Product Upgrading Research Articles

Tripartite Evolutionary Game Analysis for Manufacturing Firms, Service Providers and Consumers in Collaborative Innovation Systems

With global economic growth and intensifying market competition, Chinese manufacturing firms are under pressure to enhance product competitiveness and adapt to changing consumer demands, requiring continuous product innovation and upgrading. This study introduces a tripartite evolutionary game model involving manufacturing firms, service providers and consumers to analyze the strategic stability and the impact of various factors on decision-making within this system. The results indicate that the decisions of these actors are interdependent, with a fair distribution of innovation benefits and costs being key to stimulating innovation enthusiasm and participation. The presence of free-riding behavior affects strategic choices, and a robust system of rewards and penalties can mitigate this problem. The cost of obtaining market information also influences service providers’ participation in innovation, with high costs potentially suppressing their involvement. This research provides insights to guide collaborative innovation efforts during industrial transformation, highlighting the direct impact of service providers and consumers on the success of manufacturing upgrades.

Why do upgrades worsen consumers’ experience with their possessions?

PurposeThis paper aims to investigate how product upgrades influence consumers’ hedonic responses to currently owned products, focusing on the underlying attentional mechanism.Design/methodology/approachSix experiments were conducted, including one pilot study and five main studies, employing various stimuli and methodologies. These experiments used longitudinal designs, manipulated upgrade awareness and measured hedonic decline over time. Mediation and moderation analyses were performed to test the proposed attentional mechanism.FindingsThe studies demonstrate that awareness of product upgrades induces consumers to experience a faster hedonic decline with their current possessions. This effect occurs because upgrades prompt consumers to shift their attention away from the currently owned product. The research provides both mediation-based and moderation-based evidence for this attentional mechanism.Research limitations/implicationsThe study primarily focused on product upgrades, and future research could explore this effect in nonproduct domains and investigate potential boundary conditions.Practical implicationsThe findings have implications for both consumers and companies in managing product enjoyment and upgrade cycles. Consumers can make more informed decisions about upgrades, while companies can develop strategies to maintain customer satisfaction with current products.Originality/valueThis work offers a novel perspective on the influence of upgrades on consumer behavior by introducing an attention-based account of hedonic adaptation and the consequent upgrade phenomenon, contrasting with previous research that relied on justifiability or contrast effects.

Deep Learning Algorithm-Based Appearance Detection of Cigarette Strip Packs

Each local feature in the appearance image of cigarette packs is a key element to reflect the corresponding brand information. If only a single convolutional neural network is used, the context information of sequence data may be lost, resulting in an insufficient grasp of the overall information. In order to realize the deep-level feature extraction of the appearance of cigarette packs and realize the appearance detection of cigarette packs with higher accuracy and speed, a new method for the appearance detection of cigarette packs was proposed by combining the convolutional neural network and the cyclic neural network methods in the deep learning algorithm. The image acquisition card is used to collect the appearance images of cigarette packs, and contrast enhancement and rotation correction are performed on the collected images to effectively improve their quality and provide a good guarantee for subsequent feature extraction and detection. The preprocessed cigarette package image is input into the convolutional neural network to realize the deep-level feature extraction of the cigarette package appearance image. The cigarette package appearance features’ output from the convolutional neural network is input into the short-term and long-term memory unit, as well as the gate-controlled cyclic unit, of the corresponding recurrent neural network, in order to process time sequence information based on the efficient extraction of details from the image, retain the sequence and context information of the input data, and ultimately achieve accurate detection of the cigarette package appearance. Through experimental analysis, this method can effectively identify the appearance defects of cigarette packs, mark them immediately, and present them in an intuitive way, so that staff can quickly locate the problem and take corresponding measures. The method can detect appearance defects larger than [Formula: see text] with high accuracy. For various appearance defects, the detection rate can be guaranteed to be over 98%, providing strong support for quality control and product upgrading in the tobacco industry.

Impact of loyal and new customer segments on product upgrades: The role of quality differentiation through online reviews

Impact of loyal and new customer segments on product upgrades: The role of quality differentiation through online reviews

Integrating internal electric field in PdRh-Cu2O/Cu nanorods: Revolutionizing low-energy hydrogen production and biomass upgrading

Integrating internal electric field in PdRh-Cu2O/Cu nanorods: Revolutionizing low-energy hydrogen production and biomass upgrading

Catalytic upgrading of oil products generated by retorting Dachengzi oil shale over different catalysts

Catalytic upgrading of oil products generated by retorting Dachengzi oil shale over different catalysts

Chemical Profile and Antibacterial Activity of Vitis vinifera L. cv Graciano Pomace Extracts Obtained by Green Supercritical CO2 Extraction Method Against Multidrug-Resistant Escherichia coli Strains.

The objectives of this study were to obtain and characterise polyphenolic extracts from red grape pomace of Vitis vinifera L. cv Graciano via conventional solvent extraction (SE) and green supercritical fluid extraction (SFE) and to evaluate their in vitro antibacterial activity against susceptible and multidrug-resistant Escherichia coli strains of intestinal origin. The SE and SFE methods were optimised, and ultra-performance liquid chromatography/mass spectrometry (UPLC/QqQ-MS/MS) analysis revealed 38 phenolic compounds in the SE sample, with anthocyanins being the predominant polyphenols, and 21 phenolic compounds in the SFE samples, among which hydroxybenzoic acids and flavonols were the predominant compounds. The SE and SFE samples showed antibacterial activity against both antibiotic-susceptible and -resistant E. coli strains, and minimal inhibitory concentration values were in the range of 1-4 mg/mL. The activity was bacteriostatic in all cases, and it was shown that a higher content of total polyphenols correlated with a higher antibacterial activity of the extracts. This study shows that red grape pomace of Vitis vinifera L. cv. Graciano is a rich source of bioactive phenolic compounds that can become an important feedstock for additives and other upgraded products of biotechnological interest, which can help to modulate intestinal microbiota and combat bacterial antibiotic resistance.

Mitigating Supply Disruption Risks: Impact of Product Upgrading Strategy

Mitigating Supply Disruption Risks: Impact of Product Upgrading Strategy

Hybrid model of multimodal based on data enhancement and lumped reaction kinetics: Applying to industrial ebullated-bed residue hydrogenation unit

Industrial ebullated-bed is an important device for promoting the cleaning and upgrading of oil products. The lumped kinetic model is a powerful tool for predicting the product yield of the ebullated-bed residue hydrogenation unit (EBRH), However, during the long-term operation of the device, there are phenomena such as low frequency of material property analysis leading to limited operating data and diverse operating modes at the same time scale, which poses a huge challenge to building an accurate product yield prediction model. To address these challenges, a data augmentation-based eleven lumped reaction kinetics mechanism model was constructed. This model combines generative adversarial networks, outlier elimination, and L2 norm data filtering to expand the dataset and utilizes Kernel Principal Component Analysis-Fuzzy C-Means for operating condition partitioning. Based on the hydrogenation reaction mechanism, a single and sub operating condition eleven lumped reaction kinetics model of an ebullated-bed residue hydrogenation unit, comprising 55 reaction paths and 110 parameters, was constructed before and after data augmentation. Compared to the single model before data enhancement, the average absolute error of the sub-models under data enhancement division was reduced by 23%. Thus, these findings can help guide the operation and optimization of the production process.

Application of artificial intelligence and machine learning in poultry disease detection and diagnosis: A review

The poultry population has increased exponentially from 13.9 billion in the early 21st century to 26.56 billion by 2022 worldwide, emphasizing the vital nutritional and economic part of this section. Simultaneously, the poultry sector faces a considerable amount of tests from diseases such as avian influenza, coccidiosis, mycoplasmosis, etc. that cost the industry multibillion-dollar losses each year. The groundbreaking and revolutionary possibilities of artificial intelligence and machine learning in poultry disease detection and diagnosis are discussed in this review. By capitalizing on data from physiological and behavioral traits like movement, vocalization, body temperature, and excreta, AI algorithms can detect indications of illness and pathological conditions, which means strengthening disease management and bringing down economic losses. High-precision image and video processing, non-invasive monitoring, the use of thermal imaging, and accurate tracking of poultry to spot health issues are some of the crucial developments that have also aided in analyzing stress and other abnormalities. Incorporating new-age technologies into feasible, applicable, and economical diagnostic tools that have the potential to transform poultry well-being, enhance the welfare of poultry, and upgrade production as well as handling processes is discussed here. The upcoming prospects include global partnerships, better data analytics, and extended research or studies for the management of diseases and behavioral anomalies in all poultry species. The collaboration of AI, machine learning, and biotechnology holds colossal promise for the poultry sector, guaranteeing food safety and ensuring public health.

Advances in Tandem Strategies for CO2 Electroreduction: From Electrocatalysts to Reaction System Design

AbstractThe electrochemical reduction of CO2 (CO2RR) offers the opportunity to store renewable energy in the form of chemicals and fuels while simultaneously reducing CO2 emissions. Compared to low carbon products such as carbon monoxide and formic acid, multicarbon products have demonstrated greater value in terms of economic feasibility. To date, various strategies have been developed to enhance electrocatalytic performance, with tandem strategies emerging as a promising approach, particularly for the formation of multicarbon products. In this review, current tandem strategies regarding CO2RR are thoroughly discussed, covering electrocatalyst designs from atomic‐scale tandem electrocatalysts to macroscale electrode configurations as well as reaction system designs. Additionally, the internal reaction processes involving reduction product upgrades, the application of multi‐physical tandem fields, and the tandem reaction systems are further summarized. This review aims to provide more fundamental insights into tandem strategies for CO2RR applications and inspire more creative ideas in the research community of CO2RR in light of this promising approach with its wide versatility, diversity, and flexibility.

Financial Statement Analysis of BYD Company Limited

This paper takes BYD as an example to analyze its financial indicators and conduct a comprehensive DuPont analysis for the period from 2019 to 2023. The analysis reveals that the company achieved significant financial growth during this period. Both operating revenue and net profit showed an upward trend, with particularly strong growth momentum in 2022 and 2023, as net profit increased substantially. The gross profit margin and net profit margin also improved, indicating enhanced operational efficiency and effective cost control. At the same time, BYD's continuous increase in R&D investment has driven technological innovation and product upgrades. The total assets have continued to grow, reflecting the company's business expansion and strengthened capabilities. The cash flow position remains healthy, with net operating cash flow steadily increasing, providing stable financial support for the company. However, BYD faces challenges such as intensified market competition and fluctuations in raw material prices, which necessitate ongoing optimization of business strategies and operational models to maintain its leading position and competitiveness. Overall, BYD has performed outstandingly in the new energy vehicle sector, with promising prospects for future development.

Product upgrade and advanced quality disclosure in a supply chain

Retailers frequently preannounce the quality of upcoming products, highlighting the importance of information disclosure in supply chains. This study examines the interaction between advanced quality disclosure and product upgrades within a supply chain, where a supplier can release either upgraded products exclusively or alongside regular products in a subsequent period. Using a game-theoretical model, we investigate how the supplier’s upgrade decisions and the retailer’s disclosure strategy affect consumer behaviour, particularly strategic waiting, and product cannibalisation. Our results show that when only upgraded products are offered, the retailer discloses product quality in advance if the innovation level is high. When both regular and upgraded products are available, the retailer chooses advanced disclosure when the innovation level is either high or low. Even when the product improvement is minimal, advanced disclosure reduces cannibalisation and attracts more consumers to the upgraded products, thereby boosting the retailer’s profits. Additionally, the supplier may choose not to upgrade products when the innovation cost is moderate. These insights suggest that advanced disclosure can mitigate both cannibalisation and consumer waiting, particularly in high-tech industries or incremental innovation scenarios, such as electronics and smart home sectors, where the preannouncement of product information is crucial.

Upgrading of kraft lignin pyrolysis products: Managing sulfur impurities

Pyrolysis stands as a powerful method for the valorization of biomass like kraft lignin, an aromatic-rich and sulfur-containing polymeric by-product from the pulping industry. Sulfur in the raw matrix is released during thermal processes and is redistributed in pyrolysis products. However, the presence of sulfur lowers the quality of products due to its corrosive and toxic character. In this work, commercial activated carbons have been used as adsorbents to remove the main sulfur compounds from the gas phase (i.e., CH3SH, COS, H2S). The analysis of sulfur content in biochar has been performed through SEM-EDXS, while the sulfur content in oils has been obtained by GC–MS analysis. The adsorption of sulfur on the activated carbon bed results in an enhancement of the release of sulfur in the gas phase from 34 % without activated carbon to 44 %. Up to 88 % of H2S removal is achieved after the adsorption step, together with a reduction of the overall sulfur content in the bio-oil rich in intermediates such as guaiacols and alkylated phenols. The residual sulfur contained in the liquid product is lowered to 3 % of the total sulfur in the system, thus improving the quality of the liquid pyrolysis product. This process limits the amount of sulfur in the gas and improves its quality and market value. At the same time, this results in a redistribution of sulfur in pyrolysis products such as bio-oil, improving its range of application as a source of chemical intermediates from kraft lignin.

Ni-decorated carbon nanotubes (CNTs) derived from ethanol for electrooxidation of furan derivatives featuring H2 production.

This study explores advancements in concurrent biorefinery and hydrogen production using Ni-decorated CNTs derived from bioethanol as electrocatalysts, showing the complete conversion of furan derivatives to the desired products with 95-97% selectivity and a hydrogen production rate of 75 μmol h-1. These findings highlight the simultaneous upgrading of biomass-derived compounds and hydrogen production.

Demand cannibalization during product rollovers in the presence of strategic customers

This paper studies how different types of demand cannibalization take place during product rollovers and their effects on a firm’s profitability. Due to continuous product upgrades, demand cannibalization among various generations of products has become a common practice, raising interesting investigations from professionals and academics. Within this context, we model firms’ and customers’ decision-making as a two-period sequential game and derive equilibrium results under a dual rollover strategy, with and without a trade-in program. We find that demand cannibalization is essentially caused by customers’ strategic comparison of available purchase options. Our analysis further reveals that both the cannibalization effect and the postponement effect resulting from customers’ waiting for upgraded products are necessary for firms to improve profits. On the contrary, the postponement effect due to waiting for a discount is entirely detrimental to the firm. We propose effective launch strategies to avoid unfavorable demand cannibalization under different conditions. In addition, offering trade-in programs helps not only suppress strategic waiting, but also foster demand for an upgraded product. These strategies are particularly effective when the firm holds a greater quantity of inventory or when the degree of incremental innovation is high.

Smart city construction and quality upgrading of export products: Evidence from China

Smart cities, as an advanced form of urban development in the digital age, are likely to profoundly affect the operations of local businesses. This paper analyzes the impact of smart city construction on the quality of firms’ export products by exploiting a quasi-natural experiment based on a smart city pilot policy in China and employing the staggered difference-in-differences (DID) method. Our empirical results reveal that the implementation of the policy significantly improves the export product quality of local firms, and this finding remains quite robust after a series of sensitivity tests. Mechanism analysis shows that the improvement of innovation efficiency and the reduction of transaction costs are two effective channels through which smart city construction enhances export product quality. Heterogeneity analysis reveals that the policy effects are more prominent in high-tech industries, cities with high administrative levels, the eastern and central regions, non-state-owned firms, and general trade firms, which enjoy greater policy dividends than their counterparts.

Biocrude oil Production Upgrading by Catalytic Assisted Hydrothermal Liquefaction of Underutilized non-edible seed Biomass

Biocrude oil Production Upgrading by Catalytic Assisted Hydrothermal Liquefaction of Underutilized non-edible seed Biomass

Situation Analysis of Midea Company

Midea Group, one of the famous companies in China, is known for its outstanding achievements in the field of household appliance. Under the guidance of diversification strategy, it has successfully occupied a broad market share and shown remarkable growth potential. Looking into the future, with the in-depth development of global economic integration and the continuous progress of science and technology, Midea will continue to adhere to the innovation-led development, constantly promote the upgrading of products and services, and is committed to building the world’s leading provider of household appliances and intelligent solutions. This article is aimed to conduct a comprehensive situation analysis of Midea. In this article, the overall information of Midea will be displayed, including its ownership structure, vision, mission, and core values, etc., so that readers can have a detailed understanding of the company. In addition, Midea’s product analysis, industry and market analysis will be performed, so that its marketing situation and competitive advantages can be further analyzed. Finally, two critical success factors of Midea will be listed to identify the reasons for its current success and strong market share. Also, in order to ensure its future performance growth, industry-related concerns and market-related concerns will also be discussed to analyze some potential risks and strong competitors.

Single rollover or dual rollover: How a monopoly NEV manufacturer responds to NEV credit policy

The NEV credit policy plays a crucial role in advancing the new energy vehicle (NEV) industry and environmental conservation. This study investigates the monopoly NEV manufacturer’s optimal rollover strategy (Single Rollover (SR) vs. Dual Rollover (DR)) and pricing scheme (skimming vs. penetration) under the government’s stricter calculation criteria for NEV credit policy, considering a two-period market with strategic consumers. We find that the NEV manufacturer typically adopts a skimming pricing scheme in DR strategy, while a penetration pricing scheme in SR strategy. The choice between SR and DR strategy hinges on the government’s calculation criteria for NEV credits, product upgrade speed, and vehicle environmental performance improvement. When the calculation criterion for NEV credits is moderate, or when it is low, coupled with fast product upgrade speed and marginal improved environmental performance of the new vehicle, SR strategy is beneficial to the NEV manufacturer to avoid the cannibalization between new and old vehicles. In contrast, the NEV manufacturer can benefit from DR strategy through price discrimination between new and old vehicles to attract consumers from different market segments. However, stricter calculation criteria for NEV credits do not always result in higher social welfare.