Taking the heterogeneity of citizens into account: flood risk communication in coastal cities – a case study of Bremen

The likely manifestations of climate change like flood hazards are prominent topics in public communication. This can be shown by media analysis and questionnaire data. However, in the case of flood risks an information gap remains resulting in misinformed citizens who probably will not perform the necessary protective actions when an emergency occurs. This paper examines more closely a newly developed approach to flood risk communication that takes the heterogeneity of citizens into account and aims to close this gap. The heterogeneity is analysed on the meso level regarding differences in residential situation as well as on the micro level with respect to risk perception and protective actions. Using the city of Bremen as a case study, empirical data from n=831 respondents were used to identify Action Types representing different states of readiness for protective actions in view of flood risks. These subpopulations can be provided with specific information to meet their heterogeneous needs for risk communication. A prototype of a computer-based information system is described that can produce and pass on such tailored information. However, such an approach to risk communication has to be complemented by meso level analysis which takes the social diversity of subpopulations into account. Social vulnerability is the crucial concept for understanding the distribution of resources and capacities among different social groups. We therefore recommend putting forums and organisations into place that can mediate between the state and its citizens

Flexible Global Software Development (GSD): Antecedents of Success in Requirements Analysis

Globalization of software development has resulted in a rapid shift away from the traditional collocated, on-site development model, to the offshoring model. Emerging trends indicate an increasing interest in offshoring even in early phases like requirements analysis. Additionally, the flexibility offered by the agile development approach makes it attractive for adaptation in globally distributed software work. A question of significance then is what impacts the success of offshoring earlier phases, like requirements analysis, in a flexible and globally distributed environment? This article incorporates the stance of control theory to posit a research model that examines antecedent factors such as requirements change, facilitation by vendor and client site-coordinators, control, and computer-mediated communication. The impact of these factors on success of requirements analysis projects in a “flexible” global setting is tested using two quasi-experiments involving students from Management Development Institute, India and Marquette University, USA. Results indicate that formal modes of control significantly influence project success during requirements analysis. Further, facilitation by both client and vendor site coordinators positively impacts requirements analysis success

An Exploratory Study for Perceived Advertising Value in the Relationship Between Irritation and Advertising Avoidance on the Mobile Social Platforms

This study delves deeply into advertising avoidance research and redefines the uses and gratifications theory (U&G) as divided into (a) convenience U&G, (b) content U&G, and (c) social U&G to conduct an approach to alleviate the degree of advertising avoidance on the mobile social platforms. To carefully study the forming framework of advertising avoidance, we extract the factor irritation considered to directly impact on avoidant intention induced by perceived intrusiveness and privacy concerns. As an important previous factor in advertising research, we also test the moderating effect of perceived advertising value between irritation and advertising avoidance. Findings show that ubiquity takes a negative role on mobile social platforms and tailoring also takes different roles on perceived intrusiveness and privacy concerns; unfortunately, content U&G consist of advertising informativeness and entertainment didn’t find any significant effect; in contrast with previous study, social U&G as social interaction and social integration also show some different roles but is ambiguous. However, the positive relationship of perceived intrusiveness, privacy concerns, irritation, and advertising avoidance has been confirmed again although with a pity of insignificant moderating effect of advertising value. Management issues, theoretical contributions, limitations and future study are discussed as follow

Knowledge Reuse for Customization: Metamodels in an Open Design Community for 3d Printing

Theories of knowledge reuse posit two distinct processes: reuse for replication and reuse for innovation. We identify another distinct process, reuse for customization. Reuse for customization is a process in which designers manipulate the parameters of metamodels to produce models that fulfill their personal needs. We test hypotheses about reuse for customization in Thingiverse, a community of designers that shares files for three-dimensional printing. 3D metamodels are reused more often than the 3D models they generate. The reuse of metamodels is amplified when the metamodels are created by designers with greater community experience. Metamodels make the community's design knowledge available for reuse for customization-or further extension of the metamodels, a kind of reuse for innovation

Models of everywhere revisited: a technological perspective

The concept ‘models of everywhere’ was first introduced in the mid 2000s as a means of reasoning about the environmental science of a place, changing the nature of the underlying modelling process, from one in which general model structures are used to one in which modelling becomes a learning process about specific places, in particular capturing the idiosyncrasies of that place. At one level, this is a straightforward concept, but at another it is a rich multi-dimensional conceptual framework involving the following key dimensions: models of everywhere, models of everything and models at all times, being constantly re-evaluated against the most current evidence. This is a compelling approach with the potential to deal with epistemic uncertainties and nonlinearities. However, the approach has, as yet, not been fully utilised or explored. This paper examines the concept of models of everywhere in the light of recent advances in technology. The paper argues that, when first proposed, technology was a limiting factor but now, with advances in areas such as Internet of Things, cloud computing and data analytics, many of the barriers have been alleviated. Consequently, it is timely to look again at the concept of models of everywhere in practical conditions as part of a trans-disciplinary effort to tackle the remaining research questions. The paper concludes by identifying the key elements of a research agenda that should underpin such experimentation and deployment

ATITIUDES TOW ARDS COMPUTER GAMES FOR LEARNING AND Pl.A YER ARCHETYPES: AN EXPLORATION OF MEASURES ON PREINTERVENTION PLAYER CHARACTERISTICS FOR SERIOUS GAME-BASED INTERVENTIONS

Serious game-based interventions offer promising health outcome results with the aid of pre-intervention player tailoring and the development of measurements that evaluate pre-intervention player characteristics and subgroups. Video gaming measures can potentially provide helpful tailoring information that discerns individual video gaming preferences which could influence their overall user experience. It is critical that measures that target adolescent video gaming be psychometrically validated. There is a gap in the literature with psychometrically validated measures evaluating adolescent attitudes towards computer games for learning and gaming archetypes. Therefore the aims of this dissertation were to 1) evaluate the psychometric properties (i.e., reliability and validity) of the Attitudes Towards Computer Games for Learning (ATCGFL) adapted from Askar et al.’s Attitudes towards computer-assisted learning (CAL) scale that assessed attitudes towards computer games for learning among a sample of adolescents, and 2) explore and identify the latent class structure (LCA) of the BrainHex measure among the same sample of adolescents. Secondary data analysis of a data set extricated from the “It’s Your Game-Family” study was conducted. Participants were youth aged 11-14 years in Houston, TX, who answered self-guided questionnaires in their home. Exploratory data analysis of the ATCGFL scale was performed. Reliability testing through analyzing internal consistency and test-retest reliability were also performed with the ATCGFL scale. Then, exploratory data analysis of the BrainHex measure was performed through latent class analysis. Results from the exploratory analysis of the ATCGFL scale suggest the adapted attitudes scale supports a 3-factor solution (Satisfaction, Motivation, and Cognition). The 3-factor solution indicates the scale has a mixed quality level of internal consistency because Factor 1 and Factor 2 we have an acceptable level of internal consistency, but Factor 3 has a questionable level of internal consistency. The test-retest reliability of the ATCGFL scale was low, but significant. Last, the latent class analysis of the BrainHex measure results revealed a 3-class model (low probability of gaming element likability gamers, moderate probability of gaming element likability gamers, and high probability of gaming element likability gamers). Overall, these findings suggest the Attitudes Towards Computer Games for Learning scale and BrainHex measure both possess promising utility as measures in tandem with serious game-based interventions, and that further research to conduct confirmatory analysis with both measures is merited

Do people's user types change over time? An exploratory study

In recent years, different studies have proposed and validated user models (e.g., Bartle, BrainHex, and Hexad) to represent the different user profiles in games and gamified settings. However, the results of applying these user models in practice (e.g., to personalize gamified systems) are still contradictory. One of the hypotheses for these results is that the user types can change over time (i.e., user types are dynamic). To start to understand whether user types can change over time, we conducted an exploratory study analyzing data from 74 participants to identify if their user type (Achiever, Philanthropist, Socialiser, Free Spirit, Player, and Disruptor) had changed over time (six months). The results indicate that there is a change in the dominant user type of the participants, as well as the average scores in the Hexad sub-scales. These results imply that all the scores should be considered when defining the Hexad's user type and that the user types are dynamic. Our results contribute with practical implications, indicating that the personalization currently made (generally static) may be insufficient to improve the users' experience, requiring user types to be analyzed continuously and personalization to be done dynamically.Comment: 5th International GamiFIN Conference 2021 (GamiFIN 2021), April 7-10, 2021, Finlan

Consumers' intention to use e-money mobile using the decomposed theory of planned behavior

The purpose of this study is to understand consumers’ behavior on their intention to use e-money mobile.The study of the intention to use e-money mobile is still at the early stage in payment transaction. The e-money mobile is a new product for payment transaction that look for massive, micro, and quick means for transaction. The model that integrates in this study is the Decomposed Theory of Planned Behaviour (DTPB). In particular, it is simultaneously assesses the determinants of consumers’ intention to use e-money mobile in Indonesia which examines twelve (12) variables. The variables are attitude, awareness, subjective norm, perceived behavioral control, perceived risk, perceived security, relative advantage, complexity, social-cultural influence, family, self-confidence, and resources facilitating conditions. Based on a sample of one thousand and three hundred (1300) respondents was selected using mall-intercept method with technique sampling multistage cluster sampling and systematic random sampling in Padang, Indonesia. The Partial Least Squares Method (PLS) series PLS 2.0 M3 for algorithm and bootstrap techniques and SPSS 18 was used to test the hypothesis that has been developed. Results show that all variables had significant positive influence on the intention to use e-money mobile excluded the awareness. The awareness has positive influence but not significant on the intention to use e-money mobile. This study contributes to improve the specific theory of DTPB that generally limited to e-Commerce, e-Banking, and others social networking. The findings give more information to the issuers about the characteristic consumers and add new knowledge for academics, practioners, bank, assurance companies, airline companies and the health sector

A Framework for Personalized Content Recommendations to Support Informal Learning in Massively Diverse Information WIKIS

Personalization has proved to achieve better learning outcomes by adapting to specific learners’ needs, interests, and/or preferences. Traditionally, most personalized learning software systems focused on formal learning. However, learning personalization is not only desirable for formal learning, it is also required for informal learning, which is self-directed, does not follow a specified curriculum, and does not lead to formal qualifications. Wikis among other informal learning platforms are found to attract an increasing attention for informal learning, especially Wikipedia. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. Nevertheless, navigation on information wikis suffer from several limitations. To support informal learning on Wikipedia and similar environments, it is important to provide easy and fast access to relevant content. Recommendation systems (RSs) have long been used to effectively provide useful recommendations in different technology enhanced learning (TEL) contexts. However, the massive diversity of unstructured content as well as user base on such information oriented websites poses major challenges when designing recommendation models for similar environments. In addition to these challenges, evaluation of TEL recommender systems for informal learning is rather a challenging activity due to the inherent difficulty in measuring the impact of recommendations on informal learning with the absence of formal assessment and commonly used learning analytics. In this research, a personalized content recommendation framework (PCRF) for information wikis as well as an evaluation framework that can be used to evaluate the impact of personalized content recommendations on informal learning from wikis are proposed. The presented recommendation framework models learners’ interests by continuously extrapolating topical navigation graphs from learners’ free navigation and applying graph structural analysis algorithms to extract interesting topics for individual users. Then, it integrates learners’ interest models with fuzzy thesauri for personalized content recommendations. Our evaluation approach encompasses two main activities. First, the impact of personalized recommendations on informal learning is evaluated by assessing conceptual knowledge in users’ feedback. Second, web analytics data is analyzed to get an insight into users’ progress and focus throughout the test session. Our evaluation revealed that PCRF generates highly relevant recommendations that are adaptive to changes in user’s interest using the HARD model with rank-based mean average precision (MAP@k) scores ranging between 100% and 86.4%. In addition, evaluation of informal learning revealed that users who used Wikipedia with personalized support could achieve higher scores on conceptual knowledge assessment with average score of 14.9 compared to 10.0 for the students who used the encyclopedia without any recommendations. The analysis of web analytics data show that users who used Wikipedia with personalized recommendations visited larger number of relevant pages compared to the control group, 644 vs 226 respectively. In addition, they were also able to make use of a larger number of concepts and were able to make comparisons and state relations between concepts