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This study aims to examine the impact of national culture on classification shifting in Eastern European Member States of EU Eastern European countries (EEU) vis-à-vis the Western Member States of EU (WEU). The EEU provides a unique sample to study the quality of financial reporting that the authors measure with classification shifting given that for more than five decades they were following the model of a centrally planned economy, where market-based financial reporting was absent. Yet, the EEU transitioned to a market-based economy and completed its accession to the EU.

This study uses a panel data set of firm year observations from 1996 and 2020 that covers the full transition of EEU. This empirical analysis is based on fixed effects panel regression analysis where the authors report a plethora of identifications.

This study finds classification shifting in the EEU countries since their transition to the market-based economy, though they have no long record of market-based financial reporting. This study also notices that cultural factors are associated with classification shifting across all Member States of the EU. This study further examines the impact of interactions between cultural characteristics and special items and reveal variability between WEU and EEU. As part of the robustness analysis, this study also tests the impact of culture on real earnings management measures for both WEU vs EEU, confirming the variability of the impact of culture on earnings management.

Future research could explore the role of religion differences in WEU vis-à-vis EEU states, as they are also subject to cultural differences.

The findings are important for regulators, external monitors and investors, as they show that cultural factors affect earnings management with some variability across countries in the EU, and they should be acknowledged in policymaking.

The findings show that cultural differences between EEU and the “old” Member States of the EU could explain classification shifting.

To the best of the authors’ knowledge, this is the first study that sheds light on the impact of national culture on classification shifting in EEU of EU vis-à-vis the “old” WEU of EU.

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

Traditionally, life cycle assessment (LCA) has focused on environmental aspects, but integrating social aspects in LCA has gained traction among scholars and practitioners. This study aims to review key social life cycle assessment (SLCA) themes, namely, drivers and barriers of SLCA implementation, methodology and measurement metrics, classification of initiatives to improve SLCA and customer perspectives in SLCA.

A total of 148 scientific papers extracted from the Web of Science database were used and analyzed using bibliometric and content analysis.

The findings suggest that the existing research ignores several aspects of SCLA, which impedes positive growth in topical scholarship, and the study proposes a classification of SLCA research paths to enrich future research. This study contributes positively to SLCA by further developing this area, and as such, this research is a primer to gain deeper knowledge about the state-of-the-art in SLCA as well as to foresee its future scope and challenges.

The study provides an up-to-date review of extant research pertaining to SLCA.

The purpose of this paper is to find out which research topics and methods in information science (IS) articles are used in other disciplines as indicated by citations.

The study analyzes citations to articles in IS published in 31 scholarly IS journals in 2015. The study employs content analysis of articles published in 2015 receiving citations from publication venues representing IS and other disciplines in the citation window 2015–2021. The unit of analysis is the article-citing discipline pair. The data set consists of 1178 IS articles cited altogether 25 K times through 5 K publication venues. Each citation is seen as a contribution to the citing document’s discipline by the cited article, which represents some IS subareas and methodologies, and the author team's disciplinary composition, which is inferred from the authors’ affiliations.

The results show that the citation profiles of disciplines vary depending on research topics, methods and author disciplines. Disciplines external to IS are typically cited in IS articles authored by scholars with the same background. Thus, the export of ideas from IS to other disciplines is evidently smaller than the earlier findings claim. IS should not be credited for contributions by other disciplines published in IS literature.

This study is the first to analyze which research topics and methods in the articles of IS are of use in other disciplines as indicated by citations.

This study aims to provide a holistic review of social media influencers (SMIs) research based on a unique approach of bibliometric analysis and content analysis between 2011 and 2020. The review examines the main influential aspects, themes and research streams to identify research directions for the future.

The sample selection and data collection were done from the Scopus database. The sample dataset was refined based on the inclusion and exclusion criteria to determine the final dataset of 183 articles. The dataset was exported in the BibTeX format and then imported into the BiblioShiny app for bibliometric analysis. The content analysis was done following the theory-context-methodology framework.

The several findings of this study include (1) Co-word analysis of most used keywords; (2) Longitudinal thematic evolution; (3) The focus of the research papers as per the theory-context-methodology review protocol are persuasion knowledge model, fashion and beauty industries, Instagram and content analysis, respectively; and (4) The network analysis of the research studies is known as the co-citation analysis and depicts the intellectual structure in the domain. This analysis resulted in four clusters of the research streams from the literature and two emergent themes (Chen et al., 2010)

In general, the previous reviews in the area are either domain, method or theory-based. Thus, this study aims to complement and extend the existing literature by presenting the overall picture of the SMI research with the help of a unique combined approach and further highlighting the trends and future research directions based on the findings of this study.

The purpose of this paper is to characterize library and information science (LIS) as fragmenting discipline both historically and by applying Whitley’s (1984) theory about the organization of sciences and Fuchs’ (1993) theory about scientific change.

The study combines historical source analysis with conceptual and theoretical analysis for characterizing LIS. An attempt is made to empirically validate the distinction between LIS context, L&I services and information seeking as fragmented adhocracies and information retrieval and scientific communication (scientometrics) as technologically integrated bureaucracies.

The origin of fragmentation in LIS due the contributions of other disciplines can be traced in the 1960s and 1970s for solving the problems produced by the growth of scientific literature. Computer science and business established academic programs and started research relevant to LIS community focusing on information retrieval and bibliometrics. This has led to differing research interests between LIS and other disciplines concerning research topics and methods. LIS has been characterized as fragmented adhocracy as a whole, but we make a distinction between research topics LIS context, L&I services and information seeking as fragmented adhocracies and information retrieval and scientific communication (scientometrics) as technologically integrated bureaucracies.

The paper provides an elaborated historical perspective on the fragmentation of LIS in the pressure of other disciplines. It also characterizes LIS as discipline in a fresh way by applying Whitley’s (1984) theory.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

This study aims to provide a comprehensive bibliometric investigation of the antecedents to financial stability in Islamic banking, a transition economy with a volatile stock market focusing on banks following the Shariah approach.

The data for this analysis was extracted from the Scopus database, which combines a comprehensively crafted abstract and citation database with augmented data and linked scholarly works across various disciplines. It quickly finds relevant research and provides access to reliable data and analytical tools. This study deploys “bibliometrix 3.0,” a biblioshiny R-package for influential structure and the VOS viewer for intellectual structure.

The investigation’s main findings revealed that 1,910 documents were published from 1987 to 2022. Published manuscripts received 39,050 citations, with an average of 10.18 citations per year. However, the instructed empirical research was experienced during 2009 and 2020, while earlier periods (1987–2008) were relatively inactive where banking was considered protective in the presence of BASEL-II capital accords regulations. While the International Journal of Bank Market has been at the top of the list to publish articles related to the area under investigation, the Journal of Banking and Finance is ranked one of the most cited articles. Malaysia has been at the top of the list of countries to research Islamic Sharia compliance principles in the banking industry, and International Islamic University Malaysia has produced enough evidence in this regard. The intellectual structure provided essential foundations for future research, and the bibliometric coupling approach was used.

While most of the banking research has been conducted to determine the banking business efficiency, risk and profitability, little focus is given to financial stability and that too concerning the Islamic banks. Therefore, researchers need to investigate this horizon from an Islamic banking point of view and focus on key issues that discriminate between Islamic and conventional banks in determining their stability level.

Briefly, to the best of the authors’ knowledge, this study would be the first to provide bibliometric information about financial stability keeping in view the sample data from banks with the Shariah approach. Furthermore, the proven analysis demonstrates a novel contribution that financially stable Islamic banks might strengthen the financial industry and overall economy.

This study aims to systematically review various behavioral biases that impact an investor’s decision-making process. The prime objective of this paper is to thematically explore the behavioral bias literature and propose a comprehensive framework that can elucidate a more reasonable explanation of changes in financial markets and investors’ behavior.

Systematic literature review (SLR) methodology is applied to a portfolio of 71 peer-reviewed articles collected from different electronic databases between 2007 and 2021. Content analysis of the extant literature is performed to identify the research themes and existing gaps in the literature.

This research identifies publication trends of the behavioral biases literature and uncovers 24 different biases that impact individual investors’ decision-making. Through thematic analysis, an attribute–consequence–impact framework is proposed that explains different biases leading to individual investors’ irrationality. The study further proposes directions for future research by applying the theory–characteristics–context–methodology framework.

The results of this research will help scholars and practitioners in understanding the existence of various behavioral biases and assist them in identifying potential strategies which can evade the negative effects of these biases. The findings will further help the financial service providers to understand these biases and improve the landscape of financial services.

The essence of the current paper is the application of the SLR method on 24 biases in the area of behavioral finance. To the best of the authors’ knowledge, this study is the first attempt of its kind which provides a methodical and comprehensive compilation of both cognitive and emotional behavioral biases that affect the individual investor’s decision-making.

Natural disasters profoundly influence agricultural trade sustainability. This study investigates the effects of natural disasters on agricultural production imports in China within 2002 and 2018. This exploration estimates the mediating role of transportation infrastructure and agriculture value-added and the moderating role of government effectiveness and diplomatic relations.

This investigation uses Probit, Logit, Cloglog and Ordinary Least Squares (OLS) models.

The results confirm the mediating role of transportation infrastructure and agriculture value-added and the moderating role of government effectiveness and diplomatic relations in China. According to the findings, natural disasters in trading partners heighten the risk to the agricultural imports. This risk raises, if disasters damage overall agricultural yield or transportation infrastructure. Moreover, governments’ effective response or diplomatic ties with China mitigate the risk. Finally, the effect of disasters varies by the developmental status of the country involved, with events in developed nations posing a greater risk to China’s imports than those in developing nations.

China should devise an early warning system to protect its agricultural imports by using advanced technologies such as data analytics, remote sensing and artificial intelligence. In addition, it can leverage this system by improving its collaboration with trading partners, involvement in international forums and agreement for mutual support in crisis.