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This paper delves into evaluating the effectiveness of warning messages as a deterrent against excessive fat consumption. It examines how consumers perceive the fat content of food products when presented with two distinct label types: (1) a textual warning, providing succinct information about the fat content, and (2) a pictorial warning, offering a visual representation that immediately signifies the fat content.

Two quantitative studies were carried out. Study 1 employed a questionnaire to evaluate the efficacy of textual and pictorial warning messages on high- and low-fat food products. Similarly, Study 2 replicated this comparison while incorporating a neuromarketing instrument to gauge participants’ cerebral reactions.

Results indicate that pictorial warnings on high-fat foods significantly deter consumers’ purchasing intentions. Notably, these pictorial warnings stimulate the left prefrontal area of the cerebral cortex, inducing negative emotions in consumers and driving them away from high-fat food items.

While the influence of images over text in shaping consumer decisions is well understood in marketing, this study accentuates the underlying mechanism of such an impact through the elicitation of negative emotions. By understanding this emotional pathway, the paper presents fresh academic and managerial perspectives, underscoring the potency of pictorial warnings in guiding consumers towards healthier food choices.

1. Textual warnings do not seem to discourage high-fat product consumption.

2. A pictorial warning represents the fat content of an equivalent product.

3. Pictorial warnings decrease the intention to purchase a high-fat product.

4. Pictorial warnings determine an increase in negative emotions.

Textual warnings do not seem to discourage high-fat product consumption.

A pictorial warning represents the fat content of an equivalent product.

Pictorial warnings decrease the intention to purchase a high-fat product.

Pictorial warnings determine an increase in negative emotions.

This study investigated the anthropomorphism of the pandemic virus and its downstream effects by examining how warnings trigger viewers' risk perceptions and motivate them to pursue protection.

Three experiments were conducted. The first was a two-part (virus: anthropomorphic vs non-anthropomorphic) between-subject design that measured the participants' risk perception and compliance intention. The second experiment used a three-part (cuteness: cute vs non-cute vs control) between-subjects design. The third experiment used a three-part (cuteness: cute vs non-cute vs control) by two-part (aggressive guidance: present vs absent) between-subject design.

Anthropomorphism of the virus increased risk perception, thus influencing protective behavior and the effectiveness of warning signs, but only when the message was not perceived as cute. Aggressive messages and cute images of baby schemata enhanced compliance intention to warning guidelines.

The results provide a theoretical basis for studying the effectiveness of anthropomorphized warning signs and suggest implications for the impact of anthropomorphism on risk communication and compliance.

This study highlights that cuteness, often accompanied by anthropomorphism, may evoke inferences that reduce the effect of risk communication to induce compliance intention. Furthermore, the authors discovered that a more persuasive message appeals to mitigate the maladaptive responses to cute warnings.

This study aims to predict the financial performance of Islamic banks with sentiments of management from the textual information in annual reports.

The study uses data from 33 Islamic banks in six Islamic countries from 2006 to 2020. The authors estimate the model using the system GMM because it helps dealing with endogeneity problem, which are inherent in panel data.

The findings of the study reveal that there is a strong relationship between the sentiment expressed by management in annual reports and the current (future) financial performance of Islamic banks. The higher the positive sentiments of management, the better financial performance. In addition, the study also suggests that negative sentiments using term frequency-inverse document frequency is linked to a decrease in banks’ financial performance.

The study does not present the Islamic view on sentiment analysis in the context of Islamic scriptures due to the unavailability of a relevant dictionary.

The findings of the study suggest that developing accurate models with the help of textual information for performance prediction of Islamic banks help shareholders, regulators and policymakers avoid devastating events. Using textual information may also help reduce the information asymmetry between the management and shareholders, which may lead to more efficient bank supervision. The study can also help investors evaluate their prospective investments in the Islamic bank.

To the best of the authors’ knowledge, this study is the first of its kind that uses management sentiments for performance prediction of the Islamic banking sector. It may add a valuable contribution to the existing literature.

Using textual analysis the authors study the relationship between social media sentiments and stock markets during the COVID-19 pandemic.

The study analysis is based on a sample of 1,616,007 tweets over the period January to June 2021 for seven countries. The authors process the tweets via the VADER analyzer thereby producing both positive and negative sentiment measures.

Particularly, the authors prove that higher positivism is associated with a short-term increase in stock prices. On the other side, negativism relates inversely to stock prices with long-term impact, in the case of English-spoken countries. Notably, the study results remain robust to the inclusion of various control variables, including virtual fear and Google vaccine indexes. Finally, the authors prove that positivism is associated with higher returns and lower volatility in the short-run, while negativism is linked with lower returns in the short run.

The study analysis also has significant policy implications for researchers, investors and policymakers. First, researchers can employ our measures to quantify market sentiments and expand their research arsenal to incorporate social media trends, thus providing better explanatory power. Second, during times of severe uncertainty such as in a pandemic period, investors could beneficially take into account our textual measures and empirical results when using asset pricing models or constructing their portfolios. Third, the finding that the stock market is heavily governed by sentimental behaviors, especially during crisis periods, implies that policymakers including central banks, governments and capital market commissions must consider these sentiments before exerting their policies. In this regard, governments can effectively develop policy tools and approaches to manage recovery from the pandemic, which translates to greater long-term economic resilience. Moreover, central banks should accordingly adjust their monetary policy measures in order to stabilize financial markets, and by extension, to stop the pandemic from turning into a renewed financial crisis. For example, asset purchase program is considered the main instrument of this kind of intervention.

The authors confirm that this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere. The paper should be of interest to readers in the areas of finance.

This study investigates the relationship between the Chief Executive Officer's (CEO) overconfidence and financial reporting complexity in Iran, a context characterized by weak corporate governance and heightened managerial discretion.

The sample consists of 1,445 firm-year observations from 2010 to 2021. CEO overconfidence (CEOOC) is evaluated using an investment-based index, specifically capital expenditures. Financial reporting complexity (Complexity) is measured through textual features, particularly three readability measures (Fog, SMOG and ARI) extracted from annual financial statements. The ordinary least squares (OLS) regression is employed to test the research hypothesis.

Results suggest that CEOOC is positively related to Complexity, leading to reduced readability. Additionally, robustness analyses demonstrate that the relationship between CEOOC and Complexity is more distinct and significant for firms with lower profitability than those with higher profitability. This implies that overconfident CEOs in underperforming firms tend to increase complexity. Also, firms with better financial performance present a more positive tone in their annual financial statements, reflecting their superior performance. The findings remain robust to alternative measures of CEOOC and Complexity and are consistent after accounting for endogeneity issues using firm fixed-effects, propensity score matching (PSM), entropy balancing approach and instrumental variables method.

This study adds to the literature by delving into the effect of CEOs' overconfidence on financial reporting complexity, a facet not thoroughly investigated in prior studies. The paper pioneers the use of textual analysis techniques on Persian texts, marking a unique approach in financial reporting and a first for the Persian language. However, due to the inherent challenges of text mining and feature extraction, the results should be approached with caution.

The insights from this study can guide investors in understanding the potential repercussions of CEOOC on financial reporting complexity. This will assist them in making informed investment decisions and monitoring the financial reporting practices of their invested companies. Policymakers and regulators can also reference this research when formulating policies to enhance financial reporting quality and ensure capital market transparency. The innovative application of textual analysis in this study might spur further research in other languages and contexts.

This research stands as the inaugural study to explore the relationship between CEOs' overconfidence and financial reporting complexity in both developed and developing capital markets. It thereby broadens the extant literature to include diverse capital market environments.

This paper aims to develop a model that supports public organisations in making informed strategic decisions as to which public services are most suitable to be improved through co-creation. Thus, it first identifies the features that make public services (un)suitable for co-creation and then applies this knowledge to develop a multi-criteria decision support model for the assessment of their co-creation readiness.

The decision support model is the result of design science research. While its structure is determined by a qualitative multi-criteria decision analysis, its substance builds on a content analysis of Web of Science papers and over a dozen empirical case studies.

The model is comprised of 13 criteria clustered into two groups: service readiness criteria from the perspective of service users and service readiness criteria from the perspective of a public organisation.

The model attributes rely on a limited number of empirical cases and references from the literature review. The model was tested by only one public organisation on four of its services.

The paper shifts the research focus from organisational properties and capacity, as the key co-creation drivers and barriers, to features of public services as additional factors that affect the prospect of co-creation. Thus, it makes a pioneering step towards the conceptualisation of the idea of “service readiness for co-creation” and the development of a practical instrument that supports co-creation in the public sector.

This paper aims to explore whether the key drivers identified in digitalization policies are being prioritized by practitioners in health and social care and to what degree the goals of the policies are being enacted.

The investigation comprised two stages. First, the key drivers of digitalization in the national policies were identified. Second, a survey was disseminated to practitioners within health and social care, asking them to indicate their stance on each key driver (using Likert scales).

The findings of this paper are twofold. First, they demonstrate that practitioners more readily enact the key drivers centered around their everyday operations, such as improving services and care and increasing efficiency. Second, it shows that key drivers of a more rhetorical nature, such as “becoming the best,” do not yield benefits for practitioners.

This paper shows that for policies to have an effect in practice and to contribute to change, they should be rooted in key drivers centered around practitioners’ everyday operations, promoting specificity over abstraction.

While previous studies have involved policy analysis, few studies investigate the enactment of policies, how they are implemented and whether they contribute to changes in practice.

The primary objective of this research is to provide a precise interpretation of the constructed machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype index was constructed as well as several approaches of XAI were employed to interpret Black Box models and assess the magnitude and direction of the impact of the features used.

The research paper involved the construction of a sentiment index termed the NFT hype index, which aims to measure the influence of market actors within the NFT industry. This index was created by analyzing written content posted by 62 high-profile individuals and opinion leaders on the social media platform Twitter. The authors collected posts from the Twitter accounts that were afterward classified by tonality with a help of natural language processing model VADER. Then the machine learning methods and XAI approaches (feature importance, permutation importance and SHAP) were applied to explain the obtained results.

The built index was subjected to rigorous analysis using the gradient boosting regressor model and explainable AI techniques, which confirmed its significant explanatory power. Remarkably, the NFT hype index exhibited a higher degree of predictive accuracy compared to the well-known sentiment indices.

The NFT hype index, constructed from Twitter textual data, functions as an innovative, sentiment-based indicator for investment decision-making in the NFT market. It offers investors unique insights into the market sentiment that can be used alongside conventional financial analysis techniques to enhance risk management, portfolio optimization and overall investment outcomes within the rapidly evolving NFT ecosystem. Thus, the index plays a crucial role in facilitating well-informed, data-driven investment decisions and ensuring a competitive edge in the digital assets market.

The authors developed a novel index of investor interest for NFT assets (NFT hype index) based on text messages posted by market influencers and compared it to conventional sentiment indices in terms of their explanatory power. With the application of explainable AI, it was shown that sentiment indices may perform as significant predictors for NFT sales and that the NFT hype index works best among all sentiment indices considered.

The purpose of this paper is to construct an index for systemic risk in China.

This paper develops a systemic risk index for China (SRIC) using textual information from 26 leading newspapers in China. Our index measures the systematic risk from 21 topics relating to China’s economy and provides narratives of the sources of systemic risk.

SRIC effectively predicts changes in GDP, aggregate financing to the real economy and the purchasing managers’ index. Moreover, SRIC explains several other commonly used macroeconomic indicators. Our risk measure provides a helpful monitoring tool for policymakers to manage systemic risk.

The paper construct an index of systemic risk based on the information extracted from newspaper articles. This approach is new to the literature.

The purpose of this study is to develop an intelligent tutoring system (ITS) for programming learning based on information tutoring feedback (ITF) to provide real-time guidance and feedback to self-directed learners during programming problem-solving and to improve learners’ computational thinking.

By analyzing the mechanism of action of ITF on the development of computational thinking, an ITF strategy and corresponding ITS acting on the whole process of programming problem-solving were developed to realize the evaluation of programming problem-solving ideas based on program logic. On the one hand, a lexical and syntactic analysis of the programming problem solutions input by the learners is performed and presented with a tree-like structure. On the other hand, by comparing multiple algorithms, it is implemented to compare the programming problem solutions entered by the learners with the answers and analyze the gaps to give them back to the learners to promote the improvement of their computational thinking.

This study clarifies the mechanism of the role of ITF-based ITS in the computational thinking development process. Results indicated that the ITS designed in this study is effective in promoting students’ computational thinking, especially for low-level learners. It also helped to improve students’ learning motivation, and reducing cognitive load, while there’s no significant difference among learners of different levels.

This study developed an ITS based on ITF to address the problem of learners’ difficulty in obtaining real-time guidance in the current programming problem-solving-based computational thinking development, providing a good aid for college students’ independent programming learning.