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The motivation of value co-creation among the multi-actor in sharing economy was an important topic in interactive marketing communication research. This study investigated how customer gratitude expression leads to value co-creation of PSPs in the sharing economy, and also investigates the moderating effect of platform benevolent climate.

A three-wave field survey (Study 1) and two experiments (Studies 2 and 3) were given to respondents with sharing economy practitioners.

First, customer gratitude expression positively influenced PSP's perceived meaningful work, which in turn enhanced their value co-creation intention. Second, PSP's perceived platform benevolent climate moderated the relationship between customer gratitude expression and PSP's perceived meaningful work.

Prior research discussed PSPs' value co-creation intention mainly from the perspective of platforms and PSPs, but few considered customer-PSP interaction perspective. This study revealed how customer gratitude expression influences PSP's value co-creation intention in highly interactive digital business context, examined the boundary condition of gratitude expression, and extended the application scenarios of social information processing theory.

This study delves into the intricate relationship between specific positive and negative emotions experienced by tourists during their vacations and the corresponding emotion regulation strategies they employ. Drawing from emotion regulation theory, we examine the nuanced impact of various strategies on tourists' emotional experiences, thereby advancing our understanding of emotion dynamics in the context of tourism.

Data were gathered through an online survey and travel diaries, subsequently analyzed using linear mixed-effects models.

Our findings underscore that emotion regulation strategies exert a significant influence on both positive and negative emotions. Furthermore, we identified that different strategies correlate uniquely with specific emotions. For instance, the deployment of Expressive Suppression, Savoring, and Stimulus Control strategies notably amplifies the intensity of joy.

This study recommends that tourism managers design experiences that evoke positive emotions through curated sensory cues, storytelling, and stress-free service offerings. Tourism managers should prioritize stress-free services, guide tourists in expressing themselves, and train service providers to manage emotions effectively, thus promoting positive emotional interactions and improving overall customer satisfaction.

Theoretically, this research enriches the emotion regulation literature by contextualizing it within the tourism domain, highlighting the differential effects of regulation strategies on diverse emotional experiences. From a practical standpoint, these insights can guide practitioners in crafting targeted marketing strategies and empower tourists with knowledge to select optimal strategies for enhancing their emotional well-being during vacations.

The purpose of this paper is to explore and expand the concept of a marketing system for developing a more dynamic and nuanced understanding of marketing. The purpose of the proposed framework is to extend this literature by making salient and explicit how context, market system and value creation are theoretically interrelated. To accomplish this objective, the authors use the framework proposed by Layton (2019) as the theoretical foundation to acquire insights into the market. Particularly, they investigate how four distinct marketing systems (i.e. anarchy, structured, emergent and purposeful market systems) operate in a developing economy. In addition, the study explores the market's effects of technological advancement, sociocultural influences, historical background and political institutions, as well as the responses of political entities, firms and consumers. Also, the positive and negative effects of the various marketing systems are analyzed. Finally, the authors investigate the changing marketplace in various industrial sectors (e.g. home appliances, food, apparel/fashion and transportation) to provide marketing researchers and practitioners with insights. In essence, the study focuses on the sectors related to everyday consumption.

This analysis uses a theoretical approach to extend the understanding concept of marketing. To examine the numerous market systems in India, the authors use an approach developed by Layton (2007). This theoretical approach is intended to sensitize scholars to critical processes rather than a hypothetico-deductive analysis with a prediction goal (Turner, 1986). Epistemologically, this analysis can be classified as a form of discovery-oriented theory development (Wells, 1993).

Although all four systems (e.g. autarchic, emergent, purposeful and structured) are ingrained in India, their functionality differs from the Western system and among industries. For example, the apparel sector appears more autarchic, but the food industry is more purposeful. How the home appliance market operates demonstrates the transition from an autarchic to an emergent system. The authors also uncover additional environmental factors that impact the four types of marketing systems and moderator roles of governate agencies and nonprofit organizations. The externality and positive outcomes also emerged throughout the analysis.

This study articulates the four types of marketing systems and illustrates the environmental factors/antecedents and outcomes for the exchange and value creation. Most importantly, it adds value to the literature by emphasizing the role of government agencies and unrestricted institutions in the mechanism. It also uncovers cultural elements such as spirituality as a catalyst for exchange and value creation.

The analysis provides practitioners with insights into operating the firm in India by articulating the industrial differentiations and the exchange/value creation. Specifically, it provides a blueprint for strategic analysis that can be used prior to market entry to increase the likelihood of market entry success by understanding the nuanced differences that lead to significant operational difficulties if not properly prepared for and managed.

This study adds to our existing knowledge of marketing from a systemic standpoint. It also broadens and explicates marketing system theory by assessing the uniqueness of developing markets.

The purpose of this paper is to investigate the friction and wear mechanisms in copper-based self-lubricating composites with MoS₂ as the lubricating phase, which provides a theoretical basis for subsequent research on high-performance copper-based self-lubricating materials.

Friction tests were performed at a speed of 100 r/min, a load of 10 N, a friction radius of 5 mm and a sliding speed of 30 min. Friction experiments were carried out at RT-500°C. The phase composition of the samples was characterized by X-ray diffraction of Cu Ka radiation, and the microstructure, morphology and elemental distribution were characterized by scanning electron microscopy and energy dispersive spectroscopy. Reactants and valences formed during the wear process were analyzed by X-ray photoelectron spectroscopy.

The addition of MoS₂ can effectively improve friction-reducing and anti-wear action of the matrix, which is beneficial to form a lubricating film on the sliding track. After analyzing different changing mechanism of the sliding tracks, the oxides and sulfides of MoS₂, MoO₂, Cu₂O, CuO and Ni(OH)₂ were detected to form a synergetic lubricating film on the sliding track, which is responsible for the excellent tribological properties from room to elevated temperature.

For self-lubrication Cu–Sn–Ni–MoS₂ material in engineering field, there are still few available references on high-temperature application.

This paper provides a theoretical basis for the following research on copper-based self-lubricating materials with high performance.

With this statement, the authors hereby certify that the manuscript is the results of their own effort and ability. They have indicated all quotes, citations and references. Furthermore, the authors have not submitted any essay, paper or thesis with similar content elsewhere. No conflict of interest exits in the submission of this manuscript.

Low performance life and increased machine downtime due to wear of resistance welding copper electrode is of major concern in fin–tube resistance welding in waste heat recovery boilers. The purpose of this study is to investigate an alternative material with good wear resistance to replace the currently utilized C11000 electrolytic tough pitch (ETP) copper electrode.

In this study, a Cu-Cr-Zr ternary alloy was developed for fin-to-tube welding electrode by melting commercial grade electrolytic copper (99.9% purity) plates, chips of chromium, powder of zirconium at 1100°–1300°C, followed by hot forging and precipitation hardening at 450°–550°C to attain appropriate grain flow. Microstructures of Cu-Cr-Zr alloys were analysed using scanning electron microscopy coupled with energy-dispersive backscatter electron spectrometry.

Wear performance of Cu-Cr-Zr and C11000 ETP Cu was evaluated using pin-on-disc set-up with Taguchi’s L₈ orthogonal array. Ranking of the parameters was done, and it was observed that the material and temperature play a very significant role in controlling the wear of an electrode.

Rate of fin–tube resistance welding was increased by 26% with Cu-Cr-Zr alloy. Further investigation on effect of plasma on the metallurgical characteristics of Cu-Cr-Zr is recommended.

Tribo-mechanical performance of newly developed Cu-Cr-Zr ternary alloy was compared with C11000 ETP copper.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2023-0092/

This paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate.

Firstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved.

The method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data.

The agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.

Compared to quad-rotor unmanned aerial vehicle (UAV), the tilting dual-rotor UAV is more prone to instability during exercises and disturbances. The purpose of this paper is using an active balance tail to enhance the hovering stability and motion smoothness of tilting dual-rotor UAV.

A balance tail is proposed and integrated into the tilting dual-rotor UAV to enhance hovering stability and motion smoothness. By strategically moving, the balance tail generates additional force and moment, which can promote the rapid stability of the UAV. Subsequently, the control strategy of the UAV is designed, and the influence of the swing of the balance tail at different installation positions with different masses on the dual-rotor UAV is analyzed through simulation. The accompany motion law and the active control, which is based on cascade Proportion Integration Differentiation (PID) control to enhance the hovering stability and motion smoothness of the UAV, are proposed.

The results demonstrate that active control has obvious adjustment effectiveness when the UAV moves to the target position or makes an emergency stop compared with the results of balance tail no swing and accompany motion.

The balance tail offers a straightforward means to enhance the motion smoothness of tilting dual-rotor UAV, rendering it safer and more reliable for practical applications.

The novelty of this works comes from the application of an active balance tail to improve the stability and motion smoothness of dual-rotor UAV.

This study aims to introduce a vision-based model to generate droplets with auto-tuned parameters. The model can auto-adjust the inherent uncertainties and errors involved with the fabrication and operating parameters in microfluidic platform, attaining precise size and frequency of droplet generation.

The photolithography method is utilized to prepare the microfluidic devices used in this study, and various experiments are conducted at various flow-rate and viscosity ratios. Data for droplet shape is collected to train the artificial intelligence (AI) models.

Growth phase of droplets demonstrated a unique spring back effect in droplet size. The fully developed droplet sizes in the microchannel were modeled using least absolute shrinkage and selection operators (LASSO) regression model, Gaussian support vector machine (SVM), long short term memory (LSTM) and deep neural network models. Mean absolute percentage error (MAPE) of 0.05 and R² = 0.93 were obtained with a deep neural network model on untrained flow data. The shape parameters of the droplets are affected by several uncontrolled parameters. These parameters are instinctively captured in the model.

Experimental data set is generated for varying viscosity values and flow rates. The variation of flow rate of continuous phase is observed here instead of dispersed phase. An automated computation routine is developed to read the droplet shape parameters considering the transient growth phase of droplets. The droplet size data is used to build and compare various AI models for predicting droplet sizes. A predictive model is developed, which is ready for automated closed loop control of the droplet generation.

Drawing upon social cognitive theory, this study aims to investigate the potential predictors and consequences of social media health-misinformation seeking behavior during the coronavirus (COVID-19) pandemic.

Using a sample of 230 international students studying at Wuhan University and Beijing Language and Cultural University, China, this study employs structural equation modeling to analyze the collected data.

The results indicate that personal factors such as lack of health information literacy, environmental factors, information overload and social media peer influence have a significant effect on behavior, namely social media health-misinformation seeking behavior, which further influences outcomes, namely social media users' anxiety during the COVID-19 pandemic. In addition, both lack of health information literacy and social media peer influence have significant and direct effects on social media users' anxiety. However, the direct effect of information overload on social media users' anxiety is insignificant.

First, this study contributes to the literature on the individuals' social media health-misinformation seeking behavior, its precursors and its consequences, specifically on their mental healthcare during a pandemic situation. Second, this research is one of the pioneer studies that extend social cognitive theory to the context of social media health-misinformation seeking behavior and users' anxiety relationship.

Information opacity in donation crowdfunding activities has constrained the healthy development of China’s public welfare activities. Addressing the trust crisis and enhancing public engagement warrants further investigation. This study aims to uncover the moderating effect of activity transparency by utilizing data from 1,029 donation crowdfunding projects on the Sina Weibo Public Welfare Social Platform. In this way, we seek to elucidate the impact of donation crowdfunding events on fundraising ability.

This study selects text complexity, number of supporters, creator experience, and social capital as explanatory variables; innovatively selects the number of updates of online crowdfunding activities and total reading volume as moderating variables; selects the number of shares of crowdfunding activities as a mediating variable; and constructs a moderated mediation multiple regression model for fundraising ability.

Our findings indicate that independent variables, such as text complexity, number of supporters, and social capital, can significantly affect the dependent variable, fundraising ability. However, creator experience does not influence fundraising ability. Furthermore, social interaction has a mediating effect, whereas activity transparency has a reverse moderating effect. These results indicate that social interaction can enhance the fundraising ability of donation crowdfunding events. However, with an increase in information transparency, the fundraising ability of social media decreases.

The originality of this research is in clarifying the internal factors affecting fundraising ability through induction, making bold assumptions, and focusing on how social media’s effective interaction and activity transparency will affect public welfare crowdfunding fundraising ability.