Social Learning and Strategic Pricing with Rating Systems

Social Learning and Strategic Pricing with Rating Systems

As a modern way to conduct business in the global economic environment, e-commerce is becoming an essential component integrated with traditional business processes in enterprises. To reduce risks and increase profits in e-commerce investments and to provide the best services to their customers, enterprises have to find appropriate ways to analyze their e-commerce strategies at the business planning stage. Strategic management tools are designed for enterprises to evaluate their business strategies, and can be used to evaluate an e-commerce business plan, as well. For example, the SWOT (strengths, weaknesses, opportunities and threats) analysis is regarded as a popular way to conduct an e-commerce business plan evaluation, with business environmental scanning based on internal environmental factors (strengths and weaknesses) and external environmental factors (opportunities and threats) (Turban, King, Lee, & Viehland, 2003). To facilitate the application of the strategic management tools, different forms of applications are adopted, such as checklist (OGC, 2004), rating system (UNMFS, 2004), expert system (PlanWare, 2004) and so forth. Among these, computer-driven business simulation tools enable participants to run with virtual business processes, experiment with different strategies and compete with other supposed companies or plans in a virtual business environment. As an example, the Marketplace (ILS, 2003; IDC, 2004) is a business simulator for integrative business courses that provides decision content, including marketing, product development, sales force management, financial analysis, accounting, manufacturing and quality management. Regarding the application of computer simulation in e-commerce, the Marketplace strategic e-commerce simulation is specifically designed, and it illustrates the business concepts of an e-commerce environment, as well (ILS, 2003). For an e-commerce system simulation, Griss and Letsinger (2000) studied agent-based flexible e-commerce systems with an experimental multi-player shopping game to experiment with alternative individual and group economic strategies, and to evaluate the effectiveness of agent-based systems for e-commerce. Both academic and professional practice have proved that using computer simulation is an effective, efficient and economical way for e-commerce business plan evaluation. However, it is hard to conduct simulation based on the flowchart of business processes within the current e-commerce simulation environment as mentioned above. This actually provides a limitation for applying e-commerce simulation. In fact, computer simulation has tackled a range of business problems, leading to improving efficiency, reduced costs and increased profitability since the 1950s (Robinson, 1994). Simulation tools are on the increase in various application areas (Google, 2005) and process-oriented simulation has been increasing in popularity for business management (Swain, 2001). We believe that a process-oriented simulation for e-commerce system evaluation is more directly perceived through the human sense, and our interest is to conduct a quantitative approach to e-commerce system evaluation based on the theory of process simulation. The e-commerce system simulation is an integrative procedure to run a business processes-oriented simulation program based on both internal and external business environmental factors to demonstrate the actual results of implementing an e-commerce business model by using computer-driven software toolkits. The e-commerce system simulation is an effective, efficient and economical approach, and can be used to experiment and evaluate different e-commerce business models or plans. The adoption of e-commerce system simulation can reduce potential risks in e-commerce system development, such as the huge amount of initial investments of time and money, and the long period from business planning to system development, then to system test and operation, and finally to exact return; in other words, the proposed process-oriented e-commerce system simulation can help currently used system analysis and development methods to tell investors in a very detailed way about some keen attentions, such as how good their e-commerce system could be, how many investment repayments they could have and in which area they should improve from initial business plans. The definition of the proposed process-oriented e-commerce system simulation normalizes its procedure to apply a process simulation to experiment with an e-commerce model. In this regard, this article focuses on the adaptation of an e-commerce model into a process simulation environment by using an experimental case study. Results from this article include the conception of e-commerce system simulation, a comprehensive review of simulation methods adopted in e-commerce system evaluation and a real case study of applying simulation to e-commerce system evaluation. Furthermore, we hope that the adoption and implementation of process simulation approach can effectively support business decision-making, and improve the efficiency of e-commerce systems.

E-commerce has been around for many years. As people's needs change, many product-based and service-based e-commerce platforms have emerged. However, research on the design of e-commerce platforms related to protective gear products has been relatively unpopular. This study starts from the perspective of consumers and studies what elements need to be considered when designing e-commerce platforms for experience-based products of protective gear, and what their impact on "user satisfaction" is. This study targeted consumers aged 18 to 75 who used the platform in Hsinchu, Taiwan. A total of 300 valid questionnaires were collected, and descriptive statistical analysis, reliability analysis, correlation analysis, and regression analysis were performed using SPSS statistical software. The results of this study showed that the e-commerce platform design of protective gear products affects "user satisfaction", and the results were partially established; the "information richness" dimension, "operation practicality", and "service comfort" of the "e-commerce platform design of protective gear products" had a significant positive impact on "user satisfaction". The "system security" of "e-commerce platform design for protective gear products" has no significant impact on "user satisfaction". Keywords: Protective gear products, Platform design, User satisfaction.

PurposeThe research focuses on the crucial role of online reviews in shaping consumer trust in e-commerce platforms, examining the impact of perceived authentic and fake reviews on purchasing decisions and platform reputation. It assesses how consumers perceive review authenticity and quality and their effects on trust levels in reviews, marketplaces, and reputation systems. It also explores the relationship between trust forms and overall experiences.Design/methodologyA quantitative approach is employed, utilizing a questionnaire distributed to recent Mercado Libre buyers. To test hypotheses, data from 326 valid responses are analyzed using confirmatory factor analysis and Partial Least Squares Structural Equation Modeling (PLS-SEM).FindingsFindings reveal that fake review perception negatively affects trust in rating systems, while high-quality reviews positively influence all trust forms. Customer experience is directly impacted by trust in marketplaces and rating systems, indicating a mediation effect of trust in the rating system on the relationship between fake review perception and customer experience.Research limitations/implicationsLimitations include using a convenience sample and focusing on trust in the rating system rather than reviews or the marketplace, suggesting avenues for future research. Practical implications include recommendations to ensure review quality, enhance rating system controls, and promote review usage in the purchase process.OriginalityThe study addresses a timely and relevant gap in understanding the impact of reviews on e-commerce trust, particularly within the context of Latin America and Mercado Libre’s dominance in the region’s e-commerce landscape.

As the Chinese e-commerce market appears as a leading trend that helps to reduce cost of both consumers and sellers during transactions, decrease the development gap between rural and urban areas, and create job opportunities which lead to increment of employment rate, Taobao becomes one of the largest e- commerce platforms in China for it caters to the needs of small and medium sellers, leaving other similar platforms behind. However, as the retail sales and the number of users skyrockets each year, the obstacles that e-commerce platforms face also become more obvious. Therefore, this research attempts to narrow down things that hinder e-commerce platforms from further expanding and provide viable solutions that e-commerce platforms could try to employ. To collect information about the challenges faced by e-commerce users and the possible solutions, the research focuses on relevant literature reviews and data before and in 2021. The main concerns for the development of e-commerce platforms, specifically for Taobao, are the rating system, fraud transaction, and the logistic services. According to relevant literature, providing a descriptive survey for the rating system, setting up inspection departments, and establishing its own logistic company or creating a ranking system for logistic companies that cooperate with Taobao are all possible solutions that Taobao can refer to. This research tries to identify concerns that e-commerce users generally have for e-commerce platforms that still lack improvement. Through the combination of related articles and creative ideas, it offers hypothetically effective solutions to the challenges that are identified. The research intends to help the Chinese e-commerce market to evolve into the next level and make electronic transactions more pleasant for platform users.

A common neural network differentially mediates direct and social fear learning

This paper presents L-M-6, an innovative algorithm designed to provide statistically accurate and democratically correct movie ratings using AI. Traditional movie rating systems often fail to capture the multifaceted opinions of viewers. In contrast, L-M-6 leverages natural language processing and machine learning to analyze user reviews and extract sentiments across seven key aspects of filmmaking: cinematography, direction, story, unique concept, production design, characters, and emotions. To enhance the accuracy and relevance of the ratings, a user survey is conducted to rank these aspects based on their perceived importance. The collected data is used to assign weights to each aspect, ensuring that the most valued elements have a greater influence on the overall rating. This weighted sentiment analysis provides a more nuanced and precise rating system. Moreover, L-M-6 continuously updates scores with new reviews using a rolling mean, ensuring that the ratings remain current and reflective of audience opinions. The algorithm’s ability to dynamically adjust and accurately represent diverse viewer sentiments makes it a significant advancement over traditional rating systems. Our results demonstrate that L-M-6 offers a more comprehensive and democratic approach to movie rating, aligning closely with audience preferences and enhancing the overall reliability of movie evaluations.

A patient seller faces a sequence of buyers and decides whether to build a reputation for supplying high quality products. Each buyer does not have access to the seller's complete records, but can observe all previous buyers' actions, and some informative private signal about the seller's actions. I examine how the private signals the buyers receive affect the speed of social learning and the seller's incentives to establish reputations. When each buyer privately observes a bounded subset of the seller's past actions, the speed of social learning is strictly positive but vanishes to zero as the seller becomes patient. As a result, the patient seller receives a low payoff from building reputations, which also results in low social welfare. When each buyer observes an unboundedly informative private signal about the seller's current-period action, the speed of learning is bounded from below and a patient seller can secure high returns from building reputations. My results provide an explanation to empirical findings of reputation failures in developing countries. I also discuss the effectiveness of various policies in accelerating social learning and encouraging sellers to establish good reputations.

PurposeThe purpose of this study is to explore the antecedents of consumers’ online review intention in e-commerce platforms from a unique perspective of consumer commitment and platform design. Meanwhile, for the dual-platform strategy, i.e. providing both the web and mobile platforms simultaneously, which is widely adopted in the industry but lacks theoretical concerns, this study aims to examine the differences that platform design influences consumer commitment, consequently contributing to online review intention, between the web and mobile contexts.Design/methodology/approachA cross-sectional online survey is employed, and a structural equation model-based approach is utilized to analyze the data collected from both the website-preferred consumers (N = 167) and the mobile app-preferred consumers (N = 247).FindingsThe results indicate that instrumental support design factors and socio-emotional support factors positively influence consumer commitment, which further affect online review intention positively. Furthermore, design factors in different use contexts generate different impacts, and consumer commitment generates a greater effect on online review intention in the mobile than in the web context. Empathy is found to be an important motivator of consumer commitment in both contexts.Originality/valueTo the best of the authors’ knowledge, as one of the first attempts to capture the differences in the relationship between platform design on consumer commitment and online review intention in different use contexts within the dual-platform e-commerce, this study provides insights for e-commerce platform managers and designers to promote consumer commitment and online review engagement by prioritizing the platform design.

A typical e-commerce platform has too many similar items for sale, making it difficult for customers to choose from. User reviews left by previous buyers are worth reading to help customers make purchase choices. However, due to the large number of reviews, users cannot read all reviews to extract real useful information. In this paper, we propose a user review driven rating system, which is particularly designed for Tmall, a famous Chinese e-commerce platform, to help customers to understand the differences among similar items for finding a satisfactory one. Numerical result demonstrates that, on average, aggregated score calculated by our rating system for items is as efficient as the score given by Tmall, while our rating can differentiate items at much finer granularity by the computed multi-dimension scores.

Conventional wisdom and current research suggest that the Internet will lower electronic commerce (EC) product prices by causing intense competition among vendors. However, this does not seem to be happening. This research presents a multi-industry investigation of pricing behavior using a customized data-collecting Internet agent that we call the Time Series Agent Retriever (TSAR). We use theories of information asymmetry and Stackelberg pricing to show how Internet technology increases the ability of firms to tacitly collude to keep prices higher than expected in the presence of intense competition. Our results are developed using an econometric technique called vector autoregression (VAR). They show that Internet technology creates the potential to lower information asymmetry among Internet-based sellers. Thus, it allows rapid reaction between competitors, thereby allowing firms to avoid the intense competition predicted by current theory. We find that fast competitor reaction to the price promotions of a firm minimizes any profit derived from increased market share that the firm hopes to achieve from the lower price. This short reaction time allows Stackelberg pricing, in contrast with Bertrand-Nash pricing, which is often discussed in research on pricing in Internet-based selling. _____________________________________________________________________________________

Digital technologies enable consumers to actively participate in the product design and production process for a wide range of products, leading rise to the concept of a 'prosumer'. A significant portion of the value for such products is generated through the prosumption process, and a variety of firms are investing in building such capabilities. However, a major, largely unexplored, friction in prosumption is the customers’ effort involved to undertake a creative exercise of designing products and extracting value from it. In this study, we ask whether and how social learning, the act of showing creations made by other customers to the focal customer, can ameliorate such friction. Arguably, by showing others’ product designs to the focal customer, the firm may help the customers gain design ideas and garner knowledge about product features. Such an action is also likely to influence their belief about their own ability, namely, their self-efficacy, to design a valuable product that they would like to purchase. This implies that, if not carefully done, displaying others’ design could be detrimental to prosumption. Certain designs may be perceived to be out of the creative reach of the focal user, and therefore reduce their likelihood of designing a product and purchasing it. If social learning is effective, what can we say about the nature of images to show to different sub-groups of users? In close collaboration with an e-commerce platform specialized in customized photo products, we examine the effectiveness of social learning by means of a large scale in-vivo randomized field experiment. We exogenously vary both the availability of others’ design and the characteristics of images shown to the treated users. Our analysis shows that showing other users’ design can be highly effective in influencing the purchase and design behavior of the focal customer, but firms must choose the right customers and carefully select the type of user image design for display. We develop a novel ‘honest-bagging’ approach guided by principles of causal forests to personalize the high-dimensional treatment around which images to show to what types of users.

The resources that product design relies on are more distributed than ever along with the varying of the global design environment. More and more resources outside of enterprises are needed during the product design process. The Modern Product Design platform supporting the product design under the circumstance of distributed resources will meet the requirement of the enterprise's product design and development under such conditions, and will simplify the implementation of the IT support system for integration of design resources outside of enterprises. In this paper the characteristics and supporting technologies of product modern design platform, which supporting the distributed design resource circumstance and centering on the enterprise, are studied. The building method of the platform is presented and a prototype of the product design platform is developed. Three subsystems are included in the platform; they are product requirements analysis system, product design planning system and design knowledge management system. Many design tasks can be supported on the platform, such as product requirement analysis, concept design, detail design, and experiment. The distributing, implementing, tracking and managing of product design lifecycle tasks can also be supported on the platform. The distributed design resources could be sealed as application components to provide design services. Design work flow model and knowledge flow model are built and controlled on the design platform. The design knowledge is managed based on the Six-Dimension Knowledge Classification.

Today supply chain management has become one of the crucial factors for gaining and sustaining a competitive advantage. Enterprises that can more effectively manage their supply chain network have a higher likelihood of success in the marketplace. To this end, companies need not only make the “make” or “buy” decisions but also differentiate across potential suppliers in order to improve operational performance, and hence, supplier selection is one of the key decisions aiding effective supply chain management. Many studies have also pointed out that the integration of product and supply chain is a key factor for profitability and efficiency. However, prior studies mostly address supply chain performance after the creation of a new product; and only a few studies discuss when and how to incorporate supply chain decisions during product design. In the studies that cover product design, product family and product platform concepts are presented as enabling vehicles for mass customization, which require a considerable investment, and hence might be out of reach for small to medium size enterprises (SME). Accordingly, there is a need to develop a methodology that can consider manufacturability and supply chain issues at the product design stage. This paper presents a graph theory based optimization methodology to tackle this problem. The supplier selection issue is considered by evaluating its impact on both engineering (e.g., process planning) and operational performance (e.g., cost and time), which are then aggregated as the supply chain performance at the conceptual design stage. A case study in the bicycle industry demonstrates the advantages of this methodology. The synchronized structure of the supply chain and the product design results in simultaneous optimization of both design and supply chain decisions during the early design stages.

eBay's highly visible feedback-based rating system is also highly flawed, contributing to problems for buyers, which in turn creates problems for sellers. The well-known "market for lemons" phenomenon studied by Akerlof, and the even older Gresham's law effect, are contributing to loss of buyers' confidence in eBay, shrinking sellers' margins, contributing to the erosion of eBay's share price, and, potentially, leading to serious reductions in the value of eBay as an electronic auction site. buySAFE has created an alternative mechanism for reducing buyers' information deficit concerning sellers and their merchandise, involving a third-party certification system and bonding for qualified sellers; the rating is analogous to bond rating services such as Moody's and Standard & Poor's. Analysis of buySAFE's certification and bonding strategies for eBay sellers provides a basis for ongoing theory development related to organizational strategies that recognize the importance of information asymmetries in the digital marketplace and address resolution of consumers' concerns. buySAFE's original business model involves bonding sellers' transactions and protecting consumers for as much as $25,000. This has a number of beneficial effects on the buyers and sellers: it improves the information endowments of the buyers, it increases their willingness to pay for the goods and services offered, and it increases the margins and total revenues of the sellers. Although acceptance of buySAFE has been rapid, it has been slower than anticipated, and slower than theory would suggest. The company's executives are exploring adjusting their approach to the market and finding a way to achieve higher profitability, and working to limit their dependence upon eBay.