In recent years, with the rapid development of artificial intelligence and social media, the influence of word-of-mouth (WOM) on the diffusion of online content has become increasingly evident. Video platforms can use artificial intelligence to collect WOM data of programs and formulate corresponding pricing strategies. Based on this background, considering the impact of online WOM effects on the diffusion of on-demand content exclusive to members, this study constructs a two-stage product provision model for online video platforms, consisting of the premiere and follow-up broadcast stage. Based on expected utility theory, this research explores the pricing strategies for member-exclusive on-demand content under two profit models and analyzes the influence of program WOM attributes and program quality on optimal decision-making. The findings reveal that: When the premiere stage WOM for a program is either highly positive or negative, video platform should adopt an "advertising-dominant strategy". When the premiere stage WOM is moderate, a " fee-dominant strategy" is preferable. Higher program quality increases the platform's inclination toward the "fee-dominant strategy". The better the premiere stage WOM and program quality, the more users tend to watch during the premiere stage. Accordingly, both the program price and the platform's expected profit will vary to different degrees depending on these conditions.

As Internet technology undergoes rapid development and widespread adoption, e-commerce emerges as a pivotal component of the platform economy, permeating various facets of daily life. However, due to the influence of time, space, and other factors, the problem of integrity becomes severe in the real trading environment. As the platforms, sellers, and consumers are the main participants and their decision-making is restricted by historical experiences and contextual conditions, they exhibit constrained rationality. Utilizing evolutionary game theory, the study constructs a tripartite game model that analyses the influence of relevant parameters on the behavior of the participants. To deal with the behaviors of the participants, we built a simulation system on MATLAB to demonstrate the effects of beginning circumstances and associated parameter adjustments on the evolution outcomes for participants. Through theoretical analysis and numerical simulation analysis, we identify that the e-commerce platforms should standardize the good faith behavior of sellers by increasing the punishment, which can reduce the malicious return behavior of consumers. Sellers can mitigate the probability of fraud by improving production technology. Consumers can improve their learning to avoid returning products. This research provides a theoretical framework and decision support for e-commerce platforms, and it also promotes the long-term growth of online transactions.

The adjustment effect of the anchoring effect and online reviews on consumer cognition has grown to be a significant element influencing business pricing. This study explores the effects of online reviews and price anchoring on company pricing and profits by building an online product pricing model based on expected utility theory from the perspective of consumer purchasing psychology. The findings show that firms must take consumer anchoring psychology into account when making decisions if they want to increase revenue. Different pricing strategies are used depending on the variables associated with the quality of online reviews. The higher the sensitivity coefficient of reviews, in particular when the quality of the reviews is higher than a specified value, the bigger the profit. In the anchor point, the optimal price is rising. When a company chooses a higher price policy, the optimal price steadily decreases with the degree of anchoring.

Price is an important index of consumers' purchase choice, and the price comparison behavior of consumers in the decision-making process also affects the profit and loss of their own purchase utility to varying degrees. Based on multinomial logit(MNL) model, the reference price is incorporated into product line development and design, and the pricing decision of product line and value-added services bundle is studied. The influence mechanism of reference price effect on optimal product pricing and maximum profit is analyzed, and the deviation of strategic decision-making caused by not considering reference price effect is discussed. The results show that the reference price effect has a positive impact on the lowest price products in the product line, but has a negative impact on the high price products in the product line, the total market share of the firm and the total profit. When the reference price effect is ignored, the pricing of different quality products and services in the product line will be higher or lower, and the total market share and total profit will be higher. The results can provide theoretical support for product line design and pricing decision.

Under platform economy, prices, reviews, and sales are the three most concerned purchase decision factors for consumers. However, different customers have different sensitivity to the same decision factors. Therefore, it would be an important to study the sensitivity of the consumers to reviews, price, and sales. Based on the Multinominal Logit Model (MNL Model), this paper constructs a two-stage pricing model for new products of platform enterprises, and studies the influence of price, review and sales on enterprise profit. Then it analyzes the influence mechanism of consumers’ sensitivity to price change, product cost and consumers’ valuation of product quality on product pricing and enterprise profit. After that, it further formulates the two-stage optimal pricing strategy for product sales. Research shows that enterprises should not only consider consumers’ sensitivity to comments, price and sales volume, but also learn from the previous sales experience when making pricing strategy. The research results can provide theoretical basis and decision support for product pricing and operation management of platform enterprises.

In the freemium business model, how to price value-added services and design effective strategy to achieve the sustainability of value-added services promotion is of great significance to enterprises. By constructing a monopolistic freemium enterprise, this paper uses a Multinational Logit model (MNL model) to analyze value-added services pricing and two kinds of value-added services promotion strategies (the quality reduction strategy of basic product and the price discount strategy of value-added services) with heterogeneous sticky-users demand, and then discusses the optimal promotion strategy. The results show that: Both the quality reduction strategy of basic product and the price discount strategy of value-added services can have positive impacts on the profit of enterprise. The sticky users demand valuation plays a positive role in promoting the optimal profit of enterprise. The optimal promotion strategy is the price discount strategy of value-added services. This study can provide a theoretical basis and decision support for the operation and management of the freemium enterprises.

With the advancement of society and technology, consumers are becoming more personalized and more willing to buy new products. To meet the diverse needs of consumers, the design and development of product lines have become an important strategic issue of enterprises. Based on the consumer choice model, this paper aims at the design and development of product lines and the purchase behavior of consumers. A two-stage pricing model is constructed under the condition of bundled sales of products and services. This paper analyzes the impact that enterprises should consider products with value-added services and consumers’ purchasing behavior on product line two-stage pricing. Research shows that the level of product value-added services and the degree of enterprise strategy will have an impact on the price of the product line and the enterprise’s profit. When the service level is higher, the enterprise’s product line price and profit will increase, and when the enterprise discount higher, the enterprise’s total profit and product line price will decrease.

The quality of durable consumer goods is more and more concerned by consumers, and the development of value-added services to improve product quality has become an important way for enterprises to obtain profits. Based on the multinomial logit (MNL) model, this paper establishes a product line optimization model considering value-added services, which helps find the optimal pricing, market share and maximum profit. Through numerical experiments, the effects of the ratio of service price to product price, product quality, service quality, utility loss caused by product failure on the optimal solutions are studied. The study finds that when developing a product line, increasing the relative price of services while reducing product pricing is the optimal strategy. The research results can provide theoretical basis and decision support for the pricing of durable consumer goods and value-added services.

To meet the needs of the Internet of Things, every edge device is equipped with the functions of data collection, analysis, calculation, communication, and intelligence. Based on the consumption pattern of offline experience and online purchase, and considering the impact of product quality differences, product defects, and offline service level on customers' purchasing behavior, this paper uses the model (Multinominal Logit Model) to research customers' choice behavior and online product pricing. This paper takes the pricing of dual-channel retailers in different channels as the background, and how to maximize the retailer's profit as the goal, establishes the loss cost model of customer returns, and analyzes the influence of quality problem returns on the optimal pricing and profit of retailers in different channels. The study found that the offline service level remains at 0.24 and retailers can obtain the best profit; the optimal price decreases with the online product quality and the optimal profit increases. In the omni-channel environment, customers can buy products according to their own utility and preferences freely switch between various channels, retailers in the face of customer return this situation, can start from their own interests, provide appropriate service level, reasonable control product quality, make the optimal pricing, maximize their own profits. This study expands the theory of online product pricing from the perspective of customers behavior, provides a more flexible pricing mechanism for enterprises, and speeds up the development and application of intelligent edge computing systems.

In order to better meet the needs of the green industrial Internet of things, this paper studies the anchoring psychology of online consumers and proposes a pricing model that considers online consumers to be anchored by the product price. In this model, the cognitive bias of online consumers affected by anchoring effect is described in a utility function by using the anchoring-adjustment heuristic, and the consumer choice behavior and online product pricing strategy are studied with a Multinomial Logit Model (MNL). We study the effect of price anchoring point and anchoring degree on optimal pricing. In particular, when the cost is greater than the price anchoring point, the greater the anchoring degree is, the smaller the profit will be. This paper provides a more flexible pricing mechanism for enterprises based on historical consumption data through customer behavior analysis, and it will accelerate the construction of the green industrial Internet of things.