Different Types Of Users Research Articles

Standardized Integration of Person-Generated Data Into Routine Clinical Care

Person-generated data (PGD) are a valuable source of information on a person’s health state in daily life and in between clinic visits. To fully extract value from PGD, health care organizations must be able to smoothly integrate data from PGD devices into routine clinical workflows. Ideally, to enhance efficiency and flexibility, such integrations should follow reusable processes that can easily be replicated for multiple devices and data types. Instead, current PGD integrations tend to be one-off efforts entailing high costs to build and maintain custom connections with each device and their proprietary data formats. This viewpoint paper formulates the integration of PGD into clinical systems and workflow as a PGD integration pipeline and reviews the functional components of such a pipeline. A PGD integration pipeline includes PGD acquisition, aggregation, and consumption. Acquisition is the person-facing component that includes both technical (eg, sensors, smartphone apps) and policy components (eg, informed consent). Aggregation pools, standardizes, and structures data into formats that can be used in health care settings such as within electronic health record–based workflows. PGD consumption is wide-ranging, by different solutions in different care settings (inpatient, outpatient, consumer health) for different types of users (clinicians, patients). The adoption of data and metadata standards, such as those from IEEE and Open mHealth, would facilitate aggregation and enable broader consumption. We illustrate the benefits of a standards-based integration pipeline for the illustrative use case of home blood pressure monitoring. A standards-based PGD integration pipeline can flexibly streamline the clinical use of PGD while accommodating the complexity, scale, and rapid evolution of today’s health care systems.

User characteristics and service satisfaction of car sharing systems: Evidence from Hangzhou, China.

Car sharing has become a new mode of transport during the past two decades in the world. Its rapid growth in China has attracted a wide range of users and posed some problems. The main focus is on service efficiency and user satisfaction. To explore possible service enhancement and management intervention, this study aims at capturing the user characteristics according to different user types and scrutinizing their satisfaction with station-based one-way car sharing service. The study firstly illustrates descriptive statistics of user profile. This is followed by a study of user satisfaction influenced by user rates on staffs, the efficiency of rental process, vehicle situation, the use of credit card and their familiarity towards rental station. Furthermore, by clustering users according to the total travel time and distance during one rent, two different types of users are identified and defined as User Group A (UGA) and User Group B (UGB). To examine how fully do users utilize the shared cars, ANOVA was conducted implying family car ownership, total travel distance and main travel purpose have strong impact on total rental time for UGB, while for UGA, travel purpose and age have strong impact. Finally, ordinal logistic regression was introduced to find that for UGB, “shopping” is the main travel purpose with longer rental time, whereas for UGA, “out for business”, “shopping”, “visit friends” or “pick up others” are the main travel purposes with longer total travel time. Based on the findings, advices for operators on how to improve service quality and suggestions for government management strategy are discussed, respectively.

Rome’s GRAB—Great Bicycle Ring Route—As Complex Landscape Infrastructure

This paper aims to describe the design strategy adopted in Rome to support and enhance sustainable mobility. It is a strategy aimed at promoting new green infrastructures for urban accessibility, daily sports practice and social inclusion in a historic city, stratified and not very inclined to change. Therefore, the dissemination of this experience is useful for planning a sustainable future for heritage cities that ensures an appropriate and equitable balance between conservation and development. Sustainable mobility is now considered one of the most important challenges for metropolitan areas and large conurbations. In these terms, Rome is a weak city. The city’s great bicycle ring route (GRAB), an integral part of the Extraordinary Tourism Mobility Plan 2017-22, is a key infrastructure for increasing more sustainable and healthier modes of travel, even on a local scale. The GRAB project, whose complex infrastructure provides multiple services, differs from a simple cycle path network. Its complexity refers to an ability to attract different types of users in different types of urban contexts—historical settings, monuments, newer neighborhoods and areas of contemporary urbanization. The project results can be measured first in relation to its progress (already funded, in the executive planning phase, with the approval of the first construction sites expected by 2022). A second important result is the participation of institutional bodies and citizens’ associations, which will oversee the construction and maintenance work as well as infuse into the project a constant vitality, in a true civic ecology perspective. Third, the results are important for enhancing metropolitan area accessibility and the environmental and social re-activation of the areas crossed, achieved directly and through the project’s realization. The GRAB strategy belongs to the new generation of landscape projects that have radically changed the priorities and hierarchies of intervention in the contexts of contemporary urbanization. These projects are based on the ecological analysis of the context but are located close to the fluctuating dynamics of contemporary metropolises and the problems of exclusion and marginality—both spatial and social—linked to the very rapid ecological, economic and demographic transformations.

ReRec: A Divide-and-Conquer Approach to Recommendation Based on Repeat Purchase Behaviors of Users in Community E-Commerce

Existing studies have made a great endeavor in predicting users’ potential interests in items by modeling user preferences and item characteristics. As an important indicator of users’ satisfaction and loyalty, repeat purchase behavior is a promising perspective to extract insightful information for community e-commerce. However, the repeated purchase behaviors of users have not yet been thoroughly studied. To fill in this research gap from the perspective of repeated purchase behavior and improve the process of generation of candidate recommended items this research proposed a novel approach called ReRec (Repeat purchase Recommender) for real-life applications. Specifically, the proposed ReRec approach comprises two components: the first is to model the repeat purchase behaviors of different types of users and the second is to recommend items to users based on their repeat purchase behaviors of different types. The extensive experiments are conducted on a real dataset collected from a community e-commerce platform, and the performance of our model has improved at least about 13.6% compared with the state-of-the-art techniques in recommending online items (measured by F-measure). Specifically, for active users, with w=1 and NUA∈5,25, the results of ReRec show a significant improvement (at least 50%) in recommendation. With α and σ as 0.75 and 0.2284, respectively, the proposed ReRec for unactive users is also superior to (at least 13.6%) the evaluation indicators of traditional Item CF when NUB∈6, 25. To the best of our knowledge, this paper is the first to study recommendations in community e-commerce.

Towards Adaptive Gamification: A Method Using Dynamic Player Profile and a Case Study

The design of gamified experiences following the one-fits-all approach uses the same game elements for all users participating in the experience. The alternative is adaptive gamification, which considers that users have different playing motivations. Some adaptive approaches use a (static) player profile gathered at the beginning of the experience; thus, the user experience fits this player profile uncovered through the use of a player type questionnaire. This paper presents a dynamic adaptive method which takes players’ profiles as initial information and also considers how these profiles change over time based on users’ interactions and opinions. Then, the users are provided with a personalized experience through the use of game elements that correspond to their dynamic playing profile. We describe a case study in the educational context, a course integrated on Nanomoocs, a massive open online course (MOOC) platform. We also present a preliminary evaluation of the approach by means of a simulator with bots that yields promising results when compared to baseline methods. The bots simulate different types of users, not so much to evaluate the effects of gamification (i.e., the completion rate), but to validate the convergence and validity of our method. The results show that our method achieves a low error considering both situations: when the user accurately (Err = 0.0070) and inaccurately (Err = 0.0243) answers the player type questionnaire.

Multi-level modeling: cornerstones of a rationale

This expert voice paper presents a comprehensive rationale of multi-level modeling. It aims not only at a systematic assessment of its prospects, but also at encouraging applications of multi-level modeling in business information systems and at providing a motivation for future research. The assessment is developed from a comparison of multi-level modeling with object-oriented, general-purpose modeling languages (GPMLs) and domain-specific modeling languages (DSMLs). To foster a differentiated evaluation, we propose a multi-perspective framework that accounts, among others, for essential design conflicts, different types of users, as well as economic aspects. Besides the assessment of the additional abstraction offered by multi-level modeling, the evaluation also identifies specific drawbacks and remaining challenges. Based on the results of the comparative assessment, in order to foster the adoption and further development of multi-level modeling, we discuss the prospects of supplementing multi-level modeling languages with multi-level programming languages and suggest possible dissemination strategies customized for different groups of users. The paper concludes with an outline of future research.

AlergiaPT: A Portuguese media campaign to inspire people with allergies to make a positive change in their life.

Allergic diseases comprise a significant cause of morbidity worldwide and a substantial burden on the health and medical systems of both developed and emerging economies. Although highly prevalent, relatively severe, and largely impactful on the quality of life of patients, allergic diseases are commonly trivialized. Increasing awareness of the relevance of allergic diseases as a major public health problem might lead to an improved acknowledgment by governments and health authorities. Based on the positive impact that media campaigns might have on health-related behaviors, as well as the large use of social media by different types of users, social media might be used as a powerful tool for spreading awareness and education even more effective than traditional face-to-face communication. Therefore, we aimed to develop a social media-based communication program, the AlergiaPT, reaching all stakeholders, to increase the awareness of allergic diseases tackling the causes, prevention, control, and economic impact. The AlergiaPT will provide user-generated and interactive content toward engagement, include both long-form and short-form video productions toward education, as well as stories and time-sensitive content toward empowerment. It will be targeted to all populations, engaging different stakeholders. Contents will address the 5 campaign goals: i) allergy health is promoted; ii) tolerance is actively reinforced, and avoidance reduced; iii) treatment control and guided self-management of patients of asthma, rhinitis, food allergy, and atopic eczema are strengthened; iv) recognition and treatment of severe allergy and anaphylaxis are improved, and v) indoor air quality is promoted. Engagement on the campaign will be promoted through stepwise educational takeaways meetings using different social media, and targeting all audience groups, by promoting the organization of resources for common goals and the involvement of social media to improve public awareness. The impact of AlergiaPT will be assessed through google analytics.

Dynamic Hyperparameter Allocation under Time Constraints for Automated Machine Learning

Automated hyperparameter optimization (HPO) is a crucial and time-consuming part in the automatic generation of efficient machine learning models. Previous studies could be classified into two major categories in terms of reducing training overhead: (1) sampling a promising hyperparameter configuration and (2) pruning non-promising configurations. These adaptive sampling and resource scheduling are combined to reduce cost, increasing the number of evaluations done on more promising configurations to find the best model in a given time. That is, these strategies are preferred to identify the best-performing models at an early stage within a certain deadline. Although these time and resource constraints are significant for designing HPO strategies, previous studies only focused on parallel exploration efficiency using resource awareness. In this study, we propose a novel diversification strategy for HPO, which exploits the dynamic hyperparameter space allocation for a sampler according to the remaining time budget. We provide a simple yet effective method to accelerate the maturity of the sampler that is independent of the sampling algorithm. Compared to previous resource awareness solutions, our solution achieves better performance via both time and resource awareness. We demonstrate the performance gains of our solution on several well-known HPO benchmarks. Furthermore, we implement them to our high-performance computing AI convergence platform. Considering the different types of users, both a fully automated HPO service based on graphic processing unit (GUI) interfaces and HPO job management via python application programming interface (API) on the Jupyterlab are served on the platform, publicly.

Quantitative Analysis in Securities and Futures Company Customer Value Assessment

This paper focuses on the evaluation process of users in an Investment company in China. We find different types of users through the process of clustering, and make an evaluation on users by using regression to give them a score. These two aspects provide a standard for this company to form different strategies for different customers in order to benefit both company and users. The meaning of the project is to provide better service to the old customers that have less trading frequency, and to lower the risk of the loss of valuable new customers. We perform data cleansing to remove inactive accounts and outliers and do logarithmic transformation to reduce the influence of extreme monetary values. Because of the strong correlation between variables, it is hard to perform algorithms on original data. Thus in order to reduce the large dimension of data, we perform factor analysis to create three dimensions that represent users’ information, one relating to monetary, one to their transaction number, one to their profit. For clustering, we perform widely used K-means clustering methods. Using the elbow method, customers are clustered into four groups. The resulting four groups show one group with high trading frequency; one with large money and profit, one with large money and loss, and also one majority group with less money and trading deals. We use a regression tree to perform regression based on the reduced dimensions and their contribution. The model reaches 97% accuracy showing that monetary aspects of a user make up the most important to a company. Further discussion uses classification methods to check our clustering result and performs regression on some of the variables composing contributions to reveal more details of each dimension.

Bias and Unfairness of Collaborative Filtering Based Recommender Systems in MovieLens Dataset

Recommender Systems have become one of the most important tools for streaming and marketplace systems in recent years. Their increased use has revealed clear bias and unfairness against minorities and underrepresented groups. This paper seeks the origin of these biases and unfairness. To this end, it analyzes the demographic characteristics of a gold standard dataset and its prediction performance when used in a multitude of Recommender Systems. In addition, this paper proposes Soft Matrix Factorization (SoftMF), which tries to balance the predictions of different types of users to reduce the present inequality. The experimental results show that those biases and unfairness are not introduced by the different recommendation models and that they come from the socio-psychological and demographic characteristics of the used dataset.

사용자 중심의 응급의료 공간 디자인 요구와 발전 방향 - COVID-19에 기초한 이동형 응급병원의 비교와 융합

Serious problems such as the shortage of medical settings and materials, as well as a nosocomial infection, have been recognized in hospitals of the world during the COVID-19 pandemic. Emergency medical space was the key material guarantee for the outbreak, and its systematic planning and reserve were needed in the pre-disaster phase. The purpose of the study is to chart the orientation and road for future research on standardized emergency medical space system design methods and R&D strategies on a global scale. Improve the global resource allocation ability of disaster emergency medical care and optimize the international emergency rescue guarantee system. In this study from the perspective of user demand, three types of field hospital cases that were widely used during COVID-19 were analyzed by analogy. It was found that the three types of emergency medical space had their strengths and weaknesses in the emergency response capability and treatment efficiency. The contents and results of the study are as follows.<BR> Firstly, combined with disaster emergency management and medical-related literature research. Medical staff, CDC managers, and inpatients who were involved in the treatment in Huoshenshan Hospital, Wuhan, China were interviewed and investigated. The types, behaviors, and needs of emergency medical space users were analyzed.<BR> Secondly, combined with the needs of users, the advantages that can be used for reference in the case were analyzed and integrated, and the design requirements of emergency medical space were deduced, including the structural design requirements for the whole disaster cycle, planning and design requirements for multi-type disaster treatment strategies, and functional module design requirements according to needs of different types of users.<BR> Thirdly, the development trend of the future emergency medical space was discussed, including the establishment of a global reserve mechanism, standardized design system and research and development platform, and information system to deal with the whole disaster cycle. The purpose was to provide a systematic method and strategy reference for the future development of the globalization of emergency medical space.

Enhancing System Acceptance through User-Centred Design: Integrating Patient Generated Wellness Data.

Gestational diabetes mellitus (GDM) is a condition that appears during pregnancy and is expected to be a temporary one. While patients are encouraged to manage it themselves, research findings indicate that GDM may negatively affect the foetus; in addition, there is an increased risk of women with GDM subsequently developing Type 2 diabetes. To alleviate the risks, women with GDM are advised to maintain a record of their diet and blood glucose levels and to attend regular clinical reviews. Rather than using a paper diary, women with GDM can maintain a record of their blood glucose level readings and other relevant data using a wellness mobile application (app). However, such apps are developed for general use and may not meet the specific needs of clinical staff (physicians, dietitians, obstetricians and midwives) involved in managing GDM; for example, an app may record glucose readings but not the details of a meal taken before or after the glucose reading. Second, the apps do not permanently store the data generated by the patient and do not support the transfer of these data to a clinical system or information portal. The mobile health (mHealth) system designed and developed in this research allows one to integrate different types of user generated wellness data into a centralised database. A user-centered design (UCD) approach informed by the technology acceptance model (TAM) was adopted. This paper investigates and evaluates the effectiveness of the approach with regard to facilitating system acceptance and future adoption through an early focus on enhancing system usefulness and ease of use. The functional system requirements of the proposed system were refined through a series of interviews with the perspective of clinical users; ease-of-use and usability issues were resolved through ‘think aloud’ sessions with clinicians and GDM patients.

Users' Perception of Violence and Conflicts With Professionals in Primary Care Centers Before and During COVID-19. A Qualitative Study.

Background: Workplace violence is a social problem of special interest in both intervention and research. Among the sectors that most perceive this type of violence, health care professionals stand out. The most common type of violence for this professional group is the one perpetrated by the users or patients themselves. It has been reported that one out of every four acts of violence in the workplace occurs in the healthcare setting. Within the health sector, the Mental Health, Emergency and Primary Care services have been widely reported as being among the most vulnerable, with Primary Care being the least addressed of the three. Although the available literature is extensive, there are hardly any studies that explore from a qualitative perspective what are the sources of conflict in this sector from the perspective of the users, the most common being to work with professionals.Objective: The aim of this study is to examine those aspects derived from the organization, the professionals or the users of Primary Care that, from the users' point of view, cause violent situations and how they think these could be avoided.Method: The sample consisted of 80 users of the Primary Care services of the Health Service of Murcia. For data collection, a qualitative study was conducted through 10 focus groups and a subsequent thematic analysis of the data.Results: The results have allowed us to identify that, from an organizational point of view, the uncertainty in waiting times, the need to adapt the telematic or telephone appointment to the different types of users, or the management of emergencies in Primary Care are the aspects that cause most conflicts between users and professionals. In this sense, suggested improvements are aimed at providing information in the mobile application updated on the opening hours or maintaining the telephone appointment for those who need or request it, among many others. As for the professionals, users point out that the medical staff is perceived as distant and sometimes does not provide enough information on the health status of users. Another professional group widely addressed in the focus groups was the administrative staff, being described as lacking in communication skills, assertiveness, or empathy. Users recognize the existence of a demanding/aggressive profile among users, who makes instrumental use of violence to achieve privileges over users in general. We have also identified the profile of the user who makes use of Primary Care as a way of socializing or managing conflicts of a socioemotional nature. As proposals for this thematic block, users suggest group therapies, the use of audiovisual material complementary to the information provided by professionals or community interventions in psychoeducation.Conclusion: This study allows to explore conflicts between users and professionals from the Primary Care patients' perspective. Our results are complementary to the available evidence that has used the professional's approach to study the phenomenon of workplace violence. The identification of sources of conflict and the assessment and contribution of users on possible ways of improvement can serve as a basis for the design of prevention and intervention plans to improve the work environment in Primary Care centers.

Two-stage flexible power sales optimization for electricity retailers considering demand response strategies of multi-type users

Since the electricity market is opening up in China, it is necessary for power retailers to participate in the market and find a way to gain benefits. In this context, this paper constructs a two-stage flexible power sales optimization model with multi-type demand response strategies. First, the demand response strategies of different types of users are analyzed. Second, two-stage optimization is conducted: In the first stage, the fuzzy K-means clustering method is used to divide a day into peaks, flats, and valleys, and then, a multi-objective time-of-use pricing model is constructed with the aims of minimizing the peak–valley load ratio and maximizing the response revenue rate of users. A flexible power retailing portfolio optimization model is built considering multiple types of users in the second stage and the model is solved by the chaotic particle swarm optimization algorithm. Finally, a case study with four scenarios is conducted, where the demand response is taken as a changeable factor. The results show that (1) the price-based demand response can shave peaks and fill valleys, whilst also reducing the electricity cost, and optimization for industrial users has the optimal effect, followed by that of commercial users and agricultural users; (2) different types of demand responses have different emphases on peak shaving and valley filling, and a combination can achieve the best effect; (3) with the demand response, an electricity retailer can effectively reduce the deviation assessment cost, and increase the revenue by flexibly allocating electricity sales, without resulting in unsatisfied users. The conclusions verify the effectiveness of the proposed model.

The mediating role of substance use motives in the relationship between personality dimensions and alcohol and drug use in adolescents and young adults with mild intellectual disabilities

Adolescents and young adults with mild intellectual disabilities or borderline intellectual functioning (MID-BIF) are a high-risk group for developing substance use disorders. The aim of this study was to gain a better understanding of the mechanisms that underlie substance use in this particular population. We tested the mediating role of substance use motives in the relationship between personality dimensions and substance use-related outcomes. Self-reported data on substance use risk personality dimensions (i.e., sensation seeking, impulsivity, anxiety sensitivity, and negative thinking), substance use motives (i.e., social, enhancement, coping, and conformity motives), and substance use were obtained from 163 individuals with MID-BIF (mean age 18.9 years). Results show that coping motives played a main role in the associations between personality dimensions and substance use in adolescents and young adults with MID-BIF, with significant relationships between impulsivity and negative thinking and severity of alcohol use or drug use via coping motives. Moreover, findings indicated a relatively high risk for several substance use motives and associated substance use (disorder) in individuals high on impulsivity. Results show significant relationships between impulsivity and severity of alcohol use or drug use via social, enhancement, and coping motives. No mediating associations were found for conformity motives, nor did we find any mediation associations in individuals with high levels of anxiety sensitivity. These insights provide the possibility of tailoring interventions to specific motives that underlie substance use in different types of users with MID-BIF based on personality dimensions.

Integration of solutions and services for multi-omics data analysis towards personalized medicine

Current advances in high-throughput and imaging technologies are paving the way next-generation healthcare, tailored to the clinical and molecular characteristics of each patient. The Big Data obtained from these technologies are of little value to society unless it can be analyzed, interpreted, and applied in a relatively customized and inexpensive way. We propose a flexible decision support system called IntelliOmics for multi-omics data analysis constituted with well-designed and maintained components with open license for both personal and commercial use. Our proposition aims to serve some insight how to build your own local end-to-end service towards personalized medicine: from raw data upload, intelligent integration and exploration to detailed analysis accompanying clinical medical reports.The high-throughput data is effectively collected and processed in a parallel and distributed manner using the Hadoop framework and user-defined scripts. Heterogeneous data transformation performed mainly on the Apache Hive is then integrated into a so called ‘knowledge base’. On its basis, manual analysis in the form of hierarchical rules can be performed as well as automatic data analysis with Apache Spark and machine learning library MLlib. Finally, diagnostic and prognostic tools, charts, tables, statistical tests and print-ready clinical reports for an individual or group of patients are provided. The experimental evaluation was performed as part of the clinical decision support for targeted therapy in non-small cell lung cancer. The system managed to successfully process over a hundred of multi-omic patient data and offers various functionalities for different types of users: researchers, bio-statisticians/bioinformaticians, clinicians and medical board.

When does personalization work on social media? a posteriori segmentation of consumers

The aim of this research is to find a segment of consumers of fashion products based on their personal visions of personalization of shoppable ads on mobile social media. To meet this objective, three operational objectives are defined. First, a theoretical model is evaluated based on the stimulus-organism-response framework (S–O–R). This examines, with a PLS-SEM approach, how the stimulation of personalization will affect consumers' internal cognitive state (perceived usefulness) and consequently generates a behavioral response (intention to buy). Second, we look for fashion consumer segments based on their perception of personalization through prediction-oriented segmentation (PLS-POS). Third, the segments are explained based on three constructs that were considered important in fashion consumption through mobile social networks: purchase intention, concern for privacy, and perception of trend. The inclusion of personalization and the perception of usefulness of advertisements can greatly help the intention to purchase clothing to be understood. The application of a posterior segmentation helps to better understand the different types of users exposed to shoppable ads on mobile social networks and their relationship with the purchase intention, concern for privacy and trend. While the measures and scales were tested in a context of mobile clothing trade, the methodology can be applied to other types of products or services.

Hybrid market-based resources allocation in Mobile Edge Computing systems under stochastic information

In order to deal with the problem of user diversity in Mobile Edge Computing (MEC) resource trading market, in this paper, we propose a hybrid market-based resource transaction mechanism consisting of futures market and spot market. Two different types of users have been taken into consideration. One is registered users and another is unregistered users. In futures market, registered users pay a registration fee to the agent and use the reserved resources according to the contract signed exclusively. We design optimal contracts for the registered users by adjusting the registration fee in order to maximize the servers’ utility. In spot market, unregistered users compete with one another to purchase the resources on demand. We model the trading process as a multi-seller and multi-buyer market, and propose auction algorithms to match the asking price from servers and the bidding price from unregistered users by assigning computation resources to the users. The agent acts as the auctioneer to host the auction, and the unregistered users bid on computation resources based on the estimated valuation. We study the optimal solution under both complete and incomplete information scenarios, depending on whether the agent can observe the users’ private information. Simulation results demonstrate the existences of the asking price and registration fee for the servers to maximize utility.

Demand charges and user flexibility – Exploring differences in electricity consumer types and load patterns within the Swedish commercial sector

Demand-based charges have been employed as a tool intended to reduce electricity users’ maximum demand but there is a lack of consensus regarding their efficacy. One reason for this may be the diversity in the flexibility potential of different types of users. This study explores the flexibility potential of different types of electricity consumers in the small to medium-sized commercial sector (35-63A) in response to a compulsory demand charge. The objective is to characterize varying levels of flexibility with respect to different types of commercial users with different load patterns. A multivariate clustering technique was used to group commercial users with comparable load patterns based on a year of hourly data before the tariff change was introduced. This method was used to: (1) match users from the intervention area and reference area with similar load patterns, without losing any user data, and (2) compare how users with different load patterns react differently to the tariff change. We found clear distinctions in the types of commercial users in each cluster and their response to the tariff, demonstrating the extent to which demand flexibility may be dependent on the nature of an organization’s activities and its respective load patterns. The highest demand flexibility was found in clusters which had a large share of users in the IT sector, commerce and public administration. The lowest demand flexibility was found in the real estate and education sectors. Future research should further investigate these variations and explore the possibilities of tailoring interventions to the specific types of users.

The Life of the Party: Impact of Social Mapping in OpenStreetMap

OpenStreetMap has grown rapidly into an exemplary geowiki, where contributors collectively build an open map of the world. Official ‘mapping party’ events are organized on a regular basis to invite users to socialize, map and to engage new-comers locally. Here, we measure the direct and indirect impact of mapping parties on user contributions in both the short and long terms. We question whether this social mapping is a cause for users to become highly committed through a social bond, or is an effect of the mappers’ need to find a common social ground. We show that mapping parties have distinct effects on different types of users, with a more profound direct impact on weaker contributors and a longer term effect on heavy contributors.