Abstract:IntroductionChina is now in the post-period of COVID-19 epidemic prevention and control. While facing normalized epidemic prevention and control, consumers behavioral intention and decision-making will still be influenced by the epidemic's development and the implementation of specific epidemic prevention measures in the medium to long term. With the impact of external epidemic prevention environment and measures, consumers' channel behavior has changed. How to better promote channel integration by adopting co… Show more
“…As shown in Figure 3(d), when α 12 − β 12 > 1 and α 21 − β 21 > 1, M 3 is the saddle point and there is no stable point of competition between them [21][22][23].…”
Section: Stability Analysis Of Competing Synergistic Evolutionmentioning
confidence: 98%
“…In order to visualize the development trend of urban online taxis and taxis under different relationships, data simulation analysis was carried out using Matlab simulation software, and the model parameters were used to determine the initial scale with the number of online taxis and taxis in Xi'an in 2020. respectively X 10 = 2.5 , X 20 = 1.5 Other parameters are assumed to be γ 1 = 0.3, γ 2 = 0.2, N 1 = 3.5, N 2 = 2.5 [21]. With these parameters set constant, the evolution of the competitive synergy between net cars and taxis varies with the change of the competitive influence coefficient.…”
Section: Simulation Analysis Of Competitive Co-evolutionmentioning
In order to determine the optimal scale for urban ride-hailing services and taxis while promoting their sustainable growth, we have developed a Lotka-Volterra evolutionary model that accounts for the competitive, cooperative, and mixed dynamics between these two entities. This model is rooted in the theory of synergistic evolution and is supported by data simulation and analysis. By employing this model, we can identify the appropriate size for urban ride-hailing services and taxis when they reach equilibrium under different environmental conditions. The study’s findings reveal that the evolutionary outcomes of online ride-hailing services and traditional taxis are closely linked to the competitive impact coefficient and the cooperative effect coefficient. In highly competitive environments, intense rivalry can lead to the elimination of the less competitive party, while the dominant player ultimately attains a specific size threshold. As competition moderates, both entities can achieve a balanced and stable coexistence in the market. In cooperative environments, both online ride-hailing services and traditional taxis have more room for development, which facilitates the integration of existing and innovative business models. In environments marked by competition, the development trends of both entities mirror those in competitive settings, but cooperation can slow down the decline of the less competitive party. In conclusion, we propose strategies to foster fair competition between online ride-hailing services and traditional taxis, consider the coexistence of old and new business models, and promote their integrated development.
“…As shown in Figure 3(d), when α 12 − β 12 > 1 and α 21 − β 21 > 1, M 3 is the saddle point and there is no stable point of competition between them [21][22][23].…”
Section: Stability Analysis Of Competing Synergistic Evolutionmentioning
confidence: 98%
“…In order to visualize the development trend of urban online taxis and taxis under different relationships, data simulation analysis was carried out using Matlab simulation software, and the model parameters were used to determine the initial scale with the number of online taxis and taxis in Xi'an in 2020. respectively X 10 = 2.5 , X 20 = 1.5 Other parameters are assumed to be γ 1 = 0.3, γ 2 = 0.2, N 1 = 3.5, N 2 = 2.5 [21]. With these parameters set constant, the evolution of the competitive synergy between net cars and taxis varies with the change of the competitive influence coefficient.…”
Section: Simulation Analysis Of Competitive Co-evolutionmentioning
In order to determine the optimal scale for urban ride-hailing services and taxis while promoting their sustainable growth, we have developed a Lotka-Volterra evolutionary model that accounts for the competitive, cooperative, and mixed dynamics between these two entities. This model is rooted in the theory of synergistic evolution and is supported by data simulation and analysis. By employing this model, we can identify the appropriate size for urban ride-hailing services and taxis when they reach equilibrium under different environmental conditions. The study’s findings reveal that the evolutionary outcomes of online ride-hailing services and traditional taxis are closely linked to the competitive impact coefficient and the cooperative effect coefficient. In highly competitive environments, intense rivalry can lead to the elimination of the less competitive party, while the dominant player ultimately attains a specific size threshold. As competition moderates, both entities can achieve a balanced and stable coexistence in the market. In cooperative environments, both online ride-hailing services and traditional taxis have more room for development, which facilitates the integration of existing and innovative business models. In environments marked by competition, the development trends of both entities mirror those in competitive settings, but cooperation can slow down the decline of the less competitive party. In conclusion, we propose strategies to foster fair competition between online ride-hailing services and traditional taxis, consider the coexistence of old and new business models, and promote their integrated development.
“…Some literature uses multi-agent-based genetic algorithms to study the probabilistic economic scheduling problem of multi-energy power flow systems in the optimal scheduling system of cogeneration [15]. The scenario method is another method to describe multiple uncertain factors in the optimal scheduling system of cogeneration [16]. In addition, stochastic optimization based on chance constraints is also an uncertain optimization algorithm that is often used.…”
This work expands on previous research to offer a state-of-the-art approach for optimizing the dispatching of cogeneration systems, given the limitations faced by conventional coal-fired cogeneration units and the increasing environmental standards. Acknowledging the constraints of flexibility in winter heating, the study aims to improve unit coal use optimization and lower emissions. The paper presents a novel optimization approach for distributing electricity and heat in cogeneration units, utilizing the Deep Q-Network (DQN) algorithm. The suggested approach reduces operating expenses and improves system dependability using a sixth-order function fitting and fuzzy set space division. The study's results indicate a significant 8.96% increase in performance, demonstrating the effectiveness of the DQN-based strategy in enabling cost-effective scheduling in cogeneration systems. This research offers a road towards sustainable and effective energy use and contributes to the development of cogeneration technology. It also has potential applications in natural energy systems.
“…As illustrated in Figure 2, the current study delved deep into the diverse customer intentions associated with channel usage and adoption, shedding light on underlying mediators and moderators. Channel migration intention emerged as a focal point, where customer perceived value of a particular channel assumed a mediating role, while the costs associated with switching channels act as a moderator (Wang et al, 2023). Continuous use intention is also mediated by emotional and functional value, as well as attitude, with product involvement moderating the relationship (Geng & Chang, 2022;Lee & Kim, 2021).…”
This study systematically reviews literature to explore the Moderated Mediation Model of Customer Intention in Omnichannel Technology, particularly under the transformative influence of technologies like AI, AR, and big data analytics. Amidst evolving consumer expectations and the drastic shifts induced by the COVID-19 pandemic, the retail industry's shift from multichannel to omnichannel strategies has become crucial. This review analyzes 59 studies published between 2018 and April 2023 to discern the dynamics influencing customer intention within the omnichannel framework. Our research highlights the sparse but critical discourse on the mediating roles of perceived value and trust and the moderating effects of factors like age and product involvement. Although extensive, the literature reveals gaps, particularly in unified frameworks integrating these diverse insights. We propose a novel conceptual framework centered on these mediators and moderators to better understand and harness customer intentions toward omnichannel technology adoption. Key findings indicate that seamless integration across online and offline channels, responsive to the nuanced needs and behaviors of consumers, significantly enhances customer engagement and loyalty. This synthesis not only offers a consolidated view of the omnichannel retail landscape but also provides strategic pathways for retailers aiming to optimize customer interactions and satisfaction in a digitally dominated era. Our review contributes to the omnichannel retail literature by offering a structured overview of the factors driving customer intentions and suggesting directions for future research to bridge identified gaps, particularly through longitudinal studies and expanded demographic analyses.
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