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The expansion of lifestyle and luxury markets has necessitated new marketing techniques. Recently, brand addiction has been a new topic in luxury repurchasing. The information reported in the literature regarding the effectiveness of brand addiction is insufficient and controversial. This research aimed to assess the role of brand addiction in Islamic consumers who purchase luxury smartphone brands.

Survey responses were collected from an online sample of 384 luxury consumers in Iran. The methods were evaluated using software (smart PLS 3) to test the hypothesis.

According to the uniqueness theory, the authors completed that brand addiction and consumer relationships are different and relative in each luxury product. The results positively determine that brand addiction has a more significant impact than brand jealousy, brand love and brand experience in repurchasing luxury consumers.

This study helps expand the literature on luxury repurchases and contends that brand addiction creates a new perspective in understanding behavioral addiction.

This paper provides insights for current and future marketers and managers, especially in Iran.

This investigation is the first study on the impact of different dimensions of brand addiction on luxury smartphone repurchase intention. In this regard, the findings of the study are important in the luxury market and extend current knowledge on repurchasing luxury products such as in Iran.

The purpose of this paper is to develop a novel network and dynamic data envelopment analysis (DEA) model for evaluating sustainability of supply chains. In the proposed model, all links can be considered in calculation of efficiency score.

A dynamic DEA model to evaluate sustainable supply chains in which networks have series structure is proposed. Nature of free links is defined and subsequently applied in calculating relative efficiency of supply chains. An additive network DEA model is developed to evaluate sustainability of supply chains in several periods. A case study demonstrates applicability of proposed approach.

This paper assists managers to identify inefficient supply chains and take proper remedial actions for performance optimization. Besides, overall efficiency scores of supply chains have less fluctuation. By utilizing the proposed model and determining dual-role factors, managers can plan their supply chains properly and more accurately.

In real world, managers face with big data. Therefore, we need to develop an approach to deal with big data.

The proposed model offers useful managerial implications along with means for managers to monitor and measure efficiency of their production processes. The proposed model can be applied in real world problems in which decision makers are faced with multi-stage processes such as supply chains, production systems, etc.

For the first time, the authors present additive model of network-dynamic DEA. For the first time, the authors outline the links in a way that carry-overs of networks are connected in different periods and not in different stages.

This paper aims to examine the integration of lateral transshipment and road vulnerability into the humanitarian relief chain in light of affected area priority to address equitable distribution and assess the impact of various parameters on the total average inflated distance traveled per relief item.

After identifying comprehensive critical criteria and subcriteria, a hybrid multi-criteria decision-making framework was applied to obtain the demand points’ weight and ranking in a real-life earthquake scenario. Direct shipment and lateral transshipment models were then presented and compared. The developed mathematical models are formulated as mixed-integer programming models, considering facility location, inventory prepositioning, road vulnerability and quantity of lateral transshipment.

The study found that the use of prioritization criteria and subcriteria, in conjunction with lateral transshipment and road vulnerability, resulted in a more equitable distribution of relief items by reducing the total average inflated distance traveled per relief item.

To the best of the authors’ knowledge, this study is one of the first research on equity in humanitarian response through prioritization of demand points. It also bridges the gap between two areas that are typically treated separately: multi-criteria decision-making and humanitarian logistics.

This is the first scholarly work in Shiraz focused on the equitable distribution system by prioritization of demand points and assigning relief items to them after the occurrence of a medium-scale earthquake scenario considering lateral transshipment in the upper echelon.

The paper clarifies how to prioritize demand points to promote equity in humanitarian logistics when the authors have faced multiple factors (i.e. location of relief distribution centers, inventory level, distance, lateral transshipment and road vulnerability) simultaneously.

This study aims to investigate a locating-routing-allocating problems and the supply chain, including factories distributor candidate locations and retailers. The purpose of this paper is to minimize system costs and delivery time to retailers so that routing is done and the location of the distributors is located.

The problem gets closer to reality by adding some special conditions and constraints. Retail service start times have hard and soft time windows, and each customer has a demand for simultaneous delivery and pickups. System costs include the cost of transportation, non-compliance with the soft time window, construction of a distributor, purchase or rental of a vehicle and production costs. The conceptual model of the problem is first defined and modeled and then solved in small dimensions by general algebraic modeling system (GAMS) software and non-dominated sorting genetic algorithm II (NSGAII) and multiple objective particle swarm optimization (MOPSO) algorithms.

According to the solution of the mathematical model, the average error of the two proposed algorithms in comparison with the exact solution is less than 0.7%. Also, the algorithms’ performance in terms of deviation from the GAMS exact solution, is quite acceptable and for the largest problem (N = 100) is 0.4%. Accordingly, it is concluded that NSGAII is superior to MOSPSO.

In this study, since the model is bi-objective, the priorities of decision makers in choosing the optimal solution have not been considered and each of the objective functions has been given equal importance according to the weighting methods. Also, the model has not been compared and analyzed in deterministic and robust modes. This is because all variables, except the one that represents the uncertainty of traffic modes, are deterministic and the random nature of the demand in each graph is not considered.

The results of the proposed model are valuable for any group of decision makers who care optimizing the production pattern at any level. The use of a heterogeneous fleet of delivery vehicles and application of stochastic optimization methods in defining the time windows, show how effective the distribution networks are in reducing operating costs.

This study fills the gaps in the relationship between location and routing decisions in a practical way, considering the real constraints of a distribution network, based on a multi-objective model in a three-echelon supply chain. The model is able to optimize the uncertainty in the performance of vehicles to select the refueling strategy or different traffic situations and bring it closer to the state of certainty. Moreover, two modified algorithms of NSGA-II and multiple objective particle swarm optimization (MOPSO) are provided to solve the model while the results are compared with the exact general algebraic modeling system (GAMS) method for the small- and medium-sized problems.

This study aims to examine the effect of social influence, environmental concerns and altruism on consumer purchase intention of eco-fashion (PIEF). In addition, this study, exploring the essential behavioral outcomes influenced in marketing, seeks to deepen the existing insight in this area.

To obtain the required data, the authors surveyed a group of people with previous experience in the purchase of eco-friendly apparel. The research hypotheses were tested using the structural equation modeling technique.

According to the results, social influence had the most significant impact on PIEF. Also, environmental concerns and altruism had a significant effect on PIEF. The results further indicated that eco-fashion purchase intention, in turn, influenced consumer willingness to engage in eWOM and pay a price premium.

The results of the present study guide marketing practitioners for the segmentation of target consumers, as the information on consumers’ natural needs and desires in a socio-cultural context is of significant use to fashion managers to understand their customers deeply. This information also helps them discover better ways of designing their marketing campaigns.

This paper contributes to research by advancing an understanding of how consumers make eco-fashion consumption decisions in purchasing apparel and provides businesses with managerial insights into devising marketing strategies to promote eco-fashion consumption, which facilitates fashion companies’ development of a sustainable fashion supply chain.

Este estudio examina el efecto de los factores influencia social, preocupación medioambiental y altruismo en la intención de compra de moda ecológica (PIEF) por parte de los consumidores. Además, este estudio, que explora los importantes resultados conductuales influenciados en el área del marketing, pretende profundizar en los conocimientos existentes en esta área.

Se encuesto a un grupo de personas con experiencia previa en la compra de ropa ecológica. Las hipótesis de la investigación se contrastaron con SEM.

La influencia social fue la que más influyó en el PIEF. Asimismo, la preocupación por el medio ambiente y el altruismo tuvieron un efecto significativo en el PIEF. Los resultados indicaron además que la intención de compra de moda ecológica, a su vez, influía en la disposición del consumidor a participar en el eWOM y a pagar un sobreprecio.

Los resultados del presente estudio orientan a los profesionales del marketing en la segmentación de los consumidores objetivo, ya que la información sobre las necesidades y deseos naturales de los consumidores en un contexto sociocultural es de gran utilidad para que los gestores de la moda conozcan en profundidad a sus clientes. Esta información también les ayuda a descubrir mejores formas de diseñar sus campañas de marketing.

El artículo contribuye a la investigación al avanzar en la comprensión de cómo los consumidores toman decisiones de consumo de moda ecológica en la compra de prendas de vestir y proporciona a las empresas ideas de gestión para diseñar estrategias de marketing que promuevan el consumo de moda ecológica, lo que facilita a las empresas de moda el desarrollo de una cadena de suministro de moda sostenible.

环境关注和利他主义等因素对消费者购买环保时装意向（PIEF）的影响。此外, 这项研究探讨了在营销领域有影响的重要行为结果, 试图深化这一领域的现有见解。

我们调查了一组有购买生态友好服装经验的人使用结构方程模型（SEM）方法对研究假设进行了检验。

社会影响对PIEF的影响最大。此外, 对环境的关注和利他主义对PIEF也有重大影响。结果进一步表明, 环保时装的购买意向反向影响了消费者参与eWOM和支付价格溢价的意愿。

且结果只限于环保时装消费。

本研究的结果为营销从业者提供了关于细分目标消费群体的指导。消费者在特定社会文化背景下的自然需求和欲望等信息对于时尚经理人深入了解他们的顾客来说有重要作用, 而这些信息也有助于他们探索更好地设计营销活动的方法。

为企业提供了设计促进环保时装消费的营销策略的管理建议。这促进了时尚公司发展可持续时尚供应链, 为该领域研究做出了贡献。

The purpose of this study is to investigate important factors that help explain customer willingness to adopt mobile banking (M-banking). To this end, the unified theory of acceptance and use of technology 2 (UTAUT2) was applied and to more accurately predict customer behavioral intentions, it was attempted to extend it.

The research data were collected from 396 customers of Iranian private banks who had the experience of using M-banking. The structural equation modeling technique was used to test the research hypotheses.

Findings suggest that performance expectancy, effort expectancy, social influence, facilitating conditions, habit, hedonic motivation, perceived value and trialability are endorsed as proponents of M-banking adoption intention. On the other hand, M-banking adoption intention has also had a significant positive effect on actual use behavior and word-of-mouth (WOM). WOM has also influenced actual use behavior and mediated the relationship between M-banking adoption intention and actual use behavior.

The present study focuses on private banks, therefore, although it is sufficient, it is limited to private cases. This study contributes to the literature on M-banking services and actual use behavior. By appropriately focusing on M-banking adoption intention and the service quality provided, banks can strengthen their relationships with customers, thereby stimulating actual customer behavior such as actual use behavior and WOM.

From theoretical and managerial aspects, this study has particular value for the literature on M-services’ intention in general and banking in particular. The present study provides a conceptual framework for M-banking adoption intention, which could be used in M-banking services. In addition, this study sought to extend UTAUT2 and to examine the mediating role of WOM in actual use behavior motivation as well.

In this research, using an analytical framework on factors affecting the quality of auditing services, we consider factors that affect an expectations gap in providers and users of auditing services related to factors affecting auditing quality. Effective factors studied in this regard are professional features, including the professional role of individuals in the auditing process (auditors against preparers of financial statements), professional experience of individuals, professional rating and size of auditing firms. The first statistical society consists of certified public accountants (CPAs) working in the auditing organization and audit firms in Iran, and the second statistical society consists of all investment companies that operate under the supervision of the Tehran Stock Exchange Organization. Based on the results of the research, the role of professionals in the auditing process is not effective as an independent auditor or financial statements provider, as well as the professional experience of individuals in the expectations gap from factors affecting auditing quality, while the size and qualities of the auditing firms were influenced by the expectations gap of individuals in the field of auditing regulation.

The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.