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The main goal of this study is to develop accounting students’ solidarity with and sensitivity to cooperation for sustainable development. This study also aims to analyze the role of participatory learning activities in developing the dimensions of involvement, critical reflection and thinking analysis.

This empirical study analyzes a case study activity conducted in accounting seminars with students from different universities in Spain. After completing the activity, the students completed a questionnaire divided into four areas: sociodemographic information, involvement, critical reflection and creativity. Students also answered an open-response question that asked them to propose new activities to enhance their learning experience and contribute more to sustainable development. The study thus used mixed methods, complementing quantitative analysis with qualitative data.

The multivariate analysis obtained significant results showing that female students were more willing to help and that most students said they were more sensitive to these issues after the participatory learning activity. Furthermore, the items and dimensions analyzed revealed a positive impact of involvement, critical and creative thinking and participatory learning on accounting students’ commitment to cooperation for sustainable development. When students answered the open-response question, they proposed more activities to enhance their learning and improve the functioning of the Non-Governmental Organization’s (NGO’s) beneficiary. HEIs must design more transversal courses aligned with the Sustainable Development Goals in their social science degrees.

The study not only examines university education in sustainable development but goes a step further in trying to involve students in a real development project from a financial and accounting point of view. The study also focuses on education for sustainability, and the project invites the students to think critically, reflect and assess real situations.

This study explores working mothers’ perceptions of fairness in work and home environments through real-world social media discussions. It examines how these perceptions shifted due to the COVID-19 pandemic.

Using a naturalistic approach, we analyzed 13,560 comments from 4,076 unique users on Reddit to convert qualitative data into quantitative variables. A nonparametric factorial ANOVA was employed to assess the relationship between fairness perceptions and the impact of the COVID-19 pandemic on these perceptions in both work and home settings.

The analysis revealed that working mothers predominantly express negative perceptions of fairness in both environments. Contrary to initial expectations, the pandemic did not exacerbate these perceptions. However, conversations involving both work and home contexts showed a more positive tone, suggesting the benefits of remote work scenarios introduced by the pandemic.

This study uniquely applies sentiment analysis to naturalistic social media data, offering real-time insights into how working mothers discuss fairness. Unlike traditional survey methods, this approach captures daily lived experiences, particularly in a crisis context, revealing new aspects of the work–family conflict.

This study aims to investigate the mental health treatment journeys of stigmatized consumers using user-generated content (UGC) while also examining the role of UGC in the journey.

This study offers valuable insights from 68 distinct, stigmatized consumers through a qualitative content analysis of 73 YouTube product review videos related to ten antidepressants. Data is coded, combining inductive coding with theory to provide a nuanced interpretation. Applying the Common-Sense Model of Self-Regulation to traditional consumer journey concepts, the analysis of UGC is structured by a unique mental health treatment journey.

The findings show that consumers use UGC to destigmatize their mental health treatment by engaging in dynamic reflection throughout their journey, rather than following traditional feedback models. Unlike typical consumption patterns, where search is limited to the initial stage, these consumers search at every journey phase while sharing insights that offer valuable support to others which, sometimes they report, is reciprocated by viewers.

Theoretically, this study introduces an innovative framework blending psychological and marketing theories to address a gap in health-care service marketing literature concerning long-term mental health treatment journeys. By introducing the concept of dynamic reflection, it demonstrates how consumers actively engage in and share insights throughout their treatment process, differing from traditional feedback models, and highlights the impact of UGC on health-care service provision.

Findings could inform potential health-care provider interventions that may improve treatment effectiveness.

Although stigmatized consumers’ experiences have been examined, their treatment experiences have not been framed within a journey framework.

This study examines how social influences (need to belong and fear of missing out) and the perceived credibility of health claims affect the intention to purchase foods with health claims.

Data were obtained from a web-based survey of 844 Spaniards aged 25 years and older, with sex and age quotas. Real product images with health claims contextualized the purchase intention questions, aiding respondents who often struggle to differentiate health from promotional claims.

Both personal and social dimensions of fear of missing out (FoMO) influence the desire to belong to a healthy group. The intention to purchase food with health claims is also positively associated with the desire to belong. The results reflect the importance of FoMO and the need to belong in enhancing consumers’ tendency to purchase products with health claims. The results also reflect the importance of purchase intention in making health claims perceived as credible.

This study integrates individual aspects (belief in the credibility of health claims) with social aspects (belonging and FoMO) to analyze the intention to purchase foods with health claims. Using authentic product images to aid consumer comprehension adds an innovative dimension, addressing a gap in consumer behavior research.

本研究擬探討社會影響(歸屬的需要和錯失機會的恐懼)和保健聲稱的感知可信性，會如何影響消費者購買附有保健聲稱食品的意向。

數據取自一個網上調查，研究人員調查了844名年齡為25歲或以上的西班牙人，調查設有性別和年齡的配額。研究人員使用附有保健聲稱的真實產品形象，來設計與情境相配合關於購買意向的問題，這對難於把保健聲稱與促銷聲稱區分的應答者來說是有幫助的。

恐懼錯失機會的個人維度和社會維度兩者均會影響想隸屬於保健團體的渴望; 而消費者購買附有保健聲稱食品的意向又與這渴望成正相關。 研究結果顯示，就增強消費者購買附有保健聲稱產品的意向而言，錯失機會的恐懼，以及想隸屬的渴求均有其重要性。 研究結果亦顯示，若要為保健聲稱建立可信的聲譽，購買意向是重要的因素。

本研究透過合併個人層面 (對保健聲稱可信性的信任)與社會層面 (歸屬感和錯失機會的恐懼)，來分析消費者購買附有保健聲稱食品的意向。研究人員使用真實的產品形象，以幫助消費者理解，就此而言，這為消費者行為研究填補了空白，成為一個創新的維度。

This paper presents an original approach for learning models, partially known, of particular interest when performing source identification or structural health monitoring. The proposed procedures employ some amount of knowledge on the system under scrutiny as well as a limited amount of data efficiently assimilated.

Two different formulations are explored. The first, based on the use of informed neural networks, leverages data collected at specific locations and times to determine the unknown source term of a parabolic partial differential equation. The second procedure, more challenging, involves learning the unknown model from a single measured field history, enabling the localization of a region where material properties differ.

Both procedures assume some kind of sparsity, either in the source distribution or in the region where physical properties differ. This paper proposed two different neural approaches able to learn models in order to perform efficient inverse analyses.

Two original methodologies are explored to identify hidden property that can be recovered with the right usage of data. Both methodologies are based on neural network architecture.

This article presents a fuzzy cognitive map for the evaluation of innovation in organizations.

The purpose of this paper is to develop a model to evaluate the innovative capacity of organizations based on fuzzy cognitive maps (FCM), particularly for micro, small and medium-sized enterprises (MSMEs). The specification of the innovation evaluation model based on FCM was carried out with the “Intelligent Decision Support System” methodology. It is a six-step methodology: selection of experts, definition of concepts and relationships, model design, inference, interpretation and decision.

Our approach yielded good results in three case studies, effectively determining the level of innovation in an organization. The fuzzy cognitive maps demonstrated a high level of accuracy, with an accuracy of 82% in the Colombian case studies and 92% in the global case studies. These results highlight the effectiveness of the model for quantitatively assessing levels of innovation within organizations. Furthermore, the study revealed the most influential and essential innovative activities/variables within organizations, contributing significantly to the improvement of their operations and competitiveness.

It is important to automate the definition of the relationships between the concepts of the context and of our FCM. It is also possible to improve the behavior of the FCM by analyzing the variables with a greater impact on the level of innovation and very dynamic in the context since they are the variables to be observed in real-time to follow the evolution of the innovative behavior of an organization.

The study found that innovative activities emerged as an influential factor in organizations, essential to improving their operations and competitiveness. Our model can help in identifying areas that require improvement to impact positively organizations. By improving innovation assessment through the FCM model, organizations can anticipate higher profitability because innovations are often closely tied to revenue generation and cost savings. The tool can determine the necessity of new products or services, improve operational processes or enter new markets.

The previous results in the literature show that although there are relevant advances on this topic, there is not enough knowledge to provide clear guidelines for evaluating innovation and improving performance in an organization using intelligent systems. Also, previous works have not defined a framework for evaluating innovation in MSMEs based on FCMs. They also do not use the data of an organization to assess the key characteristics related to innovation. This work applies FCM to automate the evaluation of the process and the capacity for innovation in an organization. These are the main differences between our approach and previous studies.

The property industry has been experiencing massive transformation due to rapid technological advancement in the last decade. A large body of literature has attempted to identify the challenges confronting property professionals, with some examining the impact brought by Property Industry 4.0 on the future workforce. To effectively address the challenges, however, we need participation from diverse stakeholders, as previously proposed strategies have often only considered perspectives from specific cohorts and lacked broader engagement. Hence, this study aims to formulate comprehensive strategies to address the challenges facing our future workforce in the age of Property Industry 4.0.

For the first time, we invited a unique lineup of stakeholders to our co-design workshops. These include property leaders, property professionals, recent property graduates, property students, property academics, program directors, as well as university leadership executives, to make sure the outcomes are relevant, effective and user-oriented.

Four strategies were recommended to future-proof our property graduates: (1) enhancing AI-focused content in universities and collaborating with industry stakeholders, (2) regulating AI adoption by bodies like the Australian Property Institute (API), (3) introducing industry certifications to address ethical concerns and (4) developing students' soft skills via internships and networking events in collaboration with professional bodies.

This study is the first to employ a co-design workshop, incorporating the perspectives of all stakeholders in crafting potential solutions. These include property leaders, property practitioners, recent property graduates, property academics, property students and senior school executives (e.g. deputy dean and deputy head of education).

This study investigates the impact and role of digital twin technology in building automation (DTBA) from a sustainability viewpoint. It aims to enhance the understanding of how DTBA can boost efficiency, optimize quality and support sustainable practices in contemporary construction. By exploring the integration of DTBA with sustainable practices, the study seeks to demonstrate how DT can revolutionize building management and operations, leading to significant improvements in resource efficiency, environmental impact and overall operational excellence.

This research employs a bibliographic analysis and systematic review of 176 publications from the past five years (January 1, 2019 to December 31, 2023), focusing on the application and development of DTBA. The study methodically analyzes current trends, identifies research gaps and suggests future directions by synthesizing data from various studies, offering a comprehensive overview of the current state of DTBA research. The approach combines quantitative and qualitative analyses to provide robust insights into the advancements and challenges in the field.

The review identifies key development areas in DTBA, such as energy and environmental management, resource utilization within a circular economy and technology integration and interoperability. It highlights the necessity for further research to maximize DTBA’s potential in sustainable building automation. The findings suggest that while significant progress has been made, there is a critical need for innovations in data interoperability, predictive analytics and the integration of renewable energy sources to fully realize the benefits of DTBA in enhancing building sustainability.

This paper provides a thorough review of DTBA from a sustainability perspective, offering valuable insights into its current applications and future development potential. It serves as a crucial resource for researchers and practitioners looking to advance sustainable practices in the construction sector using DT technology. By bridging the gap between theoretical research and practical applications, the paper underscores the transformative potential of DTBA in driving sustainable development and provides a roadmap for future research and innovation in the field.

The Internet of Things-based digital twin (IoT-DT) technologies offer a transformative approach to building retrofitting for reducing operational carbon (ROC) emissions. However, a notable gap exists between the potential and adoption of the two emerging technologies, further exacerbated by the nascent state of research in this domain. This research aims to establish the best practices that innovatively strengthen the identified enablers to decisively tackle challenges, ensuring the efficient implementation of IoT-DT for ROC emissions in buildings.

This study adopted a mixed-method approach. Questionnaire data from 220 multidiscipline professionals were analysed via structural equation modelling analysis, while interview data obtained from 18 stakeholders were analysed using thematic content analysis. The findings were triangulated for cohesive interpretation.

After the analysis of questionnaire data, a structural model was established, depicting the critical challenges (inadequate data security, limited technical expertise and scalability issues) and key enablers (robust data security measures, skill development and government incentives) of implementing IoT-DT for ROC. Sequentially, analysis of in-depth interview data revealed the IoT-based DT best practices (safeguarding data, upskilling and incentivization). Upon triangulating the questionnaire and interview findings, this study explicitly highlights the potential of the established best practices to strategically strengthen enablers, thereby mitigating challenges and ensuring the successful implementation of IoT-based DT for ROC emissions in buildings.

This study provides practical guidance for stakeholders to effectively implement IoT-DT in ROC in buildings and contributes significantly to climate change mitigation.

This study examines the role of logotypes in advertising effectiveness on s-commerce platforms by analyzing the visual attention paid by the consumer to fashion branding – wordmarks or combination marks – and their subsequent recall.

The study examines the main areas of visual representation of the brand (VRB) on the Instagram network and the user’s corresponding areas of interest on a mobile-device screen. Attention and recall of the VRB are assessed in light of different classification variables (users’ gender, age and level of experience in s-commerce tools) to better understand how VRB may be leveraged by fashion retailers to encourage purchasing behavior. To achieve this objective, a mixed experiment design based on the eye-tracking methodology and a self-administered questionnaire is carried out.

The results indicate that visual attention, gender, age and s-commerce experience all contribute to determining users’ recall of the brand logo to which they are exposed on-screen. By considering the different s-commerce user profiles that exhibit different visualization behaviors, fashion retailers will be better placed to improve their online advertising campaigns and, ultimately, increase brand sales. The findings also point to promising future research directions on the effectiveness of branding strategies.

This highly innovative study provides in-depth insights into advertising effectiveness in terms of attention and recall, according to the main types of VRB for two specific s-commerce tools used by a high-street fashion brand, namely, its profile on Instagram Shop and its profile on Instagram Stories.