Development of Digital Financial Inclusion in China's Regional Economy: Evidence from Panel Threshold Models

This study aims to investigate the effect of digital financial inclusion and air pollution on economic growth for 31 Chinese provinces between 2003 and 2022 using Panel Threshold Auto-Regressive (PTAR) and Panel Smooth Transition Auto-Regression (PSTAR) models. The results show that there is a nonlinear link between digital financial inclusion and economic growth in China. For PTAR, the LnDFII thresholds are 4.264 (i.e., DFII = 71.094), and for PSTAR are 4.563 (i.e., DFII = 95.871). Below these thresholds, digital financial inclusion significantly boosts economic growth by 0.061 and 0.063 in the PTAR and PSTAR models, respectively. However, above these thresholds, the positive impact diminishes, with coefficients dropping to 0.015 and 0.004 in the PTAR and PSTAR models, respectively. Additionally, both models indicate that digital financial inclusion positively affects reducing air pollution, thereby potentially fostering economic growth. Hence, authorities should strategically implement digital technologies and strengthen collaborative efforts at the regional level to maximize these benefits.

A Novel AutoCNN Model for Stock Market Index Prediction

Stock markets have a significant impact on the economic growth of countries. Predicting stock market indices has been a complex task in recent years. Indeed, many researchers and financial analysts are keenly interested in the research area of stock market prediction. In this paper, we propose a novel framework, titled AutoCNN, based on artificial intelligence techniques, to predict future stock market indices. AutoCNN is composed mainly of three stages: (1) A Convolutional Neural Network (CNN) for Automatic Feature Extraction; (2) The Halving Grid Search algorithm combined with a second CNN model for prediction of stock indices; and (3) Evaluation and recommendation. To validate our AutoCNN, we conduct experiments on two financial datasets that are extracted in the period between 2018 and 2023, which includes several events, such as economic, health and geopolitical international crises. The performance of the AutoCNN model is quantified using various metrics. It is benchmarked against different models and it proves to have strong prediction abilities. AutoCNN contributes to emerging technologies and innovation in the financial sector by automating decision-making, leveraging advanced pattern recognition, and enhancing the overall decision support system for investors in the digital economy.

Bitcoin is an Environmental Disaster. Is the Indictment Distorted?

The literature is inundated with the claim that Bitcoin pollutes the environment. While the assertion is irrefutable, the unsettled issue is whether the indictment of environmental disaster is disproportionate or distorted. This is an empirical question; this paper evaluates it via two econometric methods. First, accepting carbon dioxide (CO2) emission as a proxy for environmental pollution, the paper quantifies the elasticity of CO2 emission in relation to electricity consumption in Bitcoin production. The results reveal that, while Bitcoin production based on conventional electricity is inelastic, carbon emission responsiveness to fossil fuel is significant. A 1% increase in Bitcoin’s usage of coal-generated electricity leads to a 1.64% surge in CO2 emission. Second, the study applies the error correction model to show that some electricity consumption shocks emanate from global coal prices driven by economic factors beyond Bitcoin’s control. This raises the question of whether Bitcoin should shoulder the entire blame for the 1.64% pollution responsiveness. Therefore, the study makes two important contributions. First, Bitcoin’s pollution impact varies according to timespan and electricity source. Second, the intensity of carbon emission from electricity consumption is aggravated by external market factors beyond Bitcoin’s control. The findings should inform policymakers and enlighten environmental advocates.

An Empirical Study of the Impact of Social Media Data Analytics on Marketing Strategy

Social Media Data Analytics (SMDA) has emerged as a dynamic and growing field across various disciplines, including marketing. However, practitioners and researchers in the marketing domain have realized that harnessing the full potential of SMDA for guiding marketing strategies necessitates a clear understanding of the relevant SMDA metrics. A significant challenge lies in the lack of clear guidance on which SMDA metrics are most relevant for enhancing marketing strategies. This study aims to empirically evaluate the impact of SMDA on marketing strategies. To achieve this goal, the study carried out a questionnaire for data collection and employs an empirical investigation using the PLS-SEM methodology. The results show that the impact of SMDA on marketing strategy depends on SMDA metrics (data type, platforms and analysis methods) and also on marketing strategy type. The results suggest a valid conceptual model introducing novel metrics for the SMDA concept. These results present a broader perspective on how SMDA affects marketing strategies and suggest that future research should focus on a specific type of marketing strategy and study SMDA metrics in a different and more in-depth way.

Marketing Strategy and Artificial Intelligence

The marketing literature highlights the growing integration of artificial intelligence (AI) into marketing strategies. Several publications show that this field is attracting increasing interest from researchers. The purpose of this article is to provide an overview of academic publications related to AI and marketing strategies, while also examining the lack of bibliometric analysis in this area. In this study, 1100 articles, published in the Scopus and Web of Science databases, were selected and, according to a consistent search procedure, were examined. A performance analysis, based on bibliometric indicators, revealed the most impactful journals, the most indexed authors according to H-index and the most cited papers. The thematic factorial map highlighted the typology of AI tools used in the field of strategic marketing, in this case the marketing strategy. It also provides a discussion, potential research avenues and recommendations for future investigations.

Clustering Social Media Data for Marketing Strategies

With the rise of social media platforms for marketing purposes, the central dilemma for researchers and policymakers lies in choosing effective data analysis tools to improve marketing decisions. In the academic literature, numerous articles have discussed clustering techniques for analysing social media data, from a perspective of data mining or social media marketing. However, few studies have attempted to synthesise results obtained from both perspectives. This research aims to (1) offer a structured overview of existing literature on clustering methods for marketing strategies and (2) compare three topic modelling techniques applied to extract the main topics evoked in the corpus of papers. Indeed, topic modelling emerges as a valuable tool for extracting relevant information from big data in general and more specifically from extensive scientific papers. Based on a thematic analysis, the extracted topics were classified according to the following categories: fields, marketing strategies and technologies. Results prove that latent Dirichlet allocation (LDA) is the most effective technique in this context. Furthermore, this study provides an overview of clustering techniques and technologies used for marketing strategies in studied fields. These findings help researchers and practitioners to select the best techniques and technologies for extracting marketing knowledge from big data.