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This paper aims to assess the direct effects of antecedents of initial trust, the mediating effect of trust and the moderating effect of demographic variables on non-adopters’ behavioral intention to use mobile banking.

The study tested the models of theory of reasoned action and theory of planned behavior to evaluate potential antecedents of trust (diffusion of trust, infrastructure quality, perceived costs, privacy and security) moderators (demographic variables) and mediators (initial trust) that will influence behavioral intention to use mobile banking. The Hayes’ Process Macro developed by Andrew F. Hayes (2013) was used as a statistical analysis in SPSS to estimates the path coefficients using multiple regression. The tool provides insights on the direct and indirect effect of the independent variable on the dependent variable through the existence of moderating variables and mediation variables.

The results show that the non-adopters of mobile banking asserted that the antecedents of initial trust played a significant influence on behavioral intention to use online banking services.

There is a dearth of literature addressing mobile banking in the Philippines. The first initial trust formation in internet banking using computer workstations and laptops in the Philippines was conducted by Chiu et al. (2016). This research fills in the gap by expanding and formulating a deeper understanding of the antecedents of initial trust that influence consumer behavioral intention that might be responsible for the slow diffusion of mobile banking services in the country. The results from this study will help financial institutions create a beneficial connection with consumers while alleviating the fears of non-adopters and enhancing their understanding of the benefits of mobile banking.

This study aims to investigate how different academic fields within and outside of international business (IB) engage with the topics of social value creation in the context of multinational enterprises (MNEs). The aim is to take stock of the main themes and offer suggestions for future research avenues.

The paper undertakes a scoping review. The authors use the Web of Science database to identify relevant articles. The database search yielded 466 articles. The NVivo software was used to code and identify key thematic areas.

The matrix analysis performed in NVivo yielded 15 main thematic areas spanning 37 research fields. However, further analysis revealed that 89 per cent of the articles originated from 13 fields. Furthermore, while IB journals represent the second-largest field home to publications related to the social value creation of MNEs, they only account for 12 per cent of the sample.

The paper responds to prior calls to reduce disciplinary silos through the performing of a thematic analysis across a multitude of research fields.

The paper aims to solve the problem of personnel intrusion identification within the limits of high-speed railways. It adopts the fusion method of millimeter wave radar and camera to improve the accuracy of object recognition in dark and harsh weather conditions.

This paper adopts the fusion strategy of radar and camera linkage to achieve focus amplification of long-distance targets and solves the problem of low illumination by laser light filling of the focus point. In order to improve the recognition effect, this paper adopts the YOLOv8 algorithm for multi-scale target recognition. In addition, for the image distortion caused by bad weather, this paper proposes a linkage and tracking fusion strategy to output the correct alarm results.

Simulated intrusion tests show that the proposed method can effectively detect human intrusion within 0–200 m during the day and night in sunny weather and can achieve more than 80% recognition accuracy for extreme severe weather conditions.

(1) The authors propose a personnel intrusion monitoring scheme based on the fusion of millimeter wave radar and camera, achieving all-weather intrusion monitoring; (2) The authors propose a new multi-level fusion algorithm based on linkage and tracking to achieve intrusion target monitoring under adverse weather conditions; (3) The authors have conducted a large number of innovative simulation experiments to verify the effectiveness of the method proposed in this article.

The paper follows Jason Cope's (2011) vision of a holistic perspective on the failure-based learning process. By analyzing the research since Cope's first attempt, which is often fragmentary in nature, and providing novel empirical insights, the paper aims to draw a new comprehensive picture of all five phases of entrepreneurial learning and their interplay.

The study features an interpretative phenomenological analysis of in-depth interviews with 18 failed entrepreneurs. Findings are presented and discussed in line with experiential learning theory and Cope's conceptual framework of five interrelated learning timeframes spanning from the descent into failure until re-emergence.

The study reveals different patterns of how entrepreneurs experience failure, ranging from abrupt to gradual descent paths, different management and coping behaviors, and varying learning effects depending on the new professional setting (entrepreneurial vs non-entrepreneurial). Analyzing the entrepreneurs' experiences throughout the process shows different paths and connections between individual phases. Findings indicate that the learning timeframes may overlap, appear in different orders, loop, or (partly) stay absent, indicating that the individual learning process is even more dynamic and heterogeneous than hitherto known.

The paper contributes to the field of entrepreneurial learning from failure, advancing Cope's seminal work on the learning process and -contents by providing novel empirical insights and discussing them in the light of recent scientific findings. Since entrepreneurial learning from failure is a complex and dynamic process, using a holistic lens in the analysis contributes to a better understanding of this phenomenon as an integrated whole.

The purpose of the current study is to assess Omani teachers’ performance on tasks related to the stages of engineering design. To achieve this, data from an engineering design test was used, and demographic variables that are correlated with this performance were identified.

This descriptive study employed a cross-sectional design and the collection of quantitative data. A sample of preservice science teachers from Sultan Qaboos University (SQU) (n = 70) participated in this study.

Findings showed low and moderate levels of proficiency related to the stages of engineering design. Differences between males and females in terms of performance on engineering design tasks were found, with females scoring higher overall on the assessment. Biology preservice teachers scored higher than teachers from the other two majors (physics and chemistry) in two subscales. There were also differences between teachers studying in the Bachelor of Science (BSc) program and the teacher qualification diploma (TQD) program.

This study provides an overview, in an Arab setting, of preservice science teachers’ proficiency with engineering design process (EDP) tasks. It is hoped that the results may lead to improved instruction in science teacher training programs in similar contexts. Additionally, this research demonstrates how EDP competency relates to preservice teacher gender, major and preparation program. Findings from this study will contribute to the growing body of research investigating the strengths and shortcomings of teacher education programs in relation to science, technology, engineering and mathematics (STEM) education.

In the study of expatriation and expatriate adaptation, there are limited studies that focus on issues faced by expatriates working in foreign countries with very distinct cultures. This study aims to explore this idea through the experiences of western expatriates working in the Kingdom of Saudi Arabia (KSA). Two research questions were posed to examine the cross-cultural issues and challenges faced by expatriates in the KSA, as well as the role of cross-cultural training in expatriate adjustment.

The study was guided by an interpretivism paradigm through a qualitative method by using a semi-structured in-depth interview approach. Interviews were conducted among 12 expatriates from the USA and UK who are currently working in KSA.

A coding technique and theoretical thematic analysis was conducted to analyze the data. The results of this study highlighted three key themes that had a considerable influence on expatriates’ adjustment, in particular: culture shock, lack of pre-departure training and the demand for an extensive cross-cultural training.

It is acknowledged that the existence of sub-cultures within the KSA would expose the respondents to varying cultural values within the community. Thus, future studies within a similar context should consider the influence of intra-cultural variations.

The findings of the study emphasized on the importance understanding the cultural gap between home and host country and the individual cultural awareness of the expatriate. It calls attention to the need for a tailored and extensive pre-departure, cross-cultural training and a collaborative effort between employees’ and managers to improve expatriates’ motivation and retention.

In the recent dynamic market, supply chain disruptions are rapidly increasing with varied customer demand, technological changes, uncertain pandemic events etc. To overcome the unexpected disruptions, supply chain of each business should be resilient and pharmaceutical supply chain (PSC) is no exception. Motivated by the challenges and unexpected pandemic disruptions, this paper aims to examine the performance indicators (PIs) of the resilient PSC and to predict the resilience level for a certain time period in the context of Bangladesh.

The aim of this paper presents a structured framework based on the Delphi method, fuzzy DEMATEL (Decision Making Trial and Evaluation Laboratory) and system dynamics (SD). The proposed methodology was validated using expert's inputs from the relevant field in Bangladesh. This study reveals the influential relationships of indicators and resilience level using fuzzy DEMATEL and SD to improve the resiliency.

Findings revealed that “Supply chain risk orientation”, “Visibility”, “Flexibility”, “Agility in supply chain” and “Collaboration” are the top five influential performance indicators for resilient PSC. The cause and effect relationship found that “IT capability”, “Flexibility”, “Supply chain network design”, “Resource availability”, “Supply chain risk orientation” and “Velocity” were in cause category which play a vital role for establishing resilient supply chain. SD approach has developed a model for predicting the resilience level of the supply chain.

This work is one of the initial contributions in the literature that has targeted on the identification and analysis of the significant PIs and predicting the resilience level of the PSC.

The purpose of this paper is twofold: first, to take stock of the current state of knowledge about inclusive value-chain development (VCD) in the context of international agricultural research; and second, to draw out the implications for future research and action.

This paper is based on a review of recent research papers authored by professionals affiliated with international agricultural research centers and their partners in Africa, Asia, and Latin America.

The studies reviewed in the paper identify the opportunities emerging from new and expanding markets for agricultural products and challenges to smallholder participation in these markets. It identifies key attributes of successful value-chain interventions, emphasizing the importance of combining value-chain approaches with other approaches, including those emerging from innovation systems and rural livelihoods frameworks. Methods are offered for evaluating complex value-chain interventions.

The paper summarizes the state of knowledge as of early 2016 in a dynamic field. Important contributions to knowledge may have been made since then.

The paper summarizes the state of knowledge in the field, and identifies emerging issues and policy implications, knowledge gaps, and priorities for future applied research.

The purpose of this paper is to review cases of artificial reefs built through additive manufacturing (AM) technologies and analyse their ecological goals, fabrication process, materials, structural design features and implementation location to determine predominant parameters, environmental impacts, advantages, and limitations.

The review analysed 16 cases of artificial reefs from both temperate and tropical regions. These were categorised based on the AM process used, the mortar material used (crucial for biological applications), the structural design features and the location of implementation. These parameters are assessed to determine how effectively the designs meet the stipulated ecological goals, how AM technologies demonstrate their potential in comparison to conventional methods and the preference locations of these implementations.

The overview revealed that the dominant artificial reef implementation occurs in the Mediterranean and Atlantic Seas, both accounting for 24%. The remaining cases were in the Australian Sea (20%), the South Asia Sea (12%), the Persian Gulf and the Pacific Ocean, both with 8%, and the Indian Sea with 4% of all the cases studied. It was concluded that fused filament fabrication, binder jetting and material extrusion represent the main AM processes used to build artificial reefs. Cementitious materials, ceramics, polymers and geopolymer formulations were used, incorporating aggregates from mineral residues, biological wastes and pozzolan materials, to reduce environmental impacts, promote the circular economy and be more beneficial for marine ecosystems. The evaluation ranking assessed how well their design and materials align with their ecological goals, demonstrating that five cases were ranked with high effectiveness, ten projects with moderate effectiveness and one case with low effectiveness.

AM represents an innovative method for marine restoration and management. It offers a rapid prototyping technique for design validation and enables the creation of highly complex shapes for habitat diversification while incorporating a diverse range of materials to benefit environmental and marine species’ habitats.

Over the past decades, many health authorities and public policy experts have traditionally relied on indicators that are dependent on a nation's economy, its health-care infrastructure advancements, and superiority in biomedical sciences and technology to predict potential infection rates should a health pandemic occur. One such commonly relied-upon indicator was that of the Global Health Security (GHS) Index. However, the coronavirus disease 2019 (COVID-19) pandemic has shown how such variables prove to be inaccurate in predicting the infection rates during a global health pandemic. Hence, this paper proposes the utilization of socio-cultural behavioral traits to predict a country's COVID-19 infection rates.

This is achieved by proposing a model involving the classification and regression tree (CART) algorithm and a Poisson regression against the six selected cultural behavioral predictors consisting of individualism, power distance, masculinity, uncertainty avoidance, long-term orientation, and indulgence.

The results show that all the selected cultural behavioral predictors are significant in impacting COVID-19 infection rates. Furthermore, the model outperforms the conventional GHS Index model based on a means squared error comparison.

The authors hope that this study would continue promoting the use of cultures and behaviors in modeling the spread of health diseases.

The authors hope that their works could prove beneficial to public office holders, as well as health experts working in health facilities, in better predicting potential outcomes during a health pandemic, thus allowing them to plan and allocate resources efficiently.

The results are a testament to the fact that sociocultural behavioral traits are more reliant predictors in modeling cross-national infection rates of global health pandemics, like that of COVID-19, as compared to economic-centric indicators.