Search results

The purpose of our study is to study the impact of chief executive officer (CEO) optimism and narcissism on the company's capital structure.

(1) The authors opt for regression, machine learning and text analysis to explore the impact of narcissism and optimism on the capital structure. (2) We analyze CEO interviews and employ three methods to evaluate narcissism: the dictionary proposed by Anglin, which enabled us to assess the following components: authority, superiority, vanity and exhibitionism; count of first-person singular and plural pronouns and count of CEO photos displayed. Following this approach, we were able to make a more thorough assessment of corporate narcissism. (3) Latent Dirichlet allocation (LDA) technique helped to find the differences in the corporate rhetoric of narcissistic and non-narcissistic CEOs and to find differences between the topics of interviews and letters provided by narcissistic and non-narcissistic CEOs.

Our research demonstrates that narcissism has a slight and nonlinear impact on capital structure. However, our findings suggest that there is an impact of pessimism and uncertainty under pandemic conditions when managers predicted doom and completely changed their strategies. We applied various approaches to estimate the gender distribution of CEOs and found that the median values of optimism and narcissism do not depend on sex. Using LDA, we examined the content and key topics of CEO interviews, defined as positive and negative. There are some differences in the topics: narcissistic CEOs are more likely to speak about long-term goals, projects and problems; they often talk about their brand and business processes.

First, we examine the COVID-19 pandemic period and evaluate how CEO optimism and pessimism affect their financial decisions under specific external conditions. The pandemic forced companies to shift the way they worked: either to switch to the remote work model or to interrupt operations; to lose or, on the contrary, attract clients. In addition, during this period, corporate management can have a different outlook on their company’s financial performance and goals. The LDA technique helped to find the differences in the corporate rhetoric of narcissistic and non-narcissistic CEOs. Second, we use three methods to evaluate narcissism. Third, the research is based on a set of advanced methods: machine learning techniques (random forest to reveal a nonlinear impact of CEO optimism and narcissism on capital structure).

This study aims to investigate two newly developed (3 + 1)-dimensional Kairat-II and Kairat-X equations that illustrate relations with the differential geometry of curves and equivalence aspects.

The Painlevé analysis confirms the complete integrability of both Kairat-II and Kairat-X equations.

This study explores multiple soliton solutions for the two examined models. Moreover, the author showed that only Kairat-X give lump solutions and breather wave solutions.

The Hirota’s bilinear algorithm is used to furnish a variety of solitonic solutions with useful physical structures.

This study also furnishes a variety of numerous periodic solutions, kink solutions and singular solutions for Kairat-II equation. In addition, lump solutions and breather wave solutions were achieved from Kairat-X model.

The work formally furnishes algorithms for studying newly constructed systems that examine plasma physics, optical communications, oceans and seas and the differential geometry of curves, among others.

This paper presents an original work that presents two newly developed Painlev\'{e} integrable models with insightful findings.

This research paper aims to optimize the calibration process for an ABB IRB 120 robot, specifically for robotic orbital milling applications, by introducing and validating the use of the observability index and telescopic ballbar for accuracy enhancement.

The study uses the telescopic ballbar and an observability index for the calibration of an ABB IRB 120 robot, focusing on robotic orbital milling. Comparative simulation analysis selects the O₃ index. Experimental tests, both static and dynamic, evaluate the proposed calibration approach within the robot’s workspace.

The proposed calibration approach significantly reduces circularity errors, particularly in robotic orbital milling, showcasing effectiveness in both static and dynamic modes at various tool center point speeds.

The study focuses on a specific robot model and application (robotic orbital milling), limiting generalizability. Further research could explore diverse robot models and applications.

The findings offer practical benefits by enhancing the accuracy of robotic systems, particularly in precision tasks like orbital milling, providing a valuable calibration method.

While primarily technological, improved robotic precision can have social implications, potentially influencing fields where robotic applications are crucial, such as manufacturing and automation.

This study’s distinctiveness lies in advancing the accuracy and precision of industrial robots during circular motions, specifically tailored for orbital milling applications. The innovative approach synergistically uses the observability index and telescopic ballbar to achieve these objectives.

Today, individuals use social media to express their opinions and feelings, which offers a living laboratory to researchers in various fields, such as management, innovation, technology development, environment and marketing. It is therefore necessary to understand how the language used in user-generated content and the emotions conveyed by the content affect responses from other social media users.

In this study, almost 700,000 posts from Twitter (as well as Facebook, Instagram and forums in the appendix) are used to test a conceptual model grounded in signaling theory to explain how the language of user-generated content on social media influences how other users respond to that communication.

Extending developments in linguistics, this study shows that users react negatively to content that uses self-inclusive language. This study also shows how emotional content characteristics moderate this relationship. The additional information provided indicates that while most of the findings are replicated, some results differ across social media platforms, which deserves users' attention.

This article extends research on Internet behavior and social media use by providing insights into how the relationship between self-inclusive language and emotions affects user responses to user-generated content. Furthermore, this study provides actionable guidance for researchers interested in capturing phenomena through the social media landscape.

This paper aims to consider the potential implications of the layering of regulation in relation to hydraulic fracturing (fracking) at the borders between the nations of the UK.

This paper uses a qualitative research method grounded in particular in legal geography to examine the existing approaches to regulating hydraulic fracturing and identify the places and their features that are constructed as a result of their intersection at the borders of the nations comprising the UK.

The current regulatory framework concerning hydraulic fracturing risks restricts the places in which the practice can occur in such a manner as to potentially cause greater environmental harm should the process be used. The regulations governing the process are not aligned in relation to the surface and subsurface aspects of the process to enable their management, once operational, as a singularly constructed place of extraction. Strong regulation at the surface can have the effect of influencing placement of the site only in relation to the place at which the resource sought reaches the surface, whilst having little to no impact on the environmental harms, which will result at the subsurface or relative to other potential surface site positions, and potentially even increasing them.

This paper is limited by uncertainty as to the future use of hydraulic fracturing to extract oil and gas within the UK. The issues raised within it would also be applicable to other extractive industries where a surface site might be placed within a radius of the subsurface point of extraction, rather than having to be located at a fixed point relative to that in the subsurface. This paper therefore raises concerns that might be explored more generally in relation to the regulation of the place of resource extraction, particularly at legal borders between jurisdictions, and the impact of regulation, which does not account for the misalignment of regulation of spaces above and below the surface that form a single place at which extraction occurs.

This paper considers the potential impacts of misaligned positions held by nations in the UK in relation to environmentally harmful practices undertaken by extractive industries, which are highlighted by an analysis of the extant regulatory framework for hydraulic fracturing.

Whilst the potential for cross internal border extraction of gas within the UK via hydraulic fracturing and the regulatory consequences of this has been highlighted in academic literature, this paper examines the implications of regulation for the least environmentally harmful placement of the process.

Because the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the stock price index from a long-memory perspective. The authors propose hybrid models to predict the next-day closing price index and explore the policy effects behind stock prices. The paper aims to discuss the aforementioned ideas.

The authors found a long memory in the stock price index series using modified R/S and GPH tests, and propose an improved bi-directional gated recurrent units (BiGRU) hybrid network framework to predict the next-day stock price index. The proposed framework integrates (1) A de-noising module—Singular Spectrum Analysis (SSA) algorithm, (2) a predictive module—BiGRU model, and (3) an optimization module—Grid Search Cross-validation (GSCV) algorithm.

Three critical findings are long memory, fit effectiveness and model optimization. There is long memory (predictability) in the stock price index series. The proposed framework yields predictions of optimum fit. Data de-noising and parameter optimization can improve the model fit.

The empirical data are obtained from the financial data of listed companies in the Wind Financial Terminal. The model can accurately predict stock price index series, guide investors to make reasonable investment decisions, and provide a basis for establishing individual industry stock investment strategies.

If the index series in the stock market exhibits long-memory characteristics, the policy implication is that fractal markets, even in the nonlinear case, allow for a corresponding distribution pattern in the value of portfolio assets. The risk of stock price volatility in various sectors has expanded due to the effects of the COVID-19 pandemic and the R-U conflict on the stock market. Predicting future trends by forecasting stock prices is critical for minimizing financial risk. The ability to mitigate the epidemic’s impact and stop losses promptly is relevant to market regulators, companies and other relevant stakeholders.

Although long memory exists, the stock price index series can be predicted. However, price fluctuations are unstable and chaotic, and traditional mathematical and statistical methods cannot provide precise predictions. The network framework proposed in this paper has robust horizontal connections between units, strong memory capability and stronger generalization ability than traditional network structures. The authors demonstrate significant performance improvements of SSA-BiGRU-GSCV over comparison models on Chinese stocks.

The paper aims to elaborate on the potential for regeneration of Bagamoyo (Tanzania) through adaptive reuse of its heritage sites. The town was the most important harbour for ivory and slaves of the East-African mainland during the 19th and early 20th century and the colonial capital of German East-Africa between 1885 and 1890. Today, it has 85,000 inhabitants who mainly live in informal settlements while stone town closer to the coast is largely abandoned with its historical buildings in a poor state of conservation.

The first part of the paper describes the history and heritage of the old stone town Bagamoyo, and how it impacts its identity. Additionally, it summarises the critical reception of the town's role in the application to UNESCO World Heritage for “The Central Slave and Ivory Trade Route”. This, in order to consider the reuse of its heritage sites more as part of a layered regeneration process than of a singular narrative for preservation. The second part presents research-by-design proposals investigating the economic, social and cultural potentialities of three spatial layers: the main street, the coastal strip and the shoreline.

The identity and therefore also urban regeneration of post-colonial towns such as Bagamoyo is the result of a complex combination of different narratives rather than of a singular one.

Bagamoyo's heritage has been studied as a driver for international tourism linked to slavery but without successful implementation. This study proposes an alternative perspective by investigating its potential for urban regeneration in line with local needs. Developed in the context of a master studio of architectural design, it presents an innovative didactic approach. Moreover, the methodology of research-by-design can be inspirational for other historical towns.

This study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics.

This study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics.

Through the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups.

This study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives.

This study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces.

This study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

The purpose of the study is to perform a scientific mapping and detect the evolution pattern of two emerging fields, organizational capabilities and sustainable supply chain management (SSCM), to detect and visualize the existing conceptual domains and identify less-explored areas.

This study uses a methodological combination involving systematic literature review and bibliometric analysis. The methodology was implemented in the following order: definition and selection of the material using an electronic database, descriptive analysis of the material, category selection using bibliographic coupling analysis by VOSviewer (clusterization), material evaluation and content analysis.

The research results clarify the intellectual structure within the academic field. The authors’ identified three main clusters: (1) sustainable capabilities and practices in supply chain management (SCM), (2) green SCM and performance and (3) information technology and innovation. The findings reveal that there is a rich field to be explored, especially regarding issues involving sustainable technological capabilities, sustainable initiatives and key resource development.

This study facilitates researchers’ and practitioners’ understanding and their ability to map the different paths and evolution of SSCM and organizational capabilities. It can encourage managers and policymakers alike to conceive new approaches to engage in the adoption of SSCM.

This work employs a singular approach to identify the intellectual knowledge and topics related to the implementation of SSCM by adopting the theoretical approach of sustainable organizational capacity. It contributes to the debate on distinguishing specific sustainable organizational capabilities from traditional capabilities.