Search results

Innovation management in the healthcare sector has undergone significant evolutions over the last decades. These evolutions have been investigated from a variety of perspectives: clusters, ecosystems of innovation, digital ecosystems and regional ecosystems, but the dynamics of networks have seldom been analyzed under the lenses of entrepreneurial ecosystems (EEs). As identified by Cao and Shi (2020), the literature is silent about the organization of resource allocation systems for network orchestration in EEs. This article investigates these elements in the healthcare sector. It discusses the strategic role played by entrepreneurial support organizations (ESOs) in resource allocation and elaborates on the distinction between sponsored and nonsponsored ESOs in EEs. ESOs are active in network orchestration. The literature explains that ESOs lift organizational, institutional and cultural barriers, and support entrepreneurs' access to cognitive and technological resources. However, allocation models are not yet discussed. Therefore, our research questions are as follows: What is the resource allocation model in healthcare-related EEs? What is the role played by sponsored and nonsponsored ESOs as regards resource allocation to support the emergence and development of EEs in the healthcare sector?

The article offers an explanatory, exploratory, and theory-building investigation. The research design offers an abductive research protocol and multi-level analysis of seven (sponsored and nonsponsored) ESOs active in French healthcare ecosystems. Field research elaborates on semi-structured interviews collected between 2016 and 2022.

This article shows explicit complementarities between top-down and bottom-up resource allocation approaches supported by ESOs in the healthcare sector. Despite explicit originalities in each approach, no network orchestration model prevails. Multi-polar coordination is the rule. Entrepreneurs' access to critical technological and cognitive resources is based on resource allocation modalities that differ for sponsored versus nonsponsored ESOs. Emerging from field research, this research also shows that sponsored and nonsponsored ESOs manage their roles in different ways because they confront original issues about organizational legitimacy.

Beyond the results listed above, the main originalities of the paper relate to the instantiation of multi-level analysis operated during field research and to the confrontation between sponsored versus nonsponsored ESOs in the domain of healthcare-related innovation management. This research shows that ESOs have practical relevance because they build original routes for resource allocation and network orchestration in EEs. Each ESO category (sponsored versus nonsponsored) provides original support for resource allocation. The ESO's legitimacy is inferred either from the sponsor or the services delivered to end-users. This research leads to propositions for future research and recommendations for practitioners: ESO managers, entrepreneurs, and policymakers.

This paper aims to help understand how adopting risk allocation criteria impacts the delivery of public–private partnership (PPP) mass housing in Nigeria with the view of promoting the adoption of PPP housing scheme in Nigeria.

The research design adopts the census sampling approach by using well-structured questionnaires distributed to stakeholders involved in PPP-procured mass housing projects, i.e. consultants, in-house professionals, contractors and the organized private sector, registered with PPP departments in the Federal Capital Territory Development Authority, Abuja, Nigeria. Sixty-three risk factors, nine risk allocation criteria and nine project delivery indices were submitted for the respondents to rank on a Likert scale of 7. Two hypotheses were formulated to test whether the risk allocation criteria impacted PPP mass housing delivery or otherwise. The study adopts partial least square-structural equation modeling to model the effect of risk on risk allocation criteria on project delivery indices and risk severity.

The finding shows that project risk allocation criteria have less effect on project delivery indices than on risk severity. The study concludes that risk allocation principles do not directly affect the delivery of PPP-procured mass housing projects. This is evident by the path coefficient of 0.724 values, which is not statistically significant at a 5% alpha protection value. The study concludes that allocating critical risk factors influences the performance of PPP-procured mass housing projects, as the path coefficient of 0.360 is also not significantly far from 0 and at a 5% alpha protection value.

The study is one of the recent studies conducted in PPP-procured mass housing projects in Nigeria owing to the novelty of procurement option in the sector. It highlights the risk factors that can jeopardize the PPP-procured mass housing project objectives. The study is of immense value to PPP actors in the sector by providing the necessary information required to formulate risk response methods to minimize the impact of the risk factors in PPP mass housing projects.

The purpose of this paper is to investigate how customer relationship management (CRM) implementation affects internal capital allocation efficiency, the efficiency with which a firm allocates its capital across its business segments.

The authors use a statistical regression method to analyze a sample of 801 unique firms in the USA from COMPUSTAT and the Computer Intelligence database. This analysis examines the relation between CRM implementation and internal capital allocation efficiency and identifies the conditions under which firms benefit more from CRM implementation. They also use instrumental variables (IVs) to address endogenous concerns with a two-stage least squares (2SLS) model.

The authors find that CRM implementation is positively related to internal capital allocation efficiency. The results are robust to the 2SLS analysis with IVs. This positive relation is more pronounced for firms with effective internal control and for those operating in highly competitive markets.

The research implies that that CRM can have a significant cross-functional effect on corporate financing and budgeting. This also suggests that when chief marketing officers plan marketing initiatives and implement CRM, they should communicate to chief financial officers not only the direct effect but also the indirect strategic benefits of such initiatives to a firm.

The authors reveal a previously overlooked aspect of marketing accountability by suggesting marketing’s impact on internal capital markets. They also enrich the body of literature on CRM benefits by showing a cross-functional benefit from marketing to finance (or capital allocation).

As corporate sustainability is systemic, it cannot be achieved without effective involvement of suppliers. This study aims to examine the drivers of supplier companies’ resource allocation to a sustainability issue that affects customer companies and society at large.

Supplier companies’ resource allocation for a sustainability issue is explained from variables at the levels of the institutional, supply chain and internal environments of a supplier company. The framework is tested with a moderated regression model on 102 supplier companies in animal-based supply chains, focussing on their resource allocation for farm animal welfare.

The findings show that supply chain factors have the strongest influence on suppliers’ resource allocation, including a strong effect of investment specificity and a U-shaped effect of chain integration. Also, significant effects from institutional variables, namely, the pressure on consumer companies, and an inverted U-shaped effect of sustainability competition are found. The innovativeness, referring to the internal environment of supplier companies, appears as another important factor for the allocation of resources to animal welfare, as a sustainability issue.

The results have implications for consumer market companies to deal with sustainability issues that require involvement of their suppliers, for supplier companies to increase their competitive positions and strengthen their relationships within the supply chain, and for policymakers seeking solutions for sustainability issues in the market domain.

While existing literature focusses mostly on the corporate sustainability of highly visible and large consumer companies, to the best of the authors’ knowledge, this study is the first to examine the drivers of supplier companies’ resource allocation for a sustainability issue, namely, animal welfare. It provides insights on what drives supplier companies, usually operating outside the spotlight, to become part of a sustainability transition.

Metaverse, that is extended reality (XR)-based technologies such as augmented reality (AR) and virtual reality (VR), are increasingly believed to facilitate fundamental human practice in the future. One of the vanguards of this development has been the consumption domain, where the multi-modal and multi-sensory technology-mediated immersion is expected to enrich consumers' experience. However, it remains unclear whether these expectations have been warranted in reality and whether, rather than enhancing the experience, metaverse technologies inhibit the functioning and experience, such as cognitive functioning and experience.

This study utilizes a 2 (VR: yes vs no) × 2 (AR: yes vs no) between-subjects laboratory experiment. A total of 159 student participants are randomly assigned to one condition — a brick-and-mortar store, a VR store, an AR store and an augmented virtuality (AV) store — to complete a typical shopping task. Four spatial attention indicators — visit shift, duration shift, visit variation and duration variation — are compared based on attention allocation data converted from head movements extracted from recorded videos during the experiments.

This study identifies three essential effects of XR technologies on consumers' spatial attention allocation: the inattention effect, acceleration effect and imbalance effect. Specifically, the inattention effect (the attentional visit shift from showcased products to the environmental periphery) appears when VR or AR technology is applied to virtualize the store and disappears when AR and VR are used together. The acceleration effect (the attentional duration shift from showcased products to the environmental periphery) exists in the VR store. Additionally, AR causes an imbalance effect (the attentional duration variation increases horizontally among the showcased products).

This study provides valuable empirical evidence of how VR and AR influence consumers' spatial bias in attention allocation, filling the research gap on cognitive function in the metaverse. This study also provides practical guidelines for retailers and XR designers and developers.

Cloud computing gives several on-demand infrastructural services by dynamically pooling heterogeneous resources to cater to users’ applications. The task scheduling needs to be done optimally to achieve proficient results in a cloud computing environment. While satisfying the user’s requirements in a cloud environment, scheduling has been proven an NP-hard problem. Therefore, it leaves scope to develop new allocation models for the problem. The aim of the study is to develop load balancing method to maximize the resource utilization in cloud environment.

In this paper, the parallelized task allocation with load balancing (PTAL) hybrid heuristic is proposed for jobs coming from various users. These jobs are allocated on the resources one by one in a parallelized manner as they arrive in the cloud system. The novel algorithm works in three phases: parallelization, task allocation and task reallocation. The proposed model is designed for efficient task allocation, reallocation of resources and adequate load balancing to achieve better quality of service (QoS) results.

The acquired empirical results show that PTAL performs better than other scheduling strategies under various cases for different QoS parameters under study.

The outcome has been examined for the real data set to evaluate it with different state-of-the-art heuristics having comparable objective parameters.

The paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.

First, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.

The paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.

The paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.

In recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.

In this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.

The results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.

This novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.

Since the performance of vehicular users and cellular users (CUE) in Vehicular networks is highly affected by the allocated resources to them. The purpose of this paper is to investigate the resource allocation for vehicular communications when multiple V2V links and a V2I link share spectrum with CUE in uplink communication under different Quality of Service (QoS).

An optimization model to maximize the V2I capacity is established based on slowly varying large-scale fading channel information. Multiple V2V links are clustered based on sparrow search algorithm (SSA) to reduce interference. Then, a weighted tripartite graph is constructed by jointly optimizing the power of CUE, V2I and V2V clusters. Finally, spectrum resources are allocated based on a weighted 3D matching algorithm.

The performance of the proposed algorithm is tested. Simulation results show that the proposed algorithm can maximize the channel capacity of V2I while ensuring the reliability of V2V and the quality of service of CUE.

There is a lack of research on resource allocation algorithms of CUE, V2I and multiple V2V in different QoS. To solve the problem, one new resource allocation algorithm is proposed in this paper. Firstly, multiple V2V links are clustered using SSA to reduce interference. Secondly, the power allocation of CUE, V2I and V2V is jointly optimized. Finally, the weighted 3D matching algorithm is used to allocate spectrum resources.

This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single robot during construction operations.

A hybrid task allocation method based on integer programming and auction algorithms, with the aim of achieving a balanced workload between two robots has been proposed. In addition, while ensuring reasonable workload allocation between the two robots, an improved dual ant colony algorithm was used to solve the dual traveling salesman problem, and the global path planning of the two robots was determined, resulting in an efficient and collision-free path for the dual robots to operate. Meanwhile, an improved fast Random tree rapidly-exploring random tree algorithm is introduced as a local obstacle avoidance strategy.

The proposed method combines randomization and iteration techniques to achieve an efficient task allocation strategy for two robots, ensuring the relative optimal global path of the two robots in cooperation and solving complex local obstacle avoidance problems.

This method is applied to the scene of steel bar tying in construction work, with the workload allocation and collaborative work between two robots as evaluation indicators. The experimental results show that this method can efficiently complete the steel bar banding operation, effectively reduce the interference between the two robots and minimize the interference of obstacles in the environment.