Wen Zhang

A mental map is considered a representation of an individual's spatial cognition. It defines an individual's choice set of plausible activity locations and influences his/her daily activity-travel patterns. Despite its importance, how individuals' activity travel patterns interact with their mental maps on a daily basis is largely unknown, mainly due to data, operation, and measurement issues. The aim of this study is to address this. A total of 57 individuals in Stockholm were asked to record a two-week travel diary and draw their familiar areas in the specified maps. The familiar areas, which in this study are considered as representative of individual mental maps, were manually drawn and transferred from graph to ASCII code in ArcGIS for modelling purposes. The recently visited activity locations were used to construct the individuals' activity spaces. The crucial determinants that related to these activity spaces and familiar areas were investigated. The marginal effect of each key variable was calculated in order to understand the magnitude of influence of each variable to the individuals' activity spaces and familiar areas. The results show that an individual's activity space is partially or completely located within the individual's familiar areas and they are strongly correlated to each other. Large activity centres, such as Stockholm's central areas and areas of Huvudsta have higher probabilities to be included in both individuals' familiar areas and activ-ity spaces than other areas that are closer to home.

The influence of corporate social performance on stakeholders is one of the focal issues in corporate social responsibility research. Using data of listed companies in China, this paper examines whether CSR behavior in the form of charitable donations garners a positive reaction from suppliers. Results derived from both level and change model regressions show that superior CSP makes it easier for a firm to obtain trade credit from suppliers, although the effect is significant only in non-state-owned enterprises. The results are robust to various measures of CSP and endogeneity tests. The results support the strategic philanthropy view and apply stakeholder theory in supply chain, that strategic CSR can help firms to attract suppliers and consolidate cooperative relationships with them, which in turn benefits the firms in terms of more trade credit financing from suppliers. This paper also shows that state-owned enterprises and non-SOEs have different CSR intentions and use CSR to achieve strategic goals in different ways. The conclusions drawn from this study provide practical guidance on CSR strategy, suggesting that CSR activities can help a firm in transition economies to enhance its corporate image, establish and consolidate the good relationship with suppliers, and obtain economic benefits or achieve long-term business objectives.

Every emerging technology has its pros and cons; health-conscious users pay more importance to healthy and environment-friendly technologies. Based on the UTAUT2 model, we proposed a comprehensive novel model to study the factors influencing consumers’ decision-making to adopt the technology. Compared to prior studies that focused on linear models to investigate consumers’ technology adoption intentions and use behavior. This study used a Structural Equation Modeling-fuzzy set qualitative comparative analysis approach to account for the complexity of customers’ decision-making processes in adopting new technology. We collected valid responses from 830 consumers, analyzed them, and evaluated them using a deep learning SEM-fsQCA technique to capture symmetric and asymmetric relations between variables. We have extensively incorporated a health-consciousness attitude as a predictor and mediator to understand better the decision-making toward technology adoption, specifically 5G technology. All the factors tested in our model are statistically significant except the economic factors. Health-consciousness attitude and behavioral intention found significant predictors and valid mediators in the process of 5G technology adoption. FsQCA provided six configurations to achieve high 5G adoption. The findings have significant practical ramifications for telecom corporations, advertisers, government officials, and key policymakers. Additionally, the study added substantial theoretical literature to technology adoption, particularly the adoption of 5G technology.

This article reports on a study that took a Dynamic Systems Theory perspective to second language motivational self system. More specifically, it investigated the influence of an Intensive English Reading course based on the Production-Oriented Approach upon the L2MSS of Chinese university English majorsfrom the DST perspective. To this end, two intact classes composed of 50 students were assigned into experimental group and control group, who responded to an L2MSS scale before and after the one-semester intervention. Eight and five students were respectively selected using the purposive sampling method from the experimental and control groups for follow-up semi-structured interviews. The quantitative results revealed that the overall and dimensional levels of L2MSS were significantly strengthened over time in the EG while kept stable in the CG. The qualitative results suggested that the enhanced Ideal L2 Self of the participants stemmed from an attractor basin that was deepened by a number of attractors encompassing Output Tasks and Peer Performance. The interview results also showed that the increased L2 Learning Experience of the participants pertained to an attractor basin that was consolidated by an array of attractors containing Output Tasks, Teacher Guidance, Group Discussion, and Peer Assessment. The findings indicated that the attractors at the subjective and social dimensions in the POA-based course collectively worked together to cause changes in L2MSS among the participants. The implications for intervening L2 motivation from a POA approach in English as a Foreign Language classrooms were discussed.

We investigated whether adults have attentional bias toward infant faces, whether it is moderated by infant facial expression, and the predictive effect of the adult attachment state on it. One hundred unmarried nulliparous college students [50 men and 50 women; aged 17–24 years ] were recruited. Each completed a self-report questionnaire—the Chinese version of the State Adult Attachment Measure, and a dot-probe task with a stimulus presentation duration of 500 ms, which used 192 black-and-white photographs of 64 people as the experimental stimuli. The results showed that, at the duration of 500 ms, individuals' attentional bias toward infant faces disappeared, regardless of the facial expression. However, when the interaction between avoidant attachment state and face was controlled, the attentional bias was significant again, and the avoidant attachment state negatively predicted individuals' attentional bias toward infant faces. This indicates that at the suprathreshold stage, there are individual differences in the attentional bias toward infant faces, and high avoidant attachment will weaken individuals' attentional bias toward infant faces. This study advances previous studies that focused only on individuals' attention to infant faces occurring at the early processing stage of attention. The results provide direction for interventions; specifically, changing the attachment state of avoidant individuals can affect their attention to infants, which may promote the establishment of parent–child relationships.

An increasing number of users have been attracted by location-based social networks in recent years. Meanwhile, user-generated content in online LBSNs like spatial, temporal, and social information provides an ever-increasing chance to study the human behavior movement from their spatiotemporal mobility patterns and spawns a large number of location-based applications. For instance, one of such applications is to produce personalized point of interest recommendations that users are interested in. Different from traditional recommendation methods, the recommendations in LBSNs come with two vital dimensions, namely, geographical and temporal. However, previously proposed methods do not adequately explore geographical influence and temporal influence. Therefore, fusing geographical and temporal influences for better recommendation accuracy in LBSNs remains potential. In this work, our aim is to generate a top recommendation list of POIs for a target user. Specially, we explore how to produce the POI recommendation by leveraging spatiotemporal information. In order to exploit both geographical and temporal influences, we first design a probabilistic method to initially detect users’ spatial orientation by analyzing visibility weights of POIs which are visited by them. Second, we perform collaborative filtering by detecting users’ temporal preferences. At last, for making the POI recommendation, we combine the aforementioned two approaches, that is, integrating the spatial and temporal influences, to construct a unified framework. Our experimental results on two real-world datasets indicate that our proposed method outperforms the current state-of-the-art POI recommendation approaches.