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.