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Emojis are quickly becoming a popular new language in social media and marketing. The capability to express emotions and make message understanding easier is one of the primary reasons for using emojis. The aim of this research was to determine the influence of perceived usefulness, perceived ease of use, trust, and involvement on customer engagement due to emojis used in digital marketing communications among Generation Z (Gen Z) in South Africa.

Following the descriptive research approach, quantitative research was used in this study. A questionnaire (self-administered) was utilized to test the effectiveness of using emojis among 1,000 young consumers. Structural equation modeling was used to test the hypotheses.

The findings of the study yielded positive relationships between the variables, namely between trust and involvement; involvement and the perceived ease of use; involvement and perceived usefulness; perceived ease of use and perceived usefulness; trust and customer engagement; perceived usefulness and customer engagement; involvement and customer engagement; customer engagement and intention to purchase; trust and intention to purchase; and perceived usefulness and intention to purchase.

This study can help organizations in emerging markets use emojis in their digital marketing communications to engage customers and stimulate intention to purchase among young people, especially the Gen Z cohort, who seek organizations and brands that understand and connect with them.

By investigating the effects of emojis in digital marketing communications, this study contributes to the customer-centric process and the literature on emoji usage while also involving a credible digital language when communicating with members of Gen Z. By extending TAM, the findings of this study contribute to the TAM literature by demonstrating that emoji usage in digital marketing communications positively influences various attitudinal associations among Gen Z consumers.

This study aims to evaluate the impact of the proposed CARICOM Octagon “High In” Warning Label (OWL), against four alternative Front-of-Package Nutrition Labels (FOPNLs): US “Facts Up Front” (FUF), UK Multi-Traffic Light (MTL), Mexican OWL and the Brazilian Magnifying Glass “High In” Warning Label, on respondents’ purchase intentions, perception of healthiness and understanding of nutritional information across and within food products.

In an online randomized control experiment, adults from eight CARICOM countries (n = 948) were randomly assigned to a control and four treatment FOPNL groups. Respondents were tasked to choose between four categories of mock products with three variations in healthfulness across and within products.

No statistically significant difference (p > 0.05) was found across FOPNL groups compared to the OWL on outcomes of purchase intentions and perception of healthfulness. Regarding the understanding of nutritional information, FUF performed the best, as participants were 1.76 times (p = 0.03) and 3.23 times (p = 0.00) more likely to correctly identify the products with the highest and lowest amount of sugar, respectively. Results were similar for products with the lowest sodium (odds ratio [OR] = 2.25, p = 0.00) and highest saturated fats (OR = 2.11, p = 0.00).

Some limitations of this study include the use of an online platform to conduct the experiment. Though this was the most cost-effective method of execution and presented many benefits, there were limitations to using this approach. Firstly, this approach may not entirely replicate the real world in store purchasing settings. Although online grocery shopping is becoming increasingly popular, in the Caribbean, most grocery purchases are made in stores. Furthermore, online surveys are more likely to lead to samples with higher educational and income levels than the average population (Bethlehem, 2010). The skewedness observed was not unique to this study and was common with similar published studies (Franco-Arellano et al., 2020; Packer et al., 2021; Talati et al., 2018). Nevertheless, all respondents were randomly assigned to groups, and it was confirmed that there were no systematic differences in the education and income levels of participants across the FOPNL groups.

Some CARICOM policy makers advocate for the use of “High In” warning labels to limit the intake of nutrients of concern (NOCs) and to encourage healthier eating habits among consumers. However, regional private sector stakeholders have expressed concern about the lack of sufficient research undertaken at a regional level, to inform the effectiveness of this model within CARICOM, and some have also expressed a preference for the use of other interpretative and reductive FOPNLs, already in use in the Region. The results of this study reveal that while interpretative FOPNLs like the Draft CARICOM Regional Standard, DCRS5 (OWL) can assist consumers in making healthier purchase decisions, it was outperformed by the MTL on perception of healthiness and by the FUF on the understanding of nutrient information. It was also noted that the DCRS5 (OWL) was more effective when choosing across products with distinct nutritional differences but performed poorly in assisting respondents with making healthy purchasing decisions when all the products contained NOCs above the relevant thresholds. This study’s findings highlight that the existing FOPNL schemes can be further enhanced for improved outcomes. This can be achieved by using a hybrid approach which includes both reductive and interpretative elements to allow for comparison across and within food products. The literature also suggests the use of colour and combining positive as well as negative elements to encourage ease of interpretation, improved understanding and healthier food choices.

A properly designed FOPNL can support consumers in making healthier food choices; however, it must be accompanied by measures to raise consumer awareness and increase the health literacy of the population to cause shifts in preferences and behavioural patterns over time. This must also be coupled with policies to make healthy food choices more affordable to the general population.

The results of this study revealed that FUF and MTL performed the best in assisting participants to correctly identify between products with the highest or lowest NOCs at the 5% significant level, and that the OWL performed poorly in assisting participants with making healthy purchasing decisions when all the products contained NOCs above the relevant thresholds.

This research delves into the dynamic realm of app design by examining the impact of app icon familiarity and authority on image fit, influencing users' app usage intention. Focusing on the distinctive circumstances of Chinese and Korean customers, the study aims to provide insightful information about how application user behavior changes.

Utilizing structural equation modeling, the study employs data from 293 Korean and Chinese consumers. The research design incorporates a thoughtful approach, including parallel translation methods, focus group interviews, and pre-experimental testing to ensure survey accuracy and validity. The study strategically selects stimuli from the Apple App Store rankings, emphasizing icon features and type considerations.

The results provide important new information about the connections between usage intention, image fit, authority, and familiarity with app icons. Notably, app icon familiarity and authority positively influence image fit. Furthermore, app icon image fit emerges as a positive predictor of usage intention, mediating the complex interplay between familiarity, authority, and intention. The study also identifies moderating effects, shedding light on the nuanced role of app icon features and types.

Originating from a comprehensive exploration of icons, this study significantly contributes to the field by exploring icon differences and uncovering the intricate mechanisms guiding users' decisions. The findings offer valuable insights for app designers, marketers, and researchers seeking a deeper understanding of user behavior in diverse cultural contexts, thereby enhancing the theoretical and practical foundations in app usability and consumer behavior.

Targeting the common functions of the Zhejiang Library website, elderly individuals were invited to complete six experimental tasks on the improved website interfaces, and subjective data (PAD emotion scale and usability evaluation) and objective data (eye movement data) were recorded to verify the effects of graphic layout and navigation position on the information-seeking experience of elderly individuals.

This study analyzes the effect of the graphic layout and navigation position of the Zhejiang Library’s website interface on the emotional state, perceived usability and information-seeking time of elderly individuals, with the aim of providing guidance and suggestions for the elderly-oriented reform of the public library website.

The experimental results show that the graphic layout has a significant effect on the emotional state and perceived usability of elderly individuals, and the navigation position has a significant effect on the information-seeking time; the interaction between graphic layout and navigation position exerts a significant effect on the information-seeking time of elderly individuals. The eye movement data show that elderly individuals have a better information-seeking experience when the top navigation bar and image-text matched arrangement are used for the interface layout.

This study adopts a new approach combining subjective data and eye movement data to evaluate the effect of the public library website’s interface layout on the information-seeking experience for older people. The findings can provide a theoretical basis and methodological support for the elderly-oriented reform of public library websites. They can also provide scientific design suggestions for age-friendly interface layouts of other Internet products and service applications.

The purpose of this paper is to develop a software-library in the R programming language that implements the concepts of the interpretive coordinate, interpretive axis and interpretive plane. This allows for the automatic and reliable interpretation of results from the multiple correspondence analysis (MCA) as previously proposed and published. Consequently, the users can seamlessly apply these concepts to their data, both via R commands and a corresponding graphical interface.

Within the context of this study, and through extensive literature review, the advantages of developing software using the Shiny library were examined. This library allows for the development of full-stack applications for R users without the need for knowledge of the corresponding technologies required for the development of complex applications. Additionally, the structural components of a Shiny application were presented, leading ultimately to the proposed software application.

Software utilizing the Shiny library enables nonexpert developers to rapidly develop specialized applications, either to present or to assist in the understanding of objects or concepts that are scientifically intriguing and complex. Specifically, with this proposed application, the users can promptly and effectively apply the scientific concepts addressed in this study to their data. Additionally, they can dynamically generate charts and reports that are readily available for download and sharing.

The proposed package is an implementation of the fundamental concepts of the exploratory MCA method. In the next step, discoveries from the geometric data analysis will be added as features to provide more comprehensive information to the users.

The practical implications of this work include the dissemination of the method’s use to a broader audience. Additionally, the decision to implement it with open-source code will result in the integration of the package’s functions by other third-party user packages.

The proposed software introduces the initial implementation of concepts such as interpretive coordination, the interpretive axis and the interpretive plane. This package aims to broaden and simplify the application of these concepts to benefit stakeholders in scientific research. The software can be accessed for free in a code repository, the link to which is provided in the full text of the study.

This article aims to contribute to the accounting knowledge literature by presenting the framework of creative accounting factors and evaluating their identified factors through an argumentation-based total interpretive structural modeling (TISM) approach.

This study adopted mixed, inductive and deductive approaches to develop an integrated framework, validate its practicability and verify its effectiveness in selected manufacturing firms listed on the Tehran Stock Exchange (TSE), respectively. In developing the framework and implementation procedure, the study employed an exploratory data collection (qualitative) approach to review the phenomenon of creative accounting factors. Then, in this study’s second phase, TISM is used to develop the framework of creative accounting design. This study used two types of theoretical sampling in the qualitative part, including theoretical and snowball sampling. Also, the participants in the TISM process in this study were specialized analysts of the TSE.

Based on the mixed method of this study, the result in the qualitative part provides the creative accounting framework of the existence of three categories. There are 6 components and 35 themes during 12 interviews. In the quantitative section, it was determined that two factors, namely the type of ownership firms and intrinsic objectivity, are the most effective drivers for the formation of creative accounting in TSE firms.

So far, it is rare to find preceding studies that have proposed, validated and practically tested an integrated creative accounting framework within the context of financial markets. Thus, the authors understand that this is the very first research focused on the development of a framework for capital market companies to continuously be competitive and could help financial decision-makers, practitioners and academicians in their perception of knowing more about the financial functions of firms.

The purpose of the research is to recognize and rank the barriers preventing Industry 4.0 (I4.0) adoption in Bangladesh’s food and beverage industries. It aims to highlight the major difficulties the sector is currently facing and offer a hierarchical framework for evaluating these barriers.

An exhaustive literature review was conducted along with expert interviews with academics and industry professionals to identify and assess the barriers. The interpretive structural modeling and Matrice d'Impacts Croisés Multiplication Appliquée à un Classement (ISM-MICMAC) approach was used to classify the identified barriers based on driving and dependent power and understand the interrelationships among them.

A total of 20 barriers to I4.0 adoption in the food and beverage sector of Bangladesh were identified. The top tier of significant barriers includes “Financial constraints,” “Lack of management support,” “Lack of research and development team,” “Lack of adequate skills in the workforce,” “Lack of digital strategy alongside resource scarcity” and “Employee resistance to change.”.

The created hierarchical framework offers a useful tool for dealing with the noted barriers and assisting with the successful adoption of I4.0 in the food and beverage sector. Businesses can overcome financial constraints by allocating enough resources and obtaining management support. By creating a focused research and development team and giving the workforce the necessary training, the lack of knowledge and skills can be overcome. By developing a thorough digital strategy and making sure that the necessary resources are available, resource scarcity can be overcome. Furthermore, effective change management methods can support the adoption of I4.0 technologies by overcoming employee resistance to change.

By concentrating specifically on the difficulties encountered by the food and beverage industries in Bangladesh as it attempts to adopt I4.0, this study contributes to the body of existing literature. The study’s originality lies in its thorough analysis of barriers and the use of the ISM-MICMAC approach to comprehend how these barriers interact with one another.

Gazelle companies are characterized by rapid growth in a short time. Identifying the determinants of this exponential expansion is important as these firms have a significant impact on the economy. They generate increased employment and investment by investors interested in new opportunities. Previous studies have failed to reach a consensus about what fosters high growth in gazelle companies as each firm’s geographical, political and economic context is different. The present research uses cognitive mapping and interpretive structural modeling (ISM) to overcome the limitations of prior investigations and identify factors that can potentially accelerate growth in gazelle companies.

Two sessions were held with an expert panel with knowledge about and experience with these firms. In the first session, data were collected to create a group cognitive map, while the second meeting comprised ISM-based analyses of the high-growth determinants identified and the causal relationships between them. A final consolidation session was held to discuss the results with two members of the Committee for Central Region Coordination and Development (i.e. Comissão de Coordenação e Desenvolvimento Regional do Centro – a public entity that grants gazelle awards in Portugal).

The analysis system created was tested, and the results demonstrate that the dual methodology used can increase our understanding of the dynamics of high-growth determinants and lead to more informed and potentially better evaluations of gazelle companies. Indeed, once high-growth determinants in gazelle companies are understood, this information can help other firms implement the same business model to achieve similarly rapid growth. The strengths and shortcomings of this new structured analysis model are also analyzed.

The authors know of no prior work reporting the integrated use of cognitive mapping and ISM in this study context.

This paper aims to investigate the thermal performance of equivalent square and circular thermal systems and compare the heat transport and irreversibility of magnetohydrodynamic (MHD) nanofluid flow within these systems.

The research uses a constraint-based approach to analyze the impact of geometric shapes on heat transfer and irreversibility. Two equivalent systems, a square cavity and a circular cavity, are examined, considering identical heating/cooling lengths and fluid flow volume. The analysis includes parameters such as magnetic field strength, nanoparticle concentration and accompanying irreversibility.

This study reveals that circular geometry outperforms square geometry in terms of heat flow, fluid flow and heat transfer. The equivalent circular thermal system is more efficient, with heat transfer enhancements of approximately 17.7%. The corresponding irreversibility production rate is also higher, which is up to 17.6%. The total irreversibility production increases with Ra and decreases with a rise in Ha. However, the effect of magnetic field orientation (γ) on total EG is minor.

Further research can explore additional geometric shapes, orientations and boundary conditions to expand the understanding of thermal performance in different configurations. Experimental validation can also complement the numerical analysis presented in this study.

This research introduces a constraint-based approach for evaluating heat transport and irreversibility in MHD nanofluid flow within square and circular thermal systems. The comparison of equivalent geometries and the consideration of constraint-based analysis contribute to the originality and value of this work. The findings provide insights for designing optimal thermal systems and advancing MHD nanofluid flow control mechanisms, offering potential for improved efficiency in various applications.

The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.

This study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.

Case validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.

This study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.