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This study aims to investigate the moderating effect of external financing needs on the relationship between the disclosure level of integrated reporting (IR) and firm value using evidence from Egypt.

This study uses a panel regression analysis for a matched sample of 50 companies listed on the Egyptian Stock Exchange (EGX), specifically from EGX100. The sample covers four years (2017–2020). The current study uses content analysis to measure IR and Tobin’s Q as a proxy for firm value.

The findings reveal a significant positive relationship between the disclosure level of IR and firm value. In addition, the authors find that external financing needs moderate the relationship between IR and firm value. It is concluded that the higher the disclosure level of IR content, the higher the firm’s value, and that this relationship strengthens in firms with high needs for external financing.

Several practical implications can be derived from the results of the current study. Policymakers and regulators can impose mandatory requirements for IR in Egypt. It also opens new insights for board members, managers, analysts and auditors in forming financing decisions based on annual reports.

The present study has a novel insight from a developing country and significant contributions to the extant literature. The study provides empirical evidence from an emerging economy and an insight into how external financing can be used for firms with different levels of IR. It also provides a comprehensive disclosure index to estimate the level of IR.

The authors propose a dairy bundle, integrating strategies to jointly maximise producer revenue and consumer utility according to the latter's preferences.

An algorithm based on a nested logit model identifies the bundle maximising producer revenue based on factors affecting consumer purchase behaviour. The data are drawn from a mall-intercept survey administered in Iran, with consumers stating a hypothetical choice among a comprehensive set of dairy products.

Demographic characteristics and marketing mix elements significantly affect consumers' preferences. An algorithm based on the estimated dissimilarity parameter determines the best bundle of dairy products, simultaneously obtaining the highest utility and the highest expected revenue.

Consumer preference and maximum producer or retail seller income are considered simultaneously. The bundling promotion strategy is widely used for food offerings and fresh foods and can be extended to other products.

我們擬根據消費者偏好，提出一個整合了多個策略的捆綁包，以使生產製作者得到最高的收入和最佳的消費者效用。

研究人員根據巢式Logit 模型的演算法確認了一個捆綁包，以使生產製作者能得到最高的收入，而這均建基於會影響消費者購買行為的各個因素。有關的數據取自於伊朗的商場內進行的攔截調查，而回應的消費者須假想他們從一整套乳製品中選擇他們會購買的產品。

研究結果顯示，人口特徵和市場營銷組合元素均會顯著地影響消費者的偏好，一個基於估算的相異性參數而建立的演算法可確認最佳的乳製品捆綁包，這演算法同時也可取得最佳的裨益和最高的預期收入。

於本研究中，研究人員同時考慮消費者的偏好和生產製作者或零售賣家的最高收入。捆綁式的促銷策略在食物供品和新鮮食品方面被廣泛使用，這策略可擴展至其他產品。

乳製品捆綁包、消費者偏好、最佳化演算法、巢式Logit 模型.

Limited by the types of sensors, the state information available for musculoskeletal robots with highly redundant, nonlinear muscles is often incomplete, which makes the control face a bottleneck problem. The aim of this paper is to design a method to improve the motion performance of musculoskeletal robots in partially observable scenarios, and to leverage the ontology knowledge to enhance the algorithm’s adaptability to musculoskeletal robots that have undergone changes.

A memory and attention-based reinforcement learning method is proposed for musculoskeletal robots with prior knowledge of muscle synergies. First, to deal with partially observed states available to musculoskeletal robots, a memory and attention-based network architecture is proposed for inferring more sufficient and intrinsic states. Second, inspired by muscle synergy hypothesis in neuroscience, prior knowledge of a musculoskeletal robot’s muscle synergies is embedded in network structure and reward shaping.

Based on systematic validation, it is found that the proposed method demonstrates superiority over the traditional twin delayed deep deterministic policy gradients (TD3) algorithm. A musculoskeletal robot with highly redundant, nonlinear muscles is adopted to implement goal-directed tasks. In the case of 21-dimensional states, the learning efficiency and accuracy are significantly improved compared with the traditional TD3 algorithm; in the case of 13-dimensional states without velocities and information from the end effector, the traditional TD3 is unable to complete the reaching tasks, while the proposed method breaks through this bottleneck problem.

In this paper, a novel memory and attention-based reinforcement learning method with prior knowledge of muscle synergies is proposed for musculoskeletal robots to deal with partially observable scenarios. Compared with the existing methods, the proposed method effectively improves the performance. Furthermore, this paper promotes the fusion of neuroscience and robotics.

This paper aims to conduct a systematic review of the literature on the quality of integrated reports (IR) and highlight the gaps in the existing research to provide directions and suggestions for future research.

This study was conducted through a systematic literature review using content analysis based on 40 papers from the Scopus, Web of Science and EBSCOhost databases on IR quality. While reading the full-text papers, the authors found six additional papers referenced by the literature being reviewed that were relevant to IR quality. Thus, there were 46 papers in the final review. The analysis begins with the definition and dimension of IR quality and theoretical lenses. Furthermore, this study outlines constructs or variables used in the previous literature.

The authors found that most studies used the quantitative method (41 papers or 89%). Five papers in the literature used qualitative methods (11%). Most researchers (34 papers or 72%) defined IR quality as consistent with the International Integrated Reporting Council framework, specifically the eight content elements. In particular, with the constructs that make up the quality of the IR, variations between researchers were found. Furthermore, there were some gaps that could be the directions for future research.

The literature that provides academic knowledge about IR quality is still limited, and research on IR is still growing. The literature review conducted by this study can provide an overview of the current research positions on the quality of IR and directions for future research in this area.

This study intends to show corporate executives a framework demonstrating the quality of corporate reporting. It can impact not only investors as a specific stakeholder group but also other stakeholder groups.

To the best of the authors’ knowledge, this study is the first literature review to examine the quality of IR, thus providing a map of current research to suggest directions for future research. Most of the previous literature reviews have been focused on integrated reporting (IR) in general and not quality.

With the rise in wine consumption, accurate wine price forecasts have significantly impacted restaurant and hotel purchasing decisions and inventory management. This study aims to develop a precise and effective wine price point and interval forecasting model.

The proposed forecast model uses an improved hybrid kernel extreme learning machine with an attention mechanism and a multi-objective swarm intelligent optimization algorithm to produce more accurate price estimates. To the best of the authors’ knowledge, this is the first attempt at applying artificial intelligence techniques to improve wine price prediction. Additionally, an effective method for predicting price intervals was constructed by leveraging the characteristics of the error distribution. This approach facilitates quantifying the uncertainty of wine price fluctuations, thus rendering decision-making by relevant practitioners more reliable and controllable.

The empirical findings indicated that the proposed forecast model provides accurate wine price predictions and reliable uncertainty analysis results. Compared with the benchmark models, the proposed model exhibited superiority in both one-step- and multi-step-ahead forecasts. Meanwhile, the model provides new evidence from artificial intelligence to explain wine prices and understand their driving factors.

This study is a pioneering attempt to evaluate the applicability and effectiveness of advanced artificial intelligence techniques in wine price forecasts. The proposed forecast model not only provides useful options for wine price forecasting but also introduces an innovative addition to existing forecasting research methods and literature.

This article investigates the impact of the COVID-19 pandemic on the consumer preference for the attributes of online food delivery (OFD) services in India. It also shows how the order size influences the consumer's willingness to pay (WTP) for the attributes of OFD services.

This work incorporates a conjoint analysis-based approach to determine the consumer preference for the attributes of OFDs such as price, delivery time, restaurant rating and packing quality during the COVID-19 pandemic. The fractional factorial design is applied for the data collection. The relative importance of the attributes and the part-worth utility of the attributes' levels have been determined. Further, the utility associated with the attributes' levels is used to find the consumer's WTP for different attributes.

The COVID-19 pandemic has changed consumer preference from price to food and packing quality in India. When the order is small, consumers exhibit a higher preference to the delivery time than packing quality. In contrast, consumers show a higher preference to packing quality than delivery time with the increase in order size. The consumer's WTP attains the highest level in case of food quality, followed by convenience and packing quality. The WTP for the attributes rises with the increase in order size.

The insights highlight the need for the online food delivery industry to redesign the business framework in the post-pandemic era. The hygiene and safety measures maintained by the consumers during the pandemic have significantly changed their purchasing behaviour, raising their preference for service quality (food and packing quality) of the OFD services apart from price.

This work determines the consumers' utility for each attribute level of OFDs, along with their relative importance. Moreover, this study contributes to the existing literature by exhibiting the impact of the COVID-19 pandemic on the consumer preference and order size on consumer's WTP for the attributes.

This study aims to examine the determinants and consequences of integrated reporting (IR) disclosures of listed non-financial firms in an emerging economy.

This study uses data from 39 listed non-financial firms that had adopted IR disclosure framework in Sri Lanka for the period from 2011 to 2018. Firm size, growth opportunity, profitability and firm age are considered significant determinants of IR disclosure, while their consequences are measured in terms of share price, Tobin’s Q, return on assets and return on equity. The authors used the results of the correlation and panel regression analyses to draw this study’s conclusions.

This study finds that firm size and age are the significant determinants of IR disclosure, which is consistent with this study’s expectations. Considering the consequences of IR disclosure, only share price and Tobin’s Q show significant results as per the panel regression analyses.

The findings of this study would be useful in the decision-making processes of existing and prospective investors, regulators, policymakers and society at large. Further, the findings of this study communicate the benefits of this new reporting paradigm in shaping their disclosures in the annual corporate reporting process.

Although existing studies attempted to examine the determinants of IR disclosure and its consequences as isolated studies, this study provides new insights by merging these two aspects into a single study and consider several determinants and consequences as well.

The effect of playfulness climate on employees in firms has been the subject of an increasing number of studies in recent years. Given the growing number of businesses that have incorporated playfulness into their operations, it is possible to enhance the task performance and innovative performance of the younger generation of workers by rationally managing playfulness, particularly when it comes to that aspect of the workplace. Based on the conservation of resources theory, this study aims to investigate how the playfulness climate in organizations influences the change self-efficacy of the millennial workers and how to enhance their task performance and innovation performance.

The authors used a quantitative approach to test the relationship between the hypotheses. The survey population for this study consisted of the millennial workers in the computer sector who are involved in research and development in China. Hierarchical regression analysis was used to test the built mediation model empirically over the course of the study's three rounds of data collection, each separated by one month. Through the collection of paired questions for leadership and their subordinates, 424 valid questionnaires were obtained.

The examination of the questionnaire results supports the study's theoretical hypothesis, which states that when millennial workers sense a more playfulness work environment, it will encourage them to develop a sense of change self-efficacy. Additionally, they will be better able to handle work-related responsibilities and come up with innovative ideas as a result of change self-efficacy, which would eventually enhance the task performance and innovation performance of millennial employees.

By introducing the mediation of change self-efficacy, this study expands on the application of the conservation of resources theory. The research on the performance of millennial employees is complemented and enhanced by investigating the relationship between the playfulness climate and employees' task performance and innovation performance from the perspective of their sense of change self-efficacy. This study also reveals that managers should foster a positive and playfulness environment in their workplaces in order to manage the performance of millennial employees.

The purpose of this study is to examine the interplay between integrated reporting quality (IRQ) and capital markets. More specifically, the authors test the impact of IRQ on stock liquidity, cost of capital and analyst forecast accuracy.

The sample consists of listed firms on the Johannesburg Stock Exchange in South Africa, covering the period from 2012 to 2020. The IRQ measure used in this study is based on data from Ernst and Young. To test the proposed hypotheses, the authors conducted a generalized least squares regression analysis.

The empirical results evince a positive relationship between IRQ and stock liquidity. However, the authors did not find a significant effect of IRQ on the cost of capital and financial analysts’ forecast accuracy. In robustness tests, it was shown that firms with a higher IRQ score exhibit higher liquidity and improved analyst forecast accuracy. Additional analysis indicates a negative association between IRQ and the cost of capital, as well as a positive association between IRQ and financial analyst forecast accuracy for firms with higher IRQ scores (TOP ten, Excellent, Good).

The study stands as one of the initial endeavors to investigate the impact of IRQ on the capital market. It provides valuable insights for managers and policymakers who are interested in enhancing disclosure practices within the financial market. Furthermore, these findings are significant for investors as they make informed investment decisions.

The purpose of the study is to investigate the impact of implementing anti-dumping duties on imported Australian wine to China in the short- and long-run, respectively.

First, the Difference-in-Differences (DID) method is used in this study to evaluate the short-run causal effect of implementing anti-dumping duties on imported Australian wine to China. Second, a Bayesian ensemble method is used to predict 2023–2025 wine exports from Australia to China. The disparity between the forecasts and counterfactual prediction which assumes no anti-dumping duties represents the accumulated impact of the anti-dumping duties in the long run.

The anti-dumping duties resulted in a significant decline in red and rose, white and sparkling wine exports to China by 92.59%, 99.06% and 90.06%, respectively, in 2021. In the long run, wine exports to China are projected to continue this downward trend, with an average annual growth rate of −21.92%, −38.90% and −9.54% for the three types of wine, respectively. In contrast, the counterfactual prediction indicates an increase of 3.20%, 20.37% and 4.55% for the respective categories. Consequently, the policy intervention is expected to result in a decrease of 96.11%, 93.15% and 84.11% in red and rose, white and sparkling wine exports to China from 2021 to 2025.

The originality of this study lies in the creation of an economic paradigm for assessing policy impacts within the realm of wine economics. Methodologically, it also represents the pioneering application of the DID and Bayesian ensemble forecasting methods within the field of wine economics.