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We explore the driving forces behind the channel choices of the manufacturer and the platform by considering asymmetric selling cost and demand information.

This paper develops game-theoretical models to study different channel strategies for an E-commerce supply chain, in which a manufacturer distributes products through a platform that may operate in either the marketplace channel or the reseller channel.

Three primary models are built and analyzed. The comparison results show that the platform would share demand information in the reseller channel only if the service cost performance is relatively high. Besides, with an increasing selling cost, the equilibrium channel might shift from the marketplace to the reseller. With increasing information accuracy, the manufacturer tends to select the marketplace channel, while the platform tends to select the reseller channel if the service cost performance is low and tends to select the marketplace channel otherwise.

All these results have been numerically verified in the experiments. At last, we also resort to numerical study and find that as the service cost performance increases, the equilibrium channel may shift from the reseller channel to the marketplace channel. These results provide managerial guidance to online platforms and manufacturers regarding strategic decisions on channel management.

Although prior research has paid extensive attention to the driving forces behind the online channel choice between marketplace and reseller, there is at present few study considering the case where a manufacturer selling through an online platform faces a demand information disadvantage in the reseller channel and sales inefficiency in the marketplace channel. To fill this research gap, our work illustrates the interaction between demand information asymmetry and selling cost asymmetry to identify the equilibrium channel strategy and provides useful managerial guidelines for both online platforms and manufacturers.

This study aims to investigate whether the contract manufacturer (CM) should take the first-mover advantage in the end-product without supplying core components to the original equipment manufacturer (OEM) immediately, or should fully squeeze the benefit of the learning effect through an amplified production quantity by letting the OEM enter the end-product market early.

The authors propose a two-period model for a supply chain consisting of a CM and an OEM where the CM has four alternative entry strategies concerning it competition to the OEM in the end-product market. For each strategy, the authors derive the equilibrium solutions of the two firms using a backward approach. Comparison leads to the CM’s final choices among the four strategies.

For both CM and OEM, the monopoly and the first-entry strategies will be dominated by either the post-entry or the simultaneous-entry strategy, and thus, their preferred strategy is chosen from the latter two. Regarding the two firms choices between the post- and simultaneous-entry strategy, the CM prefers the post-entry strategy when the OEMs brand premium is at a moderate level, whereas the OEM prefers the post-entry strategy when its brand premium is low, and the learning effect can amplify the interval for the CMs adopting the post-entry strategy as well as changes the interval for the OEMs preference related to the two strategies.

This paper is the first one to explore the optimal strategy for a CM to maximize its profit in a co-opetitive supply chain situation with a CM and an OEM. The authors believe that our paper contributes to both literature and the market.

The purpose of this paper is to design a simple and accurate a-posteriori Lagrangian-based error estimator is developed for the class of backward differentiation formula (BDF) algorithms with variable time step size, and the adaptive time-stepping in BDF algorithms is demonstrated for efficient time-dependent simulations in fluid flow and heat transfer.

The Lagrange interpolation polynomial is used to predict the time derivative, and then the accurate primary result is obtained by the Gauss integral, which is applied to evaluate the local error. Not only the generalized formula of the proposed error estimator is presented but also the specific expression for the widely applied BDF1/2/3 is illustrated. Two essential executable MATLAB functions to implement the proposed error estimator are appended for practical applications. Then, the adaptive time-stepping is demonstrated based on the newly proposed error estimator for BDF algorithms.

The validation tests show that the newly proposed error estimator is accurate such that the effectivity index is always close to unity for both linear and nonlinear problems, and it avoids under/overestimation of the exact local error. The applications for fluid dynamics and coupled fluid flow and heat transfer problems depict the advantage of adaptive time-stepping based on the proposed error estimator for time-dependent simulations.

In contrast to existing error estimators for BDF algorithms, the present work is more accurate for the local error estimation, and it can be readily extended to practical applications in engineering with a few changes to existing codes, contributing to efficient time-dependent simulations in fluid flow and heat transfer.

The purpose of this paper is to solve the abrupt deterioration of lubricant performance in high-temperature conditions.

Three silver pyrazolyl methyl pyridine complexes with different morphologies were synthesized. A four-ball tribometer was used to assess the tribological characteristics as an additive for pentaerythritol oleate both independently and compound with 1-hexyl-3-methylimidazolium bis(trifluoromethane sulfonyl)imide.

The results showed that when silver complexes and ionic liquids (IL) act independently, sheet silver complex 1 and rod silver complex 2 exhibit good lubricating performance; the optimal antifriction concentration of the ILs is 0.25 Wt.%. The tribological results of the compounds additive of ILs and silver complexes indicate that the wear scar diameter of compound 1 decreased by 16.914%, the wear volume reduced by 7.44% and the lubrication effect surpassed that of the two substances individually; rod compound 2 exhibited an antagonistic effect, intensifying wear; compound 3’s lubrication effect fell between that of the two individual components.

The compound of sheet silver complexes and ILs effectively solves the agglomeration problem of micro/nano lubricant additives. When the interface fails, self-repair is completed, improving the stability and antiwear performance of the lubricating oil.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0128

Polyaniline (PANI) has garnered attention for its potential applications in anticorrosion fields because of its unique properties. Satisfactory outcomes have been achieved when using PANI as a functional filler in organic coatings. More recently, research has extensively explored PANI-based organic coatings with self-healing properties. The purpose of this paper is to provide a summary of the active agents, methods and mechanisms involved in the self-healing of organic coatings.

This study uses specific doped acids and metal corrosion inhibitors as active and self-healing agents to modify PANI using the methods of oxidation polymerization, template synthesis, nanosheet carrier and nanocontainer loading methods. The anticorrosion performance of the coatings is evaluated using EIS, LEIS and salt spray tests.

Specific doped acids and metal corrosion inhibitors are used as active agents to modify PANI and confer self-healing properties to the coatings. The coatings’ active protection mechanism encompasses PANI’s own passivation ability, the adsorption of active agents and the creation of insoluble compounds or complexes.

This paper summarizes the active agents used to modify PANI, the procedures used for modification and the self-healing mechanism of the composite coatings. It also proposes future directions for developing PANI organic coatings with self-healing capabilities. The summaries and proposals presented may facilitate large-scale production of the PANI organic coatings, which exhibit outstanding anticorrosion competence and self-healing properties.

The purpose of this paper is to explore a new approach to solve the problem of positive and negative offset in the calculation process of integral elements, then propose a series of new grey relational degree model for cross sequences.

The definitions of cross sequences and area elements have been proposed at first. Then the concept of difference degree between sequences has been put forward. Based on the definition of difference degree between sequences, various modified grey relational degree models for cross sequences have been proposed to solve the measurement problem of cross sequence correlation relationships.

(1) The new definition of cross sequences; (2) The area element; (3) Various modified grey relational degree models for cross sequences based on the definition of difference degree between sequences.

The grey relational analysis model of cross sequences is a difficult problem in grey relational analysis. The new model proposed in this article can effectively avoid the calculation deviation of grey relational analysis model for cross sequences, and reasonably measure the correlation between cross sequences. The new model was used to analyse the food consumer price index in Shaanxi Province, clarifying the relationship between different types of food consumer price indices, some interesting results that are not completely consistent with general economic theory were obtained.

The new definition of cross sequences, the area element and various modified grey relational degree models for cross sequences were proposed.

The present experimental study aims to investigate when a food safety incident occurs, how country image influences consumers' trust and purchase intention, as well as the relationship between trust and purchase intention.

Participants (N = 1,590) were randomly allocated into one of the eight conditions [(country competence: high vs low) × (country warmth: high vs low) × (clean green image: high vs low)], read the corresponding country image descriptions, and rated measures on trust in food safety and quality, and purchase intention of fruit imported from this exporting country before and after reading a fictional food safety incident scenario.

Results showed that the food safety incident led to a significant decrease in trust and purchase intention across all conditions. However, trust in food safety and quality, and purchase intention were still higher in high competence, warmth or clean green image conditions. The decreased magnitude of trust in food safety was larger when country competence and clean green image was high, and when country warmth was low. Food safety incident caused purchase intention to become more dependent on trust in food safety than food quality.

This study provides a novel insight into the impacts of food safety incidents on consumers' responses in different country image contexts including the human-related and environment-related dimensions.

This study aims to predict overnight stays in Italy at tourist accommodation facilities through a nonlinear, single factor, stochastic model called CIR#. The contribution of this study is twofold: in terms of forecast accuracy and in terms of parsimony (both from the perspective of the data and the complexity of the modeling), especially when a regular pattern in the time series is disrupted. This study shows that the CIR# not only performs better than the considered baseline models but also has a much lower error than other additional models or approaches reported in the literature.

Typically, tourism demand tends to follow regular trends, such as low and high seasons on a quarterly/monthly level and weekends and holidays on a daily level. The data set consists of nights spent in Italy at tourist accommodation establishments as collected on a monthly basis by Eurostat before and during the COVID-19 pandemic breaking regular patterns.

Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. In addition, given the importance of accurate forecasts, many studies have proposed novel hybrid models or used various combinations of methods. Thus, although there are clear benefits in adopting more complex approaches, the risk is that of dealing with unwieldy models. To demonstrate how this approach can be fruitfully extended to tourism, the accuracy of the CIR# is tested by using standard metrics such as root mean squared errors, mean absolute errors, mean absolute percentage error or average relative mean squared error.

The CIR# model is notably simpler than other models found in literature and does not rely on black box techniques such as those used in neural network (NN) or data science-based models. The carried analysis suggests that the CIR# model outperforms other reference predictions in terms of statistical significance of the error.

The proposed model stands out for being a viable option to the Holt–Winters (HW) model, particularly when dealing with irregular data.

The proposed model has demonstrated superiority even when compared to other models in the literature, and it can be especially useful for tourism stakeholders when making decisions in the presence of disruptions in data patterns.

The novelty lies in the fact that the proposed model is a valid alternative to the HW, especially when the data are not regular. In addition, compared to many existing models in the literature, the CIR# model is notably simpler and more transparent, avoiding the “black box” nature of NN and data science-based models.

一般来说, 旅游需求往往遵循规律的趋势, 例如季度/月的淡季和旺季, 以及日常的周末和假期。该数据集包括欧盟统计局在打破常规模式的2019冠状病毒病大流行之前和期间每月收集的在意大利旅游住宿设施度过的夜晚。

本研究旨在通过一个名为cir#的非线性单因素随机模型来预测意大利游客住宿设施的过夜住宿情况。这项研究的贡献是双重的:在预测准确性方面和在简洁方面(从数据和建模复杂性的角度来看), 特别是当时间序列中的规则模式被打乱时。我们表明, cir#不仅比考虑的基线模型表现更好, 而且比文献中报告的其他模型或方法具有更低的误差。

当大量搜索强度指标被作为旅游需求指标时, 传统的旅游需求预测模型将面临挑战。此外, 鉴于准确预测的重要性, 许多研究提出了新的混合模型或使用各种方法的组合。因此, 尽管采用更复杂的方法有明显的好处, 但风险在于处理难使用的模型。为了证明这种方法能有效地扩展到旅游业, 使用RMSE、MAE、MAPE或AvgReIMSE等标准指标来测试cir#的准确性。

cir#模型明显比文献中发现的其他模型简单, 并且不依赖于黑盒技术, 例如在神经网络或基于数据科学的模型中使用的技术。所进行的分析表明, cir#模型在误差的统计显著性方面优于其他参考预测。

这个模型作为Holt-Winters模型的一个拟议模型, 特别是在处理不规则数据时。

即使与文献中的其他模型相比, 所提出的模型也显示出优越性, 并且在数据模式中断时对旅游利益相关者做出决策特别有用。

创新之处在于所提出的模型是Holt-Winters模型的有效替代方案, 特别是当数据不规律时。此外, 与文献中的许多现有模型相比, cir#模型明显更简单、更透明, 避免了神经网络和基于数据科学的模型的“黑箱”性质。

Normalmente, la demanda turística tiende a seguir tendencias regulares, como temporadas altas y bajas a nivel trimestral/mensual y fines de semana y festivos a nivel diario. El conjunto de datos consiste en las pernoctaciones en Italia en establecimientos de alojamiento turístico recogidas mensualmente por Eurostat antes y durante la pandemia de COVID-19, rompiendo los patrones regulares.

El presente estudio pretende predecir las pernoctaciones en Italia en establecimientos de alojamiento turístico mediante un modelo estocástico no lineal de un solo factor denominado CIR#. La contribución de este estudio es doble: en términos de precisión de la predicción y en términos de parsimonia (tanto desde la perspectiva de los datos como de la complejidad de la modelización), especialmente cuando un patrón regular en la serie temporal se ve interrumpido. Demostramos que el CIR# no sólo aplica mejor que los modelos de referencia considerados, sino que también tiene un error mucho menor que otros modelos o enfoques adicionales de los que se informa en la literatura.

Los modelos tradicionales de previsión de la demanda turística pueden enfrentarse a desafíos cuando se adoptan cantidades masivas de índices de intensidad de búsqueda como indicadores de la demanda turística. Además, dada la importancia de unas previsiones precisas, muchos estudios han propuesto modelos híbridos novedosos o han utilizado diversas combinaciones de métodos. Así pues, aunque la adopción de enfoques más complejos presenta ventajas evidentes, el riesgo es el de enfrentarse a modelos poco manejables. Para demostrar cómo este enfoque puede extenderse de forma fructífera al turismo, se comprueba la precisión del CIR# utilizando métricas estándar como RMSE, MAE, MAPE o AvgReIMSE.

El modelo CIR# es notablemente más sencillo que otros modelos encontrados en la literatura y no se basa en técnicas de caja negra como las utilizadas en los modelos basados en redes neuronales o en la ciencia de datos. El análisis realizado sugiere que el modelo CIR# supera a otras predicciones de referencia en términos de significación estadística del error.

El modelo propuesto destaca por ser una opción viable al modelo Holt-Winters, sobre todo cuando se trata de datos irregulares.

El modelo propuesto ha demostrado su superioridad incluso cuando se compara con otros modelos de la bibliografía, y puede ser especialmente útil para los agentes del sector turístico a la hora de tomar decisiones cuando se producen alteraciones en los patrones de datos.

La novedad radica en que el modelo propuesto es una alternativa válida al Holt-Winters especialmente cuando los datos no son regulares. Además, en comparación con muchos modelos existentes en la literatura, el modelo CIR# es notablemente más sencillo y transparente, evitando la naturaleza de “caja negra” de los modelos basados en redes neuronales y en ciencia de datos.

This study aims to identify whether green advertising and eco-labels influence the purchasing behaviors of organic products or whether the influence of such behaviors is presented through the mediating effect of the attitude and environmental awareness of millennials.

This study used a quantitative, correlational and cross-sectional design. A total of 430 millennials participated in the study. The questionnaire consisted of 20 questions, distributed across five variables. It was applied to people outside shopping centers in the city of Quito and Guayaquil, Ecuador. The results were subjected to statistical tests to determine the internal consistency of the instrument. The convergent and discriminant validity of the research model was verified through confirmatory factor analysis and structural equation modeling. SPSS 20 and AMOS 24 were used for statistical analyses.

The study identified that attitude and environmental awareness mediate the relationship between green advertising and green purchasing behaviors, as well as the relationship between eco-labels and green purchasing behaviors. Furthermore, it has been proven that green advertising directly influences the green purchasing behaviors of organic products among millennials, whereas eco-labels do not.

This study is among the initial investigations to delineate the connection between elements of green marketing and the buying patterns of organic goods among millennials in an emerging economy. The study provides answers to earlier scholarly inquiries that proposed examining the correlation among the constituents of the theorized framework.

El presente estudio tuvo como objetivo identificar si la publicidad verde y las ecoetiquetas influyen en los comportamientos de compra de productos orgánicos, o si la influencia dentro de dichos comportamientos se presenta a través del efecto mediador de la actitud y la conciencia ambiental de los millennials.

La investigación fue cuantitativa, correlacional y con un diseño transversal. Participaron en el estudio un total de 430 millennials. El cuestionario constó de 20 preguntas distribuidas en cinco variables. Fue aplicado en persona fuera de los centros comerciales en las ciudades de Quito y Guayaquil - Ecuador. Los resultados obtenidos fueron sometidos a pruebas estadísticas para determinar la consistencia interna del instrumento. La validez convergente y discriminante del modelo de investigación fue verificada mediante un Análisis Factorial Confirmatorio y Modelos de Ecuaciones Estructurales. Para el desarrollo de los análisis estadísticos, se utilizaron SPSS 20 y AMOS 24.

El estudio identificó que la actitud y la conciencia ambiental median la relación entre la publicidad verde y los comportamientos de compra, así como en la relación entre las ecoetiquetas y los comportamientos de compra. Además, se demostró que la publicidad verde influye directamente en los comportamientos de compra de productos orgánicos entre los millennials, mientras que las ecoetiquetas no.

Este artículo se encuentra entre las investigaciones iniciales para delinear la conexión entre los elementos del marketing verde y los patrones de compra de productos orgánicos entre los millennials en una economía emergente. El estudio proporcionó respuestas a consultas académicas previas que proponían examinar la correlación entre los componentes del marco teórico propuesto.

本研究旨在确定绿色广告和生态标签是否影响有机产品的购买行为, 或这种行为的影响是否通过千禧一代的态度和环境意识的中介效应表现出来。

本研究采用定量、相关性和横截面设计。共有430名千禧一代参与了研究。问卷由20个问题组成, 分布在五个变量上。问卷在厄瓜多尔基多和瓜亚基尔市的购物中心外分发。结果经过统计测试, 以确定问卷的内部一致性。通过验证性因素分析 和结构方程模型 验证了研究模型的收敛效度和区分效度。统计分析使用SPSS 20和AMOS 24软件完成。

研究表明, 态度和环境意识在绿色广告与绿色购买行为之间以及生态标签与绿色购买行为之间起到中介作用。此外, 已证明绿色广告直接影响千禧一代对有机产品的绿色购买行为, 而生态标签则没有直接影响。

本研究是最初探讨绿色营销元素与千禧一代在新兴经济体中有机产品购买模式之间关系的研究之一。该研究回答了早期学术研究提出的关于理论框架各组成部分相关性的探讨。