eventually lead to development of a generalized SI implementation framework for optimization problems in SCM.

A structured state-of-the-art literature review is presented, which explores the applications of SI algorithms in SCM. It reviews 56 articles published in peer-reviewed journals since 1999 and uses several classification schemes which are critical in designing and solving a supply chain optimization problem using SI algorithms.

The paper revels growth of swarm-based algorithms and seems to be dominant among all nature-inspired algorithms. The SI algorithms have been used extensively in most of the realms of supply chain network design because of the flexibility in their design and rapid convergence. Large size problems, difficult to manage using exact algorithms could be efficiently handled using SI algorithms. A generalized framework for SI implementation in SCM is proposed which is beneficial to industry practitioners and researchers.

The paper proposes a generic formulation of optimization problems in distribution network design, vehicle routing, resource allocation, inventory management and supplier management areas of SCM which could be solved using SI algorithms. This review also provides a generic framework for SI implementation in supply chain network design and identifies promising emerging issues for further study in this area.

Seru (cell) manufacturing system has achieved huge success in production. However, related research is limited, especially, the problem of cross-trained worker assignment. The purpose of this paper is to solve this problem for two representative seru types, divisional and rotating seru, and subsequently, compare throughput performance between the two seru types under reasonable worker-task assignment.

For the cross-trained worker assignment problem, this research presents new models aiming at maximum throughput of seru and workload balance of workers under considering skill levels (SLs) and several practical constraints. Furthermore, factorial experiments that involve four factors, the number of tasks (NT), gap of task time, SL and gap of SL, are performed to compare throughput performance between divisional and rotating seru.

First, the maximum throughput of the divisional seru is better than that of the rotating seru under suitable worker assignment. Second, in the seru which has less difference of task time, throughput performance of the rotating seru is better than the divisional seru when the NT is close to the number of assigned workers. Moreover, the influence tendency of the four factors on throughput gap between the two seru types is significant.

This research addresses the worker-task assignment for divisional and rotating seru based on their characteristics. Several findings can help decision maker select more applicable seru type according to various production environments from the perspective of optimum throughput.

Based on the factors derived from the structural embeddedness theory, the purpose of this paper is to investigate the antecedents to the adoption intention for eSCM from two perspectives: buyer and supplier. The six factors examined in this study are product complexity, product specificity, the number of partners, relationship duration, dependence disadvantage and dependence advantage.

A questionnaire was designed to collect data from Mainland China with 206 valid data received. Regression analysis was employed to test the hypotheses proposed.

The differences in the results show that product specificity and dependence disadvantage are significant determinants of eSCM adoption for buyers’ perspective, but not from that of suppliers. In addition, product complexity and dependence advantage (although negatively associated with eSCM adoption) are significant for suppliers, but not for buyers. Number of partners and relationship duration are significant determinants from both perspectives.

This research contributes to understanding on how the factors embedded in an exchange structure influence the adoption of eSCM from the angles of both the buyers and suppliers. We fill the research gap in the existing literature by recognizing the differences in the roles of the buyer and supplier regarding the antecedents to eSCM adoption.