Electronic Commerce Research最新文献

The utilization of sentiment analysis as a method for predicting stock market trends has gained significant attention recently, especially during economic crises. This research aims to assess the predictive accuracy of sentiment analysis in the stock market by constructing a reinforced model that integrates both sentiment and technical analysis. While prior studies have concentrated on social media sentiment for stock price prediction, this research introduces an enhanced model that combines sentiment analysis with technical indicators to improve the precision of stock market prediction. The study creates and evaluates predictive models for stock prices and trends using a substantial dataset of tweets from twenty prominent companies. Finally the re-enforced model has been developed and tested on the stock prices of: Apple, General Electric, Ford Motors and Amazon. The deliberate selection of these companies, each representing distinct industry sectors, serves a dual purpose. It not only facilitates a practical evaluation of our model across diverse market conditions but also ensures computational feasibility, allowing for a focused and detailed analysis of the model’s predictive accuracy and reliability in various economic landscapes. The study’s outcomes offer valuable insights into the effectiveness of the reinforced model, which combines sentiment and technical analysis to predict stock market movements, providing a more comprehensive approach to understanding market sentiment’s influence on stock prices. Furthermore, these findings contribute to the existing knowledge on stock market prediction techniques and emphasize the importance of considering multiple factors in decision-making.

Partial least squares structural equation modeling (PLS-SEM) is a popular method of data analysis in marketing, information systems research, and related fields. Unfortunately, the literature on PLS-SEM contains a number of misstatements that ascribe characteristics to PLS-SEM that it does not possess. In our study, we consider why these misstatements continue to be made and reinforced. We show how inaccuracies, omissions, repetitions of past misconceptions, and the introduction of additional misconceptions lead to the so-called Woozle effect. As an example, we use perhaps the most serious misconception about PLS-SEM namely its alleged suitability for estimating the parameters of reflective measurement models. The Woozle effect is a cumulative process by which falsehoods become established as fact, and the self-correcting mechanisms of science are suspended. Through a literature review, we identify a number of factors that are likely to have contributed to the Woozle effect in the PLS-SEM literature. For the Woozle effect to disappear, researchers need to acknowledge that PLS-SEM, in its current incarnation, is not suitable for scientific work with reflective measurement models.

In the realm of online retail, third-party sellers (TPSs) are progressively being permitted to offer their products on various platforms, often in exchange for a commission fee. This dynamic creates a natural competitive environment between the platforms and the TPSs. An increasingly prevalent strategy to enhance product competitiveness is the utilization of coupon promotions, which both platforms and TPSs have adopted. This study developed a theoretical model to study the dynamics of a retail system wherein an online platform facilitates the sale of products by a TPS, while simultaneously introducing products of differing quality levels. Four distinct promotion scenarios, based on whether the platform and TPS implement coupon offerings, are examined. The analysis outcomes reveal that coupon promotions initiated by both the platform and TPS result in reduced actual payments from consumers, particularly when the platform’s product holds a quality advantage. Surprisingly, in cases where the TPS’s product boasts a quality advantage, consumer actual payments may be higher in a promotion scenario compared to a non-promotional one. Furthermore, we demonstrate that the platform can leverage the TPS’s promotion efforts, particularly when commission fees and incremental purchases are on the higher side. Intriguingly, in situations where the TPS’s product holds a quality advantage, a co-promotion strategy, as opposed to a sole TPS-promotion approach, emerges as the optimal choice, especially when commission fees are substantial and incremental purchases are limited.

Artificial Intelligence (AI) has a growing influence in the fashion industry. In this review study, the focal points of research in AI in the context of fashion are showcased. This is achieved by quantifying the amount of research conducted in this area. Various insights, that could be useful for future studies are also provided. For each included study, the particular objective, that AI is tasked to achieve, is identified, as well as the methods and evaluation metrics that have been utilized in order to do so. A potential question which is also answered through this study, is which fashion items are targeted by researchers. Lastly, information about the utilized datasets is provided.

The Internet platform encompasses an array of software components, including mobile operating systems, transportation services, and social media networks, which synergistically form the foundation of the platform ecosystem. In this study, we have created a dynamic model that explores the competition between in-house applications (IHA) and third-party applications (TPA) within the platform ecosystem, which includes a single platform and APP developer. By utilizing simulation techniques on the dynamic model, we have identified notable factors that impact the stability of the APP competition system, including: the adjustment speed of IHA, APP heterogeneity, and TPA fees. Our findings are as follows: (1) A higher adjustment speed of IHA leads to increased system instability, resulting in a chaotic state and reduced profitability for both IHA and TPA. (2) We discovered that APP competition systems with either high or low APP heterogeneity are more prone to instability. (3) In relation to TPA fees, higher fees tend to foster greater instability within the platform ecosystem, while also increasing IHA profits. (4) We incorporate the time-delayed feedback control(TDFC) method to regulate intricate phenomena. In periods of instability, the utilization of the TDFC method can significantly mitigate the instability.

The purpose of this study is to explore the influence of board of directors characteristics on the cybersecurity disclosure (CSD) of firms listed on the London Stock Exchange. The current study used an empirical approach to data collection and analysis. The independent variable is the boards of directors’ characteristics; the dependent variables are the CSD. The study analysed 2250 observation of the UK. listed firms for the period of 2011–2020. The results of the current study show a significant and positive relationship between the extent of CSD and the board size, board independence and board meeting; in terms of board gender diversity yielded an insignificant and positive relationship with the extent of CSD. The findings indicate that firms with more independent and larger board, and high meeting frequency promote cybersecurity transparency and reduce related information asymmetric with stakeholders. The analyses have implications for policymakers, top management, corporate executives and practitioners. Firms are encouraged to restructure their board to enhance its effectiveness to better support and monitor CSD. This is the first study in the UK that examined the determinants of CSD. This adds value to the literature on CSD, in addition to contributing to an understanding of the relationship between board characteristics and CSD.

With the rising popularity of live streaming e-commerce, live streaming rooms and merchants are keen to explore and understand successful cooperative models. Some merchants require live streaming rooms to guarantee a minimum sales threshold in order to ensure their earnings. Based on certain variables, live streaming rooms can opt to utilize a "guaranteed sales" marketing strategy or a "breakthrough sales" marketing strategy. This study focuses on the live streaming e-commerce supply chain and considers factors such as streamers’ social influence and the live streaming room’s slotting allowance. We have developed a Stackelberg game model and designated the live streaming room (LSR) as the leader with partner merchants as followers. Optimal marketing effort level decisions and optimal discount rate decisions are studied under both the "guaranteed sales" and the "breakthrough sales" marketing strategies. We then consider whether variations in the slotting allowance charged by the LSR to the merchant have an impact. Our results indicate that the LSR’s optimal marketing strategy is mainly determined by the streamers’ social influence. When the streamers’ social influence is low, the LSR benefits more from the "guaranteed sales" marketing strategy; when it is high, the room benefits more from the "breakthrough sales" marketing strategy. For merchants, if there is no difference in the slotting allowance, then the merchant always makes more profit using the “breakthrough sales” strategy. If there is variation in the slotting allowance, the merchant's profit under the two strategies is uncertain. The greater the difference between the two marketing strategies, the greater the probability of choosing the "breakthrough sales" strategy. The research conclusions of this paper provide references for the strategy selection of live streaming e-commerce supply chain members and contribute to the further development of live streaming e-commerce.

This study endeavors to investigate the potential synergies resulting from the integration of artificial intelligence (AI) technology and visual communication design (VCD) in the domain of metaverse e-commerce. The primary objective of this study is to harness the power of AI technology and (VCD) to stimulate economic expansion and enhance user experiences. Primarily, this study employs a literature review approach to systematically survey existing research on the application of AI in environmental design and analyzes case studies illustrating how VCD satisfies the requirements of environmental design. Subsequently, focusing on representative Landscape Environment Design (LED) projects, the study elucidates the practices of landscape environmental design under the support of VCD and AI technologies. Finally, through the collection of economic data, such as visitor numbers and revenue, before and after the integration of AI and VCD elements in the West Lake Wetland Park, located in Hangzhou, Zhejiang Province, an empirical analysis quantifies the variations in economic growth. The study assesses the impact of VCD elements, AI design elements, and environmental design changes on economic growth in the West Lake Wetland Park. The case analysis of the West Lake Wetland Park demonstrates a 20% increase in visitor numbers within a year after the integration of AI and VCD elements, and an accompanying 30% rise in commercial activity revenue by enhancing visitor experiences. Furthermore, the amalgamation of AI and visual communication in landscape design not only promotes sustainable development and energy conservation but also propels the growth of Gross Domestic Product (GDP) in the region. During the research period, the application of AI and VCD in landscape environmental design projects contributed to an approximately 15% increase in regional GDP. Based on the research findings, it can be deduced that the integration of AI technology and VCD holds substantial potential and opportunities within the realm of metaverse e-commerce. This integration has the capacity to propel economic growth and enhance user experience significantly. Consequently, this study assumes considerable importance in facilitating the amalgamation of VCD with other disciplines and assessing the influence of intelligent visual communication LED on economic development.

As image-based communication proliferates, the business value of image sentiment analysis is rapidly growing, particularly in fields like advertising where consumers receive emotional cues through visual stimuli. However, most existing research on image sentiment analysis has focused more on developing sentiment classification models rather than exploring specific factors contributing to image sentiment. Therefore, this study proposes a methodology for extracting color palettes to represent image sentiments, emphasizing the significance of color palettes as highlighted in various studies. Previous approaches to color palette extraction have included heuristic methods, survey-based selection, or utilizing clustering algorithms like K-means clustering based on color frequencies in images. In this study, we calculate the influence scores of colors for classifying image sentiments and propose deriving representative sentiment-color palettes based on these scores. Initially, we train a multi-label classification model to predict the sentiment labels of images and then create datasets for distorted images where pixels corresponding to specific colors are removed. By comparing the model outputs obtained from these distorted images with the original dataset, we obtain quantitative influence scores of colors for classifying sentiment labels. Furthermore, we extract sentiment-color palettes consisting of four important colors for 30 different sentiments. Experimental results demonstrate higher evaluation scores compared to previous studies.

To cope with the challenge caused by the lack of physical evaluation, many e-commerce platforms introduce a showroom channel, in which consumers can feel and touch products before purchasing. Based on this background, we consider there are two strategies of an e-commerce platform to introduce a showroom channel, i.e., self-build strategy and cooperation strategy. Then we build theoretical models by characterising cost inefficiency of self-build strategy and information asymmetry of cooperation strategy in the platform supply chain. Next, we derive the optimal decisions under different strategies, and investigate the advantage of different strategies. Last, we explore the strategic introduction of the showroom channel in the platform supply chain. We find the following results. (1) Under cooperation strategy, the platform can strategically design two different cooperation contracts to the retailer, i.e., full incentive contract and partial incentive contract. Moreover, the retailer may be hurt by a higher demand due to information asymmetry under cooperation strategy. (2) Self-build strategy generates information advantage and cooperation strategy brings channel advantage, which jointly affect the optimal showroom channel strategy of the platform. To be more specific, when high-type demand of the retailer is relatively high and reservation profit of the retailer is relatively low, channel advantage dominates information advantage, then the platform will choose cooperation strategy. (3) Under some conditions, the optimal strategy of the platform is not in line with the optimal ones of the whole supply chain. Then we design a side payment contract to achieve Pareto improvement.