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The purpose of this paper is to investigate older people’s views and experiences of getting help from neighbours in order to consider whether such help is situated within neighbourliness and the implications for social care policy which seeks to harness help from neighbours.

A qualitative study in which 15 older people from the North of England were interviewed to explore relationships with neighbours, managing day to day life and experiences of getting help from neighbours.

Relationships with helper neighbours were reciprocal, dynamic and preceded the start of getting help. The help offered was not negotiated but evolved in response to changes in circumstances and was commensurate with normative views of neighbourliness, i.e. reciprocated sociability and helpfulness but also respect for privacy. Respondents were reluctant to ask for help. Underpinning such reluctance were perceptions of imposing on neighbours, suggestive of anticipated asymmetry in the give-and-take of neighbourliness.

Policy makers who see the help from neighbours as an output of household production and available as a source of informal care for older people must appreciate that whether help is offered or taken up is dependent on the development of a reciprocal relationship which itself depends on observing and respecting normative boundaries, such as in relation to help giving or receiving and due respect for privacy.

There has been little research into older people’s perspectives on getting help from neighbours despite diminishing public services and neighbours viewed as a potential source of care.

Online reviews have been indicated to play an important role in consumers’ decision-making process, as supported by numerous studies. However, none of them has considered the neighborhood effect of online reviews. The purpose of this paper is to analyze the impact of neighbor store’s reviews on central store’s, along with the moderating effects of store density and product similarity.

Using data from dianping.com, this study conducts economic analysis accounting for endogeneity.

The results show that the neighbor store’s reviews exert a negative impact on that of central stores. Nevertheless, the relationship is moderated by store density and product similarity, such that the negative effect is stronger if there are a lot of stores around the central store, or if the neighbor store and central store provide similar products.

This study is the first to investigate the neighborhood effect of online reviews.

Background: Evidence from a range of major public health incidents shows that neighbour-based action can have a critical role in emergency response, assistance and recovery. However, there is little research to date on neighbour-based action during the 2020 coronavirus pandemic. This article reports on a survey of people engaged in supporting their neighbours in weeks three and four of the UK COVID-19 lockdown.

Methods: Members of area-based and community of interest COVID-19 support groups in the Bristol conurbation were invited to complete an online survey. Of 1,255 people who clicked on the survey link, 862 responded; of these, 539 responses were eligible for analysis.

Results: Respondents reported providing a wide range of support that went beyond health information, food and medical prescription assistance, to include raising morale through humour, creativity and acts of kindness and solidarity. A substantial proportion felt that they had become more involved in neighbourhood life following the lockdown and had an interest in becoming more involved in future. Neighbour support spanned all adult age groups, including older people categorised as being at-risk to the virus. With respect to most measures, there were no differences in the characteristics of support between respondents in areas of higher and lower deprivation. However, respondents from more deprived areas were more likely to state that they were involved in supporting certain vulnerable groups.

Conclusions: As with previous research on major social upheavals, our findings suggest that responses to the viral pandemic and associated social restrictions may increase existing social and health inequalities, and further research should explore this issue in more depth.

Creating superior customer experience quality is important to firm success, but the link between customer experience quality and customer-to-customer interaction quality – a critical component of customer experience quality in mass service settings – has seldom been spotlighted. This paper aims to propose and test a theoretical model of the relationship among three types of customer-to-customer interaction quality (friend-interaction, neighboring customer-interaction and audience-interaction) and customer experience quality. They also examine these variables’ effects on customer citizenship behavior in mass service settings.

The authors collected data through a self-administered survey. The proposed relationships were tested using structural equation modeling.

Friend-interaction and audience-interaction quality perceptions significantly influence customer experience quality, with neighboring customer-interaction quality perception significant only for low communication quality. We find that enhancing customer experience quality is crucial to promoting citizenship behavior in mass service settings.

Neighboring customer-interaction quality perception has a significant effect on customer experience quality, particularly in a low communication quality situation. Therefore, service marketers should provide effective neighboring customer-interaction management schemes to enhance experience quality together with friend-interaction and audience-interaction management schemes when customers experience low communication quality. Additionally, service marketers should focus on enhancing communication quality only when anticipating low neighboring customer-interaction quality.

The findings highlight the effects of three types of customer-to-customer interaction quality on customer citizenship behavior through experience quality perception in mass service settings, and the effect of neighboring customer-interaction quality perception on customer experience quality, moderated by communication quality.

In multihop wireless networks, the number of neighbors has an important role in the network performance since links are dynamically formed between a node and its neighbors. This paper aims to investigate this issue.

The paper quantitatively studies the effects of the average number of neighbors in multihop wireless networks on the network connectivity, the number of hops needed to traverse a certain distance, which can be used to determine the hop diameter of a network, and the total energy consumed by packet transmission, which can be used to choose an optimum average number of neighbors that minimizes the energy consumption. This paper also presents an analysis of the energy consumption that can be applied to a wide range of access protocols and show the effect of a variety of factors.

Results show that the minimum average number of neighbors to guarantee the overall network connectivity depends on the size of a network coverage. There is a sharp knee in the network connectivity with decrease of the average number of neighbors, N. If the distance between a source and destination, d, is known, the number of hops needed to reach the destination is usually between d/R∼2d/R, where R is the transmission range. A larger average number of neighbors N leads to a smaller number of hops to traverse a certain distance, which in turn results in a smaller traffic load caused by relaying packets. However, a bigger N also causes more collisions when a contention medium access scheme is used, which leads to more energy consumed by packet transmission. The results show that the optimum N which minimizes the energy is obtained by balancing several factors affecting the energy.

The paper provides a useful study on the effects of the number of neighbors in multihop wireless networks.

Multi-criteria recommender systems (MC-RSs) allow users to express their preference in multiple aspects. Bayesian flexible mixture model (BFMM) is a model-based RS which extends FMM from single-criterion to MC. However, results of BFMM have a preference on different rating pattern problem. In single-criterion, FMM with decoupled normalization and W’s transposed function try to solve this problem. However, these techniques are applied to each criterion separately. Then, the relationship among criteria will be lost. This paper aims to solve different rating pattern problems and loss of the relationship between criteria.

The proposed method is combining between BFMM and rating conversion. First, mean and variance normalization is applied to make MC ratings of an active user and a neighbor lying on the same plane. After that, a pattern of each user is extracted using principal component analysis (PCA). Next, the pattern is used to convert neighbors’ MC ratings to the active user aspect. After that, converted MC ratings of neighbors are aggregated to be overall ratings using multiple linear regression (MLR). Finally, overall rating of the active user toward the target item is predicted using weighted average on the derived neighbors’ overall ratings where the similarity from BFMM acts as a weight.

The experimental results show that the proposed method where all criteria ratings are converted simultaneously can improve the performance of recommendation.

The proposed method predicts overall rating of the active user by converting MC ratings of each neighbor to the active user aspect at the same time, which can reduce the loss of the relationship between criteria.

The purpose of this paper is to propose a model for sharing network capacity on demand among different underloaded and overloaded P2P ROME‐enabled networks. The paper aims to target networks of nodes with highly dynamic workload fluctuations that may experience a burst of traffic and/or massive nodes' failure rates.

The paper shows that when locally available network capacity is not adequate for the workload requirements, the excessive capacity needs to be sought into other networks with more availability. A random flat P2P overlay of the ROME servers is used for the discovery and movement of nodes between two networks. Centralised or decentralised DHT‐based directories of available nodes cannot cope with high workload fluctuations and frequent join/leave actions of nodes. The paper also introduces semantics to refine the answers to the ones with the most appropriate nodes for the requesting network and to find the requested capacity faster and more efficiently. The behaviour of the model is simulated to evaluate with several experiments the model based on some metrics.

The paper finds that all the user queries of an overloaded underlying network are dropped if G‐ROME is not used but as G‐ROME overlay satisfies the requested capacity of a ROME server, its Chord ring size increases. In case of uniformly distributed and/or plentiful capacity over the overlay, shallow searches may give very good results. On the contrary, deeper searches are required for scarce capacity but the number of messages increases almost exponentially.

This paper provides a model for moving the required resources to the requesting job environment rather than the job and its context to the resource.

Collaborative filtering (CF), one of the most popular recommendation techniques, is based on the principle of word-of-mouth communication between other like-minded users. The process of identifying these like-minded or similar users remains crucial for a CF framework. Conventionally, a neighbor is the one among the similar users who has rated the item under consideration. To select neighbors by the existing practices, their similarity deteriorates as many similar users might not have rated the item under consideration. This paper aims to address the drawback in the existing CF method where “not-so-similar” or “weak” neighbors are selected.

The new approach proposed here selects neighbors only on the basis of highest similarity coefficient, irrespective of rating the item under consideration. Further, to predict missing ratings by some neighbors for the item under consideration, ordinal logistic regression based on item–item similarity is used here.

Experiments using the MovieLens (ml-100) data set prove the efficacy of the proposed approach on different performance evaluation metrics such as accuracy and classification metrics. Apart from higher prediction quality, coverage values are also at par with the literature.

This new approach gets its motivation from the principle of the CF method to rely on the opinion of the closest neighbors, which seems more meaningful than trusting “not-so-similar” or “weak” neighbors. The static nature of the neighborhood addresses the scalability issue of CF. Use of ordinal logistic regression as a prediction technique addresses the statistical inappropriateness of other linear models to make predictions for ordinal scale ratings data.

Empirical evidence of education spillovers in developing countries and rural contexts is scarce and focuses on specific channels. The purpose of this paper is to provide evidence of such spillovers in rural India, by evaluating the overall impact of neighbours' education on farm productivity.

The author uses cross‐sectional data from the India Human Development Survey of 2005. Spatial econometric tools are used to take into account social distance between neighbours. To be sure that the author's definition of a neighbourhood does not drive the results, three different definitions of neighbours were tested.

The results show that education spillovers are substantial: one additional year in the mean level of education of neighbours increases households' farm productivity by 2 per cent. These findings are robust to changes in specification.

The results open the way to further research. In particular, this paper does not explore the channels through which this spillover effect happens.

This paper confirms the choice of improving education in developing countries: giving a child education will certainly provide him/her with greater revenues and it may also provide his/her neighbours with greater revenues. The paper shows the importance for policy makers of taking into account education spillovers and policies' complementarity when facing political trade‐offs.

This paper is one of the few to underline that education externalities do not only exist in urban contexts and education spillovers do not only occur between workers of the manufacturing and service sectors. There are also spillovers in sectors considered as more traditional, such as agriculture.

This paper aims to develop a parallel fast neighbor search method and communication strategy for particle-based methods with adaptive smoothing-length on distributed-memory computing systems.

With a multi-resolution-based hierarchical data structure, the parallel neighbor search method is developed to detect and construct ghost buffer particles, i.e. neighboring particles on remote processor nodes. To migrate ghost buffer particles among processor nodes, an undirected graph is established to characterize the sparse data communication relation and is dynamically recomposed. By the introduction of an edge coloring algorithm from graph theory, the complex sparse data exchange can be accomplished within optimized frequency. For each communication substep, only efficient nonblocking point-to-point communication is involved.

Two demonstration scenarios are considered: fluid dynamics based on smoothed-particle hydrodynamics with adaptive smoothing-length and a recently proposed physics-motivated partitioning method [Fu et al., JCP 341 (2017): 447-473]. Several new concepts are introduced to recast the partitioning method into a parallel version. A set of numerical experiments is conducted to demonstrate the performance and potential of the proposed parallel algorithms.

The proposed methods are simple to implement in large-scale parallel environment and can handle particle simulations with arbitrarily varying smoothing-lengths. The implemented smoothed-particle hydrodynamics solver has good parallel performance, suggesting the potential for other scientific applications.