Search Results (74)

Lack of leadership succession planning in South African public hospitals places nursing leadership at great risk instead of improving healthcare. There is a significant demand for nurse managers in the Free State Province; therefore, leadership succession planning is important. The re-advertising of unfilled leadership roles, the projected volume of nurse managers who will be retiring, and the number of professional nurses opting for better international opportunities indicate the need for effective succession planning. The study aims to determine leadership succession planning for professional nurses in a selected public hospital in the Mangaung District, Free State Province. A qualitative, explorative, and descriptive research design will be used. Non-probability purposive sampling will be adopted to explore the leadership succession planning. The research participants will consist of professional nurses who are currently permanently employed within the Mangaung district, Free State Province. The sample size will be determined by data saturation. An estimated sample size of ±20 participants will be expected. Data collection will be performed through in-depth, unstructured interviews to answer the research question. A central place for interviews will be organised, and appointments will be made with participants as per their schedule or availability. Data will be analysed using Braun and Clarke’s thematic method. The conclusion and the recommendations will be based on the findings of the study. Full article

This research explores the language features used by leading consumer brands with successful marketing in their promotional messages. Coca-Cola, McDonald’s, PepsiCo, Mondelez, and Unilever were selected because they appear in Effie’s Most Effective Marketers’ Index and are active on a range of media platforms. A group of 225 marketing texts, made up of social media posts, video advertisement transcripts, and website content, was examined using a corpus-based method based on Biber’s MDA framework. The goal was to find common lexicogrammatical patterns in top consumer brands on five different dimensions. Many advertisements included personal pronouns, commands, and words that suggest possibility or necessity. The findings also show that most social media posts provided information, yet had a moderate impact on persuasion. Abstract nouns, passive voice, and formal connectors were found to make the website and press release texts the most impersonal and explicit. The research discovered that Unilever’s language was more informational and abstract, but McDonald’s language was mixed-purpose and non-abstract. Overall, the results indicate that brands use vocabulary and grammar to fit each platform, but maintain their brand identity. Thus, successful consumer brands use different lexicogrammatical patterns in various media to achieve their objectives. Full article

Online paid advertising is a dynamic form of online marketing that requires precision and a quick response to market changes. Automated tools are used to greatly simplify the process of creating and managing Pay-Per-Click (PPC) campaigns. The purpose of this research is to evaluate the impact of implementing an automated PPC management tool (Dotidot) on campaign performance in a travel agency compared with standard Google Ads campaigns. A structured multi-criteria procedure is first applied to select the most suitable tool for the Czech and Slovak markets. The core contribution of the paper is the observational case study of Dotidot and its performance comparison. The subject of the research is a comparative analysis of three PPC automated tools: Conviu, Dotidot, and BlueWinston. The significance of this topic lies in highlighting the relatively new possibilities of digital marketing and choosing the right tool for using automation. This research can also stimulate other researchers in this field and expand knowledge. The analysis is carried out using the method of systematic comparison based on established criteria, the result of which is a recommendation of the preferred tool and a brief discussion of its implementation options. The analyzed tools are used mainly on the Czech and Slovak markets and are oriented towards e-commerce (electronic commerce). In addition to e-commerce, a case study of the use of digital promotion in sports organizations is also presented. The research results in a systematic comparison of Conviu, Dotidot, and BlueWinston tools according to predefined criteria. The research also includes a brief discussion on the possibilities of implementing the recommended tool in practice, as well as an assessment of its benefits and limitations. Full article

Decision-making theory has developed over many decades at the intersection of economics, mathematics, psychology, and engineering. Its classical foundations include Bernoulli’s expected utility theory, von Neumann and Morgenstern’s rational choice theory, and the criteria proposed by Savage, Wald, Hurwicz, and others. However, in real-world contexts, decisions are made under uncertainty, incompleteness, and unreliability of information, which classical approaches do not adequately address. To overcome these limitations, modern multi-criteria decision-making methods such as Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VlseKriterijumska Optimizacija I Kompromisno Resenje (Compromise solution approach) (VIKOR), and ELimination Et Choix Traduisant la REalité (Elimination and Choice Expressing Reality) (ELECTRE), as well as their fuzzy and Z-number extensions, are widely applied to the modeling and evaluation of complex systems. These Z-number extensions are based on the concept of Z-numbers introduced by Lotfi Zadeh in 2011 to formalize higher-order uncertainty. This study introduces the Z-Regret principle, which extends Savage’s regret criterion through the use of Z-numbers. Supported by Rafik Aliev’s mathematical justifications concerning arithmetic operations on Z-numbers, the model evaluates regret not only as a loss relative to the best alternative but also by incorporating the degree of confidence and reliability of this evaluation. Calculations for the selection of digital advertising platforms in terms of performance assessment under various scenarios demonstrate that the Z-Regret principle enables more stable and well-founded decision-making under uncertainty. Full article

Introduction: Cannabidiol (CBD) is widely available over the counter for presumed medical and recreational purposes. Despite its non-psychoactive nature, CBD exhibits intrinsic pharmacological activity that may lead to potential adverse drug events (ADEs) and drug–drug interactions (DDI) with common prescription medications through cytochrome P450 inhibition. Due to their largely unregulated nature and widespread advertising, consumers who use CBD products may not be aware of these potential negative drug interactions. The purpose of this study was to determine how frequently patients who use CBD products concurrently take prescription medication with known drug–drug interaction (DDI) potential, and to identify specific therapeutic classes most commonly involved. Methods: In this cross-sectional study, a survey was distributed to patients and family members in the adult and pediatric Emergency Departments of a Level 1 Trauma Center in eastern North Carolina. Respondents reported household CBD use and selected from a list of conditions for which they take prescription medications. Results: Of 681 eligible respondents, 254 (37.3%) reported CBD use in their household (CBDUIH). Among those with CBDUIH, 69.7% reported concurrent use of 1 or more medications with a potential DDI risk. The most common categories of prescriptions were antidepressants (64.4%) and antihypertensives (41.8%), followed by agents for diabetes, hyperlipidemia, and immune disorders. Conclusions: The majority of CBD users in this population are concurrently taking medications with DDI potential, highlighting the need for patient and provider education, and improved labeling of CBD-based products to accurately reflect risks. Further study of clinically significant interactions is needed to determine which medications within these common categories have the most substantial risk of DDI. Full article

Geolocating Twitter users from social media data holds significant value in applications such as targeted advertising, disaster response, and social network analysis. However, existing social network-based geolocation methods tend to focus primarily on mention relations while neglecting other critical interactions like retweet relationships. Moreover, effectively integrating diverse social features remains a key challenge, which limits the overall performance of geolocation models. To address these issues, this paper proposes a novel Twitter user geolocation method based on multi-graph feature fusion with a gating mechanism, termed MGFGCN, which fully leverages heterogeneous social network information. Specifically, MGFGCN first constructs separate mention and retweet graphs to capture multi-dimensional user relationships. It then incorporates the Information Gain Ratio (IGR) to select discriminative keywords and generates Term Frequency–Inverse Document Frequency (TF-IDF) features, thereby enhancing the semantic representation of user nodes. Furthermore, to exploit complementary information across different graph structures, we propose a Structure-aware Gated Fusion Mechanism (SGFM) that dynamically captures differences and interactions between nodes from each graph, enabling the effective fusion of node representations into a unified representation for subsequent location inference. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art baselines in the Twitter user geolocation task across two public datasets. Full article

Purpose: The objective of this research was to assess the quality, substance, and ease of reading of online information regarding the use of 3D printing in dental practices. Materials and Methods: The search term selected was ‘3D printing in dentistry’. The first 100 websites retrieved through the Google Search Engine based on this search term were reviewed. Duplicate websites, scientific articles, social media links, videos, advertisements, and broken links were excluded from our study. The quality of the websites analyzed in this study was assessed using the DISCERN tool and JAMA benchmarks, while their readability was measured using the Flesch Reading Ease Score (FRES) and the Flesch-Kincaid Grade Level (FKGL). A significance level of p < 0.05 was established for the analysis. Results: The 75 websites reviewed were categorized into two groups: blogs and commercial sites. It was determined that commercial sites (N: 42) were the majority compared to blog websites (N: 33). Blog websites scored higher than commercial sites in the total DISCERN, DISCERN 1, and Q16 scores. In the JAMA criteria, the minimum sources criterion (n = 12) and the maximum authorship criterion (n = 71) were met. There was no statistically significant difference between the groups in terms of FRES and FKGL scores (p > 0.05). Conclusions: The quality of online information sources regarding ‘3D printers in dentistry’ worldwide is considered to be at a moderate level. The readability of the data is at a low level. There is a need for higher quality and highly readable websites about ‘3D printing in dentistry’. Full article

In the attention-driven environment of online video platforms, understanding the factors that influence content selection and channel subscriptions is crucial for creators, marketers, and platform managers. This study investigates how thumbnails, view counts, video length, genre, and the number of advertisements affect user decision-making on YouTube. Grounded in random utility theory and reference-dependent preference theory, this study conducted a choice experiment with 525 respondents and employed a combined model of rank-ordered and binary logit methods to analyze viewing and subscription behaviors. The results indicate a significant preference for thumbnails with subtitles and shorter videos. Notably, we found evidence of reference-dependent effects, whereby a higher-than-expected number of ads decreased viewing probability, while a lower-than-expected number significantly increased subscription probability. This study advances our understanding of the factors that influence user behavior on social media, specifically in terms of viewing and subscribing, and empirically supports prospect theory in the online advertising market. Our findings offer both theoretical and practical insights into optimizing video content and monetization strategies in competitive social media markets. Full article

The strategic allocation of advertising billboards has become a critical aspect of urban planning and resource management. While previous studies have explored site selection based on road network and population data, they have often overlooked the diminishing marginal returns of overlapping coverage and neglected to efficiently process large-scale urban datasets. To address these challenges, this study proposes two complementary optimization methods: an enhanced greedy algorithm based on geometric modeling and spatial acceleration techniques, and a reinforcement learning approach using Proximal Policy Optimization (PPO). The enhanced greedy algorithm incorporates population-weighted road coverage modeling, employs a geometric series to capture diminishing returns from overlapping coverage, and integrates spatial indexing and parallel computing to significantly improve scalability and solution quality in large urban networks. Meanwhile, the PPO-based method models billboard site selection as a sequential decision-making process in a dynamic environment, where agents adaptively learn optimal deployment strategies through reward signals, balancing coverage gains and redundancy penalties and effectively handling complex multi-step optimization tasks. Experiments conducted on Wuhan’s road network demonstrate that both methods effectively optimize population-weighted billboard coverage under budget constraints while enhancing spatial distribution balance. Quantitatively, the enhanced greedy algorithm improves coverage effectiveness by 18.6% compared to the baseline, while the PPO-based method further improves it by 4.3% with enhanced spatial equity. The proposed framework provides a robust and scalable decision-support tool for urban advertising infrastructure planning and resource allocation. Full article

Programmatic buying has attracted growing interest from manufacturers and has become a driving force behind the growth of digital advertising. Among various formats, mobile advertisements (ads) have emerged as a preferred choice over traditional ones due to their advanced automation, adaptability, and cost-effectiveness. Despite their increasing adoption, academic research on mobile ads remains relatively limited. Unlike conventional statistical analysis techniques, the proposed feature selection methods eliminate the need for assumptions related to data properties such as independence, normal distribution, and constant variance in regression. Additionally, feature selection techniques have recently gained traction in big data analysis, addressing the limitations inherent in traditional statistical approaches. Consequently, this study aims to determine the key success factors of mobile ads in fostering customer loyalty, offering advertisers valuable insights for optimizing mobile ad design. This study begins by identifying potential factors influencing mobile advertising effectiveness. Then, it applies Support Vector Machine Recursive Feature Elimination (SVM-RFE), correlation-based selection, and consistency-based selection methods to determine the key drivers of customer retention. The findings reveal that “Price” and “Preference” are the most significant contributors to enhancing repurchase intention. Moreover, factors such as “Language”, “Perceived Usefulness”, “Interest”, “Mobile Device”, and “Informativeness” are also essential in maximizing the effectiveness of mobile advertising. Full article

The increasing popularity of short videos has presented sellers with fresh opportunities to craft video advertisements that incorporate diverse modal information, with each modality potentially having a different influence on consumer engagement. Understanding which information is most important in attracting consumers can provide theoretical support to researchers. However, the dimensionality of the multimodal features of short video advertisements is often higher than the available data, posing specific difficulties in data analysis. Therefore, designing a multimodal analysis framework is needed to comprehensively extract and reduce the dimensionality of the different modal features of short video advertisements, thus analyzing which modal features are more important for consumer engagement. In this study, we chose TikTok as the research subject, and employed deep learning and machine learning techniques to extract features from short video advertisements, encompassing visual, acoustic, title, and speech text features. Subsequently, we introduced a method based on mixed-regularization sparse representation to select variables. Ultimately, we utilized multiblock partial least squares regression to regress the selected variables alongside additional scalar variables to calculate the block importance. The empirical analysis results indicate that visual and speech text features are the key factors influencing consumer engagement, providing theoretical support for subsequent research and offering practical insights for marketers. Full article

Introduction: Demographic disparities in musculoskeletal (MSK) health exist in the US. Racial representation in advertising has been shown to influence consumer behavior and buying patterns. Direct-to-consumer advertising that does not target a racially diverse audience may exacerbate MSK disparities by failing to reach minorities. We explore the hypothesis that minorities are underrepresented in direct-to-consumer MSK advertisements in this cross-sectional analysis. Methods: Using magazines from four databases, eight health-related magazine types were selected and advertisement categories were established. Racial distribution was analyzed using Pearson’s Chi-squared and Chi-squared tests. Fisher’s Exact test was used when >20% of cells had expected frequencies <5. Significance was set at α = 0.05. Results: Of the advertisements featuring at least one model, 68.5% featured a white-presenting model, followed by 17.6% with a black model. Further, 92.7% of advertisements were monoethnic or monoracial with an overrepresentation of white models (p < 0.001). Black models were overrepresented as athletes (p < 0.001) and underrepresented in advertisements for pain relief (p < 0.001). Hispanic/Latinx and Asian models were underrepresented across all advertisement categories (p < 0.001). Discussion: The causes of musculoskeletal health disparities are multifactorial. One potential influence is adjacent industries such as MSK health-related advertisements. When controlling for US population demographics, white models were overrepresented and minority race models were underrepresented, demonstrating racioethnic disparities in MSK advertising. Improving the racial and ethnic diversity of models within MSK advertisements may serve to improve patient perceptions of orthopaedic products and services and improve MSK disparities. Full article

This paper focuses on analyzing the relationship between the financial performance of companies and their ability to utilize modern business methods. Financial analysis was conducted using the example of the automobile manufacturer Škoda Auto, with the results providing deeper insights into the company’s financial situation. The companies examined in this study were scored and underwent regression and cluster analyses. A questionnaire focusing on the modernity of advertising in selected companies was answered by 276 respondents. Based on the findings, a model for evaluating the modernity and stability of companies was developed, combining various factors including financial indicators and the adoption of modern technologies. The results indicate that there is a relationship between financial performance and the modernization of companies, although this relationship is not always straightforward. In particular, the operating profit and current ratio emerged as important factors influencing modernization. Overall, it can be concluded that the financial performance and modernization of companies are interconnected, but their relationship is complex and requires further investigation. This paper is an important contribution to understanding company modernization and sets the stage for further studies on this issue. Full article

The recommender system (RS) has been widely adopted in many applications, including online advertisements. Predicting the conversion rate (CVR) can help in evaluating the effects of advertisements on users and capturing users’ features, playing an important role in RS. In real-world scenarios, implicit rather than explicit feedback data are more abundant. Thus, directly training the RS with collected data may lead to suboptimal performance due to selection bias inherited from the nature of implicit feedback. Methods such as reweighting have been proposed to tackle selection bias; however, these methods omit delayed feedback, which often occurs due to limited observation times. We propose a novel likelihood approach combining the assumed parametric model for delayed feedback and the reweighting method to address selection bias. Specifically, the proposed methods minimize the likelihood-based loss using the multi-task learning method. The proposed methods are evaluated on the real-world Coat and Yahoo datasets. The proposed methods improve the AUC by 5.7% on Coat and 3.7% on Yahoo compared with the best baseline models. The proposed methods successfully debias the CVR prediction model in the presence of delayed implicit feedback. Full article

The need for a consistent marketing evaluation framework has been highlighted by destination authorities, who in collaboration with academia and marketing professionals have sought to formulate methodologies for measuring the impact of their campaigns. Although several attempts have been made, no simple solution has emerged for evaluating destination marketing activities. This study draws on This is Athens & Partners to reveal the interaction that should take place when destination authorities employ external experts to implement and evaluate their marketing campaigns. The collaboration required adopting the appropriate destination marketing evaluation methodology, which is presented. The adapted methodology, formulated through consultation, resulted in the selection of the advertising tracking survey as the data collection method and the adaptation of a measurement instrument. The research findings show that destination marketing can positively influence the funnel process potential travelers consider when deciding on a travel destination, only when digital tools are combined with effective strategic marketing planning and, more recently, with references to resilience and sustainability. Insights from this paper regarding the importance of establishing an informative evaluation methodology to mitigate potential deficiencies in planned marketing initiatives may prove of added value to destination authorities and stakeholders. Full article