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This study aims to investigate how small talk interaction affects knowledge sharing, examining the mediating role of interpersonal trust (affect- and cognition-based trust) and the moderating role of perceived similarity among the mechanisms of small talk and knowledge sharing.

This research conducts complementary studies and collects multi-culture and multi-wave data to test research hypotheses and adopts structural equation modeling to validate the whole conceptual model.

The research findings first reveal two trust mechanisms linking small talk and knowledge sharing. Meanwhile, the perceived similarity between employees, specifically, strengthens the affective pathway of trust rather than the cognitive pathway of trust.

This study combines Interaction Ritual Theory and constructs a dual-facilitating pathway approach that aims to reveal the impact of small talk on knowledge sharing, describing how and when small talk could generate a positive effect on knowledge sharing. This research provides intriguing and dynamic insights into understanding knowledge sharing processes.

Purpose: This study analyses the mediating effect of parasocial interaction (PSI) in the link between hedonic motivation and impulsive buying intention (IBI) in fashion vlogging about sustainable cosmetics.

Need for the Study: Due to the mass popularity of YouTube, vlogging has led to an augmented level of PSI of vloggers with consumers, which strongly impacts a consumer’s behavioural consequences and persuades consumers to indulge in impulsive buying. Thus, marketers need to comprehend the changing behavioural patterns, including sustainable products, as this new communication medium serves the future of promotion and advertising.

Methodology: Online questionnaires were administered to 349 Gen Z female fashion vlog followers. Structural equation modelling and Hayes Process macros were employed to test the model relationships.

Findings: Results indicate that PI with the fashion vlogger partially mediates between hedonic motivation and impulse buying intention for sustainable cosmetic products. Fashion consciousness (FC) was also established as a significant moderator between all the model relationships.

Practical Implications: The findings of the study would be helpful for fashion brands in the content development of visual marketing communications, which would tap the female Gen Z consumer. Improving the PSI between the follower and the fashion vlogger can be easily enhanced by delivering the right content through the vlogger’s videos.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

Additive manufacturing also known as 3D printing is an evolving advanced manufacturing technology critical for the new era of complex machinery and operating systems. Manufacturing systems are increasingly faced with risk of attacks not only by traditional malicious actors such as hackers and cyber-criminals but also by some competitors and organizations engaged in corporate espionage. This paper aims to elaborate a plausible risk practice of designing and demonstrate a case study for the compromised-based malicious for polymer 3D printing system.

This study assumes conditions when a machine was compromised and evaluates the effect of post compromised attack by studying its effects on tensile dog bone specimens as the printed object. The designed algorithm removed predetermined specific number of layers from the tensile samples. The samples were visually identical in terms of external physical dimensions even after removal of the layers. Samples were examined nondestructively for density. Additionally, destructive uniaxial tensile tests were carried out on the modified samples and compared to the unmodified sample as a control for various mechanical properties. It is worth noting that the current approach was adapted for illustrating the impact of cyber altercations on properties of additively produced parts in a quantitative manner. It concurrently pointed towards the vulnerabilities of advanced manufacturing systems and a need for designing robust mitigation/defense mechanism against the cyber altercations.

Density, Young’s modulus and maximum strength steadily decreased with an increase in the number of missing layers, whereas a no clear trend was observed in the case of % elongation. Post tensile test observations of the sample cross-sections confirmed the successful removal of the layers from the samples by the designed method. As a result, the current work presented a cyber-attack model and its quantitative implications on the mechanical properties of 3D printed objects.

To the best of the authors’ knowledge, this is the original work from the team. It is currently not under consideration for publication in any other avenue. The paper provides quantitative approach of realizing impact of cyber intrusions on deteriorated performance of additively manufactured products. It also enlists important intrusion mechanisms relevant to additive manufacturing.

In the present study, we examined how effectively online travel agencies (OTAs) handle negative e-word-of-mouth on social media platforms like Facebook, Twitter, and Instagram. We collected data from 497 participants using survey method. To test the hypotheses formulated from the existing literature, structural equation modeling was adopted in this study. The results from structural equation modeling indicate effective handling of the negative e-word of mouth (e-WOM) on social media websites significantly affects customer satisfaction and repurchase intention. The current research work provides insight into social media recovery efforts and service fairness when handling negative e-WOM. The study recommends that customers can distinguish the differences between general efforts and adaptive complaint-handling efforts, and dissimilarities may influence satisfaction, repurchase intentions, etc. Although empathy, apology, responsiveness, and paraphrasing are considered pioneer strategies in complaint handling, customers' negative e-WOM, and firms' recovery management, but the current study is among a few to categorize OTAs' handling of negative e-WOM and complaint handling efforts in the social media environment.

The purpose of this study was to determine the factors associated with functionality, a clinical criterion that could predict frailty in the elderly people in a rural context.

This project is a cross-sectional descriptive analysis of 342 adults of age >60 years who are residents of Putumayo province in Colombia. Information regarding demographic characteristics, medical history, health perception and current illness was collected. The Mini-Mental State Examination (MMSE) protocol was used to perform cognitive evaluation; the Yesavage Geriatric Depression Scale was used to establish depressive symptoms; and the Hamilton Rating Scale was used to assess anxiety level. Questionnaire was used to evaluate performance on instrumental activities of daily living that lead to functional independence [daily life questionnaire (DLQ)]. The medical outcomes study scale was used to assess social parameters.

A moderate and negative correlation was found between the DLQ score and age (r = −0.49; 95% CI: −0.57 to −0.47), whereas a positive correlation was found with education (r = 0.17; 95% CI: 0.07–0.27). Older adults with economic independence achieved a higher score in functional performance than those with economic dependence (standardized mean difference = 0.55; 95% CI: 0.33–0.77). This study observed a moderate correlation a moderate correlation between the MMSE cognitive performance (r = 0.56; 95% CI: 0.48−0.63) and the depressive symptomatology of Yesavage Scale (r = −0.36, 95% CI: −0.44 to −0.26). Finally, the structural model determined that age (r = −0.37), economic dependence (r = −0.383) and cognitive state (r = 0.309) determine the functional component.

This study provides empirical support about older adults living in rural contexts, around the functionality variable from a multidimensional approach, highlighting the sociodemographic and cognitive variables. Consequently, the policy of social support in older adults must be oriented toward the development of a range of divergent intervention strategies.

The study deals with the assessment of functionality in the elderly people from an interdisciplinary approach in the rural setting which presents a greater risk of physical and socioeconomic vulnerability. Therefore, the community, the health professionals and the government entities should help implement active aging programs for this population.

Construction sustainability (CS) is a strategic reaction to the sustainability expectations of the construction industry's external stakeholders. The extant literature has viewed the environmental, social and economic dimensions of CS as having independent effects on financial performance. Due to the influence of common stakeholders, however, interactions in these dimensions will be present in their effect on financial performance. Accordingly, this study identifies the mechanisms of the interactions between the three CS dimensions and how they jointly affect financial performance.

Content analysis of GRI reports of 60 large construction organisations, followed by a hierarchical regression analysis was used to identify the interactions between environmental, social and economic CS in their effect on financial performance.

Economic CS was found to indirectly, and not directly, affect financial performance, the effect being mediated by both environmental and social CS. Environmental CS was found to have a strong negative effect on financial performance, whilst social CS was found to have a strongly significant positive effect on financial performance.

The motivation for engaging in CS is that investment in economic CS will have a positive effect on both environmental and social CS outcomes, which, in turn can have a combined effect on financial performance.

This is one of the first studies investigating the effect of interactions between the environmental, social and economic CS dimensions on the financial performance of construction organisations. It is also one of the first studies that applies a sociotechnical framework to this relationship.

The purpose of this paper is to ascertain the significant stimulating measures/enablers relating to the existing building regulations for promoting the adoption and overcoming the barriers to the uptake and implementation of the green building concept (GBC) in developing countries using Ghana as a case.

The quantitative research approach was used to attain the study’s goal. Purposive and snowball sampling techniques were found to be suitable for collecting data from 292 relevant stakeholders in Ghana’s construction industry. The mean score ranking technique, in conjunction with the relative importance index, was used to establish the relative ranking of, among other things, the stimulus measures for increasing green building uptake in Ghana. An exploratory factor analysis was also used to classify the most significant stimulation strategies for improving green building uptake.

“Educational programmes relevant to GBTs for developers, contractors, and policymakers,” “sufficient information on the cost and benefits of GBTs” and “mandated green building codes and regulations” were the top three listed stimulating measures to promote increasing use of green building technologies (GBTs). The enablers were classified as follows: government regulations and policies; commitment and GB research; education and publicity; and incentives and support.

The study was conducted in Ghana, a developing nation, and thus the findings and implications are peculiar to Ghana. However, the study’s findings have important practical implications for the adoption and marketing of GBCs and GBTs in other developing nations.

Prioritizing major stimulation initiatives may be beneficial in terms of overcoming the constraints to the adoption of GBCs and GBTs in developing countries.

The purpose of this study was to examine how well a strength-based program grounded in positive psychology principles can advance the practical critical thinking skills of those pursuing the teacher training course.

This study used a single-group pre-test post-test design with 35 teacher-trainees from the Bachelor of Education course. The two-and-a-half-week strength-based program used the values in action survey to identify strengths. Pre- and post-test scores, measured with the Cornell Critical Thinking Test – Level Z, underwent Statistical Package for Social Sciences analysis including paired samples t-test for subcomponent and overall composite analysis.

Analysis of the pre- and post-test scores demonstrated a statistical significance in the critical thinking scores obtained by the teacher-trainees. Post-test scores were consistently significant. Out of the elements of critical thinking, induction, meaning, observation and credibility were more prominent. Deduction and assumption identification were also having a significant effect.

Most critical thinking programs focus on evaluating specific teaching methods for improving critical thinking skills. In education, positive psychology studies often center on students’ well-being, attention spans and academic success, aligning with wellness programs. Despite the importance of strengths in positive psychology, there is a lack of research on using a strength-based approach to boost critical thinking skills. This study aims to enhance teacher-trainees’ critical thinking by leveraging their individual strengths, moving away from traditional instructional strategies.

As an effective measure for reducing energy consumption and achieving carbon neutrality, prefabricated building projects (PBPs) have attracted considerable attention in China. Although the Chinese Government has vigorously promoted PBPs, neither developers nor consumers have high recognition of PBPs. This study aimed to explore the decision-making behaviour of governments, developers and consumers in promoting the development of prefabricated buildings in China and to better optimise the incentive strategies for prefabricated buildings in China.

Based on prospect and evolutionary game theories, an evolutionary game model of three stakeholders in the development of PBPs – government, developers and consumers – was constructed. Combined with the system dynamics theory, the incentive policy behaviour and influencing factors of the three parties in the evolutionary game model were analysed.

The results showed that the initial probability of the three parties affects the decision-making behaviour of each party and that of other stakeholders. Government subsidies to developers are more sensitive than developers themselves. There is a certain threshold for the scope of government subsidies to consumers, and exceeding this threshold does not promote the development of PBPs. Based on the results, policy recommendations to the government, developers and consumers were proposed to enhance PBP development.

This study provides suggestions for governments to formulate reasonable incentive policies for prefabricated buildings and a specific theoretical basis for the sound development of prefabricated buildings.