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This study aims to review the literature on sharing economy logistics and crowd logistics to answer the three following questions: How is the literature on sharing economy logistics structured? What are the main trends in sharing economy logistics and crowd logistics? What are the future research options?

Bibliometric analysis is used to evaluate 85 articles published over the past 12 years; it identifies the top academic journals, authors and research topics contributing to the field.

The sharing economy logistics and crowd logistics literature is structured around several disciplines and highlights that some are more scientifically advanced than others in their subject definitions, designs, modelling and innovative solutions. The main trends are organized around three clusters: Cluster 1 refers to the optimal allocation of costs, prices, distribution and supplier relationships; Cluster 2 corresponds to business related crowdsourcing and international industry practices; and Cluster 3 includes the impact of transport on last-mile delivery, crowd shipping and the environment.

The study is based on data from peer-reviewed scientific journals and conferences. A broader overview could include other data sources such as books, book chapters, working papers, etc.

Future research directions are discussed in the context of the evolution from crowd logistics to crowd intelligence, and the complexities of crowd logistics such as understanding how the social crowd can be integrated into the logistics process. Our results are part of the crowd science and engineering concept and provide some answers about crowd cyber-system questions regarding crowd intelligence in logistic sector.

The rapid development of 5G technology brings the expansion of the internet of things (IoT). A large number of devices in the IoT work independently, leading to difficulties in management. This study aims to optimize the member structure of the IoT so the members in it can work more efficiently.

In this paper, the authors consider from the perspective of crowd science, combining genetic algorithms and crowd intelligence together to optimize the total intelligence of the IoT. Computing, caching and communication capacity are used as the basis of the intelligence according to the related work, and the device correlation and distance factors are used to measure the improvement level of the intelligence. Finally, they use genetic algorithm to select a collaborative state for the IoT devices.

Experimental results demonstrate that the intelligence optimization method in this paper can improve the IoT intelligence level up to ten times than original level.

This paper is the first study that solves the problem of device collaboration in the IoT scenario based on the scientific background of crowd intelligence. The intelligence optimization method works well in the IoT scenario, and it also has potential in other scenarios of crowd network.

Crowd network systems have been deemed as a promising mode of modern service industry and future economic society, and taking crowd network as the research object and exploring its operation mechanism and laws is of great significance for realizing the effective governance of the government and the rapid development of economy, avoiding social chaos and mutation. Because crowd network is a large-scale, dynamic and diversified online deep interconnection, its most results cannot be observed in real world, and it cannot be carried out in accordance with traditional way, simulation is of great importance to put forward related research. To solve above problems, this paper aims to propose a simulation architecture based on the characteristics of crowd network and to verify the feasibility of this architecture through a simulation example.

This paper adopts a data-driven architecture by deeply analyzing existing large-scale simulation architectures and proposes a novel reflective memory-based architecture for crowd network simulations. In this paper, the architecture is analyzed from three aspects: implementation framework, functional architecture and implementation architecture. The proposed architecture adopts a general structure to decouple related work in a harmonious way and gets support for reflection storage by connecting to different devices via reflection memory card. Several toolkits for system implementation are designed and connected by data-driven files (DDF), and these XML files constitute a persistent storage layer. To improve the credibility of simulations, VV&A (verification, validation and accreditation) is introduced into the architecture to verify the accuracy of simulation system executions.

Implementation framework introduces the scenes, methods and toolkits involved in the whole simulation architecture construction process. Functional architecture adopts a general structure to decouple related work in a harmonious way. In the implementation architecture, several toolkits for system implementation are designed, which are connected by DDF, and these XML files constitute a persistent storage layer. Crowd network simulations obtain the support of reflective memory by connecting the reflective memory cards on different devices and connect the interfaces of relevant simulation software to complete the corresponding function call. Meanwhile, to improve the credibility of simulations, VV&A is introduced into the architecture to verify the accuracy of simulation system executions.

This paper proposes a novel reflective memory-based architecture for crowd network simulations. Reflective memory is adopted as share memory within given simulation execution in this architecture; communication efficiency and capability have greatly improved by this share memory-based architecture. This paper adopts a data-driven architecture; the architecture mainly relies on XML files to drive the entire simulation process, and XML files have strong readability and do not need special software to read.

In recent years, with the increase in computing power, artificial intelligence can gradually be regarded as intelligent agents and interact with humans, this interactive network has become increasingly complex. Therefore, it is necessary to model and analyze this complex interactive network. This paper aims to model and demonstrate the evolution of crowd intelligence using visual complex networks.

This paper uses the complex network to model and observe the collaborative evolution behavior and self-organizing system of crowd intelligence.

The authors use the complex network to construct the cooperative behavior and self-organizing system in crowd intelligence. Determine the evolution mode of the node by constructing the interactive relationship between nodes and observe the global evolution state through the force layout.

The simulation results show that the state evolution map can effectively simulate the distribution, interaction and evolution of crowd intelligence through force layout and the intelligent agents’ link mode the authors proposed.

Based on the complex network, this paper constructs the interactive behavior and organization system in crowd intelligence and visualizes the evolution process.

Media has always been used as a key manipulator of public agendas, political beliefs and individuals’ attitudes. The purpose of this study is to investigate the impact of three adaptive media strategies on the pattern and dynamics of potential crowds.

An agent-based approach is used to simulate the three adaptive media strategies on the pattern and dynamics of potential crowds. During the experiments, the media broadcast is intensified to gather momentum for crowd movements or is lessened to maintain the budget.

The results show that a slight change in the media management strategy could lead to a radical different impact on the crowd dynamics. The results also show that a quite smart media strategy could outperform a strategy with an unlimited budget. Finally, the structure of the society shows a significant influence on the crowd dynamics than it could be inferred.

The model presents an explanatory toolkit for the crowd complexity. The results provide deep insights into the crowd formation and a basis for understanding the influence of media and the impact of its strategies on the crowd dynamics.

Cruising is one of the industries most susceptible to the current COVID-19 health crisis, due to the closed environment and the contacts between cruisers and crewmembers. This study aims to understand if the perceived crowding and the health risk perception related to the pandemic situation might threaten passengers’ intentions to cruise. The study also examines corporate reputation and trust, as well as social motivation and self-confidence, as possible predictors of consumers’ intention to cruise.

The study is based on the development of a structured questionnaire submitted online via social media. Overall, 553 individuals’ responses were used for understanding the factors that can affect consumers’ intention to cruise by performing several regression models.

The results show that the perceived crowding related to the pandemic does not seem to influence people’s intention to cruise. On the contrary, trust in the cruise company, corporate reputation, cruisers’ self-confidence and research of social motivation are positive predictors of intention to cruise, thus reducing the perceived risk’s deterring impact. The importance of such factors differs in respect of repeat and not repeat cruisers.

The study presents several managerial implications as it analyses the variables that could help cruise management cope better with COVID-19’s negative impact.

Despite the severity of COVID-19’s impact on the cruise industry, no studies have yet focussed on how the current pandemic situation may influence customers’ intention to cruise in the future.

The effectiveness of fiscal policy is an interesting field in literature of macroeconomics. The purpose of this paper is to investigate the effects of fiscal policy on economic growth under contributions from the differences in institutions and external debt levels.

The authors use panel data from 2002 to 2014 from 20 emerging markets and use GMM estimators for unbalanced panel data.

The results show positive growth effects of fiscal policy across emerging markets in the examined periods. Notably, the improvement in institutions promotes higher crowding-in effects of fiscal policy. In addition, this paper finds interesting evidences that the external debt has non-linear effects on economic growth, whereas the heterogeneous effects of fiscal policy on economic growth as positive effects in low indebted level and negative effect in high indebted level may explain the mechanism of this non-linear relationship.

This study proposes the non-linear relationship of fiscal growth effects in emerging economies under the dynamic of debt levels.

This study aims to understand customer’s assessments of neighborhood stores during the COVID-19 pandemic through the influence of in-store environmental factors on patronage intention.

Online survey with 528 participants about the last shopping trip in neighborhood retail. The authors performed data analysis using structural equation modeling techniques.

High-perceived spatial crowding negatively influences shopping experience value perceptions, while human crowding influences patronage intentions through increased perceived hedonic value.

Results suggest that purchase experience at well-known neighborhood stores during a sanitary crisis is becoming less convenience-oriented and a substitute for leisure activities due to social distancing.

The findings elucidate the social function of neighborhood convenience retailing during the COVID-19 pandemic. The results emphasize that a pleasant shopping experience arising from a good relationship with shopkeepers and other customers is more influential on patronage intention than a good product assortment and store layout.

This paper contributes to the survival of small neighborhood businesses during the financial crisis installed due to Covid-19 by helping businesses become more attractive to their consumers and competitive in the new context.

The combined context of the health crisis due to COVID-19 and neighborhood retail of an emerging country raises the need for tests to better understand established marketing theories. Based on this rationale, this work intends to replicate and extend selected previous findings to the new environment dictated by the pandemic.

This study examined the roles of public spending and population moderating characteristic structure of selected African economies on bank-based financial development through credit to private sector.

The study sampled 37 selected African economies for the years 1991–2018, and it applied a pooled mean group (PMG) estimator to account for short-run and long-run causal effects, and confirmed short-run adjustments towards the long-run convergences between the variables. Specific suitable tests were also applied.

Evidence confirms positive impacts of both capital formation and final consumption expenditures on financial development in the short run and long run. The moderation of population structures on expenditure structures help to speed up convergences.

This work attests its innovation by accounting for the separate effects of the expenditure types, the moderation effects of young and mature populations for capital and final consumption expenditure on financial development among selected economies in Africa.

The purpose of this paper is to assess both short and long-term influences of public investment on economic growth and test the hypothesis that whether public investment promotes or demotes private investment in Vietnam.

The authors use the approach of autoregressive distributed lag model and Vietnam’s macro data in the period of 1990-2016, to evaluate the short and long-term effects of public investment on economic growth and private investment. The model evaluates the impact of public investment on economic growth and private investment based on the neoclassical theories. The public investment which strongly affects economic growth is also reflected by aggregate supply and demand. Public investment directly impacts aggregate demand as a government expenditure and aggregate supply as a production function (capital factor).

The results from this research indicate that public investment in Vietnam in the past period does affect economic growth in the pattern of an inverted-U shape as of Barro (1990), with positive effects mostly occurring from the second year and negative effects of constraining long-term growth. Meanwhile, investment from the private sector, state-owned enterprises, and FDI has positive effects on short-term economic growth and state-owned capital stock has positive impacts on economic growth in both the short and long run. The estimated influence of public investment on private investment also shows a similar inverted-U shape in which public investment have crowding-in private investment short-term but crowding-out in the long run.

The empirical findings in this study can be used for conducting a more efficient policy in restructuring the state sector investment in Vietnam.

The main contributions in this study are: to evaluate the impacts of public investment on economic growth and private investment, the authors extracted public investment in infrastructure from aggregate investment of state sector (as previous studies used); the authors also uses state-owned capital stock variable including cumulative public investment and state-owned enterprises investment suggesting that this could control for the different orders of integration between the stock and flow variable and improve the experimental characteristics of the equation to a higher degree.