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Drawing on signaling theory, the purpose of this study is to explore how high-performance work systems (HPWS) interact with leader–member exchange (LMX) to predict employees' proactive behavior and job engagement. Moreover, the present study also proposes the mediating role of job engagement in the interactive effects of HPWS and LMX quality on proactive behavior.

The data of this study include 228 customer-contact employees and 44 store managers from chain store enterprises in the service sector in Taiwan. The proposed models were tested with hierarchical linear modeling and Monte Carlo simulation.

The results show a significant interactive effect of HPWS and LMX on job engagement and proactive behavior. In addition, job engagement serves as a vital mechanism linking the interactive effect of HPWS and LMX quality on proactive behavior.

This study uses signaling theory to unpack the question when and how HPWS can be more influential on employees' proactive behavior. In particular, the positive effect of HPWS on proactive behavior is more prominent only when employees enjoy high LMX quality with their respective line managers. In addition, the interactive effects of HPWS and LMX quality on proactive behavior are mediated by job engagement. The findings provide valuable theoretical and managerial contribution by integrating HRM and leadership research.

In today’s Taiwan, sha-cha sauce is an indispensable ingredient for beef hot pot and stir-fried dishes. The purpose of this paper contextualizes the history of sha-cha sauce in Tainan, the oldest city in Taiwan, and argues that sha-cha sauce, introduced by Chaoshan immigrants, has contributed to new styles and habits of beef consumption tastes and habits in the post-1949 Tainan and beyond.

This paper uses documentary materials, oral interviews and diaries to explore the relationship between beef consumption and sha-cha sauce. It begins with an historical overview of Taiwan’s beef consumption during the Japanese colonial era (1895-1945). Then, it focuses on two Chaoshan business enterprises: the Bull-Head, which makes the world’s largest “canned sha-cha sauce,” and the Xiao Haozhou, a Tainan restaurant specializing in sha-cha beef hot pot. Finally, this study analyzes Xinrong Wu, a Tainan gentry whose diary entries from 1933 to 1967 documented the changing dietary habits of beef consumption among Taiwanese.

The Chaoshan migrants played an important role in introducing the sha-cha sauce to postcolonial Tainan, and this input bolstered the beef consumption among Taiwanese. The production of sha-cha provided a reliable source of income for these migrants in Tainan, and major businesses like the Bull-Head became the international brands of Taiwanese food products.

The study, though limited to Tainan, reveals the symbiosis between popularization of sha-cha sauce and widespread beef consumption in Taiwan.

This study helps researchers examine the connection between Chinese migrations and food culture.

This paper is an original scholarly investigation of the relationship between food diet and Chaoshan migration in postcolonial Tainan.

High speed axial flow pumps are widely used in aircraft fuel systems. Conventional axial flow pumps often generate radial secondary flows at partial-load conditions which influence the flow structure and form a “saddle-shaped” region in the Q-H curve that can destabilize the operation. Thus, the “saddle-shaped” Q-H region must be eliminated. The paper aims to discuss these issues.

The swept stacking method is often used for radial flow control in turbo-machinery impeller blade design. Hence, this study uses the swept stacking method to design a high speed axial flow pump. The detached eddy simulation method and experiments are used to compare the performance of a swept blade impeller in a high speed axial fuel pump with the original straight blade impeller. Both the pump performance and internal flow characteristics are studied.

The results show separation vortices in the impeller with the straight blade design at partial-load conditions that are driven by the rotating centrifugal force to gather near the shroud. The swept geometry provides an extra force which is opposite to the rotating centrifugal force that creates a new radial equilibrium which turns the flow back towards the middle of the blade which eliminates the vortices and the “saddle-shaped” Q-H region. The swept blade impeller also improves the critical cavitation performance. Analysis of the pressure pulsations shows that the swept blade design does not affect the stability.

This study is the initial application of swept blades for axial flow liquid pumps. The results show how the swept stacking changes the radial equilibrium of the high density, high viscosity flow and the effects on the mass transfer and pressure pulsations. The swept blade effectively improves the operating stability of high speed fuel pumps.

This paper provides a comprehensive assessment of the influencing factors on knowledge transfer through meta-analysis with an emphasis on the influence of cultural contexts.

The approach involved the evaluation and analysis of 69 published empirical studies and the categorization of these studies into two groups based on different cultural contexts as described by Hofstede. A meta-analytic approach was then employed to provide a comparative analysis of the categorized studies.

The results of the meta-analysis of the influencing factors of knowledge transfer are consistent with the results obtained in most previous studies, indicating a maturation of research in this area. Influencing factors such as knowledge ambiguity, tie strength, trust, and common cognition are shown to impact knowledge transfer in different cultural contexts, particularly with regard to the individualism-low power distance and collectivism-high power distance dimensions defined by Hofstede.

This analysis was limited to the correlation between the influencing factors and the general performance in knowledge transfer and did not specifically address more detailed dimensions such as efficiency and effectiveness. In addition, this analysis was restricted to the cultural contexts of only two cultural dimensions. However, the review of this broad range of studies provided sufficient data to allow an in-depth analysis of related influencing factors and helped to illustrate and exemplify the influencing mechanisms of culture on knowledge transfer.

The results presented in this paper can help managers working in cross-cultural environments to understand the key influencing factors that affect knowledge transfer in the workplace. By understanding these factors, managers can more effectively implement methods and procedures that improve cross-cultural knowledge transfer in the work environment.

This paper provides a detailed insight into the influencing factors found between two distinctive cultural contexts and offers a fresh analysis of influencing factors with regard to knowledge transfer in a cross-cultural environment.

Assembly variations, which will propagate along the assembly process, are inevitable and difficult to analyze in Aeronautical Thin‐Walled Structures (ATWS) assembly. The purpose of this paper is to present a new method for analyzing the variation propagation of ATWS with automated riveting.

The paper addresses the variation propagation model and method by first, forming a novel Stage‐State model to represent the process of automated riveting. Second, the effect of positioning error on assembly variation is defined as propagation variation (PV), and propagation matrix of key characteristic points (KCP) is discussed. Third, the effect between the variations in each stage is defined as expansion variation (EV). According to the analysis of mismatch error and the reference transformation, the expansion matrix is formed.

The model can solve the variation propagation problem of ATWS with automated riveting efficiently, which is shown as an example of this paper.

The variation obtained by the model and method presented in this paper is in conformity with the variation measured in experiments.

The propagation variation and expansion variation is proposed for the first time, and variations are studied according to novel propagation matrix and expansion matrix.

This study aims to let researchers sort out the past, look to the future and boost further management research.

This paper takes the exploration of the application of AR in the field of management research as the starting point. Through the combing and induction of AR literature to accurately grasp the essential characteristics of AR, this paper analyzes the application prospects and possible difficulties of AR in the field of management research in China, puts forward specific suggestions and explores the specific path of the transformation of management academic research to practice from the perspective of research methods.

In research methods, empirical research and case studies have become the mainstream research methods, but there is a passive research limitation of “study what happens.” Action research is different from the standpoint of empirical research that is independent of the research object. It no longer simply treats managers as the “object of information acquisition,” but as the subject of participating in the research. Management should be oriented towards the transformation of practice, and action research (AR) that can organically integrate researchers and practitioners is just a “bridge” to break the gap between academia and business circles to establish a cooperative relationship.

This paper takes the exploration of the application of AR in the field of management research as the starting point. Through the combing and induction of AR literature to accurately grasp the essential characteristics of AR, this paper analyzes the application prospects and possible difficulties of AR in the field of management research in China, puts forward specific suggestions and explores the specific path of the transformation of management academic research to practice from the perspective of research methods.

The current study aims to deliver a consolidated view of environmental sustainability in cold supply chain performance systems (CSCPS), incorporating theoretical and empirical analysis for improving environmental standards. For this purpose, this study firstly aims to explore and analyze the various crucial challenging factors for environmental sustainability in the cold supply chain (CSC). Secondly, it discovers the most effective sustainable strategies for improving the environmental sustainability of CSCPS.

The exploration of the crucial challenging factors and the proposed sustainable strategies have been done using a systematic literature review relevant to the sustainable performance of CSC. At the same time, semi-structured brainstorming sessions were conducted with the domain professionals having an industrial and academic background to finalize the strategies. Empirical analysis has been performed using an intuitionistic fuzzy (IF) based hybrid approach of SWARA and COPRAS methods.

The key findings of the study address that “higher energy consumption during refrigerated transportation and storage” is the most crucial challenge for environmental sustainability in CSC. In addition, “managerial refrain to profit decline due to sustainability implementation” is the second most crucial challenge that hinders the adoption of sustainable practices in CSCs. Meanwhile, the governmental attention to motivating organizations for green adoption and implementation of solar energy-driven refrigeration technologies are the two most important discoveries of the study that might help in improving CSC's environmental performance.

From the implications side, the study enriches and extends the current literature content on CSC sustainability. In addition, it offers sound managerial implications by identifying the challenges that create threats among the management for sustainability adoption and suggesting the most suitable sustainable strategies, which may help the management to raise the environmental performance of their CSC. Besides having various important theoretical and managerial implications for the study, contemplation of only environmental sustainability traits as a broader perspective limits the scope of the study.

The study's main contribution is the exploration of the most crucial challenges imparting obstructions in sustainable development and sustainable strategies, which may get the interest of the CSC players, market leaders, and industrial and academic practitioners working in the domain of CSC sustainability. In addition, this study offers structured theoretical and empirical evidence for CSC's environmental sustainability, thus playing a bridging role between theoretical sustainability concepts and its practical implications in CSC industries.

This study empirically tests the relationship between supply chain finance (SCF) and firm performance. Moreover, this study also investigates the potential role of industry competition in the proposed relationship between SCF and firm performance.

A conceptual framework is developed and tested using secondary data collected from 122 non-financial listed firms on the Pakistan Stock Exchange (PSX) for the period of ten years (2012–2021). Ordinary least squares (OLS) regression analysis is performed in STATA to validate the proposed relationships.

The results highlight that SCF has a positive impact on firm performance. Moreover, industry competition positively moderates the relationship between SCF and firm performance.

This study would help firms in assessing the value of operational financing to their financially constrained suppliers/customers by adopting supply chain finance practices. Furthermore, this study will help understand the role of the competitive environment in supply chain finance decision-making.

The findings will help core firms better understand how implementing SCF benefits firm performance under high product competition, especially in emerging markets.

Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways.

As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways.

The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions.

This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.

The purpose of this study is to propose an adaptive fault-tolerant control approach based on output feedback for a class of quadrotor unmanned aerial vehicles system. In the event of a controlled actuator failure, a stable flying of the aircraft can be achieved by selecting an appropriate sliding mode surface.

Aiming at the actuator failure of quadrotor aircraft during flight in the controllable range, a dynamic surface sliding mode passive fault-tolerant controller based on output feedback is designed based on the strong robustness of sliding mode method. Due to the unknown nonlinearity dynamics and parameter uncertainties in the system, a nonlinear observer is used to estimate them online.

The stability of the suggested algorithm is established using appropriate Lyapunov functions, and the performance of the proposed control approach is demonstrated using hardware-in-the-loop simulation.

An error performance function is introduced into the controller to ensure the convergence speed and accuracy of errors are within the predetermined range. By using the norm estimation method, there is only one parameter that needs to be updated in each step of the control process, which considerably minimizes the calculation burden. Finally, the validity of the proposed control scheme is verified on the hardware-in-the-loop simulation, and the results show that the proposed control method has achieved the desired results.