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Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

This study aims to explore the relationship between promoter share pledging and the company’s dividend payout policy in India. Furthermore, this study also analyses the moderating impact of family involvement in business on the association between share pledging and dividend payout.

A sample of 236 companies from the S&P Bombay Stock Exchange Sensitive (BSE) 500 Index (2014–2023) has been analysed through fixed-effects panel data regression. For additional testing, robustness checks include alternative measures of dividend payout and promoter share pledging, as well as alternative methodologies such as Bayesian regression. Lastly, to address potential endogeneity, instrumental variables with a two-stage least squares (IV-2SLS) methodology have been implemented.

Upholding the agency perspective, a significantly negative impact of promoter share pledging on corporate dividend payouts in India has been uncovered. Moreover, family involvement in business moderates this relationship, highlighting that the negative association between promoter share pledging and dividend payouts is more pronounced in family companies. The findings are consistent throughout the robustness testing.

The present study represents a pioneering endeavour to empirically analyse the link between promoter share pledging and dividend payouts in India. It enhances the theoretical underpinnings of the agency relationship, particularly by substantiating the existence of Type II agency conflicts between majority and minority shareholders. The findings of this research bear significant implications for investors, researchers and policymakers, particularly in light of the widespread prevalence of promoter-controlled entities in India.

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

This paper aims to review the topic of “luxury fashion consumption”, a field of recent interest for academics and practitioners. However, a literature review that can map the existing knowledge and aggregate it into relevant topics and offers a research agenda for future research is still lacking.

This paper uses a systematic review and a text mining approach to analyse 73 articles on luxury fashion consumption aiming to clarify, rationalise and critically interpret the literature on luxury fashion consumption; identify the core topic, create an integrative framework of core constructs; and offer research gaps and suggest a research agenda for future studies.

From this analysis, eight major research topics are found and analysed (brand desire, authenticity, luxury markets, value perceptions, luxury retail experience, luxury brands communication, responsible consumption and sustainability and status signalling). Based on these topics and following the TCM framework, this review offers directions for future research.

This research offers a text-mining review of luxury fashion consumption to help scholars and managers further develop this field, as there is no comprehensive review on the topic exploring the themes, theories, constructs and methods used in prior studies.

Este artículo pretende revisar el “consumo de moda de lujo”, un tema de reciente interés para académicos y profesionales. Sin embargo, sigue faltando una revisión de la literatura que pueda ordenar el conocimiento existente y aglutinarlo en temas relevantes y que ofrezca una agenda de investigación futura.

Este trabajo emplea una revisión sistémica de la literatura y la minería de textos para analizar 73 artículos sobre el consumo de moda de lujo con el objetivo de (i) aclarar, racionalizar e interpretar críticamente la literatura sobre el consumo de moda de lujo, (ii) identificar el tema central, crear un marco integrador de constructos clave y (iii) presentar las lagunas de la investigación y sugerir una agenda de investigación para futuros estudios.

A partir de este análisis, se identifican y analizan ocho temas principales de investigación (el deseo de marca, la autenticidad, los mercados de lujo, las percepciones de valor, la experiencia de la venta al por menor de lujo, la comunicación de las marcas de lujo, el consumo responsable y la sostenibilidad, y la señalización de estatus). Sobre la base de estos temas y siguiendo el marco del TCM, esta revisión propone líneas para futuras investigaciones.

Esta investigación ofrece una revisión de la minería de textos sobre el consumo de moda de lujo para ayudar a los académicos y gestores a seguir desarrollando este campo, ya que no existe una revisión exhaustiva sobre el tema que explore los conceptos, teorías, constructos y métodos utilizados en estudios previos.

Revisión de la literatura

本文旨在回顾 “奢侈时尚消费”, 这是学术界和从业人员最近关注的一个话题。然而, 目前仍然未能将现有知识分类并为未来研究提供议程的文献回顾。

本文使用系统的文献综述和文本挖掘, 分析了73篇关于奢侈时尚消费的文章。此文目的是：（1）批判性地解释关于奢侈时尚消费的文献; （2）确定中心主题, 建立综合框架; （3）提出研究缺憾, 为未来的研究提出议程。

从这个分析中, 我们发现并分析了八个主要的研究主题（品牌欲望、真实性、奢侈品市场、价值认知、奢侈品零售体验、奢侈品品牌传播、负责任的消费和可持续性、以及地位信号）。基于这些主题并遵循TCM框架, 本评论提出了未来研究的方向。

目前还没有关于该主题的全面文献回顾, 以探索以前研究中使用的概念、理论、构造和方法。本研究对奢侈时尚消费的文本挖掘进行了回顾, 以帮助学者和管理者进一步发展该领域。

文献评论

The purpose of this paper is to present a framework of ideation pathways that organically extend the current stock of knowledge to generate new and useful knowledge. Although detailed, granular guidance is available in the strategy literature on all aspects of empirically testing theory, the other key aspect of theory development – theory generation – remains relatively neglected. The framework developed in this paper addresses this gap by proposing pathways for how new theory can be generated.

Grounded in two foundational principles in epistemology, the Genetic Argument and the open-endedness of knowledge, I offer a framework of distinct pathways that systematically lead to the creation of new knowledge.

Existing knowledge can be deepened (through introspection), broadened (through leverage) and rejuvenated (through innovation). These ideation pathways can unlock the vast, hidden potential of current knowledge in strategy.

The novelty and doability of the framework can potentially inspire research on a broad, community-wide basis, engaging PhD students and management faculty, improving knowledge, democratizing scholarship and deepening the societal footprint of strategy research.

Knowledge is open-ended. The more we know, the more we appreciate how much we don’t know. But the lack of clear guidance on rigorous pathways along which new knowledge that advances both theory and practice can be created from prior knowledge has stymied strategy research. The paper’s framework systematically pulls together for the first time the disparate elements of transforming past learning into new knowledge in a coherent epistemological whole.

The objective of this study was to conduct a bibliometric analysis of the existing literature on organizational deviance to assess how far this concept has progressed since its introduction in the domain of organizational behavior.

This study employs bibliometric methodologies (citation analysis, co-citation analysis and co-occurrence of author keywords) using VOSviewer. The Scopus database was used, as it is the largest database of scholarly literature.

The findings indicate the character and direction of organizational research over the past two decades. Organizational deviance due to psychological contract breach, organizational deviance in the context of organizational cynicism and organizational deviance in the context of psychological capital are the three major themes in the literature on organizational deviance. In addition, the study highlights the most significant authors, journals, institutions and nations in the field of value co-creation research as well as potential future research areas in this area.

The use of a single database and the inability to contextualize the citation structure of papers revealed by the review are limitations of this study.

This study examines the structure of the literature on organizational deviance and charts the field's evolution over time.

In recent years, there has been significant interest in adopting XR (extended reality) technologies such as VR (virtual reality) and AR (augmented reality), particularly in retail. However, extending activities through reality-mediation is still mostly believed to offer an inferior experience due to their shortcomings in usability, wearability, graphical fidelity, etc. This study aims to address the research gap by experimentally examining the acceptance of metaverse shopping.

This study conducts a 2 (VR: with vs. without) × 2 (AR: with vs. without) between-subjects laboratory experiment involving 157 participants in simulated daily shopping environments. This study builds a physical brick-and-mortar store at the campus and stocked it with approximately 600 products with accompanying product information and pricing. The XR devices and a 3D laser scanner were used in constructing the three XR shopping conditions.

Results indicate that XR can offer an experience comparable to, or even surpassing, traditional shopping in terms of its instrumental and hedonic aspects, regardless of a slightly reduced perception of usability. AR negatively affected perceived ease of use, while VR significantly increased perceived enjoyment. It is surprising that the lower perceived ease of use appeared to be disconnected from the attitude toward metaverse shopping.

This study provides important experimental evidence on the acceptance of XR shopping, and the finding that low perceived ease of use may not always be detrimental adds to the theory of technology adoption as a whole. Additionally, it provides an important reference point for future randomized controlled studies exploring the effects of technology on adoption.

The imperative to conserve resources and minimize operational expenses has spurred a notable increase in the adoption of lean manufacturing within the context of the circular economy across diverse industries in recent years. However, a notable gap exists in the research landscape, particularly concerning the implementation of lean practices within the pharmaceutical industry to enhance circular economy performance. Addressing this void, this study endeavors to identify and prioritize the pivotal drivers influencing lean manufacturing within the pharmaceutical sector.

The outcome of this rigorous examination highlights that “Continuous Monitoring Process for Sustainable Lean Implementation,” “Management Involvement for Sustainable Implementation” and “Training and Education” emerge as the most consequential drivers. These factors are deemed crucial for augmenting circular economy performance, underscoring the significance of management engagement, training initiatives and a continuous monitoring process in fostering a closed-loop practice within the pharmaceutical industry.

The findings contribute valuable insights for decision-makers aiming to adopt lean practices within a circular economy framework. Specifically, by streamlining the process of developing a robust action plan tailored to the unique needs of the pharmaceutical sector, our study provides actionable guidance for enhancing overall sustainability in the manufacturing processes.

This study represents one of the initial efforts to systematically identify and assess the drivers to LM implementation within the pharmaceutical industry, contributing to the emerging body of knowledge in this area.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

In the continuous development of high-speed railways, ensuring the safety of the operation control system is crucial. Electromagnetic interference (EMI) faults in signaling equipment may cause transportation interruptions, delays and even threaten the safety of train operations. Exploring the impact of disturbances on signaling equipment and establishing evaluation methods for the correlation between EMI and safety is urgently needed.

This paper elaborates on the necessity and significance of studying the impact of EMI as an unavoidable and widespread risk factor in the external environment of high-speed railway operations and continuous development. The current status of research methods and achievements from the perspectives of standard systems, reliability analysis and safety assessment are examined layer by layer. Additionally, it provides prospects for innovative ideas for exploring the quantitative correlation between EMI and signaling safety.

Despite certain innovative achievements in both domestic and international standard systems and related research for ensuring and evaluating railway signaling safety, there’s a lack of quantitative and strategic research on the degradation of safety performance in signaling equipment due to EMI. A quantitative correlation between EMI and safety has yet to be established. On this basis, this paper proposes considerations for research methods pertaining to the correlation between EMI and safety.

This paper overviews a series of methods and outcomes derived from domestic and international studies regarding railway signaling safety, encompassing standard systems, reliability analysis and safety assessment. Recognizing the necessity for quantitatively describing and predicting the impact of EMI on high-speed railway signaling safety, an innovative approach using risk assessment techniques as a bridge to establish the correlation between EMI and signaling safety is proposed.