Buyer's Valuation Research Articles

Buyer Valuation of Angus Bull Attributes in Wisconsin

Abstract Hedonic analysis of Wisconsin Beef Improvement Association Bull Sale and Development Program data revealed buyer preferences for calving ease, growth, production weight, and carcass merit traits. Attributes like calving ease direct Expected Progeny Differences (EPD), average daily gain, birth to yearling gain EPD, and rib-eye area consistently ranked higher and significantly influenced the bull’s sale price. Further analysis using a 2-Class Finite Mixture Model indicated distinct groups of buyers in the region who prioritized measured bull attributes and others who did not, confirming heterogeneity in buyer preferences.

Perceptions of volume home builders and the transition to net zero emissions housing in Australia

ABSTRACT This study investigated the transition to net-zero emissions (NZE) homes within the Australian volume home-building sector. A group of 18 stakeholders, including volume builders, greenfield developers and industry experts, were engaged in research to identify key barriers and potential solutions for NZE home development and delivery. Initial findings, derived from a series of interviews, indicated strong support for government initiatives offering incentives and support for this transition to NZE homes. Interviewees expressed confidence in the ability of the volume home builder sector to meet the materials and skill requirements for the delivery of NZE homes. To validate these findings, a workshop involving an expanded group of 30 industry and government stakeholders, including the initial interviewees, was conducted. The outcomes from the workshop underscored the potential value of a government-led buyers’ group in assisting volume builders to transition towards NZE homes. The study identifies three critical factors (or requirements) for the success of government-supported industry intervention: customer appeal, commercial viability and alignment with policy objectives. The findings confirm that government intervention, such as forming buyers’ groups and implementing supporting policies, is essential in facilitating the shift to NZE homes in Australia.

How do engineering suppliers create project value for buyers? A micro-foundational perspective

PurposeValue creation in engineering projects has received considerable research attention. However, buyers’ expectations regarding project value do not always translate into the actions of project team members (actors). Drawing upon resource orchestration theory to explore the micro-foundations of project value creation (PVC), we investigate how suppliers organize project team members to create project value that meets buyers’ expectations.Design/methodology/approachWe adopted two instructive engineering projects as comparative cases to analyze patterns in actor interactions in PVC.FindingsThe results reveal “social interaction-based value creation” and “structural interaction-based value creation,” which explain the PVC process from the collective-level buyer’s expectations and supplier’s value orientation, to individual-level actor interactions (mobilizing, connecting, and networking), knowledge orchestration activities (structuring, bundling, and leveraging), and finally the value creation process (identifying, configuring, and generating) in project teams to generate collective-level project benefits and values.Originality/valueWe propose a macro-approach to micro-foundations, with a focus on the buyer-expected PVC process on the supplier side. Furthermore, we explain how suppliers organize individual actions and orchestrate the individual knowledge through two patterns of individual-level actor interactions for collective-level PVC.

Granger-Causal Effects of Consumer Behavior on NFT Sales

An understanding of what influences the NFT market is valuable in such a speculative space, and predictors of directional shifts in NFT sales are beneficial to NFT users and investors alike. For these reasons, research was undertaken to determine if metrics relating to consumer behavior could predict NFT market sales. To begin, a Buyer Activity Metric and a Buyer Valuation Metric were calculated using open-access data regarding the NFT market. A three-variable vector autoregression (VAR) model was then constructed using these metrics and NFT sales data. Changes in monthly NFT sales were found to be Granger-caused by changes in both the Buyer Activity Metric and the Buyer Valuation Metric. Changes in each metric were determined to precede changes in NFT sales by up to four months. The associations were also determined to be unidirectional, indicating a clear cause-and-effect style relationship. Questions about these predictive abilities were then theoretically explored.

Correlation‐savvy sellers

AbstractA multi‐product monopolist sells sequentially to a buyer who privately learns his valuations. Using big data, the monopolist learns the intertemporal correlation of the buyer's valuations. Perfect price discrimination is generally unattainable—even when the seller learns the correlation perfectly, has full commitment, and in the limit where the consumption good about which the buyer has ex ante private information becomes insignificant. This impossibility is due to informational externalities that require information rents for the buyer's later consumption. These rents induce upward and downward distortions, violating the generalized no distortion at the top principle of dynamic mechanism design.

Non-excludable Bilateral Trade between Groups

Bilateral trade is one of the most natural and important forms of economic interaction: A seller has a single, indivisible item for sale, and a buyer is potentially interested. The two parties typically have different, privately known valuations for the item, and ideally, they would like to trade if the buyer values the item more than the seller. The celebrated impossibility result by Myerson and Satterthwaite shows that any mechanism for this setting must violate at least one important desideratum. In this paper, we investigate a richer paradigm of bilateral trade, with many self-interested buyers and sellers on both sides of a single trade who cannot be excluded from the trade. We show that this allows for more positive results. In fact, we establish a dichotomy in the possibility of trading efficiently. If in expectation, the buyers value the item more, we can achieve efficiency in the limit. If this is not the case, then efficiency cannot be achieved in general. En route, we characterize trading mechanisms that encourage truth-telling, which may be of independent interest. We also evaluate our trading mechanisms experimentally, and the experiments align with our theoretical results.

Strategic Recommendation: Revenue Optimal Matching for Online Platforms (Student Abstract)

We consider a platform in a two-sided market with unit-supply sellers and unit-demand buyers. Each buyer can transact with a subset of sellers it knows off platform and another seller that the platform recommends. Given the choice of sellers, transactions and prices form a competitive equilibrium. The platform selects one seller for each buyer, and charges a fixed percentage of prices to all transactions that it recommends. The platform seeks to maximize total revenue. We show that the platform's problem is NP-hard, even when each buyer knows at most two buyers off platform. Finally, when each buyer values all sellers equally and knows only one buyer off platform, we provide a polynomial time algorithm that optimally solves the problem.

Best practices for supplier debriefs

Although supplier debriefs play a crucial role in modern procurement, they are often overlooked in both the public and private sectors. This paper underscores their importance and outlines best practices for implementation. While public procurement mandates them, the private sector often omits this essential step. These debriefs create value for both buyers and suppliers by enhancing relationships, refining procurement strategies and mitigating risks. They also provide suppliers with constructive feedback to improve their offerings, boosting their chances in future bids. Whether conducted virtually or in person, supplier debriefs offer a structured way for suppliers to assess their bid strengths and weaknesses, which is particularly relevant when substantial effort is involved. Key elements for effective debriefs include clear notifications, timing considerations, ground rules, attendee selection, agenda structuring and appropriate communication styles. Embracing supplier debriefs fosters collaboration, trust and efficiency in competitive bidding, making them indispensable for organisations seeking success.

SIKAP, KEPUASAN DAN LOYALITAS KONSUMEN SUSU SEGAR YU-ONE MILK DI KOTA BANDAR LAMPUNG

This research aims to determine the attitudes, purchasing patterns, satisfaction, and loyalty of fresh milk consumers of Yu-One Milk at Jalan Sultan Agung, Way Halim, Kedaton District of Bandar Lampung City. The research method is a survey. The number of respondents in this research are 60 people, selected by non-probability sampling technique, namely accidental sampling. The research data are analyzed by quantitative descriptively using the Fishbein multi-attribute model, Customer Satisfaction Index (CSI) and the loyalty pyramid. Data collection was carried out in October-November 2022. The results showed that the consumers’ attitude toward multi-attribute of Yu-One Milk fresh milk was in the good category, in which score was 181.72 and the most favored attribute by consumers was friendliness services. Most of Yu-One Milk consumers (25%) bought fresh milk with chocolate flavor variants. The average frequency of purchasing Yu-One Milk fresh milk was twice per month with a purchase expenditure of IDR 51,883.00/month. The average purchases amount of Yu-One Milk fresh milk was 1,377 ml/month with the purpose of purchasing to eliminate hunger/thirst (46.67%). Yu-One Milk fresh milk consumer satisfaction was in the very satisfied category with a CSI value of 83.61. Yu-One Milk consumer loyalty was quite good in the form of an inverted pyramid. The switcher buyer value was 46.67%, the habitual buyer value was 48.83%, the satisfied buyer value was 90.00%, the liking the brand value was 86.67% and the committed buyer value was 83.33%. Keywords: attitude, consumer loyalty, fresh milk, purchase pattern, satisfaction.

Institutions, urban space, and residential markets in globalizing Shanghai: A comparative study of housing sale and rental prices

ABSTRACT China’s skyrocketing residential market has raised concerns about housing affordability in metropolitan areas. Current literature usually overlooks the various mechanisms between sale and rent, which may unfold more fundamental structural forces beyond housing location and attributes. This study constructs an analytical framework to investigate how global, regional, and local development contexts, including institutional forces, urban spatial structure, human capital, and neoclassical factors, affect apartment sales and rental prices in Shanghai, China. These factors collectively shape the city’s residential market, but renters and buyers may show different housing preferences. Such variation is highly related to their residential status, as renters may prioritize job opportunities while buyers value service amenities more. From the spatial perspective, suburban areas are more inclusive than urban centers, as globalization and institutional factors have exacerbated inequality in the residential housing market in urban centers, and homeownership is changing the patterns of segregation in Chinese cities.

Analysis of The Influence of Technology Perceived Quality, Digital Advertising and Technology Digital Experiental Marketing on Customers Intention to Purchase at Marketplace Platform Tokopedia

The study's goal is to examine how advertising, perceived quality, and experiential marketing affect consumers' decisions to buy. The judgment sampling approach is the sample technique employed in this study. Based on the assumption that respondents had shopped online, samples were chosen. One hundred samples will be collected for this study. In this study, the author employed two different types of data: primary and secondary. Using Cronbach's alpha, researchers assess a variable's dependability in this test. Multiple linear regression analyses are used in the examination of research data. Experience marketing, perceived quality, and advertising are found to have a major impact on the purchasing decisions of customers who actively use e-commerce applications, according to the research findings. The a-value of 13.4 shows the buyer's decision value when there is no influence from the experiential marketing, perceived quality, or advertising variables. The regression coefficients b1, b2, and b3 each show that a one-unit increase in the experiential marketing, perceived quality, and advertising variables will result in an increase in purchasing decisions by 0.24, 0.2, and 0.24 respectively.

Optimal mechanism for the sale of a durable good

A buyer wishes to purchase a durable good from a seller who in each period chooses a mechanism under limited commitment. The buyer's value is binary and fully persistent. We show that posted prices implement all equilibrium outcomes of an infinite‐horizon, mechanism‐selection game. Despite being able to choose mechanisms, the seller can do no better and no worse than if he chose prices in each period, so that he is subject to Coase's conjecture. Our analysis marries insights from information and mechanism design with those from the literature on durable goods. We do so by relying on the revelation principle in Doval and Skreta (2022).

Deep Retrofitting Study of Government Buildings in the UAE

This report summarizes the findings of a deep retrofitting modelling study of four government buildings across the UAE. Buildings envelope assessment, HVAC, and electrical systems design, installed conditions, maintenance practices and operation methods, and rigorous engineering analysis has been performed for the four buildings to produce calibrated energy models that confirm a good correlation between actual annual energy consumption and advanced simulated energy modeling results. IES-VE software, ASHRAE Guideline 14, and IPMVP were the basis of the energy model calibration based on actual data. Energy retrofitting options were applied to the calibrated energy models of the four government buildings where advanced computational modeling studies have been utilized to analyze different thermal envelope components and external fabric energy efficiency measures in conjunction with the actual HVAC systems. The results have shown that it may be financially challenging to implement a single envelope retrofitting measure on any of the four buildings due to high payback periods. Nevertheless, when conducting an optimization through a holistic building approach targeting the overall envelope enhancements, the financial modeling results provide significantly improved and provide reduced payback periods of the solutions, making them more attractive to investors. Based on the energy modeling results and the cost of the various proposed building envelope solutions provided by Saint-Gobain, the payback period in all cases varies between 15 to 20 years. However, huge savings in terms of carbon emissions are achieved by implementing the deep retrofitting measures, crucial to achieving carbon neutrality within the UAE built environment. Deep retrofitting is a promising segment of the execution roadmap as part of the UAE's decarbonization journey; even though it is capital-intensive upfront, it has a major impact on long-term operational savings. Moreover, it is often more cost-effective than building new and energy-efficient structures from scratch. The findings indicate that deep retrofitting can be a techno-financially viable solution if it is well-planned during major refurbishment or as part of building maintenance. It has significant potential to reduce carbon emissions while also providing a range of benefits such as operational cost savings, improved thermal comfort a feature which has a direct impact on perceived efficiency and productivity at work, indoor air quality, improved asset value, reputational enhancement, and better choice for environmentally-cautious tenants or buyers.

An integrated model for rice supplier selection strategies and a comparative analysis of fuzzy multicriteria decision-making approaches based on the fuzzy entropy weight method for evaluating rice suppliers.

Rice, being a staple food in many countries, necessitates the identification of reliable suppliers to ensure a steady supply. Consequently, it is vital to establish trustworthy vendors for various types of this essential grain who can meet stringent product quality standards. This study aims to identify, analyze, rank, and select primary rice suppliers. The study emphasizes the importance of selecting and managing suitable providers to meet customer demands, proposes a ranking model for rice suppliers, and introduces developed fuzzy MCDM techniques. It proposes an integrated model for selecting rice suppliers, considering factors related to the processes before, during, and after selecting providers within a defined framework. The outcomes shows that rice supplier selection strategy can efficiently identify reliable rice suppliers, improve buyer value, reduce procurement risk, enhance efficiency, and establish strong supply chain relationships in complex decision-making processes. To assess suppliers, the study introduces two advanced integrated approaches and compares them. The fuzzy entropy weight method (EWM) was used to determine the criteria weights. The ranking of rice suppliers was achieved using a fuzzy multi-objective optimization based on ratio analysis (MOORA), fuzzy complex proportional assessment (COPRAS), and combinations of these two methods in different approaches. The methodology supports decision-makers in a rapidly evolving global environment by assisting importers, traders, suppliers, procurement, and logistics management, particularly for non-rice-cultivating countries in rice importation and supplier selection. The numerical analysis is grounded in a real-world case study of selecting rice suppliers in Jordan. The findings reveal that the various strategies yield both similar and different results. Furthermore, the integrated method is considered the most accurate for evaluating rice imports and suppliers, aligning closely with the reality of the current situation.

P2P Energy Trading through Prospect Theory, Differential Evolution, and Reinforcement Learning

Peer-to-peer (P2P) energy tradingis a decentralized energy market where local energyprosumersact as peers, trading energy among each other. Existing works in this area largely overlook the importance of user behavioral modeling and assume users’ sustained active participation and full compliance in the decision-making process. To overcome these unrealistic assumptions, and their deleterious consequences, in this article, we propose anautomatedP2P energy-trading framework that specifically considers the users’ perception by exploitingprospect theory. We formalize an optimization problem that maximizes the buyers’ perceived utility while matching energy production and demand. We prove that the problem is NP-hard and we propose a Differential Evolution-based Algorithm for Trading Energy (DEbATE) heuristic. Additionally, we propose two automated pricing solutions to improve the sellers’ profit based on reinforcement learning. The first solution, named Pricing mechanism with Q-learning and Risk-sensitivity (PQR), is based on Q-learning. Additionally, given the scalability issues ofPQR, we propose a Deep Q-Network-based algorithm calledProDQNthat exploits deep learning and a novel loss function rooted in prospect theory. Results based on real traces of energy consumption and production, as well as realistic prospect theory functions, show that our approaches achieve 26% higher perceived value for buyers and generate 7% more reward for sellers, compared to recent state-of-the-art approaches.

Assessing and improving the outcomes of nonpoint source water quality trading policies in urban areas: A case study in Virginia

Nonpoint source (NPS) water quality trading (WQT) is a market-based approach to improving water quality. Past work has shown that these programs could increase localized pollutant loadings, in part by exporting water quality controls from urban to rural areas. Virginia's NPS WQT program has enabled thousands of transactions and may provide a model for other programs, but its impacts on urban water quality have not been thoroughly assessed. We quantify the impact of NPS WQT purchases in Virginia on water quality and hydrology in an urban catchment. We go on to assess outcomes of a policy alternative where buyers and sellers are collocated in the urban catchment. Simulation results show that NPS WQT increased total phosphorus (TP) loading by an average of 0.8 lbs TP/year for each 1.0 offsite credits purchased in the analyzed catchment. The TP loading increased in years with greater rainfall, such that TP loads were increased by up to 1.2 lbs TP/year for each offsite credit purchased. These loading increases may or may not be acceptable, depending on the cumulative number of purchases within an urban catchment and existing local water quality issues. In our policy alternative with buyers and sellers collocated in the catchment, we found that the TP increase from development was completely offset at the catchment scale, with a decrease of 4.3 lbs TP/year for each 1.0 credits purchased. This suggests that credits awarded for urban mitigation practices are undervalued compared with water quality requirements for credit purchasers. This undervaluation is a result of the Virginia trading program using one approach to compute the credit value for buyers and a different approach to compute the credit value for sellers. We demonstrate how using a single model to determine both buyer and seller credit values in urban areas could provide greater transparency and mitigate the risk of urban pollution hot spots. This work demonstrates the importance of consistency in the scale of pollutant load calculations between buyers and sellers for NPS WQT, and contributes novel insight into the implications of WQT for urban NPS pollution.

Optimal nonlinear pricing by a monopoly with smooth ambiguity preferences

This paper examines the optimal nonlinear pricing by a monopolist who has smooth ambiguity preferences and sells a single good to two types of buyers with high and low valuation. Ambiguity arises from the monopolist’s uncertainty about which of the subjective beliefs govern the unobserved types of buyers. We show that the prevalence of ambiguity aversion distorts the rent extraction-efficiency tradeoff under asymmetric information in the standard model. Specifically, the ambiguity-averse monopolist acts in a pessimistic manner with the perception that buyers are less likely to have high valuation than the objective beliefs. Compared with the standard model, low valuation buyers consume a less downward distorted quantity and high valuation buyers enjoy a greater information rent. The unit prices paid by all buyers are reduced and the ambiguity-averse monopolist earns a smaller expected profit.

DELIZIA SELF SERVICE APPLICATION USING UX DESIGN AND ANDROID STUDIO

Delizia Self Service is a tart ordering service application using the concept of Self Service Technology (SST). In this application there are 2 kinds of prospective users, in terms of customers to order tarts who can choose directly the tarts they want and even customers can also customize tarts according to their tastes. And in terms of admin which is useful for managing products, from adding products, deleting products and modifying products. Based on its function, this application provides benefits such as providing alternative means of selling tarts, increasing sales value, increasing interest, user experience, satisfaction, and comfort of buyers when ordering tarts. This journal focuses on developing application recommendations by designing the UI/UX of the Delizia Self Service Application and realizing it using Android Studio, using the System Development Life Cycle (SDLC) method with the prototype model.

The influence of non‐physicality of goods on disparities in seller–buyer valuations: A meta‐analysis

AbstractTechnological innovation is shaping products and markets to appeal to consumer enduring needs. Given the rise in non‐physicality of products and consumers' increasing participation in peer‐to‐peer markets, this research examines the relationship between non‐physicality of goods and disparities in seller–buyer valuations, a critical component of market inefficiencies. In a meta‐analysis of 598 observations across 129 articles, the authors find that non‐physical (vs. physical) goods have significantly larger seller–buyer valuation disparities (i.e., larger endowment effects). This pattern is consistent across non‐physical categories, including market and non‐market goods. The authors argue the immateriality of non‐physical goods reduces evaluability and enhances perceived complexity, leading to greater asymmetries in information processing between sellers and buyers. Congruent with construal level theory, marketing tactics associated with low categorization levels (emphasizing product features) influence consumer valuations of physical goods, but not non‐physical goods. Instead, consumer valuations of non‐physical goods are asymmetrically influenced by tactics with high categorization levels (accentuating collective identity). This research foreshadows challenges in, and provides managerial implications for, the marketing and transactions of non‐physical products.

Auctions and Negotiations in Housing Price Dynamics

Abstract We shed light on housing price inertia by investigating how the home-sale mechanism affects housing price dynamics. Using Australian data, we find that auction prices forecast better and display less momentum than negotiated prices. These findings are robust to alternative price measurements and different sample selection corrections. Motivated by micro-theory that predicts different weights for buyer and seller values in auction and negotiated prices, we decompose housing prices into two diffusion processes and interpret them as buyer value and seller value, respectively. The seller value updates much more slowly, which could be an important driver of housing price inertia.