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This paper provides a stakeholder-based rationale for firm risk reduction through diversification. While firm-specific investments from stakeholders are often important sources of firm competitive advantage and economic rents, there is a reduced incentive for stakeholders to make these investments due to the risk associated with firm-specific investments. Since the risk associated with firm-specific investments is often related to the total firm risk level, we argue that stakeholders’ difficulties in diversifying the risks associated with their firm-specific investments create incentives for risk management by firms. We test this argument in a diversification setting. Based on a sample of firms’ first acquisition moves, we find that firms are more likely to engage in risk reduction through diversification when high levels of firm-specific assets are important to the firm's operations. Several proxies for stakeholders’ specific investments are found to be significant in explaining cross-sectional variation in the extent of ex ante risk reduction in acquisitions.

The purpose of this paper is to examine how the perceived trustworthiness of a B2B service provider relates to a business customer’s intention to use digital services from that provider. The study investigates whether perceived trustworthiness, composed of ability, integrity and benevolence, explains behavioral intentions equally among all business customer segments, and how characteristics such as job level, decision-making role, technology readiness age and gender moderate these effects.

Drawing on a model of trust transfer mechanism, the study explores how perceived trustworthiness established in face-to-face interaction influences the use of digital services in making B2B purchases. Hypotheses are tested using a sample of 1,866 responses collected from customers of four B2B firms.

Ability is the most influential on the customer’s intention to transact through digital channels, while the effects of integrity and benevolence show more variation. The effect of perceived trustworthiness on the intention to use digital services is remarkably stronger among senior and middle management, high-level decision makers, the younger age segment, men and individuals high in technology readiness, compared to other segments studied.

The study contributes to the scant research on B2B customer behavior in the digital environment and incorporates individual characteristics specific to the industrial domain.

This study aims to study the radar cross-section (RCS) of an intermeshing rotor with blade pitch.

The variation of rotor blade pitch is designed into three modes: fixed mode, linear mode and smooth mode. The dynamic process of two crossed rotors is simulated, where the instantaneous RCS is calculated by physical optics and physical theory of diffraction.

Increasing the pitch angle in the fixed mode can reduce the average RCS of rotor at the given head azimuth. The RCS curve of helicopter in linear mode and smooth mode will have a large peak in the side direction at the given moment. Although the blade pitch in smooth mode is generally larger than that in fixed mode, the smooth mode is conducive to reducing the peak and mean value of helicopter RCS at the given heading azimuth.

The calculation method for analyzing RCS of intermeshing rotor with variable blade pitch is established.

This paper's purpose is to develop a meaning-based framework for customer loyalty by examining how consumers make sense of customer loyalty through meanings and metaphors.

A qualitative study based on in-depth interviews and focus group data in the retail context was conducted with Finnish customers. The data were analysed with qualitative data analysis techniques such as the constant comparative technique.

The empirical findings comprise eight loyalty meanings characterised by two dimensions. The first dimension is reflexive vs. routinised, and the second dimension is private vs. social. The loyalty types are dimensionalised through four metaphors: loyalty as freedom of choice; as being conventional and binding; and as belongingness.

The findings improve the way customer loyalty currently is understood in the retail setting. The paper proposes that customer insight that utilises thick data can be used to grasp loyalty meanings. These data are rich in context and detail, and they take into account customers' everyday lives. Utilising thick data in the form of storytelling fuels customers' meaning-making related to customer loyalty, potentially enriching their relationship with the retailer.

Customer loyalty has been driven largely by a transactional and company-centric perspective. This article presents an alternative view of customer loyalty that accounts for the variety of meanings that customers may assign to their loyalty-related thoughts and behaviours.

Untimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.

In this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.

The Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.

This study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.

In this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.

Since the beginning of 2020, economies faced many changes as a result of coronavirus disease 2019 (COVID-19) pandemic. The effect of COVID-19 on the Egyptian Exchange (EGX) is investigated in this research.

To explore the impact of COVID-19, three periods were considered: (1) 17 months before the spread of COVID-19 and the start of the lockdown, (2) 17 months after the spread of COVID-19 and the during the lockdown and (3) 34 months comprehending the whole period (before and during COVID-19). Due to the large number of variables that could be considered, dimensionality reduction method, such as the principal component analysis (PCA) is followed. This method helps in determining the most individual stocks contributing to the main EGX index (EGX 30). The PCA, also, addresses the multicollinearity between the variables under investigation. Additionally, a principal component regression (PCR) model is developed to predict the future behavior of the EGX 30.

The results demonstrate that the first three principal components (PCs) could be considered to explain 89%, 85%, and 88% of data variability at (1) before COVID-19, (2) during COVID-19 and (3) the whole period, respectively. Furthermore, sectors of food and beverage, basic resources and real estate have not been affected by the COVID-19. The resulted Principal Component Regression (PCR) model performs very well. This could be concluded by comparing the observed values of EGX 30 with the predicted ones (R-squared estimated as 0.99).

To the best of our knowledge, no research has been conducted to investigate the effect of the COVID-19 on the EGX following an unsupervised machine learning method.

This study explored how teachers change, what teachers learn and how they learn during the implementation of project-based learning through lesson study.

In this study, three university researchers, one doctoral student and six mathematics school teachers formed a lesson study team. Using a qualitative research method, this study employed a locally integrating networking strategy to combine the modified Interconnected Model of Teacher Professional Growth (IMTPG) and Bannister's framework to describe the teachers' knowledge change when participating in a lesson study on project-based learning.

The research revealed that the school teachers' knowledge about authenticity and assessment in the context of project-based learning was changed after the lesson study and how the changes were triggered.

The study demonstrates how the networking of two different theories—modified IMTPG and Bannister's framework—contributes to a better understanding of the process of teachers' collective practice, as well as the knowledge change in PjBL. This networking was done by combining the two theories, which were superimposed at the domain of practice.

This paper's purpose is to explore the current interest and knowledge that UK consumers hold around agroforestry. Despite the many reported benefits of agroforestry systems, uptake in the UK, as well as other temperate nations, has been low. As the consumer has a role to play in the transition of agriculture to methods that are more environmentally friendly it is vital to have an understanding of their perceptions. Yet to date no work has looked at agroforestry from the perspective of the UK consumer.

An online survey was conducted using a convenience sample accessed by floating a link through social media and messaging apps. The survey was also shared to the members of a private Facebook group associated with an organic vegetable box service. A mix of multiple choice and open text boxes were used. The survey received 139 responses.

Non-parametric tests indicate that this sample of UK consumers would be mostly likely to buy, and willing to pay more for, agroforestry produce; and the sample showed a split group regarding familiarity. Inductive thematic analysis of the qualitative data highlighted some important barriers to the purchase as well as capturing a snapshot of this sample's perceptions.

This paper presents, to the authors knowledge, the first set of data regarding a sample of UK consumers' perspective of agroforestry produce. The findings could bolster producers' confidence in adopting agroforestry practices, but also highlight the need for policymakers to bolster consumer support through parallel means.

Neglected tropical diseases (NTDs) are a set of infectious diseases that primarily affect low-income countries situated near the equator. Effective diagnostic tools hold the key to stemming the spread of these infectious diseases. However, specificity is a major concern associated with current diagnostic protocols. In this regard, electrochemical deoxyribonucleic acid (DNA) biosensors could play a crucial role, as highlighted by renewed interest in their research. The purpose of this study was to highlight the current scenario for the design and development of biosensors for the detection of NTDs related pathogens. This review highlights the different types of factors involved and the modifications used to enhance sensor properties.

The authors discuss the potential of electrochemical DNA biosensors as efficient, affordable diagnostic tools for the detection of pathogens associated with NTDs by reviewing available literature. This study discusses the biosensor components, mainly the probe selection and type of electrodes used, and their potential to improve the overall design of the biosensor. Further, this study analyses the different nanomaterials used in NTD-based electrochemical DNA biosensors and discusses how their incorporation could improve the overall sensitivity and specificity of the biosensor design. Finally, this study examines the impact such techniques could have in the future on mass screening of NTDs.

The findings provide an in-depth analysis of electrochemical DNA biosensors for the detection of pathogens associated with NTDs.

This review provides an update on the different types and modifications of DNA biosensors that have been designed for the diagnosis of NTD-related pathogens.