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The purpose of this paper is to assess the perceived level of Jordanian nurses’ competencies in offering care to the community during a disaster.

A correlational descriptive design was used to assess nurses’ competencies in offering care for the community during a disaster.

A total of 370 nurses (55 % males) aged 25−55 agreed to participate. The mean score of competencies of nurses who offer care to the community during the disaster was 2.11 (SD = 0.59) points. The results of correlation coefficient tests revealed a significant positive correlation between stated competencies level and nurses’ sex, receiving disaster education and training with rpb (371) = 0.13, p < 0.01; rpb (598) = 0.15, p = 0.004; rpb (598) = 0.21, p < 0.001, respectively. Furthermore, the “care of communities” subscale had a weak positive correlation with the.

Nurses play a critical role in disaster response. However, there was a gap in nurses’ competencies for disaster, which shows there is a crucial need to include disaster management courses in the nursing curriculum and update disaster management courses in hospitals based on nurses’ needs to improve their competencies during disasters.

This study aims to analyze the critical factors affecting workplace well-being at construction sites across countries with different income levels. Accordingly, this study’s objectives are to identify: critical factors affecting workplace well-being at construction sites in low-, lower-middle-, upper-middle- and high-income countries, overlapping critical factors across countries with different income levels and agreements on the critical factors across countries with different income levels.

This study identified 19 factors affecting workplace well-being using a systematic literature review and interviews with construction industry professionals. Subsequently, the factors were inserted into a questionnaire survey and distributed among construction industry professionals across Yemen, Zimbabwe, Malaysia and Saudi Arabia, receiving 110, 169, 335 and 193 responses. The collected data were analyzed using descriptive and inferential statistics, including mean, normalized value, overlap analysis and agreement analysis.

This study identified 16 critical factors across all income levels. From those, 3 critical factors overlap across all countries (communication between workers, general safety and health monitoring and timeline of salary payment). Also, 3 critical factors (salary package, working environment and working hours) overlap across low-, low-middle and upper-middle-income countries, and 1 critical factor (project leadership) overlaps across low-middle, upper-middle and high-income countries. The agreements are inclined to be compatible between low- and low-middle-income, and between low- and high-income countries. However, agreements are incompatible across the remaining countries.

This study can serve as a standard for maintaining satisfactory workplace well-being at construction sites.

To the best of the authors’ knowledge, this study is the first attempt to analyze factors affecting workplace well-being at construction sites across countries with different income levels.

This study aims to understand females’ perception on the safety level of urban alleys in Bukit Bintang area, Kuala Lumpur, Malaysia, and thus identify the common safety features present in the alleys.

The study adopted a quantitative approach, based on the descriptive analysis of the safety data, the mean and median were calculated to identify the alleys with higher safety levels and to extract their common features.

These alleys had certain safety features in common, such as open views, high walls or buildings on both sides, a sense of community or business presence, adequate lighting, human and vehicle presence, safety facilities, cleanliness and moderate greenery.

The study identified these features as key contributors to improved safety levels, which in turn enhance the perception of safety among female users. The findings might assist policymakers or urban planners in managing and building urban alleys in a more effective and safe manner.

The purpose of this study is to provide a holistic understanding of the factors that either promote or hinder the adoption of artificial intelligence (AI) in supply chain management (SCM) and operations management (OM). By segmenting the AI lifecycle and examining the interactions between critical success factors and critical failure factors, this study aims to offer predictive insights that can help in proactively managing these factors, ultimately reducing the risk of failure, and facilitating a smoother transition into AI-enabled SCM and OM.

This study develops a knowledge graph model of the AI lifecycle, divided into pre-development, deployment and post-development stages. The methodology combines a comprehensive literature review for ontology extraction and expert surveys to establish relationships among ontologies. Using exploratory factor analysis, composite reliability and average variance extracted ensures the validity of constructed dimensions. Pearson correlation analysis is applied to quantify the strength and significance of relationships between entities, providing metrics for labeling the edges in the resource description framework.

This study identifies 11 dimensions critical for AI integration in SCM and OM: (1) setting clear goals and standards; (2) ensuring accountable AI with leadership-driven strategies; (3) activating leadership to bridge expertise gaps; (4) gaining a competitive edge through expert partnerships and advanced IT infrastructure; (5) improving data quality through customer demand; (6) overcoming AI resistance via awareness of benefits; (7) linking domain knowledge to infrastructure robustness; (8) enhancing stakeholder engagement through effective communication; (9) strengthening AI robustness and change management via training and governance; (10) using key performance indicators-driven reviews for AI performance management; (11) ensuring AI accountability and copyright integrity through governance.

This study enhances decision-making by developing a knowledge graph model that segments the AI lifecycle into pre-development, deployment and post-development stages, introducing a novel approach in SCM and OM research. By incorporating a predictive element that uses knowledge graphs to anticipate outcomes from interactions between ontologies. These insights assist practitioners in making informed decisions about AI use, improving the overall quality of decisions in managing AI integration and ensuring a smoother transition into AI-enabled SCM and OM.

In this research, we seek to understand the effects of artificial intelligence (AI) and knowledge management (KM) processes in enhancing proactive green innovation (PGI) within oil and gas organizations. It also aims to investigate the moderator role of trust and sustainability in these relationships.

This paper employs a quantitative analysis. Surveys have been gathered from the middle-line managers of twenty-four oil and gas government organizations to evaluate the perceptions of the managers towards AI, KM processes, trust, sustainability measures and proactive measures toward green innovation. Analytical and statistical tools that were employed in this study, including structural equation modeling with SmartPLSv3.9, have been used to analyze the data and to examine the measurement and structural models of this study.

The study results reveal a significant and positive impact of AI utilization, KM processes and PGI within oil and gas organizations. Furthermore, trust and sustainability turn out to be viable moderators affecting, and influencing the strength and direction of AI, KM and PGI relationships. In particular, higher levels of trust and more substantial sustainability commitments enhance the positive impact of AI and KM on green innovation outcomes.

Understanding the impact of AI, KM, trust and sustainability offers valuable insights for organizational leaders and policymakers seeking to promote proactive green innovation within the oil and gas industry. Thus, organizations can increase the efficiency of sustainable product development, process improvement and environmental management by using robust AI technologies and effective KM systems. Furthermore, fostering trust among stakeholders and embedding sustainability principles into organizational culture can amplify the effectiveness of AI and KM initiatives in driving green innovation outcomes.

This study extends the current knowledge by assessing the effect of AI and KM on proactive green innovation while accounting for trust and sustainability as moderators. Utilizing quantitative methods offers a nuanced understanding of the complex interactions between these variables, thereby advancing theoretical knowledge in the fields of innovation management, sustainability and organizational behavior. Additionally, the identification of specific mechanisms and contextual factors enriches practical insights for organizational practitioners striving for a practical understanding of the dynamics of the complexities of sustainable innovation in an AI-driven era.

The purpose of this paper is to investigate the accurate monitoring and assessment of steel bar corrosion in concrete based on deep learning multi-sensor information fusion method. The paper addresses the issue of traditional corrosion assessment models relying on sufficient data volume and low evaluation accuracy under small sample conditions.

A multi-sensor integrated corrosion monitoring equipment for reinforced concrete is designed to detect corrosion parameters such as corrosion potential, current, impedance, electromagnetic signal and steel bar stress, as well as environmental parameters such as internal temperature, humidity and chloride ion concentration of concrete. To overcome the small amount of monitoring data and improve the accuracy of evaluation, an improved Siamese neural network based on the attention mechanism and multi-loss fusion function is proposed to establish a corrosion evaluation model suitable for small sample data.

The corrosion assessment model has an accuracy of 98.41%, which is 20% more accurate than traditional models.

Timely maintenance of buildings according to corrosion evaluation results can improve maintenance efficiency and reduce maintenance costs, which is of great significance to ensure structural safety.

The corrosion monitoring equipment for reinforced concrete designed in this paper can realize the whole process of monitoring inside the concrete. The proposed corrosion evaluation model for reinforced concrete based on Siamese neural network has high accuracy and can provide a more accurate assessment model for structural health testing.

“Social hesitancy” is a reluctance by people to purchase products, or engage in activities, that may benefit themselves and society. This paper aims to review and assess a visual marketing approach to this significant social marketing problem.

The authors use data visualization technology as an informational tool, visual sentiment analysis as a social text mining tool and Latent Dirichlet Allocation visual (LDAvis) modelling as a topic modelling tool to measure, assess and address social attitudes inherent in hesitancy. The paper’s hypothesis is that these technologies can help society understand the reasons for, and barriers to, hesitancy, and that visual marketing is an extremely effective approach to the hesitancy problem.

Using extensive vaccination data and results from the COVID-19 pandemic, the authors found that the visual marketing technologies were successful informational and motivational tools for social hesitancy.

Hesitancy is a social marketing concern that can have an impact on product or service promotional and motivational campaigns during a crisis. The LDA visual model, for example, can quantitatively extract and measure the social attitudes of people and identify and segment these people based on their feelings. These tools can be valuable to social marketers by helping to establish strategies for any product or service exhibiting hesitant consumer behaviour.

Using advanced visual technology, the paper contributes to social hesitancy by addressing the following question: does a visual marketing approach help social marketers understand the underlying reasons for, and help to mitigate, social hesitancy?

This study aims to examine the effect of Muslim consumers’ religiosity on their brand verdict regarding clothing brands, through the mediating role of decision-making style, brand status and brand attitude.

Structural equation modeling was used to analyze the data collected from 200 clothing buyers in Mashhad, one of Iran’s religious cities.

The results indicate that intrapersonal religiosity, compared to interpersonal religiosity, has a more significant effect on Muslim consumers’ decision-making styles, and different decision-making styles of Muslim consumers affect their brand verdict through brand status and brand attitude.

The research sample consists solely of respondents from the Islamic religion. Therefore, the impact of religiosity might differ among individuals from other religions, such as Christianity and Judaism.

This study’s findings are crucial for clothing brands, both national and international, that cater to the Muslim customers’ market. They need to consider the degree of religiosity when segmenting and targeting their market. This study shows that clothing brand marketers can best influence the brand verdict of Muslim consumers by targeting those with a brand-loyal decision-making style, focusing on their religious beliefs.

To achieve success in Iran’s Muslim market, marketers must consider their consumers’ religious beliefs and tailor their marketing plans accordingly. This study aims to investigate the impact of religiosity on consumer behavior toward brands in Iran’s Muslim market.

The study investigates the impact of various factors on the number of active investors in digital health startups. Through nine hypotheses, we examine the influence of metrics such as patents, online presence, financial aspects and company valuation on investor interest. The results reveal positive associations between these metrics and investor numbers, highlighting their role in signaling strength and attracting investment. This research enhances the understanding of investor valuation in digital health startups, emphasizing the importance of credible signals for building trust and securing funding. However, we acknowledge limitations in data analysis methods and suggest future research to explore industry signals, longitudinal trends and failed startups for comprehensive insights.

This study delves into the design methodology and approach, aiming to fill gaps in understanding investor roles in valuing digital health ventures. We focus on deciphering factors driving valuations for these startups to secure growth financing. Using signaling theory, we investigate how entrepreneurs communicate their latent strengths to bridge information gaps, aiding investment decisions. We analyze a sample of 482 healthcare startups from the Pitchbook database using Poisson regression in SPSS.

This research sheds light on the factors driving investor interest in digital health startups. Despite the critical role of entrepreneurs in patient care innovations, the relationship between investor characteristics and funding for digital health technologies still needs exploration. We examine factors influencing investor valuation in healthcare startups and identify patents, social followers and financial disclosures as pivotal elements shaping investor interest. The findings show that all factors for active investors are significant for all variables except similar unique visitors.

These results significantly enhance our understanding of investor decision-making in digital health startups. They confirm the importance of various signals, like patent activity, online presence and financial performance, in attracting investor attention. We utilize unique data sources, offering insights into investors' behavior across different funding stages. In conclusion, these findings underscore investors' crucial role in the growth and funding of healthcare tech startups, emphasizing the need for robust signals to attract investment.

This study examines the interaction between sales control systems and firm level strategic orientations and their joint effects on company performance in B2B context. Independent and joint effects of market orientation (MO), innovation orientation (IO) and sales control systems (SCS) on firm performance were analyzed.

A quantitative research methodology is adopted to compile firm-level primary data from manufacturing companies located in an organized industrial zone. Research data were collected by face-to-face surveys from 302 sales professionals. The research model and hypotheses were tested by using partial least squares (PLS) structural equation modeling (SEM) with Smart PLS 3.0.

In addition to confirming the positive effects of MO and IO on performance, data analyses revealed that SCS exert an indirect effect on company performance which is fully mediated by MO.

The research was limited to a developing country context and research data was collected from a convenient sample of B2B companies by a cross-sectional study. Cross-cultural and longitudinal studies may provide additional insights. Firm level strategic orientations and sales control systems must be examined together in an integrated way to explore their effects on company performance. The individual effects of these structures on business performance may manifest differently when they come together.

Results indicate that the sales control system setup is critical for the implementation of a market-oriented strategy. This study highlights the importance of setting a compatible sales control system to achieve organizational goals in accordance with the strategic orientations which affect the success of particular organizational strategies.

Although the current literature identifies the independent and joint effects of market orientation and innovation orientation on company performance, empirical studies probing the interaction of sales control systems with these constructs is very scarce. Understanding how sales control systems relate to strategic orientations will help design a more effective sales organization and improve company performance. This study contributes to the literature by promoting additional insights by linking sales control systems with market orientation, innovation orientation and company performance.