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Community gardens are increasingly used as interventions during the resettlement of refugees and other migrants. Little is known about how garden programs might support their mental health and wellbeing. Given the links between climate change and forced migration, community gardens are especially relevant, as they can also support climate change mitigation. This study aims to document psychosocial outcomes of gardening programs for refugees and migrants, and mechanisms leading to these outcomes.

The authors searched major databases and the grey literature up to 2021, resulting in the inclusion of 17 peer-reviewed and 4 grey literature articles in a thematic, qualitative analysis.

Four consistent themes arose from the analysis: community gardening programs promoted continuity and adaptation (81% of articles), social connectedness (81%), overall wellbeing (95%) and a sense of meaning and self-worth (67%). The results suggest that community gardens can strengthen psychosocial pillars that are key to the recovery and resettlement of refugees and migrants. The land-based and social nature of community gardening may enable connections to the land and others, nurture a sense of belonging in the host country and provide a link to the past for those from agricultural backgrounds.

Further participatory action research is needed to develop guidelines for the successful implementation of community gardens by resettlement organisations.

This review indicates that community gardens can be effective psychosocial interventions as part of a network of services supporting the resettlement of refugees and migrants.

This study aims to provide a fresh look at banks as lenders in and extending past the COVID-19 crisis, with a particular focus on examining the results of recent work by Lei et al. (2020).

The authors’ replication, as well as the original paper, uses a fixed-effects model on panel data. The authors discuss issues regarding data sources as well as use an array of panel data robustness checks to help ascertain an appropriate empirical specification for continued research of this type.

The authors show that the results of Lei et al. (2020) are sensitive to the data source, as well as the construction of the standard errors in their regression framework, with an appropriate specification uncovered through panel data statistical tests. The authors also provide some extensions to the original work by including interacted fixed-effects models and extending the sample period from 2020Q1 to 2021Q1, noting some changes in results.

The authors provide novel results on banks’ lending constraints both at the onset of the COVID-19 pandemic and shortly thereafter. The study also provides an empirical framework for future studies conducted on similar panel data sets.

In the era of mobile internet, the social Q&A community has built a large-scale and complex knowledge label network through its internal knowledge units, and the scale and structure of the network have changed over time. By analysing the structural characteristics and evolution rules of knowledge label networks, the main purpose of this study is to understand the internal mechanisms of the replacement of old and new knowledge and the expansion of knowledge element boundaries, so as to explore the realization path of knowledge management in the new era from the perspective of complex networks.

This paper uses distributed crawlers to capture 419,349 samples from the Zhihu platform. Each sample contains 33 characteristic dimensions, and the natural year is used as the sliding window to divide the whole. In this study, the global knowledge label network and 11 local knowledge label networks are first constructed. Then, the degree distribution analysis and central node exploration of the knowledge label network are carried out using the complex network method. Finally, the average shortest path and average clustering coefficient of the network are analysed by the time series method, and the ARIMA model is used to predict the evolution of the correlation coefficient.

The research results show that the dissimilation degree of the degree distribution of the knowledge label network has gradually decreased from 2011 to 2021, and the attention of users in the knowledge community has shown a trend of distraction and diversification over time. With the expansion of the scale of the knowledge label network and the transformation to an information network, the network sparsity is becoming more and more obvious, and the knowledge granularity of the Q&A community is being refined and diversified. The prediction of the correlation coefficient of the knowledge label network by the ARIMA model shows that the connection between the labels is lacking diversity and the opinion strengthening phenomenon tends to strengthen, which is more likely to form the “echo chamber effect”, resulting in mutual isolation and even opposition between different circles. The Q&A community is about to enter a mature stage, and the corresponding status of each label has been finalized. The future development trend of label networks will be reflected in the substitution between labels, and the specific structure will not change significantly.

The Q&A community model is the trend in Web 2.0 community development. This study proves the effectiveness of complex networks and time series prediction methods in knowledge label network mining in the Q&A community.

The present paper aims to reveal how different cultures, as reflected by cultural norms, traditions, and social expectations, influence food waste behaviour in different regions of the world.

A systematic multifaceted literature review was employed as a main research tool.

The focal role of education and awareness campaigns in reducing household food waste and promoting responsible food consumption behaviours is revealed. The importance of guilt, behavioural control, negative attitudes towards leftovers, and social norms are among the most important factors predicting intentions to reduce food waste. Cultural beliefs significantly shape food attitudes and waste. Tailoring sustainable practices to traditions helps to ensure food security. Embracing cultural diversity can lead to the development of effective and sustainable food consumption patterns across different parts of the world.

To the best of the authors’ knowledge, this is the first paper fully devoted to revealing how different cultural backgrounds shape food consumption habits and which marketing strategies aiming to nudge positive changes in responsible food consumption are preferred in different cultural contexts.

Protection motivation theory (PMT) explains that the intention to cope with information security risks is based on informed threat and coping appraisals. However, people cannot always make appropriate assessments due to possible ignorance and cognitive biases. This study proposes a research model that introduces four antecedent factors from ignorance and bias perspectives into the PMT model and empirically tests this model with data from a survey of electronic waste (e-waste) handling.

The data collected from 356 Chinese samples are analyzed via structural equation modeling (SEM).

The results revealed that for threat appraisal, optimistic bias leads to a lower perception of risks. However, factual ignorance (lack of knowledge of risks) does not significantly affect the perceived threat. For coping appraisal, practical ignorance (lack of knowledge of coping with risks) leads to low response efficacy and self-efficacy and high perceptions of coping cost, but the illusion of control overestimates response efficacy and self-efficacy.

First, this study addresses a new type of information security problem in e-waste handling. Second, this study extends the PMT model by exploring the roles of ignorance and bias as antecedents. Finally, the authors reinvestigate the basic constructs of PMT to identify how rational threat and coping assessments affect user intentions to cope with data security risks.

Heavy fog results in low visibility, which increases the probability and severity of traffic crashes, and fog warning system is conducive to the reduction of crashes by conveying warning messages to drivers. This paper aims at exploring the effects of dynamic message sign (DMS) of fog warning system on driver performance.

First, a testing platform was established based on driving simulator and driver performance data under DMS were collected. The experiment route was consisted of three different zones (i.e. warning zone, transition zone and heavy fog zone), and mean speed, mean acceleration, mean jerk in the whole zone, ending speed in the warning zone and transition zone, maximum deceleration rate and mean speed reduction proportion in the transition zone and heavy fog zone were selected. Next, the one-way analysis of variance was applied to test the significant difference between the metrics. Besides, drivers’ subjective perception was also considered.

The results indicated that DMS is beneficial to reduce speed before drivers enter the heavy fog zone. Besides, when drivers enter a heavy fog zone, DMS can reduce the tension of drivers and make drivers operate more smoothly.

This paper provides a comprehensive approach for evaluating the effectiveness of the warning system in adverse conditions based on the driving simulation test platform. The method can be extended to the evaluation of vehicle-to-infrastructure technology in other special scenarios.

The purpose of this paper is to reveal the pattern between government innovation funding and enterprise value creation. Many factors, including government innovation funding, R&D ability, corporate governance and some company characteristics significantly affected the efficiency of firm value creation.

This paper proposed a novel methodology based on clustering-rough sets to explore the characteristics of enterprise value creation behavior, and map the relationship between government innovation funding and enterprise value creation. The agglomerative hierarchical clustering (AHC) algorithm were used to classify firm performance and get two types of value creation efficiencies and to discretize condition attributes because the rough set theory cannot deal with continuous attributes. This paper utilized the rough sets method to realize data mining and get rules of government innovation funding and enterprise value creation.

R&D ability, proportion of independent directors, remuneration of directors, operating revenue, number of employees, price-earnings ratio, quick ratio, capital intensity and ROA were important to identify firm value creation efficiency when government funded the firms. Firms of high level of government innovation funding, high lagged R&D ratio, high remuneration of directors, low price-earnings ratio, low quick ratio, moderate capital intensity and high ROA were more likely to have high efficiency of value creation.

Since China implemented the innovation-driven development strategy, facilitating enterprise innovation has become an important way to achieve high-quality economic growth. With constantly increasing of Chinese government innovation funding, studying on the effect of government innovation funding on firm’s value creation is significant to improve the efficiency of government resource allocation. It is valuable to reveal the pattern between government innovation funding and enterprise value creation based on the value added theory. The rules obtained could be used to provide decision-making support to improve the efficiency of government innovation funding and prevent waste of government resources effectively.

Electronic waste (e-waste) such as discarded computers and smartphones may contain large amounts of confidential data. Improper handling of remaining information in e-waste can, therefore, drive information security risk. This risk, however, is not always properly assessed and managed. The authors take the protection motivation theory (PMT) lens of analysis to understand intentions to protect one's discarded electronic assets.

By applying structural equation modeling, the authors empirically tested the proposed model with survey data from 348 e-waste handling users.

Results highlight that (1) protection intention is influenced by the perceived threat of discarding untreated e-waste (a threat appraisal) and self-efficacy to treat the discarded e-waste (a coping appraisal) and (2) optimism bias plays a dual-role in a direct and moderating way to reduce the perceived threat of untreated e-waste and its effect on protection intentions.

Results support the assertions and portray a unique theoretical account of the processes that underline people's motivation to protect their data when discarding e-waste. As such, this study explains a relatively understudied information security risk behavior in the e-waste context, points to the role of optimism bias in such decisions and highlights potential interventions that can help to alleviate this information security risk behavior.

Online dating services have been growing rapidly in recent years. However, adopting these services may involve high risk and trust issues among potential users toward both online dating services and the daters they introduce to users. The purpose of this paper is to investigate how perceived benefits vs risks, and trust vs distrust affect user adoption vs non-adoption intentions toward using this rather controversial information and communications technology in the context of online dating.

Structural equation modeling was used to evaluate the research model using data from a survey of 451 single individuals.

The results indicated that perceived benefits play more essential roles in adoption, while perceived risks affect non-adoption more. Individuals' trust in online dating service predicts a major portion of the variation in user benefit perceptions, while distrust in online dating service and in daters that users might select significantly influence perceived risks. Moreover, benefit and risk perceptions can mediate the impacts of trust and distrust on both adoption and non-adoption decisions.

This study extends theories of decision-making in the use of controversial information technologies such as in the case of online dating. It investigates the coexistence of various trust and distrust beliefs as well as benefit and risk perceptions, and their different impacts on adoption and non-adoption in online dating services.

The purpose of this paper is to investigate users’ continuous adoption behaviors on mobile game playing from the perspective of situational habit formation.

Based on the literature research, a continuous adoption model for situational mobile game is proposed. And the research model is assessed based on data gathered from a sample of 226 mobile game players by employing the structural equation model methodology.

The results show that situational cues represented by availability, perceived ease of use and diversion lead to repeated performance that can be represented by flow experience and satisfaction in the situational mobile game playing context. But only flow experience and diversion influence continuous usage directly. Additionally diversion, as a critical situational variable, not only indirectly affects continuous usage intention through flow experience, but also directly affects continuous usage intention for situational mobile game playing.

Mobile game adoption has been studied from different perspectives, but most research is based on the technology acceptance model. They could not explain the common fact that young people tend to be highly motivated by mobile games and can be regarded as pro-active mobile game players, but many people play mobile games only when they are bored and need a diversion. So this study attempts to illustrate the phenomena to fill the gaps.