This study aims to investigate the contributions of online promotional marketing and online reviews as predictors of consumer product demands. Using electronic data from Amazon.com, we attempt to ...predict if online review variables such as valence and volume of reviews, the number of positive and negative reviews, and online promotional marketing variables such as discounts and free deliveries, can influence the demand of electronic products in Amazon.com. A Big Data architecture was developed and Node.JS agents were deployed for scraping the Amazon.com pages using asynchronous Input/Output calls. The completed Web crawling and scraping data-sets were then preprocessed for Neural Network analysis. Our results showed that variables from both online reviews and promotional marketing strategies are important predictors of product demands. Variables in online reviews in general were better predictors as compared to online marketing promotional variables. This study provides important implications for practitioners as they can better understand how online reviews and online promotional marketing can influence product demands. Our empirical contributions include the design of a Big Data architecture that incorporate Neural Network analysis which can used as a platform for future researchers to investigate how Big Data can be used to understand and predict online consumer product demands.
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•The platform’s combating effort has a non-monotonic effect on sellers’ profits.•Optimal combating effort level depends on the authentic firm’s production cost.•The authentic firm can ...be better off with a higher production cost.
In recent years, criticism of online marketplaces has been incessant because of the widespread presence of counterfeit goods. This study develops an analytical framework to investigate the interactions among an online marketplace, an authentic brand seller, and a counterfeiter of the brand. Both sellers exert efforts to attract sales for the brand, and the online platform determines its effort level in combating counterfeiters. Our analysis reveals several interesting insights. First, our analysis shows that the platform’s combating effort has a non-monotonic impact on both sellers’ profits. Second, the platform’s optimal combating effort level relies on the production cost of the authentic firm. The platform finds it optimal to exert maximum possible effort to combat counterfeiters when the unit cost of the authentic product is very low, and not to combat when the unit cost is very high. Third, interestingly, the authentic seller can be better off with a higher production cost due to the strategic reaction of the platform whose revenue derives from both types of sellers. The intuition and managerial implications of these insights are discussed.
Purpose
– The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user reviews ...can help predict product sales.
Design/methodology/approach
– The authors designed a big data architecture and deployed Node.js agents for scraping the
Amazon.com
pages using asynchronous input/output calls. The completed web crawling and scraping data sets were then preprocessed for sentimental and neural network analysis. The neural network was employed to examine which variables in the study are important predictors of product sales.
Findings
– This study found that although online reviews, online promotional strategies and online sentiments can all predict product sales, some variables are more important predictors than others. The authors found that the interplay effects of these variables become more important variables than the individual variables themselves. For example, online volume interactions with sentiments and discounts are more important than the individual predictors of discounts, sentiments or online volume.
Originality/value
– This study designed big data architecture, in combination with sentimental and neural network analysis that can facilitate future business research for predicting product sales in an online environment. This study also employed a predictive analytic approach (e.g. neural network) to examine the variables, and this approach is useful for future data analysis in a big data environment where prediction can have more practical implications than significance testing. This study also examined the interplay between online reviews, sentiments and promotional strategies, which up to now have mostly been examined individually in previous studies.
Reseller contract and online marketplace contract are two typical supply chain contracts provided by Online retail platforms (ORPs) in online retailing. Manufacturers can either wholesale their ...products to an ORP using the traditional reseller contract or choose an agency selling contract to sell their products directly to consumers through an online marketplace provided by the ORP. Based on a game model, this paper studies the contract choice strategy for a monopoly manufacturer facing two competing downstream ORPs. The results show that the competition intensity between the ORPs and the order-fulfilment costs critically moderates the choice decision. Specifically, for a given competition intensity (level of order-fulfilment costs), with rising order-fulfilment costs (the downstream competition intensity), the preferred mode for the manufacturer switches from the pure online marketplace mode to the hybrid mode and then to the pure reseller mode. The intuition of this lies in the interaction of the transfer of the pricing rights and the responsibility for order fulfilment. Meanwhile, the conditions to ensure the dominant equilibrium in the competition of ORPs are analysed. Finally, we extend the basic model by relaxing the assumptions about the same proportion fee rate and the fixed order-fulfilment cost.
We study a closed-loop supply chain involving e-retailer’s online marketplace (i.e., platform service) and self-run shop, which are available options for upstream manufacturer to market new and ...remanufactured products. Considering the platform fee and the order fulfilment cost, we investigate four distribution channel modes, and obtain decision regions and corresponding optimal decisions for supply chain members. Furthermore, based on theoretical and numerical analyses, impacts of the platform fee and the order fulfilment cost on optimal decisions and performance of the supply chain are investigated. Our results indicate that the two parameters show different impacts on supply chain decisions under different channel modes. We also find that the preferred channel mode changes with the two parameters from the perspective of the manufacturer or the e-retailer, the preferred channel mode is only determined by the order fulfilment cost from the perspective of consumers, and selling the new product through the e-retailer and the remanufactured product in the online marketplace is more preferred from the perspective of the environment.
Online marketplaces, such as those operated by Amazon, have seen rapid growth in recent years. These marketplaces serve as an intermediary, matching buyers with sellers, whereas control of the good ...is left to the seller. In some cases, e.g., the Amazon marketplace system, the firm that owns and manages the marketplace system will also sell competing products through the marketplace system. This creates a new form of channel conflict, which is a focus of this article. We consider a setting in which a marketplace firm operates an online marketplace through which retailers can sell their products directly to consumers. We consider a single retailer, who currently sells its product only through its own website, but who may choose to contract with Amazon to sell its product through the marketplace system. Selling the product through the marketplace expands the available market for the retailer, but comes at some expense, e.g., a fixed participation fee or a revenue sharing requirement. Thus, a key question for the retailer is whether she should choose to sell through the marketplace system, and if so, at what price. We analyze the optimal decisions for both the retailer and the marketplace firm and characterize the system equilibrium.
This paper engages discourses about the nature of creative work through a case study of creative worker freelancing on Upwork, an online marketplace. The analysis begins by contextualizing the growth ...of freelancing in a transformation of capitalism to a mode of flexible accumulation. It is framed against neoliberal narratives about the creative economy, including promises of financial success and freedom from rigid Fordism. An exploration of the characteristics of technology-mediated work on Upwork, including contract structure, wages, consistency of work, risk-management strategies, and profiles of clients and freelancers follows. The findings suggest that despite the company's emphases on efficiency, flexibility, and freedom from the physical office, freelancers face significant trade-offs in undertaking such work, notably its infrequency, barriers to high wages, and intense global competition. The discussion addresses structural drivers of precarity in the marketplace and of its ongoing success.
E-commerce platforms can either serve as product resellers or provide online marketplaces. Selecting the better of these two online distribution modes or applying the combination of them has always ...been a critical issue for a supplier. The present work aims to assist a dominant supplier manufacturing differentiated products in determining the best online mode under different distribution strategies. Starting from the selective marketing strategy, three models matching products with individual modes are respectively formulated and studied for equilibria. The research is then extended to the intensive marketing strategy, where the combination of modes for each product is addressed. Our analytical results reveal that the mode selection highly relies on a product's inherent attribute, defined as the Cost-Market-Substitution (CMS) factor. Both the commission rate and slotting fee can be functioned as important moderators for relieving the conflict of distribution modes. The optimal commission rate to maximize the win-win region is shown to be highly dependent upon the substitution intensity across the differentiated products, and hence can be attained accordingly.
•We investigate a platform supply chain with a dominant supplier manufacturing differential products.•Supplier and e-platform act contradictorily in selecting the best distribution mode for different products.•The optimal distribution mode is determined under both the selective and intensive marketing strategies.•An appropriate commission rate is derived to maximize the win-win situation for the two parties.
This paper introduces a novel platform of Waste-To-Energy Online Marketplace. The platform maintains a comprehensive catalogue of available biomass resources, detailing types, quantities, and ...geographical locations. This allows bioenergy facilities to identify and select suitable biomass feedstock based on their specific energy production requirements. Through an intuitive online marketplace, stakeholders can negotiate agreements, ensuring a streamlined and mutually beneficial exchange of biomass feedstock for bioenergy production. The online matchmaker by A.I. recommendation engine platform opens new avenues for biomass suppliers and bioenergy facilities to connect beyond traditional geographical and logistical constraints, fostering a more expansive and interconnected market. Efficient matching ensures that biomass resources are utilised optimally, reducing waste, and maximising bioenergy production. The proposed model seeks to enhance the efficiency of converting sugarcane biomass into bioenergy, leveraging digital and A.I. technologies to match biomass producers with bioenergy facilities, optimising the efficient conversion of biomass resources into renewable energy and fostering a reduction in GHG emissions associated with traditional waste disposal methods. This innovative approach has the potential to revolutionise the biomass supply chain, facilitates competitive pricing and cost-effective transactions, benefitting both biomass suppliers and bioenergy producers, promoting sustainability, efficiency, and collaboration in the journey towards a greener and more resilient energy future.
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