Types Of Sellers Research Articles

Product diversion by vertically differentiated firms

Retail sellers authorized to sell a branded product may divert some units to an unauthorized dealer thus enabling the dealer to become a direct competitor. Because these sellers vary in quality, it is often unclear what type of seller is more motivated to participate in diversion. We develop an analytical model to demonstrate how vertically differentiated sellers make diversion decisions responding to an unauthorized dealer under unique circumstances not considered by previous research. The model demonstrates that when diversion occurs, the low‐quality seller can actually earn a greater profit than the high‐quality seller. Furthermore, a higher quality differential can lead to an overall profit loss for the high‐quality seller when both sellers divert. The sellers will divert products to the competing dealer if and only if they are sufficiently similar to each other. The low‐quality seller has an incentive to divert more units in general, and is motivated to divert alone when the quality differential is not too high. Additionally, changes in vertical differentiation have nonmonotonic effects on the sellers. Overall, the results establish seller vertical differentiation as a new key variable in analyzing product diversion and demonstrate its unique consequences for the authorized sellers and the unauthorized dealer.

Institutional factors and the timing of land development: a survival analysis applied to the GZM Metropolis in Poland

PurposeThe purpose of the article is to improve the understanding of the role of institutional factors in real estate development. The authors take into account zoning (existence and type), type of right of disposal and type of buyer and seller of property in a multivariate econometric estimation. Dependent variable of the analysis is the time between acquisition of empty land and the application for a building permit, a period when many important development decisions have to be made. This indicator is closely related to debated phenomena like land hording and speculation.Design/methodology/approachThe authors estimate a Cox proportional hazard model with the time between acquisition and application for a building permit as dependent variable and institutional indicators and a number of control variables as explanatory variables. Study area is the GZM Metropolis in the South of Poland. This region shows enough variability in institutional arrangements to allow for this type of analysis.FindingsThe analysis shows that institutional factors significantly influence the real estate development process. In areas that have not issued a zoning plan, the period until the building permit application is significantly longer. When the state is involved in a transaction (as purchaser or seller), it also takes longer until the building permit application is submitted. Although the instrument is usually intended to speed up development, perpetual usufruct implies a longer period until building permit application. Because of the results the authors get for control variables and for robustness checks, the authors are confident of the results of the analysis.Originality/valueTo the authors’ knowledge, this is the first study that deals with the question how institutional factors influence the timing of real estate development. By using data for a region in Poland, the authors also add to knowledge about real estate development in CEE countries.

Analysing the digital transformation of the market for fake documents using a computational linguistic approach

The market for fake documents on the internet is a topic that has not yet been explored in depth, despite its importance in facilitating many crimes. This research explored the market of fake documents on the White House Market anonymous market with a computational linguistic methodology; more specifically using textometry. The textual corpus is composed of the data of the ads titles as well as the profiles of the sellers, which were analysed as traces of their online activities. We investigated how these remnants can help to answer general questions. What kinds of fake documents are sold? Can we distinguish types of sellers based on their selling activities or profiles? Can we link distinct vendors based on language trace similarities? The free software IRaMuTeQ was used to carry out the analysis. The results showed that the textometric methods have real potential in classification, highlighting the different products on the market, and grouping the sellers according to their offers.

USED CAR PRICE PREDICTION

Cars have become a necessity in this modern world. Every middle class family needs a vehicle or a mode of transport in order to move from one place to another. Not everyone is able to afford a new vehicle as they are costly and there’s an added cost of taxes and various other expenses by both the provider/company of the car as well as the government. Moreover, not every customer is sure of spending a sum of their wealth on a certain car. The product might not meet their needs. The solution to this problem of having a car despite not being able to afford one is met by buying and selling second hand cars. It has become its own market now. There are already numerous companies and websites and app based services that serve as a mediator or a platform for the dealing of second hand or used cars and other vehicles. Establishment of such places is easy but there is another problem that still remains- How to price the used car appropriately at a price comfortable for both the seller and the buyer? Luckily, the Used Car Price Prediction systems exist and can be developed. Users might think that it’s easy to determine the price of a used car, and whether there is even a need to have such a system. In truth, there are a lot of factors that are important in determining the price of a second hand vehicle. The quality of a vehicle deteriorates with age1 of course but that is not all. Every single vehicle is different even when it is manufactured and sold as a new product and even more so when the same vehicle is used over time. Different people may use their vehicles more or less depending on their everyday activity, making kilometers driven as one of the important factors for the price prediction. It is obvious that a vehicle which is driven for 2000 kilometers in 1 year would be priced less than a vehicle which has been driven for only 500 kilometers in 2 years. This is just one of the factors that determine the price of a used car. In our Car Price Prediction System, we have used the Year of Manufacturing (used to determine the age of the vehicle by subtracting this from the date of selling), the original maximum retail price of the vehicle (the price at which the vehicle was sold at from the manufacturing company/garage), the fuel type of the vehicle (Petrol, Diesel, CNG, Electric ; This affects the pricing severely as different fuel type engines have different prime performance periods and different rates of deterioration), Seller Type (Individual or Dealership), Transmission (Manual or Automatic), Number of past owners of the vehicle. Using all these factors2, we are going to determine which model is best to determine a price for the used vehicle. For the Car Price Prediction System, Regression models3are used since these models give the results as a continuous curve instead of a categorized value as a result. Due to this, we can use the continuous curve to determine an accurate price for each and every scenario which won’t be possible if the results obtained were in the form of a range. The final model of the system will implement the best suited algorithm and have a UI (User Interface) which make it possible for a user to be able to enter the values of these deciding factors and the system will predict the price for them. Keywords: Car price prediction, machine learning, regression analysis, linear regression, correlation analysis

From making visible to hiding. Visual representations of financial markets as tools of manipulation and active and living agents

This article shows that visual representations of financial markets not only make them visible, i.e. present and reveal certain important financial information such as the size and distribution of demand and supply, the quantity, type and intentions of sellers and buyers, trends and patterns in price movements, or the depth, liquidity and sentiment of the market, but also hide what they are supposed to reveal and make visible. What is important, they do this not only because of the ways of their production; not only because of pitfalls and errors inherent in them; not only because of framing processes they are subjected to, but also because of their interactive nature that allows financial agents to manipulate them. This fact, and above all its possibility, carries with itself important consequences for the role that the visual representations play in the functioning of capital markets. Namely, it causes that these representations, regardless of other factors, themselves become a source of uncertainty in these markets. As such, they are therefore not so much neutral and passive representations of financial exchange as rather tools for manipulation on the one hand and active and living agents on the other.

Applying topic modelling and qualitative content analysis to identify and characterise ENDS product promotion and sales on Instagram

BackgroundIncreased public health and regulatory scrutiny concerning the youth vaping epidemic has led to greater attention to promotion and sales of vaping products on social media platforms.ObjectivesWe used unsupervised machine...

Sequential Procurement with Contractual and Experimental Learning

We study the design of sequential procurement strategies that integrate stochastic and strategic information. We consider a buyer who repeatedly demands a certain good and is unable to commit to long-term contracts. In each time period, the buyer makes a price offer to a seller who has private, persistent information regarding his or her cost and quality of provision. If the offer is accepted, the seller provides the good with a stochastic quality that is not contractible. Therefore, the buyer can learn from the (strategic) acceptance decisions taken by the seller and from evaluations of the (stochastic) quality delivered whenever a purchase occurs. Hence, the buyer not only faces a tradeoff between exploration and exploitation but also needs to decide how to explore: by facilitating quality experimentation or by strategically separating seller types. We characterize the perfect Bayesian equilibria of this sequential interaction and show that the buyer’s equilibrium strategy consists of a dynamic sequence of thresholds on his or her belief on the seller’s type. When only one seller type is more efficient than the buyer’s outside option, the buyer uses one form of information: either strategic or stochastic. If both seller types are more efficient, then the buyer uses both forms of information; at the early stages of the interaction, the buyer offers high prices that incentivize trade and quality experimentation, and after gathering enough information, the buyer may advance to offering low prices that partially separate seller types. We identify the effect strategic sellers have on the buyer’s optimal strategy relative to more traditional learning dynamics and establish that, paradoxically, when sellers are strategic, the ability to observe delivered quality is not always beneficial for the buyer. This paper was accepted by Victor Martínez-de-Albéniz, operations management.

The anatomy of buyer–seller dynamics in decentralized markets

In this paper I investigate the nexus between buyer–seller dynamics, financial frictions and market efficiency in decentralized markets. To do so, I introduce financial frictions in a dynamic market with heterogeneous traders. Heterogeneously constrained buyers sequentially enter the market to acquire units of a generic good from heterogeneously endowed sellers. I characterize two closely related classes of equilibria, respectively called homogeneous equilibrium with no entry (HEWNE) and homogeneous equilibrium with entry (HEWE). Both equilibria prescribe a market where only the efficiently endowed type of seller exists in the limit. However, the two equilibria diverge in the specification of agents’ behavior subsequent to trade. In HEWNE, sellers and buyers exit the market upon successful trading. In HEWE, like in supply chains, in every period certain types of buyers replace exiting sellers, thus becoming potential sellers for subsequent waves of buyers. First, I identify the critical role of frictions in steering the complex evolution of market heterogeneity for both classes of equilibria. Secondly, I operationalize the combined study of HEWNE and HEWE to obtain sharp predictions on market efficiency for a range of empirically-relevant situations in which buyer–seller dynamics are decoupled, for example when entry of new sellers is delayed or stopped. Third, I test the theoretical findings against a simulated artificial market.

Unraveling preferences for religious ties in food transactions: A consumer perspective

AbstractFood choices are key in communicating religious identities and marking religious boundaries. Whereas the literature has focused on religion's impact on food habits, the effect of religion on food market exchanges remains largely unexplored. Using the Ethiopian milk market as a case study, we conduct a series of innovative experimental choice tasks with urban dairy consumers to elicit their preferences for milk seller characteristics. In the choice tasks, milk seller profiles are constructed using five relevant attributes: milk price, distance to the selling point, seller type, seller gender, and seller religious affiliation. We observe consumer preferences for lower milk prices, shorter distances, and dairy farmers over milk shops. When accounting for attribute non‐attendance, we find that consumers who take religion into account prefer co‐religious sellers, and that consumers who take gender into account generally prefer female sellers. Our results enrich our understanding of the role of religion in purchase behavior and market segmentation.

CLUSTER AND CONJOINT ANALYSIS FOR DETERMINING ONLINE SHOP SHOPEE CUSTOMERS PREFERENCE BASED ON E-SERVICE QUALITY

The use of e-commerce as a means of shopping is a trend that is very much in demand by many Indonesians. This makes e-service quality very important in a transaction. Customer preference is one of the main factors in shaping a business strategy. This research discusses the use of cluster analysis to segment Shopee's e-commerce customers based on sociodemographic characteristics with k-means algorithm and conjoint analysis to determine which e-service quality attributes are most important to each cluster. The sociodemographic characteristics to be analyzed are gender, education, profession, e-commerce visit, income, and last purchase from the e-commerce. The result from k-means algorithm is there are 2 groupings of customers based on their sociodemographic characteristics, cluster 1 with the majority of women members with frequent visits, while for cluster 2 with the majority of male members with frequent visits. With the result of cluster analysis, conjoint analysis help this research to find which e-service quality attributes are most important. The results are members in cluster 1 prioritize Full payment payment methods when shopping online, while members in cluster 2 prioritize star seller types when shopping online. The aspect that doesn't matter most when shopping online is fulfillment in cluster 1 and security in cluster 2.

Strategic stocking for new products to signal quality information

This paper investigates the signaling role of stocking for new experience products in a two‐period setting. The seller privately observes the product quality information, which cannot be resolved until the second selling period. We show that the stocking plays a pivotal role in signaling the quality information, and the equilibrium strategy depends highly on ordering cost and the consumer prior belief about the seller type. If the seller is unable to dynamically decide the retail prices, separating equilibrium arises more frequently and such a fixed pricing may result in a win‐win situation for both seller and consumers.

Typologies of urban wildlife traffickers and sellers

Urban wildmeat consumption can contribute to significant declines in wildlife populations, ecosystem function, and food insecurity security. Describing types of individuals involved in illegal urban wildmeat trafficking can help distinguish ordinary citizens from members of criminal organizations and urban vs. rural dimensions of the activity. This research aimed to: (1) create and apply a typology for urban wildmeat traffickers and sellers; and (2) explore linkages between types of urban wildmeat traffickers and sellers. We used focus groups with experts in Republic of the Congo, February 2019 (N = 2, n = 7–10) to achieve objectives and focused on pangolins, great apes, and dwarf crocodiles. Participants generated risk rankings for each species, typology and city; data was encoded and indexed. Results illustrate heterogeneity in actors involved in the illegal supply chain. Business sideliner and trading charity trafficker types were associated with the highest total risk to wildlife trafficking. A similar pattern of divergence was detected for seller typologies; hidden and casual sellers were associated with the greatest total risk in Pointe Noire and Brazzaville, respectively. Differentiating but not stove piping stakeholders involved in urban wildmeat trafficking will help clarify stages of illegal supply chains as well and promote thinking about new sectors to involve in interventions and solutions, particularly in urban ecosystems thought to be outside the scope to wildlife crime.

Determining the Flat Sales Prices by Flat Characteristics Using Bayesian Network Models

There are various factors affecting flat sales prices. Various characteristics of a flat play an important role in determining its sales price. In this study, a machine learning based Bayesian network was built by a restrictive structural learning algorithm using the data collected from 24 randomly selected cities in Turkey. The data consist of the sales prices and various characteristics of a flat such as number of bedrooms, building age, availability of balcony, net area, heating type, mortgageability, number of bathrooms, seller type, presence in a housing estate area and floor location. After the model validity check, a sensitivity analysis was performed for the estimated Bayesian network model and related results were provided. Some of these results indicate that sales prices of flats mostly change depending on the number of bathrooms available. Additionally, number of bedrooms, net area and floor location are also determinative about the sales prices. The lack of significant difference among the sales prices of flats that are sold by construction companies, housing estate agents or property owners is another result obtained.

Does Costly Persuasion Signal Quality?

We present a buyer-seller setup in which seller type is private information and buyer utility is state contingent. States of nature are commonly unknown and the seller can design information through a Bayesian experiment by facing a cost based on Shannon’s entropy. We show that the seller of high type has an incentive to design more precise information and is willing to bear a higher cost. Hence, when the cost is sufficiently high, separation can occur without price distortions. Differently, when the cost is excessively low, price signaling is also needed to separate.

The Impact of Platform Protection Insurance on Buyers and Sellers in the Sharing Economy: A Natural Experiment

The sharing economy has radically reshaped marketing thought and practice, and research has yet to examine whether and how platform-level buyer protection insurance (PPI) affects buyers and sellers in this economy. The authors exploit a natural experiment involving an unexpected system glitch during a PPI launch and estimate difference-in-differences models using over 5.4 million data points from a food sharing platform. Results suggest that PPI significantly increases buyer spending and seller revenue, affirming the benefits of this platform-level insurance in the sharing economy. The authors also uncover multifaceted buyer-side and seller-side responses that enable such benefits. PPI increases buyer spending by boosting product orders and variety-seeking behavior. Furthermore, it enhances seller revenue by increasing customer retention and acquisition. This work contributes to the literature by (1) putting a spotlight on the topic of PPI, a platform governance policy that reduces consumer risks and improves the efficacy of sharing platforms; (2) accounting for how PPI alters buyer and seller behaviors on a platform; (3) addressing what types of buyers and sellers benefit more or less from PPI; and (4) offering guidance for managers to improve platform reputation, marketplace efficiency, and consumer welfare in the context of the sharing economy.

Price Dispersion in Farmland Markets: What Is the Role of Asymmetric Information?

This article investigates the role played by information, search, and bargaining cost in agricultural land markets to explain price dispersion. Based on a hedonic model under incomplete information, we build a two‐tier stochastic frontier. By linking costs of being information deficient to agent characteristics such as degree of professionalism, we identify relative price effects of buyer and seller types related to information deficiency. We compile a comprehensive data set of more than 10,000 transactions in Saxony‐Anhalt, Germany, between 2014 and 2017. We find institutional sellers to achieve the lowest losses resulting from information deficiency and sell with mark‐ups, whereas other sellers incur losses. This seller group could benefit from investments in professionalism and roughly halve their cost of being information deficient. Tenant buyers can benefit from informational advantages resulting in markdowns with lowest effects in the harvest season. We conclude that Germany's policy makers can do more to support market transparency.

Investing to access an adverse selection market

I analyse a market with asymmetric information and interdependent values. Accessing the market is costly and stochastic, e.g. requires lobbying, licences or R&D. For a range of parameter values, interventions that raise production or entry costs for all seller types increase trade, investment and payoff for all types. This effect is driven by a novel feedback loop between the market composition and the investment in access.

Big Data, Natural Language Processing, and Deep Learning to Detect and Characterize Illicit COVID-19 Product Sales: Infoveillance Study on Twitter and Instagram.

BackgroundThe coronavirus disease (COVID-19) pandemic is perhaps the greatest global health challenge of the last century. Accompanying this pandemic is a parallel “infodemic,” including the online marketing and sale of unapproved, illegal, and counterfeit COVID-19 health products including testing kits, treatments, and other questionable “cures.” Enabling the proliferation of this content is the growing ubiquity of internet-based technologies, including popular social media platforms that now have billions of global users.ObjectiveThis study aims to collect, analyze, identify, and enable reporting of suspected fake, counterfeit, and unapproved COVID-19–related health care products from Twitter and Instagram.MethodsThis study is conducted in two phases beginning with the collection of COVID-19–related Twitter and Instagram posts using a combination of web scraping on Instagram and filtering the public streaming Twitter application programming interface for keywords associated with suspect marketing and sale of COVID-19 products. The second phase involved data analysis using natural language processing (NLP) and deep learning to identify potential sellers that were then manually annotated for characteristics of interest. We also visualized illegal selling posts on a customized data dashboard to enable public health intelligence.ResultsWe collected a total of 6,029,323 tweets and 204,597 Instagram posts filtered for terms associated with suspect marketing and sale of COVID-19 health products from March to April for Twitter and February to May for Instagram. After applying our NLP and deep learning approaches, we identified 1271 tweets and 596 Instagram posts associated with questionable sales of COVID-19–related products. Generally, product introduction came in two waves, with the first consisting of questionable immunity-boosting treatments and a second involving suspect testing kits. We also detected a low volume of pharmaceuticals that have not been approved for COVID-19 treatment. Other major themes detected included products offered in different languages, various claims of product credibility, completely unsubstantiated products, unapproved testing modalities, and different payment and seller contact methods.ConclusionsResults from this study provide initial insight into one front of the “infodemic” fight against COVID-19 by characterizing what types of health products, selling claims, and types of sellers were active on two popular social media platforms at earlier stages of the pandemic. This cybercrime challenge is likely to continue as the pandemic progresses and more people seek access to COVID-19 testing and treatment. This data intelligence can help public health agencies, regulatory authorities, legitimate manufacturers, and technology platforms better remove and prevent this content from harming the public.

Learning pareto optimal solution of a multi-attribute bilateral negotiation using deep reinforcement

Abstract This paper aims to design an intelligent buyer to learn how to decide in an incomplete information multi-attribute bilateral simultaneous negotiation. The buyer does not know the negotiation strategy of the seller and only have access to the historical data of the previous negotiations. Using the historical data and clustering method, the type of seller is identified online during the negotiation. Then, the deep reinforcement learning method is utilized to support the buyer to learn its optimal decision. In the complete information case, we prove that the negotiation admits a unique Nash bargaining solution with possibly asymmetric negotiation powers. In comprehensive simulation studies, the efficiency of the proposed learning agent is evaluated in different scenarios and we show that the learning negotiation with incomplete information is converged to a Pareto optimal solution. Then, using the concept of the Nash bargaining solution, the negotiation power of the buyer is assessed in negotiation.

Determinants of the retail price of illegal drugs in New Zealand

IntroductionNew Zealand has a number of unusual geographical features that may influence the price of illegal drugs including international isolation, numerous rural communities, and two separate islands (North and South Islands). Aims(1) Collect data on the prices of different drug types from all regions of New Zealand; (2) Identify independent predictors of the reported prices. MethodAn online drug survey was promoted via a targeted Facebook™ campaign. A total of 6331 respondents completed the survey. Normal regression models were constructed to identify predictors of the reported price of an ounce of cannabis, gram of methamphetamine, pill of ecstasy and tab of LSD using demographics, drug use frequency, drug market indicators, region, community size, type of seller, and location of purchase as predictor variables. ResultsHigher availability was a predicator of lower prices for cannabis (-3% per higher availability category), methamphetamine (-4%) and ecstasy (-5%). Those living in the North Island (-26%) and buying from gangs (-5%) paid lower prices for methamphetamine. Those living in rural communities reported higher prices for methamphetamine (+5%) but lower prices for cannabis (-3%). Daily cannabis users (+4%) and those on social welfare (+2%) paid higher prices for cannabis. ConclusionLower prices for methamphetamine in the North Island may reflect the concentration of methamphetamine manufacture there. The fact that gangs offer lower prices for methamphetamine suggests they do not have monopoly control of this market. Gangs may be able to offer lower prices for methamphetamine due to scale of production/trafficking and lower risk of victimisation. Higher prices for methamphetamine in rural areas may reflect less competitive markets. Lower prices for cannabis in rural communities may be due to proximity to growing locations. Daily cannabis users and those on social welfare may be less able to delay their cannabis purchases to low price harvest months.