Psychological and decision-related themes in sustainable consumption research: A bibliometric analysis

This paper comprehensively evaluates the literature on retail investor overconfidence using a framework-based systematic approach to understand the various dimensions of overconfidence bias, its effect on investing choices, and market dynamics. A systematic review of 137 publications from the Scopus database have been done to detect the research trend concerning investor overconfidence bias from its inception. An integrated ADO-TCM framework has been employed to present a systematic analysis of the theory, context, and methodologies (TCM) employed in the reviewed studies. The ADO (Antecedents, Decisions, and Outcomes) framework thoroughly examines the antecedents, decisions, and results of investor overconfidence.
The study identified four broad sets of factors contributing to investor overconfidence, as found in the existing literature. These factors include demographic characteristics, personality traits of investors, their knowledge and experience, and the features of investments and investor types. The Prospect theory is the most popular theory in the literature, with much research using secondary data and experiment-based analysis. The prospective study directions, based on the gaps in the existing literature, are as follows: further investigation into the decision-making processes of overconfident retail and professional investors is a worthwhile subject. Future research may shift their focus from financial outcome variables to non-financial outcome variables such as the impact of investor overconfidence on individuals’ stress levels, subjective financial well-being, and overall life happiness.

Dynamic capabilities theory has become one of the most widely accepted theories of business strategy. The study aims to determine the degree of development of the dynamic capabilities theory. A meta-analysis was carried out through a bibliometric analysis and a systematic literature review. Documents published in Scopus and Web of Science between 1997–2023 were analyzed using VOSviewer software. 49 documents met the analysis requirements, with a maximum of 11 published in 2022. Keyword co-occurrences were also analyzed. 222 were found, and 26 were selected with at least two co-occurrences. The documents were grouped into six clusters by these keywords and analyzed to determine the degree of development of the theory. The results have shown popularity among researchers in different science branches, indicating a high degree of development. However, the study noted a lack of theoretical consistency in elaborating constructs for measuring variables in empirical investigations. As a result, this leads to a lack of consistency and generalization of the results. Hence, the theory is not considered fully developed in terms of lack of internal coherence, falsifiability, and predictability. Tools for the empirical measurement of variables need to be theoretically and empirically validated to ensure their application in different contexts. The confrontation of results from validated measurement tools in diverse environments will contribute to developing dynamic capabilities theory.

Acknowledgment
We appreciate the support granted through the Postdoctoral Stays program by CONAHCYT.

In stock markets, technical analysis plays a vital role by offering valuable insights into price trends, patterns, and anticipated market movements, aiding investors in making well-informed decisions. This study employs bibliometric and topic modelling approaches on 589 English-language journal articles indexed in Scopus in the last two decades (from 2003 to 2023), exclusively focusing on technical analysis in stock markets. The keyword co-occurrence analysis identifies five topic clusters. The application of structural topic modelling also unravels five prominent thematic clusters, namely pattern-based forecasting, rule-based trading, algorithmic trading, techno-fundamental trading, and machine learning & sentiment analysis. The topic of pattern-based forecasting involves researching the application of various patterns or models to predict stock prices. Rule-based trading concentrates on utilizing technical analysis tools to generate buy and sell signals, aiming for profitability. The algorithmic trading cluster explores the use of algorithms to systematically execute buy and sell actions, especially in high-frequency trading scenarios. Techno-fundamental trading investigates the integration of both fundamental and technical analysis in trading and investment decisions. Lastly, machine learning & sentiment analysis focus on applying advanced machine learning techniques and sentiment analysis for predicting stock prices, highlighting the use of sophisticated methods in this domain. The three predominant topics in the dataset are "rule-based trading," "machine learning & sentiment analysis," and "algorithmic trading" constituting 26.79%, 23.52%, and 21.11% of the dataset, respectively. These findings underscore the prominence and significance of these themes within the context of the research domain.