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Ports create harmful effects on their adjacent population because ships discharge noxious gases like SO_X, NO_X, and particulate matter (PM). To tackle this problem, some ports started to control emission through regulations such as Emission Control Areas (ECA) and Reduced Speed Zone (RSZ). This paper estimates the social cost of ship emission and eco-efficiency at the Port of Incheon (POI). We further examine how the ECA and RSZ designation can reduce the social cost. The estimation is based on the activity-based approach, where ship type, engine, and movement are used to measure fuel consumption and then emission. Results suggest that the social cost of ship emission at the POI amounts to $90,805,478. The eco-efficiency of the POI, compared to the one at the Port of Las Palmas in another study, is substantially better. Under RSZ, the corresponding emission abatement values are $4,485,308, $2,642,009 and $21,932,435 from SO₂, NO_X and PM reduction, respectively. If 1.0% and 0.1% sulfur fuel are used complying with rules of the ECA, the social cost savings amount to $8,174,947 and $12,868,842 from SO₂ reduction.

The purpose of this paper is to provide a guiding framework for studying the travel patterns of PT users. The combination of public transit (PT) users’ travel data and user profiling (UP) technology to draw a portrait of PT users can effectively understand users’ travel patterns, which is important to help optimize the scheduling of PT operations and planning of the network.

To achieve the purpose, the paper presents a three-level classification method to construct the labeling framework. A station area attribute mining method based on the term frequency-inverse document frequency weighting algorithm is proposed to determine the point of interest attributes of user travel stations, and the spatial correlation patterns of user travel stations are calculated by Moran’s Index. User travel feature labels are extracted from travel data containing Beijing PT data for one consecutive week.

In this paper, a universal PT user labeling system is obtained and some related methods are conducted including four categories of user-preferred travel area patterns mining and a station area attribute mining method. In the application of the Beijing case, a precise exploration of the spatiotemporal characteristics of PT users is conducted, resulting in the final Beijing PTUP system.

This paper combines UP technology with big data analysis techniques to study the travel patterns of PT users. A user profile label framework is constructed, and data visualization, statistical analysis and K-means clustering are applied to extract specific labels instructed by this system framework. Through these analytical processes, the user labeling system is improved, and its applicability is validated through the analysis of a Beijing PT case.

Human group behavior is the driving force behind many complex social and economic phenomena. Few studies have integrated multi-dimensional travel patterns and city interest points to construct urban security risk indicators. This paper combines traffic data and urban alarm data to analyze the safe travel characteristics of the urban population. The research results are helpful to explore the diversity of human group behavior, grasp the temporal and spatial laws and reveal regional security risks. It provides a reference for optimizing resource deployment and group intelligence analysis in emergency management.

Based on the dynamics index of group behavior, this paper mines the data of large shared bikes and ride-hailing in a big city of China. We integrate the urban interest points and travel dynamic characteristics, construct the urban traffic safety index based on alarm behavior and further calculate the urban safety index.

This study found significant differences in the travel power index among ride-sharing users. There is a positive correlation between user shared bike trips and the power-law bimodal phenomenon in the logarithmic coordinate system. It is closely related to the urban public security index.

Based on group-shared dynamic index integrated alarm, we innovatively constructed an urban public safety index and analyzed the correlation of travel alarm behavior. The research results fully reveal the internal mechanism of the group behavior safety index and provide a valuable supplement for the police intelligence analysis.

Location-based games (LBGs) have afforded novel information technology (IT) developments in how people interact with the physical world. Namely, LBGs have spurred a wave of territoriality (i.e. controlling) and exploration (i.e. discovering) of augmented physical space that are driven by different social dynamics related to group formation, social connectivity and altruism. The aim of this study is to investigate this dynamic and how it is further related to the use intensity of location-based IT.

This work presents a structural equation model that connects social dimensions of play to territorial control and exploration, and playing intensity. The model was tested with psychometric data gathered from a global sample of Pokémon GO players (N = 515).

In the tested sample, players' social self-efficacy and altruism were positively associated with team identification. Team identification, in turn, was positively associated with both territorial control and exploration tendency. Territorial control had a significant relationship with playing intensity; however, exploration tendency did not. This implies territorial control is the stronger predictor of playing intensity.

The findings suggest that human primal urges to conquer and control geographical territory may surface in the digital reimagination of physical space. LBGs offer opportunities for making use of new forms of play (territorial control and exploration) in motivating locative behaviours.

This research quantifies the relationships between a social predisposition, team identification, territorial control, exploration tendency and playing intensity in the context of Pokémon GO. It contributes new knowledge to the understanding of territorial behaviour (control and exploration) in location-based IT.

This study investigates how game design, which divides players into static teams, can reinforce group polarisation. The authors study this phenomenon from the perspective of social identity in the context of team-based location-based games, with a focus on game slang.

The authors performed an exploratory data analysis on an original dataset of n = 242,852 messages from five communication channels to find differences in game slang adoption between three teams in the location-based augmented reality game Pokémon GO. A divisive word “jym” (i.e. a Finnish slang derivative of the word “gym”) was discovered, and players' attitudes towards the word were further probed with a survey (n = 185). Finally, selected participants (n = 25) were interviewed in person to discover any underlying reasons for the observed polarised attitudes.

The players' teams were correlated with attitudes towards “jym”. Face-to-face interviews revealed association of the word to a particular player subgroup and it being used with improper grammar as reasons for the observed negative attitudes. Conflict over (virtual) territorial resources reinforced the polarisation.

Game design with static teams and inter-team conflict influences players' social and linguistic identity, which subsequently may result in divisive stratification among otherwise cooperative or friendly player-base.

The presented multi-method study connecting linguistic and social stratification is a novel approach to gaining insight on human social interactions, polarisation and group behaviour in the context of location-based games.

This paper aims to investigate the formation of an inter-organizational collaboration network that made it possible to repair 2,516 mechanical respirators that were inoperative in Brazil during the first wave of the COVID-19 pandemic.

A qualitative approach was used in a single case study with semi-structured interviews. The interviewee selection process was non-probabilistic through snowball sampling.

The results suggest that society, through different social groups with their different roles, can organize itself quickly through the formation of collaborative networks, and this organizational configuration can be an alternative for facing crises where actions isolated would be insufficient or slow to urgently address complex situations.

This paper aims to (1) demonstrate that society, through different social groups with their different roles, can organize itself quickly through the formation of collaborative networks; (2) favor the understanding and dynamics of the formation of a network; and (3) contribute to a possible replication of this initiative in future contexts.

The case portrays an unprecedented formation of a collaboration network involving more than 144 organizations that mobilized quickly in a complex context of a pandemic and that generated remarkable results through the reintroduction of equipment that were responsible for the preservation of thousands of lives during the year from 2020.

Free guided walking tours are one of the most successful tourism segments in the digital platform economy. It is beginning to be associated with negative impacts in some of the destinations where it is spreading rapidly. Although the platform economy is generating increasing academic interest, the free tour model remains largely unexplored area in the literature. This study aims to examine how such activity affects cultural destinations.

Focussing on the largest Free Tours platform operating in Spain, GuruWalk, the methodology used analyses its impact in six cultural destinations on two of the sustainability dimensions: the territorial dimension and the governance, through an exploratory study.

The findings help to understand the differences that such activity generates in each destination depending on the phase of its life cycle, and to implement, if necessary, corrective measures. The research confirms that the impacts differ according to the tourist destination’s maturity, concluding that such activity contributes to the increase of tourist agglomerations and the overcrowding of cultural destinations in their middle and mature life cycles. The findings highlighted the importance of the role of local governance on free tour activity.

The main contribution is the association of the impacts they produce (especially in terms of massification) with destination life cycle phases. There were no similar precedents with a spatial or territorial analysis to reliably demonstrate not only that this activity has an impact on the territory but also what type of impact is produced.

Using cross-sectional household survey data, this paper aims to determine the impact of food price increases on poverty in Indonesia.

This paper uses the quadratic almost ideal demand system applied to the 2013 Indonesian household survey data. The impact of food price increase on household welfare is calculated using a welfare measure, compensating variation.

Three food groups with the most outstanding price impact on poverty, rice, vegetables and fish, were studied. The 20% increase in the price of each food group causes an increase in the headcount ratio by 1.360 points (rice), 0.737 points (vegetables) and 0.636 points (fish). Maintaining food price stability for these food groups is very important because the more the price increases, the more the impact on poverty. Food price policies in rural areas are also more critical than in urban areas because the impact of food price increases in rural areas is higher.

This paper does not consider the positive impact of rising food prices on food-producing households.

Implementing appropriate poverty alleviation policies through food policies for main food groups and social protection.

Promoting rural development policies and agricultural growth.

This paper contributes to the existing literature by providing empirical results regarding the impact of domestic food prices increase on poverty in Indonesia.

The purpose of this paper is to identify the way different economic sectors in Brazil use knowledge-intensive business services (KIBS) and explore which features of KIBS use are associated with better innovation outcomes.

Clusters and regression analyses were used to analyze data from the national innovation survey (PINTEC) from 2014.

The results show that most of the 55 sectors of the Brazilian economy studied make little use of KIBS, but industries in which firms that interact with KIBS providers also have better innovation performance and offer more innovative offerings. The relationship with higher education institutions and research institutes proved particularly relevant, while the interaction with consultancy firms seems to be a strategy that leaves firms “stuck in the middle”.

The outcomes confirm the arguments of the literature that the use of KIBS has positive outcomes for customer firms. More importantly, however, the paper complements the existing literature by showing that the type of KIBS used in each country is relevant to understand firms’ innovation performance. The outcomes can guide firms and public policy initiatives oriented at the articulation of the national innovation system.

There is a great reliance on fiscal incentives to sustain the automotive industry competitiveness due to several structural problems, among them the inefficiency of the supply chain. This paper aims to compare the supply chain structure of traditional automotive industry with the supply chains from South Korea and China. Based on strategic decision and transaction cost theory, this comparison seeks to exploit the factors that led to the inefficiency of automotive supply chains.

The authors used a qualitative approach and applied a multi-method research. They conducted semi-structured interviews with six executives from automakers representing the selected countries, carried individual meetings during one workshop and used secondary data from several sources.

Concepts identified in the research such as reliability, supply chain governance and automaker competencies led the authors to propose that the traditional automakers have higher transaction costs when compared to the new automakers due to the horizontal structure of their supply chain. While new competitors have vertical upstream supply chains, which indicates better profitability, traditional automotive industry is horizontal, depends on fewer Tier 1 suppliers and is disconnected from Tier 2, impacting negatively in the transaction costs and supply chain management.

This study suggests that automotive executives rethink the current upstream supply chain model by identifying the competencies required for their current and future competitiveness and implementing a vertical integration of these competencies.

This research exploited the inefficiency of supply chain as one of the explanations for the low competitiveness of the national automotive industry.