Retail Platforms Research Articles

Information sharing decision of retail platform: platform's risk aversion and competing suppliers

An increasing number of retail platforms are adopting blockchain technology to mitigate information asymmetries and share data with upstream suppliers, thereby reducing demand uncertainty. However, these platforms often engage with multiple upstream suppliers of varying product quality. This study constructs a game-theoretic model within a supply chain framework, featuring a risk-averse retail platform and two upstream suppliers of different quality levels. As the core leader of the game, the retail platform decides whether to share demand information with the two competing suppliers after implementing the technology. Using mean-variance theory, this study addresses a key question: Which types of suppliers should be included in information sharing on a retail platform? The results show that when the unit cost of information sharing is low, allowing both suppliers to share demand information is most beneficial. Conversely, when the unit cost is high, only high-quality suppliers should be included. Notably, as the unit cost of information sharing and the intensity of competition between high-quality and low-quality products increase, low-quality suppliers are excluded from information sharing. Additionally, the model indicates that the greatest social welfare can be achieved whether both high-quality and low-quality suppliers are authorized to join the information sharing network, or only high-quality suppliers are included.

Selling mode choice and return policy for advance selling in an e-commerce platform with social influence

Advance selling is increasingly employed to mitigate demand uncertainty, raise consumer awareness of products, and even exert social influence on subsequent sales. However, consumers may hesitate to pre-order products due to uncertainty about product valuation. As a response, sellers have begun implementing return policies and utilizing online retail platforms to alleviate the uncertainty. This study aims to investigate the interaction between return policies for advance selling and selling formats of e-commerce platforms considering the impact of social influence. We examine a two-period platform supply chain that includes agency selling, reselling, and hybrid selling formats with and without return policies. Using game theory models, we analyze pricing decisions and profits of each player under six possible scenarios. The results indicate that the manufacturer consistently prefers adopting a Money-Back Guarantee (MBG) return policy regardless of the selling format. However, the MBG return policy is not always advantageous for the platform, except in the case of low social influence. In such a case, the platform may implement the MBG return policy to boost advance sales, thereby enhancing the penetration power from advance sales to regular sales. Regarding selling formats, the platform and the manufacturer opt for the reselling and agency selling, respectively, when the commission rate is low; otherwise, they adopt the agency selling and hybrid selling, respectively. Furthermore, we explore the impact of critical drivers on the pricing decisions and profits within the platform supply chain. Our study offers valuable insights into implementing return policies and determining selling formats.

Retailer-Manufacturer Partnerships in E-Commerce: Dual Product Strategy and Market Share Dynamics

A new practice among online retail platforms, e.g., Amazon and Wayfair, is to offer their own private label product and a substitutable exclusive manufacturer product. We employ a game theoretic approach to examine conditions under which a retailer and a manufacturer find it optimal to enter into such a partnership. Our analysis reveals that a retailer finds it profitable to partner with a manufacturer with one of two profiles. The first is a manufacturer with low unit production and holding costs and large capacity, and the retailer gives the manufacturer most of the market share. In this case, the retailer uses a low private product price and marketing effort to pressure the manufacturer to set a low exclusive product price, which increases the manufacturer's revenue and the retailer's fees. The second is a manufacturer with a large consumer base, high unit production and holding costs, and small capacity. For this profile, the retailer takes most of the market share by offering a low private product price, and the manufacturer is unable to counteract the retailer's low private product price and marketing effort. The partnership results in a lower price when the marketing effort is costly and the retailer relies on private product price, intensifying price competition. Under some conditions, the retailer's profit may decrease in his share of the manufacturer's revenue. Also, the retailer may increase the marketing effort and decrease the private product price, not to take market share, but to pressure the manufacturer to decrease price.

Artificial Intelligence, Smart Applications and Sustainable Consumption: A Theoretical Overview

Sustainable consumption means consuming natural resources consciously, considering future generations. In today's technological age, artificial intelligence and smart applications are used to achieve sustainability goals. In this context, this article examines the impact of artificial intelligence (AI) and smart applications on promoting sustainable consumption behavior. Providing a comprehensive theoretical framework, this article explores how AI technologies support informed decision-making, maximize resource management, and deliver positive environmental impact across a variety of industries. Through a variety of examples, from energy management plans to environmentally friendly retail platforms, the effects of artificial intelligence and smart applications on sustainable consumption are highlighted. This article includes examples of smart applications used to promote sustainable consumption around the world and in Türkiye. Natural challenges that need to be overcome, such as algorithmic biases, data privacy issues and the digital divide, are also mentioned. The article offers recommendations for Türkiye, highlighting the importance of financing digital infrastructure, data privacy laws, digital literacy initiatives and innovation ecosystems, with the aim of emphasizing the importance of artificial intelligence (AI) and smart applications for sustainable consumption.

EXPRESS: From Stars to Dogs – Identifying “Out-Of-Favor” Products on E-Commerce Platforms? Data Analytic Approach to System Design

Online retail platforms are increasingly challenged by the proliferation of low-quality products, which may damage their reputation and sales. To address this problem, we propose a system architecture to proactively identify products that are likely to go "out of favor". Our approach uses historical data to extract useful information from customer ratings and textual reviews. Available data are fed into a state-of-the-art deep learning sequence model to forecast future ratings. We then analyze rating trends, extracting hyperparameters that a binary classifier uses to label products as "out of favor" or not. We tested this system on an Amazon dataset comprising nearly 800,000 observations across 2,826 electronics products. Our results show that the Long Short-Term Memory (LSTM) model excels in forecasting future product ratings compared to other benchmarks. Ablation analysis shows sentiment-related features significantly improve rating forecasts by up to 40%, with review topics adding 10% and other review characteristics 4%. Counterintuitively, topic extraction from reviews does not provide substantial benefits, despite the heavy computational resources it requires. Finally, the two-stage classification process, which leverages time-series data and rating trends, offers a more stable and robust performance than conventional single-stage methods. We provide considerations for system architecture development through robustness checks ensuring its resilience to stressors. Our experiments indicate that rating trends can change in subtle ways over time, leading a promising “star” product to turn into a liability (“dog”). E-commerce platforms can use the proposed system architecture proactively to identify and remove potentially dubious products instead of waiting to take reactive action.

The interaction between manufacturer’s showroom deployment and online selling formats

Many manufacturers selling through online retail platforms are deploying physical showrooms to facilitate consumers’ learning about their products. To uncover the effects of the manufacturer-deployed showrooms on participants in a platform-based distribution channel, we establish a game-theoretic model wherein a manufacturer sells through an online retailer under either agency selling or wholesale selling, and consumers decide sequentially on showrooming and purchasing. We find that, firstly, despite the merits of showrooms in resolving consumers’ valuation uncertainty, the deployment of showrooms may unexpectedly engender more product returns (i.e., return exacerbation effect), which hurts the retailer under wholesale selling when the consumers’ travel cost is moderate. Second, the manufacturer under each selling format tends to benefit (suffer) from showrooms in the case of low (high) travel cost; in addition, the decrease in commission rate boosts the gain from showroom deployment, making the manufacturer under agency selling more likely to deploy showrooms, especially when the travel cost is moderate. Third, in anticipation of the manufacturer’s (not) deploying showrooms and the corresponding profit impacts, the channel members may adopt varying preferences for the selling format. Notably, they have aligned preferences for agency selling with medium low commission rate, while neither prefers agency selling with relatively high commission rate and travel cost. This study sheds light on the flourishing practice of manufacturer-deployed showrooms, clarifying their impacts on the platform-based distribution channel.

Unveiling the Role of Artificial Intelligence and its Impact on Consumer Buying Behaviour in Online Fashion Retail: A Comprehensive Study

This comprehensive study delves into the intricate dynamics between Artificial Intelligence (AI) and consumer buying behaviour within the realm of online fashion retail. As the digital landscape continues to evolve, AI technologies have become increasingly integral to shaping consumer preferences and purchasing decisions. This research endeavours to elucidate the multifaceted role played by AI in influencing consumer behaviour, spanning aspects such as perceived value, ease of use and impact on purchase decisions. Through a meticulous analysis of online fashion retail platforms, this study seeks to provide valuable insights into the intricate interplay between AI innovations and consumer behaviour, thus contributing to a deeper understanding of the contemporary retail landscape.

Sell on, sell to, or sell through: The sales performance implications of brand-platform selling arrangements

Despite the growing prevalence of online retail platforms, scant research has focused on how different selling arrangements affect brand sales performance on the platform. This study explores the sales performance implications of three brand–platform selling arrangements: 1P (a brand manufacturer sells to the platform for resale), 3P (a brand manufacturer sells on the platform), and 2P (a brand manufacturer sells through the platform using a hybrid model in which the platform handles all fulfillment). Utilizing a comprehensive dataset from a major U.S. brand-aggregator platform, encompassing 21,555 brand–category observations across 17 product categories, we examine the impact of these selling arrangements on brand sales performance on the platform. The findings reveal that 2P operations are generally associated with higher sales performance than 1P operations. We find no significant difference between 2P and 3P operations. The study also shows that the effectiveness of these arrangements is contingent on brand characteristics such as operational capabilities, innovativeness, and pricing strategies. This research contributes to understanding the nuanced interplay between platform selling arrangements and brand attributes, providing strategic insights for brand managers in the evolving online retail landscape.

FinTech Credit and Entrepreneurial Growth

ABSTRACTBased on automated credit lines to vendors trading on Alibaba's online retail platform and a discontinuity in the credit decision algorithm, we document that a vendor's access to FinTech credit boosts its sales growth, transaction growth, and the level of customer satisfaction gauged by product, service, and consignment ratings. These effects are more pronounced for vendors characterized by greater information asymmetry about their credit risk and less collateral, which reveals the information advantage of FinTech credit over traditional credit technology.

Self-Preferencing and Search Neutrality in Online Retail Platforms

Self-Preferencing and Search Neutrality in Online Retail Platforms

Measuring consumer perceptions of online retail platform corporate sustainable development: a scale development and validation

PurposeOnline retail platform corporate sustainable development (ORPCSD) has garnered significant interest and appeal among consumers. However, no scale has been developed to measure consumer perceptions of ORPCSD. Therefore, this study aimed to delineate the conceptual framework and dimensions underlying these perceptions and construct a reliable and valid measurement tool.Design/methodology/approachThis study employed established qualitative and quantitative methods in two studies. In the first study, the dimensions and measurement items of consumer perceptions of ORPCSD were proposed using the grounded method. In the second study, the measurement scale was refined and validated using exploratory factor analysis, confirmatory factor analysis, and nomological validity examination.FindingsThe results indicated that consumer perception of ORPCSD consisted of three dimensions: economic, social, and environmental sustainability. The measurement scale for these dimensions comprised 25 items, demonstrating excellent psychometric properties.Originality/valueThis study contributes original insights by enhancing the current understanding of consumer perceptions of ORPCSD. Additionally, it provides researchers and managers with psychometric metrics to gauge these perceptions and offers actionable strategies for sustainable marketing initiatives.

Customer purchase intention towards agricultural products on Vietnam online retail platforms

This study aims to investigate significant factors affecting customers’ online purchase intentions towards agricultural products in Vietnam. A questionnaire survey was conducted to collect data from 250 customers who have experience in the online shopping sector. The collected data were analyzed by SPSS 26 software. The results reveal that perceived usefulness, perceived ease of use, brand reputation, subjective norm, and perceived risk are direct predictors of customers’ online purchase intention for agricultural products on Vietnam retailing platforms. Particularly, perceived usefulness is found to have the greatest impact on customers’ online purchase intention, followed by perceived risk and then brand reputation. Meanwhile, perceived risk has negative impacts, perceived usefulness and brand reputation pose positive impacts for online purchase intention towards retailing platforms. Meaningfully, this study provides significant contributions about online customer behaviour in both theoretical and practical aspects, especially in a developing area of agricultural products such as that found in Vietnam.

Optimizing Product Matching in E-Commerce with DOC2VEC: Leveraging Hierarchical Clustering Parameters Based on Product Titles

Information technology is pivotal in increasing efficiency and effectiveness in online retail, particularly in product matching. This research delves into the challenges associated with product matching in the e-commerce sector, addressing issues related to the diversity and ambiguity of product titles and the fast-paced introduction of new products to the market. As a solution, we implement a neural network-based approach. The main contribution of this research is the implementation and validation of the Doc2Vec method in the context of product matching for e-commerce products. Additionally, this study successfully identifies the optimal parameter combinations for Hierarchical Clustering, which has been tested and validated on 4,000 product title data points. The data for learning and evaluation comes from an online retail platform and includes 34,000 product names from various sectors. The research compares two Doc2Vec architectures for feature extraction from product titles and then integrates them with a Hierarchical Clustering approach to group similar products. The results indicate that the Doc2Vec model with the DBOW (Distributed Bag of Words) architecture yields a better average NMI (Normalized Mutual Information) Score than the DM (Distributed Memory) architecture.

Toward A Process Model of a Harmonious Retail Platform Ecosystem

Toward A Process Model of a Harmonious Retail Platform Ecosystem

Sourcing Channel Decision in the Presence of Platform Encroachment with Quantity Competition

The decision of sourcing channels is a critical strategy for online retail platforms (ORs) when they encroach into the product market of the third-party retailers (3PRs). Whether the products of the OR are provided by the common supplier or the one different from that of the 3PR leads to different supply chain structures. This paper focuses on the sourcing channel decision of an encroaching OR given the sourcing channel of a 3PR. The OR may source either from the common supplier, i.e., the same supplier as that of the 3PR, or from a different supplier, i.e., one different from that of the 3PR. This paper develops two game-theoretical models to examine the sourcing channel decision of an OR in quantity competition with a 3PR on the platform. The results show that the OR’s preference for the sourcing channel is significantly affected by the production cost, product substitutability and commission rate. By comparing the performances of the OR under these two sourcing channels, we find that when the production cost of the different supplier is very low, or when the product substitutability of the different supplier is low, or when the commission rate is either very low or very high, the OR prefers to source from the different supplier. Otherwise, the OR tends to source from the common supplier. We also find that the OR’s encroachment into the sales market of a 3PR by sourcing from the common supplier will always hurt the 3PR, but the platform encroachment by sourcing from the different supplier will not necessarily hurt, especially when the supplier selected by the OR has a significant disadvantage in production cost compared to those of the 3PR. Furthermore, when the OR encroaches by sourcing from the common supplier, it becomes the largest retailer in the market; otherwise, the 3PR is the largest retailer in that market. Our results also indicate that platform encroachment by different sourcing can not only expand the sales market, but also weaken the negative effects of platform encroachment on the profit of the 3PR.

Is Metaverse Intended for Purchasing? An Empirical Investigation

The metaverse, a digitally accessed immersive virtual environment, has the capacity to transform online retail by providing consumers with distinctive product experiences. This study examines how consumers behave when using metaverse platforms for purchasing. It specifically looks at how trust and perceived enjoyment play a part in influencing their desire to make a purchase. A quantitative methodology was utilized, with a total of 483 undergraduate students taking part in an experiment that entailed engaging with a metaverse retail platform called Decentraland, as well as completing a questionnaire that they administered to themselves. The results of the structural equation modelling analysis showed that the consumer's attitude towards the metaverse platform had a substantial positive effect on trust (β = 0.53) and perceived enjoyment (β = 0.39). These factors, in turn, had a favorable impact on purchase intention (β = 0.42 and β = 0.62, respectively). Furthermore, it was discovered that trust (with an indirect effect of 0.223) and perceived enjoyment (with an indirect effect of 0.241) play a major role in mediating the connection between customer attitude and purchase intention. The results enhance our comprehension of consumer behavior within the metaverse framework, highlighting the significance of cultivating trust and enjoyment to stimulate positive purchase intentions. The paper examines the consequences of the study for both theoretical and managerial perspectives. It emphasizes the importance for creators of metaverse platforms and brands to prioritize methods that build trust and boost the perceived satisfaction of users through immersive, interactive, and engaging virtual experiences. Proposed future study directions aim to overcome restrictions and broaden the scope of investigation as the metaverse continues to develop.

Are young adult consumers ready to be intelligent shoppers? The importance of perceived trust and the usefulness of AI-powered retail platforms in shaping purchase intention

Purpose The advancement of artificial intelligence (AI) has brought intelligent online shopping experiences to customers. AI-powered retail platforms deliver personalized shopping experiences through tailored recommendations, promotions and assistance. Given the increasing preference for online shopping, it is crucial to explore methods to optimize the adoption of AI-powered retail platforms. To address this, this study aims to examine the impact of technology readiness motivators (optimism and innovativeness) and inhibitors (discomfort and insecurity) on perceived trust, perceived usefulness and purchase intention toward AI-powered retail platforms. Design/methodology/approach Data were collected from 276 customers in Egypt, primarily from the millennial and Gen Z demographic segments. The collected data were then analyzed using the statistical package for social sciences (SPSS) and partial least squares structural equation modeling (PLS-SEM). Findings The findings revealed that optimism, innovativeness and discomfort significantly influence perceived trust, while optimism, insecurity and perceived trust significantly influence perceived usefulness. Both perceived trust and usefulness are significant predictors of purchase intention. Perceived trust mediates the effects of technology readiness motivators on perceived usefulness and purchase intention. Moreover, perceived usefulness mediates the effects of technology readiness motivators, insecurity and perceived trust on purchase intention. Originality/value To date, there are few investigations regarding the acceptance and adoption of AI-powered retail platforms in developing countries. Thus, this study offers valuable theoretical and practical implications in the context of smart retail technology adoption.

The interaction between manufacturer encroachment and gray market

With the rapid development of retail platforms, many manufacturers use retail platforms to encroach into the retail market (i.e., a type manufacturer encroachment) and gray market is increasingly prevalent in various industries. This paper considers a manufacturer who directly sells product 1 in the domestic market and sells product 2 through an incumbent retailer in the overseas market and a gray marketer can divert product 1 across markets without authorized (we call it gray market). In this paper, we examine the interaction between manufacturer encroachment and gray market. We find that when the domestic price of product 1 is sufficiently low, the gray marketer can successfully enter the overseas market without and with encroachment. Second, regardless of whether there is a gray market threat, the manufacturer has an incentive to encroach through retail platform when the commission rate is low. Moreover, the manufacturer’s incentive to encroach varies with the domestic price of product 1 and gray market threat. Finally, manufacturer encroachment can reduce the scale of the gray market and even eliminate the gray market under certain conditions. The gray marketer always suffers from manufacturer encroachment while the retailer can benefit from manufacturer encroachment.

Competitive coupon promotions: a theory-based model for online retail platforms and third-party sellers

Competitive coupon promotions: a theory-based model for online retail platforms and third-party sellers

Contract adoption of motivating green technological innovation in a supply chain

AbstractGreen technological innovation has been widely recognized around the world; however, consumer environmental awareness may result in uncertain market demand growth and lead to manufacturers’ risk‐averse attitude towards green technological innovation. This paper focuses on the contract adoption problem of a risk‐averse manufacturer who is responsible for green technological innovation, through a resell contract (RC) or agency contract (AC) with a retail platform. Stackelberg models are built, and the conditional value at risk method is used to measure the manufacturer's risk‐averse behavior. The differences between two contracts are compared. We find that when the commission rate is less than some threshold, the green technological level under AC is greater than that under RC regardless of the fixed cost coefficient, otherwise the green technological level under RC is higher. Further, there is the dual excellent region for AC and RC where the manufacturer can not only obtain a higher level of green technology but also achieve better utility. Finally, consumer surplus and social welfare under two contracts are performed through numerical experiments.