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This paper applies a theory of persuasion using the elaboration likelihood model to explain how communication messages from mobile banking services are interpreted by receivers to increase their trust in mobile banking. We explain how users’ security and privacy concerns moderate the central route persuasion relationship between argument quality and trust. A questionnaire survey is employed to test a research model. The findings indicate that central and peripheral route persuasion cues enforce users’ trust in mobile banking services. Users with strong privacy and security concerns are more likely to rely on the quality of arguments of the messages. Theoretical and practical implications are discussed in the paper.

Recent lockdowns over the world due to COVID19 pandemic accelerated virtualization of our social and work life. While virtualization contributes to reducing green-house-gas (GhG) through reduced mobility, it also dramatically increases energy consumption by data centers (DCs) which host large number of servers for realizing virtualization. This paper argues that current engineering-oriented studies on Green DCs need to be complemented by IS scholars to address human and organizational issues.

Introduction. Much of the research in obesity has explored the role of social networks in promoting health through social influence and selection. However, little research has examined how social networks themselves could condition health behaviours. Research began to address more detailed questions regarding peer socialization process and network properties. One of the key questions is whether behaviour association (either peer socialization or selection) is dependent on the local network structure. Examining social network position, structure, and how they interact with behaviour provide valuable insight on how to modify these behaviours. The objective of the study was to examine the moderating role of friendship network properties on the association between individual’s and friends’ weight-related behaviours among adolescents. Materials and methods. Methods are drawn from a set of analytical tools known as ‘Social Network Analysis’, which uses friendship nomination data from a complete network (socio-metric), along with reported data on diet, physical activity, sedentary behaviour, and sleep deprivation. Results. A total of 673 adolescents aged between 11-15 years, from four schools in Kingdom of Bahrain, participated in this study. Findings from binary logistic regression models suggest that there are associations between adolescents and their friends’ in multiple weight-related behaviours. There is also evidence for the moderating role of some network properties on these associations. Conclusion. Properties of friendship network are critical in conditioning associations between individuals and their friends’ weight-related behaviours. However, findings are gender specific, which has implications for gender-tailored interventions.

Part 3: Open Data: Social and Technical Aspects; International audience; The energy consumption of data centres is increasing over and over. However, there are few decision support systems introduced for data centre practitioners to use it for their daily operations, including simulating their new server installation and forecasting power consumption for the target periods. We propose a simulation model based on CloudSim, which is widely used for data centre research. Our simulation model will be tested with datasets, including IT work-loads, cooling performance, and power consumption of servers and cooling devices. In the final stage, we provide a decision support system to monitor, forecast, and optimise the power consumption of a data centre easily on the web.

Optimizing vessel speed can be considered a multi-objective optimization problem since fuel emission and service level often trade-off. Understanding such relationships is important for decision makers in maritime shipping. Majority of existing studies on solving this problem consider it as a deterministic problem. However, in real world, this problem contains stochastic variables such as port waiting time. Therefore, we need a new approach to solve the problem including stochastic variables. This paper proposes conceptual model of simulation optimization to solve stochastic multi-objective problem for speed optimization.

Part 3: Open Government and Transparency; International audience; This paper identifies main features of online games to be disclosed to the market for policy makers. It tests the positive relationships between information disclosure for online games and stock returns using event analysis method. Based on data collected from online game companies between 2004 to 2009 in South Korea, the paper finds positive and significant correlation between game rating information and stock reactions. Specifically, the positive reactions are clear when a company introduces casual games, and a company has development capability. However, we do not find any significant relationships between stock reactions and voluntary information releases after the game rating information released. The findings support the feasibility of introduction of mandatory information disclosure scheme for online game industry.

This book constitutes the proceedings of the 19th IFIP WG 8.5 International Conference on Electronic Government, EGOV 2020, held in Linkoping, Sweden, in August/September 2020, in conjunction with ...

© The Author(s) 2018. A key prediction of the Capital Asset Pricing Model (CAPM) is that idiosyncratic risk is not priced by investors because in the absence of frictions it can be fully diversified away. In the presence of constraints on diversification, refinements of the CAPM conclude that the part of idiosyncratic risk that is not diversified should be priced. Recent empirical studies yielded mixed evidence with some studies finding positive correlation between idiosyncratic risk and stock returns, while other studies reported none or even negative correlation. We examine whether idiosyncratic risk is priced by the stock market and what are the probable causes for the mixed evidence produced by other studies, using monthly data for the US market covering the period from 1980 until 2013. We find that one-period volatility forecasts are not significantly correlated with stock returns. The mean-reverting unconditional volatility, however, is a robust predictor of returns. Consistent with economic theory, the size of the premium depends on the degree of ‘knowledge’ of the security among market participants. In particular, the premium for Nasdaq-traded stocks is higher than that for NYSE and Amex stocks. We also find stronger correlation between idiosyncratic risk and returns during recessions, which may suggest interaction of risk premium with decreased risk tolerance or other investment considerations like flight to safety or liquidity requirements. We identify the difference between the correlations of the idiosyncratic volatility estimators used by other studies and the true risk metric the mean-reverting volatility as the likely cause for the mixed evidence produced by other studies. Our results are robust with respect to liquidity, momentum, return reversals, unadjusted price, liquidity, credit quality, omitted factors, and hold at daily frequency. Korea National Research Foundation through Global Research Network Program

Sentiment classification has played an important role in various research area including e-commerce applications and a number of advanced Computational Intelligence techniques including machine learning and computational linguistics have been proposed in the literature for improved sentiment classification results. While such studies focus on improving performance with new techniques or extending existing algorithms based on previously used dataset, few studies provide practitioners with insight on what techniques are better for their datasets that have different properties. This paper applies four different sentiment classification techniques from machine learning (Naïve Bayes, SVM and Decision Tree) and sentiment orientation approaches to datasets obtained from various sources (IMDB, Twitter, Hotel review, and Amazon review datasets) to learn how different data properties including dataset size, length of target documents, and subjectivity of data affect the performance of those techniques. The results of computational experiments confirm the sensitivity of the techniques on data properties including training data size, the document length and subjectivity of training /test data in the improvement of performances of techniques. The theoretical and practical implications of the findings are discussed. This study was partially funded by Korea National Research Foundation through Global Research Network Program (Project no. 2016S1A2A2912265) and EU funded project Policy Compass (Project no. 283700).

Background: An unhealthy diet, low levels of physical activity and increased participation in sedentary activities are important obesity-related behaviours that negatively impact the health of today’s youth. Friends’ health behaviours have been found to influence the individual health behaviours; however current evidence on the specific role of friendship networks on obesity-related behaviours are inconclusive. The purpose of this review is to profile the existing literature in an attempt to identify the associations between friendship networks and obesity-related behaviours among adolescents. Method: A systemized review of the literature was undertaken after a search of the Scopus database, a total of fifteen articles were eligible for inclusion. The selected publications assessed the association between friendship networks and obesity-related behaviours (diet, physical activity, and sedentary behaviour) among the youth. Results: There is consistent evidence that friends are similar in physical activity; evidence on diet and sedentary behaviour is mixed and limited. Friendship network characteristics seem to associated with obesity-related behaviours. Popularity (receiving ties) is likely to be associated with diet, network size and the proportion of active friends is tends to be associated with physical activity and finally, network density is associated with sedentary behaviour. Conclusion: Friendships are critical in shaping young people’s obesity-related behaviours. There is extensive research investigating friendship influences on diet physical activity and sedentary behaviour. Future studies should investigate whether friendship influence operates via other obesity-related behaviours that have not been explored yet such as sleep. Results from this review are informative for designing effective public health interventions because network-based promotion interventions have a promising potential. Keywords: social networks, friendship, diet, physical activity, sedentary behaviour, obesity

Electronic government services (e-services) involve the delivery of information and services to stakeholders via the Internet, Internet of Things and other traditional modes. Despite their beneficial values, the overall level of usage (take-up) remains relatively low compared to traditional modes. They are also challenging to evaluate due to behavioral, economical, political, and technical aspects. The literature lacks a methodology framework to guide the government transformation application to improve both internal processes of e-services and institutional transformation to advance relationships with stakeholders. This paper proposes a cognitive analytics management (CAM) framework to implement such transformations. The ambition is to increase users’ take-up rate and satisfaction, and create sustainable shared values through provision of improved e-services. The CAM framework uses cognition to understand and frame the transformation challenge into analytics terms. Analytics insights for improvements are generated using Data Envelopment Analysis (DEA). A classification and regression tree is then applied to DEA results to identify characteristics of satisfaction to advance relationships. The importance of senior management is highlighted for setting strategic goals and providing various executive supports. The CAM application for the transforming Turkish e-services is validated on a large sample data using online survey. The results are discussed; the outcomes and impacts are reported in terms of estimated savings of more than fifteen billion dollars over a ten-year period and increased usage of improved new e-services. We conclude with future research. The authors would like to acknowledge the funding support for the CEES project (C-E-E-S: Citizen Oriented Evaluation of E-government Services: a reference model) funded by “FP7-PEOPLE-IAAP-2008 - Marie Curie Action: Industry-Academia-Partnerships and Pathways” under grant agreement: 230658. We would like also thank the senior management of Turksat, namely President Ensar Gül and VP Halil Yeşilçimen of Turksat for their advocacy and commitment to support the implementation of the various recommendations, without them, the project would not have achieved the e-government goals. The Husni Sawwaf Chair at the American University of Beirut for supporting also the project. Also special thanks go to the Euro Excellence in Practice Award committee for selecting this work to be among finalists at EURO 2015 conference, Glasgow, UK. Finally, sincere appreciations go to the guest editors and the anonymous referees for their critical comments and support that immensely improved the clarity of the presentation of the paper. Scopus

Citizens satisfaction is acknowledged as one of the most significant influences for e-government adoption and diffusion. This study examines the impact of information quality, system quality, trust, and cost on user satisfaction of e-government services. Using a survey, this study collected 1518 valid responses from e-government service adopters across the United Kingdom. Our empirical outcomes show the five factors identified in this study have a significant impact on U.K. citizens satisfaction with e-government services. This publication was made possible by Qatar National Research Fund and the findings achieved herein are solely the responsibility of the author(s). Scopus

This paper aims to examine the potential of multi-objective optimization (MOO) as a decision support to improving sustainability in maritime shipping. We focus on environmental sustainability and the trade-offs involved with economic and operational objectives. Through a systematic approach, we review the literature on environmental sustainability, decision support and multi-objective optimization in maritime shipping. We identify the gaps and directions for future research. It is expected that the next generation of decision support systems for maritime transport will exploit the theoretical development in MOO to facilitate informed decision making in maritime supply chains considering environmental sustainability and the competing objectives.

The importance of evaluation and optimization of electronic government (e-government) services is imperative if the government organisations are to have an effective impact on the success and take-up of the services offered. Transport For London's (TFL) London Congestion Charging (LCC) is one of the innovative electronic services (e-services) introduced by the United Kingdom (UK) government to the citizens. While some studies have addressed the impact of the introduction of the congestion charge there has been a dearth of research performed to address user (citizen) satisfaction of the online LCC system. Therefore, this research seeks to measure the citizen satisfaction of using the LCC online payment system offered by TFL. The citizen satisfaction in this context is measured using the four dimensions from the COBRA framework that comprise the cost, opportunity, benefits and risk assessment constructs. This paper presents the findings of a survey of 500 users of the TFL LCC online payment system. It also reports the qualitative feedback obtained from the participants that can be used to determine the areas that need further improvement in the current LCC e-service and potential influences on user satisfaction.

This paper provides a management perspective of organisational factors that contributes to the reduction of food waste through the application of design science principles to explore causal relationships between food distribution (organisational) and consumption (societal) factors. Qualitative data were collected with an organisational perspective from commercial food consumers along with large-scale food importers, distributors, and retailers. Cause-effect models are built and “what-if” simulations are conducted through the development and application of a Fuzzy Cognitive Map (FCM) approaches to elucidate dynamic interrelationships. The simulation models developed provide a practical insight into existing and emergent food losses scenarios, suggesting the need for big data sets to allow for generalizable findings to be extrapolated from a more detailed quantitative exercise. This research offers itself as evidence to support policy makers in the development of policies that facilitate interventions to reduce food losses. It also contributes to the literature through sustaining, impacting and potentially improving levels of food security, underpinned by empirically constructed policy models that identify potential behavioural changes. It is the extension of these simulation models set against a backdrop of a proposed big data framework for food security, where this study sets avenues for future research for others to design and construct big data research in food supply chains. This research has therefore sought to provide policymakers with a means to evaluate new and existing policies, whilst also offering a practical basis through which food chains can be made more resilient through the consideration of management practices and policy decisions.