Search results

The objectives of this study are threefold: to delineate the phenomenon of “You Share, We Donate” (YSWD) campaigns and what distinguishes them from sales-based cause-related marketing; to contrast the effectiveness of YSWD and sales-based cause-related marketing campaigns and provide an explanation for the differences in the effectiveness; to explore boundary conditions of the proposed differences.

Three experiments were conducted to empirically test the differential effect of campaign formats (i.e. YSWD vs sales-based cause-related marketing), the underlying mechanism and structural as well as contextual features moderating the differential effect.

The findings suggest that YSWD messages elicit consumers’ message-sharing intentions more than traditional cause-related marketing messages. The effect is explained by consumers’ sense of empowerment and can be enhanced through donation cap non-specification. The findings further indicate that YSWD campaigns are especially fruitful in low power distance cultures.

This study contributes toward corporate donation campaign literature by focusing on the usage of social media.

From a managerial perspective, this research provides marketers with guidelines on how to choose between the two cause-related marketing campaign formats and how to enhance the effectiveness of YSWD campaigns.

This paper extends cause-related marketing literature by not only introducing the phenomenon of YSWD campaigns to the literature but also exploring strategies to enhance the effectiveness of such campaigns and shedding light on an outcome beyond the sales impact of cause-related marketing campaigns, i.e. an increase of visibility in social media. From a managerial perspective, this research provides marketers with guidelines on how to choose between the two cause-related marketing campaign formats and how to enhance the effectiveness of YSWD campaigns.

This study establishes a method for predicting ground vibrations caused by railway tunnels in unsaturated soils with spatial variability.

First, an improved 2.5D finite-element-method-perfect-matching-layer (FEM-PML) model is proposed. The Galerkin method is used to derive the finite element expression in the ub-pl-pg format for unsaturated soil. Unlike the ub-v-w format, which has nine degrees of freedom per node, the ub-pl-pg format has only five degrees of freedom per node; this significantly enhances the calculation efficiency. The stretching function of the PML is adopted to handle the unlimited boundary domain. Additionally, the 2.5D FEM-PML model couples the tunnel, vehicle and track structures. Next, the spatial variability of the soil parameters is simulated by random fields using the Monte Carlo method. By incorporating random fields of soil parameters into the 2.5D FEM-PML model, the effect of soil spatial variability on ground vibrations is demonstrated using a case study.

The spatial variability of the soil parameters primarily affected the vibration acceleration amplitude but had a minor effect on its spatial distribution and attenuation over time. In addition, ground vibration acceleration was more affected by the spatial variability of the soil bulk modulus of compressibility than by that of saturation.

Using the 2.5D FEM-PML model in the ub-pl-pg format of unsaturated soil enhances the computational efficiency. On this basis, with the random fields established by Monte Carlo simulation, the model can calculate the reliability of soil dynamics, which was rarely considered by previous models.

The first-aid courses organized by the Youth Red Cross Carinthia (Austria) had a quality problem, necessitating a professionalization in teaching and time structure. This research aimed to enhance the quality and effectiveness of these courses by implementing modified lesson studies with non-professional trainers. The paper presents the realization process, empirical research and results obtained by applying the first-aid curriculum.

Around 22 lesson study first-aid courses (14 classes with 2 cycles, 8 with 3 cycles) were conducted and evaluated in different Austrian school types. An observation sheet was created to evaluate attention and competencies. Interviews were conducted with both teachers and students to validate the results.

The research findings demonstrate that lesson studies can significantly enhance the quality and effectiveness of first-aid courses. Inexperienced and experienced first-aid teachers significantly improved their teaching skills. Newly educated first-aid teachers showed substantial improvement, leading to the introduction of an induction period and coaching opportunity within the Youth Red Cross Carinthia.

This is the first lesson study conducted in a non-academic context. It highlights the adaptation process of Carinthian first-aid courses. It illustrates how lesson studies impact lesson clarity, instructional variety, student engagement in the learning process, student outcome, student feedback and teaching effectiveness in a non-academic context. It contributes to the literature on the application of lesson study in first-aid education and provides insight into the benefits of this approach in enhancing the quality of first-aid training.

Prior research suggests that collaboration is key to sustainable community development and environmental management, and peer-to-peer learning (P2PL) may facilitate community building and collaborative learning skills. This study aims to examine the effect of P2PL on the enhancement of environmental management and sustainable development skills, community building and social capital (i.e. connectedness) and understanding of course learning objectives.

Quantitative and qualitative longitudinal survey data was collected in a sustainable development focused course offered at a large American public university that uses P2PL to explicitly facilitate community building and collaborative skills. Safety precautions and changing locational course offerings due to the COVID-19 pandemic in years 2020, 2021 and 2022 provided an opportunity to evaluate the impact of P2PL on these skills during both virtual and in-person formats. Additionally, this study compared in-course student evaluations with students taking other sustainable development-related courses with collaborative learning aspects to understand the wider effectiveness of this course structure.

This study finds that course format (virtual vs in-person) overall made no difference in either connectedness or conceptual understandings, and that students in both formats felt more connected to others than students taking other courses with P2PL. Scaffolding P2PL and supplemental peer support can yield improved connectedness and learning among students taking environmental coursework.

Sustainable development requires group collaboration and partnership building skills. Issues are consistently raised about the challenges to teaching these skills in higher education. The students and instructors in this research study identify P2PL strategies to address these challenges for in-person and virtual classroom settings.

This study examines the influence of consumer skepticism on cause-related marketing (CRM) campaign participation intentions of Generation Z consumers from emerging market perspective. This study was undertaken due to the paucity of relevant literature in the public domain to directly investigate whether and how consumers' skepticism affects CRM participation intentions, specifically in the luxury retailing context.

A survey was conducted among 552 Generation Z consumers and path analysis was used to examine the direct and indirect effects of skepticism. The mediation and moderation analysis was used to explore and test the authors' hypotheses via partial least squares structural equation modeling (PLS SEM).

The authors' findings provide empirical evidence that corporate social responsibility, religiosity and cause involvement positively affects consumer participation intentions, and this link is also established indirectly through skepticism toward the CRM campaign. These findings provide novel theoretical contributions by establishing skepticism's complex role in determining the CRM participation intention in the Generation Z consumers' context. This study further demonstrates the moderating effect of gender and luxury store format on consumer skepticism and CRM participation intentions.

The Generation z group will represent a quarter of the Asia–Pacific region's population by 2025. However, little is known about Generation z consumers' CRM participation intentions. This research would help practitioners, including luxury retailers, CRM managers and advertising professionals, to effectively design CRM campaigns. The paper contributes by highlighting the theoretical implications and managerial implications based on the current findings in the emerging market context.

This paper aims to explore how music festival organisers negotiate diversity and inclusion in marketing and promotion practices through symbolic and social boundaries.

Based on semi-structured interviews with 18 festival organisers in Rotterdam and participant observation with six festival photographers I show that symbolic and social boundaries are employed in three areas: (1) boundaries in festival format (i.e. [partially] free or ticketed), (2) boundaries in distribution partners and technologies and (3) boundaries in promotional content.

Symbolic and social boundaries are intentionally used by festival organisers to build and delineate festival audiences. Implications are drawn on current understandings of the accessibility of music festival spaces, arguing that festival research should move beyond within-space dynamics to grasp the negotiation of diversity and inclusion at festivals more fully.

While music festivals are often marketed as celebratory spaces that are “welcoming to everyone”, few studies have investigated diversity and inclusion nor marketing and promotion practices at music festivals. This study shows how festival audiences are shaped through marketing and promotion practices.

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.

Leadership assessment programs are intended to ensure that the organization retains or hires high-quality leadership. Among the many skills that must be included, executive communication is a cornerstone of effective leadership. However, there are many techniques to assessing executive communication that impose numerous advantages and disadvantages. The purpose of this study is to explore several techniques for evaluating executive communication skills in leadership assessment programs.

Building upon case studies from novel commanding officer selection efforts in the military, the current study outlines three possible areas of executive communication for leadership assessment programs: panel-based interviews, guided discussion and executive writing.

Although each technique offers some advantages, the best technique depends upon the context. Panel-based interviews can provide excellent depth in evaluating candidates, whereas executive writing focuses more upon crafting a deliberate and clear message without the ability to clarify or use nonverbal cues. Selecting an appropriate technique depends greatly upon the workload imposed on the leadership assessment team and the number of candidates available.

Leadership selection programs are often done piecemeal or based on local experience. By building upon novel efforts in military commanding officer selection, the goal is to promulgate effective executive communication techniques that will enhance leadership selection through more effective communication across all levels of leadership positions.

Key to transnational higher education (HE) cooperation is building trust to allow for seamless recognition of studies. Building on the Tuning Educational Structures initiative (2001) and lessons learnt from the Organisation for Economic Cooperation and Development (OECD)-Assessment of Learning Outcomes in Higher Education (AHELO) feasibility study, this paper offers a sophisticated approach developed by the European Union (EU)-co-financed project Measuring and Comparing Achievements of Learning Outcomes in Europe (CALOHEE). These evidence the quality and relevance of learning by applying transparent and reliable indicators at the overarching and disciplinary levels. The model results allow for transnational diagnostic assessments to identify the strength and weaknesses of degree programmes.

The materials presented have been developed from 2016 to 2023, applying a bottom-up approach involving approximately 150 academics from 20+ European countries, reflecting the full spectrum of academic fields. Based on intensive face-to-face debate and consultation of stakeholders and anchored in academic literature and wide experience.

As a result, general (overarching) state-of-the-art reference frameworks have been prepared for the associated degree, bachelor, master and doctorate, as well as aligned qualifications reference frameworks and more detailed learning outcomes/assessment frameworks for 11 subject areas, offering a sound basis for quality assurance. As a follow-up, actual assessment formats for five academic fields have been developed to allow for measuring the actual level of learning at the institutional level from a comparative perspective.

Frameworks as well as assessment models and items are highly innovative, content-wise as in the strategy of development, involving renown academics finding common ground. Its value is not limited to Europe but has global significance. The model developed, is also relevant for micro-credentials in defining levels of mastery.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.