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The purpose of this paper is to critically examine the multinational oil companies’ (MOCs) corporate social responsibility (CSR) initiatives in Nigeria. Its special focus is to investigate the impact of the global memorandum of understanding (GMoU) on mainstreaming gender sensitivity in cash crop market supply chains in the Niger Delta region of Nigeria.

This paper adopts an explanatory research design with a mixed method to answer the research questions and test the hypotheses. A total of 1,200 rural women respondents were sampled across the Niger Delta region.

Results from the use of a combined logit model and propensity score matching indicate a significant relationship between the GMoU model and mainstreaming gender sensitivity in cash crop market supply chains in the Niger Delta.

This study implies that MOCs’ CSR interventions that improve women’s access to land and encourage better integration of food markets through improved roads and increased mobile networks would enable women to engage in cash crop production.

This implies that improving access to credit through GMoU cluster farming targeted at female farmers would improve access to finance and extension services for women in cash crop production in the Niger Delta.

This research contributes to the gender debate in the agricultural value chain from a CSR perspective in developing countries and is rational for demands for social projects by host communities. It concludes that businesses have an obligation to help solve problems of public concern.

To investigate the influence of logistics and transportation workers’ perceptions of their management’s simultaneous safety and operations focus (or lack thereof) on related worker safety and operational perceptions and behaviors.

This multi-method research consisted of two studies. Study 1 aimed to establish correlational relationships by evaluating the impact of individual-level worker perceptions of operationally focused routines (as a moderator) on the relationship between worker perceptions of safety-related routines and workers’ self-reported safety and in-role operational behaviors using a survey. Study 2 aimed to establish causal relationships by evaluating the same conceptual relationships in a behavioral-type experiment utilizing vehicle simulators. After receiving one of four pre-task briefings, participants completed a driving task scenario in a driving simulator.

In Study 1, the relationship between perceived safety focus and safety behavior/in-role operational behavior was strengthened at higher levels of perceived operations focus. In Study 2, participants who received the balanced pre-task briefing committed significantly fewer safety violations than the other 3 treatment groups. However, in-role driving deviations were not impacted as hypothesized.

This research is conducted at the individual (worker) level of analysis to capture the little-known perspectives of logistics and transportation workers and explore the influence of balanced safety and operational routines from a more micro perspective, thus contributing to a deeper understanding of how balanced routines might influence worker behavior when conducting dynamic tasks to ensure safe, effective outcomes.

This study aims to develop a model based on the financial variables for better accuracy of financial distress prediction on the sample of private French, Spanish and Italian firms. Thus, firms in financial difficulties could timely request for troubled debt restructuring (TDR) to continue business.

This study used a sample of 312 distressed and 312 non-distressed firms. It includes 60 French, 21 Spanish and 231 Italian firms in both distressed and non-distressed groups. The data are extracted from the ORBIS database. First, the authors develop a new model by replacing a ratio in the original Z”-Score model specifically for financial distress prediction and estimate its coefficients based on linear discriminant analysis (LDA). Second, using the modified Z”-Score model, the authors develop a firm TDR probability index for distressed and non-distressed firms based on the logistic regression model.

The new model (modified Z”-Score), specifically for financial distress prediction, represents higher prediction accuracy. Moreover, the firm TDR probability index accurately depicts the probabilities trend for both groups of distressed and non-distressed firms.

The findings of this study are conclusive. However, the sample size is small. Therefore, further studies could extend the application of the prediction model developed in this study to all the EU countries.

This study has important practical implications. This study responds to the EU directive call by developing the financial distress prediction model to allow debtors to do timely debt restructuring and thus continue their businesses. Therefore, this study could be useful for practitioners and firm stakeholders, such as banks and other creditors, and investors.

This study significantly contributes to the literature in several ways. First, this study develops a model for predicting financial distress based on the argument that corporate bankruptcy and financial distress are distinct events. However, the original Z”-Score model is intended for failure prediction. Moreover, the recent literature suggests modifying and extending the prediction models. Second, the new model is tested using a sample of firms from three countries that share similarities in their TDR laws.

Proponents of micro-credentials envision them as vehicles for upskilling or re-skilling individuals. The study examines how integrating micro-credentials in the higher education ecosystem enhances employability. It aims to offer insights from the perspective of stakeholders who may benefit from these credentials at an institutional or individual level.

Online in-depth interviews are conducted with 65 participants from India, Nigeria, the United Arab Emirates and the United Kingdom to explore how micro-credentials can be a valuable addition to the higher education ecosystem. A multi-stakeholder approach is adopted to collect data.

The analysis highlights two possible methods of integrating micro-credentials into the higher education ecosystem. First, micro-credentials-driven courses can be offered using a blended approach that provides a flexible learning path. Second, there is also the possibility of wide-scale integration of micro-credentials as an outcome of standalone online programs. However, the effectiveness of such programs is driven by enablers like student profiles, standardization and the dynamics of the labor market. Finally, the study stipulates that micro-credentials can enhance employability.

The study's findings suggest that, for successful integration of micro-credentials, an operational understanding of micro-credentials, their enablers and strategic deliberation are critical in higher education. Institutions must identify the determinants, address technological limitations and select a suitable delivery mode to accelerate integration. However, micro-credentials can augment employability, considering the increasing emphasis on lifelong learning. An overview of the findings is presented through a comprehensive framework.

COVID-19 continues to spread, and cause increasing deaths. Physicians diagnose COVID-19 using not only real-time polymerase chain reaction but also the computed tomography (CT) and chest x-ray (CXR) modalities, depending on the stage of infection. However, with so many patients and so few doctors, it has become difficult to keep abreast of the disease. Deep learning models have been developed in order to assist in this respect, and vision transformers are currently state-of-the-art methods, but most techniques currently focus only on one modality (CXR).

This work aims to leverage the benefits of both CT and CXR to improve COVID-19 diagnosis. This paper studies the differences between using convolutional MobileNetV2, ViT DeiT and Swin Transformer models when training from scratch and pretraining on the MedNIST medical dataset rather than the ImageNet dataset of natural images. The comparison is made by reporting six performance metrics, the Scott–Knott Effect Size Difference, Wilcoxon statistical test and the Borda Count method. We also use the Grad-CAM algorithm to study the model's interpretability. Finally, the model's robustness is tested by evaluating it on Gaussian noised images.

Although pretrained MobileNetV2 was the best model in terms of performance, the best model in terms of performance, interpretability, and robustness to noise is the trained from scratch Swin Transformer using the CXR (accuracy = 93.21 per cent) and CT (accuracy = 94.14 per cent) modalities.

Models compared are pretrained on MedNIST and leverage both the CT and CXR modalities.

The study determined the role of personal values in doctor of philosophy (Ph.D.) students’ academic success in Tanzania. Specifically, it looked into the influence of openness to change values, self-enhancement values and conservation values on Ph.D. students’ academic success.

The study employed a cross-sectional survey design, in which 200 Ph.D. students from Tanzanian universities were involved by responding to a questionnaire. The relationship between the variables was determined by using structural equation modeling, and testing of the measurement model was done by confirmatory factor analysis (CFA).

The results indicate that personal values influence Ph.D. students’ academic success. Particularly, openness to change values have an ß value of 0.209 and p value of < 0.001, self-enhancement values have an ß of 0.173 and p-value of < 0.001 and conservation values have ß of 0.339 and p-value of < 0.001.

In the quest to improve Ph.D. students’ academic success, universities and Ph.D. students should foster openness to change values, self-enhancement values and conservation values in Ph.D. students.

The results of this study extend the use of the Schwartz theory of basic human values in explaining the academic success of Ph.D. students in Tanzanian universities. Past studies that applied this theory were based on secondary school and college students. Moreover, based on the author’s knowledge, this study is one of the early studies to systematically look into the role of personal values on Ph.D. students’ academic success. Thus, the study contributes to the existing literature on personal values and academic success because previous studies on this subject could not examine Ph.D. students’ success in isolation.

This study aims to determine barriers to innovation and to develop a quantitative model for the barrier to innovation in Vietnamese construction organizations of different sizes.

A literature review and discussions with experienced practitioners were implemented to determine barriers to innovation in construction organizations. The rank-based non-parametric test analyzed collected data from a questionnaire survey to examine if there were significant differences between the three groups of organizations, including small, medium and large construction organizations. The fuzzy synthetic evaluation (FSE) technique was employed to develop barrier indexes (BIs) for organizations of different sizes in Vietnam.

The findings showed 17 barriers to innovation which were categorized into four groups, including organizational, human resources, economic and market barriers. Statistical analysis revealed significant differences regarding barriers to innovation between small, medium and large construction organizations in Vietnam. The post hoc test highlighted barriers to innovation differently separated into two groups: SMEs and large construction organizations. The FSE analysis integrated the identified barriers into the comprehensive BIs for SMEs and large construction organizations. The FSE analysis illustrated that the organizational barrier is the most critical barrier for SMEs. On the other hand, the market barrier received the most significant attention in large construction organizations.

This research is one of the first integrated barriers to innovation into a comprehensive formulation. The indexes provide the decision-makers with a practical and reliable tool to evaluate barriers to innovation in construction organizations of different sizes.

The purpose of this paper is to explore a novel storytelling approach that investigates lived experience at the intersection of motherhood/caregiving and Ph.D. pursuits. The paper contributes to the feminist tradition of writing differently through the process of care that emerges from shared stories.

Using a process called heartful-communal storytelling, the authors evoke personal and embodied stories and transgressive narratives. The authors present a composite process drawing on heartful-autoethnography, dialogic writing and communal storytelling.

The paper makes two key contributions: (1) the paper illustrates a novel feminist process in action and (2) the paper contributes six discrete stories of lived experience at the intersection of parenthood and Ph.D. studies. The paper also contributes to the development of the feminist tradition of writing differently. Three themes emerged through the storytelling experience, and these include (1) creating boundaries and transgressing boundaries, (2) giving and receiving care and (3) neoliberal conformity and resistance. These themes, like the stories, also became entangled.

The paper demonstrates how heartful-communal storytelling can lead to individual and collective meaning making. While the Ph.D. is a solitary path, the authors' heartful-communal storytelling experience teaches that holding it separate from other relationships can impoverish what is learnt and constrain the production of good knowledge; the epistemic properties of care became self-evident.

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

Utilizing the theoretical foundation of the stimulus-organism-response framework, the present work developed and investigated a conceptual model. The work explores the effects of perceived risk of COVID-19 on tourists' choice hesitation and choice confidence. Furthermore, it examines the impacts of choice hesitation and choice confidence on psychological distress, which, in turn, influences purchase intentions and risk-protective behavior. Additionally, the study assesses the boundary effects of vulnerability on the association between choice hesitation, choice confidence, and psychological distress.

An online survey was administered in China during COVID-19 to assess the postulated hypotheses. We collected 491 responses using purposive sampling, and covariance-based structural equation modeling (CB-SEM) was performed to investigate the relationships.

Results show that the perceived risk of COVID-19 positively influences the choice hesitation and negatively impact choice confidence. It was also found that choice hesitation and choice confidence positively developed psychological distress, which, in turn, negatively triggered purchase intentions and positively developed risk-protective behavior. Additionally, perceived vulnerability had a significant moderating impact on the proposed relationships, strengthening psychological distress.

In the current context, this study measures bipolar behavioral outcomes using the S-O-R model. Because cognitive processes influence participation in health preventative behavior during the spread of diseases, we highlighted how the perception of risk and vulnerability to a pandemic serves as a reliable indicator of certain behaviors. This study advances understanding of how the psychological mindset of tourists copes with such circumstances. Due to the pandemic, tourists face limitations in their choices and are placing greater emphasis on adopting protective measures to mitigate associated risks.