Buyer Information Research Articles

Implementation of Quick Response Code Tags for Livestock Identification and Farm Management Based on Web and Android for Ruminant Farms

Technological advancements impact various sectors of life, including animal husbandry, which is an important part of the education curriculum. Innovations are needed to optimize livestock management, especially ruminant farming, which is crucial for producing meat and milk. SMKN 1 Bawen, with its study program focusing on ruminant farming (cows, sheep, and goats), still uses conventional recording methods, leading to inefficiencies and limited information for potential buyers. To address this, a QR Code-based livestock management and identification system was developed and integrated into a website and Android application using agile methods. Black box testing confirmed that all features function well. Stress testing on the website with 2 Mbps bandwidth showed 98% successful requests and 2% failed requests, with a data transmission speed of 52,816 bits per second and a reception speed of 268,624 bits per second. At 50 Mbps bandwidth, the system had a 100% success rate, with a data sending speed of 44,304 bits per second and a receiving speed of 343,272 bits per second, demonstrating good and stable performance across different bandwidths. The Android application, tested on a POCO X3 with 6 GB RAM and 128 GB internal storage, showed a highest CPU usage of 39%, memory usage of 326 MB, and network speeds of 10.3 KB/s for data received and 535.9 KB/s for data sent. These results indicate that the application runs optimally and is suitable for use.

PP-LEM: Efficient and Privacy-Preserving Clearance Mechanism for Local Energy Markets

In this paper, we propose a novel Privacy-Preserving clearance mechanism for Local Energy Markets (PP-LEM), designed for computational efficiency and social welfare. PP-LEM incorporates a novel competitive game-theoretical clearance mechanism, modelled as a Stackelberg Game. Based on this mechanism, a privacy-preserving market model is developed using a partially homomorphic cryptosystem, allowing buyers’ reaction function calculations to be executed over encrypted data without exposing sensitive information of both buyers and sellers. The comprehensive performance evaluation demonstrates that PP-LEM is highly effective in delivering an incentive clearance mechanism with computational efficiency, enabling it to clear the market for 200 users within the order of seconds while concurrently protecting user privacy. Compared to the state of the art, PP-LEM achieves improved computational efficiency without compromising social welfare while still providing user privacy protection.

A framework for decision making to purchase the best product using feature-based opinions

As more people express their thoughts on products on various online shopping platforms, the feelings expressed in these opinions are becoming a significant source of information for marketers and buyers. These opinions have a big impact on consumers’ decision to buy the best quality product. When there are too many features or a small number of records to analyze, the decision-making process gets difficult. A recent stream of study has used the conventional quantitative star score ratings and textual content reviews in this context. In this research, a decision-making framework is proposed that relies on feature-based opinions to analyze the textual content of reviews and classify buyer’s opinions, thereby assisting consumers in making long-term purchases. The framework is proposed in this paper for product purchase decision making based on feature-based opinions and deep learning. Framework consists of four components: i) Pre-processing, ii) Feature extraction, iii) Feature-based opinion classification, and iv) Decision-making. Web scraping is used to obtain the dataset of Smartphone reviews, which is subsequently clean and pre-processed using tokenization and POS tagging. From the tagged dataset, noun labeled words are retrieved, and then the probable product’s features are extracted. These feature-based sentences or reviews are processed using a word embedding to generate review vectors that identify contextual information. These word vectors are used to construct hidden vectors at the word and sentence levels using a hierarchical attention method. With respect to each feature, reviews are divided into five classes: extremely positive, positive, extremely negative, negative, and neutral. The proposed method may readily detect a customer’s opinion on the quality of a product based on a certain attribute, which is beneficial in making a purchase choice.

Countering Price Discrimination with Buyer Information

Countering Price Discrimination with Buyer Information

SPECIALLY PROTECTED ENVIRONMENTAL AREAS X REGISTRATION IN REAL ESTATE OFFICE

Areas with special environmental protection designations have legal regulations to ensure biodiversity conservation and sustainable resource management. Among these areas are the Private Natural Heritage Reserves (RPPNs), Legal Reserves (RLs), and Environmental Easement Areas (ASAs). This study focuses on the importance of recording these zones in property registries at the Deed Offices. This practice centralizes crucial information about the property, including judicial and administrative aspects and obligations associated with the property, ensuring transparency and information for potential buyers and interested third parties. The employed methodology involved documentary research and systematic data analysis. It emphasizes the importance of intensifying the adoption of the concentration principle in property registration, through recording, to provide greater legal security, visibility, protection of areas, and to mitigate potential fraud. This study serves as a guide for registry professionals and the general public on the legal significance of recording in protected areas.

Perancangan Sistem Informasi Penjualan Tanaman Hias pada JA Garden

This research aims to design a web-based information system for selling ornamental plants at JA Garden. This system consists of input, output, process, control, labor, and cost design stages. Each stage is detailed with the programs involved, including data entry programs for categories, products, buyer information, purchase transactions, sliders, brands, blogs, users, and messages. System output includes category recaps, products, customers, transaction reports, transaction details, goods transactions, brands list, sliders, blogs/news, and users. The sales data processing process adopts computerized applications with context, tiered and data flow diagrams. User control involves registration in the database to prevent redundant data. Application control includes user management and data security with encryption. Minimal labor required, with training for application use. The proposed costs cover system maintenance and development. The use of various web programming languages and supporting software such as XAMPP, NotePad++, and the Google Chrome browser supports the functions of this system. Suggestions involve optimizing computer use, improving human resource skills, and utilizing all software. This research is expected to provide guidance for the development and implementation of an ornamental plant sales information system at JA Garden.

Supplier bottleneck and information dissemination

AbstractThis paper investigates the capacity decisions of complementary suppliers who produce different components of a final product. The suppliers solicit private forecast information from a buyer who has more precise information regarding the market as compared to the suppliers. In this context, the lowest capacity built among suppliers—termed as effective capacity—represents the bottleneck of a supply chain, which in turn determines the throughput of the entire channel. The standard analysis based on full rationality posits that the capacity decisions of suppliers are based on their prior belief of demand, with no consideration of the buyer's information dissemination or the number of peer suppliers. We test the predictions experimentally, and our laboratory observations reject the prediction of rational model. Then, we develop a behavioral model based on suppliers' heterogeneity in the processing of demand information provided by the buyer. Our behavioral model indicates that suppliers lower their capacity levels when the number of suppliers increases, thereby exacerbating the supplier bottleneck. While the buyer may exaggerate the market demand to ensure abundant supply, interestingly, the inflation can benefit suppliers by increasing their capacity levels. In this manner, the inflation of the buyer can serve to mitigate the supplier bottleneck, thereby resulting in a win–win outcome for both the suppliers and the buyer.

Do Online Firms Individualize Search Results? An Empirical Analysis of Individualization on Amazon

Online markets offer sellers access to buyers’ information and, thus, the potential to alter prices and products accordingly. In light of this, we undertook an empirical analysis to test for individualization on Amazon.com. We collect data from individuals recruited to shop for household items. Our results indicate evidence of individualization of search results and net prices (via coupons). We found, contrary to what was expected, that demographic, geolocation, and account information play an insignificant role in individualization of search results. Thus, we conclude that individualization is based on more dynamic information, e.g., online browsing behavior. This highlights the fact that sellers’ need for (and use of) buyer information goes beyond the simple information accessible from the buyers’ accounts to a more rigorous monitoring of buyers’ online behavior.

Research on the Influence Path of Online Consumers' Purchase Decision Based on Commitment and Trust Theory.

Based on the theory of commitment and trust, this paper constructs an online consumer purchase decision model, empirically studies the common values, online trust, commitment and purchase decision, and explores the influence mechanism and path of online consumer purchase decision. The results show that common values of shopping platforms and online consumers have a significant positive impact on trust and commitment, among which the common value orientation of mutual trust and commitment compliance has the most significant impact, which reflects the core content of common values. Secondly, the confidentiality of buyer information, common value of ethics, accurate disclosure of information, and finally the attitude to solve problem;The establishment of online trust has a significant positive impact on commitment, and both online trust and commitment are positively related to purchase decision. In terms of research methods, the theory of commitment and trust is introduced into the study of online consumption on Chinese shopping platforms, which expands the scope of application of the theory of commitment and trust. The trust of online consumers is generated by their own shopping experience. The conclusion of this study provides a decision-making basis for the innovation of marketing model and has theoretical reference value.

Deep Q-Learning and Preference Based Multi-Agent System for Sustainable Agricultural Market.

Yearly population growth will lead to a significant increase in agricultural production in the coming years. Twenty-first century agricultural producers will be facing the challenge of achieving food security and efficiency. This must be achieved while ensuring sustainable agricultural systems and overcoming the problems posed by climate change, depletion of water resources, and the potential for increased erosion and loss of productivity due to extreme weather conditions. Those environmental consequences will directly affect the price setting process. In view of the price oscillations and the lack of transparent information for buyers, a multi-agent system (MAS) is presented in this article. It supports the making of decisions in the purchase of sustainable agricultural products. The proposed MAS consists of a system that supports decision-making when choosing a supplier on the basis of certain preference-based parameters aimed at measuring the sustainability of a supplier and a deep Q-learning agent for agricultural future market price forecast. Therefore, different agri-environmental indicators (AEIs) have been considered, as well as the use of edge computing technologies to reduce costs of data transfer to the cloud. The presented MAS combines price setting optimizations and user preferences in regards to accessing, filtering, and integrating information. The agents filter and fuse information relevant to a user according to supplier attributes and a dynamic environment. The results presented in this paper allow a user to choose the supplier that best suits their preferences as well as to gain insight on agricultural future markets price oscillations through a deep Q-learning agent.

A matching method for second-hand goods exchange considering loss aversion of buyer and seller in e-brokerage

The second-hand goods exchange matching problem with intelligent exchange platform is a valuable research topic, while research in the related field is relatively lacking. Thus, the goal of this paper is to develop a two-sided matching method for the second-hand goods exchange matching. In the method, not only the characteristic of loss aversion of buyers/sellers and the benefit of e-brokerage are considered, but also the suggested exchange prices that meet the requirements of buyers and sellers are determined. Specifically, the description of second-hand goods exchange matching problem is firstly presented, in which the 2-tuple including the ideal level and the lowest acceptable level is used to quantify the expected demands of buyers and sellers for attributes. Then, several models maximizing the perceived values of buyers and sellers are derived to determine the suggested exchange price of goods. Next, according to the expectation information of buyers and sellers, and the real information of goods, the perceived value matrix for each attribute of buyers and sellers can be obtained by using the prospect theory. Furthermore, a model can be constructed and solved to determine the maximum number of exchange of buyers and sellers in e-brokerage, and an optimization model with the constraint of the exchange number threshold is constructed, and the optimal exchange matching results can be obtained by solving the optimization model. Finally, an example is given to illustrate the practicality of the proposed method. The method can be used to improve the operational performance of intelligent exchange platforms.

Supplier dependence asymmetry and investment in innovation: The role of psychological uncertainty

A key source of competitive advantage for large firms accrues from investments in innovative products and processes by their suppliers, incentivized by a positive relationship climate. A fundamental hindrance lies in a condition that commonly characterizes buyer-supplier relationships: asymmetric levels of dependence between business partners. Such asymmetry has been shown to be detrimental to the relationship, enhancing the likelihood of conflict, and negatively impacting the performance of the more dependent party. Considering that such dependence asymmetry is likely to persist, large buyers face the challenge of promoting a relationship environment that provides an incentive for suppliers to invest in innovation. In this study, we propose that large, dominant buyers can influence suppliers' intentions and behaviors by mitigating the effects of their perceived riskiness and ambiguity that emerge from a situation of dependence asymmetry. Specifically, using the lens of resource dependence theory, we investigate the effects of these two psychological dimensions of uncertainty on a supplier's trust, commitment, and willingness to invest in innovation, as well as the moderating effect of a buyer's information sharing in shaping a supplier's perceived uncertainty. The model is tested with a scenario-based experiment and results are corroborated by interviews with experienced managers of retail suppliers. Our findings indicate that a supplier's dependence on a large buyer increases its perception of ambiguity and riskiness in the relationship, negatively impacting its trust, commitment, and willingness to invest in innovation. However, a buyer who shares relevant and timely information with the supplier mitigates its perceived uncertainty, promoting the supplier's positive attitudes towards the buyer and incentivizing innovation. Sharing operational and strategic information such as point-of-sales data, sales forecasts, or strategic plans, becomes a valuable strategy to powerful buyers in the pursuit of becoming a preferred customer and enhancing a supplier's resource mobilization towards the relationship.

A Framework for Online Reverse Auction Based on Market Maker Learning with a Risk-Averse Buyer

The online reverse auction is considered as a new e-commerce approach to purchasing and procuring goods and materials in the supply chain. With the rapid and ever-expanding development of information technology as well as the increasing usage of the Internet around the world, the use of an online reverse auction method to provide the required items by organizations has increased. Accordingly, in this paper, a new framework for the online reverse auction process is provided that takes both sides of the procurement process, namely, buyer and seller. The proposed process is a multiattribute semisealed multiround online reverse auction. The main feature of the proposed process is that an online market maker facilitates the seller’s bidding process by the estimation of the buyer’s scoring function. For this purpose, a multilayer perceptron neural network was used to estimate the scoring function. In this case, in addition to hiding the buyer’s scoring function, sellers can improve their bids using the estimated scoring function and a nonlinear multiobjective optimization model. The NSGA II algorithm has been used to solve the seller model. To evaluate the proposed model, the auction process is simulated by considering three scoring functions (additive, multiplicative, and risk-aversion) and two types of open and semisealed auctions. The simulation results show that the efficiency of the proposed model is not significantly different from the open auction, and in addition, unlike the open auction, the buyer information was not disclosed.

Social media and customer relationship management technologies: Influencing buyer-seller information exchanges

Due to the increasing array of sales technology, salespeople must understand how each application assists them. This study examines how business-to-business salespeople use different forms of sales technology to meet their boundary-spanning roles. Our research draws from social exchange theory and task-technology fit theory to test a model that examines how salespeople use CRM and social media technologies differentially to support competitive information collection, product information communication, and buyer information sharing. Dyadic data from industrial buyers and sellers is used to analyze the technology-behavior relationships. Our study's results reveal social media use and CRM technology both positively influence buyer-seller information exchanges; however, each technology takes a distinct route to enable the information exchange between the buyer and the seller. The results also suggest that managers need to champion the use of both technology applications to their salesforce.

Should a Sharing Platform Adopt the Bilateral Review System?

The sharing economy and platforms have gained popularity in recent years, raising concerns about demand-side information asymmetry: the cost to serve each buyer is typically unknown to sellers. To mitigate this concern, some platforms like Airbnb adopt the bilateral review system (BRS), where both buyers and sellers can rate each other, different from the traditional unilateral review system (URS). We examine the effect of different review systems on the operation of sharing platforms. We analyze a decentralized platform in which heterogeneous sellers with one-unit capacity set prices simultaneously and then get matched with buyers with different serving costs. We show that with URS, even when perfect seller information is provided, the high-quality sellers can still be driven out of the market, owing to buyer information asymmetry and the “co-production” nature of the serving cost, in contrast to adverse selection in the classic used-car market. Additionally, we identify the critical role of serving cost structure. Although BRS can alleviate the adverse selection problem with a convex cost function and benefits the platform, it is detrimental to the platform with a linear cost function. Interestingly, we find that providing buyer information could help buyers but hurt sellers. Moreover, BRS could lead to a win-win-win outcome for the platform, buyers, and sellers. Our results not only shed light on the review system design but also provide a plausible explanation for the business practice that BRS is widely used by sharing platforms like Airbnb and Fiverr.

Online Consumer Inspection on using E-shopping Service of E-commerce

The Internet has become the primary source of information for a large number of consumers and it enables consumers to share their opinions and experiences concerning goods and services. Online consumer reviews provide information and recommendations for prospective buyers and are useful for decision-making on purchases. The purpose of this study is to assess the impact of on-line consumer reviews on a decision of using e-commerce services. In this study, we tend to conduct a laboratory experiment to analyze product review helpfulness likewise as its corresponding antecedents from the product review feature perspective (i.e., source- and contentbased review features). Findings from the study are threefold. First, the results of the information analysis support the theoretical conceptualization of product review helpfulness as a formative construct. Second, the results support the notion that the source- and content-based review options have direct impact on product review helpfulness. Customers perceive customerwritten product reviews as additional useful than those written by experts; they also perceive a concrete review as additional helpful than an abstract review. Third, we discover an interaction effect of the source- and content-based options of product reviews on review helpfulness. A customer-written product review with a low level of content abstractness yields the highest perceived review helpfulness

ARMOR: A Secure Combinatorial Auction for Heterogeneous Spectrum

Dynamic spectrum allocation via auction is an effective solution to spectrum shortage. Combinatorial spectrum auction enables buyers to express diversified preferences towards different combinations of channels. Despite the effort to ensure truthfulness and maximize social welfare, spectrum auction also faces potential security risks. The leakage of sensitive information such as true valuation and location of bidders may incur severe economic damage. However, there is a lack of works that can provide sufficient protection against such security risks in combinatorial spectrum auction. In this paper, we propose ARMOR, to enable combinatorial auction for heterogeneous spectrum with privacy, which can preserve bidders’ privacy while guaranteeing the economic-robustness of the combinatorial auction. We leverage the cryptographic methods, including homomorphic encryption, order-preserving encryption, and garbled circuits, to shield the bid and location information of buyers from the auctioneer. We design a novel location protection algorithm, which allows the auctioneer to exploit spectrum reuse opportunities without knowing the exact locations of buyers. Furthermore, we propose a verifiable payment scheme based on digital signature to prevent the auctioneer from forging the payment. The extensive experiments confirm that ARMOR maintains the good performance of the combinatorial spectrum auction, in terms of buyer satisfactory ratio and social welfare, and achieves privacy preservation with acceptable computation and communication costs.

Making Money from What You Know - How to Sell Information?

Information plays a key role in many decision situations. The rapid advancement in communication technologies makes information providers more accessible, and various information providing platforms can be found nowadays, most of which are strategic in the sense that their goal is to maximize the providers’ expected profit. In this paper, we consider the common problem of a strategic information provider offering prospective buyers information which can disambiguate uncertainties the buyers have, which can be valuable for their decision making. Unlike prior work, we do not limit the information provider’s strategy to price setting but rather enable her flexibility over the way information is sold, specifically enabling querying about specific outcomes and the elimination of a subset of non-true world states alongside the traditional approach of disclosing the true world state. We prove that for the case where the buyer is self-interested (and the information provider does not know the true world state beforehand) all three methods (i.e., disclosing the true worldstate value, offering to check a specific value, and eliminating a random value) are equivalent, yielding the same expected profit to the information provider. For the case where buyers are human subjects, using an extensive set of experiments we show that the methods result in substantially different outcomes. Furthermore, using standard machine learning techniques the information provider can rather accurately predict the performance of the different methods for new problem settings, hence substantially increase profit.

Transatlantic Transactions and the Domestic Market:Agnew’s Stock Books in 1894–1895

This essay uses the case study of a prominent firm of art dealers, Thos. Agnew and Sons (Agnew’s), to present a methodological discussion of how digital tools can be used to investigate circulation and transnational exchange in the historical art market, highlighting how these tools offer new ideas and opportunities for research, but also present tensions and contradictions. The essay focuses particularly on the dynamics of Agnew’s transatlantic art trade, and seeks to present analysis of the firm’s financial data within a nuanced and contextualised historical narrative about the cross-cultural movement of art between Britain and the United States. The principal data is drawn from Agnew’s London Stock Books, and its interpretive framework builds upon a recent collaborative pilot project between the National Gallery and King’s College London, which resulted in a database that records these transactions, investigates financial information, traces works of art, and identifies the biographical information and geographical locations of buyers and sellers. The essay, ultimately, aims to clarify how a top-tier, successful, and well-established dealership operated.

Cream Skimming and Information Design in Matching Markets

Short-lived buyers arrive to a platform over time and randomly match with sellers. The sellers stay at the platform and decide whether to accept incoming requests. The platform designs what buyer information the sellers observe before deciding to form a match. We show full information disclosure leads to a market failure because of excessive rejections by the sellers. If sellers are homogeneous, then coarse information policies are able to restore efficiency. If sellers are heterogeneous, then simple censorship policies are often constrained efficient as shown by a method of calculus of variations. (JEL C78, D82, D83)