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The purpose of this paper is to comprehensively review a large and heterogeneous body of academic literature on investors' feedback trading, one of the most popular trading patterns observed historically in financial markets. Specifically, the authors aim to synthesize the diverse theoretical approaches to feedback trading in order to provide a detailed discussion of its various determinants, and to systematically review the empirical literature across various asset classes to gauge whether their feedback trading entails discernible patterns and the determinants that motivate them.

Given the high degree of heterogeneity of both theoretical and empirical approaches, the authors adopt a semi-systematic type of approach to review the feedback trading literature, inspired by the RAMESES protocol for meta-narrative reviews. The final sample consists of 243 papers covering diverse asset classes, investor types and geographies.

The authors find feedback trading to be very widely observed over time and across markets internationally. Institutional investors engage in feedback trading in a herd-like manner, and most noticeably in small domestic stocks and emerging markets. Regulatory changes and financial crises affect the intensity of their feedback trades. Retail investors are mostly contrarian and underperform their institutional counterparts, while the latter's trades can be often motivated by market sentiment.

The authors provide a detailed overview of various possible theoretical determinants, both behavioural and non-behavioural, of feedback trading, as well as a comprehensive overview and synthesis of the empirical literature. The authors also propose a series of possible directions for future research.

Authenticity has been studied from a variety of disciplinary perspectives, leading to a rich but confused literature. This study, a review, aims to compare the psychology and sociology/tourism definitions of authenticity to clarify the concept. From a psychological perspective, authenticity is a mental appraisal of an object or experience as valued leading to feelings and summative judgements (such as satisfaction or perceived value). In objective authenticity, a person values the object due to belief in an expert’s opinion, constructive authenticity relies on socially constructed values, while existential authenticity is based on one’s self-identity. The resultant achievement of a valued goal, such as seeing a valued object, leads to feelings of pleasure. Sociological definitions are similar but based on different theoretical antecedent causes of constructed and existential authenticity. The paper further discusses the use of theory in tourism and the project to develop tourism as a discipline. This project is considered unlikely to be successful and in turn, as argued, it is more useful to apply theory from other disciplines in a multidisciplinary manner. The results emphasise that it is necessary for tourism researchers to understand the origins and development of the concepts they use and their various definitions.

Purchase conversion prediction aims to improve user experience and convert visitors into real buyers to drive sales of firms; however, the total conversion rate is low, especially for e-retailers. To date, little is known about how e-retailers can scientifically detect users' intents within a purchase conversion funnel during their ongoing sessions and strategically optimize real-time marketing tactics corresponding to dynamic intent states. This study mainly aims to detect a real-time state of the conversion funnel based on graph theory, which refers to a five-class classification problem in the overt real-time choice decisions (RTCDs)—click, tag-to-wishlist, add-to-cart, remove-from-cart and purchase—during an ongoing session.

The authors propose a novel graph-theoretic framework to detect different states of the conversion funnel by identifying a user's unobserved mindset revealed from their navigation process graph, namely clickstream graph. First, the raw clickstream data are identified into individual sessions based on a 30-min time-out heuristic approach. Then, the authors convert each session into a sequence of temporal item-level clickstream graphs and conduct a temporal graph feature engineering according to the basic, single-, dyadic- and triadic-node and global characteristics. Furthermore, the synthetic minority oversampling technique is adopted to address with the problem of classifying imbalanced data. Finally, the authors train and test the proposed approach with several popular artificial intelligence algorithms.

The graph-theoretic approach validates that users' latent intent states within the conversion funnel can be interpreted as time-varying natures of their online graph footprints. In particular, the experimental results indicate that the graph-theoretic feature-oriented models achieve a substantial improvement of over 27% in line with the macro-average and micro-average area under the precision-recall curve, as compared to the conventional ones. In addition, the top five informative graph features for RTCDs are found to be Transitivity, Edge, Node, Degree and Reciprocity. In view of interpretability, the basic, single-, dyadic- and triadic-node and global characteristics of clickstream graphs have their specific advantages.

The findings suggest that the temporal graph-theoretic approach can form an efficient and powerful AI-based real-time intent detecting decision-support system. Different levels of graph features have their specific interpretability on RTCDs from the perspectives of consumer behavior and psychology, which provides a theoretical basis for the design of computer information systems and the optimization of the ongoing session intervention or recommendation in e-commerce.

To the best of the authors' knowledge, this is the first study to apply clickstream graphs and real-time decision choices in conversion prediction and detection. Most studies have only meditated on a binary classification problem, while this study applies a graph-theoretic approach in a five-class classification problem. In addition, this study constructs temporal item-level graphs to represent the original structure of clickstream session data based on graph theory. The time-varying characteristics of the proposed approach enhance the performance of purchase conversion detection during an ongoing session.

Consistency and consensus are two important research issues in group decision-making (GDM). Considering some drawbacks associated with these two issues in existing GDM methods with intuitionistic multiplicative preference relations (IMPRs), a new GDM method with complete IMPRs (CIMPRs) and incomplete IMPRs (ICIMPRs) is proposed in this paper.

A mathematically programming model is constructed to judge the consistency of CIMPRs. For the unacceptably consistent CIMPRs, a consistency-driven optimization model is constructed to improve the consistency level. Meanwhile, a consistency-driven optimization model is constructed to supplement the missing values and improve the consistency level of the ICIMPRs. As to GDM with CIMPRs, first, a mathematically programming model is built to obtain the experts' weights, after that a consensus-driven optimization model is constructed to improve the consensus level of CIMPRs, and finally, the group priority weights of alternatives are obtained by an intuitionistic fuzzy programming model.

The case analysis of the international exchange doctoral student selection problem shows the effectiveness and applicability of this GDM method with CIMPRs and ICIMPRs.

First, a novel consistency definition of CIMPRs is presented. Then, a consistency-driven optimization model is constructed, which supplements the missing values and improves the consistency level of ICIMPRs simultaneously. Therefore, this model greatly improves the efficiency of consistency improving. Experts' weights determination method considering the subjective and objective information is proposed. The priority weights of alternatives are determined by an intuitionistic fuzzy (IF) programming model considering the risk preference of experts, so the method determining priority weights is more flexible and agile. Based on the above theoretical basis, a new GDM method with CIMPRs and ICIMPRs is proposed in this paper.

In the realm of education, there has been an increasing emphasis on developing analytical thinking (AT) in the past few years. This systematic review focuses on an analysis of journal publications that have explored AT within the context of science, technology, engineering and mathematics (STEM) education. This analysis investigated four primary issues: (1) the operational definition that was used; (2) the types of theories that were used; (3) the interventions that were implemented to enhance AT skills; and (4) the research designs that were used.

To ensure a comprehensive and thorough review, we used the guidelines of preferred reporting items for systematic reviews and meta-analysis.

A comprehensive review of 28 pertinent scholarly articles reveals that scholars frequently rely on the concepts proposed by Anderson (2002), Marzano and Kendall (2008), Rodrangsee and Tuntiwongwanich (2021) and Suyatman et al. (2021) to establish a framework for delineating the competencies associated with analytical thinking (AT). Quasi-experimental designs were the most frequently used research designs in the studies analysed, followed by research and development approaches and then correlational designs. Most researchers have focused on investigating the effectiveness of problem-based learning as an intervention for improving AT skills. However, most research indicates that the theories or theoretical frameworks used to guide the research must be evident.

To the extent the authors know, this study represents the initial comprehensive examination of analytical thinking in STEM education. It presents a consolidated summary of the available evidence, assessing its quality and bringing it together in a single resource.

This paper discusses the evolution of sustainability reporting and the role of the Global Reporting Initiative (GRI) in relation to the social and environmental accounting (SEA) literature calling for a revolution in the standardization of sustainability reporting and the inherent complexities. This paper focuses on the future role of GRI in light of the changes resulting from harmonization supported by the International Sustainability Standards Board and the European Financial Reporting Advisory Group’s draft European Sustainability Reporting Directive.

Building on Bourdieu (1983, 1992) and SEA studies, the authors adopt a critical and qualitative approach to theorize power dynamics in the sustainability reporting field. After identifying the main issues arising from the complexity of the sustainability reporting standards and practices according to SEA scholars, the authors connect them with Bourdieu’s (1992, 1983) field theory to discuss the future role of GRI.

The findings suggest two distinct but intertwined roles that GRI could play in the future, namely, power related and theoretical/technical, aimed at engendering revolutionary rather than evolutionary changes in sustainability reporting.

This study offers practical implications for GRI to strengthen its future role in sustainability reporting standardization.

The limited time available to mitigate the disastrous consequences of non-sustainable business on society and the environment calls for urgently addressing the complexities of sustainability accounting to foster a positive impact on society and the environment.

The authors’ reflections reclaim the SEA literature as central to identifying sustainability complexity and Bourdieu’s (1983, 1992) notions of power as key to understanding the role of GRI in the sustainability field. Furthermore, this paper emphasizes the intersection of different critical concepts, including power, complexity, value, capital and materiality.

This paper aims to conduct an interdisciplinary systematic literature review (SLR) of fake news research and to advance the socio-technical understanding of digital information practices and platforms in business and management studies.

The paper applies a focused, SLR method to analyze articles on fake news in business and management journals from 2010 to 2020.

The paper analyzes the definition, theoretical frameworks, methods and research gaps of fake news in the business and management domains. It also identifies some promising research opportunities for future scholars.

The paper offers practical implications for various stakeholders who are affected by or involved in fake news dissemination, such as brands, consumers and policymakers. It provides recommendations to cope with the challenges and risks of fake news.

The paper discusses the social consequences and future threats of fake news, especially in relation to social networking and social media. It calls for more awareness and responsibility from online communities to prevent and combat fake news.

The paper contributes to the literature on information management by showing the importance and consequences of fake news sharing for societies. It is among the frontier systematic reviews in the field that covers studies from different disciplines and focuses on business and management studies.

The purpose of this paper was to develop a quantitative classroom observation method that is able to analyse the school day to identify Time-on-Task losses comprehensively and systematically, at a level of detail that can be used by teachers and principals to stimulate and focus practical improvement efforts.

The novel Time-on-Task Analysis (TOTA) model was developed by triangulating the conceptual framework of the Overall Equipment Effectiveness metric with the semantics and structure of the target domain. Once developed, the model was tested structurally against a time-series classroom observation data set, after which the resulting TOTA was presented to a sample of 52 education stakeholders, who then gave their perspectives of the analysis in a structured survey.

The ontological model was found to be accurate, complete and without conceptual incongruencies, and its output novel and useful by the sample of education stakeholders. Of the participants, 90.3% found the analysis to provide a new perspective, 94.2% reported that the analysis triggered improvement ideas and 80.8% thought that their school(s) could benefit from a TOTA study.

The TOTA model introduces a time-loss-focused perspective to the field of quantitative classroom observation studies, which is dominated by more sociologic- and pedagogic-focused topics. Its grounding in Overall Equipment Effectiveness also gives it a more detailed and systematic approach than the few Time-on-Task studies done to date, resulting in a model made for the “Gemba”: the school classroom.

The research on supply chain resilience (SCRES) has gained momentum after organizations have experienced more frequent and severe disruptions, especially with COVID-19 and the Russia/Ukraine conflict. Due to its potential for new practices and capability building, SCRES requires dynamic capabilities (DC) to enable an organization to prepare for, counter, and recuperate from disruptions leading to performance improvements and competitive advantage.

The present literature study seeks to enrich the theoretical debate on DC in SCRES, contributing to an advanced understanding of SCRES. Therefore, a meta-review of 83 peer-reviewed literature reviews has been conducted. Based on qualitative content analysis and abductive reasoning, relevant constructs are synthesized to facilitate theory-building for SCRES DC into a comprehensive framework.

The analysis reveals that SCRES has developed into an independent research area. Thus, resilience capabilities must be considered bundles of practices, evolving from different areas beyond supply chain risk management (SCRM). Most recent literature reviews on SCRES address more than one practice bundle applying SCRES DC as antecedents of new DC when organizational structures become more mature, leading to path dependencies when building business capabilities.

Aggregating extant literature on SCRES into a theoretical framework, the study contributes to a better understanding of the relationships between DC and SCRES practices while offering potential avenues for future research. It enriches DC theory by extending its microfoundations towards a holding/buffering dimension, which particularly accounts for the stability-based view of SCRES.