Search results

This chapter presents the principles of knowledge representation according to the MASK methodology. The authors present knowledge and the structure of knowledge as a representation. The semiotic triangle, as well as the systematic triangle, of knowledge is discussed. The MASK approach to representing knowledge is introduced, including knowledge as representation and knowledge as meaning.

Introduction: In this study, approaches based on right-tail unit root tests are used to analyze high-frequency time series. Although these approaches successfully capture unusually extreme price movements (bubbles) in financial markets, they can be biased in policymaking and forecasting. Testing the parameter stability enables the detection of both unusual price behavior and possible change points within the framework of the volatility approach. The break dates that cause the parameter change on the return series can be obtained, and the differentiation in the period can be seen.

Purpose: In this study, the analysis of periods that differ from the “changing parameter values” of the volatility process that emerged after November 2018 in the Borsa Istanbul (BIST) is made by using a new econometric approach in terms of change dates, parameter stability, and explosiveness characteristics. In this way, starting from determining periods with stable parameter values, the volatility process is tested to decide whether or not it shows an explosive feature.

Methodology: This study’s mainstay was published in February 2023. The findings reached within the framework of the knowledge provided by the technique in question will be the first in the applied literature. We used a uniform test for a mildly explosive GARCH process with double supreme statistics for BIST.

Findings: BIST is significantly affected by social and political events. This result implies that the “semi-efficient” market hypothesis for BIST needs to be re-examined in this context.

Toward sustainable development, radical green innovation (RGI) is necessary. Despite extensive research on the factors influencing green innovation, few studies have been conducted on the precursors. Based on upper echelons (UE) theory, dynamic capability (DC) theory, “stimulus-organism-response” (SOR) theory, social information processing (SIP) theory and cognitive appraisal (CA) theory of emotion, the study explores how digital leadership (DL) affects RGI and investigates the mediating effects of green organizational identity (GOI) and the moderating effects of digital threat (DT) and technology for social good (TSG), as well as the multiple concurrent causalities that trigger high RGI.

The method of combining structural equation model (SEM) and fuzzy-set qualitative comparative analysis (fs QCA) is adopted in the study. Data from 233 questionnaires were collected at two different time points.

This study's findings indicate that the four dimensions of DL can positively influence RGI and GOI partially mediates between the four dimensions of DL and RGI. DT has a negative moderating effect between DL and GOI, while TSG is positively regulated between them, DT and TSG linkage moderates the partial mediating effect of GOI in DL and RGI. Further, fs QCA is used to analyze the causal complexity of DL dimensions and GOI to RGI and nine effective configuration paths are identified. It is found that the synergy of digital thinking ability (DTA), digital detection ability (DDA), digital social ability (DSA), digital reserve ability (DRA) and GOI is crucial to high RGI. Among them, GOI core appears the most times, indicating that GOI plays a vital role in improving enterprise RGI.

This study expands the literature on leadership and innovation by constructing a framework of “DL-GOI-RGI” and exploring the transmission of GOI and the boundary effect of DT and TSG. The study used fs QCA and SEM to better understand the statistical associations and the set relations between the conjunctions and conditions.

This study explores the intricate relationship between social media communication, consumer attitude and purchase intention within the context of lifestyle category products. With the rapid proliferation of social media platforms, businesses have turned to these platforms to connect with consumers and influence their purchasing decisions. This study aims to provide an in-depth analysis of how social media communication strategies impact consumer attitudes and, in turn, influence purchase intentions.

The study employs partial least squares structural equation modeling (PLS-SEM) to analyze the data collected from a sample of consumers.

The results of this study present that lack of visibility (LV), low-efficiency levels (LEL) and unpredictable elements (UE) are ranked as the top three major risk hurdles whereas real-time information on a package’s location (LV1), putting a GPS tracking system to track last-mile journey (OT3) and users wants on time location of their package (LV2) are ranked as top three most significant criteria affecting the practices of modern last-mile logistics in e-commerce businesses.

The results of this study contribute to our understanding of how social media influences consumer behavior in the lifestyle product sector, shedding light on the underlying mechanisms that drive consumer purchasing decisions.

By constructing and testing experimentally a research model that reveals a thorough analysis of pertinent literature and identifies multiple important elements influencing consumer behavior in the lifestyle category, this paper adds to the body of knowledge on marketing. Practical ramifications for lifestyle firms are examined, along with suggestions for improving their social media tactics, in light of the findings.

The construction of homes to meet the primary needs of society results in a surge of harmful gas emissions into the ecosystem. Globally, the construction sector alone contributes a staggering 39% of gas emissions, underscoring the urgent need for green innovation to mitigate their adverse impacts. The concept of green living emerges as a strategy to address this challenge. Consequently, this research aims to delve into the influence of green brand positioning and perceived financial risk on the purchase intention of green residential properties, with attitude toward environment as a mediating variable.

This study adopts a quantitative associative approach, employing a purposive sampling technique to target consumers aged at least 21 years old who live in Indonesia. Data are collected through online questionnaires distributed via Google Forms, garnering responses from 269 participants. Data are processed using PLS-SEM with the SmartPLS 3.0 program for hypothesis testing.

The research findings reveal that both functional and emotional positioning have a significant impact on attitudes toward environmental concerns. However, green positioning does not exhibit such influence on these attitudes. Moreover, attitude toward environmental issues significantly affects purchase intentions, although perceived financial risk does not. This study also intimates that functional and emotional positioning significantly influence purchase intention, with attitude toward environment as a mediating variable.

Consumer priorities lie in the functional and emotional attributes of the green homes they purchase, while environmental consciousness needs to be heightened. However, investing in green living often entails bearing higher costs, thus creating financial constraints on consumers. This condition also calls for ongoing education and advocacy initiatives to bolster consumer awareness of green living practices, fostering consumer environmental awareness and active participation in efforts to reduce greenhouse gas emissions.

Research on green living remains limited in Indonesia due to insufficient understanding and low environmental awareness. It is imperative to address this issue to foster a deeper understanding of green concepts and promote environmentally conscious behavior on a wider scale.

This study aims to investigate issues of natural resource management (NRM), conservation and ecotourism leadership on rural livelihood in Musina Local Municipality, Limpopo Province, South Africa. It concentrated on the existing and probable effects of ecotourism and integrated rural activities and their environmental influences on climate change and vice versa, subsistence and sustainability. The study utilizes a broad literature review, focus group discussions and field observations to investigate the municipality’s biodiversity, holistic livelihoods and tourism resource and facility concerns with climate change implications in the study area, with data analyzed manually and also by means of cross tabulations and central tendencies. A harmful link between ecotourism-induced environmental impacts and integrated rural activities that retain potential climate change consequences is found to exist in the vicinity. Nevertheless, it is fathomed in line with the study findings that when tourism actions adhere to the justifications of sustainable and responsible tourism management, they can result in better ecotourism products that foster environmental sustainability and enhance rural livelihoods. Therefore, the study has identified a need for decent ecotourism facilities and activities and a managerial approach to diminish climate change hazards and spur subsistence for local communities in the municipality. Within its rationale and ability, the study presents an analysis of conservation, ecotourism management, environmental degradation and responsible tourism qualities regarding climate change consequences within the municipality.

This study aims to identify the principal changes that digitalization has brought about in the internal audit functions (IAFs), focusing on the main characteristics that have been affected by this phenomenon.

The study follows a qualitative approach, involving the thematic analysis of semistructured interviews. A total of 22 professionals working in Italian-listed companies’ IAFs were interviewed. The framework of institutional theory was used to analyze the results.

The elements most affected by the digitalization of IAFs in Italy are: the skills required in IAFs, the relationship with external consultants and the types of activities performed by internal auditors (IArs). Nevertheless, the extent of the level digitalization within the IAFs of the companies analyzed appears to vary in accordance with the level of institutional pressures experienced by the organizations.

The study contributes to the literature on IA and digitalization by delineating the principal elements that are influenced by the digitalization process of IA. Moreover, it highlights how these elements develop and evolve as the level of digitalization of the IAF changes.

Internet gaming disorder (IGD) has become a crucial concern due to its rising prevalence and potential mental health impacts, which can lead to significant psychological, social and functional impairments, affecting academic performance, relationships and overall well-being of an individual. This paper aims to evaluate the published literature on IGD, identifying its growth, prolific authors, journals, countries, collaboration patterns and emerging research hotspots.

The present study has used scientometrics and altmetrics approaches to evaluate the published IGD literature. The data was collected from the Scopus database using the keywords TITLE-ABS-KEY, “Internet Gaming Disorder” or “Gaming Addiction” by filtering the period of 2014–2023, “research articles” and “English” language. Dimensions.ai has been used to extract the altmetric attention score. The results showed a total of 1,454 documents. The analysis has been done using VoSviewer and MS Excel.

The results showed an upward trend in publications over time. Dr Mark Griffiths is the most productive author, and the Journal of Behavioral Addictions is prolific. China is the most productive country. The study found minimal collaboration between the authors. The results highlighted that IGD predominantly affects adolescent males, leading to anxiety and significant impacts on brain function.

To the best of the authors’ knowledge, studies examining IGD research using both scientometric and altmetric approaches within the specified time frame have yet to be identified.

The purpose of this paper is to investigate the accurate monitoring and assessment of steel bar corrosion in concrete based on deep learning multi-sensor information fusion method. The paper addresses the issue of traditional corrosion assessment models relying on sufficient data volume and low evaluation accuracy under small sample conditions.

A multi-sensor integrated corrosion monitoring equipment for reinforced concrete is designed to detect corrosion parameters such as corrosion potential, current, impedance, electromagnetic signal and steel bar stress, as well as environmental parameters such as internal temperature, humidity and chloride ion concentration of concrete. To overcome the small amount of monitoring data and improve the accuracy of evaluation, an improved Siamese neural network based on the attention mechanism and multi-loss fusion function is proposed to establish a corrosion evaluation model suitable for small sample data.

The corrosion assessment model has an accuracy of 98.41%, which is 20% more accurate than traditional models.

Timely maintenance of buildings according to corrosion evaluation results can improve maintenance efficiency and reduce maintenance costs, which is of great significance to ensure structural safety.

The corrosion monitoring equipment for reinforced concrete designed in this paper can realize the whole process of monitoring inside the concrete. The proposed corrosion evaluation model for reinforced concrete based on Siamese neural network has high accuracy and can provide a more accurate assessment model for structural health testing.