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Little empirical research has examined the role of culture on the internal marketing-employee satisfaction (ES) relationship, and even fewer studies focus on this subject in the airline sector. This paper aims to assess whether culture moderates the contribution of internal marketing to the satisfaction of employees from Gulf Cooperation Council (GCC) airlines.

The investigation is conducted via a structured questionnaire with 310 cabin crew members who work at three GCC airlines. Multi-group analysis was used within SmartPLS 3.0 to measure whether the impact of internal marketing on ES is moderated by cultural congruence.

The results reveal the practice of internal marketing contributes to ES in GCC airlines, while cultural congruence moderates the direct relationship between internal marketing and the satisfaction of airlines’ employees. The results indicate that airlines need to satisfy the cultural needs of employees to increase their satisfaction for successful internal marketing practice.

This study contributes towards assessing the moderation effects of cultural congruence on internal marketing practice in the airline sector. It contributes to the current literature on aviation research, as well as specific managerial implications for the airlines and managers to achieve higher competitiveness.

文化一致性是否会影响企业内部营销活动与员工满意度二者之间的关系？目前在这方面的研究还很稀缺, 而聚焦于航空领域的这一主题的研究更是少之又少。本研究以海湾合作委员会的航空公司为例, 旨在分析文化一致性因素能否调节企业内部营销活动对员工满意度的影响力。

本研究对三家海湾合作委员会的航空公司共计310名的空服人员展开了问卷调查。通过运用SmartPLS 3.0 软件的多群组分析方法来具体评估航空公司的内部营销活动对其员工满意度的影响是否会被文化一致性因素所调节。

研究结果显示, 在海湾合作委员会航空公司中, 内部营销活动的实施有助于员工满意度的提升；同时文化一致性因素可以调节航空公司的内部营销活动对其员工满意度的影响。该结果表明, 为了获得成功的内部营销实践, 航空公司需要满足员工的不同文化需求, 以期提高员工的满意度.

本研究聚焦于分析文化一致性对航空公司内部营销实践的调节效应。其不仅是对当前航空研究领域文献的补充, 同时也能够为航空公司及其管理者提供具体的管理方法, 从而获得更多的竞争优势。

el papel de la cultura en la relación de satisfacción de los empleados y el marketing interno ha sido examinada en pocas investigaciones empíricas, e incluso menos estudios se centran en este tema en el sector de las aerolíneas. Este documento tiene como objetivo evaluar si la cultura modera la contribución del marketing interno a la satisfacción de los empleados de las aerolíneas del Consejo de Cooperación del Golfo (CCG).

la investigación se real a través de un cuestionario estructurado con 310 miembros de la tripulación de cabina que trabajan en tres aerolíneas del CCG. El análisis de grupo múltiple se realizó con SmartPLS 3.0 para medir si el impacto del marketing interno en la satisfacción de los empleados es moderado por la congruencia cultural.

los resultados revelan que la práctica del marketing interno contribuye a la satisfacción de los empleados en las aerolíneas del CCG, mientras que la congruencia cultural modera la relación directa entre el marketing interno y la satisfacción de los empleados de las aerolíneas. Los resultados indican que las aerolíneas necesitan satisfacer las necesidades culturales de los empleados para aumentar su satisfacción por los logros del marketing interno.

este estudio contribuye a evaluar los efectos de moderación de la congruencia cultural en la práctica del marketing interno en el sector de las aerolíneas. Contribuye a la literatura actual sobre investigación de aviación, así como indicadores de las consecuencias específicas de gestión de personal para que las aerolíneas y los directivos logren una mayor competitividad.

This paper aims to analyze the spatiotemporal heterogeneity of regional total factor productivity (TFP) growth and explores how haze pollution and different levels of new-type urbanization affect China’s economic growth.

This paper constructs an index for evaluating the TFP growth of China’s 31 provinces by integrating slack-based measures and the Global Malmquist (GM) productivity index. Meanwhile, the panel threshold estimation method is used to examine the complex relationships among haze pollution, new-type urbanization and TFP growth.

The results reflect conspicuous spatiotemporal heterogeneity in TFP growth in China. Interestingly, the influence of haze pollution on TFP growth is limited by the “critical mass” of new-type urbanization in China. When new-type urbanization does not cross the first threshold, haze pollution has a negative but non-significant effect on TFP growth. When new-type urbanization crosses the first threshold but not the second, haze pollution has a significant positive impact on TFP growth. When new-type urbanization crosses the second threshold, haze pollution significantly and positively affects TFP growth with the strongest positive effect.

This study innovates by combining haze pollution and TFP growth and proposing an integrated framework from the perspective of new-type urbanization, providing insight into how different degrees of new-type urbanization impact the mechanism between haze pollution and TFP growth. Using panel data in China and emphasizing green development, a sustainable economy and new-type urbanization, this study contributes to the current studies on haze pollution and economic development based on developed countries.

Sensorless passive lead-through programming (LTP) is a promising physical human-robot interaction technology that enables manual trajectory demonstrations based on gravity and friction compensation. The major difficulty lies in static friction compensation during LTP start-up. Instead of static friction compensation, conventional methods only compensate for Coulomb friction after the joint velocity exceeds a threshold. Therefore, conventional start-up external torques must overcome static friction. When the static friction is considerable, it is difficult for conventional LTP to start up and make small movements. This paper aims to decrease the start-up external torque and improve the small movement performance.

This paper reveals a novel usage of a high-gain position-loop in industrial robot applications aimed at sensitively detecting external torque during start-up. Then, the static friction is partly compensated by Coulomb friction to facilitate start-up. In addition, a detailed transition method between the proposed start-up and conventional passive LTP is proposed based on a finite state machine.

Experiments are implemented on the ROKAE XB4 robot to verify the effectiveness of the proposed external torque detection. Compared with the conventional LTP method, the proposed LTP method significantly decreases the start-up external torque and facilitates small movements.

This paper proposes and verifies a novel start-up method of sensorless LTP based on a start-up external torque detection and a transition method between start-up and conventional LTP. This research improves the LTP start-up performance, especially for industrial robots with large static friction.

The purpose of this paper is to enhance control accuracy, energy efficiency and productivity of customized industrial robots by the proposed multi-objective trajectory optimization approach. To obtain accurate dynamic matching torques of the robot joints with optimal motion, an improved dynamic model built by a novel parameter identification method has been proposed.

This paper proposes a novel multi-objective optimal approach to minimize the time and energy consumption of robot trajectory. First, the authors develop a reliable dynamic parameters identification method to obtain joint torques for formulating the normalized energy optimization function and dynamic constraints. Then, optimal trajectory variables are solved by converting the objective function into relaxation constraints based on second-order cone programming and Runge–Kutta discrete method to reduce the solving complexity.

Extensive experiments via simulation and in real customized robots are conducted. The results of this paper illustrate that the accuracy of joint torque predicted by the proposed model increases by 28.79% to 79.05% over the simplified models used in existing optimization studies. Meanwhile, under the same solving efficiency, the proposed optimization trajectory consumes a shorter time and less energy compared with the existing optimization ones and the polynomial trajectory.

A novel time-energy consumption optimal trajectory planning method based on dynamic identification is proposed. Most existing optimization methods neglect the effect of dynamic model reliability on energy efficiency optimization. A novel parameter identification approach and a complete dynamic torque model are proposed. Experimental results of dynamic matching torques verify that the control accuracy of optimal robot motion can be significantly improved by the proposed model.

The biggest obstacle to the sustainable development of online brand communities is social loafing. Based on 3M Hierarchical Model of Personality, this paper aims to discuss the influence of the Big Five on social loafing in online brand communities, and consider the mediating role of dehumanization from the perspective of moral disengagement theory, so as to alleviate social loafing and promote the sustainable development of online brand communities.

The data were collected utilizing the online self-report questionnaires. Community members of mobile phone brands represented by HUAWEI and Xiaomi completed the questionnaire sets. Based on the responses of 214 participants, a structural equation modeling was applied to examine the relationship between Big Five on social loafing in online brand communities. Later, bootstrap analysis was used to further explore the indirect effect of dehumanization.

Only two personality traits, neuroticism and extroversion, have direct effect on social loafing, while agreeableness, conscientiousness and openness not. However, all the Big Five personality traits can indirectly influence social loafing through dehumanization.

This study introduces the concept of social loafing into online brand communities, and analyzes social loafing from the unique research perspective of moral disengagement theory, which can not only contribute to the development of moral disengagement theory and social loafing theory but also provide guiding points for relieving social loafing in online brand communities.

This paper focuses on assembly line performance of an automotive body shop that builds body‐in‐white (BIW) assembly utilizing about 700+ process robots. These robots perform various operations such as welding, sealing, part handling, stud welding and inspection. There is no accurate tool available for the plant personnel to predict the future throughput based on plant's data. The purpose of this paper is to provide future throughput performance prediction based on plant data using Box‐Jenkins' ARMA model.

The following data were collected for five major assembly lines. First, the assembly machine‐in‐cycle time: the assembly line machines include robots that perform various functions like load, welding or sealing and unloading parts; the manual operators loading cycle time to the production fixtures. The conveyors act as buffers in between stations, and also feed to the production cells, and carry parts from station to station. The conveyors' downtime and uptime were also part of the machine‐in‐cycle time; second, the number of units produced from the beginning to the end of the assembly line; third, the number of fault occurrences in the assembly line due to various machine breakdowns; fourth, the machine availability percentage – i.e. the machine is readily available to perform its functions (the machine blocked upstream (starving) and blocked down (downstream) state is considered here); fifth, the actual efficiency of the machine measured in percentage based on output percentage; sixth, the expected number of units at designed efficiency.

In summary, this research paper provided a systematic development of a forecast model based on Box‐Jenkin's ARMA methodology to analyze the complex assembly line process performance data. The developed ARMA forecast models proved that the future prediction can be accurately predicted based on the past plant performance data. The developed ARMA forecast models predicted the future throughput performance within 99.52 percent accuracy. The research findings were validated by the actual plant performance data.

In this study, the automotive assembly process machines (robots, conveyors and fixtures) production data were collected, statistically analyzed and verified for viable ARMA model verification. The verified ARMA model has been used to predict the plant future months' throughput with 99.52 percent accuracy, based on the plant production data. This research is unique because of its practical usage to improve production.

Hydro-viscous drive (HVD) clutches are widely used in equipment requiring soft start, such as fans and pumps, to transmit torque and adjust speed by changing the gap distance between friction pairs. This paper aims to propose a novel two-parameter evaluation method for HVD during the mixed lubrication stage. The objective is to develop an effective model that establishes the relationship between these parameters and the actual surface topography.

In the presented methods, the fractal features of the real manufacturing surface are calculated based on the power spectrum function by the ultra-depth three-dimensional microscope. After that, the hybrid friction model of the friction plate is established based on mixed elasto-hydrodynamic lubrication theory, boundary friction model and fractal theory. Then the torque and load bearing characteristics of the clutch are obtained, and the influences of the surface fractal features are investigated and discussed. Finally, the Weierstrass–Mandelbrot function is adopted for the surface topography characterization and evaluation.

The results indicate that the proposed method exhibits good accuracy, while the speed difference between the friction pair exceeds 2,500 rpm. It is concluded that this paper proposed a way to evaluate the torque and loading capacity of HVD considering the real manufacturing surface topography and is helpful for surface optimization.

The originality and value of this study lie in its development of a novel torque and load bearing capacity evaluation method for HVD in mixed lubrication stage, considering manufacturing surface topography and describing the real manufacturing surface.

As a common form of failure in industry, corrosion causes huge economic losses. At present, with the development of computational techniques, artificial intelligence (AI) is playing a more and more important role in the field of scientific research. This paper aims to review the application of AI in corrosion protection research.

In this paper, the role of AI in corrosion protection is systematically described in terms of anticorrosion materials and methods, corrosion image recognition and corrosion life prediction.

With efficient and in-depth data processing methods, AI can rapidly advance the research process in terms of anticorrosion materials and methods, corrosion image recognition and corrosion life prediction and save on costs.

This paper summarizes the application of AI in corrosion protection research and provides the basis for corrosion engineers to quickly and comprehensively understand the role of AI and improve production processes.

The purpose of this paper is to study the influence of structural parameters of oil groove (such as central angle number, depth and so on) on pressure, flow, load capacity and transmitted torque between friction pairs of hydro-viscous clutch.

According to the working process of friction pairs of hydro-viscous clutch, mathematical models of hydrodynamic load capacity and torque transmitted by the oil film were built based on viscosity-temperature property. Then analytical solutions of pressure, flow, load capacity and transmitted torque were obtained; effects of central angle of oil groove zone and friction contact zone, oil film thickness, number of oil grooves on pressure, flow, load capacity and torque were studied theoretically.

The research found that the central angle of oil groove zone, number of oil grooves and oil groove depth have similar effects on flow, which means that with the increase of central angle, number or depth of oil grooves, the flow also increases; pressure in friction contact zone and oil groove zone drops along radial direction, whereas its value in oil groove zone is higher. With the increase of the central angle of oil groove zone, pressure in friction contact zone and friction contact zone rises, and the load capacity increases, whereas the transmitted torque drops. Number of oil grooves has little effect on load capacity. When the oil film thickness increases, its flow increases accordingly, whereas the pressure, load capacity and transmitted torque drops. Meanwhile, the transmitted torque decreases with the increase of number of oil grooves, whereas the oil groove depth nearly has no effects on transmitted torque.

In this paper, mathematical models of hydrodynamic load capacity and torque transmitted by oil film were built based on viscosity-temperature property in the working process of hydro-viscous clutch, and their analytical solutions were obtained; effects of structural parameters of oil groove on transmission characteristics of hydro-viscous clutch based on viscosity-temperature property were revealed. The research results are of great value to the theory development of hydro-viscous drive technology, the design of high-power hydro-viscous clutch and relative control strategy.

Background: Artificial intelligence (AI) is a booming sector that has profoundly influenced every walk of life, and the education sector is no exception. In education, AI has helped to develop novel teaching and learning solutions that are currently being tested in various contexts. Businesses and governments across the globe have been pouring money into a wide array of implementations, and dozens of EdTech start-ups are being funded to capitalise on this technological force. The penetration of AI in classroom teaching is also a profound matter of discussion. These have garnered massive amounts of student big data and have a significant impact on the life of both students and educators alike.

Purpose: The prime focus of this chapter is to extensively review and analyse the vast literature available on the utilities of AI in health care, learning, and development. The specific objective of thematic exploration of the literature is to explicate the principal facets and recent advances in the development and employment of AI in the latter. This chapter also aims to explore how the EdTech and healthcare–education sectors would witness a paradigm shift with the advent and incorporation of AI.

Design/Methodology/Approach: To provide context and evidence, relevant publications were identified on ScienceDirect, PubMed, and Google Scholar using keywords like AI, education, learning, health care, and development. In addition, the latest articles were also thoroughly reviewed to underscore recent advances in the same field.

Results: The implementation of AI in the learning, development, and healthcare sector is rising steeply, with a projected expansion of about 50% by 2022. These algorithms and user interfaces economically facilitate efficient delivery of the latter.

Conclusions: The EdTech and healthcare sector has great potential for a spectrum of AI-based interventions, providing access to learning opportunities and personalised experiences. These interventions are often economic in the long run compared to conventional modalities. However, several ethical and regulatory concerns should be addressed before the complete adoption of AI in these sectors.

Originality/Value: The value in exploring this topic is to present a view on the potential of employing AI in health care, medical education, and learning and development. It also intends to open a discussion of its potential benefits and a remedy to its shortcomings.